University of California

UNIVERSITY OF CALIFORNIA

Los Angeles

Divergent regulation and function of Wnt/β-catenin signaling and TCF transcription

factors in pancreatic cancer

A dissertation submitted in partial satisfaction of the

requirements for the degree Doctor of Philosophy

in Cellular and Molecular Pathology

Kathleen Kershaw

2018

Kathleen Kershaw

2018 ABSTRACT OF THE DISSERTATION

Divergent regulation and function of Wnt/β-catenin signaling and TCF transcription

factors in pancreatic cancer

Kathleen Kershaw

Doctor of Philosophy in Cellular and Molecular Pathology

University of California, Los Angeles, 2018

Professor David Wayne Dawson, Chair

With steadily increasing incidence and an 8-9% 5-year survival rate, pancreatic ductal adenocarcinoma (PDA) treatment represents an urgent, unmet clinical need. Hindered by long asymptomatic growth, methods of early detection are not yet available, resulting in frequent late-stage patient presentation at which point treatment options are extremely limited. Current standards of care offer only modest increases in survival, and molecular targeted therapies have underperformed in the clinic, likely due in part to the high degree of genetic heterogeneity within PDA. Increased efforts to sequence large numbers of tumors have revealed a short list of highly mutated pathways within that heterogeneity, including the Wnt/β-catenin signaling pathway. A highly detailed understanding of the regulation and function of Wnt/β-catenin signaling in PDA

however, is lacking. Here we use an unbiased approach to uncover the existence of discrete patterns of expression for T-cell factor/lymphoid enhancer factor (TCF/LEF) family members that are linked to divergent Wnt activity and function in PDA.

Importantly, these distinct patterns were also observed in resected patient tumors where they are correlated with survival outcomes. TCF expression was found to define distinct subtypes of PDA with differential phenotypic responses to similar stimuli. Furthermore, patterns of TCF expression corresponded to differing Wnt niche growth requirements.

The predictive power of TCFs may serve as a more sensitive biomarker for stratifying patients into relevant groups for targeted clinical therapies. Additionally, the effect of

Wnt manipulation on TCF-mediated control of particular gene programs reveals vulnerabilities that could be targeted by novel therapeutics alongside Wnt, pushing PDA treatment one step closer to effective, personalized medicine.

iii

The dissertation of Kathleen Kershaw is approved.

Gay M Crooks

Timothy R Donahue

Michael Alan Teitell

David Wayne Dawson, Committee Chair

COMMITTEE PAGE

University of California, Los Angeles

2018

TABLE OF CONTENTS

ABSTRACT OF THE DISSERTATION ...... ii COMMITTEE PAGE ...... iv LIST OF FIGURES AND TABLES ...... vi LIST OF ABBREVIATIONS ...... viii ACKNOWLEDGEMENTS ...... x VITA ...... xi INTRODUCTION ...... 1 MATERIALS AND METHODS ...... 20 CHAPTER ONE: TCF7L1 IS A SUPPRESSOR OF WNT SIGNALING AND PROMOTER OF TUMOR GROWTH IN PANCREATIC CANCER ...... 33 INTRODUCTION ...... 33 RESULTS ...... 34 FIGURES ...... 48 CHAPTER TWO: TCF7 AND TCF7L2 ARE MARKERS OF ACTIVE WNT SIGNALING AND PROMOTE PANCREATIC CANCER GROWTH ...... 70 INTRODUCTION ...... 70 RESULTS ...... 70 FIGURES ...... 84 CHAPTER THREE: WNT SIGNALING HAS THE POTENTIAL TO REGULATE IMMUNE INFILTRATION IN PDA ...... 104 INTRODUCTION ...... 104 RESULTS ...... 107 FIGURES ...... 113 CHAPTER FOUR: IDENTIFICATION OF NOVEL SMALL MOLECULE COMPOUNDS TO POTENTLY INHIBIT AUTOCRINE WNT SIGNALING IN PANCREATIC CANCER ...... 119 INTRODUCTION ...... 119 RESULTS ...... 124 FIGURES ...... 131 DISCUSSION ...... 138 FIGURES ...... 153 REFERENCES ...... 154

LIST OF FIGURES

Figure 1-1. PDA exhibits discrete patterns of constitutive Wnt signaling and TCF expression. ..49 Figure 1-2. TCF expression patterns in resected pancreatic tumors correlate with patient prognosis...... 51 Figure 1-3. High TCF7L1 in tandem with low TCF7 predicts worse survival in PDA...... 52 Figure 1-4. TCFs differentially influence Wnt/β-catenin-mediated transcription in PDA...... 55 Figure 1-5. TCF7L1 protein decreases upon Wnt pathway activation...... 57 Figure 1-6. TCF7L1 overexpression promotes PDA growth...... 59 Figure 1-7. TCF7L1 silencing activates the Wnt reporter...... 60 Figure 1-8. TCF7L1 silencing has variable effects on in vitro pancreatic tumorigenicity...... 62 Figure 1-9. TCF7L1 structure and targeting areas of RNAi...... 63 Figure 1-10. TCF7L1 silencing promotes pancreatic cancer growth in vivo...... 64 Figure 1-11. Novel off-target effects of Ambion siRNA 905 targeting TCF7L1...... 67 Figure 1-12. Stable TCF7L1 depletion downregulates pro-oncogenic gene sets...... 69 Figure 2-1. TCFs are both mediators and downstream targets of Wnt/β-catenin signaling...... 85 Figure 2-2. Nuclear β-catenin expression in resected PDA correlates with improved survival. ..87 Figure 2-3. TCF7 promotes Wnt/β-catenin activity and tumor growth...... 89 Figure 2-4. TCF7L2 antagonizes Wnt/β-catenin reporter activity but promotes tumor growth in AsPC-1...... 92 Figure 2-5. TCF7L2 promotes growth in a context-dependent manner in MiaPaCa-2...... 94 Figure 2-6. TCF7 promotes growth in a context-dependent manner in MiaPaCa-2...... 95 Figure 2-7. TCF7L2 positively regulates pro-oncogenic gene programs in PDA...... 97 Figure 2-8. TCF7 positively regulates pro-oncogenic, inflammatory gene programs in PDA...... 99 Figure 2-9. Regulation of Wnt target genes varies by cell line...... 101 Figure 2-10. Paracrine Wnt signaling impacts PDA proliferation, EMT and differentiation...... 102 Figure 2-11. Refining Wnt/β-catenin transcriptional signature in AsPc-1...... 103 Figure 3-1. T-cell infiltrated and non-infiltrated subsets exist in PDA...... 114 Figure 3-2. Wnt signaling may be positively correlated with T-cell infiltration...... 115 Figure 3-3. TM4SF5 is enriched in the non-T cell infiltrated population...... 116 Figure 3-4. TCF7 and TCF7L2 negatively regulate TM4SF5...... 117 Figure 3-5. Potential cross-talk between TM4SF5 and Wnt/β-catenin...... 118 Figure 4-1. Scheme for high throughput screening to identify novel small molecule inhibitors of Wnt signaling...... 131 Figure 4-2. HTS of over 80,000 diverse compounds in the UCLA MSSR Core yields over 500 compounds with Wnt inhibitor activity as measured by reporter...... 133 Figure 4-3. MEK inhibitor AZD6244 acts synergistically with LGK974 to potently inhibit Wnt/β- catenin signaling...... 137 Figure D-1. Working model of the relationship between BAR, TCF expression, PDA subtype and Wnt niche dependency...... 153

LIST OF TABLES

Table M-1. Methods Table 1 ...... 31 Table 1-1. Multivariate proportional hazards analysis for the PDA tissue microarray...... 53 Table 1-2. Clinicopathologic characteristics and correlations for TCF7L1 High/TCF7 Low dichotomized groups in PDA tissue microarray...... 54

vii

LIST OF ABBREVIATIONS

5-FU Fluorouracil ADM Acinar-to-ductal metaplasia AIRE Transcriptional regulator autoimmune regulator APC Adenomatous Polyposis Coli APC Antigen-presenting cell ATCC American Type Culture Collection BAR β-catenin activated reporter BAR-H β-catenin activated reporter high BAR-L β-catenin activated reporter low BCBD β-catenin binding domain CAFs Cancer-associated fibroblasts CDD Collaboratory Drug Discovery CI Confidence Interval CI Combination Index CIDs PubChem Compound IDs CRC Colorectal cancer CRD Context-dependent regulatory domain CRD Cysteine-rich domain c-src Tyrosine-protein kinase Src CtBP C-terminal binding protein DAVID Database for Annotation, Visualization, and Integrated Discovery dN Dominant negative DVL Disheveled ECM Extracellular matrix EGFR Epithelial growth factor receptor EMT Epithelial-mesenchymal transition Fa Effect levels FAK Focal adhesion kinase fuBAR Found unresponsive BAR FZD Frizzled GEM Gemcitabine GEMMs Genetically engineered mouse models GLUT1 Glucose transporter SLC2A1 GO Gene Ontology GSEA Gene set enrichment analysis HCC Hepatocellular carcinoma HIER Heat-induced epitope retrieval HPDE Human pancreatic ductal epithelial cell line HR Hazard ratio IHC Immunohistochemistry IL-6 Interleukin 6 InChIKeys International Chemical Identifiers IPA Ingenuity pathway analysis viii

IPMN Intraductal papillary mucinous neoplasms KC Pdx1-Cre; LSL-KRasG12D KRT17 Keratin 17 LRP5/6 LRP5 or LRP6 co-receptor MAC Macrophage MAPK MAP kinase MCN Mucinous cystic neoplasms MCP Microenvironment Cell Population MEK MAPK Kinase MHC Major histocompatibility complex MMPs Matrix metalloproteinases mOS Median overall survival MSDCs Myeloid-derived suppressor cells MSI Microsatellite instability MSSR Molecular Screening Shared Resource NCI National Cancer Institute NGS Next-Generation Sequencing NK Natural killer (cells) NRF2 Nuclear factor erythroid 2 related factor 2 isoform 2 NSG NOD scid gamma PanIN Pancreatic intraepithelial neoplasia PDA Pancreatic Ductal Adenocarcinoma pN1 Positive lymph node PORCN Porcupine PSCs Pancreatic stellate cell pT Pathologic tumor stage QM Quasi-mesenchymal Rb Retinoblastoma RECIST Response evaluation criteria in solid tumors ROS Reactive oxygen species SAR Structure-activity relationship SDF Structure Database File shRNA Short-hairpin RNA siSPOTR siRNA Sequence Probability-of-Off-Targeting Reduction SPT Solid pseudopapillary tumor TCF/LEF T-cell factor/lymphoid enhancer factor TCGA The Cancer Genome Atlas TFBS Transcription Factor Binding Site TIL Tumor-infiltrating lymphocyte TLE Transducin-like enhancer of split TMA Tissue microarray Tregs T regulatory cells WLS Wntless WREs Wnt response elements

ACKNOWLEDGEMENTS

Thank you to all current and former members of the Dawson lab for their contributions to my work. Thank you to Dr. David Dawson for your mentorship and patience.

Thank you to our neighbors in the French and Donahue labs, especially Anthony Buzzanco and Dr. Andrew Nguyen for their support and scientific insight. Special thanks to Dr. Sam French for your mentorship, guidance, and levity.

Many thanks to Dr. Catie Grasso for your enthusiasm, collaboration and mentorship.

Thanks to Dr. Xinmin Li and Dr. Robert Damoiseaux for use of their core facilities and expertise.

Special thanks to Kristin Vallacher, Derek Jameson, CJ Brenton, Beth Bishop and Joe Carrier for empowering me to live my best life and keeping me sane.

Thank you to Woody, Ravey, Greg, Menace, Fakenews.Cameron and definitely NOT Sebas for all of your hard work and dedication. You made getting up every morning a lot easier.

Thank you to my TPE and LATC family, I would have been lost without you.

Very special thanks to my parents, Mikie Kershaw, and Jack Trinh for their enduring support during this process. It has truly meant more to me than I am capable of expressing, and it is to you that I owe any success that may come my way.

VITA EDUCATION

BS Molecular Biology 2010 Santa Clara University, Santa Clara CA

RESEARCH EXPERIENCE Graduate Student, David Dawson Laboratory, 2012-present UCLA Dept of Pathology and Lab Medicine • Examined the effect of differential TCF/LEF expression on tumor formation in pancreatic cancer • Tested genetic manipulations of TCF7L1 in a mouse orthotopic xenograft model • Identified transcriptional targets of TCF/LEF factors in pancreatic cancer with RNA-seq • Tested therapeutic agents on primary acinar cells harvested from murine pancreas • Performed high-throughput screening to discover small molecules affecting the Wnt pathway • Managed and mentored two undergraduate students in experimentation and data analysis

Research Associate, SRI International, Immunology group 2010-2012 • Developed a point of care device and assay for detection of biomarkers for radiation sickness • Assisted a multi-disciplinary team in performing an NCI60 high-throughput screen • Evaluated novel selective estrogen receptor modulators in a murine model of multiple sclerosis • Tested a novel therapeutic compound in a murine model of inflammatory bowel disease • Studied biomarkers in a murine model of rheumatoid arthritis

Research Assistant, Angel Islas Laboratory, 2009-2010 Santa Clara University Dept of Biology • Studied non-templated nucleotide addition of HIV Reverse Transcriptase

PAPERS Arensman MD, Nguyen P, Kershaw KM, Lay AR, Ostertag-Hill CA, Sherman MH, Downes M, Liddle C, Evans RM, Dawson DW. Calcipotriol targets LRP6 to inhibit Wnt signaling in pancreatic cancer. Mol Cancer Res. 2015; (11) 1509-19.

Arensman MD, Telesca D, Lay AR, Kershaw KM, Dawson DW. The CREB binding protein inhibitor ICG-001 suppresses pancreatic cancer growth. Mol Cancer Ther. 2014; (10) 2303-14.

Leoh LS, Morizono K, Kershaw KM, Chen IS, Penichet ML, Daniles-Wells RT. Gene Delivery in malignant B cells using the combination of lentiviruses conjugated to anti-transferrin receptor antibodies and an immunoglobulin promoter. J Gene Med 2014; (1-2) 11-27.

Alt C, Lam J, Harrison T, Kershaw KM, Samuelsson S, Toll L, D’Andrea A. Nociceptin/Orphanin FQ inhibition with SB612111 ameliorates dextran sodium sulfate-induced colitis. European Jour of Pharm. 2012; 683:285-293. xi

PRESENTATIONS

Kershaw KM, Ho BT, Vasquez MI, Lay AR, Dawson DW. Disruption of TCF7L1 mediated transcriptional repression promotes pancreatic tumorigenesis. AACR. 2017 April 1-5, Washington DC.

Kershaw KM, Ho BT, Vasquez MI, Lar AR, Dawson DW. Wnt signaling promotes a lipogenic gene program in pancreatic cancer through transcriptional derepression. CURE. 2017 March 17, Los Angeles, California.

Kershaw KM, Arensman MD, Ho BT, Lay AR, Dawson NA, Dawson DW. TCF7L1 constrains Wnt/ β-catenin signaling and tumor growth in pancreatic cancer. American Association for Cancer Research (AACR). 2015 April 17-21, New Orleans, Louisiana.

Kershaw KM, Arensman MD, Lay AR, Chien AJ, Dawson DW. TCF7L1 is a repressor of Wnt/β-catenin transcription and tumor growth in pancreatic adenocarcinoma. UCLA Department of Molecular and Medical Pharmacology Retreat. 2014 November 21-22, Huntington Beach, California.

Alt C, Kershaw KM, Lowell C, D’Andrea A. Hematopoietic Cell Kinase (Hck) modulates dendritic cell function. Federation of Clinical Immunology Societies (FOCis). 2012 June 20-23, Vancouver, British Columbia, Canada.

HONORS AND AWARDS Isabel and Harvey Kibel Fellowship 2012-2014 UCLA Eugene V. Cota-Robles Fellowship 2012-2014 Magna Cum Laude, Santa Clara University 2010 SCU Professor Francis Corrigan Scholarship 2007-2009 SCU T&M Carroll Scholarship 2007-2009 SCU Dean’s List 2007-2009 SCU Grant 2006-2007

xii

DIVERGENT REGULATION AND FUNCTION OF WNT/β-CATENIN SIGNALING AND TCF TRANSCRIPTION FACTORS IN PANCREATIC CANCER

INTRODUCTION

Pancreatic ductal adenocarcinoma (PDA) remains a disease of incompletely understood pathogenesis and near uniform lethality. Poised to become the 2nd leading cause of cancer death by 2030, an estimated 55,440 people are projected to be diagnosed with pancreatic cancer in 2018, nearly all of whom will die from the disease1,2. PDA has only

8-9% 5-year survival rates, which have improved by only single digits since the 1970s2.

Major clinical impediments include a typical late-stage diagnosis of most patients and a lack of robust systemic therapies.

The incidence of pancreatic cancer has been steadily rising, especially among men in western countries. While increased age, male gender, smoking, high fat diet, non-O blood group, African-American descent, chronic pancreatitis and diabetes mellitus are known risk factors3,4, obesity5, race6 and dietary consumption of sugary food and drink7 are also implicated. Familial inherited PDA only accounts for around 10% of new cases each year, thus, an aging population with increased obesity and shifting ethnicity are likely the main contributors to the spike in PDA incidence3,8. Risk factors such as obesity and diabetes are associated with dietary choices that are further correlated with increased PDA, including increased consumption of meat and sugary beverages7.

Behavioral factors such as smoking and alcohol consumption also increase the risk for

PDA and may be further exacerbated by molecular and genetic differences related to race or ethnicity6.

Due to the relatively modest association of known risk factors and a typical lengthy period of asymptomatic growth for PDA, most patients present with late stage or inoperable disease. A patient’s best chance for durable survival is surgical resection.

However, median survival time is only 20-22 months even in those patients who undergo complete surgical resection, pointing to the need for complementary forms of systemic therapy and more effective early detection. Unfortunately, even at the time of disease presentation, clinical symptoms are often generic and can be erroneously attributed to other causes. Presenting symptoms frequently include abdominal or mid- back pain, nausea, weight loss, fatigue or failure to thrive. Most efforts to develop preventative screening methods tend to focus on people in the known at-risk groups; however, the lack of sensitivity and specificity for established disease markers as well as low incidence of PDA in the general population continue to hinder these efforts.

Disappointingly, limited progress has been made to date in early detection despite recent significant research efforts in this area. Nevertheless, there is strong rationale to continue attempts to develop early detection method. Namely, computational models utilizing mutational profiles and relative proliferation rates indicate an average of 11.7 years pass between the initiating oncogenic event and the establishment of the parental clone that gives rise to the pancreas tumor, and 6.8 additional years pass before metastatic subclones are present9. Based on disease kinetics, this suggests a large

2 window of opportunity exists to effectively intervene in patients if robust early detection strategies can be developed and deployed.

Treatment of PDA.

For decades, the first line systemic treatment for PDA was single-agent chemotherapy, including fluorouracil (5-FU) or gemcitabine (GEM), nucleoside analogs that lead to inhibition of DNA replication. GEM or 5-FU remain the backbone of current standard of care or trial-based treatment approaches that employ combinations of further cytotoxic or targeted molecular agents. For example, GEM used in combination with the microtubule disassembly inhibitor nab-paclitaxel (aka Abraxane) was recently shown to modestly increase median overall survival over GEM alone (8.5 months versus 6.7 months) in the metastatic disease setting10. A second combinatorial therapy now widely used is FOLFIRINOX, which consists of 5-FU, folinic acid (to enhance the effects of 5-

FU), the topoisomerase inhibitor irinotecan, and the anti-neoplastic agent oxaliplatin.

FOLFIRINOX almost doubled median overall survival (mOS) relative to GEM alone

(11.1 versus 6.8 months)11 in metastatic PDA but with an associated higher toxicity that cannot be tolerated by all patients. Indeed, the tolerability of these cytotoxic regimens is a major barrier to their use in PDA patients who often already present with significant co-morbidities.

In the age of high-throughput sequencing that has comprehensively defined genetic mutations that are enriched in PDA12,13, molecular targeted therapies have garnered 3 significant interest. The first molecular targeted therapy approved for PDA was the epidermal growth factor receptor (EGFR) inhibitor erlotinib14, which was shown to increase mOS to 6.24 months versus 5.91 months for GEM alone in patients with locally advanced or metastatic disease without prior chemotherapy treatment, with the exception of GEM or 5-FU as sensitizing agents for radiotherapy. Only 53% of trial participants with available tumor sample were classified as EGFR positive, indicating that only a small subset of patients might actually derive benefit from erlotinib.

Additional targeted strategies for PDA are at various stages of preclinical or clinical development and include: growth factor inhibitors, angiogenesis inhibitors, tumor stroma inhibitors, and cancer stem cell inhibitors, as well as immune-targeting therapies and cancer vaccines15,16. While significant opportunity exists to develop effective targeted molecular or immune-based therapies, an improved understanding of the molecular genetics and signaling networks involved in PDA has the potential to immeasurably improve the use of these agents by better informing clinical decision-making regarding patient selection and anticipating intrinsic or evolving mechanisms of resistance.

PDA development and progression.

PDA is the most common form of pancreatic cancer and typically arises in the context of a well-described series of non-invasive precursor lesions involving either small or larger ducts. This stepwise progression of small duct dysplasia or pancreatic intraepithelial neoplasia (PanIN) has been studied in-depth in human disease and successfully recapitulated in genetically engineered mouse models (GEMMs)17–19. PanIN grade is

4 determined by the level of cytologic and architectural atypia and spans lesions from

PanIN-1 to PanIN-3 that frequently accompany full-blown PDA20. Dysplastic precursor lesions involving larger cystically-dilated pancreatic ducts include intraductal papillary mucinous neoplasms (IPMN) and mucinous cystic neoplasms (MCN)21. IPMNs arise in the main pancreatic duct or its branches and can obstruct the duct system by overgrowth or excessive production of mucin. MCNs do not directly arise or communicate with the pancreatic ductal system and are histologically defined by a mucinous epithelial lining and associated ovarian-type stroma, with much higher incidence (20:1) in women relative to men22,23. The progression of each of these precursor lesions has been shown to be accompanied by successive accumulation of key driver mutations, linking the histologic progression of PDA to its underlying molecular pathogenesis.

Molecular diversity is as synonymous with PDA as is lethality. In a 2008 study, Jones and colleagues performed one of the first of its kind global genetic analysis of 24 pancreatic cancers via exomic sequencing and reported pancreatic cancers average 63 highly heterogeneous genetic alterations12. Seeking common themes among these highly heterogeneous molecular alterations, they identified 12 core signaling pathways/processes that were genetically altered in some fashion across most of these

PDAs. This landmark study not only provided valuable insights into pancreatic tumorigenesis but highlighted the potential importance of personalized treatment approaches for this disease. In addition to the observed heterogeneity, they also described a set of high-frequency mutations that have been shown to be key molecular 5 drivers of PDA disease progression in both precursor lesions and full-blown cancer.

Activating mutations in KRAS are found in >90% of PDA and occur as an early initiating oncogenic event in most settings9,12,17,24. Loss or silencing of the tumor suppressor

CDK2NA (encoding the cell cycle regulator p16) is frequently seen as an intermediate step in dysplastic progression, while mutations in TP53 and SMAD4 occur in later stages of dysplastic progression. Subsequent whole genome sequencing studies with far larger sample numbers have reconfirmed the notion of mutations across these 12 core signaling pathways that are shared across most PDA13,25–28. It is clear this mutational landscape significantly contributes to the development and progression of disease. One example is in metastasis. In a seminal rapid autopsy study, nearly two- thirds of patients died from complications related to metastasis, while the other third died from complications related to local tumor burden. Metastatic efficiency was not tied to survival or any clinical feature; however, loss of SMAD4 highly correlated with widespread metastases and worse prognosis, exemplifying its importance in the biology of disease progression29.

Additional microarray and next-generation sequencing (NGS) transcriptomics-based analyses have provided important insights into the existence of discrete molecular subtypes of PDA that impact overall clinical behavior and patient survival13,30,31. First described by Collisson, et al, the classical subtype is defined by adhesion associated and epithelial gene expression with better prognosis. The quasi-mesenchymal (QM) subtype displayed worse survival and enrichment of mesenchymal gene expression.

Additional subtypes have been described, including those further elucidating differences in the transcriptional signature of associated stroma or inflammatory cells.

Cell of origin theories.

While the morphology of PDA is that of glandular or ductal differentiation, the cell of origin for PDA remains a subject of some controversy. Based on lineage-tracing studies, all cell types of the exocrine pancreas (i.e. acinar, centroacinar, ductal) have the capacity to give rise to PDA under the proper conditions32. One favored theory is that

PDA displays ductal morphology because it originates from acinar cells undergoing acinar-to-ductal metaplasia (ADM) in response to injury and in the context of key initiating oncogenic events (i.e., KRAS mutation). Normally, acinar cells will regenerate after injury by undergoing morphological and molecular changes that reactivate elements of embryonic development. In this process, they first become more stem-like or undifferentiated before differentiating back into acinar cells33–35. If ADM occurs in the context of concurrent oncogenic and/or epigenetic alterations, these acinar cells fail to differentiate into acinar cells and instead transdifferentiate towards a ductal morphology, as the first step of PanIN-to-PDA progression. Evidence also exists that ductal cells can give rise to PDA under specific conditions, for example in mature human ductal cells harboring oncogenic KRAS, loss of expression of the chromatin remodeler BRG1 promotes dedifferentiation of duct cells and formation of IPMNs36. Further PDA cell of origin possibilities include centroacinar cells, which are the only exocrine cells that retain Notch pathway activation in their adult state and have the capacity to

7 transdifferentiate into ductal cells and undergo dysplastic progression in the context of

PTEN mutation37. Finally, ill-defined stem cell populations in the pancreas have also been proposed to harbor tumor-initiating potential38. Ultimately, these hypotheses are not mutually exclusive and perhaps imply that various pancreas cell constituents harbor profound cellular plasticity and, in the appropriate context (i.e., oncogenic insult with or without cellular injury and inflammation), can serve as a cell of origin for PDA.

Mouse models of PDA.

While oncogenic KRAS mutation is widely considered the initiating event in almost all

PDAs, the first GEMMs expressing mutant Kras under control of acinar or ductal specific promoters failed to histologically recapitulate typical PDA. However, when a mouse employing a floxed Kras mutation at its endogenous locus was crossed with a mouse expressing CRE recombinase under control of pancreatic-specific promoters such as those regulating Pdx1 or Ptf1, this “KC mouse” developed classical PanIN, as well as PDA at low frequency after prolonged latency. Further crosses of KC mouse models with those harboring Tp53, Cdkn2a or various other mutations described in PDA result in rapid and fully penetrant PDA development. Results stemming from the development of these GEMMS have helped underscore the importance of the chronological order and strength of signaling necessary for PDA progression. For instance, modeling of the canonical arm of the Wnt/β-catenin signaling pathway in KC mice shows it regulates different phenotypes at various stages of tumor progression

(i.e., tumor initiation versus tumor maintenance).

The PDA stroma.

PDA tumor cells themselves are typically a minority of the cell population that makes up the bulk tumor. For instance, cancer-associated fibroblasts (CAFs) can frequently predominate. Once pancreatic stellate cells (PSCs) are activated to become CAFs they secrete copious amounts of collagen and extracellular matrix (ECM), resulting in a dense, desmoplastic stroma. PSCs and CAFs further regulate their own turnover and influence the extracellular microenvironment through secretion of matrix metalloproteinases (MMPs) and various cytokines. The desmoplastic reaction not only represents a mechanical barrier, but also a dynamic player in the tumor’s overall formation, progression, invasion and eventual metastases39. Dense stroma and poor vascularization contribute towards a hypoxic tumor microenvironment with poor perfusion that impedes drug delivery and immune cell infiltration. Intratumoral effector T- cells are rare, with most immune cells comprising suppressive T regulatory cells

(Tregs), polarized macrophages, and myeloid-derived suppressor cells (MSDCs). This has been shown to contribute to increased metastatic potential and worse prognosis40.

The study of the tumor microenvironment again highlights the importance of specific and sequential tuning of signaling pathways necessary for PDA progression, which is strikingly similar to how developmental pathways function in embryogenesis. It comes as no surprise then that several of the 12 core signaling pathways identified by Jones and colleagues12 are developmental pathways, including the canonical arm of the

Wnt/β-catenin signaling pathway.

The canonical Wnt/β-catenin signaling pathway.

Wnt receptor-ligand engagement can activate three different pathways: canonical

Wnt/β-catenin pathway, the non-canonical planar cell polarity (PCP) pathway and the

Wnt/Ca2+ pathway41. The canonical Wnt/β-catenin signaling pathway is an embryonic developmental pathway involved in the morphogenesis, proliferation, and differentiation of several organs, including the pancreas and is the focus of this dissertation. Genes in the pathway are highly conserved throughout evolution. While Wnt genes are lacking in single-cell organisms, they are present in sponge and sea anemone, suggesting the

Wnt pathway played a key role in the evolution of multicellular organisms42. An integral feature of Wnt/β-catenin signaling that sets it apart from other embryonic signaling pathways is its function as a directional growth factor, regulating both the shape and proliferation of growing tissues.

In the absence of Frizzled receptor (FZD) engagement by Wnt ligand, the scaffolding protein AXIN1, along with Adenomatous Polyposis Coli (APC), β-catenin, and the constitutively active serine/threonine kinases CK1α and GSK3β form a multi-protein complex in the cytosol referred to as the destruction complex. CK1α and GSK3β sequentially phosphorylate β-catenin bound by AXIN1, creating a docking site for β-

TrCP, part of an E3 ubiquitin ligase complex. This complex then ubiquitinates β-catenin, targeting it for subsequent degradation by the proteasome. This process tightly

10 regulates free levels of β-catenin to prevent its accumulation in the cytosol and subsequent translocation to the nucleus.

There are 19 Wnt ligands encoded by the human genome. Wnts are small proteins that must be palmitoylated by Porcupine (PORCN) in the endoplasmic reticulum to promote their secretion. Once Wnts are palmitoylated they can be bound by Wntless (WLS), which shepherds them through the Golgi for secretion at the plasma membrane via secretory vesicles or exosomes. Wnts are thought to primarily act at close range either in an autocrine, juxtacrine or paracrine manner. For signaling initiated through the canonical Wnt pathway, Wnt ligands bind to a heterodimeric complex that includes FZD and LRP5 or LRP6 co-receptor (hereafter collectively referred to as LRP5/6). There are

10 known FZD receptors in the human genome, each of which has a large N-terminal cysteine-rich domain (CRD) with multiple binding surfaces, including a hydrophobic groove capable of binding lipid-modified Wnt ligand43. The CRD-binding domain imparts upon each Wnt ligand the potential capacity to bind to each FZD receptor, and while there are cell or tissue-specific affinities for a particular Wnt ligand-FZD receptor interaction, there is also a degree of promiscuity. Wnt ligand engagement leads to a conformational change in the FZD-LRP5/6 complex and its progressive phosphorylation by CK1α and GSK3β. Upon LRP5/6 phosphorylation the cytoplasmic tail of LRP5/6 can recruit AXIN1, thus relocating the destruction complex to the cell membrane or membrane-bound vesicles. LRP5/6 interaction with AXIN1 is further facilitated by members of the Disheveled (DVL) family, which binds the cytoplasmic tail of FZD.

DVLs contains a DIX domain that dimerizes with a similar domain on AXIN1. Once the 11 destruction complex is bound in this altered conformational state, further phosphorylation of β-catenin is blocked. This allows newly synthesized β-catenin to freely accumulate in the cytoplasm and subsequently translocate to the nucleus where target gene expression then occurs. Importantly, β-catenin itself has no intrinsic transcriptional activity but binds co-activators and transcription factors, most notably one of four T-cell factor/lymphoid enhancer factor (TCF/LEF) transcription factors.

The TCF/LEF transcription factor family.

TCF/LEF proteins contain an N-terminal β-catenin binding domain (BCBD), context- dependent regulatory domain (CRD) and highly conserved high mobility group (HMG)

DNA binding domain that binds consensus sequences in the DNA referred to as Wnt response elements (WREs). Despite their similarities, each of the four members of the

TCF/LEF family—which includes LEF1, TCF7 (formerly TCF1), TCF7L1 (formerly

TCF3) and TCF7L2 (formerly TCF4)—have differences in function that are dictated by variations in their structural domains and the context in which they are expressed. All

TCFs can bind DNA in the absence of β-catenin and inhibit transcription. However,

TCF7L1 and TCF7L2 further interact with the transcriptional repressors Groucho and transducin-like enhancer of split (TLE)44,45 at much higher affinities than LEF1 or

TCF746. Thus, while not absolute, TCF7L1 and TCF7L2 biological effects have been more frequently linked to their ability to act as strong transcriptional repressors, while

LEF1 and TCF7 effects, by comparison, are more generally derived from their activation of transcription when bound by β-catenin 47–51.

Structural diversity in the multitude of known TCF isoforms confers complexity to their transcriptional effects either in the presence or absence of canonical Wnt signaling. For instance, naturally occurring dominant negative (dN) TCFs lacking the BCBD have been described which can compete with wild-type TCFs for DNA binding at specific chromatin loci and thus inhibit transcription despite activated Wnt signaling52–55. Further structural complexity arises from differential splicing and the use of alternative transcriptional start sites for certain TCFs, which confer divergent biochemical activity to these isoforms. For example, certain TCF7L1 and TCF7L2 isoforms possess a C-terminal binding protein

(CtBP) binding domain56. CtBP binds to NADH, linking its regulation of gene expression and cellular redox state57. CtBP is overexpressed in several solid tumors and has been ascribed pro-tumorigenic roles56,58. Hence, the ability of specific TCF isoforms to differentially engage CtBP can dictate the interplay between cellular redox status and

Wnt signaling via context-dependent changes in TCF isoform expression. Furthermore,

TCF7 and TCF7L2 possess an additional DNA binding domain called the C-clamp, composed of four highly conserved cysteine residues59 in their C-terminal region and the site of greatest heterogeneity between individual TCF isoforms. This motif has been shown to bind sequence-specific helper sites in the regulatory regions of important Wnt target genes, including LEF1 and MYC. Mutations at these helper sites leads to the inhibition of transcription of these genes upon Wnt activation60–65. The C-clamp in combination with the HMG domain creates bipartite DNA recognition which enhances the specificity of binding of individual TCFs to target DNA. Thus, the diversity of function and expression for TCFs dictates significant context-dependent transcriptional and

13 phenotypic outcomes both in the absence and presence of Wnt activation. To summarize, these sundry differences in TCFs add further diversity to an already highly complex signaling pathway that is comprised of 19 WNT ligands and 10 FZD receptors.

Even this does not begin to address the extent to which Wnt activity is further regulated via cross-talk with other signaling pathways or linked to further diverse transcriptional outcomes dictated by the interaction of β-catenin with DNA binding factors other than

TCFs.

Wnt/β-catenin signaling in development and cancer.

It is clear that canonical Wnt signaling has evolved the capacity to mediate a panoply of biological outputs essential for proper embryonic development, as well as maintenance of adult somatic tissues. These effects are crucially dependent on the precise spatial and temporal regulation of the Wnt pathway that when dysregulated leads to pathologic disease states, including cancer. Embryonic development of the pancreas provides an example of the need for precise regulation. In the specification stage of organ development, aberrant expression of activated β-catenin results in a hypoplastic pancreas lacking normal endocrine and exocrine compartments, implying Wnt signaling at this stage impedes proper specification66. Wnt/β-catenin signaling also appears to be essential for the subsequent expansion stage of pancreas development. β-catenin knockout in the early embryonic expansion phase results in reduced organ size. A concurrent reduction in TCF/LEF target gene expression and a lack of defects involving intercellular adhesion suggests this reduced growth results from the loss of canonical

Wnt/β-catenin signaling67,68. Meanwhile, inappropriate activation of β-catenin at a later stage of expansion utilizing a different Cre driver leads to a grossly enlarged pancreas with histologically normal-appearing pancreas cells. At one year of age, these transgenic mice have a 4.6-fold larger pancreas compared to their wild-type littermates, despite having similar body masses. Strong nuclear β-catenin staining in the exocrine compartment of these mice links the overgrowth to active Wnt/β-catenin signaling.

However, while enlarged, these pancreata showed no sign of neoplasia66. Furthermore, in the adult pancreas, Wnt/β-catenin signaling is dormant and only becomes activated in the context of acinar cell regeneration following injury due to pancreatitis or partial pancreatectomy.

WNT1, was first described as an oncogene in 198269. The importance of Wnt/β-catenin signaling as a key oncogenic signaling pathway garnered significant interest after inactivating mutations in APC were shown to be causal for familial adenomatosis polyposis syndrome70. Subsequent work demonstrated that APC bound β-catenin as part of the destruction complex71. APC mutations were later confirmed first by targeted sequencing and subsequent global exome sequencing to also be present in a majority of spontaneous colorectal cancers72. Further mutations in APC and other key Wnt pathway members (including β-catenin) are found not only in colorectal cancer, but also a variety of other solid tumors, including hepatocellular carcinoma, melanoma, and breast cancer to name a few73,74. Meanwhile, the importance of Wnt signaling and its association with PDA remained elusive. While genetic alterations in the pathway are found in 5-15% of PDA patients13, they are not the typical mutations found in colorectal 15 cancer or other tumors that lead to Wnt pathway hyperactivation. Nevertheless, histopathologic analysis of patient tumors suggests the Wnt pathway might be of importance in PDA. Between 10-60% of pancreas tumors exhibit strong nuclear staining of β-catenin, a widely used surrogate marker of Wnt pathway activation24,75.

More recent work with GEMMs has provided greater clarity in our understanding of the importance of Wnt signaling in PDA. In wild-type mice, β-catenin signaling is activated during acinar regeneration in response to experimentally-induced pancreatitis. In mice expressing Pdx-1 driven Cre recombinase and floxed oncogenic Kras, β-catenin signaling appears to be blocked at the analogous time during ADM. Mice engineered to express both oncogenic Kras and an activated form of β-catenin fail to undergo progression from ADM to PanIN lesions following induction of pancreatitis, and instead give rise to a tumor resembling human solid pseudopapillary tumor (SPT). SPT is morphologically and clinically distinct from PDA and is notable for the near ubiquitous presence of a mutation in CTNNB1 that encodes a constitutively active form of β- catenin76. This observation reinforces the concept that Wnt signaling must be precisely regulated in a spatiotemporal manner and at specific signaling strength for particular transcriptional and phenotype outcomes (i.e., PDA tumor initiation). For example, in the context of oncogenic Kras, only a low threshold of β-catenin signaling can be tolerated by pancreatic cells undergoing transdifferentiation from acinar cells to the ductal phenotype of PanIN, the dysplastic precursor for PDA. Thus, it is not surprising that classical Wnt mutations that result in pathway hyperactivation are not oncogenic drivers

16 in PDA. When they are found, they presumably have occurred at later stages of pre- malignant or malignant progression.

While insufficient to induce PDA on its own, activated Wnt/β-catenin signaling has been shown to be critical for tumor initiation. Zhang and colleagues created a KC mouse with inducible Dkk177, a naturally-occurring secreted inhibitor of Wnt signaling that promotes the endocytosis of LRP5/6 co-receptors to effectively stall Wnt-ligand stimulated signaling activity. If Dkk1 was induced prior to the formation of PanINs, mice developed fewer PanINs compared to their age-matched KC counterparts. This was seen in conjunction with biochemical and transcriptional evidence that the Wnt pathway was significantly inhibited by. In contrast, if Dkk1 was induced at a time point past the initial formation of PanINs, there was an equal number of PanINs relative to controls77. These

Dkk1 results were phenocopied via the delivery of a Wnt inhibitor (FZD antibody OMP-

18R5) currently being evaluated in clinical trials for PDA and other solid tumors. The ability of Dkk1 to block Wnt activation and inhibit initial PanIN formation indicates that a certain level of Wnt/β-catenin signaling is required to drive early tumorigenesis (i.e., at

ADM-PanIN) in the context of oncogenic Kras. Interestingly, this level of Wnt activity still needs to be tightly controlled. If Wnt levels are too high, ADM-PanIN progression is also blocked.

The capacity of the FZD antibody OMP-18R5 to similarly inhibit murine PanIN formation highlights the potential utility of pharmacologically targeting Wnt for PDA treatment or

17 chemoprevention. While the Wnt pathway offers ample opportunities for targeting at various steps, the development of potential therapies has only occurred in the past decade. While disruption of the TCF/β-catenin complex would be an ideal downstream target, the large binding surface makes this difficult. Likewise, tankyrase inhibitors that stabilize AXIN1 and increase degradation of β-catenin have proven to be only modestly effective in preclinical studies. Perhaps the most promising class of Wnt pathway compounds are PORCN inhibitors, which block the Wnt pathway at the level of Wnt ligand secretion. Interest in PORCN inhibitors has been raised by recent studies showing tumors with R-spondin (RSPO) fusions or RNF43 mutations are highly responsive to these class of compounds78. RNF43 is a transmembrane E3 ubiquitin ligase that targets FZD receptors for degradation and thus functionally represses Wnt signaling at the level of the cell membrane. RNF43 itself is also targeted for degradation when R-spondins bind to LGR4/5/6 receptors, with stabilization of FZD at the membrane explaining the Wnt potentiating effects of R-spondins79,80. An important caveat is that RNF43 is also an important downstream target of the Wnt pathway, so its expression and activity will predictably decrease following inhibition of Wnt ligand secretion. Indeed, there are several downstream Wnt targets that function to inhibit Wnt signaling activity. These complex mechanisms of Wnt feedback control present a particular challenge for pharmacologic approaches with the goal of long-term Wnt pathway inhibition. In actuality, a successful Wnt-targeting therapy for PDA or other cancer will likely depend on combinatorial approaches tailored not only to specific subsets of tumors (i.e. those with inactivating mutations in RNF43) but also able to

18 overcome this feedback regulation as a potential mechanism of intrinsic or acquired drug resistance.

The goal of this dissertation is to better understand the regulation and function of Wnt/β- catenin pathway effectors in PDA, with a particular focus on the importance of TCF/LEF family members in mediating the diversity of Wnt effects seen across a spectrum of patient tumors. This work introduces and offers functional insights into a novel group of prognostic and predictive clinical biomarkers that could ultimately be used to stratify disease risk and response to novel classes of Wnt inhibitors now being explored in preclinical models and early clinical trials. Distinct patterns of TCF/LEF expression are shown to be useful in defining levels of Wnt pathway activation, function, and targetability across the spectrum of PDA. While considerable research and therapeutic development remain to be accomplished, data presented here point to the feasibility of identifying and targeting subsets of PDA defined by their growth dependency on autocrine or paracrine Wnt/β-catenin signaling.

MATERIALS AND METHODS

Cell Lines and Reagents

AsPC-1, HPAF-2, MiaPaCa-2, PANC1, L cell, and L Wnt-3A cell lines were obtained from the American Type Culture Collection (ATCC, Rockville, MD). HPDE, an immortalized, non-transformed pancreatic ductal epithelial cell line was provided by Dr.

Ming-Sound Tsao (Princess Margaret Hospital, Toronto, Canada). All other PDA cell lines were provided by Dr. Eric Collisson (University of California, San Francisco). All

PDA lines were authenticated by Short Tandem Repeat testing (Laragen, Culver City,

CA). Conditioned media from L cell and L Wnt-3A cell lines were prepared as previously described81. All cell lines were maintained in DMEM + 10% fetal bovine serum (FBS) with the exception of AsPC-1 which was maintained in RPMI + 10% FBS. The GSK3β inhibitor CHIR-99021 (Tocris, Minneapolis, MN) was used at 5µM.

In vitro phenotypic assays

Anchorage-dependent growth assays were performed by plating of 2,500 cells per well in 96-well format in standard growth media with cell viability measured at 72-96 hours by MTT (ATCC, Manassas, VA) or CellTiter-Glo 2D (Promega, Madison, WI) per manufacturers’ instructions. For clonogenic assays, cells were plated at 500 cells/well

(24-well plates) as dispersed single cell suspensions in DMEM + 5% FBS. After 7-9 days colonies were fixed in 1% formalin for 5 minutes and then stained with 0.05%

Crystal Violet in 20% methanol for 1 hour on a shaker. Colonies were counted using

Image J software (version 1.48v). Anchorage-independent growth assays in liquid suspension cultures were performed in standard growth media with either 10% or 1%

20 serum using 96-well round bottom ultra-low attachment spheroid microplates (Corning,

Corning, NY). Cell viability was measured at 72 hours using CellTiter-Glo 3D assays per manufacturer’s instructions (Promega, Madison, WI). For soft agar assays, MiaPaCa-2 or PANC1 were introduced at 5,000 or 3,000 cells per well (24-well plates) as dispersed single cell suspensions in 0.4% Noble Agar overlaid on a base of 0.8% Noble Agar.

Media (DMEM + 20% fetal bovine serum) was replaced twice weekly. Colonies were visualized by staining with MTT and counted at 14-21 days. For wound healing assays, a pipette tip was used to create a uniform-width scratch on confluent cell monolayers.

Cells were cultured in standard media further supplement with L cell control or Wnt3A conditioned media at a 1:1 ratio. The percentage area of wound closure was measured at 48 hours using Image J software.

Gene knockdown and overexpression

For transient gene knockdown, cell lines were transfected with 1-20 nM siRNA using

Lipofectamine 2000 (ThermoFisher Scientific, Grand Island, NY) per manufacturer’s instructions. Virus was produced by transfecting 293T cells with 0.5 µg pSL3, 3.8 µg pSL4, 1.9 µg pSL5 and 1.9 µg of transducing vector with X-tremeGENE 9 DNA transfection reagent (Sigma, St. Louis, MO). Initial plate was split into two and media was collected every other day for four days, concentrated, and aliquoted for single use.

For stable gene knockdown, cells were infected with lentiviral short-hairpin RNA

(shRNA) to TCF7L1, TCF7L2, or a universal negative control sequence present in the

21 pLKO.1 vector backbone. Significant and stable shRNA gene knockdown of TCF7L1 was achieved in short and long-term cultures without drug selection. TCF7L2 hairpins were selected with 1 µg/ml puromycin until all mock cells had died. Efficiency of all knockdowns was confirmed by qPCR and western blot at initial infection and periodically after that.

For stable TCF7L1 overexpression full-length TCF7L1 was cloned into the pLPCX vector, kindly provided by Dr. Samuel French (University of California, Los Angeles).

Virus was produced as detailed above. Target cells were then infected and selected with 1 µg/ml puromycin until all mock cells had died. RNAi target sequences and their sources are provided in Methods Table 1.

Real-time PCR

RNA extraction, cDNA synthesis and SYBR green-based quantitative PCR with ABI-

Prism 7700 sequence detector were performed as previously described81. All gene expression values were normalized to ACTB housekeeping gene. Primer sequences are provided in Methods Table 1.

Luciferase reporter assays

Dual luciferase reporter assays (Promega) to measure β-catenin/TCF transcriptional activity were performed as previously described81. For these reporter assays, cell lines were either transiently co-transfected with reporter plasmids or stably transduced with corresponding lentiviral constructs. These constructs included a control Renilla reporter under constitutive EF1α promoter and BAR (β-catenin activated reporter, an optimized luciferase reporter with a concatemer of twelve TCF response elements) or fuBAR

(found unresponsive BAR, identical to BAR but with mutated TCF binding sites rendering it unresponsive to Wnt activation)82.

Immunoblots

Triton X-100 whole-cell lysates or subcellular fractions generated with NE-PER extraction kit (Thermo Fisher) were resolved by SDS-PAGE and transferred to nitrocellulose membranes. Membranes were blocked with 10% nonfat milk in 1%

Tween-TBS and incubated overnight at 4°C with primary antibodies, including: 1:70,000

β-catenin (Sigma-Aldrich, St. Louis, MO, #C2206), 1:1000 non-phospho (Active) β- catenin (Ser45) (Cell Signaling Technologies, Danvers, MA, CST #19807), 1:1000

TCF7 (CST #2203), 1:1000 TCF7L1 (CST #2883), 1:1000 TCF7L2 (CST #2569), 1:500

LEF1 (CST #2230), and 1:5000 ACTB (CST #8457). ECL Plus detection (GE

Healthcare Biosciences, Piscataway, NJ) was performed after 1-hour incubation with

1:5000 horseradish peroxidase (HRP)-linked secondary antibodies (CST #7074).

Immunohistochemistry (IHC)

All patient tissue and data on the PDA tissue microarray (TMA) were obtained and evaluated with prior institutional review board approval. The TMA has been previously described83 and is comprised of three separate 1 mm cores taken from each tumor. All tumors on the TMA are treatment-naïve, AJCC Stage I or II PDA resected at UCLA

Medical Center between 1991-2005. Following heat-induced epitope retrieval (HIER) in a vegetable steamer (10mM Tris-EDTA, pH 8.0), TMA sections were incubated overnight at 4°C with primary antibody, including: 1:100 anti-TCF7 (CST #2203), 1:250 anti-TCF7L1 (EMD Millipore, Billerica, MA, #ABE414) or 1:100 anti-TCF7L2 (Abcam,

Cambridge, MA, #76151). For β-catenin, HIER was performed in 10 mM sodium citrate, pH 6.0 followed by a two-hour incubation with 1:300 anti-β-catenin antibody (Sigma

C2206) at room temperature. The Envision+ Dual Link System-HRP (K4063, DAKO,

Carpenteria, CA) was used for visualization. Only those patients in which sufficient tumor was available in cores to be evaluated by each of the primary antibodies were included in the final analysis (N=133). Nuclear staining for each core was scored by two practicing surgical pathologists using a semi-quantitative nuclear histoscore (range 0-

300) representing the product of nuclear staining intensity (0-absent, 1-weak, 2- moderate, 3-strong) and percent tumor nuclei (0-100%). The median histoscore value for each patient tumor was used for all subsequent analysis, with patient tumors for each TCF dichotomized into groups for either low (histoscore ≤ 100) or high (histoscore

> 100). β-catenin was dichotomized into groups with either absent/low (histoscore < 30) or high (histoscore ≥ 30).

Microarray and RNA-sequencing for gene expression analyses

Supervised analysis of microarrays (SAM) was performed on a previously published31

Affymetrix U133 plus 2.0 oligonucleotide microarray dataset of PDA cell lines

(GSE17891) using the dChip Analysis software package84 with default parameters

(invariant set normalization with signal intensity summarized using the model-based expression index algorithm with mismatch probe option for background subtraction).

Initial gene filtering criteria was set at >10% present call and variance across samples of

0.4 < standard deviation/mean < 1,000. Differentially regulated genes were determined by comparative analysis of BAR-H versus BAR-L cell lines (selection cut-off: >3-fold difference and P < 0.05). For comparison of gene expression changes seen with

MiaPaCa-2 shCTRL versus shTCF7L1, RNA from two sets of biological replicates were provided to the UCLA Technology Center for Genomics and Bioinformatics who processed samples and hybridized to Affymetrix U133 plus 2.0 oligonucleotide microarrays. Microarray data were analyzed as described above with dChip software package to identify genes with absolute change >75 and P <0.05.

For RNA-seq analysis, total RNA was isolated from three biological replicates each of

MiaPaCa-2 or PANC1 cells transfected for 48 hours with control or TCF7L1 siRNA, or three biological replicates of AsPC-1 cells transfected for 48 hours with control, TCF7L2 or TCF7 siRNA. RNA was submitted to the UCLA Technology Center for Genomics and

Bioinformatics for sequencing. RNA sequencing libraries were constructed using the

KAPA Stranded mRNA kit then sequenced on an Illumina HiSeq3000 using the

HiSeq3000/4000 SBS kit (50 cycles). Raw reads were aligned to the UCSC Human

Genome hg19 using the Bowtie2 aligner (version 2.2.9). Read count was then performed by RSEM (version 1.2.25) with differential expression analysis performed with the edgeR Bioconductor package. Pathway and network analysis was performed using Ingenuity Pathway Analysis (IPA) software which computes a score for each network according to the fit of genes showing altered expression. For TCF7L2 splicing analysis, RNA was first prepared for paired-end sequencing, which included mRNA enrichment, cDNA generation, and end repair to generate blunt ends, A-tailing, adaptor ligation and PCR amplification. Paired-end sequencing was performed on Illumina

Hiseq3000. Reads were mapped to the latest UCSC transcript set using Bowtie2

(version 2.1.0) and gene expression levels were estimated using RSEM (version

1.2.15). Trimmed mean of M-values (TMM) was used to normalize the gene expression.

Sashimi plots were used to quantitatively visualize splice junctions for all exons in

TCF7L2 sequence reads.

Gene set enrichment analysis (GSEA, v2.2.3), a computational approach for establishing significant enrichment using pre-defined sets of genes or pathways85, was used to identify biological processes enriched upon TCF7L1 knockdown. GSEA was performed on ranked lists of differentially regulated genes using the curated set of canonical pathways (C2: CP.V5.2, 1329 gene sets) available from the Molecular

Signatures Database along with the GseaPreranked tool. Default parameters were used except for selection of classic enrichment statistic and minimum gene set of 10. GSEA- enriched gene sets (P<0.05, FDR<0.05 based on 1000 permutations) were subsequently evaluated with the GSEA leading-edge analysis tool.

KEGG, Gene Ontology (GO) and Transcription Factor Binding Site (TFBS) functional enrichment was examined using default parameters in the web-based Database for

Annotation, Visualization, and Integrated Discovery (DAVID version 6.7)86,87

(http://david.abcc.ncifcrf.gov), to identify significantly enriched biological pathways or upstream regulators upon TCF7L1, TCF7L2 or TCF7 knockdown.

Pancreatic Cancer TCGA Analysis

Gene expression and somatic mutation data for pancreatic cancer were downloaded from the TCGA. The RNA-seq gene expression data contain upper-quartile- normalized and log2-transformed RNA-seq by expectation maximization (RSEM) values summarized at gene level88. The whole-exome sequencing mutation data contains somatic mutation calls for each subject. A total of 182 pancreatic cancer sampled were analyzed. For clustering of non-T-cell infiltrated and T-cell infiltrated tumors, unsupervised hierarchical clustering of the genes was performed, clusters containing the 13 known T-cell signature transcripts (CD8A, CCL2, CCL3, CCL4,

CXCL9, CXCL10, ICOS, GZMK, IRF1, HLA-DMA, HLA-DMB, HLA-DOA, HLA-DOB) were selected for resampling-based hierarchical clustering. Genes differentially expressed between the non-T-cell infiltrated and T-cell infiltrated groups were detected using ANOVA.

Small molecule screen

A high-throughput assay was performed to screen for novel inhibitors of the Wnt/β- catenin signaling pathway as measured by BAR. This screen was optimized and performed in the UCLA Molecular Screening Shared Resource (MSSR) core facility. For the screen 25 µl of growth media (RPMI + 10% FBS) per well was dispensed into 384- well microtiter plates (Greiner One, Kremsmünster, Austria) using a Multidrop 384 dispenser (Thermo Lab Systems, Waltham, MA), followed by the addition of 0.25 µl of screen compounds dissolved in 0.5% DMSO for a final concentration of 5 µM using the

Biomek FX (Beckman Coulter, Brea, CA). Next, 25 µl of AsPC-1 stably transduced with the BAR were plated at a final concentration of 3900 cells per well using the Multidrop

384. Plates were first incubated at room temperature for 45 minutes followed by 48 hours incubation at 37°C + 5% CO2. For the final 15 minutes of incubation, 20 µl per well of 20 µg/ml Hoechst stain (Fisher, H1399) was added to each well. Imaging by the

ImageXpress Confocal (Molecular Devices, San Jose, CA) was performed to determine the number of cells as determined by Hoechst stain. Next, effects on Wnt reporter were quantified by adding 10 µl of room temperature BrightGlo (Promega, Madison, WI) diluted 1:2 per well for 2 minutes followed by luciferase measurements using the GT

Analyst Microplate reader (Molecular Devices). A compound was designated a hit in this primary screen if luminescence (normalized to Hoescht stain) was at least 3 Z-scores below the median.

Structure-Activity Relationship Analysis

Compound data from hits in the small molecule screen, including a list of physiochemical properties and identifiers, was downloaded from the UCLA Molecular

Screening Shared Resource (MSSR) Collaboratory Drug Discovery (CDD) database.

International Chemical Identifiers (InChIKeys) were submitted to the NIH Chemical

Identifier Exchange service to return a list of PubChem Compound IDs (CID) to be used for subsequent analysis of the PubChem database. Compounds failing to return a CID through this method were manually screened using other identifying information (i.e.

SMILES, IUPAC name, common name). The generated list of CIDs was then submitted to the PubChem BioAssay database

(https://pubchem.ncbi.nlm.nih.gov/assay/assay.cgi). Hits that returned as “Active” in luciferase inhibitor assays were removed from the list for further downstream in silico analysis, including the identification of predicted protein targets. In addition to this structure-activity analysis (SAR), hierarchical clustering was performed through JKlustor

(ChemAxon, Budapest, Hungary) using compound structures downloaded as a

Structure Database File (SDF) from the MSSR CDD.

Subcutaneous Xenograft Tumor Assay

Animal work was performed with oversight by the UCLA Division of Laboratory Animal

Medicine and prior approval from the UCLA Animal Research Committee. Xenograft tumors were generated by subcutaneously injecting tumor cells (5 x 105) in a suspension of 100 µl DMEM and 30% Matrigel (BD Biosciences, Franklin Lakes, NJ) into hind flanks of age-matched, NOD scid gamma (NSG) mice. Tumor sizes were

29 measured daily by calipers. All animals were sacrificed at 45 days, at which time primary tumors were excised, measured, processed and histologically analyzed by a surgical pathologist (DWD) blinded to sample identities.

Statistics

Statistical analyses were performed using SPSS 23.0 (IBM, Armonk, NY) and

GraphPad Prism 5.0 software packages. Unless otherwise indicated, all data are presented as the mean ± SEM of 3 biological repeats of one representative experiment, with experiments repeated a minimum of 2-3 times. Student t-tests were used to evaluate continuous variables, and, unless otherwise specified. Spearman rank-order correlations were used to evaluate associations between nuclear expression of TCFs and β-catenin on the PDA TMA. Pearson correlations were used to evaluate associations between gene expression and T-cell average. Chi-square tests were used to evaluate dichotomized patient groups in relation to clinical variables. Kaplan-Meier survival curves were evaluated using log-rank tests. Univariate Cox regression was performed to evaluate the prognostic significance of individual variables, while multivariate Cox regression analysis was performed in a stepwise fashion with backward selection using the Akaike Information Criterion. The level of significance for all statistical tests was defined as α=0.05.

Table M-1. Methods Table 1 qPCR Primer Sequences GENE Primer Sequence 5' → 3' ACACA FOR GCCTGACTTTTGATCCGACC REV TCCTCTCATCATTGCGCCTC ACTB FOR CCAACCGCGAGAAGATGA REV CCAGAGGCGTACAGGGATAG AXIN2 FOR GAAGCTGGAGTTGGAGAGCCGCC REV TCTCTCTTCATCCTCTCGGATCTGC CDK6 FOR TGCAGGGAAAGAAAAGTCCAATG REV TCCTCGAAGCGAAGTCCTCA FASN FOR TCGTGTTGACTTCTCGCTCC REV AAGCCGTAGTTGCTCTGTCC LEF1 FOR AGGTACAGGTCCAAGAATGACAGCT REV GCTGCCTTGGCTTTGCACGTTG SREBF1 FOR CGGCTTCAAAAATCCGCCG REV GGTGGGTCAAATAGGCCAGG TCF7 FOR AGCCAAGGTCATTGCAGAGT REV GTGGTGGATTCTTGGTGCTT TCF7L1 FOR TCGAGAAGAACAGGCCAAGT REV TGGGACAGCTGCTTTTCTCT TCF7L2 FOR AGCAAAGGTCGTAGCTGAGTG

RNAi Sequences

GENE TARGET siRNA Vendor (catalog number) TARGET SEQUENCE 5' → 3' CTNNB1 Ambion (s437) GGAUGUUCACAACCGAAUUTT FASN Ambion (s5030) GGUAUGCGACGGGAAAGUATT LEF1 Integrated DNA Technologies AAGUGCAGCUAUCAACCAGAUUCTT TCF7 #1 Integrated DNA Technologies GGAGAAGCUCUGUUUAUAAAAACAA TCF7 #2 Integrated DNA Technologies GAAAAAGAAAUGCAUUCGGUACUTA TCF7 #3 Integrated DNA Technologies CAUGUACAAAGAGACCGUCUACUCC TCF7L1 Ambion (s37905) CAGUCACCGUGAAAAAGGATT TCF7L1 Ambion (s37904) GGACAACUAUGGUAAGAATT TCF7L1 #1 Integrated DNA Technologies CCAGCACACUUGUCUAAUAAAGUTC TCF7L1 #8 Integrated DNA Technologies GGACAGCGCGUUCUUUAAAGGACCC TCF7L2 #1 Integrated DNA Technologies AACCUCAUGAUUCUACCAAAAUUTT TCF7L2 #2 Integrated DNA Technologies GAAUCAGAAACGAAUCAAAACAGCT TCF7L2 #3 Integrated DNA Technologies GAAGAAGCCCCACAUAAAGAAACCT Control Integrated DNA Technologies CGUUAAUCGCGUAUAAUACGCGUAT 31

shRNA Thermo Fisher TCF7L1 05 (TRCN0000021705) TTTATTAGACAAGTGTGCTGG Thermo Fisher TCF7L1 06 (TRCN0000021706) CCTTGGAAGAAAGTGGCACAA Thermo Fisher TCF7L1 07 (TRCN0000021707) TGAAGGAAAGTGCAGCCATTA Thermo Fisher TCF7L1 08 (TRCN0000021708) GATCGATCCAAAGACAGGAAT Themo Fisher TCF7L2 NTERM (TRCN0000262844) GCGCCAACGACGAACTGATTT Themo Fisher TCF7L2 EX10 (TRCN0000262847) CCTTTCACTTCCTCCGATTAC Themo Fisher TCF7L2 CTERM (TRCN0000262851) CCATGTGGCTACATTAGTTGA Control - CAACAAGATGAAGAGCACCAA

CHAPTER ONE: TCF7L1 IS A SUPPRESSOR OF WNT SIGNALING AND

PROMOTER OF TUMOR GROWTH IN PANCREATIC CANCER

INTRODUCTION

With an overall five-year survival rate of only 8-9%, pancreatic ductal adenocarcinoma

(PDA) now accounts for over 40,000 deaths per year in the United States1. While the current standard of care offers only modest benefit to most patients, there is hope more effective therapies will evolve from our growing understanding of the molecular and cellular underpinnings of PDA2. One therapeutic target under investigation in preclinical studies and early clinical trials is Wnt signaling, one of a small number of core pathways shown to be genetically altered in most PDA in a seminal exomic sequencing study published in 200812. A more recent study of over 450 pancreatic cancers comprehensively integrating whole-genome and deep-exome sequencing with mutation functional interaction sub-network analysis further validated Wnt to be one of the ten most highly enriched pathways altered in PDA13. Of particular clinical interest is a subset of PDA with inactivating mutations in RNF4313, a RING-type E3 ubiquitin ligase that negatively regulates Wnt signaling through its ubiquitination of Frizzled (FZD) receptors. RNF43 inactivation in PDA lines was recently shown to predict their growth dependency on Wnt and therapeutic response to LGK97478,89, an inhibitor of Wnt ligand secretion currently in clinical trials.

Essential for proper embryonic development and adult tissue homeostasis, Wnt plays crucial roles in diverse biological processes, including cell differentiation, proliferation, 33 survival, migration, and self-renewal. Wnt is also deregulated and plays important roles in the pathogenesis of various disease states, including cancer90. Wnt signaling occurs through β-catenin-dependent (or canonical) and β-catenin-independent (or non- canonical) pathways. Initiated by Wnt ligand binding to FZD receptor and LRP5/6 co- receptor, canonical Wnt signaling culminates in the stabilization and nuclear translocation of β-catenin. Once in the nucleus, β-catenin mediates gene expression through its interactions with co-activators and transcription factors, most notably the four members of the T cell factor (TCF)/lymphoid enhancer factor (LEF) family45, hereafter collectively referred to as TCFs. In pancreatic cancer, Wnt/β-catenin-dependent signaling confers stem properties to responsive tumor cells91, is essential for tumor initiation77, and promotes tumor growth and progression in established PDA24,92,93.

While Wnt pathway effectors and downstream targets have been well detailed in certain cancers such as colorectal and hepatocellular carcinoma, they are less well understood in PDA. For this study, an unbiased approach was employed to broadly define mediators and targets of Wnt signaling in PDA. Among these, TCF7L1 was found to repress Wnt transcriptional activity and facilitate pancreatic tumor growth.

Downregulated via context-specific gene silencing or in response to extrinsic Wnt ligand stimulation, TCF7L1 was found to regulate the expression of genes that dictate proliferation, transcriptional activity and response to growth factors.

RESULTS

A transcriptional signature associated with autocrine Wnt signaling in pancreatic ductal adenocarcinoma. 34

We endeavored to gain greater insight into biologically relevant effectors and targets of constitutive Wnt/β-catenin signaling in PDA. First, a large panel of PDA cell lines was transiently transfected with an optimized Wnt/β-catenin-activated reporter (BAR) or its mutated, functionally-unresponsive counterpart (fuBAR) (Fig. 1-1a). PDA lines separated into groups with absent/low (BAR-L) or higher (BAR-H) levels of constitutive reporter activity (Fig. 1-1b). Supervised analysis of PDA cell line microarray data revealed a robust set of 128 upregulated and 81 downregulated gene transcripts (>3- fold change, absolute change >100, P ≤ 0.05, false discovery rate FDR<0.2) in BAR-H lines. This list included several known Wnt mediators and targets, as well as additional genes with no prior link to Wnt signaling (Fig. 1-1b). Increasing confidence in the biological relevance of this approach, several genes (RNF43, WNT7B, GATA6, FZD5, and TCF7L2) enriched in BAR-H cells are already known effectors of canonical Wnt signaling in PDA based on studies directly perturbing their function78,81,93, as well as a genome-wide CRISPR/Cas9 screen defining fitness genes specific to Wnt growth- dependent PDA lines89.

Discrete patterns of TCF expression are observed in PDA.

In western blots, BAR-H lines had higher levels of total and activated β-catenin (Fig. 1-

1c), providing biochemical evidence that they indeed have higher levels of Wnt ligand- dependent autocrine signaling activity81,89. Interestingly, a discrete pattern of higher

TCF7L2 and lower TCF7L1 gene expression was detected in BAR-H compared to BAR-

L lines in microarray analysis (Fig. 1-1b), which we speculated could further explain their differences in β-catenin/TCF-dependent reporter activity. Western blots further

35 confirmed higher TCF7L2 and lower TCF7L1 expression in BAR-H lines relative to

BAR-L lines (Fig. 1-1c). Multiple TCF7L2 isoforms were detected in BAR-H lines, which will be discussed further in Chapter Two. BAR-L PDA very weakly expressed TCF7L2, which could only be detected after prolonged western blot exposure (data not shown).

Although TCF7 message levels were not found to be differentially regulated in microarray analysis, TCF7 protein levels were increased in BAR-H versus the majority of BAR-L lines, the notable exception being MiaPaCa-2 (Fig. 1-1c). LEF1 protein was only weakly detected in two BAR-H and one BAR-L PDA lines (Fig. 1-1c), consistent with microarray results showing LEF1 is absent in most PDA lines with only weak expression in those same three lines. For additional comparison, an immortalized, non- transformed human pancreatic ductal epithelial cell line (HPDE) that has no autocrine

Wnt reporter activity (data not shown) was included as a surrogate for comparing PDA lines to normal pancreatic ductal epithelium. HPDE predominantly expressed TCF7L1

(Fig. 1-1c), mirroring BAR-L PDA lines that lack autocrine Wnt activity.

The biological relevance of different patterns of TCF expression was next explored by immunohistochemical analysis of resected patient samples on a large PDA tissue microarray (TMA, N=133). Several tumors were found to have concurrent high TCF7L2 and low TCF7L1 expression and vice versa (Fig. 1-2a). Using semi-quantitative histoscores to measure levels of nuclear TCF expression (range 0-300), a strong positive correlation was found between TCF7 and TCF7L2 expression (r=0.602, p<0.0001, Spearman rank order), while no significant correlations were noted for either

TCF7 or TCF7L2 in relation to TCF7L1 expression. Next, tumors were dichotomized

36 into groups with either low (≤100) or higher (101-300) nuclear histoscores for each TCF.

Low TCF7 was significantly correlated with worse overall survival in univariate analysis

(Hazard Ratio, HR=1.49; 95% Confidence Interval, CI 1.01-2.21; P=0.05), as was high- grade tumor histology and positive lymph node involvement (Fig. 1-2b). TCF7L1 and

TCF7L2 dichotomized groupings did not correlate with survival (Fig. 1-2b). However, in further subgroup analysis, tumors with combined low TCF7 and high TCF7L1 were strongly associated with worse prognosis (HR=2.11, CI 1.33-3.41, P=0.002, Fig. 1-2b).

In Kaplan-Meier analysis, low TCF7/high TCF7L1 tumors had a median survival of 13.0 months relative to 26.3 months for the combination of all other subgroups (log rank

P=0.001, Fig. 1-3a-b). Low TCF7/high TCF7L1 was also an independent prognostic factor in multivariate analysis (Table 1-1) and had no significant associations with a variety of clinicopathologic factors, including patient age, gender, tumor size, histologic grade, pT stage, lymph node involvement, and margin status (Table 1-2).

TCFs differentially repress or activate Wnt/β-catenin-dependent transcription in pancreatic adenocarcinoma.

To determine if differences in TCF expression might be directly responsible for variations in Wnt reporter activity across PDA lines, RNAi-mediated knockdown of each

TCF was performed. TCF7L1 knockdown had little impact on baseline Wnt reporter activity in BAR-L lines, but dramatically augmented reporter activity in these same lines in the context of stimulation by Wnt 3A ligand or the GSK3β inhibitor CHIR 99021 (Fig.

1-4a-b). This is compatible with several studies that show TCF7L1 commonly functions as a transcriptional repressor45,94,95. LEF1 did not significantly contribute to β-

37 catenin/TCF-dependent reporter activity in PDA, as LEF1 knockdown did not impact the reporter in SUIT2 or HPAF2 (data not shown), the two BAR-H PDA lines with detectable

LEF1 expression (Fig. 1-1c). Results exploring the impacts of TCF7L2 and TCF7 on

Wnt reporter activity are addressed in Chapter Two.

TCF protein expression is regulated in turn by Wnt/β-catenin-dependent signaling.

Several Wnt target genes (i.e., AXIN2, DKKs, LEF1, etc.) exert negative or positive feedback on Wnt signaling in various contexts7. Therefore, we explored whether TCF expression patterns in PDA might further reflect their own transcriptional regulation by canonical Wnt signaling. RNAi-mediated depletion of TCF7L1 with or without Wnt 3A stimulation did not significantly alter the message levels of several Wnt targets including

AXIN2, MMP7, CCND1 and RNF43 (data not shown). Of note, Shy and colleagues recently showed that Wnt activation decreases TCF7L1 nuclear localization and protein stability in stem cells and breast cancer lines via a β-catenin-dependent, post- translational mechanism96. We explored this potential mechanism of TCF7L1 regulation in PDA by treating TCF7L1-expressing lines with Wnt 3A or GSK3β inhibitor. Both manipulations led to rapid stabilization of β-catenin, biochemical evidence indicating activation of canonical Wnt signaling (Fig. 1-5a-b). In parallel with increased β-catenin,

TCF7L1 protein levels decreased as early as 6-12 hours and more significantly at 24 hours. A greater decrease was observed with GSK3β inhibitor, in keeping with its more robust stabilization of β-catenin (Fig. 1-5a-b) and activation of the reporter (Fig. 1-4a-b).

Meanwhile, no differences in TCF7L1 message levels were observed at multiple time

38 points following Wnt 3A stimulation (Fig. 1-5c), a strong indication that TCF7L1 protein is decreased by canonical Wnt signaling via a post-transcriptional mechanism and possibly that which was described by Shy, et al96. Regardless of the precise mechanism, Wnt-mediated downregulation of TCF7L1 presumably antagonizes its function as a transcriptional repressor of β-catenin/TCF-driven gene expression (Fig. 1-

5d). Therefore, a full understanding of the extent to which TCF7L1 is regulated and impacts PDA biology hinges on its characterization at both the level of gene and protein expression.

TCF7L1 overexpression promotes pancreatic cancer growth.

The functional consequences of TCF7L1 expression in BAR-L cell lines were explored to assess whether the more aggressive phenotype seen in patient samples with higher

TCF7L1 staining might be linked to its direct involvement in promoting tumor growth or progression. First, MiaPaCa-2 and PANC1 cells were stably transduced with a retroviral construct overexpressing TCF7L1 under regulation of a CMV promoter. MiaPaca-2- pLPCX-TCF7L1 cells exhibited more modest overexpression of TCF7L1 compared to

PANC1-pLPCX-TCF7L1 (Fig. 1-6a). TCF7L1 has been generally characterized as a repressor of canonical Wnt signaling97,98,45 leading us to hypothesize its overexpression should either further repress BAR activity or have no effect given the lack of Wnt reporter activity in these lines. At baseline in the absence of Wnt stimulation, TCF7L1 overexpression led to a very subtle increase in reporter activity in PANC1 cells compared to vector control (Fig. 1-6b). However, in the context of Wnt pathway stimulation with Wnt 3a conditioned media, TCF7L1 overexpression partially inhibited

39 reporter activity in keeping with its presumed function as a transcriptional repressor (Fig.

1-6B).

Next, TCF7L1 overexpressing PDA lines were plated in a clonogenic assay to determine effects on colony formation. In both PANC1 and MiaPaCa-2, TCF7L1 overexpression promoted clonogenic growth (Fig. 1-6c) and anchorage-independent growth in soft agar (Fig. 1-6d). These findings are in line with the observation that human PDA tumors with higher TCF7L1 in the context of lower TCF7 expression were correlated with more aggressive clinical behavior (Fig. 1-2, 1-3). While our in vitro assays suggest increased levels of TCF7L1 are pro-oncogenic, the impact of TCF7L1 overexpression on the Wnt reporter was not uniform across all cell lines tested (data not shown), raising the possibility that TCF7L1 exerts its pro-oncogenic activity via regulation of genes that are not direct targets of β-catenin-mediated transcriptional activation. For instance, TCF7L1 might promote tumorigenesis by repressing tumor suppressor genes or other genes that inhibit tumor progression or response to chemotherapy.

TCF7L1 silencing inhibits pancreatic cancer growth in vivo.

We next addressed the biological function of TCF7L1 at physiologic levels of expression by measuring the effects of TCF7L1 knockdown in BAR-L PDA lines where it is expressed. Given overexpression of TCF7L1 augmented tumor growth phenotypes, we hypothesized that TCF7L1 knockdown would inhibit growth. First, we examined the effects of siRNA knockdown of TCF7L1 in short-term (3-5 day) cell viability assays in

MiaPaCa-2 and PANC1 and saw no differences in growth rates (data not shown). Next, multiple lentiviral hairpins targeting TCF7L1 (shTCF7L1 05-08) were generated to assess phenotypes requiring prolonged silencing (including clonogenic, soft agar, and xenograft assays). As with transient siRNA transfection, stable knockdown of TCF7L1 augmented Wnt reporter activity following pathway activation with Wnt 3A ligand (Fig. 1-

7a-c). Knockdown of TCF7L1 with the various hairpins had no impact on MiaPaCa-2 cell migration as measured in a cell culture wound healing assay (data not shown).

Furthermore, cell viability in liquid suspension cultures with ultra-low attachment plates was not significantly affected in MiaPaCa-2 or PANC1 (Fig. 1-8a). Soft agar growth effects for MiaPaCa-2 were inconsistent; with 2 hairpins inhibiting growth and 2 hairpins augmenting colony number (Fig. 1-8b). Stable TCF7L1 knockdown in PANC1 also showed inconsistent results for soft agar colony formation (Fig. 1-8c). Soft agar assays are dynamic in vitro measures of tumor-initiating capacity (reflected in colony number) and anoikis-resistant growth (size of individual colonies). By visual analysis we noted that most of the TCF7L1 hairpins resulted in smaller colonies relative to control, suggesting a potential impact on anoikis-resistance. This was even the case for hairpin

#7, where colony size was smaller even though there were a larger number of colonies overall. Technical limitations with image analysis have not allowed us to accurately quantify colony size with assays completed thus far; however, it appears to be a parameter that could reveal important in vitro phenotypic growth differences.

Interestingly, for both MiaPaCa-2 and PANC1, shRNAs augmenting soft agar colony number (# 6 and 7) target sequences in exons 9-10 of TCF7L1, while shRNAs inhibiting

41 soft agar colony number (#5 and #8) target sequences in exon 5 (Fig. 1-9). While only a single transcript variant has been annotated for human TCF7L1, there is a second

Tcf7l1 transcript variant lacking a 14 amino acid sequence in exon 5 that has been annotated in mouse. This region overlaps with the Groucho binding domain and has been shown to promote expression of tissue and lineage-specific markers in mouse embryonic stem cells99. Isoform analysis performed on RNA-sequencing data from

MiaPaCa-2 identified only the one annotated transcript for TCF7L1, although it is possible additional human TCF7L1 isoforms exist and were not identified here based on tools used to align sequence reads.

The inconsistency of in vitro results seen with TCF7L1 depletion using different hairpins is disappointing and raises important questions about the true biological relevance of

TCF7L1 in PDA. We currently speculate the role of TCF7L1 in these cell lines is subtle and that variable (and at times contradictory) growth effects of individual hairpins may reflect an artifact of selection of these stable knockdown lines or other spurious growth differences related to their passage in culture over time. In retrospect, an inducible hairpin would have been preferable to address the effects of TCF7L1 knockdown on in vitro growth phenotypes. Another explanation for why TCF7L1 may have little or no impact on the in vitro growth of these PDA lines is that its influence is highly context- specific, both in terms of its overall levels and timing of expression. Finally, its biological effects may be decoupled from the role of Wnt/β-catenin-mediated transcription in promoting tumorigenesis in these cell lines or PDA in general. A similar decoupling has been described in hair follicle stem cell maintenance, where TCF7L1 is a marker of

42 stem cells and essential for their self-renewal. Ectopic expression of TCF7L1 in the basal epidermis suppresses differentiation of hair follicle stem cells51. Interestingly, the phenotypes of TCF7L1-deficient mice only partially overlap with those of CTNNB1- deficient mice, suggesting TCF7L1 maintains skin epithelial stem cells via both Wnt- dependent and Wnt-independent mechanisms. TCF7L1 could also exert its pro-growth effects in PDA by skewing the balance between cancer cell differentiation and self- renewal, a phenotype that may only be assessable in an in vivo context.

Because certain pro-oncogenic phenotypes are relevant only in vivo, three MiaPaCa-2 shTCF7L1 lines and one shCtrl line were xenografted subcutaneously into NSG mice.

At the end of this xenograft study (45 days), shTCF7L1 xenografts revealed a trend

(p=0.09, 0.004, 0.005, respectively) towards decreased tumor growth as measured by tumor volume (Fig. 1-10b) and a non-significant trend toward decreased tumor weight

(Fig. 1-10a). Again, as the growth effects appear subtle, these changes may have reached statistical significance if the experiment had been better powered. Furthermore, given TCF7L1 can promote stemness and self-renewal in different contexts97,100, a limiting dilution xenograft assay would have been a better experiment to tease out potential effects related to tumor-initiating potential.

Off-target actions of Ambion siTCF7L1 905.

To determine mechanisms by which reduced TCF7L1 expression might inhibit PDA tumorigenicity, genome-wide transcriptomic analysis (RNA-seq) was performed in

MiaPaCa-2 and PANC1 following TCF7L1 knockdown using a commercially available

43 siRNA from Ambion (siRNA 905). RNA-seq analysis identified 1647 genes (762 upregulated and 885 downregulated) in PANC1 and 1197 genes (500 upregulated and

697 downregulated) in MiaPaCa-2 (≥1.5-fold change in expression, P<0.05) altered by siRNA 905 knockdown. Of these, 390 upregulated and 102 downregulated genes were shared by both cell lines (Fig. 1-11a). Gene set enrichment analysis (GSEA)85 performed using a ranked list of the 492 genes altered at least 1.5-fold by siRNA 905 knockdown in both MiaPaca-2 and PANC1 identified metabolism of lipids and lipoproteins as being positively enriched (nominal P<0.01, FDR<0.05). Gene ontology

(GO) analysis also identified several biological processes positively enriched (nominal

P<0.00001, FDR<0.05) upon siRNA 905 knockdown in both cell lines including regulation of cholesterol biosynthetic process (SREBF1, APOE, LSS), lipid biosynthetic process (ACACA, FASN, CYP1A1), and lipid metabolic process (CDS2, PISD, LIP6,

PLCD1, EPT1), again highlighting that lipid metabolism may be inhibited by TCF7L1 in

PDA (Fig. 1-11b). GO datasets negatively enriched with siRNA 905 knockdown included cell cycle (CDK6, CCND1, CCNB3), and transcription (DNMT3A, MDM2, FOSB),

(nominal P<0.001, FDR<0.05), which implicated these processes as being positively regulated by TCF7L1.

To uncover biologically relevant targets of TCF7L1-mediated gene repression linked to lipogenesis, we turned our attention to the GSEA leading-edge subset for lipid metabolism (Fig. 1-11c). This analysis revealed several key rate-limiting enzymes for fatty acid synthesis, as well as SREBF1 which encodes SREBP1-c. SREBP1-c is the master transcriptional regulator of lipid homeostasis, responsible for governing the

44 expression of key rate-limiting enzymes in lipogenesis including FASN and ACACA, as well as SREBF1 itself101. Separate qPCR validation experiments with siRNA 905 confirmed these genes were transcriptionally upregulated following TCF7L1 knockdown in both PANC1 and MiaPaCa-2 (Fig. 1-11d). Given that the RNA-seq analysis pointed to

TCF7L1 having a potential role in regulating cell cycle and key cell cycle mediators, we sought to validate some of these targets in repeat qPCR studies using not only siRNA

905, but also additional siRNAs against TCF7L1. CDK6, a member of the cyclin- dependent kinase family that phosphorylates and inactivates the tumor suppressor retinoblastoma (Rb), was decreased 2.5-fold after TCF7L1 depletion with siRNA 905. A decrease in CDK6 would presumably inhibit cell cycle via effects on pRB and G1/S checkpoint (Fig. 1-11b). While Ambion siRNA 905 downregulated CDK6 message levels by qPCR (Fig. 1-11e), three other TCF7L1 siRNAs had either no effect or instead increased CDK6 expression. Likewise, additional TCF7L1 siRNAs failed to validate the upregulation of genes involved in lipid metabolism seen with siRNA 905 (Fig. 1-11f).

Therefore, we explored the possibility that siRNA 905 may have off-target effects using a more robust bioinformatic program, siRNA Sequence Probability-of-Off-Targeting

Reduction (siSPOTR)102. Disappointingly, we identified CDK6 itself to be a potential off- target hit for siRNA 905 purchased from Ambion. This would certainly explain the knockdown of CDK6 seen only with this siRNA to TCF7L1. Given the central role that

CDK6 plays in mediating G1/S transition, and the potential multitudes of genes whose expression could be altered indirectly as a consequence of altered cell cycle progression versus TCF7L1 silencing, we were forced to abandon this RNA-seq data as representative of a TCF7L1 transcriptional signature; however, the data is deposited in

NCBI Gene Expression Omnibus (accession number GSE90926) and is a potential representation of CDK6 inhibition that may be seen in PDA with targeted inhibitors now in clinical use.

TCF7L1 regulates focal adhesion and mitogenic signaling in pancreatic cancer.

As an alternative to siRNA 905 RNA-sequencing data, we turned to microarray data generated on two paired replicates of MiaPaCa-2 stably transduced with shCtrl versus shTCF7L1 05 (Fig. 1-12). By using hairpins, we are examining the transcriptional consequences of prolonged TCF7L1 knockdown. Supervised analysis of microarray data identified a total of 262 probes (115 upregulated and 147 downregulated) in

MiaPaCa-2 (absolute change >75). GO analysis revealed several pro-growth, pro- oncogenic pathways enriched within genes downregulated by TCF7L1 depletion, including MAP kinase (MAPK/ERK) activity. MAPK/ERK is dysregulated in a number of human cancers, with activation linked to increased growth. This includes pancreatic cancer, with its near-ubiquitous oncogenic KRAS mutation. In melanoma, ERK/MAPK negatively regulates Wnt/β-catenin signaling by increasing expression of the destruction complex protein AXIN1103. Positive regulation of MAPK/ERK signaling by TCF7L1 would reinforce repression of the Wnt pathway in a pro-oncogenic manner. In contrast, in colorectal cancer, Wnt/β-catenin and MAPK/ERK signaling intersect to promote transformation through the stabilization of β-catenin that can lead to stabilization of

RAS, which initiates the MAPK/ERK signaling cascade104. While combinatorial

MAPK/ERK + Wnt/β-catenin-targeted therapies may hold promise for PDA patients, the

46 pancreas-specific effects of MAPK/ERK and Wnt signaling/TCF7L1 must be elaborated first.

FIGURES

Figure 1-1. PDA exhibits discrete patterns of constitutive Wnt signaling and TCF expression. (a) Schematic of the TCF-based Wnt/β-catenin activated reporter (pBAR) which has 12 LEF/TCF binding sites driving expression of firefly luciferase. pBAR is normalized to fuBAR, which is the same except the TCF/LEF sites are mutated, found unresponsive BAR (fuBAR). (b) Wnt reporter activity across PDA lines was measured by dual luciferase assays following co-transfection with control Renilla and BAR or fuBAR luciferase plasmids and is presented as log2 median-centered BAR/fuBAR ratios. Heatmap from supervised analysis of microarray data (GSE17891) based on dichotomization of corresponding cell lines into high (BAR-H) or low (BAR-L) reporter activity is provided along with a partial list of known Wnt pathway genes and their mean log2 change in BAR-H versus BAR-L. (c) Western blots on whole cell lysates from PDA lines, including total and activated β-catenin, individual TCFs and ACTB (loading control). All lanes are from the same western blot exposure image with the line denoting the location of a cropped lane.

Figure 1-2. TCF expression patterns in resected pancreatic tumors correlate with patient prognosis. (a) Representative immunohistochemistry for TCF7, TCF7L1, and TCF7L2 shows discrete patterns of TCF expression for three patient tumors on the PDA TMA. (b) Clinicopathologic characteristics and univariate analysis of pancreatic adenocarcinoma tissue microarray. Univariate hazards analyses were performed for patients groups dichotomized on the indicated variable. Hazard ratios (HR) and 95% confidence intervals are provided where HR > 1 indicates an increased risk of death and HR < 1 indicates a decreased risk of death for the first listed variable for each group.

a b

Figure 1-3. High TCF7L1 in tandem with low TCF7 predicts worse survival in PDA. (a-b) Kaplan Meier curves for overall survival is shown for each subgroup as indicated on the plot. Subgroup combinations were determined after first dichotomizing tumors on each individual variable.

Table 1-1. Multivariate proportional hazards analysis for the PDA tissue microarray. Variable Hazard Ratio 95% Confidence p-value Interval

High TCF7L1/Low TCF7 1.99 1.24 – 3.20 0.004

Positive lymph node (pN1) 1.82 1.21 – 2.72 0.003

Multivariate Cox proportional hazards models were used to test statistical independence and significance of multiple predictors with backward selection performed using the Akaike information criterion. A hazard ratio > 1 indicates an increased risk of death/failure for variable listed. Only variables retained in the model after backward selection process are listed in the table. Variables evaluated in the model included: High TCF7L1/Low TCF7 IHC expression, age at time of surgery (≤ 60 versus >60 years), gender, tumor size (≤ 3 cm versus > 3 cm), pathologic T-stage (pT1 versus pT2 versus pT3), histologic grade (low versus high), margin status (negative versus positive), regional lymph node involvement (pN1 versus pN0).

Table 1-2. Clinicopathologic characteristics and correlations for TCF7L1 High/TCF7 Low dichotomized groups in PDA tissue microarray.

High TCF7L1/Low All other

Clinicopathologic TCF7 combinations variable P N % N % valuea Age (yrs) ≤ 60 9 34.6 39 36.4 1.00 > 60 17 65.4 68 63.6

Gender Female 10 38.5 56 52.3 0.27 Male 16 61.5 51 47.7 Tumor size (cm) ≤ 3 13 50.0 64 59.8 0.38 > 3 13 50.0 43 40.2 Tumor grade

Low (G1+G2) 14 53.8 62 57.9 0.82 High (G3+G4) 12 46.2 45 42.1 T-stage T1 + T2 16 61.5 59 55.1 0.66 T3 10 38.5 48 44.9 N-stage

N0 11 42.3 51 47.7 0.67

N1 15 57.7 56 52.3 Margin status Negative (or unknown) 21 80.8 95 88.8 0.32 Positive (any) 5 19.2 12 11.2 a P values listed for Fisher’s Exact Test (2-sided).

Figure 1-4. TCFs differentially influence Wnt/β-catenin-mediated transcription in PDA. Indicated cell lines stably transduced with Wnt reporter were transiently transfected with control (siCtrl) or siRNAs for TCF7L1. Gene knockdown was confirmed by qPCR and western blots (WB) 48-hours after transfection. Dual luciferase assays were performed following treatment with control L cell media (LCM), Wnt 3A conditioned media (Wnt 3A), or GSK3β inhibitor Chir- 99021 (CHIR). All data are normalized to siCtrl and respective media/vehicle controls. *, P ˂ 0.001

Figure 1-5. TCF7L1 protein decreases upon Wnt pathway activation. Western blots for TCF7L1, β-catenin or ACTB (loading control) were performed on whole cell lysates collected at indicated time points following treatment of (a) MiaPaCa- 2 or (b) PANC1 with LCM, Wnt3A CM, DMSO (vehicle control), or Chiron 99021 (CHIR). (c) qPCR for TCF7L1 was performed on RNA collected at the indicated time points in parallel with MiaPaCa-2 whole cell lysates. (d) Schematic adapted from mechanism proposed by Shy and colleagues. In the absence of Wnt signaling, TCF7L1 sits on the chromatin, along with its co-repressors Grouch/TLE, blocking trans-activating TCFs from binding and repressing a subset of Wnt target genes. Upon activation of the pathway, β-catenin binds with TCF7L1 in order to remove it from the chromatin for eventual proteasomal degradation. In the absence of TCF7L1, trans-activating TCFs such as TCF7 are able to bind the DNA, and the β-catenin/TCF transcriptional complex can assemble.

Figure 1-6. TCF7L1 overexpression promotes PDA growth. Western blots for TCF7L1 or ACTB (loading control) were performed on whole cell lysates collected from MiaPaCa-2 or PANC1 WT cells, cells transfected with empty pLPCX vector, or with pLPCX-TCF7L1. (b) Dual luciferase assays were performed following treatment of PANC1-pLPCX and PANC1-pLPCX-TCF7L1 cells transiently transfected with pBAR/fuBAR reporter with control L cell media (LCM) or Wnt 3A conditioned media (Wnt 3A) for 24 hours. MiaPaCa-2 and PANC1 transduced with either empty vector (pLPCX) or vector overexpressing TCF7L1 (pLPCX-TCF7L1) were assessed for differences in (c) clonogenic assay growth or (d) soft agar growth. A representative experiment is shown for each assay. All data are the mean ± standard error of 3-4 replicates. *, P ˂ 0.002

Figure 1-7. TCF7L1 silencing activates the Wnt reporter. (a-b) MiaPaCa-2 and PANC1 were transduced with control or 1 of 4 different TCF7L1 shRNA. (c) Cells were assessed for differences in their effect on BAR. Representative experiments are shown for each in vitro assay. All data are the mean ± standard error of 3-4 replicates. *, P ˂ 0.01

Figure 1-8. TCF7L1 silencing has variable effects on in vitro pancreatic tumorigenicity. (a) MiaPaCa-2 and PANC1 were transduced with control or 1 of 4 different TCF7L1 shRNA. Cells were assessed for differences in their cell viability in liquid suspension culture using ultra-low attachment plates or (b-c) growth in soft agar. Representative experiments are shown for each in vitro assay. All data are the mean ± standard error of 3-4 replicates. *, P ˂ 0.01

Figure 1-9. TCF7L1 structure and targeting areas of RNAi. (a) The overall structure of the human TCF7L1 coding region and sites of siRNA/shRNA targeting are depicted. (b) The overall structure of the mouse TCF7L1 isoforms 1 and 2. Isoform 2 lacks exon 5.

Figure 1-10. TCF7L1 silencing promotes pancreatic cancer growth in vivo. In vivo subcutaneous xenograft tumor growth in immunodeficient NSG mice. Both tumor weight (a) and volume (b) at the termination of study are indicated. (N=12 control or 7 shTCF7L1 mice per group). *, P ˂ 0.01

Figure 1-11. Novel off-target effects of Ambion siRNA 905 targeting TCF7L1. (a) RNA-sequencing was performed on MiaPaCa-2 and PANC1 cells transiently transfected with either control siRNA (siCtrl) or siRNA against TCF7L1 (Ambion siTCF7L1 905). RNA was collected 48 hours after transfection. Heatmap shows differentially expressed genes with a fold change ≥ 1.5, p <0.05. Venn diagrams list number of differentially regulated genes in each direction shared by MiaPaCa-2 and PANC1. (b) Top enriched Ggene Ontology groups within genes either upregulated or downregulated with Ambion siRNA 905. (c) Gene set enrichment analysis (GSEA) enrichment plot for genes differentially expressed in both MiaPaCa-2 and PANC1 following TCF7L1 knockdown. (d) Expression of indicated genes determined by qPCR performed 24 hours following transfection of PANC1 or MiaPaCa-2 with control or TCF7L1 siRNA Ambion 905 (siTCF7L1 905). Each gene is normalized to expression with control siRNA. (e) qPCR performed 24 hours following transfection of PANC1 with Ambion control or IDT control (c) siRNAs, or siRNAs against TCF7L1: Ambion siTCF7L1 904 (904) Ambion siTCF7L1 905 (905), IDT siTCF7L1 #1 (#1) and IDT siTCF7L1 #8 (#8). Each gene is normalized to its supplier’s control siRNA. (f) Expression of indicated genes determined by qPCR performed 24 hours following transfection of PANC1 with IDT control (siCtrl) siRNA or siRNA against TCF7L1, IDT siTCF7L1 #1 or #8. All data points are normalized to their respective controls. *, P ˂ 0.01

Figure 1-12. Stable TCF7L1 depletion downregulates pro-oncogenic gene sets. Affymetrix u133 2.0 microarray analysis performed on two paired replicates of MiaPaCa- 2 transfected with either control (shCTRL) or TCF7L1 05 shRNA. Heatmap shows genes 33% increased or decreased in both sets of replicates. Top Gene Ontology from downregulated and upregulated gene sets as well as differentially expressed genes are highlighted.

CHAPTER TWO: TCF7 AND TCF7L2 ARE MARKERS OF ACTIVE WNT SIGNALING

AND PROMOTE PANCREATIC CANCER GROWTH

INTRODUCTION The successful deployment of novel treatments for PDA not only hinges on a detailed molecular and cellular understanding of disease pathogenesis but also on methodologies for identifying patients well-suited for specific molecular targeted therapies. The Wnt/β-catenin signaling pathway is one of ten highly enriched pathways mutated in PDA12,13 and is a focus of ongoing clinical trials. Chapter One detailed discrete TCF/LEF expression patterns that characterize PDA cell lines with or without high autocrine Wnt signaling activity. There, we focused on the phenotypic consequences of TCF7L1 expression in lines lacking appreciable autocrine Wnt signaling activity. For Chapter Two, the focus turns to TCF7L2 and TCF7, which were preferentially expressed in PDA lines with high autocrine Wnt activity. We demonstrate that while TCF7L2 acts as a repressor of Wnt transcriptional activity, it paradoxically promotes pancreatic tumor growth phenotypes. Furthermore, we demonstrate that

TCF7 is an important mediator of Wnt transcriptional activity that includes genes with wide-ranging effects on pancreatic cancer proliferation and immune cell trafficking.

RESULTS

TCF7L2 and TCF7 are mediators and downstream targets of Wnt/β-catenin signaling in PDA.

As detailed in Chapter One, a subset of PDA lines with higher autocrine Wnt signaling had higher levels of TCF7L2 and TCF7 expression as determined by gene expression profiling and/or western blot analysis (Fig. 1-1b-c). Based on this finding, we hypothesized TCF7L2 was primarily responsible for Wnt/β-catenin-mediated transcriptional activity in BAR-H lines as measured by the reporter. However, TCF7L2 knockdown was instead found to paradoxically increase autocrine and Wnt 3A ligand- stimulated reporter activity in AsPC-1 (Fig. 2-1a-b), as well as in BAR-H YAPC and

SUIT2 (data not shown). Thus, akin to TCF7L1, TCF7L2 represses β-catenin/TCF- dependent transcription from this multimerized TCF-based Wnt reporter. This is not entirely without precedent, as TCF7L2 has been shown to activate or repress Wnt transcriptional activity in a context-dependent manner. Its variable activity has been shown to be heavily influenced by context-specific interactions with different co- repressors or co-activators, as well as structural and functional differences between its multiple isoforms45,54,105–107. In contrast to TCF7L1 and TCF7L2, TCF7 knockdown partially inhibited baseline and Wnt 3A-induced reporter activity across multiple PDA lines, indicating it generally functions as a Wnt transcriptional activator in PDA (Fig. 2-

1c).

Several Wnt target genes (i.e., AXIN2, DKKs, LEF1, etc.) can exert negative or positive feedback on Wnt signaling in various contexts90. Therefore, we explored whether TCF expression patterns in PDA might reflect their own regulation by canonical Wnt signaling. To inhibit Wnt, AsPC-1 were transfected with CTNNB1 siRNA or treated with the PORCN inhibitor LGK974. Each significantly inhibited Wnt transcriptional activity as

71 confirmed by downregulation of AXIN2, an endogenous target and widely used surrogate of canonical Wnt signaling (Fig. 2-1d-e). In parallel with Wnt pathway inhibition, there was significant downregulation of TCF7 message and protein. TCF7L2 was also downregulated, albeit to a lesser extent (Fig. 2-1d-f). In contrast, Wnt stimulation with either Wnt 3a ligand or GSK3β inhibitor treatment had no effect on

TCF7, TCF7L1, or TCF7L2 message levels in BAR-L PANC1 cells (data not shown), indicating Wnt signaling regulates TCF7 and TCF7L2 expression in a context- dependent manner in PDA. Further complexity in the regulation of TCF members was highlighted by the fact that TCF7L2 knockdown augmented TCF7L1 message levels in

AsPC-1 (Fig. 2-1g). Thus, TCF7L2 expression appears to reinforce the low level of

TCF7L1 expression observed in BAR-H PDA lines by directly or indirectly inhibiting its transcription.

Higher Wnt activation in PDA tumors correlates with patient survival outcomes.

In Chapter One, TCF expression was determined by immunohistochemical analysis of a large cohort of resected PDA samples on a tissue microarray (TMA, n=133).

Histoscores were used to semi-quantitatively determine levels of nuclear TCF expression (range 0-300). Interestingly, we observed a strong positive correlation between TCF7 and TCF7L2 expression (r=0.602, p<0.0001, Spearman rank order), indicating the two are frequently co-expressed. Furthermore, when tumors were dichotomized into groups with low (≤100) or higher (101-300) nuclear histoscores for each individual TCF, high TCF7 levels were significantly correlated with improved overall survival in univariate analysis (Hazard ratio, HR =0.67, 95% Confidence Interval,

CI 0.45-0.99; P=0.047). Given our data showing TCF7 both promotes and is a downstream target of Wnt/β-catenin transcriptional activity in PDA lines, we hypothesized high Wnt activity may, in fact, portend a better prognosis in PDA. To address this, the PDA TMA was further evaluated for nuclear β-catenin expression, a widely used diagnostic marker of Wnt pathway activation in patient samples108. Most

PDA tumors in the TMA showed retention of strong membranous β-catenin expression without appreciable nuclear β-catenin (Fig. 2-2a-b). This was not unexpected as PDA clearly have lower Wnt/β-catenin signaling relative to other tumor types where genetic mutations result in constitutive hyperactivation of the Wnt pathway (i.e., colorectal or hepatocellular carcinoma). Nevertheless, those PDA tumors with high or even modest levels of nuclear β-catenin (histoscore ≥30) were found to have improved overall survival (median survival of 34.9 versus 21.5 months, log rank P=0.024, Fig. 2-2c).

Furthermore, nuclear β-catenin expression showed a nearly significant positive correlation with both TCF7 (r=0.16, P=0.07) and TCF7L2 (r=0.17, P=0.06). To summarize, these data point to a correlation between TCF7 expression and better prognosis for the subset of PDA with higher levels of canonical Wnt signaling (Fig. 1-2,

1-3). Thus, TCF7 expression may be a more sensitive IHC marker of Wnt pathway activation in PDA relative to nuclear β-catenin, whose IHC expression has been optimized in tumors that in contrast to the majority of PDA, harbor genetic mutations that constitutively hyperactivate the Wnt pathway.

An important caveat for these IHC results is that they offer a comparison between two subsets of PDA but do not imply Wnt signaling is anti-oncogenic in those tumors where

73 it is activated. Indeed, a recent genomic functional screen using CRISPR/Cas9 shows that several Wnt target genes or regulatory components, including TCF7L2, are highly enriched as fitness genes in RNF43-mutated versus RNF43-wild type PDA lines89.

Thus, while higher Wnt pathway activation portends a comparatively less aggressive clinical course, these PDA lines remain highly dependent upon Wnt signaling for their growth fitness.

TCF7 and TCF7L2 promote tumor growth in PDA cell lines with high endogenous

Wnt signaling.

Expanding on this concept that Wnt growth dependency is specific to a subset of PDA with higher Wnt activity, we performed a more in-depth investigation to determine whether differences in TCF7 and TCF7L2 expression are linked to higher Wnt signaling levels and growth promotion in BAR-H PDA lines. RNAi-mediated knockdown of TCF7 was performed in AsPC-1 using three distinct siRNAs and shown to effectively deplete

TCF7 expression individually (Fig. 2-3a) or as a pool (Fig. 2-3b). TCF7 knockdown inhibited baseline Wnt reporter activity in BAR-H lines (Fig. 2-3c), decreased colony formation in a clonogenic assay (Fig. 2-3d) and decreased short-term anchorage- dependent growth (Fig. 2-3e). Therefore, TCF7 promotes growth in PDA lines with high endogenous Wnt signaling activity.

Next, we performed a further in-depth analysis of TCF7L2, which was also highly expressed in BAR-H lines (Fig. 1-1b-c), and was shown to be an important fitness gene for RNF43-mutated PDA lines89. Three separate siRNAs that target unique locations in

TCF7L2 each reduced its protein expression and message levels, although there was some variability in the efficiency of different siRNAs in targeting different TCF7L2 protein isoforms. Multiple TCF7L2 isoforms were detected in BAR-H lines, including ~79 kDa and ~ 58 kDa isoforms, previously designated Tcf4 (E) and Tcf4 (M/S) in the literature45,109(Fig. 1-1c, 2-4f), as well as smaller isoforms. Interestingly, TCF7L2 siRNA

#2 targeting a region that corresponds to the N-terminal, β-catenin binding domain

(BCBD) of TCF7L2 (Fig. 2-4f) was less efficient in targeting protein isoforms at ~58 and

~33 kDa (green arrows, Fig. 2-4a). This difference was of functional significance in relation to the extent to which each TCF7L2 siRNA altered Wnt reporter activity. While reporter activity increased significantly with all three siRNAs (Fig. 2-4c), TCF7L2 siRNA

#2 only mildly increased BAR activity (<2-fold), while #1 and #3 dramatically increased

BAR near 10-fold (Fig. 2-4c). Intrigued by the possibility that TCF7L2 siRNA #2 may weakly target repressive isoforms of TCF7L2 and possibly a dominant-negative isoform lacking the BCBD (Fig. 2-4f), we performed paired-end RNA-sequencing to look for the differential expression of TCF7L2 transcript isoforms in the context of knockdown with each individual TCF7L2 siRNA. Splicing analysis was performed with Sashimi plots used to visualize splice junctions for all exons. Disappointingly, there were no differences in TCF7L2 transcript variants identified between the different siRNAs by this analysis (data not shown). Furthermore, all TCF7L2 isoform calls in this RNA-seq analysis corresponded to the higher protein bands seen on the western blot. No transcript variant that correlated with the faster migrating band at ~33 kDa was identified. While it is possible that individual siRNAs preferentially target specific

75 isoforms, we lack proof that our siRNAs differentially target a known or novel TCF7L2 transcript variant.

While TCF7L2 represses Wnt/β-catenin transcriptional activity as measured by the

BAR, TCF7L2 appeared to functionally promote tumorigenesis. TCF7L2 knockdown showed trends towards reduced colony formation in clonogenic assay (Fig. 2-4d) and decreased short-term cell viability as measured by Cell-Titer-Glo assay (Fig. 2-4e).

Thus, TCF7L2 pro-oncogenic effects may be decoupled from its repressive effects on generalized Wnt/β-catenin transcription, at least as measured by a reporter whose activity has shown to parallel pro-oncogenic effects in most contexts in the literature110.

TCF7L2 mediated pro-oncogenic effects in PDA are likely tied to its influence on a very specific set of gene targets linked to growth promotion. It presumably represses a subset of Wnt target genes (including the BAR), while driving the expression of other

Wnt target genes that are pro-oncogenic and/or repressing genes that otherwise antagonize tumor growth.

TCF7L2 and TCF7 mediated context-specific phenotypic effects in the absence of appreciable Wnt signaling in PDA.

To explore differences in TC7L2 function that might be ascribed to its level of expression or cellular context, we investigated the function of TCF7L2 in BAR-L

MiaPaCa-2 (Fig. 2-5a). MiaPaCa-2 express detectable TCF7L2 message and protein, albeit at lower levels than that encountered in BAR-H lines but have no biochemical or transcriptional evidence of canonical Wnt signaling. TCF7L2 siRNA knockdown in

MiaPaCa-2 marginally increased Wnt reporter activity at baseline and dramatically augmented reporter activity upon stimulation with Wnt 3a (Fig. 2-5b), consistent with its repressive activity on the reporter seen in BAR-H PDA lines. We then induced long-term knockdown of TCF7L2 in MiaPaCa-2 using lentiviral hairpins corresponding to the same loci targeted by the three siRNAs described above (and further designated here based on their target location, Fig. 2-5c). TCF7L2 knockdown diminished colony formation in a clonogenic assay (Fig. 2-5d) and decreased anchorage-independent growth in soft agar

(Fig. 2-5e). Therefore, TCF7L2 expression promotes tumor growth in PDA even in the absence of Wnt pathway activation.

Unlike most other BAR-L cell lines, MiaPaCa-2 showed appreciable expression of TCF7

(Fig. 1-1c). Therefore, we investigated TCF7 function in MiaPaCa-2 using the siRNAs described (Fig. 2-6a). TCF7 depletion had no effect on baseline Wnt reporter activity, which was not surprising as the baseline activity is essentially nil. Meanwhile, TCF7 depletion significantly inhibited reporter activity induced by Wnt3a stimulation (Fig. 2-

6b). While TCF7 knockdown had no effect on short-term anchorage-dependent growth

(Fig. 2-6c), it significantly decreased the migratory capacity of MiaPaCa-2 cells in a wound healing assay with or without Wnt 3a stimulation (Fig. 2-6d). Thus, the phenotypic effects of TCF7 in BAR-L PDA lines are partially divergent from its effects in

BAR-H PDA lines such as AsPC-1.

TCF7L2 and TCF7 positively regulate pro-growth gene programs in PDA.

To gain mechanistic insights as to how TCF7L2 and TCF7 might promote growth in

PDA, RNA-sequencing was performed in AsPC-1 following either TCF7L2 or TCF7 siRNA knockdown. A total of 665 genes (547 upregulated and 118 downregulated, ≥2- fold change in expression, P<0.05) were altered with TCF7L2 knockdown (Fig. 2-7a).

The vast majority of gene expression changes were upregulation, which appears to be in line with TCF7L2 primarily functioning as a transcriptional repressor, with the caveat that this analysis does not specifically distinguish between direct versus indirect targets of TCF7L2 repression or activation. Ingenuity pathway analysis (IPA) revealed several pathways significantly enriched for these 665 differentially expressed genes (Fig. 2-7b).

Among these were pathways that included cellular growth, proliferation, and cell cycle, consistent with our observation that TCF7L2 depletion decreased in vitro growth. KEGG pathway analysis also reassuringly showed enrichment of Wnt signaling (DKK1, FOSL1,

PORCN) and returned focal adhesion (CAV1, CAV2, BAD). and extracellular matrix- receptor interaction (COL4A2, ITGB7) as additional top enriched pathways (Fig. 2-7c-d), suggesting TCF7L2 plays roles in cell motility and cross-talk between the tumor and the extracellular microenvironment.

RNA-seq analysis identified a total of 683 genes (455 upregulated and 228 downregulated, ≥2-fold change in expression, P<0.05) altered by TCF7 knockdown (Fig.

2-8a). IPA pathways significantly enriched within these differentially expressed genes

(Fig. 2-8b) included: cancer, cell injury and abnormalities, cell to cell signaling, inflammatory response, inflammatory disease, and immune cell trafficking. IPA networks

78 significantly enriched included cellular movement (TGFBR1, WNT5B, FGF19), hematological system development and function (FYN, CSF1), and immune cell trafficking (IL1B, PYCARD, TLR4) (Fig. 2-8c). Both IPA network and UCSC

Transcription Factor Binding Sites (TFBS) database analyses highlighted NFκB as a central upstream regulator of genes that were downregulated upon TCF7 knockdown.

While there are known links between NFκB and Wnt/β-catenin signaling, including shared gene targets111, our transcriptomic data suggests that TCF7 could sit at an important intersection between NFκB and Wnt/β-catenin signaling, possibly integrating their roles in regulating immune response to cancer. Among genes decreased significantly upon both TCF7L2 and TC7 knockdown was Keratin 17 (KRT17). A known target gene of NFκB, KRT17 encodes an epithelial-specific intermediate filament protein with diverse functions that include providing structural support and stimulating signaling pathways involved in the control of epithelial growth112. KRT17 expression has been tied to poor prognosis in several cancer types113–118, but has not yet been explored in PDA.

CRISPR/Cas9 knockout of KRT17 in cell lines with high endogenous expression decreases expression of pAKT, mTOR, and the glucose transporter SLC2A1 (GLUT1).

These same genes are concurrently upregulated with KRT17 in patient samples112.

Augmenting these pathways via KRT17 may be one mechanism by which TC7 promotes growth in PDA cells. However, the polarity of regulation between NFκB and

Wnt/β-catenin signaling can differ between cell types and conditions, so a study in the relevant PDA samples will be necessary to discern a relationship between TCF7 and

NFκB. It is possible TCF7 is responsible for positively regulating genes via NFκB signaling or that TCF7 and NFκB coordinately regulate certain genes via co-occupancy

79 of their promoters. Alternatively, there could be indirect effects mediated through other genes such as KRT17. For example, in skin tumors, the expression of transcriptional regulator autoimmune regulator (AIRE) is induced by KRT17. ChIP analysis reveals

KRT17 and AIRE co-occupy promoters of several genes that have NFκB consensus motifs, including those for the T-cell chemoattractants CCL9, CCL10, and CCL11119.

Interestingly, KRT17 co-immunoprecipitated with NFκB, suggesting NFκB may functionally bridge KRT17 and AIRE on gene promoters that they co-regulate. While this represents one potential hypothetical link, an in-depth analysis of concurrent gene expression and ChIP data ultimately will need to be pursued in order to elucidate a functional link between Wnt/β-catenin signaling and NFκB that is integrated through

TCF7.

The effects of Wnt manipulation vary between PDA lines with differential TCF expression.

While PDA tumors with increased TCF7 or nuclear β-catenin expression correlated with better overall survival, this difference was on the order of months. Importantly, this does not imply Wnt signaling is tumor suppressive in PDA. Indeed, perturbations of Wnt or β- catenin in PDA with autocrine Wnt signaling indicate it promotes tumorigenesis. To gain greater insight into the biological consequences of Wnt pathway activation across PDA, we defined the transcriptional consequences of Wnt pathway activation or inhibition in different PDA contexts by gene expression microarray analysis. The transcriptional responses of AsPC-1 (BAR-H, express TCF7 and TCF7L2), MiaPaCa-2 (BAR-L, express TCF7L1 and TCF7) and PANC1 (BAR-L, express TCF7L1) lines were 80 measured either in the presence or absence of Wnt 3A ligand following CTNNB1 siRNA knockdown. To identify genes regulated in a Wnt/β-catenin-dependent manner across all three PDA lines, we determined the overlap of genes downregulated by siCTNNB1 in

AsPC-1 compared to genes upregulated by Wnt 3a but also reversed upon further knockdown with siCTNNB1 in MiaPaCa-2 and PANC1 (Fig. 2-9a). Unexpectedly, we observed not only a wide variation in Wnt/β-catenin-dependent genes, but also very few overlapping genes across the three PDA lines. Despite a dramatic activation in BAR activity, very few genes were significantly altered in response to Wnt 3A in MiaPaCa-2.

Meanwhile, there was a small overlap of Wnt-regulated genes for AsPC-1 and PANC1, the majority of which were actually downregulated by Wnt activation, while only AXIN2 and FAM125B were found to be upregulated in both cell lines (Fig. 2-9b). AXIN2 is the most widely used surrogate for Wnt/β-catenin pathway activation in the literature, thus its upregulation here was reassuring as it confirms legitimate activation of the canonical pathway with an endogenous gene target.

While canonical Wnt signaling is classically described to mediate its phenotypic effects through its direct activation of target gene expression, most expression changes we observed in BAR-L lines were genes that were decreased with Wnt 3A stimulation.

These changes were reversed upon further siCTNNB1 transfection, supporting the idea that these were indeed Wnt/β-catenin-dependent gene changes. We cannot reliably discriminate between a direct versus indirect mechanism of gene regulation based on this microarray analysis as it was performed 24 hours after ligand stimulation. However, it is possible many genes in BAR-L lines are regulated by TCF7L1 in a β-catenin-

81 dependent manner given that Wnt 3A rapidly induces β-catenin stabilization followed by reduction in TCF7L1 protein. To further establish a mechanism whereby TCF7L1 acts a key regulator of Wnt transcriptional output in BAR-L PDA, additional studies are needed to show: (1) TCF7L1 depletion reverts gene expression changes induced by Wnt3A, (2)

TCF7L1 physically associates with gene regulatory elements by chromatin immunoprecipitation, and (3) the kinetics of altered target gene expression matches the loss of TCF7L1 from their chromatin sites.

Given potentially important ramifications related to the effects of paracrine Wnt signaling in PDA, further attention was given to Wnt 3A/β-catenin-regulated genes in BAR-L lines.

Again, while the overlap of gene alteration between these two BAR-L cell lines (Fig. 2-

10) was small, they did include a subset of important downstream targets/mediators of the Wnt pathway, including AXIN2, DKK3, and LGR6. Within the larger subset of Wnt

3A/β-catenin-dependent downregulated genes we observed enrichment of gene ontology biological processes that included mesenchymal cell proliferation, epithelial- mesenchymal transition (EMT), epithelial cell differentiation and regulation of MAPK cascade. These observations offer potentially enticing mechanistic links between autocrine and/or paracrine Wnt signaling activity, potentiation of KRAS signaling through a downstream effector pathway, tumor cell differentiation, and the process of epithelial-to-mesenchymal transition.

Despite their opposite effects on the BAR, depletion of TCF7L2 or TCF7 each reduced growth in PDA cells, suggesting their control of potentially overlapping subsets of Wnt/β-

82 catenin-dependent genes. Therefore, we turned our attention to microarray data to identify genes altered at least 1.5-fold by CTNNB1 siRNA transfection in AsPC-1 (Fig.

2-11). As knockdown of β-catenin could also alter gene expression by disrupting its function as an adhesion molecule in junctional complexes at the cell membrane, we also generated a further refined list of genes altered by CTNNB1 knockdown (pathway inhibition) that were inversely regulated by Wnt 3A stimulation (pathway activation). This high confidence list of target genes encompassed approximately one-half of the hits seen with CTNNB1 knockdown and likely includes several genes directly regulated by

TCF7 or TCF7L2. Increasing our confidence in the biological validity of this list, we observed several known Wnt pathway mediators and/or target genes, as well as enrichment of several gene ontology biological process with links to Wnt signaling, including positive regulation of canonical Wnt signaling, odontogenesis, developmental processes (ureteric bud and branching duct morphogenesis), and cell fate commitment

(Fig. 2-11). These results highlight both overlapping and differential regulation of target genes that are potentially influenced by TCF expression. Understanding the precise composition of TCFs as well as their particular effects in a specific context is paramount to predicting the utility of Wnt-based therapies in the clinic.

FIGURES

Figure 2-1. TCFs are both mediators and downstream targets of Wnt/β-catenin signaling. (a) PDA lines stably transduced with Wnt reporter (BAR) were transiently transfected with control (siCtrl) or siRNAs targeting (a-b) TCF7L2 or (c) TCF7. Gene knockdown was confirmed by qPCR and/or western blots (WB) 48 hours after transfection. Dual luciferase reporter assays were performed following 24-hour treatment with control L cell media (LCM) or Wnt 3A conditioned media. All data are normalized to siCtrl or respective media/vehicle controls. (d-g) Expression of indicated genes was determined by qPCR in AsPC-1 48 hours after (d) transfection with control or CTNNB1 siRNA or (e) treatment with LGK974 or DMSO vehicle control. (f) Parallel western blots were performed on whole cell lysates. (g) qPCR for TCF7L2 and TCF7L1 was performed 48 hours after transfection of AsPC-1 with TCF7L2 siRNA. All data points are normalized to their respective controls. *, P ˂ 0.01

Figure 2-2. Nuclear β-catenin expression in resected PDA correlates with improved survival. (a) Representative immunohistochemical staining of four tumors that stratify into groups with either absent/low (histoscore <30) or higher (histoscore ≥30) nuclear β-catenin expression. In addition to increased nuclear β-catenin, tumors also demonstrate higher cytoplasmic expression with diminished membrane expression. (b) Histogram shows the distribution of median nuclear β-catenin across the PDA TMA (N=133 patient tumors). (c) Kaplan Meier survival curves for patients with either absent/low or higher positive expression levels of nuclear β-catenin expression (log rank P=0.024).

Figure 2-3. TCF7 promotes Wnt/β-catenin activity and tumor growth. AsPC-1 were transiently transfected with control siRNA (siCtrl) or three distinct siRNAs targeting TCF7 (a) individually or (b) in combination. Gene knockdown was confirmed by (a) western blot or (b) qPCR 48 hours post-transfection. (c) Dual luciferase assays were performed using AsPC-1 stably transduced with the Wnt reporter (BAR). AsPC-1 were also assayed for (d) colony forming in clonogenic assays at 7 days and (e) adherent growth as measured by cell viability in Cell-Titer-Glo assay (CTG). Data are presented as the mean ± standard error of 3-4 replicates. *, P ˂ 0.05 **, P ˂ 0.001

Figure 2-4. TCF7L2 antagonizes Wnt/β-catenin reporter activity but promotes tumor growth in AsPC-1. AsPC-1 were transfected with control (siCtrl) or TCF7L2 siRNAs targeting discrete locations, including: #1 (C-terminal), #2 (N-terminal), and #3 (exon 10). (a) TCF7L2 western blot (b) qPCR and (c) Wnt reporter assays were performed 48 hours after siRNA transfection. AsPC-1 were also assayed for (d) colony formation in clonogenic assays or (e) short-term adherent growth as measured by CelTtiter-Glo (CTG). (f) The structure of the TCF7L2 gene and its protein domains are depicted and include the β- catenin binding domain (BCBD), Groucho/TLE interaction domain (TLE), HMG DNA- binding domain (HMG), nuclear localization sequence (NLS), and variable C-terminal domain that via alternative splicing variably contains the C-clamp auxiliary DNA binding domain and/or CtBP transcriptional co-repressor binding domain. The epitope recognized by the anti-TCF7L2 antibody (CST2569) and target sequences for the three TCF7L2 siRNAs are indicated by the arrows. Gray boxes indicate exons that undergo alternative splicing and includes the highly variable region (exons 13-16) that through alternative splicing, as well as alternative splice acceptor/donor sites, gives rise to a variety of messages encoding diverse protein isoforms. TCF7L2 (E) isoforms arise from different combinations of exons 13-16 and share a common stop codon in exon 17. TCF7L2 (M/S) isoforms are shorter and arise from alternative splicing in exons 13-16 that result in messages with earlier stop codons. ΔN-TCF7L2 isoforms lacking the BCBD are encoded by messages that arise from an alternative transcriptional start site in intron 5 that encodes a variant first exon (ex1b/c, ex1e) and thus lacks exons 1-5 of the full-length gene. All data are normalized to siCtrl and are the mean ± standard error of 3-4 replicates. *, P ˂ 0.05 **, P ˂ 0.001

Figure 2-5. TCF7L2 promotes growth in a context-dependent manner in MiaPaCa-2. (a) MiaPaCa-2 cells were transiently transfected with control (siCtrl) or siRNA against TCF7L2. Gene knockdown was confirmed by qPCR 48 hours after transfection. (b) Dual luciferase assays performed on MiaPaCa-2 cells stably transduced with Wnt reporter (BAR) following siRNA transfection and 24 hours treatment with control L cell media (LCM), or Wnt 3A conditioned media (Wnt 3A). (c) MiaPaCa-2 were transduced with control or 1 of 3 different TCF7L2 shRNA targeting different locations: the n-terminal BCBD (NTERM), exon 10 (EX10) or the 3’ UTR (CTERM). Stable knockdown was confirmed by western blots (WB). (d-e) MiaPaCa-2 stable hairpins were examined for differences in non-adherent growth in soft agar. Representative experiments shown, all data are normalized to siCtrl/shCtrl or control media and are the mean ± standard error of 3-4 replicates. *, P ˂ 0.01

Figure 2-6. TCF7 promotes growth in a context-dependent manner in MiaPaCa-2. (a) MiaPaCa-2 cells were transiently transfected with control (siCtrl) or siRNA against TCF7. Gene knockdown was confirmed by qPCR 48 hours after transfection. (b) Dual luciferase assays performed on MiaPaCa-2 cells stably transduced with Wnt reporter (BAR) following siRNA transfection and 24 hours treatment with control L cell media (LCM), or Wnt 3A conditioned media (Wnt 3A). Transiently transfected MiaPaCa-2 were assayed for (c) adherent growth by MTT or (d) migration in a wound healing assay. All data are normalized to siCtrl or control media and are the mean ± standard error of 3-4 replicates. *, P ˂ 0.001

Figure 2-7. TCF7L2 positively regulates pro-oncogenic gene programs in PDA. (a) RNA-sequencing was performed on AsPC-1 cells transiently transfected with either control siRNA (siCtrl) or siRNA against TCF7L2 (siTCF7L2 CTERM). RNA was collected 48 hours after transfection. Heatmap shows differentially expressed genes with a fold change ≥ 2, p <0.05. (b) Top enriched disease and function pathways from Ingenuity Pathway Analysis (IPA). (c) Top enriched KEGG pathways within genes ≥ 2-fold decreased with TCF7L2 depletion. (d) KEGG pathway map of focal adhesion. Genes downregulated by TCF7L2 depletion are starred.

Figure 2-8. TCF7 positively regulates pro-oncogenic, inflammatory gene programs in PDA. (a) RNA-sequencing was performed on AsPC-1 cells transiently transfected with either control siRNA (siCtrl) or siRNA against TCF7 (siTCF7). RNA was collected 48 hours after transfection. Heatmap shows differentially expressed genes with fold change ≥ 2, p <0.05. (b) Top enriched disease and function pathways from Ingenuity Pathway Analysis (IPA). (c) Top enriched network from Ingenuity pathway analysis (IPA). Genes in green were ≥ 2-fold decreased with TCF7 depletion, genes in red were ≥ 2-fold increased.

100

Figure 2-9. Regulation of Wnt target genes varies by cell line. Indicated cell lines were transfected with control siRNA or siRNA against CTNNB1 for 24 hours, then treated for an additional 24 hours with control L cell media (LCM), or Wnt 3A conditioned media (Wnt 3A). RNA was collected and analyzed by Affymetrix microarray analysis (U133 2.0plus arrays). (a) Gene expression changes of at least 1.5- fold (absolute change > 75) for either AsPC-1 siCTRL versus ASPC-1 siCTNNB1 or for MiaPaCa-2 or PANC1, control L cell media (LCM) versus Wnt 3a. (b) Overlapping Wnt/βcatenin genes shared by at least two cell lines in part A.

101

Figure 2-10. Paracrine Wnt signaling impacts PDA proliferation, EMT and differentiation. Genes differentially up or downregulated 1.5-fold (absolute change > 50) by Wnt 3A treatment that are subsequently knocked down at least 50% upon CTNNB1 siRNA in MiaPaCa-2 or PANC1, analyzed by Affymetrix microarray analysis (U133 2.0plus). Known Wnt pathway mediators and enriched gene ontology pathways are listed. 102

Figure 2-11. Refining Wnt/β-catenin transcriptional signature in AsPc-1. Heatmap of genes up or downregulated 1.5-fold (absolute change >50) by CTNNB1 siRNA in AsPC-1. Differentially regulated subsets of genes and enriched pathways are highlighted.

103

CHAPTER THREE: WNT SIGNALING HAS THE POTENTIAL TO REGULATE

IMMUNE INFILTRATION IN PDA

INTRODUCTION The PDA microenvironment is characterized by a dense, desmoplastic stroma that surrounds and supports the tumor cells. Activated PSCs secrete a host of factors to facilitate tumor progression and immune evasion including nitric oxide, cytokines and

MMPs. PDA is typically considered poorly immunogenic, with a largely immunosuppressive infiltrate including M2 macrophages (MAC), Tregs and neutrophils120 while CD8+ effector T-cells are rare. During the course of PDA, pro- inflammatory signals facilitate tumor growth by activation of PSCs. Upon increasing mutational burden, acinar cells secrete interleukin 6 (IL-6) which promotes further chronic inflammation121, leading to induction of immunosuppressive phenotypes. In the absence of immune surveillance, transformation continues, allowing tumor cells to increase their expression of pro-oncogenic enzymes such as focal adhesion kinase

(FAK) which can then act on PSCs. FAK levels have been correlated with increased tumor fibrosis and decreased CD8+ T-cell infiltration122, further promoting immune escape and tumor progression.

Pro-inflammatory tumor microenvironments with increased CD8+ T-cell infiltration are associated with improved prognosis in several cancer types123. The prevailing view is that T-cell infiltration is increased in tumors with high mutational load due to the variety of neoantigens124. These tumors tend to respond favorably to checkpoint inhibition

104 therapy such as anti-CTLA4 and anti-PDL1. CTLA4 and PD1 are signaling molecules that normally prevent autoimmunity and reduce chronic inflammation by negatively regulating T-cells, however, cancer cells upregulate their expression to evade the immune system. Blocking these signaling molecules allows for potentiation of the immune response against a tumor; however, there must be functional immune cells to mount an immune response in the first place. Early trials with anti-CTLA4125 and anti-

PDL1126 in melanoma and other metastatic carcinomas reported a correlation between high mutation load and prolonged survival or increased clinical benefit (defined by

Response Evaluation Criteria in Solid Tumors [RECIST]). Overexpression of PDL1 has been seen in PDA and correlated with decreased tumor infiltrating lymphocytes (TIL) and worse survival127,128; however, trials in patients have yielded no responders129,130.

Lack of response to checkpoint inhibitors has been attributed to low mutational load; anti-PDL1 is only approved for the rare MSI-high PDA cases under a blanket approval for all MSI-high cancers. However, the relationship between mutational load and

TIL/response to checkpoint inhibitors is not absolute and can be affected by other factors such as defects in MHC presentation124. Recently, whole exome sequencing and in silico antigen prediction in long-term survivors of pancreatic cancer found that only the combination of both neoantigen number and increased CD8+ T-cell infiltrates predicted the longest surviving patients131. The authors postulate that neoantigen quality, not quantity could serve as a biomarker for PDA tumors most amenable to immunotherapies, calling the mutational load theory into question. This study also demonstrates the existence of a subset of immunogenic PDA, reaffirming earlier

105 genomic, proteomic, and transcriptomic sequencing analysis that defined an immunogenic subtype, marked by increased gene programs relating to B-cells and

CD8+ T-cells13.

CD8+ T-cells that have infiltrated a PDA tumor can be effective under the right conditions or with the help of immune checkpoint inhibitors. CD40 is a co-stimulatory molecule expressed on various antigen presenting cells (APC). Upon ligand binding,

CD40 signals through adaptor molecules to enhance T-cell activation, pro-inflammatory cytokine production, and increased co-stimulatory molecule expression132. In a subcutaneous PDA mouse study, CD40 agonists in combination with anti-CTLA4 or anti-PDL1 therapy led to tumor regression and increased overall survival, abrogating resistance to anti-CTLA4 and anti-PDL1. This reaffirms the notion that checkpoint inhibitors can be beneficial in PDA patients if the T-cells can be induced.

In searching for mechanisms to overcome immune checkpoint inhibitor resistance in melanoma, Spranger and colleagues found a correlation between activation of the

Wnt/β-catenin signaling pathway and lack of T-cell infiltration133. They demonstrated that active β-catenin results in a decrease in CCL4 expression, leading to defective recruitment of CD103+ dendritic cells to the tumor. A similar relationship between active

Wnt/β-catenin signaling and exclusion of T-cells has been described in a study of colorectal cancer (CRC), which further suggests that treatment with a Wnt inhibitor (i.e.

LGK974) could sensitize previously resistant tumors to immune checkpoint therapy134.

106

In light of the Wnt/β-catenin signaling pathway’s involvement in a subset of PDA, we sought to discern whether an analogous mechanism was relevant in pancreatic cancer.

RESULTS

A T-cell infiltrated subtype exists in pancreatic cancer.

PDA tends to have a low somatic mutational load in comparison to melanoma or lung cancer, where high mutational load has been correlated to increased T-cell infiltration.

To examine this relationship in PDA, we analyzed gene expression and somatic mutation data for 182 pancreatic cancer samples from The Cancer Genome Atlas

(TCGA). T-cell average was determined using a previously published133 set of 13 known

T-cell transcripts (CD8A, CCL2, CCL3, CCL4, CXCL9, CXCL10, ICOS, GZMK, IRF1,

HLA-DMA, HLA-DMB, HLA-DOA, and HLA-DOB). Surprisingly, T-cell average was negatively correlated with mutational load (Fig. 3-1a) suggesting that something else might be driving differences in T-cell infiltration. Categorization of the TCGA set into groups with low (non-T-cell infiltrated) versus high (T-cell infiltrated) expression of T-cell signature genes confirmed a spectrum of immune infiltration in PDA (Fig. 3-1b).

Comparative gene expression profiling revealed 182 genes increased at least 4-fold and

25 genes decreased at least 2-fold in the T-cell infiltrated cohort (Fig. 3-1b). RNA deconvolution using the Microenvironment Cell Populations-counter (MCP-counter)135 confirmed the enrichment of specific immune cell types, with greatest enrichment of B- cells and CD8+ T-cells among the ten cell types analyzed (T-cells, B-cell lineage, CD8+

T-cells, cytotoxic T-cells, NK cells, myeloid dendritic cells, monocytic lineage,

107 neutrophils, endothelial cells, and fibroblasts) (Fig. 3-1b). This data is consistent with the enriched gene programs seen by Bailey and colleagues in the immunogenic subtype of PDA13. To further confirm the existence of a T-cell infiltrated subset we examined the correlations between T-cell average and T-cell associated genes not included in the 13 gene signature including CD3, PERFORIN1 (PRF1), GRANZYME B

(GMZB) and CYTOHESIN1 (CYTH1) (Fig 3-3c). CD3, PRF1, and GMZB were all highly correlated with T-cell average while CYTH1 was moderately correlated (r= 0.805, 0.679,

0.801 and 0.422 respectively) increasing confidence that this subtype represents a true immune infiltrated population. Pathway analysis of 182 genes preferentially expressed

(at least 4-fold) in the T-cell infiltrated cohort highlighted cytokine signaling (p = 3.1 x10-

16), hematopoietic cell lineage (p = 9.5 x10-16), chemokine signaling (p = 1.3 x10-11), T- cell receptor signaling (p = 1.8 x10-8), B-cell receptor signaling (p = 0.0015), NK cell signaling (p = 0.023), and toll-like receptor signaling (p = 0.026) (Fig. 3-3c), all indicative of active immune infiltration (Fig. 3-3d).

Wnt/β-catenin signaling is positively correlated with immune infiltration in PDA.

Examining the list of genes at least 2-fold decreased in the T-cell infiltrated population

(Fig. 3-3a), we did not see evidence of strong Wnt involvement as was seen by

Spranger and Grasso in melanoma and colorectal cancer, respectively. None of the Wnt fitness genes in PDA identified by Steinhart and colleagues were significantly enriched in either subset, including AXIN2, APC, RNF43, FZD5, WNT7B, PORCN, LRP5 and

LRP6 (data not shown). However, several Wnt genes including TCF7 were positively enriched in the T-cell infiltrated population (Fig. 3-2a), suggesting that active Wnt/β- 108 catenin signaling may promote tumor infiltration in PDA. We examined the correlation between each of the TCF/LEF family members and T-cell average across the TCGA and found that TCF7 and LEF1 were moderately correlated (r = 0.584 and 0.416 respectively) while TCF7L1 displayed a weak correlation (r = 0.191) and TCF7L2 was moderately negatively correlated (r = -0.238) (Fig. 3-2c). While these results do not suggest the negative regulation of T-cell infiltration by Wnt/β-catenin signaling seen in other cancers133, the positive correlation between TCF7 expression, T-cell infiltration, and improved prognosis are provocative given that we have previously observed both

TCF7 and nuclear β-catenin to be correlated with improved overall survival in PDA (Fig.

1-2 and 2-2). To further characterize the immune signature of TCF7 expressing tumors, we analyzed its correlation to the populations included in the MCP-counter. TCF7 expression showed the highest correlation with B-cell lineage, followed by CD8+ T-cells

(Fig. 3-2b) again consistent with Bailey’s immunogenic subtype. While this suggests a relationship between TCF7 expression and TILs and B-cells across PDA, it is important to remember this data is correlative and based on genomic analysis of whole tumor samples. It is possible that the TCF7 enrichment seen in these samples is due to its expression in the immune infiltrate compartment, rather than the tumor cells themselves. This possibility is being examined by multiplex IHC of various immune cell markers on the PDA tissue microarray, with results pending. We next shifted our focus toward genes that were anti-correlated to T-cell average, thus ensuring their expression stems from the non-lymphoid cells.

109

Tetraspanin TM4SF5 expression is enriched in non-T-cell infiltrated tumors.

One of the genes found to be decreased in the T-cell infiltrated compartment was

TM4SF5, a tetraspanin that associates with integrins and various other proteins during cell adhesion and migration. It has four transmembrane domains with terminals in the cytosol which bind to proteins including focal adhesion kinase (FAK) and proto- oncogene tyrosine-protein kinase Src (c-src)136,137. Interestingly, IL-6 expression is reduced in tumors with TM4SF5 expression, and TM4SF5 induced FAK activity may allow tumor cells to escape IL-6/STAT3 mediated immune activity136 (Fig. 3-3d).

TM4SF5 also interacts with non-integrin receptors including CD44 and EGFR138,139, both of which are known targets of the Wnt pathway140,141. Additionally, the TM4SF5 activated kinase c-src possesses the ability to phosphorylate β-catenin at tyrosine position 654 (Y654), which blocks β-catenin association with α-catenin and instead permits its binding to BCL9-2142. This phosphorylation results in reduced E-cadherin bound β-catenin at the cell membrane, interfering with cell adhesion. Preferential BCL9-

2 binding has been shown to recruit transcriptional co-activators to TCF/LEF family members bound to the DNA and potentially promote Wnt signaling143. TM4SF5 expression is increased in gastrointestinal cancers including hepatocellular carcinoma

(HCC), CRC and PDA144 and is associated with decreased expression of E-cadherin and β-catenin at cell-cell contacts in human clinical samples137.

Given its ties to PDA, Wnt/β-catenin signaling, and immune evasion, we examined

TM4SF5 expression across the entire TCGA and found its expression to trend opposite of T-cell average (Fig. 3-3b), with a group of high expressers that would represent 110 relevant clinical cases for a TM4SF5-based therapy. As would be expected by decreased expression in T-cell infiltrated tumor samples, TM4SF5 was negatively correlated with T-cell average (Fig 3-3c). Most interestingly, in the RNA-seq performed in Chapter Two using siRNAs against TC7 or TCF7L2, TM4SF5 was found to be upregulated upon TCF depletion (Fig. 3-4a-b), suggesting that TCF7, TCF7L2, and by extension the Wnt/β-catenin pathway may negatively regulate TM4SF5 expression, resulting in increased immune infiltration through suppression of TM4SF5 ability to disrupt IL-6 mediated antitumorigenic and immunological effects (Fig. 3-3d). This is also consistent with previous results from a microarray of AsPC-1 cells, where we observed

TM4SF5 expression to increase after Wnt inhibition with CTNNB1 siRNA or decrease following Wnt 3A addition (data not shown). It is unclear how this relationship between

Wnt/β-catenin signaling and T-cell infiltration in PDA is opposite to those seen in melanoma and CRC, although this could reflect divergent roles for Wnt in different contexts or at different signal strengths.

Further in vitro experiments are now necessary to confirm these expression changes in response to Wnt signaling and changes in TM4SF5 biochemical effects followed by an assessment of in vivo tumor phenotypes in response to inhibition of TM4SF5.

Monoclonal antibodies against TM4SF5 have already been shown to inhibit proliferation, migration, and invasion of TM4SF5 positive HCC and CRC cells, as well as increase tumor cell lysis by antibody-mediated cellular cytotoxicity (ADCC) in the presence of NK cells145. If indeed TM4SF5 expression either directly or indirectly affects

T-cell infiltration in PDA, it would represent a novel druggable target which in

111 combination with immune checkpoint inhibitors could improve immunotherapy for pancreatic cancer.

112

FIGURES

113

Figure 3-1. T-cell infiltrated and non-infiltrated subsets exist in PDA. (a) Plot of T-cell average versus mutational load for 182 pancreatic cancer samples from TCGA (b) Plot of RNA deconvolution using MCP analysis (top). Plot of T-cell average across the TCGA (middle). Heatmap of ANOVA detected genes differentially expressed at least 4-fold in the T-cell infiltrated population (bottom). (c) Correlation between T-cell average and functional markers of immune cells: CD3ε, CYTOHESIN1 (CYTH1), PERFORIN1 (PRF1) and GRANZYME B (GZMB). (d) Enriched KEGG pathways within genes at least 4-fold increased in the T-cell infiltrated subset.

114

Figure 3-2. Wnt signaling may be positively correlated with T-cell infiltration. (a) Heatmap showing known Wnt target genes at least 1.5-fold increased in the T-cell infiltrated population (b) Pearson correlation (r) between TCF7 expression across the TCGA and each of the MCP populations. (c) Correlations plotting each of the TCF/LEFs against T-cell average.

115

Figure 3-3. TM4SF5 is enriched in the non-T cell infiltrated population. (a) Plot of T-cell average across the TCGA (top). Heatmap of ANOVA detected genes differentially expressed at least 2-fold less in the T-cell infiltrated population (bottom). (b) Waterfall plot of TM4SF5 expression (top) across the TCGA, and corresponding T-cell average in the same tumors (bottom). (c) Correlation of TM4SF5 expression across the TCGA to T-cell average. (d) Schematic of potential mechanism of TM4SF5 immune escape via decreased IL-6 signaling.

116

Figure 3-4. TCF7 and TCF7L2 negatively regulate TM4SF5. Sequencing counts of TM4SF5 from RNA-sequencing of (a-b) AsPC-1 transiently transfected with control siRNA (siCtrl) or siRNAs targeting (a) TCF7 or (b) TCF7L2 or (c) MiaPaCa-2 transiently transfected with control siRNA (SiCtrl) or siRNA targeting TCF7L1. ND = not detected. RNA-seq data are the mean ± standard error of 3-4 replicates. *, P ˂ 0.01

117

Figure 3-5. Potential cross-talk between TM4SF5 and Wnt/β-catenin. Schematic of various interactions and downstream effects of TM4SF5.

118

CHAPTER FOUR: IDENTIFICATION OF NOVEL SMALL MOLECULE COMPOUNDS

TO POTENTLY INHIBIT AUTOCRINE WNT SIGNALING IN PANCREATIC CANCER

INTRODUCTION Pancreatic ductal adenocarcinoma (PDA) remains one of the most lethal cancers in part due to the modest survival benefits and significant side effects of the existing standards of care. Until recently, the standard of care was single-agent chemotherapy, including either fluorouracil (5-FU) or gemcitabine (GEM). More recently, combinatorial strategies have been adopted in the clinic and have contributed to some improvement in survival outcomes after diagnosis. These include GEM in combination with nab-paclitaxel (aka

Abraxane) or FOLFIRINOX, a cocktail of agents that includes 5-FU in combination with folinic acid (to reduce 5-FU toxicity), irinotecan (topoisomerase inhibitor) and oxaliplatin

(anti-neoplastic). While these combinations have led to incremental improvements in survival, they also are associated with significant cytotoxicity, evolving resistance, and eventual recurrence.

In the age of rapid and cost-effective high-throughput sequencing approaches to identify actionable mutations in PDA and other tumor types12,13, molecular targeted therapies have gained increased traction. The first targeted molecular therapy approved in PDA was erlotinib, an EGFR inhibitor14; however, it offered an extremely modest survival advantage in a large phase III clinical trial where it appeared to only benefit a small subset of patients. KRAS is mutated in 90-95% of all PDA patients and a key factor in tumor initiation and maintenance77. Thus, oncogenic KRAS mutation is a highly

119 desirable, yet to date intractable, drug target. In recognition of its importance, the NCI

RAS initiative was started in 2013 with the goal to develop various methodologies to target KRAS in multiple cancers. Early progress has already described preferential downstream signaling partners of KRAS with diverging phenotypic outcomes146. RAS can activate a number of pro-survival and pro-growth signaling pathways, including

PI3K/AKT and RAF/MEK/MAPK/ERK147.Combinatorial approaches to target these downstream pathways are being evaluated in the clinic.

Most recently, significant excitement has also focused on the development of a new generation of KRASG12C-specific inhibitors with efficacy in in vivo preclinical models148.

While KRASG12C mutations occur in only a small minority of PDA, the proof of principle that KRAS can be directly targeted raises hope that further compounds will eventually be developed against those mutations occurring at higher frequency in PDA. While the adoption of KRAS inhibitors would revolutionize the PDA treatment landscape, lessons learned from the clinical targeting of key oncogenic drivers in other cancers (i.e., BRAF in melanoma), as well evidence in preclinical models using an inducible KRAS showing

PDA can escape KRAS growth dependency149, indicate their clinical efficacy will undoubtedly hinge on further therapies that account for inherent or evolving resistance mechanisms (i.e., additional oncogenic signaling pathways, metabolic dependencies, etc.)

Among targets upon which pancreatic cancer growth shows dependency that are currently being targeted in trials are drugs that block angiogenesis (VEGF), disrupt 120 pancreatic stroma (hyaluronan), block cancer stem cells (Smoothened and STAT3), and modulate tumor immune response 15,16. Immune checkpoint therapies (anti-PD1, anti-

CTLA4) have had dramatic success in melanoma and other selected cancers, but have failed to show efficacy in PDA130,150. PDA is characterized by a dense stroma that excludes immune cells from the tumor, while the immune cells that do infiltrate are largely immunosuppressive40. Efforts to enhance the immune response through adjuvant therapy (GM-CSF/GVAX, mesothelin, MUC1, CD40) are ongoing40.

Approaches that employ adoptive T-cell transfers have been limited by a lack of known neoantigens in PDA, as well as the time-intensive process it takes to generate such cells in the context of rapid PDA progression. Finally, while anti-PD1 therapy has received FDA approval for use in all MSI-high solid tumors, only a small subset of PDA tumors (1-2%) are of MSI-high phenotype.

Many other molecular targeted therapies have been tried and failed in clinical trials, despite promising preclinical results. One favored explanation for this lack of success is the robust desmoplastic stroma of PDA, which acts as a physical barrier to both immune cell infiltration and drug delivery. Stromal targeting approaches have included efforts to disrupt the stroma at the level of signaling (i.e., hedgehog inhibitor) or mechanically by enzymatically degrading hyaluronan, a key ECM molecule in the tumor stroma. These approaches have either paradoxically resulted in decreased survival or otherwise failed to significantly improve survival151,152. One reason these approaches may have failed is due to the manner in which the study cohort was enrolled. Just as heterogeneity exists within the tumor cells, it also exists in the PDA stroma30,153. The trial of pegylated

121 hyaluronidase (PEGPH20), the enzyme that degrades hyaluronan, highlights this point.

In a subset of patients, stromal hyaluronan levels are elevated and correlated with poor prognosis, increased tumor growth and decreased survival154. In a phase II study combining PEGPH20 with GEM and nab-paclitaxel, there were no differences in survival for the entire group of patients. However, when participants with higher levels of hyaluronan at the outset were analyzed, PEGPH20 was associated with an improved overall objective response rate (45% versus 31% without, includes partial responders, using RECIST). Accordingly, current phase III clinical trials with PEGPH20 have a patient selection criterion for only treating tumors with higher hyaluronan levels. In a cancer with significant heterogeneity, it is unlikely that a “one size fits all” therapy will ever prove to be useful. Instead, drugs need to be selected for different subsets of patients with specific tumor pathology and molecular signatures (i.e., personalized cancer therapy). Of these, one subset of PDA tumors are those that demonstrate growth dependency on the Wnt/β-catenin signaling pathway. Of note, mutations in

RNF43 have been shown to be a predictive marker for PDA cell lines sensitive to the

PORCN inhibitor LGK974, currently in clinical trials155.

Wnt/β-catenin signaling is one of 12 core signaling pathways frequently mutated in

PDA12. Several therapies targeting Wnt are currently in various stages of preclinical or early clinical evaluation in a variety of cancer types, including three for PDA; two that target PORCN and one that inhibits β-catenin/CBP binding. However, there remain numerous opportunities to find novel inhibitors or activators of the Wnt pathway that could be leveraged for clinical use. One common approach to identify novel

122 pharmacologic agents is through high-throughput screening (HTS) of small molecule libraries. HTS uses automated robotic systems to evaluate tens or hundreds of thousands of small compounds in a rapid and economical manner. Several factors can influence the efficiency and success of HTS, including the diversity of compounds in the library collection, the number and quality of validated hits, and the choice of informative primary and secondary functional assays. Several functional assays to screen for

Wnt/β-catenin signaling have been employed in HTS, including measures of β-catenin nuclear localization or stability, FZD receptor endocytosis, or Wnt transcriptional activity as determined by TCF/LEF-based reporters. While numerous HTS for Wnt inhibitors have been performed using ligand-stimulated 293T cells or colon cancer lines with hyperactivating mutations in the Wnt pathway, there are no published screens in relevant cell lines that mimic the autocrine Wnt activity and growth dependency we observe in our BAR-H PDA lines.

Seeking to identify novel Wnt pathway inhibitors that may be specifically and/or more robustly active in subsets of PDA with autocrine Wnt signaling activity, we chose to perform a HTS using the BAR-H PDA line AsPC-1, which also importantly harbors a mutation in RNF43. We made use of a large and diverse set of nine libraries in the

UCLA MSSR Core encompassing over 80,000 compounds. To follow effects on Wnt signaling, we used AsPC-1 cells stably transduced with our optimized luciferase-based

β-catenin activated reporter (BAR)82 featuring a concatemer of 12 TCF/LEF binding sites. Reporter activity was then normalized to nuclear Hoechst stain, to distinguish between Wnt reporter activity and changes due to loss of cell viability/toxicity.

123

RESULTS

Screen for small molecule inhibitors of Wnt/β-catenin signaling.

To identify small molecule inhibitors of the Wnt pathway in the BAR-H PDA line AsPC-1, we followed inhibition of Wnt reporter as a surrogate for efficacy against the pathway.

The basis for high levels of autocrine Wnt signaling and BAR activity in AsPC-1 is its secretion of the Wnt ligand WNT7B81, as well as its concurrent loss of feedback inhibition due to a mutation in RNF43. The potential novelty and utility of our screen lies within these key aspects of enhanced Wnt signaling in AsPC-1. Namely, unlike other assays that rely on cell lines with hyperactivating mutations in the pathway or stimulation of cells by exogenous ligand or drug, AsPC-1 can robustly respond to molecules that disrupt any level of the pathway, including: ligand secretion, ligand- receptor-coreceptor engagement, intracellular signaling events above and below the level of the destruction complex, nuclear translocation of β-catenin, and β-catenin interaction with transcription factors and/or other co-activators. Moreover, AsPC-1 Wnt activity and anchorage-independent growth are reduced after treatment with PORCN inhibitor (i.e., IWP-2 or LGK97481), which prevents the palmitoylation and secretion of

Wnt ligand. Accordingly, we used LGK974 as an internal control for robust pathway inhibition across all plates analyzed in our screen. Importantly, we also utilized LGK974 as a tool compound for several optimizations in the UCLA MSSR, including efforts to appropriately scale the screening assay into 384-well plate format. This involved optimization of manual and automated steps, including determination of appropriate cell numbers and media volumes, media conditions, length of treatments, luciferase

124 measurements and high-throughput imaging parameters of Hoechst stains for accurate cell counts.

In the final screening assay, AsPC-1 with stable BAR were plated into 384-well plates pinned with each test compound (5 µM final concentration) and incubated for 48 hours.

Cell number was assessed by Hoechst stain and high-content imaging. Next, 50% well volume of Bright-Glo was added to each well and luminescence was quantified using a luminometer. Bright-Glo (Promega) is a combined luciferase substrate and lysis buffer with a 30-minute half-life. Because it requires only a single step and is considerably less expensive than dual-luciferase reagent, Bright-Glo was ideal for use in this HTS.

Luminescence for each sample was normalized to Hoechst dye count to control for spurious changes in Wnt reporter activity due to increased cytotoxicity or variability in plated cell numbers (Fig. 4-1). In total, we screened nine libraries in the MSSR. They included Prestwick (NPW), a collection of known FDA-approved drugs; LOPAC, composed of molecules similar to pharmacologically active drugs; the NIH Clinical

Collection that includes molecules that have been used in clinical trials with known toxicology; the Microsource Spectrum Collection, a diverse collection of known drugs, experimental bioactives and natural products; The Druggable Compound set, a set of compounds targeted at kinases, proteases, ion channels and G-coupled protein receptors; the Lead Like Compound Set, custom-designed to resemble lead compounds obeying the rule of four; two Chemically Diverse Library sets, one selected for low cellular toxicity and the second for structural diversity; and the Kinase Inhibitor Library made up of known kinase inhibitors. Cumulatively, our screen included over 80,000 test

125 compounds (Fig. 4-2a). For the primary screen, we chose a cut-off for compounds inhibiting BAR activity with a Z-score of -3 or greater, with a total of 529 compounds fulfilling this criterion (Fig. 4-2b).

Secondary and tertiary screens now remain to be completed on these 529 compounds.

The secondary screens will include revalidation of compounds in dual luciferase-based

BAR assays in AsPC-1 and a second RNF43-mutated PDA line (HPAF2). These compounds will be further validated for inhibition against one or more endogenous Wnt target genes (i.e., AXIN2) by qPCR with subsequent determination of potency based on

IC50 values in dose response assays. Top hits will be explored in tertiary screens of biological activity based on our already established Wnt-dependent in vitro growth phenotypes for these PDA lines, including clonogenic and anchorage-independent growth assays81,89.

Structure-activity relationship (SAR) analysis is an in silico approach to identify common substructures or moieties of compounds with similar activity. In order to explore biological themes and potentially inform selection of lead compounds for further optimization, we performed SAR analysis on the 529 compounds from our primary screen. This analysis revealed no significant common substructure, indicating that our screened group of compounds is highly diverse and likely acts on a broad range of targets. In retrospect, this is perhaps not surprising given the multiple levels at which

Wnt signaling is regulated and the numerous physiologic process (i.e., cell cycle

126 progression) and signaling pathways (i.e., ERK, TGF-beta, Hippo, etc.) known to influence Wnt signaling activity.

Next, to identify potentially enriched targets based on our screened compounds, we used bioactivity information from the PubChem BioAssay database to generate a short list of potential targets, ranked by the number of compounds within our subset with reported activity at a concentration of 1 µM or less against a specific target. Notably, this approach identified several compounds annotated as luciferase inhibitors—these compounds will be excluded from subsequent analysis. Among many of the top compound target hits, we found enrichment for several members of the nuclear hormone receptor superfamily, perhaps not surprising given that β-catenin has been shown to bind and act as a transcriptional co-factor with many nuclear hormone receptors either directly or indirectly.

Among potential lead targets for further study, our top enriched hit was nuclear factor erythroid 2 related factor 2 isoform 2 (NRF2), a transcription factor and master regulator of genes in response to reactive oxygen species (ROS)156. Increased ROS and deregulated redox signaling are hallmarks of cancer. We previously identified NRF2 in a siRNA screen against the Wnt reporter in AsPC-1, where its depletion resulted in a 30% decrease in BAR activity (data not shown), suggesting NRF2 or its downstream targets can augment Wnt signaling. Interestingly, treatment of hepatocellular carcinoma with

LGK974 decreases NRF2 expression, leading to enhanced tumor cell sensitivity to radiation157, indicating inhibition of NRF2 in conjunction with inhibition Wnt signaling 127 may warrant further investigation. Twelve screened compounds were predicted to target

GEMININ, a nuclear protein that inhibits DNA replication and has also been shown to antagonize Wnt158. GEMININ has been shown to be required for epithelial to mesenchymal transition (EMT) of stem cells during development159, but its role in cancer has not been extensively studied. In summary, this type of data mining has identified these and other target candidates worthy of further mechanistic study. We plan to explore these potential targets through direct genetic perturbation (siRNA- or

CRISPR/Cas9-mediated loss-of-function assays) in order to establish their potential targetability and mechanisms of action in blocking the Wnt signaling pathway in PDA or more generally. These results will be useful in further prioritizing those compounds in our screen most suitable for lead compound optimization.

Finally, bioactivity analysis also revealed enrichment of inhibitors of MEK activity. MEK inhibitors are already in clinical trials for the treatment of several cancers, including

PDA. Importantly, there is known crosstalk between MEK, PI3K and Wnt signaling.

Therefore, we chose to further validate a top hit from this class of compounds in our screen, AZD6244 (Fig. 4-3a). AZD6244 (aka Selumetinib) inhibits MAPK kinase (MEK) within the RAS/RAF/MEK/MAPK/ERK signaling cascade (Fig. 4-3d). Oncogenic signaling through MEK and MAPK/ERK is a key biochemical pathway linked to the uncontrolled growth of PDA, particularly given ~95% of all PDA have KRAS mutations.

As activated ERK has been shown to phosphorylate the Wnt co-receptor LRP6 in colorectal cancer, we hypothesized pharmacologic targeting of Wnt/ β-catenin signaling in combination with MEK/MAPK/ERK signaling in tumors with RNF43 mutations may act

128 in synergy to enhance disruption of Wnt signaling by inhibiting two discrete and important regulatory nodes for the Wnt pathway. The pharmacologic interaction of

LGK974 and AZD6244 in inhibiting in vitro Wnt signaling as measured by BAR activity was determined in AsPC-1 using combinatorial dose-response curves and analysis with

Compusyn software. These results revealed combinations of LGK974 and AZD244 exerted synergistic inhibitory effects on the reporter at effect levels (Fa) less than 70%, with combination index (CI) range 0.281-0.649 for the Fa range of 0.19-0.70 inhibition.

Thus we reached a quantitative conclusion of synergism for these two compounds using the Chou-Talalay CI method (where CI < 1 synergistic, CI = 1 additive, CI > 1 antagonistic) with these in vitro conditions and drug concentration combinations160. As a caveat for this particular experiment, we were unable to further inhibit reporter activity beyond 70% at any higher concentrations of these drugs individually or in combination.

It has been shown in colorectal cancer that activated MAPK/ERK phosphorylates the

Wnt/FZD co-receptor LRP6, activating it161. Phosphorylated LRP6 recruits the destruction complex components AXIN1 and GSK3β, along with DVL to the FZD/LRP6 complex at the membrane162, freeing cytoplasmic β-catenin to translocate to the nucleus and stimulate transcription of Wnt target genes. MEK inhibition blocks activation of

MAPK/ERK, which then decreases LRP6 phosphorylation. This allows AXIN1 and

GSK3β to remain in a functional destruction complex, that degrades β-catenin, and blocks its transcriptional activity. While the potential opportunity to synergistically leverage these two patient-experienced drugs against Wnt growth-dependent tumors is exciting, a variety of biochemical and functional assays are needed to establish if this

129 combinatorial approach can enhance the anti-growth effect of these compounds or circumvent resistance that might otherwise occur if they were used individually.

130

FIGURES

Figure 4-1. Scheme for high throughput screening to identify novel small molecule inhibitors of Wnt signaling.

131

132

Figure 4-2. HTS of over 80,000 diverse compounds in the UCLA MSSR Core yields over 500 compounds with Wnt inhibitor activity as measured by reporter. (a) List of libraries and number of compounds within those libraries that were screened. (b) Breakdown of compound hits from each library based on different Z-score cut-offs. (c) List of protein targets ranked by the number of compounds from our assay that were shown to have an activity of less than 1μM in other PubChem BioAssays.

133

134

135

136

Figure 4-3. MEK inhibitor AZD6244 acts synergistically with LGK974 to potently inhibit Wnt/β-catenin signaling. (a) Molecular structure of AZD6244. (b) AsPC-1 stably transduced with Wnt reporter (BAR) were treated with serial dilutions of AZD6244 and LGK974 alone and in combination at different concentrations in relation to their IC50s (1:1, 1:4, 4:1 AZD6244 to LGK974). Dual luciferase assay was performed at 48 hours. Schematics showing (c) known biochemical crosstalk between the RAS/RAF/MEK/MAPK/ERK and Wnt signaling pathways and (d) how the use of LGK974 in combination with AZD6244 could function in synergy to inhibit canonical Wnt signaling with high efficacy. Figure adapted from McCain, J, 2013, Pharmacy and Therapeutics, 38(2), 96-98, 105-108.

137

DISCUSSION

To date, published studies exploring Wnt activity and phenotypes in PDA have mostly focused on the roles and functions of key upstream Wnt pathway mediators5, 6, 9-14. Here we have uniquely focused on the roles of TCFs as important effectors governing diverse

Wnt-mediated transcriptional and phenotypic effects across PDA. This work elaborates on the importance of TCF expression in contextualizing Wnt pathway activation and function. Furthermore, these findings have potentially important clinical implications for stratifying subsets of PDA whose divergent Wnt activity dictates clinical behavior and/or therapeutic responses.

Differential TCF expression confers complexity to Wnt transcriptional responses in PDA.

Based on their overall patterns of expression in PDA lines with (BAR-H) or without

(BAR-L) autocrine Wnt signaling, as well as the effects that perturbations in their expression have on Wnt activity and tumor phenotypes, we conclude that TCFs are important mediators of the diverse, in some instances paradoxical, effects of canonical

Wnt signaling in pancreatic cancer. While each repressed Wnt signaling as measured by the BAR reporter, the expression of TCF7L1 and TCF7L2 were inversely correlated across PDA lines. We speculate that important, but as of yet undefined, mechanisms of transcriptional or epigenetic control dictate their pattern of expression in different subsets of PDA. In addition, our data reveals their expression to be differentially influenced by canonical Wnt signaling, which serves to reinforce their inverse correlation in BAR-H versus BAR-L PDA. TCF7L1 was rapidly diminished by a post-transcriptional

138 mechanism upon exogenous Wnt stimulation, while TCF7L2 message was downregulated by perturbations that inhibit Wnt. Despite their inverse patterns of expression, TCF7L1 and TCF7L2 were shown to promote tumor phenotypes in each of their specific contexts. Finally, TCF7 was observed to be both an activator and downstream target of Wnt signaling in PDA lines with higher autocrine Wnt activity, as well as a mediator of pro-tumor phenotypes.

Our observations involving TCF members extend beyond discriminating the in vitro activity level and function of Wnt in PDA cell lines. In in vivo xenograft assays, TCF7L1 depletion in a BAR-L PDA line resulted in modest but significant differences in tumor growth rate. More compelling, we provided circumstantial evidence that these differences may also be clinically relevant in patients. In a UCLA cohort of resected

PDA tumors, high TCF7L1 in combination with low TCF7 (an immunohistochemical pattern corresponding to that seen in BAR-L PDA cell lines) was an independent predictor of worse overall survival. This result paralleled the finding that low/absent nuclear β-catenin expression in this same PDA tumor cohort was also associated with worse prognosis. Altogether, these findings imply that a subset of PDA tumors defined by lower Wnt activity is more clinically aggressive. This observation is consistent with a variety of studies in other cancer types where higher levels of Wnt signaling activity as measured by nuclear β-catenin or other surrogate markers are correlated with less aggressive disease, including breast cancer163, melanoma164, hepatocellular carcinoma165, and others. While PDA tumors with increased TCF7 or nuclear β-catenin

139 expression are correlated with better overall survival, this difference remains only on the order of months, consistent with a disease of nearly universally poor prognosis.

At first glance, it may seem counterintuitive that tumors with lower levels of Wnt—a pathway associated with the promotion of stemness, cancer metabolism, and cellular proliferation—portends more aggressive clinical behavior in PDA. However, delving deeper into factors that distinguish BAR-H and BAR-L PDA cell lines, we note a strong corresponding relationship with their respective microarray-based stratification into

“classical” (i.e., HPAC, HPAF2, SUIT2, and AsPC-1) and “quasi-mesenchymal” (QM)

(i.e., MiaPaCa-2, PANC1, DanG) subtypes31. In that seminal study by Collisson et al,

PDA cell lines and patient samples of the QM subtype displayed higher expression of mesenchymal genes and were linked to a less differentiated, more aggressive phenotype. In contrast, the classical subtype showed increased expression of adhesion- associated and epithelial genes and was associated with better patient prognosis. We propose that Wnt/β-catenin activation status in combination with established patterns of

TCF expression dictate and/or are important surrogate markers of these two biologically important transcriptional PDA states. Collisson’s analysis corresponds with microarray data presented in this dissertation showing experimental perturbations in Wnt signaling by β-catenin knockdown alone, or in combination with exogenous Wnt3A stimulation, enrich for biological processes that included mesenchymal cell proliferation, EMT, and epithelial cell differentiation. Alternatively, the classical and QM subtypes may be permissive states in which the growth promoting effects of canonical Wnt signaling and differential TCF expression status manifest discrete transcriptional and phenotypic

140 effects. For example, in the less differentiated QM subtype, TCF7L1 may be better able to exert its pro-stemness effects. Regardless, the potential for TCF expression patterns to be a clinically useful surrogate for determining Wnt growth dependency or therapeutic response to Wnt inhibitors warrants further investigation in additional patient cohorts and prospective trials.

It is important to reiterate that patient survival differences do not necessarily imply the role of Wnt signaling is to suppress pancreatic tumorigenesis. In fact, our data and those of several other labs show PDA lines with high levels of Wnt signaling are dependent upon the pathway for tumor initiation and maintenance77,81,89. This mirrors observations in other cancer types. For instance, there is a well-defined genomic subtype of hepatocellular carcinoma whose oncogenesis is driven by mutations in Wnt leading to constitutive pathway activation, but is also associated with better prognosis relative to other HCC subtypes166.

Further TCF-regulated pancreatic tumor phenotypes may only be relevant in vivo.

While trends towards decreased growth upon siRNA depletion of TCF7L1, TCF7L2 or

TCF7 were seen in vitro, profound growth perturbations were not observed. PDA xenografts with stable knockdown of TCF7L1 significantly reduced tumor volume (Fig.

1-10b) even with hairpins that showed little if any growth effects in vitro. This raises the possibility that TCFs and Wnt/β-catenin signaling promote pancreatic cancer phenotypes not readily assayed in isogenic cell cultures. These could include effects on

141 angiogenesis, immune cell modulation, tumor-stromal interactions, and stemness within the context of the tumor niche.

Important parallels can be drawn to in vivo studies in the literature where Wnt has been studied in the context of normal development and disease pathogenesis, including cancer. For example, in embryonic skin, LEF1 and TCF7L1 are both expressed in basal progenitors. Later in development, TCF7L1 is restricted to the hair follicle bulge where multipotent stem cells self-renew, while LEF1 expression is limited to the base of the hair follicle where cells differentiate51. When TCF7L1 is inappropriately expressed at the hair follicle base, differentiation is suppressed and the gene expression program begins to resemble that of uncommitted epidermis. Therefore, in the context of the hair follicle, TCF7L1 blocks differentiation to maintain a multipotent stem cell population. In the context of cancer, TCF7L1 may repress Wnt signaling-driven differentiation in order to maintain a cancer stem cell population capable of seeding distant tumors, consistent with its enriched expression in the QM subtype.

When full-thickness wounds were made in TCF7L1/TCF7L2 -null skin grafts, epithelial regeneration was blocked even once controls had fully healed. Moreover,

TCF7L1/TCF7L2 -null cells displayed an inability to form colonies in vitro51.The authors found that in early hair follicle development, phenotypes of TCF7L1/TCF7L2-null cells were less severe than those at later stages, presumably due to the concurrent expression of LEF1 in the early stage. Later in development, once LEF1 is confined to the base of the hair follicle, it is no longer able to compensate for the loss of both

142

TCF7L1 and TCF7L2. The existence of this type of redundancy offers another explanation why in vitro phenotypes may not be particularly robust. Indeed, redundant patterns of TCF/LEF expression and function are observed in several of our PDA lines.

The involvement of the Wnt/β-catenin signaling pathway in stem cells is well accepted, although its precise role regulating stemness in various contexts has yielded divergent and sometimes contradictory results. This may reflect the fact that the Wnt pathway is not only essential for stem cell maintenance, but also a key determinant of cell fate determination and decisions to differentiate and proliferate. Observations such as those described above for skin/hair follicle development point to the central roles TCFs play in providing redundancy in support of essential biological processes such as stemness, as well as contextualizing the transcriptional response to Wnt in spatiotemporal contexts. In retrospect, the impact of our TCF loss of function studies using RNAi on both in vitro and in vivo phenotypes might have been far more profound had we better anticipated these issues of redundancy and context. For example, while MiaPaCa-2 xenografts showed a modest reduction in tumor size with TCF7L1 knockdown, a limiting dilution in vivo tumor initiation assay using PANC1 may have revealed a far more dramatic phenotype linking TCF7L1 action in PDA to its known effects on stemness in development107 and breast cancer96. Likewise, instead of RNAi knockdown, the use of

CRISPR/Cas9 genome editing to fully ablate expression of a specific TCF, may have been necessary to fully unmask a TCF-dependent phenotype that was dosage- dependent. Finally, combinatorial gene depletion (i.e., simultaneous knockdown of

TCF7L1 andTCF7L2) may have been informative in addressing functional redundancy

143 between these two factors in PDA, even if one was expressed at much lower levels in the BAR-L versus BAR-H subtypes. Indeed, in retrospect, we note that even though not appreciably detectable by western blot, siRNA knockdown of TCF7L1 in BAR-H or

TCF7L2 in BAR-L PDA cell lines each augmented Wnt signaling as measured by the reporter (data not shown).

An evolving Wnt niche in PDA.

The GATA6 transcription factor offers an additional mechanistic link between PDA differentiation state and Wnt signaling. We find GATA6 to be expressed in BAR-H PDA lines (Fig. 1-1b). Interestingly, GATA6 has been shown to have a functional, subtype- specific role for the classical PDA subtype31 and promote Wnt signaling in PDA by negatively regulating the secreted Wnt antagonist Dickkopf-193. In the QM PDA subtype,

GATA6 is silenced through epigenetic regulation167. In very recent work published this past month, Seino and colleagues define three distinct subtypes of PDA patient-derived organoids with differing dependency on Wnt/R-spondin for survival. Wnt non- producing (W-) require exogenous Wnt and R-spondin for organoid growth and have no autocrine Wnt signaling. Wnt producing (W+) organoids can grow independent of exogenous Wnt ligand because they secrete their own in an autocrine manner, but may still rely on exogenous supplementation with R-spondin. Wnt and R-spondin independent (WRi) grow as organoids without the need for these exogenous supplements as they do not have or require active Wnt signaling167. The authors provide evidence supporting the hypothesis that a GATA6-dependent transcriptional program mediates Wnt dependency in PDA organoids. They also showed that GATA6 copy

144 number is correlated with overall PDA survival, a finding in line with the notion that high

TCF7L1/low TCF7 in our PDA cohort is a negative prognostic factor. Indeed, in this dissertation, we have drawn parallels between lower Wnt signaling in PDA (BAR-L) and the QM-PDA subtype—a subtype that is also Wnt-independent, GATA6-silenced, and more clinically aggressive. It is also noteworthy that in focusing on established isogenic cell lines we have undoubtedly missed an important Wnt subtype elucidated through the

Seino organoid study, the W- organoids, whose growth are dependent on supplementation with exogenous Wnt and R-spondin. Presumably, these represent tumors that have evolved growth dependency on paracrine Wnt signaling in the tumor microenvironment. Our BAR-H PDA lines correspond to W+ organoids in their capacity to support their Wnt-dependent growth via autocrine signaling. Our BAR-L PDA lines correspond to WRi organoid lines, having evolved in cultures without the need for endogenous or exogenous Wnt ligand (Fig. D-1). This is an important consideration for efforts to comprehensively study the impact of the Wnt pathway, including its drugability, in PDA. For instance, a clinical trial for the PORCN inhibitor LGK974 that now specifically enrolls PDA patients based on RNF43 mutation will exclude patients representing W- organoids, whose PDA might still be dependent on paracrine Wnt signaling. These patients would benefit from the drugs’ inhibition of Wnt ligand secretion but would not be eligible for treatment based solely on the selection criteria. In this regard, TCF expression patterns may be more sensitive in defining Wnt growth dependent states, whether due to direct autocrine signaling or indirect paracrine signaling in the tumor niche.

145

Furthermore, TCF expression and their effects on Wnt signal strength and transcriptional response may importantly contribute to cellular heterogeneity within individual PDA tumors. Ilmer and colleagues recently described discrete Wnthigh and

Wntlow subpopulations in PDA with divergent phenotypes, which include stem cell traits.

Wnthigh populations were shown to have minimal response to external Wnt stimuli; conversely, Wntlow subpopulations responded to Wnt 3A or R-spondin treatments by increasing Wnt reporter activity. The authors speculate that the biological consequences of Wnt are contextualized across cell populations within a PDA tumor that exhibit different states of Wnt responsiveness and adaptability91. This is compatible with data we present here and in previous work81 showing PDA lines with higher autocrine signaling (BAR-H) respond marginally to exogenous Wnt stimulus relative to lines without endogenous Wnt activity (BAR-L).

Genome-wide transcriptomic analysis reveals a possible relationship between

CDK6 and lipid metabolism.

Initial global transcriptomic profiling using Ambion Silencer Select siRNA 905 against

TCF7L1 showed enrichment of gene sets involved in lipogenesis, an important hallmark of cancer metabolism168. Of note, SREBF1 and its downstream transcriptional targets appeared to be targets of repression by TCF7L1, as they were reactivated upon knockdown of TCF7L1 with siRNA 905. However, in follow-up work with other TCF7L1 siRNAs, we were unable to cross-validate these hits to support the idea that TCF7L1 regulates a transcriptional program dictating lipid metabolism. We now suspect that the

Ambion si905 has an off-target knockdown effect on CDK6, leading to inhibition of cell

146 cycle progression. This raises an interesting possibility that if the upregulation of lipid metabolism genes was in fact due to CDK6 depletion and/or cell cycle inhibition, a

CDK4/6 inhibitor could be used in combination with an inhibitor of lipid metabolism in

PDA. The lipid metabolism inhibitor would target the vulnerability created by the inhibition of cell cycle, which may prevent rapid resistance. Further direct targeting of

CDK6 with additional siRNAs will need to be performed first to determine the feasibility of this idea.

Repeat global transcriptional profiling using three unique TCF7L1 siRNAs, revealed far fewer changes in gene expression than that seen with siRNA 905. However, we still noted FASN, a multienzyme protein the catalyzes fatty acid synthesis, was 1.5-fold increased upon knockdown of TCF7L1. This suggests TCF7L1 may still play a more restricted role in inhibiting some aspects of lipid metabolism. This would also coincide with a recent metabolomics profiling study showing cell lines of the classical PDA subtype (commonly BAR-H) fall into a lipogenic class, while those of the QM subtype

(commonly BAR-L) fall into a glycolytic category169. Thus, in regulating genes such as

MYC, FASN and others, Wnt/β-catenin/TCF-dependent signaling may be a key determinant in the transcriptional rewiring necessary for cancers to meet their metabolic demands, a premise gaining increasing traction in the literature. Further detailed mechanistic studies exploring the regulation of lipogenesis and glycolysis by Wnt and

TCFs are currently underway.

147

Genome-wide transcriptomic analysis reveals TCF ties to growth.

In exploring the consequences of Wnt activation across PDA by microarray and NGS, we have identified enrichment of varied biological functions due to context-specific transcriptional effects of individual TCFs (Fig. 1-12, 2-7, 2-8). For example, genes involved in focal adhesion were increased upon TCF7L1 knockdown but decreased after TCF7L2 knockdown whereas genes regulating transcription, growth, and metabolism were decreased after knockdown of either gene. The existence of both similarly and opposingly regulated gene programs shared by TCF7L1 and TCF7L2 highlights the impact relative concentrations of particular TCFs can have on the biological response to the same stimuli. As perturbation of focal adhesion is a necessary step in the metastatic spread of disease, the ability of TCF7L2 signaling to regulate focal adhesion genes suggests both a potential mechanism of Wnt-mediated oncogenic growth, as well as an important in vivo phenotype to assess in future

Wnt/PDA studies.

In future work, we plan to focus our efforts on assays that measure differences in stemness and turn greater attention to the role of TCFs in regulating other in vivo- specific tumor phenotypes, including metastasis. We also plan to move toward organoids as a platform to study PDA and Wnt in a more holistic manner. This will provide opportunities to better probe the effects of Wnt and TCF status as a determinant of intratumoral heterogeneity and paracrine cross-talk in the PDA niche. For example,

Nguyen and colleagues found that TCF7L1 expression promoted maintenance of multipotent progenitors. Paradoxically, Ilmer and colleagues found that R-spondin

148 stimulation of BAR-H cell lines led to increased stemness-associated traits such as tumor-initiating capacity. This discrepancy again points to the importance of understanding the consequences of spatialtemporal context and the ability of TCFs to impart a biological effect.

Genome-wide transcriptomic analysis reveals TCF ties to immune signaling.

Notable biological pathways enriched in IPA analysis upon TCF7 depletion included growth and metabolism, as well as immune cell signaling and trafficking. It follows that effects on growth and metabolism were identified given the in vitro growth inhibitory effects we observed withTCF7 knockdown in AsPC-1. The significance of immune pathways is less clear considering our analysis is based on gene expression changes in an isogenic PDA cell line. In light of recent work incriminating Wnt/β-catenin signaling in

T-cell exclusion from melanoma133 and CRC134 tumor microenvironments, we analyzed the TCGA PDA RNA-seq dataset for immune and T-cell infiltration markers and found a subset of PDA enriched for T-cell infiltration. Of note relative to the recent FDA approval of checkpoint inhibitors for MSI-high tumors, T-cell infiltration was negatively correlated with mutational load in PDA. We noted a subset of Wnt target genes, including TCF7 and LEF1, that were positively correlated with T-cell infiltration while expression of TM4SF5 was enriched in the non-T-cell infiltrated subset and upregulated by siRNA-mediated depletion of TCF7 and TCF7L2 in RNA-seq datasets (Fig. 3-3 and

3-4). TM4SF5 expressing HCC cells decrease IL-6 signaling through STAT3 by both binding to the IL-6 receptor and activating FAK, which inhibits STAT3, resulting in the loss of IL-6 mediated immune surveillance and subsequent T-cell infiltration136.

149

Reduction in TM4SF5 through treatment of HCC and CRC cells with a monoclonal antibody increased ADCC-mediated lysis of tumor cells145, supporting the immunosuppressive role of TM4SF5. We hypothesize that TCF7 and TCF7L2- dependent Wnt signaling directly or indirectly reduces levels of TM4SF5 in PDA cells,which in turn is permissive for increased T-cell infiltration.

While a poorly immunogenic cancer in relative terms, increased immune infiltration has been linked to better prognosis in PDA40 and amenability to checkpoint inhibitors like anti-PD-L1 and anti-CTLA4132. In the largest transcriptomic analysis of its kind to date,

PDA was stratified into four subtypes of prognostic significance. One of these was the immunogenic subtype which was principally similar to the classical subtype of PDA but further defined by the presence of immune transcripts, including enrichment of gene programs involving CD8+ T-cells and B-cells13. Interestingly, of the 10 immune cell types we extrapolated from the TCGA PDA RNA-seq data, TCF7 was strongly correlated with

CD8+ T-cells and B-cells (Fig. 3-2b). This is consistent with our work that identifies differential expression of TCF7 in BAR-H PDA lines that correlate with the classical subtype. Our working hypothesis at this point is that TCF7-dependent Wnt/β-catenin signaling is responsible for maintaining low levels of TM4SF5 expression at the membrane, which promotes increased immune surveillance and consequently better prognosis. This immune infiltration could render these tumors more susceptible to checkpoint inhibitors. Cross-talk between TM4SF5 binding partners/downstream effectors and Wnt/β-catenin signaling makes this an intriguing target for further study, especially as anti-TM5SF4 antibodies have already shown efficacy in decreasing tumor

150 growth, invasion, and migration in mouse models of HCC and CRC145. Sensitizing PDA to checkpoint inhibitors by increasing T-cell infiltration through targeted therapies has the potential to give rise to a new paradigm in the treatment of PDA.

Final conclusions.

The role of TCFs in contextualizing Wnt signaling activity and function in PDA and other cancers has been underappreciated but is clearly important. Wnt/β-catenin mediated gene expression is highly variable and context-dependent, influenced by upstream events in the membrane and cytosol, as well as downstream nuclear mediators and chromatin dynamics. The relative level of expression and chromatin distribution of

TCFs, as well as their multitude of interactions with co-activators, co-repressors, and other chromatin modifiers, are poorly understood elements that critically shape Wnt transcriptional responses in various contexts45,94,107,170,171. Here we show that differences in global cellular levels of TCFs modulate Wnt signal strength and further influence diverse transcriptional and phenotypic outcomes in PDA, ranging from perturbed focal adhesion to increased immune infiltration. The ligand-driven nature of

Wnt in PDA suggests it is druggable. We have shown through HTS that the pathway can be inhibited by a diverse range of small molecules. Increased understanding of the relevant phenotypes and their evaluation in the context of the overall tumor microenvironment has the potential to better inform patient selection criteria and future testing of novel therapeutics. Additionally, characterization of PDA subtypes with distinct clinical vulnerabilities marked by TCF expression provides a method to stratify patients

151 into relevant treatment groups in a step towards personalized therapy for pancreatic cancer.

152

FIGURES

Figure D-1. Working model of the relationship between BAR, TCF expression, PDA subtype and Wnt niche dependency.

153

REFERENCES

1. Rahib L, Smith BD, Aizenberg R, Rosenzweig AB, Fleshman JM, Matrisian LM. Projecting cancer incidence and deaths to 2030: The unexpected burden of thyroid, liver, and pancreas cancers in the united states. Cancer Res. 2014;74(11):2913-2921. doi:10.1158/0008-5472.CAN-14-0155 2. Facts C. Cancer-Facts-and-Figures-2018. 2018. 3. Ilic M, Ilic I. Epidemiology of pancreatic cancer. World J Gastroenterol. 2016;22(44):9694-9705. doi:10.3748/wjg.v22.i44.9694 4. Vincent A, Herman J, Schulick R, Hruban RH, Goggins M. Pancreatic cancer. In: The Lancet. Vol 378. NIH Public Access; 2011:607-620. doi:10.1016/S0140- 6736(10)62307-0 5. Genkinger JM, Spiegelman D, Anderson KE, et al. A pooled analysis of 14 cohort studies of anthropometric factors and pancreatic cancer risk. Int J cancer. 2011;129(7):1708-1717. doi:10.1002/ijc.25794 6. Arnold LD, Patel A V, Yan Y, et al. Are racial disparities in pancreatic cancer explained by smoking and overweight/obesity? Cancer Epidemiol Biomarkers Prev. 2009;18(9):2397-2405. doi:10.1158/1055-9965.EPI-09-0080 7. Larsson SC, Bergkvist L, Wolk A. Consumption of sugar and sugar-sweetened foods and the risk of pancreatic cancer in a prospective study. Am J Clin Nutr. 2006;84(5):1171-1176. doi:84/5/1171 [pii] 8. Midha S, Chawla S, Garg PK. Modifiable and non-modifiable risk factors for pancreatic cancer: A review. Cancer Lett. 2016;381(1):269-277. doi:10.1016/j.canlet.2016.07.022 9. Yachida S, Iacobuzio-Donahue CA. Evolution and dynamics of pancreatic cancer progression. Oncogene. 2013;32(45):5253-5260. doi:10.1038/onc.2013.29 10. Von Hoff DD, Ervin T, Arena FP, et al. Increased Survival in Pancreatic Cancer with nab-Paclitaxel plus Gemcitabine. N Engl J Med. 2013;369(18):1691-1703. doi:10.1056/NEJMoa1304369 11. Conroy T, Desseigne F, Ychou M, et al. FOLFIRINOX versus Gemcitabine for Metastatic Pancreatic Cancer. N Engl J Med. 2011;364(19):1817-1825. doi:10.1056/NEJMoa1011923 12. Jones S, Zhang X, Parsons DW, et al. Core Signaling Pathways in Human Pancreatic Cancers Revealed by Global Genomic Analyses. Science (80- ). 2008;321(5897):1801-1806. doi:10.1126/science.1164368 13. Bailey P, Chang DK, Nones K, et al. Genomic analyses identify molecular subtypes of pancreatic cancer. Nature. 2016;531(7592):47-52. 154

doi:10.1038/nature16965 14. Moore MJ, Goldstein D, Hamm J, et al. Erlotinib plus gemcitabine compared with gemcitabine alone in patients with advanced pancreatic cancer: a phase III trial of the National Cancer Institute of Canada Clinical Trials Group. J Clin Oncol. 2007;25(15):1960-1966. doi:10.1200/JCO.2006.07.9525 15. Amanam I, Chung V. Targeted Therapies for Pancreatic Cancer. Cancers (Basel). 2018;10(2):36. doi:10.3390/cancers10020036 16. Teague A, Lim K-H, Wang-Gillam A. Advanced pancreatic adenocarcinoma: a review of current treatment strategies and developing therapies. Ther Adv Med Oncol. 2015;7(2):68-84. doi:10.1177/1758834014564775 17. Ying H, Dey P, Yao W, et al. Genetics and biology of pancreatic ductal adenocarcinoma. Genes Dev. 2016;30(4):355-385. doi:10.1101/gad.275776.115 18. Sommers S.C. Murphy S.A., S. W. Pancreatic duct hyperplasia and cancer. Gastrenterology. 1954;27(82):629-640. http://www.ncbi.nlm.nih.gov/pubmed/13210601. Accessed February 19, 2018. 19. Hruban RH, Adsay NV, Albores-Saavedra J, et al. Pancreatic intraepithelial neoplasia: A new nomenclature and classification system for pancreatic duct lesions. Am J Surg Pathol. 2001;25(5):579-586. doi:10.1097/00000478- 200105000-00003 20. Hruban RH, Maitra A, Kern SE, Goggins M. Precursors to Pancreatic Cancer. Gastroenterol Clin North Am. 2007;36(4):831-849. doi:10.1016/j.gtc.2007.08.012 21. Matthaei H, Schulick RD, Hruban RH, Maitra A. Cystic precursors to invasive pancreatic cancer. Nat Rev Gastroenterol Hepatol. 2011;8(3):141-150. doi:10.1038/nrgastro.2011.2 22. Maitra A, Hruban RH. Pancreatic Cancer. Annu Rev Pathol Mech Dis. 2008;3(1):157-188. doi:10.1146/annurev.pathmechdis.3.121806.154305 23. MacGregor-Das AM, Iacobuzio-Donahue CA. Molecular pathways in pancreatic carcinogenesis. J Surg Oncol. 2013;107(1):8-14. doi:10.1002/jso.23213 24. Pasca di Magliano M, Biankin A V, Heiser PW, et al. Common activation of canonical Wnt signaling in pancreatic adenocarcinoma. Hatakeyama M, ed. PLoS One. 2007;2(11):e1155. doi:10.1371/journal.pone.0001155 25. Biankin A V., Waddell N, Kassahn KS, et al. Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes. Nature. 2012;491(7424):399-405. doi:10.1038/nature11547 26. Sausen M, Phallen J, Adleff V, et al. Clinical implications of genomic alterations in the tumour and circulation of pancreatic cancer patients. Nat Commun. 2015;6(1):7686. doi:10.1038/ncomms8686 155

27. Waddell N, Pajic M, Patch AM, et al. Whole genomes redefine the mutational landscape of pancreatic cancer. Nature. 2015;518(7540):495-501. doi:10.1038/nature14169 28. Witkiewicz AK, McMillan EA, Balaji U, et al. Whole-exome sequencing of pancreatic cancer defines genetic diversity and therapeutic targets. Nat Commun. 2015;6(1):6744. doi:10.1038/ncomms7744 29. Blackford A, Serrano OK, Wolfgang CL, et al. SMAD4 gene mutations are associated with poor prognosis in pancreatic cancer. Clin Cancer Res. 2009;15(14):4674-4679. doi:10.1158/1078-0432.CCR-09-0227 30. Moffitt RA, Marayati R, Flate EL, et al. Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nat Genet. 2015;47(10):1168-1178. doi:10.1038/ng.3398 31. Collisson EA, Sadanandam A, Olson P, et al. Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat Med. 2011;17(4):500-503. doi:10.1038/nm.2344 32. Roy N, Hebrok M. Regulation of Cellular Identity in Cancer. Dev Cell. 2015;35(6):674-684. doi:10.1016/j.devcel.2015.12.001 33. Jensen JN, Cameron E, Garay MVR, Starkey TW, Gianani R, Jensen J. Recapitulation of elements of embryonic development in adult mouse pancreatic regeneration. Gastroenterology. 2005;128(3):728-741. http://www.ncbi.nlm.nih.gov/pubmed/15765408. Accessed February 25, 2018. 34. Fendrich V, Esni F, Garay MVR, et al. Hedgehog signaling is required for effective regeneration of exocrine pancreas. Gastroenterology. 2008;135(2):621-631. doi:10.1053/j.gastro.2008.04.011 35. Morris JP, Cano DA, Sekine S, Wang SC, Hebrok M. Beta-catenin blocks Kras- dependent reprogramming of acini into pancreatic cancer precursor lesions in mice. J Clin Invest. 2010;120(2):508-520. doi:10.1172/JCI40045 36. Roy N, Malik S, Villanueva KE, et al. Brg1 promotes both tumor-suppressive and oncogenic activities at distinct stages of pancreatic cancer formation. Genes Dev. 2015;29(6):658-671. doi:10.1101/gad.256628.114 37. Stanger BZ, Stiles B, Lauwers GY, et al. Pten constrains centroacinar cell expansion and malignant transformation in the pancreas. Cancer Cell. 2005;8(3):185-195. doi:10.1016/j.ccr.2005.07.015 38. Li C, Heidt DG, Dalerba P, et al. Identification of Pancreatic Cancer Stem Cells. Cancer Res. 2007;67(3):1030-1037. doi:10.1158/0008-5472.CAN-06-2030 39. Hidalgo M. Pancreatic cancer. N Engl J Med. 2010;362(17):1605-1617. doi:10.1056/NEJMra0901557

156

40. Kunk PR, Bauer TW, Slingluff CL, Rahma OE. From bench to bedside a comprehensive review of pancreatic cancer immunotherapy. J Immunother Cancer. 2016;4(1):14. doi:10.1186/s40425-016-0119-z 41. Clevers H. Wnt/β-Catenin Signaling in Development and Disease. Cell. 2006;127(3):469-480. doi:10.1016/j.cell.2006.10.018 42. Petersen CP, Reddien PW. Wnt signaling and the polarity of the primary body axis. Cell. 2009;139(6):1056-1068. doi:10.1016/j.cell.2009.11.035 43. Janda CY, Waghray D, Levin AM, Thomas C, Garcia KC. Structural basis of Wnt recognition by Frizzled. Science. 2012;337(6090):59-64. doi:10.1126/science.1222879 44. Arce L, Pate KT, Waterman ML. Groucho binds two conserved regions of LEF-1 for HDAC-dependent repression. BMC Cancer. 2009;9(1):159. doi:10.1186/1471- 2407-9-159 45. Cadigan KM, Waterman ML. TCF/LEFs and Wnt Signaling in the Nucleus. Cold Spring Harb Perspect Biol. 2012;4(11):a007906-a007906. doi:10.1101/cshperspect.a007906 46. Chodaparambil J V, Pate KT, Hepler MRD, et al. Molecular functions of the TLE tetramerization domain in Wnt target gene repression. EMBO J. 2014;33(7):719- 731. doi:10.1002/embj.201387188 47. Kim CH, Oda T, Itoh M, et al. Repressor activity of Headless/Tcf3 is essential for vertebrate head formation. Nature. 2000;407(6806):913-916. doi:10.1038/35038097 48. Merrill BJ, Pasolli HA, Polak L, et al. Tcf3: a transcriptional regulator of axis induction in the early embryo. Development. 2004;131(2):263-274. doi:10.1242/dev.00935 49. Liu F, van den Broek O, Destrée O, Hoppler S. Distinct roles for Xenopus Tcf/Lef genes in mediating specific responses to Wnt/beta-catenin signalling in mesoderm development. Development. 2005;132(24):5375-5385. doi:10.1242/dev.02152 50. Tang W, Dodge M, Gundapaneni D, Michnoff C, Roth M, Lum L. A genome-wide RNAi screen for Wnt/beta-catenin pathway components identifies unexpected roles for TCF transcription factors in cancer. Proc Natl Acad Sci U S A. 2008;105(28):9697-9702. doi:10.1073/pnas.0804709105 51. Nguyen H, Merrill BJ, Polak L, et al. Tcf3 and Tcf4 are essential for long-term homeostasis of skin epithelia. Nat Genet. 2009;41(10):1068-1075. doi:10.1038/ng.431 52. van de Wetering M, Cavallo R, Dooijes D, et al. Armadillo coactivates transcription driven by the product of the Drosophila segment polarity gene dTCF. 157

Cell. 1997;88(6):789-799. http://www.ncbi.nlm.nih.gov/pubmed/9118222. Accessed February 25, 2018. 53. Kennell JA, O’Leary EE, Gummow BM, Hammer GD, MacDougald OA. T-cell factor 4N (TCF-4N), a novel isoform of mouse TCF-4, synergizes with beta- catenin to coactivate C/EBPalpha and steroidogenic factor 1 transcription factors. Mol Cell Biol. 2003;23(15):5366-5375. http://www.ncbi.nlm.nih.gov/pubmed/12861022. Accessed February 25, 2018. 54. Vacik T, Stubbs JL, Lemke G. A novel mechanism for the transcriptional regulation of Wnt signaling in development. Genes Dev. 2011;25(17):1783-1795. doi:10.1101/gad.17227011 55. Vacik T, Lemke G. Dominant-negative isoforms of Tcf/Lef proteins in development and disease. Cell Cycle. 2011;10(24):4199-4200. doi:10.4161/cc.10.24.18465 56. Patel J, Baranwal S, Love IM, Patel NJ, Grossman SR, Patel BB. Inhibition of C- terminal binding protein attenuates transcription factor 4 signaling to selectively target colon cancer stem cells. Cell Cycle. 2014;13(22):3506-3518. doi:10.4161/15384101.2014.958407 57. Deng Y, Liu J, Han G, et al. Redox-dependent Brca1 transcriptional regulation by an NADH-sensor CtBP1. Oncogene. 2010;29(50):6603-6608. doi:10.1038/onc.2010.406 58. Di L-J, Byun JS, Wong MM, et al. Genome-wide profiles of CtBP link metabolism with genome stability and epithelial reprogramming in breast cancer. Nat Commun. 2013;4:1449. doi:10.1038/ncomms2438 59. Ravindranath AJ, Cadigan KM. The Role of the C-Clamp in Wnt-Related Colorectal Cancers. Cancers (Basel). 2016;8(8):74. doi:10.3390/cancers8080074 60. Chang M V., Chang JL, Gangopadhyay A, Shearer A, Cadigan KM. Activation of Wingless Targets Requires Bipartite Recognition of DNA by TCF. Curr Biol. 2008;18(23):1877-1881. doi:10.1016/j.cub.2008.10.047 61. Bhambhani C, Ravindranath AJ, Mentink RA, et al. Distinct DNA Binding Sites Contribute to the TCF Transcriptional Switch in C. elegans and Drosophila. Mango SE, ed. PLoS Genet. 2014;10(2):e1004133. doi:10.1371/journal.pgen.1004133 62. Archbold HC, Broussard C, Chang M V., Cadigan KM. Bipartite Recognition of DNA by TCF/Pangolin Is Remarkably Flexible and Contributes to Transcriptional Responsiveness and Tissue Specificity of Wingless Signaling. Barsh GS, ed. PLoS Genet. 2014;10(9):e1004591. doi:10.1371/journal.pgen.1004591 63. Atcha FA, Syed A, Wu B, et al. A Unique DNA Binding Domain Converts T-Cell Factors into Strong Wnt Effectors. Mol Cell Biol. 2007;27(23):8352-8363. doi:10.1128/MCB.02132-06

158

64. Hoverter NP, Zeller MD, McQuade MM, et al. The TCF C-clamp DNA binding domain expands the Wnt transcriptome via alternative target recognition. Nucleic Acids Res. 2014;42(22):13615-13632. doi:10.1093/nar/gku1186 65. Hatzis P, van der Flier LG, van Driel MA, et al. Genome-Wide Pattern of TCF7L2/TCF4 Chromatin Occupancy in Colorectal Cancer Cells. Mol Cell Biol. 2008;28(8):2732-2744. doi:10.1128/MCB.02175-07 66. Heiser PW, Lau J, Taketo MM, Herrera PL, Hebrok M. Stabilization of beta- catenin impacts pancreas growth. Development. 2006;133(10):2023-2032. doi:10.1242/dev.02366 67. Wells JM, Esni F, Boivin GP, et al. Wnt/beta-catenin signaling is required for development of the exocrine pancreas. BMC Dev Biol. 2007;7(1):4. doi:10.1186/1471-213X-7-4 68. Murtaugh LC, Law AC, Dor Y, Melton DA. Beta-catenin is essential for pancreatic acinar but not islet development. Development. 2005;132(21):4663-4674. doi:10.1242/dev.02063 69. Nusse R, Varmus HE. Many tumors induced by the mouse mammary tumor virus contain a provirus integrated in the same region of the host genome. Cell. 1982;31(1):99-109. http://www.ncbi.nlm.nih.gov/pubmed/6297757. Accessed February 27, 2018. 70. Kinzler KW, Nilbert MC, Vogelstein B, et al. Identification of a gene located at chromosome 5q21 that is mutated in colorectal cancers. Science. 1991;251(4999):1366-1370. http://www.ncbi.nlm.nih.gov/pubmed/1848370. Accessed February 25, 2018. 71. Su LK, Vogelstein B, Kinzler KW. Association of the APC tumor suppressor protein with catenins. Science. 1993;262(5140):1734-1737. http://www.ncbi.nlm.nih.gov/pubmed/8259519. Accessed February 25, 2018. 72. Wood LD, Parsons DW, Jones S, et al. The genomic landscapes of human breast and colorectal cancers. Science. 2007;318(5853):1108-1113. doi:10.1126/science.1145720 73. Satoh S, Daigo Y, Furukawa Y, et al. AXIN1 mutations in hepatocellular carcinomas, and growth suppression in cancer cells by virus-mediated transfer of AXIN1. Nat Genet. 2000;24(3):245-250. doi:10.1038/73448 74. Rubinfeld B, Albert I, Porfiri E, Fiol C, Munemitsu S, Polakis P. Binding of GSK3beta to the APC-beta-catenin complex and regulation of complex assembly. Science. 1996;272(5264):1023-1026. http://www.ncbi.nlm.nih.gov/pubmed/8638126. Accessed February 25, 2018. 75. Zeng G, Germinaro M, Micsenyi A, et al. Aberrant Wnt/beta-catenin signaling in pancreatic adenocarcinoma. Neoplasia. 2006;8(4):279-289.

159

doi:10.1593/neo.05607 76. De La O J-P, Emerson LL, Goodman JL, et al. Notch and Kras reprogram pancreatic acinar cells to ductal intraepithelial neoplasia. Proc Natl Acad Sci U S A. 2008;105(48):18907-18912. doi:10.1073/pnas.0810111105 77. Zhang Y, Morris JP, Yan W, et al. Canonical Wnt Signaling Is Required for Pancreatic Carcinogenesis. Cancer Res. 2013;73(15):4909-4922. doi:10.1158/0008-5472.CAN-12-4384 78. Jiang X, Hao H-X, Growney JD, et al. Inactivating mutations of RNF43 confer Wnt dependency in pancreatic ductal adenocarcinoma. Proc Natl Acad Sci. 2013;110(31):12649-12654. doi:10.1073/pnas.1307218110 79. Kim K-A, Zhao J, Andarmani S, et al. R-Spondin Proteins: A Novel Link to β-catenin Activation. Cell Cycle. 2006;5(1):23-26. doi:10.4161/cc.5.1.2305 80. Schepers AG, Vries R, van den Born M, et al. Lgr5 intestinal stem cells have high telomerase activity and randomly segregate their chromosomes. EMBO J. 2011;30(6):1104-1109. doi:10.1038/emboj.2011.26 81. Arensman MD, Kovochich AN, Kulikauskas RM, et al. WNT7B mediates autocrine Wnt/β-catenin signaling and anchorage-independent growth in pancreatic adenocarcinoma. Oncogene. 2014;33(7):899-908. doi:10.1038/onc.2013.23 82. Biechele TL, Moon RT. Assaying beta-catenin/TCF transcription with beta- catenin/TCF transcription-based reporter constructs. Methods Mol Biol. 2008;468:99-110. doi:10.1007/978-1-59745-249-6_8 83. Nguyen Kovochich A, Arensman M, Lay AR, et al. HOXB7 promotes invasion and predicts survival in pancreatic adenocarcinoma. Cancer. 2013;119(3):529-539. doi:10.1002/cncr.27725 84. Li C, Wong WH. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc Natl Acad Sci U S A. 2001;98(1):31-36. doi:10.1073/pnas.011404098 85. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545-15550. doi:10.1073/pnas.0506580102 86. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44- 57. doi:10.1038/nprot.2008.211 87. Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37(1):1-13. doi:10.1093/nar/gkn923 160

88. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12(1):323. doi:10.1186/1471-2105-12-323 89. Steinhart Z, Pavlovic Z, Chandrashekhar M, et al. Genome-wide CRISPR screens reveal a Wnt-FZD5 signaling circuit as a druggable vulnerability of RNF43-mutant pancreatic tumors. Nat Med. 2017;23(1):60-68. doi:10.1038/nm.4219 90. Clevers H, Nusse R. Wnt/β-Catenin Signaling and Disease. Cell. 2012;149(6):1192-1205. doi:10.1016/j.cell.2012.05.012 91. Ilmer M, Boiles AR, Regel I, et al. RSPO2 Enhances Canonical Wnt Signaling to Confer Stemness-Associated Traits to Susceptible Pancreatic Cancer Cells. Cancer Res. 2015;75(9):1883-1896. doi:10.1158/0008-5472.CAN-14-1327 92. Wang L, Heidt DG, Lee CJ, et al. Oncogenic Function of ATDC in Pancreatic Cancer through Wnt Pathway Activation and β-Catenin Stabilization. Cancer Cell. 2009;15(3):207-219. doi:10.1016/j.ccr.2009.01.018 93. Zhong Y, Wang Z, Fu B, et al. GATA6 Activates Wnt Signaling in Pancreatic Cancer by Negatively Regulating the Wnt Antagonist Dickkopf-1. Ng IOL, ed. PLoS One. 2011;6(7):e22129. doi:10.1371/journal.pone.0022129 94. Mao CD, Byers SW. Cell-context dependent TCF/LEF expression and function: alternative tales of repression, de-repression and activation potentials. Crit Rev Eukaryot Gene Expr. 2011;21(3):207-236. http://www.ncbi.nlm.nih.gov/pubmed/22111711. Accessed February 16, 2018. 95. Yi F, Pereira L, Merrill BJ. Tcf3 functions as a steady-state limiter of transcriptional programs of mouse embryonic stem cell self-renewal. Stem Cells. 2008;26(8):1951-1960. doi:10.1634/stemcells.2008-0229 96. Shy BR, Wu C-I, Khramtsova GF, et al. Regulation of Tcf7l1 DNA Binding and Protein Stability as Principal Mechanisms of Wnt/β-Catenin Signaling. Cell Rep. 2013;4(1):1-9. doi:10.1016/j.celrep.2013.06.001 97. Lien W-H, Polak L, Lin M, Lay K, Zheng D, Fuchs E. In vivo transcriptional governance of hair follicle stem cells by canonical Wnt regulators. Nat Cell Biol. 2014;16(2):179-190. doi:10.1038/ncb2903 98. Merrill BJ, Pasolli HA, Polak L, et al. Tcf3: a transcriptional regulator of axis induction in the early embryo. Development. 2003;131(2):263-274. doi:10.1242/dev.00935 99. Salomonis N, Schlieve CR, Pereira L, et al. Alternative splicing regulates mouse embryonic stem cell pluripotency and differentiation. Proc Natl Acad Sci U S A. 2010;107(23):10514-10519. doi:10.1073/pnas.0912260107 100. Cole MF, Johnstone SE, Newman JJ, Kagey MH, Young RA. Tcf3 is an integral component of the core regulatory circuitry of embryonic stem cells. Genes Dev. 161

2008;22(6):746-755. doi:10.1101/gad.1642408 101. Jeon T-I, Osborne TF. SREBPs: metabolic integrators in physiology and metabolism. Trends Endocrinol Metab. 2012;23(2):65-72. doi:10.1016/j.tem.2011.10.004 102. Boudreau RL, Spengler RM, Hylock RH, et al. siSPOTR: a tool for designing highly specific and potent siRNAs for human and mouse. Nucleic Acids Res. 2013;41(1):e9. doi:10.1093/nar/gks797 103. Biechele TL, Kulikauskas RM, Toroni RA, et al. Wnt/β-catenin signaling and AXIN1 regulate apoptosis triggered by inhibition of the mutant kinase BRAFV600E in human melanoma. Sci Signal. 2012;5(206):ra3. doi:10.1126/scisignal.2002274 104. Jeong W-J, Yoon J, Park J-C, et al. Ras Stabilization Through Aberrant Activation of Wnt/ -Catenin Signaling Promotes Intestinal Tumorigenesis. Sci Signal. 2012;5(219):ra30-ra30. doi:10.1126/scisignal.2002242 105. Weise A, Bruser K, Elfert S, et al. Alternative splicing of Tcf7l2 transcripts generates protein variants with differential promoter-binding and transcriptional activation properties at Wnt/beta-catenin targets. Nucleic Acids Res. 2010;38(6):1964-1981. doi:10.1093/nar/gkp1197 106. Hoppler S, Kavanagh CL. Wnt signalling: variety at the core. J Cell Sci. 2007;120(Pt 3):385-393. doi:10.1242/jcs.03363 107. Lien W-H, Fuchs E. Wnt some lose some: transcriptional governance of stem cells by Wnt/ -catenin signaling. Genes Dev. 2014;28(14):1517-1532. doi:10.1101/gad.244772.114 108. White BD, Chien AJ, Dawson DW. Dysregulation of Wnt/β-Catenin Signaling in Gastrointestinal Cancers. Gastroenterology. 2012;142(2):219-232. doi:10.1053/j.gastro.2011.12.001 109. Wallmen B, Schrempp M, Hecht A. Intrinsic properties of Tcf1 and Tcf4 splice variants determine cell-type-specific Wnt/β-catenin target gene expression. Nucleic Acids Res. 2012;40(19):9455-9469. doi:10.1093/nar/gks690 110. Biechele TL, Adams AM, Moon RT. Transcription-based reporters of Wnt/beta- catenin signaling. Cold Spring Harb Protoc. 2009;2009(6):pdb.prot5223. doi:10.1101/pdb.prot5223 111. Du Q, Geller D a. Cross-Regulation Between Wnt and NF-kB Signaling Pathways. Immunopathol Dis Ther. 2011;1(3):1-18. doi:10.1615/ForumImmunDisTher.v1.i3.Cross-Regulation 112. Khanom R, Nguyen CTK, Kayamori K, et al. Keratin 17 Is Induced in Oral Cancer and Facilitates Tumor Growth. Wiche G, ed. PLoS One. 2016;11(8):e0161163. doi:10.1371/journal.pone.0161163 162

113. van de Rijn M, Perou CM, Tibshirani R, et al. Expression of Cytokeratins 17 and 5 Identifies a Group of Breast Carcinomas with Poor Clinical Outcome. Am J Pathol. 2002;161(6):1991-1996. doi:10.1016/S0002-9440(10)64476-8 114. Wang Y-F, Lang H-Y, Yuan J, et al. Overexpression of keratin 17 is associated with poor prognosis in epithelial ovarian cancer. Tumour Biol. 2013;34(3):1685- 1689. doi:10.1007/s13277-013-0703-5 115. Escobar-Hoyos LF, Shah R, Roa-Peña L, et al. Keratin-17 promotes p27KIP1nuclear export and degradation and offers potential prognostic utility. Cancer Res. 2015;75(17):3650-3662. doi:10.1158/0008-5472.CAN-15-0293 116. Cohen-Kerem R, Rahat MA, Madah W, Greenberg E, Sabo E, Elmalah I. Cytokeratin-17 as a Potential Marker for Squamous Cell Carcinoma of the Larynx. Ann Otol Rhinol Laryngol. 2004;113(10):821-827. doi:10.1177/000348940411301008 117. Ide M, Kato T, Ogata K, Mochiki E, Kuwano H, Oyama T. Keratin 17 expression correlates with tumor progression and poor prognosis in gastric adenocarcinoma. Ann Surg Oncol. 2012;19(11):3506-3514. doi:10.1245/s10434-012-2437-9 118. Chen Y, Cui T, Yang L, et al. The diagnostic value of cytokeratin 5/6, 14, 17, and 18 expression in human non-small cell lung cancer. Oncology. 2011;80(5-6):333- 340. doi:10.1159/000329098 119. Hobbs RP, Depianto DJ, Jacob JT, et al. Keratin-dependent regulation of Aire and gene expression in skin tumor keratinocytes. Nat Genet. 2015;47(8):933-938. doi:10.1038/ng.3355 120. Protti MP, De Monte L. Immune infiltrates as predictive markers of survival in pancreatic cancer patients. Front Physiol. 2013;4:210. doi:10.3389/fphys.2013.00210 121. Weber CK, Adler G. From acinar cell damage to systemic inflammatory response: Current concepts in pancreatitis. Pancreatology. 2001;1(4):356-362. doi:10.1159/000055834 122. Jiang H, Hegde S, Knolhoff BL, et al. Targeting focal adhesion kinase renders pancreatic cancers responsive to checkpoint immunotherapy. Nat Med. 2016;22(8):851-860. doi:10.1038/nm.4123 123. Barnes TA, Amir E. HYPE or HOPE: the prognostic value of infiltrating immune cells in cancer. Br J Cancer. 2017;117(4):451-460. doi:10.1038/bjc.2017.220 124. Byrne EH, Fisher DE. Immune and molecular correlates in melanoma treated with immune checkpoint blockade. Cancer. 2017;123(S11):2143-2153. doi:10.1002/cncr.30444 125. Van Allen EM, Miao D, Schilling B, et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science. 2015;350(6257):207-211. 163

doi:10.1126/science.aad0095 126. Le DT, Uram JN, Wang H, et al. PD-1 Blockade in Tumors with Mismatch-Repair Deficiency. N Engl J Med. 2015;372(26):2509-2520. doi:10.1056/NEJMoa1500596 127. Nomi T, Sho M, Akahori T, et al. Clinical Significance and Therapeutic Potential of the Programmed Death-1 Ligand/Programmed Death-1 Pathway in Human Pancreatic Cancer. Clin Cancer Res. 2007;13(7):2151-2157. doi:10.1158/1078- 0432.CCR-06-2746 128. Geng L, Huang D, Liu J, et al. B7-H1 up-regulated expression in human pancreatic carcinoma tissue associates with tumor progression. J Cancer Res Clin Oncol. 2008;134(9):1021-1027. doi:10.1007/s00432-008-0364-8 129. Brahmer JR, Tykodi SS, Chow LQM, et al. Safety and Activity of Anti–PD-L1 Antibody in Patients with Advanced Cancer. N Engl J Med. 2012;366(26):2455- 2465. doi:10.1056/NEJMoa1200694 130. Royal RE, Levy C, Turner K, et al. Phase 2 trial of single agent ipilimumab (Anti- CTLA-4) for locally advanced or metastatic pancreatic adenocarcinoma. J Immunother. 2010;33(8):828-833. doi:10.1097/CJI.0b013e3181eec14c 131. Balachandran VP, Łuksza M, Zhao JN, et al. Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer. Nature. 2017;551(7681):512- 516. doi:10.1038/nature24462 132. Winograd R, Byrne KT, Evans RA, et al. Induction of T-cell Immunity Overcomes Complete Resistance to PD-1 and CTLA-4 Blockade and Improves Survival in Pancreatic Carcinoma. Cancer Immunol Res. 2015;3(4):399-411. doi:10.1158/2326-6066.CIR-14-0215 133. Spranger S, Bao R, Gajewski TF. Melanoma-intrinsic β-catenin signalling prevents anti-tumour immunity. Nature. 2015;523(7559):231-235. doi:10.1038/nature14404 134. Catherine S. Grasso, Marios Giannakis, Daniel K. Wells, Tsuyoshi Hamada, Xinmeng Jasmine Mu, Michael Quist, Jonathan A. Nowak, Reiko Nishihara, Zhi Rong Qian, Kentaro Inamura, Teppei Morikawa, Katsuhiko Nosho, Gabriel Abril- Rodriguez, Charles Connolly, He and UP. Genetic mechanisms of immune evasion in colorectal cancer. Cancer Discov. 2018. 135. Becht E, Giraldo NA, Lacroix L, et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 2016;17(1):218. doi:10.1186/s13059-016-1070-5 136. Ryu J, Kang M, Lee M-S, et al. Cross Talk between the TM4SF5/Focal Adhesion Kinase and the Interleukin-6/STAT3 Pathways Promotes Immune Escape of Human Liver Cancer Cells. Mol Cell Biol. 2014;34(16):2946-2960.

164

doi:10.1128/MCB.00660-14 137. Lee SA, Lee SY, Cho IH, et al. Tetraspanin TM4SF5 mediates loss of contact inhibition through epithelial-mesenchymal transition in human hepatocarcinoma. J Clin Invest. 2008;118(4):1354-1366. doi:10.1172/JCI33768 138. Lee D, Na J, Ryu J, et al. Interaction of tetraspan(in) TM4SF5 with CD44 promotes self-renewal and circulating capacities of hepatocarcinoma cells. Hepatology. 2015;61(6):1978-1997. doi:10.1002/hep.27721 139. Kang M, Choi S, Jeong S-J, et al. Cross-talk between TGFβ1 and EGFR signalling pathways induces TM4SF5 expression and epithelial–mesenchymal transition. Biochem J. 2012;443(3):691-700. doi:10.1042/BJ20111584 140. Tan X, Apte U, Micsenyi A, et al. Epidermal growth factor receptor: a novel target of the Wnt/beta-catenin pathway in liver. Gastroenterology. 2005;129(1):285-302. http://www.ncbi.nlm.nih.gov/pubmed/16012954. Accessed February 25, 2018. 141. Wielenga VJM, Smits R, Korinek V, et al. Expression of CD44 in Apc and TcfMutant Mice Implies Regulation by the WNT Pathway. Am J Pathol. 1999;154(2):515-523. doi:10.1016/S0002-9440(10)65297-2 142. Brembeck FH, Rosário M, Birchmeier W. Balancing cell adhesion and Wnt signaling, the key role of beta-catenin. Curr Opin Genet Dev. 2006;16(1):51-59. doi:10.1016/j.gde.2005.12.007 143. Brembeck FH, Schwarz-romond T, Bakkers J, Wilhelm S, Hammerschmidt M, Birchmeier W. Essential role of BCL9-2 in the switch between β -catenin â€TM s adhesive and transcriptional functions service Essential role of BCL9-2 in the switch between ␤ -catenin ’ s adhesive and transcriptional functions. 2004:2225- 2230. doi:10.1101/gad.317604 144. Müller-Pillasch F, Wallrapp C, Lacher U, et al. Identification of a new tumour- associated antigen TM4SF5 and its expression in human cancer. Gene. 1998;208(1):25-30. doi:10.1016/S0378-1119(97)00633-1 145. Ahn HM, Ryu J, Song JM, et al. Anti-cancer activity of novel TM4SF5-targeting antibodies through TM4SF5 neutralization and immune cell-mediated cytotoxicity. Theranostics. 2017;7(3):594-613. doi:10.7150/thno.15629 146. Yuan TL, Amzallag A, Bagni R, et al. Differential Effector Engagement by Oncogenic KRAS. CellReports. 2018;22:1889-1902. doi:10.1016/j.celrep.2018.01.051 147. Mendoza MC, Er EE, Blenis J. The Ras-ERK and PI3K-mTOR pathways: cross- talk and compensation. Trends Biochem Sci. 2011;36(6):320-328. doi:10.1016/j.tibs.2011.03.006 148. Janes MR, Zhang J, Li L-S, et al. Targeting KRAS Mutant Cancers with a Covalent G12C-Specific Inhibitor. Cell. 2018;172(3):578-589.e17. 165

doi:10.1016/j.cell.2018.01.006 149. Donahue TR, Dawson DW. Leveraging Mechanisms Governing Pancreatic Tumorigenesis To Reduce Pancreatic Cancer Mortality. Trends Endocrinol Metab. 2016;27(11):770-781. doi:10.1016/j.tem.2016.06.009 150. Brahmer JR, Tykodi SS, Chow LQM, et al. Safety and Activity of Anti–PD-L1 Antibody in Patients with Advanced Cancer. N Engl J Med. 2012;366(26):2455- 2465. doi:10.1056/NEJMoa1200694 151. Feig C, Gopinathan A, Neesse A, Chan DS, Cook N, Tuveson DA. The pancreas cancer microenvironment. Clin Cancer Res. 2012;18(16):4266-4276. doi:10.1158/1078-0432.CCR-11-3114 152. Wong KM, Horton KJ, Coveler AL, Hingorani SR, Harris WP. Targeting the Tumor Stroma: the Biology and Clinical Development of Pegylated Recombinant Human Hyaluronidase (PEGPH20). Curr Oncol Rep. 2017;19(7):47. doi:10.1007/s11912- 017-0608-3 153. Nicolle R, Blum Y, Marisa L, et al. Pancreatic Adenocarcinoma Therapeutic Targets Revealed by Tumor-Stroma Cross-Talk Analyses in Patient-Derived Xenografts. Cell Rep. 2017;21(9):2458-2470. doi:10.1016/j.celrep.2017.11.003 154. Hingorani SR, Zheng L, Bullock AJ, et al. HALO 202: Randomized Phase II Study of PEGPH20 Plus Nab-Paclitaxel/Gemcitabine Versus Nab- Paclitaxel/Gemcitabine in Patients With Untreated, Metastatic Pancreatic Ductal Adenocarcinoma. J Clin Oncol. 2018;36(4):359-366. doi:10.1200/JCO.2017.74.9564 155. Jiang X, Hao H-X, Growney JD, et al. Inactivating mutations of RNF43 confer Wnt dependency in pancreatic ductal adenocarcinoma. Proc Natl Acad Sci U S A. 2013;110(31):12649-12654. doi:10.1073/pnas.1307218110 156. Hayes JD, Dinkova-Kostova AT. The Nrf2 regulatory network provides an interface between redox and intermediary metabolism. Trends Biochem Sci. 2014;39(4):199-218. doi:10.1016/j.tibs.2014.02.002 157. Tian D, Shi Y, Chen D, Liu Q, Fan F. The Wnt inhibitor LGK-974 enhances radiosensitivity of HepG2 cells by modulating Nrf2 signaling. Int J Oncol. 2017;51(2):545-554. doi:10.3892/ijo.2017.4042 158. Caronna EA, Patterson ES, Hummert PM, Kroll KL. Geminin Restrains Mesendodermal Fate Acquisition of Embryonic Stem Cells and is Associated with Antagonism of Wnt Signaling and Enhanced Polycomb-Mediated Repression. Stem Cells. 2013;31(8):1477-1487. doi:10.1002/stem.1410 159. Emmett LSD, O’Shea KS. Geminin Is Required for Epithelial to Mesenchymal Transition at Gastrulation. Stem Cells Dev. 2012;21(13):2395-2409. doi:10.1089/scd.2011.0483

166

160. Chou T-C. Drug combination studies and their synergy quantification using the Chou-Talalay method. Cancer Res. 2010;70(2):440-446. doi:10.1158/0008- 5472.CAN-09-1947 161. Lemieux E, Cagnol S, Beaudry K, Carrier J, Rivard N. Oncogenic KRAS signalling promotes the Wnt/β-catenin pathway through LRP6 in colorectal cancer. Oncogene. 2015;34(38):4914-4927. doi:10.1038/onc.2014.416 162. Tamai K, Zeng X, Liu C, et al. A Mechanism for Wnt Coreceptor Activation. Mol Cell. 2004;13(1):149-156. doi:10.1016/S1097-2765(03)00484-2 163. Slyper M, Shahar A, Bar-Ziv A, et al. Control of breast cancer growth and initiation by the stem cell-associated transcription factor TCF3. Cancer Res. 2012;72(21):5613-5624. doi:10.1158/0008-5472.CAN-12-0119 164. Chien AJ, Moore EC, Lonsdorf AS, et al. Activated Wnt/beta-catenin signaling in melanoma is associated with decreased proliferation in patient tumors and a murine melanoma model. Proc Natl Acad Sci U S A. 2009;106(4):1193-1198. doi:10.1073/pnas.0811902106 165. Monga SP. β-Catenin Signaling and Roles in Liver Homeostasis, Injury, and Tumorigenesis. Gastroenterology. 2015;148(7):1294-1310. doi:10.1053/j.gastro.2015.02.056 166. Khemlina G, Ikeda S, Kurzrock R. The biology of Hepatocellular carcinoma: implications for genomic and immune therapies. Mol Cancer. 2017;16(1):149. doi:10.1186/s12943-017-0712-x 167. Seino T, Kawasaki S, Shimokawa M, et al. Human Pancreatic Tumor Organoids Reveal Loss of Stem Cell Niche Factor Dependence during Disease Progression. Cell Stem Cell. http://linkinghub.elsevier.com/retrieve/pii/S1934590917305106. Published January 10, 2018. Accessed February 21, 2018. 168. Shao W, Espenshade PJ. Expanding Roles for SREBP in Metabolism. Cell Metab. 2012;16(4):414-419. doi:10.1016/j.cmet.2012.09.002 169. Daemen A, Peterson D, Sahu N, et al. Metabolite profiling stratifies pancreatic ductal adenocarcinomas into subtypes with distinct sensitivities to metabolic inhibitors. Proc Natl Acad Sci. 2015;112(32):E4410-E4417. doi:10.1073/pnas.1501605112 170. Murphy M, Chatterjee SS, Jain S, Katari M, Dasgupta R. TCF7L1 Modulates Colorectal Cancer Growth by Inhibiting Expression of the Tumor-Suppressor Gene EPHB3. Sci Rep. 2016;6(1):28299. doi:10.1038/srep28299 171. Bhambhani C, Cadigan KM. Finding a Needle in a Genomic Haystack: Genome- Wide Approaches to Identify Wnt/TCF Transcriptional Targets. In: Wnt Signaling in Development and Disease: Molecular Mechanisms and Biological Functions. Hoboken, NJ, USA: John Wiley & Sons, Inc; 2014:73-87.

167 doi:10.1002/9781118444122.ch5

168