DNA Demethylating Agents and Interferons As Regulators of Wnt/Β-Catenin Signaling in Colorectal Cancer

DNA Demethylating Agents and Interferons As Regulators of Wnt/β-Catenin signaling in Colorectal Cancer

Ilias Ettayebi

A thesis submitted in conformity with the requirements for the degree of Master of Science Department of Medical Biophysics University of Toronto

DNA Demethylating Agents and Interferons As Regulators of Wnt/β-Catenin signaling in Colorectal Cancer

Ilias Ettayebi Master of Science Department of Medical Biophysics University of Toronto 2018

Colorectal Cancer (CRC) is characterized by the accumulation of genetic and epigenetic alterations, leading to aberrant activation of pathways important in self-renewal and apoptosis. Recent reports have discovered that treatment with low doses of the DNA demethylating agent, 5-Aza-2-deoxycytidine (5-Aza-CdR), can target cancer initiating cells (CICs) through the activation of a viral sensing pathway. In this study, we show a novel intersection between RIG-I/MDA-5 and Wnt/β-catenin signaling pathways. We found that treatment of CIC enriched cancer cells with both 5-Aza-CdR, synthetic dou-ble stranded RNA, or β-catenin signaling. Furthermore, treatment with type I and III interferons reduced CIC frequency in colorectal cancer cells in vitro and in vivo. These findings may explain a novel mechanism by which 5-Aza-CdR can specifically target CIC enriched populations in colorectal cancer and highlights the importance of intact innate immune pathways in epigenetic therapy efficacy.

ii Acknowledgements

I would like to first thank my supervisor, Dr. Daniel De Carvalho for giving me the opportunity to work on such an exciting project. Your passion for science pushed me to think outside the box and inspired me throughout my studies. I would like to also thank my committee members, Dr. Catherine O’Brien and Dr. Cheryl Arrowsmith for their valuable feedback and suggestions throughout my studies. Your expertise was instrumental in guiding my project and experiments. I would like to also thank all my wonderful colleagues in the De Carvalho lab. David played an instrumental part in my early days in the lab. Thank you for not only teaching me many of the techniques that were used for my project, but also mentoring me on how to be a scientist. I would like to also thank Parinaz for her contribution to this project. We spent a lot of time together doing experiments side by side and in the process had a lot of great conversations, both science and non-science related. Thank you to Roxana, well for pretty much everything! Without you, I know things would have been much more difficult. Your willingness to lend a hand and an ear whenever I needed it is something I won’t forget. I want to also thank Helen for making my experience in the lab so fun. All those tissue culture room conversations will definitely be missed. Thank you to Frank and Tiago and Rajat for also making working in the lab so enjoyable. The amount of funny moments we had together are uncountable! I would like to thank Ankur, Fabiola, Felipe, Charles, Raymond, Isabella for all their support during the end of my studies, you all made the experience that much more enjoyable! Lastly, I would like to thank my family and friends for their moral support through this journey. To my mom and dad, who had to hear about all of my fun adventure through my master’s everyday for their life for the past 3 years, thank you for supporting me and encouraging me. I truly couldn’t have done this without you. Thank you to my two sisters who cheered me up when I was feeling down and lending out an ear when I needed it. And lastly thank you to all my friends who were there for me through my time as a student. Your constant encouragement will not be forgotten.

iii Contents

Acknowledgements ...... iii Table of Contents ...... v List of Tables ...... vi List of Figures ...... vii List of Abbreviations ...... viii Introduction 1 1.1 Colon Homeostasis ...... 1 1.1.1 The Intestinal Stem Cell (ISC) ...... 1 1.1.2 The Colon Cancer Stem Cell ...... 5 1.1.3 Targeting Colon Cancer Stem Cells ...... 7 1.2 Epigenetic Landscape of Cancer ...... 8 1.2.1 DNA Methylation ...... 8 1.2.2 Epigenetic Mediated Therapeutic Approaches ...... 11 1.3 Tumour Immunity ...... 13 1.3.1 Innate Immunity ...... 13 1.3.2 Role of Immunity in Cancer ...... 18 1.3.3 Immune Mediated Therapeutic Approaches ...... 19

2 Materials & Methods 22 2.1 Analysis of publicly available datasets ...... 22 2.2 Tissue Culture ...... 22 2.3 Treatments of cells ...... 23 2.4 Cell Growth Measurement ...... 23 2.5 Generation of TCF-GFP reporter ...... 23 2.6 Confocal Microscopy ...... 23 2.7 Proximity Ligation Assay ...... 24 2.8 Flow Cytometry Analysis ...... 24 2.9 Isolation, cDNA synthesis and RT-qPCR ...... 25 2.10 NanoString Processing and Analysis ...... 25 2.11 GST Pull down ...... 25 2.12 Immunoblotting ...... 26 2.13 Limiting Dilution Assay ...... 26 2.14 Statistical Analysis ...... 27

iv 3 Results 28 3.1 Inverse correlation between Interferon signaling and Intestinal stem cell signature in multiple colorectal cancer (CRC) data-sets ...... 28 3.2 5-Aza-2-deoxycytidine (5-AZA-CdR) Induces IRF Genes & Reduces Select Stemness Genes in CRC patient derived xenograft (PDX) Cultures . . . 28 3.3 Interferon regulatory factor 7 (IRF7) Activation Reduces Wnt Signaling . 29 3.4 IRF7 Signaling Does Not Change β-Catenin Localization ...... 29 3.5 IRF7 is in proximity but does not physically interact with β-Catenin . . 30 3.6 Immune Activation Reduces CRC Cell Growth in-vitro ...... 30 3.7 Immune Activation Reduces CIC Frequency in-vitro ...... 31 3.8 Type I & III Interferons Reduce CIC Frequency in-vivo ...... 31

4 Discussion 41 4.1 Summary & Future Directions ...... 41 4.2 Limitations ...... 47 4.3 Conclusion ...... 48

5 Appendix 49 5.1 Appendix I - Supplementary Figures & Tables ...... 49

Bibliography 63

v List of Tables

5.1 Patient derived CRC cells and their mutational status used in this study 59 5.2 RT-qPCR primers used for this study ...... 60 5.3 List of reagents, assays and cellines used for this study ...... 61 5.4 Nanostring gene expression code set used for this study ...... 62

vi List of Figures

3.1 Publicly Available RNA-Seq data of CRC tumours ...... 32 3.2 Gene expression analysis after DNA methyltransferase (DNMT)i . . . . . 33 3.3 Gene expression analysis after 5 and 10 days of culture ...... 34 3.4 Wnt activity after immune activation ...... 35 3.5 IRF7 and β-catenin Immunoﬂuorescent staining after immune activation 36 3.6 proximity ligation assay (PLA) of Interferon regulatory factor 3 (IRF3)/β- catenin after Polyinosine-polycytidylic acid (Poly(I:C)) treatment . . . . 37 3.7 PLA of IRF7/β-catenin after immune activation ...... 38 3.8 In-vitro cell growth and cancer initiating cell (CIC) frequency after immune activation ...... 39 3.9 In-vivo CIC frequency after interferon treatment ...... 40

5.1 Interferon Stimulated Gene expression analysis after DNMTi ...... 50 5.2 Interferon Stimulated Gene expression analysis after immune activation . 51 5.3 Wnt high and low activity after Poly(I:C) & IFNλ treatment ...... 52 5.4 GST-β-catenin pulldown blot for TCF4, IRF3, & IRF7 ...... 53 5.5 TCF4 chromatin immunoprecipitation (ChIP)-qPCR of Wnt target genes after IFNλ treatment ...... 54 5.6 ATF3 and CCL gene expression after 5-Aza-CdR treatment ...... 55 5.7 Wnt activity: All celllines ...... 56 5.8 Basal levels of Interferon Stimulated Gene expression in CRC cells . . . . 57 5.9 Current Working Model ...... 58

vii Abbreviations

5-AZA-CdR 5-Aza-2-deoxycytidine. v, 12, 23–26, 28–31, 33, 35, 36, 38, 39, 42–44

ALDH aldehyde dehydrogenase. 7

AML acute myeloid leukemia. 5, 8

APC adenomatosis polyposis coli. 3, 5

BER base excision repair. 9

CBC crypt base columnar. 2, 3

ChIP chromatin immunoprecipitation. vii, 54

CIC cancer initiating cell. vii, 5, 8, 39–41, 44–47

CIMP CpG island methylator phenotype. 10, 29, 31

CRC colorectal cancer. v, vii, 6, 10–12, 22–24, 28–32, 39–47

CSC Cancer Stem Cell. 5–8

DAMP danger associated molecular pattern. 13

DNMT DNA methyltransferase. vii, 8–13, 19, 20, 28–30, 41–47 dsRNA double stranded RNA. 8, 12, 15, 19, 20, 30, 41, 42, 44, 45, 48

ERV endogenous retrovirus. 12, 13

ES embryonic stem cells. 9

HDAC histone deacetylase. 12

viii IFN interferon. 15–21, 30, 31, 46

IFNAR interferon alpha receptor. 16

IFNLR interferon lambda receptor. 16

IRF3 Interferon regulatory factor 3. vii, 14, 15, 18, 30, 37, 44, 46, 47

IRF7 Interferon regulatory factor 7. v, vii, 14, 15, 20, 23, 26, 29, 30, 32, 36, 38, 43, 44, 47

ISC Intestinal Stem Cell. iv, 1, 2, 4, 28, 29, 32

LDA limiting dilution assay. 26, 27, 39, 40

LGR5 Leucine-rich repeat-containing G-protein coupled receptor 5. 2, 29, 42, 47

LINE long interspersed nuclear element. 9

LOH loss of heterozygosity. 10

LTR long terminal repeats. 9

MDA5 Melanoma Diﬀerentiation Associated protein 5. 8, 15, 19

MMP matrix metalloproteinases. 7

PAMP pathogen associated molecular pattern. 14

PDX patient derived xenograft. v, 23, 28–30, 33–35, 39, 40

PLA proximity ligation assay. vii, 30

Poly(I:C) Polyinosine-polycytidylic acid. vii, 19, 23–26, 29–31, 35–39

RIG-I retinoic acid-inducible gene I. 8, 14, 15, 19

TCGA The Cancer Genome Atlas. 28, 32

TE transposable elements. 9

TET ten eleven translocase. 13

TLR toll like receptor. 14, 15

ix TSS transcription start site. 12

VEGFA vascular endothelial growth factor A. 29, 42

x Chapter 1

Introduction

1.1 Colon Homeostasis

1.1.1 The ISC

Hierarchical crypt organization

The colon is organized in three main layers. The outer layer is made of smooth muscle and is important in the peristaltic movements of the intestine. The second layer consists of stroma, which is found between the outer muscle and the inner epithelial layer. The stroma contains compartments of the hematological, nervous, as well as the immune system. The last most luminal layer is a single cell epithelial layer. This layer plays an important role in the absorption of nutrients into the bloodstream and acts as a protective barrier against pathogens entering the body [6]. Within the epithelial layer exists a well- deﬁned architecture. This includes the simple columnar epithelium, which is folded to form a number of invaginations known as crypts [99]. Cells residing at the top of the crypt consist of several diﬀerentiated cell types including enterocytes, mucus-secreting goblet cells as well as peptide hormone secreting enteroendocrine cells. On the opposite side of the spectrum, at the base of the crypt exist the ISC and Paneth cells [7, 129].

Identifying normal ISC

Tissue stem cells have unique features that diﬀerentiate them from other cells that make up a tissue. First, tissue stem cells must be maintained over long periods of time. This requirement plays a fundamental role in maintaining the structure of the tissue over the life time of an organism. In addition, tissue stem cells must also have the ability to generate diﬀerentiated cell types of the tissue [6]. The ISC meets these requirements.

1 Chapter 1. Introduction 2

While the general architecture of the crypt has been agreed upon, there are two models that can explain the hierarchical organization of the intestinal stem cell. The first model termed the “+4 position” model, describes an ISC as the cell which is located at position 4 from the base of the crypt, with the first three cells thought to be differentiated Paneth cells [119]. This was initially described by labeling cells positioned at +4 and observing that these cells not only retain their DNA label, which is indicative of quiescence, but also could be labeled with 5-Bromo-2-deoxyuridine (BrdU), showing their ability to proliferate [120]. These seemingly conflicting results suggest that the +4 cells could retain labeled template DNA strands selectively, while segregating newly synthesized DNA strands during mitosis. Unfortunately, one of the main set backs in identifying the true position of the intestinal stem cell has been the lack of specific stem cell markers. More recently, there have been studies that have identified markers specific to the +4 cells. These include phospho-PTEN and phospho-AKT [68], Sox4 [52] , Dcamkl1 [59], and Bmi1 [129]. For example, lineage tracing studies have shown that cells that are at the +4 position and express Bmi1 have the ability to generate all epithelial lineages [129] . The second model termed the stem cell zone model, states that a mixture of cells represent the direct offspring of the crypt base columnar (CBC), which are considered to be the intestinal stem cells [12]. In this model, Paneth cells migrate downwards towards the very base of the crypt, while all other cell types migrate upward, maturing into functional epithelial cells. Similar to +4 cells, CBCs have self-renewal properties and can generate differentiated mature intestinal epithelial cells. More specifically, recent studies have shown that a population of CBCs that express CD133 and Leucine-rich repeat-containing G-protein coupled receptor 5 (LGR5) have intestinal stem cell properties, and are distinct from +4 cells [7]. This data suggests that these two types of stem cells both play an important role in regulating intestinal epithelial tissue homeostasis and self-renewal in the intestine.

Wnt Signaling

One of the major signaling pathways involved in the maintenance of the intestinal stem cell is the Wnt/β-catenin pathway [49]. This pathway is activated via secreted glyco- proteins known as Wnt ligands, and bind to the N-terminal domain of the Frizzled (Fz) receptor family [67]. For the signal transduction pathway to occur, other co-receptors such as low-density-lipoprotein-related protein 5/6 (LRP5/6) are required [67]. Once this interaction occurs, the signal is transduced to the cytoplasmic protein Dishevelled (Dsh/Dvl), where further propagation of the pathway can occur [153]. At this stage, the Wnt pathway can separate into two pathways, the canonical and non-canonical pathways. Although the non-canonical pathway plays an important role in tissue formation Chapter 1. Introduction 3 during development, it’s function in intestinal stem biology is not clear [138]. The more well studied pathway is the canonical or β-catenin dependent Wnt pathway. In the non- activated form, no Wnt ligand is bound to the Fz and LRP5/6 receptors. The lack of Wnt ligand in turn leads to the assembly of a destruction complex, which is composed of various proteins including Axin,adenomatosis polyposis coli (APC), protein phosphatase 2A (PP2A), glycogen synthase kinase 3 (GSK3) and casein kinase 1α (CK1α). The role of this complex is to ultimately degrade β-catenin through phosphorylation and protea- somal degradation [61]. On the other hand, when Wnt ligands are bound to the Fz and LRP5/6 receptors, disruption of the APC/Axin/GSK3 destruction complex occurs. More specifically, Wnt induces Dsh activity, which in turn inhibits the activity of the GSK3 kinase. Prevention of phosphorylation and degradation of β-catenin subsequently leads to stabilization and accumulation of β-catenin in the cytoplasm. Stabilized β-catenin is then able to translocate into the nucleus. Interestingly, the mechanism by which this occurs is not well known, with some evidence pointing to β-catenin’s ability to bind with other factors in the cytoplasm as a way to translocate into the nucleus [28]. Once in the nucleus however, β-catenin ultimately acts as a co-activator to multiple transcription factors, the LEF/TCF DNA-binding transcription factors being the most well studied [27]. In the intestine, the Wnt cascade is the most important pathway involved in controlling cell fate along the cryptvillus axis, with β-catenin present throughout the crypts of the intestine [8]. Interestingly, mice lacking the DNA-binding transcription factor TCF4, does not affect the differentiated compartment, but completely ablates the progenitor compartment in the intestine [88]. This suggest that an activated Wnt pathway plays essential role maintaining the intestinal stem cell compartment.

Notch

Notch has been shown to have a broad range of roles in dictating cell fate. Briefly, binding of Notch ligands to the receptors (Delta or Jagged), leads to their cleavage by γ-secretase. Cleaved receptors then translocate into the nucleus and from a transcriptional complex with RBP-jκ, ultimately leading to the activation of Notch specific genes. One of the most important genes induced is hairy/enhancer of split (Hes). In mice car- rying a single mutant Apc allele (APC min), expression of hes1 was associated with the undifferentiated cell compartment. Interestingly, the use of γ-secretase inhibitor led to the down-regulation of hes1, and subsequently led to differentiation of undifferentiated cells into post-mitotic goblet cells [147]. Later studies confirmed the localization of hes1 to CBC cells, further supporting its importance the intestinal crypts [148]. Additionally, other Notch target genes such as Olfm4 have been identified in CBC cells [148]. Chapter 1. Introduction 4

BMP

BMPs belong to the transforming growth factor β family and bind to various BMP receptor including type I (IA, IB, or ALK2) and type II (IIA or IIB) receptors. In general, BMP signaling is thought to antagonize crypt formation and ISC self-renewal [64]. Briefly, BMP receptor binding leads to phosphorylation of downstream proteins, mainly the Smad family of proteins. Phosphorylated Smads can form heterodimers which lead to their nuclear translocation and subsequent transcriptional activation [132]. Interest- ingly in the intestine, BMP signaling is thought to specifically antagonize Wnt signaling. Mechanistically, BMP signals enhance PTEN activity, and subsequently inhibit Akt activity [68]. This leads to a decrease in nuclear β-catenin and shut down of Wnt signaling in ISCs. Furthermore, studies have shown that these effects can be abrogated with the use of the BMP inhibitor, Noggin, confirming the antagonistic role of BMP signaling in ISC maintenance [68].

Hedgehog

In many aspects, Hedgehog signaling is regulated similar to Wnt signaling. In the absence of ligand, the patched family of receptors (PTCH1/PTCH and PTCH2) inhibit Smoothened (SMO) signal transducer, leading to the formation of the a degradation complex. Within this complex, GLI family members are phosphorylated by casein kinase I (CKI), glycogen synthase kinase-3β (GSK3β) and protein kinase A (PKA) and subsequently, ubiquitinated and degraded by FBXW1/βTRCP1. On the other hand, binding of ligand to the Patched receptors leads to the liberation of the SMO signal transducer. The liberated SMO can then activate the serine/threonine kinase STK36, and subsequently leading to the degradation of the GLI complex. GLI then translocates into the nucleus and activates genes including PTCH1, JAG2, and CCND2. Although Wnt an Hedgehog regulation is similar in many aspects, Hedgehog signaling has been shown to inhibits Wnt signaling in intestinal stem cells [39]. Mechanistically, hedgehog signaling activates Gli1 and leads to promoter activation of various genes including the Wnt antagonist SFRP1 [66]. Interestingly, studies have shown that overexpression of Gli1 can lead to the abrogation of β-catenin accumulation in Wnt activated cells, but this can be rescued with expression SFRP1 [66]. Therefore, hedgehog signaling is an important player in the overall signaling network of the intestinal stem cell. Chapter 1. Introduction 5

1.1.2 The Colon Cancer Stem Cell

The Cancer Stem Cell (CSC) Model

The CSC model states that tumour growth is driven by a small subpopulation of cells with characteristics resembling embryonic stem cells [90, 125, 74]. These CSCs have the ability to both self renew as well as produce differentiated cancer cells and initiate a tumour [90]. Early investigations into the cancer stem cell model began in acute myeloid leukemia (AML), when AML CSCs were injected into immunosuppressed mice and observed to initiate tumor formation [14]. These initial finding led to the cancer stem cell model to be tested in solid tumours as well, with the breast cancer stem cell being the first to be isolated [3]. Subsequently, other solid tumours have been shown to follow the CSC model, including colon cancer, which is characterized by a population of cells expressing the marker CD133 [112]. By definition, CSCs are a group of cells that are isolated from the bulk tumour and have properties mentioned above. Although some cancers have been shown to have clear distinction between this bona-fide CSC population and the remaining bulk, and can be isolated accordingly, CSC have not been isolated from all cancer types [105]. However, nearly all cancers have populations of cells that posses a “stemness” characteristic such as the ability to self-renew, and ability give rise to differentiated cells can proliferate uncontrollably [90]. Therefore, a more broad term has been adopted to include all tumor populations that have “stemness” characteristics, independent of whether they have been successfully isolated from tumours. The term for this population is the CIC. Nonetheless, these two terms are still currently being used interchangeably in a large portion of the literature.

Wnt signaling in CSCs

Studies linking Wnt signaling with colon cancer was ﬁrst described when the APC gene was discovered to be the driver in causing familial adenomatous polyposis (FAP). FAP is a hereditary syndrome characterized by the development of hundreds of adenomatous colorectal polyps, dramatically increasing the risk of progression to colorectal cancer [121]. In this syndrome, The APC gene is mutated in the germline of patients. Studies have shown that loss of Apc in mice leads to rapid activation of the Wnt pathway, characterized by increase nuclear localization of β-catenin. As a results, changes in the transcriptome of APC mutant cells lead to aberrant regulation of apoptosis, cell cycle control and self-renewal properties [130]. Not surprisingly, the vast majority of patients with colon cancer have mutations in APC or other Wnt related proteins. However, what is intriguing is the observation that not all colon cancer cells have strong Wnt activation. Chapter 1. Introduction 6

In fact, activation of Wnt signaling has been shown to be limited to a subpopulation of cells [149]. These Wnt “high” cells were shown to be enriched in CSC frequency, and able to induce tumours, compared, whereas Wnt “low” cells had very limited capacity to induce tumours [149]. This suggest that only a fraction of cells, who are high in Wnt signaling, are endowed with self renewal properties.

CSC assays

Assays to investigate CSCs have been instrumental in our understanding of the molecular and functional role of CSCs. The limiting dilution xenografting assay is a technique used to measure the CSC tumor initiating potential in primary tumors and is considered to be the gold standard [112, 137, 14, 107]. Although the limiting dilution xenografting assay is considered the highest level of evidence for the existence of a CSC, there have been in vitro based assays that have shown to be good surrogates for the measurement of a stemness phenotype [107]. In this assay, cells are serially diluted and plated to assess the number of sphere-forming cells. Another assay used in the identification of CSC is fluorescence activated cell sorting (FACS). In this method, CSCs can be enriched by sorting for certain cell surface markers known to be involved in self-renewal. For example, in CRC, CSCs have been identified using CD133, CD166, CD44, CD24, and EpCAM [112, 83, 31]. Although this method has shown to enrich for CSC populations, other CSC assays are generally used in parallel, due to the expression of these surface markers on other non-CSC populations. Lastly, the use for reporter systems to measure the activity of a signaling pathway has recently been adopted to study CSC activity. As mentioned above, the canonical Wnt pathway is a fundamental player in CSC maintenance. Indeed, studies using a TCF/LEF reporter construct in colon cancer cells showed that sorting cells based on high Wnt activation was able to enrich the cancer cells for stem cell properties, such as the ability for the cells to generate tumours in mice [149]. Additionally, this tool has been used to perform high-throughput screens for the discovery of novel Wnt inhibitors [158]. Overall, the use of a combination of these methods to assess CSC properties will help in furthering our understanding of CSCs and their role in cancer progression.

CSC niche

Similar to normal stem cells, it is thought that cancer stem cells maintain their stemness in a speciﬁc microenvironment. These environments can contain cells that promote self renewal through secretion of various molecules including growth factors and cytokines. Chapter 1. Introduction 7

For example, studies have shown that the introduction of CSCs with endothelial cells isolated from tumors enhanced tumorigenesis in mice, compared to the same CSCs in the absence of cancer associated endothelial cells. The mechanism for this observation was shown to be through a nitric oxide/Notch signaling pathway [17]. In colorectal cancer, it is thought that cancer associated myofibroblasts play an important role in CSC maintenance. Myofibrocytes were shown to be recruited into the tumour microenvironment and express matrix metalloproteinases (MMP) leading to CSC maintenance [149]. Recently, there has been more evidence supporting the role of the specific microenvironents in CSC maintenance. A recent study showed that the location of CSCs can change their sensitivity to ablation. For example, the authors found that the genetic loss of Lgr5+ CSCs from already established liver metastases caused tumours to shrink and lose the ability to regrow, whereas loss of Lgr5+ CSCs from primary tumours regrew significantly [33]. This data not only highlights the importance of the micorenviroenment as it relates to CSC maintenance properties, but also highlights its role in dictating weather CSC can be permanently targeted.

1.1.3 Targeting Colon Cancer Stem Cells

Although treatment of colorectal cancer has improved, tumour recurrence remains a major clinical challenge. As such, there has been a tremendous amount of effort invested in the development of drugs that can target various signaling pathways involved in CSC maintenance [22]. These include targeting of the anti-apoptotic factors, detoxifying enzymes, DNA repair enzymes as well as oncogenic cascades, such as the Wnt/β-catenin, hedgehog, and notch pathways [22]. For example, small molecules that disrupts Wnt signaling has been shown to decrease tumorigenicity of colon cancer [21]. Similarly, small molecule inhibition of the hedgehog pathway has shown promise in inhibiting tumor initiation in pancreatic cancer [48]. This was thought to occur through the reduction of a population of cells high in aldehyde dehydrogenase (ALDH). With the observation that a pro-inflammatory environments can expand the cancer stem cell pool in some tumours [89], there has also been a growing interest in targeting inflammatory pathway as a CSC therapy. For example, inhibiting NF-κB has been shown to sensitize chemoresistant cells to standard anticancer drugs [56]. Similar results have also been reported with the inhibition of IL-4 in colon cancer using an anti-IL-4 antibody or IL-4Ra antagonist. This led to the CSCs to become apoptotic and sensitizes them to standard anticancer [144]. Recently, activation of an interferon signature has also been shown to target CSC. Treatment of CSC enriched colorectal cancer cells with DNA demethylating agents or Chapter 1. Introduction 8 stimulators of double stranded RNA (dsRNA) sensing pathway led to a marked decrease in CIC frequency [126]. This was shown to be reliant on an intact antiviral signaling pathway, as knockdown of the dsRNA sensors Melanoma Differentiation Associated protein 5 (MDA5) and retinoic acid-inducible gene I (RIG-I), or the downstream adapter MAVS lead to a rescue in the CIC reduction [126]. Lastly, targeting cellular surface markers has also been employed as anti-CSC strategy. For example, targeting CD44, a marker differ- entially expressed between normal stem cells and AML CSCs, showed a drastic reduction in AML stem cells [82]. Therefore, targeting CSC has shown promise through various mechanisms of action. However, it is becoming more evident that targeting the CSC alone many not be sufficient to permanently destroy the tumor. The use combinatorial therapies should therefore be employed in parallel with anti-CSC therapies.

1.2 Epigenetic Landscape of Cancer

The role epigenetics plays in cancer is now well established. The ﬁrst discovery linking the two was the observation that cancer cells had a loss in DNA methylation at CpG sites compared to normal cells [47]. Since then, a plethora of other epigenetic alterations have been discovered in cancer, ranging from chromatin modiﬁers to non-coding regulatory elements. Nonetheless, DNA methylation still remains at the centre of cancer epigenetics.

1.2.1 DNA Methylation

DNA Methylation in normal homeostasis

In mammals, cytosine methylation occurs primarily in a symmetrical CpG context[166]. There are three enzymes responsible for DNA methylation deposition and maintenance. These include DNA methyltransferase 1 (DNMT1), DNMT3A and DNMT3B. [34]. DNMT1 functions as a maintenance methyltransferase, responsible for the propagation of already established DNA methylation patterns in the cell [122] . DNMT3A and DNMT3B are “de novo” methyltranferases, establishing new DNA methylation patterns during early development [110]. During development, CpGs in mammalian genomes are generally methylated, with the exception of CpG islands found at promoters of housekeeping or developmentally regulated genes, which are constitutively hypomethylated. Maintaining a hypomethylated state requires the exclusion of DNMTs which allows transcription factor binding and gene regulation [98]. During embryonic cell diﬀerentiation however, some promoters will gain de novo methylation, which is an important event in driving embryonic development. Chapter 1. Introduction 9

On the other hand, loss of DNA methylation can occur passively through the absence of methyl donors or the inactivation of the methylation machinery during successive rounds of DNA replication [157]. In addition, active de-methylation occurs through an enzymatic process of removing the methyl group from the DNA [157]. This is carried out by Ten eleven translocation (Tet) proteins which oxidize methyl-cytosines to form hydroxyl-methyl-cytosine. base excision repair (BER) proteins can detect these modiﬁed bases and replace them with unmethylated cytosine residues[157]. Although promoter methylation plays an important role in gene expression regulation, most of the mammalian genome consists of non-coding sequences with latent transcriptional potential [29]. For example, endogenous transposable elements (TE) constitute over 40% of mammalian genomes [104]. Some classes of TEs such as long interspersed nuclear element (LINE) and long terminal repeats (LTR) elements encode strong promoters that must be constitutively repressed to prevent their activity. This is maintained through a complex of DNMTs (DNMT1 and DNMT3B) and histone methyltranferases (SETDB1) [96]. Therefore methylation not only plays a critical role in regulation of gene coding sequences of the genome, but also represses TEs, thus playing an essential role in genome integrity during normal homeostasis.

DNA methylation in Self Renewal

It has been well established that depletion of CpG methylation in differentiated mammalian cells results in growth defects, cell death, and genome instability [154, 40], highlighting the role of DNA methylation in basic cellular functions of mammalian cells. However, its role in stem cell maintenance has not been fully elucidated. Early studies showed that genes involved in the maintenance of embryonic stem cells (ES), such as Oct4 and Nanog genes, are usually hypomethylated when activated and become hypermethylated during differentiation [53]. Similarly, the conversion of differentiated genes into induced pluripotent stem (iPS) cells showed a loss of methylation at ES cell-specific genes [141]. However, this observation may be specific to the study of embryonic stem cells and not other types of stem cells such as multipotent stem cells. Unlike pluripotent ES cells, multipotent stem cells are usually restricted to a particular lineage (mesodermal, endodermal, or ectodermal) but have the potential to differentiate into distinct somatic cell types with appropriate stimulation [142]. Studies analyzing genome- wide methylation changes of CpG during the conversion of human multipotent stem cells into differentiated somatic cells have shown very minor changes in DNA methylation at promoter regions. Interestingly, studies treating both multipotent and pluripotent stem cells with DNA demethylating agents resulted in spontaneous differentiation [124, 163]. Chapter 1. Introduction 10

This seemingly contradicting data highlights the complexity of epigenetic changes in the context of self renewal potential.

DNA Methylation in Cancer

Hypermethylation in CRC

It is thought that promoter DNA methylation of some tumour suppressor genes leads to inappropriate transcriptional silencing, and that this is the main mechanism aberrant methylation drives cancer. The earliest evidence for this was shown with the retinoblastoma gene (RB), where the RB promoter was found to be methylated in a large portion of retinoblastoma tumours [128]. Since then, other promoters of tumour suppressor genes have been found to be hypermethyled. These include the mismatch repair enzyme MLH1, P16, retinoic acid receptor beta (RARB), and secreted frizzled related protein (SFRP). Collectively, the molecular proﬁle of these cancers are referred to as the CpG island methylator phenotype (CIMP).

Hypomethylation in CRC

In addition to DNA hypermehtylation, DNA hypomethylation can have tumourigenic potential not related to changes gene expression. Comparing the the global levels of DNA methylation in normal and tumour cells, it has generally been found that tumour cells have less genome wide methylation than their normal counter parts [47]. One of the main mechanisms of hypomethylation in cancer is its role in promoting chromosomal instability. This is thought to occur because of the lack of methylation at certain regions in the genome, leading to an increase the likelihood of DNA breakage and recombination, or retrotransposition[139, 123]. Indeed, mice harboring hypomethylated tumours had a signiﬁcant increase in gains and losses of chromosomes, suggesting that DNA hypomethylation plays a role in chromosomal instability [43, 10]. Similarly, another group showed that mice with loss of Nf1 and inactivating mutations in the p53 tumor suppressor gene develop soft tissue sarcomas between 3 and 7 months of age and exhibited loss of heterozygosity (loss of heterozygosity (LOH)) at both gene loci. Interestingly, depleting DNMT1 (hypomorphic depletion) in these mice result in increased rates of LOH, further supporting the role of hypomethylation in promoting genomic instability [58]. Other studies have also found that hypomethylation at repetitive elements increased the risk of developing cancer as well as increased the likelihood of mortality [165]. This ﬁts well with the observation that mutations in the DNA methylation enzyme DNMT1 are found in colorectal cancers Chapter 1. Introduction 11

[85] and mutations in DNMT3A have been reported in acute myelogenous leukemia [93].

Epigenetic Addiction in Cancer

As mentioned earlier, there are ample of examples where tumour suppressor or candidate tumour suppressor genes are found to be methylated at CpG islands of their promoter regions. This process can be used as an alternative mechanism to genetic mutation and can drive tumour progression. These epigenetic changes confer survival beneﬁt to the cells, by allowing accumulation of additional genetic and/or epigenetic mutations to occur. One intriguing model by which this occurs is through the acquisition of activating mutations in oncogenes that cancer cells depend on to survive [155]. Indeed, this concept of “oncogene addiction” has been shown with the oncogene Myc, where brief inactivation of Myc induced tumours showed a sustained regression and diﬀerentiation of osteogenic sarcoma cells into mature osteocytes [81]. Similar results have also been observed with BRAF, EGFR, HER2, and RAS [134]. Similar to the acquisition of mutations, there is accumulating evidence showing that aberrant methylation in cancer cells can lead to a state of “epigenetic addiction” to oncogenic pathways. One example of this is seen in CRC, where promoter hypermethylation of the Wnt signaling antagonist, SFRP, leads to its inactivation and subsequent abnormal activation of Wnt signaling [140]. When SFRPs were re-expressed in the colon cancer cells, Wnt signaling was blocked and resulted in apoptosis [140]. Similarly, CRC cells that have mutations in one allele of both MLH1 and CDKN2A have been shown to acquire hypermethylation in the corresponding wild-type allele, leading to a functional repression of these genes [9]. In line with the above data, double knockout of DNMTs, results in reduced cell viability and complete knockout of the maintenance DNMT1 has been shown to lead to high amounts of cell death[24, 44, 37]. This suggests that cancer cells rely on methylation marks as a survival mechanism in a similar fashion to their reliance on oncogenes. Therefore, cancer cells can become reliant on epigenetic aberrations, and can use these mechanisms to drive tumourigensis.

1.2.2 Epigenetic Mediated Therapeutic Approaches

Exploiting Epigenetic Addiction in Cancer

Observations that cancer cells have aberrant changes in their epigenome compared to their normal counterparts has led to advances in epigenetic molecule discovery [50, 76]. Since DNA hypermethylation has been shown to result in abnormal silencing of several tumour suppressor genes in most types of cancer, one of the earliest epigenetic developments was that of DNA methyltranseferase inhibitors for the treatment of myelodysplastic syn- Chapter 1. Introduction 12 dromes and acute myelogenous leukemia [78]. DNMT inhibitors are cytosine analogues that become converted to deoxynucleotide triphosphates and are then incorporated in place of cytosine into replicating DNA [45]. They are thought to work through the demethylation of hypermethylated tumour suppressor genes, allowing their re-expression and restoring their function. In line with the “epigenetic addiction” model, studies have shown in CRC cells that re-expression of newly identiﬁed tumour suppressors led to a decreases in cell viability [37]. Additionally, there are other mechanisms by which DNMT inhibitors can target cancer cells. Contrary to promoters, where methylation is associated with gene repression, methylation of genic regions are generally associated with gene expression. Therefore, demethylation of these regions with DNMT inhibitors would lead to gene repression. Indeed, there is evidence suggesting a positive correlation between gene body methylation and gene expression in cancer cells [161]. Interestingly, treatment with the DNMT inhibitor, 5-AZA-CdR, induces gene body demethylation and downregulated gene expression. Genes that are downregulated at gene bodies are thought to be important in oncogenic pathways such as genes regulated by c-MYC. This would overall lead to cell growth inhibition [161]. Therefore, the use of epigenetic inhibitors may be an approach to exploit epigenetic addiction, speciﬁcally by re-activation of tumour suppressor genes and silencing of oncogenes.

DNMTi Mediated Activation of Repetitive Elements in Cancer

Another explanation for the observed cancer cell death in response to DNMTi is the observation that gene body methylation can inhibit cryptic transcription events. This is an interesting concept, and has been seen in other organisms such as plants, where cryptic initiation was shown to generate antisense transcripts that pair with mRNA transcribed from the transcription start site (TSS), and lead to double-stranded RNA (dsRNA) mediated methylation and silencing of the cryptic TSS [146]. Indeed, recent evidence has shown that repetitive elements are located in transcribed (gene body) regions, and are normally silenced by DNA methylation [126]. Interestingly, treatment of human cells with DNMT or histone deacetylase (HDAC) inhibitors can induce cryptic transcription of start sites of endogenous retrovirus (endogenous retrovirus (ERV)s)[15]. This accumulation of ERVs leads to formation of dsRNA, which in turn activates a state of “viral mimicry”. Brieﬂy, newly formed dsRNA can bind to nucleic acid sensors within the cell, and lead to the production of type I and type III interferons [126, 25, 94]. Interferons can subsequently induce hundreds of interferon stimulated genes and ultimately lead to cell death. Therefore, it is becoming apparent that there are diﬀerent mechanisms by which DNMT inhibitors can lead to tumour growth inhibition. These include re-activation Chapter 1. Introduction 13 of tumour suppressors, reduction of oncogene expression, and activation of an immune response through the inductions of repetitive elements.

Activation of DNA Demethylases in Cancer

Another mechanism by which DNA demethylation occurs is through active DNA demethylases. The ten eleven translocase (TET) family of enzymes (TET1, TET2, TET3) are demethylases that convert 5-methylcytosine to 5-hydroxymethylcytosine (5hmC). Inter- estingly, this process requires ascorbic acid (vitamin C) as a co-factor [13]. TET protein can also convert 5hmC to 5-formylcytosine and 5-carboxylcytosine which ultimately is replaced by a cytosine [79]. Therefore activation of demethylases with vitamin C is a potentially interesting strategy to change methylation patterns in cancer. Indeed, studies have shown that vitamin C in combination with DNMT inhibitors can synergistically increase DNA demethylation [97]. This can lead to increased activation of ERVs and tumour suppressor genes, and leads to more potent tumour regression. Additionally, modulation of TET activity with vitamin C also has been linked to reduction of self renewal potential in leukemia [26, 1]. Altogether, exploiting the function of aberrantly regulated epigenetic modifying enzymes may be a durable therapeutic strategy against many cancers.

1.3 Tumour Immunity

1.3.1 Innate Immunity

Cytosolic Nucleic Acid Sensing of Viruses

As a ﬁrst line of defense, cells have various mechanisms to detect foreign molecules and consequently activate an antiviral response. This involves deploying receptors to detect molecular patterns associated with extracellular or intracellular pathogens. There are two broad categories of receptors that can be activated depending on the nature/type of molecule. As the name suggests, DNA receptors detect danger associated molecular pattern (DAMP) originating form DNA where as RNA receptors can detect DAMP of RNA origin [62]. Activation of either receptor leads to production of interferons and interferon stimulated genes. Chapter 1. Introduction 14

DNA Sensing Pathways

Cytoplasmic DNA plays an important role in activating an immune response in most cell types. The first described toll like receptor (TLR) was TLR9 and was thought to sense cytoplasmic DNA from microbes [69]. However, later studies found that cells lacking TLR9 were still capable of producing an immune response, which suggested that other DNA sensors must exist [77]. Since then, various DNA sensors have been discovered. For example, Absent in Melanoma 2 (AIM2) has been shown to bind cytoplasmic DNA and initiate the assembly of the inflammasome. This leads to the production of pro-inflammatory cytokines such as IL1-β and IL-18 and inflammatory caspases such as caspase 1,4 and 5, ultimately leading to apoptosis [100]. Another important DNA sensing pathway is the Cyclic GMP-AMP synthase (cGAS)/STING pathway. Briefly, this pathway is initiated by dsDNA binding to cGAS, leading to production of the second messenger cGAMP, which activates the adaptor STING. As a result, STING activates innate immune responses by activating interferon regulatory factors such as IRF3 and IRF7, ultimately leading to the production of type I interferons. This pathway has been shown to be important not only in mediating immune responses against infection, but also in the detection of tumor-derived DNA, generating intrinsic antitumour immunity [156].

RNA Sensing Pathway

Similar to components of the DNA sensing pathway, there are receptors involved in rec- ognizing pathogen associated molecular pattern (PAMP) derived from RNA microbes. Various RNA sensors exist, and can be distinguished by ligand speciﬁcity and cellular localization. Interestingly however, these sensors generally converge on similar downstream signaling pathways. The three main RNA sensing mediators are TLRs, (RIG-I)-like receptors (RLRs) and nucleotide-binding oligomerization domain-containing (NOD)-like receptors(NLRs).

TLRs

TLRs have been the most extensively studied family of Pattern recognition receptors (PRRs) and comprise a gene family of 10 members in humans. These receptors cover a range of PAMPs involved in the recognition of parasites, fungi, bacteria, and viruses [2]. Structurally, they are transmembrane glycoprotein receptors that consist of an extracellular PAMP-binding region and an intracellular signaling region. Of the 10 TLR members, TLR2, -3, -4, -7, and -8 are thought to be of importance in the recognition of Chapter 1. Introduction 15

RNA viruses, with TLR3 being the sole TLR capable of binding double stranded RNA (dsRNA). In epithelial cells, TLR3 has been found to be localized both intracellularly and on the membrane. Similar to other RNA sensing TLRs, TLR3 is activated upon interacting with a dsRNA ligand. This leads to TLR3 dimerization, and subsequent recruitment of TIR domain-containing adapter protein inducing interferon (IFN)-β (TRIF) [159]. Interestingly, TRIF can activate two separate signaling pathways. First, TRIF can trigger a pro-inﬂammatory pathway by recruiting TRAF6 for the activation and translocation of NF-κB [103]. In addition TRIF can recruit TRAF3 leading to the activation of (TANK)-binding kinase 1 (TBK1), and IKK. The activation of this side of the pathway leads to interferon mediated anti-viral immunity through association with IRF3 [133, 51]. Therefore, TLR3 can activate both a pro-inﬂammatory as well as an antiviral signatures in response to dsRNA.

RIG-I-Like Receptors (RLRs)

RLRs are cytoplasmic proteins that sense RNA molecules. There are three proteins in this family, the retinoic acid-inducible gene I product (RIG-I), melanoma diﬀerentiation- associated antigen 5 (MDA5), and laboratory of genetics and physiology 2 (LGP2). Of the three receptors MDA5 and RIG-I have been shown to induce an antiviral response, whereas LGP2 has generally been shown to be a negative modulator of the pathway [87]. MDA5 has been shown to bind dsRNA of high molecular weight (1.5-8 kb), whereas RIG-I binds dsRNA of low molecular weight (0.2-1 kb) [86]. Nonetheless, both receptors converge on the adapter protein MAVS, and subsequently activate IRF3/IRF7. IRF activation leads to the production of type I/III interferons and activation the the JAK/STAT pathway [87].

NOD-Like-Receptors(NLR)

NLRs are cytosolic proteins that regulate inflammatory and apoptotic responses. The two main players in pathogen sensing are Nod1 and Nod2. Nod1 senses peptidoglycan containing meso-diaminopimelic acid (meso-DAP) found in gram-negative bacteria, where as Nod2 detects muramyl dipeptide, found in both gram-negative and gram-positive bacteria [18, 75]. Similar to TLRs, Nod1 and 2 activate pro-inflammatory responses through the down stream activation of NFκB. Briefly, upon ligand binding, Nod1 and Nod2 oligomerize and recruit receptor-interacting protein 2 (RIP2). The newly formed Nod1/2-RIP2 complex then recruits the inhibitor of NFκB kinase complex, which leads to activation of NFκB. Chapter 1. Introduction 16

Interferon Signaling

Type I & II Interferons

Interferons (IFNs) are cytokines that have both antiviral and anti-proliferative effects [117]. Generally thought to be the first line of defense after microbial infections, interferons are secreted from both infected and immune cells. Historically, the IFN family has consisted of two classes: type I IFNs and type II IFNs[116]. Type I interferons consist of a large family, which include IFNα, IFNβ,IFNδ,IFN, IFNτ, IFNκ, and IFN [115]. On the other hand, type II IFNs only consist of IFNγ [115]. Each class of interferons has it’s accompanying receptor. Type I IFN signals through the type I IFN receptor which is composed of two subunits, IFNAR1 and IFNAR2. Activation of interferon alpha receptor (IFNAR) leads to the association with Janus activated kinases (JAKs), specifically tyrosine kinase 2 (TYK2) and JAK1. This leads to the heterodimerization of tyrosine phosphorylation of (signal transducer and activator of transcription) STAT1 and STAT2 which forms a complex with IRF9 [117]. The formation of this complex, termed ISGF3, binds to IFN-stimulated response elements (ISREs) in the nucleus, leading to gene transcription. On the other hand, IFNγ activates type II IFN receptor, which consist of IFNGR1 and IFNGR2 [117]. This leads to the specific activation of JAK1 and JAK2, and induce the formation of STAT1/STAT1 homodimers. This complex then translocates into the nucleus and bind IFNγ activated site (GAS) leading to gene transcription [117].

Type III Interferons

Recently, a new class of IFNs has emerged, known as the IFN-λ molecules. IFN-λ1, IFN-λ2, IFN-λ3, which are also known as interleukin-29 (IL-29), IL-28A and IL-28B, respectively,[152] have similar antiviral properties to the type I and type II IFNs, but bind a distinct cell surface receptor. This receptor has two chains, IFNLR1 (also known as IL-28Rα) and IL-10Rβ10. Nonetheless, its downstream signaling cascade is similar to type I interferon. Binding of the type III IFN receptor complex by any of the three ligands leads to activation JAK1 and TYK2, which then allows for STAT1 and STAT2 dimerization and IRF9 recruitment. Like in type I mediated activation, ISGF3 complex is formed which enters the nucleus and drives the transcription of IFN-stimulated genes (ISGs). Unlike IFNAR however, which is present on almost all nucleated cells, type III IFNs are restricted to tissues with a high risk of viral exposure and infection, such as those at mucosal surfaces [164]. This is due to the restricted expression of its receptor interferon lambda receptor (IFNLR)1. In humans, this restriction seems to be mostly Chapter 1. Introduction 17

to mucosal epithelial tissues, with liver cells and some immune cells such as B cells, neutrophils, macrophages, and plasmacytoid dendritic cells also expressing IFNLR1 [164]. Interestingly, reports have showed that gut epithelial cells respond exclusively to type III IFN, and a type III IFN response is favored when the host is infected with virus that target epithelial cells [118]. Therefore, although type I and type III IFN signal through the same JAK/STAT pathway, their function is non redundant and is specific to the location/type of infection. One of the major advantage of restricted expression of IFNLR1 is the ability to activate an anti-viral response locally rather than systemically. This can be seen in a recent study that showed type III IFNs as the first IFN produced at the epithelial barrier after influenza infection [55]. The initial production of type III IFNs acted to suppress initial viral spread without activating inflammation. If the infection persisted, then type I interferon was produced, causing systemic inflammation. Mechanistically, neutrophils were thought to be responsible for the pro-inflammatory activation, but only when activated by type I IFNs, and not type III IFNs [55]. Due to these observations of significantly fewer adverse events, PEGylated IFN-λ has been developed as an alternative to PEGylated-IFNα for the treatment of hepatitis C [152]. It’s role in the treatment of respiratory infections and intestinal infection are currently being investigated [152].

Interferon Stimulated Genes

As their name suggests, interferon stimulated genes (ISGs) are a set of genes that are upregulated after interferon stimulation. ISGs have a wide range of activities, including enhancing pathogen detection, upregulation of chemokines, and activation of proapop- totic signaling pathways [131]. For example, pathogen recognition receptors (PRRs) and interferon regulated factors (IRFs) are generally present at basal levels within a cell, however are upregulated after interferon stimulation. These set of ISGs act to maintain an antiviral response until the pathogen is cleared. In addition, some ISGs have functions to restrict pathogen entry. For example, the murine myxovirus resistance 1 (Mx1) gene is induced after interferon stimulation and has been shown to sequester incoming viral components, such as nucleocapsids, thus preventing them from infecting the host [57]. Lastly, as mechanism of to avoid dysregulated IFN production, some ISGs work through desensitizing cells from IFN signaling. For example, SOCS proteins, which are induced early in the IFN response, inhibit the JAK/STAT signaling pathway by binding to activated JAK proteins. This leads to a reduction in STAT binding and therefore reduces the amount of ISGs expressed [72]. Overall, ISGs play a fundamental role in anti-viral immunity. Chapter 1. Introduction 18

1.3.2 Role of Immunity in Cancer

Inﬂammation & Cancer

It is well established that inflammation is a critical component of tumour progression, and cancer cells rely on an inflamed microenvironment to thrive. One of the earliest models by which inflammation was thought to lead to tumour formation was the idea of cancer being a wound that never heals [42]. This is thought to occur due to sustained DNA damage or mutagenic insult, leading to continued proliferation in microenvironments rich in inflammatory cells.[42]. Accumulating evidence points to the role of infections in promoting this environment. For example, infectious agents have been shown to induce the formation of reactive oxygen (ROS) and nitrogen species (NOS) by phagocytes at the site of inflammation [109]. This leads to ROS accumulation, damage to DNA, proteins and cell membranes, and favors gene transcription profiles that are involved in carcinogenesis [109]. In addition, tumour cells themselves can also produce various cytokines and chemokines that attract leukocytes and promote inflammation. This can be seen in melanoma where a plethora of chemokines can activate neoplastic cell proliferation in an autocrine manner. Indeed blocking certain chemokines has been shown to reduce melanoma cell proliferation [106], whereas chemokines overexpression has been shown to enhances their colony-forming activity [111] .

Innate Immune Sensing in Cancer

There is strong evidence pointing to the role of aberrant activation of pro-inﬂammatory immune pathways in the development of cancer. On the other hand, there is just as con- vincing evidence for the importance of a functional immune system in tumour rejection. This can be seen in studies that have shown a requirement of type I interferon production by dendritic cells in order for tumour rejection to occur [38]. Indeed, in mice defective in IFN-α/β receptor or STAT1, T cell priming was not induced after tumor injection, highlighting the importance of interferons in anti-tumour immunity [54]. Further studies have focused on more upstream mediators of innate immunity. For example, it has been shown that dendritic cells (DCs) can uptake and sense nuclear DNA released by dying cells. Additionally, T cell responses against tumours require nucleic acid signaling. This is thought to signal exclusively through the STING/cGas pathway, leading to TBK1 and IRF3 activation [156]. Overall, this data suggests an important role of an intact immune response in tumour rejection. Chapter 1. Introduction 19

1.3.3 Immune Mediated Therapeutic Approaches

Activation of cytosolic nucleic acids sensors in Cancer

With the compounding knowledge that effective rejection of tumours requires an intact innate immune system, there has been excitement surrounding the development of agonists of these pathways. Both natural and synthetic agonists of nucleic acid sensing pathways can trigger cell death in malignant cells and recruit immune cells into the tumour microenvironment [80]. One of the earlier examples of this was the use of the natural STING ligand, cGAMP, to activate an antitumour immune response in colon cancer and melanoma models. cGAMP activation was dependent on the presence of a type I interferon response [95, 35]. More recently, this has been extended to other cancers such as breast cancer and squamous cell carcinomas [108]. With accumulating evidence suggesting an anti-tumour effect of STING activation, there have been clinical trials initiated in patients with both solid and blood cancers [80]. In addition to the DNA sensing pathway, activation of the RNA sensing pathway has shown great promise. Interestingly, overexpression of components of the dsRNA sensing pathway alone, have shown tumour suppressive effects. For example, ectopically over- expressing MDA5 in prostate cancer cells has been shown to induce cancer cell death through initiation of type I IFN production [162]. Similarly, RIG-I expression has been shown to inhibit hepatocellular carcinoma (HCC) progression and can improve patient survival in response to interferon treatment [73]. These results suggest that development of molecules that can induce a dsRNA pathway may be a promising strategy to target cancer. Indeed, the use of synthetic dsRNA both extracellularly and intracellularly has been shown to have anti-tumoural effects. For example, transfection of the synethetic dsRNA Poly(I:C) can induce reactive oxygen species production, interferon-β production and leads to Caspase-8/9 mediated apoptosis [65]. Similarly, extracellular Poly(I:C) can induce apoptosis through PKC-α mediated pathways [113]. These results have lead to multiple clinical trials that are now exploring the the use of Poly(I:C) alone or in combination with other therapies, as a novel way to target cancer [101]. More recently, there has been an exciting and novel mechanism by which the dsRNA sensing pathway can be induced to trigger tumour regression. One of these mechanisms is the use of epigenetic therapies as activators of an innate immune response. Various groups have shown that treatment of cancer cells with DNA demethylating agents can activate endogenous dsRNA within the cancer cell itself, leading to the activation of an innate viral sensing pathway [126, 25]. This state of “viral mimicry” leads to the production of type I and type III interferons and leads to cell death. Furthermore, the DNMT Chapter 1. Introduction 20 inhibitor mediated antiviral response was able to deplete cancer initiating cells in a colorectal cancers and suppresses intestinal tumour organoid formation [126, 127]. These studies have led the way for examining the use of other epigenetic inhibitors as mod- ulators and antiviral immunity, with studies showing similar effect with Histone-lysine N-methyltransferase inhibitors such as SETDB1 [63, 30], and EZH2 inhibitors. In fact, the use of non-epigenetic targeted therapies have also been shown to induce dsRNA. Recent reports have shown that pharmacological depletion of cyclic dependent kinases , CDK4/6 can lead to anti-tumour immunity [60, 36]. The using abemaciclib reduced the activity of the DNA methyltransferase, DNMT1, which led to depression of endogenous retroviruses and activation of an immune response [60]. Altogether, these promising results point to an important interplay between epigenetic enzymes, viral sensing pathways, and anti-tumour immunity.

Role of interferons in Cancer

The rationale for the use of interferons as cancer therapies comes from their expression of a large amount of genes that directly affect tumour cell growth, proliferation, differentiation, and migration [114]. Very early studies showed interferons as having anti-proliferative effects by prolonging all phases of the cell cycle in breast and prostate cancer cells [5, 71]. Now, there is clear evidence that interferons can induce apoptosis through both extrinsic (caspase 8 mediated), intrinsic (cytochrome c mediated), [143], and more recently, non-death receptor mediated pathways [20]. Interestingly, silencing IRF7, a transcription factor that activates interferons has been linked to the promotion of metastasis in breast cancer [11], highlighting the importance of functional interferon signaling within the tumour cell. Other than their role in tumour cell intrinsic apoptosis, IFNs play an important role in modulating the tumour microenvironment. Studies have shown that IFN-α/β play a crucial role in the process of immunoediting, and that IFN is required for the prevention of sustained tumour growth, even when tumour cells themselves are not sensitive to IFN treatment [41]. Together, these data suggest that whether it is derived from the tumour, stromal, or immune compartment, IFN signaling may be used as a therapeutic strategy against tumours. This, in fact has been the case for some cancers. For examples, type I IFN- based therapies have shown strong efficacy against haematological cancers, with improved survival rates seen in both hairy cell leukaemia (HCL) and chronic myelogenous leukemia (CML) [117]. On the other hand, IFN based therapies in solid tumours have had mixed success. This is mostly due to toxicities from the high doses used in the trials [114]. However, with advances in formulations, such as the conjugation of interferon with Chapter 1. Introduction 21 polyethylene glycol moieties (PEG-IFN), this has mitigated some of the earlier observed cytotoxicities [150]. Overall, there is strong evidence pointing to the anti-tumoural effects of IFNs in cell lines and in-vivo models. However, the mixed results from clinical trials and the potential cytotoxic effects highlight the need to further investigate the role IFN in cancer patients. Chapter 2

Materials & Methods

2.1 Analysis of publicly available datasets

We obtained the interferon signature from [126] and a Intestinal Stem Cell Signature from [102]. RNA-seq data were obtained from SAGE Synapse for TCGA n=600, CCMS- CRC n=2922, and CRC Organoids. Clustering of the correlation matrix and Pearsons correlation on ssGSEA scores were used to evaluate relationships between the interferon and stemness signatures.

2.2 Tissue Culture

LIM1215 CRC cells were maintained in McCoy medium supplemented with 10% fetal bovine serum, 100 U/ml penicillin, 0.1 mg/ml streptomycin. Cells were grown on adher- ent tissue culture-treated plates and maintained in a humidified incubator at 37C with CO2 maintained at 5%. Cells were passaged every 3-4 days, spun down and split at a 1:5 ratio. Patient derived CIC enriched colorectal cells (POP92, POP66, POP181 and CSC73) were cultured as previously described [90].Human colorectal cancer tissue was obtained with patient consent, as approved by the Research Ethics Board at the Univer- sity Health Network. Tumor specimens were minced with a razor blade and incubated with collagenase A (3 mg mL1, Roche) for 60 min at 37C. After enzymatic digestion, samples were filtered through a 40 m cell strainer. Red blood cells were removed using ammonium chloride solution (Stemcell Technologies) for 5 min. Cells were cultured in suspension flasks with DMEM/F12 (1:1 ratio) supplemented with penicillin-streptomycin (1%), fungizone (1 g mL1), L-glutamine (2 mM), nonessential amino acids, sodium pyru- vate (1 mM), HEPES, heparin (4 g mL1), B27 supplement (GIBCO), N2 supplement

22 Chapter 2. Materials & Methods 23

(GIBCO), lipids (Sigma), EGF (20 ng mL1) and basic FGF (10 ng mL1). PDX CRC cells were cultured in suspension culture ﬂasks at 37 C in a 5% CO2-humidiﬁed incubator and were passaged weekly by dissociating the cells with trypsin. Cells were split at a 1:4 ratio.

2.3 Treatments of cells

For each experiment, cells were plated 24 hr prior treatment. At day 0, cells were treated with 0.3 µM 5-AZA-CdR, 100ng/ml of Interferon α, 100ng/ml Interferon λ, and 100ng/ml Poly(I:C). At the indicated time point, cells were harvested, counted and collect for downstream assays.

2.4 Cell Growth Measurement

Cells were seeded at 50,000 cells/ml in 6 well plates 24hrs before treatment. 24hrs later, cells were treated with 0.3 µM 5-AZA-CdR, 100ng/ml of Interferon α, 100ng/ml Interferon λ, and 100ng/ml Poly(I:C). After 4 days, cells were collected, trypsinized, and viable cells were counted using trypan blue. Relative cell growth was calculated by dividing the percentage of total number of cells in the treatment condition by the percentage of total number of cells in the mock treated control.

2.5 Generation of TCF-GFP reporter

Patient derived CRC cells were stably transduced with TCF/LEF-GFP reporter or negative control lentivirus particles (Cignal Lenti Reporter, Qiagen) and clones were selected using puromycin. After 1 week of selection, cells were cultured in suspension culture ﬂasks as previously described.

2.6 Confocal Microscopy

2.7 Proximity Ligation Assay

Cells were fixed using cold methanol for 15 min at 20C, washed three times with PBS and incubated with saturation buffer (PBS BSA 1%) for 1 hour. Primary antibody was then added (anti-IRF7, anti-IRF3, or anti-β-catenin) and incubated overnight at 4C. Cells were washed three times for 10 minutes with PBS. 8 µL of PLA probe MINUS stock, 8 µL of PLA probe PLUS stock and 24 µL of in (PBS BSA 1%) per samples were incubated for 20 minutes. 40ul of PLA probes were added to the cells and incubated for 1 hour in a pre-heated humidity chamber at 37C. PLA probe solution was then tapped off from the slide. The slide was then washed 2x 5 minutes in 1x Wash Buffer A. 40ul of 1x ligation master mix was added to each slide and incubated in a pre-heated humidity chamber for 30 minutes at 37C. The ligation solution was then tapped off from the slide and washed 2x 5 minutes in 1x Wash Buffer A. 40ul of 1x amplification master mix was then added to each slide and incubated in a pre-heated humidity chamber for 100 minutes at 37C. The amplification solution was then tapped off from the slide and washed 2x 10 minutes in 1x Wash Buffer B. One final wash with 0.01x Wash Buffer B was performed for 1 minute. slide were then mounted with mounting media containing DAPI. Confocal analysis was performed with a Zeiss LSM700 confocal microscope and images were then analyzed using the BlobFinder software (http://www.cb.uu.se/ amin/BlobFinder/). The list of antibodies used for this study are listed in Table 5.3

2.8 Flow Cytometry Analysis

Stably transduced TCF/LEF-GFP reporter patient derived CRC cells were seeded at 100,000/ml in 6 well plates and treated with 5-AZA-CdR, IFNα, IFNλ, and Poly(I:C) 24hrs later. Cells were harvsted at 7 , and 10 days post treatment, and drugs were repleneshed at day 4, and day 4 and day 7 respectivly. Cells were harvsted by dissociating spheres with trysin for 10 minutes and washed with PBS. Cells were then resuspended in in 2% FBS/PBS and were ﬁltered through a 40 m cell strainer. Cells were then analyzed Chapter 2. Materials & Methods 25 using the BD FACS Canto II system. Results and histograms were generated using FlowJo software.

2.9 Isolation, cDNA synthesis and RT-qPCR

Total RNA was purified from Patient derived CIC enriched colorectal cells pre- and post- 5-AZA-CdR, IFNα, IFNλ, and Poly(I:C) treatment using RNeasy Mini Kit (QIAGEN) and quantified by Nanodrop. One hundred ng of RNA was reverse transcribed into cDNA using SuperScript VILO Master Mix (Thermo Fischer Scientific). 5 to 10 ng of cDNA were used to perform quantitative polymerase chain reaction (qPCR) using SYBR Select Master Mix (Thermo Fischer Scientific) to quantify the interferon-stimulated genes (ISG). mRNA levels were normalized against the housekeeping gene RPLP0 . The sequences of the primers used for ISG quantification in this study are listed in Table 5.2.

2.10 NanoString Processing and Analysis

100 nanograms of total RNA from Patient derived CIC enriched colorectal cells pre- and post-5-AZA-CdR, IFNα, IFNλ, and Poly(IC) treatment were used as input material. After overnight hybridization (65C for 12hrs-30hrs in Veriti, Applied BioSystems 96 well Thermal Cycler) between target mRNA and reporter-capture probe pairs, excess probes were removed and the probe/target complexes were aligned and immobilized in the nCounter cartridge, which was then placed in a digital analyzer for image acquisition and data processing. The expression level of a gene was measured by counting the number of times the color-coded barcode for that gene was detected, and the barcode counts were then tabulated in RCC ﬁles. RCC data was analyzed using the nSolver software version 3.0 (NanoString Technologies). The results were ﬁrst normalised using the default settings for GE analysis and then by the geometric mean expression of the reference genes presented in the Custom Codeset (Table S2).

2.11 GST Pull down

4 µg Purified GST-fusion β-catenin protein was incubated with 400µg of nuclear extract diluted with low salt GSE buffer (20 mM TrisHCl pH 7.5, 200 mM NaCl, 1.5 mM MgCl2, 0.2 mM EDTA, 1 mM DTT and 0.1% Nonidet P40), and rocked for 1-2 hours at 4C. GST-protein complexes were collected with glutathione-Sepharose, washed twice with low salt GSE buffer, and eluted with Laemmli sample buffer for western blot analysis. Chapter 2. Materials & Methods 26

2.12 Immunoblotting

Cells were lysed using RIPA buffer supplemented with protease inhibitors (Roche). Pro- tein concentration was measured using BCA (Pierce, thermos scientific). 25 ug of protein lysate were loading per lane and separated by SDSPAGE. Proteins were then transferred using a semi-dry system (Transblot Turbo, BioRad) prewet nitrocellulose membrane (Bio- Rad). After transfer, membrane were saturated with Tris buffer saline Tween (50mM Tris, 150mM NaCl, 0.05% Tween 20, pH 7.6) BSA 5% for 1 hour. Membranes were then incubated with primary antibody against specific targets: rabbit anti-IRF7 (1/1000, abcam), rabbit, rabbit anti-TCF4 (1/2000 cell signaling) and mouse anti-α-β-catenin (1/2000 cell signaling), and anti-α-tubulin (1/2000 cell signaling). After 3 washing with TBST, secondary antibody were added antirabbit HRP, (1/2000, cell signaling) antigoat- HRP (1/2000, cell signaling). Signal was then revealed using an enhanced chemilumines- cence detection system (Perkin Elmer inc).

2.13 Limiting Dilution Assay

For in vitro limiting dilution assay (LDA)s, colorectal cancer cells were treated or not with 5-AZA-CdR for 1 day, or transfected with 0.5 g/ml of Poly(I:C) low and high molecular weight in LyoVec. Cells were then seeded in 96-well plates at the indicated cell doses (1, 10, 100, and 1,000 cells/well). Trypan Blue was used to exclude dead cells. For each cell dose, at least 18 wells were seeded with cells, and for the lower cell doses, at least 72 wells were plated. 4 weeks later, wells containing spheres were scored, and the number of positive wells was used to calculate the frequency of sphere-forming units using the Extreme Limiting Dilution Analysis (ELDA) software provided by the Walter and Eliza Hall Institute. (www.bioinf.wehi.edu.au/software/elda/) For in vivo LDAs, POP66 cells were treated with and without 5-AZA-CdR for 24 hours. Following, single cells suspension was obtained and diluted serially to the desired cell doses. Cells were injected subcutaneously into the ﬂanks of NSG mice. The number of tumors formed out of the number of sites injected was scored to determine the frequency of colorectal CICs calculated using the ELDA software. Animal work was carried out in compliance with the ethical regulations approved by the Animal Care Committee, University Health Network, Toronto, Ontario, Canada Chapter 2. Materials & Methods 27

2.14 Statistical Analysis

Flow cytometry, cell growth, immunoﬂourence quantiﬁcation, and and RT-qPCR assays were assessed using a paired t-test, using Prism GraphPad software (version 5). LDA statistical analyses were performed using a pairwise chi-square test within the ELDA software as previously described (Hu and Smyth, 2009). Chapter 3

Results

3.1 Inverse correlation between Interferon signaling and Intestinal stem cell signature in multiple CRC data-sets

Previous work has shown that interferon signaling can reduce stemness in colorectal cancer cells. [126]. In order to investigate the relationship between stemness and interferon activity we initially assembled and analyzed a large collection of CRC patient transcriptome datasets, from the The Cancer Genome Atlas (TCGA) (n=600) and CCMS-CRC (n=2922). Using these large data-sets, we compared the correlation of RNA expression between two signatures; an ISC gene signature (n = 49 genes) [102] and an interferon response gene-set known to be induced by DNMTi (n= 22 genes) [126]. Interestingly, we found a signiﬁcant anti-correlation between these two signatures (Figure 3.1). In order to account for the eﬀects of the tumor microenvironment, we analyzed a collection of CRC patient derived organoids and found a similar anti-correlation. Of note, the ISC gene signature has previously been shown to correlate with worse prognosis and earlier time to recurrence in CRC patients. These results suggest an anti-correlation in genes involved in immunity and self-renewal across CRC patients.

3.2 5-AZA-CdR Induces IRF Genes & Reduces Se- lect Stemness Genes in CRC PDX Cultures

In order to investigate the relationship between interferon stimulated genes and stemness related genes, we treated patient derived CRC cells with low doses of the DNA demethy-

28 Chapter 3. Results 29 lating inhibitor (DNMTi) 5-AZA-CdR. Previous work has shown that 5-AZA-CdR can induce activation of interferon stimulated genes through the activation of the double stranded RNA sensing pathway [126]. Indeed, treatment of CRC cells activated this pathway, as evident by increased mRNA expression of several interferon related genes (Figure 5.1, 5.2). In addition, we observed a reduction of a select subset of genes from the ISC signature (Figure 3.2). These included (LGR5), Ephrin type-B receptor 3 (EPHB3), and Vascular endothelial growth factor A vascular endothelial growth factor A (VEGFA). Interestingly, these were genes that also increased as Wnt activity increased, conﬁrming their association with the Wnt/β-catenin signaling pathway (Figure 3.3) . Altogether, these results show that DNMTi can regulate both interferon and stemness genes in CRC.

3.3 IRF7 Activation Reduces Wnt Signaling

To further investigate the role of activation of the double stranded RNA sensing pathway on Wnt signaling in CRC, we employed a Wnt reporter construct to measure Wnt activity in the PDX cells. Compared to the PBS treated control cells, we observed a signiﬁcant reduction is Wnt activity after DNMTi treatment (Figure 3.4). This was observed in all cells, irrespective of CIMP status. In addition, treatment of CRC cells with a potent activator of the double stranded RNA sensing pathway, Poly(I:C), as well as type I and type III interferons also reduced Wnt activation. Interestingly, the largest reduction in Wnt activity was seen at later time-points, where endogenous Wnt is at its highest, suggesting that interferon activation preferentially targets cells with high Wnt activity. Indeed, sorting CRC cells into Wnt “high” and Wnt “low” cells showed a preferential shutdown in Wnt “high” cells compared to Wnt “low” cells after Poly(I:C) and interferon treatment (Figure 5.3). Altogether, This data suggest that DNMTi and interferons can directly reduce Wnt activity in CRC cells through the activation of a “viral mimicry” response.

3.4 IRF7 Signaling Does Not Change β-Catenin Lo- calization

One of the major players in canonical Wnt activation is the transcriptional co-activator β-catenin. It has been reported that nuclear β-catenin levels are correlated with Wnt activity and our data suggests that activation of a innate immune response in CRC cells reduces Wnt signaling. Therefore, we hypothesized that nuclear β-catenin levels Chapter 3. Results 30 might decrease as a mechanism of Wnt inactivation. Immunoflouresence staining of the interferon regulated factor IRF7, and β-catenin after stimulation was performed, and showed a strong activation of IRF7 (Figure 3.5). However, nuclear β-catenin levels did not significantly change after stimulation. This was confirmed with cytoplasmic/nuclear fractionation followed by immunoblotting (Figure 3.5). This data shows that while IRF signaling can reduce canonical Wnt signaling, nuclear β-catenin is indeed still present at pre-treatment levels, and suggests its participation in Wnt inactivation may be mediated through non-traditional mechanisms.

3.5 IRF7 is in proximity but does not physically interact with β-Catenin

In order to delineate the role of β-catenin in immune activation, we reasoned that nuclear β-catenin may interact with factors related to the interferon pathway as a mechanism on Wnt inactivation. Previous reports have shown that the related interferon regulated factor, IRF3 binds β-catenin and localizes to the promoter region of IFNβ in response to synthetic dsRNA in murine macrophages [160]. Using a PLA, we observe that β-catenin does indeed interact with IRF3 after Poly(I:C) treatment in human colorectal cancer cells (Figure 3.6). Additionally, activating the viral sensing pathway with Poly(I:C), type I and type III interferons, or at later time points, with 5-AZA-CdR led to β-catenin/IRF7 interaction (Figure 3.7). However, when performing a GST-β-catenin pull-down assay, neither IRF3 nor IRF7 was visibly present, which may be due to the lack of sensitivity of the assay or the overall low levels of these proteins bound to β-catenin (Figure 5.4). Taken together, these results show both IRF3 and IRF7 operate in close proximity to β-catenin in response to immune activation.

3.6 Immune Activation Reduces CRC Cell Growth in-vitro

Previous studies have reported an immune dependent anti-proliferative role of DNMTi [126, 25]. To test weather other activators of innate immunity, or downstream effectors of the viral sensing pathway can also produce anti-proliferative phenotypes, PDX CRC cells were cultured in the presence of Poly(I:C) or type I and type III IFNs. Compared to the PBS treated control cells, we observed a significant reduction in cell growth when targeting both upstream and downstream effectors of the viral sensing pathway in all Chapter 3. Results 31

cells, irrespective of CIMP status (Figure 3.8). This was also observed in CRC cells treated with low doses of 5-AZA-CdR as previously reported [126].

3.7 Immune Activation Reduces CIC Frequency in- vitro

To further characterize the effects of immune activation on self renewal capacity, we treated 3 CIC-enriched CRC cells with 5-AZA-CdR, Poly(I:C), type I or type III IFNs and performed an in-vitro limiting dilution assay. Notably, there was a significant reduction in sphere forming frequency in all treatments compared to the mock treated cells (Figure 3.8). Interestingly, sensitivity to the downstream effector interferons was consistent across all cells, but varied compared to upstream activators such as Poly(I:C) and 5-AZA-CdR. In addition, Poly(I:C) had a less effect on CIC frequency compared to 5-AZA-CdR. This may be due to the ability of 5-AZA-CdR to maintain a sustained antiviral state through paracrine production of interferons. Altogether, these results show a direct relationship between the activation of immune signaling and reduction of cancer initiating cells in-vitro

3.8 Type I & III Interferons Reduce CIC Frequency in-vivo

Due to the observation that interferons can inhibit sphere forming frequency in-vitro, as seen with Poly(I:C) and 5-AZA-CdR [126], we reasoned that similar effects may be seen in-vivo as well. To test this, we treated 2 CIC-enriched CRC cells with type I or type III IFNs and injected tumour cells into NGS mice. After approximately four months, we observed a significant reduction is tumour initiating capacity after treatment with both type I and type III IFNs in POP92 CRC cells. However, when performing an in-vivo limiting dilution assay on POP66 CRC cells, we did not observe any significant change CIC frequency (Figure 3.9). Chapter 3. Results 32

ISC Signature IRF7 Signature (Batle - Cell Stem Cell) (DeCarvalho - Cell)

Figure 3.1: RNA expression from publically avialable data sets (top: TCGA, middle: CCMS, bottom: CRC Organoids). Data showing Inverse correlation between the Intesti- nal Stem Cell Signature and IRF7 Signature(Pearson Correlation). Left side: IRF7 gene cluster is in red, ISC gene cluster is in black. Right side: dot plot showing the ssGSEA for each sample in the data set. Chapter 3. Results 33

IRF/ISC Signature Day5 IRF/ISC Signature Day10 A.

Treatment Treatment 2 1.5 AZA AZA IRF7 NT ISG15 NT 1 Celline 1 Celline 0.5 CSC73 CSC73 POP181 POP181 IRF9 0 POP92 IRF7 0 POP92 −0.5 Signature Signature −1 −1 IRF IRF ISG15 ISC IRF9 ISC −1.5 −2

DDX58 DDX58

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Figure 3.2: Gene expression analysis in PDX colorectal cancer cells to measure interferon stimulated genes and wnt target genes. Gene expression was measured using Nanostring after cells were treated with 5-AZA-CdR for either 5 or 10 days. A. heatmap of all PDX colorectal cells measuring interferon stimulated genes and wnt genes after 5-AZA-CdR treatment. B. heatmap of CSC73 colorectal cells measuring interferon stimulated genes and wnt genes after 5-AZA-CdR treatment. C. Measurement of wnt target genes in CSC73 colorectal cancer cells over 5 and 10 days. Chapter 3. Results 34

ISC Signature A. TCF/LEF Activity 3 Celline HOXA5 CSC73 TEAD2 2 POP181 POP92 10000 MYC 1 Day PTPRO Day10 0 AXIN2 Day5 APCDD1 −1 8000

CTNNB1 −2 POP92 HOXA9 −3 EPHB3 6000

I POP181

LGR5 F DPYSL3 M CSC73 LAPTM4B 4000 CDK6 VEGFA CD44 2000 SFRP2 CPXM1 SFRP1 ZEB1 0 RUNX2 4 7 0 y y 1 BIRC5 a a y D D a D

ISC Signature

B. TCF/LEF Activity Celline CSC73 1 TEAD2 10000 Day 0.5 Day10 Day5 0 MYC 8000 −0.5

−1

ZEB1 6000

I F

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CD44 0 LGR5 4 7 0 y y 1 a a y D D a D

Figure 3.3: Gene expression analysis and GFP median fluorescence intensity mesurement in PDX colorectal cancer cells. A. heatmap of all PDX colorectal cells measuring wnt genes after 5 and 10 days in culture, and quantification of relative GFP median fluorescence intensity between 4, 7, and 10 days in culture. B. heatmap of all CSC73 colorectal cells measuring wnt genes after 5 and 10 days in culture, and quantification of relative GFP median fluorescence intensity between 4, 7, and 10 days in culture. Chapter 3. Results 35

CONTROL Day 10 (100ng/ml) Poly(I:C) Day 10 (300nM) 5-Aza-CdR

Day 10 (100ng/ml) IFNα Day 10 (100ng/ml) IFNλ Non-Infected

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Figure 3.4: GFP intensity in PDX colorectal cancer cells expressing lentivirus transduced TCF/ LEF transcriptional reporter (TCF-GFP) to monitor Wnt/β- catenin pathway activity. GFP fluorescence was measured by flow cytometry after cells were treated with 5-AZA-CdR, Poly(I:C), IFNα, and IFNλ for 4,7 & 10 days. B. Quantification of relative GFP median fluorescence intensity relative to PBS control. Chapter 3. Results 36

A. IRF7 5000 Control * IRF7 4000 * DAPI IRF7 β-catenin MERGE * *

F 3000

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Nuclear Cytoplasmic

Figure 3.5: Representative images of immunoﬂuorescent staining of LIM1215 colorectal cancer cells. A. IRF7 and β-catenin protein expression after treatment with 5-AZA-CdR, Poly(I:C), IFNα, and IFNλ (left), and quantiﬁcation of IRF7 and β-catenin staining (right). B. Whole cell lysate immunoblot of IRF7, β-catenin, and TCF4 after treatment with 5-AZA-CdR (day 5), Poly(I:C) (24hrs), and IFNλ (24hrs)(left), and nuclear and cytoplasmic immunoblot of IRF7, β-catenin, and TCF4 after treatment with 5-AZA- CdR (day 5), Poly(I:C) (24hrs), and IFNλ (24hrs)(right) Chapter 3. Results 37

CONTOL

DAPI IRF3/β-catenin MERGE

1HR Poly(I:C)

DAPI IRF3/β-catenin MERGE

Figure 3.6: Representative images of immunoﬂuorescent staining of LIM1215 colorectal cancer cells using proximity ligation assay. A. Signal indicates ¡40nm proximity of IRF3 and β-catenin after treatment with 1ug of Poly(I:C) for 1 hour. B. Quantiﬁcation of average signal per nuclei in Poly(I:C) treated and PBS control cells. Chapter 3. Results 38

A. CONTROL 24HR Poly(I:C) Day 5-AZA-CdR

IRF7/β-catenin IRF7/β-catenin IRF7/β-catenin Day 10-AZA-CdR 24HR IFNλ No Ab CONTROL

IRF7/β-catenin IRF7/β-catenin IRF7/β-catenin

Figure 3.7: Representative images of immunoﬂuorescent staining of LIM1215 colorectal cancer cells using proximity ligation assay. A. Signal indicates ¡40nm proximity of IRF7 and β-catenin after treatment with 300nM 5-AZA-CdR, 100ng/ml Poly(I:C), IFNα, and IFNλ. B. Quantiﬁcation of average signal per nuclei in treated and PBS control cells. Chapter 3. Results 39

POP92 - Day 5 POP66 - Day 5 CSC73 - Day 5

150 150 150

T T 100 100 T 100 *

O O

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* * * * * * * * * * * *

Figure 3.8: Cell growth and in-vitro CIC frequency of CRC cells after treatment with 5-AZA-CdR, Poly(I:C), IFNα, and IFNλ. A. Percentage of viable cells after 5 days of treatment with 5-AZA-CdR, Poly(I:C), IFNα, and IFNλ. B. Percentage of cancer initiating frequency of PDX derived CRC cells after 5 days of treatment with 5-AZA- CdR, Poly(I:C), IFNα, and IFNλ. CRC cells were seeded at 1000,100, 10, or 1 cell per well and in-vitro LDA was measured 4 weeks after seeding. Chapter 3. Results 40

Figure 3.9: in-vivo CIC frequency of PDX CRC cells, as measured by LDA. A. POP92 CRC cells and B. POP66 CRC cells were treated with IFNα and IFNλ for 5 days, dissociated into single cells, and injected subcutaneously into NSG mice at doses of 10,000, 1,000, 100, and 10 cells (n = 4 mice, 4 injections per mouse). Chapter 4

Discussion

4.1 Summary & Future Directions

DNMT inhibition has long been shown to have anti-proliferative eﬀects in both solid and liquid tumours. Recent evidence suggests that DNMT inhibitors can have direct eﬀects on the cancer initiating population [126]. However, the mechanism by which this occurs has not been well elucidated. In this current investigation, I show that DNMTi-induced activation of an antiviral response in patient derived CRC cells modulates the Wnt/β- catenin pathway. Additionally, these results extend to synthetic dsRNA, type I and type III interferons. Therefore, targeting the Wnt/β-catenin pathway with immune activating molecules may be a novel strategy to target colorectal cancer. Similar to the normal intestinal stem cell, colorectal cancer is organized in a hierarchical fashion, with CRC initiating cells residing at the apex of the crypt. These CICs have been shown to have similar molecular signatures to normal intestinal stem cells, and strongly correlate with poor patient outcome [102, 32, 145]. This suggests that the presence of a CIC pool characterized by a “stemness” signature is essential for tumour maintenance. On the other hand, the role of innate immunity in the context of CICs is not well characterized, with some evidence showing that CICs can reduce the expression of components of the innate viral sensing pathway as a mechanism of survival [4]. In order to investigate the link between stemness signatures and antiviral signatures, we mined RNA expression data of publicly available datasets of CRC patients and found an anti-correlation between the Intestinal Stem Cell signature [102] and Interferon signature [126] in multiple data sets. This suggests that, when a patient’s tumour is enriched in the Intestinal Stem Cell signature, the tumour also tends to be low in expression of gene present in the Interferon signature, and vice-versa. Interestingly, the Intestinal Stem Cell signature contains a group of Wnt target genes, that are especially anti-correlative, point-

41 Chapter 4. Discussion 42 ing to the an unappreciated relationship between Wnt signaling and interferon signaling. One caveat of using patient expression data is the variation in tumour purity between samples. Interestingly, RNA expression data from CRC tumour derived organoids showed a similar anti-correlation between the two signatures. With recent evidence pointing to the concordance between organoid and patient data [151], these results suggest that the presence/absence of an interferon signature in CRC may play an important role in patient outcomes. Aberrant DNA methylation is required for colorectal tumourigenesis [92]. This consists of genome wide DNA hypomethylation and site specific hypermethylation, including CpG islands of putative tumour suppressor genes. In addition, aberrant Wnt pathway signaling is involved in 90% of colorectal cancers, suggesting it plays a fundamental part of CRC initiation and maintenance. Understanding the mechanism that underpin these pathways is essential for the development of novel and targeted therapies. I show that treatment of CRC cells with a low dose (300nM) of 5-AZA-CdR can shutdown Wnt signaling, and the magnitude of inhibition is dependent on endogenous Wnt activity. In other words, higher levels of basal Wnt activity responded more robustly to DNMTi. The observation that DNMTi reduces Wnt signaling is consistent with previous studies, showing that with high dose of 5-AZA-CdR, Wnt signaling can be shut down [32]. Due to the high doses used however, the mechanism of 5-AZA-CdR mediated Wnt downregulation may not be the same as in the current study. Indeed, the mechanism that was proposed for high doses of 5-AZA-CdR was thought to be by the demethylation of Wnt pathway antagonists that were re-expressed and subsequently able to act as negative regulators of the pathway. This is in line with other reports that have shown that the inactivation of the family of Wnt antagonists, SFRP, can lead to increased Wnt signaling [140]. However, methylation changes of Wnt antagonist were not assessed in my study. Interestingly, intracellular delivery of CRC cells with synthetic dsRNA or treatment with interferon type I and type III, which have not been reported to play a role in demethylation, also showed the same effects as 5-AZA-CdR. This data suggest that the DNMTi mediated shut down of Wnt signaling may be dependent on an intact antiviral response, in addition to de-methylation of Wnt antagonists. In order to further investigate the molecular gene signatures involved in the observed antiviral mediated Wnt shut down, CRC cells were subjected to RNA expression profiling after low dose DNMTi treatment. In line with previous reports [126, 25], 300nM of 5-AZA-CdR induced a potent antiviral response, characterized by increased expression of interferon stimulated genes, and antigen presentation machinery. In addition, select stemness genes were downregulated, including LGR5, EPHB3, and VEGFA, while other Chapter 4. Discussion 43

stemness genes were unregulated, including the Wnt antagonist AXIN2. The latter finding is in agreement with previous reports showing an increase in AXIN2 after treatment with high dose (2uM) 5-AZA-CdR [32]. Interestingly, the genes that were downregulted after 5-AZA-CdR treatment were highly correlated with activation of Wnt signaling, indicating they are good surrogate markers for Wnt activity. Of note, not all CRC cells responded to DNMTi treatment with the same magnitude, with the CRC cells with the highest Wnt signal responding the most. This may be due to the fact that each CRC celline if derived from patients with varying molecular profiles. Indeed, looking at the basal level of Wnt gene expression varied between celllines. Taken together, these results show that DNMTi treatment leads to a strong activation of an antiviral gene signature within the cancer cell and an accompanying decrease in select stemness gene expression. The role of β-catenin in Wnt signaling is well established. Upon activation of the Wnt pathway, a series of signal transduction events occur leading to the accumulation of β-catenin and it’s transcription factors TCF/LEF in the nucleus [16]. In the case of most CRC, this pathway is constitutively active, characterized by high levels of nuclear β-catenin [149]. Therefore, one possible mechanism by which antiviral mediated Wnt shutdown occurs is through the degradation or exclusion/cytoplasmic shuttling of active β-catenin from the nucleus. Indeed, this has previously been achieved with selective Wnt inhibitors [21]. However, unlike Wnt inhibitors, this did not occur after DNMTi. Low doses of 5-AZA-CdR treatment increased the expression of the IRF7 protein and nuclear localization indicating that an antiviral response did occur. However, both levels and localization of β-catenin did not significantly change. This suggests that although canonical Wnt activity is being repressed, nuclear β-catenin is still present and active. These results are in line with previous reports showing that disruption of the β-catenin interaction with other co-activators can selectively shut down Wnt signaling while maintaining β- catenin expression levels [46]. Whether DNMTi can disrupt β-catenin interaction with other co-activators has not been investigated. The association of β-catenin with non-canonical transcription factors has been reported previously. For example, CRC cells with high levels of hypoxia divert canonical β-catenin/TCF4 signaling to a β-catenin/HIF-1α signaling pathway, leading to activation of HIF-1α target genes [84]. Mechanistically, HIF-1α competes with the canonical TCF4 transcription factor, leading to reduced Wnt transcriptional activity [84]. Therefore, similar to the result observed in a high hypoxia environment, one possible explanation for the reduction in Wnt transcriptional activity after DNMTi is that the presence of a high interferon environment can potentially lead to component of the interferon pathway competing for β-catenin with components of the canonical Wnt pathway. A proximity Chapter 4. Discussion 44

ligation assay, which asses proteins within 40nm vicinity from one another, showed a strong signal between IRF3 and β-catenin after synthetic dsRNA stimulation. This is in line with previous reports, showing a direct interaction between IRF3 and β-catenin upon simulation with synthetic dsRNA in mouse macrophages [160]. Since DNMTi has been shown to induce IRF7 [126], CRC cells were treated with DNMTi and assayed for proximity with PLA. 5-AZA-CdR treated cells showed a modest increase in signal at 5 days after stimulation and a strong signal after 10 days of stimulation. Interestingly, similar results were observed with stimulation of synthetic dsRNA and type I and type III interferon. This data suggest that proximity of IRF7 and β-catenin observed in the DNMTi treated CRC cells is in part due to an active interferon response. In order to asses direct interac- tions between IRF3/7 and β-catenin, a glutathione S-transferase (GST) pull-down was performed. Neither IRF3 nor IRF7 was pulled down with β-catenin after 5-AZA-CdR treatment at two different time points (Day 5 and Day 10). These results also extended to treatment with synthetic dsRNA and interferon type III. This finding is in contrast to previous reports showing a direct interaction between IRF3 and β-catenin after stimulation with dsRNA [160]. One explanation for the discrepancy between the results is the use of an overexpression vector to artificially overexpress both Hemagglutinin-tagged IRF3 and Myc-tagged β-catenin in the previous study. Taken together, these results show that activation of an innate viral sensing pathway leads to close proximity of endogenous IRF3/7 and β-catenin. However, their direct interaction in an endogenous setting has not yet been determined.

With accumulating evidence of the role that cancer initiating cells play in tumour maintenance, it is of great importance to find novel strategies to target these populations. Recent data suggests that activating antiviral immune responses within these cells may be a viable therapeutic option. Indeed, a previous study has found that activation of dsRNA sensing pathway in CRC organoids can lead to loss of tumour growth [127]. The authors reported that the use of the DNA demyethylating agent 5-AZA-CdR, induced dsRNA formation and a subsequent antiviral response. This ultimately led to CRC organoids to shrink, which is indicative of the loss of the cancer initiating pool within the CRC organoid. One disadvantage of using DNMTi as a method of activating the innate immune pathway however, is the inherent cytotoxic effects of these drugs. Therefore, the use of synthetic dsRNA or downstream effectors such as interferons may be a more attractive alternative. In order to test the effects of CIC frequency upon interferon treatment, an in-vitro limiting dilution assay was performed. Treatment with both type I and type III interferons lead to a significant reduction in CIC frequency in multiple CIC-enriched cells. These results are in line with previous reports showing a strong Chapter 4. Discussion 45

reduction in CIC frequency upon treatment with synthetic dsRNA or DNMTi [126]. As mentioned earlier, the cytotoxicity of the use of type I interferon is well documented. In fact, multiple clinical trials have been terminated due to the lack of tolerability and adverse reactions in patients. This is likely due to the fact that the receptor that type I interferon binds to is expressed on all nucleated cells. In contrast, the interferon type III receptor is primarily restricted to cells of epithelial origins. Therefore, the use of type III interferons may be a better option for treatment in pre-clinical and clinical systems. In order to test the CIC potential of type III interferon in-vivo, two patient derived CRC cells were serially injected subcutaneously into the flanks of NSG mice, and assessed for tumour growth. Out of the two CRC samples, One sample (POP92) showed a significant reduction is cancer initiation potential where as the other sample (POP66) showed no effect. One possible explanation for the differential sensitivity in CIC depletion could be due to the molecular differences between the cells used. Interestingly, POP92, which showed significant depletion in CIC frequency after type III interferon treatment has a mutation in the Wnt pathway (APC mut), where as POP66, which did not respond, is BRAF mutant. Lastly, the effect of interferons on cell growth were assessed. All CRC cells responded to interferon treatment, indicating that they all have a functional level of the interferon type I and type III receptor expression. This was confirmed with the assessment of interferon stimulated genes, which were all induced after interferon treatment. This data is in line with previous reports of the anti-proliferative activities of interferons [143]. The mechanism for the reported anti-proliferative effects range from up-regulation of Fas, Fas- ligand (FasL) and TRAIL [23], activation of p53 [143], and inhibition of cellular protein synthesis by 2-5 OAS and RNaseL pathway [136]. Due to the focus on mechanisms of cancer initiating potential, rather that tumour growth, the molecular pathway involved in the observed interferon induced tumour inhibition was not assessed. A few key questions remain. Firstly, what are the changes in chromatin occupancy of Wnt and interferon regulated transcription factors? One possible hypothesis is that in an interferon inactive state, β-catenin is localized to it’s canonical targets, leading to a maintained Wnt gene transcriptional signature and stemness phenotype. However, upon activation of an interferon signature with DNMTi, or interferons themselves, β-catenin can now re-localize to other parts of chromatin involved in interferon stimulated gene activation. This would have two direct implication. First, the reduced occupancy at Wnt target genes would reduce Wnt activity, as observed in our initial results. Second, gained occupancy at interferon loci could potentially enhance interferon gene activation. In other words, the presence on high nuclear β-catenin may function as a potentiator of interferon Chapter 4. Discussion 46

signaling. Although not evaluated in my current study, a recent report has shown that IRF3 transcriptional activity requires the assembly of β-catenin, CBP and HDAC6 to form a stable transcription initiation complex at some interferon target gene promoters [19]. Additionally, some data suggest that β-catenin levels are required for optimal IFNβ production. In this study, they show a significant increase in IFNβ production after stimulation with pppRNA and over-expression of β-catenin/LEF compared to stimulation with pppRNA alone [70]. One potential way to test this hypothesis in my model is through chromatin immunoprecipitation after stimulation with interferon. Although this was not feasible for β-catenin, treatment of CRC cells with type III interferon showed no change in TCF4 chromatin occupancy at Wnt target genes (Figure 5.5). This suggests that TCF4 localization may not be required to have an observed shut down of Wnt signaling. However, since β-catenin is important in activating Wnt target gene expression, genome-wide β-catenin chromatin occupancy is required. The use of DNMTi and interferons as a strategy to modulate Wnt activity not only has direct effects on CIC populations, but can also play a more broad role in promoting tumour clearance. One recent study highlighted the role Wnt/β-catenin signaling can play in promoting immune evasion by analyzing tumour intrinsic β-catenin levels in melanoma patients. Their results found an astonishing difference in immunotherapy response between β-catenin high and β-catenin low tumours. The mechanism by which β-catenin high tumors promoted an immune suppressive environment was through the activation of the transcription factor ATF3 and subsequent downregulation of an important immunostimulatory cytokine, CCL4. Upon ablation of β-catenin, ATF3 expression decreased with an accompanying increase in CCL4, leading to better response to immunotherapy. Therefore, modulating the β-catenin target gene ATF3, may be a strategy to re-sensitize tumours to immunotherapy. Interestingly, my results show that upon treatment of CRC cells with DNMTi and interferons, there is a decrease in ATF3 gene expression compared to mock treatment. Additionally, there was an increase in expression of CCL15,CCL20,and CCL28 after DNMTi (Figure 5.6). Therefore, DNMTi and interferons mediated shutdown of Wnt in CRC cells may have immuno-activating roles, in addition to the observed CIC depleting roles. Further investigation into the immuno-activating roles of DNMTi and interferons will be required. It has long been thought that depletion of the cancer stem cell pool is a durable and sustainable strategy to target cancer. Indeed, cancer stem cell therapies have shown promise in both in-vitro and in-vivo models [91]. However, as the the field of clonal organization in cancer stem cells advances, more complexities arise. One of the fundamental questions is, whether targeting the apex of the stem cell hierarchy is sufficient for the Chapter 4. Discussion 47 sustained loss of viability of a tumour, or can more differentiated cells gain stem cell functions upon ablation of the cancer stem cells? Recent studies have shown that upon ablation of LGR5+ CRC stem cells, more differentiated cell in fact gain plasticity. In this model, complete deletion of LGR5 did lead to significant reduction of tumor size. How- ever, the tumour eventually re-appeared [135]. Interestingly, lineage tracing experiments identified differentiated KRT20+ cells as cells able to express LGR5 and these cells had tumour initiating potential [135]. This data suggest that although some therapies can target the stem cell pool, it will be important to understand the mechanism by which differentiated cells can revert to a cell with stemness properties. In addition, using drugs that have the ability to target both populations may be important. For examples, although treatment of CRC cells was able to reduce cancer initiating frequency in both the interferon and DNMTi treated cells in my experiments, the effect of total cell growth was more subtle in the interferon treated cells. Therefore, it may be important to combine interferon treatments with other therapies that have a strong effect on the bulk of the tumor.

4.2 Limitations

One of the main limitations of the study of CRC cancer stem cells is the lack of univer- sal markers that distinguish between normal and cancer stem cells. Therefore, there is an over reliance on the use of gene expression signatures as a way to assess changes in stemness phenotypes. My initial hypothesis relies on RNA expression data from publicly available datasets. Although the signatures I used showed a strong anti-correlation, vali- dation with immunohistochemistry of interferon and stemness markers would strengthen the results. The use of knock down experiments are important in identifying causal eﬀects. Al- though previous work has shown that an intact viral sensing pathway is required for the observed loss in CIC frequency [126], whether loss of Wnt signaling is also dependent on viral sensing pathway mechanisms was not addressed. This could have been performed by the knocking down of key viral sensing genes such as MDA5, RIGI, or blocking of downstream pathways such as the JAK/STAT pathway. In addition, knocking down LRRFIP1, which has been linked to the recruitment of β-catenin to the interferon β pro- motor [160], would have also been important in assessing the causal relationship between Wnt signaling and interferon induction. Although my results show close proximity (<40nm) of β-catenin with IRF3 and IRF7 after activation of the viral sensing pathway, there is currently no evidence that these Chapter 4. Discussion 48

have any functional consequences on chromatin. In order to have conclusive evidence that Wnt and interferon genes are being modulated specifically due to β-catenin, experiments assessing changes in chromatin occupancy are required. Due to technical difficulties such as the lack of ChIP grade β-catenin antibodies, chromatin immunoprecipitaion (ChIP) experiments for β-catenin were not successful and were inconclusive. Future studies will focus on using alternative methods to ChIP to assess β-catenin occupancy. Lastly, the direct effect of synthetic dsRNA and interferons on tumour growth was only assessed in-vitro. The use of a mouse model would have strengthened my in-vitro results. More specifically, the use of immune compromised mice would have shed light on the direct anti-tumour effects of interferons, while the use of an immunocompetent mouse model would have furthered our understanding of the relationship between the tumour microenvironment, Wnt signaling and interferons.

4.3 Conclusion

The use of epigenetic therapy as a durable strategy to treat cancer has been around for decades. However, the insight into the mechanisms by which epigenetic therapy functions is still being unraveled. Recent work has shown that innate anti-viral sensing pathways play important roles in the observed responses of tumours to epigenetic therapy. Although DNA methylation inhibitors were the ﬁrst epigenetic drugs to show this phenomenon of “viral mimicry”, subsequent studies have reported similar results using other epigenetic inhibitors. It will therefore be of great interest to further study the role of epigenetic modiﬁers as they relate to the innate immune system. Additionally, the understanding of the relationship between cancer stem cell activity and interferon activity is also of interest. With accumulating evidence showing epigenetic mediated repression of endogenous retrovirus and other interferon activating transposable elements in drug tolerant cancer cell populations, understanding the interplay between stemness pathways and interferon mediated pathways will give us a more comprehensive understanding of the interaction between these seemingly unrelated pathways. Chapter 5

Appendix

5.1 Appendix I - Supplementary Figures & Tables

49 Chapter 5. Appendix 50

A. B. IRF Signature Day5 IRF Signature Day10

Treatment Treatment 2 AZA 2 AZA IRF7 NT IFIT1 NT

1 Celline 1 Celline IRF9 CSC73 MX1 CSC73 POP66 POP66 0 POP92 0 POP92 ISG15 IRF9 −1 −1

−2 DDX58 −2 TAP1

MX1 STAT2

TAP1 ISG15

STAT1 IRF7

STAT2 STAT1

IFIT1 DDX58

IFIH1 IFIH1

Figure 5.1: Gene expression analysis in PDX colorectal cancer cells to measure interferon stimulated genes. Gene expression was measured using Nanostring after cells were treated with 5-Aza-CdR for either 5 or 10 days. A. heatmap of all PDX colorectal cells measuring interferon stimulated genes after 5 days of treatment with 5-Aza-CdR. B. heatmap of all PDX colorectal cells measuring interferon stimulated genes after 10 days of treatment with 5-Aza-CdR. Chapter 5. Appendix 51

POP92 12000 A. NT n 10000

o 5-Aza-Cdr

i t

c Poly (I:C) u

d 8000 n

I IFNα

e 3000 v

i IFNβ

t a

l 2000

e IFNλ R 1000 0 5 5 7 8 1 A F 5 G D R X S I D I M D

POP181

B. 10000 NT n

o 5-Aza-Cdr i t 5000

c Poly (I:C)

d n

I IFNα

e v

i 1000 IFNβ

a l

e IFNλ

R 500

0 5 5 7 8 1 A F 5 G D R X S I D I M D

CSC73 6000 C. 4000 NT

2000 n

o 5-Aza-Cdr

i t c Poly (I:C)

u 1000

d n

I IFNα

e v

i IFNβ t

a 500 l

e IFNλ R

0 5 5 7 8 1 A F 5 G D R X S I D I M D

Figure 5.2: Gene expression analysis in PDX colorectal cancer cells to measure interferon stimulated genes. Gene expression was measured using RT-qPCR after cells were treated with 5-Aza-CdR, Poly(I:C), IFNα, and IFNλ for 5 days. Relative induction of ISGS in A. POP92 B. POP181 and C. CSC73 PDX colorectal cells. Chapter 5. Appendix 52

Figure 5.3: GFP intensity in CDC73 PDX colorectal cancer cells expressing lentivirus transduced TCF/ LEF transcriptional reporter (TCF-GFP) to monitor Wnt/β- catenin pathway activity. GFP fluorescence was sorted for 10% high and 10% low by fluorescence activated cell sorting. into A. top 10% Wnt cells (Wnt high) were treated with Poly(I:C) and IFNλ for 7 days and GFP fluorescence was measured by flow cytometry. B. bottom 10% Wnt (Wnt low) cells were treated with Poly(I:C) and IFNλ for 7 days and GFP fluorescence was measured by flow cytometry. Chapter 5. Appendix 53

Figure 5.4: Assessment binding with GST-β-catenin pulldown for TCF4, IRF3, & IRF7. Nuclear extracts were prepared from LIM1215 CRC cells treated with 5-Aza-CdR, Poly(I:C) and IFNλ and IRF3, IRF7, and TCF4 were assessed using a GST-β-catenin pulldown. A western blot demonstrates TCF4 binding to β-catenin, whereas IRF3, and IRF7 did not bind. A ponceau stained gel shows the loading levels of GST proteins. Chapter 5. Appendix 54

Figure 5.5: TCF4 ChIP-qPCR was performed using mock treated and IFNλ (72Hr) treated chromatin from LIM1215 CRC cells. TCF4 chromatin occupancy was assessed as fold enrichment over input. Indicated Wnt genes and unique location controls were used. Chapter 5. Appendix 55

A ATF3 Day10 B CCL28 Day10

P66 P92 P66 P92 ● ● ●

● ● M

6 M 4 ●

2 5 2

● 3 ● g

g ● o

o ●

4 ● 2 ● ● ● ● NT AZA NT AZA NT AZA NT AZA Treatment Treatment C CCL20 Day10 D CCL15 Day10

P66 P92 P66 P92

7.5 ● ● 4.0 ● ● ● ●

7.0 3.5 M

M ● P

P 3.0 C

6.5 C

2 2.5 g

6.0 g

2.0 5.5 ● ● ● ● ● 5.0 ● 1.5 ● NT AZA NT AZA NT AZA NT AZA Treatment Treatment

Celline ● P66 ● P92

A ATF3 Day5 B ATF3 Day10

P66 P92 P66 P92 7 ● ● 6

● ● 6 ● ●

● 5

5 M

M ●

P P

C C

4 2

2 ●

g g

o o L

L 4

●

● ● 3 ● ● ● ● ● ● ● ● ● 3 ● ● ● 2 ● ● ● ● ● 2 NT AZA IFNA IFNL NT AZA IFNA IFNL NT AZA IFNA IFNL NT AZA IFNA IFNL Treatment Treatment

Celline ● P66 ● P92

Figure 5.6: RNA expression analysis of CRC organoids. Top panel: Gene expression levels of A. ATF3, B. CCL28, C. CCL20, and D. CCL15 in POP92 and POP66 after 10 days of treatment with 5-Aza-CdR. Bottom panel : Gene expression levels of ATF3 after treatment with 5-Aza-CdR, IFNα, and IFNλ after A. 5 days and B 10 days in culture. Chapter 5. Appendix 56

CONTROL 5-Aza-CdR IFNα IFNλ

Day7

POP92

Day10

Day7

POP181

Day10

Day7

CSC73 Day10

Figure 5.7: GFP intensity in POP92, POP181, and CDC73 PDX colorectal cancer cells expressing lentivirus transduced TCF/ LEF transcriptional reporter (TCF-GFP) to monitor Wnt/β- catenin pathway activity. GFP ﬂuorescence was measured by ﬂow cytometry after cells were treated with 5-Aza-CdR, Poly(I:C), IFNα, and IFNλ for 4,7 & 10 days. Chapter 5. Appendix 57

Basal ISG Levels

200

n o

i 150

d n

I POP92

100

e v

i POP181

a l

e 50 CSC73 R

0 S L 7 5 V S F 1 A A IR G M O IS

Figure 5.8: Gene expression analysis in PDX colorectal cancer cells measuring interferon stimulated genes. Gene expression was measured using RT-qPCR at basal levels. Relative induction of ISGS in POP92, POP181 and CSC73 PDX colorectal cells. Chapter 5. Appendix 58

Figure 5.9: In CRC, activation of the Wnt pathway leads to the accumulation of β-catenin in the cytosol, leading to it’s nuclear localization. High levels on nuclear β-catenin leads to it’s association with the canonical Wnt activators, TCF/LEF, and lead to the activation of Wnt target genes that are important in promoting a “stemness” phenotype. However, upon activation of an immune response in CRC, Wnt activity is shutdown leading to increased interferon stimulated genes and reduction in stemness genes. β-catenin can interact with members of the IRF family of transcription factor as a mechanism of Wnt pathway shutdown. Chapter 5. Appendix 59

Sample Name APC TP53 BRAF KRAS NRAS TCF7L1

POP181 Stop gain G13V

POP66 V600E

CSC73 Stop gain Splicing G12A

POP92 G1678G P72R R161R

Table 5.1: Patient derived CRC cells and their mutational status used in this study Chapter 5. Appendix 60

RPLPO-S CAGACAGACACTGGCAACA

RPLPO-AS ACATCTCCCCCTTCTCCTT

ISG15-S GCCTCAGCTCTGACACC

ISG15-AS CGAACTCATCTTTGCCAGTACA

IRF7-S GTGGACTGAGGGCTTGTAG

IRF7-AS TCAACACCTGTGACTTCATGT

OASL-S GCAGAAATTTCCAGGACCAC

OASL-AS CCCATCACGGTCACCATTG

MAVS-S AGGAGACAGATGGAGACACA

MAVS-AS CAGAACTGGGCAGTACCC

DDX58-S CCAGCATTACTAGTCAGAAGGAA

DDX58-AS CACAGTGCAATCTTGTCATCC

MDA5-S CACTTCCTTCTGCCAAACTTG

MDA5-AS GAGCAACTTCTTTCAACCACAG

Table 5.2: RT-qPCR primers used for this study Chapter 5. Appendix 61

REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies IRF-7 (D2A1J)Rabbit Cell Signalimg 13014S IRF-3 antibody (FL-425) Santa Cruz Biotech. sc-9082 Stat1 (42H3) Rabbit Cell Signaling 9175S β-Catenin (L54E2) Mouse Cell Signaling 2677S Phospho-β-Catenin (Ser33/37/Thr41) Rabbit Cell Signalimg 9561S MouseAnti-β-Catenin BD biosciences 610154 β-Catenin (D10A8) XP Rabbit Cell Signaling 8480S Anti-rabbit IgG (H+L), F(ab')2 Fragment (Alexa Fluor®488 Conjugate) Cell Signaling 4412S Anti-mouse IgG (H+L), F(ab')2 Fragment (Alexa Fluor®647 Conjugate) Cell Signaling 4410S Anti-TCF7L2 Rabbit Abcam ab676151 Bacterial and Virus Strains Cignal Lenti TCF/LEF Reporter (GFP) Qiagen CLS-018G Cellines Laboratory of Dr. Catherine Colorectal Cancer PDX Cells O'Brien N/A Laboratory of Dr. Catherine LIM1215 Colorectal Cancer celline O'Brien N/A Laboratory of Dr. Catherine Colorectal Cancer Organoid Cells O'Brien Chemicals, Peptides, and Recombinant Proteins Pierce™ ECL Western Blotting Substrate Thermo Fisher Scientific 32106 RIPA Lysis and Extraction Buffer Thermo Fisher Scientific 89900 SYBR Green Master Mix Applied Biosystems 4385612 Lipofectamine™ 3000 Transfection Reagent Thermo Fisher Scientific L3000015 Halt™ Protease and Phosphatase Inhibitor Cocktail, EDTA-free (100X) Thermo Fisher Scientific 78441 ProLong® Gold Antifade Reagen Cell Signaling 9071S Human IL-29 Cell Signaling 5183LC Human Interferon-Alpha1 Cell Signaling 8927LC Polyinosinic–polycytidylic acid Sigma P1530 5-Aza-2′-deoxycytidine Sigma A3656 LyoVec™ Cedarlane LYEC-2 Critical Commercial Assays RNeasy Mini Kit Qiagen Cat# 74134 RNase-free DNase set Qiagen Cat# 79254

Subcellular Protein Fractionation Kit for Cultured Cells Thermo Fisher Scientific Cat# 78840 SS VILO MASTERMIX Thermo Fisher Scientific 11755500 SYBR SELECT MASTER MIX Thermo Fisher Scientific 4472920 Duolink® In Situ Detection Reagents Red Sigma DUO92008 Software and Algorithms GraphPad PRISM 6 GraphPad Software https://www.graphpad.com/scientific-software/prism/ R N/A https://www.r-project.org/ Intergrative Genomics Viewer (IGV) Broad Institute http://software.broadinstitute.org/software/igv/ https://imagej-nih-gov-laneproxy-stanford- ImageJ NIH edu.myaccess.library.utoronto.ca/ij/

Table 5.3: List of reagents, assays and cellines used for this study Chapter 5. Appendix 62

HUGO Gene Accession Position Target Sequence ACTB NM_001101.2 1011-1110 TGCAGAAGGAGATCACTGCCCTGGCACCCAGCACAATGAAGATCAAGATCATTGCTCCTCCTGAGCGCAAGTACTCCGTGTGGATCGGCGGCTCCATCCT APCDD1 NM_153000.4 2111-2210 GCCTGAGCAACTTCACAACAGTAATTGCACTTTAAGACAGCCTAGAGTTCTGGACGAGCGTGTTTGGTAGCAGGGATGAAAGCTAGGGCCTCTTATTTTT AXIN2 NM_004655.3 1036-1135 CTTGTCCAGCAAAACTCTGAGGGCCACGGCGAGTGTGAGGTCCACGGAAACTGTTGACAGTGGATACAGGTCCTTCAAGAGGAGCGATCCTGTTAATCCT BIRC5 NM_001168.2 1216-1315 CCATTCTAAGTCATTGGGGAAACGGGGTGAACTTCAGGTGGATGAGGAGACAGAATAGAGTGATAGGAAGCGTCTGGCAGATACTCCTTTTGCCACTGCT CD44 NM_001001392.1 430-529 ACACCATGGACAAGTTTTGGTGGCACGCAGCCTGGGGACTCTGCCTCGTGCCGCTGAGCCTGGCGCAGATCGATTTGAATATAACCTGCCGCTTTGCAGG CDK6 NM_001259.6 2405-2504 AGAGTAGTTCTCTCTAACTAGAGACAGGAGTGGCCTTGAAATTTTCCTCATCTATTACACTGTACTTTCTGCCACACACTGCCTTGTTGGCAAAGTATCC CPXM1 NM_001184699.1 1708-1807 AGCAGGTGCGCATGGGCATTGCAGGAGTGGTGAGGGACAAGGACACGGAGCTTGGGATTGCTGACGCTGTCATTGCCGTGGATGGGATTAACCATGACGT CTNNB1 NM_001098210.1 1816-1915 TCTTGCCCTTTGTCCCGCAAATCATGCACCTTTGCGTGAGCAGGGTGCCATTCCACGACTAGTTCAGTTGCTTGTTCGTGCACATCAGGATACCCAGCGC CXCL12 NM_199168.3 70-169 CCGCCCGCCCGCCCGCCCGCGCCATGAACGCCAAGGTCGTGGTCGTGCTGGTCCTCGTGCTGACCGCGCTCTGCCTCAGCGACGGGAAGCCCGTCAGCCT CXCR4 NM_003467.2 1336-1435 ATTGATGTGTGTCTAGGCAGGACCTGTGGCCAAGTTCTTAGTTGCTGTATGTCTCGTGGTAGGACTGTAGAAAAGGGAACTGAACATTCCAGAGCGTGTA DDX58 NM_014314.3 2131-2230 CTGGCATATTGACTGGACGTGGCAAAACAAATCAGAACACAGGAATGACCCTCCCGGCACAGAAGTGTATATTGGATGCATTCAAAGCCAGTGGAGATCA DHX58 NM_024119.2 848-947 GAGCACAGCCAACAGCCTTGCAAACAGTACAACCTCTGCCACAGGCGCAGCCAGGATCCGTTTGGGGACTTGCTGAAGAAGCTCATGGACCAAATCCATG DPYSL3 NM_001197294.1 121-220 GCTCGGCGCACGCTGCCGGCGGCTGCCCTTTCCGCCTCTGGGGAAGAAAAACCCGCGGGCCGCTTTCGTGCTTGAACCATGGCCTCGGGCCGGAGGGGCT EPHB3 NM_004443.3 3486-3585 GCTGGACTTTCGGACTCTTGGACTTTTGGATGCCTGGCCTTAGGCTGTGGCCCAGAAGCTGGAAGTTTGGGAAAGGCCCAAGCTGGGACTTCTCCAGGCC HOXA5 NM_019102.2 896-995 TGAGCAGTATTAGCGGATCCCGCGTAGTGTCAGTACTAAGGTGACTTTCTGAAACTCCCTTGTGTTCCTTCTGTGAAGAAGCCCTGTTCTCGTTGCCCTA HOXA9 NM_152739.3 416-515 ACGCCCGGTGCGCTCTCCTTCGCGGGCTTGCCCTCCAGCCGGCCTTATGGCATTAAACCTGAACCGCTGTCGGCCAGAAGGGGTGACTGTCCCACGCTTG IFIH1 NM_022168.2 186-285 GCTTGGGAGAACCCTCTCCCTTCTCTGAGAAAGAAAGATGTCGAATGGGTATTCCACAGACGAGAATTTCCGCTATCTCATCTCGTGCTTCAGGGCCAGG IFIT1 NM_001548.3 1441-1540 GAGAAAGGCATTAGATCTGGAAAGCTTGAGCCTCCTTGGGTTCGTCTACAAATTGGAAGGAAATATGAATGAAGCCCTGGAGTACTATGAGCGGGCCCTG IFNA1 NM_024013.1 668-767 TGACTCATACACCAGGTCACGCTTTCATGAATTCTGTCATTTCAAAGACTCTCACCCCTGCTATAACTATGACCATGCTGATAAACTGATTTATCTATTT IFNB1 NM_002176.2 611-710 ACAGACTTACAGGTTACCTCCGAAACTGAAGATCTCCTAGCCTGTGCCTCTGGGACTGGACAATTGCTTCAAGCATTCTTCAACCAGCAGATGCTGTTTA IFNL1 NM_172140.1 234-333 AGCTAGCGAGCTTCAAGAAGGCCAGGGACGCCTTGGAAGAGTCACTCAAGCTGAAAAACTGGAGTTGCAGCTCTCCTGTCTTCCCCGGGAATTGGGACCT IFNL2 NM_172138.1 590-689 TTCAACCTCTTCCGCCTCCTCACGCGAGACCTGAATTGTGTTGCCAGTGGGGACCTGTGTGTCTGACCCTCCCACCAGTCATGCAACCTGAGATTTTATT IL10RB NM_000628.3 1761-1860 TTCTACCAGATTATGGATGGACTGATCTGAAAATCGACCTCAACTCAAGGGTGGTCAGCTCAATGCTACACAGAGCACGGACTTTTGGATTCTTTGCAGT IL17RD NM_017563.3 3191-3290 TTGGTTGGCCTTCTGGTCTAAAGCTGTGTCCTGAATATTAGGGATCACAATTCACTGAAATACAGCAGTGTGTGGAGGTGATGGCCAGTTAATCTGCTGA IL22 NM_020525.4 320-419 CTATCTGATGAAGCAGGTGCTGAACTTCACCCTTGAAGAAGTGCTGTTCCCTCAATCTGATAGGTTCCAGCCTTATATGCAGGAGGTGGTGCCCTTCCTG IL22RA1 NM_021258.2 2525-2624 GACGGGTACAATAACACACTGTACTGATGTCACAACTTTGCAAGCTCTGCCTTGGGTTCAGCCCATCTGGGCTCAAATTCCAGCCTCACCACTCACAAGC IRF1 NM_002198.1 511-610 CTGTGCGAGTGTACCGGATGCTTCCACCTCTCACCAAGAACCAGAGAAAAGAAAGAAAGTCGAAGTCCAGCCGAGATGCTAAGAGCAAGGCCAAGAGGAA IRF3 NM_001571.5 1304-1403 TCATGGCCCCAGGACCAGCCGTGGACCAAGAGGCTCGTGATGGTCAAGGTTGTGCCCACGTGCCTCAGGGCCTTGGTAGAAATGGCCCGGGTAGGGGGTG IRF7 NM_001572.3 1764-1863 CGCAGCGTGAGGGTGTGTCTTCCCTGGATAGCAGCAGCCTCAGCCTCTGCCTGTCCAGCGCCAACAGCCTCTATGACGACATCGAGTGCTTCCTTATGGA IRF8 NM_002163.2 254-353 AGTTTAAAGAAGGGGACAAAGCTGAACCAGCCACTTGGAAGACGAGGTTACGCTGTGCTTTGAATAAGAGCCCAGATTTTGAGGAAGTGACGGACCGGTC IRF9 NM_006084.4 386-485 GCACTCAACAAGAGTTCTGAATTTAAGGAGGTTCCTGAGAGGGGCCGCATGGATGTTGCTGAGCCCTACAAGGTGTATCAGTTGCTGCCACCAGGAATCG ISG15 NM_005101.3 306-405 CCCGGCAGCACGGTCCTGCTGGTGGTGGACAAATGCGACGAACCTCTGAGCATCCTGGTGAGGAATAACAAGGGCCGCAGCAGCACCTACGAGGTACGGC LAPTM4B NM_018407.4 1736-1835 CTGTTCTTGTGGATCTTGTGTCCAGGGACATGGGGTGACATGCCTCGTATGTGTTAGAGGGTGGAATGGATGTGTTTGGCGCTGCATGGGATCTGGTGCC LGR5 NM_003667.2 1469-1568 CTTATGCTTACCAGTGCTGTGCATTTGGAGTGTGTGAGAATGCCTATAAGATTTCTAATCAATGGAATAAAGGTGACAACAGCAGTATGGACGACCTTCA MAVS NM_020746.3 3461-3560 ACCCACTGTTGGGGAGATTATCTACAATAACACCAGAAACACATTGGGGTGGATTGGGGGTATCCTTATGGGTTCTTTTCAGGGAACCATTGCTGGACAA MX1 NM_002462.2 1486-1585 GCCTTTAATCAGGACATCACTGCTCTCATGCAAGGAGAGGAAACTGTAGGGGAGGAAGACATTCGGCTGTTTACCAGACTCCGACACGAGTTCCACAAAT MYC NM_002467.3 1611-1710 TCGGACACCGAGGAGAATGTCAAGAGGCGAACACACAACGTCTTGGAGCGCCAGAGGAGGAACGAGCTAAAACGGAGCTTTTTTGCCCTGCGTGACCAGA OASL NM_198213.1 261-360 GGCGTTTCTGAGCTGTTTCCACAGCTTCCAGGAGGCAGCCAAGCATCACAAAGATGTTCTGAGGCTGATATGGAAAACCATGTGGCAAAGCCAGGACCTG PTPRO NM_030671.2 2771-2870 GATGTGCGCCCATCCTCCCTTGCTTCCAGATTGTTTTAGTGGGCCCTGATGGTCATTTTTCTAAACAGAGGCCCTGCTTTGTAATATGTGGCCAAGGAGA RPLP0 NM_001002.3 251-350 CGAAATGTTTCATTGTGGGAGCAGACAATGTGGGCTCCAAGCAGATGCAGCAGATCCGCATGTCCCTTCGCGGGAAGGCTGTGGTGCTGATGGGCAAGAA RUNX2 NM_001024630.3 35-134 AGAACCACAAGTGCGGTGCAAACTTTCTCCAGGAGGACAGCAAGAAGTCTCTGGTTTTTAAATGGTTAATCTCCGCAGGTCACTACCAGCCACCGAGACC SFRP1 NM_003012.3 3321-3420 CAGAGTCCTTAGTGGAGGGGTTTACCTGGAACATTAGTAGTTACCACAGAATACGGAAGAGCAGGTGACTGTGCTGTGCAGCTCTCTAAATGGGAATTCT SFRP2 NM_003013.2 841-940 GAAGGAGATAACCTACATCAACCGAGATACCAAAATCATCCTGGAGACCAAGAGCAAGACCATTTACAAGCTGAACGGTGTGTCCGAAAGGGACCTGAAG STAT1 NM_139266.1 456-555 ACAGTGGTTAGAAAAGCAAGACTGGGAGCACGCTGCCAATGATGTTTCATTTGCCACCATCCGTTTTCATGACCTCCTGTCACAGCTGGATGATCAATAT STAT2 NM_005419.3 1391-1490 GATTTGGGACTTTGGTTACCTGACTCTGGTGGAGCAACGTTCAGGTGGTTCAGGAAAGGGCAGCAATAAGGGGCCACTAGGTGTGACAGAGGAACTGCAC STAT3 NM_003150.3 2061-2160 AAAGAAGGAGGCGTCACTTTCACTTGGGTGGAGAAGGACATCAGCGGTAAGACCCAGATCCAGTCCGTGGAACCATACACAAAGCAGCAGCTGAACAACA TAP1 NM_000593.5 2076-2175 GTGGCTGCAGTGGGACAAGAGCCACAGGTATTTGGAAGAAGTCTTCAAGAAAATATTGCCTATGGCCTGACCCAGAAGCCAACTATGGAGGAAATCACAG TEAD2 NM_001256658.1 2037-2136 AACGAGATATTTATAAGTGGGTGCTAGGGTCTTGACTTTATCTCCGCTGCACAAGCAGTGTGTTGAACTTTCTAATCTCATCCCTCTCCTAAGTGAGCCC VEGFA NM_001025366.1 1326-1425 GAGTCCAACATCACCATGCAGATTATGCGGATCAAACCTCACCAAGGCCAGCACATAGGAGAGATGAGCTTCCTACAGCACAACAAATGTGAATGCAGAC ZEB1 NM_001128128.1 1451-1550 TTACAAAATGGGGTTTTCACTGGTGGTGGCCCATTACAGGCAACCAGTTCTCCTCAGGGCATGGTGCAAGCTGTTGTTCTGCCAACAGTTGGTTTGGTGT

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