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The multifaceted roles of serine/arginine repetitive matrix 4 in the development of neuroendocrine prostate… Lee, Ahn Rhi 2019

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 THE MULTIFACETED ROLES OF SERINE/ARGININE REPETITIVE MATRIX 4  IN THE DEVELOPMENT OF NEUROENDOCRINE PROSTATE CANCER  by AHN RHI LEE B.Sc. (Hon.), McGill University, 2014  A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF  THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY  in  THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Reproductive and Developmental Sciences)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  April 2019 © Ahn Rhi Lee, 2019 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled:  The multifaceted roles of serine/arginine repetitive matrix 4 in the development of neuroendocrine prostate cancer  submitted by Ahn Rhi Lee  in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Reproductive and Developmental Sciences  Examining Committee: Xuesen Dong, Urologic Sciences Supervisor  Ralph Buttyan, Urologic Sciences  Supervisory Committee Member   Supervisory Committee Member Marianne Sadar, Pathology and Laboratory Medicine University Examiner Geoffrey L. Hammond, Cellular and Physiological Sciences University Examiner  Additional Supervisory Committee Members: Colin Collins, Urologic Sciences Supervisory Committee Member Yuzhuo Wang, Urologic Sciences Supervisory Committee Member iii Abstract As the clinical burdens of a lethal and therapy-resistant subtype of prostate cancer called treatment-induced neuroendocrine prostate cancer (t-NEPC) are increasing, delineating the molecular underpinnings of t-NEPC will be paramount in developing clinical strategies for this disease course. Recently, t-NEPC-unique RNA-splicing signatures, predominately facilitated by SRRM4, have been characterized. SRRM4 is an RNA-splicing factor that promotes progenitor cell differentiation via neural-specific exon networks essential for functional reprogramming of proteins required for neurogenesis. SRRM4 can transform prostate adenocarcinoma cells into t-NEPC xenografts under castration via a neuroendocrine transdifferentiation mechanism. Given the essential roles of SRRM4 during neurogenesis, we hypothesize that SRRM4 can ultimately promote neuroendocrine reprogramming in different cell types by neural-specific exon networks that contribute to t-NEPC progression. Given the cellular heterogeneity of prostate tumours, this work investigates the functions of SRRM4 in various prostate adenocarcinoma cell lines. We show that SRRM4 ultimately promotes neural-specific transcriptome and splicing programs across all tested cell lines. We also uncover a novel mechanism whereby SRRM4 facilitates t-NEPC development via a pluripotency gene network in DU145 cells that closely recapitulates the molecular and cellular phenotypes of clinical t-NEPC. Furthermore, we characterize the downstream functional consequences of SRRM4-mediated alternative splicing of t-NEPC-unique MEAF6 and GIT1 transcripts. We report a novel facet of SRRM4 in promoting t-NEPC development—invasion and migration via MEAF6 splicing iv and focal adhesion-mediated signaling and stability via GIT1 splicing. Moreover, we reveal that the t-NEPC-specific MEAF6 isoform promotes cell proliferation and tumorigenesis. These studies demonstrate an important role of SRRM4-mediated RNA alternative splicing of MEAF6 and GIT1 in its contributions to the multifaceted processes of t-NEPC development such as cell proliferation, clonal expansion, and invasion/metastasis. This thesis work adds to an understudied field of alternative splicing and its importance in the t-NEPC disease progression. My findings suggest a role of SRRM4 and SRRM4-mediated splicing signatures (i.e. MEAF6/GIT1) as potential biomarkers of t-NEPC and support the notion that SRRM4 is an important facilitator of t-NEPC development. Ultimately this knowledge pertains to the clinical implications of SRRM4 and SRRM4-mediated splicing in informing future therapies that will be effective in detecting, preventing, or managing t-NEPC.  v Lay Summary  Hormone therapies that treat prostate cancer can promote the development of an aggressive, deadly disease called treatment-induced neuroendocrine prostate cancer (t-NEPC). t-NEPC is becoming a serious clinical threat as it affects nearly one in five men with advanced-stage prostate cancer, with its incidences predicted to rise with the extensive use of anti-hormone therapies. Unfortunately, due to the limited understanding on how t-NEPC develops, there are no effective therapies to treat this growing clinical burden. Our lab has recently discovered a key player in this development called SRRM4 that controls genes that are unique to t-NEPC tumours. This work reports that SRRM4 and the genes SRRM4 regulates work together to promote the emergence, growth, and establishment of a subset of t-NEPC tumours. The broader impact of this work pertains to the clinical implications of SRRM4 in informing future therapies that will be effective in detecting, preventing, or managing t-NEPC.  vi Preface All of the work presented henceforth was conducted in the Vancouver Coastal Health Research Institute at the Vancouver Prostate Centre, Jack Bell Research Centre and Robert NH Ho buildings. All projects and associated methods were approved by the University of British Columbia Biosafety Committee (protocol #B13-0187). All animal procedures were performed in accordance with the guidelines and regulations of the Canadian Council on Animal Care and approved by the Institutional Animal Care and Use Committee at the University of British Columbia (protocol #A18-0065). A version of Chapter 1 and Chapter 6 has been published in Frontiers in Oncology [Lee AR*, Che N*, Lovnicki JM, Dong X. (2018) Development of neuroendocrine prostate cancers by the ser/arg repetitive matrix 4-mediated RNA splicing network. Front. Oncol. 8:93. doi: 10.3389/fonc.2018.00093]. I am the corresponding co-first author and contributed to the conception as well as the design of the article and performed the necessary and required literature research to the drafting of the work. NC was involved in the early stages of concept formation as well as the first draft of the article. XD was the supervisory author on this project and was involved throughout the project in concept formation. All authors revised the work critically for important intellectual content, contributed to manuscript revision, and approved the submitted version. A version of Chapter 2 and 3 has been published in EBioMedicine [Lee AR*, Gan Y*, Tang Y, Dong X. (2018) A novel mechanism of SRRM4 in promoting neuroendocrine prostate cancer development via a pluripotency gene network. vii EBioMedicine. 35: 167-177. doi.org/10.1016/j.ebiom.2018.08.011]. I was responsible for the conception of the study as well as the manuscript composition. YG and I were co-first authors responsible for all major areas of data collection and analysis. XD was the supervisory author on this project and was involved throughout the project in concept formation. All authors revised the work critically for important intellectual content, contributed to manuscript revision, and approved the submitted version.  A version of Chapter 2 and 4 has been published in Oncotarget [Lee AR, Li Y, Xie N, Gleave ME, Cox ME, Collins CC, Dong X. (2017) Alternative RNA splicing of the MEAF6 gene facilitates neuroendocrine prostate cancer progression. Oncotarget. 8(17): 27966–27975. doi.org/10.18632/oncotarget.15854]. I was the lead investigator, responsible for all major areas of concept formation, data collection and analysis, as well as the majority of manuscript composition. NX and YL assisted with molecular and cellular biology techniques. CCC provided RNA-seq data and analysis assistance. XD was the supervisory author on this project and was involved throughout the project in concept formation. All authors revised the work critically for important intellectual content, contributed to manuscript revision, and approved the submitted version. A version of Chapter 2 and 5 has been published in Cancer Science [Lee AR, Gan Y, Xie N, Ramnarine VR, Lovnicki JM, Dong X. (2019) Alternative RNA splicing of the GIT1 gene is associated with neuroendocrine prostate cancer. Cancer Sci. 110:245–255. doi.org/10.1111/cas.13869]. I was the corresponding lead investigator, responsible for all major areas of concept formation, data collection and analysis, as well as the manuscript composition. YG aided in the analysis and scoring of RISH and IHC as well as AmpliSeq analysis and GSEA. NX performed the cloning and viii construction of overexpression plasmids as well as the RISH assays. VRR analyzed RNA-sequencing data and respective statistics for the VPC 2018 and Beltran cohorts. XD was the supervisory author on this project and was involved throughout the project in concept formation. All authors revised the work critically for important intellectual content, contributed to manuscript revision, and approved the submitted version.  ix  Abstract ................................................................................................................. iii Lay Summary ......................................................................................................... v Preface .................................................................................................................. vi Table of Contents .................................................................................................. ix List of Tables ....................................................................................................... xiv List of Figures ....................................................................................................... xv Acknowledgements ............................................................................................. xix Dedication ........................................................................................................... xxi  Introduction ........................................................................................... 1 1.1 The prostate ........................................................................................................... 2 1.1.1 Prostate biology ......................................................................................................... 2 1.1.1.1 The development of the prostate .......................................................................................... 2 1.1.1.2 The gross anatomy of the prostate ....................................................................................... 4 1.1.1.3 The cellular anatomy of the prostate ..................................................................................... 6 1.1.1.4 The physiological roles of AR in the prostate ........................................................................ 8 1.1.2 Prostate pathology ..................................................................................................... 9 1.1.2.1 Prostate adenocarcinoma (AdPC): the oncogenic roles of AR in the prostate ................... 10 1.1.2.2 Detection, diagnosis, and treatments for AdPC .................................................................. 10 1.1.2.3 Castration-resistant prostate cancer (CRPC) ..................................................................... 11 1.1.3 Mechanisms of ARPI resistance in CRPC .................................................................12 1.1.3.1 Treatment-induced neuroendocrine prostate cancer (t-NEPC) .......................................... 13 1.1.3.2 Heterogeneity of NEPC ....................................................................................................... 15 1.1.3.3 Current clinical challenges in t-NEPC ................................................................................. 16 1.2. Mechanisms of lineage plasticity: the emergence of t-NEPC .............................. 16 1.2.1 Genetic and epigenetic modifications confer plasticity of AdPC cells.........................17 1.2.2 Alternative RNA splicing confers plasticity of AdPC cells...........................................20 1.3 Ser/Arg Repetitive Matrix 4 (SRRM4) .................................................................. 23 1.3.1 The SR family and SR-related family of proteins .......................................................23 1.3.2 SRRM4 in normal development .................................................................................26 1.3.2.1 SRRM4-mediated alternative splicing confers neuronal cell differentiation and function ... 27 Table of Contents  x 1.3.3 SRRM4 in pathogenesis............................................................................................28 1.4 The multifaceted roles of SRRM4 in t-NEPC development .................................. 30 1.4.1 The multifaceted roles of SRRM4 in t-NEPC development is mediated by downstream neural-specific exon networks ............................................................................................30 1.4.1.1 SRRM4-mediated alternative RNA splicing of the REST gene facilitates NE differentiation of AdPC cells ................................................................................................................................... 33 1.4.1.2 SRRM4-mediated alternative RNA splicing of the Bif-1 gene facilitates evasion of apoptosis ......................................................................................................................................... 34 1.4.1.3 SRRM4-mediated alternative RNA splicing of putative epigenetic modifiers ..................... 35 1.4.1.4 SRRM4-mediated alternative RNA splicing of GTPases .................................................... 36 1.5 Objectives, hypotheses, and specific aims........................................................... 37 1.5.1 Overall thesis rationale and objective ........................................................................37 1.5.2 Overall hypothesis and aims .....................................................................................37 1.5.3 Specific hypotheses and aims ...................................................................................38 1.5.3.1 Aim 1 hypothesis and aims ................................................................................................. 38 1.5.3.2 Aim 2 hypothesis and aims ................................................................................................. 39 1.5.3.3 Aim 3 hypothesis and aims ................................................................................................. 40  Materials and Methods ....................................................................... 41 2.1 Cell lines and culture ............................................................................................ 41 2.2 DNA and siRNA transfections .............................................................................. 42 2.3 Construction of cell models by lentiviral approaches ........................................... 44 2.4 RT-qPCR and immunoblotting (western blot) assays .......................................... 46 2.5 RNA-ChIP assays ................................................................................................ 48 2.6 Cell morphology, cell proliferation, cell invasion and migration, colony formation, and apoptosis assays ................................................................................................ 49 2.7 FA assays ............................................................................................................ 51 2.8 CRPC TMA .......................................................................................................... 51 2.9 Human prostate cancer xenografts ...................................................................... 52 2.10 RISH and IHC assays ........................................................................................ 53 2.11 Digital image analysis and Ki-67 scoring ............................................................ 54 2.12 Ion AmpliSeq transcriptome sequencing and GSEA .......................................... 54 2.13 Gene microarray ................................................................................................ 55 2.14 Clinical, PDX, and GEMM datasets ................................................................... 56 xi 2.15 RNA-Seq analysis pipeline ................................................................................. 57 2.16 Statistics ............................................................................................................. 57  SRRM4 promotes neuroendocrine prostate cancer development via a pluripotency gene network .................................................................................. 59 3.1 INTRODUCTION ................................................................................................. 59 3.2 RESULTS ............................................................................................................ 62 3.2.1 SRRM4 induces heterogeneous t-NEPC transcriptomes in multiple cell models .......62 3.2.2 Multifarious histological features of SRRM4-transduced prostate cancer xenografts .63 3.2.3 Differential cellular impacts of prostate cancer cell models by SRRM4 ......................66 3.2.4 DuNE cells possess a pluripotency gene network distinct from the LnNE model .......67 3.2.5 SRRM4 enhances SOX2 expression in the DuNE cell model and in some GEMMs and PDX models ................................................................................................................69 3.2.6 The DuNE model represents a subset of clinical t-NEPC tumours with high SOX2 expression .........................................................................................................................72 3.3 DISCUSSION ....................................................................................................... 75  The role of alternative RNA splicing of the MEAF6 gene during neuroendocrine prostate cancer progression ...................................................... 79 4.1 INTRODUCTION ................................................................................................. 79 4.2 RESULTS ............................................................................................................ 81 4.2.1 Alternative RNA splicing of the MEAF6 transcript is associated with NEPC progression ........................................................................................................................81 4.2.2 SRRM4 regulates RNA splicing of the MEAF6 gene .................................................83 4.2.3 MEAF6-1 promotes prostate cancer cell growth and invasion ...................................86 4.2.4 MEAF6-1 transcriptome in prostate cancers ..............................................................87 4.2.5 MEAF6-1 accelerates xenograft tumour growth ........................................................89 4.3 DISCUSSION ....................................................................................................... 91  The role of alternative RNA splicing of the GIT1 gene during neuroendocrine prostate cancer progression ...................................................... 95 5.1 INTRODUCTION ................................................................................................. 95 5.2 RESULTS ............................................................................................................ 97 5.2.1 Expression profiling of GIT1 splice variants in NEPC patients, PDXs, and cell models ..........................................................................................................................................97 5.2.2 RNA splicing of GIT1 is associated with clinical NEPC tumours .............................. 100 5.2.3 SRRM4 regulates RNA splicing of GIT1 .................................................................. 103 xii 5.2.4 Transcriptome and cellular functions of GIT1 splice variants ................................... 105 5.3 DISCUSSION ..................................................................................................... 108  Conclusions ...................................................................................... 111 6.1 SUMMARY OF FINDINGS ................................................................................. 111 6.2 LIMITATIONS .................................................................................................... 113 6.2.1 Cell-context dependency ......................................................................................... 113 6.2.2 Previous knowledge of MEAF6 and GIT1 splice variants ........................................ 114 6.2.3 Challenges of NEPC in the lab ................................................................................ 116 6.2.3.1 NEPC models .................................................................................................................... 116 6.2.3.2 NEPC as a chronic disease .............................................................................................. 117 6.3 FUTURE DIRECTIONS ..................................................................................... 119 6.3.1 SRRM4 and the epigenome .................................................................................... 120 6.3.2 SRRM4 and the microenvironment ......................................................................... 121 6.3.3 Targeting SRRM4 ................................................................................................... 122 Bibliography ....................................................................................................... 126 Appendix ........................................................................................................... 140 Appendix A: Primers for RT-qPCR ........................................................................... 140 Appendix B: Antibodies ............................................................................................ 142 Appendix C: Detailed GIT1-A, GIT1-C, and SRRM4 RISH scores in CRPC TMA ... 143 Appendix D: Validation of SRRM4 expressions in the SRRM4-transduced stable cell and xenograft models .............................................................................................. 145 Appendix E: SRRM4 induces stem cell differentiation and neural fate factors in DU145 cells .......................................................................................................................... 146 Appendix F: SOX2 and AR are negatively correlated in CRPC cohorts ................... 147 Appendix G: SRRM4 and SOX2 are positively correlated in CRPC cohorts ............ 148 Appendix H: MEAF6 RNA splicing is positively correlated with SRRM4 expression and REST RNA splicing .................................................................................................. 149 Appendix I: SRRM4 regulates RNA splicing of MEAF6 ........................................... 150 Appendix J: Androgen receptor signaling does not regulate MEAF6 variant expression ................................................................................................................................. 151 Appendix K: Validation of MEAF6-1 overexpressing stable lines ............................. 152 Appendix L: MEAF6 does not facilitate neuroendocrine trans-differentiation ........... 153 xiii Appendix M: GSEA of the GIT1-C transcriptome ..................................................... 154 Appendix N: RB1 knockdown in the SRRM4-transduced PC-3 cell model does not induce a NEPC-specific phenotype .......................................................................... 155 Appendix O: Differential functions of GIT1 splice variants in FA stability under endogenous GIT1 depletion ..................................................................................... 156 Appendix P: Differential localization of the MEAF6 splice variants .......................... 158    xiv List of Tables Table 1.1 16 t-NEPC-unique genes spliced by SRRM4 ........................................... 32 Table 2.1 Primers for cloning and site-directed mutagenesis ................................... 43 Table 2.2 siRNA and plasmid information ............................................................... 44 Table 2.3 Primers used for lentiviral vector cloning ................................................. 45 Table 2.4 Nomenclature of cell models used .......................................................... 46 Table 5.1 Correlation of GIT1 splice variants with clinical diagnostic markers of AdPC and NEPC ........................................................................................................... 101 Table 5.2 Sensitivity and specificity of GIT1-A as a biomarker for NEPC ................ 103 Table 5.3 SRRM4 expression is associated with GIT1 splice variant expressions in CRPC ................................................................................................................. 104   xv List of Figures Figure 1.1 The development of the prostate: budding and branching ......................... 4 Figure 1.2 The gross anatomy of the adult prostate .................................................. 5 Figure 1.3 The cellular anatomy of the adult prostate ................................................ 8 Figure 1.4 The mechanisms of ARPI resistance in CRPC ....................................... 14 Figure 1.5 The multifaceted, interconnected mechanisms of t-NEPC development ... 23 Figure 1.6 SRRM4 promotes neural-specific exon networks that are essential for neuronal cell differentiation during neurogenesis ...................................................... 27 Figure 1.7 The multifaceted roles of SRRM4 in t-NEPC development is mediated by downstream neural-specific exon networks .............................................................. 33 Figure 3.1 SRRM4 induces heterogeneous t-NEPC transcriptomes in multiple cell models .................................................................................................................. 63 Figure 3.2 Multifarious histological features of SRRM4-transduced prostate cancer xenografts ............................................................................................................. 65 Figure 3.3 Differential cellular impacts of prostate cancer cell models by SRRM4...... 67 Figure 3.4 DuNE cells possess a pluripotency gene network distinct from the LnNE model ................................................................................................................... 69 Figure 3.5 SRRM4 enhances SOX2 expression in the DuNE cell model and in some GEMMs and PDX models....................................................................................... 71 Figure 3.6 The DuNE model represents a subset of clinical t-NEPC tumours with high SOX2 expression ................................................................................................... 74 Figure 3.7 Proposed models for SRRM4 in driving t-NEPC development through different mechanisms ............................................................................................. 75 Figure 4.1 RNA splicing of the MEAF6 gene is associated with NEPC progression ... 83 Figure 4.2 SRRM4 regulates RNA splicing of the MEAF6 gene ............................... 85 Figure 4.3 MEAF6-1 promotes prostate cancer cell growth and invasion .................. 87 Figure 4.4 MEAF6-1 function is mediated through ID1 and ID3 ................................ 89 Figure 4.5 MEAF6-1 accelerates xenograft tumour growth ...................................... 90 Figure 4.6 Differentiation and proliferation: two distinguishable and coordinated processess for AdPC and NEPC tumour establishment ............................................ 92 Figure 5.1 Expression profiling of the GIT1 splice variants in NEPC patient, PDX, and cell models ............................................................................................................ 99 Figure 5.2 RNA splicing of GIT1 is associated with clinical NEPC tumours ............. 102 Figure 5.3 SRRM4 regulates RNA splicing of GIT1 ............................................... 105 Figure 5.4 Transcriptome and cellular functions of the GIT1 splice variants ............ 107 Figure 5.5 Differential functions of the GIT1 splice variants in FA stability ............... 108 Figure 6.1 The full picture: multifaceted roles of SRRM4 in t-NEPC development is mediated by downstream neural-specific exon networks ........................................ 113 Figure 6.2 Future directions ................................................................................ 119 xvi List of Abbreviations AdNC  Adenocarcinoma Prostate Cancer with Neuroendocrine Cells AdPC  Prostate Adenocarcinoma ADT Androgen Deprivation Therapies AR Androgen Receptor ARPI  Androgen Receptor Pathway Inhibition ASD Autism Spectrum Disorder ASO Antisense Oligonucleotides Bif-1 or SH3GLB1 Bax-Interacting Factor 1 BPH Benign Prostatic Hyperplasia BrdU Bromodeoxyuridine  c-PARP Cleaved Poly(ADP-Ribose) Polymerase CI  Confidence Interval CPT Camptothecin CRPC  Castration-Resistant Prostate Cancer CRPC-Ad  Castration-Resistant Adenocarcinoma CSS Charcoal-Stripped Serum Ctrl or CTL Control Cx Castration d Day DAVID Database for Annotation, Visualization, And Integrated Discovery DEGs Differential Expression Genes DHT 5α-Dihydrotestosterone DKO Double Knock-Out DMEM Dulbecco's Modified Eagle Medium Doce Docetaxel DuNE SRRM4-Transduced DU145 Cell-Derived Neuroendocrine Prostate Cancer Model E-Cad E-Cadherin ECM Extracellular Matrix EMT Epithelial-to-Mesenchymal Transition EZH2 Enhancer of Zeste 2 Polycomb Repressive Complex 2 FA  Focal Adhesion FDR False Discovery Rate FBS Fetal Bovine Serum GEMM Genetically Engineered Mouse Model GEO Gene Expression Omnibus GO  Gene Ontology GSEA  Gene Set Enrichment Analysis HAT Histone Acetyltransferase  xvii HDAC Histone Deacetyltransferase HGPIN High Grade Prostatic Intraepithelial Neoplasia HPG Hypothalamus-Pituitary-Gonad IF Immunofluorescence IHC  Immunohistochemistry  IPA Ingenuity Pathway Analysis LE Luminal Epithelial LHRH Luteinizing Hormone Releasing Hormone LnNE SRRM4-Transduced LNCaP-Derived Neuroendocrine Prostate Cancer Model LTL  Living Tumour Laboratory MEAF6 MYST/Esa1-Associated Factor 6 NE Neuroendocrine NEPC Neuroendocrine Prostate Cancer NES Normalized Enrichment Score OD Optical Density PHF21A or BHC80 PHD Finger Protein 21A PDX Patient-Derived Xenograft PSA Prostate-Specific Antigen PTEN Phosphatase and Tensin Homolog RB1 Retinoblastoma Transcriptional Corepressor 1 REST RE-1 Silencing Transcription Factor RISH RNA In Situ Hybridization RNA-ChIP RNA-Chromatin Immunoprecipitation RRM RNA Recognition Motifs RS Arginine/Serine-Rich RT-qPCR Real-time Quantitative Polymerase Chain Reaction SC Stem Cell SCLC Small-Cell Lung Cancer SCNC Small-Cell Neuroendocrine Carcinoma or Small-Cell Prostatic Carcinoma SDS Sodium Dodecyl Sulfate SKO Single Knock-Out SMI Small Molecule Inhibitors SOX2 Sex Determining Region Y-Box 2 SR Serine/Arginine SRRM4 or nSR100 Serine/Arginine Repetitive Matrix 4 SSO Spice-Switching Oligonucleotides SYP Synaptophysin t-NEPC Treatment-Induced Neuroendocrine Prostate Cancer TKO Triple Knock-Out TMA Tissue Microarray xviii TP53 Tumour Protein P53 UGS Urogenital Sinus UTR Untranslated Region UV Ultra Violet VPC Vancouver Prostate Centre WB Western Blot wk Week WT Wild-Type   xix Acknowledgements I’d like to acknowledge my supervisor, Dr. Xuesen Dong, for providing one of the greatest learning opportunities and for all of your direction throughout the milestones of my academic development. And, for your amazing team of intelligent individuals, Ms. Ning Xie, Dr. Yinan Li, Dr. Yu Gan, Dr. Haolong Li, Dr. LiangLiang Liu, Dr. Richard Chen, Ms. Jessica Lovnicki, and Ms. Yue Yu. All your expertise, advice, attention, and smiles have been the driving force and foundation of my academic achievements.  I am eternally grateful to all the members of my supervisory committee, Dr. Colin Collins, Dr. Yuzhuo Wang, Dr. Ralph Buttyan, and Dr. Caigun Du. Thank you for generously offering your time, support, and guidance throughout the preparation and reviews of my graduate work throughout the years.  I wish to express my gratitude to my collaborators and team at the Vancouver Prostate Centre, Dr. Martin Gleave, Dr. Michael Cox, Ms. Mary Bowden, Dr. Ladan Fazli, Ms. Estelle Li, and Mr. Alireza Moeen, for their generous help, knowledge, and expertise.  I would like to also express my utmost gratitude for the support and interest of my funding agencies and sources, Prostate Cancer Foundation British Columbia and the Reproductive and Developmental Sciences program at the University of British Columbia. Your support has ensured not only the success of the proposed projects, but also the development of my professional and academic growth.  To all my graduate peers and colleagues that have given me the most productive (and unproductive) behind-the-scene collaborations and scientific brainstorming: Kevin Tam, Kush Dalal, Daksh Thaper, Mannan Nouri, Rohan Ramnarine, Mani Moniri, Justin xx Lardizabal, and Parvin Yenki. Thank you for all the academic support, advice, and for resonating with my feelings about all the absurdity that comes with graduate studies.  To my executive editor, Sahil Kumar: bless you for always editing my manuscripts (and life). To my RDS confidante, Kayleigh Campbell: thank you for the “accountability” meetings (over breakfast). And, to my partner and love, Ron Jovillar: merci for being the artist to the original dissertation drawings and for being my driver to the lab every morning and late night. Thanks for putting up with all my academic (and non-academic) frenzies! To the special people that I have always aspired to become: my grandfather, Habu, my parents (John and Angela), and the best siblings anyone can wish for, Tommy and Jay. Thank you for all your love and for being the core support and fuel to my very existence. Love you always.  xxi Dedication 계절은 바야흐로 초록의 계절(Evergreen Season)을 맞지하고 있습니다. 만좌에 참석하신 총장님을 비롯하여 각 학과 학장님 신사 숙녀 귀빈 여러분 ! 저, 이안이 입니다. 부리티쉬 쿨롬비아대학 비뇨기과 전립선 연구 학업을 졸업 하였습니다. 오늘의 제가 캐나다에서 초등, 중등, 고등, 그리고 대학을 맏치고 부리티쉬 쿨롬비아대학 에서 비뇨기과 전립선 연구을 완료 하였습니다. 그간 저명하신 교수님들의 명강의와 그리고 상급생 여러 학우들과 서로 서로 연구 토론이 일치될 때도 있으나 의견이 다를때 애로가 발생 했다가도 지혜롭게 해결하고 비약 할수있는 Vision으로 바뀌워 보람된 미래를 설계해 갔습니다. 좋고 유익한 추억을 간직 했습니다. 또한 지도 교수님들에 전폭적인 분석과 과학적인 설명등에 힘입어 저는 최선을 다 하였음으로 앞으로 아름다운 캐나다 국가을 더욱 발전 시킬수 있는 의학 연구를 제 평생에 사명으로 마음속에 새겨두었읍니다. 오늘까지 변함 없이 저를 이끌어 준 그리고 모든 교육과 국민 정신을 빛나게 연마 하여서 제가 받은 훌륭한 지, 성, 미를 아름다운 캐나다 국가 건설에 이바지 하므로써 은혜에 보답하며 고통받는 비뇨기 환자들에게 아픔없는 삶을 살어가도록 최선을 다해 갈것이며 오늘에 영광된 이 날이 있기 까지 물심 양면으로 양육해 주신 부모님과 국민 여러분게 감사 드립니다. 비뇨기 의학 연구 과학과  졸업자 이안이 드림.    1  Introduction According to 2018 GLOBOCAN estimates, the incidence of cancer is increasing in frequency across the world. It is estimated that 18.1 million new cancer cases and 9.2 million cancer deaths will have occurred globally in 2018 (1). Within these cancer statistics, prostate cancer is the most prevalent cancer diagnosed in men not only in North America, but also on a global scale. This emphasizes the heavy economical and clinical burden of prostate cancer. Unfortunately, prostate cancer is not just a singular disease; it is many diseases that are interconnected through molecular, phenotypic, and functional heterogeneity not only between patients but also within the individual. This heterogeneity is one of the greatest challenges in developing therapeutic programs for prostate cancer. The diversity in the tumour cell population gives the ultimate survival advantage of tumour cells when challenged with therapeutics as some cells can gain the ability to escape therapeutics via dormancy, metastasis, and/or resistance. Though great headway has been made in delineating the mechanisms and facilitators of prostate tumour heterogeneity, a deeper understanding of the networks involved as well as its interconnections does not exist. Therefore, to develop more effective, suitable, and personalized treatment regimens for prostate cancer patients, a more comprehensive understanding for the mechanisms involved in facilitating this heterogeneity is fundamental. 2 1.1 The prostate 1.1.1 Prostate biology 1.1.1.1 The development of the prostate The rudiments of prostate cancer biology are derived from our knowledge of normal developmental biology. Ultimately, cancer cells highjack tightly controlled and sophisticated biological processes and mechanisms driving embryonic and fetal development such as invasion, proliferation, regulation of cell death, and lineage differentiation. Thus, this subsection of the first chapter will highlight the development, cellular structures and functions, and gross anatomy of the normal prostate to better understand the basic biology of prostate cancers.  The development of secondary male structures, such as the prostate, follows embryonic sex and gonadal differentiation as its development and differentiation is dependent on the androgens produced by a functioning testis. The prostate is derived from the primitive endoderm layer. Following the differentiation of the primitive endoderm (or gut tube) into the foregut, midgut, and hindgut, the caudal end of the gut tube swells to form the cloaca or urogenital sinus (UGS), which will differentiate into the future bladder and urethra (2). By the 10th gestational week, the development of the prostate is initiated by the protrusion and budding of the UGS epithelium cells (Figure 1.1) (3). The induction of this budding process is dependent on the androgen receptor (AR) signaling in the UGS mesenchyme that surrounds the UGS epithelium (2–4). Androgens produced from the fetal testis and converted to 5α-dihydrotestosterone (DHT), a more potent derivative of testosterone, activates the AR in the UGS mesenchymal cells (5–7). Activated AR signaling is believed to promote the secretion of 3 inductive factors called andromedins (2,5,8,9). Andromedins are then proposed to promote the secretion of growth factors, such as FGF, in a precise temporal and spatial pattern. Growth factors initiate UGS epithelial bud invasion, outgrowth, and branching to definite locations that will develop and differentiate into specific zones of the prostate in the future. During branching and canalization of the UGS epithelial structures (i.e. future prostatic ducts), mesenchymal condensation of the surrounding UGS mesenchymal cells occurs, forming the stromal components of the human prostate zones (Figure 1.1) (2). By week 18 of gestation, cytodifferentiation of the UGS epithelial cells occur where definitive luminal secretory and basal cells of the future prostatic ducts appear and express differentiation markers such as cytokeratins 8/18 and 5/14, respectively (2,10). Though the role of the AR is important in the differentiation of prostate cells, the mechanisms of cytodifferentiation of the developing prostate cells remain elusive. The prostate remains immature and small following fetal development until puberty, when the maturation, growth, and reproductive functions of the prostate is established by AR-signaling. 4 Figure 1.1 Figure 1.1 The development of the prostate: budding and branching. An outline of a developing prostate. Androgens activate the AR in the UGS mesenchymal cells and is believed to promote the secretion of inductive factors called andromedins. Andromedins are proposed to then initiate UGS epithelial bud invasion, outgrowth, branching, and canalizing via growth factors. Red arrows that are blunted and pointed represent inhibitory and stimulatory signals, respectively, at branching points. AR, androgen receptor; UGS, urogenital sinus. Original drawing.  1.1.1.2 The gross anatomy of the prostate The human adult prostate is a walnut-sized secondary sex organ located underneath the bladder and ventral to the rectum. The gross anatomy of the human prostate is divided into four zones: the transition, central, peripheral, and fibromuscular zone (Figure 1.2). These zones differ in histology, ductal branching patterns, structure, and molecular features. Though the transition zone contains only <5% of the prostate 5 glandular elements, the central and peripheral zones contain 25% and 75% of the prostatic ducts, respectively (2,11). One of the main functions of this exocrine gland is to produce the prostatic fluid component of a male’s ejaculate that protects and mobilizes the sperm (2). The majority of the male ejaculate is produced by the seminal vesicles and is secreted via the ejaculatory duct. This duct empties into the craniodorsal side of the prostate and into the prostatic urethra/duct. Additionally, the muscular features of the prostate are important for providing the necessary robust propulsion of the ejaculate through the prostatic urethra and into the vaginal tract.   Figure 1.2 The gross anatomy of the adult prostate. Cross section of the prostate to illustrate its position and the locations of its four zones. Original drawing adapted from Issa M and Marshall F “Contemporary diagnosis and management of disease of the prostate” (2005). 3rd ed. Newtown, Pa: handbooks in healthcare co. Figure 1.2 6 1.1.1.3 The cellular anatomy of the prostate At the cellular level of a developed adult prostate, the epithelium compartment of the prostate is composed of luminal, intermediate, basal, neuroendocrine (NE), and stem cells (Figure 1.3). Luminal or secretory epithelial cells constitute the majority of the prostate epithelium layer and encloses the lumen of the prostatic ducts. Luminal epithelial (LE) cells perform the laborious task of producing and secreting the enzymes and proteins that make up the prostatic fluid (2). One of the many proteins secreted by the luminal secretory cells include PSA, which is an enzyme involved in liquefying the ejaculate for sperm mobility. These simple columnar cells maintain the integrity of the epithelium layer and are terminally differentiated with low proliferative activity. Basal cells are located on the basement membrane between the LE cells and make up ~10% of the cell population in the epithelium layer (2). Basal cells have a very low proliferative index of <1% (2,12). Intermediate cells, as the name would suggest, possess characteristics intermediate between the LE and basal cells of the prostate epithelium layer, such as producing both the basal- and secretory-specific cytokeratins (2). Similar to basal cells, prostate NE cells are quiescent, make up a very small population of the epithelium layer, and are wedged between columnar secretory cells on the basement membrane (2). NE cells are terminally differentiated and also reside in the urothelium of the prostatic urethra. Though the origin of NE cells is unknown, NE cells are observed in the UGS epithelium prior to the induction of prostate development and are AR-negative (13). NE cells of the prostate receive neural stimuli via heterotypic interactions with the nerve cells of the prostate stromal layer and, in turn, release hormones and bioactive peptides such as chromogranin and serotonin into the prostatic lumens, circulatory 7 system, or to the nearby LE or basal cells. These factors released by the NE cells are likely to promote the secretion, growth, and differentiation of the epithelial cells via paracrine signaling (2). Supporting the epithelium layer, the extracellular matrix (ECM) is comprised of collagen, elastin, keratin, laminin, and glycosaminoglycan. The stromal compartment of the prostate is composed of nerve, immune, smooth muscle, fibroblast, and endothelial (capillaries) cells (Figure 1.3). Analogous to the indispensable heterotypic regulation between the UGS epithelial and mesenchymal cells required for prostate development, heterotypic interactions between the epithelium and stromal compartments of the adult prostate are important for the maintenance, differentiation, growth, and function of prostate cells.   8 Figure 1.3 The cellular anatomy of the adult prostate. Illustration of the different cell types of the prostate. The arrows indicate some of the heterotypic or homotypic paracrine and autocrine communications that occur between or within the prostate cells. ECM, extracellular matrix ; LE, luminal epithelial; NE, neuroendocrine; DHT, 5α-dihydrotestosterone; PSA, prostate-specific antigen. Original drawing adapted from Berman DF et al. “Development, Molecular Biology, and Physiology of the Prostate” (2012). 11th ed. Campbell-Walsh Urology (10.1016/B978-1-4160-6911-9.00090-6).  1.1.1.4 The physiological roles of AR in the prostate Similar to the aforementioned paracrine communication between the prostate NE and LE or basal cells, long-distance endocrine factors and steroid hormones such as testosterone also regulate prostate epithelium growth, proliferation, and secretion. Unlike the role of androgens during embryonic prostate induction, in the adult prostate, AR-signaling occurs directly in the LE cells (5). Testosterone production in the adult testis is stimulated by the luteinizing hormone releasing hormone (LHRH) via the Figure 1.3 9 hypothalamus-pituitary-gonad (HPG) axis and released through the endocrine system (2). Testosterone is then taken up by prostate stromal cells and predominantly converted to DHT (Figure 1.3). Through heterotypic paracrine communications, DHT will bind to its receptor (i.e. AR) in the cytoplasm of LE cells. Dimerization of the hormone-bound AR will activate and translocate the steroid receptor to the nucleus where it will interact with chromatin remodeling complexes for preparation of transcriptional activation or repression machineries. Following the unwinding of the DNA, activated AR dimers will recruit co-factors and bind to androgen response elements within the enhancer regions of its target genes. This will then activate transcription of AR-target genes (e.g. KLK3 and Nkx3.1) encoding growth factors and secretory proteins (e.g. PSA). The AR has imperative roles in the development and maintenance of the prostate throughout a male’s lifespan. Its activity in the prostate varies during the different stages of development including fetal, puberty, and adulthood. During adulthood, AR-signaling is tightly regulated in the stromal cells to indirectly maintain functional and cellular homeostasis of the prostate epithelial cells (2,5). Consequently, disruption of this homeostasis or dysfunction of the AR can result in benign or malignant diseases.  1.1.2 Prostate pathology  In fact, the prostate gland is vulnerable to pathologies or complications that arise from aging. One of the most common prostate pathologies with a global lifetime prevalence estimated at ~26% is benign prostatic hyperplasia (BPH) (14). BPH is the benign enlargement of the prostate transition zone that surrounds the urethra, resulting 10 in lower urinary tract complications. Another frequent prostate pathology is high grade prostatic intraepithelial neoplasia (HGPIN). HGPIN is considered to be the precursor of the most common variant of prostate cancer called prostate adenocarcinoma (AdPC) as HGPIN arises from the LE cells of the prostate peripheral zone similar to AdPC (1,2). However, HGPIN preserves normal, intact prostatic ducts and does not present with infiltrative growth into the stromal compartment unlike AdPC (15). 1.1.2.1 Prostate adenocarcinoma (AdPC): the oncogenic roles of AR in the prostate According to 2018 GLOBOCAN estimates, AdPC is the most prevalent cancer diagnosed in men and the second leading cause of cancer-related deaths not only in North America, but also on a global scale (in incidences) (1). The most significant risks of AdPC are age, ethnicity, and hereditary genetics (16,17). As previously mentioned above, AdPC is the most common form of prostate cancer. The central driving mechanism of AdPC development is dysregulation and hyperactivity of the AR. In the context of cancer, this leads to the oncogenic role of the AR in the uncontrolled proliferation, survival, and invasion of prostatic LE cells. Overproduction of LE lineage-specific differentiation markers, such as cytokeratins 8 and 18, and secretion of excessive PSA ensues with hyperactivity of the AR-signaling pathway. These characteristics or hallmarks of AdPC are exploited for detection and guiding treatment regimens following diagnosis of the disease.  1.1.2.2 Detection, diagnosis, and treatments for AdPC In the last three decades, PSA has been the choice of biomarker for AdPC detection and surveillance of prostate cancer progression. In addition, physical 11 castration, chemical androgen deprivation therapies (ADT), or androgen receptor pathway inhibitors (ARPI) have been the first line of therapy for almost a century (18). These methods minimize or block AR activity in order to limit the growth and viability of AdPC tumours (19,20). ADT, such as LHRH antagonists and agonists, ultimately target the regulation of circulating androgens via the HPG axis or androgen biosynthesis (i.e. steroidogenesis) pathway. ARPI (such as AR-antagonist abiraterone acetate, which also inhibits the biosynthesis of androgens) directly target the AR-signaling pathway. More potent next-generation ARPI enzalutamide and apalutamide binds to the ligand-binding domain of the AR. Following initial diagnosis of localized prostate cancer, treatment course can include surgical resection and radiation therapy. Treatments for locally advanced prostate cancer (i.e. the spread of the disease to local structures such as the seminal vesicles) are more aggressive with the combination of ADT and radiation therapy. AdPC with metastasis to distant sites, such as to the bones (the most common site of prostate cancer spread), are treated with a combination of ARPI and chemotherapies such as docetaxel. Docetaxel is a taxane, which is a class of chemotherapy compounds that inhibit the depolymerisation of microtubules and thus the mitosis of cells.  1.1.2.3 Castration-resistant prostate cancer (CRPC) Though the aforementioned treatments are ultimately effective, 20-30% of cases result in recurrent prostate cancer or castration-resistant prostate cancer (CRPC) within 5 years from diagnosis (21–23). CRPC is treated with potent, next-generation ARPI therapies, such as enzalutamide and abiraterone acetate. Though these treatments have been shown to improve the median overall patient survival to 18.4 or 14.8 months, 12 respectively, the benefits are short-lived (24,25). Resistance to these therapies and, in turn, progression to a more lethal stage with visceral metastases inevitably occurs (24–26). At this stage, therapies are currently palliative and limited to second-line taxane chemotherapies such as cabazitaxel (20). These different degrees of aggressiveness and predisposition to recurrence demonstrate the heterogeneity of early and late stage prostate cancer. In fact, heterogeneity also extends to the CRPC tumour subtypes and to the cellular/molecular mechanisms by which prostate cancer cells utilize under the selection pressures of ARPI. 1.1.3 Mechanisms of ARPI resistance in CRPC Overall, there are three main classifications of resistance mechanisms to ARPI and chemo- or radiation therapies that have been demonstrated to date: androgen-dependent AR signaling, receptor-dependent AR signaling, and complete bypass of AR signaling (Figure 1.4). In androgen-dependent AR signaling, tumour cells can restore the AR signaling pathway by either increasing the synthesis of circulating androgens (27–29) or acquiring AR gene overexpression, amplification, and mutations that allow AR activation under attenuated levels of androgens following castration or ARPI (30–34). In contrast, tumour cells can re-gain active AR signaling that is independent of androgen ligand-mediated activation of the AR by means of the following: (i) generating constitutively active splice variants of the AR (35–37), (ii) altering the mode of actions of the AR in a receptor-dependent manner (38), or (iii) relying on the downstream signaling of other hormone receptor pathways, such as the glucocorticoid receptor (39,40). CRPC tumours that restore its AR signaling retain its LE lineage and AdPC 13 phenotype and are referred to as castration-resistant prostate adenocarcinoma (CRPC-Ad). However, a subset of tumour cells will develop mechanisms that bypass its dependency on AR signaling altogether and progress into AR “indifferent” tumours. One subtype of AR-indifferent CRPC that was recently reported by Bluemn et al. is double-negative for AR and NE markers (41). These tumours have hyperactive FGF and MAPK pathways, which have been reported to promote ARPI resistance and facilitate this tumour progression. However, the phenotypes of these double-negative prostate cancer subtypes have yet to be characterized. 1.1.3.1 Treatment-induced neuroendocrine prostate cancer (t-NEPC) Another subtype of AR-indifferent CRPC, induced by the selection pressures of ARPI, is called treatment-induced neuroendocrine prostate cancer (t-NEPC) (42–46), a subtype of neuroendocrine prostate cancer (NEPC). Generally, NEPC tumours are defined by the expression of NE markers, such as synaptophysin (SYP), chromogranin A (CHGA), and neuron-specific enolase (NSE), and the loss or low expression of LE makers, such as E-cadherin (E-Cad), PSA, and AR (45).  14 Figure 1.4 Mechanisms of ARPI resistance in CRPC. ARPI and chemo- or radiation therapies promote the emergence of various CRPC subtypes. Whether tumour cells progress to the double-negative, CRPC-Ad, or t-NEPC phenotypes depends on the molecular profiles of the cells within the heterogeneous cell population of the recurrent prostate tumour. This figure illustrates three main classifications of resistance mechanisms that have been demonstrated to date: androgen-dependent AR signaling (via increase in steroidogenesis and/or AR mutations or amplifications), receptor-dependent AR signaling (via androgen-independent AR activation), and complete bypass of AR signaling (via FGF/MAPK pathways or NE lineage switching). AR, androgen receptor; NE, neuroendocrine; T-NEPC, treatment-induced neuroendocrine prostate cancer; CRPC, castration-resistant prostate cancer; CRPC-Ad, castration-resistant prostate adenocarcinoma; AdPC, prostate adenocarcinoma; ARPI, androgen receptor pathway inhibition; NE, neuroendocrine. Figure 1.4 15 1.1.3.2 Heterogeneity of NEPC The different phenotypes and mechanisms of resistance seen within CRPC subtypes described above represent the molecular heterogeneity of prostate cancer. However, to complicate matters further, CRPC tumours do not exclusively assume one of the three different mechanisms of ARPI resistance. They can exhibit a varied range of AR or NE marker expressions, resulting in a phenotypic, functional, and molecular heterogeneity seen within the tumour (45–48). For example, it has been previously reported by several groups that NE foci are observed in anywhere from 10% to 100% of the examined CRPC-Ad patient tumours (46,49–53). However, these tumours with NE foci/differentiation have not been confirmed to progress to t-NEPC and its associated prognosis is unclear. Moreover, histopathological heterogeneity in the expression levels of AR as well as the expression of different NE and LE markers vary across different t-NEPC patient tumours (54–56). The degree of histopathological, molecular, and phenotypic heterogeneity is seen not only between CRPC subtypes, but also within individual CRPC subtypes. In fact, heterogeneity extends to the different subtypes of NEPC, which are classified by its histopathological and clinical characteristics (57,58). These NEPC subtypes include AdPC with NE differentiation (AdNC), carcinoid tumours, and small- or large-cell NEPC. Small-cell neuroendocrine carcinoma (SCNC; also known as small-cell prostatic carcinoma) is the most common subtype detected within t-NEPC patients, suggesting its emergence induced by therapies (55,56,59). SCNC tumours grow as solid sheets and exhibit typical NE cell features such as a scant cytoplasm, salt-and-pepper nuclei, 16 and a high nucleus-to-cytoplasm ratio (57,60,61). As well, a high mitotic index and areas of necrosis are often detected in SCNC.  1.1.3.3 Current clinical challenges in t-NEPC Though de novo NEPC is one of the least common variants of prostate cancer, making up 0.5-2% of all prostate cancer cases (62), t-NEPC now poses a major clinical issue with 16-25% of advanced-stage prostate cancer patients bearing this subtype (41,63). This rate of occurrence is predicted to rise with the widespread use of ARPI. Once diagnosed, patients rapidly succumb to the disease within 7 months to 2 years (64). Similar to CRPC-Ad patients, treatments are palliative and limited to second-line etoposide and platinum-based chemotherapies as there are no current targeted therapies in practice for t-NEPC patients (63,64). Moreover, there is no golden standard in detecting or diagnosing t-NEPC subtypes to meet this growing clinical burden. Presently, SYP, CHGA, and NSE are the three main NE biomarkers used to detect NEPC tumours (45). However, 10–40% of AdPC tumours are positive for these same markers, which demonstrates the relatively poor specificity of these diagnostic biomarkers (65). The gravity of an increasing frequency of an aggressive and fatal disease, whereby no effective detection or therapeutic regimens have been defined, emphasizes a need for the understanding of the etiology and molecular underpinnings of t-NEPC development.  1.2. Mechanisms of lineage plasticity: the emergence of t-NEPC These mechanisms of therapeutic resistance and tumour cell heterogeneity can be facilitated by cellular processes called lineage plasticity. Lineage plasticity is 17 described as a reprogramming mechanism whereby AdPC cells can gain the ability to transform into other phenotypes or lineages, such as mesenchymal phenotypes or NE-like lineages. These programs promote the ultimate survival advantage. In fact, recent advances in delineating the etiology of t-NEPC suggest that t-NEPC tumours arise from AdPC cells that have exploited lineage plasticity mechanisms to bypass AR-signaling via phenotypic reprogramming into AR-indifferent NE-like cells (54,63,66). Proposed reprogramming mechanisms in the emergence of t-NEPC include, but are not limited to, dedifferentiation of AdPC to stem-like cells to NE-like cells and direct NE transdifferentiation of AdPC cells to NE-like cells (63). Though the molecular underpinnings of these NE-like cellular reprogramming mechanisms remain unclear, recent studies have demonstrated that genetic, epigenetic, and RNA splicing regulations may facilitate the lineage plasticity of AdPC cells. 1.2.1 Genetic and epigenetic modifications confer plasticity of AdPC cells Although the overall genomic mutational landscapes (i.e. somatic copy number, point mutations, and polyploidy) of CRPC-Ad and t-NEPC tumours show significant overlap, genetic alterations may contribute to the lineage plasticity of cells in the emergence of t-NEPC (54,63,66). Whole-genome sequencing of CRPC-Ad and t-NEPC tumours has revealed that 55-75% of t-NEPC cases have concurrent functional mutations or deletions of the RB1 and TP53 genes, as opposed to the 15-40% of cases seen in CRPC-Ad tumours (54,67). In fact, one of the earliest genetically engineered mouse model (GEMM) of prostate cancer progression called TRAMP reveals the implications of these tumour suppressors in the emergence of t-NEPC tumours (68). 18 TRAMP GEMMs express the transforming region of SV40 large T antigen, which acts to sequester and inactivate both Trp53 and Rb1. TRAMP mice spontaneously develop prostate cancer that closely resembles the molecular and phenotypic characteristics as well as the progression of hormone-naïve prostate cancer to metastatic CRPC-Ad to t-NEPC under castration conditions. A recent study by Mu et al. has reported that knockdown of RB1 and TP53 in the human LNCaP AdPC cell line facilitates resistance to ARPI and promotes t-NEPC progression via a pluripotency gene network mediated by SOX2 (69). SOX2 is a putative developmental factor essential for self-renewal and pluripotency of embryonic stem cells. During the same time of that study, Ku et al. reports a similar resistance mechanism in their Pten/Rb1/Trp53 triple knock-out (TKO) GEMMs (70). Both groups propose a model of lineage plasticity whereby LE cells undergo dedifferentiation reprogramming into a stem-like intermediate cell that can differentiate into NE, basal, or mesenchymal lineages. A separate study has recently reported that NPp53 GEMMs, containing co-inactivation of Trp53 and Pten restricted to the adult prostate LE cells, can progress to highly aggressive and proliferative phenotypes with SCNC histopathology under ARPI (71). Lineage tracing of this progression has revealed that this t-NEPC tumour phenotype emerges from the LE cells via NE differentiation. This group proposes that SOX11, a target of Trp53 and conserved in t-NEPC patient tumours, promotes this NE differentiation downstream of SOX2. Moreover, they suggest that the SOX family of transcription factors (i.e. SOX2 and SOX11) are temporally regulated to promote lineage plasticity of LE cells to the NE cell lineage fate in the emergence of t-NEPC. However, it is also important to note the well-established functions of p53 and Rb1 as putative tumour suppressors of 19 proliferation and survival. As its genomic and functional alterations are prevalent in t-NEPC tumours, loss-of-function mutations in these genes may contribute to the high mitotic index observed in clinical t-NEPC tumours (72).  Although there is a large overlap in the genomic mutational landscapes between CRPC-Ad and t-NEPC tumours, there are significant differences in its epigenetic profiles, such as DNA methylation profiles (54,63). It is suggested that this marked difference in the genome-wide DNA methylation status between CRPC-Ad and t-NEPC tumours is primarily facilitated by a histone methyltransferase called EZH2 (54,63,73,74). EZH2 protein and mRNA levels are found to be upregulated in t-NEPC tumours. Recent findings by Dardenne et al. have reported that N-Myc and EZH2 signaling activity is tightly coupled to induce a t-NEPC molecular program (75). MYCN (encoding the N-Myc protein, a neural-specific oncogenic transcription factor) is amplified in ~40% of t-NEPC tumours (73,75). Dardenne et al. have also demonstrated that the overexpression of N-Myc increases EZH2 activity, which, in turn, represses AR signaling to facilitate t-NEPC development (75). Although further studies are required, these results suggest a potential mechanism by which N-Myc can promote an EZH2-mediated reprogramming of the epigenome to promote t-NEPC development. In addition, Ku et al. suggests that the NE lineage transition and ARPI resistance seen in their TKO GEMMs is induced by EZH2, whereas EZH2 inhibitor treatments can reverse this phenomenon (70). Though the interplay of genetic alterations and epigenetic modifications have been shown to promote lineage plasticity, and, in turn, the emergence of t-NEPC, these epigenetic mechanisms and signatures remain elusive and require further investigation. 20 While these studies propose that functional inactivation of TP53 and RB1 and/or neural-specific epigenetic reprogramming confer AdPC lineage plasticity under ARPI in the emergence of t-NEPC, other factors may be required to facilitate t-NEPC development. A prime example is brain metastasis-derived DU145 AdPC cell lines which contain inactivated mutations in TP53 and RB1 as well as an AR-null profile but present typical AdPC phenotypes (76–78). As these cells have had the ability to reside and colonize in a neuronal niche (presumably for a long time), it would not be outrageous to assume that these cells have been exposed to some degree of neural-specific epigenetic reprogramming induced by its neural niche (78–80). Though DU145 cells exhibit some stem-like characteristics and markers (81,82), they present typical AdPC phenotypes in vivo (76–78). In addition, DU145 cell-derived xenografts present typical LE markers (such as cytokeratins 8/18) and little to no expression of NE markers. These characteristics of DU145 cells suggest that, although genetic and epigenetic alterations may be important in lineage plasticity mechanisms, other factors may be required in the emergence of t-NEPC. 1.2.2 Alternative RNA splicing confers plasticity of AdPC cells Alternative RNA splicing processes are exploited by prostate cancer cells to facilitate cellular plasticity, growth, proliferation, differentiation, and treatment resistance (83). For example, in the emergence of CRPC-Ad, AR splice variants, such as ARv7 (the most frequent variant of AR observed in prostate cancers), have been shown to promote resistance to ARPI and proliferation of various cell models in a ligand-independent, constitutively active manner (84–88). In the context of t-NEPC, increased 21 mRNA expression of serine/arginine repetitive matrix 4 (SRRM4 or nsr100), a neural-specific RNA splicing factor, is uniquely seen in t-NEPC patient tumours (89–92). Importantly, our team at the Vancouver Prostate Centre has recently identified a t-NEPC-unique RNA splicing signature that is predominately facilitated by SRRM4 (90). Overexpression of SRRM4 in LNCaP AdPC cells facilitates the transformation of these cells into t-NEPC xenografts in castrated mice. Following this study, our lab has generated a series of five t-NEPC xenograft models called LnNE via long-term serial passaging from mouse to mouse (93). These LnNE tumour series reveal a model of AdPC-to-t-NEPC progression whereby the later generations/passages of the tumours present increasing expressions of NE markers and decreasing to no expression of PSA. Furthermore, we have recently found that increased expression of SRRM4 is highly correlated with therapeutic interventions such as ARPI in clinical prostate cancer tumours, suggesting its role as a mechanism of therapeutic resistance (91). Altogether, these findings propose that SRRM4 transforms AdPC cells to t-NEPC tumours via direct NE transdifferentiation to escape therapeutics in the emergence of t-NEPC. Following studies on the downstream mechanisms of SRRM4 suggest that SRRM4 facilitates t-NEPC development via reprogramming of alternative RNA splicing networks that are required for NE transdifferentiation and therapy resistance (90,94). In fact, SRRM4 and SRRM4-mediated alternative RNA splicing networks plays an essential role in the development of neural systems as well as neural-related diseases. Particularly, SRRM4-mediated splicing has been shown to be critical in reprogramming the overall function of the proteins required for neurogenesis. The resulting neural-specific protein networks are essential for facilitating neural differentiation (i.e. morphological and 22 phenotypical), neural-specific cell processes/functions (e.g. cell migration, growth, evasion of apoptosis, and proliferation during neurite development and network formations), neuronal transcription networks, and neural-specific epigenetic networks (95–100). These functions of splicing are of particular interest as AdPC to t-NEPC development also involves multifaceted, interconnected mechanisms and cellular processes of the following (Figure 1.5): i. Lineage plasticity of AdPC cells to ultimately differentiate into NE-like cell lineages, which may be facilitated by neural-specific epigenetic, transcriptional, and/or protein networks. Mechanisms of lineage plasticity may involve dedifferentiation of AdPC to stem-like cells to NE-like cells, NE differentiation via a pluripotency network, and direct NE transdifferentiation of AdPC cells to NE-like cells. ii. Evasion of apoptosis mediated by ARPI or chemotherapies. iii. Expansion of the differentiated NE cell lineages and establishment of a t-NEPC tumour.   23 Figure 1.5 The multifaceted, interconnected mechanisms of t-NEPC development. Progression from AdPC to t-NEPC requires mechanisms of apoptosis evasion to bypass and survive ARPI or chemotherapies as well as mechanisms of lineage plasticity (e.g. neural-specific transcriptional networks) to ultimately differentiate into NE-like cell lineages. In addition, cell proliferation and clonal expansion of the NE lineage cells is also required in the emergence of t-NEPC tumours. AdPC, prostate adenocarcinoma; T-NEPC, treatment-induced neuroendocrine prostate cancer; LE, luminal epithelial; NE, neuroendocrine; ARPI, androgen receptor pathway inhibitors.  1.3 Ser/Arg Repetitive Matrix 4 (SRRM4) 1.3.1 The SR family and SR-related family of proteins It has been predicted that 90–98% of the genes in the human genome have alternative splice variants, which highlights a critical role of alternative splicing processes and different mRNA or protein variants in normal cellular functions (101,102). One of the essential families of splicing factors are the serine/arginine (SR) family of proteins, such as ASF/SF2 (103,104). Members of this family contain one or two conserved RNA recognition motifs (RRM) at the N-terminus that are essential for RNA-binding specificity (103–106). In addition, SR family members contain an RS domain of Figure 1.5 24 varying sizes at the C-terminal end, which is important for protein-protein interactions and recruitment of the spliceosome complex (103–106). The SR family of proteins has diverse functions in regulating not only constitutive and alternative pre-mRNA splicing, but also post-splicing events including exportation of mRNA, nonsense-mediated mRNA decay, and translation of mRNA. On the other hand, SR-related proteins contain an RS domain, but may or may not contain a defined RRM (107). Similar to SR family of proteins, SR-related proteins are found to play a role in not only splicing, but also other fundamental cellular processes, such as chromatin remodeling, cell cycle progression, and transcription (108). Though these proteins have a diverse range of functions, its fundamental role is in the regulation of RNA splicing (95,99,109). Splicing processes can have dramatic effects on the function, localization, stability, and/or expression of the mRNA splice variants or of its resulting translated protein isoform (95,99,109). The factors essential for orchestrating splicing programs include RNA-binding domain-containing small nuclear ribonucleoproteins (snRNP), such as U1, U2, U4, U5, and U6, and SR and SR-related family of proteins (110,111). These components make up a macromolecular complex called the spliceosome. The spliceosome interacts with cis- and trans-acting factors to regulate the repression or activation of splicing (i.e. the inclusion or exclusion of exons) and to ensure correct splice-site selections. The regulation of SR and SR-related protein splicing activity and its subcellular localizations depend, in part, on the dynamic cycle of phosphorylation and de-phosphorylation of the serine residues in the RS domain (112). Phosphorylation results in protein conformational changes, whereby altering protein-protein or protein-RNA interactions. Therefore, the phosphorylation status of RS proteins have a diverse effect in mediating 25 the regulation of spliceosome complex assembly, recruitment, splicing activation, and splice-site selection (113–115). SRRM4 is particularly interesting as it has been suggested to be the source of proteomic diversity and functional complexity within the vertebrate nervous system—although its evolutionary origin is unclear (116). SRRM4 belongs to the family of SR-related proteins. It is a neural tissue- and vertebrate-restricted SR protein involved in complex alternative splicing of neural-specific exons—by binding to its conserved putative intronic 3’ UGC motif upstream of the neural-specific exon—which are essential for vertebrate nervous system development and neural cell fate differentiation of neural progenitor cells (116). In addition, SRRM4 is uncharacteristically heavy (when compared to the other members of the SR-related protein family) with a molecular weight of 100 kDa, which is likely a result of the large RS region in the protein (100,116). The presence of large RS domains makes SRRM4 more phosphorylated than its family members, which is an important characteristic of splice-site selection as mentioned above. Furthermore, the RS-rich domains of SRRM4 have also been predicted to be responsible for protein-protein and/or protein-RNA interactions required for spliceosome complex assembly to promote alternative splicing (98,100,106,117). SRRM4-mediated alternative splicing regulates the inclusion of brain-specific exons associated with neuronal development. This brain-specific exon network mediated by RNA splicing mechanisms has been proposed to be essential for neural function and development (118,119). 26 1.3.2 SRRM4 in normal development Raj et al. has recently demonstrated that, by knocking down or overexpressing SRRM4 in vivo, SRRM4 mediates the inclusion of 30-50% of conserved mouse and human brain-specific exons that manifests a unique neural splicing program (98). Additionally, in utero SRRM4 knockdown studies (i.e. knockdown of SRRM4 in embryonic stem cells) in mice has reported diminished differentiation of neural progenitor cells and neurodevelopmental defects that resulted in postnatal mortality of the mice (96). Furthermore, knockdown of SRRM4 in neural cells and in zebrafish impairs neurite morphogenesis and branching (116). Collectively, these studies propose that SRRM4-mediated neural-specific exon networks are essential in reprogramming the functions of the resulting protein isoform (Figure 1.6). Functional reprogramming ultimately alters protein-protein interaction networks that facilitate neurogenesis, promotes neuronal transcription networks, and mediates neural-specific functions and morphological processes. For example, several transcripts regulated by SRRM4-mediated alternative RNA splicing encode neuronal GTPases that are required for cytoskeletal remodeling and dendritic growth and branching (116). Though studies on the functional significance SRRM4-targeted neural-specific exons are currently limited, findings thus far have suggested that splicing-mediated functional reprogramming of the resulting protein isoforms are required to facilitate neuronal cell differentiation and functions. 27 Figure 1.6 Figure 1.6 SRRM4 promotes neural-specific exon networks that are essential for neuronal cell differentiation during neurogenesis. In neuronal cells, SRRM4 mediates the inclusion of neural-specific exon networks by binding to its putative UGC motif site. Translation of these neural-specific splice isoforms results in functionally significant neural-specific protein isoforms that are required for neuronal specific protein-protein networks, neuronal transcription networks (e.g. mediated by the truncated, dominant-negative REST4 splice isoform), and neural-specific functions (such as neurite outgrowth and networks). Ultimately, this neural-specific exon reprogramming will promote neuronal cell differentiation and maturation.  1.3.2.1 SRRM4-mediated alternative splicing confers neuronal cell differentiation and function One of the well-studied key mechanisms by which SRRM4 and SRRM4-mediated alterative splicing promotes neurogenesis and facilitates neural differentiation of neural progenitor cells is via the cross-regulation of RE-1 silencing transcription factor, REST (96,98,116,120) (Figure 1.6). REST is a transcriptional repressor of neuronal genes that binds to the RE-1 site in the regulatory region upstream of its target 28 gene. It inhibits the transcriptional activity of neuronal genes by facilitating repressive histone modifications via the recruitment of co-repressors and factors, such as HDAC1/2, coREST, and Sin3A, to the promoter region of the gene. REST and SRRM4 antagonistically regulate neurogenesis. Specifically, REST represses the transcription of genes that are important for promoting the neuronal phenotype as well as directly inhibits SRRM4 expression to prevent neurogenesis in nonneuronal cells (96). Conversely, SRRM4 facilitates the alternative splicing of the REST transcript into a splice variant called REST4 in neuronal cells. REST4 is a truncated isoform of the REST protein with reduced DNA-binding function, resulting in the liberation of the transcriptional activity of REST-repressed target genes. Moreover, it has been shown that REST4 isoforms can directly bind to REST to inhibit its function (120). In summary, to achieve neuronal cell differentiation, SRRM4-mediated alternative splicing promotes the expression of the dominant-negative isoform REST4, which compromises its canonical function in the repression of neuronal transcription networks. 1.3.3 SRRM4 in pathogenesis Based on the fundamental role of SRRM4 and its alternative splicing functions in fostering and facilitating normal neural development, it is clear that aberrant SRRM4 expression or function may promote pathogenesis. Aberrant RNA splicing has been demonstrated as a mechanism exploited by cells to promote the progression of many neurological diseases or disorders and neural-related cancers (83,95,97,100,101,121–123). For example, autism spectrum disorder (ASD) has been associated with aberrant SRRM4 expression and alternative splicing function (95). In this study, Irimia et al. 29 observes a reduced expression of SRRM4 in ASD and a distortion in the highly conserved SRRM4-mediated splicing program (primarily its regulation in the splicing of “microexons,” which are exons composed of 3-27 nucleotides) (95). They reveal that the positions of these SRRM4-mediated microexons are in regions of conserved domains and motifs that are important for essential protein–protein interactions required for neural development and differentiation. This suggests the importance of microexons in the reprogramming of protein interaction networks during neurogenesis. Moreover, altered increased expression and splicing function of SRRM4 have been associated with the progression of small cell lung cancer (SCLC), a NE cancer of the lung (123). These and other studied neurological-related diseases highlight the importance of SRRM4 and SRRM4 function in its regulation of splicing programs during disease progression. Therefore, studying the altered splicing profiles of diseases mediated by SRRM4 may provide a new avenue in investigating the molecular mechanisms and outcomes of splicing during disease progression. As previously mentioned, our recent studies have demonstrated that SRRM4 facilitates a unique RNA splicing profile within two t-NEPC patient cohorts, whereby, 24 genes are commonly spliced in all the studied t-NEPC tumours. Within the 24 genes, 21 are predicted to be regulated by SRRM4 (90). In fact, 16 of the 21 genes are known targets of SRRM4 during normal neural development and suggested to be highly enriched with neuronal functions (90,98). In fact, these alternative splice transcripts encode kinases that can activate major signaling cascades/pathways, GTPases that can promote NE-specific morphogenesis, epigenetic regulators, cell survival regulators, and neural differentiation fate factors. These processes are of particular interest as the 30 AdPC to t-NEPC progression involves the aforementioned multifaceted, interconnected mechanisms involving NE lineage differentiation, evasion of apoptosis, and clonal expansion of the newly differentiated cell populations. Therefore, characterizing the functional significance of alternative RNA splicing of these t-NEPC-unique transcripts may provide further insight on the molecular mechanisms of SRRM4-facilitated t-NEPC development. 1.4 The multifaceted roles of SRRM4 in t-NEPC development While there is limited knowledge on the function of SRRM4 in t-NEPC development (as the first study was reported just last year), our studies have thus far shown that SRRM4 facilitates 2D and 3D cell growth/proliferation, evasion of apoptosis, colony formation, NE cellular morphological alterations, and t-NEPC tumour development in vitro (90,94)—with other possible cellular or molecular functions of SRRM4 still waiting to be defined. These functions of SRRM4 have been suggested to be mediated by neural-specific transcription networks. Exploration of the downstream spliced targets of SRRM4 may uncover the molecular mechanisms by which SRRM4 orchestrates these cellular processes as well as discover novel facets of SRRM4 function in t-NEPC development and progression. 1.4.1 The multifaceted roles of SRRM4 in t-NEPC development is mediated by downstream neural-specific exon networks Within the 16 t-NEPC-unique spliced genes facilitated by SRRM4, the functional consequences of alternative splicing of only 8 of these genes have previously been characterized (Table 1.1). Of the 8, the role of alternative RNA splicing of only the REST 31 and Bif-1 transcripts have been studied in the context of t-NEPC development. Characterization of the alternative RNA splicing of REST and Bif-1 genes has revealed that its t-NEPC-unique splice variants are important in contributing to the development of t-NEPC via promoting NE lineage differentiation and evading apoptosis, respectively (Figure 1.7) (90,94). Though further studies are essential in fully understanding the molecular mechanisms involved in t-NEPC progression via SRRM4-mediated alternative splicing events, splicing of the other SRRM4-targeted transcripts may also be important in contributing to the other facets of t-NEPC development such as NE cell proliferation, clonal expansion, and regulation of a t-NEPC-specific epigenome. Excitingly, the literature reveals that several of these t-NEPC-uniquely spliced target genes have putative roles in neural-related functions such as neuronal cell differentiation, neurite branching, and synaptic signaling. Characterizing the functional significance of alternative splicing will be crucial in elucidating how splicing mechanisms contribute to the development of t-NEPC.  32 Table 1.1 16 t-NEPC-unique genes spliced by SRRM4 Gene name Proposed functions Previous literature on splice isoforms ABI1 Regulation of micropinocytosis and neuronal differentiation via cytoskeletal remodeling PMID: 20479892 ATL2 GTPase involved in Golgi and endoplasmic reticulum morphogenesis and polarity in motor neurons N/A Bif-1 (SH3GLB1) Regulator of apoptosis PMID: 29759485 PHF21A (BHC80) Component of the BRAF-HDAC complex involved in regulation/repression of transcription via deacetylation of histones PMID: 15325272 CAMTA2 Transcription activator/regulator of genes involved in cardiac cell growth N/A ERGIC3 Regulator of endoplasmic reticulum-to-Golgi trafficking N/A GIT1 GTPase-activating protein via GPCR-mediated signal transduction important in regulating the dynamics of cytoskeleton N/A MEAF6 Component of HAT complexes involved in regulation of transcription via acetylation of histones N/A MEF2D Transcription activator/regulator to promote myoblast cell fusion during myogenesis and neuronal cell differentiation PMID: 25087874 MON2 Regulator of Golgi-to-endosome trafficking N/A NSMF (NELF) Regulation of neuronal migration PMID: 24316376 PTK2 (FAK1) Tyrosine kinase involved in regulation of integrin-mediated spreading, migration, and signal transduction pathways as well as regulation of synaptic functions PMID: 12391143 PTPRF Tyrosine phosphatase receptor involved in regulation of neuronal cell growth and neurite outgrowth PMID: 12716943 REST Transcriptional regulator/repressor of NE genes PMID: 21884984 SH3GLB2 Regulator of autophagy and endocytosis N/A SPTAN1 Filamentous cytoskeletal protein to provide cell strength and structural rigidity N/A Table 1.1   33  Figure 1.7 The multifaceted roles of SRRM4 in t-NEPC development is mediated by downstream neural-specific exon networks  1.4.1.1 SRRM4-mediated alternative RNA splicing of the REST gene facilitates NE differentiation of AdPC cells  The mechanism of neuronal cell differentiation involving SRRM4-mediated alternative RNA splicing of the REST gene to its truncated REST4 splice isoform during neurogenesis is exploited in AdPC cells to promote the NE lineage cell fate during NEPC development (90,92). A recent study by Zhang et al. has revealed a positive correlation of increased SRRM4 expression, decreased REST expression, and increased REST splicing with the NE phenotype in CRPC patient tumours and patient-derived xenograft (PDX) models (89). These observations were also seen in SCLC (123). In fact, our recent report demonstrates that SRRM4 directly splices the REST transcript to facilitate NE transdifferentiation of LNCaP AdPC cells (90). These results establish a critical role of SRRM4-mediated functional reprogramming of REST in the emergence of t-NEPC, where the loss of REST-mediated transcriptional repression of Figure 1.7 34 neuronal genes is important in facilitating neural-specific transcription networks. However, one major difference between the roles of REST and SRRM4 during neurogenesis and t-NEPC development is that REST does not regulate SRRM4 expression under the context of prostate cancer cells (90). 1.4.1.2 SRRM4-mediated alternative RNA splicing of the Bif-1 gene facilitates evasion of apoptosis Like any other cancer, t-NEPC requires more than just differentiation to develop—resistance to cell death induced by ARPI, radiation, or chemotherapies is a prerequisite condition for t-NEPC to develop. In fact, our lab has recently reported that the role of SRRM4 in the evasion of apoptosis is through the alternative RNA splicing of the Bax-interacting factor 1 (Bif-1 or SH3GLB1) gene, an endophilin protein involved in apoptosis, autophagy, and mitochondrial functions (94). Specifically, Bif-1 is directly spliced by SRRM4 into two t-NEPC-unique splice variants Bif-1b and Bif-1c. These neural-specific variants have demonstrated resistance to apoptosis in prostate cancer cells. In contrast, the constitutive isoform, Bif-1a, which is highly represented in AdPC tumours, has pro-apoptotic functions when challenged with UV radiation and chemotherapy drugs. Interestingly, the inclusion of neural-specific exons in the Bif-1b and Bif-1c transcripts lays within the N-BAR domain of Bif-1 required for the activation of the intrinsic apoptosis pathway (124). This suggests that the inclusion of the exon may perturb protein-protein interactions or other related functions of the N-BAR domain, which may describe the altered functions of the Bif-1b and Bif-1c splice variants in apoptosis regulation. However, further characterization of the three Bif-1 splice variants and its associated protein networks and pathways is needed. 35 1.4.1.3 SRRM4-mediated alternative RNA splicing of putative epigenetic modifiers Of the 16 t-NEPC-unique target transcripts, the PHF21A and MEAF6 transcripts encode putative epigenetic modifiers of histone deacetylation/demethylase or acetylation, respectively. PHD finger protein 21A (PHF21A) is a component of histone deacetylase (HDAC) complexes associated with HDAC1/2 as well as histone demethylase complexes containing LSD1 and coREST that recognize and methylate unmethylated histone 3 lysine 4 (H3K4) residues to ultimately activate transcription (125). These complexes containing PHF21A mediate REST-dependent transcriptional repression of neuronal genes (125–127). It has also previously been shown that PHF21A functions as a negative modulator of REST-mediated repression of neuronal genes required for neurosecretion and neurotransmission via inhibitory binding to the REST protein (121,127). Previously, Iwase et al. has demonstrated that alternative RNA splicing of the PHF21A transcript can re-program the function of the PHF21A protein by altering its protein-protein interaction networks (such as enhanced binding to coREST and LSD1) that can facilitate a neural-specific transcription network (128). Furthermore, LSD and coREST function is important in the proliferation and population of neuronal cells during neurogenesis (129). On the other hand, the functional significance of alternatively spliced isoforms of MYST/esa1-associated factor 6 (MEAF6) has never been studied. However, MEAF6 is evolutionarily conserved from yeast to humans, suggesting the functional importance of this gene (130–132). MEAF6 is a component of 4 of the 5 MYST family of histone acetyltransferase (HAT) complexes (130–134). HAT complexes have putative roles in key fundamental nuclear processes (i.e. transcription, DNA repair, and replication) via post-translational modifications of histones and 36 transcriptional regulators, such as p53. The MYST family of HAT complexes have also been implicated in neurogenesis, where depletion of these HATs results in reduction of neuronal cell differentiation and population number (135). Though the functional significance of alternative RNA splicing of these two putative epigenetic modifiers remains to be elucidated, these findings suggest that alternative RNA splicing of these transcripts may facilitate a neural-specific epigenetic, transcriptomic, and/or protein-protein network to promote NE differentiation and proliferation of prostate cancer cells. 1.4.1.4 SRRM4-mediated alternative RNA splicing of GTPases As previously mentioned, GTPases are required for neuronal cell differentiation during neurogenesis. GTPases regulate cytoskeletal dynamics, whereby remodeling of the cytoskeleton promotes migration, dendritic growth, and neurite outgrowth in neuronal cells (116). In fact, within the 16 t-NEPC-unique spliced genes, 5 have putative roles in regulating these processes required for neuronal cell differentiation and function. Of the 5 genes, the G-protein-coupled receptor kinase-interacting protein 1 (GIT1) gene contains a GTPase-activating domain. In neural systems, GIT1 has been shown to activate signaling cascades important for regulating spine morphogenesis, neuronal cell-cell communication, and stability of synapses (136,137). Though, the functional significance of alternative splice isoforms of GIT1 has yet to be characterized, GIT1 may be important in the NE differentiation (i.e. morphological differentiation) of prostate cancer cells. 37 1.5 Objectives, hypotheses, and specific aims 1.5.1 Overall thesis rationale and objective Though we have limited knowledge on the role of SRRM4-mediated alternative splicing in t-NEPC, great progress in the investigation of this t-NEPC-unique splicing program has been made since its discovery in 2017. Excitingly, our lab has been the leading investigators in this field of unraveling the downstream functional significance of SRRM4-mediated alternative splicing in the contribution to t-NEPC development. Many ongoing investigations on the role of SRRM4-mediated alternative RNA splicing of the PTK2 and PHF21A transcripts are currently happening in our lab. A deeper investigation of the role of SRRM4 and SRRM4-mediated alternative splicing will provide a more comprehensive understanding of the molecular events of t-NEPC development. Furthermore, exploration of these mechanisms may uncover novel facets of the processes involved in t-NEPC development. As the incidences and clinical burden of t-NEPC are increasing, delineating the molecular underpinnings of t-NEPC will be paramount in developing diagnostic or therapeutic strategies that may prevent or mitigate the disease course. Therefore, the overarching objective of my thesis work is to understand the role of SRRM4 and SRRM4-mediated alternative splicing in promoting t-NEPC development and progression. 1.5.2 Overall hypothesis and aims SRRM4 is a key facilitator of neural-specific exon networks containing kinases, GTPases, epigenetic modifiers, cell survival regulators, and NE differentiation fate factors. This neural-specific exon network has been shown to be required for the 38 differentiation and function of neural progenitor and neuronal cells during neurogenesis. Therefore, we hypothesize that SRRM4 can ultimately promote NEPC programming in different AdPC cell models, whereby SRRM4-mediated neural-specific exon networks contribute to the multifaceted processes of t-NEPC development. To test this hypothesis, the following aims have been designed: Aim 1: To delineate the role of SRRM4 in promoting t-NEPC development in various prostate cancer cell models. Aim 2: To investigate the functional significance of SRRM4-mediated alternative RNA splicing of the MEAF6 gene unique to t-NEPC tumours. Aim 3: To characterize the functional significance of SRRM4-mediated alternative RNA splicing of the GIT1 gene unique to t-NEPC tumours. The specific hypotheses and aims will be discussed in the next sections. 1.5.3 Specific hypotheses and aims 1.5.3.1 Aim 1 hypothesis and aims Given the heterogeneous cellular nature of prostate tumours seen in the clinic, studying the role of SRRM4-mediated NE differentiation in a heterogeneous population of cell types would be of significant interest. Since SRRM4 is essential for the neuronal differentiation of neural progenitor and neuronal cells, we hypothesize that SRRM4 can ultimately promote a NEPC program in a panel of different cell types. To test this hypothesis, the following aims have been designed: 39 Specific aim 1: To determine the function of SRRM4 in promoting a NEPC-specific transcriptome and splicing program in vitro.  Specific aim 2: To investigate the function of SRRM4 in reprogramming various AdPC cell models into NEPC xenografts in vivo. Specific aim 3: To study the cellular functions of SRRM4-transduced cell models. Specific aim 4: To profile the transcriptome of the SRRM4-overexpressing cell models. 1.5.3.2 Aim 2 hypothesis and aims Since t-NEPC development requires epigenetic reprogramming and SRRM4-regulated putative epigenetic modifiers have previously been shown to regulate cell proliferation and population during neurogenesis, we hypothesize that the SRRM4-mediated alternative RNA splicing of the MEAF6 gene will contribute to the proliferation and tumorigenesis of prostate cancer cells. To test this hypothesis, the following aims have been designed: Specific aim 1: To characterize the expressions of the MEAF6 splice variants in patient tumours, xenograft models, and cell models. Specific aim 2: To study the functional significance of alternative RNA splicing of MEAF6 using prostate cancer cell models and its derived xenografts. 40 Specific aim 3: To investigate the transcriptome and signaling pathways regulated by the MEAF6 splice variants. 1.5.3.3 Aim 3 hypothesis and aims Given that GTPases are required in the stability of morphological differentiations and migration of neuronal cells during neurogenesis, we hypothesize that the SRRM4-mediated alternative splicing of the GIT1 gene will contribute to the NE differentiation and migration of prostate cancer cells. To test this hypothesis, the following aims have been designed: Specific aim 1: To characterize the expressions of the GIT1 splice variants in patient tumours, xenograft models, and cell models. Specific aim 2: To define the association, specificity, and sensitivity of GIT1 expression with the NEPC phenotype in CRPC patient tumours. Specific aim 3: To investigate the transcriptome and signaling pathways regulated by the GIT1 splice variants.  41  Materials and Methods 2.1 Cell lines and culture  22Rv1, C4-2, DU145, LNCaP, PC-3, VCaP, WPMY-1, and NCI-H660 cell lines were purchased from American Type Culture Collection (Manassas, VA, USA). LNCaP95 (LN95) cells were kindly provided by Dr. Alan Meeker (Johns Hopkins University, MD, USA). The BPH-1 and hTERT-Myo cell lines were provided by Dr. Simon Hayward (Vanderbilt University, TN, USA) and Dr. Jennifer Condon (Wayne State University, MI, USA), respectively. RWPE-1 and 293T cells were generously provided by Dr. Michael Cox and Dr. Ralph Buttyan, respectively, from the Vancouver Prostate Centre (VPC; Vancouver, BC, Canada). Small cell lung cancer (SCLC) cell lines NCI-H69 and NCI-H82 was kindly provided by Dr. Yuzhuo Wang from the VPC. The LnNE and DuNE cell models, along with its control cells, were established by our group (90,93,138). VCaP, PC-3, DU145, WPMY-1, and 293T cells were cultured in Dulbecco Modified Eagle Medium/High Glucose (DMEM; Hyclone; Logan, UT, USA) with 10% fetal bovine serum (FBS; Gibco; Waltham, MA, USA), whereas LNCaP, BPH-1, and 22Rv1 cells were cultured in RPMI-1640 medium (Hyclone) with 10% FBS. LN95 cells was maintained in phenol red-free RPMI-1640 medium with 10% charcoal-stripped serum (CSS; Hyclone). Under experimental conditions in Chapter 3, LNCaP cells were cultured in 5% CSS and phenol red-free RPMI-1640 medium. WPMY-1 cells were maintained in DMEM medium containing 10% FBS and 1% penicillin-streptomycin (Hyclone). NCI-H660 was cultured in HITES medium (RPMI-1640 medium containing 42 0.005 mg/ml insulin, 0.01 mg/ml transferrin, 30 nM sodium selenite, 10 nM hydrocortisone, 10 nM ß-estradiol, and 2 mM L-glutamine) with 10% FBS. NCI-H69 and NCI-H82 suspension cells were maintained with RPMI-1640 medium supplemented with 10% FBS, 100 g/ml streptomycin, and 100 units/ml penicillin. RWPE-1 cells were grown in Keratinocyte-SFM (1X) media with human recombinant epidermal growth factor 1-53 and bovine pituitary extract supplements (Gibco). Cells were all incubated in 5% CO2 at 37C. All cell lines used were tested negative for mycoplasma contamination and authenticated by short tandem repeat assays. 2.2 DNA and siRNA transfections Cloning and construction of the MEAF6 and GIT1-specific DNA plasmids were performed by Ms. Ning Xie (VPC). To construct Flag-MEAF6-1 (pCMV1 promoter), MEAF6-1 gBlock was purchased from Integrated DNA Technologies (Coralville, IA, USA) and was used as a template for PCR amplification using the Platinum® Taq DNA Polymerase High Fidelity kit (Invitrogen; Waltham, MA, USA). PCR amplification conditions were as followed: 2 min at 94°C, followed by 35 cycles of 15 sec at 94°C, 30 sec at 45°C-60°C, and 2 min at 65°C, then by 5 min at 68°C, and lastly at 10°C. pCMV1 vector and amplified cDNA was digested with BamHI and EcoRI, followed by a T4 DNA ligation reaction. The Flag-MEAF6-2 construct was generated with the Q5® Site-Directed Mutagenesis kit (New England Biolabs; Ipswich, MA, USA) using Flag-MEAF6-1 as a template and following manufacturer’s protocol. The Flag-GIT1-A plasmid (#15225) was purchased from Addgene (Cambridge, MA, USA) and was used as a template to generate the Flag-GIT1-C plasmid using the Q5® Site-Directed 43 Mutagenesis kit (New England Biolabs) following manufacturer’s protocol. Primers used for cloning and site-directed mutagenesis are listed in Table 2.1. The fidelity of all PCR-generated fragments and expression plasmids were confirmed by Sanger sequencing. All transfections were carried out using Lipofectamine® 3000 (Invitrogen) according to the manufacturer’s protocols. Detailed information on expression plasmids and siRNA targets is listed in Table 2.2 Table 2.1 Primers for cloning and site-directed mutagenesis Primer Name Forward Primer Sequence Reverse Primer Sequence MEAF6-1_EcoRI AAC GAA TTC ATG GCG ATG CAC AAC AAG GCG  MEAF6-1_BamHI CGC GGA TCC CTA ATA GTC AGC TCG TGG TTT  MEAF6-2_Cloning GAT TGA TCT GAA GTT AAA CAA AAA ACC ACG CTGTGCCGGTTTTTATTCTTTCGCTTTTTATGGC GIT1-C_Cloning CCTTGACTTATCCGAATTGGCCAAAG CTGTCAGCCATCTGTGGGATGATG Table 2.1   44 Table 2.2 siRNA and plasmid information Reagent Provider Catalogue # Negative control siRNA  Dharmacon, Lafayette, CO, USA D-001210-01-20 ON-TARGETplus SMARTpoolTM siRNA targeting SRRM4 Dharmacon J-058651-10 ON-TARGETplus SMARTpoolTM siRNA targeting SOX2 Dharmacon L-011778-00-0005 Silencer® Select Negative Control No. 1 siRNA ThermoFisher Scientific 4390843 Silencer® Select siRNA targeting MEAF6 ThermoFisher Scientific 4392420 Silencer® Select siRNA targeting ID1 ThermoFisher Scientific s7106 Silencer® Select siRNA targeting ID3 ThermoFisher Scientific s7110 Flag-SRRM4 plasmid Dr. Xuesen Dong (VPC)  Flag-U2AF65 plasmid Dr. James Manley (Columbia University)  Flag-ASF/SF2 plasmid Dr. Gourisankar Ghosh (UC San Diego)  Flag-hnRNP I plasmid Dr. Allain Frédéric (Institute for Molecular Biology and Biophysics Eidgenössiche Technische Hochshule, Switzerland)  Flag-U1A plasmid Dr. Xuesen Dong  Flag-PSF plasmid Dr. Xuesen Dong   Flag-p54nrb plasmid Dr. Benjamin Blencowe (University of Toronto)   Table 2.2 2.3 Construction of cell models by lentiviral approaches  Lentiviral expression vectors (pFUGWBW backbone) encoding Flag-SRRM4 and empty vector (Ctrl or CTL) were provided by Dr. Xuesen Dong from the VPC (90). Flag-MEAF6-1, Flag-MEAF6-2, Flag-GIT1-A, and Flag-GIT1-C plasmids were used as a template to amplify and clone the genes-of-interest into Gateway® pDONRTM201 and pFUGWBW vectors using the Platinum® Taq DNA Polymerase High Fidelity kit 45 (Invitrogen) and Gateway® Technology (Invitrogen), respectively, according to manufacturer’s protocol. Primers used for lentiviral vector cloning is listed in Table 2.3. The fidelity of all PCR-generated fragments and lentiviral expression plasmids (pDONRTM201 and pFUGWBW) were confirmed by Sanger sequencing and expressed proteins were validated using anti-flag antibody before and after application.  Table 2.3 Primers used for lentiviral vector cloning Primer Name Forward Primer Sequence Reverse Primer Sequence MEAF6_Lenti GGG GAC AAG TTT GTA CAA AAA AGC AGG CTT CAC CAT GGA TTA TAA AGA TGA TGA TGA TAA AGC GAT GCA CAA CAA GGC GGC GCC G GGG GAC CAC TTT GTA CAA GAA AGC TGG GTC CTA ATA GTC AGC TCG TGG TTT GIT1_Lenti GGGGACAAGTTTGTACAAAAAAGCAGGCTTCACCATGGATTATAAAGATGATGATGATAAAATGTCCCGAAAGGGGCCGCGAGCG GGGGACCACTTTGTACAAGAAAGCTGGGTTCACTGCTTCTTCTCTCGGGTGGTGAT Table 2.3 To package lentivirus, lentiviral expression vectors, together with the 2nd Generation Packaging Mix (Applied Biological Materials; Richmond, BC, Canada) were transfected into 293T cells. Media was changed 24 hrs post-transfection. Lentiviral particles were harvested and filtered 30 hrs later and used to transduce target cells. Transduced cells were selected by and cultured under blasticidin (Gibco) with the respective concentrations for the following cells: RWPE-1, 2 μg/ml; BPH-1, 2.5 μg/ml; LNCaP, 22Rv1, and DU 145, 5 μg/ml; PC-3, WPMY-1, and hTERT-Myo, 10 μg/ml. Exogeneous expressions of SRRM4, MEAF6-1 or MEAF6-2, and GIT1-A or GIT1-C were confirmed by RT-qPCR and immunoblotting assays as shown in Appendix Di, Appendix K, and Figure 5.4A, respectively. Nomenclature of the cell models used is shown in Table 2.4. 46 Table 2.4 Nomenclature of cell models used Used in Chapter Name Description 3 LNCaP(SRRM4) Stable overexpression of SRRM4 in LNCaP cells LNCaP(Ctrl) Stable overexpression of empty vector in LNCaP cells 22Rv1(SRRM4) Stable overexpression of SRRM4 in 22Rv1 cells 22Rv1(Ctrl) Stable overexpression of empty vector in 22Rv1 cells DU145(SRRM4) Stable overexpression of SRRM4 in DU145 cells DU145(Ctrl) Stable overexpression of empty vector in DU145 cells PC-3(SRRM4) Stable overexpression of SRRM4 in PC-3 cells PC-3(Ctrl) Stable overexpression of empty vector in PC-3 cells 4 PC-3(MEAF6-1) Stable overexpression of MEAF6-1 in PC-3 cells PC-3(MEAF6-2) Stable overexpression of MEAF6-2 in PC-3 cells PC-3(CTL) Stable overexpression of empty vector in PC-3 cells LNCaP(MEAF6-1) Stable overexpression of MEAF6-1 in LNCaP cells LNCaP(MEAF6-2) Stable overexpression of MEAF6-2 in LNCaP cells LNCaP(CTL) Stable overexpression of empty vector in LNCaP cells 5 DU145(GIT1-A) Stable overexpression of GIT1-A in DU145 cells DU145(GIT1-C) Stable overexpression of GIT1-C in DU145 cells DU145(Ctrl) Stable overexpression of empty vector in DU145 cells Table 2.4 2.4 RT-qPCR and immunoblotting (western blot) assays  Expression of mRNA was assessed by real-time quantitative polymerase chain reactions (RT-qPCR). Total RNA was extracted using TRIzol® (Ambion; Waltham, MA, USA) according to manufacturer’s protocol. 2 g of RNA was reverse-transcribed using random hexamers and Superscript IITM Reverse Transcriptase (Invitrogen) according to manufacturer’s instructions. Reactions were prepared using SYBR® Green reaction mix (Roche; Basel, Switzerland) and run on the ABI ViiA7 machine (Applied Biosystems; Burlington, ON, Canada) using their default cycle settings: 2 min at 50°C and 10 min at 95°C, followed by 45 cycles of 15 sec at 95°C and 1 min at 60°C. Relative quantification of mRNA expression was measured by calculating and comparing the Ct values of 47 housekeeping genes GAPDH or 18S to our gene-of-interest. RT-qPCR assays were carried out using three technical replicates and three independent biological replicates. Heatmaps in Chapters 3 and 5 were constructed based on the results of the qPCR. Primer information is listed in Appendix A. For immunoblotting assays, cell lysates were prepared in 50 mM Tris-HCl (pH 8.0), 150 mM NaCl, 1% NP40, 0.5% sodium deoxycholate, and 0.1% sodium dodecyl sulfate (SDS) with cOmplete™ EASYpack proteinase and PhosSTOPTM phosphatase inhibitors (Roche) followed by brief sonication to extract protein lysates. Protein concentrations were measured with the Pierce BCA Protein Assay Kit (ThermoFisher Scientific; Waltham, MA, USA) according to the manufacturer’s protocol. Equal weight of total protein (40-60 μg) were then mixed with and denatured by SDS and boiled for 5 min at 95°C. Prepared protein samples were separated by electrophoresis on an 8%-15% SDS polyacrylamide gel and transferred onto polyvinylidene difluoride membrane (Millipore; Bedford, MA, USA). Transfers were performed using the semi-dry transfer method: 40-60 min at 25 V at room temperature with the Trans-Blot® SD Semi-Dry Transfer Cell (Bio-Rad; Hercules, CA, USA). Immunoblots were probed by Pierce ECL Western Blotting Substrate (ThermoFisher Scientific) and visualized using autoradiography films via a film processor (EL-RAD; Vancouver, BC, Canada). Densitometry was performed to quantify protein expression. All immunoblotting assays were repeated in three independent experiments with one representative blot shown. -actin or vinculin served as loading controls. Antibody information is listed in Appendix B. 48 For RNA and protein extraction of xenograft tumours (performed in Chapter 4), tissues lysates were prepared in TRIzol® or lysis buffer, respectively, with beads and homogenized using the Precellys Homogenizer (Bertin Instruments, France). The supernatant was then collected and further processed for protein or RNA extraction following the standard protocols mentioned above. 2.5 RNA-ChIP assays RNA-chromatin immunoprecipitation (RNA-ChIP) assays were performed by Ms. Ning Xie and Dr. Yinan Li from the VPC as previously described (91,94). Briefly, cells were transfected with empty vector or Flag-SRRM4 plasmid. 48 hrs post-transfection, RNA-protein complexes were cross-linked with 1% formaldehyde for 10 min at 37°C and sonicated in lysis buffer containing 1% SDS, 10 mM ethylenediaminetetraacetic acid (EDTA), 50 mM Tris (pH 8.0), and protease inhibitor. 2% of the supernatant was used as input, and the remaining lysate was immunoprecipitated with the anti-Flag antibody listed in Appendix B. Eluted RNA fragments were used as templates for RT-qPCR. Primer sequences complimentary to the intron region up to 150 bp upstream of the alternatively spliced exon were designed and listed in Appendix A. Data was calculated as a percentage of input and plotted as fold changes relative to its respective control IgG.    49 2.6 Cell morphology, cell proliferation, cell invasion and migration, colony formation, and apoptosis assays The morphology of the cells was imaged by Zeiss AxioObserver Z1 light microscopy (Carl Zeiss AG; Oberkochen, Germany) at 32X magnification, or the cells were fixed with 4% paraformaldehyde, treated in 0.25% Triton X-100 for 15 min, incubated with anti-F-actin conjugated to Phalloidin-iFluorTM 488 (Abcam; Cambridge, UK), and mounted with DAPI staining mount (Vector Labs; Burlingame, CA, USA). Cells were then imaged by Zeiss LSM780 confocal microscopy (Carl Zeiss AG) at 63X magnification. To calculate the length of the cell bodies, 50 cells were randomly selected, and the length was quantified by the Image J program (139). Three independent biological replicates were performed. For 2D and 3D bromodeoxyuridine (BrdU) cell proliferation, cells were measured with the BrdU cell proliferation assay kit (Millipore) according to manufacturer’s protocol. Cells were seeded in Matrigel (Corning; New York, NY, USA) for 3D BrdU proliferation assays. 2D-proliferation rates were measured every other day for 5 days post-seeding and 3D-proliferation rates measured 24 hrs post BrdU treatment. Optical density (OD) was measured at 450 nm. 2D-proliferation rates were also measured with the CellTitre 96 AqueousOne kit (Promega; Madison, WI, USA) according to manufacturer’s protocol. 2D-proliferation rates were measured at OD of 490 nm every other day for 5 days post-seeding. Each experiment contained six technical replicates, and three independent experiments were performed.  50 For wound-healing (migration) assays, a monolayer wound was created when cells reached 100% confluency. Cell migration was subsequently captured at time point 0- and 20-hrs after wound scratch. Migration ability of cells was calculated as the migration distance from the two time points by the Image J Program. Three technical replicates and three independent biological replicates were performed. Cell invasion assays were carried out by using BD BioCoatTM Matrigel Invasion chambers (BD Biosciences; Franklin Lakes, NJ, USA) according to manufacturer’s protocol. Briefly, 1x105 cells were seeded in the chambers containing serum-free media and incubated in serum containing wells. The membrane insert was then mounted 24 hrs later with DAPI staining mount onto microscope slides and imaged with Zeiss AxioObserver Z1 microscope (Carl Zeiss AG). Cells were quantified by the Image J program (139), and invasion rates were calculated as the percentage of cell invasion. Two technical replicates and three independent biological replicates were performed. For colony formation assays, cells were seeded in 0.7% soft agar with a 1% soft agar bottom base coating. The wells containing cell colonies were stained with crystal violet after 10-14 days. The entire well was imaged by stitching 5X field images together, a feature on the Zeiss AxioObserver Z1 microscope. Colony numbers were counted according to its diameter (>100 µm or 100–300 µm and >300 µm). Three technical replicates and three independent biological replicates were performed. Apoptosis assay methods were as followed: cells were treated with either docetaxel (Doce), camptothecin (CPT), UV irradiation, or vehicle (100% ethanol). 48 hrs following treatment, protein lysates were extracted for immunoblotting following the 51 aforementioned standard protocol. Two technical replicates and three independent biological replicates were performed. 2.7 FA assays For focal adhesion (FA) assays, cells were seeded on coverslips and serum-starved the next day. Following serum-starvation, cells were treated with 10 µM of nocodazole (Sigma-Aldrich; St. Louis, MO, USA) for 4 hrs, subsequently washed away, and replenished with serum-containing medium. Cells were fixed at 0- or 120-min after the washout for IF, co-stained against GIT1 (Alexa Fluor-594; Invitrogen) and vinculin (Alexa Fluor-488; Invitrogen), and then mounted with DAPI (Vector Labs). Antibody information is listed in Appendix B. Five quadrants of the coverslip were imaged using the Zeiss AxioObserver Z1 (Carl Zeiss AG) microscope, and the ZEN program profiled the overlapping GIT1 and vinculin signals in the FA complexes. Three biological repeats were performed for each experiment and the most representative images were shown. 2.8 CRPC TMA The CRPC tissue microarray (TMA) containing 64 tissue cores from 32 patients that had undergone hormonal therapy, chemotherapy, or radiotherapy was obtained from the tissue bank at VPC. This TMA was used for Chapters 3 and 5. The histopathology of the tumour cores has previously been described by Li et al. (91). In summary, the histology is classified as typical AdPC (n=52), AdNC (atypical AdPC with 10% NE cells; n=6), or SCNC (small-cell NEPC with only NE cells; n=6). Typical AdPC is classified by the presence of compact and homogenous glandular structures, large 52 cells with prominent nucleoli, and no NE cells. Next, SCNC tumours contain only NE cell populations, grow as solid sheets, and exhibit typical NE cell features, such as scant cytoplasm, salt-and-pepper nuclei, and a high nucleus-to-cytoplasm ratio. Areas of necrosis are often present in SCNC. Finally, AdNC subtypes are histologically similar to AdPC, however, they are atypical tumours comprised of a mixed-cell population containing ≥10% NE cells and are positive for at least two NE markers. 2.9 Human prostate cancer xenografts  All animal procedures were performed in accordance with the guidelines and regulations of the Canadian Council on Animal Care and approved by the Institutional Animal Care and Use Committee at the University of British Columbia. To construct xenografts in Chapter 3, 1×106 cells of the established DU145, PC-3, LNCaP, or 22Rv1 cell models overexpressing SRRM4 or empty vector were implanted subcutaneously 1:1 with Matrigel Matrix (Corning) in bilateral flanks of 6-8-wk old male nude (Nu/Nu) mice. All mice were surgically castrated prior to tumour implantation. NCI-H660 xenografts were previously established (91). To construct PC-3 xenografts in Chapter 4, 1×106 PC-3(MEAF6-1) or PC-3(CTL) cells were implanted subcutaneously with Matrigel (1:1) in bilateral flanks of 6-8-wk old male Nu/Nu mice. Tumour volume (V = length × width × height × 0.5236) was measured three times a week and were harvested for RNA extraction, protein extraction, or paraffin embedding when tumours reached experimental endpoints (V = 1500 mm3).   53 2.10 RISH and IHC assays  Xenograft tumours were fixed in 10% paraformaldehyde, paraffin embedded, and processed for RNA in situ hybridization (RISH) or immunohistochemistry (IHC). RISH probes targeting SRRM4 (NM_194286.3; targeting 496-835 bp), GIT1-A (NM_001085454.1; targeting 944-985 bp), GIT1-C (NM_014030.3; targeting 941-983 bp), and negative control probe (targeting the dapB gene from bacteria) were designed by Advanced Cell Diagnostic (Hayward, CA, USA). RISH assays were performed by technician Ms. Ning Xie and Dr. Yinan Li from the VPC using the BaseScopeTM assay kit (Advanced Cell Diagnostic) following manufacturer’s instructions. IHC staining was conducted with the Discovery XT autostainer (Ventana Medical Systems; Oro Valley, AZ, USA) and examined with their UltraMapTM DAB kit (Ventana Medical Systems). Antibodies used for IHC are listed in Appendix B. RISH and IHC scoring methods have previously been characterized by Li et al. (91). Briefly, positive RISH signals were presented as red dots under 10X and 40X magnifications. RISH scores of 0, 1, and 2 indicate no positive signal, <20% of all cells with positive signal, and 20% of all cells with positive signal within a tissue core, respectively. IHC scores were calculated by the signal intensity (no, low, medium, and high as 0‐3, respectively) multiplied by the percentage of positive cells. IHC scores of 0.3 were considered to be strongly positive.    54 2.11 Digital image analysis and Ki-67 scoring  All stained slides were digitalized with the SL801 autoloader and Leica SCN400 scanning system (Leica Microsystems; Wetzlar, Germany) at a magnification equivalent to 40X (scale bar, 100 µm). The images were subsequently stored in the SlidePath digital imaging hub (Leica Microsystems) of the VPC. For the Ki-67 scoring performed in Chapter 3, the Aperio Image Analysis IHC menu (Leica Biosystems; Wetzlar, Germany) was used. We selected five areas of interest within the same core, defined the parameter, optimized the level of intensity, and selected positive pixel Count Algorithm for Ki-67. 2.12 Ion AmpliSeq transcriptome sequencing and GSEA  For Chapters 3 and 5, DU145(Ctrl) and DU145(SRRM4) cell models or DU145(Ctrl), DU145(GIT1-A), and DU145(GIT1-C) were extracted using the mirVanaTM RNA Isolation Kit (Ambion) according to the manufacturer’s protocol. The quality of the RNA samples was assessed by NanodropTM 2000 and 2100 Bioanalyzer (Caliper Life Sciences; Hopkinton, MA, USA). Samples were then sent for Ion AmpliSeq Transcriptome Sequencing. Ion AmpliSeq transcriptome library preparation, sequencing, and primary analyses were completed by the UBC-DMCBH Next Generation Sequencing Centre (Vancouver, BC, Canada) following the protocol detailed by Li et al. (140). In summary, cDNA was synthesized from 100 ng of total RNA using the SuperScript® VILOTM cDNA Synthesis kit (Invitrogen) and amplified with Ion AmpliSeqTM technology (ThermoFisher Scientific). cDNA libraries were diluted to 100 pM and amplified on Ion Torren OneTouchTM 2 instrument (ThermoFisher Scientific) 55 using emulsion PCR. Template libraries were then subjected for sequencing of >20,000 RefSeq transcripts using the Ion Torrent ProtonTM sequencing system (ThermoFisher Scientific). Transcriptome data from Chapter 3 is accessible under the accession number GSE118104 in the Gene Expression Omnibus (GEO) database. Transcriptome data from Chapter 5 is available at https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2Fcas.13869&file=cas13869-sup-0006-TableS4.xlsx. Gene Set Enrichment Analysis (GSEA; http://www.broadinstitute.org/gsea/index.jsp) for Chapter 3 was carried out to determine whether a defined set of genes showed concordant enrichment between two samples groups (e.g. SRRM4 vs Ctrl) or two clinical phenotypes (e.g. t-NEPC with high vs low SOX2 expression). The analyses in Chapters 3 and 5 were performed using the latest MSigDB database for each gene set collection or using gene sets curated based on published data. Heatmaps were constructed based on the results of the AmpliSeq data. GSEA analyses in Chapter 3 used whole transcriptomic data with the p-value cut-off as 0.01. 2.13 Gene microarray Gene microarray (used in Chapter 4) was performed by the Laboratory for Advanced Genome Analysis (VPC) and have previously been described in detail (141). In summary, two independent biological repeats of LNCaP(CTL) and three independent biological repeats of LNCaP(MEAF6-1) and LNCaP(MEAF6-2) cells were extracted for total RNA using the mirVanaTM RNA Isolation Kit (Ambion) according to manufacturer’s 56 protocol and subsequently hybridized to human GE 8×60 K gene expression microarray (Agilent; Santa Clara, CA, USA). The resulting data was processed by the Agilent Feature Extraction software. For statistical analyses, comparisons were performed between RNA samples with LNCaP(MEAF6-1) vs LNCaP(CTL), LNCaP(CTL) vs LNCaP(MEAF6-2), and LNCaP(MEAF6-1) vs LNCaP(MEAF6-2). Unpaired t-test with p-value cut-off of 0.05, a fold change cut-off of 1.5, and Benjamini-Hochberg multiple testing correction were applied. LNCaP(MEAF6-1) vs LNCaP(CTL) transcriptomes were then uploaded and analyzed in IPA (Ingenuity Pathway Analysis) for pathway prediction algorithms. 2.14 Clinical, PDX, and GEMM datasets  Clinical cohorts used in Chapter 3 includes the following: RNA-seq data for the Beltran 2016 cohort (CRPC-Ad, n=34; t-NEPC, n=15) was from Weill Medical College of Cornell University (New York, NY, USA) (54); RNA-seq data for the Robinson 2015 cohort (CRPC, n=118) (142) and microarray and clinical data for the Grasso 2012 cohort (CRPC, n=31) (143) were accessed through cBioPortal (144,145); microarray data for both the Varambally 2005 (CRPC, n=6) (146) and Kumar 2016 cohorts (CRPC, n=169) (147) were accessed from the GEO database, GSE3325 and GSE77930, respectively. Microarray data from the Living Tumour Laboratory (LTL) 331-7 and 331-7-R castration time-series patient-derived xenograft (PDX) models were accessed by GSE59986 (148). Sequencing data for the genetically engineered mouse models (GEMMs) including the Ku et al. (WT, wild-type, SKO, single knock-out, DKO, double knock-out, TKO, triple knock-out) (70) and Zou et al. (NPp53) cohorts (71) were 57 accessed from GSE90891 and GSE92721, respectively. RNA-seq dataset of the LnNE cell model was previously reported by our lab (93). RNA-seq datasets from the Beltran 2011 (73) and VPC 2011 (92) clinical cohorts were also used in Chapter 4. Cohort composition for the Beltran 2011 and VPC 2011 were 7 NEPC and 30 AdPC and 5 NEPC and 8 AdPC, respectively. Chapter 5 includes the VPC 2018 (NEPC, n=5, and AdPC, n=24) (149) cohort rather than the VPC 2011 cohort. cDNA samples from the LTL PDX models were shared by Dr. Yuzhuo Wang from the VPC. All specimen collection and RNA extraction protocols for these datasets are described in their associated publications. 2.15 RNA-Seq analysis pipeline Implemented in-house and published with the VPC 2018 cohort, the RNA-seq analysis pipeline and algorithms used in Chapter 5 were performed by Dr. Varune R Ramnarine (VPC) and was previously described by Ramnarine et al. (149). However, in this study, the pipeline was updated to use the Hg38 human genome build and Ensembl GRCh38.86 gene tracks. Cufflinks were also used to identify and quantify alternative splice variants present within all annotated genes. 2.16 Statistics  For Chapters 3 and 4, all results are expressed as the mean ±SD, unless otherwise stated. Statistical analyses were done using GraphPad Prism version 7.00 for Windows (GraphPad Software; La Jolla, CA, USA). One-way ANOVA or Student 2-tailed t-tests were carried out to determine differences between groups. Correlation 58 between two expression groups were computed with Pearson’s r correlation, and overall survival was estimated by Kaplan-Meier. The levels of significance were set at a p-value of 0.05, 0.01, and 0.001, denoted as *, **, and ***, respectively. For VPC and Beltran cohorts in Chapter 5, all values were log2-transformed prior to statistical testing (Figure 5.1B). All clinical group-wise comparisons were calculated using a standard Student’s 2-tailed t-test. Multiple test corrections were applied to p-values using the Bonferroni and Hochberg correction method to minimize false discovery rate. Analyses between two groups were compared via unpaired Student’s 2-tailed t-test. Pearson's χ2 test was performed to compare GIT1-A and GIT1-C RISH scores to the different tumour groups (Figure 5.2B). One-way ANOVA and Newman-Keuls multiple comparison test were performed to compare between multiple groups (Figure 5.2C and Figure 5.3D). Pearson’s r correlation analyses were performed to compare between RISH scores and the expression of NE positive markers (Figure 5.2D), as well as GIT1-A or GIT1-C expression with NEPC and AdPC marker expression (Table 5.1) or SRRM4 expression (Table 5.2). Fisher’s exact test was used to calculate the sensitivity and specificity of GIT1-A to predict the NEPC phenotype (Table 5.3). The levels of significance were set at a p-value of 0.05, 0.01, and 0.001, denoted as *, **, and ***, respectively. 59  SRRM4 promotes neuroendocrine prostate cancer development via a pluripotency gene network 3.1 INTRODUCTION Tumour cell plasticity and heterogeneity creates many challenges for prostate cancer disease management. To counteract ARPI therapies and become CRPC tumours, AdPC cells can either enhance AR signaling in the presence of castration levels of androgens and progress into CRPC-Ad tumours or, alternatively, become AR-indifferent or AR-negative and progress into t-NEPC (46,63,150,151). While the molecular mechanisms by which AdPC cells transform into t-NEPC remain elusive, recent studies have shown that the combination of AR-inhibition with genomic inactivation of TP53, RB1, and PTEN confers AdPC cells lineage plasticity to gain basal, mesenchymal, or neuroendocrine (NE) phenotypes and subsequently the development of t-NEPC tumours (69,70). These studies demonstrate that this transition from AdPC to t-NEPC can be through an intermediate pluripotent stem cell (SC)-like state. During this state, there are elevated expressions of a network of pluripotency genes including the SOX family members such as SOX2 and SOX11 which are well known for its roles in early embryogenesis, embryonic SC pluripotency, and neurogenesis (69–71,152). Given the genomic heterogeneity of prostate tumour cells, these findings highlight that AdPC cells containing certain genomic features may be prone to undergo this lineage switching to develop into t-NEPC via a pluripotency gene network. 60 However, whole-exome sequencing has revealed that patient t-NEPC and AdPC tumours have similar gene mutation landscapes (54). In vitro, AdPC cell models have been shown to undergo a LE-to-NE cell lineage switch through a transdifferentiation mechanism to initiate t-NEPC development. This NE transdifferentiation process is shown to be mediated by dysregulations of a key transcriptional repressor of neuronal genes, REST (89,153,154), epigenetic modulators, such as EZH2 (54,74,75), and microenvironment factors (e.g. cAMP, IL-6, and hypoxia) (154–157). These results emphasize that multiple non-genomic factors also play important roles during t-NEPC establishment. In fact, we have recently shown that RNA splicing mechanisms, mediated by the RNA splicing factor SRRM4, promote this NE transdifferentiation mechanism of lineage plasticity in the development of t-NEPC tumours (90). The upregulation of SRRM4 is associated with t-NEPC and predominately establishes a t-NEPC-unique RNA splicing program when compared to AdPC tumours (90). SRRM4 can transform LNCaP AdPC cells, which express functional p53 and Rb1, into t-NEPC xenografts under androgen-deprived conditions. These SRRM4-transduced LNCaP cells also exhibit global transcriptome and RNA splicing signatures similar to that of t-NEPC tumours in patients. Through inoculating these cells into castrated nude mice and serially passaging the tumours for five generations over 18 months, we have reported the establishment of a series of t-NEPC models, called LnNE (93). LnNE tumours strongly express NE markers and present a NEPC morphology. Tumours at later passages grow more aggressively and become androgen-insensitive and PSA-negative. These features mimic AdPC progression to t-NEPC in patients. Further dissection of SRRM4-mediated 61 signaling has revealed that SRRM4 can not only compromise the functions of REST to promote NE transdifferentiation (90,158,159), but also regulate RNA splicing of other transcripts such as Bif-1 to promote the evasion of apoptosis (94). Altogether, these findings indicate that SRRM4 is a facilitator of t-NEPC through the NE transdifferentiation mechanism. SRRM4 is a key regulator of neural-specific exon networks that are required for the differentiation of neural progenitor and neuronal cells during embryonic development (95,96,98–100,108,118). It exerts its actions by promoting the inclusion of neural-specific exons, generating alterative protein isoforms necessary for remodeling protein-protein interaction networks and signaling pathways that are required for neurogenesis and neuronal cell maturation and function (99). The essential developmental roles of SRRM4 in the differentiation of neural progenitor and neuronal cells lead us to hypothesize that SRRM4 can ultimately promote NEPC development in any AdPC cell-specific context (i.e. with different genetic and epigenetic backgrounds). In this study, we report that SRRM4 induces a global NEPC-specific RNA splicing signature in a panel of prostate and prostate cancer cell models. However, SRRM4 induces heterogeneous transcriptomes and phenotypes among these cell models tested. The SRRM4-transduced DU145 cells are unique by which a pluripotency gene network including SOX2 is activated. This discovery characterizes another SRRM4-mediated mechanism underlying lineage plasticity distinct from the LnNE model. This model, so called the DuNE model, recapitulates the molecular signatures of a subset of patient t-NEPC tumours expressing stem-like molecular features, 62 emphasizing its clinical relevance and application in investigating the complex molecular mechanisms of t-NEPC development. 3.2 RESULTS 3.2.1 SRRM4 induces heterogeneous t-NEPC transcriptomes in multiple cell models Given the genetic and epigenetic heterogeneity innate to prostate tumour cells, we applied lentivirus encoding SRRM4 to transduce multiple cell lines with no endogenous SRRM4 protein expression previously reported by Li et al. (90) including prostate cancer lines (LNCaP, PC-3, DU145, 22Rv1), benign prostate epithelial lines (BPH-1 and RWPE-1), prostate stromal line (WPMY-1), and uterine smooth muscle line (hTERT-Myo) (Appendix Di). SRRM4 induced a previously reported NEPC-unique RNA splicing signature of 21 neural-specific transcripts in all of the eight cell models (Figure 3.1A), suggesting that SRRM4 is functionally active as an RNA splicing factor in these cells. Furthermore, SRRM4 induced an overall increase of multiple NE markers including ASCL1, CHD2, SCG3, SYP, and SYT4 and decrease of LE markers including CDH1 (encodes the E-cadherin protein), KRT8, and Nkx3.1 (Figure 3.1B). However, the extent of the changes in NE and LE marker expression varied as shown by RT-qPCR and immunoblotting assays (Figure 3.1C). These results indicate that although SRRM4 can activate a global NEPC-specific RNA splicing program, the subsequent induction of a NEPC-unique transcriptome is heterogeneous, which may be explained by the different genetic and epigenetic backgrounds of these cell models as previously reported (160–162).  63 Figure 3.1 SRRM4 induces heterogeneous t-NEPC transcriptomes in multiple cell models. Total RNA was extracted from the AdPC (LNCaP, PC-3, DU145, 22Rv1), benign prostate epithelial (BPH-1 and RWPE-1), prostate stromal (WPMY-1) and uterine smooth muscle (hTERT-Myo) generated stable cell lines overexpressing SRRM4 or empty vector control. RT-qPCR was then performed to measure (A) the RNA splicing activity of SRRM4 in promoting the expression of neural-specific variants and decreasing expression levels of the respective constitutive variants and (B) the expression of LE and NE markers in SRRM4-overexpressing cells compared to that of the control cells. All heat maps represent the relative fold change in log10. (C) Protein lysates from all stable cell models were used to measure E-Cad, SYP, and vinculin protein levels by immunoblotting assays. Three independent biological replicates were performed for each experiment. AdPC, prostate adenocarcinoma; LE, luminal epithelial; NE, neuroendocrine; SYP, synaptophysin; E-Cad, E-cadherin.  3.2.2 Multifarious histological features of SRRM4-transduced prostate cancer xenografts SRRM4 was incapable of inducing tumorigenesis of RWPE-1 cells that were inoculated into nude mice, indicating that SRRM4 is not essential for the oncogenesis of non-malignant prostate cells—at least not within the 14-wk duration of our experiment. All tumours were confirmed to express exogenous SRRM4 using RISH assays (Appendix Dii) as there are no commercially available antibodies that effectively recognize SRRM4 via IHC. However, SRRM4-transduced AdPC cell-derived xenografts Figure 3.1 64 presented differential phenotypes (Figure 3.2). E-cadherin (or E-cad) was reduced in the SRRM4-transduced DU145 and PC-3 tumours, but not the 22Rv1 and LNCaP tumours (Figure 3.2A). Pan-cytokeratin levels were only robustly reduced in the SRRM4-transduced DU145, or DU145(SRRM4), tumours. In contrast, all xenografts strongly expressed the NE marker, SYP. The most striking difference that separated the DU145(SRRM4) tumours from the others was that the tumour cells grew in high density within the tumour mass with minimal stromal components (Figure 3.2B). All DU145(SRRM4) tumour cells were strongly Ki-67 positive. The Ki-67 index in DU145(SRRM4) tumours were 3-fold higher than that of all other xenografts (Figure 3.2C). This phenomenon is highly similar to NCI-H660 tumours, a bona fide NEPC cell model. These findings suggest that SRRM4 confers DU145 cells strong anti-apoptotic and proliferative properties even under a hypoxic microenvironment with minimal stroma support and limited nutritional supply.   65  Figure 3.2 Multifarious histological features of SRRM4-transduced prostate cancer xenografts. DU145, PC-3, LNCaP, and 22Rv1 stable cell lines overexpressing SRRM4 or its respective control cells were inoculated in bilateral flanks subcutaneously in 6-8-week-old nude mice (n=3 mice per cell line). Mice were euthanized when tumour burdens reached experimental endpoint (volume of 1500 mm3) and the tumours were harvested, fixed, paraffin embedded, and processed for IHC detection against (A) E-cad, pan-cytokeratin, SYP, and (B-C) Ki-67. (C) Previously processed NCI-H660 xenografts were also processed for IHC detection against Ki-67. Ki-67 was quantified by using the Aperio Image Analysis IHC menu (Leica Biosystems) and the results are represented on the dot plot. Results are presented as the mean ± SD (One-way ANOVA; n = 5; ***denotes p <0.001). Scale bars represent 100 μm. IHC, immunohistochemistry; E-Cad, E-cadherin; SYP, synaptophysin.  Figure 3.2 66 3.2.3 Differential cellular impacts of prostate cancer cell models by SRRM4 The cellular impacts of SRRM4 on multiple cell models were also studied. In contrast to PC-3, LNCaP, and 22Rv1 cells, only DU145(SRRM4) cells showed dramatic morphological changes (Figure 3.3A). When cultured under 2D-conditions, a reduction of the cytoplasmic to nuclear ratio and 5-fold reduction in size of the cell bodies were observed in the DU145(SRRM4) cells compared to that of its respective control, DU145(Ctrl), cells. Dendritic projections similar to that of neuronal cells were also observed, exemplified by F-actin staining. Consistent with the high Ki-67 index of DU145(SRRM4) xenografts, SRRM4 stimulated proliferation of DU145 cells, but not the other cell models under 2D-conditions (Figure 3.3B). For the reason that only the DU145(SRRM4) cell and xenograft models (designated as the DuNE models) have thus far shown unique characteristics of t-NEPC, we performed further functional assays on the DuNE cell model. DuNE cells formed spheroids on Matrigel and showed stronger cell proliferation measured by BrdU incorporation assays when compared to that of the DU145(Ctrl) cells (Figure 3.3C). Furthermore, they formed 3-times the number of colonies on soft agar compared to the DU145(Ctrl) cells (Figure 3.3D). When challenged with apoptosis inducers including UV irradiation or chemotherapy reagents docetaxel (Doce) and camptothecin (CPT), the DuNE cells showed stronger anti-apoptotic properties than that of its control cells, evidenced by the levels of cleaved PARP (c-PARP) (Figure 3.3E). These results indicate that the DuNE cell model presents cellular phenotypes similar to that of t-NEPC tumour cells.   67  Figure 3.3 Differential cellular impacts of prostate cancer cell models by SRRM4. (A) The morphology of cells was imaged by Zeiss light microscope, where the scale bars represent 20 μm. Additionally, the DU145(SRRM4), or DuNE, cells and its control cells, DU145(Ctrl), were fixed for IF against F-actin using anti-F-actin antibody conjugated to Phalloidin-iFluor 488 and mounted with DAPI staining mount. Cells were then imaged by confocal microscopy, where the scale bar represents 10 μm. 50 cells were randomly selected to measure the average cell body length in μm in the Image J program and presented on the bar graph. (B) Cells were seeded in a 96-well plate for MTS assays to determine cell viability over a 5-day time course. The DuNE cells and its respective control cells, were seeded in (C) Matrigel for 3D BrdU proliferation assays, (D) soft agar for 10-day-long colony formation assays (where colonies >100 μm were counted), or (E) 10 cm2 dish and treated the next day with Doce (20 nM), CPT (60 nM), vehicle (100% ethanol), or 10 mJ/cm2 of UV irradiation. The protein lysates were then collected 48 hrs later and used to measure c-PARP and vinculin protein levels by immunoblotting assays. Three independent biological replicates were performed for each experiment. All results are presented as the mean ± SD (Student’s 2-tailed t-test; n = 3; ***denotes p<0.001). IF, immunofluorescence; UV, ultra violet; CPT, camptothecin; Doce, docetaxel; c-PARP, cleaved PARP; OD, optical density; F-actin, filamentous actin; BrdU, bromodeoxyuridine.  3.2.4 DuNE cells possess a pluripotency gene network distinct from the LnNE model To decipher signaling networks unique to DuNE, we performed Ion AmpliSeq Transcriptome analyses. There was a total of 3339 genes that were differentially upregulated and 2694 genes that were downregulated in the DuNE cells when compared to the DU145(Ctrl) cells. Gene set enrichment analyses (GSEA) revealed that the DuNE transcriptome was significantly enriched with gene sets related to lineage plasticity, cell survival, neuron differentiation, and neural-specific cell morphogenesis (Figure 3.4A). For example, SRRM4-target genes were enriched with gene ontology Figure 3.3 68 (GO) terms of stem cell differentiation, neuron differentiation, and cell morphogenesis in neuron differentiation, but were negatively associated with the hallmark apoptosis gene set. These findings indicate that SRRM4 can re-program the AdPC phenotype of DU145 cells towards a neuronal phenotype by activating a pluripotency gene network. Further analyses were performed to compare the transcriptomes of the DuNE and LnNE cell models (Figure 3.4B). GSEA revealed that the 3883 genes unique to the DuNE cell model were uniquely enriched with gene sets related to SC differentiation, neuronal SC population maintenance, anti-apoptosis, and neural precursor cell proliferation. Furthermore, we extracted the leading-edge subsets of genes associated with DuNE phenotype from the three GSEA gene sets under the “lineage plasticity” group from figure 4A (Appendix E). Of the 59-stem differentiation and SC-related genes, we validated the mRNA expressions of 13 genes including SOX2 in the SRRM4-transduced DU145, PC-3, LNCaP, and 22Rv1 cell models by RT-qPCR (Figure 3.4C). These results demonstrate that SRRM4 regulates different transcriptomes to promote the transformation of DU145 and LNCaP AdPC cells into t-NEPC xenografts, and that SRRM4 regulates a pluripotency gene network in the DuNE model.   69  Figure 3.4 DuNE cells possess a pluripotency gene network distinct from the LnNE model. (A) Transcriptome data of the DuNE, or DU145(SRRM4), cell model and its respective control, DU145(Ctrl) (p-value cut-off as 0.01) were analyzed by GSEA based on the latest MSigDB database for each collection. GSEA revealed the enrichment of genes associated with lineage plasticity, cell survival, neural fate, and cell morphology in the DuNE cells. (B) DEGs (p-value cut-off as 0.01) caused by SRRM4 transduction were compared between the DuNE (n=6063) and LnNE (n=5925) cell models. GSEA show more enrichment of the lineage plasticity and cell survival-associated genes in distinct DEGs (n=3883) mediated by SRRM4 in the DuNE model. (C) Thirteen SC- or pluripotency-related genes were selected from the leading-edge groups associated with the DuNE phenotype in the GSEA gene sets under the ‘lineage plasticity’ subgroup from subfigure 4A. The mRNA expression of these genes in the SRRM4-overexpressing DU145, PC-3, LNCaP, and 22Rv1 cell models were compared to that of its respective control cells via RT-qPCR. Heat maps represent the relative fold change in log10. GSEA, gene set enrichment analysis; DEGs, differential expression genes; GO, gene ontology; NES, normalized enrichment score; FDR, false discovery rate; CSS, charcoal-stripped serum; SC, stem cell.  3.2.5 SRRM4 enhances SOX2 expression in the DuNE cell model and in some GEMMs and PDX models SOX2 was selected to further delineate the SRRM4-regulated pluripotency gene network as SOX2 was recently demonstrated to promote the lineage plasticity of AdPC cells during progression to t-NEPC (69,70). SOX2 protein expression was only detected Figure 3.4 70 in the DuNE cells (Figure 3.5A). We showed that the upregulation of SOX2 by SRRM4 was in both time- and dose-dependent manners when SRRM4-expressing plasmids were transiently transfected into DU145 cells (Figure 3.5B). Moreover, SOX2 was downregulated when DuNE cells were challenged with SRRM4-targeted siRNA (Figure 3.5C), and SOX2 depletion resulted in the reduction of several pluripotency gene expressions and NE markers and induction of LE markers (Figure 3.5D). The LTL 331-7/331-7-R PDXs that model the progression of AdPC to t-NEPC after castration surgery also showed positive correlations of SRRM4 with SOX2 and SYP, but negative correlations with AR expression (Figure 3.5E). Furthermore, analyses of the recently reported WT/SKO/DKO/TKO GEMMs by Ku et al. (70) revealed that SOX2 was strongly and positively correlated with SRRM4 (Figure 3.5F), and negatively associated with AR (Appendix Fi). This negative association of SOX2 with AR was also observed in four clinical CRPC patient cohorts (Appendix Fii). However, SRRM4 does not show a correlation with SOX2 expression in the NPp53 GEMMs by Zou et al. (71) (Figure 3.5G), as well as the SRRM4-transduced PC-3, LNCaP, and 22Rv1 cell models, suggesting that the activation of the SRRM4-SOX2 axis does not apply to all prostate cancer models exhibiting t-NEPC features. Together our findings support that SOX2 is a major component of the pluripotency network regulated by SRRM4 in a subset of t-NEPC tumours.    71  Figure 3.5 SRRM4 enhances SOX2 expression in the DuNE cell model and in some GEMMs and PDX models. (A) Protein lysates from the SRRM4-overexpressing DU145, PC-3, LNCaP, and 22Rv1 cell models and its respective controls were used to measure SOX2 and vinculin protein levels by immunoblotting assays. (B) For the time-dependent studies, DU145 cells were transiently transfected with 4 μg of SRRM4 or control plasmids, and total RNA was extracted at times 0, 24, and 48 hrs. For the dose-dependent studies, DU145 cells were transiently transfected with 0, 2, and 4 μg of SRRM4 or control plasmids, and total RNA was extracted after 24 hrs. SOX2 expression was detected by RT-qPCR. (C-D) DuNE, or DU145(SRRM4), cells were transiently transfected with siRNA targeting SRRM4 (C), or SOX2 (D) and extracted for RNA following transfection to detect expression levels of indicated genes. All results are presented as the mean ± SD (Student’s 2-taile t-test; n = 3; * denotes p<0.05, **p<0.01, and Figure 3.5 72 ***p<0.001). (E) The expressions of SRRM4, SOX2, AR, and SYP during the progression of AdPC (LTL331) to t-NEPC (LTL331R_NE) after castration in the PDX LTL331/331R model are shown. (F) SRRM4 and SOX2 expressions in WT/SKO/DKO/TKO GEMMs (Ku et al., 2017) are shown as well as the Pearson’s r correlation coefficient between SRRM4 and SOX2 expressions. All results are presented as the mean ± SD (Student’s 2-tailed t-test; WT/SKO, n=8; TKO/DKO, n=19; *** denotes p<0.001). (G) SRRM4 and SOX2 expressions in different phenotypic subcategories of the NPp53 GEMMs (Zou et al., 2017) are shown as well as the Pearson’s r correlation coefficient between SRRM4 and SOX2 expressions. All results are presented as the mean ± SD (One-way ANOVA; AdPC, n=8; Focal NE, n=3; Overt NE, n=2; *** denotes p<0.001, and “ns” denotes not significant). GEMMs, genetically engineered mouse models; AdPC, prostate adenocarcinoma; WT, wild-type; SKO, single knock-out; DKO, double knock-out; TKO, triple knock-out; NE, neuroendocrine. PDX, patient-derived xenograft; AdPC-to-t-NEPC, prostate adenocarcinoma to treatment-induced neuroendocrine prostate cancer; d, day; wk, week; Cx, castration; NE, neuroendocrine.   3.2.6 The DuNE model represents a subset of clinical t-NEPC tumours with high SOX2 expression To define the clinical relevance of SOX2, we found that high SOX2 expression was correlated with poor prognosis in patients after receiving hormone therapies in the Grasso et al. (143) CRPC cohort (Figure 3.6A). When exploring the RNA-seq data of four patient cohorts reported by Beltran et al. (54), Robinson et al. (142), Kumar et al. (147), and Varambally et al. (146), we observed an overall positive association of SOX2 with SRRM4 expression (Appendix G). However, we found markedly distinct expressions of SOX2 within the t-NEPC tumours from the Beltran et al. (54) cohort, the largest t-NEPC cohort containing pathological evaluation of each sample. There were five t-NEPC tumours with low SOX2 expression, t-NEPC(SOX2low), similar to that of CRPC-Ad tumours, while the remaining ten t-NEPC tumours expressed a ~17.5-fold (log2) increase of SOX2 levels, t-NEPC(SOX2high) (Figure 3.6B). These findings demonstrate that different subgroups of t-NEPC tumours exist, where a division of two subgroups is evident based on a robust difference in SOX2 mRNA expression levels. 73 To determine the clinical relevance of the DuNE model to the t-NEPC(SOX2high) subgroup, we applied GSEA to compare DuNE transcriptome to that of: 1) the top 500 differentially expressed genes between t-NEPC(SOX2high) and CRPC-Ad, 2) the top 500 differentially expressed genes between t-NEPC(SOX2high) and t-NEPC(SOX2low), and 3) the top 500 genes differentially expressed between t-NEPC(SOX2low) and CRPC-Ad (Figure 3.6C). We observed strong enrichment of the DuNE transcriptome with that of the t-NEPC(SOX2high) subgroup. Moreover, GSEA revealed that the t-NEPC(SOX2high) subgroup was enriched with gene sets associated with pluripotency and proliferation, where the leading-edge genes presented a consistent expression pattern in the DuNE cell model (Figure 3.6D). Furthermore, this SRRM4-SOX2 axis was also seen in t-NEPC patient tumour cores that have been previously characterized (91) (Figure 3.6E). In the six t-NEPC patient tumours, all were SRRM4-positive but only four were SOX2-positive. Collectively, these findings recognize two distinct subgroups of t-NEPC tumours classified by SOX2 expression. Although both subgroups have relatively high levels of SRRM4, our results support that SRRM4 may promote t-NEPC development through different mechanisms (Figure 3.7). The DuNE model represents a clinically relevant model to study SRRM4-SOX2 axis during t-NEPC progression.   74  Figure 3.6 The DuNE model represents a subset of clinical t-NEPC tumours with high SOX2 expression. (A) Overall survival Kaplan-Meier between low and high expression of SOX2 in CRPC tumours from the Grasso et al. (2012) cohort. (B) SOX2 expression was compared among CRPC tumour samples from the Beltran et al. (2016) cohort and separated into three groups, CRPC-Ad (n=34), t-NEPC expressing low level of SOX2, t-NEPC(SOX2low) (n=5), and t-NEPC expressing high level of SOX2, t-NEPC(SOX2high) (n=10). Results are presented as the mean ± SD (One-way ANOVA; *** denotes p<0.001, and “ns” denotes not significant). Expressions of SOX2 and SRRM4 in each patient sample (patient ID: “WCMC-”) are indicated as high, red, or low, blue, where the median value of the SRRM4 datasets define the cut-off. (C) GSEA revealed enrichment of the DU145(SRRM4), or DuNE, model with that of the differentially expressed top 500 genes in the 1) t-NEPC(SOX2high) tumours when compared with CRPC-Ad or 2) t-NEPC(SOX2low) and in the 3) t-NEPC(SOX2low) tumours compared with CRPC-Ad. (D) GSEA revealed the enrichment of the t-NEPC(SOX2high) tumours with well-defined gene sets named “Wong Embryonic stem cell core,” “Sarrio EMT up,” and “Benporath Proliferation,” where the leading-edge genes presented a consistent expression pattern in the DuNE cell model. (E) Six t-NEPC patient tumours were used to measure SRRM4 and SOX2 expression by applying RISH and IHC assays, respectively. Due to limited space, two SOX2-positive and two SOX2-negative tumour cores are shown. Scale bars represent 100 μm. CRPC-Ad, castration-resistant prostate adenocarcinoma; t-NEPC, treatment-induced neuroendocrine prostate cancer; GSEA, gene set enrichment analysis; NES, normalized enrichment score; FDR, false discovery rate; EMT, epithelial-to-mesenchymal transition; RISH, RNA in situ hybridization; IHC, immunohistochemistry; SYP, synaptophysin. Figure 3.6 75 Figure 3.7 Proposed models for SRRM4 in promoting t-NEPC development through different mechanisms. A schematic diagram illustrates two mechanisms of SRRM4 in promoting the development of t-NEPC tumours. AdPC, prostate adenocarcinoma; t-NEPC, treatment-induced neuroendocrine prostate cancer; ARPI, androgen receptor pathway inhibitors; NE, neuroendocrine; LE, luminal epithelial.  3.3 DISCUSSION This study identifies a novel mechanism by which SRRM4 transforms AdPC cells into t-NEPC xenografts through a pluripotency gene network containing SOX2. Importantly, this newly discovered SRRM4-SOX2 signaling axis can be stratified from a subgroup of t-NEPC in patients, emphasizing its clinical significance. Additionally, we have established a new NEPC cell and xenograft model (DuNE), which recapitulates Figure 3.7 76 the phenotypes of clinical t-NEPC tumours expressing stem-like characteristics. Together with our previously reported LnNE model, we propose that SRRM4 can facilitate the transformation of AdPC into t-NEPC tumours through either a NE transdifferentiation pathway or a pluripotency gene network. Whether a pluripotency gene network is initiated by SRRM4 likely relies on the epigenetic and genomic heterogeneity of AdPC cells. These studies highlight the complexity of SRRM4 signaling in promoting tumour progression to t-NEPC to develop therapy resistance. Heterogeneous genomic features of cancer cells within an AdPC tumour may contribute to the activation of the SRRM4-SOX2 signaling pathway in the progression of t-NEPC. Upregulation of SRRM4 and SOX2 expressions only occurs in the Pten/Rb1 DKO and Pten/Rb1/Trp53 TKO GEMMs, but not in the WT and Pten SKO GEMMs (Figure 3.5F), suggesting that Rb1-inactivation is necessary for the activation of the SRRM4-SOX2 signaling to confer the lineage plasticity of LE cells to a NE cell lineage. While the Pten/Trp53 DKO NPp53 GEMMs show either focal or overt NE differentiation (71), the upregulation of SRRM4 does not concur with SOX2 stimulation (Figure 3.5G), indicating that the Trp53-inactivation is not sufficient in activating the SRRM4-SOX2 signaling axis. However, a recent study has shown that an increase in SOX2 expression was observed in PTEN-deficient LNCaP cells with TP53- and RB1-inactivations, while SRRM4 remained negative in these cells (69). Although DU145 cells are also TP53- and RB1-deficient cells, RB1 silencing to SRRM4-transduced TP53-null PC-3 cells do not show similar phenotypes to the DuNE model (Appendix N). Collectively, these findings demonstrate that the activation of the SRRM4-SOX2 signaling axis cannot be solely explained by loss-of-function of PTEN, TP53 and RB1. This suggests that other 77 complex mechanisms in addition to the abovementioned genomic factors also contribute to the t-NEPC progression facilitated by a SRRM4-mediated pluripotency gene network. Epigenetic mechanisms may be responsible for SRRM4 and SOX2 activation. CBX8 and EZH2 are the two most overexpressed epigenetic regulators in t-NEPC clinical samples and PDXs (54,63,74). It is known that EZH2 is implicated in t-NEPC development (63,75,163). Intensive H3K27Me3 covers the coding regions of the SRRM4 and SOX2 genes in LNCaP cells and prevents the transcription of these genes. Furthermore, it has been reported that LNCaP cells cultured in an androgen-free neural/neural crest stem medium can redirect the cells into a neural-fate possibly via EZH2-mediated epigenetic reprogramming (80), indicating the importance of the tumour microenvironment in regulating tumour cell plasticity (63,79). The DU145 cell line is originally derived from an AdPC metastasis lesion to the brain (78), suggesting that the neural microenvironment of the cell niche may have altered the epigenome of the DU145 cells to be predisposed to a NE lineage switch. In addition, although both DU145 and PC-3 cells exhibit stem-like phenotypes and properties such as expressions of SC markers and self-renewal capabilities (63,81,82), only the SRRM4-transduced DU145 cells undergo a robust NE differentiation via the activation of a pluripotency gene network containing SOX2. These findings indicate that the DU145 cells possess unique epigenetic features that may contribute to the SRRM4 and SOX2 signaling axis, although the connection between epigenetic factors and the SRRM4-SOX2 axis warrant further investigations. 78 Currently, there are limited t-NEPC cell and xenograft models that can be utilized to efficiently manipulate signaling pathways and study molecular mechanisms of t-NEPC development. The only NEPC cell line, NCI-H660, is very difficult to be transfected or transduced by lentivirus and has a doubling time of >100 hrs (164). More importantly, it is unclear whether this cell line was derived from AdPC cells or primary NEPC cells. The five newly developed LnNE xenografts, replicate the prolonged transition process of AdPC progression to t-NEPC in an in vivo environment (93). However, compared to the LnNE model, the DuNE model has several advantages. The DuNE cell model develops into xenografts faster than the LnNE model and exhibits more dramatic changes in LE and NE marker expressions. Additionally, the DuNE model is AR- and PSA-negative, TP53- and RB1-null, exhibits striking neuronal-like morphologies, and mimics the molecular signatures of a subset of clinical t-NEPC (i.e. SCNC) that present stem-like characteristics as previously demonstrated (63,165,166). Moreover, plasmid transfection efficiency can be achieved as high as 80%, and these cells have a doubling time of ~15-20 hrs in 2D-conditions. DuNE cells can be adapted into 3D-culture formats (such as petri dishes and hanging-drop systems) and studied as xenografts in immunocompromised mice, all systems of which are suitable for high-throughput drug screening. In summary, our study reports a novel SRRM4-mediated mechanism underlying lineage plasticity where lineage switching is primarily mediated by a pluripotency network including SOX2, a key regulator of dedifferentiation and neural lineage-specific fate. Furthermore, the newly established DuNE model can also serve as a useful t-NEPC model to study the complexity of AdPC progression to t-NEPC.  79  The role of alternative RNA splicing of the MEAF6 gene during neuroendocrine prostate cancer progression 4.1 INTRODUCTION Next-generation ARPI prolong survival of patients with metastatic CRPC (24,25). However, a consequence of the selection pressures exerted by these more potent ARPI is that they can promote the emergence of AR-independent CRPC, one variant of which is t-NEPC. T-NEPC is reported in up to 25% of post-ARPI CRPC patients and the rate of occurrence is predicted to rise with the widespread use of ARPI (41,63). Once diagnosed, t-NEPC patients succumb to death within <2 years (64). No targeted therapy is available for t-NEPC patients, only systemic chemotherapy regimens, reflecting our limited knowledge on the molecular underpinnings of t-NEPC progression. There is accumulating evidence indicating that t-NEPC is clonally derived from AdPC cell precursors (46,54,63,66). Whole exome sequencing has shown similar mutational landscapes between t-NEPC and AdPC (54,66,67). Case studies reveal that cell populations of AdPC, AdPC with neuroendocrine differentiation, and t-NEPC co-exist in the same tumour (46,167). Intermediate morphological and phenotypical transitions exist in cancer cells between the boundaries of AdPC and t-NEPC cell populations, indicating dynamic NE differentiation processes. The transition of AdPC to t-NEPC has been reported in the LTL 331/331-R PDX models following castration, with no genotypic alterations pre- and post-castration (66). Moreover, in castrated mice, when neural RNA splicing factor SRRM4 is introduced exogenously, LNCaP AdPC cells can be transformed into t-NEPC xenografts (90). These findings collectively support that t-NEPC originates from AdPC. 80 The transition of AdPC to t-NEPC is a complicated process that may involve both cell differentiation and proliferation, two distinguishable and coordinated processes controlled by multiple genes. Luminal epithelial AR signaling is critical in the maintenance of epithelial cell differentiation in the adult prostate (2,5). As a result, AR inhibition can trigger prostate cancer cell de-differentiation to confer adaptive plasticity and promote phenotype reprogramming. While AR blockade is necessary for t-NEPC establishment, it is insufficient since <25% of ARPI-treated tumours are transformed into t-NEPC (41,63). Similarly, AdPC cells can acquire a NE phenotype when treated with microenvironment factors such as IL-6 or cAMP (155,168,169), but these cells have not been reported to be capable of forming a t-NEPC tumour. While AdPC with NE differentiation can be found in 10-100% of AdPC tumours (46,49–53), expression of NE markers is not sufficient for t-NEPC establishment. To establish a t-NEPC tumour, these NE-like differentiated cells must gain a proliferative state for clonal expansion. Targeting genes that facilitate NEPC cell proliferation would be more effective in delaying t-NEPC progression, particularly in patients under ARPI treatment. Using whole-transcriptome sequencing of AdPC and NEPC patient biopsies from the Beltran 2011 (73) and VPC 2011 (92) cohorts, we report a t-NEPC-unique RNA splicing signature that is predominantly facilitated by SRRM4. SRRM4 can transform LNCaP AdPC cells into t-NEPC xenografts, suggesting that SRRM4 regulates RNA splicing of multiple transcripts that are responsible for NE differentiation and cell proliferation processes during t-NEPC progression. In fact, we have shown that SRRM4 regulates RNA splicing of the REST transcript and re-programs its functions to confer a NE phenotype in AdPC cells (90). However, REST knockdown in LNCaP cells does not 81 facilitate the development of t-NEPC xenografts, supporting the notion that SRRM4-mediated REST alternative RNA splicing leads to NE differentiation but does not induce proliferation sufficient for clonal expansion and t-NEPC tumour establishment. In searching for other t-NEPC-unique SRRM4-target transcripts that may contribute to NEPC progression, we have found a highly expressed splice variant of MYST/Esa1-associated factor 6 (MEAF6), MEAF6-1, in NEPC tumour samples. Although a protein component of the HAT complexes (130–134), there have been no studies on the cellular functions of MEAF6. MEAF6-1 and MEAF6-2 are two protein coding transcripts from the MEAF6 gene, whereby exon 6 (30 bp) is included in MEAF6-1 but skipped in MEAF6-2 mRNA. Since increased MEAF6-1 expression is closely associated with t-NEPC, we set out to determine whether MEAF6-1 contributes to t-NEPC progression. 4.2 RESULTS 4.2.1 Alternative RNA splicing of the MEAF6 transcript is associated with NEPC progression Whole transcriptome sequencing data on AdPC and NEPC tumour samples from the VPC 2011 (8 AdPC and 5 NEPC samples) and Beltran 2011 (32 AdPC and 6 NEPC samples) cohorts were analyzed by a computational tool, COMPAS (90), which identified an increase in the ratio of MEAF6-1:MEAF6-2 in NEPC patients (p=0.0034 in the VPC cohort and p=0.0002 in the Beltran cohort), while total MEAF6 mRNA expression remained unchanged (Figure 4.1A-B). These results indicated that the inclusion of the neural-specific exon of the MEAF6 transcript is a unique feature of NEPC. RT-qPCR assays on tumour samples from PDXs further confirmed that MEAF6-82 1 mRNA levels in NEPC were about 150-fold higher than AdPC (p=0.001), while MEAF6-2 mRNA levels in NEPC were not statistically different between NEPC and AdPC (p=0.338) (Figure 4.1B). Increased MEAF6 RNA splicing was also positively correlated with elevated SRRM4 mRNA expression in both xenograft (Figure 4.1C) and clinical CRPC samples (Appendix Hi). Additionally, RNA splicing activity of the MEAF6 transcript was positively correlated with REST RNA splicing (Appendix Hii). These results collectively suggest that SRRM4 may be also be a regulator of alternative splicing of the MEAF6 transcript. In prostate cancer cell lines, MEAF6-1 was highly expressed in NEPC cell line NCI-H660 as well as SCLC cell lines NCI-H69 and NCI-H82, when compared to MEAF6-1 expression levels in AdPC cell lines (p=0.00028). In contrast, MEAF6-2 mRNA levels were not statistically different in AdPC lines from NCI-H660, NCI-H69, and NCI-H82 cell lines (Figure 4.1D). Further validation of MEAF6 protein expression could not be done because currently available antibodies cannot differentiate MEAF6 splicing variants from each other, and immunoblotting and immunohistochemistry assays were unable to recognize endogenous MEAF6 proteins. Together, these results indicate that up-regulation of the expression of MEAF6-1 splice variant is closely associated with NEPC progression.  83 Figure 4.1 Alternative RNA splicing of the MEAF6 transcript is associated with NEPC progression. (A) Illustration of MEAF6-1 and MEAF6-2 RNA. The alternatively spliced exon (exon 6) is illustrated in red, where constitutive exons are denoted in yellow. IGV was used to visualize the coverage of MEAF6 by RNA-seq reads in AdPC and NEPC patient tumours and PDXs. Grey areas represent the sequencing depth of the respective exon, where the more prominent peaks reflect the significant presence of the positioned exon. (B) MEAF6 splicing ratio (MEAF6-1:MEAF6-2 RNA-seq reads per base-pair) and MEAF6 total expression obtained from RNA-seq data of AdPC and NEPC patient tumour samples (NEPC n=5 and AdPC n=8 in VPC cohort; NEPC n=6 and AdPC n=32 in Beltran cohort) (C) Validation of RNA-seq data, Figure 1A, using RT-qPCR on RNA isolated from AdPC and NEPC PDX. (D) Profiling of mRNA copy numbers of MEAF6 splice variants in a panel of AdPC cell lines (LNCaP, LN95, PC-3, DU145, C4-2, 22Rv1 and VCaP) and NEPC cell line (NCI-H660) as well as small cell lung cancer (SCLC; NCI-H69 and -H82), which is a neuroendocrine cancer of the lung. This was done via RT-qPCR for absolute quantification of total MEAF6-1 and MEAF6-2 using a standard curve. All results are presented as the mean ± SEM (Student’s 2-tailed t-test *** denotes p<0.001 and ** denotes p<0.01). AdPC, adenocarcinoma prostate cancer; NEPC, neuroendocrine prostate cancer; VPC, Vancouver Prostate Centre; SCLC, small cell lung cancer; IGV, integrative genomics viewer; PDX, patient-derived xenograft.  4.2.2 SRRM4 regulates RNA splicing of the MEAF6 gene To determine whether SRRM4 regulates alternative RNA splicing of the MEAF6 transcript, we transiently transfected SRRM4 expression vector in LNCaP cells. SRRM4 did not alter the levels of total MEAF6 transcripts (Figure 4.2A). Instead, it induced MEAF6-1 but had no impact on MEAF6-2 mRNA levels. SRRM4 regulation of MEAF6 RNA splicing was further confirmed in SRRM4 knockdown conditions via siRNA (Appendix I). To test whether other RNA splicing factors may also regulate the inclusion of the neural-specific exon of the MEAF6  transcript, we repeated the experiments with Figure 4.1 84 a panel of splicing factors and showed that RNA splicing of the MEAF6 transcript is uniquely regulated by SRRM4 (Figure 4.2B). Furthermore, RNA-chromatin immunoprecipitation (RNA-ChIP) assays confirmed that SRRM4 is recruited to its putative 3’ UGC splice site in the intron region upstream of the neural-specific exon in the MEAF6 transcript, but not in the control intron region of the GAPDH transcript (Figure 4.2C). In addition, we found that RNA splicing or regulation of MEAF6 mRNA expression was not altered by AR signaling (Appendix J). Collectively, these results demonstrate that SRRM4 is an important regulator of MEAF6-1 splicing.   85  Figure 4.2 SRRM4 regulates RNA splicing of the MEAF6 gene. (A) Illustration of splice variant-specific primers designed to detected total MEAF6, MEAF6-1, and MEAF6-2 mRNA levels. LNCaP cells seeded in 6-well plates were transiently transfected with 4 µg of Flag-SRRM4 for 24-hrs and were isolated for RNA. RT-qPCR for absolute quantification of total MEAF6, MEAF6-1, and MEAF6-2 using a standard curve was performed. (B) LNCaP cells were transiently transfected with a panel of RNA splicing factors (hnRnp I, U1A, PSF, PTB, ASF1/SF2, SRRM4, U2AF65). Cells were then extracted for RNA. Relative quantifications of total MEAF6, MEAF6-1, and MEAF6-2 were compared to 18S via RT-qPCR. Results are presented as the mean ± SEM (one-way ANOVA; n=3; *** denotes p<0.001). (C) RNA-ChIP was performed with 293T and LNCaP cells in 10 cm2 dishes. Cells were transfected with 10 µg of Flag-SRRM4 and immunoprecipitated with anti-Flag antibody following fixation with paraformaldehyde. Eluted RNA fragments were used as templates for RT-qPCR primers antisense for intron sequence upstream of alternatively spliced exon of MEAF6-1. Position of probes for MEAF6-1 and GAPDH is indicated by the yellow star. Otherwise indicated, results are presented as the mean ± SEM (Student’s 2-tailed t-test, n=3 *** denotes p<0.001 and ** denotes p<0.01). F, forward; R, reverse; CTL, control (empty vector). Figure 4.2 86 4.2.3 MEAF6-1 promotes prostate cancer cell growth and invasion To study the biological functions of MEAF6-1 in prostate cancer cells, we constructed LNCaP and PC-3 cell lines that stably express exogenous MEAF6-1 or MEAF6-2 via lentiviral transduction (Appendix K). Neither MEAF6-1 nor MEAF6-2 changed the expression levels of NE markers CHGA, ENO2, SYP, CHGB or LE markers such as CHD1 (Appendix L). These results indicate that neither of the MEAF6 splice variants are involved in NE trans-differentiation of prostate cancer cells.  Under 2D culture conditions, BrdU incorporation assays demonstrated that enhanced expression of MEAF6-1 but not MEAF6-2 in both LNCaP and PC-3 cells induced a 40–50% increase of cell proliferation rates (Figure 4.3A). Since NEPC cells lose its epithelial morphology and favor 3D growth conditions, we also seeded MEAF6-1 and MEAF6-2 stable lines into Matrigel to allow for the formation of multi-cellular spheroids and then performed BrdU incorporation assays. We repeatedly observed that MEAF6-1 stimulated cell proliferation (Figure 4.3B). MEAF6-1-overexpressed LNCaP cells also formed significantly larger sizes and numbers of anchorage-independent colonies than control and MEAF6-2-overexpressed cells (Figure 4.3C). Furthermore, MEAF6-1 enhanced the migration and invasion rates of PC-3 cells (Figure 4.3D). Collectively, these results demonstrate that MEAF6-1 and MEAF6-2 have different biological functions in promoting prostate cancer cell proliferation and invasion that can accelerate tumour progression. 87  Figure 4.3 MEAF6-1 promotes prostate cancer cell growth and invasion. Using LNCaP or PC-3 cell lines stably expressing CTL, MEAF6-1, or MEAF6-2, (A) 2D BrdU cell proliferation assays were performed to measure proliferation of cells. BrdU results represent colorimetric quantitative measurements (OD at 450 nm wavelength) of cellular BrdU incorporation into DNA. (B) Cells were also seeded in Matrigel for BrdU proliferation assays to measure the proliferative ability under 3D conditions. (C) Soft agar assays were performed on LNCaP cells in a 6-well plate. Colonies were stained with crystal violet and counted by varied sizes (i.e. 100-300 µm or >300 µm). Images of wells were captured by Zeiss light microscope, representing one of the three independent biological replicates. (D) PC-3 stable cells were seeded in 6-well plates and incubated for 16-hrs for wound healing assays. Area of migration measured using Image J program. Cell invasion ability of PC-3 cell lines was measured using Matrigel Invasion chambers. All results are presented as the mean ± SEM (one-way ANOVA; n = 3; *** denotes p<0.001 and ** denotes p<0.01). CTL, control (empty vector); OD, optical density; BrdU, bromodeoxyuridine.  4.2.4 MEAF6-1 transcriptome in prostate cancers To profile the MEAF6-1 transcriptome, we compared gene microarrays using LNCaP(CTL), LNCaP(MEAF6-1), and LNCaP(MEAF6-2) stable lines. There were 2044 genes differentially regulated by MEAF6-1 and 2702 genes by MEAF6-2. DAVID (Database for Annotation, Visualization, and Integrated Discovery) for GO analyses indicated that the functions of both gene groups regulated by MEAF6-1 or MEAF6-2 are very similar. GO terms indicated that MEAF6-1 and MEAF6-2-regulated genes are Figure 4.3 88 associated with cell cycle, cellular response to stress, cell division, regulation of cell cycle, and DNA replication. Since MEAF6-1, but not MEAF6-2, promotes cell proliferation, anchorage-independent growth and invasion, we further stratified a group of 159 genes that were specifically regulated by MEAF6-1 (Figure 4.4A). Interestingly, ID1 and ID3 genes are the genes most upregulated by MEAF6-1. When the MEAF6-1-regulated gene list was analyzed by IPA (Ingenuity Pathway Analysis), it predicted that ID1 and ID3 gene networks were the most prominent downstream effectors for MEAF6-1 and are highly associated with cell proliferation, cell cycling, and cell migration and invasion functions (Figure 4.4B). These microarray results on MEAF6-1 transcriptome were further validated by our RT-qPCR assays showing that MEAF6-1 induced a 5-fold increase of ID1 and a 4-fold of increase of ID3 mRNA levels in LNCaP cells (Figure 4.4C). Moreover, MEAF6-1 increased protein expression levels of ID1. ID3 protein levels were not confirmed because of the poor quality of antibodies available against it. Additionally, MEAF6-1-induced ID1 and ID3 expression in LNCaP and PC-3 cells was dramatically compromised when cells were challenged by siRNA targeting MEAF6 (Figure 4.4D-E). To further confirm that ID1 and ID3 mediates MEAF6-1 functions in cell proliferation and invasion, we silenced ID1 or ID3 with siRNA and observed that ID1 and ID3 depletion significantly attenuated MEAF6-1 actions on cell proliferation and invasion (Figure 4.4F). These results indicate that ID1 and ID3 genes play important roles in mediating MEAF6-1 actions for cancer cell proliferation and invasion.   89 Figure 4.4 MEAF6-1 function is mediated through ID1 and ID3. Microarray was performed comparing LNCaP(CTL), LNCaP(MEAF6-1), and LNCaP(MEAF6-2). (A) Gene expressions with fold changes over 1.3 and an adjusted p-value of less than 0.05 from LNCaP(MEAF6-1) vs LNCaP(CTL) were overlapped with LNCaP(MEAF6-1) vs LNCaP(MEAF6-2) transcriptomes. (B) LNCaP(MEAF6-1) vs LNCaP(CTL) transcriptomes were uploaded and analyzed in IPA for pathway prediction algorithms of ID1 and ID3. Dashed arrows represent indirect regulation, where solid arrows represent direct regulation. (C) MEAF6-1-regulation of ID1 and ID3 expression was then validated via RT-qPCR. ID1 protein expression was validated by Western blot. (D) LNCaP(CTL) and LNCaP(MEAF6-1) or (E) PC-3(CTL) and PC-3(MEAF6-1) stable lines were transfected with siRNA targeting MEAF6 (siMEAF6) to study the effect of MEAF6-1 on ID1 and ID3 expression. Relative quantification of MEAF6-1, ID1, and ID3 compared to 18S via RT-qPCR. (C-E) All results are presented as the mean ± SEM (Student’s 2-tailed t-test *** denotes p<0.001). (F) PC-3(CTL) and PC-3(MEAF6-1) stable cells were transfected with control (siCTL), ID1 (siID1), or ID3 (siID3) targeted siRNA. 2D BrdU cell proliferation assays were performed, where BrdU results represent colorimetric quantitative measurements (OD at 450 nm wavelength) of BrdU incorporation into DNA. Cell invasion ability of the PC-3 stable cells with siRNA transfections were measured using Matrigel Invasion chambers. All results are presented as the mean ± SEM (one-way ANOVA; n=3; *** denotes p<0.001; ** denotes p<0.01; * denotes p<0.05). CTL, control (empty vector); OD, optical density; BrdU, bromodeoxyuridine; IPA, ingenuity pathway analysis.  4.2.5 MEAF6-1 accelerates xenograft tumour growth To study the impact of MEAF6-1 on xenograft growth, we transplanted PC-3(CTL) or PC-3(MEAF6-1) cells subcutaneously into nude mice. Tumour uptake at ~3 Figure 4.4 90 weeks was similar in both groups. However, tumour volume of PC-3(MEAF6-1) xenografts increased significantly faster than control tumours (Figure 4.5A). Mice were sacrificed once tumour burden reached experimental points and xenograft tissues were collected. RT-qPCR and immunoblotting assays validated exogenous MEAF6-1 expression in PC-3(MEAF6-1) xenografts as well as MEAF6-1 upregulation of ID1 mRNA and protein and ID3 mRNA levels (Figure 4.5B). Together with the results from Figure 4.3 and Figure 4.4, these results confirm that MEAF6-1 promotes prostate xenograft growth in part through ID1 and ID3 genes.  Figure 4.5 MEAF6-1 accelerates xenograft tumour growth. (A) 1x106 PC-3(CTL) or PC-3(MEAF6-1) were subcutaneously injected into nude mice (CTL n=16; MEAF6-1 n=20). Tumour volume was measured using a caliper. (B) Tumours were extracted and processed for RNA and protein analysis. Relative quantification of MEAF6-1, ID1, and ID3 compared to 18S via RT-qPCR. Protein expressions was detected via Western blot using antibodies against exogenous Flag-MEAF6, ID1, and loading control beta-actin. CTL, control (empty vector). Figure 4.5 91 4.3 DISCUSSION This study reports that a functionally reprogrammed MEAF6 transcript via alternative splicing by neuronal splicing factor SRRM4 facilitates prostate cancer cell proliferation, invasion, and migration, which may contribute to NEPC progression. Spliced by SRRM4, the MEAF6-1 variant does not mediate neuroendocrine differentiation of prostate cancer cells, but rather promotes cell proliferation and invasion to accelerate tumour growth. MEAF6-1 exerts these actions mainly via the ID1 and ID3 genes, which are well characterized inhibitors of cell differentiation as well as enhancers of cell proliferation and multipotency preservation (170). These findings highlight the critical role of alternative RNA splicing in NEPC progression and support SRRM4 as a key facilitator of t-NEPC. Through RNA splicing, SRRM4 simultaneously reprograms REST functions to confer a neuroendocrine phenotype in AdPC cells and re-directs MEAF6 functions to stimulate tumour cell proliferation and invasion (Figure 4.6).   92 Figure 4.6 Differentiation and proliferation: two distinguishable and coordinated processes for AdPC to NEPC tumour establishment. SRRM4 re-directs MEAF6 functions via alternative RNA splicing to stimulate tumour cell proliferation and invasion through ID1 and ID3. AdPC, prostate adenocarcinoma; NEPC, neuroendocrine prostate cancer.  To our knowledge, this is the first report that characterizes the biological functions of the MEAF6 gene with a particular focus on the actions of MEAF6 alternative RNA splicing and its role in NEPC progression. It is known that MEAF6 is a component of 4 different MYST family of HAT complexes in cells that modulate gene transcription through posttranslational modifications of histones (130–134). However, no studies have confirmed whether MEAF6 can exert enzymatic activities directly to acetylate histones or whether it serves as an adapter to recruit differential protein factors to the HAT complexes. Until this study, it was not clear whether or not MEAF6 can regulate transcription of endogenous genes. The existence of this protein in the HAT complex has only been confirmed by mass spectrometry (171); however, this method cannot differentiate between the different splice variants of MEAF6. Our studies show that the Figure 4.6 93 constitutive MEAF6-2 splice variant is ubiquitously expressed in AdPC, while MEAF6-1 is specifically expressed in NEPC patient, PDX, and cell models. While MEAF6-2 regulates a similar transcriptome as MEAF6-1, it does not affect prostate cancer cell differentiation, proliferation, or invasion, suggesting that the unique MEAF6-1-regulated transcriptome (i.e. 159 genes) is more important for prostate cancer proliferation, invasion, and tumour growth. These results are consistent with previous studies reporting that SRRM4 commonly targets “microexons” to modulate protein-interaction networks during neurogenesis, where misregulation of microexon-mediated networks is associated with neural-related disorders such as autism (95). Since SRRM4 and MEAF6-1 expression is NEPC tumour specific, we propose that SRRM4-mediated inclusion of the neural-specific exon results in the functional reprogramming of the MEAF6-1 protein, which may contribute to NEPC progression by accelerating proliferation of prostate cancer cells that have acquired neuroendocrine phenotypes. We report that MEAF6-1 facilitates NEPC progression mainly by activating the ID1 and ID3 gene networks. The ID family of proteins are inhibitors of bHLH, ETS, and PAX transcription factors and RB family members which regulate cell differentiation (172). ID protein expression is high in stem and progenitor cells and, while often downregulated during normal cell differentiation, are reactivated in cancer (170). While tumour suppressor proteins such as Rb1 and p53 inhibit ID proteins from activation (173,174), excessive ID proteins override the tumour suppressor functions of RB1 (175). Therefore, MEAF6-1-induced ID1 and ID3 may create a condition mimicking genetic inactivation of RB1 and TP53. In this context, MEAF6-1 may play a similar role as SV40 in the TRAMP mouse model, where prostate-specific expression of SV40 94 sequesters Rb1 and p53 functions and leads TRAMP tumour progression into NEPC (176,177). As a result, our findings suggest a SRRM4-MEAF6-1-Rb1/p53 axis that may contribute to NEPC progression. In summary, our studies demonstrate that SRRM4-mediated RNA splicing of the MEAF6 transcript may contribute to NEPC progression via the neural-specific MEAF6-1 splice variant, where MEAF6-1 may facilitate prostate cancer cell proliferation and tumorigenesis required for the development of and progression to NEPC.  95  The role of alternative RNA splicing of the GIT1 gene during neuroendocrine prostate cancer progression 5.1 INTRODUCTION While de novo NEPC represent <2% of all prostate cancer incidences, the increasing prevalence of t-NEPC as an outcome of selection pressures exerted by ARPI treatments is becoming a paramount clinical issue (46,62,63). Currently, t-NEPC accounts for >16% of all cases of CRPC patients (41,63). Patients with t-NEPC have a mean survival rate of ~7 months post-diagnosis, as the disease is already very aggressive and more resistant to contemporary chemo- and radiation therapies (46,64). Presently, there are no targeted therapies available to treat t-NEPC effectively due to the limited understanding of the mechanisms initiating its origin and development. Furthermore, t-NEPC is predicted to become even more prevalent considering the extensive clinical applications of ARPI therapies (63). This highlights the importance of delineating the molecular underpinnings of t-NEPC to inform future therapies that can prevent or mitigate its development. Emerging evidence suggests that t-NEPC is derived from AdPC, and this transition can occur as a result of NE differentiation. While AdPC and NEPC tumours have similar genotypes (i.e. somatic copy number, point mutations, and polyploidy), its transcriptome, epigenome, and cellular morphologies differ (46,54,59,66,67). In recent studies, we have demonstrated that a pre-mRNA splicing factor, SRRM4, can facilitate NE differentiation that transforms LNCaP AdPC cells into t-NEPC tumours by reprogramming the transcriptome via alternative RNA splicing under ARPI (90). Following this study, we have established a new t-NEPC cell model, called LnNE, which 96 utilizes the SRRM4-overexpressing LNCaP cells to create 5 generations of xenografts and cell models (93). Interestingly, this SRRM4-directed RNA splicing profile shares a similar pattern to the diverse alternative splicing patterns seen in the neural system during development, where SRRM4-spliced target genes have recognized functions that are crucial for neural programs early in development (95,96,98). Furthermore, our studies have reported that this reprogrammed RNA splicing signature is unique to NEPC patient tumours, PDX models, and cell models, demonstrating a clear variance in the phenotype of NEPC and AdPC tumours (90,94,138). The SRRM4-mediated t-NEPC-specific reprograming of the transcriptome modifies anti-apoptotic factors (e.g. Bif-1), epigenetic modifiers (e.g. PHF21A, MEAF6-1), and transcriptional regulators (e.g. REST) that are important for regulating cell survival (94), proliferation and tumorigenesis (178), and neural differentiation (90), respectively. Together, these studies propose a model in which SRRM4 facilitates t-NEPC development via alternative RNA splicing of downstream transcripts. Among the transcripts alternatively spliced by SRRM4, the G-protein-coupled receptor kinase-interacting protein 1 (GIT1) transcript is differentially spliced into GIT1-A and GIT1-C, where the GIT1-A splice variant is uniquely found in t-NEPC (90). GIT1 is a multifaceted signaling scaffold protein within the ArfGAP family of proteins that contains a conserved architecture, including an N-terminal ArfGAP domain, three ankryin repeats, a Spa2-homology domain, a coiled-coil domain, and a focal adhesion (FA) targeting domain (179). GIT1 has a variety of canonical biological functions, such as endocytosis regulation of receptors, FA regulation, cell motility, morphogenesis, angiogenesis, and neural functions (such as synapse and dendritic spine 97 morphogenesis) (179). One of the key functions of GIT1 in epithelial cells is its recruitment of FA proteins, which is important for the formation of stable invadopodia structures. Invadopodia structures utilize stable FAs to degrade the ECM and enable efficient invasion of cancer cells (180,181). In neural systems, GIT1-mediated recruitment of FA proteins activates signaling pathways important for regulating directed spine morphogenesis, cell-to-cell communication, and stability of synapses (136,137). FA-mediated signaling can involve other fundamental aspects of cell biology including survival, proliferation, and environmental sensing of the ECM, mechanical stress, growth hormones, and hypoxic conditions (i.e. oxygen and pH alteration) (182,183). Additionally, dysregulation or malfunction of GIT1 has been associated with various neural-associated diseases, such as Huntington’s disease and glioblastoma (179). Increased GIT1 expression has also been reported to promote the migration, invasion, and metastasis of liver, colon, melanoma, lung, renal, and non-small cell lung cancer cells (179,184–187). However, the function of GIT1 in the progression of prostate cancer as well as the role of GIT1 RNA splicing in any cancers is unknown and has not been previously studied. Here, we investigate the role of GIT1 alternative RNA splicing during t-NEPC progression and the function of its alternative splicing isoforms, GIT1-A and GIT1-C.  5.2 RESULTS 5.2.1 Expression profiling of GIT1 splice variants in NEPC patients, PDXs, and cell models Whole-transcriptome datasets of Beltran 2011 (73), Beltran 2016 (54), and VPC 2018 (149) patient cohorts, LTL PDX models (66,148), and LnNE (90) RNA-sequenced 98 datasets were analyzed to determine the expression of GIT1 splice variants in the various prostate cancer models. The software Integrative Genomics Viewer was used to visualize the coverage of RNA-seq reads corresponding to the GIT1 transcript. We confirmed the presence of GIT1-A, which differ from GIT1-C by an alternatively spliced 27 bp microexon (exon 8), in the NEPC models, but not the AdPC models (Figure 5.1A). All three patient cohorts confirmed a robust increase in GIT1-A expression in NEPC tumours (p<0.0001) when the expression was compared to that of AdPC or CRPC-Ad (Figure 5.1B). Interestingly, GIT1-C expression was decreased by 6- (p<0.05), 11- (p<0.01), and 13-fold (p<0.0001) in NEPC tumours when the expression was compared to AdPC subtypes in the VPC 2018, Beltran 2011, and Beltran 2016 cohorts, respectively. Although total GIT1 levels were increased in the Beltran 2011 NEPC tumours, neither the VPC 2018 or Beltran 2016 cohorts showed significant changes in its total GIT1 expression (p=0.91 and p=0.077, respectively). This inverse relationship between the expression of the two splice variants was validated by RT-qPCR in the LTL PDX models (Figure 5.1C), as well as in the NEPC cell models LnNE and DuNE (Figure 5.1D). Here, NEPC PDX and cell models expressed increased levels of GIT1-A mRNA, but decreased levels of GIT1-C, when compared to the levels in AdPC models. Noteworthy, the 331-7-R PDX and LnNE cells are t-NEPC models that differentiated from its AdPC phenotypes (331-7 and LNCaP, respectively) after castration (66,90). Furthermore, profiling GIT1-A and GIT1-C expression in a panel of different prostate cell lines (i.e. benign, AdPC, NEPC, SCLC) revealed that, overall, NEPC and SCLC cell lines expressed higher mRNA copy numbers of GIT1-A and lower mRNA copy numbers of GIT1-C, when compared to its expressions in benign and AdPC cell lines (Figure 99 5.1E). Together, these data suggest that the alternative RNA splicing of the GIT1 transcript may be associated with the progression to t-NEPC.  Figure 5.1 Expression profiling of GIT1 splice variants in NEPC patient, PDX, and cell models. (A) Illustration of the GIT1-C and GIT1-A exonic regions, where yellow represents the constitutive exons and red represents the 9-amino acid alternatively spliced microexon (exon 8). Integrative Genomics Viewer was used to visualize the coverage of GIT1 by RNA-seq reads. Grey peaks represent the sequencing depth of each respective exon. (B) Analyzed RNA-seq data of total GIT1 and splice variant expressions from VPC 2018 (AdPC n=24 and NEPC n=5), Beltran 2011 (AdPC n=30 and NEPC n=7), and Beltran 2016 (CRPC-Ad n=34 and NEPC n=15) cohorts is shown. (C-D) Relative RNA levels of GIT1 splice variants in (C) the LTL PDX models and (D) in the AdPC subtype (LNCaP) and NEPC cell models (LnNE and DuNE) was validated. (E) GIT1 splice variant expressions were profiled from a benign prostate cell line (BPH-1), a panel of AdPC cell lines (22Rv1, C4-2, DU145, LN95, LNCaP PC-3, and VCaP), a NEPC cell line (NCI-H660) and a SCLC cell line (NCI-H82) via RT-qPCR for absolute quantification of GIT1-A and GIT1-C using a standard curve. All results are presented as the mean ±SD (Student’s 2-tailed t-test; *, **, and *** represent p<0.05, p<0.01, and p<0.0001, respectively). AdPC, prostate adenocarcinoma; NEPC, neuroendocrine prostate cancer; CRPC-Ad, castration-resistant adenocarcinoma; LTL, living tumour laboratory; PDX, patient-derived xenograft; VPC, Vancouver Prostate Centre; SCLC, small-cell lung cancer. Figure 5.1 100 5.2.2 RNA splicing of GIT1 is associated with clinical NEPC tumours  Currently, there are no commercially available GIT1 antibodies to specifically detect each splice variant. As a result, we utilized RISH, a well-established alternative technique (91,94,138), on a TMA to study and confirm the expression of GIT1-A and GIT1-C. We created probes to target the unique exon 7/8 junction of GIT1-A, as well as the exon 7/9 junction of GIT1-C, to detect RNA expression levels on the TMA. The human CRPC TMA contains 64 cores: 52 AdPC, 6 AdNC, and 6 SCNC. Details of these prostate cancer subtype classifications have been previously published by our group (91,94,138). Briefly, AdPC is classified by the presence of glandular structures, large cells with a prominent nucleolus, and no NE cells. Next, SCNC tumours contain only NE cell populations, grow as solid sheets, and exhibit typical NE cell features, such as scant cytoplasm, salt-and-pepper nuclei, and a high nucleus-to-cytoplasm ratio. Finally, AdNC subtypes are histologically similar to AdPC, however, they are atypical tumours comprised of a mixed-cell population containing ≥10% NE cells and are positive for at least two NE markers. We found that GIT1-A is expressed in 20 out of 64 tissue cores (Appendix C). The 20 cores positive for GIT1-A included all 6 SCNC cores (5 of 6 had a RISH score of 2) and 5 AdNC cores (Figure 5.2A). Conversely, 51 of 52 AdPC cores were positive for GIT1-C, whereas only 2 AdNC and 1 SCNC cores were positive (RISH score of 1). Correlation studies indicate that GIT1-A expression levels increase in AdNC, and even further in SCNC, when compared to the expression in AdPC. Moreover, GIT1-C expression levels decrease in AdNC and SCNC when compared to the expression levels in AdPC (Figure 5.2B-C). GIT1-A expression showed significant positive 101 correlation (p<0.0001) with all three NE markers (CHGA, r=0.7209; SYP, r=0.7152; and CD56, r=0.7148) and negative correlation with markers of AdPC (AR, r=-0.3937, p=0.0013; and PSA, r=-0.3259, p=0.0086). Inversely, GIT1-C expression was negatively correlated with CHGA (r=-0.6193, p<0.0001), SYP (r=-0.6353, p<0.0001), and CD56 (r=-0.4158, p<0.01) expression, but positively correlated with AR expression (r=0.4105, p<0.001; Table 5.1). Furthermore, GIT1-A expression was positively correlated with the number of positive NE markers in a tissue core (r=0.7172, p<0.001), while GIT1-C expression was negatively correlated (r=-0.6473, p<0.001; Figure 5.2D).  Table 5.1 Correlation of GIT1 splice variants with clinical diagnostic markers of AdPC and NEPC. Pearson’s r correlation was performed between the expression of GIT1-A or GIT1-C and expressions of NEPC (i.e. CHGA, SYP, and CD56) or AdPC (i.e. AR and PSA) diagnostic markers used in the clinic. NEPC, neuroendocrine prostate cancer; AdPC, prostate adenocarcinoma. Correlation with GIT1-A Pearson r value p value CHGA 0.7209 <0.0001 SYP 0.7152 <0.0001 CD56 0.7148 <0.0001 AR -0.3937 0.0013 PSA -0.3259 0.0086    Correlation with GIT1-C Pearson r value p value CHGA -0.6193 <0.0001 SYP -0.6353 <0.0001 CD56 -0.4158 <0.001 AR 0.4105 <0.001 PSA 0.04045 ns Table 5.1 102  Figure 5.2 RNA splicing of GIT1 is associated with clinical NEPC tumours. (A) RISH probes targeting the exons 7/8 or exons 7/9 junction were created to detect GIT1-A or GIT1-C, respectively, in a human CRPC TMA (n=64 cores). The TMA was stained against CHGA, SYP, CD56, AR and PSA via IHC. The columns in the heatmap represents one of sixty-four cores. One representative core from each of the histologically diagnosed AdPC (n=52), AdNC (n=6), and SCNC (n=6) core is shown. Scale bars represent 25 m. (B-C) The cores were grouped accordingly to its histopathology report, and its respective RISH scores were plotted to present the (B) percentage of cores containing the same RISH score (Pearson's 𝜒2 test) or (C) average RISH score within each tumour subtype (one-way ANOVA; ***, p<0.001; “ns”, non-significant). (D) RISH scores from each core were plotted with respect to the number of positive NE markers within the same core (Pearson's r correlation). RISH, RNA in situ hybridization; TMA, tissue microarray; CRPC, castration-resistant prostate cancer; IHC, immunohisochemistry; AdPC, prostate adenocarcinoma; AdNC, adenocarcinoma prostate cancer with neuroendocrine cells; SCNC, small-cell neuroendocrine prostate cancer; NE, neuroendocrine. Figure 5.2 103 To determine the diagnostic reliability of GIT1-A to detect a NEPC phenotype, we defined AdNC and SCNC as a NEPC phenotype. Fisher’s exact test revealed that the GIT1-A sensitivity of correctly identifying the NEPC phenotype was 0.917 and the specificity was 0.827 (Table 5.2). These results indicate that while GIT1-A is detectable in NEPC cores, the low specificity suggests that AdPC tumours may also express GIT1-A. However, it is unknown whether these AdPC GIT1-A-positive cores progressed to NEPC. Overall, these collective findings indicate a strong positive association between GIT1 splice variant expression and t-NEPC development, namely that GIT1-A expression manifests largely in NEPC tumours. Table 5.2 Sensitivity and specificity of GIT1-A as a biomarker for NEPC. Fisher’s exact test was performed to determine the sensitivity and specificity of GIT1-A as a diagnostic biomarker for NEPC prediction. SCNC and AdNC is defined as NEPC in this case. NEPC, neuroendocrine prostate cancer; AdPC, prostate adenocarcinoma; SCNC, small-cell neuroendocrine prostate cancer; AdNC, adenocarcinoma prostate cancer with neuroendocrine cells.   GIT1-A + GIT1-A - Marginal Row Totals Sensitivity Specificity AdPC 9 43 52 0.917 0.827 NEPC 11 1 12   Marginal Column Totals 20 44 64 (Grand Total)    p value <0.00001    Table 5.2 5.2.3 SRRM4 regulates RNA splicing of GIT1 Based on our previous report, SRRM4 predominately facilitates the t-NEPC-unique RNA splicing signature, promoting the transformation of AdPC to a NEPC phenotype (90,138). RISH probes targeting SRRM4 were hybridized on identical CRPC TMA cores (Appendix C). SRRM4 expression was strongly correlated with GIT1-A 104 (r=0.7994, p<0.0001) and negatively correlated with GIT1-C expression (r=-0.6067, p<0.0001; Figure 5.3A & Table 5.3). To investigate how SRRM4 mediates GIT1 splicing, we overexpressed SRRM4 in LNCaP cells and detected a 5-fold increase in GIT1-A expression and a ~40% reduction in GIT1-C expression (Figure 5.3B). Transfection with siRNA in the LnNE model was used to knock-down SRRM4, which reduced GIT1-A expression by ~20% and increased GIT1-C expression by 6-fold (Figure 5.3C). Within the panel of splicing factors tested, SRRM4 was unique for its GIT1-splicing activity (Figure 5.3D). We designed a ChIP probe specific to the 3' intron splicing site upstream of the alternatively spliced microexon 8 of GIT1. Using an RNA-ChIP binding assay, we determined that SRRM4 directly binds to GIT1 and facilitates its splicing at exon 8 but does not bind to the negative control region on GAPDH (Figure 5.3E). Collectively, these results demonstrate that SRRM4 directly splices the GIT1 transcript to promote the neural-specific GIT1-A splice product. Table 5.3 SRRM4 expression is associated with GIT1 splice variant expressions in CRPC. Pearson’s r correlation was applied between the expressions of GIT1-A or GIT1-C and SRRM4 expression. CRPC, castration-resistant prostate cancer; RISH, RNA in situ hybridization.        GIT1-A RISH score    0 1 2 SRRM4 RISH score 0 42 5 0 1 2 7 1 2 0 3 4    r= 0.7994; p<0.0001   GIT1-C RISH score    0 1 2 SRRM4 RISH score 0 0 21 26 1 3 4 3 2 6 1 0    r= -0.6067; p<0.0001 Table 5.3 105 Figure 5.3 SRRM4 regulates RNA splicing of GIT1. (A) Matched TMA cores are shown to represent the associations of the expressions of SRRM4 with GIT1-A and GIT1-C. Scale bars represent 25 m. (B) LNCaP cells were transfected with 4 g of flag-SRRM4 and subsequently extracted for RNA. Relative expressions of GIT1-A or GIT1-C were compared to 18S via RT-qPCR. Primer pairs designed for unique exon junctions in GIT1-A and GIT1-C variants are illustrated. (C) NEPC cell model, LnNE, was transfected with 20 M of siRNA targeting SRRM4 or negative control siRNA to determine the splicing activity of GIT1. (D) 4 g of various RNA splicing factors or control (empty vector) were transfected into LNCaP cells and subsequently extracted for RNA. RT-qPCR was performed to compare GIT1 splice variant expression compared to 18S (one-way ANOVA; n=3; ** and *** denotes p<0.01 and p<0.001, respectively). (E) RNA-ChIP probes for the intron sequence upstream of the alternatively spliced microexon (exon 8) of GIT1 and probes for negative-control gene GADPH were created, as indicated by the yellow star. RNA-ChIP was performed with LNCaP cells transfected with 10 g of Flag-SRRM4 and immunoprecipitated with anti-Flag antibody. RNA fragments were eluted and used as templates for the antisense primers/probes via RT-qPCR. All experiments were repeated three times. Unless otherwise indicated, results are presented as the mean ±SD (Student’s 2-tailed t-test, n=3; **, p<0.01; ***, p<0.001). Ctrl, control; TMA, tissue microarray; RNA-ChIP, RNA-chromatin immunoprecipitation.   5.2.4 Transcriptome and cellular functions of GIT1 splice variants To determine the functional significance of GIT1-A and GIT1-C, we transduced DU145 cells to overexpress GIT1-A (DU145(GIT1-A)) or GIT1-C (DU145(GIT1-C)) and confirmed its expression by immunoblotting and RT-qPCR (Figure 5.4A). Using Ion AmpliSeq Transcriptome analyses, we profiled and compared the DU145(GIT1-A) or DU145(GIT1-C) transcriptome to DU145(Ctrl) (n=524 and n=755 genes, respectively) (Figure 5.4B). The transcriptomes were mostly distinct, with the exception of 177 genes Figure 5.3 106 in common. To investigate further into the biological differences between the GIT1-A and GIT1-C transcriptomes, we performed GSEA and compared the two phenotypes, revealing that the GIT1-A transcriptome was enriched with gene sets related to neural function, morphogenesis, environmental sensing, and epigenetic regulation (Figure 5.4C). The GIT1-C transcriptome was enriched with gene sets associated with general immune function and metabolism (Appendix M). Within the four subgroups enriched in the GIT1-A transcriptome, we extracted the leading-edge genes from the “GO regulation of cell adhesion mediated by integrin,” “GO cardiac epithelial to mesenchymal transition,” and “GO neuron recognition” MSigDB gene sets and generated a heatmap using GSEA software (Figure 5.4D). Among these genes identified, we validated the expression of 9 using RT-qPCR (Figure 5.4E). These results suggest that GIT1-A and GIT1-C exhibit differential functions, whereby GIT1-A facilitates progression to a neuronal transcriptome indicative of NEPC. Consistent to the GESA predictions, we confirmed that the GIT1 splice variants differentially regulate FAs (Figure 5.5). We observed that GIT1-A promoted, while GIT1-C compromised the stability of FAs, indicated by vinculin (a universal FA marker), when DU145 cells were treated with nocodazole. Collectively, our transcriptomic and FA assays indicate distinctive functional roles of GIT1 splice variants in regulating FA stability, which may contribute to NEPC development.  107  Figure 5.4 Transcriptome profiling of GIT1 splice variants. (A) DU145 stable cell lines overexpressing GIT1-A, GIT1-C, or empty vector (Ctrl) were created via lentiviral transduction and expressions were validated by immunoblot and RT-qPCR. (B-C) Transcriptomes of these cell lines (Ctrl, n=2; GIT1-A, n=3; GIT1-C, n=3) were profiled by Ion AmpliSeq Transcriptome. (B) Compared to control, genes unique to the transcriptomes of GIT1-A (n=4326) or GIT1-C (n=5003) were overlapped (n=200). (C) The transcriptomes of GIT1-A and GIT1-C were analyzed by GSEA based on the latest MSigDB database for each collection. GSEA revealed the enrichment of genes associated neural function, morphogenesis, and environmental sensing in the DU145(GIT1-A) cells. GSEA enrichment plots from these categories are presented where differential expressions of the leading-edge genes are shown in the heatmaps created by the GSEA software. (D) Expression of nine of these genes were validated and confirmed by RT-qPCR relative to the DU145(Ctrl) cell line (n=2 per cell line). Heatmap was created using the normalized z-scores of each row. All experiments were repeated three times. GSEA, gene set enrichment analysis; GO, gene ontology; NES, normalized enrichment score; NOM p-val, nominal p-value.   Figure 5.4 108  Figure 5.5 Differential functions of the GIT1 splice variants in FA stability. DU145 stable cell lines overexpressing GIT1-A, GIT1-C, or empty vector were seeded on coverslips and serum-starved the next day. They were treated with 10 M Nocodazole for 4 hrs, subsequently washed away, and replaced with serum-containing medium. Cells were fixed at 0 or 120 min after the washout, co-stained against GIT1 and vinculin, and then mounted with DAPI staining mount. Cells were imaged using the ZEISS AxioObserver Z1 microscope, where the scale bar represents 10 μm. Arrowheads indicate FA complexes. Overlapping signals between GIT1 and vinculin appear yellow. The overlapping of the two signals in a cross section (indicated by the white line) of FA complexes were profiled by the ZEN program. All experiments were repeated three times. IF, immunofluorescence; FA, focal adhesion; ZEN, ZEISS efficient navigation.  5.3 DISCUSSION Using patient tumour samples, PDX models, and cell models, we are the first to characterize the alternative RNA splicing of the GIT1 transcript in its association with Figure 5.5 109 NEPC progression. We found that an inverse correlation of high GIT1-A and low GIT1-C expressions are associated with the NEPC phenotype, when compared to GIT1 splice variant expressions in AdPC subtypes (Figure 5.1 and 5.2 & Table 5.1 and 5.2). We demonstrate that SRRM4 is an important regulator of GIT1 post-transcriptional modifications, whereby SRRM4 expression in NEPC tumours is associated with the splicing of the GIT1 transcript (Figure 5.3 & Table 5.3). From whole-transcriptome analyses, we report differential transcriptomes of GIT1-A and GIT1-C, where GIT1-A regulates gene sets that are associated with morphogenesis, neural function, environmental sensing, and epigenetic regulation (Figure 5.4). Consistent with our transcriptomic analyses, we report opposing functions of GIT1-A and GIT1-C in the stability of FAs, whereby GIT1-A-overexpressing cells promote FA stability (Figure 5.5). Altogether, our study demonstrates that SRRM4-mediated RNA splicing of the GIT1 transcript reprograms its function involving FA-mediated signaling and cell processes, which may contribute to t-NEPC development. Our current understanding of SRRM4-facilitated NEPC progression involves multiple processes, including NE differentiation, apoptosis evasion, cell proliferation, and tumorigenesis. Subsequent studies have found that these processes involved in NEPC progression are facilitated by REST, Bif-1, and MEAF6 which are functionally reprogrammed via SRRM4-mediated alternative splicing to facilitate NE differentiation (90), apoptosis evasion (94), and cell proliferation and tumorigenesis (178), respectively. However, there are many other cellular functions of SRRM4 waiting to be defined. In fact, this study is the first to report that SRRM4 regulates RNA splicing of GIT1 to reprogram its function, whereby the neural-specific GIT1-A splice variant 110 regulates genes associated with cell-adhesion processes (Figure 5.4C) and enhances FA stability (Figure 5.5). In support of this, DuNE is an SRRM4-mediated NEPC model that also expresses genes related to FA processes, suggesting that SRRM4 may modulate FA-mediated signaling and cell processes through the GIT1 splice variants. This understanding demonstrates that multiple gene networks and cellular processes are altered during NEPC progression, by which cell-adhesion gene networks are only a part of this multifaceted process. Moreover, SRRM4 splices genes that are canonical components of many epigenetic complexes, such as MEAF6 and PHF21A (90,138,178). GIT1 has also been suggested to regulate epigenetic modifications via its interaction with MAT2B, which synthesizes methyl donors for DNA and histone methylation during cancer progression (179). Additionally, our transcriptomic analyses reveal that the GIT1-A transcriptome is enriched with gene sets related to epigenetic regulation (Figure 5.4C). Though specific epigenetic regulation of GIT1 has yet to be reported, we hypothesize that SRRM4-mediated RNA splicing mechanisms may interplay with epigenetic mechanisms to reprogram AdPC cells and facilitate t-NEPC development. In conclusion, our research suggests that RNA splicing of the GIT1 transcript is associated with the progression of NEPC, evident by our comprehensive transcriptomic data, which reveals distinct molecular changes regulated by GIT1-A and GIT1-C. In addition, we confirm that GIT1 splice variants differentially regulate the stability of FAs. Since SRRM4 is a demonstrated facilitator of NEPC progression, our studies may reveal a novel function of SRRM4 in regulating FA-mediated processes through GIT1 alternative RNA splicing.  111  Conclusions 6.1 SUMMARY OF FINDINGS This dissertation work uncovers a novel mechanism by which SRRM4 mediates lineage plasticity required for t-NEPC development. We have demonstrated that SRRM4 mediates NE differentiation of AdPC cells via a pluripotency gene network including the SOX2 gene in the DuNE cell model. Given the cellular heterogeneity innate to prostate tumours seen in the clinic, these results suggest that SRRM4-mediated mechanisms of t-NEPC likely relies on the cell context (i.e. specific genetic or epigenetic backgrounds). Furthermore, we have investigated a novel SRRM4-SOX2 axis which is also present in clinical NEPC patients, PDXs, and GEMMs.  Moreover, our studies characterize the downstream mechanisms and functional consequences of SRRM4-mediated alternative RNA splicing of the MEAF6 and GIT1 transcripts. We have found that alternative RNA splicing of the MEAF6 and GIT1 transcripts are directly mediated by SRRM4 and are unique to NEPC patient tumours, PDX, and cell models. Though SRRM4-mediated RNA splicing of the MEAF6 and GIT1 transcripts does not promote NE differentiation in AdPC cells, other functions of these t-NEPC-unique splice variants have been demonstrated to contribute to the various facets promoting t-NEPC development. SRRM4-mediated splicing of the MEAF6 transcript reprograms the function of the neural-specific MEAF6-1 isoform to facilitate cell proliferation, migration, invasion, and tumorigenesis via the ID1 and ID3 genes. In fact, this study uncovers a potential novel facet involved in t-NEPC progression—invasion and migration. This cellular process is of interest as t-NEPC tumours are very aggressive and exhibit sites of metastasis in the liver and brain (26,46,188). 112 Consequently, SRRM4-mediated alterative splicing of the GIT1 transcript reprograms its function involving FA-mediated signaling and cell processes through GIT1-A, which may also contribute to the migration and invasion facets involved in t-NEPC development. Importantly, this suggests a novel function of SRRM4 in regulating FA-mediated signalling pathways or processes via GIT1 splicing. It is not entirely possible to elucidate the origin and progression of t-NEPC solely through studying the overall genomic landscape as there are very little overlap between NEPC and AdPC phenotypes. Only recently have differences in the molecular signatures beyond the overall genomic mutational profiles been discovered, such as RNA splicing signatures. However, characterizing RNA splicing signatures of diseases are not common in our field of study, as microexons are commonly ignored during whole transcriptome analyses. Therefore, these findings contribute to an understudied field of alternative splicing-mediated reprogramming of protein function—specifically the SRRM4-mediated neural-specific exon network required for the t-NEPC disease progression. This thesis work suggests a role of SRRM4 and RNA splicing signatures (i.e. MEAF6/GIT1) as potential biomarkers of t-NEPC detection and diagnosis. Moreover, these studies support the notion that SRRM4 is an important facilitator of t-NEPC development. By regulating alternative RNA splicing, SRRM4 not only stimulates AdPC lineage plasticity to the NE cell lineage, but also promotes cancer cell survival, proliferation, migration, invasion, FA-processes, and tumorigenesis via alternative RNA splicing of a neural-specific exon network (Figure 6.1).  113 Figure 6.1 Figure 6.1 The full picture: multifaceted roles of SRRM4 in t-NEPC development is mediated by downstream neural-specific exon networks. SRRM4 promotes several facets of t-NEPC development through the alternative RNA splicing of a t-NEPC-unique splicing program. SRRM4 promotes NE differentiation via the REST4 isoform of REST (90), evasion of apoptosis via the Bif-1b and Bif-1c isoforms of Bif-1 (94), proliferation and invasion via the MEAF6-1 isoform of MEAF6, and focal adhesion-mediated cell processes and signaling which may also contribute to migration and invasion of prostate cancer cells. ARPI, androgen receptor pathway inhibitors; NE, neuroendocrine; AdPC, prostate adenocarcinoma; t-NEPC, treatment-induced neuroendocrine prostate cancer.  6.2 LIMITATIONS 6.2.1 Cell-context dependency The heterogeneity of NEPC subtypes suggests that there are many complex mechanisms involved in the development of t-NEPC. Therefore, to understand the role of SRRM4 under the context of a heterogeneous cell population, we created several SRRM4-overexpressing cell models of different cell types. The results of this study (Chapter 3) demonstrate that SRRM4 function is cell-context dependent. Though striking NE phenotypic and morphologic alterations were seen in our DuNE model, SRRM4 function in PC-3 cells did not show such robust NE phenotypes. PC-3 cells are similar to DU145 cells as they are AR- and p53-null and exhibit stem-like characteristics. Even when depleting RB1 expression in the SRRM4-overexpressing 114 PC-3 cell model, there were no changes in SRRM4 function, and these cells did not present any NE morphological or phenotypical changes (Appendix N). This reveals that exclusivity of the cell lines used and cell-context dependence on the functions of our gene-of-interests may be a large limitation in the subsequent chapters. However, to by-pass this limitation, multiple prostate cancer cell lines were used to create GIT1 and MEAF6 cell lines. Though cell context-dependent phenomena are inherent, significant discordant functions of the MEAF6 and GIT1 splice variants were not seen throughout the various AdPC cell models created. However, it would be essential to investigate the functions of SRRM4, GIT1, and MEAF6 in NEPC cell models. Unfortunately, there are limited NEPC models (including one bona fide NEPC cell model) to utilize. This challenge will be discussed in the below sections. 6.2.2 Previous knowledge of MEAF6 and GIT1 splice variants Though these findings on the function of MEAF6 and GIT1 neural-specific splice variants have provided a novel contribution to the field of alternative splicing, t-NEPC, and potentially other neural-related diseases, the limited knowledge on the functional significance of these splice variants have proved to be challenging during the studies. One of the main limitations of this work is the lack of analyses on the splice variant expressions at the protein level as there are no commercially available antibodies for these specific splice variants. Characterizing and detecting the splicing activity of the transcripts in human tumour samples would provide a stronger rationale in the use of RNA splicing as a biomarker of NEPC progression and phenotype. However, to circumvent this challenge, our lab has established RISH techniques and generated specific probes to detect SRRM4 and GIT1 splice variants in clinical tumour samples. 115 As discussed in our Chapter 5 results, the expressions of SRRM4 and GIT1 splice variants are associated with the different phenotypes of prostate cancer (i.e. AdPC, AdNC, and SCNC). High, low, and no expression levels of SRRM4 and GIT1-A (i.e. a RISH score of 2, 1, and 0, respectively) are detected in SCNC, AdNC, and AdPC, respectively, whereas GIT1-C expressions reveal an opposite trend. This suggests that splicing activity of GIT1 and SRRM4 increases as the disease progresses from AdPC to SCNC, where AdNC phenotypes are suggested to be an intermediate phase of AdPC and SCNC. However, further investigation is required for the MEAF6 splice variant expressions in these CRPC tumour cohorts.  Moreover, one of the criticisms of our MEAF6 and GIT1 splice variant-overexpressing cell models is that endogenous levels of constitutive MEAF6 and GIT1 variants are naturally expressed in the prostate cancer cell lines used. Thus, this basal expression combined with the exogenous expressions may interfere with the phenotypes and functions seen in our cell models. To counteract this, it is required to deplete the endogenous mRNA expression levels in the overexpressing cell lines. One method of knockdown is through a shRNA targeting the untranslated regions (UTR) of the transcript. In fact, we have found a shRNA targeting the 3’ UTR region of the GIT1 transcript (189) and have created stable cell lines with both the depletion of endogenous GIT1 and overexpression of GIT1 splice variants (Appendix Oi). Though these cell models have produced similar results as the single overexpression cell models, revealing no changes in migration, proliferation, invasion, and differentiation, GIT1 functions in FA stability have demonstrated to be more robust in the double expression cell models when compared to sole overexpression (Appendix Oii). Unfortunately, due 116 to the difficulty in constructing shRNA targeting the UTR regions of the MEAF6 transcript, we could not perform the same experiments. However, given that the GIT1 splice variant functions did not exhibit significant differences when co-expressed with total GIT1 depletion, we suspect that MEAF6 splice variant functions under the depletion of endogenous MEAF6 will remain similar to the single overexpression cell models, if not increased. Additionally, due to the novelty of studying the splice variants of MEAF6 and GIT1, there are no commercially available siRNA or shRNA targeting the specific splice variants of MEAF6 and GIT1. This creates a challenge in investigating the functional significance of the neural-specific variants in NEPC models, which highly express the neural-specific splice variants of MEAF6 and GIT1. Though efforts have been made with the only bona fide NEPC cell model, NCI-H660, these cell lines have proved to be difficult in culturing, transfecting, and transducing.  6.2.3 Challenges of NEPC in the lab 6.2.3.1 NEPC models As previously mentioned in the discussions of my main chapters, there are limited NEPC cell and xenograft models that can be utilized to efficiently manipulate signaling pathways in order to study the molecular mechanisms of NEPC development. The only bona fide NEPC cell line, NCI-H660, is a slow growing cell line with a doubling time of >100 hrs (164), rendering cell transfections and transductions very difficult. The newly developed LnNE NEPC models have global transcription signatures and phenotypical characteristics similar to that of NEPC patient tumours (90,93). Because the tumours are generated by serially passaging for 5 generations in castrated mice, the LnNE P1 to P5 (P, passage) models replicate prolonged transition processes of AdPC 117 progression to NEPC, whereby the P5 cells and tumours, although AR positive, become PSA negative even in the presence of androgens (93). Excitingly, this thesis work has created another NEPC cell and xenograft model called the DuNE model. In comparison to the LnNE model, the DuNE cells develop into xenografts faster, and its derived xenografts present more robust LE and NE marker profiles. The DuNE model closely recapitulates the molecular and cellular phenotypes resembling SCNC subtypes of NEPC, whereby both the DuNE model and SCNC tumours express pluripotency gene networks including high SOX2 expressions. Moreover, the DuNE model may be a suitable model for high-throughput drug screening. Altogether, this newly established DuNE NEPC model adds to the small handful of available NEPC cell and tumour models and may be a clinically relevant model to study the molecular complexity of AdPC progression to NEPC.  6.2.3.2 NEPC as a chronic disease Despite great advances and efforts in t-NEPC, the diverse and dynamic changes in which tumour cells undergo to become malignant and tumorigenic remains an enigma due to its perplexity. One of the utmost challenges in delineating the molecular mechanisms of t-NEPC development is that the transition from AdPC to t-NEPC is a chronic process involving multiple intermediate phenotypes and molecular events such as genetic aberrations or epigenetic alterations (46,48,58). Furthermore, this disease progression is difficult to monitor as its aggressiveness results in a short viability from the time of diagnosis to patient death. As mentioned in the section above, to study this chronic and progressive disease, we have utilized a CRPC tumour cohort that contains AdPC, AdNC, and SCNC. Though it is unknown whether the tumours presenting AdNC 118 phenotypes progressed to SCNC, these tumours exhibit an intermediate phenotype. Yet, whether SRRM4 promotes t-NEPC in the clinic is unknown since the progression is hard to follow. Furthermore, the ARPI-induced AdPC to t-NEPC progressive LTL 331/331-R PDX model was studied to characterize the role of SRRM4 and SRRM4-mediated alternative splicing during this progression. However, as seen in Figure 3.6, we could not interpret any significant associations of the expressions of AR, SRRM4, SYP, and SOX2 with the disease progression of AdPC to t-NEPC. In fact, we only observe an increase in the expression of SOX2 and decrease in the AR expression prior to relapse (i.e. week 12 of castration), whereas SYP and SRRM4 expression spikes in the relapsed tumours. Though SRRM4 expression seems to be a result of ARPI, our lab has recently observed existing SRRM4-positive populations in ~16% of the treatment-naïve AdPC tumour cores studied, where the prevalence and expression of SRRM4 increases after therapeutic interventions such as ARPI in CRPC-Ad tumours (91). In that study, we propose that one of the earliest initial molecular events in the emergence of SRRM4-mediated t-NEPC occurs in two possible ways. One way is that the existing SRRM4-positive population of prostate cancer cells is selected for survival under the selection pressures of ARPI. Alternatively, prolonged ARPI can increase SRRM4 expression via epigenetic alterations regulated, in part, by AR. Although the specific molecular cross-talk mechanisms between AR and SRRM4 remain to be discovered, this suggests that the conjunction of ARPI and SRRM4 expression may promote CRPC-Ad progression to t-NEPC rather than the expression of SRRM4 as a passenger in the event of t-NEPC emergence. 119 6.3 FUTURE DIRECTIONS Future directions of my project certainly involve further investigation on the functions of SRRM4 as well as MEAF6 and GIT1 splice isoforms in our newly established LnNE and DuNE NEPC tumour models as well as up-and-coming 3D NEPC organoid models. Furthermore, it is also important to note that SRRM4 may facilitate t-NEPC progression in conjunction with other recognized pathways and factors important for t-NEPC development, such as epigenetic and environmental factors (Figure 6.2). In this section, further directions of this thesis work will be discussed as well as how SRRM4 may be connected to these different factors to promote t-NEPC progression.   Figure 6.2 Future directions. Given that SRRM4 splices genes (i.e. MEAF6, PHF21A, and potentially GIT1) related to epigenetic functions, we hypothesize that SRRM4 may function at the epigenetic level as well or work in conjunction or cross-regulation with epigenetic mechanisms or microenvironmental factors (such as hypoxia and cytokines). ARPI, androgen receptor pathway inhibitors; NE, neuroendocrine; AdPC, prostate adenocarcinoma; t-NEPC, treatment-induced neuroendocrine prostate cancer; SMI, small molecule inhibitors; ASO, antisense oligonucleotides; SSO, splice-switching oligonucleotides. Figure 6.2 120 6.3.1 SRRM4 and the epigenome Given that SRRM4 splices transcripts (i.e. MEAF6, PHF21A, and potentially GIT1) related to epigenetic functions, we hypothesize that SRRM4 may function at the epigenetic level as well or work in conjunction or cross-regulation with epigenetic mechanisms. For example, the constitutive PHF21A variant may facilitate altered histone deacetylation or demethylase activity as the SRRM4-mediated inclusion of the alternatively spliced exon disrupts an AT hook responsible for DNA binding, as well as one of the two predicted nuclear localization signals of PHF21A (190). Similarly, in the neural-specific MEAF6-1 mRNA, the inclusion of the neural-specific exon affects the nuclear localization signal at the C-terminus of the resulting protein. Our pilot studies on the sub-localization of the MEAF6 splice variants reveal differential localization of the splice variants (Appendix P). The ubiquitously expressed MEAF6-2 splice isoform localizes strictly within the nucleoli, whereas the t-NEPC-specific MEAF6-1 variant localizes within both the nucleus and in the nucleoli, suggesting its role in both the nucleoli and nucleus. Though the canonical function of the nucleoli is to house ribosomal biogenesis, it has recently been suggested to be a hub in the regulation of several other fundamental cellular processes such as epigenetic regulation and maintenance of genomic stability (191). Therefore, it would be interesting to profile any epigenetic changes induced by SRRM4 and/or investigate the contributions of the downstream splice variants to t-NEPC-specific epigenetic signatures. 121 6.3.2 SRRM4 and the microenvironment It is important to note that SRRM4 may facilitate t-NEPC progression in conjunction with other known pathways and factors important for t-NEPC development, such as the tumour microenvironment. For example, stress factors from therapeutics such as ARPI have been demonstrated to induce t-NEPC (46,54,83) and potentially SRRM4 expression (91) as previously mentioned (section 6.2.3.2). Although further studies are required to elucidate the complexity of the mechanisms associated with environmental factor-mediated emergence of t-NEPC, studies to date have suggested that the microenvironment is an important inducer of lineage plasticity and differentiation to the NE cell lineage. Other studies have revealed other various environmental factors important in NEPC development such as cAMP (155,157), cytokines (i.e. IL6 and IL8) (156,168), and hypoxia (154,159). However, the induction of the NE phenotype by environmental factors is ultimately reversible, suggesting that other factors or molecular mechanisms—perhaps SRRM4—are essential to the development of t-NEPC.  Mitogenic cytokines, such as IL8 and IL6 (156,168,192,193), in the tumour stromal microenvironment have been proposed to promote the emergence of t-NEPC. It has recently been demonstrated that IL6 can induce NE differentiation in LNCaP cells by suppressing REST function, whereas exogenous REST can abolish the IL6-induced NE program (168). Another study reports that depletion of REST expression is essential for IL6-induced NE differentiation (194). Interestingly, both IL6 and REST have both been implicated in hypoxia-induced NE differentiation (154). Hypoxia is another important regulator of NE differentiation in prostate cancer cells. In fact, hypoxia-122 induced NE differentiation of prostate cancer cells relies on the inhibition of REST, which is also a regulator of hypoxia-induced genes (154,195). These studies suggest a possible mechanism of tumour microenvironment factors, such as hypoxia and cytokines, and REST in promoting NE differentiation. Connecting these pathways together, it is possible that SRRM4 may interplay with the environment, where inhibition of REST function can be augmented by SRRM4 to promote the NE lineage cell fate in AdPC cells under hypoxic conditions. Consequently, it is possible that SRRM4 may act in conjunction or synergy with tumour microenvironment factors (such as IL6), regulators (i.e. regulator of hypoxia, REST), and stressors (i.e. ARPI and hypoxia) to push t-NEPC development and progression. Tumour microenvironment-induced pathways and factors have gained a lot of attention in the progression of AdPC tumours to t-NEPC as hypoxia-induced characteristics such as necrosis and high mitotic index are very common in t-NEPC tumours (46,196). Defining any mechanistic connections between the roles and mechanisms of SRRM4 and the tumour microenvironment would, not only be a novel discovery, but also provide a more intimate understanding of the t-NEPC tumour biology. 6.3.3 Targeting SRRM4 The fact that a plethora of factors have recognized involvements in facilitating or contributing to t-NEPC development, emphasizes the heterogeneity of t-NEPC at several levels. Therefore, further investigation is needed to understand how these different molecular mechanisms and players, environmental factors, and signaling pathways interplay to promote the emergence and development of t-NEPC. Ultimately this knowledge will provide insight for personalized medicine-based strategies for 123 prostate cancer patients. These efforts will rationalize SRRM4 and its t-NEPC-unique splice variants as potential diagnostic or prognostic biomarkers and SRRM4 as a therapeutic target in t-NEPC patients. In fact, SRRM4 fulfills several criteria to be a possible therapeutic target. Although SRRM4 has been shown to be critical during neural development (95,96,98,116), a partial deletion of 2710 bp of the 3’ end of the srrm4 gene (resulting in the removal of the last exon encoding the RS domain, which is important for its RNA-splicing and splice-site function) does not result in embryonic lethality in Bronx Waltzer mice (121). These mice present abnormal splicing patterns in their hearing and balance organ (i.e. hair cells) and exhibit abnormal neural behaviors such as deafness and impaired balance. However, this study reports no splicing defects in the cerebellum and neocortex (centres of the brain involved in balance, coordinated motor movements, higher brain function such as perception, cognition, and reasoning) of the mice and demonstrates that SRRM4 function is not critical in all neural tissues (121). This suggests that specific inactivation of the SRRM4 splicing activity, while maintaining the majority of the protein, may have limited side effects. Furthermore, SRRM4 expression, though high in the brain during embryonic development of the neural system, decreases in adult tissues and is rarely detected in non-neuronal cells (95,98,100). Nevertheless, it would be important to carefully design SRRM4-specific inhibitors that cannot cross the blood-brain barrier in order to limit potential off-target effects of SRRM4-targeted drugs to the nervous system. There are several ways to block SRRM4 function. Small molecule inhibitors (SMI) can either target the RNA-binding domain of SRRM4 to prevent it from 124 recognizing its RNA substrates or can directly inhibit the C-terminus domains important for the splicing activity of SRRM4. In fact, although the exact mechanism of splicing inhibition is currently unknown, a SMI compound called LMI070 is currently under clinical trials to treat spinal muscular atrophy, by binding to the RNA itself or RNA-binding domains of splicing factors (197). Before designing SRRM4-targeting SMI, the SRRM4 protein must be crystalized via microfluidics reaction technology or cryo-electron microscopy methods. However, these techniques face many challenges, such as the requirement of a large amount of protein purification and low resolution, respectively. Furthermore, the RS domain of SRRM4 is highly charged, rendering crystallization of the protein very difficult. If SRRM4 crystallization is a success, SMI that bind to either the RS domain or the C-terminus domain may be designed. Alternatively, antisense oligonucleotides (ASO) or splice-switching oligonucleotides (SSO), which can inhibit or redirect SRRM4-mediated alternative splicing events, may be effective in inhibiting SRRM4 splicing events of t-NEPC-unique transcripts (i.e. MEAF6/GIT1). In fact, the first Food and Drug Administration-approved ASO/SSO called SPINRAZA™ has shown to be effective in patients with spinal muscular atrophy, a genetic disorder whereby altered RNA splicing patterns of the survival motor neuron (SMN) transcript result in an unstable and dysfunctional SMN protein (197,198). SPINRAZA™ targets the splicing events of the SMN transcript by silencing the splicing silencer element in the intron region upstream of the alternatively spliced exon. 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Available from: http://www.ncbi.nlm.nih.gov/pubmed/28604635   140 Appendix Appendix A: Primers for RT-qPCR Appendix A Primer Name Forward Primer Sequence Reverse Primer Sequence 18S TTGACGGAAGGGCACCACCAG GCACCACCACCCACGGAATCG ABII(C)  GGCTGGTTATTTCCATGCTTGA TCACAAAATAATAGCACCTGCG ABII(NS) ATGCTTGGCTTTTAGCCACTT TCACAAAATAATAGCACCTGCG ALDH1A2 TTGCAGGGCGTCATCAAAAC ACACTCCAATGGGTTCATGTC AMIGO1 CTGCGTACACTGGATGAGTTC ACCGCCATGATGTGGTTATTG AR TCTTGTCGTCTTCGGAAATGT AAGCCTCTCCTTCCTCCTGTA ASCL1 CCCAAGCAAGTCAAGCGACA AAGCCGCTGAAGTTGAGCC ATL2(C)  TTCCTCCATCAAATTATCACCC TCTTGTGTAACCTTGTCATGGG ATL2(NS) GATGACAGTCTCTGTCGCTGTG CTGAAACACTATGGGAACAGAG BHC80(C)  AGTCTTTGAGCCAGAGCGTAAG CACTGCATTGTATTTTGGAGGA BHC80(NS) GGAAGAGAGCCAATGAGGAAC AGTAACTGGCCACTTTTTCTGC CAMTA2(C)  GCTGAAATCGCTTCTGTTCATA GTTACCGGAAGTACAAGCAGTT CAMTA2(NS) GCTGAAATCGCTTCTGTTCATA GTACAAGCAGCTGACCTGGATT CDH1 ATTTTTCCCTCGACACCCGAT TCCCAGGCGTAGACCAAGA CDH2 TGCGGTACAGTGTAACTGGG GAAACCGGGCTATCTGCTCG CDK6 CCAGATGGCTCTAACCTCAGT AACTTCCACGAAAAAGAGGCTT CHGA TAAAGGGGATACCGAGGTGATG TCGGAGTGTCTCAAAACATTCC CHGB CGAGGGGAAGATAGCAGTGAA CAGCATGTGTTTCCGATCTGG CIB1 CACGGCTTAGTGCGTCTGAG AAAGTCTGGAGAACGGGAGAT DYNLL2 ACCCTACCTGGCATTGTATCG AGCCTGACTTGAAGAGGAGGA EFNA1 TCAGGCCCATGACAATCCAC GTGACCGATGCTATGTAGAACC ENO2 CCGGGAACTCAGACCTCATC CTCTGCACCTAGTCGCATGG EPHA2 AGAGGCTGAGCGTATCTTCAT GGTCCGACTCGGCATAGTAGA ERGIC2(C)  GAAGATGCAGGAGCAGAAGAAT AGCTCTGCAAGTCATGGACGT ERGIC2(NS) CAGATACTATTGAGCAGTGCCG AAGCTCTGCAAGTCATGGATCT FOCX1 TGTTCGAGTCACAGAGGATCG ACAGTCGTAGACGAAAGCTCC FOXD3 TCACGCACCAATTCTAACGC CACGGCTTGCTTACTGAAGG GAPDH GGACCTGACCTGCCGTCTAGAA GGTGTCGCTGTTGAAGTCAGAG GAPDH exon7/8 (ChIP) GGCATGGACTGTGGTCATGAG TGCACCACCAACTGCTTAGC GIT1 exon7/8 (ChIP) CTGATGTGAAAAGGCGTGTG GCACTTTTGCCGAGATCTAAGT GIT1(C) or GIT1-C TTCTACCTCTGTGGACGCAAG AATTCGGATAAGTCAAGGCTGTC GIT1(NS) or GIT1-A CCACAGATGGCTGACAGATC TACACGTCCATGGCGAGTT HEY1 GTTCGGCTCTAGGTTCCATGT CGTCGGCGCTTCTCAATTATTC ID1 TCTGCACACCTACTAGTCACCA  GAGAAGCACCAAACGTGACC ID3 GGAGCGAAGGACTGTGAACT  CCACGCTCTGAAGAGACCTT  ID4 GGCCACTCAAGCAGCATTTG TCTGGTTGCCTGGTTAGGAC IGFBP1 GGCCATCGAGAATTGTTGCAG CCAGGGATCAGGTGAGACTG KIT TTGTGATTTTGGTCTAGCCAGAGA GTGCCATCCACTTCACAGGTAG KLK3 AGTGCGAGAAGCATTCCCAAC CCAGCAAGATCACGCTTTTGTT KRT8 TCCTCAGGCAGCTATATGAAGAG GGTTGGCAATATCCTCGTACTGT MEAF6 (ChIP) ACCAATTCTTGCTAGGGTGGCTA TCATAATCAAACATGCCAGACG MEAF6 (total) AGGAGCTGGCGGAAACATTG TGGTTGGTCAGATACCGATCC 141 MEAF6(C) or MEAF6-2 GAATAAAAACCGGCACAGGATTG AAGGGAAGCAGGGCTCTACA MEAF6(NS) or MEAF6-1 AATAAAAACCGGCACAGCCC AAGGGAAGCAGGGCTCTACA MEF2D(C)  GGGGTTAATGCATCACTTGACTG TGGCTGAGTAAACTCGGCGT MEF2D(NS) GGGGTTAATGCATCACTTGAACA TGGCTGAGTAAACTCGGCGT MON2(C)  AAATGCAAAATATAATCAGGCG GCTTTGTGACACGCTGTTTTT MON2(NS) ATCCAACTATTTGCACCGGC GCTTTGTGACACGCTGTTTTT MSX1 GGGGCAGGGAAAACACAGAT AGGGATGTTTGAGAGCCACAC NKX3-1 CCCACACTCAGGTGATCGAG GAGCTGCTTTCGCTTAGTCTT OCT3/4 GTGTTCAGCCAAAAGACCATCT GGCCTGCATGAGGGTTTCT PTK2(C)  ATGTACATCTCCAAATTGGCCT GATACTTACACCATGCCCTCAAC PTK2(NS) TACATCTCCAAATTGGCCTTCT GCCCTCAAAAAGCTATGGAATA RB1 TTGGATCACAGCGATACAAACTT AGCGCACGCCAATAAAGACAT REST(C)  TGCGTACTCATTCAGGTGAGA TCTTGCATGGCGGGTTACTT REST(NS) GCGTACTCATTCAGTGGGGTAT GATTAGAGGCCACATAACTGCAC SCG3 GTCTTCATCAACTAGACGGGACT ACAATCTTGTCAAACACGGCTC SCGN GGCCATTTCTGAGGCTAAACT GGGCTCCTGTTTTACTAACATCA SH3GLB2(C)  GAGGGCACCACAGGCATAGT AGAAGCAGCTGGGCAGATTT SH3GLB2(NS) GAGGGCACCACAGGCATAGT CAGCTCCCAGGGTGCCATAT SIX2 AAGGCACACTACATCGAGGC CACGCTGCGACTCTTTTCC SNAI2 CGAACTGGACACACATACAGTG CTGAGGATCTCTGGTTGTGGT SNAI2 TGTGACAAGGAATATGTGAGCC TGAGCCCTCAGATTTGACCTG SOX2 GCCGAGTGGAAACTTTTGTCG GGCAGCGTGTACTTATCCTTCT SRRM4 CACAAGCGACGCAGGTCAT CGGTGGCGGTGAGACTTTC SYP TTAGTTGGGGACTACTCCTCG GGCCCTTTGTTATTCTCTCGGTA SYT4 ATGGGATACCCTACACCCAAAT TCCCGAGAGAGGAATTAGAACTT TGFB2 CAGCACACTCGATATGGACCA CCTCGGGCTCAGGATAGTCT TGFBR3 TGGGGTCTCCAGACTGTTTTT CTGCTCCATACTCTTTTCGGG * C = Constitutive variant   * NS = Neuro-specific variant  * ChIP = Chromatin immunoprecipitation    142 Appendix B: Antibodies Appendix B Antibody Clone ID Cat. No. Supplier AR (IHC) N-20 sc-816 Santa Cruz B-Actin (WB) C-11 sc-1615 Santa Cruz Biotech c-PARP (WB) Asp214 9541 Cell Signaling CD56 (IHC) MRQ-42 156R-94 Milipore Sigma CHGA (IHC) EP38 AC-0037 Milipore Sigma E-Cadherin (IHC) EP6 AC-0003 Epitomics  E-Cadherin (WB) H-108 sc-7870 Santa Cruz Flag (RNA-ChIP and WB) M5 F4042 Sigma-Aldrich GIT1 (WB & IF) H-170 sc-13961 Santa Cruz ID1 (WB)  Ab134163 Abcam Ki-67 (IHC) SP6 RM-9106 Thermo Fisher Pan-Cytokeratin (IHC)  Z0622 Dako PSA (IHC) C-19 sc-7638 Santa Cruz SOX2 (IHC)  NB110-37235 Novus Biologicals SOX2 (WB) D6D9 3579S Cell Signaling SRRM4 (WB)  Sab2107518 Sigma-Aldrich SYP (IHC) D-4 sc-17750 Santa Cruz SYP (WB) D-4 sc-17750 Santa Cruz Vinculin (WB & IF) hVIN-1 V9131  Sigma-Aldrich * WB = Western blot    * IHC = Immunohistochemistry   * RNA-ChIP = RNA-chromatin immunoprecipitation * IF = Immunofluorescence    143 Appendix C: Detailed GIT1-A, GIT1-C, and SRRM4 RISH scores in CRPC TMA Appendix C Histopathology GIT1A GIT1C SRRM4 AdPC 0 0 0 AdPC 0 1 0 AdPC 0 2 0 AdPC 0 1 0 AdPC 0 1 0 AdPC 0 1 0 AdPC 0 1 0 AdPC 0 1 0 AdPC 0 1 0 AdPC 0 2 0 AdPC 0 2 0 AdPC 0 2 0 AdPC 0 2 0 AdPC 0 2 0 AdPC 0 2 0 AdPC 0 2 0 AdPC 0 2 0 AdPC 0 1 0 AdPC 0 1 0 AdPC 0 1 0 AdPC 0 1 0 AdPC 0 1 0 AdPC 0 2 0 AdPC 0 2 0 AdPC 0 2 0 AdPC 0 2 0 AdPC 0 2 0 AdPC 0 2 0 AdPC 0 2 0 AdPC 0 2 0 AdPC 0 2 0 AdPC 0 2 0 AdPC 0 1 0 AdPC 0 2 0 AdPC 0 2 0 AdPC 0 2 0 144 AdPC 0 1 0 AdPC 0 1 0 AdPC 0 1 0 AdPC 0 1 0 AdPC 0 1 0 AdPC 0 1 0 AdPC 0 1 1 AdPC 1 2 0 AdPC 1 2 1 AdPC 1 2 0 AdPC 1 2 0 AdPC 1 2 1 AdPC 1 1 0 AdPC 1 1 0 AdPC 1 1 1 AdPC 1 2 1 AdNC 0 0 1 AdNC 1 1 1 AdNC 1 1 1 AdNC 1 0 1 AdNC 1 0 2 AdNC 1 0 2 SCNC 1 0 2 SCNC 2 1 2 SCNC 2 0 2 SCNC 2 0 1 SCNC 2 0 2 SCNC 2 0 2 * AdPC = Prostate adenocarcinoma * AdNC = Prostate adenocarcinoma with neuroendocrine cells * SCNC = Small-cell prostatic carcinoma  145 Appendix D Appendix D: Validation of SRRM4 expressions in the SRRM4-transduced stable cell and xenograft models Appendix D Validation of SRRM4 expressions in the SRRM4-transduced stable cell and xenograft models. Lentiviral transduction to create SRRM4-overexpressing stable lines in hTERT-Myo, RWPE-1, WPMY-1, BPH-1, DU145, PC-3, LNCaP, and 22Rv1 parental cells were validated by (i) Western blot and RT-qPCR. Anti-flag antibody was used to detect against exogenous SRRM4 and anti-vinculin antibody was used to detect the loading control, vinculin. This validation was repeated three times, and only one representative repeat is presented. All results are presented as the mean ± SEM (ii) DU145, PC-3, LNCaP, and 22Rv1 stable cell lines overexpressing SRRM4 or its respective control cells were inoculated in bilateral flanks subcutaneously in 6-8-week-old nude mice. Tumours were harvested, fixed in 10% paraformaldehyde, paraffin embedded, and processed for RNA in situ hybridization using an anti-sense probe to detect against SRRM4 mRNA. Scale bars represents 100 μm. 146 Appendix E Appendix E: SRRM4 induces stem cell differentiation and neural fate factors in DU145 cells Appendix E SRRM4 induces stem cell differentiation and neural fate factors in DU145 cells. GSEA enrichment plots shows the correlations of the DU145(SRRM4), DuNE, vs DU145(Ctrl) dataset (n = 6033) with the GSEA gene sets in the ‘lineage plasticity’ subgroup from subfigure 4a. Thirteen stem cell marker or stem cell-related genes were selected from the leading-edge groups associated with the DuNE phenotype in the three GSEA enrichment plots to validate in subfigure 4C. GSEA, gene set enrichment analysis; GO, gene ontology; NES, normalized enrichment score; FDR, false discovery rate.          147 Appendix F Appendix F: SOX2 and AR are negatively correlated in CRPC cohorts Appendix F SOX2 and AR are negatively correlated in CRPC cohorts. (i-ii) Pearson’s r correlation coefficient between SOX2 and AR expressions obtained from GEMMs by Ku et al. (2017) (i), and human CRPC cohorts by Beltran et al. (2016), Robinson et al. (2015), Kumar et al. (2016), and Varambally et al. (2005) (ii) are shown. GEMMs, genetically engineered mouse models; CRPC, castration-resistant prostate cancer.   148 Appendix G Appendix G: SRRM4 and SOX2 are positively correlated in CRPC cohorts Appendix G SRRM4 and SOX2 are positively correlated in CRPC cohorts. Pearson’s r correlation coefficient between SRRM4 and SOX2 expressions obtained from human CRPC cohorts by Beltran et al. (2016), Robinson et al. (2015), Kumar et al. (2016), and Varambally et al. (2005) are shown. CRPC, castration resistant prostate cancer.   149 Appendix H Appendix H: MEAF6 RNA splicing is positively correlated with SRRM4 expression and REST RNA splicing Appendix H MEAF6 RNA splicing is positively correlated with SRRM4 expression and REST RNA splicing. Pearson’s r correlation coefficient between MEAF6 splicing ratio (MEAF6-1:MEAF6-2 RNA-seq reads per base-pair) and (i) SRRM4 total expression or (ii) REST splicing ratio (REST4:REST RNA-seq reads per base-pair) obtained from the Beltran CRPC patient RNA-seq dataset (NEPC n=6 and AdPC n=32). CRPC, castration-resistant prostate cancer; NEPC, neuroendocrine prostate cancer; AdPC, prostate adenocarcinoma.   150 Appendix I Appendix I: SRRM4 regulates RNA splicing of MEAF6 Appendix I SRRM4 regulates RNA splicing of MEAF6. LNCaP(SRRM4) stable cells were seeded in 6-well petri dishes in RPMI-1640 medium with 10% FBS and transfected the next day. Cells were transfected with 20 µM of control or SRRM4-targeted siRNA for 48 hrs and then collected for RNA extraction for RT-qPCR validation. Relative quantifications of total MEAF6, MEAF6-1, and MEAF6-2 were compared to 18S. FBS, fetal bovine serum; CTL, control.      151 Appendix J Appendix J: Androgen receptor signaling does not regulate MEAF6 variant expression Appendix J Androgen receptor signaling does not regulate MEAF6 variant expression. LNCaP cells were cultured in phenol red-free RPMI-1640 medium with 5% CSS for 48 hrs. Cells were then treated with DMSO (veh), 10 nM AR-agonist DHT (dihydrotestosterone), or 5 µM AR antagonist MDV3100 (Enzalutamide) for 24-hrs and subsequently collected for RNA extraction for RT-qPCR. Relative quantification of total MEAF6, MEAF6-1, MEAF6-2, and KLK3 were compared to 18S. All results are presented as the mean ± SEM (Student’s 2-tailed t-test; n=3, *** denotes p<0.001). AR, androgen receptor. CSS, charcoal-stripped serum; Veh, vehicle.    152 Appendix K Appendix K: Validation of MEAF6-1 overexpressing stable lines Appendix K Validation of MEAF6-1 overexpressing stable lines. Lentiviral transduction to create MEAF6-1 or MEAF6-2 overexpressing stable lines in (i) LNCaP and (ii) PC-3 parental cells were validated by Western blotting and RT-qPCR. Anti-flag antibody was used to detect exogenous MEAF6-1 and MEAF6-2 expression. Anti-beta-actin antibody was used as a loading control. CTL, control (empty vector); WB, Western blot.   153 Appendix L Appendix L: MEAF6 does not facilitate neuroendocrine trans-differentiation Appendix L MEAF6 does not facilitate neuroendocrine trans-differentiation. LNCaP(CTL), LNCaP(MEAF6-1), and LNCaP(MEAF6-2) cells were collected for total RNA extraction. Expression of neuroendocrine markers (i.e. CHGA, CHGB, ENO2, and SYP) and epithelial markers (i.e. REST and CDH1) were determined by RT-qPCR. Relative quantifications were compared to 18S. CTL, control (empty vector).   154 Appendix M Appendix M: GSEA of the GIT1-C transcriptome  Appendix M GSEA of the GIT1-C transcriptome. The transcriptomes of GIT1-A and GIT1-C were analyzed by GSEA based on the latest MSigDB database for each collection. The GIT1-C transcriptome was enriched with gene sets associated with immune function and metabolism. GSEA enrichment plots from these categories are presented where differential expressions of the leading-edge genes are shown in the heatmaps created by the GSEA software. GSEA, gene set enrichment analysis; GO, gene ontology; NES, normalized enrichment score; NOM p-val, nominal p-value. 155 Appendix N: RB1 knockdown in the SRRM4-transduced PC-3 cell model does not induce a NEPC-specific phenotype  Appendix N RB1 knockdown in the SRRM4-transduced PC-3 cell model does not induce a NEPC-specific phenotype. A second round of lentiviral transduction of shRNA targeting RB1 using the PC-3(Ctrl) and PC-3(SRRM4) cell lines to establish PC-3(Ctrl)(shCtrl), PC-3(Ctrl)(shRB1), PC-3(SRRM4)(shCtrl), and PC-3(SRRM4)(shRB1) stable cell models. These cell lines were validated for SRRM4 and RB1 mRNA expression by (i) RT-qPCR. (ii) RNA was extracted to measure levels of luminal epithelial (i.e. CDH1, Nkx3.1, and KRT8) and neuroendocrine markers (i.e. SYP, CDH2, and CHGA) via RT-qPCR. (iii) The morphology of these cells was imaged by Zeiss light microscope, where the scale bars represent 20 μm. (iv) RNA was extracted from these cell lines to measure levels of SOX2 mRNA via RT-qPCR. All experiments were repeated three times, and only one representative repeat is presented. All results are presented as the mean ±SEM. Ns, not significant.    Appendix N 156 Appendix O: Differential functions of GIT1 splice variants in FA stability under endogenous GIT1 depletion     i  ii  157 Appendix O Differential functions of GIT1 splice variants in FA stability under endogenous GIT1 depletion. (i) Lentiviral approaches were performed to create DU145 stable cell lines overexpressing GIT1-A, GIT1-C, or empty vector under GIT1 depletion via shRNA targeting the 3’ UTR of the GIT1 transcript. Stable cell lines were validated by Western blot and RT-qPCR. (ii) These cells were seeded on coverslips and serum-starved the next day. They were treated with 10 M Nocodazole for 4-hrs, subsequently washed away, and replaced with serum-containing medium. Cells were fixed at 0- or 120-min after the washout, co-stained against GIT1 and vinculin, and then mounted with DAPI staining mount. Cells were imaged using the ZEISS AxioObserver Z1 microscope, where the scale bar represents 10 μm. Overlapping signals between GIT1 and vinculin appear yellow. The overlapping of the two signals in a cross section (indicated by the white line) of FA complexes were profiled by the ZEN program. All experiments were repeated three times. Exp, exposure; IF, immunofluorescence; FA, focal adhesion; ZEN, ZEISS efficient navigation.    158 Appendix P: Differential localization of the MEAF6 splice variants  Appendix P Differential localization of the MEAF6 splice variants. PC-3 cells were transfected with 4 µg of plasmid encoding DsRED-tagged empty vector, MEAF6-1, or MEAF6-2. Cells were fixed for IF, and stained for α-tubulin, and then mounted with DAPI staining mount. Cells were imaged using the ZEISS AxioObserver Z1 microscope, where the scale bar represents 10 μm. All experiments were repeated three times. IF, immunofluorescence; ZEN, ZEISS efficient navigation.   Appendix P 

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