Medicine, Faculty of
DSpace
UBCV
Dourieu, Claire
Master of Science - MSc
University of British Columbia
Background: Prostate cancer is the most common cancer in Canadian men. Androgen receptor drives PCa, and androgen deprivation therapy is the standard of care for advanced PCa, initially ADT elicits a response but often leads to ADT-induced dormancy that precedes relapse to castration-resistant PCa, which is incurable. This underscores the need to better understand tumor dormancy, identify critical genes for dormant cell survival, and develop therapies to target cancer in the earlier disease state when it is more easily treated. We hypothesize that dormancy PDX models provide accurate tumor recapitulation for study of actionable disease targets through wet lab and computational methodology. We will use IL33 as an example for this study. Materials and Methods: A panel of hormone-naïve PCa PDX models were used to establish ADT-induced dormancy. Bulk RNA sequencing of PDX models were analysed to assess IL33 RNA expression. IL33 protein was examined by IHC on sections taken pre-castration and during dormancy. The same methods were applied to FVB Myc-CaP models to recapitulate the immunocompetent TME at chronological timepoints following ADT. We carried out sequencing analysis on clinical cohorts and IHC staining on ADT-treated tissue microarrays. Functional analysis was carried out using single cell sequencing of the LTL331 model in the dormant stage and clinical cohorts for correlation analysis. Results: We confirmed a significant upregulation of IL33 in PDX models at both RNA and protein levels in the dormant stage. Importantly we also found upregulation of IL33 in Myc-CaP immunocompetent mouse models after host-castration. This was also observed in several cohorts of ADT-treated clinical PCa samples, suggesting IL33 is clinically relevant. Using a single cell sequencing dataset of LTL331 model, we also discovered the enrichment of IL33 in a dormant cluster and predicted function roles of IL33 in PCa dormancy. Conclusion: Our results establish a enrichment of IL33 in ADT-induced dormancy in PCa. We confirmed IL33 upregulation across multiple PDX and immunocompetent mouse models, and established clinical relevance of IL33 upregulation following ADT. We also predicted functional roles for IL33 in dormancy by computational methods. This study has successfully established a reproducible methodology for analysis of target genes in ADT-induced dormancy.
METHOD DEVELOPMENT FOR IDENTIFICATION OF TARGETABLE GENES IN ADT-INDUCED PROSTATE CANCER DORMANCY: THE CASE OF INTERLEUKIN-33 by Claire Dourieu BSc., The National University of Ireland, Galway, 2021 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Interdisciplinary Oncology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2024 © Claire Dourieu, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Method Development for Identification of Targetable Genes in ADT-Induced Prostate Cancer Dormancy: The Case of Interleukin-33 submitted by Claire Dourieu in partial fulfilment of the requirements for the degree of Master of Science in Interdisciplinary Oncology Examining Committee: Dr. Yuzhuo Wang, Professor, Department of Urologic Sciences, UBC Supervisor Dr. Christopher Ong, Associate Professor, Department of Surgery, UBC Supervisory Committee Member Dr. Mads Daugaard, Associate Professor, Department of Urologic Sciences, UBC Supervisory Committee Member Dr. Nathan Lack, Assistant Professor, Department of Urologic Sciences, UBC Additional Examiner iii Abstract Background: Prostate cancer is the most common cancer in Canadian men. Androgen receptor drives PCa, and androgen deprivation therapy is the standard of care for advanced PCa, initially ADT elicits a response but often leads to ADT-induced dormancy that precedes relapse to castration-resistant PCa, which is incurable. This underscores the need to better understand tumor dormancy, identify critical genes for dormant cell survival, and develop therapies to target cancer in the earlier disease state when it is more easily treated. We hypothesize that dormancy PDX models provide accurate tumor recapitulation for study of actionable disease targets through wet lab and computational methodology. We will use IL33 as an example for this study. Materials and Methods: A panel of hormone-naïve PCa PDX models were used to establish ADT-induced dormancy. Bulk RNA sequencing of PDX models were analysed to assess IL33 RNA expression. IL33 protein was examined by IHC on sections taken pre-castration and during dormancy. The same methods were applied to FVB Myc-CaP models to recapitulate the immunocompetent TME at chronological timepoints following ADT. We carried out sequencing analysis on clinical cohorts and IHC staining on ADT-treated tissue microarrays. Functional analysis was carried out using single cell sequencing of the LTL331 model in the dormant stage and clinical cohorts for correlation analysis. Results: We confirmed a significant upregulation of IL33 in PDX models at both RNA and protein levels in the dormant stage. Importantly we also found upregulation of IL33 in Myc-CaP immunocompetent mouse models after host-castration. This was also observed in several cohorts of ADT-treated clinical PCa samples, suggesting IL33 is clinically relevant. Using a single cell sequencing dataset of LTL331 model, we also discovered the enrichment of IL33 in a dormant cluster and predicted function roles of IL33 in PCa dormancy. Conclusion: Our results establish a enrichment of IL33 in ADT-induced dormancy in PCa. We confirmed IL33 iv upregulation across multiple PDX and immunocompetent mouse models, and established clinical relevance of IL33 upregulation following ADT. We also predicted functional roles for IL33 in dormancy by computational methods. This study has successfully established a reproducible methodology for analysis of target genes in ADT-induced dormancy. v Lay Summary Prostate cancer is the most prevalent cancer among Canadian men and is often associated with favorable prognosis. However, some patients experience relapse and progression to treatment-resistant disease. We believe this recurrence stems from a small population of âdormantâ cancer cells that survive initial treatment and evade detection by standard diagnostic techniques. These dormant cells can reactivate months or years later, leading to aggressive, therapy resistant tumors with high mortality rates. My research focuses on understanding and targeting these elusive dormant cancer cells in order to prevent relapse of disease. By investigating the genes responsible for the survival of these cells, we aim to develop strategies to eliminate dormant cells before relapse. This approach has the potential to revolutionize prostate cancer treatment, shifting from managing recurrence to preventing it altogether, ultimately improving long term outcomes for patients. vi Preface Fulfilment of this thesis as part of the UBC Master of Science degree was carried out through the Interdisciplinary Oncology program, and was completed across two institutions: the BC Cancer Research Centre and the Vancouver Prostate Centre. The animal studies were carried out in accordance with protocol #A17-0165. The original concept for this study was developed on the basis of work carried out by Dr Nelson Wong using Myc Cap models. Histological scoring of clinical tissues was carried out with the aid of pathologist Cindy Dong. Chapter 1 was completed by me, including the literature review and production of figures. A portion of Chapter 1 is being adapted for publication relating mechanisms of entrance and maintenance of ADT induced dormancy in prostate cancer. Editing assistance was provided by Dr. Nelson Wong, Dr Yuzhuo Wang and Dr. Christopher Ong. Chapter 2 describes methodologies which were completed by myself, with assistance from several lab members, including Adam Classen (computational), Rebecca Wu (tissue processing and IHC), Dr. Hui Xu (in vivo PDX models) and Dr. Nelson Wong (in vivo Myc Cap models). RNA sequencing datasets were provided by Dr Adam Sowalsky and Dr Colin Collins. TMAs were constructed and stained by Dr Sonia Kung with the Vancouver Prostate Centre pathology core. The majority of the results in Chapter 3 were completed based on experimental processes designed by me, with input from my supervisor Dr. Yuzhuo Wang, and mentor Dr. Nelson Wong. Figure 3.1A-D was adapted from our labs paper âModeling Androgen-Deprivation Therapy-Induced Prostate Cancer Dormancy and Its Clinical Implicationsâ. doi: 10.1158/1541-7786.MCR-21-1037. Tables 3.1 and 3.2 were adapted vii from our labs paper âFramework of Intrinsic Immune Landscape of Dormant Prostate Cancerâ. doi: 10.3390/cells11091550. Constructive feedback and discussion were provided by multiple lab members including Adam Classen, Xinyao Pang, Dr. Zoe Maylin, Dr. Nelson Wong, Dr. Connie Kang, Dr. Hui Xu and Dr Yuzhuo Wang. Editing contribution was made by Dr. Yuzhuo Wang. viii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ....................................................................................................................... viii List of Tables ............................................................................................................................... xii List of Figures ............................................................................................................................. xiii List of Abbreviations ...................................................................................................................xv Acknowledgements ................................................................................................................... xvii Dedication ...................................................................................................................................... ii Chapter 1: Introduction ................................................................................................................1 1.1 Prostate Cancer ............................................................................................................... 1 1.1.1 Treatment & Recurrence ......................................................................................... 1 1.2 ADT-Induced Dormancy ................................................................................................ 2 1.2.1 Cell Cycle Regulation ............................................................................................. 3 1.2.2 Metabolic Adaptations ............................................................................................ 5 1.2.3 Modifications To Tumor Microenvironment .......................................................... 7 1.2.3.1 Immunogenic modulation ................................................................................... 7 1.2.3.2 Stromal and Extracellular Matrix Modifications ................................................ 8 1.2.3.3 Angiogenic dormancy ......................................................................................... 9 1.2.4 Genomic Instability and Epigenetics .................................................................... 10 1.2.4.1 Regulation of plasticity and dormancy associated genes .................................. 10 ix 1.2.4.2 DNA Damage.................................................................................................... 11 1.2.4.3 Epigenetic Modifications .................................................................................. 12 1.3 Interleukin-33 (IL33) .................................................................................................... 13 1.3.1 Structure & Function............................................................................................. 13 1.3.1.1 Cytokine ............................................................................................................ 13 1.3.1.2 Nuclear .............................................................................................................. 14 1.3.2 Role in Cancer....................................................................................................... 15 1.3.2.1 IL-33 in Prostate Cancer ................................................................................... 16 1.4 Rationale, Hypothesis & Objectives ............................................................................. 16 1.4.1 Rationale ............................................................................................................... 16 1.4.2 Hypothesis............................................................................................................. 17 1.4.3 Objectives ............................................................................................................. 17 Chapter 2: Methodology..............................................................................................................18 2.1 Cell Lines ...................................................................................................................... 19 2.2 PCa Patient-Derived Xenograft (PDX) Models ............................................................ 19 2.3 Human PCa Samples and Clinical Data........................................................................ 20 2.4 Histopathology and Immunohistochemistry ................................................................. 20 2.5 IHC Quantification........................................................................................................ 22 2.6 RNA microarray gene profiling .................................................................................... 22 2.7 Bioinformatics............................................................................................................... 23 2.8 Statistical Analysis ........................................................................................................ 24 Chapter 3: Results........................................................................................................................25 x 3.1 Establishment of models for the study of ADT-induced dormancy and target selection for method validation ................................................................................................................ 25 3.1.1 Patient-Derived Xenograft Models ....................................................................... 25 3.1.2 Immunocompetent Myc-CaP model ..................................................................... 29 3.1.3 Target Selection .................................................................................................... 30 3.2 Confirmation of IL33 upregulation in mouse models and indication of intrinsic role for IL33 in dormancy ...................................................................................................................... 33 3.2.1 Patient-Derived Xenograft Models ....................................................................... 33 3.2.2 Immunocompetent Myc-CaP Model..................................................................... 35 3.3 Validation of IL33 upregulation in clinical cohorts and tissue microarrays following ADTâ¦. ..................................................................................................................................... 36 3.4 IL33 is enriched in dormant clusters in PDX following castration .............................. 39 3.5 IL33 expression is associated with survival pathways in dormancy ............................ 42 3.5.1 Gene-Set Enrichment Analysis ............................................................................. 42 3.5.2 Assessment of Tumor-Stromal Crosstalk ............................................................. 45 Chapter 4: Discussion ..................................................................................................................50 4.1 Our PDX models accurately recapitulate dormancy and Myc-CaP models provide insight into the TIME in dormancy ........................................................................................... 50 4.2 Validation of IL33 upregulation in mouse models ....................................................... 50 4.2.1 Nuclear IL33 is upregulated in both PDX and Myc-CaP models during dormancyâ¦........................................................................................................................... 50 4.2.2 Upregulation of IL33 in immunocompetent and immunocompromised models indicates an intrinsic role for this gene in dormancy ............................................................ 52 xi 4.3 Clinical confirmation of IL33 upregulation in PCa ...................................................... 53 4.3.1 Nuclear IL33 is clinically relevant and is upregulated following ADT ................ 53 4.3.2 IL33 upregulation is specific to ADT treatment suggesting a role for AR in regulation .............................................................................................................................. 54 4.4 IL33 is expressed in a cluster depicting dormant characteristics .................................. 55 4.5 IL33 positive cells are relevant to the dormant phenotype, with predicted roles in anti-apoptosis, immune evasion and stemness ................................................................................. 56 4.5.1 Summary and Key Findings.................................................................................. 58 4.5.2 Limitations ............................................................................................................ 58 4.5.3 Future Directions .................................................................................................. 59 Bibliography .................................................................................................................................61 xii List of Tables Table 1: Genetic background information for LTL313B, 313H, 412, 471 and 556. .................... 26 Table 2: Upregulated genes across PDX models and Myc-CaP models during PCa dormancy. . 31 xiii List of Figures Figure 1: Project workflow: Methodology for assessment of potential gene targets in dormancy....................................................................................................................................................... 18 Figure 2A. Hematoxylin & Eosin (H&E) and IHC of LTL467 and LTL313B for Ki67 (proliferation), Caspase-3 (apoptosis), AR and PSA at three timepoints; precastration, dormancy and relapse. B. Tumor volume C. PSA D. Ki67 and Caspase3. ................................................... 28 Figure 3A. Tumor volume pre and post castration (cx) in NRG and FVB Myc-CaP models. B. IHC staining for Ki67, Casp-3 and CD45 at pre-cx, 7 days post-cx, 14 days post-cx and at relapse, in FVB. C. RNAseq of Ki67 and CD45 in NRG and FVB. ............................................ 30 Figure 4. RNAseq on PDX models 310, 313B, 313H, 412 and 556, pre-castration and 12 weeks post-castration (dormancy) depicting (left to right) IL33, proliferation markers TPX2, PCNA and MKI67, and AR markers KLK3, KLK2 and TMPRSS2. ............................................................. 32 Figure 5. Expression of IL33 in various PDX models. A. Microarray depicts fold change following castration. B. IHC depicts protein expression of IL33 pre-cx and 12 weeks post-cx. C. IHC quantification of IL33-positive cells pre-cx and 12 weeks post-cx. ..................................... 34 Figure 6A. Bulk RNAseq depicting IL33 expression in NRG and FVB mice in dormancy. B. IHC quantification for IL33 expression in FVB Myc-CaP model in dormancy. C. IHC for IL33 expression in dormancy in FVB Myc CaP mouse. ....................................................................... 36 Figure 7A. Sowalsky cohort IL33 expression pre and post ADT, and AR markers KLK2/3 with proliferation markers TPX2/MKI67. B. Highlow cohort IL33 expression in ADT treated vs untreated samples with KLK2/3. C. IHC for IL33 on TMA cores. D. TMA scores for IL33+ve cells. .............................................................................................................................................. 38 xiv Figure 8A. Dimplot of single cell clustering at each timepoint pre and post castration. B. IL33 expression by timepoint. C. IL33 expression by cluster. D. Cluster ranking by signature........... 41 Figure 9A. Single cell dimplot at 12 weeks post castration. B. Dimplot showing IL33 positive cells. C. Violin plot showing IL33 expression by cluster. D. GSEA results for IL33 positive cluster. ........................................................................................................................................... 44 Figure 10A. Dimplot of epithelial (tumor) cells and stromal cells for analysis. B. Outgoing signalling analysis in IL33+ve and -ve cells. C. Shared signalling pathways in IL33+ve and -ve cells. Cancer stromal crosstalk interactions in D. MIF, E. PROS1 and F. BMP7. G. Correlation analysis of IL33 expression in PRAD with signatures in GEPIA2. ............................................. 49 xv List of Abbreviations ABC Avidin-biotin complex ADT Androgen deprivation therapy AMPK AMP-activated protein kinase AR Androgen receptor ARPI Androgen receptor pathway inhibitor ATM Ataxia-telangiectasia mutated CAFs Cancer associated fibroblasts CRPC Castration resistant prostate cancer CSS Charcoal stripped serum DAB 3,3-diaminobenzidine tetrahydrochloride DNAPKcs DNA-dependent protein kinase catalytic subunit dsDNA Double strand DNA ECM Extracellular matrix EMT Epithelial mesenchymal transition eNOS Endothelial nitric oxide synthase EZH2 Enhancer of zeste homolog 2 FFPE Formalin-fixed paraffin embedded G-CSF Granulocyte colony stimulating factor GSEA Gene set enrichment analysis H&E Hematoxylin and eosin HCC Hepatocellular carcinoma IHC Immunohistochemistry IL1 Interleukin-1 IL1RL1 Interleukin 1 receptor-like 1 IL33 Interleukin-33 IPA Ingenuity pathway analysis LHRH Luteinizing hormone releasing hormone LMO2 LIM domain only 2 mCRPC Metastatic castration resistant prostate cancer NEPC Neuroendocrine prostate cancer NHEJ Non-homologous end-joining NHT Neo-adjuvant hormone therapy NLS Nuclear localization signal NR2F1 Nuclear receptor subfamily group 2 F member 1 OXPHOS Oxidative phosphorylation PCa Prostate cancer PDX Patient derived xenograft PSA Prostate specific antigen QC Quality control ROS Reactive oxygen species SCC Slow cycling cells TAM Tumor associated macrophages xvi TET2 Tet methyl cytosine dioxygenase 2 TMA Tissue microarray TME Tumor microenvironment Treg Regulatory T cell TSLP Thymic stromal lymphopoietin xvii Acknowledgements This thesis would not have been possible without the unwavering support from Dr. Yuzhuo Wang. His kindness and compassion throughout this process has been essential to my ability to complete this body of work, and his knowledge and expertise has been invaluable to me. Without his encouragement I donât know that I would have made it this far. He believed in me from the very beginning, and I am so grateful for everything. I also thank my committee members, Dr. Christopher Ong and Dr. Mads Daugaard for their input and assistance throughout my graduate studies. I am so grateful to have had the opportunity to learn from them and to have had their invaluable input on my project. A special thank you to Dr. Nelson Wong, who acted as a mentor throughout the project and helped tremendously both with the thesis content and constructive discussion. I am so thankful to all my lab members for their constant help, guidance and thoughtful discussions that helped to shape this project. They have shown me such kindness and have always been there to provide constructive feedback and words of motivation. They have shown so much patience while teaching me new lab techniques and without them I could never have gained the new skills I have today. They are such a brilliant group of people, and I am in awe of their determination to contribute to a cancer fear free world. A very special thank you to my lab mate Adam Classen, who helped me at every step of this masters, from coffee rants to puppy therapy to teaching me to code. Your wisdom and friendship xviii through these difficult few years has been an essential constant that helped me through, and I cannot thank you enough. Outside of the lab, I have cheerleaders both here and at home, and they deserve the biggest thanks of all. The friendships I have made here since my arrival have played a huge part in my completion of this masters, and I am eternally grateful to my Vancouver girls. An extra special mention to Jess and Ragini for absolutely everything they have done over the last two years, they are the most supportive, patient, and compassionate women I have ever met, and I so am proud to call them chosen family. At home, I cannot write this without mentioning my best friends, Catherine, Ãine and Celine. From 10000 miles away they have shown as much support as they would if they lived next door. They remind me every day how loved I am and pick me up even on my worst days. Finally, to my family. Thank you for supporting me in moving here to have this experience, for listening to my meltdowns and for loving me so much from so far away. I look forward to our reunion every single day and it has been the driving force in the last few months of this masters. Thank you, thank you, thank you, for everything. xix Dedication For Gerry Murphy & Jean Claude Dourieu.1 Chapter 1: Introduction 1.1 Prostate Cancer Prostate cancer (PCa) is the most diagnosed malignancy in Canadian men. It is estimated that 1 in 8 men will develop PCa during their lifetime, and an estimated 11% of cancer deaths in Canadian men will be attributed to PCa in 20241,2. Despite high diagnosis rates, the five-year survival for localized PCa is roughly 90%2,3. This favourable outcome has been largely attributed to the discovery of androgen receptor (AR) signalling as the driving oncogenic force in this cancer type, and the discovery of prostate-specific antigen (PSA) as a highly effective diagnostic biomarker for PCa4. The remainder of PCa patients go on to develop more aggressive disease subtypes such as castration resistant prostate cancer (CRPC), AR indifferent PCa or Neuroendocrine prostate cancer (NEPC) 5. The survival rates for metastatic CRPC (mCRPC) and NEPC are significantly lower, with five year survival rates of 31% and 14% respectively6,7. These subtypes evidently present challenges clinically as there are limited therapeutic options, and thus, far higher rates of mortality. 1.1.1 Treatment & Recurrence As PCa is primarily fueled by androgen, the standard of care for locally advanced PCa is androgen deprivation therapy (ADT)8,9. ADT functions to block the supply of dihydrotestosterone (DHT) to the tumor, either by surgical or pharmaceutical means, resulting in tumor shrinking and remission in a large proportion of PCa cases10,11. While orchiectomy was originally carried out as the primary ADT method, a myriad of therapeutics has emerged, overshadowing the need for physical castration due to their high efficacy and low invasiveness. Luteinizing hormone-releasing hormone (LHRH) agonists are one of the most prescribed androgen deprivation treatments, acting 2 to downregulate LHRH receptor function which reduces serum testosterone levels12. The first example of an LHRH agonist was Leuprolide8 . LHRH agonists sometimes induce a testosterone âflareâ as a result of initial upregulation of LHRH, and this may result in tumor growth9,13. For this reason, LHRH antagonists were introduced. LHRH antagonists such as degarelix eliminate the testosterone flare while maintaining suppression of androgen signalling, thus making them a more attractive choice for PCa patients14. Since their development, newer antiandrogen therapies have emerged which act to inhibit AR binding without reducing serum testosterone. Examples of these include enzalutamide and apalutamide, though these are used primarily in combination therapies15. Abiraterone is an androgen receptor pathway inhibitor (ARPI) which acts to block the production of AR both by the testes and by the adrenal glands. This drug may be used in combination with traditional ADT methods such as leuprolide or degarelix as a more aggressive approach to reduce PCa growth in hormone sensitive tumors8. While ADT is highly effective in many cases, a small proportion (roughly 10%) of the population experience initial tumor shrinkage and a period of approximately 2-5 years of remission, which is followed by relapse of disease3. Upon further ADT treatment these patients may experience further periods of disease latency, however the disease invariably returns with progressively shorter durations of remission16. Eventually these cases develop resistance to ADT which is then termed CRPC. The latent stage following ADT, prior to biochemical recurrence is termed ADT-induced dormancy. 1.2 ADT-Induced Dormancy The term cancer dormancy is an umbrella label used to coin a range of cellular and mechanistic processes that occur during cancer progression. Cancer dormancy as a definition, has 3 been highly disputed within scientific and clinical communities due to diversity of the dormant phenotype. The concept of dormancy was first defined in concrete terms in the early 20th century. In 1934, Willis stated that âneoplastic cells must have lain dormant in the tissues in which they were arrestedâ 17. Following this logic, in 1954 Hadfield stated that cells are in âa temporary state of mitotic arrestâ and that this might be the reason for cancer recurrence18. It is now a consensus that cancer dormancy is a non-proliferative disease state during which tumor cells are capable of evading the immune response for prolonged periods of time, prior to relapse19. Cancer dormancy can be induced by many factors including microenvironmental changes or therapeutic interventions 20. In this instance we observe a dormant state which is induced by ADT. The possible mechanisms of entrance and maintenance of this dormancy type will be discussed subsequently. 1.2.1 Cell Cycle Regulation The initial response to ADT is characterised by a dramatic reduction in AR signalling, which is essential to the growth and survival of PCa tumors8,10. ADT causes diminished levels of circulating androgens, which leads to reduction of tumor size through programmed cell death and inhibition of proliferation10. A subset of AR-dependent PCa cells may enter period of growth arrest or latency, which is denoted by decreased metabolic activity and halted cell division in response to androgen deprivation21,22. As a mechanism of survival in response to ADT-induced stress, PCa cells may enter a quiescent state of G0-G1 cell cycle arrest22. Quiescent cells remain in this state until favourable conditions arise, and they can re-enter the cell cycle. Cancer cells which enter quiescence may be considered to be in a state of dormancy, which is characterised by a lack of proliferation without dying19. 4 Similarly, a subpopulation of slow-cycling cells (SCCs) may exist within the heterogeneous PCa tumor, which are capable of surviving ADT by entering a state of low metabolic activity23 . SCCs in breast cancer have been reported to be governed by epigenetic oxidation of TET224 . This type of proliferation, albeit slow, may facilitate mutations which can contribute to the plasticity of dormant cancer cells. This heterogeneity of cell cycle is also observed in dormancy and is based on a balanced proliferation and cell death which maintains the tumor volume at a steady rate19 . In vitro, ADT has been noted to inhibit proliferation and induce cytostasis in LNCaP cell lines, though minimal apoptosis has been recorded25 . Androgen deprivation longer than six months, in this cell type, has the capacity to initiate a dormant state which has been denoted by a stable persistence of a small number of cancer cells which display a rounded morphology associated with dormant cells 25. This supports the theory that ADT-induced dormancy may be supported by a subgroup of cells which undergo a process of de-differentiation in order to survive biological stress. In line with this, a subset of LNCaP cells treated with androgen deprivation by charcoal-stripped serum (CSS) have shown to have stem-like properties with a capacity for self-renewal lacking androgen, as well as the same spherical morphology depicted by Pelliccia et al (2023) which fits with a theory of cell dedifferentiation for survival in ADT induced dormancy25. A cornerstone of PCa lies in its heterogeneity. While a majority of PCa cells rely on AR signalling for survival, some cell subsets are only partially dependent, or AR insensitive altogether. The clonal expansion of these subsets through proliferative advantage may contribute to eventual escape from the dormant phenotype, and in tandem with AR mutations, lead to the development of CRPC 26. 5 1.2.2 Metabolic Adaptations Metabolic adaptations play a crucial role in the induction of dormancy in prostate cancer, with specific metabolic pathways being upregulated in order to cope with environmental stressors such as reactive oxygen species (ROS), hypoxia, and androgen deprivation27,28. ADT impacts the AR signalling pathway, leading to decreased AR function and subsequent inhibition of cell proliferation in PCa cells. However, loss of AR signalling due to ADT can trigger activation of alternative survival pathways such as the PI3K/Akt/mTOR pathway, inhibiting pro-apoptotic proteins and promoting autophagy as mechanisms for cell survival29,30. ADT-induced upregulation of autophagy in PCa cells is associated with enhanced cell survival, therapy resistance and the development of androgen resistant phenotypes, which allow cancer cells to survive despite androgen-deprived and hypoxic conditions 30. Autophagy can be stimulated by ADT through various pathways such as HIF-1 gene expression, mTOR kinase inhibition, or lipophagy, enabling cancer cells to survive under nutrient-deprived conditions during dormancy. In 2019, the metabolic response of PCa cells to ADT was observed in Pten/Tp53 null cells31. Those that survived ADT displayed increased levels of HK2 and mitochondrial association, which may act as a survival mechanism for PCa cells following ADT. Furthermore, pAKT levels were also observed, which may drive metabolic reprogramming via HK2, inhibiting mitochondrial death pathways 31 . AMP-activated protein kinase (AMPK) is a critical signalling molecule that aids PCa survival of ADT-induced metabolic stress. AMPK functions by inhibiting ATP-consuming processes while stimulating ATP-producing pathways, thereby conserving ATP for cell survival32. Although apoptosis may be triggered by ADT, studies suggest that autophagic processes induced by the hypoxic TME might overshadow or inhibit apoptotic mechanisms33 . Lastly, dormant prostate cancer cells also exhibit a metabolic shift towards increased mitochondrial respiration and 6 oxidative phosphorylation (OXPHOS), rather than relying primarily on aerobic glycolysis following ADT. This adaptation allows for dormant cells to maintain viability and quiescence while simultaneously minimizing proliferation and division of PCa cells27. Adaptations may also aid in the exit from ADT-induced dormancy via AR signalling. The role of AR in the progression from hormone-naïve PCa to castration-resistant or metastatic PCa has been thoroughly researched, and there are many AR-mediated mechanisms of resistance to androgen deprivation which allow for relapse of disease. These include both ligand dependent mechanisms such as AR amplification, AR promiscuity and AR variants, or ligand independent mechanisms such as aberrant activation and modification of coregulators through post translational adaptation34. The reestablishment of alternative AR signalling pathways can contribute to dormancy escape by returning the driving fuel to PCa, encouraging cells to re-enter the cell cycle and continue proliferating. AR inhibition may also contribute to PCa stemness and the dormant phenotype by promoting dedifferentiation of cells35. This may be feasible given the known role of AR signalling in prostate cell differentiation and development, further contributing to the dormancy capabilities of PCa cells following ADT. Epithelial-to-mesenchymal transition (EMT) is a critical regulator of cancer progression to CRPC following ADT. EMT induction is enriched directly post-castration in LNCaP xenografts and may contribute to the entrance and maintenance of the dormant phenotype in PCa by inducing metabolic quiescence through modulation of OXPHOS and glycolysis.36â38 Further investigation of the role of EMT in ADT-induced dormancy is required to gain full understanding of the impact of this mechanism in this cancer context. ECM remodelling plays a crucial role in the interplay between prostate cancer cells and their microenvironment, influencing tumor growth, invasion and dormancy. Dormancy escape in the breast cancer context has been associated with stromal cell 7 response via IL-6 and granulocyte colony stimulating factor (G-CSF) mediated MEK signalling 39. As such, it is highly probable that stromal-cancer interactions in the PCa context may contribute to dormancy escape. However, there is huge heterogeneity of the tumor microenvironment in PCa, rendering it difficult to gain a âbig pictureâ view of the tumor-stromal cell interactions which are directly contributing to ADT-induced dormancy in PCa. 1.2.3 Modifications To Tumor Microenvironment 1.2.3.1 Immunogenic modulation Immunogenic modulation plays a pivotal role in the induction and maintenance of dormancy in PCa following ADT. The intricate interplay between the immune system and cancer cells significantly influences the initial establishment of dormancy. Notably, immune tolerance emerges as a critical mechanism enabling PCa survival during dormancy. Studies such as that by Long et al. (2020), have underscored the impact of ADT on immune cell dynamics, revealing a notable increase in CD8+ T cells within treated samples. However, this influx of immune cells, including regulatory T cells (Tregs) and M2 macrophages, may foster an environment conducive to immune tolerance by facilitating unchecked proliferation of residual cancer cells40. Increased intratumoral Treg infiltration has also been correlated with shorter progression free survival, indicating a possible role for Tregs in dormancy exit 41 . ADT can induce alarmins such as HMGB1 which act to recruit and polarize tumor associated macrophages (TAMs) towards an M2 phenotype aiding in the immune-evasive phenotype of dormant PCa 42. TAMs have also been associated with the production of VEGF in PCa tumors, contributing to angiogenesis and possible escape from dormancy 43. Therefore, the presence of immune cells within the tumor microenvironment not only influences the delicate balance between proliferation and dormancy but also confers immune-8 protective properties to SCC and quiescent cancer cells. These cells exhibit phenotypic traits that enable evasion of immune targeting, including neoantigen loss observed in hepatocellular carcinoma (HCC) cells44,45. Additionally, myeloid derived suppressor cells (MDSCs) are implicated in facilitating immune evasion during the latent phase of dormancy, with ADT-associated increases in intratumoral MDSC density further supporting this notion46. In alternative cancer contexts, MDSCs have the capacity to induce macrophage differentiation towards the immunosuppressive (M2) phenotype, and simultaneously disrupt dendritic cell antigen presentation, which enhances immune tolerance and promotes tumor progression47. Consequently, ADT induces an immunosuppressive signature characterised by downregulation of IFN response pathways, diminished expression of cytotoxicity genes in CD4 and CD8+ T cells and reduced Treg-derived expression of immune checkpoint molecules and exhaustion markers40,46,48. 1.2.3.2 Stromal and Extracellular Matrix Modifications ADT further amplifies the reciprocal interaction between PCa cells and stromal components, promoting invasion and creating a microenvironment conducive to tumor recurrence. Notably, downregulation of AR in stromal fibroblasts causes ECM protein expression dysregulation, potentially facilitating PCa cell recurrence49. Additionally, ADT-resistant PCa cells exhibit elevated expression of endothelial nitric oxide synthase (eNOS), suggesting a survival pathway for dormant PCa cells post-ADT 49. The role of eNOS in endothelial cell survival and proliferation may contribute to maintaining the pro-oncogenic phenotype of dormant PCa cells, further complicating treatment strategies. Endothelial cells also contribute to PCa cell invasion in a metastatic context by downregulating AR transactivation and signalling, promoting an AR-independent survival route that fosters relapse and treatment resistance49. 9 Cancer-associated fibroblasts (CAFs) respond to ADT by upregulating tumor-promoting cytokines such as CCL2 and CXCL8, fostering an inflammatory microenvironment conducive to relapse38. Furthermore, ADT induces overexpression of LIM domain only 2 (LMO2) in PCa-associated CAFs, promoting tumor progression and growth through paracrine release of FGF9, highlighting the multifaceted role of AR signalling in regulating tumor-stromal interactions 38. One report stressed the importance of AR signalling in stromal cells for the progression of PCa50. Lacking AR signalling in this cell type, growth factors such as FGF-2 and FGF-10 were downregulated which prevents disease progression38,50. This could pose as a contributor to dormancy maintenance following ADT 51. 1.2.3.3 Angiogenic dormancy Angiogenic dormancy emerges as a pivotal aspect of the dormant phenotype in prostate cancer, particularly following ADT52. The tumor microenvironment undergoes a significant shift, with hypoxia becoming a prominent feature. While irregular vascular networks are characteristic of many cancer types including PCa, and can contribute to hypoxic conditions, ADT exacerbates this phenomenon by further reducing local vascularization and angiogenesis52. The diminished production of pro-angiogenic factors such as VEGF and FGF, likely stemming from disrupted endothelial cell proliferation, exacerbates the hypoxic milieu53. Angiogenic dormancy serves as a protective mechanism, impeding cancer cells from outpacing apoptosis due to hypoxia within the TME. This phenomenon has been observed in disseminated tumor cells in PCa, where the absence of angiogenesis triggers the induction of dormancy, thereby restraining cancer cell proliferation54. As such, it is highly probable that ADT-induced dormancy operates under these same mechanisms to promote survival and eventual recurrence.55 Hypoxia can also promote the escape from 10 dormancy in prostate cancer via paracrine and autocrine signalling loops. Hypoxia causes upregulation of HIF1a and TGF-B1, which in turn, regulate VEGFA-165 secretion promoting increased angiogenesis, migration, and tumorigenesis in PCa 56â58. 1.2.4 Genomic Instability and Epigenetics 1.2.4.1 Regulation of plasticity and dormancy associated genes The regulation of plasticity and dormancy associated genes in prostate cancer, particularly in the context of ADT and dormancy, involves key molecular pathways and tumor suppressor genes which influence the transition between dormant and proliferative states. Analysis using Ingenuity Pathway Analysis (IPA) identified deregulated pathways associated with cell cycle, cell death, survival, cellular assembly, DNA repair and cell growth in dormant prostate cancer patient derived xenografts following castration21. Among the differentially expressed genes interacting directly with the androgen receptor, 53 out of 58 were downregulated in dormant PDXs post-castration, highlighting the impact of androgen deprivation on genes relating to dormancy induction 21. Tumor suppressor genes such as PTEN play a crucial role in regulating the entry into dormancy in prostate cancer59. In primary prostate cancer prior to ADT, loss or mutation of PTEN occurs in approximately 20% of patients, however, by the time CRPC develops the population with PTEN mutation rises to 40%31. As such, ADT may play a role in the genetic instability of PCa. Loss of PTEN function may activate pro-survival pathways through increased PI3K/AKT-mediated signalling and influence the initial transition to a dormant state 31. Multiple studies have referenced the implications of PTEN loss in PCa aggressiveness and have referenced an increased survival capacity in these cells, likely contributing to dormant capability and escape 60,61. 11 1.2.4.2 DNA Damage In vitro assays have revealed a causal effect of ADT on double-strand DNA (dsDNA) breaks, whereby in metastatic hormone-sensitive prostate cancer cells, ADT induces caspase-dependent dsDNA breaks which employ ataxia telangiectasia mutated (ATM) kinase to maintain cell viability, and as such, to survive ADT and maintain dormancy62. ADT has the capacity to sensitize PCa cells to DNA damage as AR maintains regulation of DNA-PC catalytic subunit (DNAPKcs) which are required for non-homologous end joining, and XRCC2, RAD54B and RAD51C, which are necessary for homologous recombination 6364. ADT in clinical settings has been shown to reduce Ku70 protein levels which are also heavily involved in NHEJ pathways65. Lacking androgen signalling, some PCa cells lose their capacity to repair DNA damage, and in turn, are promoted towards an pro-invasive phenotype via activation of oncogenes or loss of tumor suppressor genes66. The downregulation of DNA repair genes in PCa following ADT has also been corroborated using whole transcriptomic data in patient-derived xenograft models of dormancy.21 In general, NHEJ is a more error prone method of DNA repair, and this may contribute to genomic instability and transformation following ADT. This may be more prevalent in the quiescent cells undergoing dormancy due to lower expression of DNA damage repair genes in this cell type 67,68 . This concept has been primarily investigated in hematopoietic stem cells and as such, requires further investigation in the prostate cancer context. However, as ADT increases the population of stem-like cells while decreasing non-stem like cells, it may present a relevant mechanism of PCa survival and progression following ADT-induced dormancy. 12 1.2.4.3 Epigenetic Modifications ADT can undoubtably lead to epigenetic changes in prostate cancer cells which influence gene expression patterns 69. These changes involve alterations in methylation, histone modifications and chromatin remodeling70 . In the context of ADT-induced dormancy, the papers detailing specific epigenetic modifications are scarce, and the majority of PCa epigenetics papers discuss modifications contributing to lineage plasticity and subsequent development of NEPC 71. Alternatively, many papers reported epigenetic alterations to androgen receptor signalling which may promote AR-mediated exit from dormancy70,72,73. This may occur through binding of the known histone methyltransferase enhancer of zeste homolog 2 (EZH2) as a transcriptional coactivator of AR, which promotes resistance to ADT and possible dormancy escape74. Alternatively, histone acetyltransferase p300 can act to acetylate AR or enhance transcriptional activity to promote tumor progression following ADT 72. In alternative dormancy contexts, a known player in tumor dormancy is nuclear receptor subfamily 2 group F member 1 (NR2F1)75â77. Epigenetic modifications of NR2F1 can induce a dormant state through addition of repressive histone marks such as H3K27me3 and H3K4me3 at promoter regions of SOX9 and RARb 7678. This has been observed in metastatic prostate cancer dormancy and head and neck squamous cell carcinoma and may be applicable to the ADT-induced dormancy context also77. These epigenetic modifications collectively set the stage for a microenvironment that is less supportive of active cell growth, thus creating conditions in which prostate cancer cells may enter dormancy as a survival strategy. 13 1.3 Interleukin-33 (IL33) 1.3.1 Structure & Function IL33 is a pleiotropic cytokine and nuclear factor which is expressed in many cell types, displaying constitutive expression in endothelial cells and epithelial barrier cells79. IL33 is a member of the interleukin-1 (IL1) family, and can function as an alarmin which is released in response to cell stress, death or damage80. IL33 is well known for its role in immune responses, particularly in asthmatic and autoimmune conditions81,82. The molecular weight of IL33 is 33kDa, and structurally it is composed of 270 amino acids, featuring a B-trefoil structure83. It contains three domains, the C-terminal cytokine domain, the central domain and the homeodomain-like HTH nuclear domain80. 1.3.1.1 Cytokine The C-terminal domain is responsible for the cytokine-like functions of IL3379. Extracellularly secreted IL33 (sIL33) can bind to its receptor IL1RL1 (ST2) on the surface of a number of immune cells such as T helper 2 cells, mast cells, macrophages and Treg cells79. Salt bridge formation facilitates high affinity binding of IL33 to its receptor. ST2 forms a complex with IL-1RAcP, which, when bound to IL33, stimulates MyD88 signalling pathways and results in a proinflammatory response via NFkB activation81. IL33 can be neutralised by binding to soluble ST2, which acts as a decoy receptor. Alternatively, IL33 can be inactivated through cleavage by caspases 3 and 781,84. 14 1.3.1.2 Nuclear IL33 may be expressed in the nucleus as a result of its nuclear domain. It contains a nuclear localisation signal (NLS) that may aid in mediation or influence of gene transcription through its chromatin binding motif85. This motif allows IL33 to interact with histone proteins H2A and H2B85. It has been established that nuclear localisation of IL33 is essential for maintenance of homeostasis, and deletion of the N-terminal nuclear domain of IL33 in a knock-in mouse model resulted in elevated levels of IL33 in the serum and ST2-dependent inflammation-induced lethality86. This supports observations that nuclear localisation of IL33 may have been selected during evolution to regulate its own potent pro-inflammatory activity85. Furthermore, IL33 is capable of impairing transactivation of NFKB regulated genes such as IL1, IL6 and IL18, and has been known to regulate M1/M2 chemokine expression via mitochondrial redox-related mitophagy in human monocytes87,88. Nuclear IL33 has many context-specific roles in transcriptional regulation, both as an activator and repressor, via TF binding89. It is capable of regulating gene expression by binding to chromatin and interacting with transcriptional complexes dependent on the biological circumstances or cell types involved90. nIL33 can also control chromatin accessibility at c-jun motifs which influences its own expression91. As a repressor, IL33 can sequester nuclear NFkB, decreasing NFkB triggered gene expression to suppress proinflammatory signalling, thus acting as a negative modulator of NFkB activity92. nIL33 also inhibits expression of the RUNX1 transcription factor thymic stromal lymphopoietin (TSLP) cytokines which are induced in cases of atopic dermatitis93. This is thought to occur through formation of a nIL33/STAT3 complex which inhibits transcription93. As an activator, one report has indicated that nIL33 is capable of forming a histone complex which binds to ST2 and activates IL33/ST2 signalling94. Finally, nIL33 15 has been known to bind to chromatin recruiting histone acetyltransferase p300 for enhancement of H4 acetylation and activation of target genes95. 1.3.2 Role in Cancer In skin and pancreatic tumors, nIL33 can induce a tumor promoting environment due to chronic inflammation96. This tumor promoting effect is independent of IL33 cytokine function96. High levels of nIL33 have been documented in both skin and pancreatic epithelial cells undergoing chronic inflammation, capable of promoting TGFÃ/SMAD signalling within cancer cells96. nIL33 was found to repress SMAD6 gene expression via blocking of RUNX2 transcriptional activity, which in turn, caused upregulation of phosphorylated SMAD2/3 and p-SMAD1/596. In colorectal cancer, nIL33 is upregulated in correlation with the hypoxia marker HIF1a97. This in turn causes reduced soluble ST2 (the iI33 decoy receptor), as accumulated nuclear IL33 binds GATA3 which blocks its access to the sST2 promoter97. Decreased sST2 expression in CRC promotes tumor growth by IL33 mediated bioprocesses in the TME such as Th2 differentiation, macrophage infiltration, angiogenesis and enhancement of tumor growth97. nIL33 is capable of reducing sST2 expression in CRC cells by recruiting SUV39H1 which mediates h3K9me397. Restoration of sST2 in hypoxic tumor regions restored the microenvironment and suppressed tumor growth97. nIL33 currently has few implications in DNA damage repair, and these are largely associated with gliomas98. Overexpression of nIL33 resulted in an increase of DNA repair genes such as BRCA1, BRCA2, Rad51, FANCB and FANCD98. This is postulated to contribute to the resistance of this cell type to DNA damaging agents such as temozolomide98. Overexpression of nIL33 caused increased H2A and H2B proteins, as well as increased HMGA1 and HMGA2 in glioma98. These proteins are considered architectural TFs, and are capable of remodelling chromatin structures and 16 contributing to cancer transformation98. As such IL33 may mediate chromatin remodelling by regulation of chromatin structural proteins in these cancer contexts. 1.3.2.1 IL-33 in Prostate Cancer The role of IL33 in prostate cancer is relatively unknown, with only a handful of published papers on the topic. IL33 has been found to have higher expression in PCa patients versus controls, and is further elevated in PCa patients with higher Gleason scores, which may implicate IL33 in cancer progression99. Conversely, IL33 has been implicated in anti-tumor function in metastatic prostate cancer as it relates to immune surveillance. In this study, reduced expression of IL33 was observed which facilitated immune escape in the metastatic context, and complementation with IL33 aided functionality of MHC molecules, thus improving the anti-cancer immune response100. As yet, no studies have been published investigating IL33 following ADT treatment. 1.4 Rationale, Hypothesis & Objectives 1.4.1 Rationale Late-stage prostate cancers such as mCRPC are currently incurable. There has been modest progress in advancing treatment modalities for these fatal diseases over the last decades. The cells surviving ADT-induced dormancy are responsible for relapse and CRPC. Therefore, we propose to intercept disease progression in the earlier dormant stage such that we may prevent the development of CRPC altogether. To target the dormant stage, it is essential to have actionable disease targets. We hope to employ our PDX models for target identification and leverage that against computational analysis to establish a method for target discovery in dormancy. Targets 17 may then be used in combination with ADT to prevent the entrance to dormancy, or to prolong dormancy to a degree that PCa could be considered a chronic or âfear freeâ disease. 1.4.2 Hypothesis Dormancy PDX models provide accurate tumor recapitulation for study of actionable disease targets through combined wet lab and computational methodology. 1.4.3 Objectives Objective 1: Establishment of appropriate models for the study of dormancy and selection of an example target gene for analysis. Objective 2: Validation of target upregulation in models of PCa dormancy. Objective 3: Clinical confirmation of target expression following ADT. Objective 4: Analysis of target enrichment in dormancy using single cell sequencing. Objective 5: Functional assessment of target in the PCa dormancy context using computational analysis. 18 Chapter 2: Methodology Figure 1. Project workflow: Methodology for assessment of potential gene targets in dormancy Dormancy model establishmentTarget selectionModel validation of target expression in dormancyClinical confirmation of target expression Bioinfirmatic enrichment analysis of target in dormancyComputational prediction of target function in dormancy19 2.1 Cell Lines The mouse prostate cancer cell line Myc-CaP was obtained from Dr. Christopher Ongâs Lab at Vancouver Prostate Centre. Cell lines were cultured in Dulbeccoâs modified Eagles medium (DMEM), supplemented with 10% foetal bovine serum (FBS) in an incubator at 37ï°C with 5% CO2/air environment and tested for mycoplasma. Following this, the line was used for subcutaneous implantation in FVB and NRG mice for development of the Myc-CaP dormancy model. 2.2 PCa Patient-Derived Xenograft (PDX) Models PDX lines established using cryopreserved PDX seeds which were implanted by subrenal capsule engrafting into 6â8-week-old non-obese diabetic Rag1null IL2rgnull (NRG) mice101. Following growth, the PDX tissues were harvested and reimplanted into NRG mice which had been supplemented with testosterone. Once tumor volume reached 150mm3 the mice were randomly assigned to three cohorts, Precastration, dormancy and long-term monitoring for assessment of relapse in each PDX line. Precastration are harvested without castration, dormancy are harvested at 12 weeks post-castration and long term monitoring group were harvested either at tumor relapse to a volume of 300mm3, after 42 weeks post-castration, or once they reached a humane endpoint due to age. Dormant state at 12 weeks post-castration is confirmed by undetectable serum PSA in conjunction with low tumor volume. This may also be paired with immunostaining for Ki67 and Caspase-3 to assess reduced proliferation and apoptosis. Finally, morphological changes in castrated samples include reduced mitotic structures, hyalinized stroma, reduced glandular structures, nuclear pyknosis and cytoplasmic vacuolisation21. Ethical guidelines set out by the declaration of Helsinki were followed in this study, with informed written consent being given by 20 patients prior to obtaining the specimens for PDX establishment. The protocol was approved by the Institutional Review Board of the University of British Columbia (UBC). Animal studies were approved by the UBC Animal Care Committee and were carried out under protocol #A17-0165. 2.3 Human PCa Samples and Clinical Data Vancouver Prostate Centre tissue bank provided Human PCa samples, consisting of specimens from neoadjuvant hormonal therapy (NHT) treated groups and specimens from radical prostatectomy groups (NHT2009). The recipient block method was used for construction of the tissue microarray (TMA). Patient cores are extracted using a 0.6mm tissue punch needle from parrafin embedded blocks of donor tissue. The TMA was then assembled using a predefined map based on treatment duration or cancer stage. The tissue microarray provided consisted of approximately 180 tissue cores taken at four timepoints; untreated, NHT 1-3 months, NHT 5-12 months or CRPC. Quality control measures such as H&E, IHC and in-situ hybridization was performed to assess protein and RNA quality in the TMA. RNA expression data from the High-low Clinical trial cohort was acquired from Dr. Colin Collins102. STAR 2.6.0a was used to map RNA-seq reads to the reference database based on Ensembl GRCh38 Release 90. Additional RNAseq from the Sowalsky NHT cohort included paired pre and post ADT samples (6 months NHT), and was sequenced using Illumina NovaSeq 6000 S4 flow cells. This cohort was provided by Dr. Adam Sowalsky103. 2.4 Histopathology and Immunohistochemistry Tissue sections were bisected through the longest dimension and prepared according to standard formalin fixed paraffin embedding (FFPE) protocols and cut using a microtome. Following 21 mounting on slides, the sections were stained with Hematoxylin and eosin (H&E) for histopathology. Immunohistochemical staining was employed to localize and visualize protein expression within tissue samples for Ki67 (monoclonal mouse anti-human 2:25, Dako), Caspase 3 (rabbit monoclonal 1:50, Cell Signalling), AR (rabbit monoclonal 1:100, Abcam), PSA (rabbit monoclonal 1:200, Abcam), IL33 (Polyclonal goat anti-human AF3625 1:100 and polyclonal goat anti-mouse AF3626 1:50, RND Systems). Antibody specificity for human IL33 antibody was validated using human lymph node tissue as a positive control and mouse spleen as a negative. For mouse IL33 spleen was used as a positive control and human lymph node was used as a negative control. Ki67 was used to assess proliferation, caspase 3 was used for apoptosis and PSA was used to assess AR signalling in dormancy, as loss of these markers is indicative of the dormant state. FFPE tissue sections were deparaffinized and rehydrated through a series of xylene and graded alcohol solutions. Antigen retrieval was performed using a citrate buffer of pH 6.0 in a microwave for 20 minutes to unmask epitopes. Endogenous peroxidase activity was blocked using 3% hydrogen peroxide for 10 minutes. After blocking non-specific binding sites for 1 hour at room temperature the sections were incubated overnight at 4 degrees. The secondary antibody was added the following day, followed by avidin-biotin complex (ABC) reagent for 30 minutes. Diaminobenzidine (DAB) was used as the chromogen to visualise antibody binding. Sections were then counterstained with Hematoxylin, dehydrated and mounted with coverslips. Negative controls, omitting the primary antibody, were included to ensure specificity. Stained sections were examined under a light microscope and images were captured for analysis. Human lymph node tissue was used as IL33 staining positive control. TMA cores were scored with the aid of an independent investigator using the following formula: intensity + (% area score 3) x 3 + (% area score 2) x 2 + (% area score 1) x 1. 22 2.5 IHC Quantification IHC quantification of nuclear IL33 positive cells was observed by taking 5 random sample images from each model at both timepoints at x400 magnification. A stereology 3x3 grid method was employed and five of these grid-boxes were selected for counting â those selected were the outer corner boxes and the center box on each sample image. All IL33 positive cells within the acceptance criteria were then manually counted using a clicker and documented. Following this the remaining negative cells were also counted in the same fashion. The sum of the negative and positive cells indicates total cell count per box and a percentage or rate of positive cells could be deduced from this. These counts were carried out on 5x5 samples â resulting in 25 individual counts per timepoint. Means of these counts were converted into percentages. Stereological methods were chosen over digital image analysis due to enhanced objectivity and accuracy due to variability in staining intensity among samples, which may not be captured by automated imaging programs. 2.6 RNA microarray gene profiling Agilentâs One-Color Microarray-Based Gene Expression Analysis Low input Quick Amp Labelling v6.0 was used to process extracted RNA from PDX tissues. Cyanine-3 labelled cRNA was generated using 100ng of RNA and these were hybridized on Agilent DNA Microarray G3 Human GE 8 x 60K Microarray. Following this, arrays were scanned and processed using the Agilent DNA Microarray Scanner and Agilent Feature Extraction 10.10 respectively. Normalization was carried out using Agilent GeneSpring 11.5.1. Downstream validation of 23 microarray profiling was carried out using quantitative PCR (qPCR) and western blot to confirm observed expression changes in dormancy. 2.7 Bioinformatics Single cell sequencing of LTL331 model was used to analyze functional implications of IL33 using computational methods. R software version 4.0 was used to carry out all scseq analyses. Key packages used include Seurat, CellChat, Azimuth and FGsea. Seurat was used to allow interpretation of sample heterogeneity in single cell datasets. CellChat was employed to assess cellular crosstalk between the tumor cells and the stromal cells in our dataset. Azimuth provides organ specific cell type annotation. FGsea was used to carry out gene set enrichment analysis in R. Standard quality control (QC) procedures were followed for preprocessing of raw samples, including removal of doublets and low-quality cells, and establishment of cut-offs for mitochondrial percentage. The data was normalized using a global-scaling normalization method in R. For GSEA analysis, only the human reads were used to ensure specificity to tumor-derived IL33. The cluster expressing the highest level of IL33 was selected and subset for IL33-positive cells, and GSEA was carried out on this group at each timepoint. The databases used for GSEA were Hallmark, KEGG, GOBP and Reactome which were curated by MSigDB. For assessment of cellular crosstalk between IL33-positive cancer cells and stromal cells at each timepoint, the CellChat R package was used. Human and mouse samples were processed and merged, and the Azimuth package was employed to annotate cell types based on a curated database of Human kidney cell phenotypes. The cluster with high IL33 expression was again subset based on IL33 positive cells. Within this cluster IL33 positive cells in the human cohort were isolated, and all 24 relevant stromal cells were selected to assess crosstalk, regardless of stromal IL33 expression. This procedure was repeated with IL33-negative cells within the same cluster for comparison. 2.8 Statistical Analysis GraphPad Prism 10.0, Microsoft Excel, R version 4.0 and GEPIA2 were used to carry out all statistical analyses. Minimum p-value of <0.05 was used as a cut off. Paired t-tests were used for analysis of paired datasets (Sowalsky). Gene Set Enrichment Analysis (GSEA) was carried out using the Fgsea R package. Pearsonâs correlation was used to assess association of IL33 with various dormancy signatures in PRAD using the GEPIA2 database. Signatures were established using literature and the mSigDB database. 25 Chapter 3: Results 3.1 Establishment of models for the study of ADT-induced dormancy and target selection for method validation 3.1.1 Patient-Derived Xenograft Models We selected hormone naïve prostate adenocarcinoma derived PDX models that are responsive to ADT. In Table 3.1 we observe the genetic background of models 313B, 313H, 412, 471 and 556. The variations in genetic background strengthen the validity of our study as they show a wide range of diversity akin to clinical PCa presentation. All selected models are AR positive which is key to ADT response and induction of a dormant state following castration. SYP negativity is associated with the adenocarcinoma phenotype. TMPRSS2-ERG fusion status has been associated with PCa aggressiveness, and status variability amongst our models may provide further insight into treatment response and duration of dormancy. Furthermore, the disparity of expression of our target gene between models may be linked to specific genetic variations which would, in turn, set a path for targeted therapies in these cases. IHC staining is observed in Fig. 2.A. on 467 and 313B models. Following castration, these models undergo initial tumor regression with entrance to a dormant state. In both instances there are residual carcinoma cells present in the dormant phase as observed by H&E. The tumor volume decrease is substantial with an average reduction of 88.35% post-castration. PSA expression is significantly reduced in the dormant phase, noted in Fig. 2.C. with undetectable levels in all models. Ki67 is reduced by an average of 90% across all models and Caspase-3 is reduced by an average of 65% at 12 weeks post-castration, which is confirmative of reduced proliferation and apoptosis in dormancy. Following dormancy, the cancer growth resumes resulting in relapse of disease. On relapse, proliferation, apoptosis, and 26 androgen response is renewed indicating that the capacity for cancer progression is reinstated. The variation in Ki67 expression between models speaks to the heterogeneity of these systems, which may be associated with response to ADT and PCa aggressiveness due to genetic background. Cross-model variation may be instrumental in assessing appropriate targeted treatment modalities in the clinical setting. The maintenance of dormant characteristics, in tandem with capacity for relapse, cements the relevance of these models to the study of ADT induced dormancy. Table 1: Genetic background information for LTL313B, 313H, 412, 471 and 556. 27 28 Figure 2A. Hematoxylin & Eosin (H&E) and IHC of LTL467 and LTL313B for Ki67 (proliferation), Caspase-3 (apoptosis), AR and PSA at three timepoints; precastration, dormancy and relapse. B. Tumor volume C. PSA D. Ki67 and Caspase3. 29 3.1.2 Immunocompetent Myc-CaP model The Myc-CaP model was characterised to establish a role in the study of dormancy due to its shared molecular crossover with human prostate tumors. In this study we compared the NRG, immunocompromised group with the FVB immunocompetent model. Following subcutaneous injection and subsequent tumor growth, the mice were castrated. Lacking AR signalling we observe a rapid fall in tumor volume, followed by recurrence after approximately two weeks. This can be seen in Fig. 3A. The FVB Myc-CaP mouse model is especially informative, presenting as an immunocompetent AR positive dormancy model which may be used to study the immune landscape of PCa both prior to and post castration. Deconvolution of bulk RNAseq revealed low proliferation as seen by Fig. 3C. depicting the Ki67 graph, and high CD45 in the FVB cohort during the dormant stage. On further investigation by IHC in Fig. 3B. we observe that the Myc-CaP FVB model displays the same characteristics at the protein level, depicting low proliferation, low apoptosis, and high immune infiltration. Based on these highly relevant models, we selected an example gene for study of the ADT-induced dormancy context. A 30 Figure 3A. Tumor volume pre and post castration (cx) in NRG and FVB Myc-CaP models. B. IHC staining for Ki67, Casp-3 and CD45 at pre-cx, 7 days post-cx, 14 days post-cx and at relapse, in FVB. C. RNAseq of Ki67 and CD45 in NRG and FVB. 3.1.3 Target Selection A previous study by Wong et al. was carried out within our lab and revealed several genes which are upregulated across both PDX models and Myc models, seen in Table 3.2. A literature review highlighted Interleukin-33 as a gene of interest due to its known roles in disease progression in many cancer types as mentioned in Chapter 1. We also noted very few studies on the function of IL33 in prostate cancer, rendering it a novel target in this context. On further analysis of bulk RNAseq, we discovered significant upregulation of IL33 at 12 weeks post-castration in all five of our sequenced models (310, 313B, 313H, 412 and 556) denoted in Figure 4. The dormant phenotype is confirmed using proliferation markers TPX2, PCNA and MKI67, and AR markers KLK2, KLK3 and TMPRSS2 confirm efficacy of castration as a method of ADT at the same time B C 31 point (12 weeks post-cx). Thus providing rationale for selection of this gene for study in ADT-induced dormancy. Table 2: Upregulated genes across PDX models and Myc-CaP models during PCa dormancy. 32 Figure 4. RNAseq on PDX models 310, 313B, 313H, 412 and 556, pre-castration and 12 weeks post-castration (dormancy) depicting (left to right) IL33, proliferation markers TPX2, PCNA and MKI67, and AR markers KLK3, KLK2 and TMPRSS2. 33 3.2 Confirmation of IL33 upregulation in mouse models and indication of intrinsic role for IL33 in dormancy 3.2.1 Patient-Derived Xenograft Models Microarray data shows a three-fold increase in IL33 expression twelve weeks post-castration in LTL models 508, 556 and 331, depicted in Fig. 5A. A single fold-change increase in IL33 expression can be seen in the remaining models (310F, 313B, 313H, 412, 471, 467 & 484). Immunohistochemical staining for IL33 protein on models 508, 556 and 331 show minimal expression pre-castration and significantly increased expression at 12 post-castration Fig5.B. IHC quantification on LTL331, LTL508 and LTL556 in Fig.5C. confirm visual findings, and indicate a significant upregulation of IL33 protein expression in all models. In LTL331 nuclear IL33 expression increased from 0.3% of total cancer cells to 3.64%. In LTL508 nuclear IL33 expression increased from 2.69% to 33.25% of total cancer cells, and in LTL556 the IL33 expression increased from 0.81% to 5.22% of total cancer cells in week 12 post-cx. The variability of expression between models may be attributed to genetic heterogeneity such as TMPRSS2-ERG fusion. While LTL331 and LTL556 are positive for this mutation, LTL508 is negative for TMPRSS2-ERG fusion, which may present a contributing factor in the enhanced expression of IL33 protein in this model. While this merits further analysis, it highlights the importance of genetic background in the treatment of clinical PCa and strengthens the value of our models in this field of research. 34 Figure 5. Expression of IL33 in various PDX models. A. Microarray depicts fold change following castration. B. IHC depicts protein expression of IL33 pre-cx and 12 weeks post-cx. C. IHC quantification of IL33-positive cells pre-cx and 12 weeks post-cx. A B C 35 3.2.2 Immunocompetent Myc-CaP Model We sought to assess the impact of the immune system on IL33 expression in dormancy, as IL33 has known roles in immunomodulation. Following androgen deprivation via castration, both immunocompetent and immunocompromised Myc-CaP models (FVB & NRG respectively), displayed increased expression of IL33 at the RNA level prior to relapse (R), as seen in Fig. 6.A. IHC staining in Fig. 6.B. depicts low intensity nuclear staining of Myc CaP cancer cells at day 14 post-castration (Cx_14), indicating protein expression of IL33 in the latent disease stage. We then carried out stereological quantification shown in Fig. 6.C. which supported our visual findings, indicating an increase from 0% IL33+ve cells in the pre-castration section, to almost 10% IL33+ve cells at 14 days post-cx. These results implicate an intrinsic role for IL33 in PCa, as expression is upregulated in both immunocompetent and immunocompromised cohorts. A B 36 Figure 6A. Bulk RNAseq depicting IL33 expression in NRG and FVB mice in dormancy. B. IHC quantification for IL33 expression in FVB Myc-CaP model in dormancy. C. IHC for IL33 expression in dormancy in FVB Myc CaP mouse. 3.3 Validation of IL33 upregulation in clinical cohorts and tissue microarrays following ADT Two clinical cohorts were used to assess mRNA expression of IL33 under various conditions. The Sowalsky cohort provided bulk RNAseq of paired ADT samples pre-treatment and after 6 months of treatment. This cohort reveals statistically significant upregulation of IL33 following ADT. This validates our previous results as clinically relevant to PCa following treatment and is observed in Fig 7.A. KLK2/3/4 & TMPRSS2 depict the loss of AR signalling at this timepoint and proliferation markers MKi67 and TPX2 are shown in Fig. 7.A. to confirm a state of reduced proliferative capacity during this timepoint. Collectively these support a dormant phenotype following 6 months of ADT. The second cohort we observed is the High/Low cohort. This cohort contains unpaired ADT treated samples & an âuntreatedâ group who underwent radical prostatectomy only. The High/Low cohort indicates significant upregulation of IL33 following ADT treatment specifically, in line with downregulation of AR-related genes seen in Fig. 7.B. This implicates AR as a possible negative regulator of IL33 in the PCa context. In order to assess the C Pre-cx Cx day 7 Cx day 14 Relapse 37 protein expression of IL33 we accessed Tissue Microarrays (TMA) of clinical samples. The 2009 cohort indicate no IL33 expression in untreated samples, and upregulated nuclear expression of IL33 in cancer cells at 1-3 months of Neoadjuvant Hormonal Therapy (NHT). We again confirmed this by TMA scoring indicating significant upregulation from untreated to NHT 1-3 in Fig. 7.D. This again validates clinical relevance of nuclear IL33 expression at protein level in patient samples. The observed clustering of IL33 positive cells points to the heterogeneity of its expression and suggests a complex role for this gene in dormancy requiring further analysis at high resolution for prediction of function during this disease stage. Clustering of IL33 positive cells may indicate formation of specialized niches within the tumor. These niches could provide a supportive environment for dormant cells which would, in turn, promote survival or future reactivation. Secondly, the heterogeneity of IL33 expression may suggest a subpopulation of tumor cells which are dependent on IL33-mediated signalling for survival, rendering them more resistant to standard PCa treatments such as ADT via enrichment of the immunosuppressive niche. A 38 Figure 7A. Sowalsky cohort IL33 expression pre and post ADT, and AR markers KLK2/3 with proliferation markers TPX2/MKI67. B. Highlow cohort IL33 expression in ADT treated vs untreated samples with KLK2/3. C. IHC for IL33 on TMA cores. D. TMA scores for IL33+ve cells. B C D 39 3.4 IL33 is enriched in dormant clusters in PDX following castration To understand the role of IL33 in ADT-induced PCa dormancy, we employed single cell sequencing which was carried out on the LTL331 model. The dimensional reduction plot (Dimplot) in Fig. 8.A. depicts clustering of samples at each timepoint at a resolution of 0.5. The feature plot and violin plot below demonstrate which clusters are expressing IL33 across the dormancy timeline. Cluster 17 is primarily responsible for the expression of IL33 at each timepoint, and its expression is markedly increased at week 12 post-castration (Fig. 8.B and C). We then created functional signatures for a number of dormancy characteristics, based on MSigDB and literature review. When the clusters are ranked based on these functional signatures, the IL33-enriched cluster 17 ranks strongly for dormancy-associated traits such as positive regulation of autophagy, negative regulation of apoptosis, low proliferation and hypoxia, as seen in Fig. 8.D. Thus, we can confirm enrichment of IL-33 in dormancy using a single-cell sequencing approach. IL33 has known roles in autophagy which may confer survival benefit to dormant PCa cells by providing metabolic support under nutrient deficient conditions. Similarly, IL33 may be upregulated in hypoxic conditions through HIF1a, thus explaining possible reasons for the high ranking of the IL33-enriched cluster on these signatures. Conversely, the moderate ranking of cluster 17 in the PI3K/AKT/mTOR signature may be related to the paradoxical nature of survival in dormancy. While this pathway is essential for survival by reducing apoptosis, it is simultaneously responsible for increased proliferation. In the case of dormancy it may rank moderately as the cells require the pathway to remain viable but not to proliferate. Therefore, the PI3K/AKT/mTOR pathway may be less important to the IL33-driven cells in dormancy. 40 A B C 41 Figure 8A. Dimplot of single cell clustering at each timepoint pre and post castration. B. IL33 expression by timepoint. C. IL33 expression by cluster. D. Cluster ranking by signature. D 42 3.5 IL33 expression is associated with survival pathways in dormancy 3.5.1 Gene-Set Enrichment Analysis To assess IL33-associated survival mechanisms we selected for the IL33-expressing cluster observed in Figs. A-C. IL33 is associated with survival pathways in dormancy through several mechanisms, which are first indicated through GSEA analysis of IL33 positive cells in week 12 following castration seen in Fig 9.D. Positively enriched pathways include positive regulation of macrophage activation, positive regulation of IL4 production and regulation of T-helper type 1 immune response. Enrichment of these pathways suggest the establishment or maintenance of an immunosuppressive tumor microenvironment, allowing cancer cells to evade detection by NK and CTLs during the dormant phase. Therapies aimed at TAM reprogramming or recruitment inhibition such as inhibition of CSF1/CSF1R signalling, may disrupt IL33-driven macrophage activation and enhance anti-tumor immunity. The finding that IL33 may positively regulate IL4 production implies that inhibition of IL4 signalling may provide a viable therapeutic approach in PCa. IL4 is known for its pro-tumorigenic roles, and as such, targeting the IL4 receptor may counteract this effect. Negative enrichment of pathways such as Myc targets 1, E2F targets, and G2M checkpoints, suggest the downregulation of cell cycle processes and proliferative capacity. This is in line with the low proliferative rate observed in dormant cells due to growth arrest. The GSEA analysis also reveals downregulation of multiple pathways such as ribosomal biogenesis and rRNA metabolic processes. Reduced metabolic function may be employed by IL33 positive cells as a mechanism of energy conservation during dormancy. 43 A B 44 Figure 9A. Single cell dimplot at 12 weeks post castration. B. Dimplot showing IL33 positive cells. C. Violin plot showing IL33 expression by cluster. D. GSEA results for IL33 positive cluster. D C 45 3.5.2 Assessment of Tumor-Stromal Crosstalk Next we assessed the role of IL33 positive cells in stromal crosstalk to expand on possible functions in dormancy, the Dimplot for target cells can be seen in Fig.10.A. Cellchat computational analysis of IL33 positive cells reveals several signalling pathways which are not observed in the IL33 negative cells within the same cluster at twelve weeks post castration (Fig. 10.B). While several pathways are shared between the IL33 positive and negative cells, a number of pathways are distinct to the positive group. BMP, MIF, PROS, EGF, ACTIVIN and ncWNT are highly expressed in the IL33 positive group observed in Fig.10.C. Bone morphogenic protein (BMP), signalling appears to be largely autocrine, acting on the epithelial (cancer) cells primarily. The key genes involved are BMP7, BMPR2 and BMPR1B (Fig. 10F). BMP7-BMPR2 signalling is known in PCa, exerting roles in inhibition of proliferation and induction of temporary growth arrest in metastatic contexts. Tumor-derived macrophage migration inhibitory factor (MIF) signalling is shown to be acting on non-classical monocytes, and has been shown to be responsible for initiating monocyte and macrophage dependent angiogenesis via its receptors CD74, CD44 and CXCR4 (Fig.10D). Functional roles for MIF have also been observed in immunosuppression and stemness. Protein S (PROS) signalling in IL33 positive cells is dependent on one pathway only, the PROS1-AXL signalling pathway (Fig.10E). Functions of PROS1-AXL signalling that may contribute to dormant cell survival include activation of anti-apoptotic pathways such as Akt and Erk, and enhancement of immunosuppression via monocyte activation and influence on NFkB signalling. Collectively we observe that IL33 positive cells are associated with specific or unique survival pathways as compared with their IL33 negative counterparts in dormancy. Finally, to establish the clinical relevance of IL33 to the suggested survival mechanisms mentioned we assembled gene signatures based on MsigDB, literature search and our previous scseq ranking for correlation 46 analysis. The GEPIA2 database was used to provide clinical prostate adenocarcinoma samples derived from TCGA and GTEx databases. A significant positive correlation was observed in all four signatures, with an R value of 0.42 for correlation if IL33 with stemness, 0.65 for correlation with senescence, 0.51 for immune evasion and 0.49 for anti-apoptosis. These correlations provide several indications for the clinical setting observed in Fig. 10.G. Firstly, the association of IL33 with stemness and senescence is suggestive of the role for IL33 in maintenance of the dormant state, and the expression of IL33 in patients may serve as a prognostic biomarker for a more aggressive disease phenotype which is linked to increased stemness and immune evasion, with potential correlation to poorer patient outcomes. Secondly, the association between IL33 and immune evasion may imply a greater capacity for therapy resistance in IL33-positive cells. Targeting these tumor cells via combination therapies such as checkpoint inhibitors in tandem with IL33 antagonists may enhance tumor immunity in these patients. Thus, we can confirm the relevance of IL33 to survival mechanisms in clinical prostate cancer context. 47 M2 Macrophage Fibroblast Lymphatic Endothelial Monocyte-derived Non-classical monocyte Epithelial M2 Macrophage Fibroblast Lymphatic Endothelial Monocyte-derived Non-classical monocyte Epithelial M2 Macrophage Fibroblast Lymphatic Endothelial Monocyte-derived Non-classical monocyte Epithelial 48 D E F M2 Macrophage Fibroblast Lymphatic Endothelial Monocyte-derived Non-classical monocyte Epithelial M2 Macrophage Fibroblast Lymphatic Endothelial Monocyte-derived Non-classical monocyte Epithelial M2 Macrophage Fibroblast Lymphatic Endothelial Monocyte-derived Non-classical monocyte Epithelial M2 Macrophage Fibroblast Lymphatic Endothelial Monocyte-derived Non-classical monocyte Epithelial M2 Macrophage Fibroblast Lymphatic Endothelial Monocyte-derived Non-classical monocyte Epithelial M2 Macrophage Fibroblast Lymphatic Endothelial Monocyte-derived Non-classical monocyte Epithelial 49 Figure 10A. Dimplot of epithelial (tumor) cells and stromal cells for analysis. B. Outgoing signalling analysis in IL33+ve and -ve cells. C. Shared signalling pathways in IL33+ve and -ve cells. Cancer stromal crosstalk interactions in D. MIF, E. PROS1 and F. BMP7. G. Correlation analysis of IL33 expression in PRAD with signatures in GEPIA2. G 50 Chapter 4: Discussion 4.1 Our PDX models accurately recapitulate dormancy and Myc-CaP models provide insight into the TIME in dormancy We have confirmed that the PDX models used in this study are both appropriate and clinically accurate to disease progression, inclusive of the dormant disease phase. The wide range of models available simulate patient-patient variability in pathogenesis, and the molecular and histopathology observed in the clinical setting is maintained. This provides an essential basis for the study of both the biology of ADT-induced dormancy and the discovery of potential therapeutic targets during this stage of PCa progression. While the Myc-CaP models are based on a mouse line and may be less accurate in clinical recapitulation of progression through dormancy, the FVB model provides key information relating to the immune microenvironment and how it may operate following ADT. This is evident when we compare the variation in tumor volume in the NRG and FVB mice. It appears that the NRG tumors grow at a faster rate than the FVB tumors, which could be attributed to increased CTLs and NK cells in the TIME. These killer cells may act to slow the growth rate, counter to the NRG tumors which go unchecked by the immune system. The combined elements of the PDX and Myc-CaP models provide a âbig pictureâ view of ADT-induced dormancy which are essential to target study. 4.2 Validation of IL33 upregulation in mouse models 4.2.1 Nuclear IL33 is upregulated in both PDX and Myc-CaP models during dormancy The discovery that IL33 is upregulated in both PDX and Myc-CaP models is novel and important to our continued understanding of ADT-induced dormancy. The prevalence of this gene 51 at a nuclear level specifically during the dormant phase implicates a role for IL33 in the progression of PCa. Previous literature in the study of Glioma has revealed the essential role of the nuclear localization signature (NLS) in the contribution of IL33 to disease progression. Lacking this NLS, IL33 positive glioma cells were unable to polarize macrophages towards the M2 phenotype, thus reducing their capacity to induce an immunosuppressive TME104. This was new knowledge within the field as it was previously assumed that IL33 exerts its immunosuppressive function only through its expression as a cytokine. This glioma study also confirmed that nuclear IL33 acts as a transcriptional regulator, thus in the context of PCa we can deduce that IL33 may be exerting similar roles due to its nuclear expression in dormancy104. Transcriptional targets of nuclear IL33 in other cancer contexts include promotion of TGFb/SMAD signalling in pancreatic and skin cancers which operates by blocking RUNX2 transcription and repressing SMAD6 expression92. Additionally, nuclear IL33 can bind GATA3 in colorectal cancer which, in turn, blocks binding of IL33 to its decoy receptor sST2, resulting in promotion of tumor growth through ST2L97. These additional roles for nuclear IL33 in alternative cancer contexts further support a role for nuclear IL33 in PCa. The upregulation in both PDX and myc models highlights the importance of the gene in dormancy, it is not presenting as an isolated phenomenon, but is observed across a myriad of models. The microarray data reveals a three-fold change in three models, 331, 508 and 556. Upon review of the genetic backgrounds of these models we did not discover any one common mutation that could be attributed to the significant expression of IL33 in these models specifically though TMPRSS2-ERG fusion may be attributed to the comparatively reduced protein expression of IL33 in LTL331 and 556 models, as mentioned in Chapter 3. The variation in expression levels across models speaks to the heterogeneity of PCa, nevertheless, its omnipresent expression is suggestive of a constitutive role in cancer dormancy. Furthermore, this heterogeneity of IL33 expression in 52 dormancy may be employed in personalised medicine approaches for combination treatment in PCa. In patients with high IL33 expression and aggressive disease, IL33 antagonists may be used in combination with PD1/PDL1 checkpoint inhibitors for enhanced anti-tumor immunity which may prevent relapse of disease. Finally, it has been previously observed that IL33 is expressed at modest levels in hormone-naïve PCa, with greater expression in higher Gleason grades99. We confirm this using our models, observing some expression prior to castration, which is more prominent in particular models created using patient tissue with a higher Gleason grade e.g. 9 for 331. In CRPC or metastatic disease IL33 expression dwindles, and this has also been observed in our relapsed myc cap models, which is indicative of the fidelity of our models100. This is especially important as it leads us to conclude that IL33 upregulation is context specific to dormancy. 4.2.2 Upregulation of IL33 in immunocompetent and immunocompromised models indicates an intrinsic role for this gene in dormancy Upregulation of IL33 in the Myc CaP models, both immunocompetent and immunocompromised, is indicative of an intrinsic role for this gene in ADT-induced dormancy. Due to IL33 having known roles in immunomodulation, we anticipated that the presence of immune cells could alter the expression of this gene in dormancy. While there are some minor differences between the FVB and NRG mice, the pattern of IL33 expression remains the same. This leads us to deduce that IL33 upregulation may be intrinsic to cancer cells and is expressed regardless of immune input in this context. The comparative elevation of IL33 at day 14 post-cx may implicate a role for this gene in dormancy exit, possibly through increased angiogenesis or immune microenvironment remodeling. IL33 has been previously implicated in angiogenesis and in immune escape rendering this hypothesis probable105â107. It is evident that the nuclear expression 53 of IL33 in the Myc CaP model is less intense than that of the PDX models and this may be attributed to species-species differences in IL33 expression which have been previously documented in literature108,109. 4.3 Clinical confirmation of IL33 upregulation in PCa 4.3.1 Nuclear IL33 is clinically relevant and is upregulated following ADT Establishing the clinical relevance of IL33 expression in PCa following adt further strengthens the importance of this gene in disease progression. Previous studies have elucidated associations with high Gleason grades and IL33 expression in PCa, but as yet, no studies have referenced upregulation following androgen deprivation, rendering this a novel discovery110. Moreover, one study on metastatic PCa referenced a loss of IL33 which aids immune evasion through downregulation of MHC presentation, cementing the notion of multifaceted roles for this gene and the importance of cancer context in its functional output 100. Functional differences from ADT-treated to metastatic PCa may be attributed to the expression of IL33 as a nuclear factor or cytokine. In our clinical samples, we observe nuclear IL33 expression in cancer cells following 1-3 months of NHT. Heterogeneity of IL33 expression may pose an explanation for upregulation at this timepoint only, and a larger sample set may be required to assess this further. As IL33 is an alarmin, the expression of IL33 in this early phase may simply present as a damage response signal, however, we argue that as an alarmin IL33 would be secreted in its cytokine form, and would not be expressed in the nucleus111,112. Therefore, we propose that nuclear IL33 may play a role in survival of cancer cells following ADT. Increased understanding of the regulatory mechanisms associated with nuclear IL33 would be a valuable avenue of study in the future. 54 4.3.2 IL33 upregulation is specific to ADT treatment suggesting a role for AR in regulation While the Sowalsky cohort is highly useful due to paired pre and post-ADT samples, the Highlow cohort provides additional information surrounding the regulation of IL33 expression. This cohort is composed of ADT-treated samples and âuntreatedâ samples having undergone radical prostatectomy. As both surgery and ADT would induce a state of cellular stress or damage, it is anticipated that both would have high IL33 expression if it was acting as an alarmin111,112. However, we observe significant upregulation in IL33 expression in the ADT-treated group which may indicate a negative regulatory capacity for AR in expression of IL33. This notion is strengthened by previous discoveries of androgen responsive elements (AREs) on the promoter region of IL33 in mouse models113. Finally, a number of published papers reference a role for androgen in the expression of IL33 in the context of asthma114â116. IL33 is a known contributor to the asthmatic phenotype, and it was noted that males having undergone puberty often outgrow asthma. The reason for this has been identified as an increase in testosterone which reduces the expression of proinflammatory IL33 in the lungs117,118. While not directly related to cancer, these publications echo our findings, and support a negative regulatory role for AR in IL33 expression which merits further investigation. Finally, the expression of IL33 following ADT may act as a biomarker for ADT response, as it may imply a higher resistance to androgen deprivation, and as such would allow clinicians to alter treatment strategies or administer additional targeted therapies to reduce disease burden in these patients. 55 4.4 IL33 is expressed in a cluster depicting dormant characteristics Use of single cell sequencing allows high resolution analysis of IL33 positive cells, which is essential due to its heterogeneous expression in clinical samples, as noted earlier in the study. Separation of IL33 by timepoint provides increased understanding of the evolution of IL33 expression across the dormancy timeline and indicates that the origin of upregulation in dormancy may be primarily the result of clonal expansion. The elevated IL33 expression in week 12 post-castration in scseq confirms our previous findings and dominant expression in cluster 17 specifically furthers the notion of heterogeneity, with its expression noted in a small subgroup of the cancer population. Moreover, our establishment of dormancy-associated signatures such as autophagy, proliferation and apoptosis provide clear indication of a dormant phenotype in the IL33 positive cluster. In tandem with these, we observe medium-high ranking of cluster 17 for additional dormancy indicators such as stemness, PI3K/MTOR signalling and hypoxia, cementing a relevance for IL33 positive cells in ADT-induced dormancy. The hypoxia ranking may be especially interesting, as known hypoxia marker HIF1a has been previously linked to positive regulation of IL33 in bowel disease119. This may also be applicable to PCa, either acting separately to the AR mediated upregulation, or it may be that ADT induces hypoxic pockets within the tumor, as have been previously noted, which in turn cause upregulation of HIF1a and subsequent regulation of IL3352,120. This may present a âtwo-hitâ system of IL33 regulation, in which loss of AR signalling provides the first hit, initiating transcription of IL33, while the upregulation of HIF1a facilitates the second hit and induces translation of the IL33 protein. Previous literature has identified crosstalk between AR and HIF1a in PCa via co-immunoprecipitation assays, and ChIPseq has highlighted AR/HIF1a interaction on the promoter region of PSA121. Therefore, AR loss may drive transcription of IL33 initially, and in the hypoxic tumor pockets induced by ADT, 56 HIF1a may act to enhance IL33 translation to protein in order to maintain tumor cell survival during states of environmental stress such as dormancy. Secondly, this section of the study strengthens our argument for employment of scseq in target analysis in dormancy. This same method may be applied to any target to assess their relevance to the dormant phenotype, and it presents as a simple yet effective method to screen a number of targets in a low-cost manner prior to in depth wet lab analysis. In line with this, more specific signatures may be created using literature and the MsigDB database, to interrogate additional aspects of survival in dormancy. 4.5 IL33 positive cells are relevant to the dormant phenotype, with predicted roles in anti-apoptosis, immune evasion and stemness By separation for IL33 positive cells within the cluster with the most expression of this gene, it enables us to accurately assess the possible contributions of IL33 to the dormant phenotype. The GSEA revealed relevance to several dormancy attributes. Downregulation of cell cycle genes is typical of dormancy, and the relationship between IL33 and inhibition of cell cycle has been previously studied in gastric cancer122. In this study, recombinant IL33 caused reduced expression of proliferative genes involved in G0/G1 and G2/M checkpoints122. The downregulation of metabolic processes is also relevant in dormancy, and is considered a survival mechanism as it acts to conserve energy expenditure in dormant cells when they are in a nutrient deficient environment123,124. CellChat revealed several pathways which are unique to IL33 positive cells and may highlight functional associations of this gene in dormancy. M2 macrophage-derived MIF signalling is required for expression of angiogenic growth factors, and lacking this signalling TAMs revert to an M1-like phenotype125,126. MIF signalling has been previously implicated in PCa 57 aggressiveness and has been correlated with features of plasticity and stemness through stimulation of PGE2127. Senescence associated secretory proteins (SASPs) are thought to be regulated by MIF signalling due to surface expression of CD44. CD74 and CXCR4128. This pathway may contribute to the temporary growth arrest observed in IL33 positive dormant cells. MIF signalling is also known to activate the Akt pathway via CD44, which would impair apoptosis, though largely paracrine signalling observed in this pathway implies a greater role for MIF in survival via immunosuppression and angiogenesis as opposed to anti-apoptosis129â131. PROS signalling in IL33 positive cells is dependent on one pathway only, the PROS1-AXL signalling pathway. PROS1-AXL signalling crosstalk occurs between tumor cells, fibroblasts and monocyte-derived cells. The PROS1 AXL pathway is less studied as compared with TYRO3 or MERK, and until recently it was thought that PROS1 could not bind to AXL, instead GAS6 was the primary ligand132. In PCa, PROS1 is elevated in androgen independent cell lines such as DU145 and PC3, and is significantly associated with high grade PCa phenotypes133. In metastatic PCa dormancy, the GAS6-AXL pathway has been strongly implicated in dormant cell survival and stemness134. It is therefore possible that PROS1-AXL may retain similar functions of survival. Functions of PROS1-AXL signalling that may contribute to dormant cell survival include activation of anti-apoptotic pathways such as Akt and Erk, and enhancement of immunosuppression via monocyte activation and influence on NFkB signalling134â136. Bmp7 forms a signalling complex with BMPR2 and BMPR1B which causes phosphorylation of downstream R-SMADs which regulate various survival features including cell growth, differentiation and apoptosis137. In IL33 positive cells, BMP7 is largely acting in an autocrine manner, influencing the cancer cells. BMP7 has induced reversible senescence via MAPK14 in metastatic PCa cells, and as such may be exerting the same function to facilitate a 58 dormant phenotype138. In carcinomas BMPs have widespread tumor suppressive functions, capable of inducing growth arrest, and can synergize with other growth factors to promote or inhibit proliferation139. BMPR2 is a critical mediator of BMP7 signalling for dormancy, and knockdown of BMPR2 significantly decreases the capacity for BMP7 induced senescence138,140. BMP7 can counteract EMT and was reported to protect PCa cells from stress induced apoptosis via PI3K/Akt activation141. While the signalling pathways discussed have indicated association of IL33 positive cells with survival mechanisms in dormancy, the clinical relevance of IL33 in PRAD survival was confirmed by its strong positive correlations with signatures such as senescence, immune evasion, stemness and anti-apoptosis. Thus confirming the relevance of our target gene and streamlining avenues for further study. 4.5.1 Summary and Key Findings This study has successfully mapped out a reproducible methodology for the study of target genes and prediction of function in ADT induced dormancy. We have demonstrated the accuracy and clinical relevance of our models and illustrated a gap in the scientific field for future discoveries surrounding target genes in dormancy. Lastly, this study has successfully established enrichment of our chosen target gene, IL33, in ADT-induced dormancy, and predicted functional relevance of this gene in a novel and complex cancer context. 4.5.2 Limitations One major limitation in the study of ADT-induced dormancy lies in the lack of dormant clinical samples available for study. Dormancy occurs when a patient is in a state of clinical 59 remission, and as such few patients would undergo biopsy or tissue harvesting during this period. Secondly, the residual tumor volume is minimal in many cases, reducing the viability of these samples for analysis. This knowledge cements the importance of establishment and maintenance of high fidelity models for the accurate study of dormancy going forward. In line with this limitation, single cell sequencing datasets on patient samples in dormancy have not yet been created, though they would be hugely informative for the study of target genes in this context. Specifically relating to IL33, the pleiotropic nature of this gene presents an obstacle to our understanding of function in dormancy. While we observe nuclear expression and predict its function in transcriptional regulation or mediation, it is possible that IL33 may act simultaneously as a cytokine under certain conditions. Further understanding of these mechanisms is needed to appropriately target this gene in dormancy. 4.5.3 Future Directions There are a myriad of possible directions that could be taken in the future with the models and methodology we have established. Firstly, we may choose to analyze other targets which are upregulated during dormancy, such as IL6R, which was noted in Table 3.2. IL6R has been previously associated with dormancy in triple negative breast cancer and metastatic prostate cancer142â144. It has known roles in activation of the JAK/STAT signalling pathway which may contribute to survival during dormancy via prevention of apoptosis and promotion of a quiescent state145. The benefit of this method enables us to establish relevance and prediction of function of target genes prior to extensive in vitro studies, therefore facilitating cost efficacy, efficiency and precision for future wet lab studies. Additionally, we may apply further computational methods to our analyses, such as Monocle3, which would further our understanding of the origin and evolution 60 of target gene expression across the dormancy timeline. This would be most helpful for assessment of lineage plasticity due to de-differentiation in PCa. Comprehension of where the target gene expression originates from, be it clonal expansion or de-differentiation, may enable us to intercept or target these genes at the appropriate timepoint such that we may prevent the entrance or exit from the dormant state. To strengthen the functional inference of our methodology, we may wish to apply the same concept to additional scseq datasets based on our other PDX models such as LTL556 or LTL508. For our target gene, IL33, we have paved the way for future mechanistic studies focusing specifically on AR signalling and HIF1a using reporter assays. For functional assessment a co-culture system may be used to assess immunomodulatory effects of IL33 in vitro. By treating macrophages with recombinant IL33 we may observe the polarization capacity of IL33 and could analyze alterations in cytokine production via multiplex assay. Colony formation assays may also be used to assess the contribution of IL33 to stemness. In vivo, the establishment of a CRISPR knockout model in Myc-CaP would allow observation of the functional impact of IL33 on tumor volume, dormancy duration, and immune evasion during disease progression. 61 Bibliography 1. Fraser M, Mohammad A. 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Thesis/Dissertation
eng
Interdisciplinary Oncology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Wang, Yuzhuo, Ph.D.
Method development for identification of targetable genes in ADT-induced prostate cancer dormancy : the case of Interleukin-33
Text