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Immune cell alterations in mouse models of prostate cancer Tien, Hsing-chen Amy 2007

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IMMUNE CELL ALTERATIONS IN MOUSE MODELS OF PROSTATE CANCER by HSING-CHEN AMY TIEN B.Sc., The University of British Columbia, 2001  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY  in  THE FACULTY OF GRADUATE STUDIES  (Experimental Medicine)  THE UNIVERSITY OF BRITISH COLUMBIA December 2007 © Hsing-Chen Amy Tien, 2007  ABSTRACT Numerous studies have demonstrated that tumour cells have the ability to alter immune function to create an immune suppressed environment. This allows tumour cells to escape immune surveillance and consequently the tumour can progress. Dendritic and T cells have critical roles in immune activation and tolerance and are thus major targets of tumour-mediated immune suppression. Understanding the mechanism(s) by which tumour cells modulate the immune system will facilitate the development of immune system-based therapies for cancer treatments. In this study we sought to determine the nature of, and cellular and molecular mechanisms underlying, changes in immune status during tumour progression using mouse models of prostate cancer. Detailed analysis of the immunological status in a mouse prostate dysplasia model (12T-7slow) revealed that immune suppression accompanied tumour progression. We found that T cells isolated from tumour-bearing hosts were hypo-responsive to antigen stimulation. Furthermore, we demonstrated that CD4+CD25+ regulatory T cells were responsible, at least in part, for this alteration. Anti-CD25 antibody treatment reduced, but did not prevent, tumour growth in either a transplanted prostate tumour model or a spontaneously developing prostate tumour model. In addition, an altered dendritic cell phenotype and an elevated frequency of CD4+CD25+ regulatory T cells were observed within the tumour mass. Similar alterations were observed in the prostatespecific Pten knockout mice which develop advanced prostate adenocarcinoma. Interestingly, evidence of immune activation, such as an increased frequency of activated T cells, was detected in the tumour microenvironment in both mouse prostate tumour models. To identify factors that may play critical roles in the altered immune cell phenotype observed in the tumour microenvironment, a global gene expression profiling analysis was carried out to evaluate the changes in immune-related gene expression patterns. This analysis provided additional evidence for the co-existence of immune suppression and immune activation. Moreover, subsequent analyses suggested that one  ii  differentially expressed transcript, interferon regulatory factor 7, and its target genes might be involved in modulating immune cells and/or tumour progression. Taken together, these studies have important implications for designing specific and effective anti-tumour immune therapy strategies that involve manipulation of tumour cells, dendritic cells and regulatory T cells.  iii  TABLE OF CONTENTS Abstract.............................................................................................................................ii Table of Contents.............................................................................................................iv List of Tables ..................................................................................................................vii List of Figures............................................................................................................... viii List of Abbreviations ........................................................................................................x Acknowledgements.........................................................................................................xii CHAPTER 1 Introduction ...............................................................................................1 1.1 Immune System ....................................................................................................1 1.1.1 Immune Surveillance in the Tumour Environment ......................................1 1.1.2 Regulatory T Cells........................................................................................2 1.1.3 Dendritic Cells ..............................................................................................5 1.2 Prostate Cancer .....................................................................................................7 1.2.1 Prostate Glands .............................................................................................7 1.2.2 Prostate Cancer Incidence, Causes and Treatment .......................................8 1.2.3 Mouse Models for Prostate Cancer ..............................................................9 1.3 Immune Cell Alterations in Cancer ....................................................................10 1.3.1 Immune Suppression in the Tumour Environment.....................................10 1.3.2 CD4+CD25+ Regulatory T Cells in the Tumour Environment ...................12 1.3.3 Dendritic Cells in the Tumour Environment ..............................................14 1.4 Gene Expression Profiling..................................................................................17 1.4.1 Serial Analysis of Gene Expression ...........................................................17 1.4.2 Global Gene Expression Analyses in Cancer .............................................19 1.5 Hypotheses and Objectives.................................................................................20 1.6 References ..........................................................................................................22 CHAPTER 2 Regulatory T Cells in the Prostate Tumour Environment .......................37 2.1 Background.........................................................................................................37 2.2 Materials and Methods .......................................................................................38 2.2.1 Mice ............................................................................................................38 2.2.2 Single Cell Preparation from Primary Tissues ...........................................39 2.2.3 Phenotype Analysis by Flow Cytometry ....................................................39 2.2.4 T Cell Proliferation Assay ..........................................................................40 2.2.5 Purification and Functional Assessment of CD4+CD25+ T Cells...............40 2.2.6 Determination of Cytokine Release by Cytometric Bead Array ................41 2.2.7 Cell Lines and Culture ................................................................................41 2.2.8 Tumour Cell Transplantation and Anti-CD25 Monoclonal Antibody Treatment in vivo ........................................................................................41 2.2.9 Data Presentation and Statistical Analysis .................................................42  iv  2.3 Results ................................................................................................................42 2.3.1 Altered Phenotype of T Cells in the Prostate Tumour Microenvironment ......................................................................................42 2.3.2 Expansion of Immune Cells in the Secondary Lymphoid Tissues during Prostate Tumour Progression......................................................................43 2.3.3 Alteration of T Cell Function during Prostate Tumour Development........45 2.3.4 Depletion of CD4+CD25+ T Cells in a Tumour Transplant Mouse Model..........................................................................................................48 2.3.5 Depletion of CD4+CD25+ T Cells in a Prostate Dysplasia Mouse Model..........................................................................................................50 2.4 Discussion...........................................................................................................52 2.4.1 Mechanisms for Treg Cell Generation or Expansion in the Tumour Environment ...............................................................................................52 2.4.2 Depletion of CD4+CD25+ Treg Cells by Anti-CD25 Antibody .................53 2.5 References ..........................................................................................................56 CHAPTER 3 Dendritic Cells in the Prostate Tumour Environment .............................60 3.1 Background.........................................................................................................60 3.2 Materials and Methods .......................................................................................61 3.2.1 Mice ............................................................................................................61 3.2.2 Bone Marrow-Derived DC Culture ............................................................61 3.2.3 Phenotype Analysis by Flow Cytometry ....................................................62 3.2.4 T Cell Proliferation Assay ..........................................................................62 3.2.5 Prostate Cell Culture and Co-culture Setup................................................63 3.2.6 Data Presentation and Statistical Analysis .................................................63 3.3 Results ................................................................................................................64 3.3.1 Analysis of in vitro BM-Derived DC .........................................................64 3.3.2 Analysis of DC in the Secondary Lymphoid Tissues and Prostate ............68 3.3.3 Co-culture of BMDC with Tumour Cell Culture Supernatant ...................71 3.4 Discussion...........................................................................................................74 3.4.1 Mechanisms for DC Alteration in the Tumour Microenvironment............74 3.4.2 The Role of Altered DC during Tumour Development..............................76 3.5 References ..........................................................................................................78 CHAPTER 4 Identification of Immune Modulatory Factors Produced by Prostate Tumour Cells ..........................................................................................81 4.1 Background.........................................................................................................81 4.2 Materials and Methods .......................................................................................82 4.2.1 Prostate-Specific Pten Knockout Mouse Model.........................................82 4.2.2 Phenotype Analysis by Flow Cytometry ....................................................82 4.2.3 LongSAGE Library Construction and Data Analysis ................................83 4.2.4 Gene Validation by Quantitative RT-PCR .................................................84 4.2.5 Immunofluorescence Staining ....................................................................85 4.2.6 Western Blotting.........................................................................................85  v  4.2.7 cisRED Analysis.........................................................................................86 4.2.8 Statistical Analysis......................................................................................86 4.3 Results ................................................................................................................87 4.3.1 Altered Phenotype of Immune Cells in the Pten Knockout Prostate Microenvironment ......................................................................................87 4.3.2 Summary of the LongSAGE Prostate Libraries .........................................89 4.3.3 Immune Gene Expression in the Three SAGE Libraries............................95 4.3.4 Identification of Immune-Related Factors................................................100 4.4 Discussion.........................................................................................................109 4.4.1 Changes in Immune Cells and Transcripts during Tumour Development.............................................................................................109 4.4.2 The Roles of IRF7 in Prostate Tumour Development ..............................112 4.5 References ........................................................................................................114 CHAPTER 5 Conclusions ...........................................................................................118 5.1 Summary and Significance of the Studies ........................................................118 5.2 Limitations of the Studies.................................................................................120 5.3 Application for Future Studies .........................................................................121 5.4 References ........................................................................................................123 APPENDIX 1 Top 3 GO Terms at the Third Level of Biological Process in Each Cluster....................................................................................................124 APPENDIX 2 GoMiner Analysis of Immune System-Related Genes in Each Cluster Profile ...................................................................................................125 APPENDIX 3 Significantly Up-regulated Membrane-Related Genes in the KO Prostate Tissues .....................................................................................127 APPENDIX 4 Significantly Down-regulated Membrane-Related Genes in the KO Prostate Tissues .....................................................................................132 APPENDIX 5 Increased Level of TNFα Production in Pten KO Prostate Culture Supernatant ............................................................................................134 APPENDIX 6 Animal Care Certificates ......................................................................135  vi  LIST OF TABLES Table 2.1 Cellularity and cellular composition of various draining LN during tumour progression .....................................................................................................44 Table 2.2 CD4+CD25+ T cells from the secondary lymphoid tissues of tumour-bearing mice secrete elevated levels of IL-10 compared with CD4+CD25- T cells ....48 Table 3.1 Cellular composition of the lumbar LN at late stages of prostate tumour progression......................................................................................................70 Table 4.1 Summary of the LongSAGE mouse dorsolateral prostate libraries ...............89 Table 4.2 Significantly enriched immune-related GO terms in each cluster..................96 Table 4.3 Immune-related genes in each cluster ............................................................98 Table 4.4 Significantly up-regulated immune-related genes in the KO prostate tissues............................................................................................................102 Table 4.5 Significantly down-regulated immune-related genes in the KO prostate tissues............................................................................................................102 Table 4.6 Significantly up-regulated potential target genes of IRF7 in KO prostate ...106 Table 4.7 Significantly down-regulated potential target genes of IRF7 in KO prostate..........................................................................................................108  vii  LIST OF FIGURES Figure 1.1 Elimination, equilibrium and escape during cancer immunoediting...............1 Figure 1.2 Effects of tumour-derived factors on DC alteration in the tumour microenvironment..........................................................................................16 Figure 1.3 A schematic diagram of SAGE library construction.....................................19 Figure 2.1 Tumour-infiltrating T cells exhibit an altered phenotype .............................43 Figure 2.2 The frequency and number of CD4+CD25+ T cells is elevated in the lumbar LN of Tg mice during tumour progression .......................................45 Figure 2.3 Tg lumbar LN and spleen T cells are impaired in proliferative capacity during tumour development...........................................................................46 Figure 2.4 The suppressive activity of CD4+CD25+ T cells contributes to the decreased proliferation of Tg T cells .............................................................47 Figure 2.5 Anti-CD25 mAb treatment reduces the growth of transplanted prostate tumour cells ...................................................................................................49 Figure 2.6 Anti-CD25 mAb treatment reduces the growth of spontaneous prostate tumours ..........................................................................................................51 Figure 3.1 Tg BMDC generated in GM-CSF/IL-4 cultures show no significant phenotypic or functional differences compared to WT cells.........................65 Figure 3.2 Tg BMDC generated in Flt3L cultures show no significant phenotypic or functional differences compared with WT cells............................................67 Figure 3.3 Phenotype and function of WT and Tg spleen CD11c+ cells are similar......69 Figure 3.4 Frequency and phenotype of the CD11c+ cell population are similar in WT and Tg LNs .............................................................................................70 Figure 3.5 Tumour-infiltrating DC exhibit an altered phenotype...................................71 Figure 3.6 Exposure to tumour cell culture supernatant alters the phenotype and function of mDC ............................................................................................73  viii  Figure 3.7 Tumour supernatant impairs T cell proliferation due to effects on antigen presenting cells ..............................................................................................74 Figure 4.1 The phenotype of tumour-infiltrating immune cells is altered in Pten KO mice ...............................................................................................................88 Figure 4.2 GO analysis shows the distribution of genes at the third level of biological process in the three prostate SAGE libraries .................................................91 Figure 4.3 GO analysis shows the distribution of genes at the third level of cellular component in the three prostate SAGE libraries ...........................................92 Figure 4.4 The mean values of adjusted FOM versus the number of clusters are calculated .......................................................................................................93 Figure 4.5 Clustering analysis of transcripts shows the gene expression patterns in the three prostate SAGE libraries ..................................................................94 Figure 4.6 The number of significantly over-represented GO terms in each cluster is shown.............................................................................................................95 Figure 4.7 Irf7 is up-regulated in prostate tumour tissues ............................................103 Figure 4.8 Nuclear localization of IRF7 is detected in the Pten KO prostate tumour cells ..............................................................................................................104 Figure 4.9 Up-regulation of potential IRF7 target genes in prostate tumour tissues....105  ix  LIST OF ABBREVIATIONS 7AAD Ab Ag APC BM BMDC CD cDNA CLP CMP CpG ODN CTL CTLA4 DC DC-LAMP DMEM EDTA FACS FBS FITC Flt3 Flt3L FOM FOXP3 G-CSF GITR GM-CSF GO Het HLA IDO IFN IgG IL IMDM IRF7 ISGF3γ i.v. KO LN LPB LPS  7-Aminoactinomycin D antibody antigen antigen-presenting cells bone marrow dendritic cells derived from bone marrow cluster of differentiation complementary DNA common lymphoid progenitors common myeloid progenitors CpG motif containing oligonucleotide cytotoxic T lymphocytes cytotoxic T lymphocyte antigen 4 dendritic cells DC-lysosome-associated membrane protein Dulbecco’s Modified Eagle’s Medium ethylenediaminetetraacetic acid fluorescence activated cell sorting fetal bovine serum fluorescein isothiocyanate fms-like tyrosine kinase 3 fms-like tyrosine kinase 3 ligand figure of merit forkhead box protein P3 granulocyte colony-stimulating factor glucocorticoid-induced TNF-receptor-related protein granulocyte macrophage colony-stimulating factor gene ontology heterozygous human leukocyte antigen indoleamine 2,3-dioxygenase interferon immunoglobulin G interleukin Iscove’s modified Dulbecco’s medium interferon regulatory factor 7 interferon-stimulated gene factor 3-γ intravenous knockout lymph node(s) large fragment of the rat probasin gene promoter lipopolysaccharide  x  mAb M-CSF mDC MGC MFI MHC mPIN MSC MSP MTG NK cells ODN PB PBS PCa PCR pDC PE PGE2 PI PIN PSA Pten qRT-PCR SAGE s.c. SD SEM SV40 STAT Tag Tg TGF-β Th cells TLR TNF Tr1 TRAF6 TRAMP Treg cells VEGF WT  monoclonal antibody macrophage colony-stimulating factor myeloid DC (also known as DC1) Mammalian Gene Collection mean fluorescence intensity major histocompatibility complex mouse prostatic intraepithelial neoplasia myeloid suppressor cells macrophage-stimulating protein monothioglycerol natural killer cells oligodeoxynucleotides probasin Dulbecco’s phosphate-buffered saline prostate cancer polymerase chain reaction plasmacytoid DC (also known as DC2) phycoerythrin prostaglandin E2 propidium iodide prostatic intraepithelial neoplasia prostate specific antigen phosphatase and tensin homolog deleted on chromosome 10 quantitative real-time polymerase chain reaction serial analysis of gene expression subcutaneous standard deviation standard error of the mean Simian virus 40 Signal Transducers and Activators of Transcription SV40 large T antigen transgene or transgenic transforming growth factor-β T helper cells toll-like receptor tumour necrosis factor CD4+ Type-1 regulatory tumour necrosis factor receptor-associated factor 6 transgenic adenocarcinoma of the mouse prostate regulatory T cells vascular endothelial growth factor wild type  xi  ACKNOWLEDGEMENTS I would like to give special thank to my supervisor, Dr. Cheryl D. Helgason, for the opportunities to join her laboratory, to explore scientific research and to learn skills that may allow me to make a living, as well as for the constant support during my studies (such as helps with problem solving, critical thinking and, of course, salary support). I would also like to thank my supervisory committee members - Dr. Megan Levings, Dr. Jan Dutz and Dr. Marianne Sadar - for their advice and comments on my studies. Numerous thanks go to Dr. Lixin Xu and Dr. Brad Hoffman, both of whom provided me with a number of helps and advice on both science- and non-science-related matters during my graduate studies. I also appreciate very much the technical or moral support from many technicians, post-doctoral fellows, school professors, directed studies students, co-op students, summer research students, graduate students, and volunteers: Ms. Leanne Neill for various technical support and suggestions; Dr. Jose Rey-Ladino and Ms. Caroline Bodner for teaching me cell culture and purification techniques; Ms. Dorothy Hwang for suggestions about cell culture techniques and immunostaining, and for treating me to lunches; Ms. Joy Witzsche, Mr. Bogard Zavaglia, Ms. Ida Zhang, Ms. Krista Joris, Ms. Gina Eom, Ms. Teresa Ruiz de Algara, Dr. Hui Xue, Dr. Jun Guan, Ms. Rebecca Wu, Ms. Yuwei Wang, Ms. Nasrin Rina Mawji and Mr. Peter Cheung for help and suggestions regarding cell culture, immunostaining, microscopy, tissue sectioning, and gene expression analysis; Mr. Louis Huang and Ms. Yolanda Yang for genotyping analysis; Dr. Min Liu and Dr. Yun Zhao for many wonderful suggestions on immunostaining and gene expression analysis; Dr. Michael Cox and Sandra Krueckl for teaching me Western blot analysis; Dr. Fan Zhang and Ms. Amy Li for many helpful suggestions on Western blot analysis; Dr. Takashi Kagami for many wonderful suggestions on gene and protein studies; Dr. Akira Watahiki for suggestions about gene expression studies; Dr. Takeshi Kurita for pathological analysis and immunostaining analysis on mouse prostate tissues; Dr. Pauline Johnson for many valuable suggestions; Dr. Yuzhuo Wang for showing me prostate tissue dissection and for providing various  xii  materials and tools; Dr. Maisie Lo for helping me with the preparation of oral presentations and for helping me prepare for my thesis defense; Ms. Claire Hou for many suggestions on gene expression studies; Ms. Frann Antignano and Ms. Jennifer Cross for various ideas and suggestions on dendritic cell studies; Ms. Blanche Lo and Ms. Joanna Chan for help with cell culture and flow cytometric analyses. In addition, I would like to thank Dr. Susan Kasper and Dr. Robert Matusik for kindly providing the 12T-7slow mouse model; Dr. Norman M. Greenberg for kindly providing the TRAMP cell lines; Dr. Tak Mak and Dr. Chris Ong for kindly providing the Ptenflox/flox mice. I also like to give thanks to the staff at Animal Resource Centre at the BC Cancer Research Centre for taking care of the animals; Dr. Joseph Pagano and Dr. Shunbin Ning for providing information and suggestions regarding the interferon regulatory factor 7 studies. Moreover, I would like to thank the various funding agencies for supporting this study: Prostate Cancer Research Foundation of Canada, US Department of Defense Prostate Cancer Research Program Award DAMD17-02-1-225, CIHR/MSFHR Research Transplantation Scholarship Training Program, Genome Canada and Genome BC.  xiii  CHAPTER 1. INTRODUCTION 1.1 IMMUNE SYSTEM 1.1.1 Immune Surveillance in the Tumour Environment The concept of immune surveillance in the tumour environment, once abandoned due to the lack of strongly supportive evidence, began to be re-investigated about a decade ago. Several studies using mouse models provided evidence for the existence of immune surveillance in the tumour environment. For example, the absence of perforin or interferon-γ (IFN-γ), critical factors for inhibition of tumourigenesis, enhanced tumour formation (i.e. increased incidence) [1-5]. However, despite protective immune responses occurring in tumour-bearing hosts, tumour cells are still detected in immunocompetent individuals. Therefore, the idea of cancer immunoediting was proposed by Schreiber and colleagues [6,7]. Three processes are involved (as shown in Figure 1.1): elimination of tumour cells by immune surveillance, equilibrium of tumour cell variants without being eliminated by the immune system, and escape of tumour cells from immune surveillance. During the equilibrium and escape phases, tumour cells (possibly only some of the tumour variants) create an actively immune-suppressed environment allowing them to continue to proliferate or become invasive. The mechanism(s) by which tumour cells mediate immune suppression (or escape immune surveillance) require investigation so that anti-tumour immune responses can be enhanced.  Figure 1.1 Elimination, equilibrium and escape during cancer immunoediting. Adapted from Dunn et al. [6].  1  The immune system contains various types of cells and humoral components that have specific roles in immune surveillance, thus protecting an individual from invasion of tumour cells or diseases caused by pathogens. There are innate and adaptive immune responses in vertebrates. Innate immunity reacts in an immediate manner without identifying the specificity of the pathogen(s), while adaptive immunity has optimal responses upon recruiting memory cells (antigen-experienced cells) that recognize specific pathogen(s). There are several innate immune mediators such as macrophages, dendritic cells (DC), neutrophils, eosinophils, basophils, mast cells and natural killer (NK) cells. The adaptive immune response is mediated by T cells, B cells, and DC. In addition to immune surveillance that protects against foreign intruders, the immune system also has roles in tolerating self antigens (Ag) to prevent autoimmunity. Regulatory T (Treg) cells are known to suppress excessive immune responses, consequently preventing autoimmune responses [8-10]. Since DC and T cells have essential roles in regulating the balance between immune activation and tolerance, they are likely to be the primary targets of tumour-mediated immune suppression. Therefore, detailed analysis of T cells and DC in the tumour environment is likely to facilitate the development of effective immune system-based therapies for cancer treatment. 1.1.2 Regulatory T Cells The major role of Treg cells is to prevent excessive immune responses by establishing and maintaining tolerance to both self and foreign Ags. In humans and mice there are several subtypes of Treg cells categorized based on their phenotype, cytokine production, development, and suppression mechanisms. Naturally occurring CD4+CD25+ Treg cells constitutively express CD25 (interleukin [IL]-2 receptor α chain) at a higher level than activated CD25+ effector T cells. Naturally occurring Treg cells uniquely express the forkhead/winged helix transcription factor, forkhead box protein P3 (FOXP3), which is involved in Treg cell development and function [11-13]. CD4+ Type-1 regulatory (Tr1) cells and T helper 3 (Th3) cells are induced Treg cell subtypes. Tr1 cells produce high levels of IL-10 and/or transforming growth factor-β (TGF-β), and they require IL-10 to differentiate and function. Th3 cells secrete TGF-β and require TGF-β for their suppressive activity [1417]. Another type of Treg cells has the same phenotype as CD4+CD25+ Treg cells but they are converted from CD4+CD25- T cells in the periphery [18].  2  Naturally occurring CD4+CD25+ Treg cells develop in the thymus and constitute 510% of the peripheral CD4+ T cell population [19]. These cells specifically express FOXP3 which has an essential role in the development and function of naturally occurring Treg cells. Mutations in the Foxp3 gene, resulting in impaired CD4+CD25+ Treg cells, may cause human IPEX syndrome (i.e. immune dysregulation, polyendocrinopathy, enteropathy, Xlinked syndrome) and lethal X-linked lymphoproliferative syndrome in the scurfy mouse [20-22]. A recent study demonstrated that FOXP3 is required for the suppressive activity of Treg cells but not for lineage commitment during their development in thymus [22]. Naturally occurring CD4+CD25+ Treg cells also express cytotoxic T lymphocyte antigen 4 (CTLA4), glucocorticoid-induced tumour necrosis factor (TNF)-receptor-related protein (GITR), and CD62 ligand on their surface, and these cell surface molecules are involved in Treg cell function [19]. Moreover, IL-2 is required for the expansion and suppressive activity of CD4+CD25+ Treg cells in vitro [23]. These Treg cells inhibit self-reactive T cell activation. Although the mechanism(s) of suppression is controversial, cell-cell contact, as well as membrane-bound and/or soluble factors, such as Fas/FasL, IL-10 and TGF-β, are involved in suppression [19,24]. In addition to these mechanisms, Treg cells may cause down-regulation of co-stimulatory molecule expression on antigen-presenting cells (APC) through IL-10 and TGF-β, resulting in their impaired ability to activate effector T cells [25,26]. Tr1 cells are induced Treg cells and their unique feature is that they secrete high levels of IL-10 and/or TGF-β, but not IL-4. Tr1 cells can also produce IFN-γ. Although they lack specific cell surface markers, they express T cell activation markers, such as CD25, CD69 and CTLA-4, at similar levels to activated T cells. However, unlike activated T cells, they have a low capacity to proliferate in response to Ag stimulation [27-29]. Stimulation with allogeneic immature DC leads to induction of IL-10-producing CD4+ T cells with suppressive activity. This induction requires IL-10 production from the immature DC [30,31], which were found to exhibit plasmacytoid morphology [32]. Tr1 cells inhibit naïve and memory Th1 and Th2 cell-mediated responses in vivo and in vitro, and this suppression is through the production of the immune suppressive factors IL-10 and TGF-β [29,33]. Similar to Tr1 cells, Th3 cells are induced Treg cells. However, they specifically produce high levels of TGF-β and low levels of IL-10 and IL-4 [17,24]. It is not clear how 3  Th3 cells are induced in vivo, but a study demonstrated that hypo-reactive Foxp3+CD25+ Th3 or Foxp3+CD25- Th3 cells with suppressive activity can be differentiated from Th0 cells by TGF-β over-expression during Ag stimulation in vitro [34]. Moreover, Foxp3 expression in Th3 cells can be maintained by repetitive Ag stimulation. Unlike naturally occurring CD4+CD25+ Treg cells, Th3 cells do not require IL-2 for development and suppressive activity [34,35]. Th3 cells suppress Th1 and Th2 cells. Moreover, instead of directly inhibiting T cells, Th3 cells may produce TGF-β to induce Foxp3+CD4+CD25+ Treg cells as TGF-β can induce Foxp3 expression [34,36]. In the periphery, CD4+CD25+ Treg cells can be induced from CD4+CD25- T cells. Foxp3 is inducible in naïve CD4+CD25- T cells, which consequently convert into CD25+ Treg cells, and interestingly, TGF-β can induce Foxp3 expression in naïve CD4+CD25- T cells [36,37]. Retroviral transduction of Foxp3 into CD4+CD25- T cells can convert these cells to CD4+CD25+ T cells that display a similar phenotype to CD4+CD25+ Treg cells and have suppressive activity [21,38]. Since the generation of these induced CD4+CD25+ Treg cells is thymus-independent, the environment in which the naïve CD4+CD25- T cells reside is critical for the induction of Foxp3 expression. As demonstrated by Liu et al., tumourproduced TGF-β is responsible for the induction of CD4+CD25+ Treg cells from CD4+CD25T cells [37]. In addition, these induced CD4+CD25+ Treg cells are able to produce high amounts of IL-10 and TGF-β and to suppress CD4+CD25- T cell proliferation. In addition to CD4+ Treg cells, the immune system contains CD8+ Treg cells. The CD8+ Treg cells are similar to CD4+ Treg cells in that they are able to inhibit T cell activation, i.e. proliferation and function such as cytotoxicity [39]. These CD8+ Treg cells do not express CD28, which is expressed on naïve T cells and interacts with co-stimulatory molecules (e.g. CD80 and CD86) on the surface of APC [40]. Interestingly, one study has shown that CD8+CD28- T cells expanded in the peripheral blood of lung cancer patients and this population expressed Foxp3 [41]. Although this study did not demonstrate whether this CD8+CD28- T cell population exhibited suppressive activity, another study has demonstrated that the CD8+CD28-FOXP3+ T cell population has the ability to up-regulate inhibitory signals on endothelial cells to inhibit alloreactivity in vitro [42].  4  1.1.3 Dendritic Cells DC are the most efficient APC for activating naïve T cells and they have essential roles in regulating the balance between immune response and immune tolerance. DC express high levels of the α chain of the β2 integrin, CD11c, as well as expressing major histocompatibility complex (MHC) class II molecules and co-stimulatory molecules such as CD80 (B7.1), CD86 (B7.2) and CD40. DC present foreign Ag to T cells to initiate an immune response and present self Ag to maintain self tolerance [9,43,44]. In general, DC are divided into two groups: myeloid and plasmacytoid DC (mDC [DC1] and pDC [DC2], respectively). Both mouse mDC and pDC express CD11c but only pDC express B220 (CD45RA) [45]. Mouse DC subtypes have also been classified based on expression of cell surface molecules such as CD4, CD8α, CD11b and CD205, their distribution in lymphoid and non-lymphoid tissues, and their function [46-50]. There are three subtypes in the mouse spleen: CD4+CD8α-CD205-CD11b+, CD4-CD8α-CD205-CD11b+ and CD4-CD8α+CD205+ CD11b-. Two additional subtypes, CD8αlowCD205int and CD8αlowCD205high, can be found in the lymph nodes (LNs) [49]. Thymic DC have a CD8α+CD205highCD11blow/- phenotype [46,51]. In addition, there are epidermal Langerhans cells in the skin (with the phenotype of CD8α-CD205highCD11bhigh) , as well as CD8α+CD205highCD11blow DC (as the majority) and B220+ DC in the Peyer’s patch [51,52]. Regardless of the ultimate phenotype of the DC found in different tissues, the DC precursor is either of myeloid or lymphoid origin. About a decade ago, several studies demonstrated that CD8α- and CD8α+ DC (the 2 major subtypes of mouse DC) were derived from myeloid and lymphoid progenitors, respectively [53-55]. However, it was later demonstrated by Manz et al. that both common myeloid progenitors (CMP) and common lymphoid progenitors (CLP) are able to differentiate into CD8α- or CD8α+ DC [56]. Within the mouse bone marrow (BM) cells, portions of the CMP or CLP population express fms-like tyrosine kinase 3 (Flt3), whose ligand Flt3L, is a growth factor for hematopoietic progenitors. These Flt3+ progenitors (Flt3+Lin-Sca-1+c-kit+) have the capacity to differentiate into all subtypes of DC since both mDC and pDC are generated and expanded with in vivo Flt3L treatment [57], although CLP cells are more readily able to generate pDC since the majority of the CLP in BM express high levels of Flt3 [57,58]. Interestingly, pDC derived from different progenitors (CMP or CLP) exhibit different functions. For example, Yang et 5  al. demonstrated that pDC derived from CMP secrete IFN-α at higher levels than those derived from CLP [58]. Nevertheless, these studies proved that DC can be generated regardless of their myeloid or lymphoid origin. Moreover, these studies provide one method for generating DC in vitro: BM cells are treated with Flt3L to differentiate into both mDC and pDC [45,59-61]. Unlike Flt3L, granulocyte-macrophage colony-stimulating factor (GMCSF), together with IL-4, favors the generation of mDC only [59,62]. Of note, human DC are derived from either CD14+ monocytes or CD34+ progenitors [63-65]. After DC capture foreign Ag, they migrate to lymphoid tissues and mature. This maturation process can be mimicked in vitro by stimulation of immature DC with CD40 ligand or toll-like receptor (TLR) ligands, such as bacterial lipopolysaccharide and CpG oligodeoxynucleotides (ODN) [66]. During maturation, mouse DC up-regulate the expression of co-stimulatory molecules such as CD80, CD86, CD40 and MHC class II molecules, and human DC up-regulate the expression of CD83 and DC-lysosome-associated membrane protein (DC-LAMP) [44,67,68]. Mature DC provide two signals for T cell activation. The first signal is through Ag presented by MHC molecules that interact with T cell receptors and the second one is the interaction between co-stimulatory molecules on DC and their cognate receptors on T cells. CD8α+ DC can induce a Th1 response by secreting IL-12 and IFN-γ, while CD8α- DC can induce Th2 responses by IL-10 production [69,70]. In addition to immune response initiation, DC also have critical roles in maintaining tolerance by presenting self Ag to induce T cell deletion or anergy. It was previously assumed that immature DC circulate in the periphery and uptake self Ag to present to T cells. In the absence of “danger signals”, such as pathogen-associated molecular patterns, DC provide only one signal to naïve T cells, leading to tolerance. However, DC can also maintain peripheral tolerance through the induction and expansion of Treg cells. It has been demonstrated that repeated stimulation of naïve T cells with immature DC leads to the generation of Treg cell suppressive activity [30] and that DC can induce expansion of a CD4+CD25+ Treg cell population with suppressive function [71,72]. A recent study showed that DC, together with TGF-β, are more efficient at inducing Foxp3+CD4+ Treg cells from peripheral (LN and spleen) Foxp3-CD4+ precursor cells [73]. Interestingly, pDC, whose main function is to produce type I IFN upon viral infection, become tolerogenic when they are not stimulated and these pDC are able to induce Treg cell differentiation with suppressive  6  activity [74,75]. Several studies suggest that maturation state, rather than lineage, as well as the microenvironment are determining factors for DC to behave as activating or tolerogenic cells [76-79]. Their maturation status is likely dependent on the microenvironment since two studies have demonstrated that spleen stromal cells can support the generation of DC with regulatory properties from either mature DC or Lin-c-kit+ progenitor cells [80,81].  1.2 PROSTATE CANCER 1.2.1 Prostate Glands The prostate has important roles as an accessory reproductive gland and as a urethral gland. It produces exocrine fluid, consisting of proteins and ions, as part of the seminal fluid and provides muscle contraction for urination [82]. Although the human adult prostate does not have distinct lobular structures, based on histological features it is divided into the periurethral transition zone, the peripheral zone, the central zone and the anterior fibromuscular stroma. Unlike the human prostate, the mouse prostate is composed of clearly anatomically distinct lobes: anterior (also known as coagulating gland), ventral, dorsal and lateral lobes. The dorsal and lateral lobes are usually grouped together as the dorsolateral lobe. Human prostate also differs from mouse prostate in that the human prostate contains a more abundant amount of fibromuscular stroma that surrounds each individual gland than mouse prostate [83-85]. In human prostate, cancer (adenocarcinoma) occurs most frequently in the epithelium of the peripheral zone. Although it has been suggested that the mouse dorsolateral lobes are the most similar to the human peripheral zone, there is no clear evidence showing that one mouse prostate lobe can represent any of the human prostate zones [85]. Regardless of the differences between mouse and human prostates, the cellular composition in the prostate epithelium is similar in the two species. Prostate epithelium consists of basal cells, luminal cells and a small population of neuroendocrine cells. In normal mouse prostate the luminal cells are secretory cells that produce proteins as part of the seminal fluid. Basal cells may contain prostate stem cells that are able to differentiate into luminal secretory cells. Neuroendocrine cells are capable of secreting growth factors that promote prostate cell growth and differentiation in a paracrine manner. In addition to  7  epithelial cells, the prostate tissue also contains stromal cells that are located in a collagenous extracellular matrix. The stroma include smooth muscle cells, fibroblasts, blood cells, lymphatic cells and connective tissue cells [85-89]. 1.2.2 Prostate Cancer Incidence, Causes and Treatment Prostate cancer (PCa) is a major cause of morbidity and the second leading cause of cancer mortality among men in the Western countries, as it was statistically estimated that there will be 218,890 new cases diagnosed and 27,050 cases of mortality in the United States population in 2007 [90]. PCa is a heterogeneous disease that progresses from low- to highgrade human prostatic intraepithelial neoplasia (PIN) lesions, which are likely to be the precursors of prostatic adenocarcinoma, and ultimately to microinvasive adenocarcinoma that could lead to metastasis. However, histological examination of PCa tissues usually shows a mixture of cells with various degrees of severity (i.e. stage and grade) [85,91,92]. The initiation of PCa is attributed to many factors: dietary, environment, genetic changes and epigenetic modification [85,91]. Of note, the incidence of PCa is correlated with aging [90]. Early detection of PCa is done by digital rectal examination and/or serum prostate specific antigen (PSA) screening due to its increased production by tumour cells. Biopsy is required to determine the stage and grade of PCa. Current available treatments for localized PCa, depending on the age and health condition of patients, include watchful waiting (usually at the earliest stage), prostatectomy, external beam radiation and brachytherapy, while treatments for late-stage disease (locally advanced or metastasis) include chemotherapy and hormonal strategies. Despite the effectiveness of the therapies for localized PCa, the treatments for late-stage PCa do not result in effective outcomes in all patients. The reason for this failure is that tumour cells may progress, i.e. mutate, to become insensitive to chemotherapy or independent of androgen [91,93,94]. Novel strategies involving manipulation of the immune system to induce or enhance anti-tumour immune responses have been extensively tested as alternative approaches to specifically and effectively reduce the growth of late-stage tumours. Indeed, immune system-based therapies, such as cytokine and vaccine approaches (monoclonal antibodies or small molecular inhibitors), have been studied in mouse tumour models and clinical trials to treat various types of cancer including melanoma, renal cell carcinoma and  8  PCa [95-101]. One strategy that holds great promise is the use of DC as a vector for presenting tumour-associated Ag, thus initiating an Ag-specific anti-tumour immune response [102-104]. The specificity of this approach ensures that toxic side effects are minimal. However, the majority of cancer patients enrolled in such trials showed only partial response without complete eradication of tumour cells, even though survival rates are improved [96,105]. Therefore, a clearer understanding of the mechanism(s) by which immune function is modulated by prostate tumour cells or other types of cancer is required to devise strategies to enhance the efficacy of immune system-based therapeutic interventions. 1.2.3 Mouse Models for Prostate Cancer A variety of mouse prostate tumour models have been developed using both transgenic (Tg) and knockout (KO) approaches that target signaling pathways such as the cell cycle or a molecule such as a tumour suppressor [85]. Each model has its advantages and disadvantages. However, none has exactly the same features or characteristics as human PCa due to the complexity, heterogeneity and environmental factors involved in this disease. Three mouse models are described and used in our studies to examine the immune system in the tumour environment: the 12T-7slow Tg mice, transgenic adenocarcinoma of the mouse prostate (TRAMP) cell line and prostate-specific Pten (phosphatase and tensin homolog deleted on chromosome 10) KO mice. The 12T-7slow is a mouse model of prostate dysplasia induced using a large fragment of the rat probasin gene promoter (LPB) linked to the Simian Virus 40 (SV40) large T antigen (Tag) deletion mutant to allow consistently high levels of transgene expression in the mouse prostate cells. This mouse model starts developing multifocal hyperplastic lesions at the age of 7-8 weeks with all prostate epithelial cells becoming hyperplastic at the age of 10-12 weeks. Reactive proliferation of stromal cells may be observed when the mice are 16-17 weeks of age. The tumour cells progress to high-grade dysplasia at the age of >20 weeks. Tumour formation is observed in 100% of the male Tg offspring and tumour growth is androgen dependent. However, this model does not develop advanced adenocarcinoma and metastasis is rarely observed in these mice [87,106]. The TRAMP mouse is a Tg model as well. However, unlike the 12T-7slow model, the transgene is the SV40 early region linked to a short fragment of the probasin gene promoter. This model develops mouse PIN (mPIN) at the age of 8-12 weeks and the tumour  9  is able to develop into adenocarcinoma and metastasize when the mice are 24-30 weeks of age [107]. Although this model develops more advanced tumours than the 12T-7slow mice, the duration for tumour progression is longer than the prostate-specific Pten KO model, described below. Three cell lines have been derived from the TRAMP mice - C1, C2 and C3 - which are easily available for in vitro experiments [108]. The doubling time for C1 and C2 in an in vitro culture is approximately 24 hours, while that for C3 is 37 hours. The C1 and C2 lines, but not C3, are tumourigenic when grafted into syngeneic C57Bl/6 mice. The prostate-specific Pten KO mouse model was developed using a Cre-Lox recombination strategy. The gene encoding Cre recombinase is linked to a modified promoter from the rat probasin (PB) gene and thus expression of the Cre recombinase is prostate-specific. Tg PB-Cre mice are crossed with mice that have several exons of the Pten gene flanked by loxp sites. The homozygous KO mice begin to develop hyperplasia at the age of 4 weeks, mPIN at 6 weeks, and invasive adenocarcinoma at 9 weeks of age. These mice have the potential to develop metastases at the age of ≥ 12 weeks [109]. Unlike the homozygous Pten deletion mice, the heterozygous Pten deletion (Het) mice have a longer latency for tumour formation. The Het mice develop mPIN when they are 8 - 10 months of age. The Pten KO mouse model has numerous advantages over previous models: (1) the Pten KO mice develop more advanced tumours in a shorter time period, (2) the changes in gene expression are similar to those detected in human prostate tumours, and (3) it is more biologically relevant to human PCa since the PTEN mutation has been implicated in human PCa [109-111].  1.3 IMMUNE CELL ALTERATIONS IN CANCER 1.3.1 Immune Suppression in the Tumour Environment There is a plethora of evidence showing immune suppression in the tumour environment. Reduced infiltration of APC, such as DC and macrophages, is observed in human PCa and the reduction is associated with tumour progression [112-114]. In breast cancer patients, DC were found to be impaired in their ability to present Ag to activate T cells [115,116]. An increased number of myeloid suppressor cells (MSC; Gr-1+CD11b+) has been found in mouse models of spontaneous mammary carcinoma, as well as in cancer  10  patients [117-120]. These MSC are capable of inhibiting proliferation of activated T cells, thus resulting in impaired immune responses [118,121]. Moreover, increased apoptosis of Ag-specific or tumour-reactive T cells was detected when these T cells encounter tumourassociated B7-H1, a co-stimulatory molecule inducing activated T cell apoptosis and found expressed at a high level on tumour cells [122,123], suggesting another potential mechanism of immune suppression. In addition, Treg cells, which are able to inhibit effector T cell activation, are present at higher frequencies or numbers in different types of cancer patients [124,125]. Another mechanism that tumour cells have been reported to use to escape immune surveillance is by generating defective MHC or human leukocyte antigen (HLA) molecules on their surface, leading to ignorance by the immune cells [126-129]. Elucidating the different mechanisms of immune suppression in various types of cancer is essential for designing a novel, or improving an existing, immune system-based therapy. Numerous studies have been carried out to identify tumour-produced factor(s) responsible for immune suppression because these factors are potential targets for therapy. Tumour microenvironment-associated immunosuppressive factors such as vascular endothelial growth factor (VEGF), macrophage colony-stimulating factor (M-CSF) and IL-6 were demonstrated to have negative effects on DC differentiation and function since DC exposed to these factors acquired tolerogenic properties, consequently resulting in insufficient T cell activation [130,131]. In addition to inhibiting DC function, migration or survival, TGF-β and IL-10 are able to inhibit T cell activation [132-134]. Moreover, these two factors have also been demonstrated to induce the generation or expansion of Treg cells. A study done by Curiel et al. showed that human ovarian cancer cells produce a chemokine CCL22 that attracts Treg cells to accumulate in the tumour microenvironment and consequently suppress effector T cell activation [135]. Moreover, a recombinant mucin glycoprotein that resembles the form highly expressed on epithelial tumour cells has inhibitory effects on human DC differentiation and function [136,137]. Another immunosuppressive factor produced by, or indirectly associated with, tumour cells is indoleamine 2,3-dioxygenase (IDO), which is known to inhibit T cell proliferation by tryptophan depletion. IDO is constitutively expressed by pDC, which have been found in the tumour environment and have tolerogenic properties that allow them to activate Treg cell suppressive function [75,138-140]. In addition, a variety of human tumour cells express IDO  11  as an immune regulatory mechanism that suppresses T cell-mediated anti-tumour immune responses [141,142]. These various mechanisms of immune suppression arise due to the heterogeneity of the tumour microenvironment. Therefore, to identify all the potential factors responsible for tumour-mediated immune suppression at a particular stage of tumour progression in a certain type of tumour will facilitate the development of effective immune therapies for cancer. 1.3.2 CD4+CD25+ Regulatory T Cells in the Tumour Environment A number of studies have demonstrated that the frequency or number of CD4+CD25+ Treg cells is increased in the peripheral blood and/or tumour sites of various human cancers. These cells were detected at a higher frequency in the tumour mass of patients with late-stage ovarian cancer and with early-stage non-small cell lung cancer [143]. Treg cells infiltrating lung cancers were found to inhibit autologous, but not allogeneic, T cell proliferation in vitro [144]. An elevated frequency or number of CD4+CD25+ Treg cells was also observed within the tumour-infiltrating lymphocyte population in patients with pancreas, breast, gastric and esophageal cancers [145,146]. One study reported that CD4+CD25+ Treg cells preferentially trafficked to the tumour mass of patients with ovarian carcinoma [135]. This cell population represented approximately a quarter of the tumour-infiltrating CD4+ T cells on average, and the accumulated Treg cell population was correlated with reduced survival of patients. A similar correlation was also observed in breast cancer patients [147]. A recent study from Miller et al. demonstrated that the frequency of CD4+CD25high Treg cells was significantly increased in the peripheral blood and tumour microenvironment of PCa patients [148]. The peripheral blood from patients with ovarian cancer, pancreas, breast, head, neck, gastric and esophageal cancers has also been shown to contain a higher frequency of CD4+CD25+ Treg cells [143,145,146,149,150]. Moreover, the frequency of CD4+CD25high T cells was found to be significantly increased in LN containing metastases, compared to LN that do not contain metastases from the same melanoma patients [151], suggesting that the tumour microenvironment is responsible for the accumulation of Treg cells. Knowledge of the mechanisms responsible for the increase of Treg cells in the tumour environment is still limited. In ovarian carcinoma it has been demonstrated that the tumour cells and tumour-infiltrating macrophages secrete the chemokine CCL22, which  12  attracts Treg cells to the tumour mass [135]. In vivo blockage of CCL22 decreased the CD4+CD25+ Treg cell infiltration in the tumour. A study on human PCa also attempted to determine whether CCL22 is responsible for the increased Treg cells in cancer tissues compared with benign tissues [148]. This study showed that the PCa cells were likely to produce chemokines other than CCL22 to attract Treg cells since the culture supernatants from both cancer and benign tissues contain similar levels of CCL22. One recent study showed that mouse tumour cells such as TRAMP-C2 and RENCA (a murine renal cell carcinoma) produced higher amounts of TGF-β than non-tumourigenic cells, and the TGF-β converted CD4+CD25- T cells into CD4+CD25+ T cells that expressed FOXP3 protein and displayed suppressive activity [37]. Moreover, TGF-β may work through APC to generate Treg cells [152]. In a lung cancer study, prostaglandin E2 was able to induce Foxp3 gene and its protein expression in CD4+CD25- T cells and splenocytes, and to enhance the suppressive activity of CD4+CD25+ Treg cells as well [153]. Melanoma secreted heavy chain ferritin was another factor implicated in Treg cell induction [154]. Distribution of Treg cells in either the secondary lymphoid tissues or tumour mass may affect their development (or induction/expansion) and function. It was reported that the CD4+ T cells expressing FOXP3 directly contact CD11c+ cells in the draining LN and spleen of mice transplanted with melanoma B16 [155], and the frequency of this cell-cell contact was significantly increased in the tumour-bearing hosts. A study done by Curiel et al. demonstrated that 80% of the CD25+FOXP3+ cells were in close contact with tumourinfiltrating CD8+ T cells, suggesting this cell-cell contact was critical for the suppressive activity of Treg cells in the tumour microenvironment [135]. In addition to inhibition of CD8+ T cell function, CD4+CD25+ Treg cells isolated from cancer patients were found to inhibit the cytotoxicity mediated by NK cells in vitro [156]. Effective rejection of transplanted tumours or reduction of tumour burden has been observed following the removal of the Treg cell population in different tumour models. AntiCD25 Ab treatment has been used to remove the CD4+CD25+ Treg cell population. Removal of these cells resulted in the generation of tumour specific cytotoxic T lymphocyte (CTL) and non-tumour specific NK cells that were able to eliminate RL♂1 leukemia and B16 melanoma [157]. In vivo depletion of CD4+CD25+ Treg cells by anti-CD25 mAb (clone PC61) led to reduced tumour growth in mice inoculated with RL♂1 leukemia, MOPC-70A  13  myeloma, and Meth A fibrosarcoma, and this tumour regression was mediated by CD8+ T cells [158]. In addition to CD8+ T cells, CD4+ T cells were also required for generation of a long-term immune response following depletion of the CD4+CD25+ Treg cells in a transplanted B16F10 melanoma mouse model [159]. Interestingly, treatment with DC loaded with stressed B16F10 melanoma cells, combined with anti-CD25 Ab treatment, enhanced the effect of DC treatment alone, resulting in a long-lasting anti-tumour immune response [160]. Other types of treatment to deplete Treg cells or inhibit their suppressive function include anti-GITR Ab and cyclophosphamide [161,162]. Clinical trials demonstrated that after removal of Treg cells using DAB389IL-2 (Denileukin diftitox, ONTAK®), the antitumour immune response was enhanced in renal cell carcinoma patients vaccinated with tumour RNA-transfected DC [163]. Furthermore, Wang et al. identified a tumour Ag-specific Treg cell clone isolated from tumour-infiltrating lymphocytes of a melanoma patient [164]. The knowledge about Ag specificity of the Treg cells in the tumour environment is still limited [125]. However, to reduce the potential autoimmune responses caused by elimination of the Treg cell population, targeting the tumour-specific Treg cells may improve the overall health of tumour-bearing hosts. 1.3.3 Dendritic Cells in the Tumour Environment One of the mechanisms by which tumour cells evade the immune system is through the inhibition of DC maturation and function, consequently resulting in the lack of T cell activation. Numerous studies have shown that DC are altered in the tumour environment. For example, Troy et al. demonstrated that a decreased frequency of DC infiltration was observed in human malignant prostate tissues, compared to adjacent normal prostate tissues [112], and that only a small portion of the tumour-infiltrating DC exhibit an activated phenotype. In addition, studies have shown that human or murine prostate tumour cells, or the soluble factors they secrete, alter the maturation of BM-derived or monocyte-derived DC, inhibit their allostimulatory capacity, and induce apoptosis [165-169]. In a rat model of colon adenocarcinoma, tumour-infiltrating DC were not able to induce allogeneic T cell proliferation, compared to splenic DC, due to their deficiency of B7 co-stimulatory molecule expression [170]. Decreased expression of co-stimulatory molecules on DC was also found in the metastatic melanoma environment, resulting in immune tolerance [171]. The function  14  of pDC, a major source of IFN-α that has anti-tumour activity, was impaired due to downregulation of TLR9 on their surface in head and neck squamous cell carcinoma patients [138], and immature pDC were found localized in the peritumoural areas of melanoma [172,173]. In breast cancer, it was shown that immature DC are localized inside the tumour mass, whereas mature DC were localized in the peritumoural areas [174]. Interestingly, these mature DC were clustered with CD4+ T cells, which have been shown to produce IL-13, a factor that may stimulate tumour growth, through the involvement of DC, leading to facilitated tumour progression [174-176]. However, it was found that the accumulation of mature DC (DC-LAMP+) within the tumour mass of metastatic melanoma was correlated with a local increase of T cell activation, resulting in protective anti-tumour immune responses and an improved prognosis [177]. Therefore, whether anti-tumour immunity is initiated depends on the maturation status of DC (activated or tolerogenic), which is likely to be affected by their location in the tumour microenvironment and the degree of tumour progression. Since the tumour microenvironment has a critical role in modulating DC phenotype and/or function, identification of tumour-produced factors is important for designing effective DC-based immunotherapies for cancer treatment. It has been reported that tumour cells have both direct and indirect effects on DC either through soluble mediators or through direct cell-cell contact. As illustrated in Figure 1.2, tumour-produced cytokines, chemokines or growth factors may alter DC phenotype and function, and consequently affect the T cell population [178]. A variety of tumours, including prostate tumours, secrete soluble immunosuppressants such as VEGF, TGF-β and IL-10, which have the potential to inhibit DC differentiation, maturation and function (e.g. migration, Ag presentation and stimulatory ability), resulting in weak endogenous anti-tumour immune responses [132,179-182]. IL-10 was found to inhibit CD40 expression on DC [183]. In addition, IL-6 and M-CSF produced by human renal cell carcinoma lines and IL-6 and granulocyte colony-stimulating factor (GCSF) produced by human metastatic pancreatic cancer cell lines were also reported to have the potential to hamper DC differentiation from CD14+ monocyte or CD34+ progenitor cells, as well as their ability to stimulate allogeneic T cell proliferation [184,185]. The inhibitory activity of IL-6 on DC development was observed in progenitors isolated from multiple myeloma patients as well [186]. Tumour-derived gangliosides are also factors responsible for  15  abrogated DC differentiation [187,188] and a recent study demonstrated that gangliosides purified from human melanoma not only inhibit the allostimulatory function of epidermal Langerhans cells but also abrogate their migratory ability [189]. Of interest, in addition to direct factors produced by tumour cells, an increase in the Treg cell population may contribute to DC dysfunction since CD4+CD25+ Treg cells are able to reduce the expression of co-stimulatory molecules (CD80 and CD86) [25] and to induce the up-regulation of inhibitory molecules (e.g. B7-H3) on DC [190]. The factors mentioned above were demonstrated to either reduce co-stimulatory signals (i.e. no maturation) or reduce DC survival (e.g. no cell expansion or induced apoptosis), thus rendering them inefficient for stimulating T cell activation. Tumor-derived Cytokines/Chemokines/Growth factors  maturation of conventional DC  IL-12, IFNγ, IP-10  function of conventional DC  Endocytosis  attraction of conventional DC (loss of CXCL14)  longevity of conventional DC attraction of plasmacytoid or regulatory DC differentiation of endotheliallike DC  Motility APM component expression  Tumor Agspecific T cells  Ag procession Ag presentation B7-1/2 IDO B7-H1/H4 IL-10 TNF-α IL-8  CD4+ Treg CD8+ Treg  TUMOR PROGRESSION  TUMOR  DENDRITIC CELLS  Neovascularization  Figure 1.2 Effects of tumour-derived factors on DC alteration in the tumour microenvironment. Adapted, with modification, from Shurin et al. review [178]. The therapeutic potential of DC and the design of effective DC-based immune therapies, for cancer or transplantation, are being studied intensively [66,191]. Targeting the tumour-derived immunosuppressants to reverse the impaired function or generation of DC is 16  one way to enhance anti-tumour immune responses. For example, inhibition of IL-6 and MCSF by neutralizing antibody (Ab) restored the impaired ability of DC to stimulate allogeneic T cell proliferation [184]. Gabrilovich et al. demonstrated that administration of Ab against VEGF improved the function of DC and allowed expansion of the DC population in mouse tumour models [192]. However, recently Gabrilovich and colleagues reported a phase I clinical trial on inhibition of VEGF in various types of progressive solid tumours [193]. Although this treatment resulted in an increased level of mature DC in the peripheral blood of patients, the overall anti-tumour immune responses were not improved after the treatment. The stage of disease progression in different patients and the degree of induction of inhibitory factors are likely to increase the complexity of immune suppression in the tumour environment. In addition to targeting tumour-produced soluble factors to enhance DC function, DC themselves are used as vaccines to stimulate immune responses against cancer. DC are generated ex vivo and loaded with tumour-associated Ag as either peptides or RNA [66,194]. There have been numerous DC vaccine studies done in mouse tumour models and the first clinical study was carried out in patients with B cell lymphoma in 1996 [195]. A number of phase I or II clinical trials using DC loaded with tumour Ag, or in combination with other types of treatment, resulted in anti-tumour responses in a portion of cancer patients and increased their survival [105,196-199]. For PCa, phase III vaccine trials using Provenge®, a vaccine with autologous DC loaded with a fusion protein containing prostatic acid phosphatase and granulocyte macrophage colony-stimulating factor (GM-CSF), are ongoing [200-202]. However, these studies did not show complete regression of tumour growth. Therefore, a more comprehensive strategy targeting all the essential factors responsible for immune suppression in the tumour environment is required for developing effective immunotherapies for cancer treatment.  1.4 GENE EXPRESSION RPOFILING 1.4.1 Serial Analysis of Gene Expression Hundreds of studies have been carried out on analyses of genes or transcripts that are differentially expressed in tumour cells. Such analyses provide important information needed  17  for identification of diagnostic or prognostic markers for cancers, as well as to understand the molecular mechanisms involved in tumour progression. In the past decade, advanced technologies have been developed to allow a more thorough analysis of gene expression profiles [203]. Serial Analysis of Gene Expression (SAGE) is an effective tool to comprehensively study gene expression profiling in a quantitative manner. It was developed based on the concept that a short nucleotide sequence (i.e. 10- to 14-mer tags) is sufficient to identify a transcript uniquely. This technique provides advantages over other gene analysis methods, such as microarrays: (1) the data is digital and quantitative, so it can be easily expanded upon and available for cross comparison among different experiments; (2) novel transcripts can be identified without prior knowledge of transcript sequences [204-207]. Recently, a LongSAGE protocol, which generates 21-mer tags, was developed to map tags to genes with higher confidence [208]. With these improved and advanced techniques, researchers will be able to elucidate molecular mechanism(s) underlying the tumourmediated immune alterations in an efficient manner. Figure 1.3 illustrates the basic principle of the SAGE technique. Total messenger RNA is reverse transcribed to complementary DNA (cDNA). The cDNA is then cleaved by an anchoring enzyme at a specific nucleotide sequence, e.g. GTAC by the enzyme NlaIII. These cleaved cDNA molecules are divided into two portions and ligated to two different linkers. The next step is to cleave these linker-attached cDNA to generate short nucleotide sequences (i.e. 17-mer “tags” for LongSAGE), using a tagging enzyme, usually BsmFI. These tags are ligated to form ditags) and amplified by polymerase chain reaction (PCR) by primers specific for the linkers. Once sufficient amounts of PCR products are obtained, an anchoring enzyme is added to cleave the PCR products to generate ditags with cohensive overhangs. The ditags are then isolated, ligated to form concatamers and cloned. The cloned concatamers are sequenced to generate a list of tags which can be quantified and analyzed using a variety of computer programs [204,205].  18  A A AA A T T TTT  Anchoring enzyme  A A AA A T T TTT  Total RNA  A A AA A T T TT T  GTAC  A A AA A T T TT T  GTAC  cDNA Ligate to linkers (A and B)  CATG  A GTAC CATG  A GTAC  A A AA A T T TT T A A AA A T T TT T  CATG  A A AA A T T TT T  B GTAC CATG  A A AA A T T TT T  B GTAC  Tagging enzyme  Tagging enzyme  CATGXXXXXXXXXXXXXXXXX  CATGOOOOOOOOOOOOOOOOO  A GTACOOOOOOOOOOOOOOOOO  B GTACXXXXXXXXXXXXXXXXX  tag  tag  Ligate and amplify  B GTACXXXXXXXXXXXXXXXXX  CATGOOOOOOOOOOOOOOOOO  A GTACOOOOOOOOOOOOOOOOO  CATGXXXXXXXXXXXXXXXXX  Anchoring enzyme CATG  XXX…XXX GTACXXX…XXX  OOO…OOO GTACOOO…OOO  ditag  Concatenate and clone CATGXXX…XXX GTACXXX…XXX  …CATGOOO…OOO …GTACOOO…OOO  OOO…OOOXXX…XXXCATG OOO…OOOXXX…XXX  OOO…OOOXXX…XXXCATG… OOO…OOOXXX…XXXGTAC…  Sequencing A list of tags (with counts)  Figure 1.3 A schematic diagram of SAGE library construction.  1.4.2 Global Gene Expression Analyses in Cancer In an ovarian cancer study, several potential novel markers for diagnosis and prognosis were identified after gene expression patterns on primary tumour tissues and normal ovarian epithelial cells were compared using SAGE analysis [209]. Those identified  19  markers were confirmed by real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) on samples from a number of cancer patients, adding confidence to the data. There are a number of examples of large-scale gene expression analyses, including human melanoma and mouse prostate tumour [210,211]. The results from SAGE analysis on human melanoma samples correlated with microarray data generated in previous studies, and several genes thought to be involved in signaling related to tumour progression were identified and their expression was confirmed using immunohistochemical analysis. Similarly, both SAGE analysis and microarray studies carried out on the TRAMP mouse model confirmed previously known genes (e.g. phospholipase A2 group IIA) associated with tumour progression, as well as revealed novel genes involved in this process. An interesting study on samples obtained following different radiation treatments for cancers, using microarray techniques, provided information on the changes of gene expression patterns due to different doses of radiation [212]. The knowledge of the different responses from different treatments (i.e. molecular mechanisms) will allow researchers to develop more effective cancer therapies. Moreover, such gene expression analyses facilitate the identification of gene signatures that provide insight into the biological progression of disease development. One study based on microarray data showed that an IFN-related gene signature was expressed in approximately 40% of mammary cancer samples [213], suggesting that the IFN signaling pathway is involved in tumour development and the contribution of inflammation to tumour development [213,214]. Another study, using microarray analysis combined with confirmatory immunohistochemical analysis, on the differential gene expression between mouse prostate epithelial and stromal cells, reported signature genes associated with different functions in the two types of prostate cells [215]. The identified gene signatures are likely to provide insights required to design therapies targeting critical signaling pathways or biological properties that are tumour-specific.  1.5 HYPOTHESES AND OBJECTIVES The ultimate goal of the studies described in this thesis is to enhance the endogenous anti-tumour immune response, thus leading to tumour regression or ablation. The hypotheses underlying this work were that: (1) prostate tumour cells promote, either in a direct or  20  indirect manner, the induction of CD4+CD25+ Treg cells that, at least in part, mediate the immune suppression in prostate tumour-bearing hosts; (2) removal of the Treg cell population will enhance the anti-tumour immune response; (3) the microenvironment of the prostate tumour progressively alters the phenotype and/or functional properties of DC, thus facilitating the accumulation of Treg cells; (4) serial analysis of gene expression (SAGE) analysis on the expression changes of transcripts in prostate tumour cells will facilitate identification of critical factor(s) that modulate immune cell phenotype and function in the tumour environment. The specific objectives were as follows: (1) To determine whether the frequency, number and suppressive activity of CD4+CD25+ Treg cells are altered in a mouse model of prostate dysplasia. 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De Marzo AM, Platz EA, Sutcliffe S, Xu J, Gronberg H, Drake CG, Nakai Y, Isaacs WB, Nelson WG: Inflammation in prostate carcinogenesis. Nat Rev Cancer 2007;7:256-269. Berquin IM, Min Y, Wu R, Wu H, Chen YQ: Expression signature of the mouse prostate. J Biol Chem 2005;280:36442-36451.  36  CHAPTER 2. REGULATORY T CELLS IN THE PROSTATE TUMOR ENVIRONMENT1 2.1 BACKGROUND Regulatory T (Treg) cells have important roles in establishing and maintaining selftolerance, as well as in preventing excessive immune responses and autoimmune diseases. Treg cells have the capacity to inhibit effector T cell proliferation and function. Treg cells are divided into a number of subtypes depending on the suppressive mechanism they use, as well as their phenotype and development. Subsets of Treg cells include: naturally occurring CD4+CD25+ Treg, CD4+ type-1 regulatory (Tr1), T helper 3 (Th3), and CD4+CD25high Treg cells that have been converted from CD4+CD25- T cells in the periphery [1-3]. It has been demonstrated that Treg cells contribute to the reduced anti-tumour immune responses in cancer patients or animal models. A number of studies have reported that the frequency of CD4+CD25+ Treg cells is elevated in the peripheral blood and/or tumour microenvironment of patients with pancreas, breast, non-small cell lung, colorectal, and ovarian carcinomas, neck and head cancer, gastric and esophageal cancers [4-10]. Metastatic LN isolated from melanoma patients have an increased frequency of CD4+CD25high T cells that co-express a higher level of FOXP3 (forkhead box protein P3) and are able to inhibit CD4+CD25- T cell proliferation in vitro [11]. Recent studies have also demonstrated an increase in Treg cells in the blood and tumour mass of prostate cancer (PCa) patients [12,13]. Further evidence to suggest a functional role for the CD4+CD25+ Treg cell population in tumour progression and immune suppression is the observation that transient elimination of this suppressive cell population with antibody (Ab) treatment, such as anti-CD25 monoclonal Ab (mAb), results in effective rejection of transplanted tumours derived from leukemia, myeloma, sarcoma and B16 melanoma [14-17]. Clinical trials also showed that following removal of Treg cells by DAB389IL-2 (Denileukin diftitox, ONTAK®), the antitumour immune response was augmented in renal cell carcinoma patients vaccinated with tumour RNA-transfected DC and in melanoma patients [18,19]. Although removal of the 1  A version of this chapter has been published. Tien, A.H., Xu, L. and Helgason, C.D. (2005) Altered Immunity Accompanies Disease Progression in a Mouse Model of Prostate Dysplasia. Cancer Research. 65:2947-2955.  37  Treg cell population may cause tumour regression, only partial responses have been observed and autoimmunity may be induced. At the time of this study (2002-2004), no comprehensive analyses of Treg cell phenotype and function had been carried out in PCa patients or in mouse models of this disease. Hence, the immunologic status, and specifically the role of Treg cells, was evaluated as a function of disease progression in the 12T-7slow transgenic mouse model of prostate dysplasia. Understanding the mechanisms by which Treg cells arise during prostate tumour progression and developing effective strategies to block their generation or function may have important implications for overcoming the limitations of existing immunotherapeutic approaches to PCa treatment.  2.2 MATERIALS AND METHODS 2.2.1 Mice The 12T-7slow LPB-Tag Transgenic (Tg) mouse model of prostate dysplasia has been described previously [20,21] and was kindly provided by Dr. Susan Kasper and Dr. Robert J. Matusik (Vanderbilt University Medical Center, Nashville, TN, USA). In these mice a large promoter fragment of the rat probasin gene (LPB) is linked to the Simian virus (SV40) large T antigen (Tag) deletion mutant to deliver consistently high levels of transgene expression to the mouse prostate tissue. Tumour formation is observed in 100% of the male Tg offspring and the characteristics of tumour progression are similar to that observed in humans. Tg mouse strains were maintained by mating wild-type (WT) CD1 males with Tg females and the progeny were screened by polymerase chain reaction (PCR) analysis of tail DNA. Tissues were isolated at various stages of tumour progression, including 11 to 12 weeks of age when the entire epithelium is in the process of becoming hyperplastic, 16 to 17 weeks of age when reactive stromal proliferation is observed, and 20 to 25 weeks of age when the tumour has progressed to high-grade dysplasia. C57BL/6J recipients of the transplanted prostate cancer cell line were purchased from the in-house breeding program. All mice were bred and maintained at the British Columbia Cancer Agency Animal Resource Centre with sterilized food, water and bedding. All protocols were conducted according to guidelines set forth by the Canadian Council for Animal Care and approved by the Animal Care Committee at the University of British Columbia. 38  2.2.2 Single Cell Preparation from Primary Tissues All reagents for cell culture, preparation and purification were purchased from StemCell Technologies Inc. (STI, Vancouver, BC, Canada) unless otherwise indicated. Prostate tissues collected from WT and Tg mice were minced into small pieces and treated with collagenase (0.25% and type IA, STI; 2mL for 1 prostate) for 25-30 minutes at 37oC with the addition of 10 mM EDTA (ethylenediaminetetraacetic acid) for the final 5 minutes. The spleen and various lymph nodes (LNs) were collected from WT and Tg mice of the indicated ages. Single cell suspensions were prepared by incubating the tissues in Dulbecco’s phosphate-buffered saline (PBS) containing 2% fetal bovine serum (PBS/2%FBS), 5 mM EDTA, and 100 µg/mL hyaluronidase (Sigma, St. Louis, MO, USA) for 10 minutes at room temperature or by incubating tissue fragments with collagenase (STI) for 30 minutes at room temperature with the addition of 10 mM EDTA for the final 5 minutes. Cells (prostate, spleen or LN) were then passed through 16-gauge blunt-end needles and 70 µm cell strainers (VWR International, Mississauga, ON, Canada). 2.2.3 Phenotype Analysis by Flow Cytometry Single cell suspensions were washed 2-3 times with PBS/2%FBS and then incubated on ice for 20 to 30 minutes with fluorescein isothiocyanate (FITC)-, phycoerythrin (PE)-, or allophycocyanin-labelled monoclonal antibodies (mAb). The antibodies used for phenotype analysis included: CD11c (HL3), CD4 (L3T4, RM4-5), CD8α (Ly-2, 53-6.7), CD25 (7D4 or PC61) and CD69 (H1, 2F3) mAb (all from Becton Dickinson [BD] PharMingen, San Diego, CA, USA). Cells were then washed three times with the addition of 1µg/mL propidium iodide (PI; Sigma) in the final wash. Alternatively 1µg/mL 7-Aminoactinomycin D (7AAD; Sigma) was added during the Ab incubation for detection of dead cells. Cells were analyzed by flow cytometry using a FACSCalibur flow cytometer and CELLQuest Pro software (BD). Absolute cell numbers were calculated by multiplying nucleated, live (determined microscopically by eosin exclusion) cell numbers with the frequency of positive cells (CD4+ or CD8+) in the nucleated viable cell gate from flow cytometric analysis. The proportion of CD4+ or CD8+ cells that express the indicated markers was calculated by dividing the percentage of the population positive for the indicated marker by the percentage of the total CD4+ or CD8+ cell population. 39  2.2.4 T Cell Proliferation Assay Single cell suspensions of spleen or lumbar LN were prepared as outlined above. Cells were cultured in triplicate in round-bottom 96-well plates at 1 x 105 cells per well (with 0.2 mL medium per well) in the absence or presence of various concentrations (0.1-10 µg/mL) of purified anti-CD3 mAb (clone 145-2C11, BD). The culture medium consisted of Iscove’s Modified Dulbecco’s Medium (STI) supplemented with 10% FBS, 100 units/mL penicillin, 100 mg/mL streptomycin, 2 mM L-glutamine, and 115 x 10-6 M monothioglycerol (MTG; Sigma). Proliferation was measured by scintillation counting of each sample following addition of 1 µCi of [3H]-thymidine (Perkin-Elmer Life Science, Inc., Boston, MA, USA) per well during the final 16 to 18 hours of a 72-hour culture period at 37oC in 5% CO2. Data were plotted as either raw counts per minute (cpm) or as the cpm ratio in which mAbinduced cpm was divided by the unstimulated cpm levels to control for differences in responder T cell numbers arising from the altered T cell frequency in the starting populations. For each representative proliferation assay result, each point is the mean of triplicate determinations. 2.2.5 Purification and Functional Assessment of CD4+CD25+ T Cells For analysis of the CD4+CD25+ T cell population, CD4+ T cells were first isolated from LN cell suspensions of WT and Tg mice (≥20 weeks of age) by negative selection using the mouse CD4+ T cell isolation kit (MACS system; Miltenyi Biotec GmbH, Bergisch Gladbach, Germany). Following isolation of CD4+ T cells, cells stained with phycoerythrinCD25 mAb were purified using anti-phycoerythrin-coupled magnetic beads (Miltenyi Biotec). CD4+CD25+ T cells were obtained with purity greater than 90%. Alternatively, CD4+CD25+ T cell depletion was achieved from lumbar LN and spleen cell suspensions of WT and Tg mice (≥20 weeks of age) by fluorescence-activated cell sorting (FACSVantage, BD). The purity of the resultant cell population was >99%. In one series of experiments, spleen cells (1 x 105 cells per well) were stimulated with soluble anti-CD3 mAb (1 µg/mL) in the presence of various numbers of CD4+CD25+ T cells purified from either WT or Tg lumbar LNs. In the second series of experiments, proliferation of the LN cell population depleted of CD4+CD25+ T cells was assessed as described above. [3H]-thymidine incorporation was measured at 72 hours and results were analyzed as described above.  40  2.2.6 Determination of Cytokine Release by Cytometric Bead Array For analysis of cytokine production by CD4+ T cell subpopulations, CD4+CD25- and CD4+CD25+ T cells were purified from ≥20-week-old Tg mice using the MACS system. Purified cells were cultured at 1 - 1.5 x 106 cells/mL with plate-bound anti-CD3 mAb (2 µg/mL) plus anti-CD28 mAb (1 µg/mL) and cross-linking immunoglobulin G (IgG) mAb (1 µg/mL). Supernatant samples were collected at 24 hours [for Interleukin-2 (IL-2)] and 72 hours (IL-10 and IFN-γ). Cytokine levels were analyzed by cytometric bead array according to manufacturer’s instructions (BD PharMingen). Results are the mean ± SEM for duplicate wells for 4 to 6 samples (each representing cells pooled from 3 to 4 mice). 2.2.7 Cell Lines and Culture The hybridoma secreting the anti-CD25 mAb (clone PC61, rat IgG) was purchased from the American Type Culture Collection. The antibody was precipitated from the culture supernatant using two rounds of 40% ammonium sulfate. IgG used for the control injection was precipitated from the serum of normal rats. The murine prostate cancer cell line transgenic adenocarcinoma of mouse prostate (TRAMP)-C2 cell line [22], kindly provided by Dr. N.M. Greenberg (Fred Hutchinson Cancer Research Center, Seattle, WA, USA), was maintained in high-glucose Dulbecco’s Modified Eagle’s Medium (DMEM) with 5% NuSerum IV (BD), 5% FBS, 5 µg/mL insulin (Sigma), 10-8 M dihydrotestosterone (Sigma), 25 units/mL penicillin, 25 µg/mL streptomycin and 2 mM L-glutamine. Cultured TRAMP-C2 cells were washed three times with PBS and re-suspended to the appropriate dilution (5 x 106 cells in 0.2 mL PBS) for injection as outlined below. 2.2.8 Tumour Cell Transplantation and Anti-CD25 Monoclonal Antibody Treatment in vivo C57BL/6J mice (approximately 8 weeks old) were inoculated subcutaneously (s.c.) in the flank with 5 x 106 TRAMP-C2 tumour cells (in 0.2 mL PBS) per mouse. Rat IgG or antiCD25 mAb (clone PC61) was injected intravenously (i.v.) at a concentration of 1 mg per mouse in 0.25 mL PBS on day -1 and day +3 with respect to the day of tumour transplant. Tumour development was assessed 2 to 3 times weekly by palpation. TRAMP-C2 recipients were sacrificed for tumour weight analysis at 6 weeks post-transplant. There were 14-15 41  mice per group over 3 independent experiments. Tumours recovered from mice treated with either rat IgG or anti-CD25 mAb were fixed in 10% formalin, sectioned and stained with Hematoxylin/Eosin following standard protocols. Staining was done on 1 to 2 sections per tumour. 12T-7slow mice were treated with two doses of either control rat IgG or anti-CD25 mAb (0.65 or 1 mg per mouse; i.v. injection) at 8 or 12 weeks of age. These mice were sacrificed for analysis at 20 weeks of age. There were at least 6 mice per group in 2 to 3 independent experiments. Phenotypic analysis of the secondary lymphoid and prostate tissues, isolated at the time indicated in the figure legend, was carried out as described above except that CD25 expression was detected only using the clone 7D4 anti-CD25 mAb. 2.2.9 Data Presentation and Statistical Analysis Data are presented as mean ± SD (standard deviation) or mean ± SEM (standard error mean; only for the cytokine level analysis). All statistical analyses were carried out using a one-tailed Student’s t-test which assumes that the two samples being compared are different and that the difference results from one sample being either higher or lower than the other. Any calculated P-value was determined only for the data with n ≥ 3.  2.3 RESULTS 2.3.1 Altered Phenotype of T Cells in the Prostate Tumour Microenvironment To facilitate development of effective immune-based therapies for PCa, a comprehensive understanding of immune function during tumour progression must be achieved. The 12T-7slow prostate dysplasia mouse model mimics the early stages of human PCa progression with tumours progressing to mouse prostatic intraepithelial neoplasia (mPIN). Thus, this model is suitable for examining immune alterations caused by tumours that escape immune surveillance during the early stages of tumour development. We first investigated the immune cellular composition of the prostate tumour microenvironment in the 12T-7slow mice at a stage when the tumour has progressed to high-grade dysplasia (>20 weeks; [20]). WT mice, which have normal prostate tissues, were used as the control. Results of the dendritic cell (DC) analysis will be described in Chapter 3.  42  There were no significant changes in the frequencies of CD4+ (WT: 0.33±0.13% versus Tg: 0.49±0.17%) or CD8+ (WT: 1.11±1.29% versus Tg: 0.67±0.46%) T cells infiltrating the Tg prostate, compared to WT normal prostate tissues. Intriguingly, the proportion of CD8+ or CD4+ T cells expressing the early activation marker CD69 was elevated in the Tg prostate tissue. Of further note was the observation that the proportion of CD4+CD69- T cells expressing CD25 was also significantly increased in the tumour microenvironment (Figure 2.1A). In all cases, there was a corresponding increase in the mean fluorescence intensity (MFI; Figure 2.1B). These results suggest that the tumour microenvironment alters the phenotype of the tumour-infiltrating T cells. B. 100  **  60  **  40  *  20  **  0 CD69 CD69 CD25 CD25  CD8+  CD4+  CD4+ CD69-  MFI  Proportion (%)  A. 80  80 60 40 20 0  *  * *  CD69 CD69 CD25 CD25  CD8+  CD4+  CD4+ CD69-  Figure 2.1 Tumour-infiltrating T cells exhibit an altered phenotype. (A) The proportion of CD4+, CD8+ or CD4+CD69- T cells that express the indicated cell surface molecules. (B) Mean fluorescence intensity (MFI) of expression of the various cell surface molecules on CD4+, CD8+ or CD4+CD69- T cells. Age-matched WT mice (>20 weeks) were used as the control. Open columns, WT; closed columns, Tg. (n > 5). *, P ≤ 0.05. **, P ≤ 0.01. 2.3.2 Expansion of Immune Cells in the Secondary Lymphoid Tissues during Prostate Tumour Progression To determine if alterations in T cell phenotype extend beyond the tumour microenvironment, we analyzed the phenotype of T cells in the secondary lymphoid tissues (i.e. spleen and LN) of WT and Tg mice. As indicated in Table 2.1, the total nucleated cell number was higher in the various LNs isolated from ≥20 week-old Tg mice, and this difference reached significance for the lumbar LN, which is the draining LN closest to the prostate gland. Of note, in WT mice the size of one lumbar LN is much smaller than other types of LN (approximately 3-6 fold changes). Moreover, total nucleated cell numbers in each Tg lumbar LN increased as a function of tumour progression (Table 2.1; 6- to 8-fold in 43  Tg mice ≥16 weeks of age), but this difference was not detected in a distant LN (e.g. submandibular). The cellular compositions of the lumbar LNs were then analyzed to determine if there was a preferential expansion of selected T cell populations or if both T cell subsets expanded similarly. As shown in Table 2.1, CD4+ and CD8+ T cell numbers increased approximately 3- to 5-fold in Tg versus WT mice ≥20 weeks of age, reflecting the increased Tg LN cellularity. This expansion is due to the increased absolute cell number for each cell population since the frequencies of CD4+ and CD8+ T cells were similar. These observations suggest that tumour progression in this prostate tumour mouse model induces an in vivo expansion of T cells. Analysis of the T cell phenotype indicated that the proportion of CD4+ or CD8+ T cells expressing the early activation marker CD69 was modestly elevated in the lumbar LN of ≥20-week-old Tg mice (Figure 2.2A). Additional evidence suggesting T-cell activation was the observation that both the proportion of CD4+ T cells expressing CD25 (Figure 2.2A) and the absolute numbers of CD4+CD25+ T cells (Figure 2.2B) were significantly elevated in the lumbar LN during tumour development. Of interest, this increased frequency and number of CD4+CD25+ T cells was not seen in other LNs (i.e. submandibular, inguinal and mesenteric LN; Figure 2.2C), suggesting that expansion of this population occurs in response to the nearby tumour. Table 2.1 Cellularity and cellular composition of various draining LN during tumour progression Tg (x 106) Fold change WT (x 106) Submandibular 7.3 ± 3.5 8.7 ± 3.6 1.2 Inguinal 5.4 ± 2.3 10.0 ± 2.3 1.8 LNa Mesenteric 8.4 ± 4.7 13.7 ± 4.4 1.6 Lumbar 1.8 ± 0.8 10.5 ± 4.0 * 5.8 11-12 weeks 1.9 ± 0.4 3.2 ± 0.6 1.7 Lumbar LN 16-17 weeks 1.7 ± 0.6 12.8 ± 1.7 * 7.5 20-25 weeks 1.8 ± 0.8 10.5 ± 4.0 * 5.8 b CD4 0.93 ± 0.37 4.56 ± 0.71 * 4.9 Lumbar LN (20-25 weeks) CD8 0.83 ± 0.17 2.61 ± 0.26 * 3.2 a Cell number per LN isolated from mice ≥20 weeks of age. Statistical difference (*, P ≤ 0.01) versus WT was determined using the Student’s t-test. b Absolute numbers of cells expressing CD4 and CD8 in each lumbar LN were calculated by multiplying total lumbar LN cellularity with the percentage of viable nucleated cells expressing the indicated markers as determined by flow cytometry. Each value represents the mean ± SD (n ≥ 3 independent experiments). 44  30 25 20 15 10 5 0  **  CD69 CD25 CD69  Proportion (%)  C.  CD4+  CD4+CD25+ cell number per lumbar LN (x104)  B. Proportion (%)  A.  80  **  **  16-17  20-25  60 40 20 0 11-12  Age (weeks)  CD8+  20  **  15  *  10 5 0 Submandibular  Inguinal  Mesenteric  Lumbar  Spleen  LN  Figure 2.2 The frequency and number of CD4+CD25+ T cells is elevated in the lumbar LN of Tg mice during tumour progression. (A) The proportion of CD4+ or CD8+ T cells expressing CD25 or CD69 in lumbar LN isolated from mice ≥ 20 weeks of age. (B) Absolute numbers of CD4+CD25+ T cells in the lumbar LN at the indicated stages of tumour development. (C) The proportion of CD4+ T cells expressing CD25 in various LN and spleen from mice ≥ 20 weeks of age. Open columns, WT; closed columns, Tg. (n ≥ 3). *, P ≤ 0.05. **, P ≤ 0.01.  2.3.3 Alteration of T Cell Function during Prostate Tumour Development Expansion of phenotypically defined cell populations within the secondary lymphoid tissues does not necessarily reflect enhanced, or even normal, function. Since the increased number of activated T cells suggested that an immune response is initiated in the secondary lymphoid tissues during tumour progression, we examined the function of T cells in the spleen or lumbar LN of tumour-bearing mice. As shown in Figure 2.3 (upper panels), there was a progressive age-related decrease in the ability of Tg lumbar LN T cells to proliferate in response to anti-CD3 mAb stimulation. A similar reduction in the proliferation response was observed using Tg spleen cells (Figure 2.3 lower panels).  45  Proliferation response (cpm ratio) Spleen Lumbar LN  30  12 weeks  80  * *  20  0 0.1  40  10  20  20  0  0  0.01  0.1  10  10  0.01  **  * 0.1  60  *  40  20  20  0  0  0.01  **  1  10  *  **  1  10  80  40  1  1  20 weeks  60 40  60  * 0.1  80  80  0 0.01  **  40  10  * *  30 20  1  **  60  10  0.01  16 weeks  0.1  1  10  0.01  ** 0.1  Anti-CD3 mAb (µg/mL) Figure 2.3 Tg lumbar LN and spleen T cells are impaired in proliferative capacity during tumour development. Proliferation of lumbar LN (upper panels) and spleen (lower panels) T cells, isolated from mice of the indicated ages, in response to anti-CD3 mAb stimulation. (n ≥ 3). Solid line, WT; dashed line, Tg.  To explore the possibility that the expanded CD4+CD25+ T cell population present in the spleen and lumbar LN might be responsible for the reduced T cell proliferation, the function of this population was assessed. CD4+CD25+ T cells were purified from the lumbar LN of WT or Tg mice (≥20 weeks of age) and their ability to inhibit T cell proliferation was analyzed. As illustrated in Figure 2.4A, both the WT and Tg CD4+CD25+ T cell populations inhibited the proliferative response of naïve T cells. Under these conditions, neither the WT nor the Tg CD4+CD25+ T cell population was able to proliferate in response to anti-CD3 mAb stimulation (WT cpm: 36±13 versus Tg cpm: 40±24 for the representative data shown in Figure 2.4A). To further confirm the suppressive capacity of the CD4+CD25+ T cells in the WT or Tg lumbar LN, the T cell population from the lumbar LN was depleted of CD4+CD25+ T cells by fluorescence-activated cell sorting and cell proliferation in response to antigenic stimulation was determined. Following removal of the CD4+CD25+ T cell population, Tg T cell proliferation was restored to WT levels (Figure 2.4B), indicating that  46  the elevated number of CD4+CD25+ T cells could play a significant role in suppressing immune responsiveness in the prostate tumour environment.  B.  25000 20000  Responder  15000 10000 5000 0 0  20 +  40  60 +  80 100 3  CD4 CD25 T cells (x10 )  Proliferation response (cpm ratio)  Proliferation response (cpm)  A.  300 250 200 150 100 50 0 0  0.2  0.4  0.6  0.8  1  Anti-CD3 mAb (µg/mL)  Figure 2.4 The suppressive activity of CD4+CD25+ T cells contributes to the decreased proliferation of Tg T cells. (A) Anti-CD3 mAb-stimulated T cell proliferation following addition of CD4+CD25+ T cells purified from WT (solid line) or Tg (dashed line) lumbar LN. Horizontal dashed line represents the responder T cell proliferation in the absence of CD4+CD25+ T cells. (B) Proliferation of WT (solid line) and Tg (dashed line) lumbar LN cells depleted of CD4+CD25+ T cells. (n ≥ 3). In both cases a representative experiment is shown.  To further evaluate the properties of the altered Tg T cell populations, we determined the levels of various cytokines released following stimulation. Of note, cytokine levels were not assessed on unstimulated T cells which might be more representative of T cells residing in vitro. As indicated in Table 2.2, only moderate differences in the induced cytokine  secretion profiles of WT and Tg CD4+ T cells were observed. Hence, we separated Tg CD4+CD25+ and CD4+CD25- T cells to examine which population was responsible for the cytokine secretion. This analysis revealed that the Tg CD4+CD25+ T cell population secreted dramatically elevated levels of IL-10 compared with the CD4+CD25- T cells (Table 2.2). In contrast, they produced significantly lower levels of IL-2 and IFN-γ. Moreover, IL-10 production by WT and Tg CD4+CD25+ T cells was evaluated as well, and they produced similar levels of IL-10 (WT: 12,148±2,720 pg/5x105 cells versus 8,805±3,212 pg/5x105 cells; P=0.224) consistent with the similar levels of suppression induced by these two populations. Taken together, these results raised the possibility that Treg cells present in the  47  expanded CD4+CD25+ T cell population may play a critical role in immune suppression during prostate tumour progression. Table 2.2 CD4+CD25+ T cells from the secondary lymphoid tissues of tumour-bearing mice secrete elevated levels of IL-10 compared with CD4+CD25- T cellsa CD4+ cells (pg/mL) Tg CD4+ cells (pg/5x105 cells) Cytokine WT Tg CD4+CD25CD4+CD25+ IL-2 125 ± 37 163 ± 58 857 ± 193 288 ± 92 * IL-10 602 ± 395 1,044 ± 558 157 ± 59 8,805 ± 3,212 * 7,836 ± 1,644 16,931 ± 6,972 37,198 ± 8,536 4,628 ± 738 ** IFN-γ a Each value represents the mean ± SEM for duplicates in 3 independent experiments (4-6 independent samples in total). Statistical analysis was carried out using the Student’s t-test. *P ≤ 0.05; ** P ≤ 0.005. 2.3.4 Depletion of CD4+CD25+ T Cells in a Tumour Transplant Mouse Model  Tumour transplantation studies have implicated CD4+CD25+ Treg cells in suppression of anti-tumour immunity against a wide variety of tumours [14-17,23]. However, at the time of this study there was no evidence that they contributed to the immune suppression in prostate cancer. To address this question, mice inoculated with TRAMP-C2 prostate tumour cells were treated with anti-CD25 mAb. Both the control (rat IgG [immunoglobulin G]) and mAb-treated groups developed palpable tumours (s.c. in the flank) at approximately 3 weeks post-transplant, which grew progressively until the mice were sacrificed at 6 weeks post-transplant (Figure 2.5A). No metastases were observed in the recipient mice, and thus tumour weight was used as the primary assessment of tumour growth. In this model, anti-CD25 mAb treatment reduced the average tumour weight by approximately 28.5% (0.85±0.53g in the control IgG-treated group versus 0.61±0.43g in the anti-CD25 mAb-treated group; p=0.094, Figure 2.5B). Interestingly, anti-CD25 mAb treatment was significantly more effective on tumours weighing less than 1g than on those weighing more than 1g. To determine if the reduction in tumour growth was accompanied by an altered infiltration of immune cells into the tumour, one to two sections were sliced at random from each tumour and the number of immune cells within a designated field was estimated microscopically. As shown in Figure 2.5C, there was an increase in the frequency of tumour-infiltrating immune cells in the anti-CD25 mAb-treated group. This study suggested the involvement of CD4+CD25+ Treg cells in prostate tumour progression.  48  A.  Tumour weight (g)  B.  2.5  P=0.0937  P=0.1516  P=0.0083  2.0 1.5 1.0 0.5 0.0  IgG CD25 IgG CD25 IgG CD25 (n=14) (n=15) Tumour > 1g Tumour < 1g All tumour samples C.  IgG control  Anti-CD25 mAb  Figure 2.5 Anti-CD25 mAb treatment reduces the growth of transplanted prostate tumour cells. (A) Images of transplanted prostate tumours isolated from mice treated with anti-CD25 mAb or control rat IgG. (B) Weights of transplanted prostate tumours isolated from mice treated with anti-CD25 mAb or control rat IgG. Each point represents a tumour isolated from an individual mouse. Horizontal bars represent the average tumour weight in each treatment group. (C) Representative H/E staining showing the lymphocytic infiltration into the tumour after anti-CD25 mAb or control rat IgG treatment. Arrows indicate areas of lymphocytic infiltration. Final magnification was x200.  49  2.3.5 Depletion of CD4+CD25+ T Cells in a Prostate Dysplasia Mouse Model  Despite the successes using anti-CD25 mAb treatment to reduce the growth of transplanted tumours in this and previous reports, prior studies had not yet shown that inhibition of the Treg cell population has efficacy against spontaneously developing tumours. We therefore assessed the ability of anti-CD25 mAb treatment to reduce or prevent tumour growth in the 12T-7slow mouse model. On day 3 following mAb treatment, approximately 60% to 80% of the CD4+CD25+ T cells were depleted from the spleen and LNs of anti-CD25 mAb-treated mice, although only 15% depletion was achieved in the tumour mass (Figure 2.6A). As indicated in Figure 2.6B, mice treated at 8 weeks of age had similar tumour weights at 20 weeks regardless of the treatment given (IgG: 8.88±3.48 g versus anti-CD25 mAb: 9.42±3.90 g). The average tumour weights from either group were not significantly different from those of mice without any treatment (8.23±3.29 g). In contrast, analysis of tumours from mice treated at 12 weeks of age and examined 8 weeks post-injection revealed a 23.4% reduction in tumour weight (7.12±2.10 g for IgG versus 5.45±1.41 g for anti-CD25 mAb; P=0.025). Again, the average tumour weights of the control IgG-treated group were similar to those of untreated mice (8.23±3.29 g). These studies suggest that anti-CD25 mAb treatment may reduce tumour growth if administered at the appropriate time during tumour development and they confirm the involvement of CD4+CD25+ Treg cells in the immune suppression observed during spontaneous prostate tumour progression. We next determined the frequency and phenotype of tumour-infiltrating T cells in mice treated at 12 weeks of age. As shown in Figure 2.6C, the frequency of tumourinfiltrating CD4+ T cells was similar in the anti-CD25 mAb- and IgG-treated groups. In contrast, there was a pronounced (i.e. 50%), although not statistically significant (P=0.076), increase in the frequency of CD8+ T cells in the anti-CD25 mAb-treated group. The phenotype of these T cells was similar in the 2 groups (Figure 2.6D).  50  CD25 within CD4+ T cells (%)  A.  25 20 15 10 5 0  *  Submandibular  *  *  Inguinal  Lumbar  *  Spleen  Prostate  LN B. Tumour weight (g)  20  P=0.3832  P=0.0246  15 10 5 0  untreated IgG CD25 IgG CD25 (n=14) (n=7) (n=10) (n=11) (n=9)  Frequency (%)  C.  D.  0.6 0.5 0.4 0.3 0.2 0.1 0 CD4 CD8  Proportion (%)  8 weeks  12 weeks 80 60 40 20 0 CD69 CD25 CD69  CD4+  CD8+  Figure 2.6 Anti-CD25 mAb treatment reduces the growth of spontaneous prostate tumours. (A) The proportion of CD4+ T cells expressing CD25 (detected using the clone 7D4 antiCD25 mAb) in the secondary lymphoid tissues and prostate on day 3 following Ab (clone PC61) treatment. (n=3). *P ≤ 0.01. Open columns, rat IgG; closed columns, anti-CD25 mAb. (B) Weights of tumours from 20-week-old 12T-7slow mice without Ab treatment or treated with either rat IgG or anti-CD25 mAb at 8 or 12 weeks of age. Each point represents the tumour isolated from an individual mouse. Horizontal bars represent average tumour weight in each treatment group. (C) Frequency of CD4+ or CD8+ T cells infiltrating the prostate tumour at the time of tumour weight assessment. (D) Proportion of tumour-infiltrating CD4+ or CD8+ T cells expressing either CD69 or CD25. (n=4-6).  51  2.4 DISCUSSION 2.4.1 Mechanisms for Treg Cell Generation or Expansion in the Tumour Environment  Previous studies have shown that patients with early-stage non-small cell lung cancer and late-stage ovarian cancer have tumour-associated T cells with an activated phenotype at initial diagnosis [4]. This observation suggests that at least during the early stages of tumour development an active anti-tumour immune response is initiated. Our analyses of the 12T7slow prostate tumour mouse model support this possibility. Activated CD4+ T cells and phenotypically normal DC (discussed in Chapter 3) expanded in the Tg lumbar LNs. Similarly, activated T cells were also detected in the tumour microenvironment. Despite the indications of anti-tumour immunity, there was a progressive decline in immune responsiveness during the course of tumour development (Figure 2.3). Of note, the reduction in Tg T cell proliferation upon anti-CD3 mAb stimulation (Figure 2.3) could have arisen due to altered T cell function or a reduced number of antigen-presenting cells (APC) capable of providing sufficient signals for T cell activation. However, we observed an increased frequency of apparently phenotypically normal DC in the Tg secondary lymphoid tissues (discussed in Chapter 3, Section 3.3.2). Therefore, an alteration in T cell function was assumed to be the major factor contributing to the reduced T cell proliferation. Indeed, the results (Figure 2.4B) showed that CD4+CD25+ Treg cells were responsible, at least in part, for this alteration. There was an increased frequency of tumour infiltrating CD4+CD69- T cells expressing CD25 in the 12T-7slow prostate tumour mass. We hypothesize that these cells are Treg cells that contribute to the immune suppression in the tumour environment. At the time of these studies, there was no suitable and more specific marker, such as FOXP3, to confirm that the Treg cell population was expanded. However, it has been demonstrated that there are higher expression levels of CD25 on Treg cells than on activated T cells [24]. The CD4+CD25+ T cells that infiltrated the 12T-7slow prostate tumour indeed displayed a higher level of CD25 expression (Figure 2.1B). Moreover, studies have demonstrated that the frequency of CD4+CD25+ Treg cells is increased in the peripheral blood and/or tumour microenvironment in different types of cancer patients, and the increase in the Treg cell population contributes to immune tolerance to the tumour [4-6,8-10]. In keeping with our observations, a recent study reported that a higher frequency of CD4+CD25high T cells that  52  co-expressed FOXP3 is present in the peripheral blood and tumour mass of patients with early stage PCa [12]. The T cell population isolated from the peripheral blood of PCa patients was demonstrated to be immune suppressive in vitro. Moreover, the increased frequency of CD4+CD25+ Treg cells infiltrating the tumour microenvironment predicted poor survival in ovarian carcinoma and renal cell carcinoma patients [25,26]. These observations raise an interesting question regarding the mechanism(s) by which CD4+CD25+ Treg cells increase in the tumour environment, both within the tumour site as well as in the draining (lumbar) LNs. The exact mechanisms may be dependent on tumour type or stage of the disease. One interesting study demonstrated that in ovarian carcinoma the tumour cells and tumour-infiltrating macrophages secrete CCL22, a chemokine that attracts Treg cells to the tumour microenvironment [25]. Thus, CCL22 expression in the prostate tumour may recruit and retain Treg cells to suppress any ongoing immune responses [27]. Studies also demonstrated that prostaglandin E2 (PGE2) can induce FOXP3 expression and enhance Treg cell activity [28,29]. It has been shown that prostate tumour cells secrete PGE2, due to up-regulation of cyclooxygenase-2 [30]. Another possible mechanism is through the production of transforming growth factor-β (TGF-β). Inhibition of TGF-β by a neutralizing Ab abrogated the conversion of CD4+CD25- T cells to CD4+CD25+ Treg cells that was directly mediated by tumour-produced TGF-β [31]. Moreover, TGF-β may alter APC phenotype or function so that they induce Treg cells in the tumour environment [32]. It is known that DC incubated with PCa cells or tumour culture supernatant exhibit an immature phenotype ([33-35] and Chapter 3). Immature DC are able to induce or expand Treg cells [36]. DC that express the glucocorticoid-induced leucine zipper protein were demonstrated to induce Ag-specific Treg cells [37]. Therefore, it is tempting to speculate that DC present in the 12T-7slow model may behave as tolerogenic DC that induce Treg cells, resulting in an increase in the Treg cell frequency within the tumour microenvironment. Chapter 3 will discuss the DC population in the 12T-7slow mouse model. 2.4.2 Depletion of CD4+CD25+ Treg Cells by Anti-CD25 Antibody  Regardless of the mechanisms by which Treg cells expand in the tumour environment, it has been shown that depletion of CD4+CD25+ Treg cells using anti-CD25  53  mAb can abrogate immunologic unresponsiveness to syngeneic implanted leukemia, lymphoma, sarcoma, myeloma, and Meth A fibrosarcoma and B16 melanoma cell lines in vivo, resulting in endogenous anti-tumour immune responses [14-17]. It has been shown that  reduction of tumour mass by anti-CD25 mAb treatment may involve CD4+ or CD8+ T cells, or natural killer (NK) cells depending on the tumour type and that the immune response mediated by these cells may be affected by the immunogenicity of the tumour. Studies showed that the response is mediated by tumour antigen-specific CD8+ cytotoxic T lymphocytes (CTL) and tumour antigen-nonspecific CD4-CD8- cytotoxic cells akin to NK cells [38,39]. In the B16 melanoma model there is an enhancement of the IFNα-induced, CD8+ T cell-dependent immune response following elimination of CD4+CD25+ Treg cells in vivo [16]. It has also been shown that treatment with anti-CD25 mAb facilitates long-term  CD4+ T cell-mediated tumour immunity [17]. This evidence suggests that Treg cells play an essential role in immune suppression in at least some tumours. In our studies an increased frequency of tumour-infiltrating immune cells was observed in the TRAMP-C2 recipients treated with anti-CD25 mAb. It has not yet been determined whether this immune response is tumour Ag specific or non-specific, i.e. resulting from enhanced release of inflammatory cytokines induced by the cell death resulting from anti-CD25 mAb treatment. Regardless, a slightly elevated frequency of CD8+ T cells infiltrating the tumours was detected in the anti-CD25 mAb-treated 12T-7slow mice (Figure 2.6C). It will be important to determine the Ag specificity of these cells and further elucidate their roles in reducing tumour growth. Further studies are obviously required to understand the mechanisms by which anti-CD25 mAb treatment reduces the growth of transplanted and spontaneous prostate tumours. The study described in this chapter was the first to show that blockade of +  CD4 CD25+ Treg cells using anti-CD25 mAb reduces prostate cancer cell growth both in a prostate tumour transplant model (the TRAMP-C2 cell line) and in a spontaneous prostate tumour mouse model (the 12T-7slow mice). Moreover, it was shown that the timing of antiCD25 mAb treatment, as well as unidentified factors that regulate tumour growth, is likely to be critical in the efficacy of this treatment in reducing tumour growth. In the 12T-7slow mouse model, the expansion of Treg cells in the draining lumbar LNs or tumour microenvironment does not likely occur until 12 weeks of age or later. Therefore, treating the  54  mice too early (i.e., at 8 weeks of age) did not eliminate CD4+CD25+ Treg cells. Instead, it was most likely that activated T cells were depleted, thus accounting for the slight increase in tumour weight in the anti-CD25 mAb-treated mice. In contrast to untreated control mice, or those receiving rat serum IgG, the reduction in tumour weight induced following anti-CD25 mAb treatment at 12 weeks of age is likely due specifically to CD4+CD25+ T cell elimination. Depletion of CD4+CD25+ Treg cells may be limited to the secondary lymphoid tissues. Studies have shown that specific immune T cells in tumour-draining LN were generated following Treg depletion [14]. Because only slight depletion of the Treg population was achieved within the tumour mass (Figure 2.6A), it is not surprising that complete tumour elimination was not observed. Moreover, based on previous work in other tumour models, it is known that the frequency of Treg cells in LNs and/or peripheral blood returns to pretreatment levels approximately 1 to 3 weeks following treatment [14,15,17]. Therefore, the location (or tissue type) of Treg cell depletion and the level of depletion achieved are critical factors in determining the outcome of the treatment. Obviously improved strategies for complete and sustained removal of the tumour Ag-specific Treg cells from the tumour environment, as well as the secondary lymphoid tissues, must be developed to increase the efficacy of this treatment method. In the long term, elimination of the Treg cell population using Ab therapy may favor the generation and/or expansion of activated T cells capable of reducing tumour growth. Validation of this hypothesis will require a detailed analysis of immune function following Ab therapy at various times during tumour progression. Although these results are encouraging, our studies and others suggest that simply blocking or even eliminating CD4+CD25+ Treg cell function in vivo using anti-CD25 mAb might not be adequate to overcome immune suppression and eradicate the tumour ([14,40] and studies in this chapter). A major concern is that CD25 is also transiently expressed on activated T cells, and thus use of anti-CD25 mAb in vivo could potentially eliminate recently activated tumour Ag-specific T cells. A second concern is that this Ab treatment likely results in only temporary blockade of Treg cell expansion or function, rather than permanent. Determination of appropriate doses, administration routes, and treatment schedules may circumvent these concerns.  55  2.5 REFERENCES  1.  2. 3. 4.  5.  6. 7.  8. 9.  10. 11.  12. 13.  Oldenhove G, de Heusch M, Urbain-Vansanten G, Urbain J, Maliszewski C, Leo O, Moser M: CD4+ CD25+ regulatory T cells control T helper cell type 1 responses to foreign antigens induced by mature dendritic cells in vivo. J Exp Med 2003;198:259266. Sakaguchi S: Naturally arising Foxp3-expressing CD25+CD4+ regulatory T cells in immunological tolerance to self and non-self. Nat Immunol 2005;6:345-352. Beyer M, Schultze JL: Regulatory T cells in cancer. Blood 2006;108:804-811. 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Steitz J, Bruck J, Lenz J, Knop J, Tuting T: Depletion of CD25+ CD4+ T cells and treatment with tyrosinase-related protein 2-transduced dendritic cells enhance the interferon α-induced, CD8+ T-cell-dependent immune defense of B16 melanoma. Cancer Res 2001;61:8643-8646. Jones E, Dahm-Vicker M, Simon AK, Green A, Powrie F, Cerundolo V, Gallimore A: Depletion of CD25+ regulatory cells results in suppression of melanoma growth and induction of autoreactivity in mice. Cancer Immun 2002;2:1. Dannull J, Su Z, Rizzieri D, Yang BK, Coleman D, Yancey D, Zhang A, Dahm P, Chao N, Gilboa E, Vieweg J: Enhancement of vaccine-mediated antitumor immunity in cancer patients after depletion of regulatory T cells. J Clin Invest 2005;115:36233633. Mahnke K, Schonfeld K, Fondel S, Ring S, Karakhanova S, Wiedemeyer K, Bedke T, Johnson TS, Storn V, Schallenberg S, Enk AH: Depletion of CD4+CD25+ human regulatory T cells in vivo: kinetics of Treg depletion and alterations in immune functions in vivo and in vitro. Int J Cancer 2007;120:2723-2733. Kasper S, Sheppard PC, Yan Y, Pettigrew N, Borowsky AD, Prins GS, Dodd JG, Duckworth ML, Matusik RJ: Development, progression, and androgen-dependence of prostate tumors in probasin-large T antigen transgenic mice: a model for prostate cancer. Lab Invest 1998;78:319-333. Masumori N, Thomas TZ, Chaurand P, Case T, Paul M, Kasper S, Caprioli RM, Tsukamoto T, Shappell SB, Matusik RJ: A probasin-large T antigen transgenic mouse line develops prostate adenocarcinoma and neuroendocrine carcinoma with metastatic potential. Cancer Res 2001;61:2239-2249. Foster BA, Gingrich JR, Kwon ED, Madias C, Greenberg NM: Characterization of prostatic epithelial cell lines derived from transgenic adenocarcinoma of the mouse prostate (TRAMP) model. Cancer Res 1997;57:3325-3330. Sabel MS, Hess SD, Egilmez NK, Conway TF, Jr., Chen FA, Bankert RB: CTLA-4 blockade augments human T lymphocyte-mediated suppression of lung tumor xenografts in SCID mice. Cancer Immunol Immunother 2005;54:944-952. Kuniyasu Y, Takahashi T, Itoh M, Shimizu J, Toda G, Sakaguchi S: Naturally anergic and suppressive CD25+CD4+ T cells as a functionally and phenotypically distinct immunoregulatory T cell subpopulation. Int Immunol 2000;12:1145-1155. Curiel TJ, Coukos G, Zou L, Alvarez X, Cheng P, Mottram P, Evdemon-Hogan M, Conejo-Garcia JR, Zhang L, Burow M, Zhu Y, Wei S, Kryczek I, Daniel B, Gordon A, Myers L, Lackner A, Disis ML, Knutson KL, Chen L, Zou W: Specific recruitment of regulatory T cells in ovarian carcinoma fosters immune privilege and predicts reduced survival. Nat Med 2004;10:942-949. Siddiqui SA, Frigola X, Bonne-Annee S, Mercader M, Kuntz SM, Krambeck AE, Sengupta S, Dong H, Cheville JC, Lohse CM, Krco CJ, Webster WS, Leibovich BC,  57  27.  28.  29.  30. 31.  32. 33. 34. 35. 36.  37.  38.  Blute ML, Knutson KL, Kwon ED: Tumor-infiltrating Foxp3-CD4+CD25+ T cells predict poor survival in renal cell carcinoma. Clin Cancer Res 2007;13:2075-2081. Iellem A, Mariani M, Lang R, Recalde H, Panina-Bordignon P, Sinigaglia F, D'Ambrosio D: Unique chemotactic response profile and specific expression of chemokine receptors CCR4 and CCR8 by CD4+CD25+ regulatory T cells. J Exp Med 2001;194:847-853. Sharma S, Yang SC, Zhu L, Reckamp K, Gardner B, Baratelli F, Huang M, Batra RK, Dubinett SM: Tumor cyclooxygenase-2/prostaglandin E2-dependent promotion of FOXP3 expression and CD4+CD25+ T regulatory cell activities in lung cancer. Cancer Res 2005;65:5211-5220. Baratelli F, Lin Y, Zhu L, Yang SC, Heuze-Vourc'h N, Zeng G, Reckamp K, Dohadwala M, Sharma S, Dubinett SM: Prostaglandin E2 induces FOXP3 gene expression and T regulatory cell function in human CD4+ T cells. J Immunol 2005;175:1483-1490. Moreno J, Krishnan AV, Swami S, Nonn L, Peehl DM, Feldman D: Regulation of prostaglandin metabolism by calcitriol attenuates growth stimulation in prostate cancer cells. Cancer Res 2005;65:7917-7925. Liu VC, Wong LY, Jang T, Shah AH, Park I, Yang X, Zhang Q, Lonning S, Teicher BA, Lee C: Tumor evasion of the immune system by converting CD4+CD25- T cells into CD4+CD25+ T regulatory cells: role of tumor-derived TGF-β. J Immunol 2007;178:2883-2892. Alard P, Clark SL, Kosiewicz MM: Mechanisms of tolerance induced by TGFβtreated APC: CD4 regulatory T cells prevent the induction of the immune response possibly through a mechanism involving TGFβ. Eur J Immunol 2004;34:1021-1030. Aalamian M, Pirtskhalaishvili G, Nunez A, Esche C, Shurin GV, Huland E, Huland H, Shurin MR: Human prostate cancer regulates generation and maturation of monocyte-derived dendritic cells. Prostate 2001;46:68-75. Aalamian M, Tourkova IL, Chatta GS, Lilja H, Huland E, Huland H, Shurin GV, Shurin MR: Inhibition of dendropoiesis by tumor derived and purified prostate specific antigen. J Urol 2003;170:2026-2030. Tourkova IL, Yamabe K, Foster B, Chatta G, Perez L, Shurin GV, Shurin MR: Murine prostate cancer inhibits both in vivo and in vitro generation of dendritic cells from bone marrow precursors. Prostate 2004;59:203-213. Jonuleit H, Schmitt E, Schuler G, Knop J, Enk AH: Induction of interleukin 10producing, nonproliferating CD4+ T cells with regulatory properties by repetitive stimulation with allogeneic immature human dendritic cells. J Exp Med 2000;192:1213-1222. Hamdi H, Godot V, Maillot MC, Prejean MV, Cohen N, Krzysiek R, Lemoine FM, Zou W, Emilie D: Induction of antigen-specific regulatory T lymphocytes by human dendritic cells expressing the glucocorticoid-induced leucine zipper. Blood 2007;110:211-219. Sutmuller RP, van Duivenvoorde LM, van Elsas A, Schumacher TN, Wildenberg ME, Allison JP, Toes RE, Offringa R, Melief CJ: Synergism of cytotoxic T lymphocyte-associated antigen 4 blockade and depletion of CD25+ regulatory T cells in antitumor therapy reveals alternative pathways for suppression of autoreactive cytotoxic T lymphocyte responses. J Exp Med 2001;194:823-832.  58  39. 40.  Shimizu J, Yamazaki S, Sakaguchi S: Induction of tumor immunity by removing CD25+CD4+ T cells: a common basis between tumor immunity and autoimmunity. J Immunol 1999;163:5211-5218. Antony PA, Restifo NP: Do CD4+CD25+ immunoregulatory T cells hinder tumor immunotherapy? J Immunother (1997) 2002;25:202-206.  59  CHAPTER 3. DENDRITIC CELLS IN THE PROSTATE TUMOR ENVIRONMENT1 3.1 BACKGROUND  Dendritic cells (DC) have essential roles in the generation of T cell-mediated immune responses and in the maintenance of central and peripheral tolerance [1,2]. Studies have demonstrated that repetitive stimulation of naïve T cells with immature DC results in the generation of T cells with suppressive activity [3,4]. Additional studies suggest that DCmediated peripheral tolerance may involve the induction and/or expansion of regulatory T (Treg) cells [5]. As discussed in Chapter 2, there is an increased frequency and number of CD4+CD25+ Treg cells in the 12T-7slow tumour environment. These observations suggest that tumour cells may induce changes in DC development, phenotype and/or function, contributing to immune suppression during tumour progression. A number of studies have demonstrated that human and murine tumour cells, or the soluble factors they secrete, alter the phenotype, development or maturation of bone marrow (BM)-derived or monocyte-derived DC, inhibit their allostimulatory capacity, and/or induce apoptosis [6-10]. There is also evidence showing that tumour infiltration by DC is decreased in patients with advanced prostate cancer (PCa) [11]. Moreover, tumour-infiltrating DC exhibited a reduced ability to stimulate allogeneic T cell proliferation and express reduced levels of B7 molecules, compared to splenic DC, in a rat model transplanted with colon carcinoma [12]. It has also been shown, in one study of head and neck cancer, that tumourinfiltrating plasmacytoid DC (pDC) were unable to produce interferon-α (IFN-α) upon CpG motif containing oligonucleotide (CpG ODN) stimulation in vitro [13]. To determine whether alterations in DC development, phenotype and function in the 12T-7slow mouse model of prostate dysplasia contribute to the immune suppression observed during disease development, we evaluated DC derived from BM cells (BMDC) and isolated from the secondary lymphoid tissues (i.e. spleen and various lymph nodes [LNs]) of tumour-bearing mice at various stages of disease progression. In addition, the effects of the tumour microenvironment and factors secreted by the tumour cells on DC were examined. 1  A version of this chapter has been published. Tien, A.H., Xu, L. and Helgason, C.D. (2005) Altered Immunity Accompanies Disease Progression in a Mouse Model of Prostate Dysplasia. Cancer Research. 65:2947-2955.  60  3.2 MATERIALS AND METHODS 3.2.1 Mice  The 12T-7slow mouse model of prostate dysplasia, described in Chapter 2 (Section 2.2.1), was used in this study. Pep3b mice for allogeneic T cell proliferation assays were purchased from the in-house breeding program. Animal care was carried out as described in Chapter 2. 3.2.2 Bone Marrow-Derived DC Culture  All reagents for cell culture and preparation were purchased from StemCell Technologies Inc. (STI, Vancouver, BC, Canada). BM cells were flushed from the femurs and tibiae of mice at the indicated ages. Myeloid DC (mDC) were generated using a Granulocyte-Macrophage-Colony stimulating factor and interleukin-4 (GM-CSF/IL-4) culture system in which BM cells were grown in Iscove’s modified Dulbecco’s medium (IMDM) containing 10% fetal bovine serum (FBS; STI, Sigma [St. Louis, MO, USA], or HyClone [Fisher Scientific, Ottawa, ON, Canada]), 100 units/mL penicillin, 100 µg/mL streptomycin, 2 mM L-glutamine, 115 µM monothioglycerol (MTG; Sigma), 5% IL-4 supernatant (produced by cell line X63-IL4 kindly provided by Dr. Fritz Melchers, Max Planck Institute for Infection Biology, Berlin, Germany) and 5 ng/mL mouse recombinant GM-CSF. Cells were seeded at a density of 0.75 x 106 nucleated cells/mL in Petri dishes. Fms-like tyrosine kinase 3 ligand (Flt3L) cultures were used to generate both mDC and pDC. Red blood cells were lyzed using ammonium chloride at room temperature for 30 seconds. The cell suspension was then washed 3 times with PBS/2%FBS. Cells were cultured in RPMI 1640 supplemented with 10% FBS, 100 units/mL penicillin, 100 µg/mL streptomycin, 2 mM L-glutamine, 115 µM MTG and 100 or 150 ng/mL mouse recombinant Flt3L, and seeded at a density of 1.5-2.5 x 106 nucleated cells/mL in culture dishes. All cultures were maintained at 37oC in a humidified incubator with 5% CO2. On day 5 (GM-CSF culture) or day 8 (Flt3L culture) of culture, semi- or nonadherent immature DC were harvested for phenotype, functional or co-culture analyses. In experiments with stimulation, 1 µg/mL of bacterial lipopolysaccharide (LPS) or 5 µg/mL CpG was added overnight (18-24 hours) to the harvested and re-plated DC culture. In experiments involving co-culture with prostate culture supernatant, CD11c+ cells from the 61  GM-CSF cultures were purified using EasySepTM (STI) positive selection or StemSepTM negative selection. The purity of the CD11c+ cell preparation, assessed by flow cytometry, was approximately 95%. No purification was required for the Flt3L cultures since the majority of the cells (>90%) were CD11c+. 3.2.3 Phenotype Analysis by Flow Cytometry  The preparation of single cell suspensions from primary tissues (spleen, LNs and prostate) was described in Chapter 2 (Section 2.2.2) and cells were analyzed without prior purification. Fluorochrome-conjugated monoclonal antibodies (mAb) used for phenotype analysis in this study included: CD11c (clone HL3), CD4 (L3T4, RM4-5), CD8α (Ly-2, 536.7), CD80 (16-10A1), CD86 (GL1), CD40 (HM40-3), and major histocompatibility complex (MHC) Class II IA/IE (2G9) (Becton Dickinson [BD] PharMingen, San Diego, CA, USA). Cell staining and data analysis was done as described in Chapter 2 (Section 2.2.3). Absolute cell numbers were calculated by multiplying nucleated, live (determined microscopically by eosin exclusion) cell numbers with the frequency of positive cells (CD11c+) in the nucleated viable cell gate from flow cytometric analysis. The proportion of CD11c+ cells expressing the indicated markers was calculated by dividing the percentage of the population positive for the indicated marker by the percentage of the total CD11c+ cell population. 3.2.4 T Cell Proliferation Assay  Allogeneic T cell proliferation assays were set up by culturing SpinSepTM (STI; negative selection) purified Pep3b total or CD4+ T cells at a density of 105 cells/200µL in each well of 96-well U-bottom plates with various numbers of purified DC (range: 12620000). Culture media consisted of IMDM or RPMI 1640 containing 10% FBS, 100 units/mL penicillin, 100 µg/mL streptomycin, 2 mM L-glutamine and 115 µM MTG. Proliferation was measured by scintillation counting of each sample following addition of 1 µCi of [3H]-thymidine (Perkin-Elmer Life Science, Inc., Boston, MA, USA) per well during the final 18-24 hours of a 72-hour culture period. In the T cell proliferation assay for examination of the target cells of tumourproduced factors (Figure 3.7), total spleen cells were prepared by passing spleen tissues  62  through a 70 µm cell strainer to make a single cell suspension, whereas total T cells, i.e. spleen cells without antigen-presenting cells (APC), were prepared using SpinSepTM (negative selection for total T cells). Soluble (1 µg/mL) or plate-bound (0.5 µg/mL) antiCD3 mAb was used to stimulate spleen cells or spleen cells without APC, respectively, for 3 days. Cells were plated at a density of 105/200µL in 96-well plates. WT or Tg prostate culture supernatant was added (10% v/v) at the beginning of the culture set-up. [3H]thymidine incorporation during the last 24 hours of culture was carried out to measure proliferation. For the proliferation assay results, each point is the mean of triplicate determinations and data was plotted as raw counts per minute (cpm). 3.2.5 Prostate Cell Culture and Co-culture Setup  Prostate tissues were minced into small pieces and treated with collagenase at 37oC for 1-2 hours. Small tissue clumps were seeded onto culture plates and cultured in IMDM containing 10% FBS, 100 units/mL penicillin, 100 µg/mL streptomycin, 2 mM L-glutamine, 5 µg/mL insulin, 0.5 µg/mL hydrocortisone and 10 ng/mL cholera toxin. Culture medium was changed to IMDM with 10% FBS, 100 units/mL penicillin, 100 µg/mL streptomycin, 2 mM L-glutamine and 115 µM MTG for 24 hours to obtain conditioned medium which was stored at -80oC until use. Primary prostate cell cultures were carried out for 5-6 days. Tissue clumps that attached to the plate started to show growth by day 3-4 based on microscopic observation. Neither the cell types (using immunostaining) nor cell growth rates (by cell counting over a period of time) were examined or compared between WT and Tg prostate cell cultures. Nevertheless, the short-term culture conditioned media were assumed to resemble the respective soluble factor production in vivo. In the co-culture experiments, 10% (v/v) of the conditioned medium was added to either immature or mature BMDC cultures (0.75-0.8 x 106cells/mL) for 24 hours before phenotype and functional analyses, or to the co-culture of BMDC and total T cells during the 3-day proliferation assay. 3.2.6 Data Presentation and Statistical Analysis  Data are presented as mean ± SD (standard deviation). Statistical analysis was carried out using a one-tailed Student’s t-test which makes the assumption that the two samples 63  being compared were different and that this difference results from one sample being either higher or lower than the other. Any calculated P-value was determined only for the data with n ≥ 3.  3.3 RESULTS 3.3.1 Analysis of in vitro BM-Derived DC  We utilized the 12T-7slow mouse model to characterize DC in mice developing prostate tumours. BM cells isolated from femurs and tibiae of Tg (transgenic) and control WT (wild type) mice were cultured in medium containing the DC growth factors, GM-CSF and IL-4, to generate mDC. Cells were harvested on day 5 of culture. Microscopic examination at the time of harvest revealed no obvious differences in the morphology of cells present in the WT and Tg cultures. The number of CD11c+ cells derived from 106 BM cells was decreased moderately in the Tg culture, and the difference reached statistical significance when BM cells were isolated from the mice at the age of 21 weeks (mouse prostatic intraepithelial neoplasia [mPIN] lesion in this mouse model; Figure 3.1A). However, the phenotype of these CD11c+ cells was similar in WT and Tg mice regardless of age (Figure 3.1B, upper panel). To determine if these immature DC generated from WT and Tg BM were equally able to respond to maturation signals, we activated DC with bacterial LPS. After over-night stimulation (18-24 hours), both WT and Tg immature DC matured and had a similar phenotype regardless of age (Figure 3.1B, lower panel). Of note, the phenotype for LPS-stimulated DC that were derived from BM cells of 8-week-old mice was carried out only once due to the fact that no significantly phenotypic changes were observed in d5 cultures (WT versus Tg) derived from BM cells of 8-week-old mice or in LPS-stimulated DC derived from BM cells of 21-week-old mice (assuming any changes would be obvious when the tumour environment is more serious at an older age). Despite the lack of replicates for one data point, the mature DC were able to activate allogeneic T cell proliferation regardless of age and genotype. Although the mature Tg BM-DC exhibited a reduced ability to activate allogeneic T cells in approximately two-thirds of the time, this observation was not consistent (Figure 3.1C and data not shown).  64  CD11c+ cells  B.  0.4 0.2 0 8  12  17  21  Age (weeks)  8 21  8 21  8 21  8 21  8 21  8 21  8 21  8 21  8 21  8 21  100 80 60 40 20 0  Age (weeks)  Proliferation response (cpm)  C. 15000  8 weeks  20000  10000  **  15000  17 weeks  10000  **  5000  CD86 CD40 IA/IE  d5 culture  0.6  100 CD11c CD80 80 60 40 20 0  d5 culture + LPS  *  0.8  Frequency or Proportion (%)  Cell number per 106 BM cells (x106)  A.  5000  0  0 0  25000 20000 15000 10000 5000 0  1/100  2/100 3/100  12 weeks  ** 0  4/100 5/100  **  **  1/100  2/100 3/100  4/100 5/100  0  50000 40000 30000 20000 10000 0  1/100  2/100 3/100  21 weeks  0  1/100  2/100 3/100  4/100 5/100  **  4/100 5/100  DC:T cell ratio  Figure 3.1 Tg BMDC generated in GM-CSF/IL-4 cultures show no significant phenotypic or functional differences compared to WT cells. (A) Absolute CD11+ cell number on day 5 of culture. (B) Proportion of CD11c+ cells that express the indicated cell surface molecules before and after LPS stimulation. Open columns, WT; closed columns, Tg. (C) Allogeneic T cell proliferation responses to LPS-stimulated BMDC. (n=2-7 except n=1 for the phenotype of d5 culture + LPS DC at 8 weeks). Tg. Solid line, WT; dashed line, Tg. *, P ≤ 0.05. **, P ≤ 0.01.  65  Next, to examine whether tumour progression has any effect on lineage determination from DC progenitors, we used Flt3L cultures to generate both mDC and pDC. BM cells were collected from mice >20 weeks of age and cultured in the presence of Flt3L for 8 days. Mice of this age were used since it was expected that significant changes in DC progenitors, if there were any, would be detected at the latest stages of the disease in this 12T-7slow mouse model. The frequencies of mDC (B220- within CD11c+) were WT: 69.1±15.3% versus Tg: 65.7±22.8%, while those of pDC (B220+ within CD11c+) were WT: 30.9±15.3% versus Tg: 34.3±22.8%. There was no significant difference in the ratio of mDC to pDC in WT and Tg cultures. Moreover, the phenotypes of mDC and pDC, with or without LPS or CpG stimulation, were similar for the WT and Tg cells (Figure 3.2A). The abilities of mature mDC and pDC (LPS and CpG stimulation, respectively) to stimulate allogeneic T cell proliferation were not consistently different for WT and Tg (Figure 3.2B showing 2 sets of data). Taken together these observations suggest that prostate tumour development in vivo does not affect BM-derived DC progenitors or influence their ability to differentiate into phenotypically normal, functionally mature BMDC.  66  CD11c+ B220d8+LPS d8+CpG  A. d8  d8  CD11c+ B220+ d8+LPS d8+CpG  CD80  CD86  CD40  IA/IE  B.  d8 + LPS 60000  Test 1  25000 20000 15000 10000 5000 0  Proliferation response (cpm)  45000 30000 15000 0 0  5/100  10/100  15/100  20/100  Test 2  0  5/100  10/100  15/100  20/100  10/100  15/100  20/100  d8 + CpG 50000 40000 30000 20000 10000 0 0  20000  Test 1  15000  Test 2  10000 5000 0 5/100  10/100  15/100  20/100  0  5/100  DC:T cell ratio  Figure 3.2 Tg BMDC generated in Flt3L cultures show no significant phenotypic or functional differences compared with WT cells. (A) Expression of the indicated cell surface molecules on mDC and pDC, with or without LPS or CpG stimulation. A set of representative data is shown. Black line, WT; orange line, Tg. (B) Allogeneic T cell proliferation in response to LPS- or CpG-stimulated BMDC (2 sets of data are shown). (n=5). Solid line, WT; dashed line, Tg.  67  3.3.2 Analysis of DC in the Secondary Lymphoid Tissues and Prostate  Since the Tg BMDC generated in vitro did not exhibit significant changes in phenotype or function, next we investigated the properties of in vivo DC. Single cell suspension prepared from the secondary lymphoid tissues, including spleen and various LNs, were analyzed. Total nucleated cell number in the spleen was similar in WT and Tg mice during tumour development (Figure 3.3A). Although the frequency of CD11c+ cells was moderately higher in the Tg spleen, regardless of the stage of tumour growth, the phenotype of CD11c+ cells was similar in mice of the two genotypes (Figure 3.3B and C). The numbers of CD11c+ cells were similar as well in the spleen tissues isolated from WT and Tg mice at the age of 21 weeks (WT: 6.85±2.70 x 106 versus Tg: 9.93±6.27 x 106; P=0.20). Moreover, the ability of Tg splenic CD11c+ cells to stimulate allogeneic T cell proliferation was not different from that of WT cells (Figure 3.3D). We next analyzed DC in the lumbar LN isolated from WT or Tg mice. The CD11c+ cell frequency was similar in the WT and Tg submandibular and lumbar LNs during tumour development, although it was significantly higher in the Tg lumbar LN at the latest stage examined (20-25 weeks old; Figure 3.4A). The phenotype of the CD11c+ cells was similar in the WT and Tg lumbar LNs regardless of the age of the mice (Figure 3.4B). We then evaluated total numbers of DC in the lumbar LN. As described in Chapter 2 (Section 2.3.2), there was an expansion of total cell numbers in the Tg lumbar LN resulting in increased absolute numbers of each T cell subset. Interestingly, the number of CD11c+ cells in the Tg lumbar LN was preferentially elevated (10-fold change in CD11c+ cells versus a 5.8-fold change in total cell numbers; Table 3.1). No functional studies were carried out using the LN DC since, as in the spleen, no phenotypic differences were observed.  68  A.  B. Frequency (%)  Cell number per spleen (x106)  400 300 200 100 0  6 4 2 0  8  12  16  Age (weeks)  C. Proportion (%)  8  21  12 weeks  100 80 60 40 20 0  12  21  Age (weeks) 100 80 60 40 20 0  21 weeks  CD80 CD86 CD40 IA/IE  CD80 CD86 CD40 IA/IE  marker Proliferation response (cpm)  D.  10000 8000 6000 4000 2000 0 0  1/100  2/100  3/100  4/100  5/100  DC:T cell ratio  Figure 3.3 Phenotype and function of WT and Tg spleen CD11c+ cells are similar. (A) Absolute cell number per spleen in mice of different ages. (B) Frequency of CD11c+ cells in the viable, nucleated spleen cell population isolated from 12- and 21-week old mice. (C) The proportion of CD11c+ cells expressing the indicated cell surface molecules. Open columns, WT; closed columns, Tg. (D) Allogeneic T cell proliferation in response to LPS-stimulated splenic CD11c+ cells purified from WT (solid line) or Tg (dashed line) mice. (n=2-5 except n=1 for splenic cell numbers at 8 weeks).  69  Frequency (%)  A.  Submandibular  6 5 4 3 2 1 0  11-12 16-17 20-25  Lumbar  *  11-12 16-17 20-25  Age (weeks)  Proportion (%)  B.  100 80 60 40 20 0  12 weeks  100 80 60 40 20 0  CD80 CD86 CD40 IA/IE  21 weeks  CD80 CD86 CD40 IA/IE  Marker  Figure 3.4 Frequency and phenotype of the CD11c+ cell population are similar in WT and Tg LNs. (A) Frequency of CD11c+ cells in submandibular or lumbar LNs during tumour progression. (B) The proportion of CD11c+ cells expressing the indicated cell surface molecules on lumbar LNs from 12- and 21-week old mice. Open columns, WT; closed columns, Tg. (n=2-5 except n=1 for CD11c+ cell frequency in submandibular LNs at 16-17 weeks). *, P ≤ 0.05. Table 3.1 Cellular composition of the lumbar LN at late stages of prostate tumour progressiona Tg (x 106) Fold change WT (x 106) Lumbar LN 1.8 ± 0.8 10.5 ± 4.0 * 5.8 CD11c 0.0234 ± 0.0067 0.234 ± 0.085 * 10.0 CD4 0.926 ± 0.368 4.56 ± 0.71 * 4.9 CD8 0.825 ± 0.170 2.61 ± 0.26 * 3.2 a Cell number per LN isolated from mice ≥ 20 weeks of age. Statistical difference (*, P ≤ 0.01) versus WT was determined using the Student’s t-test. (n ≥ 3 individual experiments).  Since there were no obvious differences in BM-DC or DC isolated from the secondary lymphoid tissues, we next investigated the phenotype of prostate-infiltrating DC. Whole single cell suspension from the prostate tissues (>20 weeks of age) was analyzed without prior DC purification. Although the frequency of CD11c+ cells increased only 70  slightly in the Tg prostate tissue (WT: 0.75±0.32% versus Tg: 0.99±0.25%), the phenotype of the Tg CD11c+ cells was altered. For example, the proportion of CD11c+ cells expressing CD86 or MHC class II molecules was significantly decreased (Figure 3.5A). There was only a modest decrease in CD11c+ cells expressing CD80 or CD40. Interestingly, the mean fluorescence intensity (MFI) of CD80 was elevated in the tumour-infiltrating CD11c+ cells, whereas that of MHC class II was decreased (Figure 3.5B). Moreover, the CD80/CD86 MFI ratio was significantly higher in the Tg CD11c+ cells (Figure 3.5C).  **  2000 1500 1000  CD80 CD86 CD40 IA/IE  100 80 60 40 20 0  **  MFI  Proportion (%)  100 80 60 40 20 0  B.  Marker  *  C.  **  **  2.0  MFI ratio  A.  1.5 1.0 0.5 0.0  CD80 CD86 CD40 IA/IE  CD80/CD86  Marker  Figure 3.5 Tumour-infiltrating DC exhibit an altered phenotype. (A) The proportion of prostate-infiltrating CD11c+ cells that express the indicated cell surface molecules. (B) Expression levels of the indicated cell surface molecules on CD11c+ cells. (C) Ratio of CD80 to CD86 expression on CD11c+ cells. Open columns, WT; closed columns, Tg. (n=5-9). *, P ≤ 0.05. **, P ≤ 0.01. 3.3.3 Co-culture of BMDC with Tumour Cell Culture Supernatant  Based on the above observations, in vitro culture systems were set up to examine more closely the effects of tumour-derived factors on DC phenotype and function. WT BMderived immature mDC (CD11c+ cells purified from GM-CSF/IL-4 cultures) were incubated with supernatants from WT or Tg prostate cell cultures for 24 hours before phenotypic and functional analysis. Figure 3.6A shows that CD11c+ cells exposed to Tg culture supernatant exhibited a decreased proportion of cells expressing co-stimulatory (CD86 and CD40) and MHC II molecules. CD11c+ cells exposed to WT supernatants displayed a phenotype similar to those from control cultures (i.e. in the absence of prostate culture supernatant).  71  Furthermore, the ability of CD11c+ cells, with or without being matured by LPS, to stimulate allogeneic T cell proliferation was impaired following exposure to Tg prostate culture supernatant (Figure 3.6B left panel). This effect was more profound when the Tg prostate culture supernatant was present during the co-culture (Figure 3.6B right panel). Further examination revealed that addition of the Tg prostate cell culture supernatant only during the T cell proliferation assay also reduced T cell proliferation (Figure 3.6C). To distinguish whether the tumour-produced soluble factors target DC or T cells, total spleen cells (presence of APC) plus soluble anti-CD3 mAb or total T cells (absence of APC) plus plate-bound anti-CD3 mAb were treated with either WT or Tg prostate culture supernatant. As indicated in Figure 3.7, the Tg prostate cell culture supernatant significantly inhibited T cell proliferation only in the presence of APC, suggesting the tumour-secreted soluble factors target DC rather than T cells.  72  100  Proportion (%)  A.  80 40  *  20 0 CD80  B.  CD86  CD40  IA/IE  Marker  BMDC/supernatant *  15000  Proliferation response (cpm)  *  *  60  10000  *  BMDC/supernatant + supernatant 15000  *  *  ** ** 10000 **  5000  5000  0  0 0  25000 20000 15000 10000 5000 0  2/100  *  4/100 6/100  8/100 10/100  BMDC/supernatant/LPS * *  2/100  4/100  6/100  0  8/100 10/100  25000 20000 ** 15000 10000 ** 5000 0 0  Proliferation response (cpm)  DC:T cell ratio 20000 15000 10000 5000 0  2/100  4/100 6/100  8/100 10/100  BMDC/supernatant/LPS + supernatant  **  0  C.  **  **  *  **  2/100  **  4/100  *  6/100  8/100 10/100  **  * * 0  2/100  4/100  6/100  8/100 10/100  DC:T cell ratio  Figure 3.6 Exposure to tumour cell culture supernatant alters the phenotype and function of mDC. (A) The proportion of CD11c+ cells expressing the indicated cell surface molecules. Gray columns, medium only; open columns, WT supernatant; closed columns, Tg supernatant. (B) Representative data showing allogeneic T cell proliferation in response to BMDC exposed to prostate culture supernatant (10% v/v). Co-cultures were set up with immature BMDC (upper panels) or LPS-stimulated (over-night stimulation) mature BMDC (lower panels), in the absence (left panels) or presence (right panels) of prostate culture supernatant during the proliferation assay. (C) Supernatant was added only during the proliferation assay. Solid line, WT supernatant; dashed line, Tg supernatant. (n=5). *, P ≤ 0.05. **, P ≤ 0.01.  73  Proliferation response (cpm)  Total spleen cells (presence of APC)  Total T cells (absence of APC) P=0.411  140000 120000 100000 80000 60000 40000 20000 0  Supernatant:  P=0.027  WT  Tg  WT  Tg  Figure 3.7 Tumour supernatant impairs T cell proliferation due to effects on antigenpresenting cells. Total spleen cells or total T cells isolated from WT mice were stimulated with anti-CD3 mAb in the presence of WT or Tg prostate culture supernatant (10% v/v). Each point is an average from triplicate results. The horizontal bar represents the average from 3 determinations.  3.4 DISCUSSION 3.4.1 Mechanisms for DC Alteration in the Tumour Microenvironment  In this study, it was demonstrated that BMDC precursors isolated from 12T-7slow prostate dysplasia mice can develop into phenotypically and functionally normal DC in vitro. This observation is in contrast to Tourkova et al.’s studies that suggested tumour cells are able to influence DC development from BMDC precursors and their function [8]. However, in those studies prostate tumour cells were present in the BM cultures during the generation of DC, suggesting that tumour-related factors or cell-cell interactions between tumour cells and DC progenitors are required to alter DC development. Similarly, we observed no alterations in the phenotype or function of DC present in the secondary lymphoid tissues of tumour-bearing mice, but observed phenotypic alterations in the DC infiltrating the tumour tissues as well as the DC treated with primary prostate tumour culture supernatant. Together, these observations suggest that factors that tumour cells may secrete into the circulation are not present at sufficiently high levels in the BM or lymphoid tissues to alter DC development, phenotype, or function.  74  Interestingly, there was an expansion of phenotypically normal CD11c+ DC cells in the draining lumbar LNs of Tg mice, but not in distant submandibular LNs, at the latest stages of tumour progression studied in 12T-7slow model. This observation, together with the expansion of activated T cells in the Tg lumbar LNs (Chapter 2, Section 2.3.2), suggests that there was initiation of an anti-tumour immune response. A similar activation of the immune system during tumour development has also been observed in non-small cell lung cancer, ovarian cancer and hepatic colorectal cancer metastasis [14,15]. These observations support the hypothesis of a tumour cell elimination phase during cancer immunoediting [16]. In contrast to observations with BMDC and DC isolated from the secondary lymphoid tissues, the frequency of tumour-infiltrating DC was increased slightly in the 12T7slow prostate tissues and these cells exhibited an immature phenotype as evidenced by reduced proportions of DC that express CD86 and MHC class II molecules. Studies by Troy et al. indicated that minimally activated DC infiltrated the tumour mass of PCa patients and renal cell carcinoma patients, even though there were lower numbers of DC recruited into these tumours [11,17]. These conflicting observations may be due to analysis at different stages of tumour progression in the human cancer patients versus the mouse models. Despite the difference in the level of DC recruitment, tumour-infiltrating DC were found to be immature or tolerogenic in various tumours, such as colon adenocarcinoma, metastatic melanoma, prostate tumour and breast cancer [12,18-21]. Immature or tolerogenic DC do not have the ability to induce effective anti-tumour immunity and instead induce immune tolerance. A number of mechanisms have been proposed to account for the altered DC phenotype and number observed in the tumour environment. For example, tumour cells may alter DC through soluble factors or membrane-bound molecules. Consistent with our studies, DC exposed to human or murine prostate tumour cells or their secretions were unable to mature, became incapable of stimulating allogeneic T cell proliferation, and/or underwent apoptosis [7,8,22]. Our study also suggests that soluble factors in tumour culture supernatants may affect DC function more profoundly than T cells (Figure 3.7). Of note, there were no outliers in the three data points for the total T cells treated with Tg culture supernatant, based on Grubbs’ test [23]. Therefore, the change in averaged cpm values is not statistically significant in the total T cell cultures treated with WT versus Tg prostate culture  75  supernatants, but it is significant in the total spleen cell cultures. It should be noted that the use of allogeneic T cell proliferation assays to assess the Ag presentation ability of DC is perhaps not the most relevant measure of function. Using syngeneic T cells together with tumor associated Ag would be a more biologically relevant approach to examine DC function. There are a number of tumour cell-produced soluble immune suppressive factors responsible for DC alteration. In a colon adenocarcinoma study, tumour-produced IL-10 reduced CD40 expression on DC surface, resulting in inhibition of IL-12 production that is essential for DC maturation [24]. Similarly, it was also demonstrated that IL-10 inhibits human DC generation from monocytes in vitro [25]. Transforming growth factor-β (TGF-β) is another factor, secreted by tumour cells, that is able to induce DC apoptosis or inhibit DC homing [26,27]. Interestingly, CD4+CD25+Foxp3+ Treg cells were shown to down-regulate the expression of CD80, CD86 and CD40 on DC, as well as to inhibit their IL-12 production. It has been proposed that this suppression involves TGF-β and IL-10 [28,29]. Tumourderived prostaglandin E2 (PGE2) was found at high levels in culture supernatants generated from various human solid tumour tissues, and this factor was found to inhibit DC maturation [9]. Vascular endothelial growth factor (VEGF), another potential factor, inhibits DC differentiation from monocytes or CD34+ progenitor cells [30]. 3.4.2 The Roles of Altered DC during Tumour Development  It has been shown that the tumour-infiltrating DC induce a partial state of T cell tolerance to the tumour cells, suggesting that alterations in DC survival or maturation prevent them from activating Ag-specific T cells [12]. The decreased levels of MHC class II molecules observed in this study (Figure 3.5B) may also have a role in tolerance induction. Since tumour-infiltrating DC and tumour culture supernatant-exposed DC exhibited an immature phenotype, it is possible that these DC can induce Treg cells, which in turn inhibit effector T cell activation [3,31]. However, it should be noted that immature DC induce Tr1 cells, but do not expand CD4+CD25+ Treg cells as we saw in the 12T-7slow model [3]. In addition, immature or tolerogenic DC are not able to express sufficient co-stimulatory signals and to produce IL-12 or IFN-α, resulting in T cell anergy or apoptosis [1]. Tumourinfiltrating DC may express high levels of indoleamine 2,3-dioxygenase (IDO), leading to reduced T cell activation through tryptophan depletion [32,33]. IDO transcripts were found at  76  a higher level in 12T-7slow prostate tissues by PCR analysis (data not shown), suggesting IDO may be involved in the immune suppression in this prostate dysplasia mouse model. Moreover, it has been demonstrated that CD80 and CD86 differentially modulate the suppressive activity and population expansion of human CD4+CD25+ Treg cells [34]. Interestingly, the expression ratio of CD80 to CD86 was increased significantly on the 12T7slow tumour-infiltrating DC (Figure 3.5C), suggesting that the DC phenotypically altered within the tumour environment may enhance the suppressive activity of the Treg cells found within the tumour tissues. DC vaccination for cancer therapy has been introduced in the clinic. Therefore, elucidating the effects of tumour cells on DC and understanding the multiple roles of DC, either in initiating immune activation or in inducing immune suppression, during tumour progression is important for designing effective therapeutic strategies. Furthermore, identifying the immune modulatory factors produced by tumour cells will be useful for selecting critical targets to enhance anti-tumour immune responses.  77  3.5 REFERENCES  1. 2. 3.  4. 5.  6. 7.  8. 9.  10.  11. 12. 13.  14.  Steinman RM, Hawiger D, Nussenzweig MC: Tolerogenic dendritic cells. Annu Rev Immunol 2003;21:685-711. 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IDENTIFICATION OF IMMUNE MODULATORY FACTORS PRODUCED BY PROSTATE TUMOUR CELLS1 4.1 BACKGROUND  An increased level of immune suppression has been observed during tumour development in numerous models, as well as in humans. In our previous studies on the 12T7slow mouse model of prostate dysplasia, T cells and dendritic cells (DC) (Chapters 2 and 3, respectively) were found to be altered in the tumour environment. T cells isolated from tumour-bearing mice were hypo-responsive to antigen (Ag) stimulation and we observed that an increased frequency (and number) of CD4+CD25+ regulatory T (Treg) cells was responsible for this reduced responsiveness. While the DC isolated from the secondary lymphoid tissues of tumour-bearing mice exhibited a normal phenotype, the DC infiltrating the tumour tissue or exposed to tumour culture supernatant exhibited an altered phenotype. A number of studies have demonstrated that prostate tumour cells produce soluble (or cell surface membrane-bound) factors that modulate DC development, phenotype and function [1-5]. As a result, these modulated DC may expand Treg cell numbers or enhance their suppressive activity [6-8]. Although numerous immune suppressive factors produced by tumour cells have been identified, inhibition of these factors results in only a partial response (Chapter1, Section 1.2.2). This observation suggests that additional factors produced by the tumour cells must be involved in the inhibition of immune function. Identification of these additional tumour-produced factors is required for developing more effective strategies to enhance anti-tumour immune responses. Serial analysis of gene expression (SAGE) allows for a quantitative, comprehensive, and comparative analysis of the transcriptome including known, as well as unknown transcripts. Such large-scale gene expression profiling is a useful tool to identify transcripts that are either up- or down-regulated at a particular stage of a biological process or pathological development [9,10]. A number of studies have utilized this technique to identify differentially expressed genes in tumour cells, thus revealing candidate biomarkers for early detection or prognosis of tumours. It has also been shown that SAGE analysis provides an 1  A version of this chapter will be submitted for publication. Tien, A.H., Hoffman, B.G., Ruiz de Algara, T., Chan, J.S. and Helgason, C.D. Serial Analysis of Gene Expression to Identify Immune Modulatory Factors Produced by Prostate Tumour Cells.  81  important association between molecules involved in signaling pathways and tumour progression [11]. To date, there have been no comprehensive gene expression analyses to identify differential expression of immune-related transcripts in prostate tumour. We therefore used SAGE analysis to identify differentially expressed transcripts that are likely to have critical roles in the modulation of immune cell phenotype and function. Gene expression clustering and gene ontology (GO) analyses were used to aid in the identification of association between immune-related genes and prostate tumour growth.  4.2 MATERIALS AND METHODS 4.2.1 Prostate-Specific Pten Knockout Mouse Model  The prostate-specific Pten knockout (KO) tumour mouse model has been previously described [12]. The Ptenflox/flox mice [13] were kindly provided by Dr. Tak Mak (Campbell Family Institute for Breast Cancer Research, Toronto, ON, Canada) and Dr. Chris Ong (Jack Bell Research Centre, Vancouver, BC, Canada). The PB-Cre4 mice were obtained from the Mouse Models of Human Cancers Consortium Repository. Cre recombinase expression is under the control of a derivative of the rat probasin (PB) promoter, ARR2PB, and Cre expression is restricted to prostate epithelial cells [14]. To generate the Ptenflox/floxPBCre+ mice used in these studies, PB-Cre4 male mice were bred with Ptenflox/flox female mice. The Ptenflox/-PBCre+ male progeny were then crossed with Ptenflox/flox female mice to produce the  F2 male mice (Ptenflox/floxPBCre+ as KO, Ptenflox/-PBCre+ as heterozygous [Het] and PBCreas wild-type [WT]). Genotype was determined using polymerase chain reaction (PCR) analysis of tail DNA. This model starts developing multifocal hyperplasia at 4 weeks of age, mouse prostatic intraepithelial neoplasia (mPIN) at 6 weeks, invasive adenocarcinoma at 9 weeks and may develop metastasis when they are ≥ 12 weeks old. The Pten Het mice may develop mPIN at the age of 8-10 months. C57Bl/6J mice were obtained from the in-house breeding program of the Animal Resource Centre at the BC Cancer Research Centre. Animal care was carried out as described in Chapter 2, Section 2.2.1. 4.2.2 Phenotype Analysis by Flow Cytometry  Single cell preparation and antibody (Ab) staining for phenotypic analyses was carried out as described in Chapters 2 and 3 (Sections 2.2.2, 2.2.3 and 3.2.3). Prostate cells 82  were stained with the following fluorochrome-conjugated antibodies (BD PharMingen): CD11c (HL3), CD80 (16-10A1), CD86 (GL1), CD40 (HM40-3), MHC class II IA/IE (2G9), Gr-1 (RB6-8C5), B220 (RA3-6B2), CD11b (Mac1; M1/70), CD4 (L3T4, RM4-5), CD8α (Ly-2, 53-6.7), CD25 (7D4 or PC61) and CD69 (H1, 2F3). 4.2.3 LongSAGE Library Construction and Data Analysis  Dorsolateral lobes of WT (C57Bl/6J; 10 weeks of age), as well as the prostatespecific Pten KO and Het mice (9 weeks of age; n=3) were dissected and tissues were sent to the Genome Sciences Centre of the BC Cancer Agency for LongSAGE library construction as part of the Mouse Atlas of Gene Expression project (www.mouseatlas.org) [15]. Data was analyzed using the DiscoverySpace version 4 program, available to download from http://www.bcgsc.ca/bioinfo/software/discoveryspace/ [16]. A 95% quality cut-off (i.e. a tag has >95% probability of being correct) was used and the data was filtered to remove duplicate ditags and the linker sequences (linker sequences=TCGGACGTACATCGTTT and TCGGATATTAAGCCTAG). Venn-Table analyses were carried to determine tag distributions in the three SAGE libraries. Tags up- or down-regulated significantly in the KO library were identified by comparison analyses between 2 libraries (i.e. KO versus Het or KO versus WT) with confidence threshold = 95. Tag mapping was carried out using the RefSeq (http://ncbi.nlm.nih.gov/RefSeq/),  Ensembl  (http://www.ensembl.org/index.html)  and  Mammalian Gene Collection (MGC; http://mgc.nci.nih.gov) databases with criteria: Mus musculus genes (Taxon ID=10090). Unambiguously mapped transcripts were identified  based on the criteria: Mus musculus genes with the frequency-sense for tag = 1 and position for tag >1. Gene Ontology (GO) analysis was carried out using DiscoverySpace to categorize the known transcripts mapped based on the RefSeq database. The RefSeq database was used for majority of the analyses since analysis using this database revealed the most number of mapped genes (Section 4.3.2). The RefSeq database was also used to search tag sequences for genes of interest that were required for analysis, and the search was carried out based on the criteria: frequency-sense=1, tag length=17, and the definition contains Mus musculus. The tag counts of these candidates in the three libraries were then determined using VennTable analysis.  83  Clustering analysis of tags was performed as described by Wang et al. [17]. In brief, only tags with unambiguously-mapped known transcripts (using the RefSeq database) with normalized tag counts >5 in any of the three libraries were included in the clustering analysis. To determine an appropriate number of clusters, the mean values of adjusted Figure of Merit (FOM) were calculated for the normalized tag count data using the k-means algorithm. This calculation was performed using the MultiExperiment Viewer program, provided by the Institute for Genomic research (available at http://www.tm4.org/), with 10 runs and a maximum of 20 clusters. A graph of the mean of adjusted FOM values versus number of clusters was plotted. Based on the results, a cluster number of 10 was chosen. Clustering of normalized tags was then performed based on the PoissonC algorithm [18], using the SAGE Data Analysis program (http://genome.dfci.harvard.edu/sager/). GoMiner analysis (High-Throughput analysis available at http://discover.nci.nih.gov/ gominer/) was carried out by queries of genes in each cluster against all genes in the RefSeq database. Parameters were set up as default except M. musculus was used for the Organism and All/Gene ontology was set as the Category. 4.2.4 Gene Validation by Quantitative RT-PCR  RNA from the dorsolateral lobes of KO and Het prostate tissues was prepared using the TRIzol protocol (Invitrogen, Carlsbad, CA, USA) and 1µg of RNA was reverse transcribed to cDNA (complementary DNA) using either SuperScript III (Invitrogen) or the QuantiTect Reverse Transcription kit (QIAGEN, Mississauga, ON, Canada). An ABI7500 real-time PCR system (7500HT Fast Real-Time PCR System; Applied Biosystems, Foster City, CA, USA) and SYBR Green supermix (Applied Biosystems) were used for quantitative real-time-PCR (qRT-PCR). Each PCR reaction was done in duplicate. Primers were designed using the Primer3 v.0.3.0 program (available on the website http://frodo.wi.mit.edu/cgibin/primer3/primer3_www.cgi), BLAST searched for specificity, and synthesized by Invitrogen. The primer sequences were: Irf7 forward: CCTCTTGCTTCAGGTTCTGC, reverse: GCTGCATAGGGTTCCTCGTA; Ubc forward: AGGTCAAACAGGAAGACAGA CGTA, reverse: TCACACCCAAGAACAAGCACA; St14 forward: AATGATGTGTGTGG GTTTCCTC, reverse: GAACATTCGCCCATCTTTCTC; Psmb8 forward: GCTATTGCTT  84  ATGCTACCCACA, reverse: TCACCCAACCGTCTTCCTTCAT; Cxcl16 forward: CCAA GACCAGTGGGTCCGTGAA, reverse: GTACTGG CTTGAGGCA AATGTT. 4.2.5 Immunofluorescence Staining  Dissected prostate tissues were fixed in 4% paraformaldehyde (Sigma, St. Louis, MO, USA) at 4oC overnight, and immersed in 30% sucrose (Sigma) solution before being frozen slowly in OCT (Optimal cutting temperature, Somagen Diagnostics, Edmonton, AB, Canada) on dry ice and stored at -80oC until use. Sections (6µm) were prepared using a Cryo-Star HM560 cryostat (MICROM International GmbH, Walldorf, Germany). Nonenzymatic antigen retrieval was performed using a microwave in 10mM sodium citrate (Sigma) buffer, pH6.0, for 20 minutes. Once cooled, the samples were washed with dH2O (5 minutes x2) and then with Dulbecco’s phosphate-buffered saline (PBS; 5 minutes x2) and the sections were penetrated by treatment with 0.1% Triton® X-100 (Sigma), prepared in blocking solution: PBS containing 3% bovine serum albumin (BSA fraction V, Roche Diagnostics GmbH, Mannheim, Germany; PBS/3%BSA), at room temperature for 3 minutes. Sections were then washed with PBS (5 minutes x2) before being incubated in blocking solution at room temperature for 1 hour. A rabbit anti-mouse IRF7 polyclonal Ab (1:50 dilution in blocking solution; Santa Cruz Biotechnology, Santa Cruz, CA, USA or Zymed, Invitrogen) was used for IRF7 detection (4oC, overnight). The sections were washed in PBS (5 minutes x3) and then incubated with an Alexa488-conjugated donkey anti-rabbit IgG Ab at room temperature for 1 hour. The sections were washed in PBS (10 minutes x4), stained with Hoechst33342 (Invitrogen), and mounted with GelTol Mounting medium (Immunon Thermo Shandon, Pittsburgh, PA, USA). Images were taken using a ZEISS Axiovert40 CFL fluorescence microscope (Carl Zeiss Canada Ltd., Toronto, ON, Canada). 4.2.6 Western Blotting  Dorsolateral lobes were snap-frozen and stored in liquid nitrogen until analysis. Samples were lyzed in RIPA buffer (50mM Tris, 150mM NaCl, 1% Nonidet P-40, 1% Na deoxycholate and 0.1% sodium dodecyl sulphate [SDS]) and homogenized by sonication. Proteins in the whole cell lysate were separated by 10% SDS-polyacrylamide gel electrophoresis (Invitrogen XCell system). After electrophoresis, proteins were transferred to  85  polyvinylidene fluoride (PVDF) membrane (Immobilon-P PVDF membrane, Millipore, Billerica, MA, USA). Blocking was carried out using 5% milk (in TBS [137mM NaCl, 2.68mM KCl, 25mM Tris-base, pH7.4] containing 0.02% Tween-20) at room temperature for 1 hour. A rabbit anti-IRF7 polyclonal Ab (1:1000 in 5% milk; Zymed) was used to detect IRF7 on the blot (4oC, overnight). A goat anti-actin polyclonal Ab (1:1000; Santa Cruz) was used for Actin detection. After washes (10 minutes x3 with TBS/0.02% Tween20), the blot was incubated with HRP-conjugated anti-rabbit or anti-goat secondary Ab (Santa Cruz) at room temperature for 1 hour. The blot was washed (5 minutes x5-6) before treatment with enhanced chemiluminescence (ECL) detection reagents (Amersham/GE Healthcare Life Sciences, Piscataway, NJ, USA) and exposure to Kodak film. 4.2.7 cisRED Analysis  The cis-Regulatory Element Database (cisRED) Mouse 3.1 database, available at http://www.cisred.org and originally described by G. Robertson et al. [19], was used to search for mouse genes whose promoter region contained putative IRF7A binding sites. This query was carried out with Discovery p-value thresholds of 0.1. The results were provided as a list of genes with accession numbers. The RefSeq database was used to generate SAGE tag sequences for each gene as described above (Section 4.2.3) and Venn-Table analysis was carried out to determine the tag counts of these genes in each of the three prostate SAGE libraries. Target genes up- or down-regulated significantly in the tumour tissues were identified based on the results from comparison analysis. 4.2.8 Statistical Analysis  Data are presented as mean ± SD (standard deviation). Statistical analysis was carried out using a one-tailed Student’s t-test, unless otherwise indicated. The one-tailed test was performed with the assumption that the two samples being compared were different and that this difference results from one sample being either higher or lower than the other. Significance from the comparison analysis was based on a 95 confidence threshold (determined by DiscoverySpace).  86  4.3 RESULTS 4.3.1 Altered Phenotype of Immune Cells in the Pten Knockout Prostate Microenvironment  A number of studies, including our own work (Chapters 2 and 3), have demonstrated that prostate tumour cells have a profound effect on the phenotype and function of dendritic and T cells. The prostate-specific Pten KO mouse model is useful for the study of the mechanisms responsible for immune suppression in the prostate tumour environment since the PTEN alteration has been implicated in human prostate tumours [20,21]. Moreover, these mice develop more advanced tumours than observed in other models and the changes in gene expression patterns are similar to those seen in human prostate tumours [12]. Because the immunological status of the Pten KO mouse model has not been studied in detail, flow cytometric analysis was first carried out to examine the phenotype of DC infiltrating the prostate tissues at the stage when the mice are developing adenocarcinoma (i.e. 9 weeks of age). The frequency of CD11c+ cells was significantly elevated in the KO prostate tissue compared to the Het (Het: 1.54±0.36% versus KO: 3.81±1.99%; P=0.033). Moreover, the proportion of CD11c+ cells expressing CD80, CD40 and MHC class II was decreased significantly in the KO (Figure 4.1A). Interestingly, the frequencies of both plasmacytoid DC (pDC; CD11c+B220+) and myeloid suppressor cells (MSC; Mac1+Gr1+) were increased significantly in the KO tissues (Figures 4.1B and 4.1C, respectively). In contrast, and consistent with the studies in the 12T-7slow mice, the phenotype of DC isolated from the lumbar lymph nodes (LNs) of tumour-bearing mice was not altered significantly (Figure 4.1D), even though the frequency of CD11c+ cells was significantly elevated in the KO lumbar LNs (Het: 1.31±0.56% versus KO: 2.26±0.51%; P=0.041). Phenotypic analysis was carried out on T cells as well. The frequencies of both prostate-infiltrating CD4+ and CD8+ T cells were elevated significantly in the KO mice (CD4+ T cells: Het: 0.14±0.06% versus KO: 0.26±0.08%, P=0.006; CD8+ T cells: Het: 0.16±0.08% versus KO: 0.29±0.08%, P=0.004). Similar to the tumour-infiltrating T cells in the 12T-7slow mouse model, the proportion of CD8+ or CD4+ T cells expressing CD69 was increased in the Pten KO prostate tumour tissues (Figure 4.1E). Moreover, the proportion of CD4+ or CD4+CD69- T cells expressing CD25 was significantly increased within the tumour mass. However, unlike the 12T-7slow mice, the Pten KO mice do not have significantly  87  increased frequencies of CD4+ or CD8+ T cells in the lumbar LNs compared to the Het mice (CD4+ T cells: Het: 20.9±11.3% versus KO: 32.1±3.1%, P=0.112; CD8+ T cells: Het: 17.0±7.0% versus KO: 20.1±4.1%, P=0.270). In addition, the KO mice do not have an altered T cell phenotype in the lumbar LNs (Figure 4.1F). Taken together, these observations suggest that the tumour microenvironment has a critical role in modulating the phenotype, and possibly function, of tumour-infiltrating immune cells in the prostate-specific Pten KO  Proportion within CD11c+ cells (%)  A.  100 80 60 40 20 0  B.  * **  *  Proportion within CD11c+ cells (%)  mouse model. 50 40 30 20 10 0  D.  **  6 4 2 0  100 80 60 40 20 0 CD80 CD86 CD40 IA/IE  Mac1+Gr1+ 80 60 40 20 0  F. 25  * *  **  CD69 CD69 CD25 CD25  CD8+  CD4+  CD4+ CD69-  Proportion (%)  Proportion (%)  E. 100  B220  Proportion within CD11c+ cells (%)  C.  Frequency (%)  CD80 CD86 CD40 IA/IE  **  20 15 10 5 0 CD69 CD69 CD25 CD25  CD8+  CD4+  CD4+ CD69-  Figure 4.1 The phenotype of tumour-infiltrating immune cells is altered in Pten KO mice. (A) The proportion of prostate-infiltrating CD11c+ cells that express the indicated cell surface molecules. (B) Frequency of pDC within the CD11c+ cell population infiltrating the prostate. (C) Frequency of MSC in the prostate tissues. (D) The proportion of lumbar LN CD11c+ cells that express the indicated cell surface molecules. (E) The proportion of prostate-infiltrating T cells that express CD69 or CD25. (F) The proportion of lumbar LN T cells that express CD69 or CD25. Open columns, Het; closed columns, KO. Data is presented as mean ± SD. (n ≥ 4). *, P ≤ 0.05. **, P ≤ 0.01.  88  4.3.2 Summary of the LongSAGE Prostate Libraries  To identify potential factors that contribute to the altered immune cell frequency and phenotype in the prostate tumour microenvironment, the gene expression profiles of the prostate tissues were analyzed using SAGE. Three LongSAGE libraries were generated from mouse prostate dorsolateral lobes: WT C57Bl/6J (10 weeks old), as well as Pten KO and Het (9 weeks old). The age difference was due to the data availability for analysis. In addition, it was assumed that the change in gene expression pattern is minimal in the prostate tissues of adult WT mice with age. Information regarding the three libraries is summarized in Table 4.1. A total of 296,903 tags were sequenced. After stringent quality control, these libraries consist of 189,889 tags corresponding to 44,809 different tag types. Of these, 24,152 (53.9%) tag types mapped to known transcripts using the RefSeq, MGC and Ensembl transcript databases. There were 20,657 tag types (46.1%) that could not be mapped to any known transcripts and 84.6% (17,467 tags) of these tags were singletons that are likely to be error tags. Of the 24,152 mapped tags, 4,843 tags (20.1%) were unambiguously mapped to known transcripts (i.e. the tag maps to only 1 transcript in the sense direction). These tags represent 3,452 genes suggesting there is alternative splicing.  Table 4.1 Summary of the LongSAGE mouse dorsolateral prostate libraries Library Tags Total tag Total tag Replicate Tissue a a name sequenced counts types ditags (%) SM026 WT C57Bl/6J (12 weeks) 119,964 73,932 22,806 21.17 SM220 Pten Het (9 weeks) 68,526 40,310 14,728 25.18 Pten KO (9 weeks) 108,413 75,647 23,790 14.89 SM219 a Refers to tag counts or types after 95% quality cut-off and removal of duplicate ditags and linkers.  Using only the RefSeq database, 12,584 out of 16,575 (75.9%) mapped tags in all three libraries were unambiguously mapped to known transcripts and these tags represent 7,773 genes. After Gene Ontology (GO) analysis using DiscoverySpace, there are 4,431 (56.7%) GO terms directly associated with these genes. In addition, there are 72 GO terms for genes categorized at the third level under biological process and 127 at the third level  89  under cellular component. As indicated in Figure 4.2, genes related to “cellular metabolic processes” are the most abundant in the three libraries, suggesting that metabolic activity is likely being carried out constantly in the prostate tissues. GO analysis also revealed that 1.87% (score) of the genes are associated with the GO term “immune response” (Figure 4.2). Interestingly, genes involved in “biosynthetic process” are relatively highly expressed in the three libraries since its “weighted score,” which includes the expression level into the score calculation, is higher than its “score” for association. Moreover, 29.7% (score) of the genes are associated with the GO term “membrane,” and it is the third most abundant group at the third level under cellular component (Figure 4.3).  90  cellular metabolic process regulation of cellular process cell organization and biogenesis cell communication multicellular organismal development anatomical structure development biosynthetic process cell development protein localization anatomical structure morphogenesis cell cycle response to stress catabolic process cell adhesion cell proliferation nitrogen compound metabolic process embryonic development  1.87%  response to external stimulus  2.01%  immune response cell motility  0  10 20 30 40 Association (%)  50  Figure 4.2 GO analysis shows the distribution of genes at the third level of biological process in the three prostate SAGE libraries. The unambiguously-mapped transcripts (7,773 genes) in the three libraries were categorized at the third level of biological process GO terms. Closed column, the percentage of genes associated with a given term (score); open column, the percentage of genes associated with a given term and the relative expression level of these constituents (weighted score). The top 20 groups are shown.  91  intracellular intracellular organelle membrane  29.7%  extracellular space  32.3%  ribonucleoprotein complex transcription factor complex vesicle cell projection extracellular matrix (sensu Metazoa) cell surface  0  10  20 30 40 50 Association (%)  60  Figure 4.3 GO analysis shows the distribution of genes at the third level of cellular component in the three prostate SAGE libraries. The unambiguously-mapped transcripts (7,773 genes) in the three libraries were categorized at the third level of cellular component GO terms. Closed column, the percentage of genes associated with a given term (score); open column, the percentage of genes associated with a given term and the relative expression level of these constituents (weighted score). The top 10 groups are shown.  To determine differences in the gene expression profiles, clustering analysis of tags in the three libraries was performed as described in Materials and Methods. A total of 2,164 tags matched the criteria for analysis. A FOM analysis using the k-means algorithm was carried out to determine the appropriate number of clusters for the three libraries. Ten was chosen based on the reduced variation shown at the point where the rate of decline with respect to the numbers of clusters is decreased (Figure 4.4). Next, a 10-cluster analysis was performed based on the PoissonC algorithm (Figure 4.5A). As indicated in Figure 4.5B, cluster 4 contains the most genes. GO analysis, at the third level under biological process, on the genes present in cluster 4 revealed that 48.9% of these genes are involved in “cellular metabolic process” (Appendix 1). Since the genes in cluster 4 are expressed at similar levels amongst the three libraries, they are likely being carried out constantly in prostate cells and may not be associated with tumour growth. However, genes in clusters 1, 2 and 3 are likely  92  to be associated with the tumour since their transcripts are detected at higher levels in the KO prostate tissues than in WT or Het tissues. In these clusters, genes that are associated with the GO terms “cell communication” and “cellular metabolic process” are the majority at the third level of biological process (Appendix 1). These groups include several oncogenes such as Ets2, Nras, Rab11a, Rab3d, Rab14, Rab18, Rab27b, Raf1and Rela, suggesting their  expression may be involved in tumour growth in the prostate-specific Pten KO mouse model. For the genes in clusters 8, 9 and 10, the inhibition or down-regulation of their expression may be essential for tumour growth. In these three clusters, the majority of genes are associated with the GO term “multicellular organismal development” (Appendix 1). Associated with this GO term, Wif1 and Nkx3.1 are two of the examples that are demonstrated as putative tumour suppressor genes [22,23]. Interestingly, the genes in cluster 5 are expressed at high levels in the Het prostate tissues and may be involved in the initiation of tumour growth. However, this cluster contains the least number of genes (Figure 4.5B), suggesting these genes may be essential for the initiation of tumour development. One example is Mapre3 (microtubule-associated protein, RP/EB family, member 3), which is  Mean Adjusted FOM  involved in cell division and cell cycle [24].  1.00 0.75 0.50 0.25 0.00 0  5  10  15  20  Number of Clusters Figure 4.4 The mean values of adjusted FOM versus the number of clusters are calculated. A total of 2,164 tags for the three libraries were included in the calculation. The calculation was carried out with 10 iterations. Data is presented as mean ± SD.  93  Percentage  A. 1  2  3  4  5  6  7  8  9  10  B.  (3.2%) (10.1%) 9  10  1  (8.6%) 2 (12.5%)  (10.4%) 8 (5.5%) 7 3 (16.5%)  (12.0%) 6 5  (1.3%)  4 (19.9%)  Figure 4.5 Clustering analysis of transcripts shows the gene expression patterns in the three prostate SAGE libraries. (A) Ten clusters of gene expression patterns. A 10-cluster analysis was performed using the PoissonC algorithm on the unambiguously mapped tags (2,164 tags) with tag count >5 in any of the three libraries. The Y axis indicates the relative tag abundance in percent. (B) Distribution of genes in each cluster. The percentage of genes in each cluster is shown in parenthesis. The percentage was calculated by dividing the number of genes in one cluster with the total number of genes in all 10 clusters.  94  4.3.3 Immune Gene Expression in the Three SAGE Libraries  The clustering analysis of tags in the three prostate libraries and GO analysis also revealed the changes in the immune-related transcripts in prostate tumour microenvironment. Our first approach was to use GoMiner analysis to evaluate whether an immune-related GO term is over-represented in each cluster. Figure 4.6 shows that cluster 4 has the most abundant number of significantly enriched GO terms (i.e. 171 terms), while clusters 1 and 5 have none. GO terms associated with “cytoplasm” or “intracellular transport” are highly significantly enriched in each cluster except clusters 1 and 5. Table 4.2 lists only the immune-related GO terms that are significantly over-represented in one cluster based on the “false discovery rate” (i.e. <0.05). Interestingly, these over-represented immune-related GO terms are associated with cytokine production, antigen (Ag) presentation, Ag processing and T and NK cell differentiation, and are found only in clusters 7 and 9, in which genes are down-regulated in the KO prostate tissues. This finding suggests that down-regulation of molecules involved in Ag presentation and processing is likely to be one of the mechanisms for tumour cells to escape the immune surveillance in the Pten KO prostate tumour mouse model. The genes associated with the enriched immune-related GO terms are also listed in Table 4.2. Amongst these genes, only Azgp1 (alpha-2-glycoprotein 1, zinc) under the Ag presentation (cluster 9) is significantly down-regulated in the KO prostate tissues, compared  Number of GO terms  with either WT or Het tissues.  200 150 100 50 0 1  2  3 4 5 6 7 8 Cluster group number  9 10  Figure 4.6 The number of significantly over-represented GO terms in each cluster is shown.  95  Table 4.2 Significantly enriched immune-related GO terms in each cluster False Cluster Total Changed c Enrichment discovery group GO category Depth genesa genesb rated number GO:0001816_cytokine_production 7 3 64 4 9.43 0.039 GO:0019882_antigen_presentation  9  a  4  GO:0019884_antigen_presentation__ 5 exogenous_antigen GO:0030333_antigen_processing N/A GO:0001865_NK_T_cell_differentiat 9 ion GO:0048003_antigen_presentation__ 6 lipid_antigen GO:0048007_antigen_presentation__ 6 exogenous_lipid_antigen GO:0051136_regulation_of_NK_T_ 4 cell_differentiation GO:0051138_positive_regulation_of_ 5 NK_T_cell_differentiation  Genes Gata3, Nfat5, Elf1, Stat5a Dpm1, Psme1, Ap3d1, H2-Q10, Ap3b1, Azgp1  38  6  12.94  0.0014  22  4  14.90  0.0071  Dpm1, Psme1, Ap3d1, Ap3b1  28  4  11.71  0.027  Dpm1, Ap3d1, H2-Q10, Ap3b1  3  2  54.64  0.035  Ap3d1, Ap3b1  3  2  54.64  0.035  Ap3d1, Ap3b1  3  2  54.64  0.035  Ap3d1, Ap3b1  3  2  54.64  0.035  Ap3d1, Ap3b1  3  2  54.64  0.035  Ap3d1, Ap3b1  Total genes refer to all the Mus musculus genes in the RefSeq database (47979 genes). Changed genes refer to the number of genes belonging to the indicated GO term category in the cluster. c Enrichment is the ratio of the percentage of Changed Genes to the percentage of Total Genes. d The False Discovery Rate refers to the significance of the data and was determined by GoMiner analysis. The values need to be lower than 0.05 to be considered significant. b  96  Another approach to evaluate the changes in immune-related genes is to directly analyze the immune-related genes in each cluster using GO analysis. In each cluster, genes belong to GO terms containing the word “immune” are listed in Table 4.3, even though these GO terms (e.g. immune response and immune cell activation) are not significantly overrepresented in each cluster from GoMiner analysis (Appendix 2). As indicated in the table, cluster 1 contains more immune-related genes (4.8% of all genes in cluster 1; corresponding to 8 genes: Ifit1, Isgf3g, Bcar1, Psmb8, Isg15, Irf7, Cxcl16 and Oas2) than other clusters. Based on the SAGE tag counts, four of these genes (Ifit1, Psmb8, Irf7 and Cxcl16; shown in Table 4.4 of Section 4.3.4) are significantly up-regulated in the KO prostate tissues, compared with either WT or Het tissues. Moreover, clusters 1, 2 and 3 contain 55.9% of all the immune-related genes found in all the clusters (19 out of 34 genes). Of these 19 genes, there are three chemokine genes (Cxcl16, Ccl21b and Cxcl14) and four interferon (IFN)related genes (Ifit1, Isgf3g, Irf7 and Irf2). In clusters 7, 8 and 9, which contain downregulated transcripts in the KO prostate tissues, there are 9 immune-related genes and four of them are associated with major histocompatibility complex (MHC) molecules (i.e. B2m, H2Aa, H2-Ab1 and H2-Q10 as listed in Table 4.3). The down-regulation of these genes, despite  a lack of statistical significance, may be responsible for the reduced recognition of tumour cells by the immune surveillance. In addition, there are no immune-related genes belonging to clusters 5 and 10. Such distribution of immune-related genes in all the 10 clusters, with cluster 1 containing the majority of immune-related genes, suggests that changes in the immune gene expression are linked to the presence of prostate tumour.  97  Table 4.3 Immune-related genes in each clustera  Cluster group  1  2  Accession  Gene name Significanceb  NM_008331  Mus musculus interferon-induced protein with tetratricopeptide repeats 1 (Ifit1), mRNA.  Ifit1  Yes  NM_008394  Mus musculus interferon dependent positive acting transcription factor 3 gamma (Isgf3g), mRNA.  Isgf3g  No  NM_009954  Mus musculus breast cancer anti-estrogen resistance 1 (Bcar1), mRNA.  Bcar1  No  NM_010724  Mus musculus proteasome (prosome, macropain) subunit, beta type 8 (large multifunctional peptidase 7) (Psmb8), mRNA.  Psmb8  Yes  NM_015783  Mus musculus ISG15 ubiquitin-like modifier (Isg15), mRNA.  NM_016850  Mus musculus interferon regulatory factor 7 (Irf7), mRNA.  NM_023158  Mus musculus chemokine (C-X-C motif) ligand 16 (Cxcl16), mRNA.  NM_145227  Mus musculus 2'-5' oligoadenylate synthetase 2 (Oas2), mRNA.  NM_009778  Mus musculus complement component 3 (C3), mRNA.  NM_013810  Mus musculus drebrin-like (Dbnl), mRNA.  NM_018851  Mus musculus SAM domain and HD domain, 1 (Samhd1), mRNA.  NM_008391  Mus musculus interferon regulatory factor 2 (Irf2), mRNA.  NM_009735  Mus musculus beta-2 microglobulin (B2m), mRNA.  Isg15 Irf7 Cxcl16 Oas2 C3 Dbnl Samhd1 Irf2 B2m  No Yes Yes No Yes No No No Yes  Mus musculus serine (or cysteine) peptidase inhibitor, clade G, member 1 (Serping1), mRNA. Mus musculus complement component 1, q subcomponent, beta polypeptide (C1qb), mRNA.  Serping1  No  C1qb  No  NM_011124  Mus musculus chemokine (C-C motif) ligand 21b (Ccl21b), mRNA.  NM_019568  Mus musculus chemokine (C-X-C motif) ligand 14 (Cxcl14), mRNA.  NM_024255  Mus musculus hydroxysteroid dehydrogenase like 2 (Hsdl2), mRNA.  NM_053109  Mus musculus C-type lectin domain family 2, member d (Clec2d), mRNA.  Cc121b Cxcl14 Hsdl2 Clec2d  No No No Yes  NM_009776  3  Definition  NM_009777  98  Cluster group  4  6 7  8  9  Accession  Definition  Gene name Significanceb  NM_007573  Mus musculus complement component 1, q subcomponent binding protein (C1qbp), mRNA.  C1abp  No  NM_010239  Mus musculus ferritin heavy chain 1 (Fth1), mRNA.  NM_019568  Mus musculus chemokine (C-X-C motif) ligand 14 (Cxcl14), mRNA.  Fth1 Cxcl14  No No  NM_025624  Mus musculus proteasome maturation protein (Pomp), mRNA.  NM_026988  Mus musculus parathymosin (Ptms), mRNA.  Pomp Ptms  No No  NM_011018  Mus musculus sequestosome 1 (Sqstm1), mRNA.  NM_019786  Mus musculus TANK-binding kinase 1 (Tbk1), mRNA.  Sqstm1 Tbk1  No No  NM_007843  Mus musculus defensin beta 1 (Defb1), mRNA.  Defb1  Yes  NM_009735  Mus musculus beta-2 microglobulin (B2m), mRNA.  B2m  No  NM_010378  Mus musculus histocompatibility 2, class II antigen A, alpha (H2-Aa), mRNA.  H2-Aa  No  NM_013499  Mus musculus complement receptor related protein (Crry), mRNA.  NM_207105  Mus musculus histocompatibility 2, class II antigen A, beta 1 (H2-Ab1), mRNA.  NM_010391  Mus musculus histocompatibility 2, Q region locus 10 (H2-Q10), mRNA.  NM_011103  Mus musculus protein kinase C, delta (Prkcd), mRNA.  NM_011189  Mus musculus proteasome (prosome, macropain) 28 subunit, alpha (Psme1), mRNA.  Crry H2-Ab1 H2-Q10 Prkcd Psme1  No No No No No  No Bcap31 Immune-related genes were selected from GO terms that contain the word “immune” (i.e. immune response and innate immune response). b Significance was determined from the comparison analyses using DiscoverySpace, i.e. with a 95 confidence threshold for either upor down-regulation in the KO prostate tissues, compared with either WT or Het tissues (up-regulation for clusters 1, 2 and 3; downregulation for clusters 8 and 9). NM_012060  a  Mus musculus B-cell receptor-associated protein 31 (Bcap31), mRNA.  99  4.3.4 Identification of Immune-Related Factors  GO analysis on all tags in the three libraries revealed that there are 144 genes (1.87% in Figure 4.2) associated with GO terms containing the word “immune” (i.e. immune response, innate immune response and immunoglobulin mediated immune response). Of these 144 genes, 5 of them are significantly up-regulated in the KO prostate tissues and 1 is significantly down-regulated, compared with either Het or WT tissues (Tables 4.4 and 4.5). These 5 up-regulated genes belong to either cluster 1 or cluster 3, which make up 8.6% and 16.5% of the unambiguously-mapped genes, respectively (Table 4.2 and Figure 4.6A). As listed in Table 4.5, the only significantly down-regulated immune-related gene that is in the KO tissues is Defb1 (defensin β1). Since membrane-bound proteins on the tumour cell surface may have roles in altering immune cells (through cell-cell contact), membrane-associated genes were analyzed as well. There are 2,293 genes associated with the GO term “membrane” at the third level of cellular component. Of these, 47 are significantly up-regulated in the KO prostate tissues (Appendix 3) and 16 are down-regulated (Appendix 4). Interestingly, Cxcl16 and Clec2d are found upregulated in both immune- and membrane-related gene lists. In the immune-related gene list, although Irf7 (Interferon regulatory factor 7) is expressed in all three tissues (WT, Het and KO), it is dramatically up-regulated in the KO tissues among the 5 up-regulated genes (10- or 38-fold increase with respect to Het or WT prostate). Moreover, since IRF7 is a transcription factor implicated to have oncogenic properties in Epstein-Barr virus-infected central nervous system lymphoma [25], it was selected to investigate further. Up-regulation of Irf7 in KO prostate tissues was confirmed using qRT-PCR. As shown in the left panel of Figure 4.7A, higher levels of Irf7 transcripts were detected in KO prostate tissues (3 individual mice) than in Het mice (P=0.010), and the average fold change was 11.6. Interestingly, Irf7 transcripts were also expressed at a higher level in the 12T7slow Tg mice, a model of prostate dysplasia, compared to WT mice, consistent with involvement of IRF7 at the early stages of tumour development (Figure 4.7A right panel). In addition, Western blot analysis showed that the IRF7 protein is elevated in the KO prostate tissues, compared to either WT or Het prostate tissues, regardless of the age of mice (9 or 11 weeks old; Figure 4.7B left and right panel, respectively). After normalization with actin, the densitometry analysis revealed that there was an average 1.5 fold change (i.e. statistically  100  significantly increase) in IRF7 protein levels in KO tissues, compared with WT and Het, regardless of age (Figure 4.7C). Since IRF7 translocates from the cytoplasm to the nucleus upon its activation, immunofluorescence staining was carried out to determine IRF7 localization. IRF7 was localized to both the cytoplasm and nucleus in the tumour epithelial cells (Figure 4.8), but only to the cytoplasm in the normal epithelial cells (either WT or Het tissues), although nuclear localization was not observed in every tumour cell (KO2 sample in Figure 4.8). The nuclear localization of IRF7 in a portion of the tumour cells suggests that it may be activated in these cells.  101  Table 4.4 Significantly up-regulated immune-related genes in the KO prostate tissues  Sequence  Position Accession  AAGGAAGACGGTTGGGT  1  NM_010724  ATTCCTACCCAGTTTCC  1  NM_008331  CTGTCTTGAGACAAAGT  2  NM_023158  GACCTGGGTCAGTGGCC  1  NM_016850  ATTGGGGGAGGGGAGGG  1  NM_053109  a  Definition  Gene name  Mus musculus proteosome (prosome, macropain) subunit, beta type 8 (large Psmb8 multifunctional peptidase 7) (Psmb8), mRNA. Mus musculus interferoninduced protein with Ifit1 tetratricopeptide repeats 1 (Ifit1), mRNA. Mus musculus chemokine (CX-C motif) ligand 16 Cxcl16 (Cxcl16), mRNA. Mus musculus interferon regulatory factor 7 (Irf7), Irf7 mRNA. Mus musculus C-type lectin domain family 2, member d Clec2d (Clec2d), mRNA.  Tag countsa  Fold change  Cluster number  WT  Het  KO  1  0  0  11.90  -  -  1  0  0  11.90  -  -  1  0  0  11.90  -  -  1  1.35  4.96 51.56  10.39  38.12  3  6.76 19.85 44.95  2.26  6.65  KO/Het KO/WT  Tag counts refer to tag numbers per 100,000 tags (normalized to total tag counts after quality control).  Table 4.5 Significantly down-regulated immune-related genes in the KO prostate tissues  Sequence  Position Accession  Definition  Gene name  Cluster number  Tag countsa WT  Het  KO  Fold change KO/Het KO/WT  GAGGATTCTGTCTCCGC  6  NM_007843  Mus musculus defensin beta 1 (Defb1), mRNA.  Defb1  7  81.16 124.04 23.79  0.19  0.29  ACATCCAAAAAAAAAAA  1  NM_007843  Mus musculus defensin beta 1 (Defb1), mRNA.  Defb1  7  39.23 44.65 5.29  0.12  0.13  a  Tag counts refer to tag numbers per 100,000 tags (normalized to total tag counts after quality control).  102  Pten KO  15 10  10  5  5  0  0 1  2  3  1  Het  2  3  KO  2  1  2  3  Tg  75  W T H et 1 H et 2 KO 1 KO 2 KO 3  W T2 H et KO 1 KO 2 KO 3  11 weeks  50 kDa  Actin 9 weeks  11 weeks  P=0.018  P=0.023  2.0  2.5 2.0 1.5 1.0 0.5 0.0  1.5 1.0 0.5 KO3  KO2  KO1  Het2  KO3  KO2  KO1  Het  WT2  WT1  0.0 WT  C.  3  WT  IRF7  Relative Intensity  1  9 weeks W T1  B.  12T-7slow  15  Het1  Relative Quantification  A.  Figure 4.7 Irf7 is up-regulated in prostate tumour tissues. (A) Relative levels of Irf7 transcripts were determined using qRT-PCR. Prostate tissues were isolated from 3 individual mice of each genotype (9 weeks old). Het1 was used as a control sample and Ubc was used as a housekeeping gene for the internal control. (B) IRF7 protein level was determined by Western blot analysis. Each lane represents prostate cell lysates from an individual mouse (9 weeks old). Actin was used as an internal control. (C) Relative quantification of protein levels was carried out by measuring the intensities of each band and normalizing to actin. Data is presented as mean ± SD from 3 different measurements for each band (on the same blot), and normalized to WT1 for 9 weeks or WT for 11 weeks.  103  Hoechst33342  IRF7-Alexa488  Merged  WT  Het  KO1  KO2  Figure 4.8 Nuclear localization of IRF7 is detected in the Pten KO prostate tumour cells. Arrows indicate the cells with cytoplasmic IRF7 expression in WT, Het and KO2 tissues, and the cells with both cytoplasmic and nuclear IRF7 expression in KO1 tissue. These prostate tissues were dissected from mice at the age of 9 weeks. Final magnification is 400x. Zoom-in images (4x) are placed in the upper-right corner in each picture. (n ≥ 3).  Since IRF7 is a transcription factor, its target genes are likely to be involved in tumour growth and/or they may have direct effects on immune cells. Although IRF7 regulates type I interferon (IFN) production, the tag counts for detectable Ifn transcripts were low and no significant differences were found amongst the prostate SAGE libraries,  104  suggesting that there are alternative targets in prostate epithelial cells. In order to identify potential targets of IRF7, we used the cisRED database to generate a list of mouse IRF7 target genes whose promoter region may contain putative IRF7 binding sites. This analysis revealed 1,843 potential target genes. A total of 1,326 tags for these genes were detected in the three libraries. Among these transcripts, 14 genes were significantly up-regulated (Table 4.6) and 7 were significantly down-regulated (Table 4.7) in KO, compared with either Het or WT prostate. Intriguingly, Psmb8 and Cxcl16, which are significantly up-regulated immunerelated or membrane-related transcripts in the KO prostate tissues, are also target genes of IRF7 (Tables 4.4 and 4.6). The increased levels of Psmb6 and Cxcl16 transcripts were confirmed by qRT-PCR (Figure 4.9). Moreover, the up-regulation of another IRF7 target gene, St14 (suppression of tumourigenicity 14), was also confirmed by qRT-PCR (Figure  Relative Quantification  4.9). Psmb8  Cxcl16  P=0.0021  P=0.00045  5  12  4  2.5 2.0  8  3 2  1.5 1.0  4  1 0  0.5  0 1  2  Het  3 1  2  KO  3  St14 P=0.021  0.0 1 2 3 1 2 3  Het  KO  1 2 3 1 2 3  Het  KO  Figure 4.9 Up-regulation of potential IRF7 target genes in prostate tumour tissues. Relative levels of Psmb8, Cxcl16 and St14 transcripts were determined using qRT-PCR. Prostate tissues were isolated from 3 individual mice of each genotype (9 weeks old). Het1 was used as a control sample (i.e. baseline) and Ubc was used as a housekeeping gene for the internal control.  105  Table 4.6 Significantly up-regulated potential target genes of IRF7 in KO prostate  Sequence  Position Accession  AAGGAAGACGGTTGGGT  1  NM_010724  CTGTCTTGAGACAAAGT  2  NM_023158  GCCCACACATAATGGAC  2  NM_023386  GCTTCAAGATATTAAAG  6  NM_025294  AAGACCCACCTGCAGGG  1  NM_011678  ATGGCATCAGGCTTTGG  1  NM_024499  GGTACCTTACTTTCCTC  1  NM_001002 004  AGGTCCTGTGGGATTTC  1  NM_011150  Definition  Gene name  Mus musculus proteosome (prosome, macropain) subunit, beta type 8 (large Psmb8 multifunctional peptidase 7) (Psmb8), mRNA. Mus musculus chemokine (C-X-C motif) ligand 16 Cxcl16 (Cxcl16), mRNA. Mus musculus receptor transporter protein 4 Rtp4 (Rtp4), mRNA. Mus musculus gene trap locus F3b (Gtlf3b), Gtlf3b mRNA. Mus musculus ubiquitin specific peptidase 4 Usp4 (proto-oncogene) (Usp4), mRNA. Mus musculus small glutamine-rich tetratricopeptide repeat Sgta (TPR)-containing, alpha (Sgta), mRNA. Mus musculus RIKEN cDNA 2610507B11 gene 2610507 (2610507B11Rik), B11Rik mRNA. Mus musculus lectin, galactoside-binding, soluble, 3 binding protein Lgals3bp (Lgals3bp), mRNA.  Tag countsa  Fold change  Cluster number  WT  Het  KO  1  0  0  11.90  -  -  1  0  0  11.90  -  -  1  1.35  0  13.22  -  9.77  1  1.35  0  10.58  -  7.82  2  4.06  2.48  17.19  6.93  4.24  2  4.06  2.48  17.19  6.93  4.24  1  2.71  4.96  30.40  6.13  11.24  2  6.76  9.92  43.62  4.40  6.45  KO/Het KO/WT  106  Sequence TAATGTTGCTAGAGTGA  Position Accession 1  NM_009864  CACACCTGGATACAGGA  11  NM_011176  GATATTTTTTTTTGGGG  1  NM_139149  TTGGTGAAGGAAAAAGC  1  NM_021278  CTTCCCGGCTCCACTTC  1  NM_133214  ACTCGGAGCCAGCAGAG  6  NM_009790  a  Tag countsa  Fold change  Definition  Gene name  Cluster number  WT  Mus musculus cadherin 1 (Cdh1), mRNA.  Cdh1  1  5.41  12.40 43.62  3.52  8.06  3  6.76  7.44  25.12  3.37  3.71  3  13.53 14.88 43.62  2.93  3.23  3  114.97 94.27 252.49  2.68  2.20  3  10.82 14.88 35.69  2.40  3.30  3  12.17 29.77 60.81  2.04  5.00  Mus musculus suppression of tumourigenicity 14 St14 (colon carcinoma) (St14), mRNA. Mus musculus fusion, derived from t(12;16) Fus malignant liposarcoma (human) (Fus), mRNA. Mus musculus thymosin, beta 4, X chromosome Tmsb4x (Tmsb4x), mRNA. Mus musculus cDNA sequence BC017612 BC017612 (BC017612), mRNA. Mus musculus calmodulin 1 (Calm1), mRNA.  Calm1  Het  Tag counts refer to tag numbers per 100,000 tags (normalized to total tag counts after quality control).  KO  KO/Het KO/WT  107  Table 4.7 Significantly down-regulated potential target genes of IRF7 in KO prostate  Sequence  Position Accession  ACAAATAAACCAACTTT  1  NM_016906  AGACATTGGTCATTAGG  1  NM_025360  TAGAATCAAATATTAGA  1  NM_009537  GAGGAAGGGGAAGGGGA  1  NM_139300  TGCCCCACGGGGTTCAC  1  NM_024440  TATGACTATGTTGACAG  1  NM_138652  AACTTTTCATTTGGAGT  3  NM_183190  a  Definition  Gene name  Mus musculus Sec61 alpha 1 subunit (S. Sec61a1 cerevisiae) (Sec61a1), mRNA. Mus musculus transmembrane emp24 Tmed3 domain containing 3 (Tmed3), mRNA. Mus musculus YY1 transcription factor (Yy1), Yy1 mRNA. Mus musculus myosin, light polypeptide kinase Mylk (Mylk), mRNA. Mus musculus Der1-like domain family, member 3 Derl3 (Derl3), mRNA. Mus musculus ATPase, H+/K+ transporting, nongastric, alpha Atp12a polypeptide (Atp12a), mRNA. Mus musculus membranespanning 4-domains, Ms4a5 subfamily A, member 5 (Ms4a5), mRNA.  Cluster number  Tag countsa WT  Het  Fold change KO  KO/Het KO/WT  4  79.80 81.87 43.62  0.53  0.55  7  63.57 66.98 29.08  0.43  0.46  7  9.47  9.92  1.32  0.13  0.14  7  22.99 39.69  3.97  0.10  0.17  5  20.29 59.54  2.64  0.04  0.13  7  24.35 17.37  0  0  0  9  14.88  0  0  0  7.44  Tag counts refer to tag numbers per 100,000 tags (normalized to total tag counts after quality control).  108  4.4 DISCUSSION 4.4.1 Changes in Immune Cells and Transcripts during Tumour Development  Similar to the tumour-infiltrating immune cells in the 12T-7slow prostate dysplasia mouse model, the T cells and DC that infiltrate the prostate tumour tissues in the Pten KO mice are altered. Interestingly, consistent with the results in the 12T-7slow mice (Figure 2.1A, Section 2.3.1), activated T cells were observed in the Pten KO mice (Figure 4.1E). Although there was an increased level of activated T cells within the tumour mass, an increased frequency of CD4+CD69- cells expressing CD25, which may contain the Treg cell population, was detected. Similarly, an increased frequency of MSC, known to inhibit T cell activation [26], was also detected. Moreover, the phenotype of the tumour-infiltrating DC was altered in such a way (i.e. decreased expression of MHC class II and co-stimulatory molecules) that they may not be able to provide sufficient co-stimulation signals for T cell activation. Although numerous studies have shown increased levels of immune suppression in different types of tumours (reviewed in Chapter 1, Section 1.3), anti-tumour immune responses may occur spontaneously (i.e. without immune-related treatments). In support of this, antibody responses can be detected in cancer patients, including melanoma and prostate cancer [27-29]. The results from others, together with ours, suggest that immune activation and immune suppression may co-exist in tumour-bearing hosts, depending on the stage of tumour progression, as in the “equilibrium” process of cancer immunoediting ([30,31] and Section 1.1.1 in Chapter 1). The changes observed in immune-associated genes detected during tumour development using SAGE analysis may at least partially account for the observed changes in the immune surveillance. GO terms such as “Ag presentation” and “Ag processing” are significantly over-represented in the cluster with genes down-regulated in the tumour tissues (cluster 9 in Table 4.2 of Section 4.3.3), and several genes associated with MHC molecules are down-regulated in the tumour tissues as well, in spite of the lack of significance. These results, together with previous studies (Chapter 1, Section 1.3.1; [32-35]), suggest that deficiency in Ag presentation, at gene or protein level, contributes to the immune suppression in the tumour microenvironment. However, slightly more than half (55.9%; Table 4.3 in Section 4.3.3) of the immune-related transcripts detected in all 10 clusters belonged to clusters 1, 2 and 3 (up-regulation of genes in KO tissues) with cluster 1  109  containing more immune response-related genes than the other clusters. These results suggest that immune responses may be occurring in the prostate tumour environment. However, since the frequency of immune cells infiltrating the prostate tissues is much lower than that of prostate and stromal cells, the transcripts of immune-related genes are more likely to be produced by the prostate or stromal cells suggesting that they may provide a means by which the tumour alters immune cell phenotype and function to evade destruction by the immune system. Interestingly, both GO analysis using DiscoverySpace and GoMiner analysis (Table 4.3 in Section 4.3.3 and Appendix 2) revealed that there are no immune-related transcripts in cluster 5, which contains transcripts up-regulated in Het tissues. This result suggests that immune-related genes may not be involved prior to tumour formation. In the list of immune-related genes in clusters 1, 2 and 3 (19 genes; Appendix 1), there are 3 chemokine genes (Cxcl16, Ccl21b, and Cxcl14) and 4 IFN-related genes (Ifit1, Isgf3g, Irf7, and Irf2). In addition to effectively attracting lymphocytes to the tumour site, the  chemokine CXCL16 was found to be expressed at high levels in colorectal cancer cell lines and patient samples, and therefore it is likely to be a potential prognostic biomarker [36,37]. It has been demonstrated that over-expression of CXCL14 can inhibit tumour growth in oral carcinoma and prostate cancer [38,39]. Therefore, the recruitment of lymphocytes to the tumour site by CXCL16 and the suppression of tumour growth by CXCL14 provide supportive evidence for immune activation occurring in the Pten KO prostate tumour microenvironment. Interestingly, a previous study on different cell types of prostate tissues from normal mice and from the Pten KO prostate tumour, using microarray analysis and immunohistochemistry analysis, revealed that CXCL12 and its receptor, CXCR4, were expressed at a higher level in tumour epithelial cells than normal cells, and it was suggested that the up-regulation of this chemokine and its receptor may allow the tumour cell to migrate and become invasive [40]. However, the Cxcl12 and Cxcr4 transcripts were not detected in the Pten KO prostate SAGE library used in our studies. This difference can likely be attributed to the fact that the prostate tissues were isolated from different stages of tumour development and/or enrichment of a particular cell type was used (epithelial cells in the Berquin et al. study [40]). The tags for known immune modulatory transcripts, such as Vegfa, Il6, Il10, Tgfb1, Tgfb2, Tgfb3 and Csf1 (for macrophage colony-stimulating factor [M-CSF]) as discussed in  110  Section 1.3.1 of Chapter 1, were not significantly changed in the Pten KO prostate libraries, compared to WT or Het (i.e. counts of detected tags ranged from 1 to 3). This discrepancy may be due to the difference in tumour stage examined or an insufficient sequencing depth for tag detection. Although several IFN-related genes are up-regulated in the prostate tumour, no Ifn transcripts were detected in the KO SAGE data. Although we cannot exclude the possibility that this is due to an insufficient sequencing depth to detect IFN, it is likely that the IFN-related transcripts have roles in processes other than IFN signalling in the prostate tumours. A more detailed discussion of this point follows in Section 4.4.2. Genes associated with the enriched immune-related GO terms in clusters 7 and 9, as well as the immune-related transcripts present in clusters 8 and 9 (i.e. genes down-regulated in the KO; Tables 4.2 and 4.3) may be involved in immune suppression in a passive manner. Alternatively, they may actively contribute to ignorance of the tumour by the immune system. Six genes are associated with GO term “Ag presentation” (Dpm1, Psme1, Ap3d1, H2-Q10, Ap3b1 and Azgp1; as listed in Table 4.2). Four of the immune-related genes are  associated with MHC molecules (B2m, H2-Aa, H2-Ab1 and H2-Q10; as listed in Table 4.3). Although their expression patterns belong to clusters 7, 8 or 9, they are not significantly down-regulated, except Azgp1, in the tumour tissues at this stage of tumour progression. However, this result suggests that the Pten KO prostate tumour cells may down-regulate their own genes that are involved in Ag presentation, resulting in escape from recognition by immune cells, as demonstrated in various studies (Chapter 1, Section 1.3.1; [32-35]). The gene Defb1 (defensin β1) was the only immune response-related gene found in the significantly down-regulated clustering profiles (i.e. cluster 7). Its down-regulation is consistent with a previous study carried out using human prostate tumour samples [41]. It has not only anti-microbial activity, but also chemoattractive properties that recruit immune cells to sites of inflammation [42]. As such, down-regulation of defensin may be one of the mechanisms by which the tumour reduces recruitment of effector cells to the tumour sites. Taken together these observations imply that the changes in immune gene expression patterns may be associated with prostate cancer, possibly reflecting changes in the balance of immune activation and immune suppression in the tumour environment.  111  4.4.2 The Roles of IRF7 in Prostate Tumour Development  Among the significantly up-regulated transcripts, Irf7 was chosen for further studies because it was the most highly up-regulated in the KO prostate tissues amongst the five immune-related genes (Table 4.4) and it is a transcriptional regulator that has oncogenic function in Epstein-Barr virus transformed central nervous system lymphoma [25]. In addition to its oncogenic properties, IRF7 plays an important role in regulating the production of Type I IFN [43]. IRF7 induces IFN production upon viral infection and it is constitutively expressed by pDC, which are a major source of IFN production. Although the frequency of pDC is significantly increased in the prostate tumour tissue (Figure 4.1B), it is unlikely that the increased frequency of pDC is sufficient to result in a significant change in transcript expression. Moreover, the nuclear localization of IRF7 (Figure 4.8) suggests that IRF7 is activated in the prostate tumour epithelial cells since IRF7 is translocated to the nucleus upon activation [44]. Although 4 transcripts (Ifit1, Isgf3g, Irf7, and Irf2) involved in IFN signaling were up-regulated in the KO prostate tissue (Table 4.3), there were low counts of detected Ifn tags (either for Type I or Type II IFN) in the three mouse prostate SAGE library data and no significant difference among the three libraries. This observation suggests that Irf7 may have roles that are not involved in IFN production by the tumour. In support of this possibility, neither IRF9 (also known as interferon-stimulated gene factor 3-γ [ISGF3γ]), which forms a complex with STAT1 (Signal Transducers and Activators of Transcription 1) and STAT2 to induce Irf7 expression in the IFN signaling pathway [43,45,46], nor MyD88 and TRAF6 (tumour necrosis factor receptor-associated factor 6), which interact with IRF7 to induce IFN expression [47-49], were significantly up-regulated in the Pten KO prostate tissues. Since the roles of IRF7 in the prostate tumour microenvironment were not likely related to IFN production, putative target genes were identified using a bioinformatics approach. Interestingly, Cxcl16, identified as one of the significantly up-regulated immunegenes, is a potential target of IRF7. The up-regulation of Cxcl16, although not confirmed by qRT-PCR yet, may contribute to the recruitment of tumour-infiltrating cells, which were shown to increase in the Pten KO prostate (Section 4.3.1). Another interesting potential target is the suppression of tumourigenicity 14 (St14) gene. It is also known as matriptase and it has been found over-expressed in a variety of tumour cell lines or tissues, including  112  prostate, breast, and ovarian cancers [50]. Matriptase can activate macrophage-stimulating protein (MSP), which has a role in promoting cell migration, thus possibly leading to metastasis [51,52]. Hence, the up-regulated St14 may contribute to the invasiveness of prostate tumour cells in the Pten KO mice. Interestingly, it has been shown that human IRF7 expression is induced by the proinflammatory cytokine tumour necrosis factor-α (TNF-α) [53]. We detected a higher level of TNF-α in the supernatant of Pten KO primary prostate tissue cultures than in those of WT or Het tissues (data in Appendix 5), using cytometric bead array analysis as described in Section 2.2.6 of Chapter 2. TNF-α is known for its dual roles in inhibiting as well as promoting tumour growth, depending on the levels used for treatment of cancer or endogenous production in the tumour environment [54,55]. Although the role of TNF-α in the KO prostate tumour cells is not clear, its over-expression may contribute to the upregulation of Irf7 expression. Moreover, the dual roles of TNF-α may contribute to the coexistence of immune activation and suppression in the tumour microenvironment. Taken together, these observations suggest that IRF7 may be a candidate target for cancer therapy or cancer detection since it may have oncogenic potential and its putative target genes are known to be involved in tumour progression. Moreover, these studies illustrate the utility of large-scale gene expression profiling studies to logically identify candidates for further biological analyses.  113  4.5 REFERENCES  1. 2.  3.  4. 5. 6.  7. 8. 9. 10. 11.  12.  13.  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Anderson GM, Nakada MT, DeWitte M: Tumor necrosis factor-α in the pathogenesis and treatment of cancer. Curr Opin Pharmacol 2004;4:314-320. Szlosarek P, Charles KA, Balkwill FR: Tumour necrosis factor-α as a tumour promoter. Eur J Cancer 2006;42:745-750.  117  CHAPTER 5. CONCLUSIONS 5.1 Summary and Significance of the Studies  These studies were carried out to provide the first comprehensive analyses of changes in the immune system as a function of tumour progression in a mouse model of prostate dysplasia (the 12T-7slow model), as well as in a mouse model of prostate cancer (Pten knockout [KO]), with the goal of identifying relevant changes that may contribute to immune suppression during prostate tumour progression. A second goal of these studies was to begin to elucidate the underlying mechanisms for any observed changes. Together, these observations were anticipated to provide important insights required for the development of more effective immune system-based therapies for the treatment of prostate cancer (PCa). The studies in the 12T-7slow transgenic (Tg) mouse model of prostate dysplasia revealed abundant evidence of immune suppression in the tumour environment. T cells isolated from the secondary lymphoid tissues, particularly from lumbar lymph nodes (LNs), of tumour-bearing mice exhibited a reduced proliferative capacity in response to antigen (Ag) stimulation. This hypo-responsiveness was attributed, at least in part, to the increased frequency and number of CD4+CD25+ regulatory T (Treg) cells. Removal of this population, both in vitro and in vivo, restored the function of the hypo-responsive T cells and reduced tumour growth, respectively. Although the dendritic cell (DC) number increased in the draining LNs of tumour-bearing mice, their phenotype (at least for the markers examined) was not different from those isolated from WT mice. In contrast, DC infiltrating the prostate tumour tissues exhibited an altered phenotype, suggesting that they may be impaired in their ability to provide sufficient activation signals for effector T cells. A similar alteration in DC phenotype was observed in the prostate-specific Pten KO mouse model. Finally, an increased frequency of CD4+CD69- T cells expressing CD25, a population including the Treg cells, was detected in the tumour-infiltrating lymphocyte populations in both mouse models. In spite of all the evidence pointing toward immune suppression in the prostate tumour environment, there was also expansion of phenotypically normal DC and an increased frequency of activated T cells in the tumour environment, suggesting that an active immune response may be occurring. Further support for the co-existence of immune suppression and activation in the tumour-bearing hosts came from the gene expression profiling analyses, as the analyses reveled the down-regulation of genes involved in Ag presentation detected in 118  gene expression clusters 7, 8 or 9, as well as the up-regulation of genes involved in immune response detected in clusters 1, 2 and 3. Taken together, these results support the concept of immunoediting during tumour development [1,2]. Autoimmune disease may accompany the treatment used to reverse (or inhibit) immune suppression [3]. Factors determining the extent or levels of immune suppression and activation may largely depend on the stage of tumour progression [3,4]. The imbalance between immune suppression and activation will result in either promoting or inhibiting tumour development. As such, understanding the mechanisms responsible for both immune suppression and tumour-specific immune activation is necessary to develop appropriate and effective immunotherapies for cancer treatment so that anti-tumour immune responses can be enhanced without causing autoimmunity. Prior to our studies, others had demonstrated that anti-CD25 monoclonal antibody (mAb) treatment to deplete the CD4+CD25+ Treg cell population was able to reduce the growth of a variety of transplanted tumour cells. Our studies provided the first experimental evidence that such a therapeutic approach was also capable of reducing tumour growth in a mouse model that spontaneously develops prostate tumours (the 12T-7slow model). Of note, intravenous (i.v.) administration of anti-CD25 mAb allowed only partial depletion of the CD4+CD25+ T cells within the tumour mass, but almost complete depletion in the secondary lymphoid tissues. Since there was an approximately 23% regression of tumour growth, this observation suggests that the route of treatment is a critical factor for the treatment outcome. These studies also suggest that understanding the mechanisms by which Treg cells arise during tumour progression and developing effective strategies to block their generation or function may have important implications for overcoming the limitations of existing immunotherapeutic approaches for PCa treatment. Our studies, using a large-scale gene expression profiling analysis, suggest that IRF7 may be a potential target for immunotherapy of localized prostate tumours. Although it may not have immune-related roles (i.e. involvement in interferon production) in the prostate tumour environment, its up-regulation and activated status in prostate tumour cells may be associated with the oncogenic potential of the tumour cells [5]. Interestingly, several of its putative target genes were expressed at a higher level in prostate tumour tissues and some of them have been shown to have roles in promoting tumour growth (Section 4.4.2 in Chapter  119  4). These results imply that using a global gene expression profiling approach allows researchers to examine biological questions from many different aspects at the beginning of studies (since it is not likely only one factor that determines all the consequences in a heterogeneous environment during tumour progression). 5.2 Limitations of the Studies  We attempted to evaluate the phenotype of DC isolated from the secondary lymphoid tissues of prostate tumor-bearing mice, but we observed no significant differences based on the cell surface molecules examined. However, since only a limited number of markers were examined we cannot exclude the possibility that the DC isolated from 12T-7slow Tg or Pten KO secondary lymphoid tissues, particularly the lumbar LNs, have alterations in the expression of molecules that were not examined. Hence, using a large-scale gene expression profiling analysis on those cells may provide a clearer picture about the changes, if any, in the DC phenotype. The studies of DC cultured in vitro with prostate tumour culture supernatants did not show highly significant changes in DC phenotype when compared with the changes observed in the tumour-infiltrating DC (Figures 3.5, 3.6 and 4.1). This difference may reflect the degradation of tumour-secreted soluble factors during storage or during the 24-hour culture period. A continuous supply of tumour-derived factors (i.e. prostate culture supernatant), or a combination of cell contact and soluble factors, may provide results more consistent with those from the in vivo studies. Different types of immune cells may express the same molecules on their surface. For example, one DC subtype expresses CD8α as do cytotoxic T lymphocytes (CTL). The expression of a particular marker by various cell types may cause difficulty with data interpretation. For example, as shown in Figure 2.1 (Section 2.3.1 in Chapter 2) the tumorinfiltrating CD8+ cells may include both T cells and DC. Such a problem can be resolved using multiple-color flow cytometric analysis. Another limitation of this study is the different databases used by the different analysis programs for evaluating the SAGE data. For example, gene ontology (GO) analysis using the DiscoverySpace program, which is based on the RefSeq database, revealed slightly different results from the GoMiner analysis - different numbers of immune response-related genes were found in the two analyses. Despite these differences, the data was still considered  120  useful since both analyses showed similar results. Hence, it is extremely important to be aware of the differences amongst the various databases. Frequent updates of the information may provide a more accurate analysis result. In addition, for the gene analysis studies, changes at the transcriptional level may not always reflect the changes at the protein level or the activation status of a protein. The additional use of a proteomic approach would provide results with more confidence.  5.3 Application for Future Studies  One study on treatment for melanoma demonstrated that the combination of Treg cell depletion with DC loaded with tumour Ag improved the effects of treatment with DC alone [6]. Our studies on the immunological status of both Treg cells and DC have important implications for designing effective anti-tumour immunotherapy strategies that involve manipulation of both Treg cells and DC. For example, removing Treg cells specifically in the tumour microenvironment and delivering activated DC that are able to present tumourspecific Ag may be an effective therapeutic approach for enhancing anti-tumour immune responses in prostate tumour-bearing hosts. Schedule, doses and routes of delivering these potential anti-tumor immune mediators, depending on the types of cancer and stages of the disease, are critical factors for enhancement of an anti-tumor immune response. For example, for localized tumors intratumoural injection, rather than intravenous injection, of anti-CD25 mAb to deplete CD4+CD25+ Treg cells may enhance the efficacy of depletion. Moreover, the roles of IRF7 and its potential target genes (Table 4.6 on Section 4.3.4) on prostate tumor epithelial cells or immune cells require further investigation. The DNA binding activity of IRF7 on potential target genes of interest, such as Psmb8 and Cxcl16, must be confirmed by chromatin immunoprecipitation and quantitative real-time polymerase chain reaction (qRT-PCR). Since IRF7, PSMB8 and CXCL16 are suggested to be involved in tumor progression or lymphocyte recruitment [5,7,8], inhibition of IRF7 or its target genes could be another approach to inhibit prostate tumour growth. Alternatively, modulation of these factors may enhance an anti-tumor immune response. Inhibition of the gene expression or functional activity of these factors, using techniques such as small interfering RNA knockdown or simply a kinase inhibitor [9,10], in the primary prostate tumour cells will be  121  necessary to examine the effects of these molecules on tumour growth or immune modulation (such as recruitment of Treg cell and alteration of DC phenotype). The analysis of gene expression profiling on the whole prostate tissues might limit our understanding of changes in gene expression patterns in specific types of cells since the whole tissue consists of heterogeneous cell population. Purification or enrichment of prostate epithelial tumour cells or immune cells (such as prostate tumour-infiltrating DC) for global gene expression analysis may be helpful to evaluate the specific expression of genes and their roles in a particular type of cells. Such analysis will aid to the understanding of the type, as well as the mechanisms, of interaction between prostate tumour cell and immune cells. Regardless of the uses of different types of cells for studying the interaction amongst different types of cells, approaches using global gene expression profiling on tumour tissues, particularly clustering and gene ontology analyses, allow us to focus on the essential alterations in tumour environment. For example, our study shows that amongst immunerelated genes, the genes associated in Ag presentation are down-regulated in the KO prostate tissues at the disease stage examined. Therefore, immunotherarpy targeting the deficiency in Ag presentation, rather than other mechanisms of immune suppression (e.g. recruitment of immune suppressor cells), may be more beneficial for initiating an anti-tumour immune response at the particular time point of disease progression. The studies investigating T cells, Treg cells and DC in the prostate tumour environment (Chapters 2 and 3) provide important evidence that the tumour microenvironment is an essential factor for altering the immune system, either in immune suppression or immune activation. A large-scale gene expression profiling analysis on the prostate tumour tissue (i.e. tumour microenvironment) also provides additional evidence for the co-existence of immune suppression and immune activation. Targeting alterations in the tumour microenvironment, rather than in a systemic approach, to inhibit the immune suppression factors, and allowing the endogenous anti-tumour immune response to function, will be an efficient strategy for cancer treatment.  122  5.4 References  1. 2. 3. 4. 5.  6. 7. 8.  9. 10.  Dunn GP, Bruce AT, Ikeda H, Old LJ, Schreiber RD: Cancer immunoediting: from immunosurveillance to tumor escape. Nat Immunol 2002;3:991-998. Dunn GP, Old LJ, Schreiber RD: The three Es of cancer immunoediting. Annu Rev Immunol 2004;22:329-360. Kim R, Emi M, Tanabe K: Cancer immunosuppression and autoimmune disease: beyond immunosuppressive networks for tumour immunity. Immunology 2006;119:254-264. Kim R, Emi M, Tanabe K, Arihiro K: Tumor-driven evolution of immunosuppressive networks during malignant progression. Cancer Res 2006;66:5527-5536. Zhang L, Zhang J, Lambert Q, Der CJ, Del Valle L, Miklossy J, Khalili K, Zhou Y, Pagano JS: Interferon regulatory factor 7 is associated with Epstein-Barr virustransformed central nervous system lymphoma and has oncogenic properties. J Virol 2004;78:12987-12995. Prasad SJ, Farrand KJ, Matthews SA, Chang JH, McHugh RS, Ronchese F: Dendritic cells loaded with stressed tumor cells elicit long-lasting protective tumor immunity in mice depleted of CD4+CD25+ regulatory T cells. J Immunol 2005;174:90-98. Heink S, Fricke B, Ludwig D, Kloetzel PM, Kruger E: Tumor cell lines expressing the proteasome subunit isoform LMP7E1 exhibit immunoproteasome deficiency. Cancer Res 2006;66:649-652. Yamauchi R, Tanaka M, Kume N, Minami M, Kawamoto T, Togi K, Shimaoka T, Takahashi S, Yamaguchi J, Nishina T, Kitaichi M, Komeda M, Manabe T, Yonehara S, Kita T: Upregulation of SR-PSOX/CXCL16 and recruitment of CD8+ T cells in cardiac valves during inflammatory valvular heart disease. Arterioscler Thromb Vasc Biol 2004;24:282-287. Wiznerowicz M, Szulc J, Trono D: Tuning silence: conditional systems for RNA interference. Nat Methods 2006;3:682-688. Smith EJ, Marie I, Prakash A, Garcia-Sastre A, Levy DE: IRF3 and IRF7 phosphorylation in virus-infected cells does not require double-stranded RNAdependent protein kinase R or IκB kinase but is blocked by Vaccinia virus E3L protein. J Biol Chem 2001;276:8951-8957.  123  Appendix 1. Top 3 GO terms at the third level of biological process in each cluster  Cluster group 1 2 3 4 5 6 7 8 9 10  GO accession  GO term  Associationa (%)  Number of genes  GO:0007154 GO:0007275 GO:0009653 GO:0044237 GO:0007154 GO:0007275 GO:0044237 GO:0050794 GO:0016043 GO:0044237 GO:0008104 GO:0007154 GO:0051301 GO:0007049 GO:0006952 GO:0009058 GO:0007154 GO:0008104 GO:0007275 GO:0007049 GO:0001816 GO:0044237 GO:0007275 GO:0007155 GO:0009058 GO:0007275 GO:0007049 GO:0007275 GO:0006950 GO:0007049  cell communication multicellular organismal development anatomical structure morphogenesis cellular metabolic process cell communication multicellular organismal development cellular metabolic process regulation of cellular process cell organization and biogenesis cellular metabolic process protein localization cell communication cell division cell cycle defense response biosynthetic process cell communication protein localization multicellular organismal development cell cycle cytokine production cellular metabolic process multicellular organismal development cell adhesion biosynthetic process multicellular organismal development cell cycle multicellular organismal development response to stress cell cycle  14.37 8.98 8.38 40.23 12.41 10.15 45.79 18.54 15.73 48.91 10.65 8.96 3.57 3.57 3.57 12.55 11.76 11.37 9.65 4.39 3.51 47.71 9.63 4.13 11.59 7.25 5.31 12.96 12.96 5.56  24 15 14 107 33 27 163 66 56 202 44 37 1 1 1 32 30 29 11 5 4 104 21 9 24 15 11 7 7 3  a  Association refers to the percentages of genes associated with the indicated GO term under the third level of biological process.  124  APPENDIX 2. GoMiner analysis of immune system-related genes in each cluster profile a  Cluster group number  1  2  3  4 6  GO category GO:0050778 positive regulation of immune response GO:0050776 regulation of immune response GO:0006955 immune response GO:0006959 humoral immune response GO:0045321 immune cell activation GO:0042088 T-helper 1 type immune response GO:0050777 negative regulation of immune response GO:0042087 cell-mediated immune response GO:0050778 positive regulation of immune response GO:0050776 regulation of immune response GO:0045087 innate immune response GO:0006959 humoral immune response GO:0006955 immune response GO:0001911 negative regulation of immune cell mediated cytotoxicity GO:0001910 regulation of immune cell mediated cytotoxicity GO:0001909 immune cell mediated cytotoxicity GO:0045087 innate immune response GO:0006959 humoral immune response GO:0006955 immune response GO:0045321 immune cell activation GO:0045321 immune cell activation GO:0006955 immune response GO:0006959 humoral immune response GO:0045087 innate immune response GO:0006955 immune response  Total genesb 57 90 506 82 135 25 25 28 57 90 45 82 506 4 8 10 45 82 506 135 135 506 82 45 506  Changed Enrichmentd genesb 2 2 9 1 1 1 1 1 2 3 1 1 6 1 1 1 2 2 8 1 2 7 1 1 3  3.57 2.26 1.81 1.24 0.75 2.70 2.70 2.41 2.37 2.25 1.50 0.82 0.80 12.10 6.05 4.84 2.15 1.18 0.77 0.36 0.61 0.57 0.50 1.48 0.39  False discovery ratee 0.60 0.69 0.57 0.88 0.91 0.73 0.73 0.75 0.67 0.61 0.80 0.88 0.89 0.50 0.57 0.62 0.66 0.79 0.89 0.91 1.17 1.15 1.17 0.93 1.05  125  Cluster group number  GO category  Total genesb  Changed Enrichmentd genesb  False discovery ratee 0.51 0.55 0.56 0.40 0.62 0.66 0.70 0.71 0.82 0.86 0.90 0.90 0.90 0.57 0.64 0.69 0.58 0.83 0.80  16 1 9.43 25 1 6.04 28 1 5.39 7 90 3 5.03 57 1 2.65 506 4 1.19 135 1 1.12 135 3 1.71 45 1 1.71 57 1 1.35 8 82 1 0.94 506 6 0.91 90 1 0.86 57 2 2.88 135 3 1.82 9 90 2 1.82 506 9 1.46 82 1 1.00 10 506 2 1.34 a Immune-related genes were selected from GO terms that contain the word “immune”. b Total genes refer to all the Mus musculus genes in the Refseq database (47979 genes). c Changed genes refer to the number of genes belonging to the indicated GO term category in the cluster. d Enrichment is the ratio of the percentage of Changed Genes to the percentage of Total Genes. e The False Discovery Rate refers to the significance of the data and was determined by GoMiner analysis. The values need to be lower than 0.05 to be considered significant. GO:0001776 immune cell homeostasis GO:0042088 T-helper 1 type immune response GO:0042087 cell-mediated immune response GO:0050776 regulation of immune response GO:0050778 positive regulation of immune response GO:0006955 immune response GO:0045321 immune cell activation GO:0045321 immune cell activation GO:0045087 innate immune response GO:0050778 positive regulation of immune response GO:0006959 humoral immune response GO:0006955 immune response GO:0050776 regulation of immune response GO:0050778 positive regulation of immune response GO:0045321 immune cell activation GO:0050776 regulation of immune response GO:0006955 immune response GO:0006959 humoral immune response GO:0006955 immune response  126  APPENDIX 3. Significantly up-regulated membrane-related genes in the KO prostate tissues  Sequence  Position Accession  Definition Mus musculus 24dehydrocholesterol reductase (Dhcr24), mRNA. Mus musculus p21 (CDKN1A)-activated kinase 1 (Pak1), mRNA.  TCCCTGTGGTTCTGGGG  1  NM_053272  CTCAGAGGTCCTACTGC  1  NM_011035  CTGTCTTGAGACAAAGT  2  NM_023158  TGCTGGAGACCTCAGAC  1  NM_027988  GACTGCGCTTCTTGTCC  1  CTCTCCGTTTATAGAGC  1  GCCCACACATAATGGAC  2  NM_023386  Mus musculus receptor transporter protein 4 (Rtp4), mRNA.  CCCCCCGAGACCCTGAG  13  NM_019517  Mus musculus beta-site APP-cleaving enzyme 2 (Bace2), mRNA.  Mus musculus chemokine (C-X-C motif) ligand 16 (Cxcl16), mRNA.  Gene name  Tag countsa Cluster number WT Het KO  Fold change KO/Het KO/WT  Dhcr24  1  0  0  18.51  -  -  Pak1  1  0  0  18.51  -  -  Cxcl16  1  0  0  11.90  -  -  1  0  0  11.90  -  -  1  0  0  10.58  -  -  1  1.35  0  15.86  -  11.73  Rtp4  1  1.35  0  13.22  -  9.77  Bace2  1  2.71  0  23.79  -  8.80  Mus musculus NADPH oxidase organizer 1 Noxo1 (Noxo1), mRNA. Mus musculus tumour necrosis factor receptor NM_013749 superfamily, member 12a Tnfrsf12a (Tnfrsf12a), mRNA. Mus musculus prostate NM_028216 stem cell antigen (Psca), Psca mRNA.  127  Sequence  Position Accession  ACCTGCGCCTTCGTGGG  1  NM_008144  GATTTAAAAGGAGCCCC  1  NM_027211  CTGACCTGGACCCTGCT  9  NM_011671  GGCTTAAGTAGGAAAGT  1  NM_010730  CAAGAGGTTGTTCTGAG  1  NM_008557  GTGGTGTGCTCCTGCAA  1  NM_011082  CCAACTCTGCCCCAGGT  1  NM_008486  TTCAGAGAATAAAATGC  1  NM_008609  AAATTTAAATTCTGTCC  1  NM_023270  GACATCAAGTCCAGGCT  1  NM_008471  Definition Mus musculus Bernardinelli-Seip congenital lipodystrophy 2 homolog (human) (Bscl2), mRNA. Mus musculus annexin A13 (Anxa13), mRNA. Mus musculus uncoupling protein 2 (mitochondrial, proton carrier) (Ucp2), mRNA. Mus musculus annexin A1 (Anxa1), mRNA. Mus musculus FXYD domain-containing ion transport regulator 3 (Fxyd3), mRNA. Mus musculus polymeric immunoglobulin receptor (Pigr), mRNA. Mus musculus alanyl (membrane) aminopeptidase (Anpep), mRNA. Mus musculus matrix metallopeptidase 15 (Mmp15), mRNA. Mus musculus ring finger protein 128 (Rnf128), mRNA. Mus musculus keratin 19 (Krt19), mRNA.  Gene name  Tag countsa Cluster number WT Het KO  Fold change KO/Het KO/WT  Bscl2  1  1.35  0  10.58  -  7.82  Anxa13  2  5.41  0  17.19  -  3.18  Ucp2  2  9.47  0  23.79  -  2.51  Anxa1  1  5.41  2.48  42.30  17.05  7.82  Fxyd3  2  31.11  7.44  89.89  12.08  2.89  Pigr  1  5.41  4.96  50.23  10.12  9.28  Anpep  1  1.35  2.48  21.15  8.53  15.64  Mmp15  2  4.06  2.48  21.15  8.53  5.21  Rnf128  2  6.76  2.48  21.15  8.53  3.13  Krt19  1  28.40 29.77 248.52  8.35  8.75  128  Sequence  Position Accession  TATCCTGAATGTCCCCC  1  TATGCCTGTCAATGACC  1  GAAGCCTGGCCTAGTTA  4  ACTTTAAATACTCGAAT  1  ACTTTGATATCTGCTTT  1  GGGTTCGTCTCTTTGGA  3  TGTTCTCCATATCCCCC  2  AGCATTCATACGAAGAC  1  CACCTTGGTGCAGAAAC  8  GAGGACTGCCACCCCTC  1  CAGGGCCTCACGGCCGG  2  Definition  Mus musculus lymphocyte NM_010738 antigen 6 complex, locus A (Ly6a), mRNA. Mus musculus lymphocyte NM_010741 antigen 6 complex, locus C1 (Ly6c1), mRNA. Mus musculus polymeric NM_011082 immunoglobulin receptor (Pigr), mRNA. NM_146010  Mus musculus tetraspanin 8 (Tspan8), mRNA.  Gene name  Tag countsa Cluster number WT Het KO  Fold change KO/Het KO/WT  Ly6a  1  20.29 39.69 313.30  7.89  15.44  Ly6c  1  4.06  19.85 155.99  7.86  38.44  Pigr  1  1.35  2.48  18.51  7.46  13.68  Tspan8  2  6.76  2.48  18.51  7.46  2.74  2  4.06  2.48  17.19  6.93  4.24  2  4.06  2.48  17.19  6.93  4.24  2  17.58  9.92  67.42  6.79  3.83  2  12.17  9.92  64.77  6.53  5.32  2  12.17  4.96  31.73  6.39  2.61  2  70.33 49.62 296.11  5.97  4.21  2  24.35 17.37 97.82  5.63  4.02  Mus musculus LPSNM_019980 induced TN factor (Litaf), Litaf mRNA. Mus musculus ORM1-like NM_024180 2 (S. cerevisiae) (Ormdl2), Ormdl2 mRNA. Mus musculus lymphocyte NM_008529 antigen 6 complex, locus E Ly6e (Ly6e), mRNA. Mus musculus keratin 8 NM_031170 Krf8 (Krt8), mRNA. Mus musculus NM_019631 transmembrane protein 45a Tmem45a (Tmem45a), mRNA. Mus musculus lymphocyte NM_008529 antigen 6 complex, locus E Ly6e (Ly6e), mRNA. Mus musculus tumourassociated calcium signal NM_008532 Tacstd1 transducer 1 (Tacstd1), mRNA.  129  Sequence  Position Accession  CCTCAGGGATCCTTGGC  24  NM_025895  GTGAGCGACATAGGTTC  1  NM_010688  CACCCTCTCATTTTGCC  1  NM_145516  CCATAAAAGACCGATCC  25  NM_019806  TTATGGAATTGATTTGC  1  NM_009846  AGGTCCTGTGGGATTTC  1  NM_011150  TAATGTTGCTAGAGTGA  1  NM_009864  CACACCTGGATACAGGA  11  NM_011176  GGTTTGGGGGCGGGGGT  1  NM_010593  GAAACCAGGAAGAGGCA  1  NM_133681  Definition  Gene name  Mus musculus mediator of RNA polymerase II transcription, subunit 28 Med28 homolog (yeast) (Med28), mRNA. Mus musculus LIM and SH3 protein 1 (Lasp1), Lasp1 mRNA. Mus musculus pleckstrin homology domain containing, family B Plekhb2 (evectins) member 2 (Plekhb2), mRNA. Mus musculus vesicleassociated membrane Vapb protein, associated protein B and C (Vapb), mRNA. Mus musculus CD24a Cd24a antigen (Cd24a), mRNA. Mus musculus lectin, galactoside-binding, Lgals3bp soluble, 3 binding protein (Lgals3bp), mRNA. Mus musculus cadherin 1 Cdh1 (Cdh1), mRNA. Mus musculus suppression of tumourigenicity 14 St14 (colon carcinoma) (St14), mRNA. Mus musculus junction Jup plakoglobin (Jup), mRNA. Mus musculus tetraspanin 1 (Tspan1), mRNA.  Tspan1  Tag countsa Cluster number WT Het KO  Fold change KO/Het KO/WT  2  5.41  4.96  26.44  5.33  4.89  2  6.76  4.96  25.12  5.06  3.71  2  6.76  4.96  25.12  5.06  3.71  2  10.82  4.96  25.12  5.06  2.32  2  71.69 39.69 189.04  4.76  2.64  2  6.76  9.92  43.62  4.40  6.45  1  5.41  12.40 43.62  3.52  8.06  3  6.76  7.44  25.12  3.37  3.71  3  10.82 12.40 37.01  2.98  3.42  3  78.45 62.02 169.21  2.73  2.16  130  Sequence GGGTGTGCTTTTGTACA  Position Accession 1  NM_016887  TTTCAAGGGAGGAGGCT  1  NM_023168  TAGCTGTAACGGGGGGC  1  NM_144900  ATTGGGGGAGGGGAGGG  1  NM_053109  GAAATATATGTTATTTC  1  NM_175015  ACTCGGAGCCAGCAGAG  6  NM_009790  TATTGGCTCTGCTTGGT  1  NM_007750  CAGGACTCCGTTTCCTT  1  NM_009128  a  Tag countsa Cluster number WT Het KO  Fold change  Definition  Gene name  Mus musculus claudin 7 (Cldn7), mRNA.  Cldn7  3  17.58 22.33 59.49  2.66  3.38  Grina  3  16.23 17.37 43.62  2.51  2.69  Atp1a1  3  33.81 34.73 84.60  2.44  2.50  Clec2d  3  6.76  19.85 44.95  2.26  6.65  Atp5g3  3  37.87 29.77 64.77  2.18  1.71  Calm1  3  12.17 29.77 60.81  2.04  5.00  Cox8a  3  59.51 84.35 153.34  1.82  2.58  Scd2  3  40.58 66.98 104.43  1.56  2.57  Mus musculus glutamate receptor, ionotropic, Nmethyl D-asparateassociated protein 1 (glutamate binding) (Grina), mRNA. Mus musculus ATPase, Na+/K+ transporting, alpha 1 polypeptide (Atp1a1), mRNA. Mus musculus C-type lectin domain family 2, member d (Clec2d), mRNA. Mus musculus ATP synthase, H+ transporting, mitochondrial F0 complex, subunit c (subunit 9), isoform 3 (Atp5g3), mRNA. Mus musculus calmodulin 1 (Calm1), mRNA. Mus musculus cytochrome c oxidase, subunit VIIIa (Cox8a), mRNA. Mus musculus stearoylCoenzyme A desaturase 2 (Scd2), mRNA.  Tag counts refer to tag numbers per 100,000 tags (normalized to total tag counts after quality control).  KO/Het KO/WT  131  APPENDIX 4. Significantly down-regulated membrane-related genes in the KO prostate tissues  Sequence  Position Accession  ACAAATAAACCAACTTT  1  NM_016906  AGACATTGGTCATTAGG  1  NM_025360  CAGCTGTCTTTGCTAAC  1  NM_026160  CTGAATGCATTCCAGCC  1  NM_033370  TCATCTTTAACTACAAG  1  NM_007591  TATTCTGCCCCCACATA  1  NM_013478  CTCCTTCCAAACTCTCC  8  TTGCAAAAAGCCATAAT  7  AATGCTTAGCCTTCAGG  6  Definition  Gene name  Mus musculus Sec61 alpha 1 subunit (S. Sec61a1 cerevisiae) (Sec61a1), mRNA. Mus musculus transmembrane emp24 Tmed3 domain containing 3 (Tmed3), mRNA. Mus musculus microtubule-associated protein 1 light chain 3 beta Map1lc3b (Map1lc3b), mRNA. Mus musculus coatomer protein complex, subunit Copb1 beta 1 (Copb1), mRNA. Mus musculus calreticulin (Calr), mRNA.  Mus musculus alpha-2glycoprotein 1, zinc (Azgp1), mRNA. Mus musculus membrane NM_008604 metallo endopeptidase (Mme), mRNA. Mus musculus NM_008548 mannosidase 1, alpha (Man1a), mRNA. Mus musculus membrane NM_008604 metallo endopeptidase (Mme), mRNA.  Cluster number  Tag countsa WT  Het  Fold change KO  KO/Het KO/WT  4  79.80 81.87 43.62  0.53  0.55  7  63.57 66.98 29.08  0.43  0.46  7  21.64 22.33  7.93  0.36  0.37  7  21.64 22.33  7.93  0.36  0.37  Calr  9  114.97 54.58 18.51  0.34  0.16  Azgp1  9  120.38 44.65 10.58  0.24  0.09  Mme  9  14.88  9.92  1.32  0.13  0.09  Man1a  10  155.55 29.77  3.97  0.13  0.03  Mme  7  12.17 24.81  2.64  0.11  0.22  132  Sequence  Position Accession  TGCCCCACGGGGTTCAC  1  TATGACTATGTTGACAG  1  TTTTGTACTTGGTACAC  1  AAGCTAATAAAATTCTT  1  AACTTTTCATTTGGAGT  3  TTGATGTGGAATTACAT  2  CAGTGGTGAACTAAGTA  1  a  Definition  Gene name  Mus musculus Der1-like NM_024440 domain family, member 3 Del3 (Derl3), mRNA. Mus musculus ATPase, H+/K+ transporting, NM_138652 nongastric, alpha Atp12a polypeptide (Atp12a), mRNA. Mus musculus RIKEN cDNA 2900064A13 gene 2900064 NM_133749 (2900064A13Rik), A13Rik mRNA. Mus musculus prostaglandinNM_008969 Ptgs1 endoperoxide synthase 1 (Ptgs1), mRNA. Mus musculus membranespanning 4-domains, NM_183190 Ms4a5 subfamily A, member 5 (Ms4a5), mRNA. Mus musculus prominin 2 NM_178047 (Prom2), transcript variant Prom2 2, mRNA. Mus musculus coatomer protein complex, subunit NM_017477 Copg gamma (Copg), transcript variant 1, mRNA.  Cluster number  Tag countsa WT  Het  Fold change KO  KO/Het KO/WT  5  20.29 59.54  2.64  0.04  0.13  7  24.35 17.37  0  0  0  7  8.12  12.40  0  0  0  7  13.53  9.92  0  0  0  9  14..88 7.44  0  0  0  9  12.17  7.44  0  0  0  7  6.76  7.44  0  0  0  Tag counts refer to tag numbers per 100,000 tags (normalized to total tag counts after quality control).  133  APPENDIX 5. Increased level of TNFα production in Pten KO prostate culture supernatanta  P=0.0089 P=0.008  TNFα (pg/mL)  200 150 100 50 0 WT  Het  KO  a  Levels of TNFα were measured by cytometric bead array (n=2-4 individual mice and duplicates were carried out for each sample). Statistical analysis was done using a one-tailed Student’s t-test.  134  APPENDIX 6. Animal Care Certificates  135  136  

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