Medicine, Department of
DSpace
UBCV
Li, Yue
Doctor of Philosophy - PhD
University of British Columbia
Metastasis, the leading cause of cancer-related death, remains a major challenge in cancer treatment. Epithelial-to-mesenchymal transition (EMT) is a well-known driver in metastasis, endowing cancer cells with enhanced migration and invasion properties. While EMT is often triggered by transcriptional reprogramming of gene expression, the contribution of post-transcriptional regulations, such as alternative polyadenylation (APA), remains largely uncharted. APA has gained attention due to its unique role in regulating 3’-untranslated regions (3’UTRs). Cleavage and polyadenylation specific factor 1 (CPSF1) is a core APA component, which facilitates recognition of the canonical poly(A) signals. However, its involvement in metastasis has yet to be thoroughly investigated. Through unbiased bioinformatics interrogation, we identified elevated CPSF1 expression across multiple distinct cancer types, which correlated with metastasis and unfavorable clinical outcomes. Functionally, CPSF1 gain-of-function studies revealed enhanced cancer cell proliferation and motility, while genetic abrogation of CPSF1 impaired cancer cell migration, invasion and EMT. CPSF1-knockout cancer cells exhibited a biased shift towards lengthened 3’UTRs in many gene transcripts through Quantitative APA (QAPA) analysis, including the key EMT regulators such as TGFBR1 and SOX9. Using ultrasound-guided sub-renal capsule xenograft models and orthotopic bladder xenograft models, we demonstrated that CPSF1-deficiency impaired tumor growth, invasion, and metastasis. Additionally, we found that CPSF1 was required to maintain CPSF complex stability by preventing ubiquitin-mediated degradation of the complex components. Collectively, our research uncovers dual function of CPSF1 in maintaining the mesenchymal features and CPSF complex stability in cancer cells. These insights position CPSF1 as a potential therapeutic target in cancer treatment.
CHARACTERIZATION OF CPSF1-MEDIATED ALTERNATIVE POLYADENYLATION IN CANCER by Yue Li M.Sc., Zhengzhou University, 2017 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Interdisciplinary Oncology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) May 2025 © Yue Li, 2025 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: CHARACTERIZATION OF CPSF1-MEDIATED ALTERNATIVE POLYADENYLATION IN CANCER submitted by Yue Li in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Interdisciplinary Oncology Examining Committee: Dr. Mads Daugaard, Associate Professor, Department of Urologic Sciences, UBC Supervisor Dr. Yuzhuo Wang, Professor, Department of Urologic Sciences, UBC Supervisory Committee Member Dr. Colin Collins, Professor, Department of Urologic Sciences, UBC Supervisory Committee Member Dr. Gregor Reid, Associate Professor, Department of Pediatrics, UBC University Examiner Dr. Alice Mui, Associate Professor, Department of Biochemistry and Molecular Biology, UBC University Examiner iii Abstract Metastasis, the leading cause of cancer-related death, remains a major challenge in cancer treatment. Epithelial-to-mesenchymal transition (EMT) is a well-known driver in metastasis, endowing cancer cells with enhanced migration and invasion properties. While EMT is often triggered by transcriptional reprogramming of gene expression, the contribution of post-transcriptional regulations, such as alternative polyadenylation (APA), remains largely uncharted. APA has gained attention due to its unique role in regulating 3’-untranslated regions (3’UTRs). Cleavage and polyadenylation specific factor 1 (CPSF1) is a core APA component, which facilitates recognition of the canonical poly(A) signals. However, its involvement in metastasis has yet to be thoroughly investigated. Through unbiased bioinformatics interrogation, we identified elevated CPSF1 expression across multiple distinct cancer types, which correlated with metastasis and unfavorable clinical outcomes. Functionally, CPSF1 gain-of-function studies revealed enhanced cancer cell proliferation and motility, while genetic abrogation of CPSF1 impaired cancer cell migration, invasion and EMT. CPSF1-knockout cancer cells exhibited a biased shift towards lengthened 3’UTRs in many gene transcripts through Quantitative APA (QAPA) analysis, including the key EMT regulators such as TGFBR1 and SOX9. Using ultrasound-guided sub-renal capsule xenograft models and orthotopic bladder xenograft models, we demonstrated that CPSF1-deficiency impaired tumor growth, invasion, and metastasis. Additionally, we found that CPSF1 was required to maintain CPSF complex stability by preventing ubiquitin-mediated degradation of the complex components. Collectively, our research uncovers dual function of CPSF1 in maintaining the mesenchymal features and CPSF complex stability in cancer cells. These insights position CPSF1 as a potential therapeutic target in cancer treatment. iv Lay Summary APA is a post-transcriptional regulation that can produce mRNA isoforms with various lengths. Currently, there are few studies investigating the potential impact of CPSF1-mediated APA on tumor invasion and metastasis. The research presented in this thesis demonstrates that CPSF1 activates changes in specific molecular networks that enhance the migration and invasion of cancer cells, leading to increased metastasis. v Preface Data presented in this thesis was conducted as part of my PhD program from July 2019 to September 2024. I was responsible for the concept, design, experimentation and analysis of the data presented in the “Results” of Chapters 3, 4, 5 and 6. Dr. Mads Daugaard is the principal investigator for all data presented in this thesis. Animal experimentation was performed according to the UBC Animal Care Committee and Canadian Council of Animal Care guidelines with UBC Animal Care Committee ethics certificate A22-0206. No artificial intelligence was used in the research as well as thesis writing. vi Table of Contents Abstract ............................................................................................................................. iii Lay Summary ................................................................................................................... iv Preface ................................................................................................................................ v Table of Contents ............................................................................................................. vi List of Tables ..................................................................................................................... x List of Figures ................................................................................................................... xi List of Abbreviations ..................................................................................................... xiii Acknowledgements ...................................................................................................... xviii CHAPTER 1: Introduction .............................................................................................. 1 1.1 Metastasis ............................................................................................................ 1 1.1.1 Steps in Metastasis.............................................................................................. 1 1.1.2 EMT in Metastasis .............................................................................................. 6 1.2 Alternative polyadenylation in cancer .............................................................. 12 1.2.1 Types of alternative polyadenylation ............................................................... 13 1.2.2 Mechanisms of alternative polyadenylation ..................................................... 14 1.2.3 APA factors involved in cancer ........................................................................ 17 1.3 Thesis hypothesis and aims ............................................................................... 20 CHAPTER 2: Materials and Methods .......................................................................... 22 2.1 Bioinformatics analysis ..................................................................................... 22 2.2 Cell lines ........................................................................................................... 22 2.3 Cell cycle analysis............................................................................................. 22 2.4 Western blot ...................................................................................................... 23 2.5 Plasmids ............................................................................................................ 23 2.6 Oligos and antibodies ........................................................................................ 25 vii 2.7 Lentivirus production ........................................................................................ 27 2.8 Transfection and lentivirus transduction ........................................................... 27 2.9 Cell growth assay .............................................................................................. 28 2.10 Wound healing assay ........................................................................................ 28 2.11 Incucyte spheroid invasion assay ...................................................................... 28 2.12 Transwell migration and invasion assay ........................................................... 28 2.13 Reverse transcription polymerase chain reaction (RT-PCR) and quantitative PCR (qPCR) .................................................................................................................. 29 2.14 QAPA ................................................................................................................ 30 2.15 Rapid Amplification of cDNA Ends (RACE) .................................................. 31 2.16 Immunoprecipitation (IP).................................................................................. 32 2.17 Ubiquitination assay.......................................................................................... 33 2.18 Animal study ..................................................................................................... 33 2.19 H&E Staining .................................................................................................... 34 2.20 Statistical analysis ............................................................................................. 34 CHAPTER 3: Pan-cancer analysis of APA factors ..................................................... 36 3.1 Background ....................................................................................................... 36 3.2 Rationale ........................................................................................................... 37 3.3 Results ............................................................................................................... 38 3.3.1 Gene alteration and expression landscape of all APA factors in solid tumor . 38 3.3.2 Pan-cancer analysis of CPSF1 expression levels in solid tumors .................... 41 3.3.3 Metastasis and clinical outcomes of CPSF1 ..................................................... 43 3.3.4 Drug sensitivity analysis of CPSF1 .................................................................. 46 3.4 Conclusion ........................................................................................................ 48 CHAPTER 4: Characterization of CPSF1-mediated APA in EMT .......................... 49 viii 4.1 Background ....................................................................................................... 49 4.2 Rationale ........................................................................................................... 50 4.3 Result ................................................................................................................ 50 4.3.1 Establishing CPSF1 CRISPR knockout cell lines ............................................ 50 4.3.2 Effect of CPSF1 on cell proliferation ............................................................... 52 4.3.3 Effect of CPSF1 depletion on cancer cell migration, invasion, and EMT ....... 56 4.3.4 Effect of CPSF1 knockout on alternative polyadenylation patterns ................. 59 4.4 Conclusion ........................................................................................................ 64 CHAPTER 5: The Role of CPSF1 in Maintaining CPSF Complex Stability ............ 66 5.1 Background ....................................................................................................... 66 5.2 Rationale ........................................................................................................... 69 5.3 Result ................................................................................................................ 70 5.3.1 Effect of CPSF1 depletion on other CPSF components ................................... 70 5.3.2 CPSF1-deficieny induced CPSF complex destabilization due to Ubiquitin mediated degradation ................................................................................................. 73 5.4 Conclusion ........................................................................................................ 75 CHAPTER 6: The role of CPSF1 in tumor growth, invasion, and metastasis ......... 76 6.1 Background ....................................................................................................... 76 6.1.1 Current in vivo models for metastasis............................................................... 76 6.2 Rationale ........................................................................................................... 79 6.3 Results ............................................................................................................... 80 6.3.1 Investigating the role of CPSF1 in Tumor Invasion ......................................... 80 6.3.2 Investigating the role of CPSF1 in Tumor Metastasis ...................................... 84 6.4 Conclusion ........................................................................................................ 90 CHAPTER 7: Discussion, limitation, future directions, and conclusion ................... 91 ix 7.1 Discussion ......................................................................................................... 91 7.2 Limitation .......................................................................................................... 99 7.3 Future directions ............................................................................................. 101 7.4 Concluding remarks ........................................................................................ 103 References ...................................................................................................................... 105 x List of Tables Table 1: List of oligos ....................................................................................................... 25 Table 2: List of antibodies ................................................................................................. 26 Table 3: PCR reaction ....................................................................................................... 29 Table 4: qPCR reaction ..................................................................................................... 30 Table 5: 3’-RACE first-strand cDNA synthesis ................................................................ 32 Table 6: 3’-RACE PCR reaction ....................................................................................... 32 Table 7: 3’UTR lengthening gene in all three CPSF1 KO clones .................................... 62 xi List of Figures Figure 1.1 Overview of Metastatic Cascade ....................................................................... 2 Figure 1.2 Tumor cell migration and invasion models ....................................................... 4 Figure 1.3 The process of EMT .......................................................................................... 8 Figure 1.4 Canonical and non-canonical TGF-β signaling pathways. ...............................11 Figure 1.5 Three types of APA. ......................................................................................... 14 Figure 1.6 3’UTR-APA. .................................................................................................... 15 Figure 1.7 First come, first served model for alternative polyadenylation. ...................... 16 Figure 1.8 Sequential model for alternative polyadenylation. .......................................... 17 Figure 3.1 APA machinery involved in cleavage and polyadenylation. ........................... 36 Figure 3.2 schematic workflow for chapter 1. .................................................................. 38 Figure 3.3 Gene alteration and expression landscape of eighteen APA factors in TCGA pan-cancer analysis. .......................................................................................................... 39 Figure 3.4 Single nucleotide variation landscape of all APA factors in pan-cancer using TCGA data. ....................................................................................................................... 40 Figure 3.5 CPSF1 expression in pan-cancer analysis. ...................................................... 42 Figure 3.6 CPSF1 expression in normal, tumor and metastatic samples. ......................... 44 Figure 3.7 Kaplan-Meier Survival analysis of CPSF1...................................................... 45 Figure 3.8 Drug sensitivity test of CPSF1. ....................................................................... 47 Figure 4.1 Constructing CPSF1 CRISPR plasmid. ........................................................... 51 Figure 4.2 Establishing and validating CPSF1 CRISPR knockout cell line. .................... 52 Figure 4.3 Effect of CPSF1 depletion on cell proliferation. ............................................. 53 Figure 4.4 Effect of CPSF1 overexpression on cell proliferation. .................................... 54 Figure 4.5 Effect of CPSF1 depletion on cell cycle. ......................................................... 55 Figure 4.6 Effect of CPSF1 depletion on DNA damage. .................................................. 56 Figure 4.7 Effect of CPSF1 depletion on cancer cell migration and invasion via incucyte wound healing assay. ........................................................................................................ 57 Figure 4.8 Effect of CPSF1 depletion and overexpression on cancer cell migration and invasion via transwell assay. ............................................................................................. 58 Figure 4.9 Effect of CPSF1 knockout on cancer cell invasion via 3D spheroid invasion assay. ................................................................................................................................. 59 xii Figure 4.10 Effect of CPSF1 knockout on EMT .............................................................. 59 Figure 4.11 Effect of CPSF1 knockout on 3’UTR processing.......................................... 61 Figure 5.1 Diagram illustrating the ubiquitination pathway. ............................................ 67 Figure 5.2 Illustration showing deubiquitylation process. ................................................ 68 Figure 5.3 Effect of CPSF1 deletion on WDR33, CPSF2, CPSF3, FIP1L1 and CPSF4 protein level. ..................................................................................................................... 71 Figure 5.4 Cycloheximide chase assay for CPSF2, CPSF3, CPSF4, FIP1P1 and WDR33 upon CPSF1 knockdown................................................................................................... 72 Figure 5.5 Effect of proteosome inhibition on reduced levels of WDR33, CPSF2, CPSF3, FIP1L1 and CPSF4 ........................................................................................................... 73 Figure 5.6 Effect of CPSF1 depletion on WDR33, CPSF2, CPSF3, FIP1L1 and CPSF4 ubiquitination level. .......................................................................................................... 74 Figure 6.1 Summary of mouse models used for studying metastasis. .............................. 77 Figure 6.2 Effect of CPSF1 knockout on tumor growth in a subcutaneous model .......... 81 Figure 6.3 Effect of CPSF1 knockout on tumor invasion via sub-renal capsule model. .. 84 Figure 6.4 Effect of CPSF1 knockout on tumor metastasis in a sub-renal capsule mouse model................................................................................................................................. 86 Figure 6.5 Effect of CPSF1 knockout on tumor growth in orthotopic urinary bladder tumor model. ..................................................................................................................... 87 Figure 6.6 Effect of CPSF1 knockout on tumor metastasis in orthotopic urinary bladder tumor model. ..................................................................................................................... 89 Figure 7.1 Schematic model of CPSF1 mediated alternative polyadenylation in cancer progression. ....................................................................................................................... 92 Figure 7.2 CRISPR/Cas9 screen for CPSF1 regulators. ................................................. 102 xiii List of Abbreviations ABHD10 Abhydrolase domain containing 10 ABLIM1 Actin binding lim protein 1 ACC Adrenocortical carcinoma ACER3 Alkaline ceramidase 3 AFF1 Alf transcription elongation factor 1 AFF4 Alf transcription elongation factor 4 AFTPH Aftiphilin AML Acute myeloid leukemia ANKRD10 Ankyrin repeat domain 10 ANOVA Analysis of variance APA Alternative polyadenylation ATF4 Activating transcription factor 4 ATM Ataxia telangiectasia mutated ATP Adenosine triphosphate ATP6V1A Atpase h+ transporting v1 subunit a BBX Bobby sox homolog BCL2L1 Bcl2 like 1 BCL9 B-cell cll/lymphoma 9 BLCA Bladder cancer BNIP2 Bcl2 interacting protein 2 BOD1L1 Biorientation of chromosomes in cell division 1 like 1 BRCA Breast invasive carcinoma CACYBP Calcyclin binding protein CAS9 Crispr associated protein 9 CCLE Cancer cell line encyclopedia CCND1 Cyclin d1 CDC7 Cell division cycle 7 CDIP1 Cell death-inducing p53 target 1 CDS Coding sequence CESC Cervical squamous cell carcinoma CFAP36 Cilia and flagella associated protein 36 CFI Cleavage factor Im CFII Cleavage factor iim CFL2 Cofilin 2 CHMP4C Charged multivesicular body protein 4c CLP1 Cleavage and polyadenylation factor i subunit 1 CNV Copy number variation COAD Colon adenocarcinoma CPSF4 Cleavage and polyadenylation specificity factor 4 xiv CPSF Cleavage and polyadenylation specificity factor CPSF1 Cleavage and polyadenylation specificity factor 1 CPSF2 Cleavage and polyadenylation specificity factor 2 CPSF3 Cleavage and polyadenylation specificity factor 3 CPSF5 Cleavage and polyadenylation specificity factor 5 CPSF6 Cleavage and polyadenylation specificity factor 6 CPSF7 Cleavage and polyadenylation specificity factor7 CPTAC Clinical proteomic tumor analysis consortium. CRISPR Clustered regularly interspaced short palindromic repeats CSTF Cleavage stimulation factor CSTF1 Cleavage stimulation factor subunit 1 CSTF2 Cleavage stimulation factor subunit 2 CSTF3 Cleavage stimulation factor subunit 3 CTDNEP1 CTD nuclear envelope phosphatase 1 CTNNB1 Catenin beta 1 CTRP C1q/TNF-related protein DIP2A Disco-interacting protein 2 homolog a DLBC Diffuse large b-cell lymphoma DTT Dithiothreitol DUB Deubiquitinase EIF1AY Eukaryotic translation initiation factor 1a, y-linked EIF2B3 Eukaryotic translation initiation factor 2b subunit gamma EIF4E3 Eukaryotic translation initiation factor 4e family member 3 EMT Epithelial-mesenchymal transition ERK Extracellular signal-regulated kinase ESCA Sophageal carcinoma FACS Fluorescence-activated cell sorting FAK Focal adhesion kinase FAM149A Family with sequence similarity 149-member A FDR False discovery rate FIP1L1 Factor interacting with papola and cpsf1 GAPDH Glyceraldehyde-3-phosphate dehydrogenase GBM Glioblastoma GDSC Genomics of drug sensitivity in cancer GEMM Genetically engineered mouse model GENI Gene enrichment identifier GEPIA2 Gene expression profiling interactive analysis 2 GFM2 GTP dependent ribosome recycling factor mitochondrial GFP Green fluorescent protein GMPR2 Guanosine monophosphate reductase 2 GO Gene ontology xv GSCA Gene set cancer analysis GSEA Gene set enrichment analysis GSK Glycogen synthase kinase GTP Guanosine triphosphate HAT Histone acetyltransferase HCC Hepatocellular carcinoma HLCS Holocarboxylase synthetase HMG High mobility group HMGB1 High mobility group box 1 HNSC Head and neck squamous cell carcinoma HS2ST1 Heparan sulfate 2-o-sulfotransferase 1 HTATSF1 Hiv-1 tat specific factor 1 IC50 Half maximal inhibitory concentration IER2 Immediate early response 2 IL1 Interleukin 1 INTS11 Integrator complex subunit 11 IP Immunoprecipitation IQGAP1 IQ motif containing gtpase activating protein 1 IVIS In vivo imaging system JNK C-jun n-terminal kinase JUNB Jun b proto-oncogene KDM5D Lysine demethylase 5d KEGG Kyoto encyclopedia of genes and genomes KICH Kidney chromophobe KIF18B Kinesin family member 18b KIRC Kidney renal clear cell carcinoma KIRP Kidney renal papillary cell carcinoma KRAS Kirsten rat sarcoma viral oncogene homolog LAML Acute myeloid leukemia LGG Lower grade glioma LIHC Liver hepatocellular carcinoma LPP Lim domain containing preferred translocation partner in lipoma LPS Lipopolysaccharide LUAD Lung adenocarcinoma LUSC Lung squamous cell carcinoma MAT2A Methionine adenosyltransferase 2a MDM2 Mouse double minute 2 homolog ME3 Malic enzyme 3 MESO Mesothelioma MIB1 Mindbomb e3 ubiquitin protein ligase 1 MJD Machado-josephin domain proteases xvi MMP Matrix metalloproteinase MNX1 Motor neuron and pancreas homeobox 1 NARS2 Asparaginyl-trna synthetase 2, mitochondrial NOTUM Notum pectinacetylesterase homolog NR1D2 Nuclear receptor subfamily 1 group d member 2 NSCLC Non-small cell lung cancer NTD N-terminal domain NUDT21 Nudix hydrolase 21 NUP35 Nucleoporin 35 ORF Open reading frame OV Ovarian cancer PAAD Pancreatic adenocarcinoma PABPN1 Polyadenylate-binding protein nuclear 1 PAGE Polyacrylamide gel electrophoresis PAM Protospacer adjacent motif PARP Poly (adp-ribose) polymerase PCF11 Cleavage and polyadenylation factor 11 PCGF5 Polycomb group ring finger 5 PCR Polymerase chain reaction PDAC Pancreatic ductal adenocarcinoma PDSS2 Prenyl diphosphate synthase subunit 2 PDX Patient-derived xenograft PEX2 Peroxisomal biogenesis factor 2 PI3K Phosphoinositide 3-kinase PITX1 Paired like homeodomain 1 PLS3 Plastin 3 PPP2R3B Protein phosphatase 2 regulatory subunit b’’ beta PPP6R3 Protein phosphatase 6 regulatory subunit 3 PRAD Prostate adenocarcinoma PRELID1 Preli domain containing 1 PRPF4 Pre-mRNA processing factor 4 PTEN Phosphatase and tensin homolog QAPA Quantitative alternative polyadenylation RAP1GDS1 Rap1 gtpase-gdp dissociation stimulator 1 3'RACE 3’ rapid amplification of cDNA ends REEP5 Receptor accessory protein 5 RIPA Radio-immunoprecipitation assay RNAP RNA polymerase RREB1 Ras responsive element binding protein 1 SARC Sarcoma xvii SDS Sodium dodecyl sulfate SHF SRC homology 2 domain containing f SKCM Skin cutaneous melanoma SLC16A13 Solute carrier family 16-member 13 SLC20A1 Solute carrier family 20-member 1 SLC41A2 Solute carrier family 41-member 2 SLUG Snail family transcriptional repressor 2 SMAD2 Mothers against decapentaplegic homolog 2 SMAD3 Mothers against decapentaplegic homolog 3 SMAD4 Mothers against decapentaplegic homolog 4 SNAIL1 Snail family transcriptional repressor 1 SNP Single nucleotide polymorphism SNV Single nucleotide variant SOX9 SRC-box transcription factor 9 SPCS1 Signal peptidase complex subunit 1 STAD Stomach adenocarcinoma STXBP6 Syntaxin binding protein 6 TGFBR1 Transforming growth factor beta receptor 1 THCA Thyroid carcinoma THYM Thymoma TIMM17A Translocase of inner mitochondrial membrane 17a TME Tumor microenvironment TMEM70 Transmembrane protein 70 TRAF6 TNF receptor associated factor 6 TRMT10C tRNA methyltransferase 10c TSPYL1 Testis specific protein y-like 1 TUBB Tubulin beta class i UPP1 Uridine phosphorylase 1 UPS Ubiquitin-proteasome system USP22 Ubiquitin specific protease 22 UTR Untranslated region WDR33 Wd repeat domain 33 ZEB1 Zinc finger e-box binding homeobox 1 xviii Acknowledgements I want to express my deepest gratitude to my supervisor, Dr. Mads Daugaard, for the support, mentorship, and guidance throughout this challenging yet rewarding doctoral journey. Dr. Daugaard’s insightful feedback, encouragement, and patience have been invaluable in shaping my research. I am deeply grateful to being a member of the Daugaard lab for pursuing my research interests. I am also deeply grateful to my committee members, Dr. Yuzhuo Wang and Dr. Colin Collins, for their invaluable time and guidance. Dr. Wang's expertise in sub-renal capsule tumor model provided crucial insights into my research, while Dr. Collins's guidance in bioinformatics significantly enhanced the rigor and clarity of my work. Their diverse perspectives and constructive suggestions have been instrumental in shaping this thesis. In addition, I would like to extend my sincere thanks to Dr. Beibei Zhai, Dr. Igor Moskalev, Dr. Tingting Feng, Dr. Lin Gao, Sarah Truong, and Jian Gao for their invaluable technical support and guidance throughout this project. Their assistance was important in overcoming numerous challenges and achieving success. Lastly, I am deeply thankful to my parents for their efforts and sacrifices, which have paved the way for me to pursue my PhD. Their love, support, and wisdom are invaluable to me. My love for them is immense, and I will continue to make them proud as I step into the next chapter of my life. This journey would be impossible without the support and encouragement of these individuals. I am deeply grateful for their contributions and will forever cherish the memories and lessons learned during my 5-year PhD. xix To my dad and mom, For their love and support… 1 CHAPTER 1: Introduction 1.1 Metastasis In 2023, more than 200,000 Canadians were diagnosed with cancer and around 80,000 Canadians perished because of cancer [1]. Cancer continues to be the top cause of disease-related death despite the introduction of novel therapies, such as PD-1 inhibitors [2]. Early detection of cancer could significantly increase patients’ survival rate; however, late diagnosis drastically worsens the clinical outcome, which is often due to the occurrence of metastasis. Metastatic tumors, the secondary tumors developed at distant sites, are much more difficult to treat than primary tumors [3]. Genetic heterogeneity between primary tumors and metastases is one of the key reasons for treatment failures [4]. Additionally, unlike primary tumors, which can be removed by surgery, metastatic tumors often require systemic chemotherapy that can potentially cause adverse effects to the body. To develop effective and targeted anti-metastasis therapies, it is necessary for researchers to understand the nature of metastasis [5]. Metastasis has also been a tough topic in the research community [6]. A key challenge in research for understanding metastasis lies in its inherent complexity. Metastasis is rooted in a mixture of biological events such as EMT, immune evasion, and angiogenesis. Another challenge is the lack of pre-clinical models that accurately mimic these biological events. To address these challenges, it's crucial to understand every step of metastatic progression. 1.1.1 Steps in Metastasis The process of metastasis can be viewed as a five-step cascade: migration and invasion, intravasation, circulation, extravasation, and colonization (Figure 1.1) [7]. Cancer cells must overcome various challenges in order to settle down at distant sites. Below is a brief discussion of each step. 2 Figure 1.1 Overview of Metastatic Cascade Schematic representation of the sequential steps of the metastatic cascade. The first step (1) is migration and invasion where cancer cells migrate and invade surrounding tissues. The second step (2) is intravasation where cancer cells enter in the bloodstream by crossing blood vessel. The third step (3) is circulation where cancer cells circulate around in the blood and lymphatic system. The fourth step (4) is extravasation which is the opposite way of intravasation. The final step of metastasis is colonization where cancer cells establish secondary tumors at distant sites. Figure created with Biorender (https://www.biorender.com/). 3 Migration and invasion The initial step in the metastatic cascade is migration and invasion. During this step, cancer cells exhibit dynamic interactions with the extracellular matrix (ECM) via cell surface receptors, which causes changes in their cellular morphology [8]. These changes enable cancer cells to penetrate the basement membrane and invade adjacent tissues [9-11]. Different cell surface receptors and ECM compositions can lead to different types of migration and invasion movement. Generally, there are two major types of tumor cell migration and invasion: single-cell migration/invasion and collective migration/invasion (Figure 1.2) [12]. In single-cell migration/invasion mode, cancer cells move individually as cell-cell interactions are lost [13]. This type of movement can be further divided into amoeboid-like or mesenchymal-like movement [10]. Amoeboid movement is the most primitive but highly efficient mode which resembles the movement of Dictyostelium discoideum. Key features of amoeboid-like movement include a round but highly deformable shape, absence of cell-cell adhesion, and the ability to degrade the surrounding matrix [14]. This type of movement has been observed in many cancers including breast cancer [15], lung cancer [16], prostate cancer [17] and melanoma [18]. It is worth noting that the amoeboid movement mainly relies on cell dynamic morphology changes rather than breaking down ECM [19]. The mesenchymal-like movement, on the other hand, is often associated with epithelial-to-mesenchymal transition (EMT), which will be discussed in the subsequent section. During mesenchymal-like movement, tumor cells change into elongated, spindle-like shapes with pseudopod protrusions and filopodia [20]. Unlike the amoeboid movement, mesenchymal-like movement is characterized by cytoskeletal contractility, attachment to the ECM, and breakdown of the nearby matrix [95]. 4 Figure 1.2 Tumor cell migration and invasion models Two major cancer migration models: single cell migration and collective migration. Single cell migration can be further divided into amoeboid movement and mesenchymal movement. Figure created with Biorender (https://www.biorender.com/). In addition to single cell migration/invasion, cancer cells can also migrate collectively as a coordinated group. This type of movement is called collective cell migration/invasion, which has been observed in many types of cancer such as melanoma, breast cancer, rhabdomyosarcoma, and fibroblast cancer [21, 22]. In this mode, there are two different cell populations: leader cells and follower cells. Leader cells are often located at the leading front of this multicellular unit, forming a finger-shaped structure [103]. They act as navigators, leading the way for the remaining cells to follow [23]. Leader cells often exhibit mesenchymal-like phenotypes and secret MMPs to degrade the ECM. Follower cells, on the other hand, move alongside leader cells as hitchhikers [104]. When collective movement occurs, leader cells actively engage with the surrounding matrix through integrins-mediated adhesion, driving the entire cell cluster forward [24]. The follower cells, in turn, push forward with contractile forces at their rear end, keep the whole tumor cell cluster moving in the same direction [25]. Intravasation Intravasation is the process where tumor cells penetrate blood vessels or lymphatic vessels. To enter into the circulation system, tumor cells must cross the endothelial barrier surrounding these vessels [26]. To achieve this, tumor cells secret degradation factors such 5 as vascular endothelial growth factor (VEGF), MMPs and other cytokines, to increase the permeability of the endothelial barrier [27, 28]. Circulation and circulating tumor cells Once intravasation is achieved, cancer cells enter into the bloodstream as circulating tumor cells (CTCs). This phase is considered as the rate-limiting step of the whole metastasis process [29]. The bloodstream is an extremely harsh environment for CTCs to survive [30]. First, physical stress from blood flow can easily destroy the cellular structure of CTCs [31]. To survive, CTCs can adapt streamline shapes or activate protective signaling pathways to reduce the physical pressure from blood flow. Second, CTCs are constantly under attack from the immune system when they circulate in the bloodstream. Immune cells such as natural killer cells and macrophages actively monitor the bloodstream and eliminate any foreign objects like CTCs [32]. To avoid attack by the immune system, CTCs are able to attach to platelet cells or modify their surface antigens to evade immunosurveillance [33]. Third, CTCs experience increased levels of reactive oxygen species (ROS) due to anoikis, which is a type of programmed cell death induced by detachment from the primary tumor. CTCs manage to mitigate oxidative stress by regulating antioxidant pathways or activating stress respond pathways [34]. Extravasation After managing to survive in the circulatory system, CTCs need to extravasate from blood vessels to establish secondary tumors. This step starts with the attachment of CTCs to the endothelial cells lining the blood vessels [35]. Similar to the intravasation step, CTCs then breach the endothelium barrier by secreting degradation factors such as VEGF, MMPs, and other cytokines. After crossing the endothelium, CTCs invade the surrounding tissues and establish a new site for tumor growth [36]. Colonization The last step of metastatic cascade is colonization. After extravasating from the bloodstream, tumor cells begin to settle down at a new site. In order to proliferate in the new environment, these cells must be able to adapt to the new oxygen and nutrient levels [37]. However, tumor cells sometimes remain dormant where they stop proliferating and become metabolically inactive. This dormancy is a major challenge for cancer treatment, 6 as dormant cells can evade anti-cancer therapies and later reactivate to cause relapse [38]. 1.1.2 EMT in Metastasis It would be inadequate to discuss metastasis without mentioning EMT. EMT is a fundamental biological event that is observed during embryonic development. Cancer cells often hijack EMT to enhance their motility and metastasize to distant location [39]. EMT is a dynamic process that is characterized by different EMT states based on changes in the expression of EMT markers [40]. These changes are often driven by specific transcription factors and signaling pathways related to EMT (Figure 1.3) [41]. Below follows a brief discussion of EMT markers, EMT stages, EMT-related transcription factors and signaling pathways involved in the EMT process. EMT markers The Cadherin family is a group of calcium-dependent cell adhesion proteins. Among them, E-cadherin is a classic epithelial marker that mediates cell-cell adhesion [42]. In contrast, N-cadherin is predominantly found in mesenchymal cells. The decrease of E-cadherin and increase of N-cadherin are key indicators of EMT in cancer [43]. This “cadherin switch” towards N-cadherin expression results in elevated cell motility, decreased cell polarity, and weak cell-cell adhesion [44]. Zonula occludens (ZO) proteins, such as ZO-1, ZO-2, and ZO-3, are also epithelial markers that are often found at tight junctions among epithelial cells [45]. During EMT, the downregulation of ZO proteins is associated with an increase in cell migration and invasion capacity [46]. Vimentin is a canonical mesenchymal marker that maintains cellular structural integrity [47]. It is often found in fibroblasts and other mesenchymal cell types. Upregulation of vimentin facilitates reorganization of the cytoskeleton, providing better structural support for cell migration [48]. High level of vimentin is associated with elevated cell migration and invasion capacities. Matrix metalloproteinases (MMPs) are proteins that break down the extracellular matrix (ECM), facilitating the penetration of cancer cells through surrounding tissues [49]. They are also recognized as mesenchymal markers during EMT. EMT state 7 The initial state of EMT is the epithelial state. In this state, tumor cells exhibit apical-basal polarity and tight cell-cell junctions that allow them to maintain structural integrity [50]. However, the polarized structure restricts their migration and invasion ability [51, 52]. In the epithelial state, tumor cells mainly express E-cadherin and other epithelial markers, which are important for cell adhesion [53]. As tumor cells progress towards the mesenchymal state, they gradually lose their tight cell-cell adhesions and polarity, and transform into an elongated, spindle-like shape [54]. This change in morphology makes tumor cells highly invasive and enables them to migrate through the ECM into surrounding tissues [48, 55]. Key mesenchymal markers include N-cadherin, vimentin, and MMPs. Given the distinct features of epithelial and mesenchymal cell states, one could assume that EMT is a simple, binary process. However, the discovery of partial EMT—where cells display both epithelial and mesenchymal characteristics—has added another state to the EMT process [56]. In a recent breast cancer study, partial EMT cells contributed significantly to lung metastasis and chemoresistance, but full EMT cells were observed less frequently at distant metastasis sites, indicating that full EMT transition may not be necessary for metastasis [57]. However, there are two major limitations of the partial EMT theory to consider. First, the degree of partial EMT can vary among different types of cancer and even among individual cells within the same tumor [58]. Second, the partial EMT state is often dynamic and transient, making it difficult for researchers to accurately characterize the key feature of partial EMT states [59]. Therefore, more research is needed to further elucidate key characteristics and molecular mechanisms underlying partial EMT. 8 Figure 1.3 The process of EMT EMT is a process where epithelial cells acquire mesenchymal phenotype. In the epithelial state (1), cells display apical-basal polarity and tight cell-cell junctions, primarily expressing E-cadherin and other epithelial markers crucial for cell adhesion. In the partial EMT stage (2, 3, 4), cells exhibit both epithelial and mesenchymal characteristics, marked by a loss of apical-basal polarity and gain of “front-back” polarity. In the mesenchymal stage (5), cells fully transition to a mesenchymal state, losing epithelial characteristics and gaining migratory and invasive properties. Figure created with Biorender (https://www.biorender.com/). 9 EMT-related transcription factors EMT-related transcription factors are the main regulators of EMT [60]. There are three types of transcription factors associated with EMT: zinc finger-binding transcription factors like SNAIL, zinc finger E-box binding homeobox factors like ZEB, and basic helix-loop-helix factors like TWIST [40]. The SNAIL family includes SNAIL1 and SNAIL2 that contain four to six zinc finger binding domains. These domains are responsible for recognizing the E-box region in the E-cadherin promoter, causing suppression of E-cadherin transcription. The reduced level of E-cadherin further leads to a loose junction between cells [61]. The TWIST family comprises Twist1 and Twist2. Twist1 activates N-cadherin transcription by binding to its E-box regulatory sequence located within the N-cadherin promoter. This binding activates the transcription process that leads to increased protein levels of N-cadherin [62]. The role of Twist2 on EMT is context-dependent, with evidences suggesting that Twist2 can either promote or inhibit EMT in different cell types [63, 64]. The ZEB family contains ZEB1 and ZEB2, both of which have two clusters of zinc finger domains and one homeodomain [65]. Similar to SNAIL family, ZEB1 and ZEB2 directly bind to E-boxes in the E-cadherin promoter, suppressing the expression of E-cadherin. In addition to these canonical EMT transcriptional factors, other transcription factors like SOX9 also contribute to the activation of EMT. SOX9, one of several SOX transcription factor family members, has three distinct domains: a dimerization domain, a DNA-binding domain containing a high mobility group (HMG) box, and a transactivation domain [66]. Dysregulation of SOX9 is associated with EMT in many cancers [67, 68]. For example, SOX9 is involved in inducing phenotype switching and EMT-associated plasticity in pancreatic cancer and breast cancer[69, 70]. Another study demonstrates that SOX9 directly binds to the promoters of genes such as SLUG, Claudin-1, β-catenin, and ZEB1, to facilitate EMT [71, 72]. Signaling pathways in EMT Behind activation of EMT transcription factors are the cellular signaling pathways. When signals from the extracellular matrix bind to tumor surface receptors of tumor cells, a cascade of intracellular signaling events is initiated, ultimately leading to the activation 10 of EMT transcription factors [73]. In this section, we explore two signaling pathways that are involved in EMT: the transforming growth factor-β (TGF-β) signaling pathway and the Wingless-INT (WNT) signaling pathway. The TGF-β signaling pathway was one of the first pathways reported to be involved in EMT [74]. This pathway is mainly activated by three ligands: TGF-β1, TGF-β2 and TGF-β3 [75]. When TGF-β ligands attach to the extracellular domain of the TGF-β type II receptor (TGFβR2), TGF-β type I receptor (TGFβR1) is recruited, forming a heterodimer complex with TGFβR2 [76]. TGFβR2 then activates TGFβR1 by phosphorylating the cytoplasmic kinase domain of TGFβR1 [77]. The activated TGFβR1 subsequently phosphorylates the intracellular transcriptional secondary messengers Smad2 and Smad3 [78]. These phosphorylated Smad2 and Smad3 proteins, along with Smad4, form a heteromeric complex that translocate into the nucleus to activate the expression of EMT-related transcriptional factors like Snail1, Twist1/2 and ZEB1/2 [79, 80]. The activated EMT-related transcription factors can also enhance the expression of TGF-β ligands. This creates an autocrine signaling loop that keeps activating the TGF-β pathway, driving the EMT [81]. In addition to the canonical TGFβ/Smad signaling pathway, there are non-canonical TGF-β signaling pathways such as the TGFβ/MAPK pathway, the TGFβ/PI3K/AKT/mTOR pathway, and the TGFβ/Rho-like GTPase-dependent pathway [82]. Non-canonical pathways often work together with canonical Smad signaling to facilitate EMT (Figure 1.4) [83]. Recently, research has shed light on the unique role of TGF-β in partial EMT state [84]. For example, one study discovered that TGF-β reduced transition timing from the epithelial-state to a partial-EMT-state but had little effect on the transition from partial-EMT-state to mesenchymal-state [85]. Another study identified TGF-β as a master regulator of partial EMT by analyzing 18 primary human head and neck squamous cell carcinoma (HNSCC) samples using single-cell RNA-seq [86]. 11 Figure 1.4 Canonical and non-canonical TGF-β signaling pathways. In the canonical pathway, TGF-β binds to the TGF-β receptor complex, leading to the phosphorylation and activation of Smad2 and Smad3. The activated Smad2/3 complex together with Smad4 translate into nucleus and activates EMT transcriptional factors, ultimately promoting EMT. In addition to the canonical pathway, non-canonical pathways include TGFβ/MAPK pathway, TGFβ/PI3K/AKT/mTOR pathway and TGFβ/Rho-like GTPases. These non-canonical pathways can act in concert with the canonical pathway to EMT. Figure created with Biorender (https://www.biorender.com/). 12 The WNT signaling pathway is another key driver in regulating EMT [87]. It is initiated when WNT ligands bind to Frizzled receptors on the cell surface [88]. This binding activates the Dishevelled protein, which prevents β-catenin from degradation by APC, Axin, and GSK3β [89]. The accumulated β-catenin then translocate into the nucleus where it works as a co-transcriptional activator with TCF/LEF [90]. This β-catenin/TCF/LEF complex subsequently binds to the promoter of Snail, Twist, and Zeb, thereby initiating their transcription and further promoting EMT. These transcription factors can in turn enhance the WNT/β-catenin signaling pathway by creating a positive feedback loop like TGF-β autocrine signaling loop [91]. 1.2 Alternative polyadenylation in cancer APA, first reported in the 1980s, has become a key post-transcriptional mechanism in regulating gene expression [92]. APA is based on the selection of different polyadenylation sites within the 3’UTR of pre-mRNAs, generating different 3’UTR isoforms of the same gene [93]. These 3' UTR isoforms can significantly impact mRNA stability, translation efficiency, and subcellular localization, ultimately influencing protein expression levels and cellular behavior. It is estimated that more than 70% of human genes undergo APA, significantly contributing to the diversity of the transcriptome in eukaryotic cells [94]. Early studies in the 90s revealed the impact of APA on several oncogenes [95]. For example, 3′UTR shortening of cyclin D1 increases the expression of cyclin D1 protein and promotes tumor cell growth [78]. Despite having the same open reading frame (ORF), short isoforms of cyclin D1 mRNA transcripts can evade regulatory control by microRNAs such as miR-16-1, miR-15 and miR17/20a and other RNA binding proteins [96]. In another study, researchers examined PRELID1, a gene involved in negative regulation of apoptotic process [97]. By using polyadenylation site sequencing (PAS-seq), they detected a major decrease in the long PRELID1 3′UTR isoforms in ER-positive breast cancer cells as compared to normal breast cells. The increased level of short PRELID1 3′UTR isoforms resulted in elevated stability and translation efficiency, ultimately facilitating tumor growth. With the development of high-throughput RNA sequencing technologies over the past decade, it has become clear that there is a strong association between APA and cancer 13 [98]. Many studies have reported that cancer cells seem to prefer the short 3' UTR isoform of transcripts over long 3' UTR isoforms [99]. For instance, one study of triple-negative breast cancer shows that a significant proportion of genes associated with tumor cell proliferation mainly present as short 3’UTR isoforms [100]. This phenomenon has also been observed in the context of metastasis, hinting a potential connection between APA and tumor metastasis [101]. For instance, in one study of metastatic breast cancer, researchers observed that the majority of expressed genes exhibited shortened 3′UTRs [102]. Global shortening of 3′ UTR isoforms is not just observed in cancer cell lines; it has also been observed in cancer patients [103]. One research group developed Dynamic Analysis of Alternative Polyadenylation from RNA-seq (DaPars) to quantify polyadenylation usage in TCGA datasets. Their findings revealed that numerous tumor-associated genes undergo 3′UTR shortening through APA [104]. Given the significant link between APA and cancer progression, it seems reasonable to examine APA in greater detail. Recently, APA has also been recognized for its contributions to metastasis, with many studies illustrating its influence. Dysregulation of APA allows key metastasis-promoting genes to escape miRNA-mediated repression, ultimately promoting multiple steps in the metastatic cascade[105]. For example, a shorter NET1 isoform exhibited increased migration and invasion in gastric cancer. Research in triple negative breast cancer showed that CPSF4, a key APA factor, promotes metastasis by upregulating MDM4 and inducing EMT[106]. Similarly, a renal carcinoma study has demonstrated that PABPN1-mediated APA contributes to tumor metastasis of kidney cancer by suppressing SGPL1 And CREG1[107]. Understanding the role of APA as a critical regulatory hub is important for tumor metastasis. Below is a comprehensive discussion of the different types of APA, the intricate mechanisms that regulate APA, and the key factors involved in APA during cancer development. 1.2.1 Types of alternative polyadenylation There are three major types of APA defined based on where the APA events take place (Figure 1.5). Firstly, exonic APA is a type of APA that occurs within exons, resulting in mRNA isoforms with different coding sequences. Secondly, intronic APA refers to APA that happens within an intron. This type of APA can lead to inclusion or exclusion of intronic sequences. Lastly, 3' UTR APA happens within the 3' UTR of pre-mature mRNA 14 (Figure 1.6). It is the most common type of APA, creating mRNA isoforms with different lengths of 3' UTRs without affecting the coding region [108]. The length variations in the 3'UTRs can affect mRNA stability and translocation, eventually impacting gene expression [109]. An interesting observation regarding 3′UTR-APA is its global shortening among many cancer types [104], necessitating the further investigation of regulatory mechanisms of 3′UTR APA. Figure 1.5 Three types of APA. 3’UTR-APA refers to APA occurring at the 3’UTR. Intronic-APA means APA happening within an intron. Exonic-APA indicates APA taking place within an exon. The proximal PAS is indicated by a red arrow, while the distal PAS is indicated by a pink arrow. The resulting mRNA isoforms differ in their 3' untranslated regions (3'UTRs) and coding sequences, which can impact gene expression regulation and protein function. Figure adapted from [108] 1.2.2 Mechanisms of alternative polyadenylation A critical question in 3’UTR APA is: how to choose between proximal and distal poly(A) sites? This decision is made by several factors, including strength of the poly(A) signal, the upstream UGUA element and downstream U/GU elements [110]. In general, two models have been proposed to describe how to choose between proximal and distal poly(A) sites. The first model is called “First come, First served,” where the selection of proximal Poly(A) site is independently of the distal Poly(A) site [111]. The second model is called “sequential polyadenylation” where distal APA sites are preferentially processed initially and then the proximal poly(A) site may or may not be subsequently processed depending on strength of proximal poly(A) signal [112]. 15 Figure 1.6 3’UTR-APA. 3’UTR-APA leads to the production of two mRNA isoforms with different 3’ untranslated regions. Unlike short 3’UTR isoforms, long 3’UTR isoforms have many (RBPs) and microRNAs (miRNAs) regulatory sites. The poly(A) tail is denoted as AAAAA. Figure adapted from [113]. First-come-First-served Model The "First-come, First-served" model proposes a straightforward mode for poly(A) site selection. In this mode, proximal poly(A) site is processed before the distal one simply because it is encountered and transcribed first by the RNA polymerase II [114]. Because of this, the speed of RNA polymerase II is key in this model. One study explored the role of two transcriptional anti-terminators, SCAF4 and SCAF8, in regulating APA. Removing these proteins was found to favor transcription termination at the proximal poly(A) site [115]. Another study revealed that the inactivation of DNA methyltransferases led to an activated CTCF, which is a DNA-binding factor. Enhanced CTCF activity could impede RNAP II progression, effectively halting it at the proximal poly(A) site in specific genes, thereby increasing the utilization of the proximal poly(A) site [116]. These studies collectively support the first-come-first-served model where proximal site is selected when the transcription elongation rate is slow (Figure 1.7). 16 Figure 1.7 First come, first served model for alternative polyadenylation. In the First come, First served" model, if the proximal poly(A) site is stronger than the distal poly(A) site, more short mRNA isoforms will be produced. However, if the proximal poly(A) site is weak and the RNA polymerase II elongation rate is slow, the proximal site will have a time advantage. This advantage gives the proximal site enough time to be processed by APA machinery, increasing the likelihood that the proximal site will be selected over the distal one. Figure adapted from [111, 117]. Sequential Polyadenylation Model The "Sequential polyadenylation" model offers a different perspective on how to select between different APA sites. Unlike the “first come, first served” model, which suggests a single cleavage event, the sequential polyadenylation model describes APA as a two-cut step process [112]. According to this theory, the distal poly(A) site is prioritized for processing co-transcriptionally (Figure 1.8) because the distal poly(A) site is always stronger than proximal poly(A) site. This initial processing at the distal site generates longer RNA transcripts that can be retained temporarily inside nucleus. These detained 17 long pre-mRNA isoforms then serve as the precursors for generating short isoforms if the proximal poly(A) sites are able to be recognized by APA machinery [118]. The "Sequential polyadenylation" model suggests that short isoforms can arise from further processing of initially longer transcripts. Figure 1.8 Sequential model for alternative polyadenylation. In the sequential model, because distal PAS is always stronger than proximal PAS, distal PAS (indicated by the pink arrow) is processed in the first place, therefore generating longer pro-mRNA transcripts. These long pre-mRNA transcripts can either be exported to the cytoplasm or temporarily retained within the nucleus. If retained in the nucleus, these long pre-mRNAs can undergo additional processing at the proximal PAS (indicated by the red arrow) only if the proximal poly(A) site is able to be recognized by APA machinery . This further cleavage and polyadenylation result in shorter mRNA isoforms. Figure adapted from [111]. 1.2.3 APA factors involved in cancer As discussed previously, dysregulation of APA has been associated with oncogenesis. While dysregulated APA could be due to RNA polymerase II activity, 18 transcription and splicing factors, and abnormal expression of APA components, can all contribute to potential dysregulation of APA [119]. Even a small change in the expression level of these APA factors can disrupt the delicate balance of APA [120]. Several APA factors are directly involved in tumor growth and metastasis. In the following section, we discuss certain APA factors that contribute to cancer progression. CFIm25 As one of the CFIm complex components, CFIm25 plays a key role in recognizing the upstream UGUA and GUAU elements in 3’UTRs [121, 122]. Beyond its role in mRNA processing, CFIm25 has emerged mainly as tumor suppressor in cancer development and progression [100]. For example, the depletion of CFIm25 enhances migration and invasion in colon cancer cells and induces shift from distal to proximal poly(A)site usage [123]. In bladder cancer, overexpression of CFIm25 suppresses bladder cancer growth, migration and invasion, while knockdown of CFIm25 has the opposite effects [124]. In cervical cancer, CFIm25 regulates the expression of WNT10B, IL-1A, and HMGB1 via APA. Downregulation of CFIm25 leads to an increase in WNT10B and HMGB1 transcripts with longer 3’UTRs, thereby reducing their protein expression level [125]. CFIm25 is downregulated in liver tumor samples compared with normal liver tissues and loss of CFIm25 leads to abnormal liver cancer cell proliferation [126]. Interestingly, in blood cancers such as leukemia, knockdown of CFIm25 inhibits cell proliferation and induces apoptosis through p-ERK regulation [127]. These data suggest a context-dependent role of CFIm25 in cancer where it acts as a tumor suppressor in solid tumors but as an oncogene in hematologic malignancies. CPSF3 CPSF3, a member of the MβL superfamily, catalyzes the cleavage reaction for pre-mature mRNAs [128, 129]. As the only APA factor with cleavage capacity, CPSF3 has gained a lot of attention due to a reported association with cancer progression. Many studies have observed that elevated CPSF3 levels are associated with poor patient outcomes in esophageal and lung cancers [130, 131]. Furthermore, the impact of CPSF3 loss on cancer cells is profound. For example, CPSF3 depletion inhibited cell proliferation through disrupting core histone mRNA processing in pancreatic ductal cancer cells [132]. 19 Beyond its impact on proliferation, CPSF3 knockout significantly impaired migration capacity of liver and breast cancer cells. Mechanistically, this was attributed to a shift from proximal to distal poly(A) site usage [133-135]. Additionally, the CPSF3 inhibitor Jet-607, has been shown to decrease cell proliferation and trigger apoptosis in AML and Ewing's sarcoma lines [136]. This may be due to the prevention of newly synthesized pre-mRNA release, leading to read-through transcription and the accumulation of DNA-RNA hybrid R-loop structures [137]. These findings collectively underscore the critical role of CPSF3 in cancer progression. FIP1L1 FIP1L1, a key subunit of the CPSF complex, interacts with poly(A) polymerase (PAP) and anchors itself to the CPSF complex through the ZF4 and ZF5 domains of CPSF4 [138, 139]. FIP1L1 has been implicated in many cancers, especially acute myeloid leukemia (AML). Overexpression of FIP1L1 in leukemia cell lines led to 3’UTR lengthening of many leukemia-associated genes such as NRAS, BAALC and MAPKAPK3 [140]. This lengthening further exposed more miRNA binding sites, which increased miRNA-mediated degradation and lowered protein levels. CSTF2 CSTF2, an important component of CSTF complex, directly recognizes GU-rich downstream element through its RNA recognition motif near the N-terminal [141]. Its hinge domain is critical for the interactions with CSTF3 [142, 143]. Recently, CSTF2 has been associated with progression of several cancer types such as lung cancer, bladder cancer, and esophageal cancer [144]. One study using DNA microarrays demonstrated that CSTF2 expression was notably high in lung cancer tissue as compared to normal lung tissues [145]. Knockdown of CSTF2 in lung cancer cell lines resulted in a reduced cell viability [146]. Similar findings have also been observed in bladder cancer. CSTF2 promoted bladder cancer proliferation and induced the 3’UTR shortening of RAC1, a well-known oncogene involved in cell proliferation, migration and invasion [147]. This is due to reduced AFF1/AFF4-mediated transcriptional elongation rates following CSTF2 depletion [148]. In esophageal squamous cancer, CSTF2 induced the shortening of the 3' UTR of BID, leading to increased BID protein expression. Conversely, silencing CSTF2 20 resulted in lengthening of the BID 3'UTR, concurrently leading to a decrease in BID protein levels. This alteration in BID expression impacted cellular apoptosis and contributed to tumor progression [149]. PCF11 As a subunit of the CFIIm complex, PCF11 interacts with the C-terminal domain of RNA polymerase II [150], initiating transcription termination and facilitating nuclear export of mRNA [151]. It has also been related to cancer progression, particularly in neuroblastoma and breast cancer. In neuroblastoma, PCF11 regulated the WNT signaling pathway, which was involved in cell proliferation, apoptosis, and migration. High level of PCF11 in neuroblastoma has been linked to poor tumor patient outcomes [152]. In breast cancer, PCF11 is a downstream target of CD44, a cell adhesion marker involved in tumor invasion and metastasis. Activation of CD44 can trigger upregulation of PCF11 through the MAPK/ERK signaling pathway. This upregulation of PCF11 can further contribute to the malignant progression of breast cancer [153]. 1.3 Thesis hypothesis and aims EMT is a critical process that empowers cancer cells with invasive and metastatic capabilities. Understanding the root cause of EMT allows us to develop novel therapeutic strategies. While extensive studies have focused on the critical role of transcription factors in orchestrating EMT, the contribution of post-transcriptional regulations, particularly APA, remains largely unexplored. APA, an important pre-mRNA 3’ end processing step, affects mRNA stability, localization, and protein translation by generating mRNA isoforms with different 3'UTRs in length. Our previous study has described that CFIm25-mediated APA could dampen EMT [123]. However, the APA machinery is a large complex with 19 distinct proteins, many of which remain poorly understood. Given the complexity of the APA machinery, we hypothesized that certain APA components promote EMT by altered gene expression through biased poly(A) site usage, thereby driving tumor invasion and metastasis. This project aimed to identify and characterize EMT-promoting APA factors and investigate the underlying molecular activities of such factors supporting tumor progression. To test our hypothesis, we proposed the following specific research aims. Aim 1: To identify potential oncogenic APA components. 21 For this aim, we conducted an unbiased bioinformatics analysis of genetic alterations and expression levels of all 19 APA factors across all major cancer types using large-scale cancer genomics and transcriptomics datasets. We then prioritized APA factors that were frequently amplified, overexpressed, or mutated in different cancer types and identified potential oncogenic APA component(s). Furthermore, we performed survival analysis to evaluate the correlations between the potential oncogenic APA gene expression and clinical outcomes in various cancers. Aim 2: To characterize the role of potential oncogenic APA factor(s) in cancer and elucidate the underlying molecular mechanism-of-action in vitro. We examined the biological function of the potential oncogenic APA factor(s) identified in Aim 1 by CRISPR knockout and RNAi-mediated knockdown in cancer cells. Then, we employed RNA-seq followed by QAPA analysis to explore the APA event changes driven by the depletion of the potential oncogenic APA factor(s). In addition, we characterized the corresponding APA-regulated target genes and the related signaling pathways in cancer cells for disease progression. Aim 3: To investigate the interplay between potential oncogenic APA factor(s) and other APA components. For this aim, we examined the mRNA and protein expression levels of additional APA factors upon depletion of our identified oncogenic APA factor. Furthermore, we investigated whether the changes in protein expression were mediated through ubiquitin-proteasome pathway. Aim 4: To evaluate the effect of potential oncogenic APA factor(s) on tumor growth, invasion, and metastasis in vivo. Using ultrasound-guided inoculation of luciferase-labeled cancer cells, we established both sub-renal capsule and orthotopic bladder tumor models in nude mice. Tumor growth was monitored by ultrasound imaging and in vivo imaging system (IVIS). These mouse models enabled us to investigate the impact of our identified oncogenic APA factor on tumor growth, invasion, and metastasis. 22 CHAPTER 2: Materials and Methods 2.1 Bioinformatics analysis The genetic alteration analysis of eighteen APA factors was carried out via cBioPortal platform (https://www.cbioportal.org/) using TCGA PanCancer Atlas Studies with 10,967 samples from 32 cancer types. Gene Expression Profiling Interactive Analysis 2 (GEPIA2) (http://gepia2.cancer-pku.cn/) was used to evaluate gene expression profiles of the APA factors across various cancer types. The expression of CPSF1 mRNA and protein between tumor and normal tissues across different cancer types was assessed using TIMER2.0 (http://timer.cistrome.org/) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) datasets, respectively. Through TNMplot analysis (https://www.tnmplot.com/), gene chip data were used to compare CPSF1 expression in normal, primary tumor, and metastatic tissues in colon, liver, lung and prostate cancers. Correlation analysis between CPSF1 mRNA expression and survival was performed by Kaplan-Meier plotter (http://kmplot.com/). 2.2 Cell lines Human colon cancer cell line HCT116 and human osteosarcoma cell line U2OS were purchased from ATCC (CCL-247 and HTB-96). UM-UC-3 and UM-UC-15 bladder cancer cell lines were kindly provided by Dr. Peter Black (UBC; Vancouver Prostate Centre). HEK293T cell line was obtained from Takara Bio (632180). HCT116-CPSF1-KO and UM-UC-3-CPSF1-KO cell lines were established as described in section 4.3.1 . HCT116 cells were cultured in McCoy’s 5A medium (ATCC; Cat#30-2007) with 10% fetal bovine serum (FBS; Gibco; Cat#A5256701). U2OS and HEK293T were cultured in DMEM (Gibco; Cat# 11965092) plus 10% FBS. UM-UC-3 and UM-UC-15 were maintained in Minimum Essential Medium (Gibco; Cat#11095080) with 1% non-essential amino acids (Gibco; Cat#11140050), 1% sodium pyruvate (Gibco; Cat# 11360070) and 10% FBS. All cells were incubated in a humidified atmosphere at 37°C with 5% CO2 and were routinely tested with mycoplasma-free. 2.3 Cell cycle analysis Cells were harvested by trypsinization at 50% confluency, resuspended in 0.5 ml phosphate-buffered saline (PBS) and gently incubated with 1.125 ml 100% ethanol to 23 achieve a final concentration of 70%. The mixture was then incubated on ice for 30 mins for fixation. After fixation, cells were pelleted at 2000 rpm for 5 mins, resuspended in 600 µl propidium iodide (PI) staining solution (100 µg/ml PI, MilliporeSigma, P4864-10ML; 0.1 mg/ml RNase A, Thermo Fisher Scientific, EN0531; 0.05% Triton X-100, MilliporeSigma), and incubated at 37°C for 40 mins in the dark. After incubation, 3 ml PBS was added to the cell suspension and centrifugated at 2000 rpm for 10 mins. The cells were then resuspended in 500 µl PBS for flow cytometric analysis using FACS Canto II (BD Biosciences). The flow cytometry data was analyzed using FlowJo V10.4.2 to assess cell cycle phases. 2.4 Western blot Whole cell lysates were collected in RIPA buffer (Thermo Scientific, Cat# 89900) or EBC buffer (50 mM Tris-HCl, 120 mM NaCl, 0.5% Nonidet P-40 Substitute, 5 mM EDTA) plus protease inhibitor (cOmplete™ protease inhibitor cocktail, Roche) and phosphatase inhibitor (PhosSTOP™ EASYpack, Roche). Lysates were then sonicated (10 cycles with 10 secs on and 10 secs off) using Bioruptor® sonication system (UCD-300, Diagenode). The protein concertation was determined by Pierce™ BCA Protein Assay Kit (Thermo Fisher Scientific). Equal amounts of cellular protein (50-120 µg) were mixed with 1X loading buffer, denatured using heat blocker, and loaded into SDS-polyacrylamide gel for electrophoresis. Proteins were transferred to the nitrocellulose membrane using a wet transfer system for 1-2 hrs or overnight. Membranes were then blocked for 1 hr in Odyssey® TBS blocking buffer (LI-COR Biosciences). Subsequently, membranes were incubated with corresponding primary antibodies (Table 2) overnight at 4°C, followed by incubation with appropriate secondary antibodies (Table 2) for 1 hr at room temperature. Membranes were washed three times with TBST for 10 mins and visualized by Odyssey imaging (LI-COR Biosciences) system. 2.5 Plasmids All plasmids were amplified in NEB® Stable Competent E. coli (high efficiency; Cat#C3040H) following manufacturer’s transformation protocol. Briefly, 1-2 µl plasmid DNA (100 ng) was added to 50 µl competent cells on ice for 30 mins. The cells were then heat-shocked at 42°C water bath for 30 secs and recovered on ice for 5 mins. Next, 950 µl 24 LB medium was added and incubated at 37°C for 1 hr in shaking incubator at 250 rpm. The competent cells (50-100 µl) were spread on pre-warmed LB agarose culture plates containing 100 µg/ml ampicillin or 25 µg/ml kanamycin for resistant-clone selection with incubation for 14-16 hrs at 37°C. Selected colonies were transferred and grown overnight in 40 ml LB medium with 100 µg/ml ampicillin or 25 µg/ml kanamycin. The plasmids were harvested using QIAGEN Plasmid Midi Kit according to manufacturer’s protocol. SOX9-overexpression plasmid and a control plasmid were purchased from VectorBuilder (VB220519-1275qkb and VB900122-2888czv). CPSF1-overexpression plasmid, TGFBR1-overexpression plasmid and a corresponding empty vector were purchased from OriGene Technologies (RC203519, RG219514 and PS100010). LentiCRISPRv2 plasmid was purchased from Addgene (52961). To generate the CPSF1-knockout construct, a single guide RNA (sgRNA; 5’-TTTGGACAGCCCACTACACA-3’) targeting the exon 23 of CPSF1 was cloned into the lentiCRISPRv2 vector following the protocol developed by Dr. Feng Zhang’s group[154]. In brief, lentiCRISPRv2 plasmid was digested with BsmBI (Thermo Fisher Scientific; FD0454). followed by gel purification using QIAquick Gel Extraction kit (QIAGEN; 28704). CPSF1-sgRNA was phosphorylated and annealed using T4 polynucleotide kinase (New England Biolabs; M0201) and T4 ligation buffer (New England Biolabs; B0202S). The annealed sgRNA oligos and the digested plasmid were then ligated using Quick Ligase (New England Biolabs; M2200). The ligated reaction (1-5µl) was transformed into NEB stable competent cells (New England Biolabs; C3040), and the subsequent resistant colonies were purified and sequenced by Sanger sequencing (Genewiz Inc.) using hU6-F primer (5'- GAGGGCCTATTTCCCATGATT-3'). 25 2.6 Oligos and antibodies Oligos and antibodies used in this thesis were listed in Table 1 and Table 2. Table 1: List of oligos RT-qPCR primers RPL32-F CCCCTTGTGAAGCCCAAGA RPL32-R GACTGGTGCCGGATGAACTT CPSF2-F GGAAGCATGTTCACCAGATTGA CPSF2-R CTTTCCGACAGCATACGGGA CPSF3-F ATGTCTGCGATTCCTGCTGAG CPSF3-R ATCCCACAGTCGAGCATTATTTT CPSF4-F TGTCCGTTTCGCCACATCAG CPSF4-R TTGGTCATGTCATACTCATGCAG WDR33-F TAAGCGACCTGATTTTGCACA WDR33-R TGGTTTTTCGGTTCACAGCTTT CPSF1-F CGCAGCTCTACGTGTACCG CPSF1-R GGACATGACGTTGCCAAAGAA CRISPR oligos (adapters in red/green) CPSF1-sgRNA-F CACCGTTTGGACAGCCCACTACACA CPSF1-sgRNA-R AAACTGTGTAGTGGGCTGTCCAAAC CRISPR-KO validation primers CPSF1-KO-F CTGTATGGGGATTCGGGCTC CPSF1-KO-R GGGGGAAGGCCTCGTAGATA 3’RACE primers TGFBR1-F CAGCCACAGATACCATTGATATTGCTCCAAACCAC TGFBR1-nest F CAAACGTGCTGACATCTATGCAATGGGCTT siRNAs CPSF1-si1 QIAGEN, SI00353094 CPSF1-si3 QIAGEN, SI04307996 Control siRNA (siCon) Thermo Fisher Scientific, Cat# 12935300 Negative Control siRNA (20 nmol) Qiagen, 1027310 26 Table 2: List of antibodies Target Manufacturer Catalog # CPSF1 Bethyl Laboratories A301-580A CPSF1 ABclonal A17144 CPSF2 Proteintech 17739-1-AP CPSF3 Proteintech 11609-1-AP CPSF4 Proteintech 15023-1-AP WDR33 Proteintech 22614-1-AP FIP1L1 Bethyl Laboratories A301-462A Phospho-histone H2A.X (Ser139) Cell Signaling Technology 2577 Anti-histone H2A.X Abcam ab11175 E-Cadherin Cell Signaling Technology 3195 N-Cadherin Abcam ab76011 Vimentin Proteintech 10366-1-AP TGFBR1 ABclonal A22151 TGFBR1 ABclonal A2124 SOX9 ABclonal A19710 Phospho-Smad2 (Ser465/Ser467) Cell Signaling Technology 18338 Phospho-Smad2 (Ser465/Ser467) ABclonal AP0925 Phospho-Smad3 (Ser423/Ser425) ABclonal AP0727 Smad2 ABclonal A7699 Smad3 ABclonal A19115 β-actin Thermo Fisher Scientific AM4302 GAPDH Cell Signaling Technology 5174 IRDye® 800CW donkey anti-rabbit IgG LI-COR Biosciences 926-32213 IRDye® 680RD donkey anti-mouse IgG LI-COR Biosciences 926-68072 27 2.7 Lentivirus production HEK293T cells (2.5x106 cells) were plated into a 10 cm dish and cultured at 37°C overnight. On the following day, transfection was performed with lentiviral packaging plasmids (1.5 µg pMD2.G and 4.5 µg psPAX2; Addgene; 12259 and 12260), vector of interest (6 µg), and X-tremeGENE™ HP DNA transfection reagent (MilliporeSigma; XTGHP-RO) following manufacturer's protocol. Forty-eight hrs after transfection, virus-containing supernatant was collected by centrifuging at 1000 rpm for 10 mins at room temperature. Subsequently, the supernatant was filtered through 0.45 µm Steriflip filter (MilliporeSigma, cat# SCGP00525) to remove cellular debris and stored in -80°C. 2.8 Transfection and lentivirus transduction Transient transfection of overexpression plasmids for SOX9, TGFBR1 and CPSF1 was performed using X-tremeGENE™ HP DNA transfection reagent (MilliporeSigma; XTGHP-RO) or lipofectamine 3000 (Thermo Fisher Scientific, L3000001) according to manufacturer’s protocol . Small interfering RNAs (siRNAs; Table 2) were delivered into the cells by lipofectamine RNAiMAX (supplier, cat#13778100). Forward transfection was performed based on manufacturer’s protocol For lentiviral transduction, 3.0×105 HCT116 or 2.0×105 UM-UC-3 were seeded into 60 mm dishes. After 24 hrs of incubation, the appropriate amount of virus supernatant containing construct-of-interest based on optimal MOI, along with corresponding serum-free medium, was added into the dishes. Two to three days post-transduction, fresh medium was replaced with appropriate puromycin concentration according to the puromycin killing curve. The puromycin selection continued for 10 days with medium replaced with puromycin every 2 days. After selection, the cells were subjected to Florescence-activated Cell Sorting (FACS) into 96-well plates. Single cell clones were cultured from 96-well plates to 24-well plate and finally 6-well plates. Genomic DNA of each clone was extracted from single-cell clones using Quick-DNA Midiprep Plus Kit (Zymo Research; D4075), and CRISPR-CPSF1-KO was validated by Sanger sequencing with primers in Table 1. 28 2.9 Cell growth assay For crystal violet proliferation assay, HCT116 and UM-UC-3 cells were seeded into 6-well plate at the density of 200 cells/well and cultured in the appropriate medium for 13 days. After that, the medium was removed and the solution of 0.01% crystal violet in methanol was used to fix and stain the cells. Images were taken with scanner and cell clones were counted visually. Statistical analysis was done via student t-test. For proliferation assay using Incucyte® system, HCT116 (1500 cells/well) and UM-UC-3 (2000 cells/well) were seeded into 96-well plates and cultured in the appropriate medium for 4 days. Cell confluency was monitored and acquired by Incucyte® Live-Cell Imaging an Analysis instrument (Sartorius) at 0, 24, 48, 72 and 96 hrs. 2.10 Wound healing assay HCT116 (1.2×105 cells/well) and UM-UC-3 (6×104 cells/well) were seeded into Incucyte® Imagelock 96-well plates (Sartorius, BA-04856), and incubated at 37 °C for 6 hrs. Mitomycin C was added 2 hrs before making scratchwounds by Incucyte® Woundmaker Tool (Sartorius, 4563) according to manufacturer’s protocol [155]. Detached cells were removed by washing twice with medium. The Incucyte® Scratch Wound Analysis Software Module (Sartorius, 9600-0012) was used to acquire images at 2-hr interval and analyze the wound confluency. 2.11 Incucyte spheroid invasion assay UM-UC-3-control and -CPSF1-KO cells (1×103 cells) were seeded in a 96-well round bottom ultra-low attachment microplate (Sartorius, BA-04856) and incubated for 3 days. After cell spheroids formed, 200 µl Matrigel was added into each well. The ultra-low microplate was then placed in Incucyte® Live-Cell Imaging and Analysis System to monitor the spheroid growth and invasion over 14 days using 4X lens. Invasion areas were analyzed using Incucyte® Spheroid Analysis Software Module (Sartorius, 9600-0019). 2.12 Transwell migration and invasion assay Cell migration and invasion were assessed using Transwell 24-well plates (Corning,3422) with or without Matrigel matrix. HCT116 (1.2×105 cells), UM-UC-3 (4×104 cells), U2OS (4×104 cells), and UM-UC-15 (6×104 cells) in the appropriate serum-free medium were seeded into the inserts (with or without Matrigel). The bottom wells 29 were filled with 10% FBS medium. After 24 hrs of incubation, the cells at the upper surface of the insert were gently wiped off using cotton swab, while the cells at the bottom were fixed with 100% methanol for 10 mins, followed by staining with 0.01% crystal violet solution for 10 mins. 3 images per group were taken with EVOS 3000M (Thermo Fisher) and cell clones were quantified by ImageJ. Statistical analysis was done with student t-test. 2.13 Reverse transcription polymerase chain reaction (RT-PCR) and quantitative PCR (qPCR) Total RNA from cultured cells was extracted by TRIzol reagent (Thermo Fisher Scientific, 15596018) following manufacturer’s instruction. Briefly, 0.5 ml TRIzol was added to lyse the cells for 5 mins at room temperature. An equal amount of chloroform (Milliporesigma, cat# 301485) was added to the Eppendorf tubes. The mixture was then shaken vigorously for 15 secs, incubated for 5 mins at room temperature and centrifuged at 12,000g for 10 mins at 4 °C. The upper aqueous phase was transferred to a new tube and mixed with 500 µl isopropanol (supplier, cat# 383910025 ) for 10 mins of incubation at 4 °C. Total RNA was pelleted by centrifugation at 12,000g for 10 mins at 4 °C. RNA pellet was then washed with 75% ethanol. Air-dry the RNA pellet and resuspend in RNase-free water. For RT-PCR, RNA was converted to complementary DNA (cDNA) using LunaScript® RT SuperMix Kit (New England Biolabs, E3010) according to manufacturer’s protocol. Then, the PCR reaction was set up as in Table 3. Table 3: PCR reaction PCR master mix 2X SeqAmp PCR buffer (µl) 25 Forward primer (pmol) 15 Reverse primer (pmol) 15 cDNA (ng) 500 SeqAmp DNA polymerase (µl) 1 Sterilized distilled water (µl) Up to 50 Thermal cycling program 94°C 1 min 98°C 10 secs, 30 cycles 60°C 15 secs, 30 cycles 68°C 30 secs/kb, 30 cycles 30 The reaction of qPCR was performed on ViiA7 Real-Time PCR system (Applied Biosystems) using Luna® Universal qPCR master mix (New England Biolabs, M3003) following manufacturer’s protocol. Target gene mRNA expression was normalized to human RPL32 RNA using 2(–ΔΔCT) relative quantification method[156]. The qPCR master mix formulation is in Table 4. Table 4: qPCR reaction qPCR master mix Luna Universal Master Mix 5 µl Forward primer (10 µmol/l) 0.25 µl Reverse primer (10 µmol/l) 0.25 µl cDNA 200 ng Sterilized distilled water Up to 10 µl 2.14 QAPA The detailed protocol was in [123]. In brief, the mRNA 3′UTR isoforms were measured using transcripts per million (TPM). To categorize genes into different APA patterns, we looked at the log2(fold change) and Z scores between CPSF1 knockout and control using the following equation: Z = √n�TPMmeanKO − TPMmeanCTRL� ∕ �TPMvarKO + TPMvarCTRL log2 FC = log2 TPMmeanKOTPMmeanCTRL Three replicates were used for RNA-seq. We calculated the average and standard deviation of TPM based on these replicates. We then categorized the shift patterns into seven groups using Z scores and log2(fold change) in TPM: “Increased Lengthened”: increase in abundance of a longer (i.e. not the shortest) 3’ UTR isoform in the mutant shortest) 3’UTR isoform in the knockout “Decrease lengthened”: decrease in abundance of a longer (i.e. not the shortest) 3’ UTR isoform in the knockout. “Decrease Shorten”: decrease in abundance of the shortest 3’ UTR isoform in the knockout. 31 “Increased shorten”: increase in abundance of the shortest 3’ UTR isoform in the knockout. “Switch Lengthen”: In the knockout, a longer 3’ UTR isoform for a given protein-coding gene increases which is accompanied by a decrease in abundance of the shortest 3’ UTR isoform. “Switch Shorten”: In the knockout, the shortest 3’ UTR isoform for a given protein-coding gene increases which is accompanied by a decrease in abundance of a longer 3’ UTR isoform. “Complex Genes”: 2 or more 3’ UTR isoforms undergo significant changes in abundance which do not fall into one of the above cases. Plot of log2 of change in TPM expression of the proximal 3’ UTR isoforms between knockout and control (x-axis) vs. Z-score (y-axis), with isoforms colored by change in Proximal Polyadenylation Usage (ΔPPAU), excluding cases where |log2FC| = Infinite If the proximal 3’ UTR isoform is higher in knockout than control, log2FC >0 If the proximal 3’ UTR isoform is lower in knockout than control, log2FC <0 Stringent cut-offs to determine significant changes in isoform expression are listed: |log2(FC)|>1.5 and |Z score|>2. 2.15 Rapid Amplification of cDNA Ends (RACE) The 3’-RACE assay was conducted using SMARTer RACE 5’/3’ kit (Takara Bio, 634858) following manufacturer’s instruction. NCBI Primer-Blast (https://www.ncbi.nlm.nih.gov/tools/primer-blast/index.cgi) was used to design gene-specific primers (GSPs). The primers were 20-25 nt in length with 50-60% GC content and over 60 °C in melting temperature. Takara proprietary sequence was excluded when calculating primer melting temperature for primer design. For 3’-RACE first-strand cDNA synthesis, the master mix and reaction were shown in Table 5. 32 Table 5: 3’-RACE first-strand cDNA synthesis 3’-RACE first-strand cDNA master mix and reaction Incubate at 42°C for 90 mins, then 70°C for 10 mins 5X First-strand buffer 5 µl DTT 100mM 0.25 µl dNTPs 20mM 0.25 µl RNase inhibitor 20 U SMARTScribe Reverse Transcriptase Up to 10 µl 1µg RNA Incubate at 72°C for 3mins, then 42 °C for 2 mins 1 µl 3’-CDS primer A Up to 12 µl distilled water 12 µl The 3’-RACE PCR master mix and reaction were shown in Table 6. After the 3’-RACE PCR, the products were run on 1% agarose gel. Table 6: 3’-RACE PCR reaction 3’-RACE PCR master mix and reaction 20 cycles of 94°C for 30 secs, 68°C for 30 secs and 72°C for 3 mins 3’-RACE cDNA 2.5 µl 10X Universal Primer A Mix (UPM) 5 µl 3’ GSP (10 µM) 1 µl H2O Up to 50 µl PCR-grade H2O 15.5 µl 2X SeqAmp buffer 25 µl SeqAmp DNA Polymerase 1 µl The 3’-RACE nested PCR was performed with Universal Primer Short and nested GSPs according to the manufacturer’s protocol. 2.16 Immunoprecipitation (IP) IP was performed using protein A/G PLUS-agarose reagent (Santa Cruz Biotechnology, sc-2003). Cell lysates were collected in IP lysis buffer (Thermo Fisher Scientific, 87787) or RIPA buffer supplemented with protease inhibitor (Roche, 04693116001) and sonicated for 5 secs. One-tenth of the cell lysates were saved as input group, and the remaining lysates were pre-treated by adding 20 µl A/G PLUS-agarose beads for 1 hrs at 4 °C. Cell lysates were divided into two equal aliquots. One aliquot was incubated with the target protein antibody at the recommended dilution, while the other aliquot was incubated with 2 μL rabbit IgG control antibody (Cell Signaling Technology, 2729) overnight at 4°C. The next day, 25 μL protein A/G PLUS-agarose beads were added 33 into both cell lysates and incubated for 1 hr at 4 °C. The beads were then pelleted, washed three times with lysis buffer, and subjected to western blot analysis. 2.17 Ubiquitination assay Cell lysates were collected in 160 µl RIPA buffer (adding protease inhibitors just before lysing the cells), sonicated for 5 secs, and kept on ice for 30 mins. The lysates were centrifugated at 15000 rpm for 10 mins at 4 °C, and the supernatant was collected. Forty µl supernatant was saved as input, and the remaining 120 µl was mixed with same amount of 10% SDS and incubated on ice. After 30 mins, a 1 ml RIPA buffer was added to achieve a final concentration of 0.1% SDS, and 20 µl A/G PLUS-agarose beads were added to pre-incubate cell lysates for 1 hr at 4 °C. Targeted protein antibodies were added to the lysates and incubated overnight. The next day, 25 µl A/G PLUS-agarose beads were added and incubated for 4 hrs at 4 °C. Beads were subsequently washed with RIPA buffer containing protease inhibitors, and 10 µl 4× loading buffer was added for western blot. 2.18 Animal study All Nu/J male mice were purchased from Jackson Laboratory. All animal experiments were conducted under the UBC Animal Care Committee ethics certificate A22-0206 and were following Canadian Council of Animal Care guidelines. For orthotopic inoculation of bladder cancer cells, UM-UC-3-luc-Con and UM-UC-3-luc-CPSF1-KO1/2 lines were injected into the bladder wall under ultrasound guidance. Fifty µl Matrigel (Corning) was injected between muscle layer and mucosa layer to create space where 1×105 tumor cells would be injected into. Tumor growth was monitored by both bioluminescence and ultrasound using IVIS2000 Imaging System (Caliper Life Sciences) and Vevo770 High-Resolution In Vivo Micro-Imaging System (FujiFilm), respectively. Tumor bioluminescence signals were recorded twice at 10-20 mins post-injection of D-luciferin (BD Biosciences, cat# L2916) to reach bioluminescence plateau. Ultrasound imaging and measurements were done by Vevo770 v3.0.0 software. Tumors were collected at experimental/humane endpoint. For sub-renal capsule tumor model, UM-UC-3-luc-Con and UM-UC-3-luc-CPSF1-KO1 cell lines were inoculated under ultrasound guidance into the sub-renal capsule in nude mice. Thirty µL Matrigel was injected between the renal capsule and 34 parenchyma to create space for tumor cells inoculation. Tumor cells (1×105 cells ) were resuspended in 30 µl Matrigel and injected into space. For metastatic study, tumors were collected at around 1000 mm3 in both control and knockout groups. For local invasion study, the mice were euthanized when tumors reached at 55 to 70 mm3 for both groups. For subcutaneous tumor model, 1×106 HCT116-Con and HCT116-CPSF1-KO1/2/3 cell lines were resuspended in 50 μL mixture of Matrigel and PBS (1:1) and injected subcutaneously into the right flank of the nude mice. Tumor growth was measured twice a week by calipers to calculate the volumes. The mice were sacrificed when reaching humane endpoint (tumor size at around 1000 mm3). 2.19 H&E Staining Tissues collected from mice were fixed immediately into 10% formalin. Post-fixation, the tissues underwent dehydration through a graded series of ethanol, ranging from 70% to 100%. The tissues were then cleared using xylene for paraffin infiltration. After cleaning, tissues were embedded in paraffin wax, maintaining a temperature 2–4 °C above the melting point of the wax. The paraffin-embedded tissues were cut into 5 µm slices using a microtome and subsequently placed on slides. Deparaffinization of those slides was performed using 100% xylene, followed by rehydration with 100% ethanol and a series of gradient ethanol concentrations (70–95%), and finally distilled water. The slides were rinsed in water and incubated with hematoxylin for 10 mins. After staining, the slides were placed under running tap water for 2 mins. The slides were then treated briefly with 1% hydrochloric acid and 1% ammonia water, followed by an immediate rinse with water. The sections were then immersed in eosin for 10 mins, rinsed with water, and air-dried. The slides were dehydrated through graded ethanol, cleared in xylene, and cover slipped. The prepared slides were analyzed under a microscope (Olympus, Corporate Parkway, Center Valley, PA, USA) at magnifications of ×4 and ×10. 2.20 Statistical analysis Statistical analyses in this thesis were performed using R and GraphPad. All in vitro experiments were repeated three times. Student t test was carried out to calculate statistical significance between two groups while the one-way ANOVA test was utilized to detect three groups difference. Statistical analysis for animal study was indicated in the relevant 35 section of chapter 6. The p-values <0.05 were deemed to be statistically significant (*p<0.05; **p<0.01; ***p<0.001; ****p<0.0001). 36 CHAPTER 3: Pan-cancer analysis of APA factors 3.1 Background As introduced in chapter 1, APA is an important post-transcriptional regulatory mechanism that generates isoforms of pre-mature mRNA with different 3’UTR in length. It is orchestrated by the APA machinery that consists of 19 proteins organized in four core protein sub-complexes: CPSF, CFIm, CSTF, and CFIIm [157] . The CPSF complex is responsible for recognizing polyadenylation signals such as canonical AAUAAA or its variants like AUUAAA. This complex also mediates pre-mature mRNA cleavage at cutting sites located 10-20 nucleotides downstream of the poly(A) site [158]. Structurally, the CPSF complex consists of two subunits: the mammalian cleavage factor (mCF) and the mammalian polyadenylation specificity factor (mPSF) (Figure 3.1) [159, 160]. mCF contains CPSF2 (CPSF100), CPSF3 (CPSF73) and is responsible for the cleavage reaction [161]. mPSF, composed of CPSF1 (CPSF160), CPSF4 (CPSF30), WDR33, and FIP1L1, directly recognizes the poly(A) signal and promotes poly(A) polymerase (PAP)-mediated polyadenylation. Figure 3.1 APA machinery involved in cleavage and polyadenylation. CFIm complex can directly recognize the upstream element like UGUA-rich element located about 40 nucleotides upstream of the poly(A) signal. CPSF complex binds to poly(A) signal and cleaves pre-mRNA at the cutting site. CPSF complex consists of CPSF1, CPSF2, CPSF3, CPSF4, WDR33 and FIP1L1. The downstream GU-rich element is often located around 100 nucleotides downstream of poly(A) signal and can be recognized by the CSTF complex. Figure adapted from [162]. 37 The CFIm complex is a heterotetrametric complex consisting of one CFIm59 (or CPSF7), one CFIm68 (or CPSF6), and two CFIm25s (or CPSF5, NUDT21) (Figure 3.1). Two CFIm25 proteins form a dimer with each protein interacting with either CFIm59 or CFIm68. Importantly, the CFIm complex directly binds to the upstream element UGUA motif through CFIm25 subunits independently of other APA components [163, 164]. The CSTF complex is a heterotrimeric complex composed of CSTF3 (CSTF77), CSTF2 (CSTF64), and CSTF1 (CSTF50) (Figure 3.1) [165]. The main function of CSTF is to facilitate pre-mRNA 3’-end cleavage by directly recognizing downstream element like U-rich or G/U-rich region [166]. Additionally, the CSTF complex also interacts with RNA polymerase II, coupling mRNA processing to transcription [167]. The CFIIm complex, composed of PCF11 and CLP1, is less characterized as compared to other complexes [168]. This complex is mainly involved in transcription termination and disassembly of elongation complex by bridging the C-terminal domain CTD of RNA polymerase II to the nascent transcript [169]. It is important to note that the interactions between the CFIIm complex and other APA complexes are weak and transient [170, 171]. 3.2 Rationale Emerging studies have highlighted the critical role of APA components in many human diseases, especially cancer [172, 173]. Our previous research demonstrated a conserved role of CFIm25-mediated APA in regulating Rac function both in worms and human cells [123]. However, the APA machinery is a sophisticated complex of 19 distinct proteins and many of them remain poorly understood. Therefore, a comprehensive analysis of the individual APA components in the context of cancer development and progression is an attractive starting point. Here, in this chapter we aimed to systematically characterize all 19 APA components across different types of cancer using TCGA datasets and other online public databases. By analyzing the expression patterns, genetic alterations, and clinical associations of these components, we aimed to identify APA factors with specific and potentially critical roles in cancer progression (Figure 3.2). We hypothesized that a specific APA component may be particularly associated with tumorigenesis, metastasis and poor patient outcomes. 38 Figure 3.2 schematic workflow for chapter 1. The chapter initiated with a pan-cancer screening analysis (left) to identify potential target. The candidate APA factor then subjected to three downstream analyses: expression profiling, survival analysis and drug sensitivity analysis to evaluate the role of CPSF1 in cancer. 3.3 Results 3.3.1 Gene alteration and expression landscape of all APA factors in solid tumor To search for potential oncogenic APA factors, we first characterized the copy number variation of all genes encoding APA factors as cancers often arise from genetic mutations. We found that CPSF1 exhibited the highest copy number variation (8% pan-cancer) as compared to other APA factors, primarily through gene amplification (Figure 3.3 A). We then examined mRNA expression of all APA components in pan-cancer via GEPIA2 (Figure 3.3 B). Notably, the absolute CPSF1 mRNA expression level was higher than that of other APA factors in almost all tumor types, which was consistent with a recent study [174]. Additionally, we explored the single nucleotide variation of APA components. We found that CPSF1 exhibited the second highest single nucleotide mutation frequency at 24%, compared to other APA factors (Figure 3.4 A). The single nucleotide variant (SNV) percentage heatmap revealed that CPSF1 harbored the most deleterious mutations in colon cancer, skin cancer, and uterine corpus endometrial carcinoma (Figure 3.4 B). Based on these findings, CPSF1 was selected as a potential candidate for further evaluation due to its frequent genetic alterations and elevated expression across multiple cancer types. 39 Figure 3.3 Gene alteration and expression landscape of eighteen APA factors in TCGA pan-cancer analysis. (A) CBioPortal analysis of the 18 APA components in 10,967 samples across 32 cancer types from TCGA dataset. Genetic alterations were shown as indicated. (B) Heatmap depicting mRNA expression levels of the APA factors in TCGA cancer samples. Data was acquired using GEPIA2 with Multiple Genes Comparison module. 40 Figure 3.4 Single nucleotide variation landscape of all APA factors in pan-cancer using TCGA data. (A) Mutation landscape plot displaying the single nucleotide variation (SNV) frequency distribution of WDR33, CPSF1, FIP1L1, CSTF3, CPSF6, CPSF2, CPSF3, CSTF2, CPSF7 and CSTF1 in pan-cancers via GSCA (https://guolab.wchscu.cn/GSCA/ ). The SNV data of 874 samples from 30 cancer types were collected from the TCGA database. Seven types of mutation were included in the analysis: Missense_Mutation, Nonsense_Mutation, Frame_Shift_Ins, Splice_Site, Frame_Shift_Del, In_Frame_Del, In_Frame_Ins. These types of mutations were often referred to as deleterious mutations. The side bar plot represented the percentage of variants in each gene, while the top bar plot shows the variants in each sample. (B) Heatmap showing the percentage of single nucleotide variations (SNVs) of CPSF1 for specific cancer types. The color intensity corresponded to mutation frequency, and each cell represented the number of mutated samples. A ‘0’ or blank cell indicated no mutations in the gene coding region or other regions of a specific gene. 41 3.3.2 Pan-cancer analysis of CPSF1 expression levels in solid tumors To investigate the potential role of CPSF1 in cancer, we first systematically analyzed its expression in tumor and normal tissues in pan-cancer using TCGA datasets. Our analysis revealed that CPSF1 mRNA expression was upregulated in 18 out of 33 cancer types, including Bladder Urothelial Carcinoma (BLCA), Breast Invasive Carcinoma (BRCA), Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC), Cholangiocarcinoma (CHOL), Colon Adenocarcinoma (COAD), Esophageal Carcinoma (ESCA), Glioblastoma Multiforme (GBM), Head and Neck Squamous Cell Carcinoma (HNSC), Kidney Chromophobe (KICH), Kidney Renal Clear Cell Carcinoma (KIRC), Kidney Renal Papillary Cell Carcinoma (KIRP), Liver Hepatocellular Carcinoma (LIHC), Lung Adenocarcinoma (LUAD), Lung Squamous Cell Carcinoma (LUSC), Prostate Adenocarcinoma (PRAD), Rectum Adenocarcinoma (READ), Stomach Adenocarcinoma (STAD), Thyroid Carcinoma (THCA), and Uterine Corpus Endometrial Carcinoma (UCEC) (Figure 3.5 A). Unfortunately, we were unable to analyze Adrenocortical Carcinoma (ACC), Diffuse Large B-Cell Lymphoma (DLBC), Lower Grade Glioma (LGG), Acute Myeloid Leukemia (LAML), Mesothelioma (MESO), Ovarian Serous Cystadenocarcinoma (OV), Uterine Carcinosarcoma (UCS), and Uveal Melanoma (UVM) due to the lack of normal samples in the database. To minimize potential bias in the analysis resulting from insufficient normal tissue samples, we further compared CPSF1 mRNA levels in tumor samples and the corresponding adjacent normal tissues. CPSF1 mRNA expression remained significantly elevated in most cancer types, such as BLCA, BRCA, CHOL, COAD, ESCA, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, PRAD, READ, STAD, THCA, and UCEC (Figure 3.5 B). Additionally, large-scale proteomics data analysis using UALCAN datasets also revealed a significant upregulation of CPSF1 protein expression in all available cancer types when compared to normal tissues. (Figure 3.5 C). Collectively, these findings suggest that CPSF1 is consistently upregulated across multiple cancer types, indicating broad and fundamental roles in oncogenesis. 42 Figure 3.5 CPSF1 expression in pan-cancer analysis. (A) The differential expression of CPSF1 mRNA between tumor and normal tissues across all TCGA cancer types were analyzed by TIMER2.0 and graphed by SRplot (http://www.bioinformatics.com.cn/SRplot ). The statistical significance was computed by the Wilcoxon test (*: p-value < 0.05; **: p-value <0.01; ***: p-value <0.001) (B) The mRNA Expression of CPSF1 in tumor and corresponding adjacent normal tissues from TCGA database was visualized by SRplot (http://www.bioinformatics.com.cn/SRplot ). The statistical significance was computed by the Wilcoxon test (*P < 0.05, **P < 0.01, and ***P < 0.001). (C) The protein expressions of CPSF1 across pan-cancers were generated by UALCAN platform with CPTAC dataset (https://ualcan.path.uab.edu/analysis-prot.html ). *P < 0.05; **P < 0.01; ***P < 0.001. 43 3.3.3 Metastasis and clinical outcomes of CPSF1 To investigate a potential role of CPSF1 in tumor metastasis, we compared CPSF1 expression in normal, tumor, and metastatic tissues of different cancer types using the TNMplot database. We found that CPSF1 expression was significantly elevated in tumor and metastatic tissues as compared to normal tissues in colon, liver, lung, and prostate cancers (Figure 3.6 A-D). These findings demonstrate a correlation between increased CPSF1 expression and tumor metastasis in these cancer types. To examine a potential prognostic significance of CPSF1 expression, we conducted Kaplan-Meier survival analyses using available datasets with patient outcome information. Patients with high CPSF1 expression had worse overall survival rates in KIRC, LIHC, SARC, and lung cancer (Figure 3.7 A-D). In colon cancer and PRAD, high levels of CPSF1 were associated with poor relapse-free or disease-free survival (Figure 3.7 E-F). Additionally, increased CPSF1 expression in gastric cancer patients was linked to low progression-free survival (Figure 3.7 G). Collectively, these results demonstrate that upregulated CPSF1 serves as an independent prognostic factor in patients with SARC, LIHC, KIRC, lung cancer, colon cancer, gastric cancer and PRAD. 44 Figure 3.6 CPSF1 expression in normal, tumor and metastatic samples. Violin plots comparing CPSF1 gene expression levels (gene chip data) in normal, tumor, and metastatic tissues across different cancer types using TNMplot database (https://tnmplot.com/analysis/ ): (A) Colon, (B) Liver, (C) Lung, and (D) Prostate. The statistical significance was computed by the Dunn's test *P < 0.05; **P < 0.01; ***P < 0.001. 45 Figure 3.7 Kaplan-Meier Survival analysis of CPSF1. (A-G) Prognostic analysis of CPSF1 in patients with kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), sarcoma (SARC), lung cancer, and gastric cancer using the Kaplan-Meier Plotter (https://www.kmplot.com/analysis/). Patients were split into two groups based on the expression levels: high expression (upper 50%) and low expression (lower 50%). Disease-free survival analysis of CPSF1 in patients with prostate adenocarcinoma (PRAD) was conducted using GEPIA2 (http://gepia2.cancer-pku.cn/#index). The statistical significance was computed by the Log-Rank test. 46 3.3.4 Drug sensitivity analysis of CPSF1 To identify potential drugs for CPSF1-driven cancer, we integrated drug sensitivity data from the GDSC and CTRP via GSCA platform [175]. A correlation analysis between CPSF1 expression levels and the IC50 values of various drugs was performed. Our findings revealed that high level CPSF1 mRNA expression was associated with low IC50 of many DNA damage repair (DDR) inhibitors CP466722 (an ATM/ATR inhibitor), AZD7762 (a novel cell cycle checkpoint inhibitor) (Figure 3.8). This finding indicates that cancer cells exhibiting high levels of CPSF1 expression may be more sensitive to DDR inhibitors, highlighting a potential therapeutic opportunity in CPSF1-high cancers. This topic is further discussed in section 7 as a future perspective on the work presented in this thesis. 47 Figure 3.8 Drug sensitivity test of CPSF1. (A-B) The correlation analysis between CPSF1 expression and IC50 of various drugs from GDSC and CTRP databases via Gene Set Cancer Analysis (GSCA, https://guolab.wchscu.cn/GSCA/#/drug ). Blue bubbles represented negative correlations which meant a higher CPSF1 expression was associated with low IC50. The deeper the color, the stronger the correlation. Bubble size was positively correlated with the FDR significance. The black outline border indicated FDR ≤ 0.05. The drugs are ranked by the integrated level of correlation coefficient and FDR of searched genes. In detail, firstly, drug-gene pairs with an 48 absolute correlation coefficient >0.1 and FDR<0.05 remained. The -log10FDR and the absolute value of the correlation coefficient were then multiplied to calculate a score for each gene-drug pair. Finally, the sum of the scores was calculated for each drug. GDSC, Genomics of Drug Sensitivity in Cancer; FDR, false discovery rate; CTRP, Cancer Therapeutics Response Portal. 3.4 Conclusion This chapter aimed to identify potential oncogenic APA factors via bioinformatics approaches. Through our bioinformatics analysis, we identified CPSF1 as a highly upregulated APA component in multiple cancer types. Moreover, CPSF1 is significantly upregulated in metastatic samples compared to normal tissues. High CPSF1 expression is associated with poor patient prognosis, including decreased overall survival, relapse-free survival, and progression-free survival. To explore potential therapeutic strategies targeting CPSF1, we performed drug sensitivity test and identified several DNA repair inhibitors that may exhibit increased efficacy in cancer cell lines with high CPSF1 expression. In conclusion, these findings identify CPSF1 as possible pro-oncogenic factor in cancer that warrants further investigation in intro and in vivo. Moreover, the link between CPSF1 and metastasis may offer pharmacologically targetable vulnerabilities to be exploited for the treatment of metastatic disease. We discuss those findings in relation to current literature in chapter 7. 49 CHAPTER 4: Characterization of CPSF1-mediated APA in EMT 4.1 Background EMT and APA EMT is a biological process often observed during embryonic development, tissue repair, and wound healing [176]. Aberrant EMT is often observed in pathological conditions, particularly in cancer. Dysregulated EMT enables cancer cells to reduce cell-cell adhesion and enhance migratory and invasive properties, thereby promoting tumor local invasion and metastasis [51]. This phenotype switch is often driven by transcriptional reprogramming of gene expression due to activation of transcription factors [177]. However, the role of post-transcriptional regulation, such as APA, in EMT remains poorly explored. APA is an important post-transcriptional process that generates mRNA isoforms with different 3'UTR in length. These 3' UTR isoforms harbor different micro-RNA or RNA binding protein sites that can influence the mRNA stability, localization, and translational efficiency of affected mRNAs [178]. Consequently, APA could regulate many aspects of cellular homeostasis such as cell proliferation, differentiation, and apoptosis [130, 179-182]. Many observations support the role of APA in modulating EMT. For example, shortening of the 3' UTR of ZEB1 has been shown to increase its expression and promote EMT [183, 184]. Given the intricate nature of both EMT and APA, it is necessary to investigate the precise mechanisms underlying APA-mediated regulation of EMT and identify key factors involved in the process. CPSF1: a key regulator of APA CPSF1 is a core APA component that has three beta-propeller domains (BPA, BPB, and BPC) and a C-terminal domain (CTD) [185]. It facilitates the recognition of poly(A) signal and recruits other factors to initiate cleavage and polyadenylation [186-189]. In addition to its role in mRNA processing, CPSF1 has been implicated in other biological processes such as in immune responses to pathogens [190]. During viral infections, macrophages exhibit high expression levels of CPSF1, leading to altered APA patterns and host immune response [191]. Additionally, emerging evidence suggests that CPSF1-mediated APA plays a significant role in the development and progression of several 50 malignancies, such as ovarian cancer and acute myeloid leukemia [192, 193]. However, less is known about its role regarding the motility of cancer cells and EMT. 4.2 Rationale In the previous chapter, we have identified CPSF1 as potential oncogenic factor, demonstrating its association with poor clinical outcomes in various cancers and elevated expression in both primary tumors and metastatic tissues. While previous studies have suggested an oncogenic role of CPSF1 in promoting tumor growth, its specific impact on EMT, a key step in migration and invasion, has not been characterized. This knowledge gap necessitates further investigation into the precise mechanisms by which CPSF1 regulates EMT. Therefore, in this chapter we aimed to assess the biological functions of CPSF1, especially the impact of CPSF1 on EMT, with an attempt to understand the underlying molecular mechanism. Based on the data from chapter 1, I hypothesized that CPSF1-mediated APA facilitates EMT and cancer cell migration and invasion in vitro. 4.3 Result 4.3.1 Establishing CPSF1 CRISPR knockout cell lines Three CPSF1 knockout plasmids with different sgRNA were constructed using the lentiCRISPRv2 vector system (Figure 4.1 A) and were validated via Sanger sequencing. The sequences of sgRNA1, sgRNA2, and sgRNA3 within the recombinant plasmids are depicted in (Figure 4.1 B). HCT116 and UM-UC3 cells were transfected with three different CPSF1 knockout plasmids (sgRNAs) respectively; however, sgRNA1 and sgRNA2 plasmids exhibited little to no knockout efficiency. Therefore, subsequent experiments were conducted using the sgRNA3 plasmid. To validate CPSF1 knockout cells, genomic DNA was extracted from single-cell clones and was then subjected to Sanger sequencing. The results indicated that around 30 nucleotides were deleted in the knockout cells compared to the wild-type parental cells (Figure 4.2 A-B). Western blot analysis confirmed that CPSF1 protein expression was completely ablated in both HCT116 and UM-UC3 cells (Figure 4.2 A-B). 51 Figure 4.1 Constructing CPSF1 CRISPR plasmid. (A). Backbone of lentiCRISPRv2 plasmid. Key components were: U6 promoter, EF1-1α promoter for CAS9, CAS9 coding sequence (CDS), puromycin resistance CDS, BsmBI and restriction site for sgRNA insert. (B). Sanger sequencing with U6 commercial primer. Highlights were CPSF1-sgRNA1, sgRNA2 and sgRNA3 sequences ligated into lentiCRISPRv2 plasmid. CPSF1-sgRNA1, sgRNA2 and sgRNA3 were designed based on Human CRISPR Knockout Pooled Library (Brunello). 52 Figure 4.2 Establishing and validating CPSF1 CRISPR knockout cell line. (A) Western blot and Sanger sequencing validation of CPSF1 knockout in HCT116 cells which were transfected with CPSF1-sgRNA-3 plasmid targeting exon 23. (B) Western blot and Sanger sequencing validation of CPSF1 knockout in UM-UC-3 cells which were transfected with CPSF1-sgRNA-3 plasmid targeting exon 23. 4.3.2 Effect of CPSF1 on cell proliferation We next sought to investigate the functional role of CPSF1 in cancer cell proliferation using genetic knockout, and overexpression methodology. Incucyte proliferation and colony formation assays indicated that the knockout of CPSF1 reduced cell proliferation capacity in HCT116 and UM-UC3 while overexpression of CPSF1 enhanced cell proliferation capacity in HCT116 and UM-UC3 (Figure 4.3 A-B and Figure 4.4 A-C). Increased cell proliferation is often linked to changes in cell cycle profiles [194]. Therefore, we next explored the cell cycle distribution following CPSF1 knockout in HCT116 and UM-UC3. Flow cytometry analysis demonstrated an increased proportion of cells in G0/G1 phase in HCT116 knockout, UM-UC3 knockout, compared to their corresponding wild type or control cells (Figure 4.5). Gene Set Enrichment Analysis (GSEA) of TCGA data revealed upregulation of 53 DNA damage repair pathways in tumors with high CPSF1 expression (Figure 4.6 A). Consistent with this finding, western blot analysis showed an elevated phospho-H2AX signal, a DNA damage marker, in CPSF1 knockout cells (Figure 4.6 B). These results suggest that CPSF1 might play a role in DDR and genomic stability. Figure 4.3 Effect of CPSF1 depletion on cell proliferation. (A) Cell proliferation using Incucyte Live Cell Analysis System for HCT116 and UM-UC-3 cell lines. Confluency measurements were taken every 24 hours. Data are represented as mean confluence of 4 wells, along with the standard error of the mean. Colony counts were analyzed by Student’s t-test. *, P < 0.05; **, P < 0.01 and ***, P < 0.001. (B) Colony formation assay of indicated cells with CPSF1 knockout. Cells were cultured for 14 days, followed by staining with 0.1% crystal violet. Colonies containing more than 50 cells were selected. Colony counts were analyzed by Student’s t-test. *, P < 0.05; **, P < 0.01 and ***, P < 0.001. 54 Figure 4.4 Effect of CPSF1 overexpression on cell proliferation. (A) Western blot analysis of CPSF1 protein level upon overexpression in HCT116 and UM-UC-3 cells. (B) Incucyte cell proliferation analysis upon overexpressing CPSF1 in HCT116 and UM-UC-3 cells. Confluency measurements were taken every 24 hours for 4 days. Data were represented as mean confluence of 3 or 4 wells, along with the standard error of the mean. Colony counts were analyzed by Student’s t-test. *, P < 0.05; **, P < 0.01 and ***, P < 0.001 (C) Colony formation assay of indicated cells with CPSF1 overexpression. Cells were cultured for 12 days, followed by staining with 0.1% crystal violet. Colonies containing more than 50 cells were selected. Colony counts were analyzed by Student’s t-test. *, P < 0.05; **, P < 0.01 and ***, P < 0.001 55 Figure 4.5 Effect of CPSF1 depletion on cell cycle. Cell cycle distribution upon CPSF1 depletion was analyzed by flow cytometry using PI staining in HCT116 (A), UM-UC3 (B). The FACS data were analyzed with FlowJo software and were plotted with GraphPad Prism 5 software; Statistical analysis was performed by Student’s t-test*, P < 0.05; **, P < 0.01 and ***, P < 0.001 compared with the corresponding control. Data presented three independent experiments. 56 Figure 4.6 Effect of CPSF1 depletion on DNA damage. (A) Gene Set Enrichment Analysis (GSEA) of DNA repair pathways using TCGA data for BLCA and COAD cohorts. Analysis was performed using the GENI platform (https://yoavshaul-lab.shinyapps.io/gsea-geni/). High CPSF1 expression was associated with upregulated DNA repair pathways. (B) Western blot analysis of phosphorylated H2AX protein levels in HCT116 and UM-UC3 cells following CPSF1 knockout. 4.3.3 Effect of CPSF1 depletion on cancer cell migration, invasion, and EMT To investigate the role of CPSF1 in cancer cell migration and invasion, we first performed Incucyte wound healing assay to assess migratory capacity. The data showed that CPSF1 knockout in HCT116 and UM-UC3 cell lines decreased the wound healing rate (Figure 4.7 A-B). We also performed the transwell migration and invasion assay. The results showed that CPSF1 knockout inhibited the migration and invasion capabilities in HCT116 and UM-UC3 cell lines (Figure 4.8 A-B). Conversely, CPSF1 overexpression enhanced the migratory and invasive abilities of HCT116 and UM-UC3 cells (Figure 4.8 C-D). Additionally, the 3D Spheroid invasion assay also revealed reduction in invasive capacity of UM-UC3 cells upon CPSF1 knockout (Figure 4.9 A-B). Given the known association between EMT and cell migration and invasion, we 57 examined the expression levels of EMT markers upon CPSF1 knockout. As shown in (Figure 4.10), western blot analysis showed that CPSF1 knockout upregulated the epithelial marker E-cadherin in HCT116 cells and downregulated the mesenchymal markers N-cadherin and vimentin in UM-UC3 cells. It's important to note that the baseline expression level of E-cadherin in UM-UC3 was markedly low and the expression level of N-cadherin and vimentin in HCT116 was also minimal. Figure 4.7 Effect of CPSF1 depletion on cancer cell migration and invasion via incucyte wound healing assay. (A-B). Cell migration was analyzed using the Incucyte scratch wound module. The indicated cells were treated with mitomycin C for two hours before creating the scratch. The bottom panel showed representative images of wound healing in the indicated cells. Yellow areas in 0 hour indicated the original scratch wound , while dark blue areas in 12 hours represented wound healing areas. The migration rate was quantified by wound confluency (%) as mean ± SD, n=4 biologically independent replicates. Statistical analysis was performed by Student’s t-test *, P < 0.05; **, P < 0.01 and ***, P < 0.001 compared with the corresponding control. 58 Figure 4.8 Effect of CPSF1 depletion and overexpression on cancer cell migration and invasion via transwell assay. Transwell migration and invasion assays were performed in the indicated cell lines following CPSF1 knockout (A-B) or overexpression (C-D). The left panel showed representative images of the indicated cell lines with/without CPSF1 knockout or knockdown or overexpression. The right panel presented quantitative results of migration and invasion assays from triplicate experiments. Statistical analysis was performed by Student’s t-test *, P < 0.05; **, P < 0.01 and ***, P < 0.001 compared with the corresponding control. 59 Figure 4.9 Effect of CPSF1 knockout on cancer cell invasion via 3D spheroid invasion assay. (A-B) UM-UC3 cells (1,000 cells/well) were seeded in an ultra-low attachment round-bottom 96-well plate and cultured for 72 hours to form spheroids. The spheroids were then embedded into Matrigel (4.5 mg/ml). Red outlines indicated the invading front. Time-course plots displayed the bright field area (whole spheroid or invading cell areas, μm²) per well over time (days), with data collected every two days. Images were captured at 4x magnification. Data points represent mean ± SEM, n=3 wells. Statistical analysis was performed by Student’s t-test *, P < 0.05; **, P < 0.01 and ***, P < 0.001 compared with the corresponding control. Figure 4.10 Effect of CPSF1 knockout on EMT Representative western blot images of E-cadherin, N-cadherin, and Vimentin expression in HCT116 (left) and UM-UC3 (right) cells after CPSF1 knockout. 4.3.4 Effect of CPSF1 knockout on alternative polyadenylation patterns To study the impact of CPSF1 on alternative polyadenylation patterns, we conducted high-depth RNA-seq on both wild-type and CPSF1-knockout HCT116 cells (Figure 4.11 A). We used the quantification of alternative polyadenylation (QAPA) to quantify the relative abundance of short and long 3′UTR isoforms in HCT116 wild type cells and HCT116 CPSF1-knockout cells [172]. The QAPA analysis demonstrated an 60 increase in the abundance of long 3'UTR isoforms, characterized by decreased abundance of shortened isoforms, increased abundance of the long isoform, and a shift from short to long isoforms (Figure 4.11 B). Among these 3'UTR lengthening transcripts in all three HCT116 CPSF1-knockout cell lines (Table 7) were SOX9 and TGFBR1, both of which are well-known key regulators of EMT (Figure 4.11 C)[195, 196]. To validate the impact of CPSF1 on TGFBR1 3'UTR length, we performed 3'RACE. The results confirmed a shift towards TGFBR1 long 3'UTR isoforms upon CPSF1 knockdown, indicating that CPSF1 promotes the use of proximal polyadenylation sites of TGFBR1 (Figure 4.11 D). Moreover, western blot analysis demonstrated decreased protein levels of TGFBR1 and SOX9 in CPSF1 knockout cells (Figure 4.11 E). Concomitantly, we observed a reduction in the phosphorylation levels of SMAD2 and SMAD3, key mediators of TGF-β signaling, in CPSF1 knockout cells. (Figure 4.11 F). These findings suggest that CPSF1 regulates EMT by modulating the expression of TGFBR1 and SOX9 through APA. 61 Figure 4.11 Effect of CPSF1 knockout on 3’UTR processing. (A) Flow chart overview of RNA-seq performed in HCT116 wild-type and CPSF1 knockout cell lines. Sequencing was carried out using three independent replicates for statistical analysis. 62 (B) Classification of APA-regulated genes into categories based on changes in isoform usage. “Increased Lengthened”: increase in abundance of a longer 3’ UTR isoform in the CPSF1 knockout cells. “Decrease Lengthened”: decrease in abundance of a longer 3’UTR isoform in the CPSF1 knockout cells. “Decrease Shorten”: decrease in abundance of the shortest 3’UTR isoform in the CPSF1 knockout cells. “Increased shorten”: increase in abundance of the shortest 3’UTR isoform in the CPSF1 knockout cells. “Switch Lengthen”: In the knockout cells, a longer 3’UTR isoform for a given protein-coding gene increases which was accompanied by a decrease in abundance of the shortest 3’UTR isoform. “Switch Shorten”: In the CPSF1 knockout cells, the shortest 3’UTR isoform for a given protein-coding gene increases which was accompanied by a decrease in abundance of a longer 3’ UTR isoform. “Complex Genes”: two or more 3’UTR isoforms undergo significant changes in abundance which do not fall into one of the above cases. (C) Venn diagram depicting the overlap in the number of genes with 3’UTR lengthening isoform among three CPSF1 knockout HCT116 cell lines. (D) Abundance of transcripts of different TGFBR1 3’UTR identified by 3'-RACE in HCT116. More isoforms with short TGFBR1 3’UTR were found in the negative control group, while more transcripts with long TGFBR1 3’UTR isoforms were found in CPSF1 knockdown group. (E) Western blot analysis of TGFBR1 and SOX9 protein levels after CPSF1 knockout in the indicated cell lines. (F) Western blot analysis of phospho-SMAD2 and phosphor-SMAD3 in the indicated cells after CPSF1 knockout in the indicated cell lines. Table 7: 3’UTR lengthening gene in all three CPSF1 KO clones KO1 and KO2 and KO3 Gene Name Full Name ABHD10 Abhydrolase Domain Containing 10 ABLIM1 Actin Binding LIM Protein 1 ACER3 Alkaline Ceramidase 3 AFF4 AF4/FMR2 Family Member 4 AFTPH Aftiphilin ANKRD10 Ankyrin Repeat Domain 10 ATF4 Activating Transcription Factor 4 ATP6V1A ATPase H+ Transporting V1 Subunit A BBX Bobby Sox Homolog BCL2L1 BCL2 Like 1 BCL9 B-Cell CLL/Lymphoma 9 BNIP2 BCL2 Interacting Protein 2 BOD1L1 Biorientation Of Chromosomes in Cell Division 1 Like 1 CDC7 Cell Division Cycle 7 CDIP1 Cell Death Inducing P53 Target 1 CFL2 Cofilin 2 CHMP4C Charged Multivesicular Body Protein 4C 63 EIF1AY Eukaryotic Translation Initiation Factor 1A, Y-Linked EIF4E3 Eukaryotic Translation Initiation Factor 4E Family Member 3 FAM149A Family With Sequence Similarity 149 Member A HLCS Holocarboxylase Synthetase HS2ST1 Heparan Sulfate 2-O-Sulfotransferase 1 IER2 Immediate Early Response 2 IQGAP1 IQ Motif Containing GTPase Activating Protein 1 KDM5D Lysine Demethylase 5D KIF18B Kinesin Family Member 18B LPP LIM Domain Containing Preferred Translocation Partner In Lipoma ME3 Malic Enzyme 3 MIB1 Mindbomb E3 Ubiquitin Protein Ligase 1 NOTUM Notum Pectin acetylesterase Homolog NR1D2 Nuclear Receptor Subfamily 1 Group D Member 2 NUP35 Nucleoporin 35 PCGF5 Polycomb Group Ring Finger 5 PDSS2 Prenyl Diphosphate Synthase Subunit 2 PITX1 Paired Like Homeodomain 1 PPP2R3B Protein Phosphatase 2 Regulatory Subunit B’’ Beta PPP6R3 Protein Phosphatase 6 Regulatory Subunit 3 PRPF4 Pre-MRNA Processing Factor 4 RAP1GDS1 RAP1 GTPase-GDP Dissociation Stimulator 1 RREB1 Ras Responsive Element Binding Protein 1 SHF Src Homology 2 Domain Containing F SLC16A13 Solute Carrier Family 16 Member 13 SOX9 SRY-Box Transcription Factor 9 SPECC1L Sperm Antigen with Calponin Homology and Coiled-Coil Domains 1 Like STRN3 Striatin 3 STXBP6 Syntaxin Binding Protein 6 TGFBR1 Transforming Growth Factor Beta Receptor 1 TGM2 Transglutaminase 2 UPP1 Uridine Phosphorylase 1 USP22 Ubiquitin Specific Peptidase 22 CACYBP Calcyclin Binding Protein CFAP36 Cilia and Flagella Associated Protein 36 CTDNEP1 CTD Nuclear Envelope Phosphatase 1 64 GFM2 GTP Dependent Ribosome Recycling Factor Mitochondrial HTATSF1 HIV-1 Tat Specific Factor 1 MNX1 Motor Neuron and Pancreas Homeobox 1 PEX2 Peroxisomal Biogenesis Factor 2 PLS3 Plastin 3 REEP5 Receptor Accessory Protein 5 SLC20A1 Solute Carrier Family 20 Member 1 SLC41A2 Solute Carrier Family 41 Member 2 SPCS1 Signal Peptidase Complex Subunit 1 TIMM17A Translocase Of Inner Mitochondrial Membrane 17A TRMT10C TRNA Methyltransferase 10C TSPYL1 TSPY Like 1 TUBB Tubulin Beta Class I DIP2A DIP2 Disco-Interacting Protein 2 Homolog A EIF2B3 Eukaryotic Translation Initiation Factor 2B Subunit Gamma GMPR2 Guanosine Monophosphate Reductase 2 NARS2 Asparaginyl-tRNA Synthetase 2 RAB6A RAB6A, Member RAS Oncogene Family TMEM70 Transmembrane Protein 70 4.4 Conclusion In this chapter, we aimed to examine the biological function of CPSF1 in cancer, with a particular focus on its role in EMT and the underlying mechanism. Consistent with previous studies, we observed that CPSF1 knockout reduced cancer cell proliferation and induced G1 phase arrest. Importantly, we found that CPSF1 depletion impaired tumor cell migration and invasion capacity and inhibited EMT. To investigate the molecular mechanisms behind EMT inhibition caused by CPSF1 deficiency, we performed RNA-seq and analyzed the APA alterations upon CPSF1 loss. Our analysis revealed an increase in the abundance of long 3'UTR isoforms upon CPSF1 knockout. Of all the transcripts with lengthened 3’UTR, we characterized two EMT modulators: TGFBR1 and SOX9. CPSF1 ablation downregulated TGFBR1 and SOX9 expressions and inhibited TGF-β/Smad2/3 signaling pathway. In conclusion, our study provides novel insights into the role of CPSF1-mediated APA in cancer progression. CPSF1 is required to maintain mesenchymal phenotype, a 65 hallmark of cancer cell invasion and metastasis, by modulating TGFBR1 and SOX9 via APA. Therefore, inhibiting CPSF1 could be a potential therapeutic strategy aiming at suppressing EMT and migration and invasion capacity. A more detailed discussion of these findings in relation to the literature is presented in Chapter 7. 66 CHAPTER 5: The Role of CPSF1 in Maintaining CPSF Complex Stability 5.1 Background Ubiquitination Ubiquitination is a dynamic post-translational modification that is responsible for context-dependent protein activity, signaling, and degradation [197]. It is mediated by ubiquitin, which can form polyubiquitin chains via one of seven lysine residues (K6, K11, K27, K29, K33, K48, and K63) or N-terminal methionine [198]. Different types of ubiquitin linkage have different biological functions. For example, K63-linked, K27-linked, M1-linked and K33-linked ubiquitination are critical for DNA repair and immune signaling such as NF-κB signaling pathway [199-202]. In contrast, K48-linked polyubiquitination primarily serves as a signal for proteasome-mediated protein degradation [203]. The formation of polyubiquitin chains is a multi-step enzymatic process involving three key enzyme classes: E1, E2, and E3 enzymes [204, 205]. First, E1 ubiquitin-activating enzyme activates ubiquitin in an ATP-dependent manner [206]. Second, E1 transfers the activated ubiquitin to the E2 ubiquitin-conjugating enzyme to form E2~Ub thioester intermediate [207]. Finally, E3 ubiquitin protein ligase recognizes the specific target protein and transfers ubiquitin from the E2~Ub complex to the specific target protein (Figure 5.1) [208]. Ubiquitin molecules are repeatedly added to the substrate through these steps to form a polyubiquitin chain [209]. If the ubiquitin chain is built through lysine 48 (K48) linkage, the chain serves as a signal for the proteasome to recognize and degrade the ubiquitin-tagged protein. The proteasome complex consists of 20S subunits as proteolytic cores and 19S subunits as regulatory particles [210]. The 19S subunit of the proteasome recognizes substrate proteins tagged with long K48-linked ubiquitin chain and directs them to the 20S subunit. The 20S subunit, containing proteolytic active sites, then cleaves the unfolded protein into smaller peptides [211]. 67 Figure 5.1 Diagram illustrating the ubiquitination pathway. During ubiquitination process (1), ubiquitin is activated by the enzyme E1 in the presence of ATP, resulting in the formation of a ubiquitin-AMP intermediate and the release of inorganic pyrophosphate (PPi). The activated ubiquitin is then transferred to the E2 enzyme. The E3 enzyme facilitates the transfer of ubiquitin from E2 to the target protein, creating a polyubiquitin chain on the substrate protein. E3 ligases, the largest and most diverse family of ubiquitination enzymes, determines the specificity of the ubiquitination process. During degradation process (2), the polyubiquitinated protein is recognized by the 26S proteasome, where it is degraded into smaller peptides. Finally, free ubiquitin molecules are released and recycled for further use in the ubiquitination process. Figure created with Biorender (https://www.biorender.com/). Deubiquitylation Deubiquitylation is the reverse process of ubiquitination that removes ubiquitin from targeted substates (Figure 5.2) [212]. It is mainly mediated by deubiquitinating enzymes (DUBs), which catalyze the hydrolysis of peptide bond at the glycine 76 residue of ubiquitin [213]. DUBs can be further divided into five group: ubiquitin-specific proteases (USP), ubiquitin C-terminal hydrolases (UCH), ovarian tumor proteases (OTU), Machado-Josephin domain proteases (MJD), and zinc-finger containing ubiquitin peptidase (ZUP) [214, 215]. The USP family is the largest family of DUBs , characterized by their conserved Cys-box and His-box catalytic domains [216]. USP14, USP7 and USP5 mainly dissemble K48-linked polyubiquitin chains from certain substrates like p53 and cyclin D1 [217, 218]. The UCH family are papain-like thiol proteases. UCH37 is known for cleaving K48-linked chains during proteasomal degradation [219]. The OTU family is characterized by a conserved catalytic triad consisting of cysteine, histidine, and aspartate residues [220]. MJD and ZUP families are less common but still play an important role in 68 deubiquitylation. In addition to removing ubiquitin from target proteins, DUBs also help recycle ubiquitin molecules for reuse and proofread ubiquitination process to ensure accuracy [221]. The balance between ubiquitination and deubiquitylation is important to keep cellular homeostasis. Disruption in this balance can lead to a variety of human diseases including cancer [222]. Figure 5.2 Illustration showing deubiquitylation process. Deubiquitinating enzymes (DUBs) modulate ubiquitin signals by detaching ubiquitin chains from substrates, thereby preventing further degradation by proteasome. Figure created with Biorender (https://www.biorender.com/). Ubiquitination in Cancer Recently, dysregulation of ubiquitination has been implicated in many human diseases such as cancer [223]. It has been proved that ubiquitination can activate oncogenic signaling pathways [224]. One example is the activation of mTORC1 signaling pathway via ubiquitination [225]. E3 ligase TRAF6 can facilitate K63-linked polyubiquitination of mTOR by interacting with signaling adaptor protein p62 [226]. Ubiquitination can also promote oncogenesis by interrupting DNA damage repair. BRCA1/BARD1 ubiquitinates histones at sites of DNA damage, which serves as signal for recruitment of DNA repair proteins. Loss of BRCA1 would result in increased genomic instability [227]. Additionally, dysregulation of ubiquitination can also impact cell cycle progression by modulating the levels of cyclin-dependent kinase, thereby promoting cancer cell proliferation. Normally, the ubiquitin-proteasome system degrades excessive cyclin-dependent kinase inhibitors, allowing smooth transition from G1 phase to S phase. However, dysregulation of 69 ubiquitination disrupts this process, leading to G1 cell cycle arrest and enhanced cell proliferation [228]. Ubiquitination and APA As discussed in previous chapter, abnormal APA has been recognized as a potential contributor to cancer development, with 3’UTR shortening being a hallmark across multiple cancer types. While the activation of oncogenes and oncogenic signaling through APA has been described for several cancer types, the underlying mechanisms regulating APA activity remain largely unclear. A recent study has provided novel insights into the role of ubiquitination events in dysregulated APA [229]. They discovered that a cancer-specific ubiquitin E3 ligase, MAGE-A11-HUWE1, was upregulated in cancer and was responsible for PCF11 degradation. The loss of PCF11 by MAGE-A11-HUWE1 mediated ubiquitination facilitated proximal polyadenylation sites usage, leading to the widespread shortening of mRNA 3'UTRs. This observation highlights a critical role of ubiquitination in controlling APA, emphasizing the need for further investigation to better understand the role of ubiquitination in APA in the context of cancer progression. 5.2 Rationale CPSF1, a core component of the polyadenylation machinery, has traditionally been considered as the key protein responsible for recognizing the poly(A) signal. However, recent studies challenged this notion by showing that CPSF1 was just a scaffold protein to facilitate the recognition of poly(A) signal by other essential APA factors [230]. This raises the question of CPSF1's true function despite being the largest components in APA machinery. In addition, several studies observed that CPSF1 depletion led to decreased protein levels of other CPSF factors without affecting their mRNA levels, implying the important role of CPSF1 in maintaining the integrity of the CPSF complex. However, the mechanisms regulating CPSF complex integrity remains to be elucidated. Given the important role of ubiquitination in protein degradation, I hypothesized that CPSF1 was required to maintain CPSF complex stability by preventing the ubiquitin mediated protein degradation pathway. In this chapter, we aimed to investigate the underly mechanism of CPSF1 in stabilizing the CPSF complex. 70 5.3 Result 5.3.1 Effect of CPSF1 depletion on other CPSF components The CPSF complex contains CPSF1, WDR33, CPSF2, CPSF3, FIP1L1 and CPSF4 (Figure 5.3A). Western blot analysis demonstrated a substantial decrease in the protein levels of WDR33, CPSF2, CPSF3, FIP1L1, and CPSF4 upon knockdown of CPSF1 (Figure 5.3 B) and one study reported that mRNA levels of these components were barely changed after CPSF1 knockdown[231]. These findings indicate that the reduced protein level of WDR33, CPSF2, CPSF3, FIP1L1 and CPSF4 are most likely due to protein degradation following CPSF1 depletion. Next, we explored the protein stability of WDR33, CPSF2, CPSF3, FIP1L1 and CPSF4 by CHX chase assay. The results showed that knockdown CPSF1 decreased the protein stability of WDR33, CPSF2, CPSF3, FIP1L1 and CPSF4 (Figure 5.4). Overall, these findings suggest that CPSF1 is required for maintaining the stability of other CPSF complex members. 71 Figure 5.3 Effect of CPSF1 deletion on WDR33, CPSF2, CPSF3, FIP1L1 and CPSF4 protein level. (A) Diagram depicting the structure of CPSF complex containing WDR33, CPSF2, CPSF3, FIP1L1 and CPSF4 and CPSF1 based on [130, 138]. (B) Western blot analysis of protein levels of CPSF1, WDR33, CPSF2, CPSF3, FIP1L1 and CPSF4 in indicated cell lines after CPSF1 knockdown in HCT116, UM-UC3, U2OS and UM-UC15 cells. 72 Figure 5.4 Cycloheximide chase assay for CPSF2, CPSF3, CPSF4, FIP1P1 and WDR33 upon CPSF1 knockdown. Cycloheximide chase assay examining CPSF2, CPSF3, CPSF4, FIP1P1 and WDR33 following CPSF knockdown. HCT116 cells were transfected with CPSF1 siRNA1 for 24 or 48 hours. Following transfection, cells were treated with 200 mM cycloheximide and collected at 0, 5, 10 and 20 hours. CPSF2, CPSF3, CPSF4, FIP1P1 and WDR33 protein levels were analyzed by western blot. 73 5.3.2 CPSF1-deficieny induced CPSF complex destabilization due to Ubiquitin mediated degradation To further elucidate the mechanism of CPSF complex protein degradation after CPSF1 knockdown, we first examined the role of the ubiquitin-proteasome system. MG132, a proteasome inhibitor, was able to rescue the decreased protein levels of WDR33, CPSF2, CPSF3, FIP1L1, and CPSF4 upon CPSF1 knockdown in HCT116 cells (Figure 5.5). Furthermore, ubiquitination assays indicated that CPSF1 depletion increased the ubiquitination level of WDR33, CPSF2, CPSF3, FIP1L1 and CPSF4 in HCT116 cells (Figure 5.6). Taken together, these data suggest that CPSF1 is required to maintain CPSF complex integrity by preventing ubiquitin-proteasome mediated degradation of complex components. . Figure 5.5 Effect of proteosome inhibition on reduced levels of WDR33, CPSF2, CPSF3, FIP1L1 and CPSF4 HCT116 cells were transfected with CPSF1 siRNA1 for 72 hours and then treated with DMOS, or 20 μM MG132 for an additional 2 hours. WDR33, CPSF2, CPSF3, FIP1L1 and CPSF4 protein levels were detected by western blot. 74 Figure 5.6 Effect of CPSF1 depletion on WDR33, CPSF2, CPSF3, FIP1L1 and CPSF4 ubiquitination level. HCT116 cells were transfected with scramble-siRNA or CPSF1-siRNA1 for 24, 48 or 72 hours. Cells were treated with MG132 for an additional 2 hours and then subjected to immunoprecipitation with WDR33, CPSF2, CPSF3, FIP1L1 and CPSF4 antibodies, followed by immunoblotting with the indicated antibodies. 75 5.4 Conclusion In this chapter, we investigated the role of CPSF1 in maintaining the stability of the CPSF complex. Our findings demonstrated that CPSF1 was essential for the stability of other CPSF complex members, including WDR33, CPSF2, CPSF3, FIP1L1, and CPSF4. Depletion of CPSF1 led to a decrease in the protein levels of these factors. Treatment with the proteasome inhibitor MG132 effectively rescued the CPSF1 depletion-induced decrease in protein levels of these CPSF components. Additionally, ubiquitination assays further demonstrated an increase in the ubiquitination level of WDR33, CPSF2, CPSF3, FIP1L1, and CPSF4 upon CPSF1 loss. Therefore, we conclude that CPSF1 is required for stabilizing the CPSF complex by preventing its ubiquitination and degradation by the proteasome. These findings shed light on the role of CPSF1 for maintaining CPSF complex integrity. We discuss these findings in relation to the current literatures in chapter 7. 76 CHAPTER 6: The role of CPSF1 in tumor growth, invasion, and metastasis 6.1 Background Metastasis is a complex process that has a series of intricate biological events [232]. Recent discoveries in the genetic and molecular aspects of metastases have underscored its complexity, yet these discoveries have also highlighted significant challenges for scientists [233]. One major challenge lies in the heterogeneity between metastatic and primary tumors. This heterogeneity makes it difficult to predict the behavior of metastatic cells and their response to therapeutic interventions [234]. Furthermore, the lack of reliable pre-clinical models that could accurately mimic the diverse aspects of metastasis, significantly hinders our understanding of this complex process [235]. To bridge this gap, it is necessary to develop pre-clinical models that reflect the complexity of metastasis in vivo. The use of in vivo models, particularly mouse models, can provide important insights into the biological and molecular aspects of metastasis and potentially reveal new therapeutic targets [236]. 6.1.1 Current in vivo models for metastasis In vivo models for studying metastasis can be classified into two categories: non-mammalian models and mammalian models. While non-mammalian model organisms, such as Caenorhabditis elegans and Drosophila melanogaster, offer valuable insights into fundamental aspects of cell motility and invasion, these models are not widely used because of their low similarity to human metastasis[237]. As a result, mammalian models, especially murine models, have been established as the gold standard for studying the biology of tumor formation, growth, and metastasis [238]. This section mainly discusses two different types of mouse models based on the immune capabilities: immunocompetent and immunocompromised mouse models (Figure 6.1). 77 Figure 6.1 Summary of mouse models used for studying metastasis. Immunocompetent mice can be used for genetically engineered models and cell-line-derived xenografts, while immunocompromised mice mainly are used for cell-line-derived xenografts and patient-derived xenografts (PDX). Injection routes include tail vein, intraperitoneal, intracardiac, orthotopic, and ectopic [239]. Figure was created with Biorender (https://www.biorender.com/). 78 6.1.1.1 Immunocompetent murine models of metastasis Immunocompetent mice are often used to generate genetically engineered mouse models (GEMMs), which are important tools for studying cancer metastasis [240]. GEMMs are designed to incorporate certain genetic modifications that spontaneously lead to the development of metastases [241]. This model reflects all stages of metastasis, offering invaluable opportunities to investigate the entire metastatic process. The first GEMM model was developed for breast cancer, a malignancy notorious for its aggressive tumor growth and widespread metastasis [241, 242]. GEMM models have then been expanded to other cancer types such as head and neck cancer and small cell lung cancer [243, 244]. Immunocompetent mice can also be used for cell-line derived xenografts via tail vein, intraperitoneal, and intracardiac injections. However, these models can only mimic the final steps of metastasis, neglecting the importance of the cell migration and invasion stages. 6.1.1.2 Immunocompromised murine models of metastases Immunocompromised mice are frequently used in research due to their weak immune system. This immune deficiency allows for successful inoculation of human tumor cells or tissues without significant immune rejection. Based on the origin of the tumor, these models can be broadly categorized into two types: cell line-derived xenografts (CDX) and patient-derived xenografts (PDX) [245]. 6.1.1.3 Cell line-derived xenografts using immunocompromised mice CDX models using immuno-compromised mice are a valuable platform for studying metastasis. It involves injecting tumor cells into immuno-compromised mice at certain sites. These cell lines can be manipulated by gene knockout or knock-in techniques to investigate the impact of specific genetic alterations on metastatic potential [246]. The injection sites or implanting sites of tumor cells significantly influence the pattern of metastasis [247]. For example, tail vein injection typically results in lung metastasis because the tumor cells often are trapped within the pulmonary capillaries [191]. Intracardiac injection disperses the tumor cells throughout the entire body, potentially leading to metastases in the bones, brain, or liver [192]. A significant limitation of these conventional injection methods is their inherent neglect of the tumor invasion stage. To 79 address this limitation, orthotopic injection has been developed where tumor cells are implanted into their original tissue. This model mimics the natural course of tumor growth and metastasis [248]. Similar to orthotopic injections, ectopic injections involve placing tumor cells in a location different from their natural site. It can be used to study tumor metastasis but subcutaneous injection is not suitable for this purpose, as it often fails to induce metastatic progression [249]. CDX models have inherent limitations too. Since cultured cells in a 2D environment may not retain the characteristics of the original tumor, CDX models are less accurate in representing original tumor phenotypes as compared to PDX models. 6.1.1.4 Patient-derived xenografts using immunocompromised mice PDX models offer a more physiologically relevant approach to studying cancer compared to CDX models. In PDX models, a mix of a patient’s tumor cells or pieces of the tumor are implanted directly into immunodeficient mice via subcutaneous injection, orthotopic injection, or sub-renal capsule grafting [250]. Among these, the sub-renal capsule PDX model has been successfully used to study prostate tumor local invasion [251]. A key advantage of PDX models over CDX is the ability to maintain the original tumor’s complexity, including genetic diversity and stromal components [252]. However, it may lose the complexity of patient tumors when they are passaged from mouse to mouse or maintained as PDX cell lines. 6.2 Rationale In chapter 4, we uncovered a pro-EMT role of CPSF1 in vitro, which sparked our interest in exploring the impact of CPSF1 in vivo, particularly on tumor invasion and metastasis. However, due to the complexity of metastasis itself, there was no standardized murine model that could fully mimic all the critical steps of metastasis in the context of CPSF1 manipulation. As mentioned in the above, while PDX model combined with sub-renal capsule inoculation is attractive as the model for tumor invasion and metastasis study, demanding surgery technique limits its widespread use [253]. Therefore, I was in need for a more efficient technique that could avoid the requirement for open surgery for inoculation. Recently, ultrasound guided techniques for mouse models have been described and developed, not only because they are less invasive for the animals, but also because they 80 allow for continuous monitoring of tumor growth. Thus, it was reasonable to replace open surgical inoculation with ultrasound-guided techniques for sub-renal capsule implementation of tumor cells. In this chapter, I aimed to establish ultrasound guided inoculation for sub-renal capsule models to avoid invasive surgery and use ultrasound imaging system to monitor tumor behaviors in real time. By using sub-renal capsule models combined with orthotopic bladder xenograft models, I aimed to evaluate the impact of CPSF1-loss on tumor growth, invasion, and metastasis. Based on the role of CPSF1 in migration, invasion, and EMT regulation elucidated in chapter 4, I hypothesized that CPSF1 deficiency impaired tumor growth, invasion, and metastasis in vivo. 6.3 Results 6.3.1 Investigating the role of CPSF1 in Tumor Invasion To explore the impact of CPSF1 deficiency on tumor growth, we first employed a subcutaneous xenograft mouse model using HCT116 wild-type and CPSF1 knockout cell lines (HCT116 CPSF1-KO1, HCT116 CPSF1-KO2, HCT116 CPSF1-KO3) (Figure 6.2 A). We monitored tumor growth by measuring the length, height and width of tumors with a caliper. Our results showed that the tumors formed by CPSF1 knockout cell lines grew smaller and slower than those formed by wild type cells (Figure 6.2 B). This suggests that CPSF1 knockout impairs tumor growth potential of HCT116 cells in vivo. 81 Figure 6.2 Effect of CPSF1 knockout on tumor growth in a subcutaneous model (A) Schematic overview of mice subcutaneous model. HCT116 wild type, HCT116 CPSF1-KO1, HCT116 CPSF1-KO2 and HCT116 CPSF1-KO3 were injected subcutaneously into the left flank side of nude mice. Each group had 10 mice, and all mice were sacrificed on the same day. (B) Subcutaneous tumor volume was measured with caliper at different time points. Quantification was carried using student t-test. *, P < 0.05; **, P < 0.01 and ***, P < 0.001 The sub-renal capsule xenograft model is a powerful platform for investigating tumor invasion and metastasis due to its unique vascular environment and high rate of blood and lymphatic flow [254]. However, current grafting technology involves a surgical procedure to open the abdominal wall, which can be quite invasive and time-consuming. To address these limitations, we have developed an ultrasound-guided sub-renal capsule inoculation model. Given the strong mesenchymal feature of UM-UC3 cells, characterized by high expression of N-cadherin and Vimentin, we selected this cell line for our tumor invasion study. In contrast, HCT116 cells only expressed epithelial markers like E-cadherin, which may exhibit less aggressive invasion capacity. Luciferase-labeled UM-UC3 wild type and CPSF1-KO1 were injected under ultrasound guidance into the sub-renal capsule and tumors were monitored by ultrasound and an IVIS imaging system. CPSF1-KO2 group exhibited minimal growth in the sub-renal model and therefore was excluded from further analysis. To rule out the differences in tumor growth, we collected tumors at similar tumor size for all the mice which were 55-70 mm3 (Figure 6.3 A). To quantify tumor invasion, we analyzed tumor ultrasound images, defining areas of tumor invasion as (i) a lack of clear border between tumor and kidney tissue, and (ii) a jagged area between tumor and 82 kidney. Compared to wild-type tumors of similar sizes, CPSF1-knockout tumors exhibited a significant reduction in invasion area, characterized by a smoother border between tumor and kidney tissues (Figure 6.3 B-D) in ultrasound images. Consistently, H&E staining also demonstrated low invasion distance in CPSF1-KO group (Figure 6.3 E-F). These findings demonstrate that depletion of CPSF1 diminishes the invasive potential of UM-UC3 cells in the sub-renal capsule xenograft model. 83 84 Figure 6.3 Effect of CPSF1 knockout on tumor invasion via sub-renal capsule model. (A) Schematic overview of mice sub-renal capsule xenograft tumor model experiment. Cancer cells were injected into mouse sub-renal capsule via ultrasound guide. Tumors were then monitored every week by ultrasound imaging and IVIS imaging. (B) Representative ultrasound images of mice tumor inside the sub-renal capsule acquired using Vevo lab 3.1. For the invasion study, 13 wild type mice and 10 CPSF1-KO1 mice were quantified for tumor invasion area quantification due to appropriate tumor position. All mice were sacrificed once the tumors reached 55 to 70 mm3 based on ultrasound 3D volume calculation. (C) Quantification of tumor invasion area in ultrasound. Tumor invasion areas were defined as a mixed population of both tumor and kidney. Statistical analysis was performed by Wilcoxon rank sum test *P<0.05; **P<0.01, ***P<0.001. (D) Tumor growth dynamics of sub-renal capsule mouse model for invasion study using UM-UC3 wild type and CPSF1-KO1 cells. Tumor volumes were calculated by Vevo lab 3.1. (E) Representative hematoxylin and eosin (H&E) staining images of UM-UC3 derived xenografts implanted in the renal subcapsular space in mice. There is a distinct boundary between the surrounding kidney tissue (salmon pink) and xenograft tissue (violet) for UM-UC3 wild type (top panel) and CPSF1 knockout (KO; bottom panel). The scale bar represents 0.5 mm. (F) Quantification of average local invasion distance by tumor cells for WT (n=13) and CPSF1 KO (n=10). Statistical analysis was performed by Wilcoxon rank sum test *P<0.05; **P<0.01, ***P<0.001. 6.3.2 Investigating the role of CPSF1 in Tumor Metastasis To determine the impact of CPSF1 on tumor metastasis, we once again employed the mice sub-renal capsule model. To ensure comparable tumor burden across groups and enough time to form metastasis, we harvested tumor at a volume of 1000 mm³ for both groups (Figure 6.4 A-B). Mice injected with wild-type UM-UC3 cells exhibited significantly higher rates of metastasis to distant organs compared to those injected with CPSF1-KO1 cells (Figure 6.4 C-D). Common metastases in mice included kidney, spleen, lymph node and adipose tissue (Figure 6.4 C). The tumor growth difference can be ruled out because tumors were harvested at similar tumor size between the two groups. These findings indicate that CPSF1 deficiency inhibits tumor metastasis in this model. 85 86 Figure 6.4 Effect of CPSF1 knockout on tumor metastasis in a sub-renal capsule mouse model. (A) Schematic overview of mice sub-renal capsule xenograft tumor model experiment.Cancer cells were injected into mouse sub-renal capsule via ultrasound guide. Tumors were then monitored every week by ultrasound imaging and IVIS imaging. (B) Tumor volume dynamics of sub-renal capsule mouse model for metastasis study using UM-UC3 wild type and CPSF1-KO1 cells. Tumor volumes were calculated by Vevo lab 3.1 (C) Representative IVIS images of the whole body and major organs showing metastasis without primary tumors. Luciferase-labeled UM-UC3 wild type (n=7) and UM-UC3 CPSF1-KO1 (n=8) were included for the analysis. 3 mice from wild type, 2 from UM-UC3 CPSF1-KO1 group were exclude because of sudden death and improper inoculation during the course of experiment. The tumors were monitored by ultrasound and IVIS imaging system over the time course of the experiment. Mice were euthanized once tumor volume reached around 1000 mm3, and metastatic lesions were then detected after primary kidney tumors were removed. (D) Quantification of bioluminescence of metastases sites from mice in each group. Statistical analysis was performed by Wilcoxon rank sum test *P<0.05; **P<0.01, ***P<0.001. Orthotopic models are useful for studying metastasis because they mimic the tumor cells’ original environment, allowing for a more physiologically relevant assessment of the metastatic process [255]. To further investigate the impact of CPSF1 on metastasis, we established orthotopic bladder cancer xenografts using UM-UC3 wild type, CPSF1 KO1 and KO2 cells. The cells were inoculated into the muscle layer of the mice bladder walls, guided by ultrasound. IVIS imaging and ultrasound imaging were employed to monitor tumors every week (Figure 6.5 A) and tumors were collected on the same day. Bioluminescence imaging (BLI) analysis revealed significantly slower tumor growth in the CPSF1-KO1 and KO2 groups compared to wild-type cells (Figure 6.5 B-C). This finding was corroborated by ultrasound-based tumor volume measurements, which also reported significantly smaller tumor volumes in the CPSF1-knockout groups (Figure 6.5 D-F). Metastatic lesions were assessed by IVIS imaging. Consistent with findings from sub-renal capsule xenografts model study, mice inoculated with wild-type UM-UC3 exhibited significantly higher rates of metastasis to distant organs compared to those with CPSF1-KO1 and CPSF1-KO2 UM-UC3 (Figure 6.6 A-B). Metastasis sites included lymph node, stomach, intestine, testicles, and spleen (Figure 6.6 A and C). These findings demonstrate that CPSF1 deficiency inhibits tumor growth and metastasis in an orthotopic bladder cancer model. 87 Figure 6.5 Effect of CPSF1 knockout on tumor growth in orthotopic urinary bladder tumor model. (A) Schematic overview of orthotopic urinary bladder tumor model. Luciferase-labeled UM-UC3 wild type (n=6), UM-UC3 CPSF1-KO1 (n=7) and UM-UC3 CPSF1-KO2 (n=6) cells were orthotopically implanted into the bladder wall of nude mice by ultrasound-guided injection. The tumor was monitored by ultrasound and IVIS imaging system over the time course of the experiment. On Day 35, all mice were euthanized, and metastatic lesions were detected after primary bladder tumors were resected. 88 (B) Representative bioluminescence images of UM-UC3 wild type and CPSF1-knockout group on day 7, day 21 and day 35 after tumor cell implantation in orthotopic bladder model. All images were plotted on the same scale. (C) Bioluminescence intensity of each group shown in radiance, normalized to the background signal of orthotopic bladder model. Data represented the mean radiance ± SEM. Statistical analysis was carried out using two-way ANOVA with Tukey’s multiple comparisons test indicated ***p < 0.001. (D) Individual tumors were collected on Day 35. 6 tumors from wild type group, 7 tumors from CPSF1-KO1 group and 6 mice from CPSF1-KO2 group were included for the analysis. The rest tumors were excluded either due to sudden mouse death before endpoint or improper inoculation. (E) Representative ultrasound images of mice tumor in bladder acquired from VevoLab 3.1. (F) Tumor volume dynamics for wild type, CPSF1-KO1 and CPSF1-KO2 group. Measurements obtained from 3D ultrasound images. Statistical analysis was performed by Kruskal-Wallis’s test *P<0.05; **P<0.01, ***P<0.001.* p < 0.05, ** p < 0.01, *** p < 0.001 . 89 Figure 6.6 Effect of CPSF1 knockout on tumor metastasis in orthotopic urinary bladder tumor model. (A) Detection of metastasis using IVIS imaging. Luciferase-labeled UM-UC3 wild type (n=6), UM-UC3 CPSF1-KO1 (n=7) and UM-UC3 CPSF1-KO2 (n=6) cells were orthotopically implanted into the bladder wall of nude mice by ultrasound-guided injection. Bioluminescence Images were taken once primary tumors were removed. Individual organs were placed along with mice body. (B) Quantification of bioluminescence of metastases sites from mice in each group. * p < 0.05 by Kruskal-Wallis’s test. (C) Representative H&E image of lymph nodes and adipose tissues containing metastatic cells. 90 6.4 Conclusion In this chapter, we aimed to investigate the role of CPSF1 on tumor growth, local invasion, and metastasis. We established, for the first time, an ultrasound guided inoculation and monitoring system for the sub-renal capsule model. This technique proved to be feasible and suitable for sub-renal capsule and murine bladder wall implantation models. No mice exhibited signs of illness after tumor cells implantation via ultrasound guided injection, and tumors were monitored via ultrasound imaging to avoid missing the best experiment endpoint. Subsequently, we determined the impact of CPSF1 deficiency on tumor growth, local invasion and metastasis via subcutaneous, sub-renal capsule, and orthotopic xenograft models. In all three models, CPSF1 knockout resulted in inhibition of tumor growth. Importantly, in the sub-renal capsule and orthotopic models which better mimic the in vivo tumor microenvironment, CPSF1 deficiency impaired tumor invasion and significantly reduced metastatic lesions. These in vivo findings underscore the critical role of CPSF1 in promoting tumor growth, invasion and metastasis. The consistent inhibition of tumorigenesis across multiple models indicates CPSF1 as a potential therapeutic target for cancer therapy. We discuss these findings in the context of current literature in Chapter 7. 91 CHAPTER 7: Discussion, limitation, future directions, and conclusion 7.1 Discussion Dysregulation of EMT is a key driver in the progression of cancer, especially in the context of metastasis [256]. This dysregulation is often driven by genetic mutations and non-genetic reprogramming [257, 258]. Unlike genetic mutations, which alter the DNA sequence, non-genetic reprogramming controls gene expression without modifying the DNA structure [259]. In fact, it is considered as one of the hallmarks of cancer. Among the mechanisms of non-genetic reprogramming, APA has gained a lot of attention recently due to its unique post-transcriptional regulatory role in tumorigenesis and metastasis [260]. APA can modulate the expression of oncogenes and tumor suppressor genes by regulating their mRNA 3’UTR [261]. In our previous study, we reported that CFIm25-mediated APA suppressed Ras-dependent signaling by controlling 3’UTR usage of many transcripts [123]. Additionally, CFIm25 inhibited cancer cell proliferation and dampened cancer cell migration by suppressing EMT [262]. Recently, emerging evidence has highlighted a close connection between APA and EMT [177, 184]. Based on these findings, we hypothesized that specific APA factor could activate EMT by regulating 3’UTR usage of EMT-related genes. To identify potential APA factors, we first focused on comprehensive characterization of expression profile for all APA factors in pan-cancer datasets. For the first time, we examined the expression pattern of all APA components in a pan-caner context. Importantly, we identified CPSF1 as one of the most highly expressed APA factors across multiple cancer types. This suggests a potential role for CPSF1 in driving tumorigenesis and metastasis. Further analysis demonstrated that high CPSF1 expression is a potential predictor of high metastatic behavior and poor clinical outcome. Mechanistically, we demonstrated that CPSF1 is required for cancer cells to maintain a mesenchymal phenotype. By characterizing 3’UTR-APA events following CPSF1 loss, we identified TGFBR1 and SOX9 as downstream targets of CPSF1-mediated APA. Using our novel ultrasound imaging-guided murine model, we further saw that CPSF1 knockout impaired tumor growth, local invasion, and metastasis. Beyond its role in tumor growth and metastasis, our study also unraveled a new role of CPSF1 as a stabilizer for the CPSF sub-complex. 92 These results indicate the important role of CPSF1 in regulating mesenchymal cell states (Figure 7.1), positioning it as a potential therapeutic target for tumor invasion and metastasis. Below are the discussion points for chapter 3, 4, 5 and 6. Figure 7.1 Schematic model of CPSF1 mediated alternative polyadenylation in cancer progression. A high level of CPSF1 stabilizes the CPSF complex, promoting efficient APA. Under these conditions, the APA machinery generates shorter 3'UTR isoforms of genes like TGFBR1 and SOX9. These shorter isoforms are less susceptible to miRNA-mediated degradation, leading to increased protein expression and activation of EMT. Conversely, low level of CPSF1 destabilizes the CPSF complex, leading to increased ubiquitination and degradation. This results in aberrant APA, generating longer 3'UTR isoforms of TGFBR1 and SOX9. These longer isoforms are more prone to miRNA-mediated degradation, resulting in decreased protein expression and inhibition of EMT. 93 Discussion for Chapter 3 and 4 Our bioinformatic and in vitro studies in chapter 3 and 4 explored the clinical and biological role of CPSF1 in cancer. We demonstrated that CPSF1 is widely upregulated in almost all types of cancer, but its association with poor prognosis and metastasis appears to be context dependent. This discrepancy may be attributed to the limitations in the sample sizes of database. Previous studies have reported CPSF1 as a potential biomarker for liver and gastric cancers [263, 264]. However, the detailed biological functions of CPSF1 in cancer have been scarcely investigated. Investigating its role in cancer is particularly important for two main reasons. First, the widespread upregulation of CPSF1 in various cancer types suggests that it not only promotes tumor growth but also drives other biological processes that contribute to cancerous behavior, such as metastasis. Second, targeting CPSF1 with a specific drug could lead to a universal treatment for multiple cancer types, offering a broad-spectrum cancer therapy. Unlike previous studies that relied on shRNA-mediated CPSF1 knockdown, we employed CRISPR to generate CPSF1-knockout cell lines which could provide more definitive information of its role in cancer. Our findings show that CPSF1 plays a role in regulating cell proliferation, migration, and cell cycle progression. These results align with previous reports in triple-negative breast cancer (TNBC) and head and neck cancer where CPSF1 knockdown decreases cell viability as well as migration and invasion capacity [265, 266]. However, we observed several discrepancies. First, G1 phase cell cycle arrest upon CPSF1 loss in colorectal cancer cells and bladder cancer cells, which is inconsistent with observations from an ovary cancer study [135]. This difference warrants further investigation into the molecular mechanisms driving this cell-type-specific response. Second, we also noticed variations in proliferation rates between individual CRISPR clones. It could be explained by limitations in the sample size during single cell clone selection, and the CRISPR off-target effect. Even though we sequenced the targeted genomic region to confirm genomic deletion, whole genome sequencing would be necessary to detect any potential off-target modifications that could influence clonal proliferation. While our results provide valuable insights to the role of CPSF1 in cancer progression, the molecular mechanisms driving its common upregulation in cancer remain 94 unexplored. Our bioinformatic analysis revealed a high frequency of CPSF1 copy number amplification across multiple cancer types, suggesting that genomic amplification may contribute to the elevated mRNA expression of CPSF1. However, the correlation between CPSF1 copy number and mRNA level varied across cancer types, indicating that additional mechanisms such as transcriptional or post-transcriptional regulation may also be involved. Further studies are needed to fully elucidate the drivers of CPSF1 upregulation in cancer. Our study has mechanistically connected APA to EMT. Genetic modification of several APA factors has been reported to disrupt APA patterns and potentially influence EMT [267-269]. However, the underlying mechanism by which CPSF1 influences EMT is not understood. Our findings have bridged this gap by showing that CPSF1-mediated APA promotes EMT via the TGF-β signaling pathways. Specifically, we demonstrate that CPSF1 knockout causes 3’UTR long isoforms being more predominant than short isoforms, accompanied by decreased cell proliferation and motility. This is consistent with the global APA observation across various cancers that 3′UTR lengthening is negatively correlated with cancer progression [270]. However, we observed minimal overlap (73 3'UTR lengthening genes) among three HCT116 CPSF1 knockout clones, which may indicate an indirect connection between CPSF1 loss and 3’UTR lengthening. To prove this, targeted rescue experiments are necessary. For example, overexpressing TGFBR1 and SOX9 in CPSF1 knockout or knockdown cell lines could help determine if there is a direct connection between CPSF1 and 3’UTR lengthening (which was finished in the prepared manuscript). Enhanced CLIP (eCLIP) or Photoactivatable Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP) for CPSF1 could also directly address this question. From QAPA analyses, we identified TGFBR1 as the direct downstream target of CPSF1 via APA. TGFBR1, a key receptor in TGF-β/SMAD signaling pathway, has been proved to be involved in EMT and metastasis [272]. In the early stages of cancer, TGF-β signaling can act as tumor suppressor by inhibiting cell proliferation [273]. However, in later stages, it can promote tumor progression and metastasis by enhancing cell migration, invasion, and immune evasion [274]. Currently, several TGFBR1 inhibitors, such as LY2157299, are undergoing clinical trials [275]. Our findings suggest that targeting CPSF1 could indirectly modulate TGFBR1 levels via APA, potentially offering a synergistic 95 therapeutic approach in combination with TGFBR1 inhibitors in cancers with hyperactivated TGF-β signaling. Interestingly, recent work has shown that knockdown of CFIm25 can increase TGFBR1 expression, indicating opposing regulatory roles between CPSF1 and CFIm25 [276]. These opposite effects on TGFBR1 underscore the intricate interplay among different APA factors. A similar phenomenon has also been reported for another two APA factors where CFIm59 depletion leads to 3′UTR lengthening of PTEN but CFIm68 loss causes 3′UTR shortening of PTEN [277]. It is worth noting that CFIm59 and CFim68 belong to the same sub-complex while CPSF1 and CFIm25 are from two different subcomplexes, suggesting opposite roles between two sub-complexes. It would be interesting to further investigate the interplay between the CPSF and CFIm subcomplexes to elucidate their coordinated regulatory mechanisms in controlling gene expression. Besides TGFBR1, we also identified SOX9 as the target of CPSF1 through APA. SOX9 is a transcriptional factor that is also involved in EMT [278]. It has been suggested as a promising target for the treatment of metastatic cancer [279]. However, directly targeting SOX9 might be challenging due to its essential role in normal tissues. By targeting CPSF1, we can indirectly modulate SOX9 expression, potentially providing a more targeted and less toxic therapeutic approach [280]. Although we were unable to pinpoint the exact microRNAs or RNA-binding proteins regulating TGFBR1 and SOX9, it is well-established that the long 3'UTRs are more prone to microRNAs or RNA-binding proteins mediated degradation due to more regulatory sites, ultimately affecting mRNA stability and function. In addition to elucidating the biological functions of CPSF1, we have provided additional rationale for investigating CPSF1 as a drug target. Currently, large-scale pharmacogenomic databases such as Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Therapeutics Response Portal (CTRP) have facilitated discovery in cancer therapeutics development[281]. Given few studies about therapeutics targeting APA machinery, we conducted large-scale drug sensitivity analysis of for CPSF1 using GDSC and CTRP databases. Our analysis revealed a negative correlation between CPSF1 expression and IC50 of several DDR inhibitors, such as AZD7762 and CP-466722 [282, 96 283]. This observation raises the possibility for a potential synergistic effect between CPSF1 inhibitors and DDR inhibitors. Furthermore, the negative correlation between CPSF1 expression and the IC50 values of DDR inhibitors suggests that CPSF1 might serve as a biomarker for predicting the efficacy of DDR inhibitors. However, it is important to note that these findings are computational. Further investigation through in vitro and in vivo experimental studies is needed to test the therapeutic potential of DDR inhibitors for CPSF1, given the potential challenges posed by the pleiotropic roles CPSF1. Taking together, we have demonstrated the importance of CPSF1-mediated APA in EMT and cell motility. Thus, targeting CPSF1 could offer a new therapeutic strategy for cancer treatment. Discussion for Chapter 5 Our results in chapter 5 highlight the central role of CPSF1 in maintaining stability of CPSF complex. This complex is indispensable for proper mRNA processing, specifically recognizing the poly(A) signal and cleaving nascent pre-mRNA [284]. Major progress has been made recently in understanding the three-dimensional structure of the CPSF complex, revealing distinct features of individual APA components. Specifically, the CPSF complex can be divided into two subcomplexes based on different biological functions. The mPSF subcomplex contains CPSF1, WDR33, CPSF4 and FIP1L1, which recognizes poly A signal [138]. The mCF subcomplex, composed of CPSF2 and CPSF3, is responsible for cleaving nascent mRNA. While cryo-EM studies have denied the role of CPSF1 in direct recognition of poly(A) signal, our new findings indicate a new role for CPSF1 in maintaining the structural integrity and proper functioning of the CPSF complex [285]. Studies have demonstrated that loss of CPSF1 leads to downregulation of all other CPSF components at the protein level, but not at mRNA level[231]. This observation indicates that CPSF1 plays a post-translational regulatory role in maintaining CPSF complex stability [286]. Supporting this hypothesis, our study demonstrated that CPSF1 protects the CPSF complex from ubiquitin-mediated degradation, yielding a plausible explanation for its essential role in maintaining CPSF complex integrity. However, more efforts are needed to identify specific E3 ubiquitin ligases or DUBs that are involved in CPSF1 mediated stabilization of the CPSF complex [208]. Also, it is tempting to speculate 97 that CPSF sub-complexes may function as a co-stabilizing complex, where each component helps stabilize the others. In support of such a possibility, knocking down individual CPSF component leads to a decrease in the protein levels of other CPSF components, implying interdependence within this complex [286]. In summary, we have described a new role of CPSF1 as a stabilizer for CPSF sub-complex components, explaining the importance of CPSF1 in APA-based gene regulation. Discussion for Chapter 6 The development of reliable animal models is important for studying the process of metastasis. Our work in this chapter has established a novel murine model that addresses several limitations of traditional approaches [287]. Historically, in vivo studies in the scientific community have often utilized mouse models, such as trail vein injection, intracardiac injection, or intraperitoneal injection models for metastasis studies [288, 289]. However, these models hardly mimic the natural course of metastasis, especially the first step of tumor metastasis [290]. In order to better simulate the initial step of tumor metastasis, researchers have developed orthotopic or ectopic implantation methods. Among these, the sub-renal capsule model has emerged as a better choice due to sufficient blood and nutrition access to support invasion and metastasis development [291]. Despite its advantages, the traditional inoculation method for sub-renal capsule model often requires sophisticated open surgery, restricting its wide application in research. Our ultrasound-guided injection technique overcomes this limitation by providing a minimally invasive inoculation approach. Ultrasound-guided injections have been previously developed for orthotopic models in various organs, such as mouse bone, bladder, prostate, and liver [292, 293]. To our knowledge, this is the first ultrasound-guided injections in non-surgical sub-renal mouse models that have been reported. It has several advantages over traditional open surgery implantation for sub-renal capsule model. First, its minimally invasive nature significantly lowers the risk of harm to the animals, when compared to invasive open surgery. Throughout our experiments, no mice experienced greater than 5% loss in body weight or any signs of sickness. Our model represents the principles of best animal care: replacement, reduction and refinement. Second, the efficiency of ultrasound-guided injections offers practical benefits. The inoculation procedure is significantly faster than traditional open surgery. In our experience, each inoculation of tumor cells only takes 98 about 7 minutes. This increased efficiency allows for processing a larger number of mice in a shorter time period. Third, the precision of ultrasound-guided injections ensures accurate tumor cell inoculation, directly improving the consistency and reproducibility of experimental readouts. Fourth, the ultrasound monitoring system provides real-time information for researchers to determine optimal experimental endpoints. For tumor local invasion models, where the timing of experimental endpoint is critical, traditional tumor monitoring methods often rely solely on humane endpoint definitions, which carry great risk of missing the best experimental endpoint. Our use of ultrasound imaging addresses this issue by enabling real-time monitoring of tumor growth and invasion, allowing us to make informed decisions about when to terminate the experiment and collect tissue samples before reaching humane endpoints. Furthermore, combining ultrasound imaging with bioluminescence imaging could improve the accuracy of tumor assessments. While the ultrasound imaging system is highly effective for visualizing tumor growth at later stages, it is less capable of detecting inoculated cancer cells immediately after injection. This limitation is likely due to the fact that only a small portion of the injected cells could survive the initial implantation, making them difficult to detect with ultrasound alone. To overcome this limitation, we combine the ultrasound imaging system with bioluminescence imaging, which is a powerful technique to monitor tumor growth at the early stage. However, we notice that bioluminescence imaging data does not always align with tumor volume. This discrepancy often arises at a late stage of tumor growth when hypoxia occurs inside the tumor. The combination of ultrasound and bioluminescence imaging provides a more comprehensive and accurate depiction of tumor behavior than either method alone. In conclusion, our ultrasound-guided sub-renal capsule model represents progress in preclinical tumor invasion and metastasis model development. Through our novel animal model, we have uncovered the pro-invasion and pro-metastasis role of CPSF1. Current studies on APA primarily focus on its impact on tumor growth, but this only represents one aspect of the complex nature of cancer. Metastasis, a critical determinant of patient survival probability, has received far less attention within the context of dysregulated APA. Our study demonstrates the critical role of CPSF1-mediated APA in driving metastasis. However, given its essential role in APA machinery as indicated in chapter 5, CPSF1 loss could exert pleiotropic effects on cellular physiology. 99 Therefore, the observed impairment in invasion and metastasis could be due to a general reduction in cellular fitness rather than solely reflecting a direct role for CPSF1 in tumor invasion and metastasis. To confirm the specific effect on tumor invasion and metastasis for CPSF1, targeted rescue experiments in vivo would be needed. For instance, restoring the expression of TGFBR1 or SOX9 without 3'UTRs in CPSF1-knockout cells, followed by assessment of their metastatic potential in a xenograft model, could address this question. 7.2 Limitation Our study on the biological function of CPSF1 has several limitations that warrant further investigation. First, although we have demonstrated a high expression pattern of CPSF1 in cancer samples compared to normal tissues, we have not explored the underlying molecular mechanisms driving the upregulation of CPSF1 in cancer. This upregulation could be a result of a variety of factors such as genetic mutations, epigenetic modifications, or dysregulation in transcriptional and post-transcriptional regulatory networks. Understanding the potential mechanism driving high levels of CPSF1 in cancer is important to develop effective therapeutic strategies targeting CPSF1. Second, we have shown that CPSF1 is required to maintain the stability of the CPSF subcomplex. However, given the interplay among CPSF complex components, it is plausible that a co-stabilization mechanism exists within this complex, where each component contributes to the stability of the others. This concept has been observed in other protein complex, such as the single-stranded DNA-binding complex (SOSS), where the stability of the complex dependent on the interactions between individual subunits. Third, while we have demonstrated that CPSF1 prevents other CPSF components from ubiquitin-mediated degradation, we haven't identified the specific ubiquitin ligase or deubiquitinating enzyme associated with CPSF1. Recently, one study has identified an APA-specific E3 ligase, which could regulate APA by targeting certain APA components [229]. Fourth, our study did not explore the gain-of-function effects of CPSF1. While we primarily focused on the loss-of-function to demonstrate its oncogenic role, it is equally important to investigate the gain-of-function effects of CPSF1. Overexpression of CPSF1 could provide extra insights to how increased levels of CPSF1 influence APA and cancer progression. Fifth, the concept of the EMT is rapidly evolving. It is now believed that EMT is a dynamic process with various hybrid 100 states between the extreme epithelial and mesenchymal states [295]. While our research has demonstrated that CPSF1 knockout inhibits EMT, we have not explored these intermediate EMT states. This represents a major limitation in our study. Hybrid EMT states exhibit both epithelial and mesenchymal capabilities, thereby enhancing the metastatic potential of cancer cells [296]. These hybrid states may also have distinct phenotypes that provide survival advantages in different microenvironments [297]. Understanding the role of CPSF1 in these intermediate stages of EMT is important for developing targeted therapies aimed at inhibiting metastasis. Lastly, our key observation is that the depletion of CPSF1 is associated with altered APA patterns, including 3’UTR lengthening of genes such as TGFBR1 and SOX9, leading to impaired invasion and metastatic potentials of tumor. However, it is currently not clear whether these specific CPSF1-mediated alterations are sufficient or directly responsible for the observed invasive and metastatic suppression. The impaired invasion and metastasis in the xenograft model following CPSF1 knockout could potentially be due to pleiotropic loss of fitness. To establish a direct connection, experimental controls such as KD/KO complementation studies (rescue studies) are essential. We acknowledge that our drug sensitivity test for CPSF1 is not sufficient to identify a viable therapeutic drug. First, our analysis primarily focused on cell line expression, but tumors are heterogeneous and may respond differently to any given treatment. Second, our study is limited to computational analysis using data from the GDSC and CTRP database. Further in vitro and in vivo experiments are needed to fully assess drug efficacy, toxicity, pharmacokinetics, and pharmacodynamics. Although beyond the scope of this thesis, addressing these limitations will be important for developing effective and rational cancer treatment targeting CPSF1. While our ultrasound-guided sub-renal inoculation mouse xenograft offers many advantages in terms of tumor cell inoculation, it also presents certain disadvantages. First, this murine model requires a high-resolution ultrasound machine, which is not only expensive but also requires route maintenance to ensure optimal performance. Second, a strong understanding of anatomic structure and sonography is necessary. During inoculation, operators need to accurately interpret ultrasound images to locate the sub-renal capsule. Similarly, during tumor monitoring, operators must be able to accurately 101 determine the invasion front between the tumor and the kidney, which is crucial for determining the optimal experimental endpoint. Third, achieving consistent and reliable injections requires specific skills. The angle of the needle and the depth of the injection need to be carefully adjusted based on the ultrasound imaging guidance. Even small deviations can lead to different tumor inoculation sites. For example, if the needle is inserted too deeply, tumors may form on top of the kidney, resulting in minimal interaction between the tumor and the kidney tissue. Such inaccuracies can potentially impact the experimental outcomes. Fourth, it can be time-consuming in achieving proficiency in these skills and inexperienced researchers may inject improperly and potentially cause harm to the mice. Finally, quantification of the tumor invasion front and tumor volume can be challenging. Poor ultrasound image quality can significantly reduce the accuracy of these measurements. Since ultrasound images are recorded while mice are under anesthesia, the breathing patterns of the mice can greatly affect image quality. This can compromise the data accuracy and reliability of the data. Therefore, ensuring high-quality ultrasound imaging is important for obtaining accurate and reproducible measurements. 7.3 Future directions One future direction for investigating molecular mechanisms underlying CPSF1 up-regulation in cancer could involve genome-wide CRISPR knockout screening. This technique allows for the systematic identification of novel regulators that contribute to elevated expression of the gene of interest (a method established in the Daugaard Lab) [298]. To begin with, a GFP-based reporter system could be developed to monitor endogenous CPSF1 expression. This reporter system helps to assess CPSF1 level before and after knockout screening. Next, a genome-wide CRISPR knockout screening could be conducted using an sgRNA library targeting every gene in the genome. Following CRISPR knockout screening, cells exhibiting altered GFP levels would be collected to analyze its sgRNA through high-throughput sequencing. By identifying sgRNAs that are significantly enriched or depleted, we can pinpoint the upstream regulatory molecules of CPSF1. We believe that due to high knockout efficiency, minimal off-target effects, and comprehensive genome coverage, this approach will be able to help us map the regulatory approach networks that control CPSF1 expression. 102 Figure 7.2 CRISPR/Cas9 screen for CPSF1 regulators. GFP cells that express CPSF1 under the control of endogenous promoter are transduced with a CRISPR lentiviral library. KO of genes affecting CPSF1 expression causes altered GFP which will be analyzed by high-throughput sequencing. Those genes analyzed by high-throughput sequencing will be potential regulators of CPSF1. A gain of function study for CPSF1 could be another important future direction. While our loss of function study for CPSF1 has identified TGF-β signaling pathways, it may not fully capture the complex downstream effects of CPSF1 overexpression. By overexpressing CPSF1 in relevant cell models, we can directly investigate the signaling pathways that are upregulated following CPSF1 overexpression. This approach would accurately mimic the tumor progression seen in cancer patients, providing a more relevant platform to study tumorigenesis, metastasis, and further to develop potential therapeutic strategies targeting CPSF1. To further explore the role of CPSF1 in maintaining CPSF-complex stability, a promising future direction involves identifying potential E3 ubiquitin ligases or DUBs. One promising approach is the use of mass spectrometry combined with immunoprecipitation (IP-MS) [299]. This method allows for the precise identification of a wide range of proteins interacting with CPSF1, including E3 ubiquitin ligases and DUBs. We believe that these detailed protein interactions analyses will help us gain a comprehensive understanding of E3 ligases interacting with CPSF1 and shed light on the mechanisms underlying CPSF complex stability. To gain a deep understanding of the impact of CPSF1 on the dynamics of EMT, one promising future direction could involve applying single-cell RNA sequencing (scRNA-seq) to our ultrasound guided sub-renal model. This cutting-edge technique allows for tracing cell lineages and identifying cell types through high-resolution profiling of gene 103 expression at single cell level [300]. By collecting samples at different time points such as before-invasion, during-invasion, and after-invasion from our sub-renal model and analyzing them by scRNA-seq, we could visualize the dynamic changes in EMT states and characterize the potential role of CPSF1 in these EMT states. We believe that this detailed analysis is important to understand the real nature of EMT, especially its intermediate phases. More importantly, by mapping gene expression profiles before, during and after invasion, we can also uncover the specific regulatory networks and signaling pathways regulated by CPSF1. Another future direction could involve addressing blurred images from ultrasound imaging systems by employing artificial intelligence (AI) to repair and analyze these images. AI, particularly deep learning-based AI, has become a rapidly advancing field, and its application in medical imaging is increasingly popular. In fact, there are several AI imaging products on the market such as Snapcyte™ [301] and Incucyte® [302]. We believe that deep learning-based AI can be of great help in removing these background noises. By doing so, the ultrasound images will be much clearer and more precise. This improvement in image quality is particularly important in animal studies, where precise imaging is essential for identifying tumor boundaries and assessing tumor progression. Moreover, AI can also assist in identifying mice’s anatomical structures, making the inoculation much easier without relying on personal experience. 7.4 Concluding remarks Overall, the work presented in this thesis has made significant contributions to our understanding of tumor metastasis and APA. We have demonstrated the dual role of CPSF1 as a pro-metastatic regulator in cancer metastasis and a stabilizer for CPSF complex in APA regulation. Specifically, Chapters 3 and 4 have explored the clinical implication, biological function and molecular mechanisms by which CPSF1 maintains the mesenchymal phenotype. Chapter 5 has investigated the role of CPSF1 in maintaining CPSF complex stabilization. Chapter 6 has examined the invasion and metastasis potential of CPSF1 in vivo using ultrasound guided sub-renal capsule model orthotopic bladder mouse model. Our findings have laid a solid groundwork for the development of targeted therapies against CPSF1. By uncovering the regulatory roles of CPSF1 in cancer metastasis and alternative polyadenylation, we have opened a new avenue for cancer therapeutic 104 interventions. We believe the continuation of the efforts presented in this thesis will lead to novel therapeutic strategies for late-stage patients with tumor metastasis. 105 References [1] X.Y. Wang, M.N. Rosen, R. Chehade, A. Sahgal, S. Das, E. Warner, R. Saskin, B. Zhang, H. Soliman, K.K.W. Chan, K.J. 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Thesis/Dissertation
eng
Interdisciplinary Oncology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Daugaard, Mads
Characterization of CPSF1-mediated alternative polyadenylation in cancer
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