The Open Collections website will be unavailable July 27 from 2100-2200 PST ahead of planned usability and performance enhancements on July 28. More information here.

UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

Gene expression in prostate cancer Romanuik, Tammy Lee 2008

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Notice for Google Chrome users:
If you are having trouble viewing or searching the PDF with Google Chrome, please download it here instead.

Item Metadata

Download

Media
24-ubc_2008_fall_romanuik_tammy_lee.pdf [ 4.32MB ]
Metadata
JSON: 24-1.0067001.json
JSON-LD: 24-1.0067001-ld.json
RDF/XML (Pretty): 24-1.0067001-rdf.xml
RDF/JSON: 24-1.0067001-rdf.json
Turtle: 24-1.0067001-turtle.txt
N-Triples: 24-1.0067001-rdf-ntriples.txt
Original Record: 24-1.0067001-source.json
Full Text
24-1.0067001-fulltext.txt
Citation
24-1.0067001.ris

Full Text

GENE EXPRESSION IN PROSTATE CANCER by TAMMY LEE ROMANUIK B.Sc., The University of British Columbia, 2003 A THESIS SUBMITTED TN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Pathology and Laboratory Medicine) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) OCTOBER 2008 © Tammy Lee Romanuik, 2008 ABSTRACT Development and maintenance of the prostate is dependent on androgens and the androgen receptor. The androgen pathway continues to be important in prostate cancer. Here, we evaluated the transcriptome of prostate cancer cells in response to androgen using Long Serial Analysis of Gene Expression (L0ngSAGE) libraries. We identified 35 genes with novel associations to androgen signalling and validated 24 of these genes using quantitative real time-polymerase chain reaction (qRT-PCR). These genes were: ARL6IF5, BL VRB, C]9orf48, C]orfJ22, C6orf66, CAMK2NJ, CCNI, DERA, ERRFI], GLUL, GOLFH3, HMJ3, HSP9OB], MANEA, NANS, NIPSNAP3A, SLC4JA], SOD], SVIF, TAOK3, TCP], TMEM66, USP33, and VTAJ. The physiological relevance of these expression trends was evaluated in vivo using the LNCaP Hollow Fibre model. There is no cure for castration-recurrent prostate cancer (CRPC), and the mechanisms underlying the disease are not known. To address this problem, we assayed the transcriptome of LNCaP human prostate cancer cells as they progress to castration-recurrence in vivo using replicate L0ngSAGE libraries. We identified 96 novel genes consistently differentially expressed in CRPC. The expression profiles support a role for the transcriptional activity of the androgen receptor genes (CCNH, CUEDC2, FLNA, and FSMA 7), steroid synthesis and metabolism genes (DHCR24, DHRS7, ELO VL5, HSDJ 7B4, and OPRKJ), neuroendocrine cell genes (ENO2, MAOA, OPRK], SJOOA]O, and TRPM8), and proliferation genes (GAS5, GNB2L], MT-ND3, NKX3-], PCGEM], PTGFR, STEAFJ, and TMEM3OA) in castration-recurrence. Screening for prostate cancer using serum levels of prostate-specific antigen has resulted in the over-treatment of indolent disease. Novel diagnostic and prognostic markers for prostate cancer are required. To address this need, the levels of 27 transcripts were investigated with qRT-PCR. Expression of POP3 (100 kb from EST CF140309) was prostate-specific, with restricted expression of ADAM2, POP1 (50 kb from AK000023), POP4 (truncated TMEFF2), POP 10 (intron ofADAM2), ELO VL5, RAMP], and SPON2. ELO VL5, NGFRAP1, POP5 (intron of NCAM2), POP8 (intron of EFNA5), RAMP], SPON2, and TMEM66 were differentially expressed between laser microdissected tumour and normal clinical samples of prostatic tissue. 11 These studies suggest that ADAM2, ELO VL5, POP 1, POP3, POP4, POP 10, RAMP], and SPON2 may be good candidates for biomarkers of prostate cancer. UI TABLE OF CONTENTS ABSTRACT ii TABLE OF CONTENTS iv LIST OF TABLES viii LIST OF FIGURES x LIST OF ABBREVIATIONS xi ACKNOWLEDGEMENTS xvii DEDICATION xviii CO-AUTHORSHIP STATEMENT xix CHAPTER I INTRODUCTION I 1.1 THE NORMAL AND DISEASED PROSTATE 1 1.1.1 Mortality, morbidity, and risk factors of prostate cancer 1 1.1.2 Normal prostate 1 1.1.3 Cytopathology and pathophysiology 3 1.2 DETECTION, DIAGNOSIS, AND MONITORiNG OF PROSTATE CANCER 4 1.2.1DRE 4 1.2.2PSA 4 1.2.3 Biopsy 5 1.2.4 Other clinical tests 5 1.2.5 TNM staging 6 1.2.6 Gleason grading 6 1.3 TREATMENT OF PROSTATE CANCER 7 1.3.1 Localized 7 1.3.2 Metastatic 8 1.4 CASTRATION-RECURRENCE 8 1.5 MODELS OF PROSTATE CANCER 9 1.5.1 Cell lines 9 1.5.2 In vivo models 10 1.5.3 Human tissue 11 1.6 GENE EXPRESSION 13 1.6.1 Gene expression analyses of prostate cancer 13 1.6.2 Methods to evaluate gene expression 14 iv 1.7 RESEARCH HYPOTHESIS AND OBJECTIVES 17 1.7.1 Background summary 17 1.7.2 Hypothesis and objectives 17 1.8 REFERENCES 22 CHAPTER II REGULATION OF THE TRANSCRIPTOME BY THE ANUROGEN AXIS N PROSTATE CANCER 40 2.1 INTRODUCTION 40 2.2 MATERIALS AND METHODS 41 2.2.1 Cell culture 41 2.2.2 Long serial analysis of gene expression 41 2.2.3 Quantitative real-time polymerase chain reaction 43 2.2.4 LNCaP Hollow Fibre model 44 2.3 RESULTS 45 2.3.1 Summary of L0ngSAGE libraries 45 2.3.2 Tag frequency and transcript abundance 46 2.3.3 Mapping distribution of LongSAGE tags 46 2.3.4 Differential gene expression 48 2.3.5 Validation of changes in gene expression in response to androgen 49 2.3.6 Cell-type specificity of gene expression 50 2.3.7 In vivo changes in gene expression in response to androgen-deprivation 50 2.4 DISCUSSION 51 2.5 CONCLUSION 55 2.6 REFERENCES 73 CHAPTER III GENE EXPRESSION ASSOCIATED WITH iN VIVO PROGRESSION TO CASTRATION-RECURRENT PROSTATE CANCER 80 3.1 INTRODUCTION 80 3.2 MATERIALS AND METHODS 82 3.2.1 Cell culture 82 3.2.2 Animals 82 3.2.3 In vivo LNCaP Hollow Fibre model 83 3.2.4 RNA sample generation, processing, and quality control 83 V 3.2.5 Quantitative real-time polymerase chain reaction 83 3.2.6 LongSAGE library production and sequencing 84 3.2.7 Gene expression analysis 84 3.3 RESULTS 85 3.3.1 LongSAGE library construction and composition 85 3.3.2 LongSAGE library and tag clustering 86 3.3.3 Gene ontology enrichments analysis 87 3.3.4 Consistent differential gene expression associated with progression of prostate cancer... 88 3.4 DISCUSSION 91 3.4.1 Support for or against the proposed models of castration recurrent prostate cancer 92 3.5 CONCLUSION 101 3.6REFERENCES 114 CHAPTER IV EXPRESSION CHARACTERISTICS OF NOVEL BIOMARKERS OF PROSTATE CANCER 137 4.1 INTRODUCTION 137 4.2 MATERIALS AND METHODS 140 4.2.1 Cell culture 140 4.2.2 Clinical samples 140 4.2.3 RNA preparation for gene expression analysis 141 4.2.4 Relative quantitation of gene expression 141 4.2.5 Statistical analysis 142 4.3 RESULTS 142 4.3.1 Tissue-specificity of gene expression 142 4.3.2 Androgen regulation of gene expression 144 4.3.3 Characterization of gene expression in prostate cancer 145 4.4 DISCUSSION 146 4.5 CONCLUSION 152 4.6 REFERENCES 164 CHAPTER V CONCLUSION AND RECOMMENDATIONS FOR FUTURE WORK 172 5.1 CONCLUSION AND FUTURE DIRECTIONS 172 5.2 REFERENCES 176 vi HA SLIS[IVDIJIflT[3S3IH1IXfflNIIJV LIST OF TABLES CHAPTER I Table 1.1 Definition of TNM 19 Table 1.2 Androgen-regulated genes and biomarkers or therapeutic targets of prostate cancer identified by gene expression analyses 20 CHAPTER II Table 2.1 Primer sequences and amplification product sizes for candidate transcripts 56 Table 2.2 Composition of LongSAGE libraries 57 Table 2.3 Characteristics of L0ngSAGE tag frequency distribution 58 Table 2.4 L0ngSAGE tag mappings 59 Table 2.5 Number of tag types found to be significantly differentially expressed between R1881 and vehicle libraries 60 Table 2.6 LongSAGE tags corresponding to genes known to increase expression in response to androgen in LNCaP cells 61 Table 2.7 LongSAGE tags corresponding to genes known to decrease expression in response to androgen in LNCaP cells 63 Table 2.8 L0ngSAGE tags corresponding to genes not previously reported to increase expression in response to androgen in LNCaP cells 64 Table 2.9 LongSAGE tags corresponding to genes not previously reported to decrease expression in response to androgen in LNCaP cells 65 CHAPTER III Table 3.1 Composition of LongSAGE libraries 102 Table 3.2 Number of tag types consistently and significantly differentially expressed among all three biological replicates and between conditions 103 Table 3.3 Top five enrichments of functional categories of tags consistently and significantly differentially expressed among all three biological replicates and between stages of prostate cancer 104 Table 3.4 Gene expression trends of L0ngSAGE tags that consistently and significantly altered expression in CR prostate cancer 105 Table 3.5 Characteristics of genes with novel association to castration-recurrence in vivo.... 108 viii CHAPTER IV Table 4.1 Information on the samples used for laser microdissection and gene expression analysis, and the patient’s they were taken from 153 Table 4.2 Primer and probe sequences for qRT-PCR of candidate transcripts 154 Table 4.3 Review of expression trends of candidate genes 155 ix LIST OF FIGURES CHAPTER I Figure 1.1 Modified Gleason grading system 21 CHAPTER II Figure 2.1 Relationship between LongSAGE library compositions 66 Figure 2.2 Confidence intervals highlight expressed tag types with non-linear relationships between LongSAGE libraries 67 Figure 2.3 Androgen regulation of genes as measured by qRT-PCR 68 Figure 2.4 Differential expression of candidate genes in LNCaP, DU145, and PC-3 cells 71 Figure 2.5 Androgen regulation of genes in the LNCaP Hollow Fibre model of prostate cancer 72 CHAPTER III Figure 3.1 qRT-PCR analysis of KLK3 gene expression during hormonal progression of prostate cancer to castration-recurrence 109 Figure 3.2 Clustering of the nine LongSAGE libraries in a hierarchical tree 110 Figure 3.3 Ten K-means clusters are optimal to describe the expression trends present during progression of prostate cancer to castration-recurrence 111 Figure 3.4 K-means clustering of tag types with similar expression trends 112 Figure 3.5 Gene ontology enrichments of the five major expression trends 113 CHAPTER IV Figure 4.1 Laser microdissection of normal and tumour prostate tissue 156 Figure 4.2 Specificity of gene expression for human prostate cancer 157 Figure 4.3 Specificity of gene expression for normal prostate tissue 158 Figure 4.4 Regulation of gene expression by androgen 159 Figure 4.5 Candidate biomarkers are differentially expressed between normal and tumour prostate 161 Figure 4.6 Transcript expression in tumour tissue correlate with circulating levels of serum PSA in the patient 163 x LIST OF ABBREVIATIONS ACPP prostate acid phosphatase ACTH adrenocorticotropic hormone ADAM2 ADAM metallopeptidase domain 2 Akt protein kinase B AMD 1 adenosylmethionine decarboxylase 1 AQP3 aquaporin 3 AR androgen receptor AREs androgen response elements ARF 1 ADP-ribosylation factor 1 ARL6IP5 ADP-ribosylation like factor-6 interacting protein 5 AS androgen-sensitive BAD BCLXL/BCL2 associated death promoter BAX BCL2-associated X protein BCL-2 B-cell CLL/lymphoma 2 BCL2L1 BCL2-like 1 BNIP3 BCL2/adenovirus El B 1 9kDa interacting protein 3 BTG 1 anti-proliferative b-cell trans location gene 1 CAK cyclin-dependent activating kinase CAMK2 calcium/calmodulin-dependent kinase II CAMK2N1 calcium/calmodulin-dependent protein kinase II inhibitor 1 CCND 1 cyclin Dl CCNH cyclin H CCT2 chaperonin containing TCP 1 subunit 2 CD151 CD151 molecule CD44 CD44 molecule CDKN 1 A cyclin-dependent kinase inhibitor 1 A CDKN1B cyclin-dependent kinase inhibitor lB CDKN2A cyclin-dependent kinase inhibitor 2A cDNA complementary deoxyribonucleic acid CHGA chromogranin A CHGB chromogranin B xi ChIP-seq chromatin inimunoprecipitation-sequence CI confidence interval CR castration-recurrent CRIB cdc42/Rac interacting and binding CRPC castration-recurrent prostate cancer CT computed tomography CUEDC2 CUE-domain-containing-2 Cx castration DHCR24 24-dehydrocholesterol reductase DHRS7 dehydrogenase/reductase SDR-family member 7 DHT dihydrotestosterone DNA deoxyribonucleic acid DRE digital rectal exam ds double stranded DSTN destrin (actin depolymerizing factor) EASE expression analysis systematic explorer EBRT external beam radiation therapy EFNA5 ephrin-A5 EGF epidermal growth factor ELOVL5 elongation of long chain fatty acids family member 5 ENO2 gamma neuronal enolase 2 ERAD endoplasmic reticulum associated degradation ERK extracellular signal-regulated kinase ERRF 11 ERBB receptor feedback inhibitor EZH2 enhancer of zeste homolog 2 FBS fetal bovine serum FFPE formalin-fixed and paraffin-embedded FGFRL 1 fibroblast growth factor receptor like 1 FHIT fragile histidine triad gene FISH fluorescence in situ hybridization FLNA filamin A GAPDH glyceraldehyde-3-phosphate xii GAS5 growth arrest specific 5 on chromosome 1 GLO1 glyoxalase I GLUE glutamate-ammonia ligase GNB2L1 guanine nucleotide binding protein beta polypeptide 2 like 1 GO Gene Ontology GOLPH3 golgi phosphoprotein 3 GRE 10 growth factor receptor bound protein 10 GSTP- 1 glutathione-S-transferase P1 HES6 hairy and enhancer of split 6 HGF hepatocyte growth factor HGNC HUGO gene nomenclature committee HM 13 Histocompatibility (minor) 13 HMGB2 high mobility group box 2 HMGCSI 3-hydroxy-3-methylglutaryl-Coenzyme A synthase 1 HNl hematological and neurological expressed 1 HPA hypothalamus-pituitary-adrenal HPGD hydroxyprostaglandin dehydrogenase 1 5-(NAD) HSD 174 hydroxysteroid (17-beta) dehydrogenase 4 HSD17B3 hydroxysteroid (17-beta) dehydrogenase 3 HSD17B5 hydroxysteroid (17-beta) dehydrogenase 5 HSP9OB 1 heat shock protein 90 kDa beta member I IGF- 1 insulin-like growth factor 1L6 interleukin 6 JNKISAPK c-Jun N-terminal kinase/stress-activated protein kinase kb kilobases KEGG Kyoto encyclopedia of genes and genomes KLK3 kallikrein 3 = PSA KPNB 1 karyopherinlimportin beta 1 LCM laser capture microdissection EDT linker-derived tag LHRH leutinizing hormone releasing hormone LongSAGE long serial analysis of gene expression xiii MANEA mannosidase, endo alpha MAOA monoamine oxidase A MAPK mitogen activated protein kinase MARCKSL1 MARCKS-like 1 MDK midkine MGC mammalian gene collection MGMT O-6-methylguanine-DNA methyltransferase MNE mean normalized expression MRI magnetic resonance imaging MT-ND3 NADH ubiquinone oxidoreductase chain 3 NANS n-acetylneuraminic acid synthase NCAM2 neural cell adhesion molecule 2 NCOA1 nuclear receptor coactivator 1 NCOA2 nuclear receptor coactivator 2 NFkB nuclear factor kappa B NGFRAP 1 nerve growth factor receptor associated protein 1 NIPSNAP3A nipsnap homologue 3A NKX3-1 NK3 homeobox 1 NTS neurotensin ODC 1 omithine decarboxylase I OPRKI opioid receptor kappa 1 OR odds ratio PC prostate cancer PCGEM 1 prostate specific non-coding gene PCOTH prostate collagen triple helix PCR polymerase-chain reaction PGK 1 phosphoglycerate kinase 1 P13K phosphoinositide-3-kinase PTA proliferative inflammatory atrophy P11(3 CD phosphoinositide-3-kinase catalytic delta polypeptide PIN prostatic intra-epithelial neoplasia PKA protein kinase A xiv POP 1 transcript 100 kb from mRNA AK000023 POP2 transcript 4 kb from mRNA AL832227 POP3 transcript 50 kb from EST CF140309 POP4 transcript from the intron of TMEFF2 POP5 transcript from the intron of NCAM2; accession D0668384 POP6 transcript from the intron of FHIT POP7 transcript from the intron of TNFAIP8 POP8 transcript from the intron of EFNA5 POP9 transcript from the intron of DSTN POP 10 transcript from the intron of ADAM2; accession D0668396 POP1 1 transcript 87 kb from EST BG194644 POP 12 transcript from the intron of EST BQ226050 PPP2CB protein phosphatase 2 catalytic subunit beta isoform PPP2R 1 A protein phosphatase 2 regulatory subunit A alpha Pre-Cx pre-castration PROM 1 prominin 1 PSA prostate-specifc antigen = KLK3 PSCA prostate stem cell antigen PSMA prostate-specifc membrane antigen PSMA7 proteasome macropain subunit alpha type 7 PTEN phosphatase and tensin homolog PTGFR prostaglandin F receptor PTHrP parathyroid honnone-related protein QF quality factor qRT-PCR quantitative real-time polymerase chain reaction R1881 methyltrienolone; synthetic androgen RAD responsive to androgen-deprivation RAMP 1 receptor (calicitonin) activity modifiing protein 1 RB 1 retinoblastoma 1 RefSeq reference sequence RNA ribonucleic acid RT reverse transcriptase xv s.c. subcutaneous S100A1O S100 calcium binding protein AlO SAGE serial analysis of gene expression SBDS Shwachman-Bodian-Diamond syndrome SD standard deviation SDHA succinate dehydrogenase complex subunit A, flavoprotein 5H3 Src-homology-3 shortSAGE short serial analysis of gene expression SLC25A4 solute carrier family 25 member 4 SLC25A6 solute carrier family 25 member 6 SLC4IA1 solute carrier family 41, member 1 SOD1 superoxide dismutase 1 SPON2 spondin 2 SQLE squalene epoxidase Src v-src sarcoma ss single stranded SSH suppressive subtractive hybridization STEAP 1 six tnsmembrane epithelial antigen of the prostate 1 SVIP small VCP/p97-interacting protein TAOK3 tao kinase 3 TCP 1 T-complex 1 TMEFF2 transmembrane protein with EGF-like and two follistatin-like domains 2 TMEM3OA cell cycle control protein 50A TMEM66 transmembrane protein 66 TNFAIP8 tumor necrosis factor, alpha-induced protein 8 TNM tumour-node-metastasis TP53 tumour protein p53 TRPM8 transient receptor potential cation channel subfamily M member 8 VTAI vps2O-associated 1 Wnt Wingless tyrosine 3 monooxygenase/tryptophan 5 monooxygenase activation protein YWHAQ theta polypeptide xvi ACKNOWLEDGEMENTS I would like to thank my supervisor, Dr. Marianne Sadar, for being an inspiring mentor and role model. Her guidance, support, and dedication have been instrumental to my growth as a young scientist. To her, I will always be grateful. Members of Marianne’s laboratory and department have also been instrumental to my education; providing excellent technical help, feedback, and discussions that have contributed to the success of this thesis. In particular, I would like to acknowledge some current and past laboratory and department members: Nasrin (Rina) Mawj i, Dr. Richard Sobel, Dr. Steven Quayle, Dr. Mohammad Qadir, Jas Khattra, Yongjun Zhao, Jean Wang, Theresa L’Heureux, Iran Travakoli, Gang Wang, Helen Chiu, Janice Wortman, Dr. Joanne Johnson, Ainsley Clement, Dr. Simon Haile, Dr. Jae Kyung Myung, Dr. Katie Meehan, Dr. Barbara Comuzzi, and Heidi Hare. I would also like to thank: my Supervisory Committee, Dr. Wan Lam, Dr. Marco Marra, Dr. Robert Holt, and Dr. Cheryl Wellington for critical assessments, helpful comments, and advice; and my Comprehensive Exam Committee, Dr. Wan Lam, Dr. Yuzhuo Wang, Dr. Marcel Bally, and Dr. Miaden Korbelik, for admitting me to candidacy. My thesis would not be possible without the love and support of my friends and family. I appreciate my friends Elise LaRue, Kristy Garbutt, Julie Boylan, Marlo Small, and Tanya Dayman for their patience during this busy time of my life. I would like to thank my sisters, Crystal and Juliane Romanuik, for calling and visiting often throughout my studies. My grandparents, Sophie and Andy Nicholson, the late Peter Moleschi, and the late Ann and John Romanuik, were my inspiration to practice cancer research. I am indebted to my parents, Colleen and Dayle Romanuik, for always believing in me and encouraging me in my pursuits. Last, but not least, I would like to acknowledge my finance, Ryan Giraud, for the laughter and balance in my life. xvii DEDICATION To my grandparents: Sophie and Andy Nicholson & In memory of: Peter Moleschi, & Ann and John Romanuik xviii CO-AUTHORSHIP STATEMENT The experiments described within this thesis were conceived, designed, conducted, and analyzed by me, Tammy L. Romanuik, and Dr. Marianne D. Sadar. All manuscripts were written by me and Dr. Marianne D. Sadar. A number of additional people contributed to the work in each of the chapters as outlined below. CHAPTER II Mr. Gang Wang generated the total RNA for the construction of the SAGE libraries. Dr. Marco Marra provided support for the SAGE library construction with sequencing by Dr. Robert A. Holt. Dr. Steven J.M. Jones aided in the analysis of data. Technical assistance was provided by Jean Wang (animal experiments), and Angelique Schnerch (whole library mappings). CHAPTER III Dr. Marco Marra was responsible for SAGE library construction and sequencing. Olena Morozova (tag clustering) and Allen Delaney (library clustering) aided in bioinformatic analysis. Technical assistance was provided by Jean Wang (animal experiments). CHAPTER IV Dr. Takeshi Ueda provided the clinical samples with medical history and Dr. Thomas Thomson, performed the pathology review. Technical assistance was contributed by Theresa L’Heureux and Iran Travakoli (laser microdissection), Dr. Margaret Sutcliffe (pathology) and Lorena Barclay (tissue sections). Statistical support was provided by Dr. Nhu Le. xix CHAPTER I INTRODUCTION 1.1 THE NORMAL AND DISEASED PROSTATE 1.1.1 Mortality, morbidity, and risk factors of prostate cancer Prostate cancer is the most common malignancy in Canadian men, and the third leading cause of cancer death1.In Canada, the lifetime probability of developing prostate cancer is one-in-seven, while the lifetime probability of dying from prostate cancer is one-in-twenty-seven’. In 2008, it is estimated that 24,700 Canadian men will be diagnosed with prostate cancer, and another 4,300 will die from this disease’. Worldwide, there were 679,000 new cases of prostate cancer in 20022. Prostate cancer is a disease of the aged. The majority of new cases are diagnosed in men aged 60-69 (39%), while the majority of deaths by prostate cancer occur in men aged greater than 80 years (53%; estimated statistics for 2008)’. In the United States, the incidence of prostate cancer in African Americans is 70% higher than in Caucasians, and Caucasians have higher morbidity rates than Asians2.In contrast, autopsy studies indicate that the prevalence of prostate cancer is relatively uniform among men from different countries and races3. In addition to age and race, family history and diet (intake of fat, vitamin D) are also risk factors for prostate cancer3. 1.1.2 Normal prostate Function and anatomy The prostate is a walnut-sized gland located just below the bladder, adjacent to the rectum, and flanked by the seminal vesicles. It surrounds the urethra and acts as a sphincter to regulate the release of urine from the bladder4.However, the primary function of the prostate is to produce and store secretions. Constituents of prostate secretions include: acid phosphatase, albumin, cholesterol, fibrinolytic enzymes, plasminogen activator, spermidine, and proteolytic enzymes such as prostate-specific antigen (PSA)57.The smooth muscle cells of the prostate contract to propel the expulsion of semen during ejaculation7. Prostate secretions are released into the lumen of prostatic ducts by luminal secretory epithelial cells. The ducts of the prostate are comprised of luminal secretory epithelial cells and rare neuroendocrine cells. These cells are located along the basal epithelial cells and basal lamina, and are surrounded by stromal smooth muscle cells8.Markers for these cell types include cytokeratins 8 and 18 (luminal epithelial cells), chromogranin A and serotonin (neuroendocrine cells), keratin 5 and p63 (basal epithelial cells), and smooth muscle actin (stromal cells)8’9 Stromal smooth muscle cells and luminal epithelial cells express the androgen receptor’°. Regulation by the androgen pathway The development and maintenance of the prostate are regulated by such as testosterone, and the more potent androgen dihydrotestosterone (DHT). The majority of androgens are produced by the testes (95%), while the remaining 5% are synthesized by the adrenal glands’2.Without testicular androgens, it is believed the concentration of circulating adrenal androgens is insufficient for prostate growth and survival’3.Androgens are derivatives of cholesterol and easily pass through the lipid-rich bilayer of the plasma membrane and bind to intracellular androgen receptor (AR). The AR protein is 110 kDa (919 amino acids). It contains a carboxy-terminal ligand binding domain, amino-terminal transactivation domain, and central deoxyribonucleic acid (DNA) binding domain in the hinge region complete with a nuclear localization signal. The AR gene is located on the X chromosome qll-12 with 8 coding exons spanning 2.7 kb’4. In the cytosol, the ligand binding domain of the AR is accessible to androgen due to the chaperone activity of heat-shock proteins . Heat shock proteins also prevent pre-mature nuclear localization of the AR’6, and degradation of the AR by the proteasome’7.Upon binding ligand, AR becomes phosphorylated, changes conformation, and is released from the heat-shock proteins’6.AR homo-dimerizes in a head-to-tail configuration’8homes to the nucleus’9via its nuclear localization signal, and binds to androgen response elements (AREs) of DNA20.The AR dimer interacts with a palindrome DNA sequence nGriACnnimnGTnCn, where ‘n’ is any nucleoticle2126.Once bound to DNA, the AR recruits co-activators (e.g., Tip6O)27 or co repressors (e.g., NCoR)28 to regulate gene expression. Direct or indirect targets of the androgen signalling axis (i.e., pathway) have functions in cell growth29,survival’3,differentiation”, and secretion30. However, the complete spectrum of genomic targets of the androgen-axis, (i.e., 2 genes regulated by androgens), still remain to be discovered. Identification of the genes whose expressions are regulated by androgens is required to elucidate the mechanisms underlying the growth and survival of normal and cancer cells. 1.1.3 Cytopathology and pathophysiology PIA Focal atrophy is a term used to describe the reduced cytoplasm of secretory epithelial cells. Simple or post-atrophic hyperplasia is described as proliferative inflammatory atrophy (PTA) if it is accompanied by inflammation (lymphocyte penetration) and proliferation (Ki67 staining)31. Some groups have observed a transition from PTA to adenocarcinoma32,and PIA to high grade prostatic intra-epithelial neoplasia33,suggesting that PTA may be a precursor lesion to prostate cancer. PIN Prostatic intra-epithelial neoplasia (PIN) is suggested by Bostwick to be a precursor lesion of prostate cancer34.PIN is closely associated with prostate cancer and featured by the proliferation of secretory epithelial cells34.This contrasts with normal prostatic ducts and acini that contain single layers of secretory epithelial cells. Furthermore, the secretory epithelial cells of PIN exhibit enlarged nuclei and nucleolus34.PiN is described as either high grade or low grade. In low grade PIN, the basal cell layer is intact, there is some cell stratification, but a small nucleolus. In contrast, high grade PIN has a partially disrupted basal cell layer, cell stratification, and a prominent nucleolus . Prostate cancer Prostate cancer is a disease of the luminal epithelial cells. Prostate cancer is completely devoid of basal epithelial cells as indicated by the absence of p63 staining, thereby distinguishing it from P1N3436.Prostate cancer is invasive and has the ability to metastasize to other tissues, while PiN does not. In prostate cancer, cell morphology and tissue architecture are altered (see Section 1.2.6 Gleason grading). Prostate cancer cells are slow growing. Therefore, at the time of detection, it is likely the patient has been living with prostate cancer for many years (e.g., 15-20 3 years). The implementation of PSA screening in 1986 (see Section 1.2.2 PSA) has resulted in most prostate cancer being detected while it is small and localized37.Of the men whose cancer metastasizes, as much as 83% of cases involve bone38.Palliative treatment for metastatic prostate cancer includes androgen-deprivation therapy. However, eventually metastatic prostate cancer becomes fatal. 1.2 DETECTION, DIAGNOSIS, AND MONITORING OF PROSTATE CANCER 1.2.1 DRE Most prostate cancer is detected using a combination of the digital rectal exam (DRE) and PSA testing39.DRE involves manual palpitation of the prostate through the walls of the rectum to gauge the size, firmness, and shape of the prostate. This is possible due to the proximal placement of the prostate to the rectum. One limitation to DRE is that the physician can only feel the surface of the prostate that faces the rectum, although the majority of prostate cancers do arise in this region4.Moreover, the test is subjective to the physician performing it. Approximately 25-50% of the cases of prostate cancer detected by DRE are no longer localized39.When used alone, DRE testing has not reduced mortality from prostate cancer40. 1.2.2 PSA Prostate-specific antigen (PSA), also known as human kallikrein 3 (KLK3), is a member of the kallikrein family of genes41.All fifteen kallikrein genes are located in a cluster on chromosome 19q133-4. The expression of PSA is restricted to the prostate42 and to humans. PSA gene expression is regulated by androgens4345 with at least three androgen response elements located in the promoter and enhancer regions46.The PSA gene was first cloned in l989. PSA protein is a serine protease5synthesized by the prostate that functions to liquefy seminal fluid6.The concentration of PSA in semen is between 0.5 and 2 mg/mL48.In healthy men, a small fraction of this PSA leaks from the prostate into the bloodstream. However, with prostate adenocarcinoma there is a breakdown of the basement membrane, loss of the basal cell layer, lack of cellular polarity, and collapsed architecture of prostatic ducts, which results in significantly more PSA released into the bloodstream49.Serum PSA levels exceeding 4 ng/mL 4 are suggestive of cancer and warrant further investigation and referral to an urologist. Since 1986 when PSA was first approved for detection of prostate cancer, prostate cancers are now detected at an earlier stage while they are still small and localized. However, with PSA screening many clinically insignificant cancers that will not impact the mortality of the patient are also detected50.Better prognostic markers are needed to distinguish those cancers requiring radical treatment from indolent disease. PSA is moderately specific (93%) and poorly sensitive (24%) as a biomarker for prostate cancer detection51.In addition to carcinoma of the prostate, PSA is expressed in normal prostate tissue and benign prostatic hypertrophy, and levels of circulating PSA are affected by age, race, and body mass49.Levels of serum PSA correlate with tumour volume52 and are used to monitor prostate cancer response to therapy. However, exceptions have been reported where PSA and response to therapy can be discordant49’53 Novel biomarkers for early and late stage prostate cancer are also needed. 1.2.3 Biopsy When prostate cancer is suspected, a needle biopsy is used to confirm and aid diagnoses. The number of biopsies taken from patients can vary between six to fourteen. While greater than six biopsies do increase detection rates, studies show that prognosis is not affected by the number of biopsies, and therefore the clinical management of prostate cancer remains unchanged55. Pathology is used in conjunction with other clinical tests to determine the aggressiveness of the disease and clinical stage of prostate cancer (see Sections 1.2.5 TNM staging and 1.2.6 Gleason grading). Biopsy is often coupled with a transrectal ultrasound to guide biopsy needles. 1.2.4 Other clinical tests A computed tomography (CT) scan or magnetic resonance imaging (MRI) may be used to identif,’ and monitor metastatic lesions in high risk patients. These radiographic imaging systems are not sensitive enough to detect metastases in asymptomatic patients56.CT entails the construction of a digital three dimensional image assembled from multiple X-rays. The majority (—9l%) of prostate cancer metastases to the bone are osteoblastic (bone forming)57;CT scans are useful in visualising these bone lesions. In contrast to CT, MRI does not use ionizing radiation and provides better contrast for soft tissues, making it particularly well suited to visualizing tumours58. 5 1.2.5 TNM staging The Tumour-Node-Metastasis (TNM) system for staging cancers is used to describe the spread of the disease. The most recent guidelines for TNM staging were published in 2002 by Greene and summarized by Chang6°as it pertains to prostate cancer. Briefly, the ‘T’ refers to the extent of the primary tumour in the prostate, and if applicable, also the invasion of the seminal vesicles or other nearby structures; the ‘N’ describes the involvement of the tumour within the lymph nodes; and the ‘M’ describes the extent or location of distant metastases such as bone or non- regional lymph nodes. The detailed definition of the TNM staging system is presented in Table i.i. Importantly, the TNM staging system has been divided into clinical and pathologic classification. Clinical staging refers to the staging that was determined before therapeutic intervention and is based on results obtained from DRE, transrectal ultrasound, and Gleason grading information of biopsy tissue. Serum PSA levels and imaging results (if available) may also be incorporated into determining the clinical stage of prostate cancer. Clinical staging does not change even if subsequent pathologic staging information is conflicting. Pathologic staging is based on the histological information obtained from tissue removed surgically, that can include assessment of regional lymph node, bladder, and rectal involvement60. 1.2.6 Gleason grading The Gleason grading system was first described in 196661, and modified in 1967, 1974, and The Gleason grading system is used by pathologists to describe the degree of differentiation of prostate tissue retrieved with biopsy, prostatectomy, or occasionally by transurethral resection of the prostate. Five Gleason patterns (grades) describe the tissue architecture, with pattern five representing the least structured tissue. The sum of the two most prevalent Gleason grades is referred to as the Gleason score. The definitions of Gleason patterns have transformed somewhat over the years to accommodate modern advances in prostate cancer diagnosis65.In 2005 the International Society of Urological Pathology came to a consensus on an improved Gleason grading system65’6 The Gleason grading system as it is used today is depicted and summarized in Figure 1.166. 6 The Gleason score is prognostic6‘and strongly associated with aggressive prostate cancer. When combined with information of TNM stage and pre-treatment levels of serum PSA, Gleason score can segregate patients who are at low, intermediate, and high risk of PSA failure following first- line treatment for localized cancer. Low risk patients have Gleason scores 6 and a TNM stage Ti c/T2a with a PSA level 10 ng/mL; intermediate risk patients have Gleason scores of 7 and a TNM stage T2b with a PSA level >10 ng/mL, but 20 ng/mL; high risk patients have Gleason scores 8 or a TNM stage T2c or a PSA level > 20 ng/mL67. 1.3 TREATMENT OF PROSTATE CANCER 1.3.1 Localized Patients with prostate cancer localized within the prostatic capsule are eligible for active surveillance, radical prostatectomy, or radiotherapy. Active surveillance may be a good choice for men with a life expectancy less than 10 years (those over the age of 65) and prostate cancer that is low in Gleason grade and volume. In active surveillance the progression of the cancer is closely monitored using tests such as the DRE, measurement of serum PSA levels, and biopsy sampling. Within eight years, 34% of men who choose active surveillance will develop metastatic disease68. Radical prostatectomy is the surgical removal of the entire prostate and sufficient nearby tissue to ensure negative surgical margins. In 1887, McGill of the Leeds General Infirmary was reportedly the first surgeon to completely remove a prostate for the treatment of prostatic disease69.Today, radical prostatectomies are performed either at retropubic or perineal incision sites, openly or laproscopically, and with or without the use of a robot70.Possible complications of surgical intervention include infection, incontinence, and impotence71.Radical prostatectomy is an excellent treatment for prostate cancer and only 15% of men may experience biochemical recurrence at eight years following surgery (defined as rising PSA levels)72. Brachytherapy is a treatment option for low risk patients that involves the implantation of radioactive pellets (iodine’25 or palladium’°3)into the prostate. With the help of CT images and transrectal ultrasound, the radioactive seeds are guided into place. Brachytherapy is considered 7 comparable to prostatectomy at treating prostate cancer. However, with brachytherapy only 7% of men experience biochemical recurrence at eight years of follow-up73. External beam radiation therapy (EBRT) differs from brachytherapy in that the radiation is administered externally. To minimize the damage to surrounding normal tissue, typically lower doses of radiation are used compared with brachytherapy. Neoadjuvant therapy with chemotherapeutic agents or androgen-deprivation (more on this below) has been proposed to improve the cytotoxicity of EBRT on prostate cancer cells74. 1.3.2 Metastatic There is no cure for metastatic prostate cancer, although palliative treatment is available in the form of androgen-deprivation therapy. In the late 19th century it was observed that orchiectomy (removal of the testes) causes atrophy of the prostate gland in dogs75.The castration of men for treatment of an enlarged prostate soon followed76,but it wasn’t until the mid 20th century that Huggins and Hodges showed that the growth and survival of the prostate requires androgens, the male sex hormones produced by the testes’3. Several treatment options constitute androgen-deprivation therapy. In addition to orchiectomy, available drugs for hormone manipulation include antiandrogens (e.g., flutamide, bicalutamide, and nilutamide), 5 ‘aipha-reductase inhibitors (e.g., finasteride), leutinizing hormone releasing hormone agonists (e.g., leuprolide, goserelin, and buserelin), and ketoconazole71.An alternative approach to androgen-deprivation is intermittent androgen suppression which involves cycling androgen-deprivation therapy between breaks in treatment. Following androgen-deprivation, restoration of physiological androgen levels cause tumour cells to differentiate77.It is assumed that this treatment regime may delay prostate cancer progression. 1.4 CASTRATION-RECURRENCE The majority (-85%) of the men who receive androgen-deprivation therapy initially exhibit a positive response because their tumours are androgen-stimulated71.Unfortunately, all men who receive androgen-deprivation therapy display biochemical recurrence within l 8 months78,and 8 succumb to the disease within another —l 8 months7880.The mechanisms underlying progression to castration-recurrence are unknown; however, there is evidence to suggest that the AR is still active in this stage of the disease81.Proposed mechanisms underlying castration-recurrent prostate cancer that involve the AR include AR hypersensitivity to low androgen concentrations due to gene amplification82’83, changes in AR co-regulators84’5 intraprostatic de novo synthesis of androgen86or metabolism of AR ligands from residual adrenal androgens87’8 AR promiscuity of ligand specificity due to mutations89,and activation of the AR by alternative signalling initiated by growth factors, cytokines, or kinases (does not require the ligand binding domain)9092. Levels of serum PSA typically correlate with tumour volume during progression52.However, this association is less reliable in castration-recurrent prostate cancer49.Therefore, there is a need to identif’ novel diagnostic and prognostic biomarkers of castration-recurrent prostate cancer. 1.5 MODELS OF PROSTATE CANCER 1.5.1 Cell lines Tissue culture provides the flexibility to test conditions that would be challenging to achieve in vivo, and the ability to have strict control of variables and systematically isolate factors to test a hypothesis. Primary cultures of prostate cancer cells have proven difficult to maintain partly due to their slow rate of proliferation. The establishment of the first prostate cancer cell lines was in the late 1970s and early 1980s from prostate cancer metastases9395.These cell lines, LNCaP, PC- 3, and DU145, remain the most commonly used prostate cancer cell lines96. LNCaP cells were isolated from lymph node metastases from a 50-year-old Caucasian man with prostate cancer ‘. LNCaP cells express the AR and PSA43,and are sensitive to androgens93. DU145 cells were isolated from a brain metastasis of a 69-year-old Caucasian man with prostate cancer and lymphocytic leukemia95,while PC-3 cells were isolated from a lumbar metastasis of a 69-year-old Caucasian man. Unlike LNCaP cells, both DU145 and PC-3 cells are androgen insensitive because they do not express AR and do not respond to androgens9496.Other prostate cancer cell lines are reviewed by Sobel and Sadar96’98 9 1.5.2 In vivo models In vivo models are a powerful tool to investigate prostate cancer under physiological conditions. The Balb/C Nu/Nu mouse is the most common background used to host human xenografts99. Rejection of the foreign human xenograft is curbed because these mice are immuno compromised; lacking a thymus due to a mutation in the Foxnl gene’°°. Balb/C Nu/Nu mice have been used to create a xenograft model with the LNCaP cell line facilitated by addition of a reconstituted basement membrane product, Matrigel101.LNCaP cells form tumours at the site of injection, but rarely metastasize96.Intriguingly, the LNCaP xenograft model progresses to castration-recurrence in castrated hosts, mimicking the hormonal progression of prostate cancer observed in patients’°2.Hormonal progression may be monitored with serum PSA testing from tail vein blood samples, and levels correlate with tumour volume in this model’°2.Due to significant vascularization, one problematic feature of the LNCaP xenograft model is that it is not possible to separate the tumour tissue from the host tissue sufficiently for down-stream molecular analysis. As a solution to this contamination problem, the LNCaP Hollow Fibre model was developed103. In the LNCaP Hollow Fibre model, LNCaP cells suspended in media and Matrigel are injected into hollow fibres, sealed by heat on each end of the fibre, and implanted subcutaneously onto the back of immuno-compromised mice. This compartmentalization, using the fibre walls as a barrier, physically separates the prostate cancer cells from the host cells. Importantly, the diffusion of proteins, metabolites, oxygen, and other factors into or out of the fibre is not impeded. Unlike the LNCaP xenograft model, the LNCaP Hollow Fibre model can be used to harvest pure populations of prostate cancer cells suitable for molecular analysis (Chapters II and III) from the same host mouse over a time-course experiment. The retrieval of hollow fibres involves only minor surgery thereby allowing retrieval of serial sets of samples. The LNCaP Hollow Fibre model mimics hormonal progression of prostate cancer, as measured by serum PSA levels’03 and facilitates molecular analysis of samples during different stages of progression. 10 1.5.3 Human tissue Sources Cell lines and in vivo models play important roles in basic research. However, samples isolated from human tissue represent the ultimate setting for investigation. For the study of localized, androgen-sensitive prostate cancer, human prostate samples retrieved at the time of prostatectomy are ideal. Both nonnal tissue and tumour tissue may be retrieved from the same patient. However, localized prostate cancer is often multifocal104 and normal tissue adjacent to tumour tissue may be altered compared to normal cells from a benign prostate. For the study of advanced, metastatic, and/or castration-recurrent prostate cancer, human prostate samples are not readily available. Treatment offered to patients with these stages of cancer is limited to palliative radiation or androgen-deprivation therapy. Treatment options do not include surgery because it would be ineffective at treating disseminated disease. Therefore, ethically it would be inappropriate to subject patients to surgery that is not of benefit to them. Moreover, the majority of metastasic prostate cancer is located in the bone. Samples of late-stage prostate cancer are generally from locally advanced disease obtained from rapid autopsy’°5. Preservation Human tissue obtained from patients at surgery or rapid autopsy will quickly decay and become unsuitable for laboratory research without sufficient preservation. The most abundant source of preserved tissue is formalin-fixed and paraffin-embedded (FFPE). These specimens are located in tissue repositories of hospitals for archival purposes. Because the tissues are stored for several years, they are accompanied by detailed clinical information including patient outcomes. Due to the abundance of samples and the wealth of patient information, these tissues are ideal for retrospective molecular studies’°6.One limitation to using archival FFPE tissue for molecular studies is the difficulty in obtaining patient consent. Much success has been achieved with the immunohistochemical analysis of FFPE tissues, and some success with DNA based assays (in situ hybridization and sodium bisulfite/hydroquinone DNA modification and polymerase chain reaction (PCR) with methylation specific primers). In contrast, RNA analysis using FFPE tissue has been challenging because ribonucleic acid (RNA) is sheared by the mechanical stresses of paraffin embedding, and formalin fixation causes RNA methylol modifications and cross-links 11 protein amino groups to RNA nucleotides’°7.For these reasons, frozen tissue is optimal for tissue preservation and isolation of RNA if transcript analysis is desired. Regrettably, there are very few tissue repositories that keep archival tissue at sub zero temperatures. Therefore, only prospective studies that anticipate analysis would be impeded by the FFPE preservation method, set-aside fresh tissue for freezing. It is easier to obtain consent in prospective studies, however, it may take up to 10 years to obtain follow-up medical history of patients. Laser microdissection Prostate cancer is heterogeneous38.Often times tissue collected at the time of prostatectomy contains both normal tissue and multifocal carcinoma’04.These foci may have arisen from independent tumours or represent the spread of the primary tumour108.Laser microdissection is a powerful method to isolate the cells of interest from a mosaic of other cell types, and is an essential step that precedes molecular analysis that is sensitive to contamination. While laser microdissection of prostate cancer tissue will yield samples that primarily contain neoplastic luminal epithelial cells, adjacent normal laser microdissected tissue samples will be a mixture of luminal and basal epithelial cells. Early microdissection techniques were imprecise and were only suitable for the basic manipulation of large specimens. They could not be utilized for the separation of specific cell types. As the technique evolved, manual dissection tools such as micromanipulators’°9allowed for greater control of dissections, but were impractical, tedious, and inefficient. In the early 1970s, researchers began experimenting with lasers as a means to isolate cells. It was not until 1996 that modern day ‘laser capture microdissection (LCM)’ was first described by Emmert Buck et al”° as developed by the National Institute of Health. Briefly, cell(s) of interest may be selected for and cut out with a laser and isolated using a thermosensitive film. Upon heating with the laser, the film will adhere to cells adjacent to it allowing selected cells to be physically separated from unselected tissue. Upon reconstitution in a DNA, RNA, or protein buffer, selected cells gently dissociate from the film and can be manipulated as desired”°. An improvement on LCM is available from Molecular Machines & Industries. In this improvement, cells are captured onto the lids of Eppendorf tubes via a membrane intermediate. This feature 12 eliminates the step of transferring the film with adherent cells to a tube for further manipulation, and thus reduces the probability of introducing contamination. 1.6 GENE EXPRESSION 1.6.1 Gene expression analyses of prostate cancer Biomarkers and therapeutic targets of prostate cancer have been identified by differential gene expression analyses by comparing samples that represent: 1) tumour versus normal tissue” 1130; 2) high versus low Gleason grade’31’3;3) progressive versus latent cancer’33’134. 4) metastatic versus localized cancer’21’135-138. and 5) castration-recurrent versus androgen-sensitive 38, 121, 130, 132-134, 137, 139-160 Select genes representing biomarkers or therapeutic targets of prostate cancer are presented in Table 1.2. Genes regulated by androgens have also been identified by differential gene expression analyses of prostate cancer cells43’86 161-177 Select androgen responsive genes are presented in Table 1.2. The TMPRSS2-ETS family of gene fusions, for example, were discovered in prostate cancer’78 using cancer outlier profile analysis to identif’ over-expressed genes from a subset of microarray studies of the Oncomine database’79.Fusions between the androgen-regulated gene TMPRSS2 and the ETS gene family of transcription factors result in the androgen regulation of transcription factors’78.Several lines of evidence suggest that the TMPRSS2-ETS family of gene fusions andlor accompanying deletions are associated with clinicopathological indicators’80such as biochemical progression following prostatectomy’81’182 and metastatic hormone refractory prostate cancer’83’ The TMPRSS2-ETS fusions are not specific to prostate cancer, however, as they are also found in PiN lesions. Therefore, TMPRSS2-ETS fusions are an early event in the development of prostate cancer. 13 1.6.2 Methods to evaluate gene expression PCR In 1983 one of the most influential biochemical and molecular biology techniques of the 20th century was conceived, polymerase chain reaction (PCR). The first report of PCR was published in Science in 1985185, for which Kary Mullis won the 1993 Nobel Prize in Chemistry. In PCR, double-stranded (ds)DNA is melted to single stands (ss) to generate templates for synthesis. Next, primers complementary to the target sequence anneal to the template. Finally, DNA synthesis is extended along the length of the template. This three-step process is repeated for several cycles. PCR amplifies DNA exponentially when there are excess reagents. In this exponential phase there is a direct relationship between the number of amplicons at any given PCR cycle and the amount of the starting templates from the first PCR cycle. As the reaction proceeds to completion, reagent starvation causes the relationship between the concentration of DNA and the cycle number to become linear and eventually plateau. The protocol for PCR can be modified and used to quantitate gene expression. Quantitative real-time (qRT)-PCR’86’9°is very sensitive at detecting differences in transcript expression because it monitors product amplification throughout all phases of PCR, including the exponential phase. qRT-PCR methodologies differ by their detection chemistries. The two most popular methods of qRT-PCR are described here. Sybr green is a cyanine dye that fluoresces when it binds the minor groove of dsDNA. The fluorescence is proportional to the concentration of DNA and has a broad dynamic range that spans over 6 orders of 19I Sybr green is used for the detection of amplicons in qRT PCR’88.The main feature of sybr green qRT-PCR is that it can detect products amplified from any primer set and even non-specific products. In contrast to the sybr green method, TaqMan qRT-PCR’89’190 detects only the transcripts of interest. The specificity lies in the probe, which is complementary to the transcript at a location between the forward and reverse primers. The probe is equipped with a 5’ fluorophore (e.g., FAM or TET) and 3’quencher (e.g., BHQ-1). When the fluorophore and quencher are in close proximity, the quencher absorbs the fluorescence of the fluorophore, preventing detection by the qRT-PCR machine. However, when DNA is amplified, the probe is broken apart by the 5’ nuclease activity of Taq DNA polymerase. When the fluorophore and quencher are released, the emission spectra from the fluorophore is no longer extinguished. Therefore, the amount of fluorescence is proportional to the number of 14 specific PCR products. Advantages to this method include the specificity of detecting only transcripts of interest, as well as the possibility to multiplex reactions in single wells when unique fluorophores are used As described, qRT-PCR is an excellent method for quantitation of transcript expression. However, for whole transcriptome expression studies, high through-put platforms like complementary (c)DNA’92or oligonucleotide microarrays’93are more suitable. Miroarrays Microarrays consist of hundreds to thousands of probes that represent individual annotated or predicted transcripts. On a cDNA array, the cDNA probes are spotted onto a solid surface such as glass. This is in contrast to oligonucleotide arrays in which the probes are synthesized directly on the chip using photolithography (e.g., Aff’metrix Gene Chip)’93.Typically, cDNA samples to be compared for relative transcript expression on cDNA arrays are labelled with different fluorophores (e.g., Cy5 or Cy3), mixed together, and co-hybridized to fixed probes on the slide. The degree of hybridization (and fluorescence) is related to the amount of transcript in the sample. Importantly, differential changes in gene expression that are identified with this method are relative. Oligonucleotide arrays are different, in that expression is normalized to standards to generate absolute expression values that may be compared across studies. One drawback to both cDNA and oligonucleotide microarrays is that they require a priori knowledge of the sequences of transcripts to design the probes. Therefore, the study is limited to the probes contained on the chip. Subtractive Hybridization Suppressive subtractive hybridization (SSH)’94 is a technique that enriches for rare differentially expressed transcripts using the principles of suppressive PCR. SSH does not require a priori knowledge of transcript sequences. The method consists of two steps, the normalization step that equalizes the abundance of cDNAs within the test population, and the subtraction step that eliminates the common sequences between the test and the control populations. One limitation of SSH is that the technique is not quantitative. 15 SAGE Serial Analysis of Gene Expression (SAGE)’95’196 offers advantages over the other methods described thus far for evaluating transcript levels. Compared to qRT-PCR, SAGE is more high- throughput, permitting the analysis of potentially all the polyadenylated transcripts of the transcriptome. The transcriptome is the set of all messenger RNA (mRNA) molecules, or transcripts, produced in one or many cells. In contrast to cDNA microarrays, SAGE transcript expression information is in absolute units (i.e., tag counts), so it may be compared to other experiments performed at a different time and in a different laboratory. Dissimilarly to oligonucleotide microarrays, SAGE does not require a priori knowledge of transcript sequence information, and is not limited to a certain number of transcripts. Finally, unlike SSH, SAGE provides information regarding the degree of differential expression between transcripts. SAGE is based on the concept that a short nucleotide sequence called a tag is almost always sufficient to map to the transcriptome. Furthermore, the number of times a tag is observed is related to the expression level of the transcript it represents. Briefly, the steps involved in the method of SAGE are: 1) restriction enzyme digestion of cDNA by the Nla III anchoring enzyme to generate CATG overhangs; 2) separation of the sample into two equal parts, and ligation of cDNA to unique adapters that bind complementary to the Nla III cut-site; 3) restriction enzyme digestion of cDNA by the Bsm j195 (or Mme Tin L0ngSAGE)’96tagging enzyme that recognizes and binds to a sequence in the adapters and cuts the eDNA 14 (i.e., in shortSAGE)’95or 21 (i.e., in L0ngSAGE)’96basepairs downstream to create tags; 4) the two unique adapter-bound tag species are mixed and ligated together to create ditags; 5) ditags are amplified by PCR with primers specific to the unique adapters; 6) ditags are released from the adapters by restriction enzyme digestion with Nla III anchoring enzyme; 7) ditags are concatenated into a long chain for cloning into a bacterial vector and propagation; 8) clones are sequenced; 9) tags are counted; and lastly 10) the tag sequences are mapped to the transcriptome to reveal their identity. The two methods of SAGE mentioned above are called short’95 and long’96 in reference to the length of the tag that is generated. The longer the tag length, the greater the probability the tag will map unambiguously to the transcriptome. This is an advantage over shorter tags, because ambiguous mappings are non-informative. 16 1.7 RESEARCH HYPOTHESIS AND OBJECTIVES 1.7.1 Background summary Prostate cancer is the most common malignancy in Canadian men, and the third leading cause of cancer death. Androgens are important for the development and maintenance of the prostate gland and continue to play a central role in all stages of prostate cancer. The complete spectrum of genomic targets of androgen signalling have yet to be elucidated and will aid in the understanding of the mechanisms involved in prostate biology and pathology. The androgen regulated gene PSA, is used as a biomarker for the screening and monitoring of prostate cancer. PSA testing has resulted in the detection and over-treatment of clinically insignificant disease. New prognostic markers are urgently needed to delineate which cancers will progress to incurable late-stage cancer, known as castration-recurrent prostate cancer. Gene expression signatures of prognostic markers may be enriched in castration-recurrence, thereby providing justification to search for them in this context. The mechanisms underlying progression of prostate cancer to castration-recurrence are unknown. Gene expression profiling will yield support for or against the proposed models of castration-recurrent prostate cancer. 1.7.2 Hypothesis and objectives The over-arching hypothesis is that the application of L0ngSAGE will catalogue gene expression signatures that are indicative of the mechanisms underlying the growth and progression of prostate cancer, and reveal potential biomarkers of prostate cancer. The objectives of this thesis were to determine the regulation of the transcriptome by the androgen-axis in prostate cancer, identify the gene expression profile associated with in vivo progression of prostate cancer to castration-recurrence, and delineate the expression characteristics of novel biomarkers of prostate cancer. These objectives were met with the following Specific aims: 1. Utilize L0ngSAGE to identify transcripts differentially expressed in LNCaP human prostate cancer cells maintained in vitro and treated with, or without androgen. Use qRT-PCR to validate the expression trends in the in vivo LNCaP Hollow Fibre model following androgen-deprivation. This aim will reveal genomic targets of the androgen signalling axis (Chapter II). 17 2. Employ LongSAGE to identify transcripts differentially expressed in the in vivo LNCaP Hollow Fibre model during hormonal progression to castration-recurrent prostate cancer. This aim will reveal gene expression signatures representative of castration-recurrence, and substantiate proposed models of castration-recurrent prostate cancer (Chapter III). 3. Evaluate gene expression profiles of candidate biomarkers of castration-recurrent prostate cancer using qRT-PCR to determine their regulation by androgen, their specificity to the prostate and cancer, and levels of expression in clinical samples obtained by prostatectomy. This work will characterize candidate genes for their potential to be biomarkers of prostate cancer (Chapter IV). 18 Table 1,1 Definition of TNIvI Primary Tumor (T) Clinical TX Primary tumor cannot be assessed TO No evidence of primary tumor TI Clinically inapparent tumor neither palpable nor visible by imaging Tla Tumor incidental histologic finding in 5% or less of tissue resected TI b Tumor incidental histologic finding in more than 5% of tissue resected Tlc Tumor identified by needle biopsy (e.g., because of elevated PSA) T2 Tumor confined within prostate* T2a Tumor involves one-half of one lobe or less T2b Tumor involves more than one-half of one lobe but not both lobes T2c Tumor involves both lobes T3 Tumor extends through the prostate capsule** T3a Extracapsular extension (unilateral or bilateral) T3b Tumor invades seminal vesicle(s) T4 Tumor is fixed or invades adjacent structures other than seminal vesicles: bladder neck, external sphinchter, rectum, levator, muscles, and/or pelvic wall *Note: Tumor found in one or both lobes by needle biopsy, but not palpable or reliably visible by imaging, is classified as Tic. **Note: Invasion into the prostatic apex or into (but not beyond) the prostatic capsule is classified not as T3 but as T2. Pathologic (pT) pT2* Organ confined pT2a Unilateral, involving one-half of one lobe or less pT2b Unilateral, involving more than one-half of one lobe but not both lobes pT2c Bilateral disease pT3 Extraprostatic extension pT3a Extraprostatic extension** pT3b Seminal vesicle invasion pT4 Invasion of bladder, rectum *Note: There is no pathologic TI classification. *sNote: Positive surgical margin should be indicated by an RI descriptor (residual microscropic disease). Regional Lymph Nodes (N) Clinical NX Regional lymph nodes were not assessed NO No regional lymph node metastasis NI Metastasis in regional lymph node(s) Pathologic pNX Regional nodes not sampled pNO No positive regional nodes pN I Metastases in regional node(s) Distant Metastasis (M)* MX Distant metastasis cannot be assessed (not evaluated by any modality) MO No distant metastasis Ml Distant metastasis Mla Non-regional lymph node(s) Mlb Bone(s) Mic Other site(s) with or without bone disease *Note: When more than one site of metastasis is present, the most advanced category is used. pMIc is most advanced. Abbreviations: TNM, tumor-node-metastasis; PSA, prostate-specific antigen. Used with the permission of the American Joint Committee on Cancer (AJCC), Chicago, Illinois. The original source for this material is the AJCC Cancer Staging Manual, Sixth Edition (2002) published by Springer Science and Business Media LLC, www.sprinaerlink.com. 19 Table 1.2 Androgen-regulated genes and biomarkers or therapeutic targets of prostate cancer identified by gene expression analyses Gene Classification Reference(s) Therapeutic Targets AKR)C3 Gene expression signature is different between castration-recurrent and androgen- 142 stimulated PC AR 142, 144, 159 FGFR1 152 1L6 143 MMP9 142 NKX3.J 142, 144, 155. 159 NR4AJ “ 144 PJK3CD 140 PPP3CA 140, 153 TMEFF2 153, 155 GSK-3 beta Gene expression signature is different between metastatic and localized PC 121 NR4AJ 137 CTSD Gene expression signature is different between early and late stage PC 134 HSDI 7B4 Gene expression signature is different between high and low Gleason grade 131 HSDI 7B4 Gene expression signature is different between tumour and normal 1 14, 120, 127 Biomarkers KLK3 Prognostic: predicts response to therapy 49 Hepsin Prognostic: predicts relapse 1 13, 124 PIM-1 124 AMA CR Prognostic: predicts progression to castration-recurrent PC 121, 139, 155 AZGPI ‘ 133 Chromogranin A “ 130, 132 Iv!UCJ 133, 156 TMPRSS2-ETS ‘ 178, 181, 183, 184 TRPM8 ‘ 142, 144, 150 EZH2 Prognostic: predicts progression to metastatic PC 121, 135 KLK3 Monitoring: is indicative of response to therapy 49 PSMA Monitoring: is indicative of metastases 138 AMA CR Diagnostic: differentiates between tumour and normal 114, 116, 117, 119, 127, 131, 148 GSTP-I “ 128, 129 KLK3 ‘ 49 PCA3 ‘ 123 Androgen-regulated genes AC?? 43 B2M ‘ 171 CAMK2 ‘ 166 FKBP5 “ 163, 170 KLK3 ‘ 43 NKX3.1 ‘ 174 PDEF “ 175 PMEPAJ 176 PSMA ‘ 177 TMPRSS2 ‘ 173 = same as above PC = prostate cancer 20 Figure 1.1 Modified Gleason grading system. Pattern 1: Circumscribed nodule of closely packed but separate, uniform, rounded to oval, medium-sized acini (larger glands than pattern 3). Pattern 2: Like pattern I, fairly circumscribed, yet at the edge of the tumor nodule, there may be minimal infiltration. Glands are more loosely arranged and not quite as unifrom as Gleason pattern 1. Pattern 3: Discrete glandular units; typically smaller glands than seen in Gleason pattern I or 2; infiltrates in and among nonneoplastic prostate acini; marked variation in size and shape; smoothly circumscribed small cribriform nodules of tumor. Pattern 4: Fused microacinar glands; ill-defined glands with poorly formed glandular lumina; large cribriform glands; cribriform glands with an irregular border: hypernephrornatoid. Pattern 5: Essentially no glandular differentiation, composed of solid sheets, cords, or single cells; comedocarcinoma with central necrosis surrounded by papillary, cribriform, or solid masses. Used with the permission of Lippincott Williams & Wilkins (Wolters Kluwer Health). Epstein, J.I., Allsbrook, W.C., Amin, MB., Egerad, L.L. Update on the Gleason grading system for prostate cancer. Results from an international consensus conference of urologic pathologists, AdvAnat Pathol 2006, 13:57-59 21 1.8 REFERENCES 1. Canadian Cancer Society/National Cancer Institute of Canada C: Cancer statistics 2008. Edited by Toronto, Canada, 2008 2. Parkin DM, Bray, F., Ferlay , J., Pisani, P.: Global Cancer Statistics, 2002, CA Cancer J Clin 2005, 55:74-108 3. Bostwick DG, Burke HB, Djakiéw D, Euling 5, Ho SM, Landolph J, Morrison H, Sonawane B, Shifflett T, Waters DJ, Timms B: Human prostate cancer risk factors, Cancer 2004, 101:2371-2490 4. McNeal JE: The zonal anatomy of the prostate, Prostate 1981, 2:35-49 5. Watt KW, Lee P1, MTimkulu T, Chan WP, Loor R: Human prostate-specific antigen: structural and functional similarity with serine proteases, Proc Natl Acad Sci U S A 1986, 83 :3 166-3 170 6. Lilja H, Oldbring J, Rannevik G, Laurell CB: Seminal vesicle-secreted proteins and their reactions during gelation and liquefaction of human semen, J Clin Invest 1987, 80:28 1- 285 7. Kumar VL, Majumder PK: Prostate gland: structure, functions and regulation, Tnt Urol Nephrol 1995, 27:231-243 8. Marker PC, Donjacour AA, Dahiya R, Cunha GR: Hormonal, cellular, and molecular control of prostatic development, Dev Biol 2003, 253:165-174 9. van Leenders GJ, Gage WR, Hicks JL, van Balken B, Aalders TW, Schalken IA, De Marzo AM: Intermediate cells in human prostate epithelium are enriched in proliferative inflammatory atrophy, Am J Pathol 2003, 162:1529-1537 10. Long RM, Morrissey C, Fitzpatrick JM, Watson RW: Prostate epithelial cell differentiation and its relevance to the understanding of prostate cancer therapies, Clin Sci (Lond) 2005, 108:1-11 11. Cunha GR, Ricke W, Thomson A, Marker PC, Risbridger G, Hayward SW, Wang YZ, Donjacour AA, Kurita T: Hormonal, cellular, and molecular regulation of normal and neoplastic prostatic development, J Steroid Biochem Mol Biol 2004, 92:22 1-236 12. Geller J: Rationale for blockade of adrenal as well as testicular androgens in the treatment of advanced prostate cancer, Semin Oncol 1985, 12:28-3 5 22 13. Huggins C, Hodges C: Studies on prostatic cancer: The effect of castration, of estrogen and of androgen injection on serum phosphatases in metastatic carcinoma of the prostate, Cancer Res 1941, 293-297 14. Gnanapragasam VJ, Robson CN, Leung HY, Neal DE: Androgen receptor signalling in the prostate, BJU mt 2000, 86:1001-1013 15. Fang Y, Fliss AE, Robins DM, Caplan AJ: Hsp9O regulates androgen receptor hormone binding affinity in vivo, J Biol Chem 1996, 271:28697-28702 16. Veldscholte J, Berrevoets CA, Zegers ND, van der Kwast TH, Grootegoed JA, Mulder E: Hormone-induced dissociation of the androgen receptor-heat-shock protein complex: use of a new monoclonal antibody to distinguish transformed from nontransformed receptors, Biochemistry 1992, 31:7422-7430 17. Vanaja DK, Mitchell SH, Toft DO, Young CY: Effect of geldanamycin on androgen receptor function and stability, Cell Stress Chaperones 2002, 7:55-64 18. Wong CI, Zhou ZX, Sar M, Wilson EM: Steroid requirement for androgen receptor dimerization and DNA binding. Modulation by intramolecular interactions between the NH2-terminal and steroid-binding domains, J Biol Chem 1993, 268:19004-19012 19. Georget V, Lobaccaro TM, Terouanne B, Mangeat P, Nicolas JC, Sultan C: Trafficking of the androgen receptor in living cells with fused green fluorescent protein-androgen receptor, Mol Cell Endocrinol 1997, 129:17-26 20. Ham J, Thomson A, Needham M, Webb P, Parker M: Characterization of response elements for androgens, glucocorticoids and progestins in mouse mammary tumour virus, Nucleic Acids Res 1988, 16:5263-5276 21. Claessens F, Verrijdt G, Schoenmakers E, Haelens A, Peeters B, Verhoeven G, Rombauts W: Selective DNA binding by the androgen receptor as a mechanism for hormone- specific gene regulation, J Steroid Biochem Mo! Biol 2001, 76:23-30 22. Rennie PS, Bruchovsky N, Leco KJ, Sheppard PC, McQueen SA, Cheng H, Snoek R, Hamel A, Bock ME, MacDonald BS, et a!.: Characterization of two cis-acting DNA elements involved in the androgen regulation of the probasin gene, Mo! Endocrinol 1993, 7:23-36 23. Riegman PH, Vlietstra RJ, van der Korput JA, Brinkmann AO, Trapman J: The promoter of the prostate-specific antigen gene contains a functional androgen responsive element, Mol Endocrinol 1991, 5:1921-1930 23 24. Roche PJ, Hoare SA, Parker MG: A consensus DNA-binding site for the androgen receptor, Mol Endocrinol 1992, 6:2229-2235 25. Verrijdt G, Haelens A, Claessens F: Selective DNA recognition by the androgen receptor as a mechanism for hormone-specific regulation of gene expression, Mol Genet Metab 2003, 78:175-185 26. Nelson CC, Hendy SC, Shukin RJ, Cheng H, Bruchovsky N, Koop BF, Rennie PS: Determinants of DNA sequence specificity of the androgen, progesterone, and glucocorticoid receptors: evidence for differential steroid receptor response elements, Mol Endocrinol 1999, 13:2090-2107 27. Brady ME, Ozanne DM, Gaughan L, Waite I, Cook S, Neal DE, Robson CN: Tip6O is a nuclear hormone receptor coactivator, J Biol Chem 1999, 274:17599-17604 28. Cheng 5, Brzostek S, Lee SR, Hollenberg AN, Balk SP: Inhibition of the dihydrotestosterone-activated androgen receptor by nuclear receptor corepressor, Mol Endocrinol 2002, 16:1492-1501 29. Balk SP, Knudsen KE: AR, the cell cycle, and prostate cancer, Nucl Recept Signal 2008, 6:eOOl 30. Berns EM, de Boer W, Mulder E: Androgen-dependent growth regulation of and release of specific protein(s) by the androgen receptor containing human prostate tumor cell line LNCaP, Prostate 1986, 9:247-259 31. De Marzo AM: The pathology of human prostatic atrophy and inflammation Edited by Chung LW, Isaacs, W.B., and J.W.Simons. Totowa, NJ, Humana Press Inc., 2007, 33-48 32. Franks LM: Atrophy and hyperplasia in the prostate proper, J. Pathol. Bacteriol. 1954, 68 :6 17-62 1 33. De Marzo AM, Marchi VL, Epstein JI, Nelson WG: Proliferative inflammatory atrophy of the prostate: implications for prostatic carcinogenesis, Am J Pathol 1999, 155:1985- 1992 34. Bostwick DG, Brawer MK: Prostatic intra-epithelial neoplasia and early invasion in prostate cancer, Cancer 1987, 59:788-794 35. Shin HJ, Ro JY: Prostatic intraepithelial neoplasia: a potential precursor lesion of prostatic adenocarcinoma, Yonsei Med J 1995, 36:215-231 36. Parsons JK, Gage WR, Nelson WG, De Marzo AM: p63 protein expression is rare in prostate adenocarcinoma: implications for cancer diagnosis and carcinogenesis, Urology 2001, 58:619-624 24 37. Ung JO, Richie JP, Chen MH, Renshaw AA, D’Amico AV: Evolution of the presentation and pathologic and biochemical outcomes after radical prostatectomy for patients with clinically localized prostate cancer diagnosed during the PSA era, Urology 2002, 60:458- 463 38. Shah RB, Mehra R, Chinnaiyan AM, Shen R, Ghosh D, Zhou M, Macvicar GR, Varambally 5, Harwood J, Bismar TA, Kim R, Rubin MA, Pienta KJ: Androgen independent prostate cancer is a heterogeneous group of diseases: lessons from a rapid autopsy program, Cancer Res 2004, 64:9209-92 16 39. Small EJ: Prostate cancer: who to screen, and what the results mean, Geriatrics 1993, 48 :28-30, 3 5-28 40. Krahn MD, Mahoney JE, Eckman MH, Trachtenberg J, Pauker SG, Detsky AS: Screening for prostate cancer. A decision analytic view, Jama 1994, 272:773-780 41. Yousef GM, Diamandis EP: Human tissue kallikreins: a new enzymatic cascade pathway?, Biol Chem 2002, 383:1045-1057 42. Wang MC, Valenzuela LA, Murphy GP, Chu TM: Purification of a human prostate specific antigen, Invest Urol 1979, 17:159-163 43. Henttu P, Liao SS, Vihko P: Androgens up-regulate the human prostate-specific antigen messenger ribonucleic acid (mRNA), but down-regulate the prostatic acid phosphatase mRNA in the LNCaP cell line, Endocrinology 1992, 130:766-772 44. Henttu P. Vihko P: Steroids inversely affect the biosynthesis and secretion of human prostatic acid phosphatase and prostate-specific antigen in the LNCaP cell line, J Steroid Biochem Mol Biol 1992, 41:349-360 45. Montgomery BT, Young CY, Bilhartz DL, Andrews PE, Prescott JL, Thompson NF, Tindall DJ: Hormonal regulation of prostate-specific antigen (PSA) glycoprotein in the human prostatic adenocarcinoma cell line, LNCaP, Prostate 1992, 2 1:63-73 46. Shang Y, Myers M, Brown M: Formation of the androgen receptor transcription complex, Mol Cell 2002, 9:601-610 47. Lundwall A: Characterization of the gene for prostate-specific antigen, a human glandular kallikrein, Biochem Biophys Res Commun 1989, 161:1151-1159 48. Stenman UH: Prostate-specific antigen, clinical use and staging: an overview, Br J Urol 1997, 79 Suppl 1:53-60 49. Lilja H, Ulmert D, Vickers AJ: Prostate-specific antigen and prostate cancer: prediction, detection and monitoring, Nat Rev Cancer 2008, 8:268-278 25 50. McGregor M, Hanley JA, Boivin JF, McLean RG: Screening for prostate cancer: estimating the magnitude of overdetection, Cmaj 1998, 159:1368-1372 51. Thompson TM, Ankerst DP, Chi C, Goodman PJ, Tangen CM, Lucia MS, Feng Z, Parnes HL, Coltman CA, Jr.: Assessing prostate cancer risk: results from the Prostate Cancer Prevention Trial, J Natl Cancer Inst 2006, 98:529-534 52. Stamey TA, Yang N, Hay AR, McNeal JE, Freiha FS, Redwine E: Prostate-specific antigen as a serum marker for adenocarcinoma of the prostate, N EngI J Med 1987, 3 17:909-9 16 53. Nishio R, Furuya Y, Nagakawa 0, Fuse H: Metastatic prostate cancer with normal level of serum prostate-specific antigen, mt Urol Nephrol 2003, 35:189-192 54. Leibovici D, Spiess PE, Agarwal PK, Tu SM, Pettaway CA, Hitzhusen K, Millikan RE, Pisters LL: Prostate cancer progression in the presence of undetectable or low serum prostate-specific antigen level, Cancer 2007, 109:198-204 55. Grossklaus DJ, Coffey CS, Shappell SB, Jack GS, Cookson MS: Prediction of tumour volume and pathological stage in radical prostatectomy specimens is not improved by taking more prostate needle-biopsy cores, BJU Tnt 2001, 88:722-726 56. Flanigan RC, McKay TC, Olson M, Shankey TV, Pyle J, Waters WB: Limited efficacy of preoperative computed tomographic scanning for the evaluation of lymph node metastasis in patients before radical prostatectomy, Urology 1996, 48:428-432 57. Berruti A, Dogliotti L, Gorzegno G, Torta M, Tampellini M, Tucci M, Cerutti S, Frezet MM, Stivanello M, Sacchetto G, Angeli A: Differential patterns of bone turnover in relation to bone pain and disease extent in bone in cancer patients with skeletal metastases, Clin Chem 1999, 45:1240-1247 58. Bottomley PA: In vivo tumor discrimination in a rat by proton nuclear magnetic resonance imaging, Cancer Res 1979, 39:468-470 59. Greene FL, Page, D.L., Fleming, T.D., et al.: Cancer Staging Manual. Edited by New York, N.Y., Springer-Verlag, 2002, p 60. Chang SS, Amin MB: Utilizing the tumor-node-metastasis staging for prostate cancer: the sixth edition, 2002, CA Cancer J Clin 2008, 5 8:54-59 61. Gleason DF: Classification of prostatic carcinomas, Cancer Chemother Rep 1966, 50: 125-128 62. Mellinger GT, Gleason D, Bailar J, 3rd: The histology and prognosis of prostatic cancer, JUrol 1967, 97:331-337 26 63. Gleason DF, Mellinger GT: Prediction of prognosis for prostatic adenocarcinoma by combined histological grading and clinical staging, J Urol 1974, 111:58-64 64. Gleason DF: Histological grading and clinical staging of prostatic carcinoma. Edited by Tannenbaum M. Philadelphia, Lea & Feibiger, 1977, 17 1-198 65. Epstein JI, Allsbrook WC, Jr., Amin MB, Egevad LL: The 2005 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma, Am J Surg Pathol 2005, 29:1228-1242 66. Epstein JI, Allsbrook WC, Jr., Amin MB, Egevad LL: Update on the Gleason grading system for prostate cancer: results of an international consensus conference of urologic pathologists, Adv Anat Pathol 2006, 13:57-59 67. DAmico AV, Whittington R, Malkowicz SB, Schultz D, Blank K, Broderick GA, Tomaszewski JE, Renshaw AA, Kaplan I, Beard CJ, Wein A: Biochemical outcome after radical prostatectomy, external beam radiation therapy, or interstitial radiation therapy for clinically localized prostate cancer, Jama 1998, 280:969-974 68. Klotz L: Active surveillance for prostate cancer: for whom?, J Clin Oncol 2005, 23:8 165- 8169 69. Watson FS: The Operative Treatment of the Hypertrophied Prostate, Ann Surg 1889, 9:1- 27 70. Walsh PC, DeWeese TL, Eisenberger MA: Clinical practice. Localized prostate cancer, N Engl J Med 2007, 357:2696-2705 71. Goldenberg SL, and I.M. Thompson: All you need to know to take an active part in your treatment. Edited by Glegg C. Vancouver, BC, Gordon Soules Book Publishers Ltd., 2001, 269 p 72. Pound CR, Partin AW, Eisenberger MA, Chan DW, Pearson JD, Walsh PC: Natural history of progression after PSA elevation following radical prostatectomy, Jama 1999, 281: 1591-1597 73. Zelefsky MJ, Kuban DA, Levy LB, Potters L, Beyer DC, Blasko JC, Moran BJ, Ciezki JP, Zietman AL, Pisansky TM, Elshaikh M, Horwitz EM: Multi-institutional analysis of long-term outcome for stages TI -T2 prostate cancer treated with permanent seed implantation, Int J Radiat Oncol Biol Phys 2007, 67:327-333 74. Torres-Roca JF: The role of external-beam radiation therapy in the treatment of clinically localized prostate cancer, Cancer Control 2006, 13: 188-193 27 75. White JW: II. The Present Position of the Surgery of the Hypertrophied Prostate, Ann Surg 1893, 18:152-188 76. Fremont-Smith F: A case of obstructive hypertrophy of the prostate treated by castration, Ann Surg 1894, 52-55 77. Gleave M, Goldenberg SL, Bruchovsky N, Rennie P: Intermittent androgen suppression for prostate cancer: rationale and clinical experience, Prostate Cancer Prostatic Dis 1998, 1:289-296 78. Crawford ED, Eisenberger MA, McLeocl DG, Spaulding JT, Benson R, Don FA, Blumenstein BA, Davis MA, Goodman PJ: A controlled trial of leuprolide with and without flutamide in prostatic carcinoma, N Engl J Med 1989, 321:419-424 79. Petrylak DP, Tangen CM, Hussain MH, Lara PN, Jr., Jones JA, Taplin ME, Burch PA, Berry D, Moinpour C, Kohli M, Benson MC, Small EJ, Raghavan D, Crawford ED: Docetaxel and estramustine compared with mitoxantrone and prednisone for advanced refractory prostate cancer, N Engl J Med 2004, 351:1513-1520 80. Tannock IF, de Wit R, Berry WR, Horti J, Pluzanska A, Chi KN, Oudard S, Theodore C, James ND, Turesson I, Rosenthal MA, Eisenberger MA: Docetaxel plus prednisone or mitoxantrone plus prednisone for advanced prostate cancer, N Engl J Med 2004, 35 1:1502-1512 81. Feldman BJ, Feldman D: The development of androgen-independent prostate cancer, Nat Rev Cancer 2001, 1:34-45 82. Visakorpi T, Hyytinen E, Koivisto P, Tanner M, Keinanen R, Palmberg C, Palotie A, Tammela T, Isola J, Kallioniemi OP: In vivo amplification of the androgen receptor gene and progression of human prostate cancer, Nat Genet 1995, 9:40 1-406 83. Ford OH, 3rd, Gregory CW, Kim D, Smitherman AB, Mohier JL: Androgen receptor gene amplification and protein expression in recurrent prostate cancer, J Urol 2003, 170:1817-1821 84. Gregory CW, He B, Johnson RT, Ford OH, Mohler IL, French FS, Wilson EM: A mechanism for androgen receptor-mediated prostate cancer recurrence after androgen deprivation therapy, Cancer Res 2001, 61:4315-4319 85. Chmelar R, Buchanan G, Need EF, Tilley W, Greenberg NM: Androgen receptor coregulators and their involvement in the development and progression of prostate cancer, Tnt J Cancer 2007, 120:719-733 28 86. Holzbeierlein J, Lal P, LaTulippe E, Smith A, Satagopan J, Zhang L, Ryan C, Smith S, Scher H, Scardino P, Reuter V, Gerald WL: Gene expression analysis of human prostate carcinoma during hormonal therapy identifies androgen-responsive genes and mechanisms of therapy resistance, Am J Pathol 2004, 164:217-227 87. Mostaghel EA, Nelson PS: Intracrine androgen metabolism in prostate cancer progression: mechanisms of castration resistance and therapeutic implications, Best Pract Res Clin Endocrinol Metab 2008, 22:243-258 88. Labrie F: Adrenal androgens and intracrinology, Semin Reprod Med 2004, 22:299-309 89. Veldscholte J, Berrevoets CA, Ris-Stalpers C, Kuiper GG, Jenster G, Trapman J, Brinlcmann AO, Mulder E: The androgen receptor in LNCaP cells contains a mutation in the ligand binding domain which affects steroid binding characteristics and response to antiandrogens, J Steroid Biochem Mol Biol 1992, 41:665-669 90. Culig Z, Hobisch A, Cronauer MV, Radmayr C, Trapman J, Hittmair A, Bartsch G, Klocker H: Androgen receptor activation in prostatic tumor cell lines by insulin-like growth factor-I, keratinocyte growth factor, and epidermal growth factor, Cancer Res 1994, 54:5474-5478 91. Hobisch A, Eder IE, Putz T, Horninger W, Bartsch G, Klocker H, Culig Z: Interleukin-6 regulates prostate-specific protein expression in prostate carcinoma cells by activation of the androgen receptor, Cancer Res 1998, 5 8:4640-4645 92. Nazareth LV, Weigel NL: Activation of the human androgen receptor through a protein kinase A signaling pathway, J Biol Chem 1996, 271:19900-19907 93. Horoszewicz JS, Leong SS, Kawinski E, Karr JP, Rosenthal H, Chu TM, Mirand EA, Murphy GP: LNCaP model of human prostatic carcinoma, Cancer Res 1983, 43:1809- 1818 94. Kaighn ME, Narayan KS, Ohnuki Y, Lechner JF, Jones LW: Establishment and characterization of a human prostatic carcinoma cell line (PC-3), Invest Urol 1979, 17: 16-23 95. Stone KR, Mickey DD, Wunderli H, Mickey GH, Paulson DF: Isolation of a human prostate carcinoma cell line (DU 145), Int J Cancer 1978, 21:274-281 96. Sobel RE, Sadar MD: Cell lines used in prostate cancer research: a compendium of old and new lines--part 1, J Urol 2005, 173:342-359 29 97. Horoszewicz JS, Leong SS, Chu TM, Wajsman ZL, Friedman M, Papsidero L, Kim U, Chai LS, Kakati S, Arya SK, Sandberg AA: The LNCaP cell line--a new model for studies on human prostatic carcinoma, Prog Clin Biol Res 1980, 37:115-132 98. Sobel RE, Sadar MD: Cell lines used in prostate cancer research: a compendium of old and new lines--part 2, J Urol 2005, 173:360-372 99. Corey E, and R.L. Vessella: Xenograft models of human prostate cancer. Edited by Chung LW, Isaacs, W.B., and J.W.Simons. Totowa, NJ, Humana Press Inc., 2007, 3-32 100. Isaacson IHaC, B.M.: Mouse news letter, report 1962, 27-3 1 101. Gleave M, Hsieh JT, Gao CA, von Eschenbach AC, Chung LW: Acceleration of human prostate cancer growth in vivo by factors produced by prostate and bone fibroblasts, Cancer Res 1991, 51:3753-3761 102. Gleave ME, Hsieh JT, Wu HC, von Eschenbach AC, Chung LW: Serum prostate specific antigen levels in mice bearing human prostate LNCaP tumors are determined by tumor volume and endocrine and growth factors, Cancer Res 1992, 52:1598-1605 103. Sadar MD, Akopian VA, Beraldi E: Characterization of a new in vivo hollow fiber model for the study of progression of prostate cancer to androgen independence, Mol Cancer Ther 2002, 1:629-637 104. Meiers I, Waters DJ, Bostwick DG: Preoperative prediction of multifocal prostate cancer and application of focal therapy: review 2007, Urology 2007, 70:3-8 105. Rubin MA, Putzi M, Mucci N, Smith DC, Wojno K, Korenchuk S, Pienta KJ: Rapid (“war1’)autopsy study for procurement of metastatic prostate cancer, Clin Cancer Res 2000, 6:1038-1045 106. Datta MWaAAK-B: Tissue microarrays in prostate cancer research. Edited by Chung LW, Isaacs, W.B., and J.W.Simons. Totowa, NJ, Humana Press Inc., 2007, 49-62 107. Masuda N, Ohnishi T, Kawamoto S, Monden M, Okubo K: Analysis of chemical modification of RNA from formalin-fixed samples and optimization of molecular biology applications for such samples, Nucleic Acids Res 1999, 27 :4436-4443 108. Cheng L, Song SY, Pretlow TG, Abdul-Karim FW, Kung HJ, Dawson DV, Park WS, Moon YW, Tsai ML, Linehan WM, Emmert-Buck MR, Liotta LA, Zhuang Z: Evidence of independent origin of multiple tumors from patients with prostate cancer, J Natl Cancer Inst 1998, 90:233-237 109. Chambers R: A Micromanipulator for the Isolation of Bacteria and the Dissection of Cells, J Bacteriol 1923, 8:1-5 30 110. Emmert-Buck MR, Bonner RF, Smith PD, Chuaqui RF, Zhuang Z, Goldstein SR, Weiss RA, Liotta LA: Laser capture microdissection, Science 1996, 274:998-100 1 111. Waghray A, Schober M, Feroze F, Yao F, Virgin J, Chen YQ: Identification of differentially expressed genes by serial analysis of gene expression in human prostate cancer, Cancer Res 2001, 61:4283-4286 112. Xu J, Stolk JA, Zhang X, Silva SJ, Houghton RL, Matsumura M, Vedvick TS, Leslie KB, Badaro R, Reed SG: Identification of differentially expressed genes in human prostate cancer using subtraction and microarray, Cancer Res 2000, 60:1677-1682 113. Luo J, Duggan DJ, Chen Y, Sauvageot J, Ewing CM, Bittner ML, Trent JM, Isaacs WB: Human prostate cancer and benign prostatic hyperplasia: molecular dissection by gene expression profiling, Cancer Res 2001, 61:4683-4688 114. Ernst T, Hergenhahn M, Kenzelmann M, Cohen CD, Bonrouhi M, Weninger A, Kiaren R, Grone EF, Wiesel M, Gudemann C, Kuster J, Schott W, Staehler G, Kretzler M, Holistein M, Grone HJ: Decrease and gain of gene expression are equally discriminatory markers for prostate carcinoma: a gene expression analysis on total and microdissected prostate tissue, Am J Pathol 2002, 160:2169-2180 115. Chaib H, Cockrell EK, Rubin MA, Macoska JA: Profiling and verification of gene expression patterns in normal and malignant human prostate tissues by cDNA microarray analysis, Neoplasia 2001, 3:43-52 116. Ashida S, Nakagawa H, Katagiri T, Furihata M, Iiizumi M, Anazawa Y, Tsunoda T, Takata R, Kasahara K, Miki T, Fujioka T, Shuin T, Nakamura Y: Molecular features of the transition from prostatic intraepithelial neoplasia (PIN) to prostate cancer: genome wide gene-expression profiles of prostate cancers and PINs, Cancer Res 2004, 64:5963- 5972 117. Rhodes DR, Barrette TR, Rubin MA, Ghosh D, Chinnaiyan AM: Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer, Cancer Res 2002, 62:4427-4433 118. Latil A, Bieche I, Chene L, Laurendeau I, Berthon P, Cussenot 0, Vidaud M: Gene expression profiling in clinically localized prostate cancer: a four-gene expression model predicts clinical behavior, Clin Cancer Res 2003, 9:5477-5485 119. Li HR, Wang-Rodriguez J, Nair TM, Yeakley TM, Kwon YS, Bibikova M, Zheng C, Zhou L, Zhang K, Downs T, Fu XD, Fan TB: Two-dimensional transcriptome profiling: 31 identification of messenger RNA isoform signatures in prostate cancer from archived paraffin-embedded cancer specimens, Cancer Res 2006, 66:4079-4088 120. Stamey TA, Warrington JA, Caldwell MC, Chen Z, Fan Z, Mahadevappa M, McNeaI JE, Nolley R, Zhang Z: Molecular genetic profiling of Gleason grade 4/5 prostate cancers compared to benign prostatic hyperplasia, J Urol 2001, 166:2171-2177 121. Varambally 5, Yu J, Laxman B, Rhodes DR, Mehra R, Tomlins SA, Shah RB, Chandran U, Monzon FA, Becich MJ, Wei JT, Pienta KJ, Ghosh D, Rubin MA, Chinnaiyan AM: Integrative genomic and proteomic analysis of prostate cancer reveals signatures of metastatic progression, Cancer Cell 2005, 8:393-406 122. Chetcuti A, Margan 5, Mann S, Russell P, Handelsman D, Rogers J, Dong Q: Identification of differentially expressed genes in organ-confined prostate cancer by gene expression array, Prostate 2001, 47:132-140 123. GroskopfJ, Aubin SM, Deras IL, Blase A, Bodrug 5, Clark C, Brentano 5, Mathis J, Pham J, Meyer T, Cass M, Hodge P, Macairan ML, Marks LS, Rittenhouse H: APTIMA PCA3 molecular urine test: development of a method to aid in the diagnosis of prostate cancer, Clin Chem 2006, 52:1089-1095 124. Dhanasekaran SM, Barrette TR, Ghosh D, Shah R, Varambally 5, Kurachi K, Pienta KJ, Rubin MA, Chinnaiyan AM: Delineation of prognostic biomarkers in prostate cancer, Nature 2001, 4 12:822-826 125. Magee JA, Araki T, Patil 5, Ehrig T, True L, Humphrey PA, Catalona WJ, Watson MA, Milbrandt J: Expression profiling reveals hepsin overexpression in prostate cancer, Cancer Res 2001, 61:5692-5696 126. Bull JH, Ellison G, Patel A, Muir G, Walker M, Underwood M, Khan F, Paskins L: Identification of potential diagnostic markers of prostate cancer and prostatic intraepithelial neoplasia using cDNA microarray, Br J Cancer 2001, 84:1512-1519 127. Luo JH, Yu YP, Cieply K, Lin F, Deflavia P, Dhir R, Finkeistein 5, Michalopoulos G, Becich M: Gene expression analysis of prostate cancers, Mol Carcinog 2002, 33 :25-35 128. Goessl C, Muller M, Heicappell R, Krause H, Straub B, Schrader M, Miller K: DNA based detection of prostate cancer in urine after prostatic massage, Urology 2001, 58:335- 338 129. Hoque MO, Topaloglu 0, Begum S, Henrique R, Rosenbaum E, Van Criekinge W, Westra WH, Sidransky D: Quantitative methylation-specific polymerase chain reaction 32 gene patterns in urine sediment distinguish prostate cancer patients from control subjects, J Clin Oncol 2005, 23:6569-6575 130. Febbo PG, Sellers WR: Use of expression analysis to predict outcome after radical prostatectomy, J Urol 2003, 170:S11-19; discussion S19-20 131. True L, Coleman I, Hawley S, Huang CY, Gifford D, Coleman R, Beer TM, Gelmann E, Datta M, Mostaghel E, Knudsen B, Lange P. Vessella R, Lin D, Hood L, Nelson PS: A molecular correlate to the Gleason grading system for prostate adenocarcinoma, Proc Nati Acad Sci U S A 2006, 103:10991-10996 132. Singh D, Febbo PG, Ross K, Jackson DG, Manola J, Ladd C, Tamayo P, Renshaw AA, D’Amico AV, Richie JP, Lander ES, Loda M, KantoffPW, Golub TR, Sellers WR: Gene expression correlates of clinical prostate cancer behavior, Cancer Cell 2002, 1:203-209 133. Lapointe J, Li C, Higgins JP, van de Rijn M, Bair E, Montgomery K, Ferrari M, Egevad L, Rayford W, Bergerheim U, Ekman P, DeMarzo AM, Tibshirani R, Botstein D, Brown P0, Brooks JD, Pollack JR: Gene expression profiling identifies clinically relevant subtypes of prostate cancer, Proc Nati Acad Sci USA 2004, 101:811-816 134. Morgenbesser SD, McLaren RP, Richards B, Zhang M, Akmaev VR, Winter SF, Mineva ND, Kaplan-Lefko PJ, Foster BA, Cook BP, Dufault MR, Cao X, Wang CJ, Teicher BA, Klinger KW, Greenberg NM, Madden SL: Identification of genes potentially involved in the acquisition of androgen-independent and metastatic tumor growth in an autochthonous genetically engineered mouse prostate cancer model, Prostate 2007, 67:83-106 135. LaTulippe E, Satagopan J, Smith A, Scher H, Scardino P, Reuter V, Gerald WL: Comprehensive gene expression analysis of prostate cancer reveals distinct transcriptional programs associated with metastatic disease, Cancer Res 2002, 62:4499- 4506 136. Ramaswamy 5, Ross KN, Lander ES, Golub TR: A molecular signature of metastasis in primary solid tumors, Nat Genet 2003, 33:49-54 137. Chandran UR, Ma C, Dhir R, Bisceglia M, Lyons-Weiler M, Liang W, Michalopoulos G, Becich M, Monzon FA: Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process, BMC Cancer 2007, 7:64 138. Horoszewicz JS, Kawinski E, Murphy GP: Monoclonal antibodies to a new antigenic marker in epithelial prostatic cells and serum of prostatic cancer patients, Anticancer Res 1987, 7:927-935 33 139. Bismar TA, Demichelis F, Riva A, Kim R, Varambally S, He L, Kutok J, Aster JC, Tang J, Kuefer R, Hofer MD, Febbo PG, Chinnaiyan AM, Rubin MA: Defining aggressive prostate cancer using a 12-gene model, Neoplasia 2006, 8:59-68 140. Wei Q, Li M, Fu X, Tang R, Na Y, Jiang M, Li Y: Global analysis of differentially expressed genes in ancirogen-independent prostate cancer, Prostate Cancer Prostatic Dis 2007, 10:167-174 141. Assikis VJ, Do KA, Wen S, Wang X, Cho-Vega JH, Brisbay S, Lopez R, Logothetis CJ, Troncoso P, Papandreou CN, McDonnell TJ: Clinical and biomarker correlates of androgen-independent, locally aggressive prostate cancer with limited metastatic potential, Clin Cancer Res 2004, 10:6770-6778 142. Stanbrough M, Bubley GJ, Ross K, Golub TR, Rubin MA, Penning TM, Febbo PG, Balk SP: Increased expression of genes converting adrenal androgens to testosterone in androgen-independent prostate cancer, Cancer Res 2006, 66:28 15-2825 143. Best CJ, Gillespie JW, Yi Y, Chandramouli GV, Perimutter MA, Gathright Y, Erickson HS, Georgevich L, Tangrea MA, Duray PH, Gonzalez S, Velasco A, Linehan WM, Matusik RJ, Price DK, Figg WD, Emmert-Buck MR, Chuaqui RF: Molecular alterations in primary prostate cancer after androgen ablation therapy, Clin Cancer Res 2005, 11:6823-6834 144. Tamura K, Furihata M, Tsunoda T, Ashida S, Takata R, Obara W, Yoshioka H, Daigo Y, Nasu Y, Kumon H, Konaka H, Namiki M, Tozawa K, Kohri K, Tanji N, Yokoyama M, Shimazui T, Akaza H, Mizutani Y, Miki T, Fujioka T, Shuin T, Nakamura Y, Nakagawa H: Molecular features of hormone-refractory prostate cancer cells by genome-wide gene expression profiles, Cancer Res 2007, 67:5 1 17-5 125 145. Zeliweger T, Ninck C, Bloch M, Mirlacher M, Koivisto PA, Helm HJ, Mihatsch MJ, Gasser TC, Bubendorf L: Expression patterns of potential therapeutic targets in prostate cancer, Tnt J Cancer 2005, 113:619-628 146. Fromont G, Chene L, Vidaud M, Vallancien G, Mangin P, Fournier G, Validire P. Latil A, Cussenot 0: Differential expression of 37 selected genes in hormone-refractory prostate cancer using quantitative taqman real-time RT-PCR, Tnt J Cancer 2005, 114:174- 181 147. Bibikova M, Chudin E, Arsanjani A, Zhou L, Garcia EW, Modder J, Kostelec M, Barker D, Downs T, Fan JB, Wang-Rodriguez J: Expression signatures that correlated with Gleason score and relapse in prostate cancer, Genomics 2007, 89:666-672 34 148. Kumar-Sinha C, Chinnaiyan AM: Molecular markers to identify patients at risk for recurrence after primary treatment for prostate cancer, Urology 2003, 62 Suppi 1:19-3 5 149. Stephenson AJ, Smith A, Kattan MW, Satagopan J, Reuter VE, Scardino PT, Gerald WL: Integration of gene expression profiling and clinical variables to predict prostate carcinoma recurrence after radical prostatectomy, Cancer 2005, 104:290-298 150. Henshall SM, Afar DE, Hiller J, Horvath LG, Quinn DI, Rasiah KK, Gish K, Willhite D, Kench JG, Gardiner-Garden M, Stricker PD, Scher HI, Grygiel JJ, Agus DB, Mack DH, Sutherland RL: Survival analysis of genome-wide gene expression profiles of prostate cancers identifies new prognostic targets of disease relapse, Cancer Res 2003, 63 :4196- 4203 151. Glinsky GV, Glinskii AB, Stephenson AJ, Hoffman RM, Gerald WL: Gene expression profiling predicts clinical outcome of prostate cancer, J Clin Invest 2004, 113:913-923 152. Devilard E, Bladou F, Ramuz 0, Karsenty G, Dales JP, Gravis G, Nguyen C, Bertucci F, Xerri L, Birnbaum D: FGFR1 and WT1 are markers of human prostate cancer progression, BMC Cancer 2006, 6:272 153. Quayle SN, Hare H, Delaney AD, Hirst M, Hwang D, Schein JE, Jones SJ, Marra MA, Sadar MD: Novel expressed sequences identified in a model of androgen independent prostate cancer, BMC Genomics 2007, 8:32 154. Amler LC, Agus DB, LeDuc C, Sapinoso ML, Fox WD, Kern 5, Lee D, Wang V, Leysens M, Higgins B, Martin J, Gerald W, Dracopoli N, Cordon-Cardo C, Scher HI, Hampton GM: Dysregulated expression of androgen-responsive and nonresponsive genes in the androgen-independent prostate cancer xenograft model CWR22-R1, Cancer Res 2000, 60:6134-6141 155. Chen Q, Watson JT, Marengo SR, Decker KS, Coleman I, Nelson PS, Sikes RA: Gene expression in the LNCaP human prostate cancer progression model: progression associated expression in vitro corresponds to expression changes associated with prostate cancer progression in vivo, Cancer Lett 2006, 244:274-288 156. Pfundt R, Smit F, Jansen C, Aalders T, Straatman H, van der Vliet W, Isaacs J, van Kessel AG, Schalken J: Identification of androgen-responsive genes that are alternatively regulated in androgen-dependent and androgen-independent rat prostate tumors, Genes Chromosomes Cancer 2005, 43 :273-283 157. BubendorfL, Kolmer M, Kononen J, Koivisto P, Mousses 5, Chen Y, Mahiamaki E, Schraml P. Moch H, Willi N, Elkahloun AG, Pretlow TG, Gasser TC, Mihatsch MJ, 35 Sauter G, Kallioniemi OP: Hormone therapy failure in human prostate cancer: analysis by complementary DNA and tissue microarrays, J Natl Cancer Inst 1999, 91:1758-1764 158. Mousses S, Wagner U, Chen Y, Kim JW, BubendorfL, Bittner M, Pretlow T, Elkahloun AG, Trepel TB, Kallioniemi OP: Failure of hormone therapy in prostate cancer involves systematic restoration of androgen responsive genes and activation of rapamycin sensitive signaling, Oncogene 2001, 20:6718-6723 159. Gregory CW, Hamil KG, Kim D, Hall SH, Pretlow TG, Mohler JL, French FS: Androgen receptor expression in androgen-independent prostate cancer is associated with increased expression of androgen-regulated genes, Cancer Res 1998, 58:5718-5724 160. Mohler JL, Morris TL, Ford OH, 3rd, Alvey RF, Sakamoto C, Gregory CW: Identification of differentially expressed genes associated with androgen-independent growth of prostate cancer, Prostate 2002, 51:247-255 161. Nelson PS, Clegg N, Arnold H, Ferguson C, Bonham M, White J, Hood L, Lin B: The program of androgen-responsive genes in neoplastic prostate epithelium, Proc Natl Acad Sci USA 2002, 99:11890-11895 162. Oosterhoff JK, Grootegoed JA, Blok U: Expression profiling of androgen-dependent and -independent LNCaP cells: EGF versus androgen signalling, Endocr Relat Cancer 2005, 12:135-148 163. Velasco AM, Gillis KA, Li Y, Brown EL, Sadler TM, Achilleos M, Greenberger LM, Frost P, Bai W, Zhang Y: Identification and validation of novel androgen-regulated genes in prostate cancer, Endocrinology 2004, 145:3913-3924 164. Wang G, Jones SJM, Marra MA, Sadar MD: Identification of genes targeted by the androgen and PKA signaling pathways in prostate cancer cells, Oncogene 2006, 165. Segawa T, Nau ME, Xu LL, Chilukuri RN, Makarem M, Zhang W, Petrovics G, Sesterhenn IA, McLeod DO, Moul JW, Vahey M, Srivastava S: Androgen-induced expression of endoplasmic reticulum (ER) stress response genes in prostate cancer cells, Oncogene 2002, 21:8749-8758 166. Xu LL, Su YP, Labiche R, Segawa T, Shanmugam N, McLeod DG, Moul JW, Srivastava 5: Quantitative expression profile of androgen-regulated genes in prostate cancer cells and identification of prostate-specific genes, Tnt J Cancer 2001, 92:322-328 167. Clegg N, Eroglu B, Ferguson C, Arnold H, Moorman A, Nelson PS: Digital expression profiles of the prostate androgen-response program, J Steroid Biochem Mol Biol 2002, 80: 13-23 36 168. Coutinho-Camillo CM, Salaomi 5, Sarkis AS, Nagai MA: Differentially expressed genes in the prostate cancer cell line LNCaP after exposure to androgen and anti-androgen, Cancer Genet Cytogenet 2006, 166:130-138 169. DePrimo SE, Diehn M, Nelson TB, Reiter RE, Matese J, Fero M, Tibshirani R, Brown P0, Brooks JD: Transcriptional programs activated by exposure of human prostate cancer cells to androgen, Genome Biol 2002, 3:RESEARCHOO32 170. Febbo PG, Lowenberg M, Thorner AR, Brown M, Loda M, Golub TR: Androgen mediated regulation and functional implications of fkbp5 1 expression in prostate cancer, J Urol 2005, 173:1772-1777 171. Waghray A, Feroze F, Schober MS, Yao F, Wood C, Puravs E, Krause M, Hanash S, Chen YQ: Identification of androgen-regulated genes in the prostate cancer cell line LNCaP by serial analysis of gene expression and proteomic analysis, Proteomics 2001, 1:1327-1338 172. Romanuik TL, Wang, G., Holt, R.A., Jones, S.J.M., Marra, M.A., and M.D. Sadar: Regulation of the transcriptome by the androgen-axis in prostate cancer, In preparation 173. Lin B, Ferguson C, White JT, Wang 5, Vessella R, True LD, Hood L, Nelson PS: Prostate-localized and androgen-regulated expression of the membrane-bound serine protease TMPRSS2, Cancer Res 1999, 59:4180-4184 174. Prescott JL, Blok L, Tindall DJ: Isolation and androgen regulation of the human homeobox cDNA, NKX3.1, Prostate 1998, 35:71-80 175. Oettgen P, Finger E, Sun Z, Akbarali Y, Thamrongsak U, Boltax J, Grail F, Dube A, Weiss A, Brown L, Quinn G, Kas K, Endress G, Kunsch C, Libermann TA: PDEF, a novel prostate epithelium-specific ets transcription factor, interacts with the androgen receptor and activates prostate-specific antigen gene expression, 3 Biol Chem 2000, 275: 1216-1225 176. Xu LL, Shanmugam N, Segawa T, Sesterhenn IA, McLeod DG, Moul JW, Srivastava S: A novel androgen-regulated gene, PMEPA 1, located on chromosome 20q 13 exhibits high level expression in prostate, Genomics 2000, 66:257-263 177. Israeli RS, Powell CT, Fair WR, Heston WD: Molecular cloning of a complementary DNA encoding a prostate-specific membrane antigen, Cancer Res 1993, 53:227-230 178. Tomlins SA, Rhodes DR, Perner S, Dhanasekaran SM, Mehra R, Sun XW, Varambally S. Cao X, Tchinda J, Kuefer R, Lee C, Montie JE, Shah RB, Pienta KJ, Rubin MA, 37 Chinnaiyan AM: Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer, Science 2005, 310:644-648 179. Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T, Pandey A, Chinnaiyan AM: ONCOM1NE: a cancer microarray database and integrated data- mining platform, Neoplasia 2004, 6:1-6 180. Cooper CS, Campbell C, Jhavar S: Mechanisms of Disease: biomarkers and molecular targets from microarray gene expression studies in prostate cancer, Nat Clin Pract Urol 2007, 4:677-687 181. Nam RK, Sugar L, Yang W, Srivastava 5, Klotz LH, Yang LY, Stanimirovic A, Encioiu E, Neill M, Loblaw DA, Trachtenberg J, Narod SA, Seth A: Expression of the TMPRSS2:ERG fusion gene predicts cancer recurrence after surgery for localised prostate cancer, Br J Cancer 2007, 97:1690-1695 182. Nam RK, Sugar L, Wang Z, Yang W, Kitching R, Klotz LH, Venkateswaran V, Narod SA, Seth A: Expression of TMPRSS2:ERG gene fusion in prostate cancer cells is an important prognostic factor for cancer progression, Cancer Biol Ther 2007, 6:40-45 183. Perner S, Demichelis F, Beroukhim R, Schmidt FH, Mosquera JM, Setlur S, Tchinda J, Tomlins SA, Hofer MD, Pienta KG, Kuefer R, Vessella R, Sun XW, Meyerson M, Lee C, Sellers WR, Chinnaiyan AM, Rubin MA: TMPRSS2:ERG fusion-associated deletions provide insight into the heterogeneity of prostate cancer, Cancer Res 2006, 66:8337-8341 184. Mebra R, Tomlins SA, Yu J, Cao X, Wang L, Menon A, Rubin MA, Pienta KJ, Shah RB, Chinnaiyan AM: Characterization of TMPRSS2-ETS gene aberrations in androgen independent metastatic prostate cancer, Cancer Res 2008, 68:3584-3590 185. Saiki RK, Scharf S, Faloona F, Mullis KB, Horn GT, Erlich HA, Arnheim N: Enzymatic amplification of beta-globin genomic sequences and restriction site analysis for diagnosis of sickle cell anemia, Science 1985, 230:1350-1354 186. Higuchi R, Dollinger G, Walsh PS, Griffith R: Simultaneous amplification and detection of specific DNA sequences, Biotechnology (NY) 1992, 10:413-417 187. Higuchi R, Fockler C, Dollinger G, Watson R: Kinetic PCR analysis: real-time monitoring of DNA amplification reactions, Biotechnology (N Y) 1993, 11:1026-1030 188. Morrison TB, Weis JJ, Wittwer CT: Quantification of low-copy transcripts by continuous SYBR Green I monitoring during amplification, Biotechniques 1998, 24:954-958, 960, 962 38 189. Heid CA, Stevens J, Livak KJ, Williams PM: Real time quantitative PCR, Genome Res 1996, 6:986-994 190. Gibson UE, Heid CA, Williams PM: A novel method for real time quantitative RT-PCR, Genome Res 1996, 6:995-100 1 191. Invitrogen: Platinum SYBR green qPCR superMix-UDG with ROX. Edited by Burlington, ON, Canada, 2005 192. Schena M, Shalon D, Davis RW, Brown P0: Quantitative monitoring of gene expression patterns with a complementary DNA microarray, Science 1995, 270:467-470 193. Pease AC, Solas D, Sullivan EJ, Cronin MT, Holmes CP, Fodor SP: Light-generated oligonucleotide arrays for rapid DNA sequence analysis, Proc Natl Acad Sci U S A 1994, 91:5022-5026 194. Diatchenko L, Lau YF, Campbell AP, Chenchik A, Moqadam F, Huang B, Lukyanov 5, Lukyanov K, Gurskaya N, Sverdlov ED, Siebert PD: Suppression subtractive hybridization: a method for generating differentially regulated or tissue-specific cDNA probes and libraries, Proc Natl Acad Sci U S A 1996, 93 :6025-6030 195. Velculescu VE, Zhang L, Vogeistein B, Kinzler KW: Serial analysis of gene expression, Science 1995, 270:484-487 196. Saha 5, Sparks AB, Rago C, Akmaev V, Wang CJ, Vogelstein B, Kinzler KW, Velculescu VE: Using the transcriptome to annotate the genome, Nat Biotechnol 2002, 20:508-512 39 CHAPTER II REGULATION OF THE TRANSCRIPTOME BY THE ANDROGEN-AXIS IN PROSTATE CANCER* 2.1 INTRODUCTION Androgens mediate their effect through the androgen receptor (AR) and together they play integral roles in the development and maintenance of the prostate. In the absence of a functional androgen-axis during development, the prostate will fail to form’. The size of the prostate increases with the elevation of levels of androgens in males during puberty2.Androgens promote proliferation, differentiation, and survival of prostate cells’ and are also associated with prostate carcinogenesis. Men that have used excess androgens in the form of anabolic steroids have a higher incidence of prostate cancer35.Association of prostate cancer with levels of androgens has also been reported in rodents6’7• Reduction of androgen in humans or dogs before puberty by castration is associated with decreased incidence of prostate cancer8’. Castration of adult males causes apoptosis of prostatic epithelium, involution and reduction of the prostate’°’2.Thus the prostate gland is an androgen-dependent organ where androgens are the predominant mitogenic stimulus’3.The dependency of the prostate epithelium on androgens provides the underlying rationale for treating prostate cancer with chemical or surgical castration (androgen deprivation)’4. The AR is a ligand-activated transcription factor’5 that regulates transcription of genes that contain androgen response elements (AREs) in the upstream or downstream regulatory regions of the promoter andlor enhancer. Kallikrein 3 (KLK3) is an example of a gene that contains numerous functional AREs that the AR interacts with to increase transcription in response to androgens’619.KLK3, also known as prostate-specific antigen (PSA), is the main tumour marker for prostate cancer and has been used clinically for 15 years20.Serum levels of PSA correlate with tumour volume21.However, as a screening and monitoring tool for prostate cancer, serum PSA levels are subject to false positives and false negatives20. A version of this chapter will be submitted for publication. Romanuik, TL., Wang, G., Holt, RA., Jones, SJM., Marra, MA., Sadar, MD. Regulation of the transcriptome by the androgen-axis in prostate cancer. In preparation. 40 Identification of the genes that change in expression in response to androgen in prostate cells is essential for the understanding of androgen-dependency of the normal prostate and the proliferation, survival, and hormonal progression of prostate cancer. Here, we apply Long Serial Analysis of Gene Expression (L0ngSAGE)22to create transcript libraries of prostate cancer cells maintained in the presence or absence of androgen. We describe 24 genes never before identified or validated to alter expression in response to androgen treatment. These genes were: ARL6IP5, BLVRB, C]9orf48, Clorf]22, C6orf66, CAMK2NJ, CCNI, DERA, ERRFI], GLUL, GOLFH3, HMI3, HSP9OB], MANEA, NANS, NIPSNAP3A, SLC4JA], SOD], SVIP, TAOK3, TCPI, TMEM66, USP33, and VTAJ. Statistically significant changes in expression ofARL6IP5, CAMK2N], ERRFIJ, HSF9OBJ, and TAOK3 in response to reduced levels of circulating androgens were measured using in vivo samples. 2.2 MATERIALS AND METHODS 2.2.1 Cell culture LNCaP human prostate cancer cells (American Type Culture Collection, Bethesda, MD, USA) were maintained in RPMI- 1640 media (Stem Cell Technologies, Vancouver, BC, Canada) supplemented with 10% v/v fetal bovine serum (FBS; HyClone, Logan, UT, USA), 100 units! mL penicillin and 100 units/mL streptomycin (antibiotics; Invitrogen, Burlington, ON, Canada). DU145 and PC-3 human prostate cancer cells were maintained in DMEM (Stem Cell Technologies) supplemented with 10% v/v FBS and 5% v/v FBS, respectively with antibiotics. All cells were maintained at 37°C with 5% CO2. 2.2.2 Long serial analysis of gene expression RNA sample generation 1 x 106 LNCaP cells were seeded in 10 cm-diameter dishes. The next day, cells were serum starved for 48 hours and then treated for 16 hours with 10 nM synthetic androgen R1881 (also known as methyltrienolone; PerkinElmer, Woodbridge, ON, Canada), or solvent (vehicle) control, ethanol (final concentration 2.85 x 1 0 %). Total RNA was extracted using TRIZOL Reagent (Invitrogen) following the manufacturer’s instructions. RNA quality and quantity were assessed using the Agilent 2100 Bioanalyzer (Agilent Technologies, Mississauga, ON, Canada) and RNA 6000 Nano LabChip kit (Caliper Technologies, Hopkinton, MA, USA). L0ngSAGE library production LongSAGE22libraries were constructed with 5 ig of total RNA using the Invitrogen I-SAGE Long kit and protocol with alterations as previously published23.Briefly, double-stranded eDNA was synthesized from total RNA and digested with Nla III. The sample was split in half and linkers type I and II were added and ligated to Nia III overhangs. An Mme I digestion resulted in 17-2 1 base-pair (bp) LongSAGE tags. The tags with unique linkers were combined and ligated together to form ditags. Ditags (131 bp) were amplified with primers designed to recognize sequences within linkers type I and II using PCR. This scale-up PCR was performed in 48 wells of a 96 well plate (50 jiL/well) using a 1120th dilution of template eDNA and 25 and 27 cycles of PCR (R1881 and vehicle LongSAGE library, respectively). Following an Nla III digestion to remove the linkers, the 36 bp ditags were concatenized. Concatemers sized 1300-1700 bp were digested with NIa III (1 minute) to increase the efficiency of cloning into pZErO- 1 vectors. Cloned concatemers were transformed into One Shot TOP 10 Electrocompetent Escherichia coli and colonies were picked with the Q-Pix robot (Genetix) and cultured in 2x Yeast-Tryptone media with 50 .Ig/mL zeocin and 7.5% (v/v) glycerol. Sequencing Glycerol stocks of transformed bacteria were used to inoculate larger cultures for alkaline lysis plasmid preparation24.Plasmid preparations were separated by agarose gel electrophoresis and visualized by ultraviolet light and sybr green. l/24 BigDye v3.1 terminator cycle sequencing reactions were performed with tetrad thermal cyclers (BioRad, Waltham, MA, USA) and visualized with capillary DNA sequencers, models 3700 and 3730x1 (Applied Biosystems, Foster City, CA, USA). Each library was sequenced to a depth of- 100,000 L0ngSAGE tags. Flanking vector sequences were removed and the LongSAGE tags were extracted from each sequence read. On average, 34 and 38 LongSAGE tags were sequenced in each read (R1881 and vehicle libraries, respectively). Sequence data were filtered for non-recombinant clones. Gene expression analysis L0ngSAGE expression data was analyzed with DiscoverySpace 3.2.4 and 4.01 software25 (http://www.bcgsc.ca/bioinfo/software/discoveryspace/). Duplicate ditags (identical copies of a ditag) and singletons (tags counted only once) were retained for analysis. Sequence data were 42 filtered for bad tags (tags with one N-base call) and linker-derived tags (artifact tags). Only LongSAGE tags with a sequence quality factor (QF) greater than 95% were included in analysis23.Where indicated, a clustering algorithm was used to amalgamate 1-off tags (tags one bp incorrect from a complete map to a transcript) with likely ‘parent’ tags to improve the mapping capability of LongSAGE tags by apparently reducing PCR/sequencing errors23.This clustering algorithm altered the number of tag types (i.e., species) without changing the total number of tags. In instances where clustering was used, the 95% QF cutoff was not. To filter data for candidate transcript validation, a p-value cutoff (p 0.001) was employed according to the Audic and Claverie test statistic26.The Audic and Claverie statistical method was used to identify differentially expressed tags between L0ngSAGE libraries because the method takes into account the sizes of the libraries and tag counts. LongSAGE tags that mapped ambiguously to more than one gene, and tags that differed by less than 2-fold were excluded from the candidate list. L0ngSAGE tags were mapped to reference sequence (RefSeq; May 2005) and Ensembi Gene (v3 1 .35d), unless otherwise stated. 2.2.3 Quantitative real-time polymerase chain reaction qRT-PCR was performed on TRIZOL-extracted RNA from LNCaP (10% serum or ± Ri 881), DU145 (10% serum) and PC-3 (5% serum) cells maintained in vitro, and LNCaP cells maintained in the in vivo Hollow Fibre model27 (see below). Contaminating genomic DNA was removed from in vitro RNA samples using DNA-free or TURBO DNA-free (Ambion, Austin, TX, USA). Input RNA (1 fig) was reverse transcribed with SuperScript III First Strand Synthesis kit (Invitrogen). A 10 L qRT-PCR reaction included 1 p1 of template cDNA (0.1 iL for limited LNCaP Hollow Fibre samples), lx Platinum SYBR Green qPCR SuperMix-UDG with ROX (Invitrogen) and 0.3 pM each of forward and reverse intron-spanning primers that produce products between 85-115 bp in size (see Table 2.1 for primer sequences). qRT-PCR reactions were cycled as follows in a 7900HT Sequence Detection System (Applied Biosystems): 50 °C for 2 mm, 95 °C for 2 mm, (95 °C for 0.5 mm, 55-56°C for 0.3-0.5 mm, and 72°C for 0.5 mm) for 40-45 cycles, 95°C for 0.25 mm, 60°C for 0.25 mm, and 95°C for 0.25 mm. All qRT-PCR reactions were performed in technical triplicates for each of at least three biological replicates. cDNAs (from different conditions) and genes [target and reference (glyceraklehyde-3-phosphate, GA PDH) ] to be directly compared were assayed in the same instrument run. No-template reactions (negative controls) were run for each gene to ensure that DNA had not contaminated 43 the qRT-PCR reactions. Only qRT-PCR data with single-peak dissociation curves were included in this analysis. Efficiency checks were performed for each primer pair in each cell line. PCR products were sequenced to verify the identity of quantified transcripts. The two-tailed, two- sample Student’s T-tests were performed to identify significant differences in transcript expression. The F-test was used to identify unequal variance among samples to be compared. 2.2.4 LNCaP Hollow Fibre model Animals Five-week-old male athymic BALB/c Nude mice were obtained from Taconic Farms (Hudson, NY, United States of America) and kept in the British Columbia Cancer Research Centre (Vancouver, BC, Canada). Mice were maintained on a Harlan/Teklad irradiated diet with a constant supply of autoclaved water and housed in cages (three animals/cage) at 2 1°C± 3°C with light/dark cycling (light between 6 AM and 6 PM). All animal experiments were performed according to a protocol approved by the Committee on Animal Care of the University of British Columbia. Hollow fibre model Polyvinylidene difluoride hollow fibres (Mr 500,000 molecular weight cutoff; 1-mm internal diameter; Spectrum Laboratories, Rancho Dominguez, CA, USA) were prepared and implanted as previously described27.Briefly, LNCaP human prostate cancer cells (3 x l0 cells) at passage 47 (provided by Dr. L.W.K. Chung at the Emory University School of Medicine, Atlanta, GA, USA) were injected into hollow fibres. The fibres were sealed and subcutaneously (s.c.) implanted into mice. Seven days post fibre implantation (day zero), mice were either castrated or left intact as controls. Blood was drawn via the tail vein each week to measure serum KLK3 levels to monitor the response to castration. Serum KLK3 levels were determined by enzymatic immunoassay kit (Abbott Laboratories, Abbott Park, IL, USA). Bundles of fibres were removed at day zero (Pre-Cx; four fibres) and day 10 (Cx; four fibres). Total RNA was isolated immediately from cells harvested from the fibres. Compromised fibres that were contaminated with mouse cells, as indicated by an infiltration of red blood cells that was determined by visual inspection, were not used in this study. 44 2.3 RESULTS 2.3.1 Summary of LongSAGE libraries LongSAGE was employed to obtain quantitative gene expression profiles of human prostate cancer cells treated with or without synthetic androgen Ri 881. LNCaP human prostate cancer cells were chosen as the model cell line for evaluating androgen signalling because they express a functional AR, respond to androgens, they can be grown in vitro as a monolayer or in vivo as a xenograft or in the Hollow Fiber mode12729.LNCaP cells have been used extensively in prostate cancer research. The time of 16 hours for treatment and concentration ofRl88i (10 nM) are optimal for induction ofKLK330. LongSAGE libraries were sequenced to a total of 121,760 (Rl 881) and 103,391 (vehicle) tags (Table 2.2). The libraries were filtered on several levels to leave only useful tags for analysis. First, bad tags were removed if they contained at least one N-base call in the LongSAGE tag sequence. Notably, when bad tags were filtered the percentages of duplicate ditags in the R1881 and vehicle LongSAGE libraries were 6% and 5%, respectively. Early SAGE studies suggest duplicate ditags likely represent polymerase chain reaction (PCR) artifacts due to the low probability the same two tags will ligate together to form ditags31.However, with deep LongSAGE library sequencing and highly expressed transcripts, this random probability is greater32. A recent study33 suggests that discarding duplicate ditags in LongSAGE analysis may introduce bias affecting the fold differences in tag expression between libraries for all tags observed at a frequency >(113-224)/100,000. Therefore, to avoid introducing this bias we opted to retain duplicate ditags. PHRED software was used to call bases for the sequencing of the LongSAGE tags34’ PHRED has a small, but significant error rate in base-calls. To ascertain which tags potentially contained these erroneous base-calls, we calculated a tag sequence quality factor (QF) and probability23.The second line of filtering removed L0ngSAGE tags with probabilities less than 0.95 (QF <95%). Linkers of known sequence were introduced into SAGE libraries as primers for amplifying ditags prior to concatenation31.These linker sequences were designed so they do not map to the human genome. At a low frequency, linkers ligate to themselves creating linker-derived tags (LDTs). These LDTs do not represent transcripts and are removed from the L0ngSAGE libraries. After filtering, there were 97,981 total useful tags representing 23,828 tag sequences in the R1881 L0ngSAGE library, and 85,861 total useful tags representing 24,592 tag sequences in the vehicle L0ngSAGE library. Due to redundancy in the 45 expressed sequences, the combined number of useftil tag types in the R1881 and vehicle LongSAGE libraries was 38,574. The remainder of the data analysis in this manuscript was carried out using this filtered data. 2.3.2 Tag frequency and transcript abundance Tag frequency spanned over three orders of magnitude corresponding to transcript abundance of 5 to 8,746 copies per cell (based on minimum and maximum observed tag counts of 1 and 1714; see Table 2.3 legend for explanation of calculations). The distribution of LongSAGE tag frequencies per 100,000 tags revealed the majority (64 and 67%) of tag types in each LongSAGE library (Ri 881 and vehicle, respectively) were singletons (tags counted only once). This result was consistent with other published SAGE libraries reporting 66% singletons36.Singletons can represent very low abundance transcripts (s 5 transcript copies per cell) or PCR/sequencing errors. Estimates indicate that less than 17% of LongSAGE tags in a library contain PCR/sequencing errors37.Coincidently, 17% of the total tags in the R1881 and vehicle L0ngSAGE libraries roughly equal the number of singletons in each LongSAGE library (Table 2.3). Although initial estimates suggest 6.8-10% of shortSAGE tags contain PCRlsequencing errors, more recent experimental evidence suggests the actual error rate is much lower ( 2%)38. This implies that an error rate of 17% may also be an overestimate for LongSAGE tags. Tag types counted 2-4 times per 100,000 tags (10-20 transcript copies per cell) and 5-9 times per 100,000 tags (25-45 transcript copies per cell) were the second and third most common groups of tag types, respectively. Generally, high frequency tags were less common. The majority of total tags in each LongSAGE library were derived from a few tag types detected between 10-99 times per 100,000 tags (50-495 transcript copies per cell). 2.3.3 Mapping distribution of L0ngSAGE tags When mapped tags (v3 8 Ensembl) were clustered to amalgamate 1-off tags (see Materials and Methods, Gene Expression Analysis for a description) and tags that mapped ambiguously were removed, the tag types in the Rl88i and vehicle LongSAGE libraries represented 7,484 genes and 7,441 genes, respectively (Table 2.4). Tag types that mapped ambiguously constituted 13% (R188i and vehicle), while 36% (R1881) and 35% (vehicle) of tag types did not map to the genome (Table 2.4). Due to the fact that these tags were clustered, the majority of the tags that 46 did not map to the genome probably represent true unannotated transcripts rather than PCR/sequencing errors. Approximately 28% of tags in each LongSAGE library mapped to the opposite strand of known genes. These LongSAGE tags either represent transcription from previously undescribed coding regions or true antisense transcripts. Each L0ngSAGE library contained tags representing transcripts from 32% of the genes in the Ensembl gene database. This percentage is indicative of the depth of coverage of the transcriptome achieved with LongSAGE. Alternatively, this percentage indicates that one third of known Ensembi genes were expressed in LNCaP cells under these experimental conditions. This percentage is substantial when considering tag types from the Mouse Atlas Project (8.55 million total LongSAGE tags generated from 72 libraries of mouse development) mapped to 57% of the Ensembl transcript database23.Approximately 63% (R1881) and 61% (vehicle) of the genes that mapped to Ensembl’s database were associated with more than one tag type to suggest that most gene expression was represented by transcript variants which is consistent with previous observations23.When the mapped LongSAGE tags (Reference Sequence -RefSeq- May 18, 2006) were clustered to amalgamate 1-off tags and tags that mapped ambiguously were removed, 53% of tags mapped solely to known exons, 9% solely to known introns (novel transcript variants), and 38% to intergenic regions (novel genes or transcript variants). The two most abundant tag types in the LongSAGE libraries were shared by both libraries. The first highly abundant L0ngSAGE tag mapped to human mitochondrial NADH ubiquinone oxidoreductase chain 4. The protein product of this gene transfers electrons from NADH to ubiquinone to generate adenosine triphosphate as metabolic energy. Using the Ensembl database, the second most abundant LongSAGE tag mapped to a non-coding gene of human mitochondria. In contrast to the higher abundance classes, the lower abundance classes were enriched for LongSAGE tags that mapped to genes with functions in regulating transcription (Table 2.3). This is particularly significant because the percentages of LongSAGE tags that mapped to the genome in the lower abundance class were reduced compared to the higher abundance classes (Table 2.3). Together this implies that the number of tags that map to genes with a function in transcription may be underestimated, as low abundance tags may be underrepresented. 47 2.3.4 Differential gene expression Venn analysis identified that 36% and 38% of tag types were exclusive to the R1881 and vehicle LongSAGE libraries, respectively (Figure 2.1). The unique expression of tag types indicates differential expression depending upon androgen stimulation. Unfortunately, the biological relevance of this differential expression was complicated by the fact that 85% (Ri 881) and 88% (vehicle) of these exclusive LongSAGE tags were singletons. Consistent with our observation that low abundance tags did not map as readily to the genome, exclusive tags also did not map as readily as tags shared between both libraries. Only 17% and 15% of tags exclusive to R1881 and vehicle LongSAGE libraries, respectively, mapped unambiguously sense to RefSeq, in contrast to 39% of shared tags. A scatter plot illustrates observed tag counts in L0ngSAGE libraries relative to the confidence intervals (CIs; 95%, 99%, and 99.9%) of respective p-values (p 0.05, 0.01, and 0.001) by Audic and Claverie statistics26 (Figure 2.2). A significant number (891) of tags were differentially expressed (p 0.05) between the two LongSAGE libraries (Figure 2.2 and Table 2.5) even though these 891 tags represented a minority (2%) of all tag types. LongSAGE tags statistically (p 0.001) differentially represented between the libraries were enriched in the higher abundance classes compared to the lower abundance classes (Table 2.3). Additionally, 90% of the LongSAGE tags were statistically (p 0.001) differentially represented between the libraries with 2-fold differences, compared to only 17% of tags with p-values greater than 0.001 (p > 0.00 1). A stringent p-value cutoff (p 0.001) was employed prior to validation of changes in expression of a gene in response to androgen. LongSAGE tags that were differentially expressed, but mapped ambiguously to more than one gene, andlor differed by less than 2-fold between the treatment groups, were excluded from analysis. Application of these criteria reduced the LongSAGE tags from 131 to 93. These 93 tags represented 87 genes. Analysis of differentially expressed L0ngSAGE tags revealed that 54 LongSAGE tags that mapped to 52 genes were previously known to change in expression in the direction observed in response to androgen in prostate cancer cells. Of these, the expression of4i genes increased as expected, including the well-known androgen-regulated gene, KLK3 (Table 2.6). The expression of 11 genes decreased 48 in response to androgen, and were consistent with previous reports (Table 2.7). Genes previously not reported to alter expression in response to androgen in prostate cancer cells were represented by 39 LongSAGE tags. These tags represented the expression of 20 genes that were increased, excluding non-coding and intergenic regions, (Table 2.8), and expression of 15 genes that were decreased (Table 2.9) in response to androgen. The 93 tags were represented by 87 genes because one tag did not map (Table 2.8) and two tags mapped to intergenic regions of human mitochondrial genome (Tables 2.8 and 2.9). Three genes were represented twice in the tables (CAMK2NJ, PPAF2A, and SORD). One gene, KRT8, was categorized in both the known and not previously known categories due to the sense of the mapping (Tables 2.6 and 2.9). 2.3.5 Validation of changes in gene expression in response to androgen Quantitative real time-polymerase chain reaction (qRT-PCR) was used to validate changes in gene expression in response to androgen of 39 (13 known; 26 novel) of the 87 total genes identified by L0ngSAGE. Of the 35 genes previously not reported to change expression in response to androgens in prostate cancer cells, only 26 were quantified by qRT-PCR, because technical limitations and gaps in the transcriptome databases prevented the analysis of 9 genes. That is, specific qRT-PCR primers could not be designed due to repetition in the genome, or because the tag mapped to an unannotated transcript variant. There were 24 of the 26 (92%) novel genes that displayed statistically significant differential expression in response to androgen as measured by qRT-PCR (Figure 2.3A). BL VRB, C19orf48, Clorfl22, ERRFIJ, GLUL, GOLPH3, HMJ3, HSP9OB], NANS, SLC4]A], TAOK3, TCPJ, TMEM66, and USP33 all increased levels of expression in response to androgen, while ARL6IP5, C6orf66, CAMK2N], CCNI, DERJ4, MANEA, NIFSNAP3A, SODJ, SVIP, and VTAJ decreased in response to androgen (Figure 2.3A). Under the experimental conditions and primers used, we did not measure statistically significant changes in expression of FRNPIP and CAPNSJ. A false discovery rate (FDR)39 of 29% was expected of the L0ngSAGE data based on the Audic and Claverie p-value 0.001. This FDR represents the anticipated percentage of type I errors (i.e., false positives). We observed only 2/26 (8%) false positives, suggesting that the other filter parameters (e.g., ? 2-fold difference in expression level) may have the increased the chances of validation by qRT-PCR. Moreover, the expression trends for all 13 genes known to change expression in response to androgen in prostate cancer cells correlated between the LongSAGE and qRT-PCR data. ADAMTSJ, CENPN, CREB3L4, FKBP5, KLK3, LRIGJ, NCAPD3, PAKJIPJ, and RHOU all 49 increased levels of expression in response to androgen while CXCR 7, NTS, FRKA CB, and ST7 decreased in response to androgen (Figure 2.3B). 2.3.6 Cell-type specificity of gene expression To determine if expression of candidate genes was unique to LNCaP cells, we assayed for constitutive levels of expression of 18 known and novel candidate genes in prostate cancer cell lines DU1454°and PC-341 using qRT-PCR (Figure 2.4). Genes chosen included those that both increased (ADA MTSJ, CAPNS], CENPN, CREB3L4, ERRFIJ, FKBF5, HSP9OBJ, KLK3, LRJG], NCAPD3, PAK]IPJ, and TAOK3) and decreased expression in response to androgen (ARL6IP5, CAMK2N1, CCNI, CXCR7, PRKACB and ST7). No obvious trends were observed depending on whether expression of the genes increased, or decreased, in response to androgen. All genes tested, except ERRFIJ , were expressed at a lower level in PC-3 and DU145 cells relative to LNCaP cells. This suggests that the majority of genes that alter levels of expression in response to androgen were enriched in LNCaP cells relative to PC-3 and DU145 cells. These data are consistent with both DU 145 and PC3 cells being androgen-insensitive and lacking a functional AR40’1. 2.3.7 In vivo changes in gene expression in response to androgen-deprivation The LNCaP Hollow Fibre model combined with qRT-PCR was employed to capture in vivo gene expression representative of physiological levels and castrated levels of androgen (Figure 2.5). We expected that the genes that had increased levels of expression in vitro in response to androgens, would decrease expression in vivo in response to castration (androgen-deprivation). Conversely, we expected that the genes that had decreased levels of expression in vitro in response to androgens, would increase expression in vivo in response to castration. These in vivo results would be consistent with androgen-responsiveness of the candidate genes. Of the candidate genes examined, 13 of 16 genes showed significant changes in gene expression in response to androgen-deprivation (Figure 2.5). As anticipated, expression of ARL6IP5, CAMK2N], CXCR7, and ST7 increased, while CENPN, CREB3L4, ERRFIJ, FKBF5, KLK3, LRIGJ, NCAFD3, PAKJIPJ, and TAOK3 decreased levels of expression in response to castration. No significant changes in gene expression in vivo was measured for ADAMTSJ, HSP9OB], or PRKA CB, suggesting that in vivo, other factors may influence their expression. 50 Alternatively, the expression kinetics of each specific gene and half-life of its transcript may vary considerably. The time of harvesting samples and measuring changes in expression of genes in response to androgen-deprivation was at 10 days in vivo compared to 16 hr in vitro in response to addition of androgens (10 nM R1881). Different levels of androgen may also have profound effects on proliferation and differentiation. Physiological levels of androgen in male Nude mice may be considerably lower than the levels used in vitro. Androgen at 10 nM inhibits proliferation of LNCaP cells in vitro while 0.1 nM is optimal for proliferation42 2.4 DISCUSSION Androgens are essential for the growth, development and maintenance of the prostate. The importance of androgens and AR continues throughout most stages of prostate cancer and provides a therapeutic pathway for clinical intervention. Androgen-deprivation and drugs that block the transcriptional activity of the AR provide treatments for locally advanced and metastatic prostate cancer. Unfortunately, these forms of therapy are not curative and the malignancy will progress to a stage that no longer responds to androgen-deprivation therapies. Identification of genes whose expression changes in response to androgen and androgen deprivation in prostate cancer will reveal the genes and pathways involved in the proliferation, survival and potentially hormonal progression of this disease. Here, we created LongSAGE libraries to obtain quantitative gene expression profiles of LNCaP human prostate cancer cells treated with, or without, androgen and revealed the following: 1) 33,385 tag types in the R1881 L0ngSAGE library and 31,764 tag types in the vehicle LongSAGE library; 2) the majority (64% to 67%) of tag types in each LongSAGE library were singletons which may represent very low abundance transcripts ( 5 transcript copies per cell); 3); when mapped tags were clustered and ambiguous mappings were removed, the tag types in the R1881 and vehicle LongSAGE libraries represented 7,484 genes and 7,441 genes, respectively; 4) 53% of tags mapped solely to known exons, 9% solely to known introns (novel transcript variants), and 38% to intergenic regions (novel genes or transcript variants); 5) the most highly abundant L0ngSAGE tag mapped to human mitochondrial NADH ubiquinone oxidoreductase chain 4 involved in metabolic energy; 6) the lower abundance classes were enriched for genes with functions in regulating transcription; 7) 87 genes were differentially 51 expressed by two-fold (p 0.00 1) in response to androgen representing 0.34% of the total tag types (131 differentially expressed tag types / 38,574 total tag types); 8) novel androgen regulated genes (direct or indirect) identified and validated were ARL6IPS, BL VRB, Cl 9orf48, C]orfl22, C6orf66, CAMK2N], CCNJ, DERA, ERRFI], GLUL, GOLPH3, HMJ3, HSP9OBJ, MANEA, NANS, NIFSNAP3A, SLC4]A], SOD], SVIP, TAOK3, TCP], TMEM66, USP33, and VTA1; 9) expression of ADAMTS], ARL6IP5, CAMK2NJ, CAPNSI, CENPN, CREB3L4, CCNI, CXCR7, FKBP5, HSP9OB], KLK3, LRIG], NCAPD3, PAKJIPJ, PRKACB, ST7, and TAOK3 was increased in LNCaP cells compared to prostate cancer cells lacking a functional AR; and 10) significant differences in levels of expression of ARL6IP5, CAMK2NJ, CENPN, CREB3L4, CXCR7, ERRFI], FKBP5, KLK3, LRIGJ, NCAPD3, PAK]IPJ, ST7, and TAOK3 were measured in vivo in response to androgen-deprivation. We identified 87 genes with statistical significant differences in levels of expression, with 35 genes identified here for the first time as showing changes in expression in response to androgen in prostate cancer cells. Expression trends were validated for 26 of these 35 genes using qRT PCR. These studies confirmed that levels of expression of 24 genes (ARL6IP5, BL VRB, C]9orf48, Clorfl22, C6orf66, CAMK2N], CCNJ, DERA, ERRFI], GLUL, GOLPH3, HM13, HSP9OB], MANEA, NANS, NIPSNAP3A, SLC4JA], SOD], SVIP, TAOK3, TCP], TMEM66, USF33, and VTA]) respond to androgen in prostate cancer cells. The products of these genes are involved in amino acid and protein synthesis, cofactor transport, protein trafficking, response to oxidative stress, as well as signalling pathways that regulate gene expression, proliferation, apoptosis, and differentiation. Androgen alters the expression of genes whose protein products may affect local glutamine concentrations in prostate cancer cells. Glutamine is the most common amino acid present in mammalian blood43.This amino acid is involved in a variety of cellular processes such as metabolism, apoptosis, proliferation, and protein synthesis/degradation44.Glutamate-ammonia ligase (GLUL) and solute carrier family 41, member 1 (SLC4]A1) transcripts were increased in response to androgen. A function of GLUL is to metabolize glutamine from glutamate and ammonia45.The activity of GLUL is dependent on binding to divalent cations such as Mg2. Interestingly, SLC4 1 Al is a putative membrane protein that mediates preferential Mg2 uptake in epithelial cells46. Expression of ADP-ribosylation like factor-6 interacting protein 5 (ARL6IP5) 52 was decreased in response to androgen. Inferred by homology to a rat protein, it probably inhibits glutamate uptake into cells47’48 Taken together, these results suggest that androgen signalling promotes the synthesis of glutamine. In response to androgen, there were alterations in expression of genes whose protein products function in protein folding, alteration, degradation, and transport. For example, androgen increased levels of expression of T-complex 1 gene (TCPJ), which is an essential molecular chaperone that resides in the cytosol and aids in the folding of cytoskeletal and cell cycle proteins49.TCP1 has been shown to be over-expressed in human colorectal cancer50. Histocompatibility (minor) 13 (HM13), n-acetylneuraminic acid synthase (NANS), and mannosidase, endo alpha (MANEA), function to alter protein structure. HM 13 is an endoplasmic reticulum membrane protein that proteolyzes signal peptides and generates epitopes recognizable by the immune system51,and NANS catalyses the synthesis of sialic acid to generate glycoproteins52.While expression of genes for these proteins was increased by androgen, expression of M4NEA was decreased. MANEA is a golgi apparatus membrane protein thought to hydrolyse the glucosyl unit from glycosylated mannosidase53.Androgen lowered gene expression of small VCP/p97-interacting protein (S VIP), which inhibits endoplasmic reticulum associated degradation (ERAD). Thus, with decreased SVIP there should be increased ERAD which degrades misfolded or unfolded proteins to prevent accumulation of aberrant proteins54 and is consistent with increased protein synthesis in prostate epithelial cells in response to androgen. Androgen also increased expression of golgi phosphoprotein 3 (GOLFH3), a membrane protein of the golgi stack that is suggested to regulate protein trafficking55 and decreased expression of vps2o-associated 1 (VTAI) and nipsnap homologue 3A (NIPSNAP3A) that have roles in multivesicular body sorting56 and vesicular transport (inferred by homology to a Caenorhabditis elegans protein)57’, respectively. The main molecular chaperone of the endoplasmic reticulum is heat shock protein 90 kDa beta member 1 (HSP9OB 1 In this study, expression of HSP9OBJ was increased in vitro, but we did not detect change in expression in vivo in response to androgen. The low 1.6-fold change in expression of HSP9OB1 in response to androgen in vitro, may be insufficient to overcome the biological variation in vivo. HSP9OB 1 mediates folding, assembly, and secretion of proteins60.It can protect cells from apoptosis6t, present malfolded proteins to the proteosome59,and aid in antigen presentation via MHC class I molecules59.Expression of HSP9OB] is induced by metabolic stress [glucose starvation, 53 estrogen, and interleukin 6 (IL-6)]59.Overall, these results indicate the androgen signalling axis regulates protein production and transport. Androgens regulate gene expression of proteins involved in signal transduction pathways. Tao Kinase 3 (TAOK3) is a member of the sterile 20-family of kinases62.These kinases are often involved in mitogen activated protein kinase (MAPK) pathways, c-Jun N-terminal kinase/stress activated protein kinase (JNKJSAPK) and extracellular signal-regulated kinase (ERK). JNKJSAPK and ERK pathways are usually activated in response to stress signals and pro- inflammatory cytokines, respectively. Signalling through JNKJSAPK and ERK pathways result in mammalian cellular responses such as proliferation, differentiation andlor apoptosis63. Whether TAOK3 is a positive or negative regulator of MAPK pathways is controversial64’5 In our study, there was an association between relatively high levels of TAOK3 gene expression and androgen in prostate cancer cells both in vitro and in vivo. ERBB receptor feedback inhibitor I (ERRFIJ) gene expression was increased in response to androgen which is consistent with reports of this gene being induced by growth factors, stress, and hormones66.ERRFIJ gene expression in AR-negative DU145 and PC-3 cells was not significantly different, but displayed a trend of increased ERRFI] expression compared to AR- positive LNCaP cells. The levels of expression of ERRFIJ had a trend that correlated to doubling time of the cell lines, with PC3 cells doubling faster than DU145 which is quicker than LNCaP67. These results suggest that levels of expression ofERRFIJ may be due to increased proliferation, and not AR. Immediate, early response ERRFI1 gene encodes a non-kinase adaptor protein containing a cdc42/Rac interacting arid binding (CRIB) domain, Src-homology-3 (SH3) domain binding motif, and a 14-3-3 protein binding motif66.The biological significance of the SH3 and 14-3-3 binding motifs have yet to be determined. However, the CRIB domain of ERRFI1 has been shown to negatively regulate Cdc42, epidermal growth factor (EGF) receptor, and hepatocyte growth factor (HGF) signalling, while positively regulating nuclear factor kappa B (NFKB) signalling66.Each of these pathways has an effect on MAPK signalling. CAMK2N 1 is an inhibitor to calcium/calmodulin-dependent kinase II (CAMK2)68.CAMK2 is a well characterized ubiquitously and highly expressed protein involved in a plethora of cellular 54 processes. The action of CAMK2 is most studied in neurons, where CAMK2 is thought to be involved in gene expression, cell signalling, ion-channel function, cytoskeletal interactions and morphology69.Interestingly, CAMK2 gene expression has been shown to increase in response to androgens70.Therefore, the down-regulation of gene expression of its inhibitor, CAMK2N 1, by androgens, as shown here, could result in increased CAMK2 activity in the presence of androgens. CAMK2N] gene expression in vivo increased following castration of the hosts. Interestingly, we detected two LongSAGE tags for CAMK2N] each of which were decreased in response to androgen, but each with distinct levels and fold-change. These two tags probably represent alternative splicing of CAMK2N] with each transcript variant differentially regulated. Androgen elicits oxidative stress in LNCaP prostate cancer cells71.However, it is not yet clear if this occurs simply as a byproduct of induction of proliferation, or a more direct relationship between AR and expression of genes involved in the regulation of oxidative stress. Here, we provide support for the latter. Superoxide dismutase 1 (SOD 1) is an enzyme capable of converting free superoxide radicals to molecular oxygen or hydrogen peroxide72 and protecting cells from oxidative damage. In vitro SOD] expression was decreased by androgens. Interestingly, expression of SOD] is lower in prostatic intraepithelial neoplasia (PIN) and prostate cancer relative to benign prostatic tissue73,suggesting that defense against superoxide radicals is compromised. These clinical data support the suggestion that dietary supplements of antioxidants may aid in the prevention of prostate cancer74. 2.5 CONCLUSION Delineation of the molecular basis of androgen action in the prostate requires identification of genes and pathways. Here, we report 24 genes that alter levels of expression in response to androgen in prostate cancer cells that are involved in protein synthesis and trafficking, response to oxidative stress, transcription, proliferation, apoptosis, and differentiation. These genes are potentially critical for the function and maintenance of the prostate and represent targets for clinical intervention. 55 Table 2.1 Primer sequences and amplification product sizes for candidate transcripts I Exons according to Ensembi CATCTTCATACTGCAAAGCACTG 10 10-11 Gene RefSeq Access. No. Forward Primer (5’-3’) Reverse Primer (5’-3’) Product Size Exons I AD.4 lviTSI NM_006988 ACTGCAAGGCGTAGGACAG CCACAAGCATGGTTTCCAC 92 1-2 ARL6JP5 NM_006407 CATG1TPGGAGGAGTCATGG GAGGTfCCGAAGTCTCAACG 91 2-3 BL IRB NM 000713 GAAGTACGTGGCTGTGATGC CCAGGTCATG3TIGGAGATG 113 4-5 ClorfI22 NM 198446 AGCTCCTGGACACCATCG GCTCCAGGTTTGGCTGAGAC 103 2-3 C19orf48 NMI 99249 AAGGGCCTGACCATCACTC ACGCCTAGGCAGGAAACAG 96 1-2 C6orf66 NM_U 14165 AAAGATGAAAAGCTGCTGTCG CTGAATTCCTFCGGCTCTTG 113 2-3 CAMK2NI NM_018584 TGCAGGACACCAACAACTFC GCACGTCATCAATCCTATCATC 114 1-2 CAPNS? NM_00 1003962 AGATGGCACTGGACAAATCC TCCTATAGCAAGGCAGTGAGG 106 10-11 CCiV1 NM_006835 TCATTCCTGATTGGCTTTCTC GAAAGGTGATGTGCCACAAG 103 6-7 CENPN NM_0i 8455 ATACACCGCTFCTGGGTCAG TGCAAGCTTTCTTCA’ITFCG 99 6-7 CREB3L4 NM_130898 TTCCAGAGTCGACCAGAAGC TGTTACGTCCTFGTGGGTCA 87 9-10 CXCR7 NM_0203 11 CCCGGAGGTCA3TFGATfG GCTGATGTCCGAGAAGTTCC 87 1-2 DERA NM_U 15954 AGTGGCTGAAGCCAGAACTC AAGCTGCATATCTTCCAGTCAC 99 8-9 ERRFI1 NM 018948 CCGATAACCATGGCCTACAG AITCATCGGAGAGA1TFGG 87 3-4 FKBP5 NM_0041 17 CGCAGGATATACGCCAACAT GAAGTCITCTFGCCCATFGC 86 I 1-12 GAPDH NM_002046 CTGACTTCAACAGCGACACC TGCTGTAGCCAAATTCGTTG I 4 8-9 GLUL NM_0U.2065 TGCCATACCAACTTCAGCAC TGCCGCTTGCTFAGTTTCTC 89 6-7 GOLPH3 NM 022 130 CTCCAGAAACGGTCCAGAAC CCACCAGGT1TrTAGCTAATCG 114 3-4 HMI3 NMI 78580 GGCCAA000AGAAGTGACAG ATGCCTCTGTTCCCTC1TFG 95 10-11 HSP9OBJ NM 003 299 GCATCTGATTACCTFGAATFGG TGGGCTCCTCAACAGTUC 115 6-7 KLK3 NM_U0 1648 CCAAGITCATGCTGTGTGCT CCCATGACGTGATACCTTGA Ill 4-5 LRIGI NM_U 15541 GACGGCTGTGAAGAAAAAGC CTGTGGAGTCCGGGTGATAC 92 18-19 MAIVEA NM 02464 I TAGCAATCGAGATGATCAAAAC AAGAGCATTGCCAGTCTTCG 109 4-5 NANS NMOI 8946 CGGTCAGTGCGTCTFGTG A3TfFCACTTTGGCCACCAC 113 5-6 NAPD3 NM_0 15261 GGGCGCTTCTFACTCTCCTC GGGTGAGAA]TITFC1TFCTFGG 98 16-17 NIPSNAP3A NM_0 15469 CCATGAGGATCCCAGAGTTG TCAGTGGTGAAAACGATGTAGG 101 5-6 N7T NM 006183 CCACAAAATCTGTCACAGCAG CC’ITFCCATIlTFGTCA]lTCC 89 3-4 PAKIIPI NM_UI 7906 CGTGTCTTGGAGTGTGGCTA AGGCTCCTTTTFGCCAATTT 113 9-10 PRKACB NM_182948 GCCACGACAGATfGGAflG AATTGCTGGTATCTCCAGAGC 89 9-10 PRIVPIP NM_024066 CCTCAGCCTGCAACACATAG AAGCCTCGATAGGCGAGTG 92 6-7 RHOU NM 021205 CCCGTGAGACTCCAACTCTG TGAAGCAGAGCAGGAAGATG 100 2-3 SLC4IAI NM_173854 GCACACCACCCTCACACTC TCCAGTCTGCGATGTACAGG 89 10-il SOD? NM_000454 CCCAGGHAACCCAGAACG ACCCCTGCTTG1TFGUGTC 88 4-5 ST7 NM_UI 8412 CGGAACTTATGGGGGTCTFC ACAGACTGGATGGGAGGATG 102 14-15 SVJP NM_I48893 AGGGYTCTCAAGCTGTCGTC TGCAAGC1TFGCTC]TTTCTC 101 1-2 TAOK3 NM 016281 CGCAGAGCACACC1TGAG CGCTCTTGCC1TFCCAATAG 98 20-21 TCPI NM_U3U752 TGTGGCCGATGTGTCTATTG ACC1TEGCCCAAGTCATCTG 109 II -12 Tlv1EM66 NMUI 6127 GGGCAGCTATFCGGTATGITC TGCATCCAGTGHTGACTCC 110 5-6 USP33 NM_2U 1624 AAATGTGGTAATGTGATGCTFAGG GGTCGCAGGATAACTFCAGG I 3 23-24 VTA I NMU1 6485 CGCAC11TFCAATACAATTTCC 56 Table 2.2 Composition of LongSAGE libraries Library Rl881 Vehicle Unfiltered Total Tags 121,760 103,391 No. ofBsd Tags 528 383 Total Tags 121,232 103,008 TagTypes 33,385 31,764 No. of Duplicate Ditags 6,763 5,193 % of Duplicate Ditags 5.579 5.041 Average QF’of Tags 89.64 89.67 No. of Tags QF<95% 22,816 17,095 Total Tags 98,416 85,913 Tag Types 23,830 24,594 Total Tags Combined 184,329 Tag Types Combined 38,576 No. ofLDTsType1 219 34 No. ofLDTsType 11 216 18 Total Tags 97,981 85,861 Tag Types 23,828 24,592 Total Tags Combined 183,842 Tag Types Combined 38,574 QF, Quality Factor s LDTs, Linker-derived Tags 57 Table 2.3 Ck trfsfT -.,,CAflF t dt,tinn Tag Count per 100,000’ l 2-4 5-9 10-99 100-999 ?l,000Tag Frequency & . . u Abundance Transcript Copies per Cell 5 10-20 25-45 50-495 500-4,995 5,000 % Transcript Abundance in Cell 0.00 1 0.002-0.004 0.005-0.009 0.01-0.099 0.1-0.999 I R188l TotalTags 15,141 13,985 11,055 32,800 21,971 3.029 TagTypes 15,141 5,464 1,703 1,417 101 2 . Total Tags 16,562 10,229 11.633 26,466 18,453 2,518Vehicle TagTypes 16,562 4,427 2,195 1,313 93 2 % of Tags that Map to Transcription Factors,Z 9.14 8.94 7.95 6.0 0 0 % of Tags that Map 29.40 57.82 76.22 83.1 85 lOO % ofTags Significantly Differentially Expressed60 0.4 1.45 16.17 25.38 58.12 100 i Tag count per 100,000 = (observed tag count/total tags in the libraiy) x 100,000 U Transcript copies per celIu= (observed tag count/total tags in the library) x 500,000 % Transcript abundance in cellu (transcript copies per cell/500,000) x 100% w Calculation based on —500,00() transcripts in a cell (Hastie and Bishop 1976) % of tags that map as transcription factors = (no. of genes with “transcription regulation acivity”/no. of genes with unambiguous sense mappings and GO terms) x 100% Mapped unambigously sense to RefSeq and subjected to Gene Ontology (GO) analysis Tag types front each tag frequency class ofRl88l and vehicle L0ngSAGE libraries were combined X % of tags that map = (no. of genes with sense mappings/combined total tag types) x 100% 13 Mapped sense (md. ambiguous) to RefSeq y One tag was mapped sense using Ensembl gene s % of tags significantly differentially expressed = (no. of significantly differently expressed tag types in class/combined total tag types in class) x 100% a Statistics according to the Audic and Claverie test statistic (p 0.001) 58 Table 2.4 T , X Lb No. of Tag Types that Mapped No. of Tag Types that Mapped No. of Tag Types that Total No. of Tag Typesrat) Unambiguously to (Genes) Ambiguously Did Not Map (Clustered)Y Rl88l 14,587 (7,484) 3,754 10,215 28,556 Vehicle 13,626 (7,441) 3,286 9,066 25,978 X Ensembl gene (v38) was used for mapping j’ Clustering amalgamated I -off tags with likely ‘parent’ tags to correct for PCR/sequencing errors Clustering altered the number of tag types without changing the total number of tags in the libraries 59 Table 2.5 Number of tag types found to be significantly differentially expressed between R1881 and vehicle libraries ° Direction of Change p 0.001 p 0.01 p 0.05 Up Regulated 83 196 455 Down Regulated 48 .L2.fl 436 Total 131 316 891 % of All Tag Types 0.34% 0.82% 2.31% ci Statistics according to the Audic and Claverie test statistic 60 Table 2.6 L0ngSAGE tags corresponding to genes known to increase expression in response to androgen in LNCaP cells°’° Tags/lOO 0011 d,t Fold cd RefSeq/Ensernbl LoneSAGE Ta Sequence Vehicle RI 881 Change Access. No. GTGACAAGTGACAGAGT 1 19 20 NM 007011 ACGTCACCAI1TFTAAC 1 24 20 NM 004457 TACTTTATAAGTATFGG 14 59 4.2 NM 006988 TAGCTCTATGGGGGGAG 35 75 2.1 NM 000701 GTTGTGGTTAATCTGGT 48 109 2.3 NM 004048 ACTTAAGGAACTTATCT 14 42 3.0 NM 015415 AAAGGAAAATAAAAATF 3 27 9 NM 018455 CTGTGATGTGACTCCTG 5 30 6 NM_030806 CAGATGAGATGTGAGCT 5 33 7 NM 130898 TGTTTATCCTAAACTGA 21 115 5.5 NM 020548 TCCCCGTGGCTGTGGGG 106 356 3.36 NM 014762 GAAATPAGGGAAGCCTT 9 34 4 NM 015036 AGATCCTACTTAGTATG 16 51 3.2 NM 004462 GGATGGGGATGAAGTAA 50 366 7.3 NMOO 1648 CCTCCAGCTACAAAACA 35 223 6.4 NM 002273 TAAAATATTGAAGTGTC ND6 42 40 NM 015541 40 NM 015261 70 NM 006096 2.9 NMO 14445 2.9 ENSG00000 196930 ATGCAGCCATATGGAAG 20 208 10 NM 002539 GCCAAGGGGCCAGCTGC 17 45 2.6 NM 002541 TAATI1TFACTTTGTAC 5 39 8 NM 017906 TATGTAATATGC1TFCT 27 164 6.1 NM 003711 AAACACCAACAACTGGG 5 31 6 NM 003711 GCGCTGGAGTGAGATGG 59 126 2.1 NM 031287 GGA1TFGAACATATGAA ND 13 10 NM 033 102 ACCTFGTGCCCGATFCT 47 238 5.1 NM 003 104 HGNC Gene Symbol Description ABHD2 Abhydrolase domain containing 2, transcript variant I .4c’SL3 Acyl-Co.A synthetase long-chain family member 3, transcript variant I ADAMTS1 * ADAM metallopeptidase with thrombospondin type I motif, I ATP]AI ATPase, Na+IK+ transporting, alpha I polypeptide, transcript variant I B2M Beta-2-microglobulin BRP44 Brain protein 44 CENPIV’ Centrorneric protein N CIot-j21 Chromosome 1 open reading frame 21 CREB3L4 cAMP responsive element binding protein 3-like-4 DBJ Diazeparn binding inhibitor (GABA receptor modulator, acyl-Coenzyme A binding protein) DHC’R24 24-dehydrocholesterol reductase EVDODI Endonuclease domain containing I FDFTI Farnesyl-diphosphate farnesyltransferase I PKBP5 FK506 binding proteinS K(NMA I Potassi urn large conductance calcium- activated channel, subfamily M. alpha member i, transcript variant 2 KLK3* Kallikiein 3, (prostate-specific antigen), KRT8 LRIGI* transcript variant I Leucine-rich repeats and immunoglobulin-like domains I ,VcAPD3* Non-SMC condensin complex subunit D3 A’DRGI N-myc downstream regulated gene I N/A9 Stress-associated ER protein I N/A9 Similar to Vesicle-associated membrane protein-associated protein A mRNA Ornithine decarboxylase I Oxoglutarate (alpha-ketoglutarate) dehydrogenase (Ii poarnide), nuclear gene encoding mitochondi-ial protein, transcript variant I PAKIIPI* PAKI interacting protein I PPAP2A Phosphatidic acid phosphatase type 2A, transcript variant I PPAP2A Phosphatidic acid phosphatase type 2A isoforms I and 2 transcript variant I SF3B5 Splicing factor 3B, subunit 5, lOk.Da SLC45A3 Solute carrier family 45, member 3 SORD Sorbitol dehydrogenase GTFCCAGTGAGGCCAAG 3 50 ACCTAGCCACTGCTGGG 1 24 20 NM_004117 20 NM 002247 TCCCTGAGCACCATTGC ND 35 GGACTTFCCHCCCTCT I 72 TTTAGGTAAACGAAAGC 19 56 AGGTTTTGCCTCATTCC 13 38 Keratin 8 ODCI OGDH GTG1TFACGTGATCCAC I 18 20 NM 004578 RAB4A RAB4A, member RAS oncogene family TATGTATAAATGGACCT ND 16 20 NMO2 1205 RHOU4 Ras homolog gene family, member U TTTGAAATGAGGTCTGT 14 48 3.4 NM 002970 SAT Spermidine/spenitineNt acetyltransferase GCAACAGCAATAGGATT 3 22 7 NM_0 14302 SEC61G Sec6l gamma subunit, 61 Table 2.6 continued Tagc/l 1)1) (100 Fold RefSeq/Ensembl HGNC Approved LongSAGE Tag Sequence Vehicle R1881 Change Access. No. Gene Symbol Description AAAATCTGCCACTCAGG ND 12 10 NM 003 104 SORD Sorbitol dehydrogenase GTGCAGGGAGACATCTG 3 55 20 NMO 12391 SPDEF SAM pointed domain containing ets transcription factor TTAAGGGATGATGGCTT ND 12 10 NM_024636 STEAP4 STEAP family member 4 TACTACAGCTATA11TG 6 52 3.3 NMOI 6192 TMEFF2 Transrnemhrane protein with EGF-like and 2 follistatin-like domains 2 TGATGTCTGGTCTGAAT 1 17 20 NM 020 182 T’vIEPAJ Transmembrane, prostate androgen induced RNA, transcript variant I CAAATAAATTATGCGAT 5 64 lO NM 005656 TMPRSS2 Transmembrane protease, serine 2 TGAAAAGCTTAATAAAT 7 28 4 NM 005079 TPD52 Tumor protein D52, transcript variant 3 TTAAAGATFTAGACACC 10 36 3.6 ENSG00000 140416 TPMI Tropomyosin I apha chain TTCTCTACACAATTGTA 6 36 6 NM_006022 TSC22DI TSC22 domain family, member 1, transcript variant I a Statistics according to the Audic and Claverie test statistic (p 0.001) h ND, not detected c ND tags were assigned a value oil when calculating fold change d Appropriate significant figures are displayed o Ambiguously mapped tags and tags with a fold change less than 2-fold have been excluded from the table q N/A = there is no HGNC approved gene symbol for this tag Tag count per 100,000 = (observed tag count/total tags in the library) x 100,000 (ln cases where a tag mapped to >1 transcript variant of the same gene the RefSeq accession number for transcript variant I was displayed * Gene further characterized in this paper 62 Table 2.7 L0ngSAGE tags corresponding to genes known to decrease expression in response to androgen in LNCaP cells°”1 TagcJlIlO.000 d,t Fold d RefSeq/Ensenibl HGNCL0ngSAGE Tag Sequence Vehicle R188l ChangeC Access. No. Gene Symbol Description9 CAAAAGCTTATTCTTGT 29 3 - 10 NM_0 16613 C4o,f18 Chromosome 4 open reading frame 18, transcript variant 2 TCACACAGTGCCTGTCG 19 I -20 NM 020311 C’XCR7* Chemokine orphan receptor I ACAAACCCCCACCCCAG 41 7 -6 NM 013330 AIME7 Non-metastatic cells 7, protein expressed in, transcript variant I, Nucleoside diphosphate kinase AATCTCTCAATTATAGG 34 9 -4 NM 006183 NTS5 Neurotensin ATCAACTGGAGGCTCAG 15 NDb -20 NM 005013 NUCB2 Nucleobindin 2 CCAAAATFAGGAAAAAC 15 I -20 NM 002577 P.4K2 p21 (CDKNIA)-activated kinsae TTACGTTTGGGAAAAAT 19 2 -9 NM 032971 PCDHI Ii Protocadherin 11 Y-linked, transcript variant ak TGACTTTGGTGCCGTTA 12 ND -10 NM_003629 PIK3R3 Phosphoinoaitide-3-kinase, regulatory subunit 3 (p55, gamma) AGCAAATATGTCAAGGG 47 16 -2.9 NM_I 82948 PRKAcB5 Protein kinase, cAMP-dependent, catalytic, beta, transcript variant I GACTATFCCATATTAAA 27 I -30 NM_0l8412 ST75 Suppression of tumorigenicity 7. transcript variant A GAGGG1TFTAAATGGAG 79 9 -9 NM_001077 UGT2BI7 UDP glucuronosyltransferase 2 family, polypeptide BI 7 a Statiatica according to the Audic and Claverie test statistic (p 0.001) h ND. not detected c ND tags were assigned a value of I when calculating fold change c/Appropriate significant figures are displayed jNegative fold change value indicates down-regulation in response to R188l k Tag has a single base pair permutation, insertion, or deletion with respect to gene a Ambiguously mapped tags and tags with a fold change less than 2-fold have been excluded from table /Tag count per 100,000 = (observed tag count/total tags in the library) x 100,000 In cases where a tag mapped to >1 transcript variant of the same gene the RefSeq accession number for transcript variant I was displayed * Gene further characterized in this paper 63 1,fl Table 2.8 LongSAGE tags corresponding to genes not previously reported to increase expression in response to androgen in LNCaP cells Tagc/I(V)000 ‘‘ Fold RefSeq/Ensembl HGNC L0ngSAGE Tag Sequence Vehicle RI 881 Change’4 Access. No. Gene Symbol Description9 TCTTTATTAGAAAAAAA ND b 16 20 NMO 14265 ADAM28 ADAM metallopeptidase domain 28, transcript variant I k AGGAGCAAAGGAAGGGG 51 107 2.1 NM_000713 BLVRB* Biliverdin reductase B (flavin reductase(NADPH)) TTTTGGGGGCTTTTAGC 16 44 2.8 NM 198446 CIorfI22* Chromosome 1 open reading frame 122 GGGCCCCAAAGCACTGC 22 69 3.1 NM 199249 C)9opf48* Chromosome 19 open reading frame 48 CCCCAGTFGCTGATCTC 24 60 2.5 NM_001003962 cAPIVSI* Calpsin, small subunit I, transcript variant 2 CTFAAGAAAAATGCACT 1 23 20 NM_018948 ERRFII * ERBB receptor feedback inhibitor I TACAGTATGTTCAAAGT 13 52 4.0 NM 002065 GLUL* Glutamate-ammonia igase (glutamine synthetase), transcript variant 1g,i TTAATAGTGGGGC1TrC 10 39 3.9 NM_022 130 GOLPH3* Golgi phosphoprotein 3 (coat protein) GCCAGGGCGGGCCACTG ND 16 20 NM_I 78580 HMJ3* Histocompatibility (minor) 13, transcript variant 2’ GAGGAAGAAGAAGCAGC ND 14 10 NM 003299 HSP9OBI* Heat shock protein 9OkDa beta (Grp94), member I GGCAAGGGGGGTCCCCA 1 20 20 NM 002273 KRT8 Keratin 8m ACTCCAAAAAAAAAAAA 41 81 2.0 XM_376 154 Similar to 40S ribosomal protein S15 (RIG protein), transcript variant I GGGTTGGCTT’GAAACCA 6 30 5 ENSG000002IOI51 N/A Non-coding predicted mitochondrial gene’° GAGAGCTCCCGTGAGTG 72 122 1.7 NC_001807 N/A Intergenic region of mitochondrial genome TCGGACGTACATCGTTA 40 223 5.6 No map N/A N/A GCAAAAAAATCAAGTCT 22 66 3.0 NM_018946 NANS* N-acetylneuraminic acid phosphate synthase(sialic acid synthase) TCTT[TAGCCAATTCAG 2 36 20 NM_006 167 NKX3-I NK3 transcription factor related, locusl TACTTTTGGCCTGGCTG 6 35 6 NM_I 73854 SLC4IA] * Solute carrier family 41, member I GAGAGCCTCAGAATGGG 5 26 5 NM_016281 TAOK3* TAO kinase 3 GAAGTTATGAAGATGCT 41 106 2.6 NM_030752 TCPI T-complex protein I, transcript variant I CAGTTCTCTGTGAAATC 40 93 2.3 NM 016127 TME/t,166* Transmembrane protein 66 ATGGCTTFGTTTTGGTT ND 14 10 NM_201624 USP33* Ubiquitin specific protease 33, transcript variant 2 a Statistics according to the Audic and Claverie test statistic (p 0.001) b ND. not detected c ND tags were assigned a value of I when calculating fold change d Appropriate significant figures are displayed e Gene family, but not this family member, previously described to change expression in response to androgens g Protein known to change expression in reponse to androgens 6 Gene known to change expression in response to androgens, but in the opposite direction as reported here i Gene known to change expression in response to androgens in cells other than prostate k Tag has a single base pair mutation , insertion , or deletion with respect to gene map niTag maps to the strand opposite of the gene ii Ambiguously mapped tags and tags with a fold change less than 2-fold have been excluded from the table p NC 001807, refers to the complete genorne ofmitoehondria in humans All mitochondrial genes in the RefSeq database are assigned the same accession number by NCBI q N/A, there is no HGNC approved gene symbol or description for this tag / Tag count per 100,000 = (observed tag count/total tags in the library) x 100,000 tp In cases where a tag mapped to >1 transcript variant of the same gene the RefSeq accession number for transcript variant I was displayed Gene further characterized in this paper 64 Table 2.9 LongSAGE tags corresponding to genes not previously reported to decrease expression in response to androgen in LNCaP cells°’ Tagc/l 00.000 d,t Foldc,af RefSeq/Ensembl HGNC L0ngSAGE Tag Sequence Vehicle Rl881 Change Access. No. Gene Symbol Description GTCTAGAATCTGTACCC 29 8 -4 NM_006407 ARL6JP5* ADP-ribosylation-like factor 6 interacting protein 5 TCAAGAGCCGAAGGAAT 12 ND -10 NM_0 14165 C6orf66* Chromosome 6 open reading frame 66 GTATTTGCAAAAATGCC 118 24 -4.9 NMOI 8584 CAMK2NI * Calcium/calmodulin-dependent protein kinase II inhibitor I AAAAGAGAAAGCACTFT 30 5 -6 NM_018584 C’AMK2NI Calcium/calmodulin-dependent protein kinase 11 inhibitor .1 TTATAACTGAATTTAGT 51 11 -4.6 NM 006835 CCNI Cyclinl i,h GCCAGGAGAAGGGACAG 34 7 -5 NP 775809 CNBDJ N/A1 TGGTACTCATTTCAGGC 12 ND -10 NMO 15954 DERA* 2-deoxyribose-5-phosphate aldolase hornolog AATCATAATGGATTCTF 16 ND -20 NM 024641 MANE.4* Mannosidase, endo-alpha CTAAGACTTCACCAGCC 19 2 -10 ENSG000002 10082 Non-coding predicted mitochondrial rRNA genek CATTTGGTAI1TTCGTC 30 8 -4 NC_00l807” N/A Intergenic region of mitochondrial genome GTATTTCAGTGTCTGTC 33 9 -4 NMO 15469 NIPSNAP3A * Nipsnap homolog 3A GTGTGTGGTGCCCCCAG 23 5 -5 NM_024066 PRIVPIP* Prion protein interacting protein GTGTTAACCAGCTAAAG 122 60 -2.0 NM 002948 RPLI5 Ribosomal protein L15 GCACAAGAAGA’FFAAAA 58 25 -2.3 NR002746 SNORD47 Small nucleolar RNA, C/D box 47 on chromosome I AAAAAGCAGATGACTTG 77 37 -2.1 NM 000454 SODI* Superoxide dismutase 1, soluble (aniyotrophic lateral sclerosis I (adult)) GTTTGGTTATAAATTCT 26 3 -10 NM_148893 SVIP* Hypothetical protein DKFZp3I3A2432, transcript variant I TATTAGAGAATGAAAAG 17 2 -9 NIvI 016485 VTAJ* VPS2O-associated I homologue ci Statistics according to the Audic and Claverie test statistic (p 0.001) 6 ND, not detected c ND tags were assigned a value of I when calculating fold change d Appropriate significant figures are displayed h Gene known to change expression in response to androgens, but in the opposite direction as reported here i Gene known to change expression in response to sndrogens in cells other than prostate jNegative fold change value indicates down-regulation by RI 881 k Tag has a single base pair permutation, insertion, or deletion with respect to gene ,nTsg maps to the strsnd opposite of the gene it Ambiguously mapped tags and tags with a fold changeless than 2-fold have been excluded from the table p NC_00I 807 ret’ers to the complete genome of mitochondria in humans All mitochondrial genes in the RefSeq database are assigned the same accession number by NCBI q N/A = there is no HGNC approved gene symbol for this tag tTag count per 100,000 (observed tag count/total tags in the library) x 100,000 (phi cases where a tag mapped to >1 transcript variant of the same gene the RefSeq accession number for transcript variant 1 was displayed * Gene further characterized in this paper 65 R1881 Figure 2.1 Relationship between LongSAGE library compositions. This Venn Diagram shows the tag types and genes exclusive to, and shared by each LongSAGE library, R1881 and vehicle. Tags were mapped unambiguously sense to RefSeq transcripts and redundant mappings were removed. Singletons are tags counted only once in each library, but may be common to both libraries. 66 0_______________________________________ 0 5 0 50 100 500 1000 Vehicle Library (L0ngSAGE tags counts) Figure 2.2 Confidence intervals highlight expressed tag types with non-linear relationships between LongSAGE libraries. Scatter plot dots represent tag types and their placement on the axis indicates the frequency of observation in either of the LongSAGE libraries. Tag types that fall outside the confidence interval (Cl) lines are statistically significantly differentially expressed (Audic and Claverie test statistic); outer line, 99.9% CI; middle line, 99% CI; and inner line, 95% CI. 67 Figure 2.3 Androgen regulation of genes as measured by qRT-PCR. A Candidate genes not previously implicated to change expression in response to androgens in prostate cancer cells: ARL6IP5, BL VRB, C19orf48, CJorfl22, C6orf66, CAMK2NI, CAPNSI, CCNJ, DERA, ERRFJ], GLUL, GOLPH3, HMJ3, HSP9OBI, MANEA, NANS, NIPSNAP3A, PRNPIP, SLC4IA], SOD], SVIP, TAOK3, TCPI, TMEM66, USP33, and VTAJ; and B Genes known to change levels of expression in response to androgens: ADAMTS1, CENFN, CREB3L4, CXCR 7, FKBP5, KLK3, LRIGJ, NCAPD3, NTS, PAK1IPI, PRKACB, RHOU, and ST7. LNCaP cells were treated for 16 hours prior to harvesting RNA, and analysing mRNA levels by qRT-PCR. Fold-change was calculated by normalizing the mean normalized expression (MNE) of transcripts in R188 I-treated cells to the mock vehicle-treated cells. In doing this, the vehicle treatment fold-change became one and standard deviation (SD) zero. Error bars represent ± SD for biological sextuplets. [*] Asterisk indicates significant differential gene expression according to the Two Sample Student’s T-test (p 0.05) for unequal variance. 68 1.2 0.8 C-) .9 0.4 0.2 0.0 SLC4IA I 02 020 C 0.8 C-) 9 0.4 0.2 0.0 -- _________ ________ Vehicle R1881 GOLPI-f3 02 -c C-) 0 - 0 Vehicle R1881 VANS 2.5 c 2.0 020 .5 C-) •0 0 0.5 0.0 1- I Vehicle SOD! 1.2 ARL6IP5 CI 9o,f48 2.0 1.5 1.0 0.5 0.0 1.2 o 1.0C 0.8 0.6 .9 0.4 0.2 1.2 V ci, 1.0 C 0.8 C-) 0 9 0.4 0.2 0.0 Vehicle R1881 Cóorf66 R1881 CCNI .2 0.0 .._________ - Vehicle R1881 * ERRFII GLUL ** 3.0 J2.5 .c 2.0 l.5 1.0 0.5 0.0 Vehicle R1881 HSP9OBI 9 C) 02* :02 0’2 0 3.0 1.8 2.5 c 2.0 1.2 9 l.0 I - 0.6 0.5 0.4 00 0.2 Vehicle R1881 0.0 NIPSNAP3A 2.0 Vehicle 1.2 Vehicle PRI’/PIP 5 * - 0 R1881 1.2 1 Sl’IP • I I 50.6.I I 0.4 I I * - 02 ________ C 0.5 0.0 -. 0.0 Vehicle R1881 Vehicle R1881 TAOK3 3.5 3.0 2.5 2.0 IS o 1.0 0.5 0.0 Vehicle R1881 25 20 - 15- _Li.. Vehicle R1881 TCPI * 1.0 020 002 D 0.6 0.4 0.2 0.0 Vehicle R1881 TMEM66 USP33 1.22.5 2.0 C 02 C-) 1.0 0.5 0.0 VTA I Vehicle R1881 Figure 2.3A 69 3 ADAMTS] C 01 Vehicle 0 CEJVPN 5 * 1.2 10. 0 0 — Vehicle l4 * 12 _______ j) 10 8 •0 2. 0 Vehicle R1881 8 15 C) 12 •0 0 9 6 0 I I Vehicle R188l FK B PS 50 40 30 ‘0 -0 C) [0 0 L Vehicle R1881 LR1GI 0.8 L) 0.6 0.4 — 0.2 0.0 CREB3L4 Vehicle Rl88l CXCR7 T c -C C-) •0 0 C)- 1.0 0.8 C) 0.4 - 0.2 0.0 Vehicle R1881 KLK3 7 C - ). ) -c C_)4 0 0 * 7-. J C- 31 -o o — .L l ____ Vehicle Rl 881 Vehicle RI 881 NCAPD3 NTS PAKIJPI PRKACB RHOU Vehicle R1881 1.2 ST7 Vehicle * R188lVehicle Figure 2.3B 70 ADAMTSI ARL6IP5 CAMK2WI Figure 2.4 Differential expression of candidate genes in LNCaP, DU145, and PC-3 cells. LNCaP, DU145, and PC-3 cells were analyzed by qRT-PCR using probes for ADA MTSJ, ARL6IP5, CAMK2N], CAPNSJ, CCNJ, CENPN, CREB3L4, CXCR7, ERRFII, FKBPS, HSLP9OB1, KLK3, LRIG1, NCAPD3, PAKIIPJ, PRKACB, ST7, and TAOK3. Error bars represent ± SD for biological triplicates. [*j Asterisks indicate the significant differential gene expression in each cell line compared to LNCaP cells according to the Two-Sample Student’s T-test (p 0.05) for equal (unpaired) or unequal variance as determined appropriate with the F-test 2.OE-03 l.6E-03 l.2E-03 8.0E-04 4OE-04 fl (..flfl CAPNSI I .2E-02 0.25 0.20i J u 8.OE-03 * *z zI 4.0E-03 0.10, . * 0.05 * 0.0E+00 —-— 0.00 LNCaP DU145 PC3 LNCaP DU145 PC3 LNCaP DU145 PC3 : 0.35 - 0.025 1• 0.020 .i I * 0.20 0.015 * . * 0.15 - 0.010I I I * * 0.005 * -—.- . — . 0.00 0.000 -- LNCaP DU145 PC3 LNCaP DU145 PC3 LNCaP DU145 PC3 CCAII CENPN0.07 0.06 0.05 z 0.03 0.02 0.01 0.00 I .E-02 8.E-03 6.E-03 4.E-03 2.E-03 I 0.E÷00 CREB3L4 CXCR7 ERRFJI6.E-03 6.E-04 5.E-03 5.E-04 4.E-03 3.E-03 Z 2.E-03 2.E-04 • O.E * LNCaP DU145 PC3 LNCaP DU145 PC3 LNCaP DU145 PC3 0.30 KLK31.2.E-03 FKBP5 1-ISP9OBI0.09I.0.E-03: T 0.08 RE-04 ...L 0.07 0.06Z 6.E-04 Z 0.05 4.E-04 8-8 * 2.E-04 • j 002 * o.E+ooL 8:8 - .- - LNCaP DU145 PC3 LNCaP DU145 PC3 LRIG] .VCAPD3 0.25 0.20 Z 0.15 0.10 0.05 * * 2.5E-03 2.OE-03 LJ I.5&03 I.OE-03 *5.OE-04 * 0.OE+00 — LNCaP DU145 PC3 2.5E-03 2.OE-03 1.5E-03 1.0E03 5.0E04 0.OE+00 ENCaP DU145 PC3 PAKIIPI 5.E-03 4.E-03 3.E03 z 2.E-03 LNCaP DU145 PC3LNCaP DU145 PC3 PRKACB ST7 T.40K3 0.005 0.025 5.E-03 0.004 0.020 4.E-03 0.003 . 0.015 3.E-03 0.002 0.010 2.E-03 * *0.001 0.005 * 1.E-03 * . * 0.000 0.000 •——--. 0.E00L LNCaP DU145 PC3 LNCaP DU145 PC3LNCaP DU145 PC3 71 -c 1.6 12 * 2.0 *1.41 10 J.2 8 - 0. ______ cl.2r U 0.81 6 06! 0.81 04 0.4 0.2 — 0.2 ______ nfl! 0 --_______ — 0.0 Pre-Cx Cx Pre-Cx 1.4 1.21 C C 101 _____ C C -C - - 0.4 0.2 I o.ol— Cx Figure 2.5 Androgen regulation of genes in the in vivo Hollow Fibre model of prostate cancer. LNCaP cells from the Hollow Fibre model were analyzed by qRT-PCR using probes for ADAMTSJ, ARL6IP5, CAMK2NI, CENPN, CREB3L4, CXCR7, ERRFII, FKBP5, HSP9OB], KLK3, LRIGI, NCAPD3, PAKIIPJ, PRKACB, ST7, and TAOK3. Cx, castrated mice, 10 days post castration, n 12; Pre-Cx, pre-castration, day 0 of castration, n = 15. Exception: LRIG1 gene expression in Cx samples was represented by 11 mice. Fold-change was calculated by normalizing the mean normalized expression (MNE) of transcripts in the Pre-Cx sample to the castrate sample. In doing this, the Cx sample fold-change became one and standard deviation (SD) zero. Error bars represent ± SD. [*] Asterisks indicate the sigiificant differential gene expression with respect to Cx according to the Two-Sample Student’s T-test (p 0.05) for unequal variance. ADAMTSI 1.6 1.41 121 - 101 08cI10.61 - 0.41 0.2 I 0.0 1 Pre-Cx CREB3L4 HSPQOBI CAMK2N] CENPN 12 1.0 0.8 L)0.6. * 0.4 U 0.2 _________ 0.0 —- Pre-Cx Cx Pre-Cx Cx ERRFII FKBP5 Cx NCAPD3 Pre-Cx Cx TA 0K3 KLK3 LRIGI Pre-Cx Cx PAKI/PI PRKACB 72 2.6 REFERENCES 1. Cunha GR, Ricke W, Thomson A, Marker PC, Risbridger G, Hayward SW, Wang YZ, Donjacour AA, Kurita T: Hormonal, cellular, and molecular regulation of normal and neoplastic prostatic development, J Steroid Biochem Mol Biol 2004, 92:221-236 2. Yong EL, Lim J, Qi W, Ong V, Mifsud A: Molecular basis of androgen receptor diseases, Ann Med 2000, 32:15-22 3. Roberts JT, Essenhigh DM: Adenocarcinoma of prostate in 40-year-old body-builder, Lancet 1986, 2:742 4. Jackson JA, Waxman J, Spiekerman AM: Prostatic complications of testosterone replacement therapy, Arch Intern Med 1989, 149:2365-2366 5. Guinan PD, Sadoughi W, Aisheik H, Ablin RJ, Airenga D, Bush TM: Impotence therapy and cancer of the prostate, Am J Surg 1976, 13 1:599-600 6. Noble RL: The development of prostatic adenocarcinoma in Nb rats following prolonged sex hormone administration, Cancer Res 1977, 37:1929-1933 7. Noble RL: Sex steroids as a cause of adenocarcinoma of the dorsal prostate in Nb rats, and their influence on the growth of transplants, Oncology 1977, 34:138-141 8. Wilson JD, Roehrborn C: Long-term consequences of castration in men: lessons from the Skoptzy and the eunuchs of the Chinese and Ottoman courts, J Clin Endocrinol Metab 1999, 84:4324-4331 9. Wilding G: The importance of steroid hormones in prostate cancer, Cancer Surv 1992, 14:113-130 10. Bruckheimer EM, Kyprianou N: Apoptosis in prostate carcinogenesis. A growth regulator and a therapeutic target, Cell Tissue Res 2000, 301:153-162 11. Isaacs JT: Antagonistic effect of androgen on prostatic cell death, Prostate 1984, 5:545- 557 12. Wu CP, Gu FL: The prostate in eunuchs, Prog Clin Biol Res 1991, 370:249-255 13. Isaacs JT, Scott WW, Coffey DS: New biochemical methods to determine androgen sensitivity of prostatic cancer: the relative enzymatic index (REI), Prog Clin Biol Res 1979, 33:133-144 14. Huggins C, Hodges C: Studies on prostatic cancer: The effect of castration, of estrogen and of androgen injection on serum phosphatases in metastatic carcinoma of the prostate, Cancer Res 1941, 293-297 73 15. Yamamoto KR: Steroid receptor regulated transcription of specific genes and gene networks, Annu Rev Genet 1985, 19:209-252 16. Shang Y, Myers M, Brown M: Formation of the androgen receptor transcription complex, Mol Cell 2002, 9:601-610 17. Wolf DA, Schulz P. Fittler F: Transcriptional regulation of prostate kallikrein-like genes by androgen, Mo! Endocrino! 1992, 6:753-762 18. Schuur ER, Henderson GA, Kmetec LA, Miller JD, Lamparski HG, Henderson DR: Prostate-specific antigen expression is regulated by an upstream enhancer, J Biol Chem 1996, 271:7043-7051 19. Cleutjens KB, van der Korput HA, van Eekelen CC, van Rooij HC, Faber PW, Trapman J: An androgen response element in a far upstream enhancer region is essential for high, androgen-regulated activity of the prostate-specific antigen promoter, Mol Endocrinol 1997, 11:148-161 20. Small EJ: Prostate cancer: who to screen, and what the results mean, Geriatrics 1993, 48:28-30, 35-28 21. Grossk!aus DJ, Shappell SB, Gautam 5, Smith JA, Jr., Cookson MS: Ratio of free-to- total prostate specific antigen correlates with tumor volume in patients with increased prostate specific antigen, J Urol 2001, 165:455-458 22. Saha S, Sparks AB, Rago C, Akmaev V, Wang CJ, Vogelstein B, Kinzler KW, Velculescu VE: Using the transcriptome to annotate the genome, Nat Biotechnol 2002, 20:508-5 12 23. Siddiqui AS, Khattra J, Delaney AD, Zhao Y, Astell C, Asano J, BabakaiffR, Barber 5, Beland J, Bohacec 5, Brown-John M, Chand S, Charest D, Charters AM, Cullum R, Dhalla N, Featherstone R, Gerhard DS, Hoffman B, Holt RA, Hou J, Kuo BY, Lee LL, Lee S, Leung D, Ma K, Matsuo C, Mayo M, McDonald H, Prabhu AL, Pandoh P, Riggins GJ, de Algara TR, Rupert JL, Smailus D, Stott J, Tsai M, Varhol R, Vrljicak P, Wong D, Wu MK, Xie YY, Yang G, Zhang I, Hirst M, Jones SJ, Helgason CD, Simpson EM, Hoodless PA, Marra MA: A mouse atlas of gene expression: large-scale digital gene-expression profiles from precisely defmed developing C57BL/6J mouse tissues and cells; Proc Nati Acad Sci U S A 2005, 102:18485-18490 24. Yang GS, Stott JM, Smailus D, Barber SA, Balasundaram M, Marra MA, Holt RA: High throughput sequencing: a failure mode analysis, BMC Genomics 2005, 6:2 74 25. Robertson N, Oveisi-Fordorei M, Zuyderduyn SD, Varhol RJ, Fjell C, Marra M, Jones S, Siddiqui A: DiscoverySpace: an interactive data analysis application, Genome Biol 2007, 8 :R6 26. Audic 5, Claverie JM: The significance of digital gene expression profiles, Genome Res 1997, 7:986-995 27. Sadar MD, Akopian VA, Beraldi E: Characterization of a new in vivo hollow fiber model for the study of progression of prostate cancer to androgen independence, Mol Cancer Ther 2002, 1:629-637 28. Horoszewicz JS, Leong SS, Chu TM, Wajsman ZL, Friedman M, Papsidero L, Kim U, Chai LS, Kakati 5, Arya SK, Sandberg AA: The LNCaP cell line--a new model for studies on human prostatic carcinoma, Prog Clin Biol Res 1980, 37:115-132 29. Gleave M, Hsieh JT, Gao CA, von Eschenbach AC, Chung LW: Acceleration of human prostate cancer growth in vivo by factors produced by prostate and bone fibroblasts, Cancer Res 1991, 51:3753-3761 30. Sadar MD: Androgen-independent induction of prostate-specific antigen gene expression via cross-talk between the androgen receptor and protein kinase A signal transduction pathways, J Biol Chem 1999, 274:7777-7783 31. Velculescu VE, Zhang L, Vogeistein B, Kinzler KW: Serial analysis of gene expression, Science 1995, 270:484-487 32. Khattra J, Delaney AD, Zhao Y, Siddiqui A, Asano J, McDonald H, Pandoh P, Dhalla N, Prabhu AL, Ma K, Lee 5, Ally A, Tam A, Sa D, Rogers S, Charest D, Stott J, Zuyderduyn 5, Varhol R, Eaves C, Jones 5, Holt R, Hirst M, Hoodless PA, Marra MA: Large-scale production of SAGE libraries from microdissected tissues, flow-sorted cells, and cell lines, Genome Res 2007, 17:108-116 33. Emmersen J, Heidenbiut AM, Hogh AL, Hahn SA, Welinder KG, Nielsen KL: Discarding duplicate ditags in LongSAGE analysis may introduce significant error, BMC Bioinfonnatics 2007, 8:92 34. Ewing B, Green P: Base-calling of automated sequencer traces using phred. II. Error probabilities, Genome Res 1998, 8:186-194 35. Ewing B, Hillier L, Wendi MC, Green P: Base-calling of automated sequencer traces using phred. I. Accuracy assessment, Genome Res 1998, 8:175-185 75 36. Margulies EH, Kardia SL, Innis JW: A comparative molecular analysis of developing mouse forelimbs and hindlimbs using serial analysis of gene expression (SAGE), Genome Res 2001, 11:1686-1698 37. Akmaev VR, Wang CJ: Correction of sequence-based artifacts in serial analysis of gene expression, Bioinformatics 2004, 20:1254-1263 38. Chen J, Sun M, Lee S, Zhou G, Rowley JD, Wang SM: Identifying novel transcripts and novel genes in the human genome by using novel SAGE tags, Proc Natl Acad Sci U S A 2002, 99:12257-12262 39. Storey JD: A direct approach to false discovery rates, J.R. Statist. Soc. B 2002, 64:479- 498 40. Stone KR, Mickey DD, Wunderli H, Mickey GH, Paulson DF: Isolation of a human prostate carcinoma cell line (DU 145), Tnt J Cancer 1978, 21:274-281 41. Kaighn ME, Narayan KS, Ohnuki Y, Lechner JF, Jones LW: Establishment and characterization of a human prostatic carcinoma cell line (PC-3), Invest Urol 1979, 17: 16-23 42. Berns EM, de Boer W, Mulder E: Androgen-dependent growth regulation of and release of specific protein(s) by the androgen receptor containing human prostate tumor cell line LNCaP, Prostate 1986, 9:247-259 43. Newsholme P, Procopio J, Lima MM, Pithon-Curi TC, Curi R: Glutamine and glutamate- -their central role in cell metabolism and function, Cell Biochem Funct 2003, 21:1-9 44. Curi R, Lagranha CJ, Doi SQ, Sellitti DF, Procopio J, Pithon-Curi TC, Corless M, Newsholme P: Molecular mechanisms of glutamine action, J Cell Physiol 2005, 204:392- 401 45. Eisenberg D, Gill HS, Pfluegl GM, Rotstein SH: Structure-function relationships of glutamine synthetases, Biochim Biophys Acta 2000, 1477:122-145 46. Goytain A, Quamme GA: Functional characterization of human SLC41A1, a Mg2+ transporter with similarity to prokaryotic MgtE Mg2+ transporters, Physiol Genomics 2005, 21:337-342 47. Lin CI, Orlov I, Ruggiero AM, Dykes-Hoberg M, Lee A, Jackson M, Rothstein JD: Modulation of the neuronal glutamate transporter EAAC 1 by the interacting protein GTRAP3-18, Nature 2001, 410:84-88 76 48. Butchbach ME, Lai L, Lin CL: Molecular cloning, gene structure, expression profile and functional characterization of the mouse glutamate transporter (EAAT3) interacting protein GTRAP3-18, Gene 2002, 292:81-90 49. Spiess C, Meyer AS, Reissmann 5, Frydman J: Mechanism of the eukaryotic chaperonin: protein folding in the chamber of secrets, Trends Cell Biol 2004, 14:598-604 50. Coghlin C, Carpenter B, Dundas SR, Lawrie LC, Telfer C, Murray GI: Characterization and over-expression of chaperonin t-complex proteins in colorectal cancer, J Pathol 2006, 210:351-357 51. Lemberg MK, Martoglio B: Requirements for signal peptide peptidase-catalyzed intramembrane proteolysis, Mol Cell 2002, 10:735-744 52. Hao J, Balagurumoorthy P, Sarilla S, Sundaramoorthy M: Cloning, expression, and characterization of sialic acid synthases, Biochem Biophys Res Commun 2005, 338: 1507-15 14 53. Hardt B, Volker C, Mundt S, Saiska-Navarro M, Hauptmann M, Bause E: Human endo alphal,2-mannosidase is a Golgi-resident type II membrane protein, Biochimie 2005, 87: 169-179 54. Ballar P, Zhong Y, Nagahama M, Tagaya M, Shen Y, Fang 5: Identification of SVIP as an endogenous inhibitor of endoplasmic reticulum-associated degradation, J Biol Chem 2007, 282:33908-33914 55. Bell AW, Ward MA, Blackstock WP, Freeman HN, Choudhary JS, Lewis AP, Chotai D, Fazel A, Gushue TN, Paiement J, Palcy S, Chevet E, Lafreniere-Roula M, Solari R, Thomas DY, Rowley A, Bergeron JJ: Proteomics characterization of abundant Golgi membrane proteins, J Biol Chem 2001, 276:5152-5165 56. Azmi I, Davies B, Dimaano C, Payne J, Eckert D, Babst M, Katzmann DJ: Recycling of ESCRTs by the AAA-ATPase Vps4 is regulated by a conserved VSL region in Vtal, J Cell Biol 2006, 172:705-7 17 57. Buechler C, Bodzioch M, Bared SM, Sigruener A, Boettcher A, Lapicka-Bodzioch K, Aslanidis C, Duong CQ, Grandl M, Langmann T, Dembinska-Kiec A, Schmitz G: Expression pattern and raft association of NIPSNAP3 and NIPSNAP4, highly homologous proteins encoded by genes in close proximity to the ATP-binding cassette transporter Al, Genomics 2004, 83:1116-1124 58. Lee AH, Zareei MP, Daefler S: Identification of a NIPSNAP homologue as host cell target for Salmonella virulence protein SpiC, Cell Microbiol 2002, 4:739-750 77 59. Csermely P, Schnaider T, Soti C, Prohaszka Z, Nardai G: The 90-kDa molecular chaperone family: structure, function, and clinical applications. A comprehensive review, Pharmacol Ther 1998, 79:129-168 60. Chen B, Piel WH, Gui L, Bruford E, Monteiro A: The HSP9O family of genes in the human genome: insights into their divergence and evolution, Genomics 2005, 86:627-637 61. Reddy RK, Lu J, Lee AS: The endoplasmic reticulum chaperone glycoprotein GRP94 with Ca(2+)-binding and antiapoptotic properties is a novel proteolytic target of calpain during etoposide-induced apoptosis, J Biol Chem 1999, 274:28476-28483 62. Strange K, Denton J, Nebrke K: Ste2O-type kinases: evolutionarily conserved regulators of ion transport and cell volume, Physiology (Bethesda) 2006, 21:61-68 63. Zhang W, Liu HT: MAPK signal pathways in the regulation of cell proliferation in mammalian cells, Cell Res 2002, 12:9-18 64. Tassi E, Biesova Z, Di Fiore PP, Gutkind JS, Wong WT: Human JIK, a hovel member of the STE2O kinase family that inhibits INK and is negatively regulated by epidermal growth factor, J Biol Chem 1999, 274:33287-33295 65. Zhang W, Chen T, Wan T, He L, Li N, Yuan Z, Cao X: Cloning of DPK, a novel dendritic cell-derived protein kinase activating the ERK 1 /ERK2 and JNKISAPK pathways, Biochem Biophys Res Commun 2000, 274:872-879 66. Zhang YW, Vande Woude GF: Mig-6, signal transduction, stress response and cancer, Cell Cycle 2007, 6:507-5 13 67. Sobel RE, Sadar MD: Cell lines used in prostate cancer research: a compendium of old and new lines--part 1, J Urol 2005, 173:342-3 59 68. Zhang J, Li N, Yu J, Zhang W, Cao X: Molecular cloning and characterization of a novel calcium!calmodulin-dependent protein kinase II inhibitor from human dendritic cells, Biochem Biophys Res Commun 2001, 285:229-234 69. Coibran RJ: Targeting of calcium/calmodulin-dependent protein kinase II, Biochem J 2004, 378:1-16 70. Xu LL, Su YP, Labiche R, Segawa T, Shanmugam N, McLeod DG, Moul JW, Srivastava 5: Quantitative expression profile of androgen-regulated genes in prostate cancer cells and identification of prostate-specific genes, mt j Cancer 2001, 92:322-328 71. Ripple MO, Henry WF, Rago RP, Wilding G: Prooxidant-antioxidant shift induced by androgen treatment of human prostate carcinoma cells, J Natl Cancer Inst 1997, 89:40-48 78 72. McCord JM, Fridovich I: Superoxide dismutase. An enzymic function for erythrocuprein (hemocuprein), J Biol Chem 1969, 244:6049-6055 73. Bostwick DG, Alexander EE, Singh R, Shan A, Qian 3, Santella RM, Oberley LW, Yan T, Zhong W, Jiang X, Oberley TD: Antioxidant enzyme expression and reactive oxygen species damage in prostatic intraepithelial neoplasia and cancer, Cancer 2000, 89:123- 134 74. Sonn GA, Aronson W, Litwin MS: Impact of diet on prostate cancer: a review, Prostate Cancer Prostatic Dis 2005, 8:304-3 10 79 CHAPTER III GENE EXPRESSION ASSOCIATED WITH IN VIVO PROGRESSION TO CASTRATION-RECURRENT PROSTATE CANCER* 3.1 INTRODUCTION Systemic androgen-deprivation therapy by orchiectomy or agonists of gonadotropic releasing hormone are routinely used to treat men with metastatic prostate cancer to reduce tumour burden and pain1. This therapy is based on the dependency of prostate cells for androgens to grow and survive2.The inability of androgen-deprivation therapy to completely and effectively eliminate all metastatic prostate cancer cell populations is manifested by a predictable and inevitable relapse, referred to as castration-recurrent prostate cancer (CRPC)3.CRPC is non-responsive to most conventional cancer therapies and fatal to the patient within 16-18 months of onset46. The mechanisms underlying progression to CRPC are unknown. However, there are several models to explain its development. One such model indicates the involvement of the androgen signalling pathway3’ Key to this pathway is the androgen receptor (AR) which is a steroid hormone receptor and transcription AR mediates androgen-regulated gene transcription”. The AR is found inactive and stabilized by heat-shock proteins in the cytoplasm of prostate cells’2 Upon binding of androgen, the phosphorylation status and conformation of the AR changes, thereby presumably releasing the heat-shock proteins’2.The AR dimerjzes’3, translocates to the nucleus’4,binds to androgen response elements (AREs) of DNA’5,and recruits co-factors to regulate gene expression’6.The resulting changes in gene expression promote proliferation’7,survival2,differentiation’8,and secretion’9. Mechanisms of progression to CRPC that involve or utilize the androgen signalling pathway include: hypersensitivity due to AR gene amplification20’21. changes in AR co-regulators [e.g., nuclear receptor coactivator 1 (NCOA 1) and nuclear receptor coactivator 2 (NCOA 2)] 22, 23; intraprostatic de novo synthesis of androgen [i.e, 3-hydroxy-3-methylglutaryl-Coenzyme A synthase 1 (HMGCSJ), and squalene epoxidase (SQLE)]24 or metabolism of AR ligands from * A version of this chapter will be submitted for publication. Romanuik, TL., Morozova, 0., Delaney, A., Marra, MA., Sadar, MD. Gene expression associated with in vivo progression to castration-recurrent prostate cancer. In preparation. 80 residual adrenal androgens [e.g., hydroxysteroid (17-beta) dehydrogenase 3 (HSDJ 7B3) and hydroxysteroid (17-beta) dehydrogenase 5 (HSDJ 7B5)]2’26; AR promiscuity of ligand specificity due to mutations27;and ligand-independent activation of AR by growth factors [protein kinase A (P1(A), interleukin 6 (1L6), and epidermal growth factor (EGF)]2830. Activation of the AR can be determined by assaying for the expression of target genes [e.g., prostate-specific antigen (PSA; gene also known as kallikrein 3; KLK3)11,prostate acid phosphatase (ACPF)31,and NK3 homeobox 1 (NKX3-1)32]. The neuroendocrine model of CRPC entails the transdifferentiation of normal luminal secretory epithelial prostate cells into neuroendocrine-like prostate cancer cells33.Transdifferentiation is encouraged by androgen-deprivation therapy, as the length of treatment time in patients correlates with the prevalence of neuroendocrine-like prostate cancer cells34’ Moreover, LNCaP adenocarcinoma of the prostate cells transdifferentiate into neuroendocrine-like cells following: androgen-deprivation in vivo36; or treatment with the catalytic subunit of PKA37, 1L638, or EGF39. In contrast to normal neuroendocrine prostate cells, neuroendocrine-like prostate cancer cells do not express basal cell markers33,are associated with metastasis and death40’1,and over-express the B-cell CLL/lymphoma 2 (BCL-2) anti-apoptotic oncogene42. Neuroendocrine-like prostate cancer cells do not express AR or KLK3, but do express neuroendocrine markers gamma neuronal enolase 2 (ENO2), neurotensin (NTS) chromogranin A (CHGA), and chromogranin B (CHGB)33’ Neuroendocrine-like prostate cancer cells secrete hormones serotonin, parathyroid hormone-related protein (PTHrP), and bombesin33’“s. A stem cell model for CRPC has been proposed’. Stem cells have an extensive capacity for self-renewal due to the signalling of Hedgehog, Notch, and Wingless (Wnt) pathways45.The tumour suppressor genes phosphatase and tensin homolog (PTEN) and tumour protein p53 (TP53) have also been implicated in stem cell self-renewal45.Self renewal makes stem cells an attractive candidate for the origin of prostate cancer. Support for the presence of adult prostate stem cells is evidenced by the regeneration of the prostate following replacement of androgens in castrated rodents46.Prostate stem cells may be selected for following androgen-deprivation. Recently, gene expression profiling has revealed putative markers of prostate cancer stem cells such as prostate stem cell antigen (PSCA), anti-proliferative b-cell translocation gene 1 (BTG]), 81 1L6, and hydroxyprostaglandin dehydrogenase 1 5-(NAD) (HPGD)47.Prostate cancer stem cells may express CD44 molecule (CD44) and prominin 1 (PROM])48,but not AR or KLK349. The final suggested model of CRPC involves an imbalance between cell growth and cell death in the prostate8.Both processes may occur at the cellular level to potentially result in a net proliferation to increase tumour burden. Genes involved in the regulation of the cell cycle [e.g., cyclin Dl (CCND]), cyclin-dependent kinase inhibitor 1 A (CDKI’J]A), cyclin-dependent kinase inhibitor lB (CDKJ”/JB), retinoblastoma 1 (RB])] and cell survival/death [BCL2, BCL2- associated X protein (BAX), BCL2-like 1 (BCL2L]), and TP53] are prognostic factors for prostate cancer50’1. Here, we describe long serial analysis of gene expression (L0ngSAGE) libraries52’3made from RNA sampled from biological replicates of the in vivo LNCaP Hollow Fibre model of prostate cancer as it progresses to the castration-recurrent stage. Gene expression signatures that were consistent among the replicate libraries were applied to the models of CRPC to determine which of the suggested mechanisms underly progression. 3.2 MATERIALS AND METHODS 3.2.1 Cell culture. LNCaP human prostate cancer cells (provided by Dr. L.W.K. Chung at the Emory University School of Medicine, Atlanta, GA, USA) were maintained in RPMI-1640 media (Stem Cell Technologies, Vancouver, BC, Canada) supplemented with 5% v/v fetal bovine serum (FBS; HyClone, Logan, UT, USA), 100 units/ mL penicillin and 100 units/mL streptomycin (Invitrogen, Burlington, ON, Canada). Cells were maintained at 37°C with 5% CO2. 3.2.2 Animals. Six-week-old male athymic BALB/c Nude mice were obtained from Taconic Farms (Hudson, NY, United States of America) and kept in the British Columbia Cancer Research Centre animal facilities (Vancouver, BC, Canada). Mice were maintained on a HarlanlTeklad irradiated diet with a constant supply of autoclaved water and housed in cages (three animals/cage) at 21°C ± 3°C with light/dark cycling (light between 6 AM and 6 PM). All 82 animal experiments were performed according to a protocol approved by the Committee on Animal Care of the University of British Columbia. 3.2.3 In vivo LNCaP Hollow Fibre model. LNCaP cells (3 x i07 at passage 43) suspended in media (50% v/v) and extracellular matrix (Matrigel; 50% v/v; BD Biosciences, Mississauga, ON Canada) were injected into Polyvinylidene difluoride hollow fibres (Mr 500,000 molecular weight cutoff; 1-mm internal diameter; Spectrum Laboratories, Rancho Dominguez, CA, USA). The fibres were heat-sealed prior to subcutaneous (s.c.) implantation into mice. Seven days post fibre implantation, mice were castrated to reduce levels of circulating androgens. Blood was drawn from the tail vein each week to measure levels of serum PSA to monitor the response to androgen-deprivation therapy and onset of castration-recurrence. Serum PSA levels were determined by enzymatic immunoassay kit (Abbott Laboratories, Abbott Park, IL, USA). Fibres were removed on three separate occasions representing different stages of hormonal progression that were androgen-sensitive (AS), responsive to androgen-deprivation (RAD), and castration- recurrent (CR). Samples were retrieved immediately prior to castration, as well as 10 and 72 days post-surgical castration. 3.2.4 RNA sample generation, processing, and quality control. Total RNA was isolated immediately from cells harvested from the in vivo Hollow Fibre model using TRIZOL Reagent (Invitrogen) following the manufacturer’s instructions. Genomic DNA was removed from RNA samples with DNaseI (Invitrogen). RNA quality and quantity were assessed by the Agilent 2100 Bioanalyzer (Agilent Technologies, Mississauga, ON, Canada) and RNA 6000 Nano LabChip kit (Caliper Technologies, Hopkinton, MA, USA). 3.2.5 Quantitative real-time polymerase chain reaction. To confirm that the transcriptomes of the samples reflect the different stages of prostate cancer progression (AS, RAD, and CR), KLK3 mRNA expression trends were measured by quantitative real time-polymerase chain reaction (qRT-PCR) prior to LongSAGE library construction. Input RNA (0.5 tg) was reverse transcribed to cDNA with SuperScript III First Strand Synthesis kit (Invitrogen). A 10 iL reaction included 1 jtl of template cDNA, lx Platinum SYBR Green qPCR SuperMix-UDG with ROX (Invitrogen) and 0.3 iM each of forward and reverse intron-spanning primers (KLK3, 83 F’: 5’ -CCAAGTTCATGCTGTGTGCT-3’ and R:’ 5’ -CCCATGACGTGATACCTTGA-3’; glyceraldehyde-3-phosphate (GAPDH), F’: 5’ -CTGACTTCAACAGCGACACC-3’ and R:’ 5’- TGCTGTAGCCAAATTCGTTG-3’) that produce products 111 and 114 bp in size, respectively. Reactions were cycled as follows in a 7900HT Sequence Detection System (Applied Biosystems): 50 °C for 2 mm, 95 °C for 2 mm, (95 °C for 0.5 mm, 55 °C for 0.3-0.5 mm, and 72 °C for 0.5 mm) for 40-45 cycles, 95°C for 0.25 mm, 60°C for 0.25 mm, and 95°C for 0.25 mm. Reactions for biological replicates were performed in technical triplicates. cDNAs (from different time-points along progression) and genes (target-KLK3 and reference-GAPDR) to be directly compared were assayed in the same instrument run. Reactions without template (negative controls) were run for each gene to ensure that DNA had not contaminated the reactions. Only data with single-peak dissociation curves were included in this analysis. Efficiency checks were performed for each primer pair in each cell line. Products were sequenced to verif’ the identity of quantified transcripts. 3.2.6 L0ngSAGE library production and sequencing. RNA from the hollow fibres of three mice (biological replicates) representing different stages of prostate cancer progression (AS, RAD, and CR) were used to make a total of nine LongSAGE libraries. LongSAGE53 libraries were constructed and sequenced at the Genome Sciences Centre, British Columbia Cancer Agency. Five micrograms of starting total RNA was used in conjunction with the Invitrogen I- SAGE Long kit and protocol, with alterations as previously published54. 3.2.7 Gene expression analysis. LongSAGE expression data was analyzed with DiscoverySpace 4.01 software55 (http://www.bcgsc.cai’bioinfo/software/discoveryspace/). Sequence data were filtered for bad tags (tags with one N-base call) and linker-derived tags (artifact tags). Only LongSAGE tags with a sequence quality factor (QF) greater than 95% were included in analysis. The phylogenetic tree was constructed with a distance metric of 1 -r (where “r” equals the Pearson correlation coefficient). The Pearson correlation is a measurement of similarity used to correlate variables (e.g., LongSAGE libraries). Here, it was used as a similarity measurement because it is not sensitive to scaling or differences in average expression level. Correlations 84 were computed (including tag counts of zero) using the Regress program of the Stat package written by Ron Perlman, and the tree was optimized using the Fitch program56 in the Phylip package57.Graphics were produced from the tree files using the program TreeView’8. Tag clustering analysis was performed using the Poisson distribution-based K-means clustering algorithm. The K-means algorithm clusters tags based on count into ‘K’ partitions, with the minimum intracluster variance. PoissonC was developed specifically for the analysis of SAGE data59. The java implementation of the algorithm was kindly provided by Dr. Li Cai (Rutgers University, NJ, USA). An optimal value for K (K=lO) was determined as described60. 3.3 RESULTS 3.3.1 LongSAGE library construction and composition RNA isolated from the LNCaP Hollow Fibre model was obtained from at least three different mice (13N, 15N, and 13R; biological replicates) at three stages of cancer progression that were androgen-sensitive (AS), responsive to androgen-deprivation (RAD), and castration-recurrent (CR). To confirm that the samples represented unique disease-states, we determined the levels of KLK3 mRNA, a biomarker that correlates with progression, using quantitative real time polymerase chain reaction (qRT-PCR; Figure 3.1). As expected, KLK3 mRNA levels dropped in the stage of cancer progression that was RAD versus AS (5 8%, 49%, and 37%), and rose in the stage of cancer progression that was CR versus RAD (229%, 349%, and 264%) for mice I 3R, 1 5N, and I 3N, respectively (Figure 3.1). Therefore, we constructed nine LongSAGE libraries, one for each stage and replicate. Each LongSAGE library was sequenced to 310,072 - 339,864 tags for a combined total of 2,931,124 tags that were filtered prior to analysis (Table 3.1). First, ‘bad tags’ were removed because they contain at least one N-base-call in the L0ngSAGE tag sequence. The sequencing of the LongSAGE libraries was base-called using PHRED software61’2 Tag sequence-quality factor (QF) and probability were calculated to ascertain which tags contain erroneous base calls63.The second line of filtering removed LongSAGE tags with probabilities less than 0.95 (QF < 95%). Linkers were introduced into SAGE libraries as known sequences utilized to 85 amplifi ditags prior to concatenation52.At a low frequency, linkers ligate to themselves creating linker-derived tags (LDTs). These LDTs do not represent transcripts and were removed from the L0ngSAGE libraries. A total of 2,305,589 useful tags represented by 263,197 tag types remained after filtering. Data analysis was carried out on this filtered data. 3.3.2 LongSAGE library and tag clustering The LongSAGE libraries were hierarchically clustered and displayed as a phylogenetic tree. In most cases, L0ngSAGE libraries made from the same disease stage (AS, RAD, or CR) clustered together more closely than LongSAGE libraries made from the same biological replicate (mice 1 3N, 1 SN, or 1 3R; Figure 3.3). This suggests the captured transcriptomes were representative of disease stage with minimal influence from biological variation. Identification of groups of genes that behave similarly during progression of prostate cancer was conducted through K-means clustering of tags using the PoissonC algorithm59.For each biological replicate (mice 1 3N, 1 5N, or 1 3R), all tag types were clustered that had a combined count greater than ten in the three libraries representing disease stages (AS, RAD, and CR) and mapped unambiguously sense to a transcript in reference sequence (RefSeq; February 28th, 2008)64 using DiscoverySpace4 software55.By plotting within cluster dispersion (i.e., intracluster variance) against a range of clusters, K (Figure 3.3), we determined that ten clusters best embodied the expression patterns present in each biological replicate. This was decided based on the inflection point in the graph (Figure 3.3). K-means clustering was performed over 100 iterations, so that tags would be grouped in clusters determined to fit best most often. The most common clusters are displayed in Figure 3.4. In three instances, there were similar clusters in only two of the three biological replicates. Consequently, changes in gene expression during progression were represented in 11 patterns (Figure 3.4). Differences among expression patterns for each biological replicate may be explained by biological variation, the probability of sampling a given LongSAGE tag, andJor imperfections in K-means clustering (e.g, variance may not be a good measure of cluster scatter). 86 3.3.3 Gene ontology enrichment analysis We conducted Gene Ontology (GO)65 enrichment analysis using Expression Analysis Systematic Explorer (EASE)66 software to determine whether specific GO annotations were over-represented in the K-means clusters. Enrichment was defined by the EASE score (p-value 0.05) generated during comparison to all the other clusters in the biological replicate. This analysis was done for each biological replicate (mice 1 3N, 1 5N, or 1 3R). To enable visual differences between the 11 expression trends, the clusters were amalgamated into five major trends: 1) up during progression; 2) down during progression; 3) constant during progression; 4) expression peak at the stage of cancer progression that was RAD; and 5) expression valley at the stage of cancer progression that was RAD (Figure 3.4). To be consistent, the GO enrichment data was combined into five major trends which resulted in redundancy in GO terms. To simplif,’ the GO enrichment data, similar terms were pooled into representative categories. Categorical gene ontology enrichments of the five maj or expression trends are shown in Figure 3.5. These data indicate that steroid binding, heat shock protein activity, de-phosphorylation activity, and glycolysis all decreased in the stage that was RAD, but increased again in the stage that was CR. Interestingly, steroid hormone receptor activity continues to increase throughout progression. Both of these expression trends were observed for genes with GO terms for transcription factor activity or secretion. The GO categories for genes with kinase activity and signal transduction displayed expression trends with peaks and valleys at the stage that was RAD. The levels of expression of genes involved in cell adhesion rose in the stage that was RAD, but dropped again in the stage that was CR. Altogether, genes with functional categories that were enriched in expression trends revealed that the AR signalling pathway was perturbed during progression of prostate cancer to castration-recurrence (Figure 3.5). For example, GO terms steroid binding, steroid hormone receptor activity, heat shock protein activity, chaperone activity, and kinase activity could represent the cytoplasmic events of AR signalling. GO terms transcription factor activity, regulation of transcription, transcription corepression activity, and transcription co-activator activity could represent the nuclear events of AR signalling. AR-mediated gene transcription may result in splicing and protein translation, to regulate general cellular processes such as 87 proliferation (and related nucleotide synthesis, DNA replication, oxidative phosphorylation, oxioreductase activity, and glycolysis), secretion, and differentiation. It should be noted, however, that both positive and negative regulators were represented in the GO enriched categories (Figure 3.5). Therefore, a more detailed analysis was required to determine if the pathways represented by the GO-enriched categories were promoted or inhibited during progression to CRPC. Moreover, many of the GO enrichments that were consistent with changes in the AR signalling pathway were generic, and could be applied to the other models of CRPC. 3.3.4 Consistent differential gene expression associated with progression of prostate cancer Pair-wise comparisons were made between L0ngSAGE libraries representing the transcriptomes of different stages (AS, RAD, and CR) of prostate cancer progression from the same biological replicate (mice 1 3N, 1 5N, or 1 3R). Among all three biological replicates, the number of consistent significant differentially expressed tag types were determined using the Audic and Claverie test statistic67 at p 0.05, p 0.01, and p 0.00 1 (Table 3.2). The tags represented in Table 3.2 were included only if the associated expression trend was common among all three biological replicates. The Audic and Claverie statistical method is well-suited for L0ngSAGE data, because the method takes into account the sizes of the libraries and tag counts. Tag types were counted multiple times if they were over, or under-represented in more than one comparison. The number of tag types differentially expressed decreased by 231 - 267% as the stringency of the p-value increased from p 0.05 to 0.001. Tag types consistently differentially expressed in pair-wise comparisons were mapped to RefSeq (March 4th, 2008). Tags that mapped anti-sense to genes, or mapped ambiguously to more than one gene were not included in the functional analysis. GO, Kyoto Encyclopedia of Genes and Genomes (KEGG; v45 .0)68 pathway, and SwissProt (v 13.0)69 keyword annotation enrichment analyses were conducted using EASE (vl.21; March 1 1th, 2008) and FatiGO (v3; March 11th, 2008)° (Table 3.3). This functional analysis revealed that the expression of genes involved in signalling increased during progression, but the expression of genes involved in 88 protein synthesis decreased during progression. Cell communication increased in the stage that was RAD but leveled off in the stage that was CR. Carbohydrate, lipid and amino acid synthesis was steady in the RAD stage but increased in the CR stage. Lastly, glycolysis decreased in the RAD stage, but was re-expressed in the CR stage (Table 3.3). Tag types differentially expressed between the RAD and CR stages of prostate cancer were of particular interest (Table 3.4). This is because these tags potentially represent markers for CRPC andlor are involved in the mechanisms of progression to CRPC. These 193 tag types (Table 3.2) were mapped to databases RefSeq (July 9th 2007), Mammalian Gene Collection (MGC; July 9t1, 2007)’, or Ensembl Transcript or genome (v45.36d)72.Only 135 of the 193 tag types were relevant (Table 3.4) with 48 tag types that mapped ambiguously to more than one location in the Homo Sapiens transcriptome/genome, and another 10 tag types that mapped to Mus musculus transcriptome/genome. Mus musculus mappings may be an indication of minor contamination of the in vivo LNCaP Hollow Fibre model samples with host (mouse) RNA. These 135 tag types represented 114 candidate genes with 7 tag types that did not map to the genome, 5 tag types that mapped to unannotated genomic locations, and 9 genes that were associated with more than one tag type. Table 3.4 shows the L0ngSAGE tag sequences and tag counts per million tags in all nine libraries. Tags were sorted into groups based on expression trends. These trends are visually represented in the ‘trend legend’ for interpretation. Mapping information was provided where available. We cross-referenced these 114 candidate genes with 28 papers that report global gene expression analyses on tissue samples from men with ‘castration-recurrent’, ‘androgen independent,’ ‘hormone refractory,’ ‘androgen-ablation resistant,’ ‘relapsed,’ or ‘recurrent’ prostate cancer, or animal models of castration-recurrence73’°°.The candidate genes were identified with HUGO Gene Nomenclature Committee (HGNC)10’approved gene names, aliases, descriptions, and accession numbers. The gene expression trends of 18 genes of 114 genes were previously associated with CRPC. These genes were: ACFF, ADAM2, AMA CR, AMDJ, ASAHi, DHCR24, FLNA, KLK3, KPNBJ, PLA2G2A, RPL]3A, RPL35A, RFL37A, RPL39, RFLP2, RFS2O, STEAP2, and TACC (Table 3.4). To our knowledge, the gene 89 expression trends of the remaining 96 genes have never before been associated with CRPC (Tables 3.4 and 3.5). A literature search helped to gauge the potential of these 96 genes to be novel biomarkers or therapeutic targets of CRPC. The results of this literature search are presented in Table 3.5. We found 31 genes that encode for protein products that are known, or predicted, to be plasma membrane bound or secreted extracellularly (Bioinformatic Harvester; May 14th, 2008)102. These genes were: ABHD2, AQP3, B2M C]9orf48, CD]5], CXCR7, DHRS7, ELOVL5, ENDOD], ENO2, FGFRL], GNB2L], GRBJO, HLA-B, MARCKSL], MDK, NATJ4, NELF, OPRK], 0R5]E2, PLCB4, PTGFR, RAMP], S]OOA]O, SPON2, STEAPJ, TFPI, TMEM3OA, TMEM66, TRPM8, and VPSJ3B. Secretion of a protein could facilitate detection of the putative biomarkers in blood, urine, or biopsy sample. Twenty of the candidate genes are known to alter their levels of expression in response to androgen. These genes were: B2M, BTG], C]9orf48, CAMK2NJ, CXCR7, EEF]A2, ELO VL5, ENDOD], HSD]7B4, MAOA, MDK, NKX3-1, ODC], P4HA], PCGEM1, PGK], SELENBP], TMEM66, TPD52, and TRPM824’5 103-114 Genes regulated by androgen may be helpful in determining the activation status of AR in CRPC. Enriched expression of a protein in prostate tissue could be indicative of whether a tumour is of prostatic origin. Five of these 96 genes are known to be over-represented in prostate tissu&°8’115 117 These genes were: NKX3-], PCGEM], SPON2, STEAP1, and TPD52. Twenty genes (ABHD2, BNIP3, EEF]A2, GALNT3, GLO], HSD17B4, MARCKSL1, MDK, ODd, 0R5]E2, PCGEM], PCOTH, PP2CB, RPS]8, SELENBP], SLC25A4, SLC25A6, STEAP], TPD52, and TRPM8) have known associations to prostate cancer88’1 18-134 Six genes (C]orJ8O, CAMK2N], GLO], MAOA, PGKJ, and SNX3) have been linked to high Gleason grade89’135, 136, and eight genes (B2M, CD 15], COMT, GALNT3, ODC], PCGEM], PCOTH, and TPD52) have been implicated in the ‘progression’ of prostate cancer89,and 15 more genes (CD]5], CXCR7, DHRS7, GNB2L], HES6, HN], NKX3-], PGK], PIK3CD, RPL]], RPSJ], SF3A2, TK], TPD52, and VPS]3B) in the metastasis of prostate cancer137’138 90 3.4 DISCUSSION Genes that change levels of expression during hormonal progression may be indicative of the mechanisms involved in CRPC. Large-scale gene expression analyses have been performed with tissue samples from men with advanced prostate cancer7389,and animal or xenograft models of CRPC90’°°. Here we provide the most comprehensive gene expression analysis to date of prostate cancer with approximately 3 million tags sequenced using in vivo samples at various stages of hormonal progression. The LNCaP Hollow Fibre model’39 mimics the hormonal progression observed clinically in response to host castration as measured by levels of expression of PSA’39’140 The stages of progression include: AS, RAD, and CR. The LNCaP Hollow Fibre model overcomes some of the limitations problematic in other studies, such as host contamination of prostate cancer cells. LNCaP human prostate cancer cells were grown in hollow fibres that were implanted subcutaneously into immunocompromised mice. The fibres are impervious to the movement of cells into or out of the fibre, but porous to proteins and metabolites’”. The fibres permit the isolation of pure populations of prostate cancer cells. Moreover, the progression of cells to CRPC may be followed within the same host mouse over time, because the retrieval of a subset of fibres entails only minor surgery’41.The power to evaluate progression within the same mouse minimizes biological variation. Furthermore, the model involves the growth of a human cell line in vivo to potentially minimize heterogeneity in the sample. Deeply sequenced L0ngSAGE libraries52’3were made using RNA sampled from the in vivo LNCaP Hollow Fibre model of prostate cancer as it progresses from the AS to CR stage. We used this LNCaP atlas to identif’ changes in gene expression that may provide clues of underlying mechanisms resulting in CRPC. Suggested models of CRPC involve: the AR3; steroid synthesis and metabolism9;neuroendocrine prostate cancer cells43;prostate cancer stem cells’; andlor an imbalance of cell growth and cell death8. 91 3.4.1 Support for or against the proposed models of castration-recurrent prostate cancer Androgen receptor (AR) Transcriptional activity of AR The AR is suspected to continue to play an important role in the hormonal progression of prostate cancer. The AR is a ligand-activated transcription factor with its activity altered by changes in its level of expression or by interactions with other proteins. Here, we identified changes in expression of 5 genes that are known, or suspected, to impact the transcriptional activity of the AR in CRPC versus RAD. Cyclin H (CCNJ-f), and proteasome macropain subunit alpha type 7 (PSMA 7) displayed increased levels of expression, while CUE-domain-containing- 2 (CUEDC2), filamin A (FLNA), and high mobility group box 2 (HMGB2) displayed decreased levels of expression. The expression trends of CCNH, CUEDC2, FLNA, and PSMA 7 in CRPC may result in increased AR signalling through mechanisms involving protein-protein interactions or altering levels of expression of AR. CCNH protein is a component of the cyclin dependent activating kinase (CAK). CAK interacts with the AR and increases its transcriptional activity 142 Over-expression of the proteosome subunit PSMA7 promotes AR transactivation of a PSA-luciferase reporter’43.A fragment of the protein product of FLNA negatively regulates transcription by AR through a physical interaction with the hinge region’44.CUEDC2 protein promotes the degradation of progesterone and estrogen receptors145.These steroid receptors are highly related to the AR, indicating a possible role for CUEDC2 in AR degradation. Thus, decreased expression of FLNA or CUEDC2 could result in increased activity of the AR. Decreased expression of HMGB2 in CRPC is predicted to decrease expression of at least a subset of androgen-regulated genes that contain palindromic AREs’46.Here, genes known to be regulated by androgen were enriched in expression trend categories with a peak or valley at the RAD stage of prostate cancer progression. Specifically, 8 of the 13 tags (62%) exhibiting these expression trends ‘E’, ‘F’, ‘J’, ‘K’, or ‘L’ represented known androgen-regulated genes, in contrast to only 22 of the remaining 122 tags (18%; Tables 3.4 and 3.5). Overall, this data supports increased AR activity in CRPC, which is consistent with re-expression of androgen regulated genes as previously reported99. 92 Steroid synthesis and metabolism In addition to changes in expression of AR or interacting proteins altering the transcriptional activity of the AR, recent suggestion of sufficient levels of residual androgen in CR prostate tissue provides support for an active ligand-bound receptor147.The AR may become re-activated in CRPC due to the presence of androgen that may be synthesized by the prostate de novo9 or through thern conversion of adrenal androgens2426.In a phase I clinical trial, abiraterone acetate reduced levels of testosterone and PSA in the serum of patients with CRPC’48.Abiraterone acetate inhibits CYPJ 7 to prevent the synthesis of steroids. Together, these data support continued AR signalling in CRPC . Here, the expression of 5 genes known to function in steroid synthesis or metabolism were significantly differentially expressed in CRPC versus RAD. Of these genes, 24-dehydrocholesterol reductase (DHCR24), dehydrogenase/reductase SDR-family member 7 (DHRS7), elongation of long chain fatty acids family member 5 (ELOVL5), hydroxysteroid (17-beta) dehydrogenase 4 (HSD] 7B4), and opioid receptor kappa 1 (OPRKJ) displayed increased levels of expression. The expression trends ofDHCR24, DHRS7, ELOVL5, HSDJ 7B4, and OPRK] in CRPC may result in increased steroid synthesis or metabolism. Steroid synthesis and metabolism is controlled by the hypothalamus-pituitary-adrenal (HPA) axis. Leutinizing hormone releasing hormone (LHRH) is secreted by the LHRH cells of the hypothalamus. LHRH stimulation causes the pituitary gland to release leutinizing hormone and adrenocorticotropic hormone (ACTH), which acts on the testes and adrenal gland to produce androgens (testosterone and androstenedione, respectively). OPRKJ gene product functions in the HPA axis. The OPRKI agonist, U50488H, stimulates the release of ACTH from the pituitary gland in rhesus monkeys. This action is blocked by the OPRK1-specific antagonist, norbinaltorphimine. It should be noted that mice do not produce the enzyme necessary for the synthesis of adrenal steroids’49,and so castrated mice presumably would not produce androstenedione upon stimulation of OPRKI and release of ACTH. Cholesterol is the precursor of steroid hormones. The enzyme DHCR24 converts desmosterol to cholesterol in the final step of cholesterol synthesis’50.DHRS7 and RoDH-like 3-alpha hydroxysteroid dehydrogenase are retinoid pathway enzymes and SDR family members’51.In addition to its role as a retinol dehydrogenase, RoDH-like 3-alpha hydroxysteroid 93 dehydrogenase converts 3-alpha androstanediol (a weak androgen) to 5-alpha dihydrotestosterone (a potent androgen)’52.Although a role for DHRS7 in androgen metabolism has not been established, it is possible this enzyme may also exhibit promiscuous substrate specificity. The androgen metabolic pathway enzyme HSD17B4 converts testosterone (a relatively potent androgen) to androstenedione (a relatively weak androgen)’52.ELOVL5 protein functions in fatty acid synthesis, and may be important in the synthesis of the male effect pheromone in goats’53’154 Overall, increased levels of expression ofDHCR24, DHRS7, ELOVL5, HSDJ 7B4, and OPRK] may be indicative of the influence of adrenal androgens, or the local synthesis of androgen, to reactivate the AR to promote the progression of prostate cancer in the absence of testicular androgens. Neuroendocrine Androgen-deprivation induces neuroendocrine differentiation of prostate cancer. Here, the expression of 8 genes that are associated with neuroendocrine cells were significantly differentially expressed in CRPC versus RAD. Of these genes, ENO2 (see above), OPRK] (see above), Sl00 calcium binding protein AlO (S]OOA]O), and transient receptor potential cation channel subfamily M member 8 (TRPM8) displayed increased levels of expression, and hairy and enhancer of split 6 (HES6), karyopherinlimportin beta 1 (KPNB]), monoamine oxidase A (MAOA), and receptor (calicitonin) activity modifying protein 1 (RAMP]) displayed decreased levels of expression. The expression trends of ENO2, MAQA, OPRK], S100AJO, and TRPM8 in CRPC may be indicative of neuroendocrine differentiation. The protein product of ENO2 is a marker for neuroendocrine differentiation in prostate cells43.Neuroendocrine-like prostate cancer cells secrete hormones such as serotonin and neurotensin43Si OOA 10 protein mediates transport of serotonin receptors (5-HTIB) to the plasma membrane’55.MAOA is a mitochondrial enzyme that inactivates the neurotransmitter serotonin’ 56 Down-regulation of MA OA in CRPC may render the cells sensitive to serotonin signalling. TRPM8 is a membrane channel protein that regulates the secretion of neurotensin in the neuroendocrine pancreatic tumour cell line, BON’ ‘. Finally, the role of OPRK] in promoting ACTH release (see steroid synthesis and metabolism section above) also supports this neuroendocrine model’58.However, some inconsistencies were 94 observed here. Decreased expression of KPNBJ, RAMP], and HES6 in CRPC is contrary to the neuroendocrine model of prostate cancer. PTHrP is expressed by prostatic neuroendocrine PTHrP possesses both paracrine and intracrine signalling properties. Intracrine PTHrP signalling involves shuttling between the cytoplasm and the nucleus’60.KPNB] gene encodes an intracellular transport receptor for PTHrP that is important for intracrine signalling’61. Moreover, RAMP 1 forms a receptor for calcitonin gene-related peptide, a neuroendocrine hormone43,with calcitonin receptor like receptor’62.Finally, HES6 expression is a marker of the neuroendocrine phenotype in the prostate cancer models CR2 TAg’63 and Cre LoxP TP53 (PE - I-), RB] (PE /)164 Overall, however, there was more support for, than against, the neuroendocrine model of CRPC. Increased expression of genes associated with neuroendocrine differentiation in response to androgen deprivation in vivo is consistent with neuroendocrine differentiation of LNCaP xenografts in castrated mice36. Stem cell The protein products of 6 genes that have been associated with stem cells were significantly differentially expressed in CRPC versus RAD. Of these genes, aquaporin 3 (AQP3), BTG] (see above), and spondin 2 (SPON2) displayed increased levels of expression, and CD151 molecule (CD]5]), HES6 (see above), and hematological and neurological expressed 1 (HN]) displayed decreased levels of expression. The expression trends ofAQP3, BTGJ, and SPON2 support a role for stem cells in CRPC. AQP3 is a marker for progenitor cells of the fetal airway’65. Expression of the BTG] gene is altered in putative prostate cancer stem cells47. SPON2 is a secreted activator of Wnt’66.Wnt signalling is thought to be important for self-renewal in intestinal stem cells45.However, the expression trends of CDJ5], HES6, and HN] do not support a role for stem cells in CRPC. Recently, HN1 has been identified as a nerve regeneration-associated gene in newts and mice’67’168 HES6 protein is an effector of Notch’69. The Notch signalling pathway is important for the self-renewal of human mammary stem cells. CD]5] is expressed on transient amplifjing epithelial cells in human adult airway tissue’70. Taken together, it remains unclear if the stem cell model of CRPC was represented in the LNCaP Hollow Fiber model. It should be noted, however, that the existence of prostate cancer stem cells is controversjal’71.Further studies are necessary to confirm the markers of this population, if it indeed exists. 95 Proliferation and survival The protein products of 31 genes that are associated with cell growth, cell cycle arrest, cell death, and/or survival were significantly differentially expressed in CRPC versus RAD. Of these genes, adenosylmethionine decarboxylase 1 (AMD 1), BCL2/adenovirus El B I 9kDa interacting protein 3 (BNIP3), BTGJ(see above), calciumlcalmodulin dependent protein kinase 2 inhibitor 1 (CAMK2N]), fibroblast growth factor receptor like 1 (FGFRL]), glyoxalase I (GLO]), NADH ubiquinone oxidoreductase chain 3 (MT-ND3), nerve growth factor receptor associated protein 1 (NGFRAPJ), NKX3-1 (see above), prostate specific non-coding gene (PCGEM]), protein phosphatase 2 catalytic subunit beta isoform (PFP2CB), prostaglandin F receptor (FTGFR), S]OOAJO (see above), solute carrier family 25 member 4 (SLC25A4), six transmembrane epithelial antigen of the prostate 1 (STEAFJ), cell cycle control protein 50A (TA’IEM3OA), transmembrane protein 66 (TMEM66), TRPM8 (see above), and WDR45L (see above) displayed increased levels of expression. In contrast, chaperonin containing TCP 1 subunit 2 (CCT2), growth arrest specific 5 on chromosome 1 (GAS5), guanine nucleotide binding protein beta polypeptide 2 like 1 (GNB2LJ), growth factor receptor bound protein 10 (GRBJO), MARCKS like 1 (M4RCKSLJ), midkine (MDK), omithine decarboxylase 1 (ODd), prostate collagen triple helix (PCOTH), phosphoinositide-3-kinase catalytic delta polypeptide (PIK3CD), protein phosphatase 2 regulatory subunit A alpha (PFP2R]A), solute carrier family 25 member 6 (SLC25A 6), and tyrosine 3 monooxygenase/tryptophan 5 monooxygenase activation protein (theta polypeptide; YWHAQ) displayed decreased levels of expression. Proliferation The gene expression trends of GAS5, GNB2LJ, MT-ND3, NKX3-1, PCGEMI, PTGFR, STEAP], and TMEM3OA were in agreement with the presence of proliferating cells in CRPC. GAS5 is a small nucleolar RNA that is required for growth arrest in The expression of GNB2LJ mRNA was decreased in CRPC compared to RAD. The GNB2LJ gene encodes the RACK 1 adapter protein. Over-expression of GNB2L1 in NIH3T3 cells inhibits insulin-like growth factor (IGF- 1)-mediated proliferation and promotes cell adhesion’73.Moreover, over- expression of GNB2LI in MCF7 cells negatively regulates IGF-I-induced activation/phosphorylation of protein kinase B (Aict), reducing cell survival following etoposide treatment’74.Over-expression of GNB2LJ suppresses v-src sarcoma (Src) activity, and delays 96 progression through the cell cycle in colon cancer cells’75. It is conceivable that reduced levels of expression of GNB2LJ may increase the proliferation and survival of cells. MT-ND3 is a human-encoded mitochondrial gene important for oxidative phosphorylation. Expression of MT ND3 is indicative of metabolically active cells. NKX3-1 is a tumour suppressor. NKX3-1 mutant mice develop PIN-like lesions’76,and over-expression of NKX3-] results in reduced proliferation and enhanced NKX3-l protein antagonizes Akt and stabilizes TP53177.Expression of NKX3-1 mRNA in vitro is significantly reduced in the castration- recurrent C4-2 prostate cancer cell line, compared to androgen-sensitive LNCaP prostate cancer cell line93. Interestingly, we observed that a transcript anti-sense to NKX3-1 was highly expressed in the stages of cancer progression that were AS and CR, but not RAD. Anti-sense transcription may hinder gene expression from the opposing strand’78,and therefore, represents a novel mechanism by which NKX3-1 expression may be silenced. PCGEM1 mRNA was highly expressed in CRPC versus RAD. PCGEM] is a prostate-specific, non-coding transcript that promotes cell proliferation and colony formation in LNCaP cells’. FCGEM] also rescues LNCaP cells from doxorubicin-induced apoptosis. Reduced expression of TP53 and CDKI’/JA are observed in PCGEM]-over-expressing cells’80.Together with prostaglandin D2, PTGFR enhances the proliferation of prostate cancer cells via the phosphoinositide-3-kinase (PI3K)/Akt TMEM3OA were in agreement with proliferating cells in CRPC. TMEM3OA is the human homolog of yeast CdcSOp. Cdc5Op is required for the localization of Bnilp, a protein important for microtubule regulation in asymmetric cell division of budding yeast’82.STEAP 1 protein functions in cellular communication and promotes the growth of LAPC9 prostate However, some inconsistencies include the expression trends of BTG], FGFRL], and PCOTH and that may be associated with non-cycling cells. Decreased proliferation is associated with increased expression of BTGJ’84 and FGFRL]’85.Based upon the increased proliferation of LNCaP cells with increased expression of PCOTH, the decreased expression illustrated herein would be expected to be associated with reduced 86 Overall, there was more support at the transcriptome level for proliferation than not, which was consistent with increased proliferation in the LNCaP Hollow Fiber model’39. 97 Cell survival Gene expression trends of GLO1, Si OOA i 0, TRPM8, and PI3KCD suggest cell survival pathways are active following androgen-deprivation and/or in CRPC. Expression of GLO], Si OOA i 0, and TRPM8 were elevated in the stage of cancer progression that was CR versus RAD. GLO1 promotes resistance to apoptosis in response to etoposide and adriamycin in leukemia cells’87,and S100A1O binds Bcl-xL/Bcl-2 associated death promoter (BAD) and inhibits its induction of apoptosis in rodent ovarian cells’88.TRPM8 is required for the survival of LNCaP }589 possibly through regulation of intracellular calcium stores’90,which function to promote growth arrest and apoptosis in prostate cancer cells’91.LNCaP cells decrease proliferation in response to androgen-deprivation. The mechanism is suggested to involve constitutively active PI3KJAkt pathway due to the deletion of the PTEN tumour suppressor’92’ The PI3KJAkt pathway is the predominant survival pathway in LNCaP cells’94.The PI3KIAkt pathway inhibitors, wortmannin or LY294002, cause apoptosis of LNCaP cells under conditions of androgen-deprivation’94.The expression of PI3KCD was highest in the RAD stage of prostate cancer and was consistent with an active PI3KJAkt pathway following androgen deprivation. While expression trends for these genes support the observed increased tumour burden observed in CRCP, gene expression trends of CAMK2NI, CCT2, MDK, TMEM66, and YWJ-IAQ may oppose such suggestion. The levels of CAMK2NJ and TMEM66 mRNA were increased in CRPC versus RAD. Over-expression of TMEM66 in human embryonic kidney cells promotes apoptosis’95,and CAMK2N1 protein inhibits the signalling of CAMK2, a kinase that supports cell survival of prostate cells’96. In contrast, the levels of CCT2, MDK, and YWHAQ mRNA were decreased in CRPC versus RAD. CCT2 is a protein chaperone indispensable for cell survival’97.MDK is a cytokine that participates in cell growth, survival, angiogenesis, migration, and transformation’98.siRNA knock down of MDK gene expression in LNCaP cells results in enhanced tumour necrosis factor alpha-induced cell death’. In contrast to earlier reports in which MDK gene and protein expression was determined to be higher in late stage cancer94’‘, we observed a drop in the levels of MDK mRNA in CRPC versus RAD. MDK expression is negatively regulated by androgen96.Therefore, the decreased levels of MDK mRNA in CRPC may suggest that the AR is reactivated in castration-recurrence. The relatively low level of expression of YWHAQ mRNA in CRPC was not consistent with cell survival. YWHAQ binds BAD and prevents it from inducing apoptosis in rodent ovarian cells’88. 98 YWHAQ also binds and negatively regulates pro-apoptotic Bax protein200.Taken together, these data neither agree nor disagree with the activation of survival pathways in CRPC. Other The significance of the gene expression trends ofAMD], BNIP3, GRB]O, MARCKSLJ, NGRAPJ, ODd, PPP2CB, PPP2RJA, SLC25A4, SLC25A6, and WDR45L that function in cell growth or cell deathlsurvival were not straightforward. For example, BNIP3 and WDR45L, both relatively highly expressed in CRPC versus RAD, may be associated with autophagy. BNTP3 promotes autophagy in response to hypoxia201,and the WDR45L-related protein, WIPI-49, co localizes with the autophagic marker LC3 following amino acid depletion in autophagosomes202. Autophagy, or “self-eating” is the process of digesting cellular components in response to nutrient deprivation. Autophagy has two possible outcomes. Breakdown of cellular components may provide temporary “food” for a nutrient-deprived cell and promote cell survival. Alternatively, if nutrient-deprivation persists, self-eating may lead to cell death203.It is not known if BNIP3 or putative WDR45L-associated autophagy results in cell survival or death. Levels of expression of NGFRAFJ were increased in CRPC versus RAD. The protein product of NGFRAPJ interacts with p75 (NTR). Together they process caspase 2 and caspase 3 to active forms, and promote apoptosis in 293T cells204.NGFRAPI requires p75 (NTR) to induce apoptosis. However, LNCaP cells do not express p75 (NTR), and so it is not clear if apoptosis would occur in this cell type205. Studies on NGFRAPJ in the prostate are lacking. The levels of expression of MARCKSL1 and GRB]O were relatively low in CRPC versus RAD. Over- expression of MARCKSL] results in increased proliferation of retinal cells, but has no effect on NIH3T3 cells206.Moreover, the adapter protein GRB 10, may positively or negatively regulate insulin-like growth factor signalling, depending on the presence of the milieu of GRB 10- interacting 7208 These data indicate that the effects of expression of MARCKSLJ and GRB1O are cell-type specific. Pairs of genes (AMDJ and ODd, PPP2CB and PPP2RJA, and SLC25A4 and SLC25A6) from the same pathways, exhibit differential expression trends in CRPC versus RAD, and yield conflicting information regarding the proliferative andlor survival status of CRPC. ODC 1 and ADM 1 are rate limiting enzymes of the polyamine pathway. In the prostate, ADM 1 catalyzes 99 the conversion of s-adenosylmethionine to decarboxylated s-adenosylmethionine, and ODC 1 catalyzes the conversion of ornithine to putrescine. Spermidine synthase catalyzes the reaction between putrescine and an acetyl group from decarboxylated s-adenosylmethionine to create spermidine. Upon the addition of another acteyl group from decarboxylated s adenosylmethionine, spermidine is converted to spermine by spermine synthase. The significance of polyamines in prostate cancer is not clear209 as spermidine and putrescine promote proliferation, and spermine is associated with differentiation210’1.ODC1 protein over- expression has been observed in human prostate cancer specimens, however, the degree of over- expression lessens in CR tissue212.This is consistent with our observation that ODCJ mRNA levels progressively decreased during progression to castration-recurrence. The significance of decreasing levels of ODCI mRNA and increasing levels ofAMDJ mRNA in CRPC is not known. However, simultaneous reduced expression of ODC] and ADM] in prostate cancer cells results in reduced proliferation213.PPP2CB and PPP2R1A encode isoforms of the catalytic and regulatory subunits of protein phosphätase 2 (PPP2), respectively214.The contradictory expression of these subunits may reflect the dual nature of PPP2. PPP2 is essential for cell survival, but emerging evidence suggests it may also be a tumour suppressor215 SLC25A4 and SLC25A6 are highly related ADP/ATP carriers of the mitochondrial membrane. These proteins are key players in oxidative phosphorylation, but they also participate in the formation of the mitochondrial transition pore, a complex required for the release of cytochrome C and the induction of apoptosis216.The opposite regulation of SLC25A4 and SLC25A6 in CRPC may represent the antagonizing roles of mitochondria in cell growth and cell death. Overall, genes involved in cell growth and cell death pathways were altered in CRPC. Increased tumour burden may develop from a small tip in the balance when cell growth outweighs cell death. Unfortunately, the contributing weight of each gene is not known, making predictions difficult based on gene expression alone of whether proliferation and survival were represented more than cell death in this model of CRPC. It should be noted that LNCaP cells are androgen sensitive and do not undergo apoptosis in the absence of androgens. The proliferation of these cells tend to decrease in androgen-deprived conditions, but eventually with progression begin to grow again mimicking clinical CRPC. Similarly, increased proliferation is observed in CR samples from the LNCaP Hollow Fibre model’39. 100 3.5 CONCLUSION Here, we describe the LNCaP atlas, a compilation of LongSAGE libraries that catalogue the transcriptome of human prostate cancer cells as they progress to CRPC in vivo. Using the LNCaP atlas, we identified differential expression of 96 genes that were associated with castration-recurrence in vivo. These genes were characterized for their potential to be therapeutic targets of CRPC. Moreover, changes in gene expression profiles were identified that support a role for the AR, steroid synthesis and metabolism, neuroendocrine cells, and increased proliferation in castration-recurrence. The gene expression trends neither supported nor discounted a role for stem cells, or an imbalance of cell death in CRPC. 101 Table 3.] Composition of LongSAGE libraries Library Sl885 S1886 Sl887 Sl888 S1889 S1890 S1891 S1892 S1893 Mouse-Condition 13N-AS5 l3N-RADt l3N-CR l5N-AS 15N-RAD 15N-CR 13R-AS 13R-RAD l3R-CR Unfiltered TotalTags 310516 318,102 339,864 338210 310,072 326,870 337,546 314,440 335,504 No.ofBadTags 955 1,010 1,083 1,097 983 737 900 744 832 TotalTags 309,561 317.092 338,781 337,113 309.089 326,133 336,646 313,696 334,672 TagTypes 79,201 96,973 99.730 81,850 84,499 88,249 79,859 91,438 90,675 No. ofDuplieate Ditags 19,761 12,220 12,678 21,973 17,471 12,836 24,552 12,786 13,127 % of Duplicate Ditags 6.38 3.85 3.74 6.52 5.65 3.94 7.29 4.08 3.92 Average QF of Tags 0.85 0.88 0.87 0.86 0.89 0.88 0.88 0.80 0.87 No. ofTags QF<0.95% 63,057 62,872 71,576 68,993 54,627 54,470 68.981 101,215 69,647 Total Tags 246,504 254,220 267,205 268,120 254,462 271,663 267,665 212,481 265,025 TagTypes 52,033 67,542 66,748 52,606 59,374 64,985 53.715 54,682 64,837 Total Tags Combined 2,307,345 Tag Types Combined 263,199 No. of LDTsTypel 124 72 174 179 84 186 164 118 30] No.ofLDTsTypell 19 9 54 56 33 40 60 24 59 Total Tags 246,361 254.139 266,977 267,885 254,345 271,437 267,441 212,339 264.665 TagTypes 52,031 67,540 66,746 52,604 59,372 64,983 53,713 54,680 64,835 Total Tags Combined 2,305,589 Tag Types Combined 263,197 * AS, Androgen-sensitive t RAD, Responsive to androgen-deprivation CR, Castration-recurrent . QF, Quality Factor 1 LDTs, Linker-derived Tags 102 Table 3.2 Number of tag types consistently and significantly differentially expressed among all three biological replicates and between cOnditions* Comparison Change p S 0.001 pS 0.01 pS 0.05 ASt vs. RAD Up in RAD 21 44 83 Down in RAD 6 105 145k Total 89 149 232 RAD vs. CR Up in CR 24 45 89 Down in CR 46 59 .1.04 Total 70 104 193 AS vs. CR Up in CR III 167 294 Down in CR 117 101 259 Total 238 335 550 * Statistics according to the Audic and Claverie test statistic t AS, Androgen-senaitive PAD, Responsive to androgen-deprivation , CR, Castration-recurrent 103 Table 3.3 Top five enrichments of functional categories of tags consistently and significantly differentially expressed among all three biological replicates and between stages of prostate cancer* Top 5 Got categories P-value: Top 5 KEGG § annotations P-value Top 5 SwissProt annotations P-value I! AS vs. RAD: Up in RAD ¶ Cell communication 2.E-02 Stilbene, coumarine and lignin biosynthesis I .E-02 Antioxidant 7.E-04 Extracellular 2.E-02 Butanoate metabolism 2.E-02 Cell adhesion 5.E-03 Extracellular matrix 2.E-02 2,4-Dichlorobenzoate degradation 2.E-02 Signal 6.E-03 Synaptic vesicle transport 3.E-02 Cell adhesion molecules (CAMs) 2.E-02 Fertilization 7.E-03 Synapse 4.E-02 Alkaloid biosynthesis II 5.E-02 Amyotrophic lateral sclerosis 7.E-03 . ASvs.RAD:DowninRAD Glycolysis 3.E-05 Glycolysis /Gluconeogenesis 3.E-05 Glycolysis 3.E-07 Glucose catabolism l.E-04 Ribosome 2.E-03 Pyrrolidone carboxylic acid 8.E-05 Hexose catabolism I .E-04 Carbon fixation 3.E-03 Pyridoxal phosphate 2.E-04 Hexose metabolism 2.E-04 Fructose and mannose metabolism 2.E-02 Gluconeogenesis 3.E-04 Monosaccharide catabolism 2.E-04 Urea cycle and metabolism of amino groups 3.E-02 Coiled coil 5.E-03 RAD vs. CR: Up in CR Acid phosphatase activity 4.E-02 gamma-Hexachlorocyclohexane degradation 5.E-03 Lyase 2.E-03 Lyase activity** 7.E-02 Glycolysis I Gluconeogenesis 3.E-02 Immune response 5.E-03 Carbohydrate metabolism** 9.E-02 0-Glycan biosynthesis 5.E-02 Signal 6.E-03 Extracellular** l.E-0l Ether lipid metabolism** 6.E-02 Glycolysis 7.E-03 Catabolism** l.E-Ol Phenylalanine, tyrosine and tryptophan 6.E-02 Progressive extemal ophthalmoplegia l.E-02 biosynthesis ** RAD vs. CR: Down in CR Cytosolic ribosome 2.E-09 Ribosome 2.E-l 1 Ribosomal protein 6.E-l0 Large ribosomal subunit 1 .E-07 Urea cycle and metabolism of amino groups I .E-02 Ribonucleoprotein 3.E-08 Cytosol 2.E-07 Arginine and proline metabolism 4.E-02 Acetylation I.E-OS Cytosolic large ribosomal subunit 2.E-07 Type 11 diabetes mellitus** l.E-0l Elongation factor l.E-03 Protein biosynthesis 2.E-07 Phenylalanine metabolism** l.E-Ol rRNA-binding 2.E-03 AS vs. CR: Up in CR Synapse 4.E-03 Butanoate metabolism 2.E-03 Glycoprotein 2.E-03 Extracellular 5.E-03 Ascorbate and aldarate metabolism 2.E-02 Vitamin C 7.E-03 Transition metal ion binding 7.E-03 Phenylalanine metabolism 2.E-02 Lipoprotein I .E-02 Metal ion binding 2.E-02 Linoleic acid metabolism 2.E-02 Signal l.E-02 Extracel lular matrix 2.E-02 gamma-Hexachlorocyclohexane degradation 2.E-02 Heparin-binding I .E-02 AS vs. CR: Down in CR Cytosolic ribosome 4.E-12 Ribosome 2.E-09 Acetylation 2.E-07 Biosynthesis 7.E-l I Carbon fixation 9.E-04 Ribosomal protein l.E-06 Macromolecule biosynthesis 2.E-lO Glycolysis / Gluconeogenesis 3.E-03 Glycolysis 7.E-05 Protein biosynthesis I.E-08 Glycosphingolipid biosynthesis - lactoseries 4,E-02 Ribonucleoprotein 8.E-05 Eukaryotic 43S preinitiation complex 2.E-08 Glutamate metabolism** 8.E-02 Protein biosynthesis I .E-04 * Statistics according to the Audic and Claverie test statistic (p 0.05) t GO, Gene Ontology P-value represents the raw EASE (Expression Analysis Systematic Explorer) score § KEGG, Kyoto Encyclopedis of Genes and Genornes Unadjusted p-value was computed using FatiGO ¶ AS, androgen-sensitive; RAD, responsive to androgen-deprivation; CR. castration-recurrent ** Not statistically significant (p a 0.05) 104 Table 3.4 Gene expression trends of LongSAGE tags that consistently and significantly altered expression in CR prostate cancer* 13N 15N 13R AS RADII CR AS RAD CR AS RAD CR Tag Sequence S1885 S1886 S1887 S1888 S1889 S1890 S1891 S1892 S1893 Trendt Gene** Accession TCTAGAGAACACTGTGC 121- 79 382 7 67 136 7 52 200 A ACPP NM_001099 TAATTTTTCTAAGGTGT 101 311 648 119 397 895 120 546 918 A CIORF8O ENS000000186063 TGAGAGAGGCCAGAACA 8 39 150 4 39 144 7 33 95 A N/A Genomic CTCATAAGGAAAGGTrA 637 952 1680 653 1170 1540 688 1620 1930 A RNF2O8 BC090061 GATTTCTATTTGTTTTT 89 169 446 116 208 339 86 311 555 A SERJNC.5 ENSG00000I64300 GTTGGGAAGACGTCACC 426 571 742 273 417 741 262 363 495 A STEAPI NM 012449 GAGGATCACTTGAGGCC 191 299 449 134 189 589 187 203 314 B AMACR BC009471 1TGTrGATFGAAAA’ITI’ 219 197 528 273 197 479 232 391 586 B AMD11- NM_001634 ITFGCTrFTGTIT1’GTT 53 16 169 34 51 129 7 28 72 B AQP3 NM_004925 GTrCGACTOCCCACCAG 45 28 101 52 47 122 34 42 106 B ASAHi if NM_177924 TAATAAACAGGTTITrA 426 232 648 332 315 700 138 250 491 B ASAHJf4 NM_177924 TCACAGCTGTGAAGATC 85 110 277 161 71 258 310 438 945 B BTGJ NM_001731 AAAAGAGAAAGCACITI’ 24 75 199 19 35 85 15 90 552 B CAMK2NI NM_0l8584 CAAAACAGGCAGCTGGT 4 71 169 15 83 162 37 75 268 B CAMK2N1ifNM_018584 AGGAGGAAGAATGGACT 33 59 187 49 67 247 26 42 223 B CCNH NM_001239 1TITAAAAATATAAAAT 89 83 243 97 130 269 64 170 382 B COMT NM_000754 GAATGAAATAAAAAATA 134 252 626 209 240 357 116 160 272 B DHRS7 NM_016029 AAAGTGCATCC’ITI’CCC 118 146 318 153 220 394 288 231 646 B FGFRL) NM_001004356 AAACTGAATAAGGAGAA 24 51 236 19 51 438 19 146 283 B GALNT3 NM_004482 ITPAAGGAAACA1TI’GA 4 4 75 4 4 81 0 0 57 B GALNT3if NM 004482 CCAACCGTGCTrGTACT 191 327 521 202 279 534 172 363 510 B GLOJ NM 006708 GAGGGCCGGTGACATCT 300 378 1170 321 476 1230 254 447 1030 B H2AFJ NM_177925 TATCATTAT1TfTACAA 57 63 161 67 63 181 75 94 181 B HSDI 784 NM_000414 AATGCACTrATGTrI’GC 16 8 64 22 16 77 19 28 98 B N/A No map ACCTCGCAGGGGAGAG 0 0 19 0 4 41 0 5 34 B N/A Genomic ATAACCTGAAAGGAAAG 0 16 56 7 4 74 0 28 87 B N/A No map GTGATGTGCACCTGTI’G 0 0 38 4 0 30 0 5 45 B N/A No map G1TFGGAGGTACTAAAG 20 43 94 34 87 169 34 90 234 B N/A Genomic TITfCAAAAATFGGAAA 0 35 180 7 4 59 0 19 61 B N/A No map GAAAAA1TTAAAGCTAA 394 397 569 433 598 788 853 862 1060 B NGFRAPI NM_206917 CAAATrCAGGGAGCACA 0 4 139 4 16 228 0 14 136 B OPRK1 NM_000912 CTATTGTCTGAACTGA 0 8 109 0 12 70 0 9 227 B 0R51E2 BC020768 ATGCTAATTATGGCAAT 4 12 75 4 8 74 0 5 57 B PCGEMI NR_002769 CAGAAAGCATCCCTCAC 4 43 195 0 16 111 7 33 264 B PLA2G2A NM_000300 TAATTTTAGTGCTTTGA 16 75 154 37 59 162 4 57 132 B PTGFR NM_000959 TTGTTTGTAAATAGAAT 0 12 94 0 4 162 0 14 72 B QKI NM_206853 TAAACACTGTAAAATCC 0 4 75 0 4 66 0 0 42 B QKItt NM_206853 AGCAGATCAGGACACTT 20 35 112 15 16 140 15 42 98 B SiOOAiO NM_002966 CTGCCATAACTTAGATT 37 55 161 93 63 192 56 99 264 B SBDS NM_016038 TGGCTGAG1TI’KITITr 20 24 79 41 8 96 4 42 147 B SFRS2B NM_032102 GAAGATIAATGAGGGAA 126 142 277 108 130 402 101 188 325 B SNX3 NM_003795 ATGGTACTAAATGT1T 16 47 124 37 28 88 11 19 76 B SPIREJ NM_020148 TATATATTAAGTAGCCG 45 39 101 45 75 133 41 75 178 B STEAP2 NM_152999 CAACAATATATGCTTTA 24 32 82 75 32 136 26 99 212 B STEAP2if NM_152999 TTTCATtGCCTGAATAA 24 43 150 34 59 114 22 61 178 B TACC1 NM_006283 TGGCCAGTCTGC1TC 8 16 67 4 4 77 0 5 38 B TMEM3OA ENSG00000I 12697 ATATCACTfCTTCTAGA 12 4 26 7 4 26 0 52 140 C ADAM2if NM 001464 ATGTGTGTTGTA1TfTA 812 338 768 1010 315 1020 269 702 865 C BN1P3 NM_004052 CCACGTTCCACAGTTGC 601 291 599 530 346 700 381 339 559 C ENO2 NM_00l975 CTGATCTGTGTTTCCTC 16 0 26 0 4 4! 19 0 34 C HLA-B BC013187 AGCCCTACAAACAACTA 382 441 596 508 456 619 400 631 1010 C MT-Iv’D3 ENSG00000198840 ATATTTTC1TrGTGGAA 20 12 90 7 0 48 4 0 23 C N/A No map CAAGCATCCCCGTTCCA 2400 2130 2440 2730 1720 2250 1020 2010 2340 C N/A ENSG000002I 1459 GYrGTAAAATAAACTTT 118 83 172 228 87 247 112 203 378 C N/A Genomic TTGGATTTCCAAAGCAG 12 0 19 0 0 33 0 0 26 C N/A Genomic TC1TFTAGCCAATTCAG 138 181 420 381 326 468 389 334 457 C A’KX3-Itt NM_006167 TGATTGCCCTTTCATAT 73 39 86 86 39 107 108 99 181 C P4HA1 NM_000917 GTAACAAGCTCTGGTAT 28 16 56 49 24 66 11 19 72 C PJA2 NM 014819 105 Table .4 cnntinued 13N 15N 13R AS RAD CR AS RAD CR AS RAD CR Tag Sequence S1885 Sl886 S1887 Sl888 S1889 S1890 S1891 S1892 S1893 Trend Gene Accession ACAGTGCTTGCATCCTA 85 75 139 108 98 203 101 118 196 C PPP2CB NM_004156 AGGCGAGATCAATCCCT 57 39 101 37 24 122 131 66 268 C PSMA7 NM_002792 TATT1TGTA1TFA]TFT 73 59 180 93 51 Ill 22 94 253 C SLC25A4 NM 001151 TTATGGATCTCTCTGCG 1050 1260 1820 1140 1300 2260 1990 1010 1530 C SPON2 NM_012445 CAG1TCTCTGTGAAATC 767 515 1060 855 503 914 467 608 1200 C TMEM66 NM_016127 AAATAAATAATGGAGGA 138 59 255 82 118 284 165 90 159 C TRPM8 NM 024080 ATGTFTAATTTTGCACA 61 87 154 157 59 195 217 85 344 C WDR45L NM 019613 GGGCCCCAAAGCACTGC 861 543 1180 1020 657 1590 1240 739 937 E C19orf48 NM 199249 TCCCCGTGGCTGTGGGG 1670 1390 2290 1740 1410 1720 3370 970 1180 E DHCR24± BC004375 GCATCTGITrACATTrA 487 201 345 444 208 468 684 226 423 E ELOVL5 NM_021814 GAAATI’AGGGAAGCCTT 317 153 311 310 181 542 359 193 298 E ENDOD1 XM_290546 GGATGGGGATGAAGTAA 2780 1160 4780 2950 1350 3620 2930 1230 1890 E KLK3t NM_001648 TGAAAAGCTFAATAAAT 313 142 322 474 181 332 273 179 314 E TPDS2 NM_001025252 GTTGTGGTTAATCTGGT 1770 634 1270 1800 806 1190 2480 659 960 F B2M NM_004048 GAAACAAGATGAAATTC 4380 1170 2260 5300 1110 2720 3750 2220 2830 F PGKJ NM 00029! AGCACCTCCAGCTGTAC 2150 1130 648 2060 1560 939 1560 1200 722 G EEF2 NM 001961 GCACAAGAAGATTAAAA 536 228 124 762 425 195 838 278 174 G GAS5 NR_002578 CCGCTGCGTGAGGGCAG 451 169 56 429 197 44 516 94 0 G HES6 NM_018645 GCCCAGGTCACCCACCC 585 55 4 519 79 7 456 66 0 G L0C644844 XM_927939 ATGCAGCCATATGGAAG 2650 386 82 2470 216 129 1210 259 98 G ODC1 NM_002539 CGCTGGTTCCAGCAGAA 1420 811 479 1250 959 553 800 589 374 0 RPLII NM_000975 AAGACAGTGGCTGGCGG 2650 1730 1220 2460 1860 1350 2120 1630 1270 G RPL37A± NM_000998 TTCTrGTGGCGCTTCTC 925 543 217 1030 708 273 1130 419 306 0 RPSJJff NM_001015 GGTGAGACACTCCAGTA 463 252 165 485 346 192 363 245 159 G SLC25A6 NM_001636 AGGT1TTGCCTCATTCC 982 515 281 1200 491 243 688 782 166 H ABJ-1D2 NM_00701 I TGAAGGAGCCGTCTCCA 317 272 187 392 295 199 366 259 140 H ATP5G2 NM_001002031 CTCAGCAGATCCAAGAG 191 185 67 254 232 66 142 231 79 H C17orf45 NM 152350 CTGTGACACAGCTTGCC 308 397 172 209 307 125 295 226 110 H CCT2 NM_006431 TCTGCACCTCCGCTTGC 495 606 277 426 570 276 366 471 204 H EEFIA2 NM 001958 GCCCAAGGACCCCCTGC 114 114 38 138 98 41 101 42 4 H FLNAI NM 001456 TTATGGGATCTCAACGA 564 425 180 642 452 317 430 490 253 H GNB2LI NM_006098 TCTGCAAAGGAGAAGTC 81 102 38 105 87 26 165 80 30 H HMGB2 NM_002129 CTI’GTGAACTGCACAAC 268 228 124 231 177 103 273 160 57 H 1-INI NM 016185 TCTGAAGTTTGCCCCAG 313 291 150 254 299 155 187 226 72 H MAQA NM 000240 TTAATTGATAGAATAAA 483 350 199 422 287 103 273 235 83 H MAQA NM_000240 GGCAGCCAGAGCTCCAA 1200 1260 420 1050 672 350 681 819 23 H MARCKSL] NM 023009 CCCTGCCTTGTCCCTCT 353 240 112 310 263 107 176 193 102 H MDK NM_001012334 CTGTGGATGTGTCCCCC 649 476 169 459 389 214 430 297 117 H N/A No map CTCCTCACCTGTATTTT 1120 771 262 1220 979 313 666 730 261 H RPLI3A NM 012423 GCAGCCATCCGCAGGGC 1980 1770 809 2300 1730 928 2150 1570 1020 H RPL28 NM_000991 GGATTTGGCCTTTTFGA 3470 2070 1370 4170 2910 1540 2800 2870 2500 H RPLP2+$ NM 001004 TCTGTACACCTGTCCCC 2320 1670 850 1930 1880 825 2130 1490 1120 H RPSII NM 001015 GCTETTAAGGATACCGG 1510 1050 626 1860 1120 593 1550 1550 960 H RPS2O NM_001023 CCCCAGCCAGTCCCCAC 921 519 281 788 664 357 1100 438 291 H RPS3 NM 001005 CCCCCAATGCTGAGGCC 89 138 26 90 94 30 90 80 30 H SF3A2 NM_007165 GCCGCCATCTCCGAGAG 195 102 30 168 118 55 172 108 30 H TKT NM_001064 GGCCATCTCTTCCTCAG 349 307 202 317 346 173 277 254 121 H YWHAQ NM_006826 AGGCTGTGTTCCTCCGT 16 39 11 34 67 22 26 38 8 I ACI’l NM_000666 TGCCTCTGC0000CAGG 446 649 427 399 664 424 501 462 317 I CD15J NM_004357 GGCACAGTAAAGGTGOC 175 216 142 332 350 173 456 316 204 I CUEDC2 NM_024040 TCACACAGTGCCTGTCG 49 71 7 30 47 15 34 66 4 I CXCR7 NM_001047841 TGTGA000AAGCTGCTT 53 87 15 67 102 52 52 90 42 1 FKBP1O BC016467 TGCTTTGC1TCATTCTG 28 63 26 22 79 26 49 118 61 I GRBJO NM_005311 GTACTGTATGCTTGCCA 170 212 82 134 153 88 123 188 113 I KPNBI NM_002265 GTGGCAGTGGCCAGTTG 106 193 97 123 173 96 94 137 76 I N/A ENS000000138744 G000AGCCCCGGGCCCG 61 63 26 30 51 18 34 57 0 I NATI4 NM_020378 TGflCAGGACCCTCCCT 28 67 26 60 63 26 60 28 0 I NELF NM_015537 TITrCCTGGGGATCCTC 41 130 15 37 87 33 56 104 45 I PCOTH NM 001014442 106 Table 3.4 l3N l5N 13R AS RAD CR AS RAD CR AS RAD CR Tag Sequence Sl885 S1886 S1887 Sl888 Sl889 S1890 S1891 S1892 S1893 Trend Gene Accession GAAACCCGGTAGTCTAG 41 75 4 37 75 26 52 151 30 I PLCB4 NM_000933 GTCTGACCCCAGGCCCC 126 205 82 119 193 103 157 179 38 I PPP2RIA NM_014225 GGCCCGAGTTACTITI’C 231 150 75 161 232 136 142 160 45 I RPL35Aff NM_000996 GTrCGTGCCAAATTCCG 881 696 390 1100 712 523 497 782 461 1 RPL35A NM_000996 TTACCATATCAAGCTGA 877 535 311 1130 598 405 636 791 578 I RPL39 NM_001000 GCTGCAGCACAAGCGGC 268 244 127 45 216 125 157 71 11 1 RPSI8tt NM_022551 AGCTCTTGGAGGCACCA 203 319 206 142 421 243 269 259 162 I SELENBPJ NM_003944 TGCTGGTGTGTAAGGGG 69 102 45 82 87 37 105 75 30 I SH3BP5L NM_030645 GAGAGTAACAGGCCTGC 191 150 71 112 181 111 108 165 64 I SYNC] NM_030786 CTGAAAACCACTCAAAC 394 508 225 306 547 236 310 381 200 I TFPI NM_006287 TAAAAAAGG1TFCATCC 183 248 127 86 130 66 142 268 87 I TFPI NM_006287 CTCCCTCCTCTCCTACC 28 32 4 30 39 7 71 24 0 1 TKI NM_003258 CATTTTCTAATTTTGTG 544 744 236 407 771 181 288 664 185 J N/A No map TGA1TCACTCCACTC 3480 5260 3910 3700 6110 3590 3040 5960 2600 K MT-C03 ENSG00000198938 ‘ITFCTGTCTGGGGAAGG 130 236 82 123 201 111 101 188 113 K PIK3CD NM_005026 GCCGCTACTI’CAGGAGC 256 370 199 224 330 169 142 316 38 K RAMP] NM_005855 ATGGTTACACTITI’GGT 93 161 94 75 208 118 60 226 95 K UTX NM_021140 CACTACTCACCAGACGC 2820 3900 3020 2740 4290 2440 2620 3120 1260 K VPS13Bff ENSG00000132549 CTAAGACTTCACCAGTC 7120 11000 9730 6390 10900 8330 3610 8870 7850 L N/A ENSG000002IOO82 * Statistics according to tlse Audic and Claverie teat statistic (p 0.05) t Tag count per 1 million = (observed tag count/total tags in the library) x 1,000,000 Trends are descibed from A to L in the trend legend below. For some genes the trend is indistinguishable between two possibilties. In addition to p-value considerations, significantly different trends were also required to display uniform directions of change in each biological replicate. AS, Androgen-sensitive 1 RAD. Responsive to androgen-deprivation ¶ CR, Castration-recurrent ** Genes were represented by HGNC approved nomenclature whes available. Non-HGNC gene names were not italicized. tt Tag maps antisense to gene Gene is known to display this expression trend in castration-recurrence § Accession numbers were displayed following the priority (where available): RefSeq>Mammalian Gene Collection>Ensembl Gene If the tag mapped to more than one transcript variant of the same gene, the accession number of the lowest numerical transcript variant was displayed. A ‘ETTh D C T’ G C rr—, j _______ ifl ifl pH it- B C E H C ‘ K C .2+ ‘ * .+ .2f * .2f II — II - _______ w , AS RAD CR AS RAD CR C C F —— a ‘— Lit1itll-itH AS RAD CR AS RAD CR AS RAD CR AS RAD CR Trend Legend: AS RAD CR AS RAD CR AS RAD CR AS RAD CR AS RAD CR AS RAD CR 107 Table 3.5 Characteristics of genes with novel association to castration-recurrence in vivo S or Reg. Spec. Associated with S or Reg. Spec. Associated with Gene5 PMt byA toPt CaPI1 GG1Pro’MetsttCR Gene PM byA toP CaP GG Prog. Mets CR ABHD2 PM - - yt - - - - NKX3-I - yt Y - - - Y - ACY] - - - - - - - ODCI - yt - yt - yI. - yt AQP3 PM OPRKI PM - - .4TP5G2 - - 0R51E2 PM - - yt - - - - B2M S&PM yt yt Y-J P4HAI - Y - - - - - - BNIP3 - - YI- - PCGEM1 - yt y yt - yt - - BTG] - - - -. - - - PCOTH - - Y yt - yt - - C17orf45 - - PGK] - yt - - yt - yt I- - C19orf48 S Yt PIK3CD - - - - - - yt yt CIorJSO yl’ PJA2 - - - - - - - - CAMK2N] Y yt PLCB4 PM - - - - - - - CCNH PPP2CB - - - Y4’ - - - - CCT2 PPP2RJA - - - - - - - - CDI5] PM yt yt PSMA7 - COMT y-i PTGFR PM - CUEDC2 QKI - CXCR7 PM y1’ yt yt RAMP) PM - - - - - - - DHRS7 PM Y’I’ R1’/F208 - - - - - - - - EEFIA2 yt yt RPLJI - - - - - - Y-J’ - EEF2 RPL28 - - - - - - - - ELOVL5 PM Y RPSJI - - - - - - y.4. - ENDODI S yt RPS]8 - - - yl’ - - - - ENO2 PM RPS3 - - - - - - - - ENSG000002IOO82 SIOOA]O PM - - - - - - - ENSG000002I 1459 SBDS - - FGFRLI PM SELENBP] - Y’ - Y1- - - - - FKBPJO SERINC5 - - - - - - - - GALNT3 yt Y- SF3A2 - - - - - - yt - GAS5 SFRS2B - - - - - - - - GLO] Yt yl’ SH3BP5L - - - - - - - - GNB2LI PM yt SLC25A4 - - - yt - - - - GRB1O PM SLC25A6 - - - yt - - - - H2AFJ SNX3 - - - - yt - - - HES6 yt yt SPIRE] - - - - - - - - 1-ILA-B PM SPON2 S - I - - - - - HMGB2 yt STEAPI PM - y yt - - - - HNI yt SYNC] - - - - - - - - HSD]7B4 yt yt TFPI S - L0C644844 TKI - - - - - - yt - MAOA Y Yt TKT - - - - - - - - MARCKSLI PM yt TMEM3OA S&PM MDK S&PM y. yt yt TMEM66 S&PM yt MT-C03 TPD52 - Yt Y yt - yt y-I- - MT-ND3 TRPM8 PM yt - yt - - - y. NAAA - yturx - - - - - - - - NAT14 PM VPSJ3B PM - - - - - yt - NELF PM WDR45L - - - - - - - - NGFRAPJ YWHAQ - - - - - - - * Genes were represented by HGNC approved nomenclature when available. Non-HGNC gene names were not italicized t S or PM, gene product is thought to he secreted (S) or localize to the plasma membrane (PM) Reg. by A, gene expression changes in response to sndrogen in prostate cells § Spec. to P. gene expression is specific to- or enriched in- prostate tissue compared to other tissues CaP, gene is differentially expressed in prostate cancer compared to normal, benign prostatic hyperplasia, or prostatic intraepithelial neoplasia ¶ GG, gene is differentially expressed in higher Gleason grade tissue versus lower Gleason grsde tissue Prog., gene expression correlates with late-stage prostate cancer or is a risk factor that predicts progression It Mets, gene expresion is associated with pI-ostste cancer nietastsis in human samples or in rico models CR, gene is associated with castration-recurrent prostate csncer in human tissue or in v/co models, but exhibits an opposite trend of this report§ Y, yes; t, high gene expression; -ij, low gene expression 108 Mouse 13N Mouse 15N Mouse 13R 0.7 1 CR 0.8 1 0.507l 0.4 0.4 AS 0.5 I 0.3 0.2 0.2 0.2 0.]0.] 0.1 0• 0• 0 0 10 72 0 10 0 10 Time Post Cx (days) Time Post Cx (days) Time Post Cx (days) Figure 3.1 qRT-PCR analysis of KLK3 gene expression during hormonal progression of prostate cancer to castration-recurrence. RNA samples were retrieved from the in vivo LNCaP Hollow Fibre model at different stages of cancer progression that were: AS, androgen-sensitive, day zero (just prior to surgical castration and 7 days post-fibre implantation); RAD, responsive to androgen-deprivation, 10 days post-surgical castration; and CR, castration-recurrent, 72 days post-surgical castration. MNE, mean normalized expression, calculated by normalization to glyceraldehyde-3-phosphate (GAPDJ-f). Error bars represent ± standard deviation of technical triplicates. Each mouse represents one biological replicate. CR 72 72 109 S1888/ 15N-AS S1886/ 13N-RAD S1893/i3RCR Figure 3.2 Clustering of the nine LongSAGE libraries in a hierarchical tree. The tree was generated using a Pearson correlation-based hierarchical clustering method and visualized with TreeView. LongSAGE libraries constructed from similar stages of prostate cancer progression (AS, androgen-sensitive; RAD, responsive to androgen-deprivation; and CR, castration-recurrent) cluster together. 13N, l5N, and 13R indicate the identity of each animal. 13R-AS 0.1 110 40000 35000 C 30000 25000 20000 C 5 15000 10000 5000 0 0 5 10 15 20 25 Figure 3.3 Ten K-means clusters are optimal to describe the expression trends present during progression of prostate cancer to castration-recurrence. K-means clustering was conducted over a range of K (number of clusters) from K2 to K20 and the within-cluster dispersion was computed for each clustering run and plotted against K. The within-cluster dispersion declined with the addition of clusters and this decline was most pronounced at K= 10. The graph of within-cluster dispersion versus K shown here is for mouse 1 3N, but the results were similar for mice 15N and 13R. No. of Clusters 111 Mouse 13N Mouse 15N Mouse 13R Figure 3.4 K-means clustering of tag types with similar expression trends. PoissonC with K=10 (where K = number of clusters) was conducted over 100 iterations separately for each biological replicate (mice 1 3N, 1 5N, and 13R) and the results from the iterations were combined into consensus clusters shown here. Plotted on the x-axes are the long serial analysis of gene expression (LongSAGE) libraries representing different stages of prostate progression: AS, androgen sensitive; RAD, responsive to androgen-deprivation; and CR, castration-recurrent. Plotted on the y axes are the relative expression levels of each tag type, represented as a percentage of the total tag count (for a particular tag type) in all three LongSAGE libraries. Different colours represent different tag types. Each of the ten clusters for each biological replicate are labeled as such. ‘No equivalent’ indicates that a similar expression trend was not observed in the indicated biological replicate. Eleven expression patterns are evident in total and are labeled on the left. K- means clusters were amalgamated into five major expression trends: ‘up’ during progression, ‘down’ during progression, ‘constant’ during progression, expression ‘peak’ in the RAD stage, and expression ‘valley’ in RAD stage. a > 100 Cluster 1 No Equivalent AS RAD CR 100- Cluster 1 100 Cluster 2 __ 2o’1j AS RAD CR AS RAD CR 100 100 80 Cluster 2 80 Cluster 3 3 No Equivalent AS RAD CR AS RAD CR 100 100 100 80 Cluster 3 80 Cluster 3 80 Cluster 4 r0 r_____ AS RAD CR AS RAD CR AS RAD CR 100 100 100 80 Chi,ter 4 80 Cluster 4 80 ClusterS 5 1 _______________ _______________ AS RAD CR AS RAD CR AS RAD CR 100 Cluster 5 100 ClusterS 100 Cluster 60 60 60 8 6o 0 0 AS RAD CR AS RAD CR AS RAD CR 100- Cluster 6 100 Cluster 6 No Equivalent AS RAD CR AS RAD CR 100- Cluster 7 Cluster 7 Cluster 7 82g 2!I_l 2g t 2g AS RAD CR AS RAD CR AS RAD CR 100 100 80 Cluster 8 80 Cluster 8 ___ ___ ________________ AS RAD CR AS RAD CR 100 100- 80 Cluster 9 80- Cluster 9 _______________ AS RAD CR AS RAD CR 100 100- 80 Cluster 10 80- Cluster 10 60 i 60- 40 40- 20! 20- 0 0- AS RAD CR AS RAD CR 112 Figure 3.5 Gene ontology enrichments of the five major expression trends. Plotted on the x-axis are Gene Ontology (GO) categories enriched in one or more of the five major expression trends. On the z-axis the five major expression trends are: ‘up’ during progression, ‘down’ during progression, ‘constant’ during progression, expression ‘peak’ in the RAD stage, and expression ‘valley’ in RAD stage. The y-axis displays the number of biological replicates (number of mice: 1, 2, or 3) exhibiting enrichment. The latter allows one to gauge the magnitude of the GO enrichment and confidence. 113 3.6 REFERENCES 1. Sharifi N, Gulley JL, Dahut WL: Androgen deprivation therapy for prostate cancer, Jama 2005, 294:238-244 2. Huggins C, Hodges C: Studies on prostatic cancer: The effect of castration, of estrogen and of androgen injection on serum phosphatases in metastatic carcinoma of the prostate, Cancer Res 1941, 293-297 3. Feldman BJ, Feldman D: The development of androgen-independent prostate cancer, Nat Rev Cancer 2001, 1:34-45 4. Crawford ED, Eisenberger MA, McLeod DO, Spaulding JT, Benson R, Doff FA, Blumenstein BA, Davis MA, Goodman PJ: A controlled trial of leuprolide with and without flutamide in prostatic carcinoma, N Engl J Med 1989, 321:419-424 5. Petrylak DP, Tangen CM, Hussain MH, Lara PN, Jr., Jones JA, Taplin ME, Burch PA, Berry D, Moinpour C, Kohli M, Benson MC, Small EJ, Raghavan D, Crawford ED: Docetaxel and estramustine compared with mitoxantrone and prednisone for advanced refractory prostate cancer, N Engi J Med 2004, 35 1:1513-1520 6. Tannock IF, de Wit R, Berry WR, Horti J, Pluzanska A, Chi KN, Oudard S, Theodore C, James ND, Turesson I, Rosenthal MA, Eisenberger MA: Docetaxel plus prednisone or mitoxantrone plus prednisone for advanced prostate cancer, N Engi J Med 2004, 351: 1502-1512 7. Scher HI, Sawyers CL: Biology of progressive, castration-resistant prostate cancer: directed therapies targeting the androgen-receptor signaling axis, J Clin Oncol 2005, 23:8253-8261 8. So A, Gleave M, Hurtado-Col A, Nelson C: Mechanisms of the development of androgen independence in prostate cancer, World J Urol 2005, 23:1-9 9. McPhaul MJ: Mechanisms of prostate cancer progression to androgen independence, Best Pract Res Clin Endocrinol Metab 2008, 22:373-3 88 10. Yamamoto KR: Steroid receptor regulated transcription of specific genes and gene networks, Annu Rev Genet 1985, 19:209-252 114 11. Riegman PH, Vlietstra RJ, van der Korput JA, Brinkmann AO, Trapman J: The promoter of the prostate-specific antigen gene contains a functional androgen responsive element, Mol Endocrinol 1991, 5:1921-1930 12. Veldscholte J, Berrevoets CA, Zegers ND, van der Kwast TH, Grootegoed JA, Mulder E: Hormone-induced dissociation of the androgen receptor-heat-shock protein complex: use of a new monoclonal antibody to distinguish transformed from nontransformed receptors, Biochemistry 1992, 3 1:7422-7430 13. Wong CI, Zhou ZX, Sar M, Wilson EM: Steroid requirement for androgen receptor dimerization and DNA binding. Modulation by intramolecular interactions between the NH2-terminal and steroid-binding domains, J Biol Chem 1993, 268:19004-19012 14. Georget V, Lobaccaro JM, Terouanne B, Mangeat P, Nicolas JC, Sultan C: Trafficking of the androgen receptor in living cells with fused green fluorescent protein-androgen receptor, Mol Cell Endocrinol 1997, 129:17-26 15. Ham J, Thomson A, Needham M, Webb P, Parker M: Characterization of response elements for androgens, glucocorticoids and progestins in mouse mammary tumour virus, Nucleic Acids Res 1988, 16:5263-5276 16. Shang Y, Myers M, Brown M: Formation of the androgen receptor transcription complex, Mol Cell 2002, 9:601-610 17. Balk SP, Knudsen KE: AR, the cell cycle, and prostate cancer, Nucl Recept Signal 2008, 6:eOOl 18. Cunha GR, Ricke W, Thomson A, Marker PC, Risbridger G, Hayward SW, Wang YZ, Donjacour AA, Kurita T: Hormonal, cellular, and molecular regulation of normal and neoplastic prostatic development, J Steroid Biochem Mol Biol 2004, 92:22 1-236 19. Bems EM, de Boer W, Mulder E: Androgen-dependent growth regulation of and release of specific protein(s) by the androgen receptor containing human prostate tumor cell line LNCaP, Prostate 1986, 9:247-259 20. Visakorpi T, Hyytinen E, Koivisto P, Tanner M, Keinanen R, Palmberg C, Palotie A, Tammela T, Isola J, Kallioniemi OP: In vivo amplification of the androgen receptor gene and progression of human prostate cancer, Nat Genet 1995, 9:401-406 115 21. Ford OH, 3rd, Gregory CW, Kim D, Smitherman AB, Mohier JL: Androgen receptor gene amplification and protein expression in recurrent prostate cancer, J Urol 2003, 170:1817-1821 22. Gregory CW, He B, Johnson RT, Ford OH, Mohier JL, French FS, Wilson EM: A mechanism for androgen receptor-mediated prostate cancer recurrence after androgen deprivation therapy, Cancer Res 2001, 61:4315-4319 23. Chmelar R, Buchanan G, Need EF, Tilley W, Greenberg NM: Androgen receptor coregulators and their involvement in the development and progression of prostate cancer, Tnt J Cancer 2007, 120:719-733 24. Holzbeierlein J, Lal P, LaTulippe E, Smith A, Satagopan J, Zhang L, Ryan C, Smith S, Scher H, Scardino P, Reuter V, Gerald WL: Gene expression analysis of human prostate carcinoma during hormonal therapy identifies androgen-responsive genes and mechanisms of therapy resistance, Am J Pathol 2004, 164:217-227 25. Mostaghel EA, Nelson PS: Intracrine androgen metabolism in prostate cancer progression: mechanisms of castration resistance and therapeutic implications, Best Pract Res Clin Endocrinol Metab 2008, 22:243-258 26. Labrie F: Adrenal androgens and intracrinology, Semin Reprod Med 2004, 22:299-309 27. Veldscholte J, Berrevoets CA, Ris-Stalpers C, Kuiper GG, Jenster G, Trapman J, Brinlcmann AO, Mulder E: The androgen receptor in LNCaP cells contains a mutation in the ligand binding domain which affects steroid binding characteristics and response to antiandrogens, J Steroid Biochem Mol Biol 1992, 4 1:665-669 28. Culig Z, Hobisch A, Cronauer MV, Radmayr C, Trapman J, Hittmair A, Bartsch G, Kiocker H: Androgen receptor activation in prostatic tumor cell lines by insulin-like growth factor-I, keratinocyte growth factor, and epidennal growth factor, Cancer Res 1994, 54:5474-5478 29. Hobisch A, Eder IE, Putz T, Horninger W, Bartsch G, Klocker H, Culig Z: Interleukin-6 regulates prostate-specific protein expression in prostate carcinoma cells by activation of the androgen receptor, Cancer Res 1998, 5 8:4640-4645 30. Nazareth LV, Weigel NL: Activation of the human androgen receptor through a protein kinase A signaling pathway, J Biol Chem 1996, 271:19900-19907 116 31. Henttu P, Vihko P: Steroids inversely affect the biosynthesis and secretion of human prostatic acid phosphatase and prostate-specific antigen in the LNCaP cell line, J Steroid Biochem Mol Biol 1992, 41:349-360 32. He WW, Sciavolino PJ, Wing J, Augustus M, Hudson P, Meissner PS, Curtis RT, Shell BK, Bostwick DG, Tindall DJ, Gelmann EP, Abate-Shen C, Carter KC: A novel human prostate-specific, androgen-regulated homeobox gene (NKX3.1) that maps to 8p2l, a region frequently deleted in prostate cancer, Genomics 1997, 43:69-77 33. Yuan TC, Veeramani S, Lin MF: Neuroendocrine-like prostate cancer cells: neuroendocrine transdifferentiation of prostate adenocarcinoma cells, Endocr Relat Cancer 2007, 14:53 1-547 34. Ito T, Yamamoto 5, Ohno Y, Namiki K, Aizawa T, Akiyama A, Tachibana M: Up- regulation of neuroendocrine differentiation in prostate cancer after androgen deprivation therapy, degree and androgen independence, Oncol Rep 2001, 8:1221-1224 35. Hirano D, Okada Y, Minei S, Takimoto Y, Nemoto N: Neuroendocrine differentiation in hormone refractory prostate cancer following androgen deprivation therapy, Eur Urol 2004, 45:586-592; discussion 592 36. Burchardt T, Burchardt M, Chen MW, Cao Y, de la Taille A, Shabsigh A, Hayek 0, Dorai T, Buttyan R: Transdifferentiation of prostate cancer cells to a neuroendocrine cell phenotype in vitro and in vivo, J Urol 1999, 162:1800-1805 37. Cox ME, Deeble PD, Bissonette EA, Parsons SJ: Activated Y,5’-cyclic AMP-dependent protein kinase is sufficient to induce neuroendocrine-like differentiation of the LNCaP prostate tumor cell line, J Biol Chem 2000, 275:13812-13818 38. Qiu Y, Robinson D, Pretlow TG, Kung HJ: Etk/Bmx, a tyrosine kinase with a pleckstrin homology domain, is an effector of phosphatidylinositol 3’-kinase and is involved in interleulcin 6-induced neuroendocrine differentiation of prostate cancer cells, Proc Natl Acad Sci U S A 1998, 95:3644-3649 39. Kim J, Adam RM, Freeman MR: Activation of the Erk mitogen-activated protein kinase pathway stimulates neuroendocrine differentiation in LNCaP cells independently of cell cycle withdrawal and STAT3 phosphorylation, Cancer Res 2002, 62:1549-1554 117 40. Cheville JC, Tindall D, Boelter C, Jenkins R, Lohse CM, Pankratz VS, Sebo TJ, Davis B, Blute ML: Metastatic prostate carcinoma to bone: clinical and pathologic features associated with cancer-specific survival, Cancer 2002, 95:1028-1036 41. Roudier MP, True LD, Higano CS, Vesselle H, Ellis W, Lange P, Vessella RL: Phenotypic heterogeneity of end-stage prostate carcinoma metastatic to bone, Hum Pathol 2003, 34:646-653 42. Segal NH, Cohen RJ, Haffejee Z, Savage N: BCL-2 proto-oncogene expression in prostate cancer and its relationship to the prostatic neuroendocrine cell, Arch Pathol Lab Med 1994, 118:616-618 43. Abrahamsson PA: Neuroendocrine differentiation in prostatic carcinoma, Prostate 1999, 39:135-148 44. Isaacs JT: The biology of hormone refractory prostate cancer. Why does it develop?, Urol Clin North Am 1999, 26:263-273 45. Korkaya H, Wicha MS: Selective targeting of cancer stem cells: a new concept in cancer therapeutics, BioDrugs 2007, 21:299-310 46. Collins AT, Maitland NJ: Prostate cancer stem cells, Eur J Cancer 2006, 42:1213-1218 47. Bimie R, Bryce SD, Roome C, Dussupt V, Droop A, Lang SH, Berry PA, Hyde CF, Lewis JL, Stower MJ, Maitland NJ, Collins AT: Gene expression profiling of human prostate cancer stem cells reveals a pro-inflammatory phenotype and the importance of extracellular matrix interactions, Genome Biol 2008, 9:R83 48. Collins AT, Berry PA, Hyde C, Stower MJ, Maitland NJ: Prospective identification of tumorigenic prostate cancer stem cells, Cancer Res 2005, 65:10946-1095 1 49. Maitland NJ, Collins AT: Prostate cancer stem cells: a new target for therapy, J Clin Oncol 2008, 26:2862-2870 50. Chakravarti A, Zhai GG: Molecular and genetic prognostic factors of prostate cancer, World J Urol 2003, 21:265-274 51. Quinn DI, Henshall SM, Sutherland RL: Molecular markers of prostate cancer outcome, Eur J Cancer 2005, 41:858-887 118 52. Velculescu VE, Zhang L, Vogelstein B, Kinzler KW: Serial analysis of gene expression, Science 1995, 270:484-487 53. Saha S, Sparks AB, Rago C, Akmaev V, Wang CJ, Vogeistein B, Kinzler KW, Velculescu VE: Using the transcriptome to annotate the genome, Nat Biotechnol 2002, 20:508-512 54. Romanuik TL, Wang, G., Holt, R.A., Jones, S.J.M., Marra, M.A., and M.D. Sadar: Regulation of the transcriptome by the androgen-axis in prostate cancer, In preparation 55. Robertson N, Oveisi-Fordorei M, Zuyderduyn SD, Varhol RJ, Fjell C, Marra M, Jones S, Siddiqui A: DiscoverySpace: an interactive data analysis application, Genome Biol 2007, 8:R6 56. Fitch WM, Margoliash E: Construction of phylogenetic trees, Science 1967, 155:279- 284 57. Felsenstein J: Numerical methods for inferring evolutionary trees, Q. Rev. Biol. 1982, 57:379-404 58. Page RD: TreeView: an application to display phylogenetic trees on personal computers, Comput Appi Biosci 1996, 12:357-358 59. Cai L, Huang H, Blackshaw 5, Liu JS, Cepko C, Wong WH: Clustering analysis of SAGE data using a Poisson approach, Genome Biol 2004, 5 :R5 I 60. Blackshaw 5, Harpavat 5, Trimarchi J, Cai L, Huang H, Kuo WP, Weber G, Lee K, Fraioli RE, Cho SH, Yung R, Asch E, Ohno-Machado L, Wong WH, Cepko CL: Genomic analysis of mouse retinal development, PLoS Biol 2004, 2:E247 61. Ewing B, Green P: Base-calling of automated sequencer traces using phred. II. Error probabilities, Genome Res 1998, 8:186-194 62. Ewing B, Hillier L, Wendi MC, Green P: Base-calling of automated sequencer traces using phred. I. Accuracy assessment, Genome Res 1998, 8:175-185 63. Siddiqui AS, Khattra J, Delaney AD, Zhao Y, Astell C, Asano J, BabakaiffR, Barber S, Beland J, Bohacec 5, Brown-John M, Chand 5, Charest D, Charters AM, Cullum R, DhalIa N, Featherstone R, Gerhard DS, Hoffman B, bit RA, Hou J, Kuo BY, Lee LL, Lee S, Leung D, Ma K, Matsuo C, Mayo M, McDonald H, Prabhu AL, Pandoh P, 119 Riggins GJ, de Algara TR, Rupert JL, Smailus D, Stott J, Tsai M, Varhol R, Vrljicak P, Wong D, Wu MK, Xie YY, Yang G, Zhang I, Hirst M, Jones SJ, Helgason CD, Simpson EM, Hoodless PA, Marra MA: A mouse atlas of gene expression: large-scale digital gene-expression profiles from precisely defined developing C57BL/6J mouse tissues and cells, Proc Nati Acad Sci USA 2005, 102:18485-18490 64. Pruitt KD, Tatusova T, Maglott DR: NCBI reference sequences (RefSeq): a curated non- redundant sequence database of genomes, transcripts and proteins, Nucleic Acids Res 2007, 35:D61-65 65. Ashbumer M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium, Nat Genet 2000, 25:25- 29 66. Hosack DA, Dennis G, Jr., Sherman BT, Lane HC, Lempicki RA: Identifying biological themes within lists of genes with EASE, Genome Biol 2003, 4:R70 67. Audic S, Claverie JM: The significance of digital gene expression profiles, Genome Res 1997, 7:986-995 68. Kanehisa M, Araki M, Goto S. Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, Yamanishi Y: KEGG for linking genomes to life and the environment, Nucleic Acids Res 2008, 36:D480-484 69. Bairoch A, Apweiler R: The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000, Nucleic Acids Res 2000, 28:45-48 70. Al-Shabrour F, Minguez P, Tarraga J, Medina I, Alloza E, Montaner D, Dopazo J: FatiGO +: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments, Nucleic Acids Res 2007, 35:W91-96 71. Strausberg RL, Feingold EA, Grouse LH, Derge JG, Klausner RD, Collins FS, Wagner L, Shenmen CM, Schuler GD, Altschul SF, Zeeberg B, Buetow KH, Schaefer CF, Bhat NK, Hopkins RF, Jordan H, Moore T, Max SI, Wang J, Hsieh F, Diatchenko L, Marusina K, Farmer AA, Rubin GM, Hong L, Stapleton M, Soares MB, Bonaldo MF, 120 Casavant TL, Scheetz TE, Brownstein MJ, Usdin TB, Toshiyuki 5, Caminci P, Prange C, Raha SS, Loquellano NA, Peters GJ, Abramson RD, Mullahy SJ, Bosak SA, McEwan PJ, McKeman KJ, Malek JA, Gunaratne PH, Richards S, Worley KC, Hale S, Garcia AM, Gay U, Hulyk SW, Villalon DK, Muzny DM, Sodergren EJ, Lu X, Gibbs RA, Fahey J, Helton E, Ketteman M, Madan A, Rodrigues S, Sanchez A, Whiting M, Madan A, Young AC, Shevchenko Y, Bouffard GG, Blakesley RW, Touchman JW, Green ED, Dickson MC, Rodriguez AC, Grimwood J, Schmutz J, Myers RM, Butterfield YS, Krzywinski MI, Skalska U, Smailus DE, Schnerch A, Schein JE, Jones SJ, Marra MA: Generation and initial analysis of more than 15,000 full-length human and mouse cDNA sequences, Proc Nati Acad Sci USA 2002, 99:16899-16903 72. Hubbard TJ, Aken BL, Beal K, Ballester B, Caccamo M, Chen Y, Clarke L, Coates G, Cunningham F, Cutts T, Down T, Dyer SC, Fitzgerald 5, Fernandez-Banet J, Graf 5, Haider 5, Hammond M, Herrero J, Holland R, Howe K, Howe K, Johnson N, Kahari A, Keefe D, Kokocinski F, Kulesha E, Lawson D, Longden I, Melsopp C, Megy K, Meidl P, Ouverdin B, Parker A, Prlic A, Rice 5, Rios D, Schuster M, Sealy I, Severin J, Slater G, Smedley D, Spudich G, Trevanion 5, Vilella A, Vogel J, White 5, Wood M, Cox T, Curwen V, Durbin R, Femandez-Suarez XM, Flicek P, Kasprzyk A, Proctor G, Searle S, Smith J, Ureta-Vidal A, Birney E: Ensembi 2007, Nucleic Acids Res 2007, 35:D610-617 73. Bismar TA, Demichelis F, Riva A, Kim R, Varambally S, He L, Kutok J, Aster JC, Tang 3, Kuefer R, Hofer MD, Febbo PG, Chinnaiyan AM, Rubin MA: Defining aggressive prostate cancer using a 12-gene model, Neoplasia 2006, 8:59-68 74. Shah RB, Mehra R, Chinnaiyan AM, Shen R, Ghosh D, Zhou M, Macvicar OR, Varambally 5, Harwood 3, Bismar TA, Kim R, Rubin MA, Pienta KJ: Androgen independent prostate cancer is a heterogeneous group of diseases: lessons from a rapid autopsy program, Cancer Res 2004, 64:9209-9216 75. Wei Q, Li M, Fu X, Tang R, Na Y, Jiang M, Li Y: Global analysis of differentially expressed genes in androgen-independent prostate cancer, Prostate Cancer Prostatic Dis 2007, 10:167-174 76. Assikis VJ, Do KA, Wen S, Wang X, Cho-Vega JH, Brisbay 5, Lopez R, Logothetis CJ, Troncoso P, Papandreou CN, McDonnell TJ: Clinical and biomarker correlates of 12 androgen-independent, locally aggressive prostate cancer with limited metastatic potential, Clin Cancer Res 2004, 10:6770-6778 77. Stanbrough M, Bubley GJ, Ross K, Golub TR, Rubin MA, Penning TM, Febbo PG, Balk SP: Increased expression of genes converting adrenal androgens to testosterone in androgen-independent prostate cancer, Cancer Res 2006, 66:28 15-2825 78. Best CJ, Gillespie JW, Yi Y, Chandramouli GV, Perimutter MA, Gathright Y, Erickson HS, Georgevich L, Tangrea MA, Duray PH, Gonzalez S, Velasco A, Linehan WM, Matusik RJ, Price DK, Figg WD, Emmert-Buck MR, Chuaqui RF: Molecular alterations in primary prostate cancer after androgen ablation therapy, Clin Cancer Res 2005, 11:6823-6834 79. Tamura K, Furihata M, Tsunoda T, Ashida S, Takata R, Obara W, Yoshioka H, Daigo Y, Nasu Y, Kumon H, Konaka H, Namiki M, Tozawa K, Kohri K, Tanji N, Yokoyama M, Shimazui T, Akaza H, Mizutani Y, Miki T, Fujioka T, Shuin T, Nakamura Y, Nakagawa H: Molecular features of hormone-refractory prostate cancer cells by genome-wide gene expression profiles, Cancer Res 2007, 67:5117-5125 80. Chandran UR, Ma C, Dhir R, Bisceglia M, Lyons-Weiler M, Liang W, Michalopoulos G, Becich M, Monzon FA: Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process, BMC Cancer 2007, 7:64 81. Zellweger T, Ninck C, B loch M, Mirlacher M, Koivisto PA, Helm HJ, Mihatsch MJ, Gasser TC, Bubendorf L: Expression patterns of potential therapeutic targets in prostate cancer, Tnt J Cancer 2005, 113:619-628 82. Fromont G, Chene L, Vidaud M, Vallancien G, Mangin P. Fournier G, Validire P. Latil A, Cussenot 0: Differential expression of 37 selected genes in hormone-refractory prostate cancer using quantitative taqman real-time RT-PCR, mt J Cancer 2005, 114: 174-181 83. Bibikova M, Chudin E, Arsanjani A, Zhou L, Garcia EW, Modder J, Kostelec M, Barker D, Downs T, Fan JB, Wang-Rodriguez J: Expression signatures that correlated with Gleason score and relapse in prostate cancer, Genomics 2007, 89:666-672 122 84. Kumar-Sinha C, Chinnaiyan AM: Molecular markers to identify patients at risk for recurrence after primary treatment for prostate cancer, Urology 2003, 62 Suppi 1:19-35 85. Stephenson AJ, Smith A, Kattan MW, Satagopan J, Reuter VE, Scardino PT, Gerald WL: Integration of gene expression profiling and clinical variables to predict prostate carcinoma recurrence after radical prostatectomy, Cancer 2005, 104:290-298 86. Henshall SM, Afar DE, Hiller J, Horvath LG, Quinn DI, Rasiah KK, Gish K, Willhite D, Kench JG, Gardiner-Garden M, Stricker PD, Scher HI, Grygiel JJ, Agus DB, Mack DH, Sutherland RL: Survival analysis of genome-wide gene expression profiles of prostate cancers identifies new prognostic targets of disease relapse, Cancer Res 2003, 63:4 196- 4203 87. Glinsky GV, Glinskii AB, Stephenson AJ, Hoffman RM, Gerald WL: Gene expression profiling predicts clinical outcome of prostate cancer, J Clin Invest 2004, 113:913-923 88. Febbo PG, Sellers WR: Use of expression analysis to predict outcome after radical prostatectomy, J Urol 2003, 170:S11-19; discussion S19-20 89. Lapointe J, Li C, Higgins JP, van de Rijn M, Bair E, Montgomery K, Ferrari M, Egevad L, Rayford W, Bergerheim U, Ekman P, DeMarzo AM, Tibshirani R, Botstein D, Brown P0, Brooks JD, Pollack JR: Gene expression profiling identifies clinically relevant subtypes of prostate cancer, Proc Natl Acad Sci U S A 2004, 101:811-816 90. Devilard E, Bladou F, Ramuz 0, Karsenty G, Dales JP, Gravis G, Nguyen C, Bertucci F, Xerri L, Birnbaum D: FGFR1 and WT1 are markers of human prostate cancer progression, BMC Cancer 2006, 6:272 91. Quayle SN, Hare H, Delaney AD, Hirst M, Hwang D, Schein JE, Jones SJ, Marra MA, Sadar MD: Novel expressed sequences identified in a model of androgen independent prostate cancer, BMC Genomics 2007, 8:32 92. Amler LC, Agus DB, LeDuc C, Sapinoso ML, Fox WD, Kern S, Lee D, Wang V, Leysens M, Higgins B, Martin J, Gerald W, Dracopoli N, Cordon-Cardo C, Scher HI, Hampton GM: Dysregulated expression of androgen-responsive and nonresponsive genes in the androgen-independent prostate cancer xenograft model CWR22-R1, Cancer Res 2000, 60:6134-6141 123 93. Chen Q, Watson JT, Marengo SR, Decker KS, Coleman I, Nelson PS, Sikes RA: Gene expression in the LNCaP human prostate cancer progression model: progression associated expression in vitro corresponds to expression changes associated with prostate cancer progression in vivo, Cancer Lett 2006, 244:274-288 94. Morgenbesser SD, McLaren RP, Richards B, Zhang M, Akmaev VR, Winter SF, Mineva ND, Kaplan-Lefko PJ, Foster BA, Cook BP, Dufault MR, Cao X, Wang CJ, Teicher BA, Klinger KW, Greenberg NM, Madden SL: Identification of genes potentially involved in the acquisition of androgen-independent and metastatic tumor growth in an autochthonous genetically engineered mouse prostate cancer model, Prostate 2007, 67:83-106 95. Kuruma H, Egawa S, Oh-Ishi M, Kodera Y, Satoh M, Chen W, Okusa H, Matsumoto K, Maeda T, Baba S: High molecular mass proteome of androgen-independent prostate cancer, Proteomics 2005, 5:1097-1112 96. Pfundt R, Smit F, Jansen C, Aalders T, Straatman H, van der Vliet W, Isaacs J, van Kessel AG, Schalken J: Identification of androgen-responsive genes that are alternatively regulated in androgen-dependent and androgen-independent rat prostate tumors, Genes Chromosomes Cancer 2005, 43:273-283 97. BubendorfL, Kolmer M, Kononen J, Koivisto P, Mousses S, Chen Y, Mahiamaki E, Schraml P, Moch H, With N, Elkahioun AG, Pretlow TG, Gasser TC, Mihatsch MJ, Sauter G, Kallioniemi OP: Hormone therapy failure in human prostate cancer: analysis by complementary DNA and tissue microarrays, J Natl Cancer Inst 1999, 91:1758-1764 98. Mousses S, Wagner U, Chen Y, Kim JW, BubendorfL, Bittner M, Pretlow T, Elkahloun AG, Trepel JB, Kallioniemi OP: Failure of hormone therapy in prostate cancer involves systematic restoration of androgen responsive genes and activation of rapamycin sensitive signaling, Oncogene 2001, 20:6718-6723 99. Gregory CW, Hamil KG, Kim D, Hall SH, Pretlow TG, Mohler JL, French FS: Androgen receptor expression in androgen-independent prostate cancer is associated with increased expression of androgen-regulated genes, Cancer Res 1998, 58:57 18-5724 100. Mohler JL, Morris TL, Ford OH, 3rd, Alvey RF, Sakamoto C, Gregory CW: Identification of differentially expressed genes associated with androgen-independent growth of prostate cancer, Prostate 2002, 51:247-255 124 101. Wain HM, Bruford EA, Lovering RC, Lush MJ, Wright MW, Povey S: Guidelines for human gene nomenclature, Genomics 2002, 79:464-470 102. Liebel U, Kindler B, Pepperkok R: ‘Harvester’: a fast meta search engine of human protein resources, Bioinformatics 2004, 20:1962-1963 103. Nelson PS, Clegg N, Arnold H, Ferguson C, Bonham M, White J, Hood L, Lin B: The program of androgen-responsive genes in neoplastic prostate epithelium, Proc Nati Acad Sci USA 2002,99:11890-11895 104. Oosterhoff JK, Grootegoed JA, Blok U: Expression profiling of androgen-dependent and -independent LNCaP cells: EGF versus androgen signalling, Endocr Relat Cancer 2005, 12:135-148 105. Velasco AM, Gillis KA, Li Y, Brown EL, Sadler TM, Achilleos M, Greenberger LM, Frost P, Bai W, Zhang Y: Identification and validation of novel androgen-regulated genes in prostate cancer, Endocrinology 2004, 145 :3913-3924 106. Wang G, Jones SJM, Marra MA, Sadar MD: Identification of genes targeted by the androgen and PKA signaling pathways in prostate cancer cells, Oncogene 2006, 25:7311-23 107. Segawa T, Nau ME, Xu LL, Chilukuri RN, Makarem M, Zhang W, Petrovics G, Sesterhenn IA, McLeod DG, Moul JW, Vahey M, Srivastava 5: Androgen-induced expression of endoplasmic reticulum (ER) stress response genes in prostate cancer cells, Oncogene 2002, 21:8749-8758 108. Xu LL, Su YP, Labiche R, Segawa T, Shanmugam N, McLeod DG, Moul JW, Srivastava 5: Quantitative expression profile of androgen-regulated genes in prostate cancer cells and identification of prostate-specific genes, Tnt J Cancer 2001, 92:322-328 109. Clegg N, Eroglu B, Ferguson C, Arnold H, Moorman A, Nelson PS: Digital expression profiles of the prostate androgen-response program, J Steroid Biochem Mol Biol 2002, 80:13-23 110. Coutinho-Camillo CM, Salaorni 5, Sarkis AS, Nagai MA: Differentially expressed genes in the prostate cancer cell line LNCaP after exposure to androgen and anti-androgen, Cancer Genet Cytogenet 2006, 166:130-138 125 111. DePrimo SE, Diehn M, Nelson JB, Reiter RE, Matese J, Fero M, Tibshirani R, Brown P0, Brooks ID: Transcriptional programs activated by exposure of human prostate cancer cells to androgen, Genome Biol 2002, 3 :RESEARCHOO32 112. Febbo PG, Lowenberg M, Thorner AR, Brown M, Loda M, Golub TR: Androgen mediated regulation and functional implications of fkbp5 1 expression in prostate cancer, J Urol 2005, 173:1772-1777 113. Meehan KL, Sadar MD: Quantitative profiling of LNCaP prostate cancer cells using isotope-coded affinity tags and mass spectrometry, Proteomics 2004, 4:1116-1134 114. Waghray A, Feroze F, Schober MS, Yao F, Wood C, Puravs E, Krause M, Hanash S, Chen YQ: Identification of androgen-regulated genes in the prostate cancer cell line LNCaP by serial analysis of gene expression and proteomic analysis, Proteomics 2001, 1: 1327-1338 115. Srikantan V, Zou Z, Petrovics G, Xu L, Augustus M, Davis L, Livezey JR, Connell T, Sesterhenn IA, Yoshino K, Buzard GS, Mostofi FK, McLeod DG, Moul JW, Srivastava S: PCGEM1, a prostate-specific gene, is overexpressed in prostate cancer, Proc Natl Acad Sci US A 2000, 97:12216-12221 116. Hubert RS, Vivanco I, Chen E, Rastegar 5, Leong K, Mitchell SC, Madraswala R, Zhou Y, Kuo J, Raitano AB, Jakobovits A, Saffran DC, Afar DE: STEAP: a prostate-specific cell-surface antigen highly expressed in human prostate tumors, Proc Natl Acad Sci U S A 1999, 96:14523-14528 117. Wang R, Xu J, Saramaki 0, Visakorpi T, Sutherland WM, Zhou J, Sen B, Lim SD, Mabjeesh N, Amin M, Dong JT, Petros JA, Nelson PS, Marshall FF, Zhau HE, Chung LW: PrLZ, a novel prostate-specific and androgen-responsive gene of the TPD52 family, amplified in chromosome 8q2 1.1 and overexpressed in human prostate cancer, Cancer Res 2004, 64:1589-1594 118. Waghray A, Schober M, Feroze F, Yao F, Virgin J, Chen YQ: Identification of differentially expressed genes by serial analysis of gene expression in human prostate cancer, Cancer Res 2001, 61:4283-4286 126 119. Xu J, Stolk JA, Zhang X, Silva SJ, Houghton RL, Matsumura M, Vedvick TS, Leslie KB, Badaro R, Reed SG: Identification of differentially expressed genes in human prostate cancer using subtraction and microarray, Cancer Res 2000, 60:1677-1682 120. Singh D, Febbo PG, Ross K, Jackson DG, Manola J, Ladd C, Tamayo P, Renshaw AA, DAmico AV, Richie JP, Lander ES, Loda M, KantoffPW, Golub TR, Sellers WR: Gene expression correlates of clinical prostate cancer behavior, Cancer Cell 2002, 1:203-209 121. Luo J, Duggan DJ, Chen Y, Sauvageot J, Ewing CM, Bittner ML, Trent JM, Isaacs WB: Human prostate cancer and benign prostatic hyperplasia: molecular dissection by gene expression profiling, Cancer Res 2001, 61:4683-4688 122. Ernst T, Hergenhahn M, Kenzelmann M, Cohen CD, Bonrouhi M, Weninger A, Klaren R, Grone EF, Wiesel M, Gudemann C, Kuster J, Schott W, Staehler G, Kretzler M, Holistein M, Grone HJ: Decrease and gain of gene expression are equally discriminatory markers for prostate carcinoma: a gene expression analysis on total and microdissected prostate tissue, Am J Pathol 2002, 160:2169-2180 123. Chaib H, Cockrell EK, Rubin MA, Macoska JA: Profiling and verification of gene expression patterns in normal and malignant human prostate tissues by cDNA microarray analysis, Neoplasia 2001, 3:43-52 124. Ashida 5, Nakagawa H, Katagiri T, Furihata M, Iiizumi M, Anazawa Y, Tsunoda T, Takata R, Kasahara K, Miki T, Fujioka T, Shuin T, Nakamura Y: Molecular features of the transition from prostatic intraepithelial neoplasia (PIN) to prostate cancer: genome wide gene-expression profiles of prostate cancers and PINs, Cancer Res 2004, 64:5963- 5972 125. Rhodes DR, Barrette TR, Rubin MA, Ghosh D, Chinnaiyan AM: Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer, Cancer Res 2002, 62:4427-443 3 126. Latil A, Bieche I, Chene L, Laurendeau I, Berthon P, Cussenot 0, Vidaud M: Gene expression profiling in clinically localized prostate cancer: a four-gene expression model predicts clinical behavior, Clin Cancer Res 2003, 9:5477-5485 127. Li HR, Wang-Rodriguez J, Nair TM, Yeakley JM, Kwon YS, Bibikova M, Zheng C, Zhou L, Zhang K, Downs T, Fu XD, Fan JB: Two-dimensional transcriptome profiling: 127 identification of messenger RNA isoform signatures in prostate cancer from archived paraffin-embedded cancer specimens, Cancer Res 2006, 66:4079-4088 128. Stamey TA, Warrington JA, Caldwell MC, Chen Z, Fan Z, Mahadevappa M, McNeal JE, Nolley R, Zhang Z: Molecular genetic profiling of Gleason grade 4/5 prostate cancers compared to benign prostatic hyperplasia, J Urol 2001, 166:2171-2177 129. Varambally S, Yu J, Laxman B, Rhodes DR, Mebra R, Tomlins SA, Shah RB, Chandran U, Monzon FA, Becich MJ, Wei JT, Pienta KJ, Ghosh D, Rubin MA, Chinnaiyan AM: Integrative genomic and proteomic analysis of prostate cancer reveals signatures of metastatic progression, Cancer Cell 2005, 8:393-406 130. Chetcuti A, Margan S, Mann S, Russell P, Handelsman D, Rogers J, Dong Q: Identification of differentially expressed genes in organ-confined prostate cancer by gene expression array, Prostate 2001, 47:132-140 131. Dhanasekaran SM, Barrette TR, Ghosh D, Shah R, Varambally S, Kurachi K, Pienta KJ, Rubin MA, Chinnaiyan AM: Delineation of prognostic biomarkers in prostate cancer, Nature 2001, 4 12:822-826 132. Magee JA, Araki T, Patil S, Ehrig T, True L, Humphrey PA, Catalona WJ, Watson MA, Milbrandt J: Expression profiling reveals hepsin overexpression in prostate cancer, Cancer Res 2001, 61:5692-5696 133. Bull JH, Ellison G, Patel A, Muir G, Walker M, Underwood M, Khan F, Paskins L: Identification of potential diagnostic markers of prostate cancer and prostatic intraepithelial neoplasia using cDNA microarray, Br J Cancer 2001, 84:1512-1519 134. Luo JH, Yu YP, Cieply K, Lin F, Deflavia P, Dhir R, Finkelstein S, Michalopoulos G, Becich M: Gene expression analysis of prostate cancers, Mol Carcinog 2002, 33:25-3 5 135. Gleason DF, Mellinger GT: Prediction of prognosis for prostatic adenocarcinoma by combined histological grading and clinical staging, J Urol 1974, 111:58-64 136. True L, Coleman I, Hawley 5, Huang CY, Gifford D, Coleman R, Beer TM, Gelmann E, Datta M, Mostaghel E, Knudsen B, Lange P, Vessella R, Lin D, Hood L, Nelson PS: A molecular correlate to the Gleason grading system for prostate adenocarcinoma, Proc Natl Acad Sci U S A 2006, 103:10991-10996 128 137. LaTulippe E, Satagopan J, Smith A, Scher H, Scardino P, Reuter V, Gerald WL: Comprehensive gene expression analysis of prostate cancer reveals distinct transcriptional programs associated with metastatic disease, Cancer Res 2002, 62:4499- 4506 138. Ramaswamy 5, Ross KN, Lander ES, Golub TR: A molecular signature of metastasis in primary solid tumors, Nat Genet 2003, 33:49-54 139. Sadar MD, Akopian VA, Beraldi E: Characterization of a new in vivo hollow fiber model for the study of progression of prostate cancer to androgen independence, Mol Cancer Ther 2002, 1:629-637 140. Gleave ME, Hsieh JT, Wu HC, von Eschenbach AC, Chung LW: Serum prostate specific antigen levels in mice bearing human prostate LNCaP tumors are determined by tumor volume and endocrine and growth factors, Cancer Res 1992, 52:1598-1605 141. Hollingshead MG, Alley MC, Camalier RF, Abbott BJ, Mayo JG, Maispeis L, Grever MR: In vivo cultivation of tumor cells in hollow fibers, Life Sci 1995, 57:13 1-141 142. Lee DK, Duan HO, Chang C: From androgen receptor to the general transcription factor TFIIH. Identification of cdk activating kinase (CAK) as an androgen receptor NH(2)- terminal associated coactivator, J Biol Chem 2000, 275:9308-9313 143. Lin HK, Altuwaijri S, Lin WJ, Kan PY, Collins LL, Chang C: Proteasome activity is required for androgen receptor transcriptional activity via regulation of androgen receptor nuclear translocation and interaction with coregulators in prostate cancer cells, J Biol Chem 2002, 277:36570-36576 144. Loy CJ, Sim KS, Yong EL: Filamin-A fragment localizes to the nucleus to regulate androgen receptor and coactivator functions, Proc Natl Acad Sci U S A 2003, 100:4562- 4567 145. Zhang PJ, Zhao J, Li HY, Man JH, He K, Zhou T, Pan X, Li AL, Gong WL, Jin BF, Xia Q, Yu M, Shen BF, Zhang XM: CUE domain containing 2 regulates degradation of progesterone receptor by ubiquitin-proteasome, Embo J 2007, 26:1831-1842 146. Boonyaratanakornkit V, Melvin V, Prendergast P, Altmann M, Ronfani L, Bianchi ME, Taraseviciene L, Nordeen SK, Allegretto EA, Edwards DP: High-mobility group chromatin proteins 1 and 2 functionally interact with steroid hormone receptors to 129 enhance their DNA binding in vitro and transcriptional activity in mammalian cells, Mol Cell Biol 1998, 18:4471-4487 147. Mohler JL, Gregory CW, Ford OH, 3rd, Kim D, Weaver CM, Petrusz P, Wilson EM, French FS: The androgen axis in recurrent prostate cancer, Clin Cancer Res 2004, 10:440-448 148. Attard G, Reid AH, Yap TA, Raynaud F, Dowsett M, Settatree 5, Barrett M, Parker C, Martins V, Folkerd E, Clark J, Cooper CS, Kaye SB, Dearnaley D, Lee G, de Bono JS: Phase I Clinical Trial of a Selective Inhibitor ofCYP17, Abiraterone Acetate, Confirms That Castration-Resistant Prostate Cancer Commonly Remains Hormone Driven, J Clin Oncol 2008 149. van Weerden WM, Bierings HG, van Steenbrugge GJ, de Jong FH, Schroder FH: Adrenal glands of mouse and rat do not synthesize androgens, Life Sci 1992, 50:857-86 1 150. Waterham HR, Koster J, Romeijn GJ, Hennekam RC, Vreken P, Andersson HC, FitzPatrick DR, Kelley RI, Wanders RJ: Mutations in the 3beta-hydroxysterol Delta24- reductase gene cause desmosterolosis, an autosomal recessive disorder of cholesterol biosynthesis, Am J Hum Genet 2001, 69:685-694 151. Haeseleer F, Palczewski K: Short-chain dehydrogenases/reductases in retina, Methods Enzymol 2000, 3 16:372-383 152. Penning TM: Hydroxysteroid dehydrogenases and pre-receptor regulation of steroid hormone action, Hum Reprod Update 2003, 9:193-205 153. Momozawa Y, Takeuchi Y, Kitago M, Masuda K, Kakuma Y, Hashizume C, Ichimaru T, Mogi K, Okamura H, Yonezawa T, Kikusui T, Mon Y: Gene expression profiles linked to the hormonal induction of male-effect pheromone synthesis in goats (Capra hircus), Biol Reprod 2007, 77:102-107 154. Kitago M, Momozawa Y, Masuda K, Wakabayashi Y, Date-Ito A, Hagino-Yamagishi K, Kikusui T, Takeuchi Y, Mon Y: Localization of the candidate genes ELOVL5 and SCD1 for ‘male effect’ pheromone synthesis in goats (Capra hircus), J Reprod Dev 2007, 53: 1329-1333 130 155. Svenningsson P, Chergui K, Rachieff I, Flajolet M, Zhang X, El Yacoubi M, Vaugeois JM, Nomikos GG, Greengard P: Alterations in 5-HT 1 B receptor function by p11 in depression-like states, Science 2006, 311:77-80 156. Zhao H, Nolley R, Chen Z, Reese SW, Peehl DM: Inhibition of monoamine oxidase A promotes secretory differentiation in basal prostatic epithelial cells, Differentiation 2008 157. Mergler S, Strowski MZ, Kaiser 5, Plath T, Giesecke Y, Neumann M, Hosokawa H, Kobayashi S. Langrehr J, Neuhaus P, Plockinger U, Wiedenmann B, Grotzinger C: Transient receptor potential channel TRPM8 agonists stimulate calcium influx and neurotensin secretion in neuroendocrine tumor cells, Neuroendocrinology 2007, 85:81 - 92 158. Pascoe JE, Williams KL, Mukhopadhyay P, Rice KC, Woods JH, Ko MC: Effects of mu, kappa, and delta opioid receptor agonists on the function of hypothalamic-pituitary- adrenal axis in monkeys, Psychoneuroendocrinology 2008, 33:478-486 159. Iwamura M, Wu G, Abrahamsson PA, di SantAgnese PA, Cockett AT, Deftos U: Parathyroid hormone-related protein is expressed by prostatic neuroendocrine cells, Urology 1994, 43:667-674 160. Jans DA, Thomas RJ, Gillespie MT: Parathyroid hormone-related protein (PTHrP): a nucleocytoplasmic shuttling protein with distinct paracrine and intracrine roles, Vitam Horm 2003, 66:345-384 161. Lam MH, Thomas RJ, Loveland KL, Schilders 5, Gu M, Martin TJ, Gillespie MT, Jans DA: Nuclear transport of parathyroid hormone (PTH)-related protein is dependent on microtubules, Mo! Endocrinol 2002, 16:390-401 162. Conner AC, Simms J, Barwell J, Wheatley M, Poyner DR: Ligand binding and activation of the CGRP receptor, Biochem Soc Trans 2007, 35:729-732 163. Hu Y, Wang T, Stormo GD, Gordon JI: RNA interference of achaete-scute homolog 1 in mouse prostate neuroendocrine cells reveals its gene targets and DNA binding sites, Proc Natl Acad Sci U S A 2004, 101:5559-5564 164. Vias M, Massie CE, East P, Scott H, Warren A, Zhou Z, Nikitin AY, Neal DE, Mills IG: Pro-neural transcription factors as cancer markers, BMC Med Genomics 2008, 1:17 131 165. Avril-Delplanque A, Casal I, Castillon N, Himirasky J, Puchelle E, Peault B: Aquaporin 3 expression in human fetal airway epithelial progenitor cells, Stem Cells 2005, 23:992- 1001 166. Kazanskaya 0, Glinka A, del Barco Barrantes I, Stannek P, Niehrs C, Wu W: R Spondin2 is a secreted activator of Wnt/beta-catenin signaling and is required for Xenopus myogenesis, Dev Cell 2004, 7:525-534 167. Zujovic V, Luo D, Baker HV, Lopez MC, Miller KR, Streit WJ, Harrison JK: The facial motor nucleus transcriptional program in response to peripheral nerve injury identifies Hnl as a regeneration-associated gene, J Neurosci Res 2005, 82:581-591 168. Goto T, Hisatomi 0, Kotoura M, Tokunaga F: Induced expression of hematopoietic- and neurologic-expressed sequence 1 in retinal pigment epithelial cells during newt retina regeneration, Exp Eye Res 2006, 83:972-980 169. Pissarra L, Henrique D, Duarte A: Expression of hes6, a new member of the Hairy/Enhancer-of-split family, in mouse development, Mech Dev 2000, 95:275-278 170. Hajj R, Baranek T, Le Naour R, Lesimple P, Puchelle E, Coraux C: Basal cells of the human adult airway surface epithelium retain transit-amplifying cell properties, Stem Cells 2007, 25:139-148 171. Masters JR. Kane C, Yamamoto H; Ahmed A: Prostate cancer stem cell therapy: hype or hope?, Prostate Cancer Prostatic Dis 2008 172. Mourtada-Maarabouni M, Hedge VL, Kirkham L, Farzaneh F, Williams GT: Growth arrest in human T-cells is controlled by the non-coding RNA growth-arrest-specific transcript 5 (GAS5), J Cell Sci 2008, 121:939-946 173. Hermanto U, Zong CS, Li W, Wang LH: RACK1, an insulin-like growth factor I (IGF-I) receptor-interacting protein, modulates IGF-I-dependent integrin signaling and promotes cell spreading and contact with extracellular matrix, Mol Cell Biol 2002, 22:2345-2365 174. Kiely PA, Sant A, O’Connor R: RACK1 is an insulin-like growth factor 1 (IGF-1) receptor-interacting protein that can regulate IGF- 1-mediated Akt activation and protection from cell death, J Biol Chem 2002, 277:22581-22589 132 175. Mamidipudi V, Dhillon NK, Parman T, Miller LD, Lee KC, Cartwright CA: RACK1 inhibits colonic cell growth by regulating Src activity at cell cycle checkpoints, Oncogene 2007, 26:29 14-2924 176. Kim MJ, Bhatia-Gaur R, Banach-Petrosky WA, Desai N, Wang Y, Hayward SW, Cunha GR, Cardiff RD, Shen MM, Abate-Shen C: Nkx3.1 mutant mice recapitulate early stages of prostate carcinogenesis, Cancer Res 2002, 62:2999-3004 177. Lei Q, Jiao J, Xin L, Chang CJ, Wang 5, Gao J, Gleave ME, Witte ON, Liu X, Wu H: NKX3.1 stabilizes p53, inhibits AKT activation, and blocks prostate cancer initiation caused by PTEN loss, Cancer Cell 2006, 9:367-378 178. Morey C, Avner P: Employment opportunities for non-coding RNAs, FEBS Lett 2004, 567:27-34 179. Petrovics G, Zhang W, Makarem M, Street JP, Connelly R, Sun L, Sesterhenn IA, Srikantan V, Moul JW, Srivastava S: Elevated expression of PCGEM1, a prostate- specific gene with cell growth-promoting function, is associated with high-risk prostate cancer patients, Oncogene 2004, 23:605-6 11 180. Fu X, Ravindranath L, Tran N, Petrovics G, Srivastava 5: Regulation of apoptosis by a prostate-specific and prostate cancer-associated noncoding gene, PCGEM 1, DNA Cell Biol 2006, 25:135-141 181. Wang 5, Yang Q, Fung KM, Lin HK: AKR1C2 and AKR1C3 mediated prostaglandin D(2) metabolism augments the PI3KJAkt proliferative signaling pathway in human prostate cancer cells, Mol Cell Endocrinol 2008, 289:60-66 182. Katoh Y, Katoh M: Identification and characterization of CDC5OA, CDC5OB and CDC5OC genes in silico, Oncol Rep 2004, 12:939-943 183. Challita-Eid PM, Morrison K, Etessami 5, An Z, Morrison KJ, Perez-Villar JJ, Raitano AB, Jia XC, Gudas JM, Kanner SB, Jakobovits A: Monoclonal antibodies to six transmembrane epithelial antigen of the prostate-i inhibit intercellular communication in vitro and growth of human tumor xenografts in vivo, Cancer Res 2007, 67:5798-5805 184. Matsuda 5, Rouault J, Magaud J, Berthet C: In search of a function for the TIS21/PC3/BTGI/TOB family, FEBS Lett 2001, 497:67-72 133 185. Trueb B, Zhuang L, Taeschler S, Wiedemann M: Characterization of FGFRL1, a novel fibroblast growth factor (FGF) receptor preferentially expressed in skeletal tissues, J Biol Chem 2003, 278:33857-33865 186. Anazawa Y, Nakagawa H, Furihara M, Ashida S, Tamura K, Yoshioka H, Shuin T, Fujioka T, Katagiri T, Nakamura Y: PCOTH, a novel gene overexpressed in prostate cancers, promotes prostate cancer cell growth through phosphorylation of oncoprotein TAF-IbetaJSET, Cancer Res 2005, 65 :4578-4586 187. Sakamoto H, Mashima T, Kizaki A, Dan S, Hashimoto Y, Naito M, Tsuruo T: Glyoxalase I is involved in resistance of human leukemia cells to antitumor agent- induced apoptosis, Blood 2000, 95:3214-32 18 188. Hsu SY, Kaipia A, Zhu L, Hsueh AJ: Interference of BAD (Bcl-xL/Bcl-2-associated death promoter)-induced apoptosis in mammalian cells by 14-3-3 isoforms and P11, Mol Endocrinol 1997, 11:1858-1867 189. Zhang L, Barritt GJ: Evidence that TRPM8 is an androgen-dependent Ca2+ channel required for the survival of prostate cancer cells, Cancer Res 2004, 64:8365-8373 190. Thebault S, Lemonnier L, Bidaux G, Flourakis M, Bavencoffe A, Gordienko D, Roudbaraki M, Delcourt P, Panchin Y, Shuba Y, Skryma R, Prevarskaya N: Novel role of cold/menthol-sensitive transient receptor potential melastatine family member 8 (TRPM8) in the activation of store-operated channels in LNCaP human prostate cancer epithelial cells, J Biol Chem 2005, 280:39423-39435 191. Vanden Abeele F, Roudbaraki M, Shuba Y, Skryma R, Prevarskaya N: Store-operated Ca2+ current in prostate cancer epithelial cells. Role of endogenous Ca2+ transporter type 1, J Biol Chem 2003, 278:15381-15389 192. Carson JP, Kulik G, Weber MI: Antiapoptotic signaling in LNCaP prostate cancer cells: a survival signaling pathway independent of phosphatidylinositol 3’-kinase and Akt/protein kinase B, Cancer Res 1999, 59:1449-1453 193. Vlietstra RJ, van Alewijk DC, Hennans KG, van Steenbrugge GJ, Trapman J: Frequent inactivation of PTEN in prostate cancer cell lines and xenografts, Cancer Res 1998, 58:2720-2723 134 194. Lin J, Adam RM, Santiestevan E, Freeman MR: The phosphatidylinositol 3’-kinase pathway is a dominant growth factor-activated cell survival pathway in LNCaP human prostate carcinoma cells, Cancer Res 1999, 59:2891-2897 195. Mannherz 0, Mertens D, Hahn M, Lichter P: Functional screening for proapoptotic genes by reverse transfection cell array technology, Genomics 2006, 87:665-672 196. Rokhlin OW, Taghiyev AF, Bayer KU, Bumcrot D, Koteliansk VE, Glover RA, Cohen MB: Calcium/calmodulin-dependent kinase II plays an important role in prostate cancer cell survival, Cancer Biol Ther 2007, 6:732-742 197. Spiess C, Meyer AS, Reissmann S, Frydman J: Mechanism of the eukaryotic chaperonin: protein folding in the chamber of secrets, Trends Cell Biol 2004, 14:598-604 198. Kadomatsu K, Muramatsu T: Midkine and pleiotrophin in neural development and cancer, Cancer Lett 2004, 204:127-143 199. You Z, Dong Y, Kong X, Beckett LA, Gandour-Edwards R, Melamed J: Midkine is a NF-kappaB-inducible gene that supports prostate cancer cell survival, BMC Med Genomics 2008, 1:6 200. Nomura M, Shimizu S, Sugiyama T, Narita M, Ito T, Matsuda H, Tsujimoto Y: 14-3-3 Interacts directly with and negatively regulates pro-apoptotic Bax, J Biol Chem 2003, 278:2058-2065 201. Mellor HR, Harris AL: The role of the hypoxia-inducible BH3-only proteins BNIP3 and BNIP3L in cancer, Cancer Metastasis Rev 2007, 26:553-566 202. Proikas-Cezanne T, Waddell S, Gaugel A, Frickey T, Lupas A, Nordheim A: WIPI lalpha (W1P149), a member of the novel 7-bladed WIPI protein family, is aberrantly expressed in human cancer and is linked to starvation-induced autophagy, Oncogene 2004, 23:9314-9325 203. Hippert MM, O’Toole PS, Thorburn A: Autophagy in cancer: good, bad, or both?, Cancer Res 2006, 66:9349-9351 204. Mukai J, Hachiya T, Shoji-Hoshino 5, Kimura MT, Nadano D, Suvanto P, Hanaoka T, Li Y, Irie 5, Greene LA, Sato TA: NADE, a p75NTR-associated cell death executor, is involved in signal transduction mediated by the common neurotrophin receptor p75NTR, J Biol Chem 2000, 275:17566-17570 135 205. Krygier S, Djakiew D: Molecular characterization of the loss of p75(NTR) expression in human prostate tumor cells, Mol Carcinog 2001, 31:46-55 206. Zhao J, Izumi T, Nunomura K, Satoh S, Watanabe S: MARCKS-like protein, a membrane protein identified for its expression in developing neural retina, plays a role in regulating retinal cell proliferation, Biochem J 2007, 408:51-59 207. Riedel H: GrblO exceeding the boundaries of a common signaling adapter, Front Biosci 2004, 9:603-618 208. Lim MA, Riedel H, Liu F: GrblO: more than a simple adaptor protein, Front Biosci 2004, 9:387-403 209. Schipper RG, Romijn JC, Cuijpers VM, Verhofstad AA: Polyamines and prostatic cancer, Biochem Soc Trans 2003, 3 1:375-380 210. Heby 0, Emanuelsson H: Role of the polyamines in germ cell differentiation and in early embryonic development, Med Biol 1981, 59:417-422 211. van der Graaf M, Schipper RG, Oosterhof GO, Schalken JA, Verhofstad AA, Heerschap A: Proton MR spectroscopy of prostatic tissue focused on the detection of spermine, a possible biomarker of malignant behavior in prostate cancer, Magma 2000, 10:153-159 212. Young L, Salomon R, Au W, Allan C, Russell P, Dong Q: Ornithine decarboxylase (ODC) expression pattern in human prostate tissues and ODC transgenic mice, J Histochem Cytochem 2006, 54:223-229 213. Li W, Liu X, Wang W, Sun H, Hu Y, Lei H, Liu G, Gao Y: Effects of antisense RNA targeting of ODC and AdoMetDC on the synthesis of polyamine synthesis and cell growth in prostate cancer cells using a prostatic androgen-dependent promoter in adenovirus, Prostate 2008, 68:1354-1361 214. Janssens V. Goris J: Protein phosphatase 2A: a highly regulated family of serine/threonine phosphatases implicated in cell growth and signalling, Biochem J 2001, 353 :417-439 215. Mumby M: PP2A: unveiling a reluctant tumor suppressor, Cell 2007, 130:21-24 216. Palmieri F: The mitochondrial transporter family (SLC25): physiological and pathological implications, Pflugers Arch 2004, 447:689-709 136 CHAPTER IV EXPRESSION CHARACTERISTICS OF NOVEL BIOMARKERS OF PROSTATE CANCER* 4.1 INTRODUCTION Prostate cancer is the most commonly diagnosed cancer, and the third leading cause of cancer death in Canadian men1.Twenty-seven per 100,000 deaths in men were from prostate cancer in Canada in 20042. This number is approximately 5-times that of Japan at 5.5 per 100,000 deaths. In the USA, 86 men die from prostate cancer each day. However, these numbers only represent the 2.5 to 3% of men who die from the disease from the 10% of men over 50 years who will have clinical progression. Autopsy studies indicate that 30% of men over the age of 50 have malignant cells in their prostate3.The European Study of Screening and the Prostate Cancer Prevention Trial indicate that screening for prostate cancer elevates the incidence rate4 with increases in the ratio of incidence to mortality from 2.5:1 to 17:1. This suggests that a substantial proportion of men with clinically insignificant disease are being over-treated. In other words, their disease will never cause morbidity or mortality. The current treatments for organ-confined malignancy are brachytherapy5,external beam radiation6,or radical prostatectomy7.These forms of therapy can produce significant morbidity such as incontinence and impotence and are not effective for disease that has spread outside the prostatic capsule. Only palliative therapy is available for disseminated disease which requires reducing levels of testosterone (androgen) and/or using antiandrogens8. Thus, there is an urgent need for selective intervention to spare those men from receiving unnecessary treatment, but still provide radical curative treatment to those men who will develop clinically significant disease. Currently there are no prognostic tools that can distinguish aggressive tumours from latent tumours. Prostate-specific antigen (PSA) has been utilized as a serum biomarker to monitor and screen for prostate cancer since 1986 and 1994, respectively’0.A recommendation for biopsy has been set at an arbitrary serum PSA level of 4 ng/mL. However, at this threshold, PSA is moderately specific and poorly sensitive as a biomarker for detection of prostate cancer. Specificity is defined as the percentage of men without prostate cancer who have serum PSA levels under the * A version of this chapter has been submitted for publication. Romanuik, TL., Ueda, T., Le, N., Thomson, T., Sadar, MD. Expression characteristics of novel biomarkers of prostate cancer. Submitted. 137 4 ng/mL threshold’°. For PSA, this percentage is 93%” In addition to carcinoma of the prostate, PSA is expressed in normal prostate tissue, prostatitis, and benign prostatic hyperplasia, and levels of circulating PSA are affected by age, racial background, physical activity, and body mass’2.Digital rectal exam, transrectal ultasound, and prostate biopsy may also cause increases in serum PSA levels’3.In contrast to specificity, sensitivity is defined as the percentage of men with prostate cancer who have serum PSA levels over the 4 ng/mL threshold’°. For PSA, this percentage is 24%”. Furthermore, 27% of men with borderline serum PSA levels (3.1-4 ng/mL) have detectable prostate cancer by biopsy’4. Serum PSA levels correlate with the degree of dissemination’5’16 and aggressiveness’6of prostate cancer. For example, serum PSA levels >10 ng!mL are associated with a high pathological stage (odds ratio (OR) 1.7) and high Gleason sum (i.e., 7-10; OR 1.9), respectively, compared to PSA levels <4 ng/mL16.Following radical prostatectomy or brachytherapy, 7-15% of prostate cancers will exhibit biochemical recurrence at eight years of follow-up as defined by rising PSA levels’7’18 However, approximately one percent of prostate cancer patients will develop metastases following first-line therapy concomitant with serum PSA levels 2ng/mL’9. Therefore, measurement of serum PSA levels is inadequate for monitoring progression for a small subset of patients. Patients receiving androgen-deprivation therapy for disseminated disease will relapse and their disease will progress to the terminal, castration-recurrent prostate cancer for which there is no effective treatment2022.Initial response to androgen-deprivation therapy is measured by PSA nadir. PSA nadir is prognostic of the time it takes to reach castration-resistant prostate cancer and death23. However, it is unknown whether pre-treatment serum PSA levels can predict response to androgen-deprivation therapy. Therefore, there is a great need for novel prognostic markers of castration-recurrent prostate cancer. These limitations of PSA emphasize the need for new biomarkers to accurately detect, monitor, and predict the aggressiveness of prostate cancer. In particular, biomarkers that are prognostic and/or signify the propensity to rapidly develop advanced disease are required. Such biomarkers may stem from gene expression studies using in vivo models of advanced prostate cancer. Here, 138 we characterize the expression of genes and novel non-coding transcripts that were previously identified by Long Serial Analysis of Gene Expression (L0ngSAGE)24and Subtractive Hybridization25technologies using samples from the in vivo LNCaP Hollow Fibre model26.This model allows the analyses of gene expression at various stages of hormonal progression in castrated hosts. LongSAGE can theoretically sample nearly all the transcripts present in a transcriptome27.Both L0ngSAGE and Subtractive Hybridization technologies can be used to discover unannotated transcripts. We chose 27 differentially expressed transcripts for further investigation to resolve the feasibility of their clinical utility in the diagnosis, prognosis, imaging, or treatment of prostate cancer. The transcripts were chosen based on their novelty and/or the ability of their gene product to be secreted or expressed on the cell surface. Genes previously identified by LongSAGE and examined here are ADAM metallopeptidase domain 2 (ADAM2), calcium/calmodulin-dependent protein kinase II inhibitor I (CAMK2N]), 24-dehydrocholesterol reductase (DHCR24), elongation of long chain fatty acids family member 5 (ELO VL5), glyoxalase 1 (GLOI), MARCKS-like I (MARCKSLJ), nerve growth factor receptor associated protein 1 (NGFRAPJ), phosphoglycerate kinase 1 (PGK]), proteasome macropain subunit alpha type 7 (PSM4 7), receptor activity modif,ing protein 1 (RAMP]), Shwachman-Bodian-Diamond syndrome (SBDS, spondin 2 (SPON2), transmembrane protein 30A (TMEM3OA), transmembrane protein 66 (TMEM66), and tyrosine 3- monooxygenase/tryptophan 5-monooxygenase activation protein theta polypeptide (YWHA Q). Subtractive Hybridization is particularly well suited for the identification of differentially expressed low abundance transcripts28.The twelve novel transcripts identified as differentially expressed in the LNCaP Hollow Fibre model using Subtractive Hybridization generally have low protein-coding potential and poor conservation across species25.These transcripts are referred to as POP 1 through 12: POP 1, transcript 100 kilobases (kb) from mRNA AK000023; POP2, transcript 4 kb from mRNA AL832227; POP3, transcript 50 kb from EST CF140309; POP4, transcript from the intron of transmembrane protein with EGF-like and two follistatin like domains 2 (TMEFF2); POP5, transcript from the intron of neural cell adhesion molecule 2 (NCAM2; accession D0668384); POP6, transcript from the intron of fragile histidine triad gene (FHIT); POP7, transcript from the intron of tumor necrosis factor, alpha-induced protein 8 (TNFAIP8); POP8, transcript from the intron of ephrin-A5 (EFNA5); POP9, transcript from the intron of actin depolymerizing factor destrin (DSTN); POP 10, transcript from the intron of 139 ADAM2 (accession D0668396); POP1 1, transcript 87 kb from EST BG194644; and POP12, transcript from the intron of EST BQ22605025.The expression of these 27 transcripts was measured in a variety of cell types and tissues, including laser microdissected human prostatectomy samples, to characterize these genes and assess their feasibility as potential biomarkers for prostate cancer. 4.2 MATERIALS AND METHODS 4.2.1 Cell culture Cell lines were maintained in RPMI-1640 media (LNCaP, 22Rv1, and COS1 cells), DMEM media (PC-3, DU145, and RKO cells), or MEM media (MG63, CV1, HEPG, and MCF7 cells). Cells were obtained from the American Type Culture Collection, Bethesda, MD, USA, with the exceptions of RKO, COS 1, and MCF7, which were kindly provided by I. Tai (Genome Sciences Centre, BC Cancer Agency (BCCA)), J. Vielkind (Cancer Endocrinology, BCCA), and M. Bally (Advanced Therapeutics, BCCA), respectively. All media (Stem Cell Technologies, Vancouver, BC, Canada) was supplemented with 100 units/mL penicillin and 100 units/mL streptomycin (Invitrogen, Burlington, ON, Canada), as well as 10% v/v fetal bovine serum (FBS; HyClone, Logan, UT, USA) with the exception of PC-3 and 22Rvi cells which only received 5% v/v FBS. Cells were maintained at 37°C with 5% CO2. For androgen treatments, LNCaP cells (1 x 106) were seeded in 10 cm diameter dishes. The next day, cells were serum-starved for 48 hours and then treated for 16 hours with 10 nM synthetic androgen Ri 881 (PerkinElmer, Woodbridge, ON, Canada), or vehicle control, ethanol (final concentration 2.85 x i0” %) in serum-free media. 4.2.2 Clinical samples Frozen prostate specimens from 84 patients who had undergone radical prostatectomies were received by our laboratory in OCT compound. Informed consent was obtained from each patient participating in the study according to guidelines set forth by the UBC BCCA Research Ethics Board. Prostatectomy specimens were accompanied by information including the age of the patient, prior treatment history, serum PSA levels prior to surgery, and TNM clinical and pathological stage. This information is summarized in Table 4.1 for all samples used in this paper. Any patient who had received pre-surgical hormone ablation treatment was excluded. 140 Tissue blocks were sectioned (8pm) onto membrane slides and fixed in 70% ethanol. H&E slides were made every 10-15 membrane sections for pathology review and used as guidance for laser microdissection. Selected membrane slides were stained with hematoxylin, dehydrated, and subsequently laser microdissected for epithelial cells by iCut Mlvii AG (MMI Molecular Machines & Industries AG, Glattbrugg, Switzerland) (Figure 4.1). 4.2.3 RNA preparation for gene expression analysis Total RNA from cell lines was harvested using TRIZOL Reagent (Invitrogen) following the manufacturer’s instructions. Total RNA from normal human tissue (adrenal gland, bone marrow, brain (cerebellum), brain (whole), fetal brain, fetal liver, heart, kidney, liver, lung, placenta, prostate, salivary gland, skeletal muscle, testis, thymus, thyroid gland, trachea, uterus, and spinal cord) was obtained commercially from Clontech (Mountain View, CA, USA). Total RNA from sections of human prostate tissue was extracted using the RNA Easy Micro Kit (Qiagen, Mississauga, ON, Canada) and concentrated by speed vacuum centrifugation (SPD IIIV Speed Vac, Thermo Electron Corporation). Contaminating genomic DNA was removed from RNA samples by TURBO DNA-free (Ambion Inc., Austin, TX, USA) or DNase I from the RNA Easy Micro Kit. RNA quality and quantity was assessed using the NanoDrop ND- 1000 (NanoDrop Technologies mc, Wilmington, DE, USA) and the Agilent 2100 Bioanalyzer (Agilent Technologies, Mississauga, ON, Canada) with RNA 6000 Nano LabChip kit (Caliper Technologies, Hopkinton, MA, USA). RNA of poor quality (RIN <2.8) and insufficient quantity (< 531 ng) was not used in this study. 4.2.4 Relative quantitation of gene expression Input RNA was reverse transcribed with SuperScript III First Strand Synthesis kit (Invitrogen). For most RNA samples, a quantity of 0.5 jig was used in the reverse transcriptase (RT) reaction, but for limited sample quantities, such as those from the laser microdissected prostate tissue, 0.ljig or 0.05 jig of RNA was used. A 10 jiL qRT-PCR reaction consisted of ljil of template cDNA, lx TaqMan Universal PCR Master Mix (Applied Biosystems, Foster City, CA, USA), and 0.9 jiM each of forward and reverse primers and 0.25 jiM of TaqMan probe (FAM-BHQ- 1 141 or TET-BHQ-1; Integrated DNA Technologies Inc., San Diego, CA, USA) that produce specific PCR products ranging in size between 85-23 5 bp (see Table 4.2 for primer and probe sequences). qRT-PCR reactions were cycled as follows in a 7900HT Sequence Detection System (Applied Biosystems): 50 °C for 2 mm, 95 °C for 10 mm, and 45 cycles of 95°C for 0.25 mm followed by 60°C for 1 mm. All qRT-PCR reactions were performed using technical triplicates. cDNAs (from different conditions/patients) and genes (target and reference) to be directly compared were assayed in the same instrument run. Glyceraldehyde-3-phosphate (GAPDI-I) was used as a reference gene for all experiments except the androgen regulation experiment in which succinate dehydrogenase complex, subunit A, flavoprotein (SDHA) was used instead. These reference genes were chosen for their stability across samples and their level of expression. Reactions without template were run for each gene to ensure that DNA had not contaminated the qRT-PCR reactions. Efficiency checks were performed for each primer pair. 4.2.5 Statistical analysis To identify significant changes in gene expression in response to androgen we used the Two- Sample Student’s T-test for unequal variance. Non-parametric methods were employed with data that was sampled from non-normal distributions. For gene expression analysis on RNA from laser microdissected prostatic tissue, the Spearman’s correlation test was used to identify associations to patient age or PSA levels, and the Kruskal Wallis test was used to identify significant differences between gene expression in normal and tumour tissue, or TNM stages of cancer. A p-value cut-off of 0.05 was employed for all tests. 4.3 RESULTS 4.3.1 Tissue-specificity of gene expression To characterize tissue-specific expression of genes compared to carcinoma of the prostate, we employed Taq Man29 quantitative real time-polymerase chain reaction (qRT-PCR) using RNA isolated from five human prostate cancer cells (LNCaP, MDA-PCa-2B, 22Rv1, PC-3, and DU145) and non-prostate human cancer cell lines that included: MG63, osteosarcoma cells; RKO, colon carcinoma cells; HEPG, hepatocellular carcinoma cells; MCF7, mammary adenocarcinoma cells; and large T-antigen transformed, and normal monkey kidney cells (COS1, and Cvi, respectively). Gene expression was displayed relative to the levels of 142 expression of each transcript in LNCaP cells. Cells were maintained in tissue culture under individualized conditions for optimal growth to gauge constitutive levels of gene expression. Expression of genes ADAM2 and POP]] were relatively specific for LNCaP cells (Figure 4.2). ADAM2 and POP] 0 (intron of ADAM2) showed differences in expression in HEPG cells suggesting tissue-specific expression of splice variants of ADAM2. MARCKSL1, POP], POP2, POP3, POP4, POP5, POPJ2, and SPON2 were enriched in human prostate cancer cell lines compared to all other human cancer cell lines tested (Figure 4.2). Genes with these enriched expression trends have the potential to be markers for prostate cancer, while ubiquitously expressed genes may be general cancer markers, provided there is differential expression between cancer and normal cells. To address tissue-specific expression of the 27 genes in benign tissues, we measured levels of transcripts in 20 human tissue samples (adrenal gland, bone marrow, cerebellum, whole brain, fetal brain, fetal liver, heart, kidney, liver, lung, placenta, prostate, salivary gland, skeletal muscle, testis, thymus, thyroid gland, trachea, uterus, and spinal cord). Gene expression was displayed relative to the levels in normal human prostate tissue. POP3 was the only transcript to exhibit exclusive expression in normal prostate tissue (Figure 4.3), suggesting it is a prostate- specific gene. This was consistent with expression of POP3 predominantly in LNCaP, MDA PCa-2B, and 22RV1 cells that express androgen receptor and low expression in all other cell lines examined (Figure 4.2). Some genes were expressed at a level on par with that of the normal prostate in the adrenal gland (ELOVL5) and testis (ELOVL5 and POP1; Figure 4.3). Interestingly, both adrenal glands and testes produce androgens that are essential for regulating the growth of the prostate30. POP1 had relatively specific expression in prostate cancer cell lines with similar expression patterns to POP3, while ELOVL5 had broad expression across most cell lines (Figure 4.2). ADAM2 and POP 10 (intron of ADAM2) showed similar expression patterns in prostate, placenta, testis with the exception of expression of only ADAM2 in thymus tissue (Figure 4.3). This data supports the tissue-specific expression of splice variants of ADAM2. POP4 (splice variant of TMEFF2) was expressed in the prostate, brain, and prostate cancer cells only expressing the androgen receptor. Both RAMP] and SPON2 had relatively restricted expression in prostate and uterine tissues (Figure 4.3). Notably, men do not have 143 uterine or placental tissue. SPON2 had expression relatively specific for prostate cancer cell lines (LNCaP and MDA-PCa-2B) while RAMP] was also highly expressed in MG63 osteosarcoma cells (Figure 4.2). Together, these data suggest that ADAM2, ELO VL5, POP 1, POP3, POP4, POP 10, RAMP], and SPON2 have relatively restricted expression patterns in adult male prostate. 4.3.2 Androgen regulation of gene expression The androgen signalling axis plays an important role in the growth, survival, and differentiation of the prostate3’ Treatment for locally advanced and metastatic prostate cancer includes androgen-deprivation therapy. Thus, it is essential to determine if levels of expression of any of the 27 genes are altered by androgen. To do this, levels of expression of these genes were assessed in prostate cancer cells with androgen receptor (LNCaP, MDA-PCa-2B, and 22Rv1) and without a functional androgen receptor (PC-3 and DU 1 45)3438• Expression of ADAM2, CAMK2N], DHCR24, MARCKSLJ, NGFRAP1, POP1, POP3, POP4, POP5, POP7, POP8, POP 10, POP 11, SPON2, and TMEM66 transcripts were enriched in prostate cancer cell lines with a functional androgen axis (compare levels in LNCaP, MDA-PCa-2B and 22Rv1 to PC3 and DU145 cells in Figure 4.2). Although MCF7 mammary carcinoma cells express the androgen receptor at low levels39,activation of the endogenous androgen signalling axis has not been documented40.With this potential lack of androgen signalling, expression of only ADAM2, MARCKSL], POP 1, POP3, POP4, POP5, POPIO, POP1 1, and SPON2 were not obviously elevated in MCF7 cells (Figure 4.2). Of these genes, expression of ADAM2, POP 1, POP3, POP 10 and SPON2 were generally restricted to the prostate. Differential expression of these 27 genes in response to androgen was also measured in LNCaP cells treated with 10 nM of synthetic androgen Ri 881 for 16 hours. Expression of ii genes (DHCR24, ELOVL5, GLO], PGK], POP4, POP6, POP7, POP8, SPON2, TMEM66, and YWHAQ) increased, while significant decreases in expression of 5 genes (ADAM2, CAMK2N], POP5, POP 10, and POP1 1) were detected (Figure 4.4). Androgen regulation of genes MARCKSLJ, NGFRAP], POP1, POP2, POP3, POP9, POP12, PSMA7, RAMP], SBDS, and TMEM3OA were not detected (Figure 4.4). Although expression of MARCKSLJ, NGFRAPJ, POP 1, and POP3 were elevated in prostate cancer cells with endogenous androgen receptor 144 compared to those cells without a functional receptor, no evidence supports that these genes are regulated by androgen. Enhanced expression ofELOVL5, GLO], PGKJ, POP6, and YWHAQ in response to androgen, while lacking enrichment in prostate cancer cells with endogenous androgen receptor compared to those without a functional receptor, suggests that these genes may be regulated by non-specific downstream effects of androgens such as changes in proliferation, metabolism, and/or differentiation. 4.3.3 Characterization of gene expression in prostate cancer To determine if levels of any of the 27 transcripts were altered in prostate cancer compared to benign prostate epithelial cells, total RNA was isolated from 28 laser microdissected samples of prostate obtained by radical prostatectomy from Japanese prostate cancer patients. Laser microdissection was employed because prostate cancer is typically a heterogeneous disease4’ with multiple foci42. These studies revealed that levels of expression of RAMP] and SPON2 were significantly increased, while levels of expression of ELO VL5, NGFRAP1, POP5, POP8, and TMEM66 were significantly decreased in malignant compared to normal epithelial prostate cells (Figure 4.5; the Kruskal Wallis test, p 0.05). MARCKSL], POP2 and POP 10 were borderline significantly increased (p<0.l), while PSMA 7 was borderline decreased (Figure 4.5). Borderline significant changes in expression of genes indicate that analysis using a larger sample size may be required to achieve statistical significance. No significant differences in expression were measured between malignant compared to normal epithelial prostate cells for ADAM2, CAMK2N], DHCR24, GLO], PGKJ, POP1, POP3, POP4, POP6, POP7, POP9, POPI 1, POP 12, SBDS, TMEM3OA, and YWHAQ (Figure 4.5). To determine if the levels of expression of genes in tumour tissue samples correlated to patient age, PSA level’0 and stage of the disease (Tumour-Node-Metastasis, TNM)43 only levels of each transcript in the tumour samples were utilized in statistical analyses. No association between the expression of any of the candidate genes and the age of the patient was detected using Spearman’s correlation (p 0.05). Only borderline significance was obtained between TNM stage of prostate cancer (clinical or pathological) and the expression for RAMP] (p = 0.07) and POP 12 (p = 0.09) as assessed with the Kruskal Wallis test (p 0.05). Additional patient samples are also required to test associations in transcript levels with Gleason grade or 145 score44.However, expression of PGKJ and POP9 were independently positively correlated with high serum PSA levels (Figure 4.6; Spearman’s correlation, p 0.05), with borderline significance for RAMP] (p = 0.07) and POP8 (p = 0.08). Men with high levels of serum PSA at the time of first line therapy (e.g., prostatectomy) have a greater risk of prostate cancer recurrence10.The expression of POP9, PGK], and possibly POP8 and RAMP], may be prognostic due to a correlation with serum PSA levels prior to surgery. 4.4 DISCUSSION Gene expression studies have been performed using prostate tissue in attempts to identify prognostic markers. The first major report was from the University of Michigan where 10k cDNA arrays were used to probe more than 50 normal and neoplastic prostate specimens. This study identified hepsin and PIM-] to be over-represented in prostate cancer. The results were also validated at the protein level using 700 clinical samples45.Despite over-expression in prostate cancer, surprisingly reduced or absent levels in cancer were associated with increased risk of relapse after prostatectomy45.Thus, the expression profiles of hepsin and PIM-] will be difficult to interpret and apply for clinical decisions, thereby emphasizing the need to identify and characterize better markers for prostate cancer. Here, genes and novel non-coding transcripts previously identified to be differentially expressed in an in vivo model of hormonal progression of prostate cancer were characterized and revealed the following: 1) prostate-specific expression of POP3 and restricted tissue expression of ADAM2, POP1, POP4, POP1O, ELO VL5, RAMP], and SPON2; 2) changes in expression of ADAM2, CAMK2NI, DHCR24, ELO VL5, GLO], PGK1, POP4, POP5, POP6, POP7, POP8, POP 10, POP 11, SPON2, TMEM66, and YWHAQ, in response to androgen; 3) differential levels of expression of ELO VL5, NGFRAPJ’, POP5, POP8, RAMP], SPON2, TMEM66 and possibly MARCKSL], POP2, POP 10, and PSM47 between clinical samples of normal and malignant prostate tissue; and 4) correlation with clinical parameters and levels of PGK], POP9 and possibly POP8, POP 12, and RAMP] transcripts. Due to the broad expression of MARCKSL], NGFRAPI, PGK], POP2, POP5, POP8, POP9, POP 12, PSMA7, and TMEM66 across many tissues, these genes or transcripts have limited application as biomarkers for prostate cancer. A summary of results is presented in Table 4.3. 146 ELOVL5 was broadly expressed across most cell lines examined, yet showed restricted expression in benign tissues to the prostate, adrenal, and testis. Enhanced levels of expression of ELO VL5 in cell lines could be interpreted to be associated with malignancy. Yet, when comparing levels of expression in malignant versus benign prostate epithelial cells, levels were significantly decreased in the tumours. Notably, all of the candidate transcripts in this paper were identified in an in vivo model for castration-recurrent prostate cancer, and not from primary tumours. Therefore, differential expression between tumour and normal tissue from prostatectomy patients was not necessarily expected. Curiously, levels of expression increased in response to androgen. This result is confirmed by a previous publication46.ELOVL5 protein functions in fatty acid synthesis. Importantly, lipogenesis is important for the synthesis of androgen precursors. Recently, local production of androgen has been implicated in castration- recurrent prostate cancer47. Levels of POP 1 and POP3 transcripts did not change in response to androgen, yet were detected specifically in prostate cancer cell lines that expressed androgen receptor. These transcripts were not expressed in prostate cancer cells that did not express androgen receptor or by any of the other cell lines tested. In addition to the prostate, POP1 was also expressed in the testis, while POP3 expression was specific for the prostate, both benign and tumour. No differences in levels of expression of POP1 and POP3 were measured in benign or malignant epithelial cells from clinical samples. It should be noted that although tissue samples were laser microdissected, basal cells are likely present in the normal samples. By definition of prostate cancer, the tumour samples would not contain basal cells. The contribution of the basal cells to the expression trends observed here remain to be determined. For these reasons, differential expression between primary tumours and normal tissue may not be observed. POP1 and POP3 represent non-coding transcripts for mRNA AK000023 (POP 1) and transcript 50 kb from EST CF140309 (POP3)25.Non-coding transcripts display a diverse array of functions including the regulation of expression of other genes. Sense non-coding transcripts can silence gene expression by recruiting chromatin remodelling complexes that methylate and deacetylate histones of specific genomic sequences such as XIST. Alternatively, intergenic non-coding transcripts may promote the expression of the surrounding gene by recruiting chromatin remodelling complexes that demethylate and acetylate histones in the wake of RNA polymerase II (e.g., XITE transcript action on the TSIX gene). Moreover, steric hindrance of sense transcription via antisense 147 transcription, and formation of RNA hybrids between non-coding transcripts and target transcripts may also lead to transcriptional suppression. Double stranded RNAs may result in RNA interference, RNA masking, RNA hyperediting, and degradation48These non-coding POP transcripts are not considered to be microRNAs because their sequences range between 155-231 bp in length. Thus, further investigation of POP 1 and POP3 non-coding transcripts in prostate biology and pathology is warranted based upon their prostate-specific expression and potential to regulate gene expression. POP4 had restricted expression to the prostate and brain, and was detected only in prostate cancer cells that expressed androgen receptor. Increased levels of POP4 transcript were measured in response to androgen, but no differences were measured between normal and malignant prostate epithelial cells. POP4 is protein-coding for a truncated isoform of the transmembrane protein TMEFF249.The truncation of TMEFF2 eliminates the transmembrane domain, creating a secreted isoform49.Full-length TMEFF2 protein has been associated with castration-recurrent prostate cancer50,and is currently being targeted by antibodies for the treatment of metastatic prostate cancer51’52• Since POP4 is a secreted form of TMEFF2 that is not elevated in prostate cancer compared to normal prostate, it is unclear if serum levels would provide any additional clinical information to serum PSA. POP 10 (intron of ADAM2) expression was restricted to the prostate, testis, and placenta. ADAM2 exhibited the same restricted expression, but was additionally detected in the thymus. Curiously, although POP 10 was borderline significantly increased in clinical samples of prostate cancer, while ADAM2 was not, expression ofPOPlO and ADAM2 were relatively restricted to LNCaP cells with decreased levels measured in the other prostate cancer cell lines tested. Levels ofPOP1O (and ADAM2) were decreased in response to androgen. Together, these data suggest that these two variant transcripts may share common androgen-response element(s) in the regulatory regions to modulate transcription. However, tissue specificity was observed for these two transcripts which support additional mechanisms to regulate transcription or mRNA processing. 148 Expression of RAMP] was significantly higher in tumour compared to normal prostate tissue from prostatectomy patients, indicating it has the potential to be detected at tumour sites. Consistent with the restricted expression of RAMP] in the prostate and uterus, RAMP] was expressed in all prostate cancer cell lines examined, except DU 145. Of the non-prostate cell lines examined, RAMP] was only expressed in MG63 cells. The protein product of RAMP] is expressed on the plasma membrane, indicating it has the potential to be detected at tumour sites. Since no changes in expression were detected in response to androgens, this protein could potentially be used to monitor andlor image metastatic prostate cancer in patients regardless of whether they are receiving androgen-deprivation therapies. An example of this can be drawn from prostate-specific membrane antigen (PSMA) that is expressed on the plasma membrane53. PSMA is a prostate-specific biomarker54used clinically for detection of recurrent prostate tumours and locate metastases55.A radiolabelled antibody, 1n-capromab or Prostascinct, binds to PSMA at the site of soft-tissue metastatic prostate cancer, and is visualized by an immunoscintigraphy scan53.Detection of metastatic cancer using imaging, has the advantage of being minimally invasive. Moreover, it can be performed repeatedly for the monitoring of disease. Expression of SPON2 increased in response to androgen, was elevated in tumour versus normal prostate, and was restricted to the prostate and uterus. However, unlike RAMP 1 expression trends, SPON2 was not expressed broadly across all prostate cancer cell lines with detection only in LNCaP and MDA-PCa-2B cells. These data may imply that expression of SPON2 may not be uniformly expressed by all prostate cancer and limited to only a subset of cancers. SPON2 gene expression is reported to be variable among non-laser microdissected samples of prostate cancer obtained by radical prostatectomy56.Prostate-specific expression of SPON2 has also been suggested from studies that interrogated publicly available SAGE databases56. However, some discrepancies exist in the literature for whether expression of SPON2 is elevated in tumour versus normal prostate tissue. No significant differences in levels of SPON2 protein among primary tumour, lymph node metastases, bone metastasis, or locally recurrent tumours in castration-recurrent patients were detected using formalin-fixed, paraffin-embedded tissue57. In contrast, levels of SPON2 transcript is significantly increased in castration-recurrent prostate cancer from the LNCaP Hollow Fibre model24.Whether these discrepancies stem from sample preparation or methods of detection, remains to be determined. Application of radiolabelled 149 antibodies(86Y-19G9) to SPON2 protein has been proposed for the detection of prostate cancer with successfully imaging of LNCaP xenografts using positron emission tomography (PET)57. Surprisingly, despite the fact SPON2 is a secreted protein, it was detected in close proximity to the tumour; perhaps because it weakly associates with plasma membranes57.Despite this sequestration in tumours, elevated levels of SPON2 protein may be detectable in the serum of prostate cancer patients. Similar to PSA, expression of SPON2 is prostate-specific. However, SPON2 is potentially superior to PSA as a serum marker because of its elevated expression in malignancy compared to normal cells as shown here. Recently a sandwich enzyme-linked immunosorbent assay for SPON2 protein was used for the diagnosis and early detection of ovarian tumours58. Numerous markers of prostate cancer have been described as androgen-regulated. For example, fusions between the un-translated region of the androgen-regulated gene TMFRSS2 and the ETS gene family (whose protein products are transcription factors) result in the deleterious misregulation of groups of genes in response to hormone59.TMPRSS2-ETS family of gene fusions are associated with biochemical progression following prostatectomy60’and metastatic, castration-recurrent prostate cancer62’3 Measurement of TMPRSS2-ETS transcript highlights that some biomarkers of prostate cancer may only be detected at the level of DNA or RNA. Unfortunately, genomic and transcript detection has thus far been restricted to use with biopsy tissue, which is invasive and inconvenient to sample. Recent studies have shown that tumour cells of metastatic, castration-recurrent prostate cancer are shed into the circulation and can be isolated and interrogated by DNA or RNA molecular analysis6567.Genomic amplification of the androgen receptor has been detected by fluorescence in situ hybridization (FISH) in circulating tumour cells of patients with castration-recurrent prostate cancer65.This approach to biomarker detection may potentially be applied to non-coding POPs. It should be noted that the levels of POP transcripts were very low (12 qRT-PCR cycle thresholds higher than glyceraldehyde-3-phosphate (GAPDH); data not shown), indicating that an assay more sensitive than FISH may be required for their detection. Reverse transcriptase-PCR analysis has been successfully applied to samples of circulating tumour cells from men with metastatic prostate cancer67.It is conceivable that the assay could be adapted for qRT-PCR for improved sensitivity. 150 Circulating tumour cells may also be used as a source for detection of protein expression65.The androgen-affected transcripts ADAM2, ELO VL5, and TMEM66 were enriched in castration- recurrent prostate cancer24,and code for plasma membrane proteins. Their over-expression may be detectable via immunohistochemistry of fixed circulating tumour cells based on the previous success of a similar study65. Detection of non-coding POPs is not restricted to blood samples. In fact, exfoliated tumour cells are also present in urine. Recently, detection of the non-coding transcript PCA3 in whole urine has been used to improve predictions of prostate biopsy outcome using the PROGENSA PCA3 assay68’9 This assay is to be used in conjunction with PSA, as PCA3 mRNA levels are normalized to PSA mRNA levels in the urine. Due to an association with pre-treatment PSA levels, the development of urine assays for POP9, and possibly POP8, might yield useful prognostic information when applied to clinical samples. Typically an assay for decreased gene expression is more challenging to design than for increased gene expression, because high quality controls are needed to interpret the results. Gene expression may be assessed by measuring the levels of expression of transcripts, or by testing for epigenetic markings such as methylation and acetylation at promoters. The advantage of evaluating epigenetics as opposed to transcript levels, is that a positive result can be obtained for decreased gene expression. For example, gene silencing of glutathione-S-transferase P1 (GSTP- 1) due to promoter methylation is present in >90% of prostate cancer, but infrequent in benign tissue. Promoter methylation has been detected using quantitative methyl specific PCR in urine sediments of prostate cancer patients with a specificity of 98% and sensitivity of 78%70. Interestingly, the gene EFNA5 is a target for promoter methylation and gene silencing in non- Hodgkin’s lymphoma71.The POP8 transcript is expressed from an intron of EFNA5, and the expression of POP8 was lower in tumour compared to normal prostate tissue. These data suggest that the promoter of POP8 may also be silenced due to promoter methylation and may be a target for epigenetic assays. Biomarkers often perform better when they are part of a panel of genes. For example, the promoter methylation of GSTP-J indicates prostate cancer with a specificity of 100% and 151 sensitivity of 87% when used in combination with cyclin-dependent kinase 2A (CDKN2A), ADP-ribosylation factor 1 (ARFJ), and o-6-methylguanine-DNA methyl transferase (MGM1)72. This is a significant improvement over GSTP-] alone. Moreover, high gene expression of enhancer of zeste homolog 2 (EZH2) indicates aggressive disease and poor survival for prostate cancer patients73.EZH2 protein is responsible for the transcriptional silencing of numerous genes, via recruitment of the histone deacetylase, in metastatic prostate cancer. However, gene silencing is insufficient to account for all gene expression changes that are expected to occur during the progression of prostate cancer74.The combination of gene expression profiles of PCA3, prostein, and transient receptor potential cation channel subfamily M member 8 (TRPM8), worked in concert with EZH2 to provide additional prognostic power in a study of 106 patients with matched prostatectomy samples75.Although the differentially expressed genes NGFRAP], POP5, POP8, and TMEM66 in tumour versus normal prostate samples were not also prostate-specific, when used in combination with prostate restricted genes ADAM2, POP 1, POP3, POP4, POP 10, ELO VL5, RAMP], and SPON2 in a biomarker assay, the panel of genes may be clinically useful. 4.5 CONCLUSION In summary, 27 potential biomarkers of prostate cancer were characterized for prostate-specific expression, regulation by androgen, and expression in clinical samples of prostate cancer. POP3 was prostate-specific with restricted expression of ADAM2, POP 1, POP4, POP1O, ELOVL5, RAMP], and SPON2. The expression of ADAM2, CAMK2N], DHCR24, ELOVL5, GLO], PGK], POP4, POP5, POP6, POP7, POP8, POP1O, POP1 1, SPON2, TMEM66, and YWHAQ genes changed in response to androgen. ELO VL5, NGFRAP], POP5, POP8, RAMP], SPON2, and TMEM66 were significantly differentially expressed between laser microdissected tumour and normal prostatic tissue, and PGKI and POP9 were positively associated with pre prostatectomy serum PSA levels. Together, these studies suggest that ADAM2, ELO VL5, POP 1, POP3, POP4, POP 10, RAMP], and SPON2 may be good candidates for biomarkers of prostate cancer. 152 1 A 59 9.7 ++2 3 B 64 19.0 4 0 5 C 71 24.2 6 7 D 68 9.5 E 64 19.1 F 71 5.5 G 69 25.1 H 67 6.4 1 64 7.7 J 70 29.9 K 62 10.0 L 63 15.6 M 74 5.2 N 70 14.1 O N/A N/A p 74 5.7 Q 69 8.0 R 68 22.2 S 73 20.3 _______________________ Gleason Grade N/Att 2b N N/A T 3+4 Ic 3b N N/A T 4+4 2b 2b N N/A T 3+4 2a 3b N N/A T 5+4 2a 3b T 3+4 2b 2b N N/A Ic N/A N N/A N N/A T 4+3 2a 2a N N/A T 4+4 2a 2a N N/A 2b 3b T 3+3 2a 2b N N/A 2a 2b T 3+4 2b 2b N N/A 2b 3b T 4+3 N/A N/A T 3+4 N N/A T 3+5 2a 2a N N/A 2b 3b N N/A 2a 3a N N/A 3a 3b T 4+4 Gleason Sum * N/A 7 N/A 8 N/A 7 N/A 9 7 N/A N/A N/A 7 N/A 8 N/A 6 N/A 7 N/A 7 7 N/A 8 N/A N/A N/A 8 * Sample No., sample number labeled ‘1 ‘to ‘28’ t Patient ID, patient identification labeled ‘A’ to ‘S’ PSA, prostate-specific antigen serum levels upon diagnosis § Stage, Tumor Node Metastses (TNM) staging system N (Normal), normal prostate tissue; T (Tumor), tumor prostate tissue ¶ Gleason Grade, grading system to describe degree of differentiation of tumor tissue cells. Gleason grading was applied to the slide of tissue used for laser microdissection by a trained pathologist ** Gleason Sum, cummulative score of the two most prominent Gleason Grades present on the slide of tissue tt N/A, not applicable or not available same as above Table 4.1 Information on the samples used for laser microdissection and gene expression analysis, and the patient’s they were taken from Sample Patient Age PSA Stage § Normal or No.* ID (years) (ng/mL) Clinical Pathological Tumor II 9 10 II 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 153 Table 4.2 Primer and probe sequences for qRT-PCR of candidate transcripts Gene Forward Primer (5’-3’) Probe (5’-3’) Reverse Primer (5’-3’) ADAM2 TGGTGAAAGTTAATTTCCAAAGG ATTCAAGCGATGAGCAACCT TCATGGCATCTCTGTTGTCC CAMK2NI TGCAGGACACCAACAAC]1’C AGCAAGCGGGTFGT1’ATFGA GCACGTCATCAATCCTATCATC D!-!CR24 GAGGCAGCTGGAGAAG1TI’G TGCTGTATGCCGACTGCTAC CTFGTGGTACAAGGAGCCATC ELOVL5 GTTTGTCGTCAGTCCCTI’CC CGTCCATACCTCTGGTGGAA TGGTCTGGATGATTGTCAGC GAPDH CTGACTTCAACAGCGACACC CGACCACTTTGTCAAGCTCA TGCTGTAGCCAAATTCGTTG GLOI AAAGGTTTGAAGAACTGGGAGTC AAGGCCTGGCATTTATTCAA TTCAATCCAGTAGCCATCAGG MARCKSL) GCAGCCAGAGCTCCAAGG CCAACGGCCAGGAGAATG AAGTCTCCATTGCTTFTCACG NGFRAPI GTCACTCGCGTCTGGCTAC AAAGCGGAGCAGGTCTGC GCCGCGGAGACACTTAGC PGK] GAAGGGAAGGGAAAAGATGC CGAGCCAGCCAAAATAGAAG GACATCCCCTAGCTTGGAAAG POP I * AAGCTCTTGCTAGGCATGTAGG CCTGGACAGCCCATTCTTTA TTT000TAGACATTTCCCC POP2 GGAGGATCAACAGCAGCATT CAACTGTGCTCCATTGACGT GGTATCAUGAGGCTGGGTG POP3 TATGGTGTGCCA1TFCTGGA CCG1TFGCATCTCTGAGTGA GTGGAACAAAATCCCCTCCT POP4 CCCTI’GTGCAAATGGGTFA TCATTFATGATAGCCACACATGA TTGTTCCCTFCACTCTTTTGTTC POP5 1TFGGAAAGGTGAGCCTCTG CAUG1TI’GGGCAGGAGAGT AAAGAAGTGGACGTGGCAC POP6 mAAGTGGTFCAGCACACAAAAC CAAAAGGATGACCTTGGGAA TGATGACflCCTfGTG1TFAACAAA POP7 TfGGTTTCTGGACCCTTTrG AAAGCTTGAGGGTGGTGATG CAGAAGAGCAGGGTGGGTAG POP8 1TFCGGTTCCTFTCCTCTTC CCCACATTCCATI’TCAAACA ATTCCTTTATGGCTTGAAGGGT POP9 CCTGTTTCCCAGTCACACCT TI’AACAATTCCCAAGCACCC ATTTGTCTI’CCACCACAGGC POPI 0 TTGCTAGGGAAAAGCAGCAT TFCTTCACCAAACTCTCTAAAACAGA GAATCATAAGGCAGCCTCCTT POPI 1 GTTCGCTCTTGGCTTFGAAC TTCCCTGTCCCCTAACTCCT TTI’GCCTTrTGCAGAATGTG POPI 2 TGTGACAAAATGGGAGGACA GCTTGGAGTTGCAAGCA CAGAAAAGTGTATGGCA000A PSMA 7 CGTCAAGAAGGGCTCGAC AAGAAGTCAGTGGCCAAACTG CGCACTGTTCTTTCATCCTG RAMP! CCTCACCCAGTFCCAGGTAG CAGGACCATCAGGAGCTACA CATGTGCCAGGTGCAGTC SBDS CGCCTGCTACAAAAACAAGG CGTGGAAAAAGACCTCGAT CAAACACTGAGTGGGTCTGC SDHA ACCAGGTCACACACTGTTGC ACATGGAGGAGGACAACTGG CCTGTGGTGTCGTAGAAATGC SPON2 CCCAGCA000ACAATGAG TGTAGACAGCGCCTCAGTFC CACAGTCCCCAGGACGAC TMEM3OA GGATGTGACACCHGCT1TFG CCATFAACTTCACACTGGAAAAG ACGTAACGACGATGG11TFG TMEM66 GGGCAGCTATI’CGGTATGTI’C CGAAAACCAGAACTGCATCA TGCATCCAGTGTTTGACTCC YWHAQ CTGAGATCCATCTGCACCAC AGCCAATGCAACTAATCCAGA ACCGGAAGTAATCACCCTI’C * Genes were represented by HGNC approved nomenclature when available. Non-HGNC gene names were not italicized. 154 Table 4.3 Review of expression trends of candidate genes Gene Ex. in CRPC Ex. in RAD En. in AR + vs. RAD* vs. AS t CaP cells En. in CaP En. in Normal Assoc. wI Ex. in Tumor Cell Lines** Prostatett PSA .t vs. Normallili Y N NS Reg. S or PM En. in by A § Prot. II Humant ADAM2 t NS Y till PM Y Y CAMK2IVJ t t Y 4 N N N N N NS DHCR24 11 4 y fill N Y N N N NS ELOVL5 t 4 N PM N N Y N GLO] t NS N t N N N N N NS MARCKSLJ .1. N5 Y N PM Y Y N N NS(1’ ¶11) NGFRAPI t NS Y N N N N N N 4 PGKI f N tITlI N N N N NS POPI*** t UD Y N NA Y Y Y N NS POP2 t UD N N NA Y Y N N NS POP3 t UD Y N NA Y Y Y N NS POP4 t UD 1’ NA Y Y Y N NS POP5 t UD Y 4 NA Y Y N N 4 POP6 t UD N 1’ NA Y N N N NS POP7 t UD Y t NA Y N N N NS POPS t UD Y t NA Y N N N 4 POP9 t UD N N NA N N N Y+ NS POPIO t UD Y 4 NA Y N Y N NS POPII 1’ UD Y 4 NA Y Y N N NS POPI2 t UD N N NA Y Y N N NS PSMA7 1’ NS N N N N N N N NS RAMP! 4 t N N PM Y N Y N SBDS t NS N N N Y N N N NS SPON2 t NS Y t S Y Y N t TMEM3OA t NS N N S&PM Y N N N NS TMEM66 t NS Y t S&PM N N N N YWHAQ 4 NS N t N N N N N NS * Ex. in CRPC vs. RAD, Gene expression in castration-recurrent prostate cancer versus responsive to androgen-deprivation in the LNCaP Hollow Fiber model t Ex. in RAD vs. AS, Gene expression in the stage of prostate cancer that is responsive to androgen-deprivation versus androgen-sensitive in the LNCSP Hollow Fiber model En. in AR-e CaP cells, Gene expression is enriched in androgen receptor positive prostate cancer cells versus androgen receptor negative prostate cancer cells § Reg. by A, Expression of gene is regulated by androgen; Arrow indicates direction of regulation with sndrogen Ii S or PM Prot, Gene product is a secreted (S) or plasma membrane (PM) protein ¶ En. in Human; Gene expression is enriched in human cell lines versus monkey kidney cells En. in CaP Cell Lines, Gene expression is enriched in prostate cancer cell lines versus cell lines of cancer from other organ sites ti En. in Normal Prostate, Gene expression is enriched in normal human prostate tissue versus other normal human tissues : Assoc. w/ PSA, Gene expression in laser microdissected ssmple is associated with serum prostate-specific antigen levels measured from the patient at the time of diagnosis of prostate cancer; ‘+‘ indicates a positive association § Ex. in Tumor vs. Normal, Gene expression in tumor tissue versus normal prostate tissue Y, yes; N, no; NS, no significant difference; NA, not applicable; UD, undetermined; 1’, higher expression; -1. , lower expressionll! Known Genes were represented by HGNC approved nomenclature when available. Non-HGNC gene names were not italicized. 155 Figure 4.1 Laser microdissection of normal and tumour prostate tissue. Selected prostate epithelial cells were cut at 20x magnification using laser power and collected onto adhesive caps. Images show tissue prior to cutting (A and D), post-cutting (location of cut is highlighted yellow; B and E), and remaining post capture on an adhesive cap (C and F). Images A-C represent normal tissue, while images D-F represent tumour tissue. 156 —C” () ‘0 Q Q c C L) L) ADAM2 CAMK2NJ DHCR24 ELOVL5 GLOI MARCKSLJ NGFRAP1 PGK1 POPI POP2 POP3 POP4 POPS POP6 POP7 POP8 POP9 POP 10 POP1 1 POP 12 PSMA 7 K4MPJ SBDS SFON2 TMEM3OA TMEM66 YWHAQ Cancer Cell Lines of Other Human Cancers Fold tt 5.0 Monkey Kidney Cells 2.OSx<5 No Change 0.5 <x <2.0 I-0.2<x0.5 440.2 x Figure 4.2 Specificity of gene expression for human prostate cancer. RNA was isolated from LNCaP, MDA PCa-2B, 22Rv1, PC-3, DU145, MG63, RKO, HEPG, MCF7, COS1, and CV1 cells and analyzed by qRT-PCR using primers and probes for ADAM2, CAMK2NI, DHCR24, ELOVL5, GLOI, MARCKSLJ, NGFRAP1, PGK1, POP1, POP2, POP3, POP4, POP5, POP6, POP7, POP8, POP9, POP1O, POP1 1, P0P12, PSMA7, RAMPI, SBDS, SPON2, TMEM3OA, TMEM66, and YWHAQ. Heat map indicates the average degree of fold-change in gene expression relative to LNCaP cells of three biological replicates.Non-HGNC gene names were not italicized. Cell Lines of Human Prostate 157 .-5 0 . . •0 - - I V I •0 0V - 0 • E I 0 0 V Q V .0 .00 — - - r., c’ H H H H No Change 0.5 <x <2.0 i’•0.2<x0.5 140.2x Figure 4.3 Specificity of gene expression for normal prostate tissue. RNA was obtained commercially from normal human tissues (adrenal gland, bone marrow, cerebellum, whole brain, fetal brain, fetal liver, heart, kidney, liver, lung, placenta, prostate, salivary gland, skeletal muscle, testis, thymus, thyroid gland, trachea, uterus, and spinal cord) and analyzed by qRT-PCR using primers and probes for ADAM2, CAMK2N1, DHCR24, ELOVL5, GLOI, MARCKSLI, NGFRAP1, PGKI, POP1, POP2, POP3, POP4, POP5, POP6, POP7, POP8, POP9, POP1O, POP11, POP12, PSMA7, RAMPI, SBDS, SPON2, TMEM3OA, TMEM66, and YWHAQ. Heat map indicates the degree of fold-change in gene expression relative to prostate tissue. Non-HGNC gene names were not italicized. ADAM2 CAMK2NJ DHCR24 ELOVL5 GLO] MARCKSLJ NGFRAPJ PGKI POPI Fold tt—x5.0 1’ 2.0 x< 5.0 158 Figure 4.4 Regulation of gene expression by androgen. RNA was harvested from LNCaP cells that were treated with Ri 881 and analyzed by qRT-PCR. Candidate biomarkers assayed for gene regulation by androgen include: ADAM2, CAMK2NJ, DHCR24, ELOVL5, GLO], MARCKSL], NGFRAPI, PGKJ, POP1, POP2, POP3, POP4, POP5, POP6, POP7, POP8, POP9, POP 10, POP1 1, POP 12, PSMA7, RAMP], SBDS, SPON2, TMEM3OA, TMEM66, and YWHAQ. Fold-change was calculated by normalizing the mean normalized expression (MNE) of transcripts in R188 1-treated cells to the mock vehicle-treated cells. In doing this, the vehicle treatment fold-change became one and standard deviation (SD) zero. Error bars represent ± SD for six biological replicates. [*] Asterisk indicates significant differential gene expression according to the Two-Sample Student’s T-test (p E 0.05) for unequal variance. Non-HGNC gene names were not italicized. 159 ,4DAM2 CAMK2NI ELOVL5 GLO]DHCR24 1.5- 7,5. 3 * I ° 2- J * c * u U :‘:‘ 2.5- I Vehicle R188l Vehicle R1881 Vehicle R1881 Vehicle R1881 Vehicle R1881 M,4RCKSLI IVGFRAPI PGKI POPI POP2 * 2.0 1.5 I l.5 I 1 ‘ 1.0 —---‘1.0.0.5 .E os0.0 . 0.0Vehicle Rl881 Vehicle R188l 2.0 5 0.) 0.0 - 00-- - -Vehicle R1881 Vehicle Rl881 0 Vehicle Rl88l POPIO POP1I POPI2 1.5.5 1.2 1.0’ -U 04 os T CE’’l .1 _ 0.2 0 Vehicle Rl881 0.0 Vehicle Rl88l Vehicle R188l Vehicle R188l Vehicle Rl88l PSM.47 RAMPI 2.0. 1.5 - 0) .) o.sI o.5 0.0’ . 0.0’Vehicle Rl88l Vehicle RN 1.5 75. 2.5 POPS Pops 1.2 POP7 I Vehicle Rl881 0.0 Vehicle R188l POP9 2.0 l.5 C C, 1.2- SBDS YWHAQ TMEM3OA 2.0 4 e 3 C C, ___ U 2 Vehicle Rl88l Vehicle Rl881 Vehicle Rl88l Figure 4.4 TMEM66 4 * 2.5 i ____ . (2.0 Vehicle R188l * 160 Figure 4.5 Candidate biomarkers are differentially expressed between normal and tumour prostate. RNA was isolated from laser microdissected normal and tumour human prostate tissue (n=19; one of each randomly selected from each patient with available tissue) and analyzed by qRT-PCR using primers and probes for ADAM2, CAMK2NI, DHCR24, ELOVL5, GLO], MARCKSL], NGFRAP], PGK1, POPI, POP2, POP3, POP4, POP5, POP6, POP7, POP8, POP9, POP1O, POP1 1, POP12, PSMA 7, RAMP], SBDS, SPON2, TMEM3OA, TMEM66, and YWHAQ. Plotted on the y-axis is the mean normalized expression (MNE) of candidate genes against the x-axis of normal and tumour tissue. A p-value cut-off of p 0.05 (Kruskal Wallis test) was used to determine statistically significant differential gene expression. Box and Whisker plots display the median MNE (bolded line), the first and third quartile range (the box), minimum and maximum non-outlier values (whiskers), and outliers (open circles). Non-HGNC gene names were not italicized. 161 ADAM2 CAMK2NI DHCR24 ELOVL5 GLOI 0 I I 0 00 I 16—I I I 2.5—i I I __ — r1w I—i-- ______I-.- ___ 14_I_i__ 01 I I I j lO _____ _____ _____ I i1.sl _____ _____ ___ I I —1--I I 2.0 ‘ PJ I 4 2.Oj I _ 6 I ______ H__iE__II 2—I ______ 1.0 I 2 — I 1.011 I ___ I —1-- I 0.5 4-i oH- I __ 01 _ __________ ____ I _ _ __ __ ____ I — I Normal Tumour Normal Tumour Normal Tumour Normal Tumour Normal Tumour p = 0.33 p = 0.20 p = 0.17 p = 0.03 p = 0.76 MARCKSLI NGFRAPI PGKJ POPI POP2 2.0 I II 6] I I __ l5I rn I4—i ‘ I 2.51 0 0 15—1 15 2.0—I’ 151 I 8 I I I’ I1.0 1.0 I II 3 i I 5 _ _____ z 1.5 fl 1w 1w 0 wlOl 10 ‘ _______ _ _ __ __ 1 Ii 0.5 _ I 0.5 II 2_____ 0 __ I _1_- I Li —‘-- I 0-I Normal Tumour Normal Tumour Normal Tumour Normal Tumour Normal Tumour p = 0.06 p = 0.04 p = 0.15 p = 0.46 p = 0.07 POP3 POP4 POP5 POP6 POP7 14 I 3.Si 0 12r 0 25-I 0 I 10-I rn I13. I I I I 0 6-1 w 0 1w I 2.5 —r— I 8—j ‘ r— I I 8 I I5 2. i I61I I I I I1w 4-4 I I0I 0 Ii:I I 4-lI I I I6 Iz I I ‘ Iz II I L ‘ ___ _ _ _ I 2—I_______ _ 5—I ___ i I h1 2 ______I I 1 0.5 - I O I 0. I _ __ 01 _ ___ __ ___ _ __ _ _ _____ Normal Tumour Normal Tumour Normal Tumour Normal Tumour Normal Tumourp = 0.51 p = 0.81 p = 0.03 p = 0.37 p = 0.74 POP8 POP9 POPIO POPII POPI2 I 2.5 o I 2.0-i I 12—I 0 I 20 1 0101 0 I I I ,_-l0—II 2.0—I I I 171) I— IL—’ IZ I41 0 Iwlo 6 I 10] 81 I I Il.5 ii i 151 10 Iz Ii w 6 1w IuI II8 1w I I 2 _____ ___________ ___________ ___________ ___________ 511 I0.5 I — 0 H i0 0 - — 0 — I 0 1 oIl Normal Tumour Normal Tumour Normal Tumour Normal Tumour Normal Tumour p 0.03 p = 0.81 p = 0.06 p = 0.77 p 0.55 PSMA7 RAv1PI SBDS SPON2 TMEM3OA w i I I I 2.0 0 I 50 ‘ I 2.0—I —rw6 I I I4 i Im 41 0 j I 2.5 0 0 —r- I 0 I 8 —,-- I ii I 60 I 2.51 0 I ‘ I l.5 —-•- I II 1.511 _ 20 I II i.oI l I, 2 I ‘110 1 Ii 2 — ‘ I 1.0 I I II I I _..L. 1 0.51 .___— I—‘—- —i-- I o.s —‘--- I —— I I .—_ Normal Tumour Normal Tumour Normal Tumour Normal Tumour Normal Tumourp = 0.06 p = 0.04 p = 0.20 p = 0.00 p 0.27 TMEM66 YWHAQ 104 I 041II 61 I Figure 4.5 0. ii Normal Tumour Normal Tumour p=0.05 p=O.89 1.56 z5 c) Figure 4.6 Transcript expression in tumour tissue correlate with circulating levels of serum PSA in the patient. RNA was isolated from laser microdissected human prostate tumour tissue (n=1 1; one randomly selected from each patient with available tissue and PSA information) and analyzed by qRT-PCR. Plotted against serum PSA levels of the patient, mean normalized expression (MNE) of PGK] or POP9 in tumour tissue were statistically significantly associated according to Spearman’s correlation test (p 0,05).Non-HGNC gene names were not italicized. 10 15 20 25 30 Serum PSA (ne/mL) p=O.05 Serum PSA (na/mL) p=O.OI 163 4.6 REFERENCES 1. Canadian Cancer Society, National Cancer Institue of Canada, Statistics Canada, Provincial/Territorial Cancer Registries, Health Canada: Canadian Cancer Statistics, 2006 2. Canadian Cancer Society, National Cancer Institute of Canada, Statistics Canada, Provincial/Territorial Cancer Registries, Canada H: Canadian Cancer Statistics, 2004 3. Sakr WA, Grignon DJ, Haas GP, Schomer KL, Heilbrun LK, Cassin BJ, Powell J, Montie JA, Pontes JE, Crissman JD: Epidemiology of high grade prostatic intraepithelial neoplasia, Pathol Res Pract 1995, 191:838-841 4. Thompson TM, Goodman PJ, Tangen CM, Lucia MS, Miller GJ, Ford LG, Lieber MM, Cespedes RD, Atkins IN, Lippman SM, Carlin SM, Ryan A, Szczepanek CM, Crowley JJ, Coitman CA, Jr.: The influence of finasteride on the development of prostate cancer, N Engi J Med 2003, 349:215-224 5. Beyer DC: The evolving role of prostate brachytherapy, Cancer Control 2001, 8:163-170 6. Horwitz EM, Hanlon AL, Hanks GE: Update on the treatment of prostate cancer with external beam irradiation, Prostate 1998, 37:195-206 7. Menon M, Shrivastava A, Tewari A: Laparoscopic radical prostatectomy: conventional and robotic, Urology 2005, 66:101-104 8. Miyamoto H, Messing EM, Chang C: Androgen deprivation therapy for prostate cancer: current status and future prospects, Prostate 2004, 61:332-353 9. Sharifi N, Gulley JL, Dahut WL: Androgen deprivation therapy for prostate cancer, Jama 2005, 294:238-244 10. Lilja H, Ulmert D, Vickers AJ: Prostate-specific antigen and prostate cancer: prediction, detection and monitoring, Nat Rev Cancer 2008, 8:268-278 11. Thompson TM, Ankerst DP, Chi C, Goodman P3, Tangen CM, Lucia MS, Feng Z, Parnes HL, Coltman CA, Jr.: Assessing prostate cancer risk: results from the Prostate Cancer Prevention Trial, J Nati Cancer Tnst 2006, 98:529-534 164 12. Bostwick DG, Burke HB, Djakiew D, Euling S, Ho SM, Landoiph J, Morrison H, Sonawane B, Shifflett T, Waters DJ, Timms B: Human prostate cancer risk factors, Cancer 2004, 101:2371-2490 13. Bunting PS: A guide to the. interpretation of serum prostate specific antigen levels, Clin Biochem 1995, 28:221-241 14. Thompson IM, Pauler DK, Goodman PJ, Tangen CM, Lucia MS, Parnes HL, Minasian LM, Ford LG, Lippman SM, Crawford ED, Crowley JJ, Coitman CA, Jr.: Prevalence of prostate cancer among men with a prostate-specific antigen level <or =4.0 ng per milliliter, N Engi J Med 2004, 350:2239-2246 15. Stamey TA, Yang N, Hay AR, McNeal JE, Freiha FS, Redwine E: Prostate-specific antigen as a serum marker for adenocarcinoma of the prostate, N Engi J Med 1987, 3 17:909-9 16 16. Pinsky PF, Andriole G, Crawford ED, Chia D, Kramer BS, Grubb R, Greenlee R, Gohagan JK: Prostate-specific antigen velocity and prostate cancer gleason grade and stage, Cancer 2007, 109:1689-1695 17. Pound CR, Partin AW, Eisenberger MA, Chan DW, Pearson JD, Walsh PC: Natural history of progression after PSA elevation following radical prostatectomy, Jama 1999, 28 1:1591-1597 18. Zelefsky MJ, Kuban DA, Levy LB. Potters L, Beyer DC, Blasko JC, Moran BJ, Ciezki JP, Zietman AL, Pisansky TM, Elshaikh M, Horwitz EM: Multi-institutional analysis of long-term outcome for stages Ti -T2 prostate cancer treated with permanent seed implantation, Tnt J Radiat Oncol Biol Phys 2007, 67:327-333 19. Leibovici D, Spiess PE, Agarwal PK, Tu SM, Pettaway CA, Hitzhusen K, Millikan RE, Pisters LL: Prostate cancer progression in the presence of undetectable or low serum prostate-specific antigen level, Cancer 2007, 109:198-204 20. Crawford ED, Eisenberger MA, McLeod DG, Spaulding JT, Benson R, Dorr FA, Blumenstein BA, Davis MA, Goodman PJ: A controlled trial of leuprolide with and without flutamide in prostatic carcinoma, N Engi J Med 1989, 32 1:419-424 21. Petrylak DP, Tangen CM, Hussain MH, Lara PN, Jr., Jones JA, Taplin ME, Burch PA, Berry D, Moinpour C, Kohli M, Benson MC, Small EJ, Raghavan D, Crawford ED: 165 Docetaxel and estramustine compared with mitoxantrone and prednisone for advanced refractory prostate cancer, N Engi J Med 2004, 351:1513-1520 22. Tannock IF, de Wit R, Berry WR, Horti J, Pluzanska A, Chi KN, Oudard S, Theodore C, James ND, Turesson I, Rosenthal MA, Eisenberger MA: Docetaxel plus prednisone or mitoxantrone plus prednisone for advanced prostate cancer, N Engi J Med 2004, 35 1:1502-1512 23. Kwak C, Jeong SJ, Park MS, Lee E, Lee SE: Prognostic significance of the nadir prostate specific antigen level after hormone therapy for prostate cancer, J Urol 2002, 168:995-1000 24. Romanuik TL, Morozova, 0., Delaney, A., Marra, M.A., and M.D. Sadar: Gene expression associated with in vivo progression to castration-recurrent prostate cancer, In preparation 25. Quayle SN, Hare H, Delaney AD, Hirst M, Hwang D, Schein JE, Jones SJ, Marra MA, Sadar MD: Novel expressed sequences identified in a model of androgen independent prostate cancer, BMC Genomics 2007, 8:32 26. Sadar MD, Akopian VA, Beraldi E: Characterization of a new in vivo hollow fiber model for the study of progression of prostate cancer to androgen independence, Mol Cancer Ther 2002, 1:629-637 27. Saha 5, Sparks AB, Rago C, Akmaev V, Wang CJ, Vogelstein B, Kinzler KW, Velculescu VE: Using the transcriptome to annotate the genome, Nat Biotechnol 2002, 20:508-512 28. Diatchenko L, Lau YF, Campbell AP, Chenchik A, Moqadam F, Huang B, Lukyanov S. Lukyanov K, Gurskaya N, Sverdlov ED, Siebert PD: Suppression subtractive hybridization: a method for generating differentially regulated or tissue-specific cDNA probes and libraries, Proc Natl Acad Sci U S A 1996, 93:6025-6030 29. Heid CA, Stevens J, Livak KJ, Williams PM: Real time quantitative PCR, Genome Res 1996, 6:986-994 30. Geller J: Rationale for blockade of adrenal as well as testicular androgens in the treatment of advanced prostate cancer, Semin Oncol 1985, 12:28-35 166 31. Balk SP, Knudsen KE: AR, the cell cycle, and prostate cancer, Nuci Recept Signal 2008, 6:eOOl 32. Huggins C, Hodges C: Studies on prostatic cancer: The effect of castration, of estrogen and of androgen injection on serum phosphatases in metastatic carcinoma of the prostate, Cancer Res 1941, 293-297 33. Cunha GR, Ricke W, Thomson A, Marker PC, Risbridger G, Hayward SW, Wang YZ, Donjacour AA, Kurita T: Hormonal, cellular, and molecular regulation of normal and neoplastic prostatic development, J Steroid Biochem Mol Biol 2004, 92:22 1-236 34. Horoszewicz JS, Leong SS, Kawinski E, Karr JP, Rosenthal H, Chu TM, Mirand EA, Murphy GP: LNCaP model of human prostatic carcinoma, Cancer Res 1983, 43:1809- 1818 35. Kaighn ME, Narayan KS, Ohnuki Y, Lechner JF, Jones LW: Establishment and characterization of a human prostatic carcinoma cell line (PC-3), Invest Urol 1979, 17:16-23 36. Stone KR, Mickey DD, Wunderli H, Mickey GH, Paulson DF: Isolation of a human prostate carcinoma cell line (DU 145), mt Cancer 1978, 21:274-281 37. Sramkoski RM, Pretlow TG, 2nd, Giaconia JM, Pretlow TP, Schwartz S, Sy MS, Marengo SR, Rhim JS, Zhang D, Jacobberger 3W: A new human prostate carcinoma cell line, 22Rv1, In Vitro Cell Dev Biol Anim 1999, 35:403-409 38. Navone NM, Olive M, Ozen M, Davis R, Troncoso P, Tu SM, Johnston D, Pollack A, Pathak 5, von Eschenbach AC, Logothetis CJ: Establishment of two human prostate cancer cell lines derived from a single bone metastasis, Clin Cancer Res 1997, 3:2493- 2500 39. Horwitz KB, Costlow ME, McGuire WL: MCF-7; a human breast cancer cell line with estrogen, androgen, progesterone, and glucocorticoid receptors, Steroids 1975, 26:785- 795 40. Beilin 3, Ball EM, Favaloro 3M, Zajac 3D: Effect of the androgen receptor CAG repeat polymorphism on transcriptional activity: specificity in prostate and non-prostate cell lines, J Mol Endocrinol 2000, 25:85-96 167 41. Shah RB, Mehra R, Chinnaiyan AM, Shen R, Ghosh D, Zhou M, Macvicar GR, Varambally 5, Harwood J, Bismar TA, Kim R, Rubin MA, Pienta KJ: Androgen independent prostate cancer is a heterogeneous group of diseases: lessons from a rapid autopsy program, Cancer Res 2004, 64:9209-92 16 42. Meiers I, Waters DJ, Bostwick DG: Preoperative prediction of multifocal prostate cancer and application of focal therapy: review 2007, Urology 2007, 70:3-8 43. Chang SS, Amin MB: Utilizing the tumor-node-metastasis staging for prostate cancer: the sixth edition, 2002, CA Cancer J Clin 2008, 58:54-59 44. Epstein JI, Allsbrook WC, Jr., Amin MB, Egevad LL: Update on the Gleason grading system for prostate cancer: results of an international consensus conference of urologic pathologists, Adv Anat Pathol 2006, 13:57-59 45. Dhanasekaran SM, Barrette TR, Ghosh D, Shah R, Varambally S, Kurachi K, Pienta KJ, Rubin MA, Chinnaiyan AM: Delineation of prognostic biomarkers in prostate cancer, Nature 2001, 412:822-826 46. Nelson PS, Clegg N, Arnold H, Ferguson C, Bonham M, White J, Hood L, Lin B: The program of androgen-responsive genes in neoplastic prostate epithelium, Proc Nati Acad Sci US A 2002, 99:11890-11895 47. McPhaul MJ: Mechanisms of prostate cancer progression to androgen independence, Best Pract Res Clin Endocrinol Metab 2008, 22:373-3 88 48. Morey C, Avner P: Employment opportunities for non-coding RNAs, FEBS Lett 2004, 567:27-34 49. Quayle SN, Sadar MD: A truncated isoform of TMEFF2 encodes a secreted protein in prostate cancer cells, Genomics 2006, 87:633-637 50. Glynne-Jones E, Harper ME, Seer>’ LT, James R, Anglin I, Morgan HE, Taylor KM, Gee JM, Nicholson RI: TENB2, a proteoglycan identified in prostate cancer that is associated with disease progression and androgen independence, Tnt J Cancer 2001, 94: 178-184 51. Afar DE, Bhaskar V, Ibsen E, Breinberg D, Henshall SM, Kench JG, Drobnjak M, Powers R, Wong M, Evangelista F, O’Hara C, Powers D, DuBridge RB, Caras I, Winter R, Anderson T, Solvason N, Stricker PD, Cordon-Cardo C, Scher HI, Grygiel JJ, 168 Sutherland RL, Murray R, Ramakrishnan V, Law DA: Preclinical validation of anti TMEFF2-auristatin E-conjugated antibodies in the treatment of prostate cancer, Mol Cancer Ther 2004, 3:921-932 52. Zhao XY, Schneider D, Biroc SL, Parry R, Alicke B, Toy P, Xuan JA, Sakamoto C, Wada K, Schuize M, Muller-Tiemann B, Parry G, Dinter H: Targeting tomoregulin for radioimmunotherapy of prostate cancer, Cancer Res 2005, 65:2846-2853 53. Elgamal AA, Holmes EH, Su SL, Tino WT, Simmons SJ, Peterson M, Greene TG, Boynton AL, Murphy GP: Prostate-specific membrane antigen (PSMA): current benefits and future value, Semin Surg Oncol 2000, 18:10-16 54. Horoszewicz JS, Kawinski E, Murphy GP: Monoclonal antibodies to a new antigenic marker in epithelial prostatic cells and serum of prostatic cancer patients, Anticancer Res 1987, 7:927-93 5 55. Bander NH: Technology insight: monoclonal antibody imaging of prostate cancer, Nat Clin Pract Urol 2006, 3:216-225 56. Edwards S, Campbell C, Flohr P, Shipley J, Giddings I, Te-Poele R, Dodson A, Foster C, Clark J, Jhavar 5, Kovacs G, Cooper CS: Expression analysis onto microarrays of randomly selected cDNA clones highlights HOXB 13 as a marker of human prostate cancer, Br J Cancer 2005, 92:376-38 1 57. Parry R, Schneider D, Hudson D, Parkes D, Xuan JA, Newton A, Toy P, Lin R, Harkins R, Alicke B, Biroc 5, Kretschmer PJ, Halks-Miller M, Klocker H, Zhu Y, Larsen B, Cobb RR, Bringmann P, Roth G, Lewis JS, Dinter H, Parry G: Identification of a novel prostate tumor target, mindin!RG- 1, for antibody-based radiotherapy of prostate cancer, Cancer Res 2005, 65:8397-8405 58. Simon I, Liu Y, Krall KL, Urban N, Wolfert RL, Kim NW, McIntosh MW: Evaluation of the novel serum markers B7-H4, Spondin 2, and DcR3 for diagnosis and early detection of ovarian cancer, Gynecol Oncol 2007, 106:112-118 59. Tomlins SA, Rhodes DR, Perner 5, Dhanasekaran SM, Mebra R, Sun XW, Varambally 5, Cao X, Tchinda J, Kuefer R, Lee C, Montie JE, Shah RB, Pienta KJ, Rubin MA, Chinnaiyan AM: Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer, Science 2005, 3 10:644-648 169 60. Nam RK, Sugar L, Yang W, Srivastava 5, Klotz LH, Yang LY, Stanimirovic A, Encioiu E, Neill M, Loblaw DA, Trachtenberg J, Narod SA, Seth A: Expression of the TMPRSS2:ERG fusion gene predicts cancer recurrence after surgery for localised prostate cancer, Br J Cancer 2007, 97:1690-1695 61. Nam RK, Sugar L, Wang Z, Yang W, Kitching R, Klotz LH, Venkateswaran V, Narod SA, Seth A: Expression of TMPRSS2:ERG gene fusion in prostate cancer cells is an important prognostic factor for cancer progression, Cancer Biol Ther 2007, 6:40-45 62. Perner 5, Demichelis F, Beroukhim R, Schmidt FH, Mosquera JM, Setlur S, Tchinda J, Tomlins SA, Hofer MD, Pienta KG, Kuefer R, Vessella R, Sun XW, Meyerson M, Lee C, Sellers WR, Chinnaiyan AM, Rubin MA: TMPRSS2:ERG fusion-associated deletions provide insight into the heterogeneity of prostate cancer, Cancer Res 2006, 66:8337-8341 63. Mehra R, Tomlins SA, Yu J, Cao X, Wang L, Menon A, Rubin MA, Pienta KJ, Shah RB, Chinnaiyan AM: Characterization of TMPRSS2-ETS gene aberrations in androgen independent metastatic prostate cancer, Cancer Res 2008, 68:3584-3590 64. Schiffer E: Biomarkers for prostate cancer, World J Urol 2007, 25:557-562 65. Shaffer DR, Leversha MA, Danila DC, Lin 0, Gonzalez-Espinoza R, Gu B, Anand A, Smith K, Maslak P, Doyle GV, Terstappen LW, Lilja H, Heller G, Fleisher M, Scher HI: Circulating tumor cell analysis in patients with progressive castration-resistant prostate cancer, Clin Cancer Res 2007, 13:2023-2029 66. Danila DC, Heller G, Gignac GA, Gonzalez-Espinoza R, Anand A, Tanaka E, Lilja H, Schwartz L, Larson 5, Fleisher M, Scher HI: Circulating tumor cell number and prognosis in progressive castration-resistant prostate cancer, Clin Cancer Res 2007, 13:7053-7058 67. Cho KS, Oh HY, Lee EJ, Hong SJ: Identification of enhancer of zeste homolog 2 expression in peripheral circulating tumor cells in metastatic prostate cancer patients: a preliminary study, Yonsei Med 32007, 48:1009-1014 68. Deras IL, Aubin SM, Blase A, Day JR, Koo S, Partin AW, Ellis WJ, Marks LS, Fradet Y, Rittenhouse H, Groskopf 3: PCA3: a molecular urine assay for predicting prostate biopsy outcome, 3 Urol 2008, 179:1587-1592 170 69. GroskopfJ, Aubin SM, Deras IL, Blase A, Bodrug S, Clark C, Brentano S, Mathis J, Pham J, Meyer T, Cass M, Hodge P, Macairan ML, Marks LS, Rittenhouse H: APTIMA PCA3 molecular urine test: development of a method to aid in the diagnosis of prostate cancer, Clin Chem 2006, 52:1089-1095 70. Goessl C, Muller M, Heicappell R, Krause H, Straub B, Schrader M, Miller K: DNA- based detection of prostate cancer in urine after prostatic massage, Urology 2001, 58:335-338 71. Shi H, Guo J, Duff DJ, Rahmatpanah F, Chitima-Matsiga R, Al-Kuhlani M, Taylor KH, Sjahputera 0, Andreski M, Wooldridge JE, Caldwell CW: Discovery of novel epigenetic markers in lymphoma, Carcinogenesis 2007, 28 :60-70 72. Hoque MO, Topaloglu 0, Begum S, Henrique R, Rosenbaum E, Van Criekinge W, Westra WH, Sidransky D: Quantitative methylation-specific polymerase chain reaction gene patterns in urine sediment distinguish prostate cancer patients from control subjects, J Clin Oncol 2005, 23:6569-6575 73. Varambally S, Dhanasekaran SM, Zhou M, Barrette TR, Kumar-Sinha C, Sanda MG, Ghosh D, Pienta KJ, Sewalt RG, Otte AP, Rubin MA, Chinnaiyan AM: The polycomb group protein EZH2 is involved in progression of prostate cancer, Nature 2002, 419:624- 629 74. Zetter BR, Banyard J: Cancer. The silence of the genes, Nature 2002, 4 19:572-573 75. Schmidt U, Fuessel S, Koch R, Baretton GB, Lohse A, Tomasetti 5, Unversucht 5, Froehner M, Wirth MP, Meye A: Quantitative multi-gene expression profiling of primary prostate cancer, Prostate 2006, 66:1521-1534 171 CHAPTER V CONCLUSION AND RECOMMENDATIONS FOR FUTURE WORK 5.1 CONCLUSION AND FUTURE DIRECTIONS The over-arching hypothesis of this thesis was that the application of Long Serial Analysis of Gene Expression (LongSAGE) would catalogue gene expression signatures that are indicative of the mechanisms underlying the growth and progression of prostate cancer, and reveal potential biomarkers of prostate cancer. To address this hypothesis, we determined the regulation of the transcriptome by the androgen-axis in prostate cancer because the androgen pathway is important in prostate cancer and provides a means for clinical intervention. Next, we identified the gene expression profile associated with in vivo progression of prostate cancer to castration-recurrence because there is no cure for castration-recurrent prostate cancer (CRPC), and the mechanisms underlying the disease are not known. Finally, we determined the expression characteristics of novel biomarkers of prostate cancer because screening for prostate cancer using serum levels of prostate-specific antigen has resulted in the over-treatment of indolent disease. Therefore, novel diagnostic and prognostic markers for prostate cancer are needed. In Chapter II, we evaluated the transcriptome of prostate cancer cells in response to androgen using deep sequencing of LongSAGE libraries. There were 131 tags (87 genes) that displayed statistically significant (p 0.00 1) differences in expression in response to androgen. Many of the genes identified by LongSAGE (3 5/87) have not been previously reported to change expression in the direction or sense observed. The expression trends of 24 novel genes were validated using quantitative real time-polymerase chain reaction (qRT-PCR). These genes were: ARL6IP5, BL VRB, C19orf48, Clorf]22, C6orf66, CAMK2N], CCNL DERA, ERRFIJ, GLUL, GOLPH3, HMJ3, HSP9OB], MANEA, NANS, NIPSNAP3A, SLC4JA1, SOD 1, SVIP, TAOK3, TCPJ, TMEM66, USP33, and VTA]. The physiological relevance of these expression trends was evaluated in vivo using the LNCaP Hollow Fibre model. Novel androgen-responsive genes identified here participate in protein synthesis and trafficking, response to oxidative stress, transcription, proliferation, apoptosis, and differentiation. These processes may represent the molecular mechanisms of androgen-dependency of the prostate. Genes that participate in these pathways may be targets for therapies or biomarkers of prostate cancer. 172 A limitation to the study is the inability to determine whether genes are direct or indirect targets of androgen receptor. Androgens (i.e., 0.1 nM Ri 881) can stimulate proliferation. The effects of proliferation and androgen may be indistinguishable. Here, we used 10 nM R1881 to stimulate the cells. At this physiological concentration, LNCaP proliferation is minimal’. Therefore, the changes to the transcriptome likely represent the effects of androgen and not proliferation. However, the contribution of other transcription factors cannot be discounted. Future work could include chromatin immuno-precipitation sequence (ChIP-seq) analysis. ChIP-seq is a combination of ChIP2 and next generation sequencing3’‘. ChIP-seq is a method for evaluating the binding sites of a transcription factor in the genome. With antibodies specific for the androgen receptor, genomic DNA that interacts with the transcription factor may be enriched and sequenced. Once the genomic sequences are mapped back to the genome, sites of androgen receptor binding may be identified. ChIP-seq has been successfully applied to interferon gamma- stimulated HeLa cells to evaluate STAT 1 binding5.By cross-referencing ChIP-seq and LongSAGE data, true androgen-regulated genes may be separated from down-stream signalling events. In Chapter III, we assayed the transcriptome of LNCaP human prostate cancer cells as they progress to castration-recurrence in vivo using replicate LongSAGE libraries. We refer to these libraries as the LNCaP atlas. We identified 96 novel genes consistently differentially expressed in CRPC. We characterized these genes for their potential to be new therapeutic targets or biomarkers of CRPC, and found that 31 genes have protein products that are either secreted or are located at the plasma membrane, 20 genes changed expression in response to androgen, and 5 genes have enriched expression in the prostate. Furthermore, expression of 20, 6, 8, and 15 genes have previously been linked to prostate cancer, Gleason grade, progression, and metastasis, respectively. The expression profiles of castration-recurrence neither supported nor discounted a role for stem cells genes (AQP3, BTGJ, CD]51, HES6, HNJ, and SPON2), or cell survival genes (AMD], BNIP3, CAMK2N1, CCT2, GLO], GRBJO, MARCKSL], MDK, NGFRAP], ODd, PIK3CD, PPP2CB, FFP2R]A, S]OOA]O, SLC25A4, SLC25A6, TMEM66, TRPM8, WDR45L, and YWHA Q) in CRPC. However, the expression profiles of castration-recurrence support a role for the transcriptional activity of the androgen receptor genes (CCNH, CUEDC2, FLNA, and PSM4 7), steroid synthesis and metabolism genes (DHCR24, DHRS7, ELO VL5, HSDJ 7B4, and OPRKJ), neuroendocrine cell genes (ENO2, MAOA, OPRKJ, SJOOA]O, and TRPM8), and 173 proliferation genes (GAS5, GNB2L1, MT-ND3, NKX3-1, PCGEM], PTGFR, STEAP], and TMEM3OA) in castration-recurrence. LongSAGE libraries were constructed and sequenced to generate replicate gene expression profiles representative of three stages of prostate cancer progression: androgen-sensitive (AS), responsive to androgen deprivation (RAD), and castration-recurrent (CR). In this study, we focused on gene expression that was different between RAD and CR stages. However, the data may be analysed from other angles. These LongSAGE libraries will be submitted to the publicly accessible database gene expression omnibus6.Researchers may download the complete libraries (referred to as the LNCaP atlas) to test their hypotheses. We envision researchers querying the LNCaP atlas for their gene-of-interest to determine its level of expression during different stages of prostate cancer progression. In our laboratory, it would be of interest to cross-reference the genes that were identified as differentially expressed between the AS and RAD stages of cancer progression with the androgen-regulated genes that were identified in Chapter II. For those androgen-responsive genes not validated in the LNCaP Hollow Fibre model, this in vivo LongSAGE data would be an excellent resource to determine the in vivo relevance of gene regulation. Also of interest to us, would be to compare the gene expression of the AS and CR stages. Although we found support for the model that the androgen receptor is reactivated in castration-recurrence, we also identified evidence of a neuroendocrine and proliferative phenotype. Therefore, by comparing gene expression in the stages of prostate cancer that are AS and CR, one may be able to enrich for genes representing non-androgen receptor-mediated mechanisms of CRPC. In Chapter IV, we determined the levels of expression of 27 novel biomarkers of prostate cancer and included several that encode for plasma membrane proteins (ADAM2, ELOVL5, MARCKSL], RAMP], TMEM3OA, and TMEM66), secreted proteins [SF0N2, TMEM3OA, TMEM66, and truncated TMEFF2 (referred to as POP4)j, intracellular proteins (CAMK2N1, DHCR24, GLO], NGFRAP], FGK1, PSMA7, SBDS, and YWHAQ), as well as non-coding transcripts referred to as POP 1 (transcript 100 kilobases (kb) from mRNA AK000023), POP2 (transcript 4 kb from mRNA AL832227), POP3 (transcript 50 kb from EST CF140309), POP5 (transcript from the intron of NCAM2, accession D0668384), POP6 (transcript from the intron of FH1T), POP7 (transcript from the intron of TNFAIP8), POP8 (transcript from the intron of 174 EFNA5), POP9 (transcript from the intron ofDSTN), POP 10 (transcript from the intron of ADAM2, accession D0668396), POP 11 (transcript 87 kb from EST BG194644), and POP12 (transcript from the intron of EST BQ226050). Expression of POP3 was prostate-specific, with restricted expression ofADAM2, POPI, POP4, POP1O, ELO VL5, RAMP], and SPON2. The expression of ADAM2, CAMK2N], DHCR24, ELOVL5, GLO], PGK], POP4, POP5, POP6, POP7, POP8, POP1O, POPI 1, SPON2, TMEM66, and YWHAQ changed in response to androgen. ELOVL5, NGFRAPJ, POP5, POP8, RAMP], SPON2, and TMEM66 were significantly differentially expressed between laser microdissected tumour and normal clinical samples of prostatic tissue, and PGKI and POP9 were positively associated with pre-prostatectomy serum PSA levels. These results suggest that ADAM2, ELO VL5, POP1, POP3, POP4, POP1O, RAMP!, and SPON2 may be good candidates for biomarkers of prostate cancer. The next step to characterizing these genes as potential biomarkers of prostate cancer would be to correlate gene expression with prognosis. Future studies will include greater numbers of laser microdissected tumour and normal prostatic tissue. The 28 patient tissue specimens used here were insufficient to identify an association between levels of gene expression and Gleason grade, currently the mainstay prognostic tool used in the clinic. If prognostic significance is achieved with any or several of the candidate biomarkers, then pre-clinical validation of a PROSTAChip will follow. Biomarkers often perform better as part of a panel of genes (e.g., GSTP-1 and EZHZ)710.Therefore, the miniature design of the PROSTAChip, a microarray containing probes that correspond to the candidate biomarkers, is ideal for limited sample volume, such as that retrieved at biopsy. If the PROSTAChip is shown to perform better at identifying subtypes of cancers with the propensity to progress to advanced disease, then the PROSTAChip has a significant potential for application in the clinic. The overall contribution of this thesis to the field of prostate cancer research is the identification and characterization of potential biomarkers and therapeutic targets of prostate cancer. 175 5.2 REFERENCES 1. Berns EM, de Boer W, Mulder E: Androgen-dependent growth regulation of and release of specific protein(s) by the androgen receptor containing human prostate tumor cell line LNCaP, Prostate 1986, 9:247-259 2. Solomon MJ, Larsen PL, Varshavsky A: Mapping protein-DNA interactions in vivo with formaldehyde: evidence that histone H4 is retained on a highly transcribed gene, Cell 1988, 53:937-947 3. Bennett S: Solexa Ltd, Pharmacogenomics 2004, 5:433-438 4. Margulies M, Eghoim M, Altman WE, Attiya S, Bader JS, Bemben LA, Berka J, Braverman MS, Chen YJ, Chen Z, Dewell SB, Du L, Fierro JM, Gomes XV, Godwin BC, He W, Helgesen S, Ho CH, Irzyk GP, Jando SC, Alenquer ML, Jarvie TP, Jirage KB, Kim JB, Knight JR, Lanza JR, Leamon JH, Lefkowitz SM, Lei M, Li J, Lohman KL, Lu H, Makhijani VB, McDade KE, McKenna MP, Myers EW, Nickerson E, Nobile JR, Plant R, Puc BP, Ronan MT, Roth GT, Sarkis GJ, Simons JF, Simpson JW, Srinivasan M, Tartaro KR, Tomasz A, Vogt KA, Volkmer GA, Wang SH, Wang Y, Weiner MP, Yu P, Begley RF, Rothberg JM: Genome sequencing in microfabricated high-density picolitre reactors, Nature 2005, 437:376-380 5. Robertson G, Hirst M, Bainbridge M, Bilenky M, Zhao Y, Zeng T, Euskirchen 0, Bernier B, Varhol R, Delaney A, Thiessen N, Griffith OL, He A, Marra M, Snyder M, Jones 5: Genome-wide profiles of STAT 1 DNA association using chromatin immunoprecipitation and massively parallel sequencing, Nat Methods 2007, 4:651-657 6. Oue N, Hamai Y, Mitani Y, Matsumura 5, Oshimo Y, Aung PP, Kuraoka K, Nakayama H, Yasui W: Gene expression profile of gastric carcinoma: identification of genes and tags potentially involved in invasion, metastasis, and carcinogenesis by serial analysis of gene expression, Cancer Res 2004, 64:2397-2405 7. Hoque MO, Topaloglu 0, Begum 5, Henrique R, Rosenbaum E, Van Criekinge W, Westra WH, Sidransky D: Quantitative methylation-specific polymerase chain reaction gene patterns in urine sediment distinguish prostate cancer patients from control subjects, J Clin Oncol 2005, 23:6569-6575 176 8. Goessi C, Muller M, Heicappell R, Krause H, Straub B, Schrader M, Miller K: DNA- based detection of prostate cancer in urine after prostatic massage, Urology 2001, 58:335- 338 9. Varambally S, Dhanasekaran SM, Zhou M, Barrette TR, Kumar-Sinha C, Sanda MG, Ghosh D, Pienta KJ, Sewalt RG, Otte AP, Rubin MA, Chinnaiyan AM: The polycomb group protein EZH2 is involved in progression of prostate cancer, Nature 2002, 419:624- 629 10. Zetter BR, Banyard J: Cancer. The silence of the genes, Nature 2002, 4 19:572-573 177 APPENDIX I Ethics certificates The University of British Columbia Biohazard Approval Certificate The Principal Investigalor/Course Director is responsible for ensuring that all research or course work involving biological hazards is conducled in accordance with the Health Canada, Laboratory Biosafety Guidelines, (2nd Edilion 1996). Copies of the Guidelines (1996) are available through the Blosafety Office, Department of Health, Safety and Environment, Room 50 - 2075 Wesbrook Mall. UBC. Vancouver. BC, V6T 1Z1, 822-7596, Fax: 822-6650. Approval of the UBC 8iohazards Committee by one 01: Chair, Biosafety Committee Manager. Biosafety Ethics Director, Office of Research Services This certificate is valid for one year from the above start or approval date (whichever is later) provided there is no change in the experimental procedures. Annual review is required. A copy of this certificate mi,rsi be displayed in your facility. Office of Reseatcfl Seivices 102. 6190 A9ronomy Road. Vancouve, V6T 1Z3 Phone: 604-827-5111 FAX: 604-822-5093 PROTOCOL NUMBER: 1107-0047 INVESTIGATOR OR COURSE DIRECTOR: Sadar, Marianne DEPARTMENT: Medicine PROJECT OR COURSE TITLE: Genoniic and proteomic analysis of androgen independent prostate cancer APPROVAL DATE: 08-04-11 APPROVED CONTAINMENT LEVEL: 2 FUNDING AGENCY: National Institutes of Health 178 THE UNIVERSITY OF BRITISH COLUMBIA ANIMAL CARE CERTIFICATE Application Number: A05-1794 Investigator or Course Director: Marianne Sadar Department: Medicine, Department of Animals: Mice Male athnic Nude mice, BALB/c Strain 1801 Start Date: November 1,2005 Approval January 4,2008Date: Funding Sources: Funding National Instttutcs ot HealthAgency: Funding Title: Genomie and proteomic analysis of androgen independent prostate cancer Funding Health CanadaAgency: Proteomics associated with the progression of prostate cancer to androgenFunding Title: mdependence. Unfunded title: N/A The Animal Care Committee has examined and approved the use of animals for the above experimental proec I This certificate is valid for one year from the above start or approval date (whichever is later) provided there is no change in the experimental procedures. Annual review is required by the CCAC and some granting agencies. A copy of Ibis CerrifIcHie must be displnved in your animal facility. 001cc of Research Services and Administration 102, 6190 Agronomy Road, Vancouver, BC V6T 1Z3 Phone: 604.827-5111 Fax: 604$22-5093 179 _____ UBC BCCA Research Ethics Board UBC Fairrrtont Medical Building (6th Floor) 614 - 750 West Broadway • BC Can gency Vancouver, BC V5Z 1H5 Tel. (604) 877-6284 Fax. (604) 708-2132 E-mail: reb@bccancer.bc.ca University of British Columbia - British Columbia Cancer Agency Website: http://www.bccancer.bc.ca > Research Research Ethics Board (UBC BCCA REB) t ics RISe: http:llrise.ubc.ca Certificate of Expedited Approval: Annual Renewal 1RINCIPAL INVESTIGATOR: INSTITUTION I DEPARTMENT: REB NUMBER: BCCA/Genome Sciences Centertananne Sadar BCCA) H 05-60099 NSTITUTION(S> WHERE RESEARCH WILL BE CARRIED OUT: N/A Ither locations whore the research will be conducted: N/A PRINCIPAL INVESTIGATOR FOR EACH ADDITIONAL PARTICIPATING BCCA CENTRE: N/A SPONSORING AGENCIES AND COORDINATING GROUPS: anadian Institutes of Health Research (CIHR) ROJECT TITLE: Development Of Custom Array For The Prognosis Of Prostate Cancer APPROVAL DATE: EXPIRY DATE OF THIS APPROVAL: PAA#: H05-60099-A00 June 12, 2008 June 12, 2009 CERTIFICATION: 1. The membership of the UBC BCCA REB complies with the membership requirements for research ethics boards defined in Division 5 of the Food and Drug Regulations of Canada. 2. The UBC BCCA REB carries out its functions in a manner fully consistent with Good Clinical Practices. 3. The UBC 8CCA REB has reviewed and approved the research project named on this Certificate of Approval including any associated consent form and taken the action noted above. This research project is to be conducted by the provincial investigator named above. This review and the associated minutes of the UBC BCCA REB have been documented electronically and in writing. The UBC BCCA Research Ethics Board has reviewed the documentation for the above named project. The research study as presented in documentation, was found to be acceptable on ethical grounds for research involving human subjects and was approved for renewal by the UBC BCCA REB. I I First Vice-Chair Second Vice-Choir If you have any questions, please call: Bonnie Shields, Manager, BCCA Research Ethics Board: 604-877-6284 or e-mail: rebbccancer.bc.ca Dr. George Browman. Chair: 604-877-6284 or e-mail: gbrowman@bccancer.bc.ca Dr. Joseph Connors, First Vice-Chair: 604-877-6000-ext. 2746 or e-mail: jconnorsbccancer.bc.ca Dr Lynne Nakashima, Second Vice-Chair: 604-707-5989 or e-mail: lnakas@bccancer.bc.ca 180

Cite

Citation Scheme:

        

Citations by CSL (citeproc-js)

Usage Statistics

Share

Embed

Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                        
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            src="{[{embed.src}]}"
                            data-item="{[{embed.item}]}"
                            data-collection="{[{embed.collection}]}"
                            data-metadata="{[{embed.showMetadata}]}"
                            data-width="{[{embed.width}]}"
                            data-media="{[{embed.selectedMedia}]}"
                            async >
                            </script>
                            </div>
                        
                    
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:
https://iiif.library.ubc.ca/presentation/dsp.24.1-0067001/manifest

Comment

Related Items