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GRB10 as a key regulator contributing to the development of castration-resistant prostate cancer Hao, Jun 2019

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GRB10 AS A KEY REGULATOR CONTRIBUTING TO THE DEVELOPMENT OF CASTRATION-RESISTANT PROSTATE CANCER by Jun Hao B.Eng., Shanghai Jiao Tong University, 2012  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Interdisciplinary Oncology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2019 © Jun Hao, 2019 ii  Committee Page The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: GRB10 as a key regulator contributing to the development of castration-resistant prostate cancer submitted by Jun Hao  in partial fulfillment of the requirements the degree of Doctor of Philosophy in Interdisciplinary Oncology Examining Committee: Yuzhuo Wang, Department of Urologic Sciences Supervisor  Christopher John Ong, Department of Urologic Sciences Supervisory Committee Member  Marianne Sadar, Department of Pathology and Laboratory Medicine University Examiner Youwen Zhou, Department of Dermatology and Skin Science University Examiner Additional Supervisory Committee Members: Colin Collins, Department of Urologic Sciences Supervisory Committee Member  William Wei-Guo Jia, Division of Neurosurgery Supervisory Committee Member  iii  Abstract  Prostate cancer is the most commonly diagnosed cancer and the leading cause of cancer-related deaths in North American men. Androgen-deprivation therapy (ADT) is the standard treatment for patients with either locally-advanced or metastatic prostate cancer (PCa). While most PCa patients initially respond to androgen ablation, progression to recurrent castration-resistant prostate cancer (CRPC) commonly occur. Current therapies for CRPC, e.g. next-generation ADT including Enzalutamide and Abiraterone, can extend patients’ lives but are not curative as resistance to their use eventually emerges. As such, there is an urgent need to identify hitherto unrecognized but critical molecular mechanisms driving CRPC, which may in turn lead to novel treatments that can be used in combination with ADT for more effective therapy.   In this doctoral dissertation, we examined the transcriptome profiling data of hormone-naïve prostate cancer (HNPC) patient-derived xenografts (PDXs) for gene expression changes at various time points after castration. Particular attention was given to expression changes that appeared early during CRPC development, indicative of genes acting as potential CRPC drivers. Eighty genes were found to be significantly upregulated at the CRPC stage, while 7 of them also showed elevated expression before CRPC development. Among the latter, Growth Factor Receptor Bound Protein 10 (GRB10), was the most significantly and consistently upregulated gene. Moreover, we found that GRB10 expression was elevated in clinical CRPC compared to HNPC in several clinical cohorts. Functionally, we found that GRB10 knockdown markedly reduced prostate cancer cell proliferation and AKT activity. Further investigation suggests that iv  GRB10 is transcriptionally regulated by androgen receptor through an androgen responsive element located in GRB10’s intron. Mechanistically, through unbiased immunoprecipitation-mass spectrometry (IP-MS), we demonstrated that GRB10 could directly bind to and reduce the expression of protein phosphatase 2A (PP2A), a well-established tumor suppressor. These data suggest that GRB10 plays an important role in CRPC development and progression.  This research demonstrates the functional roles of GRB10 in CRPC development and defines its molecular mechanisms driving ADT resistance. This study improves our understanding of the mechanisms underlying prostate cancer progression, paving the road for developing therapeutic agents that would improve the efficacy of current CRPC treatments.  v  Lay Summary  Prostate cancer is the leading cause of cancer-related deaths in North American men. While most prostate cancer patients initially respond to androgen-deprivation therapy (ADT), the cancer commonly develops into an ADT-resistant cancer called castration-resistant prostate cancer (CRPC). By analyzing prostate cancer animal models, we found that GRB10 can promote CRPC development. Furthermore, CRPC patient samples tend to have more of this gene present. Decreasing the amount of this protein expression was sufficient to suppress CRPC growth and inhibit the activity of key regulators of CRPC development. We also demonstrated that the GRB10 protein can bind with and reduce the amount of a well-known tumor suppressor. Our findings strongly suggest that GRB10 is an important regulator contributing to CRPC development and progression. This study improves our understanding of the function of GRB10 and its associated machinery in regulating CRPC development. It potentially establishes a foundation for developing novel therapeutics for CRPC. vi  Preface  Prostate cancer specimens were obtained from patients following a protocol approved by the Clinical Research Ethics Board of the University of British Columbia, the Vancouver Coastal Health Research Institute, and the BC Cancer Agency (all patients signed a consent form approved by the Ethics Board). Animal care and experiments were performed in accordance with the guidelines of the Canadian Council and the Animal Care Committee of the University of British Columbia. The protocols for animal studies were approved by the Animal Care Committee of the University of British Columbia (A17-0165, A19-0032).   A version of Chapter 3 and Chapter 4 has been previously published in [Jun Hao], Xinpei Ci, Hui Xue, Rebecca Wu, Xin Dong, Stephen Yiu Chuen Choi, Haiqing He, Yu Wang, Fang Zhang, Sifeng Qu, Fan Zhang, Anne M. Haegert, Peter W. Gout, Amina Zoubeidi, Colin Collins, Martin E. Gleave, Dong Lin, and Yuzhuo Wang. Patient-derived hormone-naive prostate cancer xenograft models reveal GRB10 as an AR-repressed gene driving the development of castration-resistant prostate cancer. European Urology. 2018.(73):949-960. I was mainly involved in study concept and design, acquisition of data, analysis and interpretation of data, drafting of the manuscript, and performing statistical analysis. Hui Xue and Rebecca Wu were responsible for the animal work involved in the study. Anne M. Haegert and Colin Collins were in charge of the profiling and processing of the transcriptome data. Xin Dong and Lin Dong scored and analyzed the immunohistochemistry staining of the tumor samples. Stephen Yiu Chuen Choi and Peter vii  W. Gout gave critical reviews of the manuscripts. Dong Lin and Yuzhuo Wang supervised the study and were involved throughout the project.   A version of Chapter 5 is being prepared for publication. [Jun Hao], Xinpei Ci, Hui Xue, Stephen Yiu Chuen Choi, Anne M. Haegert, Colin Collins, Dong Lin, and Yuzhuo Wang. GRB10 mediates increased glycolysis in CRPC through inhibition of PP2A. I am mainly involved in study concept and design, acquisition of data, analysis and interpretation of data, drafting of the manuscript, and performing statistical analysis. Anne M. Haegert and Colin Collins are in charge of the profiling and processing of the transcriptome data. Stephen Yiu Chuen Choi is involved in reviewing the manuscripts. Dong Lin and Yuzhuo Wang supervised the study and were involved throughout the project.    viii  Table of Contents Abstract ......................................................................................................................... iii Lay Summary ................................................................................................................. v Preface .......................................................................................................................... vi Table of Contents ....................................................................................................... viii List of Tables ............................................................................................................... xii List of Figures ............................................................................................................ xiii List of Abbreviations ................................................................................................... xv Acknowledgements .................................................................................................. xxiv Dedication ................................................................................................................. xxvi Chapter 1 Introduction .................................................................................................. 1 1.1 Prostate Cancer ..................................................................................................... 1 1.1.1The Prostate ..................................................................................................... 1 1.1.2 Prostate Cancer .............................................................................................. 4 1.1.3 Screening, Diagnosis and Grading of Prostate Cancer ................................... 5 1.1.4 Clinical Management of Prostate Cancer ........................................................ 6 1.1.5 Castration-Resistant Prostate Cancer (CRPC) ............................................... 8 1.2 Models of Prostate Cancer .................................................................................. 10 1.2.1 Prostate cancer cell lines .............................................................................. 10 1.2.2 Genetically engineered prostate cancer mouse models ................................ 12 1.2.3 Patient-derived xenograft models .................................................................. 14 1.3 Growth Factor Receptor-Bound Protein 10 (GRB10) ........................................... 15 1.3.1 Growth Factor Receptor-Bound Protein Family ............................................. 15 ix  1.3.2 Molecular Function of GRB10 ....................................................................... 17 1.3.3 Role of GRB10 in Human Cancers ............................................................... 21 1.4 Thesis Theme and Rationale ............................................................................... 22 1.5 Hypotheses and Specific Aims ............................................................................ 24 Chapter 2 Materials and Methods .............................................................................. 26 2.1 SRC grafting and development of transplantable HNPC lines ............................. 26 2.2 Histopathology and Immunohistochemistry ......................................................... 26 2.3 Gene Set Enrichment Analysis (GSEA) ............................................................... 27 2.4 Cell lines and Stable GRB10-knockdown/overexpression cell line construction using viral-based approaches .................................................................................... 28 2.5 Transient GRB10 knockdown using siRNA ......................................................... 29 2.6 Total RNA Isolation, Reverse Transcription and Quantitative Real Time-PCR (qRT-PCR) .......................................................................................................................... 29 2.7 Western Blotting .................................................................................................. 30 2.8 MTS cell proliferation and EdU assay .................................................................. 30 2.9 Cell cycle analysis ............................................................................................... 31 2.10 Colony formation assay ..................................................................................... 31 2.11 Immunoprecipitation assay ................................................................................ 32 2.12 Immunofluorescence (IF) and proximity ligation assay (PLA) ............................ 33 2.13 Clinical Relevance Analysis ............................................................................... 33 2.14 Chromatin Immunoprecipitation (ChIP) .............................................................. 34 2.15 Luciferase reporter plasmid construction and dual luciferase reporter assay .... 34 2.16 Transient AR knockdown using siRNA .............................................................. 37 x  2.17 Gene expression data profiling .......................................................................... 37 2.18 Ingenuity Pathway Analysis (IPA) ...................................................................... 38 2.19 Liquid chromatography–mass spectrometry (LC-MS) ........................................ 38 2.20 Statistical analysis ............................................................................................. 39 Chapter 3 Identification of GRB10 as a potential driver for the development of CRPC ...................................................................................................................................... 41 3.1 Introduction .......................................................................................................... 41 3.2 Results ................................................................................................................. 44 3.2.1 Early elevated expression of GRB10 in Prostate Cancer PDX models ......... 44 3.2.2 Elevated expression of GRB10 in clinical CRPC tissues .............................. 52 3.2.3 GRB10 gene silencing reduces in vitro cell proliferation of prostate cancer cells ............................................................................................................................... 54 3.2.4 Overexpression of GRB10 promotes PCa proliferation and resistance to ARPI treatment in vitro .................................................................................................... 61 3.2.5 A Role of GRB10 in regulating AKT signaling ............................................... 62 3.3 Discussion ........................................................................................................... 68 Chapter 4: Discovery of GRB10 as an AR transcriptionally repressed gene ......... 72 4.1 Introduction .......................................................................................................... 72 4.2 Results ................................................................................................................. 74 4.2.1 GRB10 is an AR-repressed gene in AR+ PCa cells ...................................... 74 4.2.2 Expression of GRB10 was upregulated by anti-androgen treatment in PCa mouse models and clinical PCa samples ............................................................... 76 xi  4.2.3 AR represses expression of GRB10 by binding to an androgen responsive element located in its intron ................................................................................... 79 4.2.4 ARE2 located in GRB10’s intron is a silencer regulated by AR ..................... 81 4.3 Discussion ........................................................................................................... 83 Chapter 5: Elucidation of GRB10 function in the development of CRPC ............... 86 5.1 Introduction .......................................................................................................... 86 5.2 Results ................................................................................................................. 88 5.2.1 Knockdown of GRB10 led to a significant change in the transcriptomic landscape of prostate cancer cells ......................................................................... 88 5.2.2 Discovery of GRB10 interactome in prostate cancer ..................................... 91 5.2.3 GRB10 binds to and inhibits PP2A activity in prostate cancer ...................... 92 5.3 Discussion ........................................................................................................... 95 Chapter 6 Conclusions ............................................................................................. 100 6.1 Summary of Findings ......................................................................................... 100 6.2 Limitations and Future Directions ...................................................................... 103 6.3 Conclusion and Significance .............................................................................. 105 Bibliography .............................................................................................................. 109 Appendices ................................................................................................................ 152 Appendix A: Primers used in the study (5'→3') ........................................................ 152 Appendix B: Significantly upregulated genes identified in PCa PDX models ........... 154 Appendix C: Gene Ontology gene sets significantly enriched by GRB10 knockdown ................................................................................................................................. 157  xii  List of Tables Table 3.1 Summary table of the PDX lines used in the study ……………………………...43 Table 3.2 The summary of IHC scores in LTL-313B PDX models at various time points after host castration ……………………………………………………………………………46 Table 5.1 Proteins which potentially interacted with GRB10 as detected by LC/ MS …………………………………………………………………………………………...….92   xiii  List of Figures Figure 1.1 The anatomy of the human prostate ………………………………………………2 Figure 1.2. The structure of the human GRB10 protein and its reported interacting proteins …………………………………………………………………………………………21 Figure 1.3. Hypothetical characteristics of a CRPC driving gene………………………….25 Figure 2.1 Illustration of structure of the pGL3-Basic-PSAluc plasmid……………………36 Figure 3.1 Use of hormonal-naïve prostate cancer PDX models to identify potential CRPC driving genes …………………………………………………………………………………..45 Figure 3.2 Identification of GRB10 as a potential driver of CRPC development …………48 Figure 3.3 Elevated expression of GRB10 in prostate cancer PDX ……………………….51 Figure 3.4. Elevated expression of GRB10 in clinical CRPC tissues ……………………..53 Figure 3.5. Construction of GRB10 stable knockdown LNCaP and C4-2 cells …………..55 Figure 3.6. GRB10 knockdown inhibited the proliferation and colony formation abilities of LNCaP and C4-2 cells ………………………………………………………………………...56 Figure 3.7. GRB10 knockdown inhibits DNA synthesis and cell cycle progression of prostate cancer cells …………………………………………………………………………..58 Figure 3.8. GRB10 knockdown markedly inhibits the proliferation, DNA synthesis and colony formation of ENZ-resistant MR49F prostate cancer cells …………………………60 Figure 3.9. Functional study of GRB10 in promoting ARPI resistance ……………………62 Figure 3.10. GRB10 knockdown reduced AKT activity in prostate cancer cells …………63 xiv  Figure 3.11. GRB10 directly interacted with AKT in prostate cancer cells ………………..65 Figure 3.12. GRB10 expression was negatively correlated with PTEN copy number in clinical prostate cancers ………………………………………………………………………67 Figure 4.1. GRB10 is an AR-repressed gene in AR+ prostate cancer cells ………………75 Figure 4.2. GRB10 is an AR-repressed, ADT-inducible-gene in AR+ prostate cancers .………………………………………………………………...……………………...78 Figure 4.3. AR represses expression of GRB10 by binding to an ARE located in its Intron …………………………………………………………………………...……………….80 Figure 4.4. AR represses expression of GRB10 by binding to an ARE located in its Intron ……………………………………………………………………………...…………….83 Figure 5.1. Transcriptome analysis of GRB10-regulated pathways ………………………90 Figure 5.2. The interaction between GRB10 and PP2A inhibited PP2A activity ……..…..94 Figure 6.1. The role of GRB10 in the development of CRPC …………………………….107  xv  List of Abbreviations &: and °C: degrees Celsius >: greater than <: less than μg: microgram μm: micrometre μM: micromolar %: percent ±: plus or minus ×: times  18F-FDG: 2-deoxy-2-[fluorine-18] fluoro- D-glucose ABI: Abiraterone ABL: Tyrosine-protein kinase ABL1 ADT: Androgen-deprivation therapy AKT: Protein kinase B ALL: Acute lymphoblastic leukemia AML: Acute myeloid leukemia APA: Apalutamide xvi  AR: Androgen receptor ARE: Androgen responsive element ARPI: AR pathway inhibitors ATP: Adenosine triphosphate BCA: Bicinchoninic acid BCL-2: B-cell lymphoma 2-encoded protein BCR: Breakpoint cluster region protein BPH: Benign prostate hyperplasia BPS: Between proline rich region and src homology 2 domain BRN2: POU domain, class 3, transcription factor 2 CD44: Cluster of differentiation 44 CDH1: Cadherin-1 CDK: Cyclin-dependent kinase cDNA: Complementary deoxyribonucleic acid ChIP: Chromatin immunoprecipitation CK5: Cytokeratin 5 CK8: Cytokeratin 8 CK14: Cytokeratin 14 CK18: Cytokeratin 18 xvii  CML: Chronic myeloid leukemia CO2: Carbon Dioxide CRISPR: Clustered regularly interspaced short palindromic repeats CRPC: Castration-resistant prostate cancer CSS: Charcoal stripped serum DAPI: 4',6-diamidino-2-phenylindole nuclear stain DAR: Darolutamide DRE: Digital rectal examination DEPTOR: DEP domain-containing mTOR-interacting protein EDTA: Ethylenediaminetetraacetic acid EdU: 5-ethynyl-2-deoxyuridine EGFR: Epidermal growth factor receptor ENZ: Enzalutamide ERBB2: Steroid hormone receptor ERR2 ERK: Extracellular signal–regulated kinases FAK: Focal adhesion kinase FBS: Fetal bovine serum FDR: False discovery rate FGFR: Fibroblast growth factor receptor xviii  FKBP5: Peptidyl-prolyl cis-trans isomerase FKBP5 FLT3: Receptor-type tyrosine-protein kinase FLT3 FLU: Flutamide FOXA1: Forkhead box A1 GAPDH: Glyceraldehyde-3-phosphate dehydrogenase GATA2: GATA binding protein 2 GEO: Gene expression omnibus GEMM: Genetically engineered mouse models GM: Grb and Mig GRB7: Growth factor receptor-bound protein 7 GRB10: Growth factor receptor-bound protein 10 GRB14: Growth factor receptor-bound protein 14 GSEA: Gene set enrichment analysis H&E: Haematoxylin and eosin HEAT: Huntington-Elongation-A subunit-TOR HNPC: Hormone-naïve prostate cancer (HNPC) JNK: Mitogen-activated protein kinase Ki-67: Marker of proliferation Ki-67 KIT: Mast/stem cell growth factor receptor Kit xix  KLK3: Kallikrein peptidase 3 IF: Immunofluorescence IGFR: Insulin-like growth factor receptor IgG: Immunoglobulin G IP: Immunoprecipitation IPA: Ingenuity pathway analysis IR: Insulin receptor IRS1: Insulin receptor substrate 1 LC: Liquid chromatography LHRH: Luteinizing hormone-releasing hormone M: Metastasis MEK1: Mitogen-activated protein kinase kinase 1 MET: Hepatocyte growth factor receptor mL: millilitre mM: millimolar mm: millimetre mm3: cubic millimetre MRI: Magnetic resonance imaging mRNA: Messenger ribonucleic acid xx  MS: Mass spectrometry MSigDB: Molecular Signatures Database MSKCC: Memorial Sloan Kettering Cancer Center mTOR: Mechanistic target of rapamycin kinase MTS: 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium MYC: Myc proto-oncogene protein  N: Node NE: Neuroendocrine NEDD4: E3 ubiquitin-protein ligase NEDD4 NEPC: Neuroendocrine prostate cancer NES: Normalized enrichment score NKX3.1: NK3 homeobox 1 NIL: Nilutamide nM: nanomolar nm: nanometre NOD/SCID: Nonobese diabetic/severe combined immunodeficiency OXPHOS: Oxidative phosphorylation p53: Tumor protein p53  p63: Tumor protein p63  xxi  PAGE: Polyacrylamide gel electrophoresis PB: Probasin protein PCa: Prostate cancer PCR: Polymerase chain reaction PDGF: Platelet-derived growth factor PDGFR: Platelet-derived growth factor receptor PDX: Patient-derived xenograft  PEG10: Retrotransposon-derived protein PEG10 PET/CT: Positron emission tomography/ computed tomography PFA: Paraformaldehyde pH: potential of hydrogen PH: Plechstrin homology PHLPP: PH domain leucine-rich repeat-containing protein phosphatase PI: Propidium iodide PI3K: Phosphatidylinositol 3-kinase PIN: Prostatic intraepithelial neoplasia PLA: Proximity ligation assay PP2A: Serine/threonine-protein phosphatase 2A PR: Proline Rich xxii  PSA: Prostate-specific antigen PTEN: Phosphatase and tensin homolog qPCR: Quantitative polymerase chain reaction RA: Ras-associating RAF1: RAF proto-oncogene serine/threonine-protein kinase RAS: GTPase Ras protein RB: Retinoblastoma transcriptional corepressor 1 RNA: Ribonucleic acid RPTOR: Regulatory-associated protein of mTOR RTK: Receptor tyrosine kinase  SC: Subcutaneous  SDS: Sodium dodecyl sulfate SEM: Standard error of the mean SH2: Src homology 2 SHC: SHC-transforming protein SMAD4: Mothers against decapentaplegic homolog 4 SRC: Subrenal capsule STAT5: Signal transducer and activator of transcription 5 SV40: Simian Virus 40 xxiii  T: Tumor TCA: Tricarboxylic acid cycle TCGA: The Cancer Genome Atlas Program TEC: Tyrosine-protein kinase Tec TNM: Tumor, node, metastasis TRAMP: Transgenic adenocarcinoma of the mouse prostate TSC2: Tuberous Sclerosis Complex 2 TSS: Transcription start site UGM: Urogenital mesenchyme  UGS: Urogenital sinus  VEGFR2: Vascular endothelial growth factor receptor 2   xxiv  Acknowledgements  A journey towards a PhD degree is never easy and can never be completed without the kind help from all the fantastic people around me.   First, I would like to take this chance to express my heartfelt gratitude to my mentor, Dr. Yuzhuo Wang. YZ took me in during my darkest time. I was confused and frustrated about what to do after finishing my undergraduate degree. He enlightened me and helped me discover what I really wanted to pursue in my future life. Over the years, YZ showed me how to think, how to question, and how to act like a research scientist. More importantly, the trust and the confidence he gave me encouraged me to be a more independent researcher. The freedom he granted me enabled me to explore various projects I was interested in. Despite the mistakes I made as a rookie during these years, I learned from these lessons, and they became precious gifts for my life. I am also grateful to YZ for getting me involved in the grant application process. This experience provided me opportunities to practice my scientific writing skills and pushed me to reshape my project with the help of critical reviewer comments. All of these contributed to my publications and my thesis and will continue to be essential to my future career. I would also like to extend my sincere gratitude to my supervisory committee, Dr. Christopher Ong, Dr. William Jia, and Dr. Colin Collins. Thank you so much for your kind comments and suggestions, and also for your time and constructive reviews of this thesis.  xxv   I would also like to thank all past and current lab mates. Thanks to Dr. Peter Gout for his tremendous help in improving my English writing. Without your help, I could not get this far. Many thanks to Hui, Rebecca, Yuwei, and Hongwei for their countless efforts in developing the PDX models. They were foundational for my studies. Thanks to Fang and Dong for your scientific advice for my project. I still remember the days when Dong, Xindy and I were working together, screening potential targets. Thank you so much. You helped me determine the target and guided me throughout the project. Thanks to Ting for her guidance through all the basic lab techniques. Your help led to my first publication. Thank you, Stephen, you are the real hero behind all my proposals and papers. I am so happy to witness your happiness and to have you in mine. Special thanks to my dear Xinpei, you are more like a supervisor than a colleague to me. It is your generous support and guidance that helped me move forward. Do you remember that Christmas night we resubmitted the revised manuscript? My research can never go smoothly without you. I am also thankful for all the previous academic sisters and brothers who have worked with me: Iris, Sifeng, Pier-Luc, Abhijit, Xinya, Sarah. The list goes on. I would also like to extend my appreciation to the financial support from Mitacs and the BC Cancer Foundation, as well as the Chinese Government Award for Outstanding Self-Financed Students Abroad.    Last but not least, I would particularly like to express my profound thanks to my parents and auntie, whose constant support, both mentally and physically, are the keys to all my achievements. I am and will always be grateful for all the love and support you give me. xxvi  Dedication     gÉ Åç ÑtÜxÇàá  9  \Ç ÅxÅÉÜç Éy Åç zÜtÇwÑt   1  Chapter 1 Introduction 1.1 Prostate Cancer 1.1.1The Prostate  The prostate is the largest reproductive exocrine gland in males. The primary function of the prostate gland is to produce and secrete prostatic fluid, which is slightly alkaline and accounts for 30% of the seminal fluid, which protects sperm and increases its mobility [1, 2]. In adult males, the prostate is the size of a walnut, surrounds the urethra, and is located directly under the bladder [3].    Anatomically, the human prostate can be divided into 4 zones: the Peripheral Zone, the Central Zone, the Transition Zone, and the Anterior Fibro-muscular Stroma [4] (Fig 1.1). The Peripheral Zone accounts for about 70% of the total of the prostate, and 70%-80% of prostate cancers develop in this area [5-7]. The Central Zone represents about 25% of the prostate gland. While prostate carcinoma developing in the Central Zone is rare (about 2.5%), they are proven to be more aggressive [8]. The Transition Zone is one of the smallest regions of the prostate. Since both the glandular epithelial and stromal cells in this area constantly proliferate, benign prostate hyperplasia (BPH) mainly arises from this zone [9]. The Anterior Fibro-muscular Stroma region accounts for only 5% of the total prostate and is mainly composed of smooth muscle cells, fibrous stroma, and fewer glandular epithelial cells [8, 10].    2   Figure 1.1. The anatomy of the human prostate The human prostate is located under the bladder with the prostatic urethra and the ejaculatory duct passing through. The human prostate is divided into four anatomical zones: the Peripheral Zone, the Central Zone, the Transition Zone, and the Anterior Fibro-muscular Stroma.   The prostate contains tubuloalveolar glands, with pseudostratified columnar epithelium embedded in the fibromuscular stroma [7]. The secretory function of the prostate comes from the epithelium, which is composed of tall columnar luminal secretory cells, low-cuboidal basal cells, and very few neuroendocrine (NE) cells [11]. The luminal secretory cells are responsible for the secretion of prostatic fluids. These cells are highly responsive to androgen stimulation and express cytokeratin 8 and 18 (CK8 and CK18), prostate-specific antigen (PSA), and androgen receptor (AR) [12]. Basal cells form a 3  single-cell layer surrounding the luminal cells and express basal cell markers such as cytokeratin 5 and 14 (CK5 and CK14), p63 and CD44 [12]. NE cells are rare and scattered among the basal cells. NE cells have no AR expression and thus do not respond to androgens. These cells express chromogranin A, serotonin and various neuropeptides [11, 13]. Emerging evidence suggests that there is another cell type in the prostate epithelium, known as the intermediate or transit-amplifying cells [12, 14, 15]. These cells were reported to be prostate progenitor/ stem cells, which share molecular features with embryonic urogenital sinus epithelial cells expressing both luminal and basal cell markers such as CK5 and CK18 [14].   The human prostate develops from the endoderm of the urogenital sinus (UGS) during the 10th-week of gestation and fully matures after puberty [3]. The development of the human prostate is dependent mainly on the androgen secreted by the Leydig cells in the fetal testes [16]. AR in the urogenital mesenchyme (UGM) is stimulated by androgens, which activates paracrine growth factors, cytokines, and hormones to induce epithelial budding, proliferation, and differentiation of the urogenital epithelium. The UGM differentiates into interfascicular fibroblasts and smooth muscles [3]. AR in the UGM but not the UGE is required for prostate differentiation, as evidenced by early studies using tissue recombination techniques. The androgen sensitivity of the UGM determined the fate of prostatic glandular formation, regardless of the origin of the epithelium component [17-20].   4  1.1.2 Prostate Cancer  Prostate carcinoma (PCa) is the most commonly diagnosed non-skin cancer in North American men [21] and the second-most commonly diagnosed cancer in men worldwide [22]. According to the latest Global Burden of Disease Study, about 1.5 million new PCa cases were diagnosed, with more than 336,000 PCa-related deaths were reported in 2015 worldwide [23]. In the United States, approximately 174,000 men will be diagnosed with PCa in 2019, and 31,620 men are estimated to die from it [21]. By January 1, 2019, the total number of patients who are alive with a history of PCa is projected to be more than 3.6 million in the US alone, and by the year 2030, this number will increase to more than 5 million [24]. In Canada, PCa is still the most commonly diagnosed cancer in men with an estimated 21,300 new cases in 2017 (Canadian Cancer Statistics 2017: http://www.cancer.ca/Canadian-Cancer-Statistics-2017-EN). The overall five-year survival of PCa is among the highest of all cancers. It is greater than 95% in Canada and closer to 97% in the United States [24, 25]. However, the five-year survival rate drops dramatically to 30% when cancer is diagnosed at a later stage (distant metastasis) [24, 25].    Most PCa is adenocarcinoma, which arises from prostatic epithelial cells [26-28]. Rare histological variants can include NE carcinoma [29]. In the clinic, PCa is a multi-focal and heterogeneous disease. Varying degrees of prostatic intraepithelial neoplasia (PIN) and multiple isolated foci of prostatic carcinoma with distinct genetic alterations can be found in a single prostate [30-32]. The vast majority of PCa, particularly treatment-naïve PCa, express AR and rely on androgen signalling for growth and survival. Therefore, 5  androgen-deprivation therapy (ADT) is the most effective treatment for early-stage PCas [33]. However, PCa will inevitably progress into castration-resistant prostate cancer (CRPC), which is currently incurable [34].  1.1.3 Screening, Diagnosis and Grading of Prostate Cancer  Recent studies of PCa screening oppose population-wide screening for PCa based on the fact that potential harms of  over-treatment outweigh the potential benefits of early diagnosis [35]. Symptoms of early-stage PCa include frequent or difficulty with urination and presence of blood in urine or seminal fluid. Late-stage PCa with bone metastasis result in bone pain, weight loss, and fatigue [36]. For many asymptomatic patients, the rise of serum PSA levels and the detection of abnormal prostatic growth via prostate digital rectal examinations (DRE) during routine physical examinations suggest the possibility of PCa [37, 38]. PSA is a kallikrein-associated serine protease, which is known as kallikrein peptidase 3. In humans, it is encoded by the KLK3 gene and its expression is regulated by androgen and AR [39]. PSA is secreted by the healthy prostate, but is released into the bloodstream when the cancerous lesions destroy the prostatic structure [40]. However, since the elevation of serum PSA and abnormal DRE results could come from BPH and prostatitis, the gold standard of PCa diagnosis is still the histopathologic evaluation of prostate needle biopsies [37]. Usually, a prostate biopsy will contain 8-12 core samples depending on the area and the transrectal ultrasound result. Pathologists will then examine the fixed and haematoxylin and eosin (H&E) stained core samples [41]. The cancerous area will be graded based on the Gleason grading system developed by Dr. Donald F. Gleason in the 1970s [42]. The Gleason grading system scores the 6  histopathological feature of the tumor based on the glandular pattern from 1 being the most differentiated to 5 being the most undifferentiated glandular structure [43]. Typically, the most common score and the next-most common score will be combined to get a final Gleason score of a particular biopsy sample [44, 45]. The Tumor, Node, Metastasis (TNM) stage is a cancer staging system used for patient stratification [44, 46]. Tumor (T) describes the size of the tumor and whether the tumor has invaded nearby tissues. T1 is a primary tumor that not visible by imaging. T2 is a tumor confined within prostate.  T3 is a tumor extends through the prostatic capsule. T4 is a tumor invaded into adjacent tissues other than seminal vesicles [44, 46]. Node (N) is determined by whether the PCa has spread to the regional lymph node. N0 stands for no regional lymph node metastasis, and N1 refers to cancer metastasis to lymph nodes [44, 46]. Metastasis (M) is used to describe the distant metastatic status of the tumor. M0 means no sign of distant metastasis. M1a shows metastasis to nonregional lymph nodes. M1b shows metastasis to bone(s). M1c shows metastasis to other organs [44, 46].     1.1.4 Clinical Management of Prostate Cancer  The clinical management of PCa depends on the potential risk that a particular tumor presents to the patient. Since the staging of PCa, the treatment options, and the treatment outcomes vary, a proper risk stratification system is needed for the better management of PCa. The risk stratification systems are different worldwide, but the vast majority of them are based on a combination of Gleason score, TNM stage, and the serum PSA level at diagnosis. D’ Amico risk classification, which is adopted by both American Urological Association and the European Association of Urology, is one of the most widely 7  used risk stratification systems to evaluate the potential risk of PCa recurrence following radical prostatectomy and external-beam radiotherapy, two routine therapies used against localized PCas [47-49]. The D’ Amico system groups patients into three risk groups, low-risk, intermediate-risk, and high-risk, based on all three grading systems mentioned above. Low-risk PCas are cancers with T1/T2a, PSA ≤10 ng/ ml, and Gleason score ≤6. High-risk cancers are cancers having any one of the following: T2c or higher, PSA >20 ng/mL, or Gleason 8–10. Any cancers in between are grouped as intermediate-risk cancers [47].    Different treatment options are then recommended based on the risk groups. Active surveillance is recommended to low-risk, localized PCa patients [50, 51]. Active surveillance refers to no immediate treatment since the prostate tumor is unlikely to harm the patient. This approach can reduce the side effects caused by any invasive treatment [52]. However, a strict follow-up, including PSA blood tests and DREs, is required to monitor the disease progression. Treatment may be needed if the tumor progresses rapidly. For high-risk PCa patients, immediate intervention is required, which includes surgery (radical prostatectomy) and radiation therapy (prostate brachytherapy and external beam radiotherapy) [53, 54]. These two therapies are considered curative since the 5-year survival rate at this stage is more than 99% [24, 25].   Since Dr. Huggins revealed that PCas are largely dependent on androgens for growth and survival [55], ADT has become the standard treatment for locally advanced 8  or metastatic PCas [55, 56]. Surgical castration and luteinizing hormone-releasing hormone (LHRH) agonists and antagonists, which inhibit testicular androgen production, are traditional approaches to diminish androgen level systemically [57]. Recent randomized controlled trials support several new strategies to be used for metastatic, castration-sensitive PCas, including the use of chemotherapy docetaxel [58, 59], Abiraterone (ABI) plus Prednisone [60, 61], Enzalutamide (ENZ) [62], and Apalutamide (APA) [63].  1.1.5 Castration-Resistant Prostate Cancer  ADT is the most effective therapy for advanced PCa. However, treatment resistant PCa, termed CRPC, inevitably develops within two years of treatment [64]. CRPC is an extremely lethal disease, with an approximate median overall survival of two years [34, 65, 66].   The exact molecular mechanism underlying CRPC development remains unclear, while several known factors contribute to this event. AR is one of the most critical factors mediating the development of CRPC. According to recent large-scale clinical PCa genomic screening, the occurrence of AR genetic alterations, including AR mutation and AR amplification, is dramatically higher in CRPC (nearly 60%) compared to hormone-naïve prostate cancer (HNPC) (less than 2%) [67-73]. Mechanistically, AR gain-of-function mutations, AR splicing variants such as AR-V7, and activation of AR co-factors were reported and demonstrated to help PCa regain AR signaling, contributing to the 9  androgen-independent progression of CRPC [74-82]. Besides hyper-activated AR, increased intratumoral dihydrotestosterone (DHT) levels also contribute to the development of CRPC. It is reported that the intratumoral DHT concentration can reach as high as 1nM, which is sufficient to activate AR [83-85]. Emerging evidence suggests that intratumoral DHT is produced de novo through a ‘backdoor’ pathway using precursors secreted by the adrenal gland [86-88]. Mechanistically, in CRPC patient tumors, a key enzyme catalyzing the rate-limiting step in such ‘backdoor’ DHT synthesis, 3β-hydroxysteroid dehydrogenase type 1 (3βHSD1), was reported to harbour a gain-of-function mutation [89]. Several other mechanisms were proposed to promote the development of CRPC, including the overexpression of Bcl-2 [90, 91], the activation of the PI3K/AKT pathway [92, 93], the overexpression of glucocorticoid receptor [94-96], and the transdifferentiation to neuroendocrine prostate cancer (NEPC) [97, 98].    Docetaxel was the only effective drug for CRPC treatment [99] until 2011, when a randomized phase III clinical trial showed that ABI increased survival in mCRPC patients [100]. Since then, several new AR pathway inhibitors (ARPIs) have been developed and approved for the treatment of both non-metastatic and metastatic CRPC such as ENZ [101-103], and Darolutamide (DAR) [104]. Other than ARPIs, Cabazitaxel was approved as a second-line chemotherapy for Docetaxel-resistant PCa. Compared to Docetaxel, Cabazitaxel has a lower binding affinity to the adenosine triphosphate-dependent drug efflux pump P glycoprotein 1, which exports Docetaxel and causes treatment resistance [105, 106]. Despite the success in developing new therapeutic agents for the management of CRPC, none of the treatment options is curative and treatment resistance 10  invariably occurs [107]. Therefore, there is still an urgent need to better understand the disease and develop more effective therapies.    1.2 Models of Prostate Cancer 1.2.1 Prostate cancer cell lines   Cell lines are immortalized cultured cells that can proliferate indefinitely. Cancer cell lines are the most feasible and easy-to-manipulated models to study the development and progression of cancers. Commonly used PCa cell lines include both PCa cells derived from patient samples and from PCa xenografts. The LNCaP cell line was initially isolated from a needle aspiration biopsy of a left supraclavicular lymph node from a 50-year-old Caucasian metastatic PCa patient [108]. LNCaP cells are responsive to androgen treatment and show significant growth inhibition in the androgen-ablation culture system. At the molecular level, LNCaP expresses AR and PSA. The AR in LNCaP contains a T877A mutation, which leads to an altered binding specificity to different steroids and anti-androgens [109]. PTEN in LNCaP is also deleted in one allele and mutated in the other, resulting in no PTEN protein expression [110]. A number of LNCaP sublines have been developed over the years, with C4-2 being of the most popular. The C4 line was isolated from a xenograft tumor 4 weeks following host castration. The xenograft was developed by co-inoculating LNCaP with the MS human osteosarcoma fibroblast cell line into an intact athymic male nude mouse. Later, the C4-2 line was derived from the C4+MS xenograft established in a castrated host. C4-2 cells were able to form tumors in castrated mice without MS co-injection [111, 112]. Thus, C4-2 was considered an androgen-independent CRPC subline of LNCaP. Another subline of 11  LNCaP used in the study is MR49F. It was isolated from a third-generation LNCaP xenograft tumor under long-term ENZ treatment (10 mg/kg/day). The xenograft was harvested and single cell suspensions grown under continuous ENZ pressure (10 µM). MR49F cells express both AR and PSA protein [113],[114].   22Rv1 and VCaP are two AR+ PCa cell lines commonly used in PCa studies. 22Rv1 was derived from an androgen-dependent patient-derived xenograft (PDX) model, CWR22, after undergoing multiple rounds of host castration-induced regression and relapse [115, 116]. Compared to LNCaP, 22Rv1 expresses wild-type PTEN but contains a Q331R p53 mutation. AR in 22Rv1 contains a H874Y mutation and an in-frame duplication of its exon 3, both contributing to its androgen independent growth [117-119]. It also expresses a high level of AR-V7, which is an AR splicing variant thought to be the critical regulator driving its androgen-independent growth [115, 119, 120]. VCaP was established from a bone metastasis lesion from a 59-year-old CRPC patient [121]. VCaP has wild-type but amplified AR. VCaP cell line also represents a good model for studying TMPRSS2-ERG gene fusion, which was presented in 40% to 50% of clinical prostate cancers [73, 122]. It also harbours an A248W p53 mutation [119, 121]. Both 22Rv1 and VCaP were able to form tumors in castrated mice, suggesting they are capable of androgen-independent growth.   PC-3 and DU 145 are two of the oldest PCa cell lines, being established in 1979 and 1978 respectively [123, 124]. PC-3 was isolated and established from a bone metastasis from a 62-year-old patient [123], and DU 145 cells were derived from a brain 12  metastasis [124]. Interestingly, neither cell line expresses AR. Evidence suggests that the DNA hypermethylation located at the AR promoters in PC-3 and DU 145 cells silenced their expression [117]. Therefore, these two cell lines do not respond to either androgen stimulation or androgen-ablation treatment and should be used to study AR- PCas.   1.2.2 Genetically engineered prostate cancer mouse models  Genetically engineered mouse models (GEMMs), or transgenic mouse models, are another type of model commonly used to study the malignant progression of PCa. Compared to PCa cell lines, GEMMs can recapitulate PCa development in living tissues and can represent various stages of PCa including its establishment, progression, and metastasis. Up to now, there are more than a dozen GEMMs that have been developed by either prostate-specific induction of oncogenes or deletion of tumor suppressors [125, 126]. Transgenic adenocarcinoma of the mouse prostate (TRAMP) is one of the first reported GEMM of PCa [127]. In this model, a small fragment prostate-specific rat probasin (Pb) gene promoter (-426 to +28, sPB) was used to drive the expression of Simian Virus 40 (SV40), which is a polyomavirus gene coding oncogenic large T and small t antigen. The androgen dependence of the model is variable, while the relapsed tumors after host castration are 100% synaptophysin positive [128] and later found to be a small cell NE carcinoma [129]. A similar model, LADY, was then established using a large fragment of the Pb gene promoter (12kb in size, LPB) to express the SV40 large T antigen [130]. Consistent with the finding using the TRAMP model, all metastatic LADY models were found to be NE tumors [131]. Recent evidence in genomic NEPC studies and in mouse NEPC development studies revealed that the loss of p53 and Rb tumor 13  suppressors could lead to the development of NEPC [132-134]. Since SV40 large T antigen was known to bind to and inhibit p53 and Rb, this suggests that these two models recapitulate the development of AR- NEPCs.   Along with p53 and Rb, phosphatase and tensin homolog deleted on chromosome 10 (PTEN) is one of the most critical tumor suppressors and one of the most frequently mutated/deleted genes in human PCas [135]. The establishment of a prostate-specific deletion of Pten PCa GEMM was reported in 2003 [136]. The model was established by crossing Ptenloxp/loxp mice with ARR2PB-Cre4 transgenic mice where Cre expression was driven by a modified, higher-sensitivity PB promoter containing an extra copy of androgen responsive region [136, 137]. The prostate-specific homozygous Pten deletion led to the development of invasive PCa (100% of cases), and these tumors responded to castration treatment as indicated by increased apoptosis [136]. The establishment of conditional Pten knockout mice supported a number of studies that generally focused on the involvement of particular genes in the development and progression of PCa, e.g., crosses with Tp53 deletion [138], Smad4 deletion [139], and Nkx3.1 deletion [140].    Myc overexpression and amplification is another recurrent feature observed in PCa [141-143]. Transduction of Myc was able to give rise to cancers in various mouse tissues [144-146] including PCa [147]. Prostate-specific Myc transduction (Hi-Myc) was achieved by using the ARR2/probasin-Myc plasmid in a similar approach to the PB-Cre4 transgenic line. Hi-Myc resulted in prostate intraepithelial neoplasia that later developed into invasive prostate adenocarcinomas (19/20 of cases). The Hi-Myc transgenic line also responds to 14  castration, as indicated by a low proliferation index (low Ki67 staining) and low AR expression [147].   1.2.3 Patient-derived xenograft models  Despite the advantages of PCa cell lines and GEMMs, there are still limitations. For examples, while cell lines are easy to use at a low cost, they lack tumor heterogeneity and cancer-stromal interactions. GEMMs are in vivo models representing tumor development in living tissues. However, they are murine PCas and are more focused on a particular type of genetic alteration as introduced to develop the model. More importantly, none of these models recapitulate the development of CRPC from HNPC. PCa cell lines are derived from mCRPC patients, and the response of GEMMs to castration cannot fully reflect the shrinkage-to-relapse progression. Therefore, better models are needed to study this process.    PDX models are promising tools to fulfill this demand. PDX models are established by grafting patients’ tumor tissue specimens into immune-compromised mice. In this way, PDX models can retain the genetic and gene expression features of the original tumors [148, 149], preserve the stromal components [150], and more importantly, mimic the response to treatments [151]. However, the development of PCa PDX models has long been a challenge due to the low take rates and long latency times [152, 153]. Groups from around the world established several strategies to improve the take rate of PCa PDXs, including coating patient’s tumors with Matrigel [154-156], or using mouse seminal 15  vesicle mesenchyme [157]. While they were improvements, these methods still involve the introduction of artificial or mouse-origin stroma, which may affect tumor biology. Additionally, the site of implantation is another aspect worth considering. Subcutaneous (SC), orthotopic, and subrenal capsule (SRC) are popular sites to establish PDX models. Compared to SC and orthotopic xenografts, grafting tumor tissues under mice kidney capsule is more successful, which could be due to the high vascularization of the kidney, allowing the xenografts to get enough nutrients, growth factors and hormones [158-161]. In fact, several independent groups have reported that, grafting patients’ tumors at SRC sites gave the highest take rate compared to grafting at SC or orthotopic sites [152, 162, 163]. The SRC PDX models were derived from either primary or metastatic tumors at various stages ranging from HNPC to mCRPC [151, 152]. Evidence suggests that these high fidelity SRC PDX models are able to retain the histopathological and molecular features of the original tumors [98, 151]. More importantly, these models recapitulate the tumor progression. In particular, the development of CRPC from HNPC was observed in a number of these SRC HNPC PDX lines, which spontaneously gave rise to recurrent CRPC tumors after host castration [151, 164].   1.3 Growth Factor Receptor-Bound Protein 10 (GRB10) 1.3.1 Growth Factor Receptor-Bound Protein Family  In this doctoral dissertation, we identified growth factor receptor-bound protein 10 (GRB10) is a potential driver for the development of CRPC. GRB10 belongs to the Grb (Growth factor receptor-bound) protein family, which consists of three family members, GRB7, GRB10, and GRB14 based on their conserved protein structure. All of the three 16  proteins contains an N-terminal Proline Rich (PR) region, a central GM (Grb and Mig) region that contains a Ras-Associating (RA) domain, a Pleckstrin Homology (PH) domain, and a Between PH and SH2 (BPS) domain, and a C-terminal Src Homology 2 (SH2) domain [165]. The multidomain structure of Grb proteins enables them to interact with a variety of proteins at the same time and allows them to function as scaffold adaptor proteins. The Grb proteins were all originally identified as EGFR binding proteins by yeast two-hybrid screening of a bacterial expression library using autophosphorylated EGFR as a bait [166]. Back in 1992, GRB7 was the first protein in the family to be discovered. It contains an SH2 domain responsible for its interaction with EGFR [166]. In 1995, another Grb protein, GRB10, was identified by the same research group using a NIH/3T3 cDNA library [167]. GRB10 was found to be highly-related to GRB7 and highly-similar to the previously reported C. elegans gene mig-10 and mouse gene meg1 (meiosis expressed gene 1), suggesting an involvement in meiosis [167-169]. Later in 1996, GRB14 was identified and cloned using a human breast epithelial cell cDNA library [170]. The identification, characterization, and isolation of Grb proteins revealed that these three family members shared a similar but unique protein structure, allowing them to regulated diverse downstream signaling pathways.    Since GRB7 was isolated using EGFR, a known oncogene, as bait, the study of GRB7 function was focused on cancer since the beginning. The human GRB7 is located on chromosome 17q12 and is in close proximity to ERBB2, a driver and therapeutic target for many human cancers including breast cancer, ovarian cancer, colorectal cancer, and non-small-cell lung cancer [171-174]. Thus, GRB7 is within the ERBB2 amplicon and is 17  usually coamplified and coexpressed with ERBB2 in human cancers [175, 176]. Upon interaction with ERBB family proteins, GRB7 activates its downstream signal cascades such as Shc, Ras, ERK1/2, JNK, and FAK, to promote tumor cell proliferation, resistance to apoptosis, cell migration, and tissue invasion [177-180].    GRB14, on the other hand, was reported to be more associated with endocrine signaling and has a higher affinity to IR and FGFR but not EGFR [181]. It is shown that the interaction between GRB14 and IR or FGFR inhibits their function, possibly through competition for its downstream effector binding sites [181-183]. Several studies have reported that GRB14 is associated with cancer, but the role of GRB14 is still inconclusive. GRB14 has been reported to be overexpressed in some clinical cancer specimens and cancer cell lines [184-186]. However, another report suggested a higher expression of GRB14 was a favorable prognostic marker [187]. Thus, the function of GRB14 can vary depending on the type of cancer and the proteins it interacts with.  1.3.2 Molecular Function of GRB10  The GRB10 protein was identified in 1995 by the same group that identified GRB7. They used the same protocol, probing a bacterial expression library derived from mouse NIH/3T3 cells with tyrosine autophosphorylated carboxyterminus of EGFR [167]. Later that year, human GRB10 was identified by using the cytoplasmic domain of human insulin receptor (IR) as bait to screen human liver and Hela cDNA libraries [188]. Northern blot analysis suggested that the gene was highly expressed in human skeletal muscles and 18  adipose tissues [188]. Since then, the study of GRB10 function has largely focused on its role in IR and IGFR (insulin-like growth factor I receptor) signaling. However, the role of GRB10 in insulin or IGF-I induced signaling remains inconclusive and is dependent on the particular cellular context and experimental approach. On the one hand, both microinjecting and using cell membrane-penetrating GRB10 SH2+BPS domain peptides inhibited insulin and IGF-I mediated mitogenesis in NIH/3T3 and baby hamster kidney fibroblasts [189-191]. It was suggested that GRB10 function was impaired via competition between introduced peptides and endogenous GRB10. These effects were proven to be specific since introducing of these peptides could not affect PDGF-mediated cell proliferation [189]. Moreover, overexpression of GRB10 in human Rat-1 fibroblasts, rat L6 myoblasts, and rat PC12 pheochromocytoma cells increased insulin-mediated PI3K catalytic activity and the subsequent metabolic response including glycogen synthesis, glucose and amino acid uptake, and lipogenesis [192]. On the other hand, GRB10 was also reported to be a negative regulator of IR and/or IGFR signaling. The interaction of GRB10 and IR in Chinese hamster ovary (CHO) cells, mouse embryonic fibroblast cell line 3T3-L1, and primary rat hepatocytes physically blocked the binding between IRS and IR and/or IGFR, which in turn led to decreased phosphorylation of IRS1 and its downstream signaling pathways including PI3K and AKT [188, 193-195].   In addition to EGFR, IR and IGFR, GRB10 has been reported to interact with a number of other cell surface receptors, many of which are receptor tyrosine kinases (RTK). The interaction of GRB10 with ligand-activated platelet-derived growth factor receptor (PDGFR) through its SH2 domain mediates the function of PDGFR and is critical for 19  PDGF-induced mitogenesis [189, 196]. Vascular endothelial growth factor receptor-2 (VEGFR2) is another well-studied RTK that has been reported to bind to GRB10 [197, 198]. Evidence suggests that GRB10 protects VEGFR2 from NEDD4-induced degradation by constitutively interacting with NEDD4, leading to enhanced VEFG-mediated MAPK activation [197, 198]. Some other RTKs were also reported to bind to GRB10, but the function of these interactions have not been elucidated. They include proto-oncogene tyrosine-protein kinase receptor Ret, EPH receptor B1, fibroblast growth factor receptor 1, and hepatocyte growth factor receptor Met [189, 199, 200].   Other than RTKs, GRB10 interacts with several other cell-signaling transduction kinases as well. Tyrosine-protein kinase Tec, which regulates T cell development, function and differentiation [201], was reported to be negatively regulated by its interaction with GRB10 [202]. Several proteins involved in Mitogen-activated protein kinases (MAPK) pathway, including NRAS, RAF1, and MEK1, were reported to directly bind to GRB10 [203-205]. The MAPK pathway is responsible for the regulation of gene expression, cell proliferation and apoptosis [206]. The interaction of GRB10 with these signaling cascades indicates GRB10’s role as a key regulator in such cellular processes. The PI3K/ AKT pathway is another major signaling pathway regulating cell division and programmed cell death [207]. The interaction of GRB10 with PI3K and AKT is long-established [192, 208-210]. However, the role of these interactions is still elusive and could be cell context-dependent. GRB10 has been shown to positively regulate AKT signaling by directing AKT to the cell surface when AKT is activated [208, 209]. Two phospho-proteomics studies 20  suggested that GRB10 was a substrate of mTORC1, with its upregulation leading to decreased AKT activity in TSC2-/- mouse embryonic fibroblasts [211, 212].   21   Figure 1.2. The structure of the human GRB10 protein and its reported interacting proteins. The human GRB10 protein contains an N-terminal Proline Rich (PR) region, a central GM (Grb and Mig) region consisting of a Ras-Associating domain (RA), a Pleckstrin Homology (PH) domain, and a Between PH and SH2 (BPS) domain, and a C-terminal Src Homology 2 (SH2) domain. Through its functional domains, GRB10 can interact with multiple pathways and proteins and plays a critical role in cell signaling transduction. N: the amino-terminus; C: the carboxyl-terminus; aa: amino acids.  1.3.3 Role of GRB10 in Human Cancers  In human cancers, GRB10 is mostly studied in leukemia. GRB10 expression was reported to be significantly upregulated in acute myeloid leukemia (AML), and its elevated expression is correlated with cancer relapse and poor prognosis [210, 213]. Moreover, higher expression of GRB10 was also observed in the arsenic trioxide-resistant Jurkat acute T cell leukemia cell line, suggesting that its elevated expression could drive treatment resistance [214]. Mechanistically, GRB10 was found to directly bind to an 22  oncogenic tyrosine kinase fusion protein, BCR-ABL, which is found in all chronic myeloid leukemia (CML) as well as a subset of AML and acute lymphoblastic leukemia (ALL) [215, 216]. The interaction supports the function of BCR-ABL, while abrogation of GRB10 could reduce cancer cell proliferation and induce apoptosis both in vitro and in vivo [215, 216]. FLT3, a growth factor receptor involved in hematopoiesis and a commonly mutated gene contributing to AML development [217], was also reported to interact with GRB10 [210]. It has been shown that the interaction between FLT3 and GRB10 promote AKT phosphorylation and STAT5 activation, which increased AML cell proliferation, survival, and colony formation [210].   The role of GRB10 in solid cancers is under-studied. In several large-scale microarray studies of clinical samples, we, along with other groups, found that GRB10 was upregulated in colorectal cancer [218, 219] and CRPC [164, 220] but downregulated in thyroid cancer [221], hepatocellular carcinoma [222], and adrenocortical tumors [223]. However, the precise functions and the molecular mechanisms of GRB10 in these tumors remain unclear. In this dissertation, we are the first to report the involvement of GRB10 in PCa, the regulation of its expression, and the mechanism of GRB10-mediated CRPC development.  1.4 Thesis Theme and Rationale  CRPC poses a significant challenge in the clinical management of advanced PCa. Although increasingly-powerful ARPIs such as ENZ and ABI have shown promising 23  results in the clinic, the cancers commonly return in a lethal, resistant form. As a result, there is an urgent need for a deeper understanding of the molecular mechanisms driving CRPC development such that more highly effective therapeutics can be developed.   To study the mechanisms of CRPC and to develop new therapeutics, reliable, clinically-relevant cancer models are of paramount importance. We have established over 45 high-fidelity PCa PDX models derived from clinical specimens [151]. Importantly, we overcame the significant obstacles in developing PDX models from clinical HNPC tissues and have successfully developed 11 HNPC PDXs (http://www.livingtumorlab.com/index.html). These clinically-relevant models not only retain the histopathological and molecular characteristics of the parental patient tumors, they also recapitulate the clinical disease progression from HNPC to CRPC after host castration [151, 164]. Therefore, these “next-generation” models provide unique tools for studying mechanisms underlying CRPC development and discovering novel drivers for the disease.   By comparing the gene expression data between the paired HNPC and CRPC PDX models, we have identified GRB10 as a potential driver of CRPC development. Next, we validated our finding that upregulation of GRB10 in CRPC was clinically relevant. We further investigated the pro-proliferation function of GRB10 in PCa cell lines. We were also the first to confirm that GRB10 is transcriptionally regulated by the androgen receptor. Finally, we explored the function of GRB10 in PCa by studying the transcriptomic changes 24  induced by GRB10-knockdown, describing the GRB10 interactome, and clarifying the function of these interactions in the development of CRPC.   The overall objective of this study is to discover the role of GRB10 in the development of CRPC and to elucidate the associated molecular mechanisms involved in driving the deadly disease.  1.5 Hypotheses and Specific Aims  The hypotheses of what constitutes a CRPC driving gene are as follows: (1) CRPC driving genes are commonly expressed in CRPC, (2) CRPC driving genes are upregulated following castration as an early and continuous event, and (3) CRPC driving genes remain upregulated after CRPC development. (Fig 1.3)   25   Figure 1.3. Hypothetical characteristics of a CRPC driving gene The expression of CRPC driving genes should be upregulated after castration treatment as an early and continuous event.   Accordingly, we propose the following Specific Aims:  Specific Aim 1: To identify potential CRPC driving genes using high-fidelity HNPC-to-CRPC PDX models; Specific Aim 2: To investigate the functional role of the identified driving gene;  Specific Aim 3: To explore the mechanisms associated with the identified driving gene by analyzing its direct upstream/ downstream pathways and its role in biological processes in PCa. 26  Chapter 2 Materials and Methods 2.1 SRC grafting and development of transplantable HNPC lines All 10 PDX PCa tissue lines used in this study were established and maintained in non-obese diabetic (NOD)/SCID mice (NOD.CB17-Prkdcscid/J). PCa specimens were obtained from patients following a protocol approved by the Clinical Research Ethics Board of the University of British Columbia and the BC Cancer Agency (all patients signed a consent form approved by the Ethics Board). Patient HNPC tissues were cut into pieces (1 × 3 × 3 mm3) and grafted into the SRC site of male NOD/SCID mice supplemented with testosterone. After 3 to 6 months (or earlier if reaching humane endpoint is reached), the host mice were sacrificed in a CO2 chamber. Tumors were then harvested and regrafted into another set of NOD/SCID mice at the SRC site. At each passage, a sample of the xenografts were also harvested, measured, and fixed for histopathologic analysis.  2.2 Histopathology and Immunohistochemistry  Tumor specimens were fixed in 10% neutral-buffered formalin and embedded in paraffin. Blocks were cut into 5 µm-thick sections and mounted on glass slides. For histopathology analyses, the slides were de-waxed in Histoclear (National Diagnostic) and hydrated in graded alcohol and water for hematoxylin and eosin staining. IHC staining was carried out using anti-AR (Abcam, ab108341, 1:100), anti-PSA (Santa Cruz, sc-7638, 1:100) and anti-Ki67 (Thermo Fisher, RM-9106, 1:100). Biotinylated secondary antibody (Vector Laboratories) peroxidase-linked avidin/biotin complex reagents (Vector Laboratories) and diaminobenzidine (DAB, Sigma-Aldrich) were used for staining.  27    The staining intensities of Ki67, AR and PSA were evaluated independently by two pathologists, Dr. Dong Lin and Dr. Xin Dong. Ki67 was scored by averaging the percentage of positively stained tumor cells in 10 random high-power fields. The final expression index of AR and PSA was calculated by multiplying the intensity of staining (0 = no staining, 1 = low, 2 = moderate, and 3 = high) with the percentage of positively stained tumor cells.  2.3 Gene Set Enrichment Analysis (GSEA)  Gene Set Enrichment Analysis (GSEA) (http://www.broad.mit.edu/gsea/) was used to determine whether AR signaling was significantly and concordantly different between PDX tumors before and after host castration [224]. Microarry normalization was performed by Dr. Anne M. Haegert. Normalized transcriptomic data were used for GSEA analysis. The gene sets used in ths study were downloaded from the Molecular Signature Database (MSigDB) provided by GSEA.   GSEA was also used to determine whether a defined set of genes show significant, concordant differences between two treatment groups (e.g. shC vs. shGRB10). All GSEA analyses in those studies used whole transcriptomic data without expression level cut-offs as the expression datasets. Normalized values were used to generate pre-ranked gene lists based on p-value from lowest to highest. Unbiased analysis was performed using the latest MSigDB database for each collection. Gene set permutation was applied. 28  False discovery rate (FDR) q values were calculated using 1000 permutations, and a geneset was considered significantly enriched if its normalized enrichment score (NES) has an FDR q below 0.05.  2.4 Cell lines and Stable GRB10-knockdown/overexpression cell line construction using viral-based approaches  LNCaP, C4-2, and 293T cell lines were obtained from the American Type Culture Collection (ATCC). LNCaP and C4-2 cells were maintained in RPMI-1640 medium (Hyclone) supplemented with FBS (10%) (Gibco). 293T cells were maintained in DMEM medium (Hyclone) containing 5% FBS (Gibco). ENZ-resistant MR49F PCa cells were maintained in RPMI-1640 medium with 5% FBS, supplemented with 10 μM ENZ [3].   Two shRNAs targeting GRB10 were obtained from Dharmacon (TRCN0000063683 and TRCN0000063686). Lentiviruses containing GRB10-targeting shRNA were produced by transient transfection of 293T cells using the Lipofectamine 3000 following the manufacturer’s protocol (Thermo Fisher). Culture media of 293T cells were collected 72 hours after transfection and then filtered and applied to LNCaP and C4-2 cultures for transduction. GRB10-knockdown cells were selected and maintained using 1 μg/ml puromycin (Gibco).   The GRB10 coding sequence was cloned from the plasmid “pRK5 Flag Grb10 wt long”, a gift from Dr. David Sabatini (Addgene plasmid # 37481) [211], using primers listed in Appendix A. It was then inserted into the pLNCX retroviral vector (Clontech). 29  Retroviruses expressing GRB10 were produced by transient transfection of 293T cells. Culture media of 293T cells were collected 72 hours after transfection and then filtered and applied to LNCaP for transduction. GRB10-overexpressed cells were selected and maintained using 150 ug/ml Hygromycin B (Thermo Fisher).  2.5 Transient GRB10 knockdown using siRNA  SMARTpool ON-TARGETplus GRB10 siRNA (Dharmacon) was used to transiently knock down GRB10 in MR49F cells. Lipofectamine RNAiMAX was used following the manufacturer’s protocol. Cell proliferation assays were performed one day following transfection.  2.6 Total RNA Isolation, Reverse Transcription and Quantitative Real Time-PCR (qRT-PCR)  Total RNA was extracted from cultured cells or PDX tissues using the RNeasy Mini Kit (Qiagen) following the manufacturer’s protocol. One µg of total RNA was used to synthesize cDNA using the QuantiTect Reverse Transcription Kit (Qiagen). qRT-PCR was performed using the ABI ViiA 7 Real-Time PCR system (Applied Biosystems) with KAPA SYBR Fast Universal Master Mix (Kapa Biosystems). Primers used in the study are listed in Appendix A. Relative gene expression was calculated using the 2-∆∆ct method with GAPDH as an internal reference gene.   30  2.7 Western Blotting  Total protein from cultured cells or PDX tissues was isolated using RIPA lysis buffer (50 mM Tris-HCl pH 7.4, 150 mM NaCl, 1% IGEPAL, 0.5% Na-deoxycholate, 0.1% SDS) supplemented with complete protease inhibitor cocktail (Roche) and PhosSTOP phosphatase inhibitor (Roche). Protein concentration was determined using the BCA protein assay (Thermo Fisher Scientific). Total protein samples were run on an 8% SDS polyacrylamide gel, transferred to a PVDF membrane with 0.45 µm pores (Millipore), and then incubated with primary antibodies overnight at 4℃ for Western blotting. The following antibodies were used: anti-GRB10 (Cell Signaling Technology, #3702, 1:1000), anti-AKT (Cell Signaling Technology, #2920, 1:1000), anti-phosphoSer473-AKT (Cell Signaling Technology, #4060, 1:2000), anti-Actin (Sigma, #A2066, 1:2000), anti-Vinculin (Sigma, V4505, 1:1000), anti-PP2Aa (Cell Signaling Technology, #2041, 1:1000), antiPP2Ab (Cell Signaling Technology, #2209, 1:1000), and antiPP2Ac (Cell Signaling Technology, #2259, 1:700).  2.8 MTS cell proliferation and EdU assay  Viable cell numbers were determined using the MTS CellTitre 96 Aqueous One Solution Cell Proliferation Assay (Promega) and Click-iT EdU Alexa Fluor 488 Imaging Kit (Thermo Fisher) following the manufacturer’s protocols. For the MTS assay, cells were seeded into a 96-well culture plate (3000 cells/ well) either with regular medium or 10 μM ENZ-containing medium. The absorbance of reduced MTS was measured once every two days at O.D. 490 nm. The relative absorbance was calculated based on Day 0 readings. For the EdU assay, cells were seeded into a Nunc 8-well chamber slide 31  (Thermo Fisher) (10000 cells/ well) with either regular medium or 10 μM ENZ-containing medium. Two days later, the cells were fixed and stained with EdU following the manufacturer’s protocol and imaged with an AxioObserver Z1 microscope (Zeiss) (10x magnification; 10 fields). EdU-positive cells (green), and Hoechst-stained cells (blue) were counted using ImageJ software (NIH, USA, http://rsbweb.nih.gov/ij/).   2.9 Cell cycle analysis  LNCaP, C4-2, and MR49F cells with GRB10 or control knockdown were seeded in six-well plates either with complete medium or with medium containing 10 μM ENZ. The cells were collected 72 hours later, washed twice with ice-cold PBS, fixed with ice-cold 70% ethanol overnight, and incubated with 25 μg/ml RNase (Sigma) and 50 μg/ml propidium iodide (PI, Invitrogen) at room temperature for 30 min. The DNA content was measured using FACSCanto II (BD) flow cytometry and analyzed with the FlowJo software (Tree Star).  2.10 Colony formation assay  Cells were seeded into a 12-well culture plate (100 cells/ well) for a 4-week incubation. They were then fixed with 4% paraformaldehyde (PFA) (Sigma) and stained with 0.5% crystal violet (Sigma). Stained cells were imaged with a digital camera and the number of colonies were counted manually.  32  2.11 Immunoprecipitation assay  An immunoprecipitation (IP) assay was performed using LNCaP cells transfected with Flag-GRB10 and AKT. The plasmids used for the transfections were plasmid “pRK5 Flag Grb10 wt long”, a gift from Dr. David Sabatini (Addgene plasmid # 37481) [211], and plasmid “pcDNA3.1-HA AKT1”, kindly provided by Jaewhan Song (Addgene plasmid # 78778) [225]. Both transfections were carried out using Lipofectamine 3000 (Thermo Fisher) following the manufacturer’s protocols. For the IP assay, GRB10/AKT and Flag/AKT transfected LNCaP cells were washed and lysed with NETN buffer (100 mM NaCl, 20 mM Tris-HCl pH8.0, 0.5 nM EDTA, 0.5 % v/v IGEPAL [Sigma]). Protein concentrations were then determined using the BCA protein assay (Thermo Fisher Scientific) and 5% of the input was collected. Then, 1 mg of protein from each sample was incubated with FLAG M2 magnetic beads (Sigma) at 4℃ overnight. The next day, magnetic beads were collected using a magnetic stand and washed 5 times with ice-cold NETN buffer. The beads were then boiled with SDS-PAGE sample-loading buffer for 3 min and analyzed via Western Blotting.    For endogenous IP, monoclonal mouse anti-GRB10 (C-11, Santa Cruz) was used. To block the interference of IgG heavy/ light chain on Western Blotting detection of target protein, VeriBlot for IP Detection Reagent (ab131366, Abcam) was used as secondary antibody.  33  2.12 Immunofluorescence (IF) and proximity ligation assay (PLA)  For IF assays, cells were counted and seeded into a Nunc 8-well chamber slide (Thermo Fisher) (5000 cells/ well) and incubated for 2 days with either regular medium or 10 μM ENZ-containing medium. Cells were then fixed, permeabilized and stained with both anti-GRB10 (1:50, Cell Signaling Technology, #3702), and anti-AKT (1:100, Cell Signaling Technology, #2920) antibodies; Secondary antibodies with Alexa Fluor 488 (1:1000) were obtained from Thermo Fisher. Slides were mounted using DAPI mounting solution (Vector Laboratories).    PLA assays were performed using the Duolink In Situ Red Starter Kit Mouse/Rabbit (Sigma) following the manufacturer’s protocol. In brief, cells were treated, fixed, permeabilized, and incubated with primary antibody following the same protocol as used for the IF assay. The next day, secondary probe incubation, ligation, amplification reactions, and mounting were carried out following the manufacturer’s suggestions. Both IF and PLA slides were imaged using an LSM 780 Confocal Microscope (Carl Zeiss).  2.13 Clinical Relevance Analysis  Clinical cohorts used in this study included Grasso et al. 2012 [67], TCGA 2015 [73], Varambally et al. 2015 [226], Ross-Adams et al. 2015 [227], and Robinson et al. 2015 [68]. Gene expression, protein expression, DNA copy number and clinical information of the TCGA cohort was obtained through cBioPortal [228, 229]. Gene expression and its correlation to progression free survival from the Cambridge [227], Stockholm [227], and MSKCC [230] cohort was obtained and visualized by camcAPP 34  [231]. For other cohorts, gene expression data were accessed from the Gene Expression Omnibus (GEO) database.   2.14 Chromatin Immunoprecipitation (ChIP)  ChIP assays were performed using the ChIP-IT High Sensitivity Kit (Active Motif) following the manufacturer’s protocol. LNCaP cells were treated with either ethanol (vehicle control) or 10 nM R1881 for 16 hours before the assay. Cells were cross-linked with 1% formaldehyde (Sigma) for 10 min at room temperature. Chromatin was sonicated with a Microtip sonicator (Model 120, Fisher) to an average length of 400~800 bp. Sheared chromatin was then incubated with Pierce Protein A/G Agarose (Thermo Fisher) for 30 min at 4°C to remove nonspecific binding. 10% Input was collected. One µg Anti-AR antibody (Millipore, 17-10489) or normal rabbit IgG (Santa Cruz, sc2027) was used to immunoprecipitate cleared chromatin at 4°C overnight. The next day, precipitated chromatin samples were incubated for 3 hours with 20 µl Pierce protein A/G magnetic beads (Thermo Fisher) at 4°C followed by washing with washing buffer (5 times). Reverse cross-linking and DNA purification was performed using the ChIP-IT High Sensitivity Kit (Active Motif) following the manufacturer’s protocol. The ChIP-PCR primers used are listed in Appendix A.  2.15 Luciferase reporter plasmid construction and dual luciferase reporter assay  The pGL3-Basic plasmid (Promega) was used as a backbone for luciferase reporter construction. The reported PSA enhancer and promoter region (-5752 to +10) was cloned and inserted using HindIII restriction enzyme into the pGL3-Basic (Fig. 2.1) 35  [232]. Briefly, potential AR binding sites in GRB10 intron regions, ARE1 (chr7: 50677642-50678150), ARE2 (chr7: 50657378-50657948), and ARE3 (chr7: 50653818-50654513), were cloned from 293T genomic DNA by PCR with primers listed in Appendix A. A PSA luciferase reporter plasmid containing the PSA promoter region PSAluc-ARE1, PSAluc-ARE2 and PSAluc-ARE3 were constructed by inserting amplified and purified ARE1, ARE2 and ARE3 PCR products into the multiple cloning sites of the PSA luciferase reporter plasmid at the 5’ end of the PSA promoter insert. The restriction enzymes combinations of Nhe1/Mlu1, Mlu1/Sac1, and Xho1/Nhe1 (Thermo Fisher) were used. PSAluc-ARE2 plasmids with point mutations and deletions were amplified using PCR with primers listed in Appendix A.    36   Figure 2.1 Illustration of structure of the pGL3-Basic-PSAluc plasmid As illustrated, the pGL3-Basic-PSAluc luciferase reporter plasmid was constructed based on pGL3-Basic plasmid (Promega). The reported PSA enhancer and promoter region (-5752 to +10) was cloned and inserted using HindIII restriction enzyme into the multiple cloning site of the pGL3-Basic plasmid.   For dual luciferases reporter assays, 293T cells were seeded into 24-well culture plates (50000 cells /well) in triplicates for each experimental condition. 0.2 µg of luciferase reporter, 0.4 µg of pSG5-AR and 5 ng of pGL4.70 Renilla reporter (Promega) were 37  transfected together using Lipofectamine 3000 (Thermo Fisher). Cells were incubated with either EtOH or 10 nM R1881 overnight. Dual luciferase reporter assay was performed using Dual-Luciferase Reporter Assay System (Promega) following the manufacturer’s instructions. Luciferase activity was measured using the Infinite 200Pro Microplate Reader (Tecan). Transfection efficiency was normalized to Renilla luciferase activity.  2.16 Transient AR knockdown using siRNA  SMARTpool ON-TARGETplus AR siRNA (Dharmacon) was used to transiently knock down AR in LNCaP cells. Lipofectamine RNAiMAX was used to perform the transfection following the manufacturer’s protocol. RNA samples were collected two days after transfection.   2.17 Gene expression data profiling  Total RNA was extracted from stable GRB10-knockdown and control LNCaP cells using the RNeasy Mini Kit (Qiagen) following the manufacturer’s protocol. The quality of the RNA samples was checked with the Agilent 2100 Bioanalyzer. Only samples with RNA Integrity Number (RIN) ≥8.0 were used for one-color labelling using Agilent’s One-Colour Microarray-Based Gene Expression Analysis Low Input Quick Amp Labelling v6.0 (Agilent Technologies). Total RNA (100 ng) was used to generate cyanine-3-labelled cRNA. Two replicates from each sample group (shGRB10- or shControl-treated cells) were hybridized on Agilent SurePrint G3 Human GE 8x60K Microarray v2 (Design ID 039494). Arrays were scanned with an Agilent DNA Microarray Scanner at a 3 µm scan 38  resolution and data were processed with Agilent Feature Extraction 11.0.1.1. Processed signals were quantile normalized with Agilent GeneSpring 12.0. RNA quality control and microarray analysis were performed by the Laboratory for Advanced Genome Analysis at the Vancouver Prostate Centre, Vancouver, Canada.  2.18 Ingenuity Pathway Analysis (IPA)  The gene expression microarray dataset of GRB10-knockdown and control LNCaP cells were analyzed to identify upstream regulatory genes and significantly enriched Diseases and Bio Functions using the Ingenuity Pathway Analysis tool (IPA, Qiagen) following the developer’s manual. Differentially expressed genes were selected based on the criteria that their student’s t-test p-value between groups (i.e. shC vs. shGRB10) were below 0.05.   2.19 Liquid chromatography–mass spectrometry (LC-MS)  The LC-MS/MS assays were conducted at the Proteomics Core Facility at the University of British Columbia. Briefly, the immunoprecipitated samples on FLAG magnetic beads were boiled in SDS-PAGE loading buffer and ran in a 10% PAGE gel. Gel lanes were excised and destained overnight. Proteins in these gel pieces were in-gel digested [233] and cleaned up on STAGE tips [234]. After digestion, peptides from different samples were reductively dimethylated with differing isotopolouges of formaldehyde as described [235]. Triplex protein quantification based on stable isotope labeling by peptide demethylation was applied to cell and tissue lysates [235]. This 39  imparts a light (flag only samples) or heavy (flag-GRB10 samples) mass tag on the lysines and N-termini of peptides, which then can be mixed in equal ratios and analyzed concurrently in the mass spectrometer. Two ug of resuspended samples were loaded onto a nanoflow-LC-MS/MS system (Bruker Impact II Q-Tof, with Proxeon EasyLC system, featuring in-house packed 400mm x 50um integrated emitter columns) and run with 90-minute H2O/ACN gradients. The mass spectrometer was operated in data-dependant MS/MS mode, with up to 15 MS/MS scans triggered from every MS scan. Data files were searched against the Uniprot human database and quantified using MaxQuant software v1.6.0.13 [236] with tryptic digestion specificity, default values for Bruker QToF data (including 1% FDR), and the duplex demethylation quantitation mode.  2.20 Statistical analysis  Statistical analysis was performed using the GraphPad Prism software. The statistical difference between two groups was analyzed using the Student’s t-test (2-tailed, unpaired). A linear correlation between two groups was assessed using the Pearson correlation (95% confidence interval). Graphs for clinical data were presented in vertical scatter plots or box-and-whisker plots. In the vertical scatter plots, the middle line and error bars represent the mean and standard error of the mean (SEM) respectively. In the box-and-whisker plots, the middle line represents the median, the box represents the 5%-95% percentile values, and the whiskers show the highest and lowest values in the group. The Kaplan-Meier method was used for estimating progression-free survival curves. The Log-rank test was used to compare the curves between two groups of patients stratified on the basis of GRB10 mRNA expression. Gene correlation analysis was also graphed 40  and calculated via Graphpad. Results with p-values lower than 0.05 are considered statistically significant. The level of significance is indicated by * for p<0.05, ** for p<0.01, *** for p<0.001, and **** for p<0.0001.  41  Chapter 3 Identification of GRB10 as a potential driver for the development of CRPC 3.1 Introduction  As AR signaling is the major contributor to the progression of HNPC, ADT is the most commonly used treatment for advanced PCa [55, 56]. However, in most cases, the tumor recurs in an androgen-independent manner within two years. These androgen-independent recurrent tumors were termed hormone-refractory PCas or, more recently, CRPCs. CRPC patients are treated with increasingly powerful ARPIs such as ENZ, DAR, and ABI. Unfortunately, these new therapies are only marginally effective [237]; as a result, there is an urgent need for a better understanding of molecular mechanisms driving the development of CRPC and for developing more effective therapeutics.   To better understand the mechanism of CRPC development, we aimed to identify potential CRPC driving-genes. In particular, the elevated expression of these genes will be evident initially and sustained into CRPC. Such genes would have a more critical function in CRPC development by inducing bypass pathways to circumvent the AR pathway, as opposed to genes whose elevated expression is solely involved in CRPC aggressive growth. Therefore, we proposed that CRPC driver genes should: (1) be upregulated in CRPC, (2) show elevated expression before and during the progression from HNPC to CRPC; and (3) be functionally essential for CRPC development.   42   To study this complex and challenging problem, a highly clinically-relevant model system is required to recapitulate the entire process of CRPC development and allow for the identification of early drivers of castration resistance. PDX models of HNPC that could develop into CRPC following ADT are powerful tools to identify such drivers [151, 157]. In our laboratory, using the SRC grafting technique, we have established over 45 transplantable PCa PDX lines modelling HNPC, CRPC, and NEPC tumors (www.livingtumorlab.com) [151]. In this study, we applied host castration to 10 of our HNPC PDX lines (Table 3.1). Following castration, seven of these lines developed recurrent CRPC tumors spontaneously. By longitudinally analyzing the gene expression profiles of these PDX lines before castration, at 12-weeks following castration, and after CRPC development, we obtained evidence that the GRB10 gene fulfills the above criteria as a CRPC driver.    The GRB10 gene encodes an adaptor protein, GRB10 (growth factor receptor-bound protein 10), that modulates the coupling of specific signaling pathways to cell surface receptor kinases [238]. Here, we provided evidence that GRB10 could be a driver of CRPC development and hence a potential therapeutic target for the disease.  43   Table 3.1 Summary table of the PDX lines used in the study. AC: Adenocarcinoma; ND: Not detected.    Original patient tumor information Transplantable tumor line information PDX ID Diagnosis AR PSA Doubling time (days) Androgen sensitivity AR PSA PTEN Development of CRPC after host castration LTL310 AC + + 20-32 Yes + + -/- + LTL311 AC + + ~10 Yes + + -/- + LTLT313B AC + + 11-20 Yes + + -/- + LTLT313H AC + + 11-13 Yes + + -/- + LTL418 AC + + 17-19 Yes + + +/+ + LTL467 AC + + 14-25 Yes + + -/- + LTL471 AC + + 6-11 Yes + + +/+ ND LTL484 AC + + 8-10 Yes + + -/- + LTL508 AC + + 9-13 Yes + + +/+ ND LTL556 AC + + 11-15 Yes + + -/- ND 44  3.2 Results 3.2.1 Early elevated expression of GRB10 in Prostate Cancer PDX models To elucidate the molecular mechanisms underlying CRPC development, we applied castration to 10 HNPC PDX models established in our laboratory (Table. 3.1, Fig. 3.1A). Castration of tumor-bearing mice leads to a marked reduction in tumor volume within the first week accompanied by a substantial drop in host serum PSA levels (Fig. 3.1B). This mirrors the clinical response of PCas to ADT. Within a few months after castration, 7 of these tumor lines, such as the LTL-313B line, gave rise to castration-resistant tumors with increased host serum PSA levels (Fig. 3.1B). On the histopathological level, we checked the expression of AR, PSA, and Ki67 in LTL-313B samples collected before host castration, at various time points after host castration, and in recurrent tumors. After the first week of host castration, AR had diffused to the cytoplasm, and PSA and Ki67 expression had markedly decreased, indicative of reductions in AR transcriptional activity and cell proliferation (Fig. 3.1C, Table 3.2). After tumor recurrence (approximately four months later), the scores of these markers had returned beyond pre-castration levels, showing even higher expression of AR and PSA (Fig. 3.1C, Table 3.2).    45   Figure 3.1 Use of hormone-naïve prostate cancer PDX models to identify potential CRPC driving genes (A) As illustrated, surgical castration of a mouse bearing SRC PDXs (pink) of a patient’s HNPC led to an initial reduction in tumor volume. A few months later, the tumor recurs, i.e. CRPC development. (B) Volumes of HNPC LTL-313B xenografts and mouse serum PSA levels at various time points before, during, and after castration-induced CRPC development. (C) Hematoxylin and eosin staining of LTL-313B tumor sections, as well as the levels of Ki67, AR and PSA in tumor sections (determined via 46  immunohistochemistry) at various time points before host castration and after host castration, and in recurrent tumors. Scale bar indicates 100 µm.   Pre Cx 1w Cx 3w Cx 6w Relapse Ki67 (%) 5.62±0.46 0.14±0.07 0.20±0.06 2.21±0.42 5.80±0.36 AR (score) 2.7 1.1 1.2 2.1 2.85 PSA (score) 2.3 0.1 0.1 0.2 2.9 Table 3.2 The summary of IHC scores in LTL-313B PDX models at various time points after host castration. The percentage of Ki67 positive cells and the IHC scores of AR and PSA in each tumor sample, with representative images shown in Fig. 3.1. Ki67 positive percentage was calculated by the ratio of the number of positively stained cells to the total cell number, shown with mean ± SEM. The IHC score of AR and PSA was calculated by multiplying the intensity of staining (0 = no staining, 1 = low, 2 = moderate, and 3 = high) with the percentage of positively-stained tumor cells. Ten random high-power fields were used.  Among the 10 HNPC PDX lines we used in the study, 7 of them developed into CRPC after host castration (Table 3.1). To identify potential driver genes of CRPC development, we first analyzed gene expression differences between the 7 pairs of CRPC tumor lines and their parental HNPC lines. Consistently upregulated genes in all seven pairs were then ranked based on their statistical significances between pooled HNPC 47  lines and CRPC lines. Eighty genes were identified to be significantly upregulated in CRPC (p<0.05; Fig. 3.2A, Appendix B). Next, we checked the expression of these 80 genes in all 10 HNPC PDX lines at 12 weeks after castration. At this time point, the tumors have not fully relapsed to CRPC, as indicated by the sustained inhibition of AR signaling suggested by GSEA (Fig. 3.2C-F). Of these 80 genes, GRB10 was the top-ranked gene based on the statistical significances between HNPCs and 12-week castrated samples (Fig. 3.2B, Appendix B). Therefore, this datum demonstrates that GRB10 is not only upregulated in CRPC tumors but is also increased before CRPC fully develops, suggesting that it is a potential early driver of CRPC development (Fig. 3.2D).   48   49  Figure 3.2 Identification of GRB10 as a potential driver of CRPC development (A) A heat map showing that 80 genes were significantly upregulated in in 7 CRPC PDX lines compared to their parental HNPCs. (B) A heat map showing GRB10 as the most consistently upregulated gene following host castration of the 80 identified genes. Genes ranked according to p-value (Student’s t-test). A detailed list of genes and gene expression changes used in Figures 3.2A and 3.2B is shown in Appendix B. (C-F) Sustained inhibition of AR signalling in PDX tumors at 12 weeks post host-castration as shown by GSEA. AR–related gene sets were analyzed using transcriptomic data of 10 pooled HNPC PDX lines at pre-castration and 12 weeks following castration. (G) A flow chart summarizing the strategy used to identify the potential early driver of CRPC development. Briefly, genes which are consistently upregulated in the CRPC tumour compared to their own parental HNPC tumour in the 7 pairs of PDX models were selected. The p-value of each gene comparing pooled CRPC to HNPC PDX lines was calculated. Eighty genes were found to be consistently and significantly upregulated in CRPC. Next, the p-values of these 80 genes when comparing HNPC PDX tumors at 12 weeks following castration to their parental lines were determined. Finally, among these 80 genes, GRB10 was identified to be the most significantly upregulated gene (lowest p-value) at 12 weeks following castration. Cx: host castration treatment; NES: normalized enrichment score; p: nominal p-value.  Next, we validated our findings from high-throughput microarray data using RT-PCR and Western Blot. GRB10 mRNA expression was significantly elevated in post-castration PDX samples (Fig. 3.3A). Furthermore, we checked GRB10 mRNA expression 50  in the LTL-313B tumor line during the development of CRPC. As shown in Figure 3.3B, the GRB10 mRNA expression was markedly elevated following castration and remained at a high level during CRPC development and at the CRPC stage (i.e. in LTL-313BR tumor tissue). Similarly, GRB10 protein levels were significantly higher in CRPC samples than in the parental samples (Fig. 3.3C, D).    51   Figure 3.3 Elevated expression of GRB10 in prostate cancer PDX (A) Effects of host castration on the expression of GRB10 in multiple PDX HNPC lines as determined by qRT-PCR; p-values for each tumor line were determined relative to its parental tumor. The results are presented as means ± SEM. (B) GRB10 and PSA mRNA expression in the LTL-313B tumor line were determined by qRT-PCR at various time points after host castration; p-values for each sample were determined relative to parental tumors. Results are presented as means ± SEM. (C) GRB10 protein expression in LTL-313B, LTL-418, and LTL-484 tumor tissues before castration, 10 weeks after castration and after CRPC development were assessed by Western Blotting. (D) Relative band density of Western Blotting results shown in Fig. 3.3C were quantified using ImageJ (NIH, USA, https://imagej.nih.gov). GRB10 protein expression was normalized by Actin as 52  internal control. Bar graphs show mean ± SEM. The p-values were calculated by unpaired two-tail Student’s t-tests comparing tumor samples collected 12 weeks following castration and recurrent CRPC tumors with parental untreated tumors based on three replicated experiments. Pre: pre-castration; Cx: castration; wk: weeks after castration; R: CRPC.   3.2.2 Elevated expression of GRB10 in clinical CRPC tissues  To further validate whether our discovery that GRB10 is elevated in CRPC was clinically relevant, we checked the expression of GRB10 in publically available clinical gene expression data [9]. In reported clinical PCa cohorts, we found that the expression of GRB10 in clinical CRPC samples was significantly elevated compared to HNPC and benign prostate samples [67, 226, 227] (Fig. 3.4A-C). Additionally, patients exhibiting elevated GRB10 expression show a shorter progression-free survival time, which suggests that the expression of GRB10 could be a potential prognostic biomarker (Fig. 3.4D).   53   Figure 3.4. Elevated expression of GRB10 in clinical CRPC tissues (A-C) GRB10 mRNA expression of benign prostate tissues, HNPC tissues, and CRPC tissues were derived from Grasso et al. 2012 (A), Varambally et al. 2002 (B), and Ross-Adams et al. 2015 (C) clinical PCa cohorts. The vertical scatter plots show means ± SEM. (D) Kaplan-Meier plots indicate disease-free survival times of PCa patients grouped according to GRB10 expression. Significance between the groups was analyzed by the log-rank test.; DFS: disease-free survival; HR: hazard ratio; ns: not significant. 54   3.2.3 GRB10 gene silencing reduces in vitro cell proliferation of prostate cancer cells To study the function of GRB10 in PCa, we performed GRB10-knockdown using two independent GRB10-targeting shRNAs which target the GRB10 extron and 3’UTR. First, we checked the expression of GRB10 in seven commonly used benign prostate and PCa cell lines. We found that GRB10 was expressed at the highest level in AR+ LNCaP and C4-2 cells (Fig. 3.5A). As such, we subsequently utilized these two cell lines to investigate GRB10’s cellular functions. We constructed GRB10 stable knockdown LNCaP and C4-2 cells using two independent shRNAs and validated the knockdown efficiency at both the mRNA and protein levels (Fig. 3.5B-D).   55   Figure 3.5. Construction of GRB10 stable knockdown LNCaP and C4-2 cells (A) Endogenous GRB10 mRNA expression in multiple PCa cell lines was determined using qPCR and normalized relative to BPH1. Cells were supplemented with 10% FBS. Bar graph shows mean ± SEM. (B-D) Effects of GRB10 knockdown on the expression of GRB10 at both the mRNA and protein level in LNCaP and C4-2 cells.  Then, we examined the function of GRB10 in LNCaP and C4-2 cells. Knockdown of GRB10 led to inhibition of LNCaP and C4-2 cell proliferation (Fig. 3.6A, B). Combining GRB10 knockdown and treatment with ENZ led to increased inhibition of cell proliferation (Fig. 3.6A, B). Moreover, knockdown of GRB10 restricted the ability of these PCa cells to form colonies, especially when combined with ENZ treatment. (Fig. 3.6C, D). 56   Figure 3.6. GRB10 knockdown inhibited the proliferation and colony formation abilities of LNCaP and C4-2 cells (A, B) The potential function of GRB10 in the proliferation of LNCaP and C4-2 cell lines was assessed using stable GRB10 knockdown (KD1, KD2) and control (shC) cells by means of the MTS cell proliferation assay. The effect of ENZ (10 µM) treatment (in red) was also determined. O.D. values at 490 nm were normalized based on Day 0 readings. Results are presented as means ± SEM. (C, D) Effects of GRB10 knockdown on colony formation ability were determined; cells were seeded into 6-well plates (100 cells/well) and incubated for 4 weeks. Colonies were then stained with crystal violet and imaged; representative images are shown. Bars (colony number of each sample) show means ± SEM. shC: control shRNA stable transfected cells; KD1: GRB10 stable knockdown cell line 1; KD2: GRB10 stable knockdown cell line 2; ENZ: Enzalutamide.  57  We further investigated the role of GRB10 in cell proliferation and found that GRB10 was critical for cell cycle progression. Knockdown of GRB10, regardless of ENZ treatment, led to decreased DNA synthesis as indicated by decreased number of cells undergoing DNA replication (Fig. 3.7A, B). Consistantly, GRB10 knockdown induced G0/G1 cell cycle arrest as measured by increased population of cells in G0/G1 phase by flow cytometry (Fig. 3.7C-F). These data indicated that the decrease in proliferation of GRB10-knockdown cells could be caused by delayed cell cycle progression.    58   59  Figure 3.7. GRB10 knockdown inhibits DNA synthesis and cell cycle progression of prostate cancer cells (A, B) Effects of GRB10 knockdown on DNA synthesis were determined using an EdU assay; cells were incubated for 4 hours with 10 μM EdU. EdU-labeled cells (green) and total cell numbers counterstained with DAPI (blue) were counted with 10 images taken at 10-fold magnification. Bars (EdU positive/total cells) show means ± SEM. Representative images are shown with a 100-μm scale bar. (C, E) Effects of GRB10 knockdown on cell cycle progression in LNCaP (C) and C4-2 (E) cells were measured using propidium iodide staining and flow cytometry. Data are presented as mean ± SEM. (D, F) Cell cycle distributions were analyzed and visualized using the FlowJo software. Percentages of cells in G0/G1 phase and G2/M phase are labeled. shC: control shRNA stable transfected cells; KD1: GRB10 stable knockdown cell line 1; KD2: GRB10 stable knockdown cell line 2; ENZ: Enzalutamide; EdU: Ethynyl Deoxyuridine; DAPI: 4',6-Diamidino-2-Phenylindole.   Since ARPI resistance commonly develops, we then focused on studying whether GRB10 plays a role in ARPI resistance. MR49F is an ENZ-resisant PCa cell line isolated from a LNCaP xenograft after multiple rounds of ENZ treatment [113]. Transient depletion of GRB10 led to significant inhibition of cell proliferation, DNA synthesis, and cell cycle progression similar to what we observed in LNCaP and C4-2 cells (Fig. 3.8A-E).  60   Figure 3.8. GRB10 knockdown markedly inhibits the proliferation, DNA synthesis and colony formation of ENZ-resistant MR49F prostate cancer cells (A) Effects of GRB10 knockdown on GRB10 protein expression in ENZ-resistant MR49F cells as determined by Western Blotting (B) The potential function of GRB10 in the proliferation of the MR49F cell line was determined using transient GRB10 knockdown (siGRB10) and control (shC) cells. Transient GRB10 knockdown (siGRB10) and control (siControl) cells were kept in complete media containing 10 µM ENZ. O.D. values at 490 nm were normalized based on Day 0 readings. Results are presented as means ± SEM. 61  (C,D) MR49F cells were incubated for 4 hours with 10 μM EdU. EdU-labeled cells (green) and total cell numbers counterstained with DAPI (blue) were counted in 10 images taken at 10-fold magnification. Bars (EdU positive/total cells) show means ± SEM. Representative images are shown with a 100-μm scale bar. (E) Effects of GRB10 knockdown on cell cycle progression in MR49F cells were measured using propidium iodide (PI) staining and flow cytometry. Data are presented as mean ± SEM. EdU: Ethynyl Deoxyuridine; DAPI: 4',6-Diamidino-2-Phenylindole.  3.2.4 Overexpression of GRB10 promotes PCa proliferation and resistance to ARPI treatment in vitro To determine the function of GRB10 in driving ARPI resistance, we ectopically expressed GRB10 in LNCaP, an androgen-sensitive PCa cell line with a low endogenous GRB10 level, and treated them with ENZ. Notably, GRB10 overexpression partially led to ENZ resistance as indicated by an MTS cell proliferation assay (Fig. 3.9A, B). Together, these results suggest that GRB10 is a driver of CRPC development. GRB10-targeting therapy could thus enhance the anti-tumor activity of ARPIs and suppress the development of CRPC.    62    Figure 3.9. Functional study of GRB10 in promoting ARPI resistance (A, B) The effects of GRB10 overexpression on driving ENZ resistance was assessed in LNCaP cells stably overexpressing GRB10 (+GRB10, in red) or control (+C, in green) as confirmed by Western Blotting. Cell proliferation was determined using the MTS assay.  3.2.5 A Role of GRB10 in regulating AKT signaling  AKT is a well-established driver of CRPC [239]. As reported in other systems, manipulating the expression of GRB10 can lead to either enhanced or reduced AKT activity [7]. Here, we observed that the knockdown of GRB10 in LNCaP, C4-2, and ENZ-resistant MR49F cells consistently led to a decrease in S473 phosphorylation levels of AKT. Similar results were also observed following ENZ treatment (Fig. 3.10A, B). These data suggest that increased expression of GRB10 promotes AKT activation.    63   Figure 3.10. GRB10 knockdown reduced AKT activity in prostate cancer cells  (A) The effects of GRB10 knockdown on phosphorylated AKT levels in LNCaP and C4-2 were determined by Western Blotting using stable GRB10 knockdown (KD1, KD2) and control (shC) cell lines. Total protein was isolated from cells treated with or without 10 µM ENZ for 3 days. (B) The effects of GRB10 knockdown on phosphorylated AKT levels in ENZ-resistant MR49F cells were determined by Western Blotting using transient GRB10 knockdown (siGRB10) and control (siC) cells. Cells were maintained in regular medium with 10 µM ENZ. ENZ: Enzalutamide.   Since GRB10 is an adaptor protein, we tried to determine whether GRB10 can bind to AKT in PCa cells using a co-immunoprecipitation (Co-IP) assay. GRB10 was able to precipitate AKT, indicating binding of GRB10 to AKT (Fig. 3.11A). An immunofluorescence (IF) assay further indicated that endogenous GRB10 (green) and AKT (red) were co-localized in the cytosol and on the plasma membrane. Furthermore, ENZ treatment led to increased cell membrane co-localizations (Fig. 3.11B). This suggests that, upon ENZ treatment, the AKT and GRB10 complex moved towards the 64  plasma membrane where AKT would be activated. In situ interaction between GRB10 and AKT was also confirmed by proximity ligation assay (PLA) (Fig. 3.11B). Consistent with IF results, more PLA signals were found on the plasma membrane when cells were treated with ENZ. The data suggest that GRB10 can bind to AKT and lead to its activation.   65   Figure 3.11. GRB10 directly interacted with AKT in prostate cancer cells  (A) The binding of GRB10 to AKT was confirmed by Co-IP. LNCaP cells expressing Flag-GRB10/AKT or Flag/AKT were collected and lysed with NETN buffer. Five percent of input was collected for each sample. One mg of each protein sample was incubated with FLAG magnetic beads. Then GRB10 and AKT were detected via Western Blotting. (B) AKT (green) and GRB10 (red) localizations in LNCaP cells treated with DMSO (vehicle control) or 10 µM ENZ were determined using immunofluorescence staining. Representative images are shown with a 10 μm scale bar. Interaction between AKT and GRB10 was also validated using a proximity ligation assay. Specific protein-protein interaction signals are shown in red. ENZ: Enzalutamide; IP: immunoprecipitation; WB: Western Blotting.  66   We then further elucidated the association of GRB10 expression and AKT activation in multiple PCa clinical cohorts. In the TCGA and Grasso cohorts [67, 73], GRB10 upregulation was significantly correlated with PTEN deletion, a well-documented genetic alteration initiating AKT activation in clinical CRPCs (Fig 3.12A, B). Furthermore, GRB10 expression was found to be significantly upregulated in the PTEN-deleted samples following treatment with ABI or ENZ [68] (Fig. 3.12C), which is consistent with our previous findings. The data support the hypothesis that upregulated expression of GRB10, together with PTEN deficiency, promotes AKT activation and leads to CRPC development. As such, GRB10 may be considered a critical driver of CRPC.   67   Figure 3.12. GRB10 expression was negatively correlated with PTEN copy number in clinical prostate cancers  (A) A difference in GRB10 mRNA expression between PTEN diploid and PTEN-deleted clinical samples as indicated by box-and-whisker plots. Gene expression data were collected from the TCGA 2015 patient cohort. (B) A Pearson correlation between GRB10 mRNA expression and PTEN copy number was established. Data were extracted from the Grasso et al. 2012 patient cohort; the trend line is shown in red. Copy number 0 indicates PTEN diploid status. Negative copy number indicated PTEN deletion. (C) A difference in GRB10 mRNA expression between PTEN-deleted CRPC patients treated with or without ABI/ENZ as indicated using box-and-whisker plots. Gene expression data were collected from the Robinson et al. 2015 patient cohort. 68   3.3 Discussion  Development of treatment resistance to first-line hormonal therapy, e.g., orchiectomy, LHRH agonists or antagonists, and more recently, ABI, is currently a major challenge in the management of advanced PCa. This is further exacerbated by the fact that next-generation ARPIs such as ENZ and APA only have marginal efficacy [237]. In efforts to develop more effective therapies, it is critical to improve the understanding of the molecular mechanisms underlying the development of CRPC. A major hurdle of PCa research has been the lack of clinically relevant cancer models and, in particular, models representing clinical HNPCs. Unlike other research groups using methods that recombine patient-derived tissues with mouse seminal vesicle mesenchyme [157], our laboratory has established over 45 transplantable PCa PDX lines including HNPC, NEPC, and castration-resistant prostate adenocarcinoma by directly grafting patient tumors under the mouse renal capsule. Importantly, this technique allows the xenografts to retain key features of the original patients’ tumors, including tumor heterogeneity, the tumor microenvironment, tissue architecture, and cancer-stromal interactions. These PDX models also mimic patients’ treatment responses, as evident by spontaneous CRPC development with minimal artificial manipulation [151]. While organoid culturing provides an opportunity to study advanced patient-derived PCa cells in vitro [240], it does not allow the development of HNPC models at present,. Additionally, recent studies presented at the 2019 AACR Annual Meeting (Potentials and pitfalls of organoid technology to study PCa by Dr. Yu Chen and Patient-derived organoid models of bladder cancer by Dr. Michael M Shen) suggests that the treatment response of these patient-derived organoids 69  is significantly altered by the abundant growth factors and hormones added to the culture system, limiting their usage in studying treatment response and resistance. GEMMs of PCa are powerful tools for studying mechanisms underlying CRPC development [136, 147]. These models have an advantage over PDX models which are developed in mice lacking a functional immune system. This makes GEMMs more suitable to study cancer-immune interaction. However, GEMMs do not reflect the intertumoral genomic heterogeneity of the disease [67, 68, 73]. In this study, we were able to identify common molecular events occurring in CRPC development regardless of specific genetic background by using seven pairs of hormone-naive PDX models and their related castration-induced CRPC models.    Our study shows that GRB10 is critical for AR+ PCa cell proliferation, which is consistent with another independent publication suggesting that GRB10 is a pro-proliferative protein in PCa [220]. In AR- PCa cell lines such as PC-3 and DU145, where AR expression is diminished due to other mechanisms [241], the expression of GRB10 is much lower (Fig. 3.5A). This suggests that other mechanisms such as FGFR [242] or polycomb repressor complex 1 [243] might be involved in treatment resistance in such scenarios. Thus, GRB10 can be considered a universal ARPI resistance-driving gene in AR+ PCa.    GRB10 has been shown to regulate AKT activation but with contradictory observations in different contexts [238]. For example, GRB10 has been reported to disrupt the interaction between insulin receptor (IR) and insulin receptor substrate 1 (IRS1) 70  in mouse embryonic fibroblasts, eventually leading to inhibition of insulin-induced AKT activation [212]. However, in human leukemic cell lines Mo7e and K562, GRB10 has been reported to enhance AKT activation independent of PI3-K activity [208, 209]. Our findings in the present study clarify that increased expression of GRB10 promotes AKT activation in PCa. Interestingly, we also found that elevated expression of GRB10 co-occurs with PTEN deletion. Consistently, higher endogenous expression of GRB10 was found in PTEN deleted AR+ PCa cell lines (Fig. 3.5A). This evidence suggests that PTEN deletion could initiate the baseline expression of GRB10.    AKT pathways have been reported to be critical for CRPC development and progression [239]. When ARPI is applied, the dramatic upregulation of GRB10 further enhances the activation of AKT, providing proliferative and survival advantages to the residual tumor cells. This provides the cells with enough time to accumulate the genomic alterations we observed clinically in terminal CRPC, which includes AR mutation, AR amplification and TP53 mutation, which ultimately results in the development of ARPI resistance. This mechanism could potentially explain the enrichment of PTEN-deletion in CRPC patients [67, 227, 230]. However, treatment of CRPC based on targeting only the AKT pathway has had limited effect [244, 245]. As such, targeting GRB10, a potential upstream mediator of AKT, could improve the current treatment of CRPC. Consistent with our findings, a recent publication reported the pro-proliferative function of GRB10 in PCa. In their study, AKT activity was also found to be increased by ectopic expression of GRB10, which could promote the tumor growth in vivo [220].  71   Other than GRB10, there are some other genes shown upregulation at post-castration time points among the 80 genes we identified to be significantly upregulated in 7 pairs of HNPC/CRPC PDX tumor lines (Appendix B). For example, FGFR2 expression was elevated at both post-castration and CRPC time points compared to parental HNPC PDX lines.   72  Chapter 4: Discovery of GRB10 as an AR transcriptionally repressed gene 4.1 Introduction  AR is critical for PCa development and progression [246], and ADT is the most effective treatment in the management of PCa. However, treatment resistance invariably develops. The alterations of AR including AR amplification, gain-of-function mutations, and constitutively active AR splicing variants have been observed in more than half of clinical CRPCs [67, 68]. These AR alterations suggest that AR is still one of the major drivers of prostate cancer lethality. The newly-developed next generation ARPIs such as ABI, ENZ, and DAR, are only marginally effective. Interestingly, the success of sequential usage of the anti-androgen drugs mentioned above suggests that ARPI resistance is dependent on AR as well.    The AR is a nuclear receptor as well as a transcription factor. Upon binding to its ligand, usually dihydrotestosterone, in the human prostate, AR translocates into the nucleus where it forms a homo-dimer [247] and binds to androgen responsive elements. This either induces or suppresses downstream gene transcription, depending on the co-factors it binds with [248]. For years, AR was well-studied for its role in transcriptional initiation. To induce transcription of AR target genes, pioneer transcription factors such as FOXA1 and GATA2 are first recruited to open the condensed chromatin. Then, AR binds to androgen responsive elements (ARE) located around the promoter, which leads to the recruitment of RNA polymerase II to the transcription start site (TSS). Other 73  transcription factors such as CBP and SRC-1 are AR co-factors that form a complex with AR to induce the transcription of its target genes. AR-induced genes are reported to be involved in a variety of functions such as normal prostate development, lipid metabolism, cell proliferation, differentiation and apoptosis [246]. For example, PSA (KLK3), one of the best-known AR-induced genes, is considered not only as a marker of AR activity, but also as a commonly-used biomarker indicating PCa diagnosis, progression, and recurrence [249].    Besides inducing gene transcription, AR can also act as a transcriptional repressor. By combining transcriptome analysis of androgen-inhibited genes with chromatin immunoprecipitation-sequencing (ChIP-seq)-based whole genome ARE location analysis, a wide range of genes were discovered to be directly transcriptionally repressed by AR. AR has been reported to repress of both cancer-promoting and tumor-suppressing genes, e.g., DEPTOR [250], CDH1 [251], MET [252], BRN2 [253], PEG10 [98], and AR itself [254]. Since ADT is the most-commonly used treatment for advanced PCa, it is of great importance to study whether a particular gene is directly regulated by AR.   In the previous chapter, we demonstrated that GRB10 expression was significantly upregulated after host castration in HNPC PDX models (Fig 3.3). In clinical CRPC, ENZ and ABI treatment would further increase the expression of GRB10 (Fig 3.4). Taken together, the consistent and marked response of GRB10 to AR signaling inhibition suggested that GRB10 expression was regulated by AR. In this section, we discover and 74  validate that GRB10 is transcriptionally repressed by AR through an androgen responsive element located in GRB10’s intron.  4.2 Results 4.2.1 GRB10 is an AR-repressed gene in AR+ PCa cells  To address AR regulation of GRB10, we used the AR+ lineage-related human PCa cell lines LNCaP and C4-2 and subjected them to androgen deprivation and AR blockade. In androgen-dependent LNCaP cells, ablation of androgen led to increases in both GRB10 mRNA and protein expression; the upregulated expression could be reduced by addition of the synthetic androgen R1881 (Fig. 4.1A-C). These results are consistent with previous studies of androgen regulation using high-throughput technology [255, 256] (Fig. 4.1D, E). In androgen-independent but AR-signaling active CRPC C4-2 cells, treatment with the AR antagonist ENZ also led to the upregulation of GRB10 expression (Fig. 4.1F-H). We also found that transient depletion of AR by siRNA can upregulate GRB10 expression (Fig 4.1I, J). All of these data indicate that AR itself is a direct repressor of GRB10 expression.    75  Figure 4.1. GRB10 is an AR-repressed gene in AR+ prostate cancer cells (A-B) Effects of androgen deprivation on GRB10 expression in LNCaP cells (by culturing in CSS medium) were determined by qRT-PCR and Western Blotting. To some cultures, 10 nM of AR ligand R1881 was added. P-values for each sample were determined based 76  on untreated controls (*), cells cultured in CSS medium for 3 days (#), and cells cultured in CSS medium for 7 days (†). Bar graphs show means ± SEM. (C) Relative band densities of Western Blotting results shown in Fig. 4.1B were quantified using ImageJ (NIH, USA, https://imagej.nih.gov). GRB10 protein expression was normalized to Vinculin as internal control. Bar graphs show mean ± SEM. The p-values for each sample were determined based on untreated controls (*), cells cultured in CSS medium for 3 days (#), and cells cultured in CSS medium for 7 days (†) and calculated by unpaired two-tail Student’s t-tests based on three replicate experiments. (D, E) GRB10 mRNA expression upon AR stimulation in LNCaP cells was analyzed with two publically available microarray datasets [255, 256]. Bar graphs show means ± SEM. (F, G) Relative GRB10 mRNA and protein expression in C4-2 cells were determined after treatment with 10 µM ENZ. Bars show means ± SEM. (H) Relative band densities of Western Blotting results shown in Fig. 4.1G were quantified using ImageJ (NIH, USA, https://imagej.nih.gov). GRB10 protein expression was normalized to Vinculin as internal control. Bar graphs show mean ± SEM. (I, J) Effects of AR knockdown on GRB10 expression in LNCaP cells were determined by qPCR and Western Blotting. Bars show means ± SEM.  4.2.2 Expression of GRB10 was upregulated by anti-androgen treatment in PCa mouse model and clinical PCa samples  Next, we checked the expression of Grb10 in the Pb-Cre4;Ptenflox/flox;R26ERG/ERG murine PCa model with available gene expression data [257]. Grb10 was significantly elevated in mice subjected to castration (Fig. 4.2A). Furthermore, in multiple PCa clinical cohorts [67, 73, 226, 227, 230], the expression of GRB10 in PCa samples tends to be 77  negatively correlated with PSA expression (Fig. 4.2B, C). In addition, we found that GRB10 expression was significantly upregulated in patients’ samples collected after neoadjuvant ADT compared with clinical HNPCs (Fig. 4.2D) [258]. This upregulation of GRB10 expression is in agreement with our findings in PDX models. All of these results suggest that AR directly regulates the expression of GRB10.   78   Figure 4.2. GRB10 is an AR-repressed, ADT-inducible-gene in AR+ prostate cancers (A) Grb10 mRNA expression in intact and castrated Ptenflox/flox, R26ERG transgenic mouse models constructed by Chen et al, 2013 [257]. The vertical scatter plots show means ± SEM. (B, C) Pearson correlation between GRB10 and PSA mRNA expression was determined using transcriptomic data of Grasso et al. 2012 and TCGA 2015 clinical PCa cohorts. (D) GRB10 mRNA expression in clinical PCa samples before and after 7-day neoadjuvant ADT was analyzed using the Shaw et al. 2016 patient cohort [258]. The scatter plots show means ± SEM. ENZ: Enzalutamide; ns: not significant.  79  4.2.3 AR represses expression of GRB10 by binding to an androgen responsive element located in its intron  AR is a transcription factor which directly binds to DNA to initiate or repress downstream gene transcription. To examine whether GRB10 is transcriptionally regulated by AR, we analyzed a reported AR chromatin immunoprecipitation-DNA sequencing (ChIP-seq) dataset [259]. Three potential AR-binding sites were identified to be located in the GRB10’s intron region (intron 2 and intron 3), i.e. ARE1, ARE2 and ARE3 (Fig. 4.3A). To confirm the occupancy of AR on these predicted AREs, we performed ChIP-PCR assays on LNCaP cells treated with 10 nM R1881 or ethanol (control). The treatment with R1881 resulted in significant AR enrichment at all three AREs (Fig. 4.3B). Consistently, similar AR enrichment on the GRB10 gene was also observed in other AR ChIP-seq datasets [255, 260]. Clinically, the occupancy of AR on the GRB10 gene was enriched in untreated PCa patients’ samples and decreased in ADT-responsive patients’ samples (Fig. 4.3C) [260].    80   Figure 4.3. AR represses expression of GRB10 by binding to an ARE located in its Intron (A) Potential androgen responsive elements (AREs, yellow) and their locations in the GRB10 introns (green) were identified by analyzing reported ChIP-seq data [259]. Based on the reported consensus sequences of AREs and useing a computational analysis tool (http://consite.genereg.net/cgi-bin/consite), a potential ARE appears to be located within 81  each of the 3 potential AR-binding sites. Three potential AREs are labeled based on their distances to the transcription start site (TSS; black arrow). Genomic location was aligned based on GRCh38 assembly. (B) AR occupancies at several DNA locations were measured using ChIP-PCR. The PSA enhancer region (PSA) was used as a positive control whereas an AR non-binding site was used as a negative control (NC). Bars show means ± SEM. (C) AR enrichment at identified androgen responsive elements on the GRB10 gene and on the PSA gene enhancer was analyzed using publically available ChIP-seq data of clinical samples [260]. Ch7: chromosome 7; ARE: androgen responsive element; NC: negative control; UT: untreated; TR: treatment responsive; CR: castration-resistant.  4.2.4 ARE2 located in GRB10’s intron is a silencer regulated by AR   To further confirm the biological function of AR binding at these potential AREs, we performed a dual luciferase reporter assay. Here, we constructed luciferase reporters by inserting the three potential AREs as cis-regulatory elements into separate PSA luciferase reporters whose expression was regulated by the PSA promoter. The relative luciferase signals from the reporter plasmid into which ARE2 had been inserted (PSAluc-ARE2) was significantly reduced by treatment with R1881; this was not observed for the reporter plasmids containing ARE1 or ARE3 (Fig. 4.4A). We then constructed two modified PSAluc-ARE2 reporter plasmids in which the potential ARE sequence was either mutated or deleted. These modifications led to the rescue of the luciferase signal (Fig. 4.4B). Taken together, the data demonstrate that AR can act as a transcriptional 82  repressor of GRB10 by binding to an ARE located in the GRB10 intron region (chr7: 50657497-50657511).  83   Figure 4.4. AR represses expression of GRB10 by binding to an ARE located in its Intron  (A) The transcriptional inhibitory functions of potential AREs are indicated by reduced luciferase reporter activity. Luminescence units were normalized using Renilla luciferase signal. Bars show means ± SEM. (B) Transcriptional activation abilities of ARE mutants (sequences are shown) were assessed by dual luciferase reporter assay. Nucleotides in red are sequences of consensus ARE, mutated ARE, and deleted ARE respectively. Bars show means ± SEM. ns: not significant.  4.3 Discussion  It is well-accepted that AR is responsible for most CRPC progression. Multiple potent ARPIs have been shown to extend the survival of CRPC patients [261]. However, treatment resistance invariably develops, and in most cases AR is still promoting cancer progression [246]. Here, we provided evidence that GRB10 is transcriptionally repressed 84  by AR. This finding is echoed by a recent study in adrenocortical tumors where they found GRB10 to be upregulated by cetrorelix acetate, a gonadotropin-releasing hormone receptor antagonist [223]. Thus, ARPIs, including androgen ablation and AR blockade, would increase the expression of GRB10, which in turn eventually mediates the development of treatment resistance.    With regards to crosstalk between AR and AKT, it has been shown that ADT can lead to increased AKT activity in PCa cells [239]. In this connection, FKBP5, an AR-responsive gene, has been reported to promote dephosphorylation of AKT in PCa through recruiting PHLPP phosphatase [262], which in turn leads to increased AKT activation during ADT. Many studies have shown that AR is highly expressed and transcriptionally active in CRPC (summarized in [246]), suggesting that decreased AKT activity in CRPC can be expected as a result of recurrent FKBP5 expression. However, following the development of CRPC, AKT is still highly active [239]. Therefore, a better understanding of this crosstalk is required to explain the discrepancy. In the present study, the findings that GRB10 is transcriptionally repressed by AR and mediates the activation of AKT can fill this gap between AR and AKT. Following castration, the expression of GRB10 is elevated, resulting in the activation of AKT. When CRPC has developed, the effect of AR on enhancer elements is stimulated by low, castrate-levels of androgen. However, its effect on suppressor elements remains inhibited [254], leading to consistent upregulation of GRB10 and promotion of AKT activation. This notion is supported by our observations that treatments with ENZ and ABI will further increase GRB10 expression in CRPC. As a 85  result, our findings provide a molecular bridge between AKT and AR, suggestive of a critical role for GRB10 in the development of CRPC.   As a key regulator of the IGFR pathway, GRB10 expression has also been linked to Type II diabetes [263]. In transgenic mouse models, overexpression of GRB10 has also been shown to lead to insulin resistance [264, 265]. Interestingly, when PCa patients are treated with ADT, higher incidences of type 2 diabetes and obesity are observed [266-269]. This effect can be partially explained by our finding that GRB10 is an AR repressed gene. Therefore, GRB10-targeting therapy may benefit patients not only by controlling PCa progression, but also by improving the patients’ quality of life.  86  Chapter 5: Elucidation of GRB10 function in the development of CRPC 5.1 Introduction  Despite the successes of controlling tumor growth by first-line hormonal therapy such as surgical or chemical castration, in most cases, CRPC will  develop [237]. Interestingly, androgen signaling is reactivated in CRPC, as evident by the biochemical recurrence of PSA, an AR regulated gene. This phenomenon suggests that further androgen blockade will benefit patients. This led to the development of next-generation ARPIs, including ENZ, ABI, APA, and DAR [270]. However, these drugs are only marginally effective [237], and the mechanism underlying the development of castration/ ARPI resistance remains unclear.    In the previous chapters, we have provided evidence that GRB10 is a potential driver gene for the development of CRPC. GRB10 encodes Growth Factor Receptor Bound Protein 10 (GRB10), which is an adaptor protein interacting with various specific signaling cascades. It plays different biological and pathological functions in a context-dependent manner. GRB10 was reported to directly interact with key components in signal transduction pathways including receptor tyrosine kinases (IR, IGFR, EGFR, FDGFR, KIT, VEGFR2), non-receptor tyrosine kinases (BCR-Abl fusion), serine/threonine kinases (PI3K, Raf1), ubiquitin ligase (NEDD4), and growth hormone receptors [238, 271]. Moreover, it was also reported that GRB10 can regulate AKT, a well-accepted driver of CRPC development [239, 262]. Biologically, GRB10 knockdown in cervical squamous cell cancer and BCR-ABL-positive leukemia cell lines significantly 87  inhibited cell growth [216], while GRB10-knockout mice were reported to have markedly elevated muscle mass [272]. Therefore, it is our interest to study the function of elevated GRB10 expression as observed in our PDX models during CRPC development. In this chapter, we performed a multi-omics study of GRB10 by analyzing the transcriptomic change after GRB10 knockdown and the interactome of GRB10 in PCa cells in order to reveal the molecular mechanisms underlying GRB10-promoted CRPC development.   Protein phosphatase 2 (PP2A) is of particular interest among the various GRB10-interacting proteins we discovered using high-throughput LC-MS/MS. PP2A is a ubiquitously-expressed serine/threonine phosphatase heterotrimeric protein complex. It consists of a structural “A” subunit, a regulatory “B” subunit, and a catalytic “C” subunit [273]. The A subunit is the scaffold of the protein complex and consists of 15 Huntington-Elongation-A subunit-TOR (HEAT) repeats. It provides the docking sites for the B and C subunits. The C subunit is the catalytic subunit of PP2A responsible for the serine/threonine phosphatase activity. The A and C subunits are highly conserved throughout eukaryotes and contain two isoforms each, encoded by different genes with the alpha isoform being the most predominantly expressed. The B subunit, on the other hand, consists of 4 distinct families of isoforms, i.e., B55 family (B), B56 family (B’), PR72 family (B’’), and PR93/PR110 family (B’’’). To date, there are 13 isoforms of B subunits described, all of which are responsible for the regulation of PP2A to target specific substrates and subcellular localizations [273, 274]. Thus, the diversity of B subunits allow PP2A to control multiple cellular processes at the same time.   88   PP2A has been reported to be a tumor suppressor that dephosphorylates and regulates multiple pro-survival and proliferation signaling cascades. These include, but are not limited to, AKT, SRC, ERK, and AR [275-279]. PP2A expression is downregulated in multiple human malignancies including breast cancer, lung cancer, colorectal cancer, hepatocellular cancer, ovarian cancer, cervical cancer, endometrial cancer, bladder cancer, PCa, and leukemia [280-287]. Here, we investigate the interaction between GRB10 and PP2A and the role of GRB10 in regulating PP2A in PCa.  5.2 Results 5.2.1 Knockdown of GRB10 led to a significant change in the transcriptomic landscape of prostate cancer cells  To study the function of GRB10 in PCa cells, we first performed an RNA microarray analysis using a control (shCntl) and two independent GRB10-knockdown (shGRB10) LNCaP cells. As a positive control, GRB10 expression was shown to decrease more than 7-fold in the shGRB10 group compared to the shCntl group. Overall, there are a similar number of genes significantly upregulated (n=1370) or downregulated (n=1460) at a threshold of p<0.05. To investigate which pathways were regulated by GRB10, we utilized the GSEA and unbiasedly analyzed the transcriptome upon GRB10 knockdown unbiasedly. First, in hallmark gene sets, ten cancer-related hallmarks including androgen response, mTORC1 signaling, and glycolysis were significantly downregulated by GRB10-knockdown. However, none of the hallmark gene sets were found to be upregulated by GRB10-knockdown at a threshold of FDR<0.05 (Fig 5.1A). As for Gene Ontology (GO) gene sets, many more molecular functions and biological process-related 89  pathways were inhibited by GRB10-knockdown rather than being promoted (Appendix C). The top-ranked GO pathways inhibited by GRB10-knockdown included sterol biosynthesis, antigen processing and presentation, and small molecule biosynthetic process.    Next, we used Ingenuity Pathway Analysis (IPA) to further analyze the connection between the most significantly altered-genes after GRB10-knockdown (p<0.05). Based on the fold changes of these genes, IPA predicted that the Diseases and Bio Functions including cell death of tumor cells (p=1.74E-3) and cell death of cancer cells (p=3.54E-3), were significantly increased by GRB10 knockdown, while genes related to pelvic tumor (p=6.44E-12), lung tumor (p=1.30E-3), and respiratory tumor (p=5.32E-3) were significantly decreased. Consistent with GSEA, RPTOR (the major component of mTORC1) was predicted to be inhibited by GRB10 knockdown (activation z score =-2.887, Fig 5.1B). Overall, these analyses suggest that GRB10 is a key regulator in promoting multiple cancer progression-related pathways in PCa.    90   Figure 5.1. Transcriptome analysis of GRB10-regulated pathways (A) Genes were ranked based on the significance p-value significance between control LNCaP and GRB10-knockdown LNCaP cells. Gene ranking was then uploaded on GSEA (http://software.broadinstitute.org/gsea/index.jsp) to identify GRB10-knockdown enriched gene sets. Bar chart showed Hallmark Gene Sets which were significantly downregulated by GRB10 knockdown (FDR<0.05). (B) Genes that were significantly affected by GRB10 knockdown (p<0.05) were uploaded to Ingenuity Pathway Analysis (https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis/) for discovery of potential upstream regulators regulated by GRB10. Several genes significantly downregulated by GRB10 knockdown were downstream of RPTOR, suggesting that RPTOR was inhibited by GRB10 knockdown.  91  5.2.2 Discovery of GRB10 interactome in prostate cancer  Given the fact that GRB10 is an adaptor protein, it is known to interact with various specific signaling cascades and has different context-dependent biological and pathological functions in a context-dependent manner. To understand the interactome of GRB10 in CRPC, we performed an unbiased, semi-quantitative, isotope dimethylated, liquid chromatography-mass spectrometry (LC-MS) analysis of GRB10-associated immunoprecipitated complexes derived from LNCaP and C4-2 cells. We precipitated Flag-GRB10 and discovered high-confidence, potential GRB10-binding partners. In the precipitated samples from both LNCaP and C4-2, GRB10 is among the top-ranked proteins detected based on the ratio between Flag-GRB10 samples and Flag control samples. Another protein, 14-3-3, which was previously reported to interact with GRB10, was also detected (Table 5.1). These positive controls suggest that our findings are reliable. Among all the common proteins identified by IP-MS, some were chaperone proteins such as heat shock protein 70 family protein 8 (HSPA8), heat shock protein 70 family protein 5 (HSPA5), and heat shock protein 70 family protein 1A (HSPA1A) (Table 5.1). The other group of proteins detected include cell skeleton-related proteins such as tubulin and actin. Interestingly, several metabolism-related proteins, e.g., glyceraldehyde-3-phosphate dehydrogenase, pyruvate kinase, ADP/ATP translocase 2, ATP synthase, and D-3-phosphoglycerate dehydrogenase, were also identified to be potential GRB10-bound proteins (Table 5.1). This suggests that GRB10 is closely involved in cell metabolism pathways and regulating cell proliferation and survival.    92  Protein names Gene names Ratio H/L normalized GRB10 GRB10 43.638 PP2AB PPP2R2A 29.346 HS71A HSPA1A 20.699 HSP7C HSPA8 17.752 PP2AA PPP2R1A 17.539 GAPDH GAPDH 13.103 BiP HSPA5 11.529 1433G YWHAG 9.9941 1433Z YWHAZ 7.5171 ADT2 SLC25A5 5.8242 ATPA ATP5A1 4.9334 PKM PKM 4.8438 PP2AC PPP2CA 4.3147 PHGDH PHGDH 3.4016 1433E YWHAE 2.4893 ATPB ATP5B 1.943 Table 5.1. Proteins which potentially interacted with GRB10 as detected by LC/ MS. Proteins were ranked based on the ratio of signal detected in Flag-GRB10 and Flag-control cells. H: heavy (Flag-GRB10); L: light (Flag-control).  5.2.3 GRB10 binds to and inhibits PP2A activity in prostate cancer  Among all the proteins that potentially interact with GRB10 in PCa cells, we found that all three subunits of PP2A, i.e., Serine/threonine-protein phosphatase 2A 65 kDa regulatory subunit A alpha isoform (gene name PPP2R1A), Serine/threonine-protein phosphatase 2A 55 kDa regulatory subunit B alpha isoform (gene name PPP2R2A), and Serine/threonine-protein phosphatase 2A catalytic subunit alpha isoform (gene name PPP2CA), were detected at a high confidence level. Further, PP2AB is the highest ranking protein besides GRB10 itself (Table 5.1). By Co-IP of ectopically-expressed 93  GRB10, we validated that GRB10 interacts with PP2A in C4-2 cells (Fig 5.2A); this association has never been reported before. Furthermore, in C4-2 cells, we also confirmed endogenous binding between GRB10 and PP2A (Fig. 5.2B).   Since PP2A is a well-established tumor suppressor [280], we explored whether GRB10-PP2A binding affects the status of PP2A in CRPC. We examined the PP2A protein level in GRB10 knockdown and their matched control cells. We observed that both transient and stable GRB10 knockdown increased the protein expression of PP2Ac, the subunit responsible for its phosphatase activity (Fig. 5.2C). Consistently, overexpression of GRB10 dramatically decreased the protein expression of PP2Ac in LNCaP cells (Fig. 5.2D). Interestingly, we did not see GRB10 knockdown affecting the mRNA levesl of the two isoforms (PPP2CA and PPP2CB) encoding PP2Ac when comparing GRB10 knockdown cells with control cells (Fig. 5.2E). These data suggest that the interaction between GRB10 and PP2A inhibited PP2A activity in CRPC.   94  Figure 5.2. The interaction between GRB10 and PP2A inhibited PP2A activity. (A) Binding of GRB10 to all three subunits of PP2A was confirmed by Co-IP. C4-2 cells expressing Flag-GRB10 or Flag-control were collected and lysed with NETN buffer. Five percent of input was collected for each sample. One mg of protein from each sample was incubated with FLAG magnetic beads. GRB10 and PP2A subunits were detected via Western Blotting. (B) Endogenous binding of GRB10 to PP2A was detected by Co-IP performed in C4-2 cells. Five mg of protein lysates were incubated with normal mouse IgG or anti-GRB10 antibody and then with protein A/G magnetic beads. Five percent of input was collected. GRB10 and PP2A subunits were detected via Western Blotting. (C) Effects of GRB10 knockdown on the protein levels of the PP2A C subunit in LNCaP were determined using stable GRB10 knockdown (KD1, KD2) and control (shC) cell lines by Western Blotting. (D) Effects of transient GRB10 overexpression on the protein levels of the PP2A C subunit in LNCaP were determined using Western Blotting. (E) Effects of GRB10 knockdown on the mRNA expression of PPP2CA (encoding PP2A C subunit) in 95  LNCaP were determined based on the microarray performed in Chapter 5.2.1. The mRNA expression was normalized to shControl.  5.3 Discussion  In the previous chapters, we discussed the potential role of GRB10 as a driver gene for CRPC development. To support this hypothesis, we analyzed the transcriptomic changes induced by GRB10 knockdown. We found that knockdown of GRB10 significantly downregulated the Androgen_Response gene set, suggesting knockdown of GRB10 decreased AR activity. AR, the key mediator of androgen response signaling, is still the dominant driver of CRPC progression even with continuous ADT and more potent ARPIs. In fact, the occurrence of AR genetic alterations is significantly increased from less than 2% in HNPC [67, 73] to around 60% in terminal CRPC [67, 68, 70]. This phenomenon indicates that GRB10 could support AR function during early CRPC development as it undergoes the pressure of first-line hormonal therapy. When CRPC is established, higher expression of GRB10 also assists AR in cancer progression until the PCa cells accumulate the enough genomic aberrations that finally lead to AR amplification or AR mutation.    Glycolysis is one of the other gene sets inhibited by GRB10-knockdown. Consistent with our previous findings, glycolysis is the downstream pathway regulated by both AR signaling and AKT signaling [255, 288], which were both inhibited by GRB10 knockdown. Glucose metabolism in PCa is dramatically different than that in other types 96  of cancer. Back in the 1920s, Dr. Otto Heinrich Warburg and his team observed that cancer cells convert glucose to lactic acid instead of preferring the more energy-efficient tricarboxylic acid (TCA) cycle and oxidative phosphorylation (OXPHOS). This occurred regardless of the presence of oxygen. It became known as aerobic glycolysis or the Warburg Effect and is a hallmark of cancer [289]. Thus, high consumption of glucose is one of the most distinctive traits of cancer cells compared to healthy tissue. It is the scientific basis upon which 2-deoxy-2-[fluorine-18] fluoro- D-glucose-positron emission tomography/ computed tomography (18F-FDG PET/CT) is used to diagnose cancer and detect its recurrence [290]. However, in PCa, and particularly in HNPC, FDG PET/CT is not clinically applicable due to limited sensitivity/ specificity [291]. The reason could arise from the unique function of the human prostate gland to produce citrate [292]. Clinical observations indicate that human semen contains 240~1,300-fold higher concentrations of citrate compared to blood plasma. This citrate is produced and secreted by healthy prostatic epithelial cells [293] and mainly functions to maintain the pH of the seminal fluid. To produce large amounts of citrate, prostate epithelial cells intake a large amount of glucose and treat citrate as an end-product rather than as an intermediate in glucose metabolism. This is distinct from other cell types, which uses citrate for energy production through the TCA cycle and OXPHOS. Therefore, healthy prostate epithelial cells have a low level of activity in the TCA cycle and OXPHOS but a relatively high intake of glucose to maintain its citrate production. In malignant prostate cells, glucose metabolism changes dramatically. PCa cells start to further catalyze citrate through the TCA cycle and OXPHOS to produce ATP and promote lipid and amino acid synthesis [292]. Eventually, as PCa progresses into mCRPC (metastatic CRPC), the TCA cycle and OXPHOS shut 97  down and aerobic glycolysis increases. This results in the acidification of the tumor microenvironment and the accumulation of building blocks leading to a higher proliferation rate and tumor aggressiveness [292]. In this clinical circumstance, 18F-FDG PET/CT has been shown to be an effective, non-invasive approach for the detection of PCa recurrence and metastasis [294, 295]. Here, we show that GRB10 is functionally important in glycolysis, which suggests that the upregulation of GRB10 in CRPC mediates increased aerobic glycolysis and promotes PCa progression and aggressiveness.    To further elucidate the molecular mechanism of GRB10-mediated CRPC development, we investigated the GRB10 interactome in PCa cells by using Co-IP coupled with LC/MS/MS. We found that two major groups of proteins were associated with GRB10. One group of proteins were metabolism-related proteins such as glyceraldehyde-3-phosphate dehydrogenase (GAPDH), pyruvate kinase (PKM), ADP/ATP translocase 2 (SLC25A5), ATP synthase subunit alpha (ATP5F1A), and D-3-phosphoglycerate dehydrogenase (PHGDH). GAPDH and PKM are major enzymes involved in the glycolysis pathway [296, 297]. PHGDH is an enzyme which catalyzes the oxidation of one of the glycolysis intermediates, 3-phopho-D-glycerate (3-PG), to 3-phosphooxypyruvate (p-Pyr), which can be used to biosynthesize L-serine and glycine [298]. The other two enzymes, ATP5F1A and SLC25A5, are located in the mitochondria and are involved in OXPHOS and energy generation [299, 300]. Overall, these proteins that we discovered to potentially interact with GRB10 are closely related to cellular metabolism and energy production. This suggests that one of the functions of GRB10 is regulating cell metabolism. Our finding could support a hypothesis that knockdown of 98  GRB10 downregulates overall glycolysis activity, sterol biosynthesis, and small molecule biosynthetic processes.   Another group of GRB10-interacting proteins we identified is the Hsp70 family of chaperone proteins, including heat shock protein 70 family protein 8 (HSPA8), heat shock protein 70 family protein 5 (HSPA5), and heat shock protein 70 family protein 1A (HSPA1A). Hsp70 family proteins are chaperone proteins which assist in the proper folding of unfolded proteins or refolding of misfolded proteins [301]. Evidence suggests that Hsp70 protects cancer cells from apoptosis induced by various cellular stresses and therapies such as reactive oxygen species, radiotherapies, and chemotherapies [301]. Thus, targeting Hsp70 has been suggested to be an effective therapeutic approach for treatment-resistant cancers including PCa [302, 303]. Based on this evidence, we suspect that the upregulation of GRB10 and the interaction of GRB10 with Hsp70 could promote the survival of treatment-resistant dormant PCa cells and eventual development of CRPC.   PP2A is a top-ranked protein complex that we identified to be associated with GRB10 in PCa cells. PP2A has long been reported to play a tumor suppressive role in cancer development and progression [280]. PP2A can dephosphorylate a variety of substrates of cyclin-dependent kinases (CDK) to inhibit cell cycle progression [304-306]. In lung and colon cancer, 15% of the primary clinical tumors harbour somatic alterations in PP2A, resulting in the instability of PP2A protein complex and leading to deregulated cell cycle control [283]. PP2A is also involved in Wnt signaling, where PP2A dephosphorylates GSK3β and decreases the expression of β-catenin to inhibit Wnt 99  signaling [307]. In addition, PP2A could regulate cell apoptosis. PP2A could dephosphorylate c-Jun and p53, two critical apoptosis-associated proteins, and inhibit the transcription of pro-apoptotic genes [308]. In PCa, PTEN-deletion is one of the most frequent genomic alterations in both primary and metastatic PCas. PTEN loss causes PI3K/ AKT signaling hyperactivation and promotes tumorigenesis and treatment-resistance in PCa [239, 262]. PP2A is a known inhibitor of AKT and dephosphorylates AKT at T308 [277, 309]. Therefore, activating PP2A has been suggested to be a potential therapeutic strategy in the management of PCa [310-312].    In this chapter, we provide evidence that GRB10 expression could regulate PP2A protein expression, suggesting that GRB10 could be an upstream regulator inhibiting its activity. Since PP2A is a general tumor suppressor which dephosphorylates and deactivates vital proteins involved in cell proliferation, metabolism, and apoptosis in multiple cancer types including PCa, the fact that GRB10 controls PP2A protein expression shows that GRB10 is a potential universal driver for the development and progression of CRPC.   100  Chapter 6 Conclusions 6.1 Summary of Findings  CRPC remains an extremely aggressive, incurable disease even though the effectiveness of ADT and recently approved Cabazitaxel, ABI, ENZ, APA, and DAR are effective. Therefore, a continued targeting of the AR pathway may not be the ultimate solution, and other factors should be taken into consideration to manage the development of CRPC. During CRPC development, particularly before the tumor progresses into the androgen-independent stage, signaling pathways other than the AR pathway should be activated to protect cancer cells and promote its proliferation. Therefore, in this doctoral study, we hypothesized that CRPC driving genes should fulfill the following criteria: (1) CRPC driving genes should be commonly expressed in CRPC; (2) CRPC driving genes should be upregulated following ADT as an early and continuous event; (3) CRPC driving genes should be upregulated after CRPC development, and lastly (4) CRPC driving genes should be functionally critical to cell growth and survival. Our hypothesis focuses more on the early time points during the CRPC development, since the genes upregulated only when CRPC is developed may be a consequence of CRPC aggressiveness but not a driver of its development.   In Chapter 2, following our hypothesis, we identified GRB10 as a potential driving gene for the development of CRPC. Firstly, we demonstrated that HNPC PDX models are suitable tools to study the development of CRPC, especially, the lines are capable of spontaneous recurrence into to CRPC following host castration. When castration is 101  applied to the tumor-bearing mice, the tumor shrinks and serum PSA level decreases significantly. On the molecular level, AR signaling was markedly inhibited as indicated by the decreased expression of AR regulated genes and signatures. By analyzing the transcriptomic data acquired before, during, and after the development of CRPC, we found that GRB10 is the top-ranked gene upregulated both during and after CRPC development. We further validated our identification of GRB10 in multiple independent studies, showing that GRB10 expression is elevated after ADT in genetically-engineered murine PCa models. With the analysis of clinical PCa cohorts, we further confirmed that GRB10 was significantly upregulated in clinical CRPC and its elevated expression was correlated with poor treatment outcomes. In view of these findings, we investigated the function of GRB10 in PCa cell lines. Knockdown of GRB10 using two independent shRNAs led to significant decreased cell proliferation. The reduction of GRB10 expression also inhibited cell cycle progression as indicated by decreased DNA synthesis and accumulation of cells at G0/1 stage. To further address the potential driver role of GRB10 in CRPC development, we overexpressed GRB10 in LNCaP androgen-sensitive PCa cell lines and observed that the ectopic expression of GRB10 promoted LNCaP androgen-independent proliferation. Mechanistically, given that AKT is well-reported to be one of the significant drivers of CRPC development, and given that GRB10 was reported to regulate AKT in other cell contexts, we examined the role of GRB10 in regulating AKT activity in PCa. Evidence suggest that increased expression of GRB10 during CRPC development promotes the activation of AKT, which mediates the survival and proliferation of PCa cells. Overall, the data indicate that GRB10 is a potential driver 102  of CRPC development and that a combination of ADT and GRB10-silencing could be a novel strategy to delay the development of PCa androgen-independent growth.    In Chapter 3, to explain the mechanism behind the upregulation of GRB10 during CRPC development, we looked into the upstream regulators of GRB10 in PCa. Since AR is a transcription factor and ADT induced the upregulation of GRB10 in PCa PDX models, we suspected that AR repressed the expression of GRB10. We first manipulated AR activity in PCa cell lines using androgen ablation, AR antagonist, ENZ, and the AR activator R1881. We found that the expression of GRB10 was associated with AR activity, where AR inhibition increased GRB10 expression while AR activation led to decreased GRB10 expression. Clinical evidence supported our findings, where GRB10 was upregulated after neoadjuvant ADT and the expression of GRB10 and KLK3 (PSA) was significantly negatively correlated. Through a thorough analysis of publically available AR ChIP-seq data, we identified three potential androgen-responsive elements (AREs) located in the intron of GRB10. ChIP-PCR suggests all three potential AREs could interact with activated AR. Interestingly, we also found AR enrichment at the putative AREs in clinical untreated and CRPC cases but not in treatment responsive cases, suggesting that our findings were clinically relevant. Further validation of potential AREs using luciferase reporter assay confirmed that GRB10 was transcriptionally repressed by AR through an ARE located in the intron of GRB10 (chr7: 50657497-50657511).   In Chapters 4 and 5, we focused on the potential mechanisms of GRB10-mediated CRPC development. Since GRB10 is an adaptor protein that provides scaffolding for 103  multiple signaling pathway cascades and regulates their functions, we used multi-omics analyses to elucidate its functional partners in PCa cells. Firstly, we analyzed the transcriptomic changes induced by GRB10 knockdown. Our data suggest that reduced GRB10 expression led to a dramatic decrease in several PCa progression-related gene sets including androgen response, mTORC1 signaling, and glycolysis. Next, we performed LC/MS to investigate the interactome of GRB10 in PCa cells. Interestingly, PP2A, a well-established tumor suppressor, was found to interact with GRB10. We further provided evidence that the interaction of GRB10 and PP2A decreased PP2A expression, which suggests that partial, if not all of the function of GRB10, was achieved through the regulation of PP2A.  6.2 Limitations and Future Directions  The future directions of this study are considered closely with regards to the limitation of the current study. In terms of the findings that GRB10 is regulated by AR, assays were performed in limited PCa cell lines. Given that PCa is a heterogeneous disease, the conclusion drawn from only PCa cell lines may be biased. We could use our collections of over 40 PCa PDX models to validate our findings. One of the advantages of our PCa PDX models is that these models are transplantable. We can thus acquire enough cancer tissue samples for ChIP experiments, overcoming the common problem that patient tissue samples are of too limited quantity for satisfactory protein-based assay.  104   In this study, we proposed that GRB10 is a potential driver for the development of CRPC. To this end, we overexpressed GRB10 and demonstrated that the ectopic expression of GRB10 could promote LNCaP androgen-independent growth. However, the level of GRB10 overexpression we tested is much higher than that in CRPC. Since GRB10 is negatively regulated by AR, we could use the CRISPR-Cas9 technique to knockout the androgen-responsive element we discovered, which repressed the expression of GRB10. By doing so, we can monitor the function of GRB10 in a more clinically relevant manner and demonstrate its driver role in the development of CRPC.   In Chapter 4, we discovered that AR can transcriptionally regulate GRB10 expression through an ARE located in GRB10 intron. However, the precise mechanism of AR regulating GRB10 is not conclusive as only luciferase reporter assays were performed. To better prove AR represses GRB10 expression through the identified ARE, more assays need to be used. For example, as previously mentioned, we could use the CRISPR-Cas9 technique to knockout the ARE. AR can no longer regulate GRB10 expression if the repression was conducted through that regulatory elements. Furthermore, the repression function of AR is supported be co-repressor factors such as LSD1, EZH2, and Rb1 [248, 254, 313-316]. Thus, we can perform a rapid immunoprecipitation mass spectrometry of endogenous proteins (RIMS) to identify the potential co-factors of AR in this case [317].   In Chapter 5, we identified PP2A as a GRB10-interacting protein using an unbiased IP-MS in PCa cells. We then established that GRB10 downregulates PP2A 105  protein expression. Although we have ruled out by our findings that this is achieved through a reduction in mRNA expression, there remains multiple ways to reduce PP2A protein expression, including alternating post-translational modification, protein degradation and protein synthesis. To further elucidate the precise mechanism of GRB10-regulated PP2A expression, we will study whether the expression GRB10 would affect the protein turnover of PP2A.  6.3 Conclusion and Significance  In summary, this doctoral study led to the identification of GRB10 as a potential driving gene of CRPC development. We have validated its significant upregulation in multiple clinical CRPC cohorts, and its elevated expression was associated with disease aggressiveness. We further confirmed its transcriptional suppression by AR, and established the effects of its knockdown on impeding CRPC cell proliferation. The overexpression of GRB10 also led to increased androgen-independent growth. Mechanistically, knockdown of GRB10 induced significant changes at the transcriptome level and downregulated multiple pathways related to androgen independence. Through unbiased immunoprecipitation-mass spectrometry (IP-MS), we demonstrated that GRB10 could directly bind to protein phosphatase 2A (PP2A), a well-established tumor suppressor. Moreover, GRB10 can suppress PP2A expression. These data suggest that GRB10 plays an important role in CRPC development and progression.  106   The study provides a new theory of CRPC development, where ADT, whether through androgen ablation or AR antagonists, induces the expression of GRB10, an AR repressed gene. Upregulation of GRB10 increased many cancer progression-related pathways, including PI3K/AKT/mTOR, AR signaling, and glycolysis. Meanwhile, the elevated expression of GRB10 interacts with PP2A and decreases its protein expression. Taken together, these changes on the molecular level led to increased cell proliferation and survival, which eventually promotes the development of CRPC. (Fig 6.1)   107   Figure 6.1. The role of GRB10 in the development of CRPC A schematic model summarizing the mechanisms by which increased expression of GRB10 after ARPI (ADT/AR signaling inhibitor) mediates the development of CRPC and potentially ARPI resistance through enhancing AKT activity and inhibiting PP2A. FLU: Flutamide, BIC: Bicalutamide, NIL: Nilutamide, ENZ: Enzalutamide, APA: Apalutamide, DAR: Darolutamide, LHRHi: Luteinizing hormone-releasing hormone inhibition, ABI: Abiraterone.  108   In conclusion, this study increased our understanding of the mechanisms underlying the development of CRPC, identifying GRB10 as a potential driver for the development of CRPC and providing a novel therapeutic target to lead to improved therapy of advanced PCa.  109  Bibliography [1] Cunha GR, Donjacour AA, Cooke PS, Mee S, Bigsby RM, Higgins SJ, et al. The endocrinology and developmental biology of the prostate. Endocr Rev. 1987;8:338-62. [2] Lilja H. Structure and function of prostatic- and seminal vesicle-secreted proteins involved in the gelation and liquefaction of human semen. Scand J Clin Lab Invest Suppl. 1988;191:13-20. [3] Hayward SW, Cunha GR. The prostate: development and physiology. Radiol Clin North Am. 2000;38:1-14. [4] Myers RP. 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Nature protocols. 2016;11:316-26.   152  Appendices Appendix A: Primers used in the study (5'→3') qRT-PCR primers GRB10_F CAGACACCTCGCAGTCAACA GRB10_R TATCGTTCACCAGGGCTTCC PSA_F CACCTGCTCGGGTGATTCTG PSA_R CCACTTCCGGTAATGCACCA GAPDH_F CACCAGGGCTGCTTTTAACTC GAPDH_R GACAAGCTTCCCGTTCTCAG FGFR2_F AACAGTTTCGGCTGAGTCCA FGFR2_R CCCAAAGCAACCTTCTCCCA FILIP1_F TGCACCGAGATGCCATTCTT FILIP1_R GGTGCGCCTATGACACTTCT NPC2_F GCGGTTCTGTGGATGGAGTT NPC2_R CGGCCTTGCTGCTTTTAGAC ChIP-PCR primers PSA_ChIP_F GCCTGGATCTGAGAGAGATATCAT PSA_ChIP_R ACACCTTTTTTTTTCTGGATTGTTG ARE1_ChIP_F GGCCATCTAAAAGTCAATACTGG ARE1_ChIP_R TGGACATGTTCGCACGTGTG ARE2_ChIP_F AGACCAGGGACTGTGTGA ARE2_ChIP_R TATGAGGAAATCCCCGAAAAG ARE3_ChIP_F AGGAAGCTGAGCATGGAAGAG ARE3_ChIP_R AGAAGCTGGGGACATGCTG NC_ChIP_F TAGGCGCTCACTGTTCTCTC NC_ChIP_R CGTTGACTCCGACCTTCAC Plasmid construction primers ARE1_Nhe1F CTAGCTAGCTGACAGGGAATAGCTGCTTAATTG ARE1_Mlu1R CGACGCGTCGGAATGATCTGGTTGACTGACAGAAG ARE2_Mlu1F CGACGCGTCGTTCCCGCGGCTCCTGGCATGGC ARE2_Sac1R CGAGCTCGCTCTTAACTGGCTGAAGGACAAG ARE3_Xho1F CGACGCGTCGGAATGATCTGGTTGACTGACAGAAG ARE3_Nhe1R CTAGCTAGCTAGAAACCTCACAGAAAACAGAACTGA ARE2mut_F GAGAGGAATGTGCGCCGTTGGGTGGGGGTCCCATTTAAATCTTTTC ARE2mut_R GAAAAGATTTAAATGGGACCCCCACCCAACGGCGCACATTCCTCTC ARE2del_F GAGAGGAATGTGCGCCGGTTCCCATTTAAATCTTTTC ARE2del_R GAAAAGATTTAACGCCGGTTCCCATCACATTCCTCTC 153  GRB10_Hind3_F CCCAAGCTTATGGACTACAAGGATGACGATGACA GRB10_Cla1_R CCATCGATTCATAAGGCCACTCGGATGCAG    154   Appendix B: Significantly upregulated genes identified in PCa PDX models  CRPC vs HNPC Cx 12wk vs Pre Cx GeneSymbol Fold change (log2) p value Fold change (log2) p value BASP1P1 0.974234 0.001241 0.25094063 0.474518696 BTBD9 0.767076 0.002725 0.73120973 0.032053459 CBX7 0.836602 0.003684 1.07930445 0.004057882 BAALC-AS1 0.984251 0.003897 0.41719876 0.312893912 BASP1 1.444986 0.004285 0.62175365 0.354049428 PTPRB 1.131169 0.004567 1.13152512 0.017908128 MED12 0.32725 0.00485 0.28956356 0.303586835 GRB10 1.054059 0.006795 1.74549185 0.00043506 POMGNT1 0.498621 0.006895 0.3962618 0.019258141 HOXA-AS3 2.386454 0.007151 1.19388985 0.111174706 PLD1 1.890935 0.007947 1.60342177 0.070812192 CDK10 0.634572 0.00821 -0.26044984 0.451509505 PRDM2 0.323889 0.008721 1.92351539 0.001393164 LOC101927479 1.15497 0.009638 0.45951144 0.242875852 DNAH8 2.143747 0.009989 -0.6489366 0.367007726 MED15 0.331679 0.010651 -0.29861478 0.168187976 LINC00663 0.501226 0.010932 0.10501498 0.678097869 VAMP2 0.566094 0.012016 0.39472445 0.088336064 ZRSR2 0.400643 0.0133 0.41544505 0.046497415 FNDC3B 0.899199 0.013684 0.6489845 0.025500728 TOM1 0.499679 0.013775 0.4706437 0.047637542 MKNK1 0.542426 0.014099 0.2214761 0.310666601 NENF 0.761291 0.014474 0.69035545 0.008220115 OTUD5 0.332642 0.016133 -0.55583017 0.053283534 LINC00643 1.678318 0.016207 0.56770056 0.378151363 LIMK2 0.376589 0.017562 0.2978218 0.302594742 ESPN 2.01749 0.018427 -0.33650754 0.495860204 C5orf63 0.516815 0.018568 0.3471672 0.000647732 FGFR2 1.289497 0.019653 1.41575859 0.026530083 SEPT1 1.214467 0.020271 0.51650596 0.443993228 BRE 0.42859 0.021026 0.24240835 0.266698833 CDHR1 1.52512 0.021632 1.14731598 0.118427825 SSBP3 0.69001 0.021879 0.814047 0.049316794 155  DUOXA1 1.762907 0.022037 0.53212992 0.269646057 GJD3 0.490907 0.022881 -1.11086832 0.135730577 LOC152225 2.350453 0.023087 0.73519615 0.250534699 SP4 0.511433 0.023184 0.43473935 0.099438657 EBF4 0.876654 0.023229 0.71372057 0.11525054 EMC10 0.412999 0.023584 0.45085502 0.161904268 RHBDD2 0.442733 0.02405 0.11177525 0.701237243 PBXIP1 0.705326 0.02433 0.70903787 0.075512198 LOC101929106 1.21292 0.025912 0.24246477 0.545058196 CAB39 0.655879 0.02623 0.73165455 0.045293911 NSF 0.493317 0.026453 0.1582931 0.535081 UPK3A 1.739387 0.027497 1.09910185 0.080759931 SIDT2 0.717035 0.028195 0.4574249 0.127045893 DDX3X 0.407582 0.02824 0.5273518 0.053450508 SPINT2 0.444534 0.028771 0.34422085 0.117363594 MBNL3 1.074781 0.029928 0.84307561 0.109739636 COPS7A 0.398459 0.030489 0.3398831 0.071632345 MSX1 0.579057 0.030644 -0.31265983 0.606518481 NPC2 1.331313 0.032504 1.4892755 0.011806133 PCDHGA8 0.770709 0.036243 0.96374625 0.017656256 CYP21A2 0.726514 0.036745 0.46004437 0.038546997 LOC100131756 0.979815 0.03685 0.98513414 0.072224675 TMEM200A 2.739402 0.036902 -0.56397554 0.709329625 RASAL2 0.543557 0.036979 0.52933918 0.151569838 SNX21 0.515421 0.037095 0.36645297 0.124625199 ELK4 0.442271 0.038015 -0.2765354 0.337032689 NDFIP1 0.551982 0.038466 0.24093392 0.340507978 CLCA3P 1.069014 0.038706 0.17389814 0.55148463 EDN1 1.35617 0.039794 -0.28907882 0.536295379 NTRK1 0.959147 0.041172 0.66419466 0.052857788 NPC1L1 0.900588 0.041319 0.35648643 0.286005508 TTTY6 1.964533 0.041414 -0.67347316 0.0856616 ETV3 0.386611 0.042505 -0.08717923 0.584383632 KPTN 0.575059 0.042638 -0.20138991 0.541106697 NUDT18 0.85235 0.042967 -0.22454569 0.608975976 RREB1 1.007622 0.04352 -0.15603478 0.618328712 ZFAND2B 0.509727 0.043571 0.3005792 0.139246811 FNDC4 1.525577 0.0446 0.43648605 0.286584628 DBNL 0.388396 0.044847 0.17144461 0.355126515 FUNDC1 0.530143 0.044854 -0.1277144 0.609052158 156  FAM153B 1.110716 0.045019 -0.34404239 0.699252668 TTTY11 0.859317 0.046391 -0.37903546 0.434020099 FILIP1 1.6701 0.047046 1.68412107 0.003904824 TPRG1 0.730384 0.048326 0.34986219 0.084512059 NFATC2 0.799431 0.048657 0.63716362 0.065832651 IDS 0.51157 0.048863 0.31647899 0.485725614 MAN2B2 0.497536 0.049269 -0.19639271 0.557799515    157  Appendix C: Gene Ontology gene sets significantly enriched by GRB10 knockdown NAME  SIZE  NES  NOM p  FDR q GO_STEROL_BIOSYNTHETIC_PROCESS  39  2.449  0.000  0.000 GO_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_ANTIGEN_VIA_MHC_CLASS_I  85  2.345  0.000  0.000 GO_SMALL_MOLECULE_BIOSYNTHETIC_PROCESS  385  2.165  0.000  0.004 GO_ALCOHOL_BIOSYNTHETIC_PROCESS  99  2.162  0.000  0.004 GO_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_EXOGENOUS_PEPTIDE_ANTIGEN_VIA_MHC_CLASS_I  62  2.160  0.000  0.003 GO_REGULATION_OF_CELLULAR_AMINE_METABOLIC_PROCESS  77  2.142  0.000  0.003 GO_FATTY_ACID_BIOSYNTHETIC_PROCESS  97  2.141  0.000  0.003 GO_GLUTATHIONE_DERIVATIVE_METABOLIC_PROCESS  21  2.123  0.000  0.003 GO_REGULATION_OF_FATTY_ACID_BIOSYNTHETIC_PROCESS  28  2.107  0.000  0.004 GO_ERAD_PATHWAY  71  2.097  0.000  0.004 GO_LIPOPROTEIN_BIOSYNTHETIC_PROCESS  80  2.067  0.000  0.007 GO_NUCLEOSIDE_BISPHOSPHATE_BIOSYNTHETIC_PROCESS  16  2.057  0.000  0.008 GO_THIOESTER_BIOSYNTHETIC_PROCESS  47  2.032  0.000  0.012 GO_ER_ASSOCIATED_UBIQUITIN_DEPENDENT_PROTEIN_CATABOLIC_PROCESS  59  2.030  0.000  0.011 GO_THIOESTER_METABOLIC_PROCESS  70  2.002  0.000  0.016 GO_LIPID_BIOSYNTHETIC_PROCESS  466  2.000  0.000  0.016 GO_LIPOPROTEIN_METABOLIC_PROCESS  112  1.996  0.000  0.016 GO_FATTY_ACYL_COA_METABOLIC_PROCESS  46  1.985  0.000  0.018 GO_STEROL_METABOLIC_PROCESS  106  1.980  0.000  0.018 GO_ANTIGEN_PROCESSING_AND_PRESENTATION  188  1.971  0.000  0.019 GO_CARBOHYDRATE_BIOSYNTHETIC_PROCESS  104  1.968  0.000  0.019 GO_COFACTOR_BIOSYNTHETIC_PROCESS  152  1.966  0.000  0.019 GO_STEROID_BIOSYNTHETIC_PROCESS  94  1.956  0.000  0.022 GO_COENZYME_BIOSYNTHETIC_PROCESS  114  1.954  0.000  0.022 GO_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_ANTIGEN  161  1.952  0.000  0.021 GO_MONOSACCHARIDE_BIOSYNTHETIC_PROCESS  48  1.933  0.000  0.027 GO_FATTY_ACID_TRANSPORT  47  1.918  0.000  0.033 GO_MEMBRANE_LIPID_BIOSYNTHETIC_PROCESS  107  1.904  0.000  0.039 GO_NUCLEOSIDE_BISPHOSPHATE_METABOLIC_PROCESS  32  1.903  0.000  0.038 GO_ORGANIC_HYDROXY_COMPOUND_BIOSYNTHETIC_PROCESS  143  1.902  0.000  0.037 GO_PHOSPHATIDYLSERINE_METABOLIC_PROCESS  25  1.894  0.000  0.040 GO_REGULATION_OF_CELLULAR_RESPIRATION  20  1.894  0.002  0.039 GO_ALDITOL_PHOSPHATE_METABOLIC_PROCESS  31  1.882  0.002  0.045 GO_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_ENDOGENOUS_ANTIGEN  14  1.873  0.002  0.050 GO_CHROMATIN_SILENCING_AT_RDNA  34  ‐2.160  0.000  0.013 GO_REGULATION_OF_HEMATOPOIETIC_PROGENITOR_CELL_DIFFERENTIATION  38  ‐2.079  0.000  0.047 GO_TELOMERE_CAPPING  28  ‐2.054  0.000  0.047 158  NES: normalized enrichment score; NOM p: nominal p-value; FDR q: false discovery rate q-value.   

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