Hepatocyte Differentiation and Hepatocellular Carcinoma

HEPATOCYTE DIFFERENTIATION AND HEPATOCELLULAR CARCINOMA:

RATIONALE FOR P53 INDEPENDENT THERAPY

FRANCIS O ENANE

Submitted in partial fulfillment of the requirement for the degree of Doctor of

Philosophy

Dissertation Advisor

Yogen Saunthararajah, MD

Department of Molecular Medicine

Cleveland Clinic Lerner College of Medicine

CASE WESTERN RESERVE UNIVERSITY

May 2017

CASE WESTERN RESERVE UNIVERSITY

SCHOOL OF GRADUATE STUDIES

We hereby approve the dissertation of

Francis O Enane

Candindate for Doctor of Philosophy Degree*.

Committee Chair: Peter Scacheri, PhD

Committee member: Angela Ting PhD

Committee member: Xiaoxia Li, PhD

Committee member: Alok Khorana, MD

Committee member: Yogen Saunthararajah, MD

Date of defense: December 19th 2016

*We also certify that written approval has been obtained for any proprietary

material contained therein

Dedication

I dedicate this work to approximately 17.5 million global cancer patient population as of the year 2016. I strongly believe that the scientific and medical communities will continue to work coherently to identify mechanisms to provide better cure rates of cancer, to reduce the economic burden to families affected, and to define psychological and emotional challenges experienced by patients and their families. The work performed in this dissertation is a small contribution to that objective and paves the way to understand new therapeutic mechanisms in hepatocellular carcinoma. In the modern technical and highly skilled society - and with sufficient financial and political support - there will be a time when patients will live long extended lives despite their cancer diagnosis.

Table of contents List of Tables ……………………………………………………………………….1 List of Figures ………………………………………………………………………2 Acknowledgements………………………………………………………………...6 Abstract .…………………………………………………………………………….8

Introduction .……………………………………………………………………..10 Hepatocellular carcinoma.……………………………………………………….10 Genomic and epigenetic alterations of hepatocellular carcinoma …………..17 The essential role of epigenetic mechanisms in normal versus cancer cell physiology …………………………………………………………………………20 Current therapies, treatment failures, and opportunities for novel therapeutic strategy ……………………………………………………………………………24 Hepatocyte Differentiation.. .…………………………………………………….26

Chapter 1 …………………………………………………………………………29 Several Genetic Alterations In Hepatocellular Carcinomar Disrupt GATA4- Mediated Transactivation To Suppress Hepatocyte And Preserve Precursor Fate…………………………………………………………………………………29 Abstract ……………………………………………………………………………30 Introduction and Rationale ………………………………………………………30 Results …………………………………………………………………………….38 Discussion ………………………………………………………………………...56 Summary and Significance ……………………………………………………...61 Methods Chapter 1 ……………………………………………………………....64

Chapter 2 …………………………………………………………………………78 Transcriptional Corepressors are Logical Molecular Targets for p53- Independent Differentiation Therapy in Hepatocellular carcinoma …………78 Abstract .…………………………………………………………………………..79 Introduction and Rationale ……………………………………………………...80 Results .……………………………………………………………………………86 Discussion.………………………………………………………………………...98 Summary and Significance .……………………………………………………104 Methods Chapter 2 .…………………………………………………………….106

Future Studies.…………………………………………………………………112

Bibliography.……………………………………………………………………116

List of Tables

Table i: Common risk factors associated with liver cancer and geographic distribution………………………………………………………………………………14

Table 1: Liver differentiation genes suppressed in

HCC……………………………………………………………………………………..55

List of Figures

Figure i: Global geographic variations of liver cancer incidence and mortality…………………………………………………………………………………11

Figure ii: Current treatment failures converge to p53 pathway alterations that confer resistance and toxicit…………………………………………………………………………………….23

Figure iii: Key factors involved in the progression of hepatocyte differentiation…………………………………………………………………………...25

Figure 1: Model 1: Genetic alterations of GATA4 mediated transactivation impair hepatocyte differentiation………………………………………………………………………….37

Figure 2: GATA4 is a candidate tumor suppressor gene on chromosome

8p………………………………………………………………………………………..38

Figure 3: GATA4 deletion and MYC gain are hallmarks of

HCC………………………………………...... 39

Figure 4: Reduced GATA4 expression in HCC from multiple independent studies………………………………………………………………………………...... 40

Figure 5: Atypical HCC containing a rare germ-line GATA4 mutation…………………………………………………………………………………41

Figure 6: Mutant GATA4 does not interact with mediator complex……………………………………...... 43

Figure 7: Conditional deletion of one Gata4 allele produced a fatty, proliferative liver phenotype…………………………………………………………………………44

Figure 8: Gene expression analysis demonstrated persistent expression of hepatocyte precursor and impaired expression of hepatocyte genes in Gata4 hapoinsufficient mice…………………………………………………………………46

Figure 9: Hepatocyte precursor genes……………………………………………………………………...... 47

Figure 10: Hundreds of liver differentiation genes are also suppressed in HCC compared to normal liver……………………………………………………………………………...... 50

Figure 11: Introduction of GATA4 into GATA4-haploinssuficient HCC cells induced terminal hepatocyte differentiation…………………………………………………………………………..51

Figure 12: Frequent inactivation of GATA4 and cofactors in

HCC……………………………………………………………………………………..52

Figure 13: Liver differentiation genes are suppressed in all histological grades of

HCC……………...... 54

Figure 14: Key transcription factor drivers of hepatocyte terminal differentiation are suppressed in HCC with GATA4 alterations and/or HCC with SWI/SNF alterations………………………………………………………………………………56

Figure 15: Chapter 1 Graphical summary: Several genetic alterations in HCC target the GATA4 transactivation pathway……………………………………………………………………………….62

Figure 16: Curable versus incurable disseminated solid tumors…………………………………………………………………………………..81

Figure 17: Model 2: Copressors aberrantly recruited to FOXA1/2 are logical targets for pharmacologic inhibition………………………………………………………………………………85

Figure 18: FOXA1 and FOXA2 bind to HNF4A enhancer in

HCC……………………………………………………………………………………..86

Figure 19: Copy number loss of GATA4 by 8p deletion in

PLC……………………………………………………………………………………87

Figure 20: Wild-type GATA4 promotes stronger FOXA2-coactivator interaction in

HCC……………………………………………………………………………………..89

Figure 21: Wild-type GATA4 impair FOXA1 interaction with DNMT1 in

HCC……………………………………………………………………………………..90

Figure 22: Reintroduction of coactivators in HCC promote cell cycle exit by differentiation…………………………………………………………………………91

Figure 23: Inhibition of DNMT1 induce cell cycle exit by differentiation in P53 mutant HCC…………………………………………………………………………….92

Figure 24: Inhibition of DNMT1 shifts balance of corepressor and coactivators towards coactivators in HCC……………………………………………………………………………………..94 Figure 25: Inhibition of DNMT1 does not impair transcription factor interaction in

HCC……………………………………………………………………………………..96

Figure 26: Inhibition of DNMT1 induces HCC tumor regression in vivo………………………………………………………………………………………97

Figure 27: Chapter 2 Model

Summary……………………………………………………………………...... 104

Acknowledgements

I would like to thank my mother, Jacinta Tionyi and father, Peter Enane for bringing me into this world, and for guiding me into the person I am. Thank you also to my grandmother Eunice Tionyi, for raising me through the roughest times of development. Without you, I will not be here today.

Thank you to my mentor Dr. Yogen Saunthararajah, the current and previous members of the lab and to the department of translational hematology and oncology research at the Taussig cancer center of Cleveland Clinic Ohio. Dr.

Saunthararajah provided me with the highest level of training together with the necessary funding and resources to succeed in such a complicated area of investigation. His kind, honest and strong scientific rigor has been the greatest influence to my professional development. Special thank you also to Dr. Xiaorong

Gu, Dr. Ebrahem Quteba, Dr. Kwok Peng, Dr. Reda Mahfouz and Dr. Shunji

Egusa for their everyday input, advice and guidance. Thank you to the collaborators, Dr. Jaraslow Maciejewski, Dr, Tomas Radivoyecitch, Dr. Han

Chong, Dr. Timothy Shuein Wai Ho and Dr. Mark Brown. Your leadership has greatly influenced my scientific and academic maturity.

Thank you also to my thesis committee members Dr. Peter Scacheri, Dr.

Angela Ting, Dr. Xiaoxia Li, Dr. Alok Khorana, and Dr. Pierre Triozzi for their professional input on this dissertation. They have consistently encouraged me and contributed to the strength of the science performed here. Thank you to the

6 molecular medicine PhD program of Cleveland clinic learner college of Medicine of Case Western Reserve University for providing me with the educational platform that contributed to the success of this work. I acknowledge all the friends that I have made in this program and special thank you to Dr. Marcia Jarret, Dr.

Robert Fairchild and Dr. Jonathan Smith for their professional development programs. I would like to acknowledge also Dr. Carol De la Mote and her former student Dr. David Hill for introducing me to the molecular medicine program in the summer of 2009 at the Gordon Research Conference on proteoglycans. Your guidance will forever remain with me.

Finally, thank you to my beloved wife Dr. Leslie Enane. We have spent 5 and half years living in different regions of the world. This was not easy! Thank you for loving me, for understanding my special interests in research and for all the patience during this extended period. Your love, strength and health is my motivation for success.

Hepatocyte Differentiation and Hepatocellular Carcinoma: Rationale for p53

Independent Therapy

Abstract

FRANCIS O ENANE

The era of genomic revolution illustrated that cancer is a genetic disease where alterations impair specific cellular pathways. For instance, cancer cells frequently physically remove cell death genes (i.e., TP53, CDKN2A) to impair apoptotic pathways. However, most systemic treatments of cancer are typically designed to engage cell cycle exit by apoptosis. This approach selects for apoptosis-resistant cancer cells and is toxic to normal cells. There is clear need for p53 independent non-apoptosis therapies in cancer.

To proliferate, cells must execute a complex duplication and separation of entire cells: a process coordinated by the transcription factor MYC, whose growth and division function is conserved throughout metazoan evolution. Multicellular organisms are unique however, by potently antagonizing MYC function by genetic methods. This allows differentiation into layers of cells, tissues, organs and organ systems each with specialized biological function. The evolutionary process of specialization is uniquely regulated, and arises from stem cells that acquire tissue/lineage specification signals, attaining exponential outgrowth, while systemically differentiating into various tissue precursors. Differentiation, therefore routinely restrains the exponential growth of committed tissue

8 precursors through potent physiologic antagonization of MYC and MYC related programs. The aberrant growth and division illustrates that differentiation derangement is emblematic of cancer. Hepatocellular carcinoma (HCC) - including that histologically classified as ‘well-differentiated’ by light microscopy - displays lower expression of hundreds of specialized liver differentiation genes compared to non-malignant liver. Suppression of so many differentiation genes suggests disruption to the upstream core master transcription factor circuit that coordinates physiologic programs to terminate MYC function.

We demonstrate how deletion/mutation of GATA4 - a master transcription factor of hepatocyte differentiation - explained differentiation impediment of HCC, while loss of function of its key co-activators (e.g., ARID1A, ARID2, SMARCA4, and SMARCAD1), appeared to be a cause in the rest. Genetic alterations in the

GATA4 pathway contribute to HCC phenotype of deranged differentiation and its corollary persistent proliferation. Suppression of terminal differentiation driving genes was by epigenetic methods providing a therapeutically actionable pathway through novel p53 independent epigenetic methods. In vitro and in vivo evaluation of this approach was non-cytotoxic to normal cells but significantly impaired HCC growth.

Introduction

Hepatocellular carcinoma

The most frequent tumors observed in the liver are usually metastatic lesions from other tissues. Hepatocellular carcinoma (HCC) is a primary malignancy of the liver - arising from malignant hepatocytes - and is the most commonly diagnosed primary liver tumor in ~80% of the diagnosis. The second most frequent primary malignancy in the liver is cholangiocarcinoma- arising from hepatobiliary branch – which is normally seen in about 20% of cases1. The estimated overall survival of primary HCC is 16-20 month from the time of diagnosis. While all other common cancers (Lung, Breast, and prostate cancers) have a declining mortality rate, recent evidence by National Cancer Institute

Surveillance, Epidemiology End Results (SEER), suggest that HCC death rate increased by 2.8% in men and 3.4% in women per year from 2008-2012 in U.S2.

Of global cancer incidence and mortality, liver cancer represents 6% and 9% respectively, and has an estimated annual death rate of 700,000, making it the second most lethal cancer in the world1. In the United States, the American

Society of Clinical Oncology recently estimated the annual cost of HCC to be

$437 million per year in aggregate, which amounts to approximately $31,641 per patient. HCC is thus a growing public health problem that requires urgent scientific/clinical attention and global government level support in order to combat the public health threat.

Disease epidemiology

The incidence of

HCC has a unique global variation

(Figure i). Unlike all cancers, the most predominant high- risk cases of primary liver cancer are usually in the eastern hemisphere Figure i. Global Geographic variation of liver cancer incidence and Mortality: Data generated from existing registries by and in countries with GLOBOCAN. A) Age adjusted incidence of all other cancers with the exception of skin cancers. B) Age adjusted incidence of liver cancer. less economic C) Quantification of liver cancer incidence as of 2012 D) Liver cancer mortality rates as of 2012. development. Even though the incidence is relatively low in the western hemisphere, there has been a consistent gradual rise in new diagnosis since 1970’s3. In the United States, the incidence has doubled within the past two decades and HCC is thus projected to be the third most common cancer in the U.S. by 2030. The most well characterized disease etiologies include Hepatitis B (HBV) or C (HCV) and afflotoxins, in Asia, Africa, and South America (Table i). Various reports demonstrate an association with oral use of contraceptives in Europe and United

States; however, these are relatively controversial and require more investigation4. Alcohol abuse is a common risk factor worldwide (Table i).

Various other comorbidities include Non-alcoholic statohepatitis (NASH), which is the liver manifestation of metabolic syndrome, thereby linking obesity, insulin resistance and diabetes as emerging new risk factors of HCC5.

Hepatocellular carcinoma distribution among gender, age and race/ethnicity

The global rates of HCC are 3-fold higher in men than females1. This risk is higher in endemic regions such as China and Sub-Sahara Africa, which have a

5-fold increase of HCC incidence in men. Although we do not fully understand why such disparities exist, there are suggestions that men are more likely to have chronic HBV, HCV and excessive alcohol abuse6. Hormonal variations such as the tropic effect of androgens in men or the protective effect of estrogen through inhibition of interlukine 6 in women may also contribute to HCC gender variations7.

Most HCC arise in the background of chronic liver disease. For this reason, elderly patients with a prolonged liver disease are more likely to develop

HCC. A variety of studies among Asian and European population suggest an average age at presentation of 50 and 60 years respectively, while the average age of presentation in African settings is 33 years.

Similarly, there is a wide spread racial/ethnic variation in all global HCC

(Figure i C). The highest rates of HCC are in Asia. Among the Asian population,

Chinese have the highest incidence, followed by the Japanese; while Indians have low incidence rates. In Africa, the incidence is highest in West and Central

Africa regions. Population analysis studies in North America have also demonstrated similar ethnic variations, where Asia pacific Islanders (API) have the highest incidence. An interesting trend in these analyses however demonstrate a declining HCC incidence in API of North America, but an increasing incidence in blacks/African Americans and Hispanics. HCC rates are also projected to increase in non-Hispanic whites and it is estimated that HCC rates among whites may surpass all other races by 20308.

Effects of immigration on incidence of hepatocellular carcinoma

Migrants from endemic areas contribute to increased rates of HCC in their host countries: Chinese immigrants in North America are 3.1-11 times likely to develop HCC compared to North American born individuals while West African immigrants to England and Wales have 32-fold risk of HCC compared to native born individuals9,10. A comprehensive study among Canadian population recently revealed that while most other cancers (Stomach, Breast, Colon e.t.c,) had almost equal incidence among the Chinese immigrants and Canadian individuals, the incidence of liver cancer was highest among recent Chinese immigrants11. This may be due to the related infection with hepatitis B/C, the most frequent underlying risk factors of HCC that happen in early childhood and largely seen in high endemic regions. More studies are however needed to establish if there are existing genetic predisposing factors.

Table i: Common risk factors associated with liver cancer and geographic distribution

TYPE OF HEPATOCELLULAR GEOGRAPHIC CHOLANGIOCARCINOMA CONDITION CARCINOMA REGIONS

South and East Chronic HBV* Asia Sub Sahara infection Africa

EPIDEMIC Chronic HCV* Liver flukes Japan & North infection Africa

Aflatoxin exposure China and West Africa

Global (common in Eastern Europe, Chronic alcohol Primary sclerosing U.S., U.K) consumption cholangitis

COMMON Asia and West Non-alcoholic fatty Recurrent pyogenic hemisphere liver disease cholangitis countries

Chronic HCV infection Hereditary Europe haemochromatosis Chronic HBV infection

RARE

α-1-Antitrypsin Non-alcoholic fatty liver deficiency disease *Hepatitis B (HBV) and C (HCV) virus The risk factors of hepatocellular carcinoma vary based on geographic region. In regions with epidemic HCC prominent risk factors are HBV, HCV and environmental toxins e.g., aflatoxins. Areas where HCC is common tend to have risk factors that include chronic alcohol consumption and non-alcoholic fatty liver disease. Rare HCC cases are associated with hereditary haemochromatosis and α-1-Antitrypsin deficiency, among individuals with European decent. Variations also exist for cholangiocarcinoma.

Pathophysiology of hepatocellular carcinoma

Pathogenesis of HCC is a multifactorial process involving direct/indirect contribution of environmental, infectious, nutritional and metabolic/endocrine factors. In most cases, HCC occurs in the background of chronic liver disease.

Liver cirrhosis - the replacement of normal liver tissue with scar tissue – is linked to HCC. The two well-known infectious risk factors include chronic HBV and HCV

14 viruses. While HBV can directly convert normal liver cells into proliferative HCC cells, HCV promotes HCC in the background of other liver disease such as liver fibrosis, steatosis or cirrhosis. The variation in the incidence of HCC across geographical regions - including in areas without HBV/HCV viruses - also suggest causative environmental factors that may contribute to the disease.

Studies have linked consumption of raw fish, mushrooms and peanuts to increased aflatoxins in China and West Africa, where exposure to aflatoxins further increase the risk. Regions in Eastern Europe also tend to have high incidence of HCC, where the most common risk factor is alcohol abuse. The liver manifestation of metabolic syndrome such as Nonalcoholic steatohepatitis

(NASH) is associated with HCC-genesis especially in the western hemisphere and in Japan.

Methods for detection of hepatocellular carcinoma

Individuals with chronic liver disease have an increased risk of hepatocellular carcinoma and require frequent screening, especially those in geographical regions with high incidence of HCC. In areas where screening methods are limited, chronic liver diseases frequently advance into HCC1,3.

Currently there is no standard marker established for early detection of HCC. The most common screening methods measures serum marker α-fetoprotein3.

Another widely used diagnostic marker is Prothrombin induced by vitamin K deficiency or antagonist II (PIVKA-II) and des-gamma- carboxy prothrombin, none of which are approved as standard practice. While more research to determine methods for early detection is required, the current methods use

15 tomography and magnetic resonance imaging for detection of dysplastic nodules.

Such methods are still unable to detect low grade to high-grade HCC lesions3,12.

There is currently no known serum marker for detection of cholangiocarcinoma the second most common primary malignancy of the liver. For cases where lesions are possibly detected, frequent evaluations are needed even after treatment to determine disease recurrence.

Preventative measures for hepatocellular carcinoma

The prevention of viral associated HCC can be achieved using antiviral drugs or vaccine immunization. There has been a gradual drop of HBV associated HCC in global regions where the vaccine usage has been successfully implemented. For instance, Thailand, Taiwan and various other south and east Asian countries, there is significant drop in new cases of HBV associated HCC in children born after the implementation of the vaccine compared to those born before the implementation6. In cases where the adherent of the vaccine was unsuccessful, a higher risk for HCC was observed, thereby linking the drop in new diagnosis directly to the vaccine. Studies are now projecting that treatment of chronic HCV infection will further contribute to a drop in HCC incidence13. Lack of alcohol dependence significantly lowers the incidence of HCC associated with chronic alcohol consumption. While there is limited knowledge on HCC cases associated with non-alcoholic fatty liver disease, and no current preventative measures have been characterized, it is suggested that obesity may have a role and that lack of obesity therefore may contribute to a reduction in incidence. Where chronic liver disease is experienced, individuals are required to receive frequent health

16 screening for potential detection of early tumors/neoplasia. Individuals residing in areas with environmental toxins e.g., aflatoxins may consider reducing consumption of raw food.

Genomic and epigenetic alterations of hepatocellular carcinoma

Genomic changes

Genomic alterations are the modifications at the DNA level that may or may not alter the overall function of genes. Such alterations can be grouped into germline variation – alterations that occur in the germ cell and can be observed in every cell where the gene is expressed - or somatic changes, which are acquired by a cell, and can be passed to daughter cells. Like most other cancers, the underlying molecular characterization underlying progression from normal hepatocytes into proliferative hepatocellular carcinoma is related to multiple acquired gene alterations and structural chromosome aberrations. The most frequent somatically mutated genes in primary HCC include: TP53, CTNNB1,

ARID1A, ARID2, SMARCA4, HNF1A, MYC14,15. Known structural changes at the chromosome level include copy number losses (CNV-loss) of chromosome 1p 4q

6q 8p and 17p, while copy number gains (CNV-gain) are frequently seen on chromosomes 1q and 8q16. The rates of variation in these alterations also sometimes differ across geographic regions. For instance, the frequency of

CTNNB1 variations tends to be higher in Caucasian or non-HBV driven HCC17.

The most universal alterations in HCC as demonstrated by multiple studies including data from the cancer genome atlas (TCGA) is CNV-Loss of

17 chromosome 8p, and CNV-Gain of the other end of chromosome 8 (8q) that encompass the MYC gene12,18,19. The second most frequent alterations are on chromosome 17p (TP53), with various somatic mutations of TP53 gene.

Despite all these frequent alterations, the key tumor suppressor genes that drive malignant conversion in HCC are unknown - hence the non-existing mouse model of primary HCC. Trp53 conditional knockout mouse induce HCC at an old age (9-11month)20, and frequently require carcinogenic supplementation for accelerated HCC outgrowth21. Although constitutively active mutations of

CTNNB1 are considered carcinogenic in human HCC, mice expressing mutant version of Ctnnb1 or overexpressing wild-type Ctnnb1 get hepatomegaly but not

HCC22-24. Neither TP53, nor CTNNB - genes frequently mutated in HCC - are on chromosome 8p, the most frequently deleted chromosome arm in HCC suggesting other genes are involved in HCC-genesis. In this dissertation, we modeled loss of chromosome 8p by evaluating the most vital mode of cell cycle exit (differentiation) by evaluating GATA4 loss, the only hepatocyte master transcription factor identified in micro-deleted segments of chromosome 8p. We hypothesized that to become cancerous, hepatocytes avoid differentiation cell cycle exits, by genetic alteration of early lineage specifying master transcription factors, a process that efficiently turns off hundreds of hepatocyte differentiation genes by epigenetic methods. We evaluated the GATA4 transactivation pathway by studying key components of the hepatocyte enhanceosome (multi- protein complexes) to establish alterations that can provide novel molecular

18 targets for pharmacologic inhibition to treat HCC by engaging differentiation genes instead of frequently physically unavailable apoptosis gene networks.

Genomic alterations by etiologic factors

Hepatitis B virus infection promotes HCC by three mechanisms. i)

Integration of viral DNA into host genome is linked to HCC by inducing chromosome level genomic instability25. ii) Viral integration at specific sites contributes to insertion mutations (e.g., activation of retinoic acid β-receptor, or cyclin A activation, and alterations of hTERT genes)25-27. iii) HBV integration can also lead to expression of viral protein HBX which alters cell proliferation and viability27,28. Regions with endemic HBV have reported familial HBV aggregation.

These are mostly due to familial HBV acquired by perinatal transmission of the virus during birth29. More studies also suggest that familial aggregation may be due to gene(s) with autosomal inheritance pattern that could contribute to increased rates of HCC in presence of HBV30,31. There are no known genetic alterations associated with Hepatitis C viral infections. Conversion of aflatoxin B1

(AFB1) into its active metabolite - exo-9.9-epoxide - has been linked to accumulation of DNA adducts and DNA damage32,33. Transversion mutations GC to TA have been linked to AFB1 where the most commonly reported mutation is on codon 249 (R249S) of TP53 seen in ~50% of HCC cases with AFB134.

Hepatic iron overload is a known risk factor of hepatocellular carcinoma.

This is especially common among individuals (frequent among European ancestry) that have hereditary hemochromatosis with alterations in HFE gene

(High Iron fe) located on chromosome 6p21.3. HCC develops in approximately 8-

10% of these individuals accounting for approximately 45% of HCC related death35.

The essential role of epigenetic mechanisms in normal versus cancer cell physiology

Normal cells

The term ‘epigenetics’ was coined by Conrad Waddington in the 1940’s and refers to mitotically/meiotically heritable changes in states of gene expression that do not alter DNA sequences36. Three common mechanisms of epigenetic changes have become apparent and include genomic DNA changes e.g., cytosine methylation, chemical modifications e.g, histone tail changes, and non-coding alterations e.g., microRNA (miRNA) regulation. For every cell division, epigenetic changes are passed unto the daughter cell, which has been demonstrated to maintain “cellular memory” retaining the epigenetic change in the daughter cell and subsequent cells hence forth37. Normal cells rely on epigenetics to respond to a variety of cellular signals by modifying chromatin packaging to create transient/permanent, global/local, and condensed/decondensed chromatin structure modulating the accessibility to key enzymatic machinery regulating gene expression.

DNA methylation epigenetic changes in normal hepatocytes versus HCC

Key proteins in DNA methylation epigenetic alterations include DNA methyl transeferase (DNMTs), which catalyze addition of methyl groups to 5’ cytosine nucleotides (-CH3). Methylation in CpG Island in gene promoters is

20 carcinogenic through gene silencing of tumor suppressor genes. DNA methylation promotes transcriptional gene silencing in two ways. i) CpG methylation sterically hinders accessibility of transcription factors to the DNA binding sites of target gene promoters38. ii) Direct binding of methyl CpG binding domain (MBD) – containing proteins to the methylated DNA induce transcription repression39. Studies of genome wide methylation profiling have demonstrated extensive DNA methylation changes in HCC versus normal liver tissue.

Hypermethylated gene promoters of APC, RASSFIA, CDKN2A and FZD7 can discriminate HCC tumors from normal liver tissue. Another set of hypermethylated genes (e.g., NAT2, CSPG2 and DCC) were exclusively associated with HBV-related HCC40. Various studies have demonstrated DNA methylation of tumor suppressor genes (TSGs) where functional studies have illustrated that modification of observed epigenetics changes directly alters HCC- genesis40-43.

Histone modification in HCC

Histone tails are targets for posttranslational modifications such as acetylation and methylation of lysines and arginines, serine threonine phosphorylation and lysine ubiquitination. Such modification can either turn genes on or off, by establishing gene expression through modification of DNA accessibility. Tight interactions between histones and DNA is disrupted by histone acetyl transferases (HATs) while histone deacytlase erase acetyl groups.

Histone methylation can contribute to gene silencing or gene activation depending on the lysine/argine residue modified. Although H3K23me3 is linked

21 to HCC genesis41, more studies are need to identify actionable histone modifications in HCC.

Epigenetic chromatin modifiers in HCC

Chromatin modifying enzymes such as polycomb repressor complex1/2

(PRC1 and PRC2), can contribute to heritable epigenetic gene silencing. PRC complexes are involved in maintaining cellular lineage commitment programs and stem cell pluripotency. Core proteins of PRC1 include BMI, RING1A /B and

CBX1, while co-proteins of PCR2 include SUZ12, EZH1/2, and EED1. PRC1 complexes are not associated with HCC genesis. Elevated expression of EZH2 is common in a variety of tumors including HCC compared to non-tumor samples44.

Clinically high levels of EZH2 are common in aggressive tumors with poor prognosis. Functional studies have also demonstrated that EZH2 suppresses

WNT-catenin antagonists thereby promoting Wnt/β-catenin signaling, a pathway with known oncogenic role in HCC44,45. Other studies have also demonstrated that HBV viral protein HBx down regulates PRC2 co-protein SUZ12 in human

HCC and in woodchucks infected in HBV46,47. Evaluation of potential therapeutic gains of targeting PRC2 complex in HCC needed.

MicroRNAs in HCC

MicroRNAs (miRNA) are non-coding small RNA (ncRNA) of about 20-23 nucleotide lengths are known. Initially discovered to have an important regulatory role in plants and in animals, where they are involved in targeting mRNA for cleavage/degradation or repression of translation, miRNA have gained

22 considerable interest in human physiological processes. By regulating post- transcriptional gene activity, miRNAs influence diverse cellular process including proliferation, apoptosis, cell fate and differentiation48. To turn off genes, miRNA recognize and bind 3’ untranslated regions of targeting mRNA to inhibit mRNA translation49. Aberrant miRNA activity is also evident in cancer. Aberrant expression profiles of miRNA is common in the development and progression of

HCC50. In studies screening large miRNA libraries, miR-122, is an abundant miRNA in HCC. Studies have shown that mi-R122 can promote propagation, stability and replication of HCV virus in HCC51. Interestingly, miR-122 exerts a complete opposite effect on HBV virus, where it has been shown to directly bind to

HBV viral Figure ii. Current treatment failures converge to p53 pathway genome and alterations that confer resistance and toxicity. The major problems of cancer therapy are resistance and toxicity. First and second line of negatively therapy converge into one common pathway of apoptosis induction/engagement of p53 system: TP53 gene is the most frequently regulate HBV mutated gene in cancer including HCC. Coactivators e.g., CDKN2 are replication52. similarly mutated, while co-repressors e.g., MDM2 are frequently upregulated. Induction of p53 is thus futile approach to therapy.

It is also evident that miRNAs can have tumor suppressor role in HCC. For instance, miR-138 is frequently down regulated in HCC52 and it has been shown to recognize and bind cyclin D3 to induce cell cycle arrest53. In HBV associated

HCC, miR-26a and miR-26b, are observed to have increased expression in women, where they have a protective role on HCC-genesis, while these miRNAs are down-regulated in HCC54.

Current therapies, treatment failures, and opportunities for novel therapeutic strategy

The current FDA approved drug for treatment of advanced HCC, sorafenib, improves survival by 2-3 month55. A small number HCC cases, younger individuals under the age of 55, qualify for potentially curable tumor resection/transplantation, but even in these individuals 5-year mortality exceeds

50%56. One reason for these poor outcomes is that master regulators of cell cycle exit by apoptosis (e.g., TP53, p16/CDKN2A) are often mutated and/or deleted in HCC57. Thus, most systemic treatments for cancer, which are typically designed to induce cell cycle exit by apoptosis, select for apoptosis-resistant

HCC cells58 while apoptosis-susceptible normal cells are simultaneously destroyed, thereby leading to toxicity that constrains the intensity and duration of therapy (Figure ii). Clearly, there is need for better treatment approach that can enhance patient overall survival without increased cytotoxicity.

It is imperative to define mechanism of resistance to current treatments in order to understand/develop better rationale of therapy and avoid excessive reliance on traditional therapeutic methods, with the same final intended therapeutic effect (Figure ii). Most approaches of cancer therapy today were designed prior to the genomic revolution, and the overall therapeutic goal was to

24 promote killing of the cancer cells59,60. The physiological pathway regulating cell death is definitively known as apoptosis; the mechanism by which abnormal cells within the milieu of normal cells undergo self-suicide in the interest of protecting the existence entire organ. Like all cellular pathways, various genetic programs control apoptosis. The master transcription factor TP53 regulates activation of hundreds of apoptosis genes (Figure ii); this gene is also frequently inactivated in >60% of HCC but not in normal liver61. Moreover, co-factors (e.g., CDKN2A) in the TP53-pathway are also frequently inactivated: Co-repressors such as MDM2 that negatively regulate the expression of p53 are also overly active in the tumor57,58,62,63. Therefore, HCC cells - which are physiologically prone to undergo apoptosis - are evolutionary unique by alterations in the biological pathways apoptosis (Figure ii). The frequency of alteration of the apoptosis pathway, clearly illustrates that targeted interventions attempting to engage the p53 pathway are toxic to normal cells, which have intact apoptosis genetic machinery and are resisted by cancer cells, which have impaired apoptosis gene networks

(Figure ii). Therefore, treatment failure of HCC and many other cancers is resistance and toxicity; which confer a proliferative advantage of cancer cells while simultaneously killing normal cells59,60,64.

Mechanisms for novel therapy

Since curing HCC by promoting apoptosis is futile, new therapies should be p53 independent to avoid cytotoxicity to normal cells. Such therapies should be centered on the ability to alter the proliferative phenotype of the cancer - a pathway that is driven by the transcription factor MYC - but retain the integrity of

25 normal cell i.e. therapeutic index. In HCC mouse models where MYC is antagonized by genetic methods, tumor outgrowth is usually abrogated19,65,66, however direct inhibition of MYC is biochemically complicated as MYC plays a role in every cell of the body and no known drugs have been designed to directly target MYC as a form of any therapy in human disease67,68. In this dissertation, we propose that genes that physiologically antagonize MYC are epigenetically suppressed and can be turned on if the underlying molecular mechanism of how those genes become silenced is understood. For this reason, we designed studies focused on defining key MYC antagonist genes – hepatocyte terminal differentiation genes - and specifically the underlying molecular mechanism of how such genes are epigenetically silenced in HCC. We defined key molecular targets that can be pharmacologically inhibited to promote HCC cell cycle exits by engaging epigenetically suppressed terminal differentiation gene networks.

Hepatocyte differentiation

The key master transcription factors in the hepatocyte differentiation pathway have been defined 69-72 (Figure iii). Hepatocyte master transcription factors include GATA4/6, SOX9, FOXA1 and FOXA2 and have been demonstrated by studies of forced lineage conversion as vital transcription factor players of hepatocyte differentiation pathway 70,71. GATA4 has an important role in generating cells with hepatocyte phenotype72. How all these factors coordinate programs of differentiation however, is not known. One hypothesis is that a handful of transcription factors (2-4) interact to recruit co-activators, to provide an

26 active gene environment that promotes activation of multiple downstream target differentiation genes. The downstream differentiation gene network can also be Figure iii. Key factors involved in the progression of divided into two layers i) hepatocyte differentiation. Lineage conversion studies have evaluated key factors involved in hepatocyte precursor/progenitor cells differentiation. Upstream mediators are master transcription and ii) terminally factors expressed at the endoderm level and include GATA4, FOXA1 and FOXA2 as the major players needed differentiated cells. for hepatic commitment

Precursors are distinctive from terminally differentiated cells by exponential proliferation phenotype mediated by the master transcription factor MYC.

Therefore MYC is also highly expressed and transcriptionally active in lineage committed hepatocyte progenitors65,68,73-75. This high MYC activity in hepatocellular carcinoma12,19,66,67,73,76 exemplifies that HCC cells are hepatocyte precursors with failed terminal differentiation.

Physiologically, terminal differentiation gene networks antagonize MYC function on cell growth and division of lineage-committed precursors, to induce terminal hepatocyte differentiation77-80. Since so many terminal differentiation genes are needed to antagonize MYC and generate mature liver cells that can mediate cell/tissue homeostasis, it is energetically unfavorable for cancer cells to alter each individual terminal differentiation gene. Thus we hypothesized that suppression of hepatocyte terminal differentiation genes is by genetic inactivation

27 of key early lineage specifying master transcription factors or cofactors that dictate cell fate, such that removal/alterations of few subset of upstream mediators sufficiently alters expression of hundreds of downstream targets by epigenetic methods. This approach of failed lineage commitment is most evident in liquid tumors such as Acute Myeloid Leukemia, which have accumulation of proliferative myeloid cells in the bone marrow that have failed maturation or

Myeloidysplastic syndrome Leukemia, which have accumulation of immature blast cells in the bone marrow81. Dysregulation of differentiation programs is more evident and easily detectable for liquid tumors cancers where the phenotypic consequence of failed differentiation is identifiable by light microscopy smears and detection of cell surface markers by flowcytometry. However, understanding differentiation status of solid tumors is challenging and more complicated. Several transcription factors have been demonstrated to be altered in liquid tumors where failed differentiation is evident e.g., RUNX182, NPM183.

Promoting differentiation as mechanism of non-cytotoxic p53independent therapy in leukemia has become more apparent in recent years84-88. Therefore, a greater understanding of the underlying biology of impaired hepatocyte differentiation in

HCC will provide opportunities for novel therapy.

Chapter 1

Several Genetic Alterations In Hepatocellular Carcinoma Disrupt GATA4- Mediated Transactivation To Suppress Hepatocyte And Preserve Precursor Fate

Francis O. Enane1, Wai Ho Shuen2, Xiaorong Gu1, Ebrahem Quteba1, Bartlomiej

Przychodzen1 Hideki Makishima1, Juraj Bodo1, Joanna Ng2, Chit Lai Chee2,

Rebecca Ba2, Lip Seng Koh2, Janice Lim2, Rachael Cheong2, Marissa Teo2,

Zhenbo Hu1, Jaroslaw Maciejewski1, Tomas Radivoyevitch3, Alexander Chung4,

London Lucien Ooi4, Yu Meng Tan4, Peng Chung Cheow4, Pierce Chow4, Chung

Yip Chan4, Kiat Hon Lim5, Lisa Yerian6, Eric Hsi6, Han Chong Toh2$, Yogen

Saunthararajah1$

1 Translational Hematology and Oncology Research, Taussig Cancer Institute,

Cleveland Clinic, USA

2 Division of Medical Oncology, National Cancer Centre Singapore, Singapore

3 Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio

4 Department of Hepato-pancreato-biliary and Transplant Surgery, Singapore

General Hospital, Singapore

5 Department of Pathology, Singapore General Hospital, Singapore

6 Clinical Pathology, Pathology Institute, Cleveland Clinic, USA

Abstract

The most frequent structural deletion in hepatocellular carcinoma (HCC) is of chromosome 8p suggesting key tumor suppressor genes are located on this chromosome arm. There are approximately 500 refseq genes on chromosome

8p, but none of them are frequently mutated in HCC. Therefore, identification of

8p tumor suppressor genes is currently unknown. We analyzed minimal commonly deleted 8p regions in HCC, which implicated GATA4, a master transcription factor regulator of hepatocyte epithelial lineage-fate, as a candidate tumor suppressor 8p gene in HCC. Conditional Gata4 deletion in mice produced liver morphology and gene expression indicating failed hepatocyte epithelial- differentiation and persistent precursor phenotype. HCC mimicked this gene expression profile, even cases morphologically classified as ‘well-differentiated’.

HCC without 8p deletion contained a rare germline mutation (GATA4 V267M) that abrogated GATA4 interactions with a coactivator MED12, or had inactivating mutations in other GATA4 coactivators (e.g., ARID1A, ARID2, SMARCA4).

GATA4 re-introduction into GATA4 haploinsufficient HCC cells, or ARID1A re- introduction into ARID1A-deficient/GATA4-intact HCC cells, activated hundreds of hepatocyte genes and quenched the proliferative precursor program. In sum, loss-of-function of GATA4-mediated transactivation in HCC suppresses epithelial-fate transition thereby sustaining replicative precursor phenotype.

Introduction and Rationale

The liver is the only visceral organ known to have a remarkable regenerative capacity. A variety of features can trigger routine replenishment of

30 the liver including daily wear and tear that range from chemical/mechanical injury, surgical resection, or insult from foreign agents e.g., hepatitis viruses.

Hepatocellular carcinoma (HCC) is a primary malignancy of the liver that avoids this unique feature of liver homeostasis; generating highly proliferative and relentless HCC cells. Several signaling pathways that trigger liver regeneration have been defined. One pathway - the WNT-CTNNB1 (β-catenin) signaling pathway - is a common mitogenic trigger89. Missense mutations observed in 20-

40% of most Caucasian background HCC constitutively activate β-catenin90,91.

However, induction of constitutive β-catenin signaling alone fails to promote HCC in mice23,24,92,93. This suggests that besides initiation of cell growth and division, neoplastic evolution from normal hepatocyte into HCC may also be related to pathways that turn off cell growth and division signals. For instance, apoptosis is the physiologic pathway that is conserved to terminate cycling of unhealthy cells by triggering initiation of self-suicide in order to protect the entirety of the organ/organ system. Thus, the master transcription factor that regulate apoptosis genes p53 (TP53) is frequently mutated and/or deleted in ~60% of all HCC.

Known co-activators in this pathway (e.g., CDKN2A) are also frequently altered by inactivation16 while co-represors (e.g., MDM2) that negatively regulate the expression of p53 are frequently gained and overly active in HCC57.

The key features that make eukaryotes unique is multi-cellularity and cell specialization initiated by lineage differentiation of transient amplifying progenitors. Therefore, transit amplification of the MYC gene - a master transcription factor that is evolutionary conserved to regulate progenitor growth

31 and division94 – is a hallmark of cancer and hepatocellular carcinoma19,68. In physiology, MYC function is potently antagonized by hundreds of terminal differentiation genes65-67,78. More important, MYC inhibition has been demonstrated not to alter liver tissue regeneration and homeostasis65. It is thus plausible that cancer cells – so as to retain unregulated MYC function - must impair pathways of terminal differentiation whose physiological role is to antagonize MYC function of exponential cell growth and division.

In this chapter, we explore deeply the molecular mechanism responsible for hepatocyte differentiation derangement and how this implores growth advantage of lineage committed HCC cells (Figure 1: Model). The most frequent genetic loss observed in greater than 60% of HCC cases (n = 385) is heterozygous deletion of the short arm of chromosome 8 (8p deletion). Neither

CTNNB1 (located at chromosome 3p) nor TP53 (located at chromosome

17p)16,95, are at this locus, demonstrating that other genes/pathways are affected to transform hepatocytes into HCC. The short arm of chromosome 8 (8p) has approximately 500 annotated genes, none of which have frequent inactivating tumor suppressor signature alterations in HCC or other cancers. The lack of mutations suggests that haploinsufficiency of 8p tumor suppressor genes is enough to contribute to transformation, and probably, complete loss-of-function by mutation of remaining alleles (‘Knudson two-hit’) might be deleterious.

Unfortunately, the lack of mutations also complicates the important task of identifying the key tumor suppressor genes/pathways on this chromosome arm.

An alternative clue that assists with this task, however, is that occasionally,

32 instead of the usual deletion of the entire chromosome 8p, there are smaller deletions targeting the segment from 8p22 to 8p23 (‘minimal commonly deleted region’), removing tens instead of hundreds of genes96. There have been a few attempts at functional interrogation of this more limited catalogue of genes. In one study, FGL1 (LFIRE/HFREP-1), that encodes for a fibrinogen family protein, was identified as a candidate 8p tumor suppressor gene, because of its location at 8p22, its lower expression in HCC versus adjacent non-cancerous liver, and because its further knock-down in HCC cells by antisense oligonucleotides increased proliferation, while its exogenous introduction had the opposite effect97.

Nonetheless, its expression is not clearly decreased in HCC with versus without

8p deletion, and the pathways by which FGL1 might contribute to deregulated growth and HCC remain unknown. In other studies, DLC1 on chromosome

8p22, that encodes for a GTPase-activating protein, was implicated as a tumor suppressor gene based on a range of observations similar to that generated for

FGL1, but also by experiments in which its knockdown by shRNA in embryonic p53-null/Myc-overexpressing liver progenitor cells, followed by transplantation into livers of recipient mice, accelerated development of HCC98,99. Elements of a pathway were also identified, since Dlc1 knock-down increased RhoA-GTP levels, and introduction of constitutively active RhoA into the p53-null/MYC- augmented liver progenitor cells also accelerated HCC-genesis98. In murine knock-out studies, however, neither Fgl1 nor Dlc1 deficiency produced a liver phenotype – Fgl1 knockout mice had no liver phenotype100, and Dlc1 haploinsufficient mice (complete knock-out is embryonic lethal) have no overt

33 phenotype, liver or otherwise101. Thus, these candidate tumor suppressor genes, although possibly contributing to HCC-genesis, do not appear to have a usual central role in liver growth regulation, to warrant the ‘most-frequently-deleted’ status of chromosome 8p in HCC.

In several organ systems, a hierarchy of stem cells, highly proliferative committed progenitors (transit amplifying cells) and terminally differentiated cells is well chronicled to mediate tissue homeostasis. In such organ systems (e.g., myeloid), neoplastic transformation is of precursors in the hierarchy

(stem/progenitor cells), and is characterized by disrupted differentiation, the process that otherwise routinely terminates exponential precursor growth. The liver contrasts with these organs in that even mature epithelial cells (hepatocytes and cholangiocytes) seem to respond to mitotic triggers and mediate regeneration74. On closer examination, however, even though cellular subsets are difficult to separate morphologically, elements of a cellular hierarchy are present – e.g., a sub-set of more replicative liver cells with hepatoblast characteristics such as high expression of Tbx3, low expression of specialized hepatocyte differentiation genes and a diploid genome have been shown to mediate the bulk of liver regeneration - differentiating into cells with a usual hepatocyte gene expression profile and polyploid genome74,89. Furthermore, as in other organ systems, there are indications that HCC represents differentiation- impediment and transformation of a precursor: inactivation of Myc in a murine model of HCC terminated proliferation via differentiation into hepatocytes and biliary epithelial cells102; some liver cancers combine HCC and

34 cholangiocarcinoma characteristics, suggesting transformation of a bipotential progenitor103; HCCs phenocopy liver precursor cells upon detailed immunophenotyping, e.g., expressing CD34104; HCC has a near-diploid genome as per hepatoblast-like cells, not polyploid as per mature hepatocytes; in a murine model of HCC, the cancers arose from cells adjacent to the central vein, the location of hepatoblast-like cells89; progression of HCC in mice correlated with decreased expression of several hepatocyte differentiation-driving transcription factors (e.g., Hnf4a)105,106; decreased Hnf4a expression has also been shown to be a feature of HCC in rats and humans, with HNF4A exercising anti-proliferative effects on HCC cells107. However, HNF4A is not recurrently deleted or mutated in HCC, and whether human HCC needs to avoid differentiation-mediated growth control, or how this is achieved, is still unclear.

Relatively few of the hundreds of transcription factors expressed in cells are master determinants of lineage differentiation, most dramatically shown by lineage-conversion studies 18. Such studies have identified that GATA4 is an essential master transcription factor driver of hepatocyte fate and differentiation, operating upstream of HNF4A and other hepatocyte differentiation-driving transcription factors not just during liver development, but also in liver regeneration18,108-110. Here, we show that GATA4, located in the minimal commonly deleted region at chromosome 8p23.1-p22, is a key tumor suppressor gene, and that its deletion or mutation explains avoidance by HCC cells of everyday differentiation related cell cycle exits. HCC cases without 8p deletion also demonstrated disrupted GATA4 function, via germ-line loss-of-function

35 mutation of GATA4 or inactivating mutations in key GATA4 coactivators. Murine modeling and replacement experiments confirmed that GATA4 loss-of-function preserves precursor phenotype by impeding cell-fate transition. Notably, other highly recurrent, co-occuring structural alterations in HCC also center on master transcription factors: chromosome 8q gains center on MYC, the master transcription factor driver of proliferation102,111, while chromosome 17p deletions center on TP53, the master transcription factor regulator of apoptosis - apoptosis like epithelial-fate dominantly antagonizes MYC to terminate proliferation, explaining selection pressure in neoplastic evolution for efficient suppression of both programs despite MYC amplification.

Figure 1. Model 1: Genetic alterations of GATA4 mediated transactivation impair hepatocyte differentiation

Key Observations

 Master transcription factors (GATA4/FOXA1/2) recruit coactivators to activate hepatic genes.  Hepatic genes maintain liver homeostasis and antagonize MYC to terminate precursor growth  Deletion of GATA4 and mutation of various coactivators are frequent in primary HCC  These alterations impair physiologic mechanisms to antagonize MYC

RESULTS

Copy number loss at chromosome 8p impair the GATA4 gene

Loss of 8p was the most frequent structural deletion event in our HCC series from Singapore (24/55 - 44% of cases), and the most frequent structural deletion in the large TCGA series of patients with HCC (245/360 - 68% of cases) (Figure

2A). One patient had a small deleted 8p segment that contained only 14 genes

(Figure 2B). Of these 14 genes, 2 were candidate tumor suppressor genes

(TSGs) NEIL2 and GATA4; Neil2 knockout mice have no disease phenotype112, therefore only GATA4 (master

Figure 2. GATA4 is a candidate tumor suppressor gene on chromosome 8p. A) Chromosome gains and losses in HCC. HCC (n=55 Singapore series, n=360 TCGA series) karyotyped by SNP array. Blue plot = frequency of loss, red = frequency of gain. B) The smallest minimal deleted region observed in a patient with HCC. Of the 14 genes in this region, GATA4, with higher expression in normal liver than in HCC, and with decreased expression with 8p deletion was candidate TSGs. Neil2 knockout mice have no phenotype112. P-value Wilcoxon test

38 transcription factor driver of hepatocyte differentiation) had gene expression characteristics expected of candidate TSG - lower expression in HCC with 8p deletion than without and in HCC versus normal liver (n= 46 pairs of HCC and adjacent non-malignant liver analyzed) (Figure 2B).

Analysis of multiple liver cancer cell lines (n = 27) demonstrated frequent copy number losses mapping to GATA4 locus on 8p (Figure 3A-B). The long arm of chromosome 8 (8q) had frequent copy number gains that mapped to the MYC gene locus (Figure 3A-B). Overall GATA4 mRNA expression - by RNA sequencing of The Cancer Genome Atlas (TCGA) data - showed a strong mRNA-GISTIC correlation (R=0.5382, pvalue<0.0001), (Figure 3D) (GISTIC score = quantified measure of an aberration and the frequency of the aberration across all samples). Reduced GATA4 expression was also in three independent studies (Figure 4).

Figure 3. GATA4 deletion and MYC gain are hallmarks of HCC. A) Copy number loss at chromosome 8p aligned to the GATA4 locus while copy number gain at chromosome 8q aligned to MYC locus representative images for JHH2, JHH4, JHH5, and SNU475 are shown. B) Quantified HCC Cell lines n=27. C) GATA4 mRNA expression strongly correlated with GATA4 GISTIC score, mRNA measured by RNA sequencing, Data downloaded from the cancer genome atlas (TCGA). GISTIC: quantified size of aberration and how frequent the aberration occur across all sample

Atypical HCC containing a rare germ-line GATA4 mutation

GATA4 is a master transcription factor driver of hepatocyte commitment and differentiation69,70,109, whose alteration will subsequently affect hundreds downstream differentiation genes. Thus, we sequenced all coding regions of

GATA4 from HCC and paired

Figure 4. Reduced GATA4 expression in HCC from non-cancerous liver (Sanger multiple independent studies. Data from Gene expression omnibus (GEO database) with accession and targeted next generation numbers GSE14323 - HCV associated HCC, sequencing). In cases where we GSE25097 HCC of varying stages from early to advanced HCC and GSE6764 HCV-induced HCC at did find GATA4 mutations, DNA different stages was analyzed. All three studies demonstrated a significant reduction of GATA4 from peripheral blood expression in HCC relative to normal liver. mononuclear cells was also sequenced to evaluate the germ-line. Remarkably, we found an identical germ- line GATA4 mutation (GATA4 V267M) in 2 cases of HCC without 8p deletion

(frequency 2/51, 4%) (Figure 5A). The amino-acid altered by this recurrent mutation is highly conserved across species. This mutation was not seen in germ-line sequencing of healthy ethnic Chinese individuals from Singapore

(n=99), nor in other ethnic groups from Singapore113. However, this mutation has been noted as a very rare germ-line variant in a larger series of ethnic Han

Chinese in China (4/957, 0.4%) where it has been implicated as a cause of congenital heart disease114,115 (Figure 5B).

The over-representation of GATA4 V267M in our HCC patient series compared to the ethnic Han Chinese population (p=0.002, Chi-square test) suggested pathogenic significance.

Moreover, both HCC cases with this mutation were similar to each other but atypical to human HCC. Cases with

GATA4 mutation were

females; global HCC Figure 5. Atypical HCC containing a rare germ-line GATA4 mutation. A) GATA4 germ-line missense mutation (V267M) rates are 3-fold higher in found in 2/51 patients (4%). Sanger and targeted deep males116. Neither patient sequencing of HCC, adjacent non-malignant liver and peripheral blood mononuclear cells. B) Frequency of this mutation in had hepatitis B or C, nor healthy individual populations from Singapore and China. C) HCC cases with GATA4 mutation had near-normal karyotype. other risk factors such GATA4 mut/del was not linked with hepatitis B infection or with as liver cirrhosis or a pathologic stage. Patients with mutated GATA4 were females. Karyotype analyzed by SNP array and imaged by Integrated history of heavy alcohol Genome Viewer (IGV). PathStage = American Joint Committee on Cancer staging. HepBsAg = Hepatitis B antigen. Del = use, and perhaps most GATA4 locus deletion. notable, neither case had major structural chromosome abnormalities (including an absence of 8p deletion), contrasting clearly with almost all the other HCC cases (Figure 5C).

GATA4 V267M does not recruit the mediator complex

The epidemiologic and cytogenetic data showed above circumstantially implicated GATA4 V267M as a pathogenic event in HCC-genesis. Unlike gene deletion, however, missense alteration is not obviously a loss of function event.

However, when we introduced similar amounts of wild-type GATA4 protein and

GATA4 V267M protein into GATA4 haplo-insufficient HCC cells (PLC) using expression vectors, the mutant GATA4 was less active than wild-type GATA4 in slowing HCC cell growth, in downregulating MYC or upregulating p27/CDKN1B, and more important in activating HNF4A and CEBPD, the bonafide MYC antagonists (Figure 6A-C). We therefore looked for biochemical reasons for decreased transactivating function of GATA4 V267M. Decreased DNA-binding was not the explanation, since in DNA-binding assays, GATA4 V267M bound

GATA-response elements with equal affinity as wild-type GATA4 (Figure 6D).

Nor was GATA4 V267M mislocalized to the cytoplasm instead of nucleus.

Amino-acid 267 is located between the zinc-finger DNA-binding domains of

GATA4, a region thought to mediate protein-protein interactions. Thus, we comprehensively analyzed the GATA4 V267M and wild-type GATA4 protein interactomes by immunoprecipitation (from exogenously transfected HCC cells,

PLC) followed by liquid chromatography tandem mass spectrometry (LC/MSMS).

The unbiased proteomic analysis revealed an absence of the mediator complex in the GATA4 V267M protein interactome, contrasting remarkably with the wild- type GATA4 interactome (Figure 6E). This finding was confirmed by

42 immunoprecipitation-Western blot for mediator 12 (MED12) in triplicate (Figure

6F).

Figure 6. Mutant GATA4 does not interact with mediator complex. A) HNF4A and CEBPD expression measured by qRT- PCR. B) Changes of c-MYC, p27/CDKN1B by GATA4 WT or V267M. C) Cell proliferation. D) DNA-binding of GATA4 and GATA4 V267M to GATA response elements. PLC cells

transfected with expression vectors for flag-GATA4 or flag-GATA4 V267M. The DNA probes and bound protein were pulled down with streptavidin and protein detected by Western blot against flag. E) Liquid chromatography tandem mass spectrometry (LCMS/MS) analysis of the GATA4 interactome. GATA4 and GATA4 V267M were immunoprecipitated from transfected PLC cells using anti-flag antibody and the protein interactome was analyzed by LC/MSMS. F) Absence of mediator from the GATA4

V267M interactome by western blot analysis.

Conditional Gata4 haploinsufficiency induced persistent precursor proliferation and impaired hepatocyte differentiation

To examine if chromosome 8p loss targets the GATA4 gene,

Gata4 conditional haploinsufficiency mice were generated by crossing Gata4fl/fl mice to albumin promoter creatinine recombinase (Alb- cre) mice, to produce progeny with conditional deletion of one

Gata4 allele in the liver

(Gata4wt/∆) (Figure 7A). These Figure 7. Conditional deletion of one Gata4 allele produced a fatty, proliferative liver phenotype. A) mice demonstrated Genotyping of the Gata4 allele. B) Gata4 protein reduction in liver but not other tissues in Gata4wt/∆ (liver substantially decreased Gata4 haploinsufficiency) C) Gata4wt/∆ mice had systemic obesity (n=11). Arrow = liver and D) strikingly enlarged, protein levels only in the liver fatty livers. E) Liver histopathology in Gata4fl/fl versus compared to Gata4fl/fl mice Gata4wt/∆ mice. Proliferation by immunohistochemistry for KI67 arrows = fatty changes in the HE stain and (Figure 7B). At formal positive staining for KI67. F) Increased Myc protein in Gata4wt/∆ versus Gata4fl/fl livers. Bar graph= Myc/actin phenotyping at 3 and 8 months, quantification. the Gata4wt/∆ mice (n=11) had strikingly enlarged, fatty livers and systemic obesity on a normal diet (Figure 7C-

E). Hepatocyte proliferation was assessed by immunohistochemistry for Ki67 and

Western blot for Myc. These markers of cell growth and division were

44 substantially increased in Gata4wt/∆ versus Gata4fl/fl livers (Figure 7F). In later stages, the adult mice developed spontaneous hepatic lymphoma tumors but not hepatocellular carcinoma. Development of the hepatic lymphoma may be linked to the response of accumulating lipid deposition in the liver. Moreover, these finding also illustrate that more than one genetic event is required to drive tumorigenesis in the pathogenesis of HCC. One possibility could be genetic alteration of the TP53 gene that will not just alter cell cycle exit pathways of apoptosis, but also promotes genetic instability (Figure ii), which is profound in cancer cells.

Since Gata4 has a known central role in driving hepatocyte differentiation – including upregulating specialized lipid metabolism genes and genes which antagonize Myc - we used gene expression analyses to evaluate for deranged differentiation as a potential underlying basis for the liver phenotype. RNA- sequencing was used to compare the gene expression profiles of Gata4 wild- type (Gata4fl/fl) and Gata4 liver haploinssuficient mice (Gata4wt/∆). Gata4wt/∆ liver cells demonstrated higher expression of hepatocyte precursors genes (Figure

8A). Hepatocyte precursor genes were significantly higher expressed at

Figure 8. Gene expression analysis demonstrated persistent expression of hepatocyte precursor and impaired expression of hepatocyte genes in Gata4wt/∆ mice. A) RNA sequence analysis of precursor and hepatocyte genes in Gata4fl/fl and Gata4wt/∆ B) Analysis of stage specific gene expression of hepatic genes by quantitative real time PCR for precursor markers, hepatocyte precursor transcription factors (TFs) and Terminal hepatocyte TFs.

46 interdemediate stages of development than in terminal hepatocytes (Figure 9).

By contrast, specialized hepatocyte epithelial function genes, such as lipid metabolism genes and genes that antagonize Myc, were substantially less expressed in Gata4wt/∆ compared to Gata4fl/fl liver.

Key hepatocyte precursor marker genes Afp,

Axin2, and Cd34 were measured by qRT PCR and were elevated in Gata4wt/∆ livers, affirming the hepatocyte precursor predicted by gene expression analysis (Figure 8A-B). Moreover, gene expression of key transcription factors at various stages of hepatocyte development was measured by qRT-PCR to compliment RNA sequence data. Hepatocyte precursor transcription factors operating at early stages of hepatocyte epithelial-differentiation, identified by others during

Figure 9. Hepatocyte hepatocyte generation in vitro, suggested precursor genes. preservation of hepatocyte commitment but Developmental stage specific genes were downloaded from impeded maturation, since expression levels of GEO database with accession number GSE13149. commitment and early hepatocyte-differentiation Intermediate genes were precursor genes: not expressed driving transcription factors Hnf6, Hhex and Tbx3 in terminal hepatocytes were preserved (Figure B) , while late-differentiation driving transcription factors

Hlf, Nr1h4 and Hnf4a were significantly less expressed in Gata4wt/∆ compared to versus Gata4fl/fl (Figure 8B).

Hundreds of liver differentiation genes are suppressed in human HCC compared to normal liver

We then looked to see if the gene expression pattern of HCC resembled that of

Gata4wt/∆ liver, by analyzing genes differentially expressed in HCC versus adjacent non-cancerous liver tissue (n=46 pairs). By microarray gene expression analysis, approximately 700 genes were consistently less expressed (mean expression <66%) in HCC than in paired non-malignant liver (Figure 10A). The top tissue expression association of these suppressed genes was ‘liver’ genes, that is, almost half of these genes (362/736, 49%) were specialized liver differentiation genes, such as members of the cytochrome p450 family (Figure

10B, Table 1) (p<1x10-16, Benjamini corrected, DAVID gene ontology analysis).

We therefore evaluated functional liver genes with differential pattern of expression in Gata4wt/∆ mice versus HCC. Interestingly, a higher number of differentially expressed genes commonly observed in HCC versus Gata4wt/∆

(Figure 10B). Functional evaluation of these liver differentiation genes demonstrated a high representation of lipid metabolism genes, cytochrome P50 genes, and coagulation genes, potent functions of the liver (Figure 10B-C). This pattern of suppression of hundreds of specialized liver differentiation genes compared to normal liver is also a feature of HCC that appears “well- differentiated” by light microscopy (American Joint Committee on Cancer histologic grade 1) (Figure 13). 11% of genes with higher expression in HCC versus normal were hepatocyte precursor genes (Figure 9, 10A).

Demonstrating a cause-effect role for GATA4 in the regulation of these genes, exogenous correction of GATA4 levels in the HCC cell line PLC that contains an 8p deletion (transfection with an expression vector) substantially increased expression of 272/362 (75%) liver differentiation genes that were suppressed in HCC compared to normal liver (Figure 11A) (microarray gene expression analysis). Of particular interest are liver terminal-differentiation genes that antagonize MYC-function and terminate proliferation; the hepatocyte differentiation factors HNF4A and CEBPD have been shown to have such actions80,105-107,117,118 GATA4 introduction activated both HNF4A and CEBPD quantified by qRT-PCR (Figure 11B). As expected, this was accompanied by downregulation of MYC protein, upregulation of p27/CDKN1B protein (the cyclin- dependent kinase inhibitor that mediates cell cycle exits by differentiation)

(Figure 11C), and decreased cell growth without early apoptosis (Figure 11D).

The downregulation of MYC protein and anti-proliferative effects were produced even though the transfected HCC cells (HepG2 and PLC) have copy number gains at the MYC locus; PLC are p53-null in addition119.

Figure 10. Hundreds of liver differentiation genes are also suppressed in HCC compared to normal liver. A) Gene expression in HCC versus paired adjacent non-malignant liver using microarray (n=46 pairs). Of 1547 genes 1.5-fold higher expressed in HCC compared to normal liver 166 (11%) genes had a tissue expression association with liver precursors. Of 736 genes 1.5-fold less expressed in HCC than normal liver, 362 (49%) were specialized liver differentiation genes (DAVID gene ontology analysis). B) Overlap in genes increased or suppressed in HCC versus non-cancerous livers and in Gata4wt/∆ versus Gata4fl/fl livers. C) Mean expression of categories of specialized hepatocyte-epithelial genes (lipid metabolism, cytochrome p450 and coagulation) in HCC versus non-cancerous livers and in Gata4wt/∆ versus Gata4fl/fl livers.

Figure 11. Introduction of GATA4 into GATA4-haploinsufficient HCC cells induced terminal hepatocyte differentiation. Expression vectors for GATA4 or empty vector control were transfected into GATA4 haploinsufficient (8p deleted) HCC cells (PLC). RNA was harvested for microarray gene expression analysis 96 hours after transfection. A) Heatmap = Probeset intensity values for 353 liver differentiation genes suppressed in primary HCC compared to non-cancerous liver. Four independent transfections and experiments. B) The MYC-antagonists HNF4A and CEBPD were activated by GATA4 introduction. Gene expression by qRT-PCR (expression relative to non-transfected cells). E) Changes in MYC and p27/CDKN1B protein levels were consistent with terminal differentiation. Western blot. F) GATA4 transfection reduced proliferation of PLC and HepG2 HCC cells. Cell counts by automated cell counter.

Major GATA4 co-factors are frequently inactivated in HCC.

Genetic knock-out of a key coactivator belonging to the SWI/SNF family

(Smarcb1) from mice has been shown to potently suppress terminal hepatocyte differentiation and increase hepatocyte proliferation120. Thus, it is Figure 12. Frequent inactivation of GATA4 and co-factors in HCC. A) Coactivators highly represented in the GATA4 notable that SWI/SNF interactome. B) SWI/SNF co-activators in A frequently mutated (inactivating mutations) in HCC. Sequencing and karyotyping of coactivators are Singapore HCC (n=51). C) ARID1A was the most frequently abundantly represented in altered coactivator and was mutated in GATA4 wild-type HCC cell line HepG2. C) Re-introduction of wild-type ARID1A the GATA4 protein reduced proliferation D) Increased protein levels of p27 and decreased MYC. E) Induced expression of HNF4A and CEBPD. interactome (Figure 12 F) Induced morphologic changes of epithelial-differentiation A). Moreover, several of (increased cytoplasmic to nuclear ratio of ARID1A transfected cells). Giemsa-stain 96 hours after transfection. G) these coactivators Differentiation was quantified by flow cytometry with forward scatter shift (cell size) observed in ARID1A but not (SMARCAD1, ARID1A, baseline/control cells. and SMARCA4) were genetically inactivated in HCC at a similar high rate in the

Singapore (Figure 12 B) and TCGA series (Data not shown). The gene for

HNF1A, another differentiation-driving transcription factor that operates downstream of GATA4, was also frequently mutated in both the Singapore and

TCGA series (Figure 12A, B). Thus SMARCAD1, ARID1A etc. are cofactors for

GATA4, and given the results from the murine knock-out study120, this suggested

SWI/SNF deficiency might impact the GATA4 pathway and suppress expression of liver differentiation genes, e.g., in HCC with intact GATA4. Consistent with the idea that coactivator inactivation will negatively impact GATA4-mediated gene activation, there was similar suppression of key differentiation factors downstream of GATA4 in both GATA4- and

ARID1A/SMARCAD1/ARID2/SMARCA4-deficient HCC; this suppression was greater than that observed in HCC without these alterations, in both the

Singapore and TCGA series (Figure 14). Moreover, re-introduction of ARID1A by expression vector into ARID1A-mutant/GATA4-intact HCC cells (HepG2) activated key hepatocyte genes, downregulated MYC, upregulated p27/CDKN1B, induced morphologic changes of epithelial-differentiation, and terminated proliferation (Figure 10C-G). In sum, several genetic alterations in

HCC negatively impact GATA4-mediated transactivation and hepatocyte differentiation.

Figure 13. Liver differentiation genes are suppressed in all histological grades of HCC. HCC RNA sequence data (n=321) from TCGA (LIHC) was downloaded and gene expression pattern of terminal liver differentiation genes was analyzed relative to normal liver n=50. Liver differentiation genes were suppressed across all HCC grades.

Table 1. Liver differentiation genes suppressed in HCC. 362/736 genes that were expressed at lower levels in HCC compared to non-malignant liver from the same patients (1.5-fold more expressed in non-malignant liver) were classified as liver differentiation genes (UniGene). This was a significant over-representation (362/736, p<1x10-16, Benjamini corrected, DAVID gene ontology analysis) HMGCS2 UGT2B11 AQP9 CFI FAM99A HABP2 NR1I3 SLC17A1 HAAO UGT2B17 ARG1 CRHBP FETUB HYAL1 OIT3 SLC17A2 HMGCL UGT3A1 ASGR1 CRYAA FGA HAO1 OTC SLC19A3 ABAT UGP2 ASPG CUX2 FGB HSD11B1 ORM1 SLC2A2 HPD LOC645277 BHMT CNGA1 FGG HSD17B13 PON1 SLC22A7 PFKFB1 ATF5 BHMT2 CTH FGL1 HSD17B2 PON3 SLC22A1 A1CF ACSL1 BAAT CBS FCN2 HSD17B6 PGLYRP2 SLC22A10 ABCA9 ACSL5 BASP1 CSAD FCN3 LOC255167 PPARGC1A SLC25A18 CRP ACSM2A BBOX1 CFTR FMO3 ACAA2 PBLD SLC25A37 CLEC1B ACSM2B BCHE CDA FST IGLL1 PCK1 SLC27A5

CLEC4G ACOT12 CPS1 CYP1A1 FTCD ID2 PCK2 SLC38A4 CLEC4M ACADL CA2 CYP1A2 FBP1 IGF2 PHYH SLC39A5 CD14 ACADSB CA5A CYP2A6 FAH IGFALS PIPOX SLC39A14 CD160 ADCY1 CES1 CYP2B6 GCGR ITIH1 PLAC8 SLC4A4 COBLL1 AFM CPB2 CYP2C18 GCKR IL1RAP PLG SLC46A3 DEPDC7 AGXT CPN1 CYP2C19 GNE IL1RL1 PDGFRA SLC6A12 DNAJC12 AGXT2 CPN2 CYP2C9 G6PC IL1B KCNJ16 SLC7A2 ERRFI1 AGXT2L1 CNDP1 CYP2E1 GLUD1 IL18RAP PRODH2 SLCO1B1 FXYD1 ADH1B CAT CYP2J2 GPT2 IYD PROM1 SLCO1B3 GPR128 ADH4 CCL14, CCL15 CYP26A1 GOT1 JMJD5 PROC SORD GRAMD1C ALDH1L1 CCL23 CYP3A4 GPT KLKB1 PROS1 SORL1 GCH1 ALDH6A1 CCL3L3 CYP3A43 GLS2 KRTCAP3 PROZ SAT2 GOLSYN ALDH8A1 CXCL2 CYP3A7 GSTA1 KHK PPP1R1A STAB2 H19 AOX1 CETP CYP39A1 GSTA2 LIFR PDK4 SRD5A2 HGFAC AKR1D1 CHRD CYP4A11 GCAT LECT2 QDPR STOM IQGAP2 AKR7A3 C1orf168 CYP4F2 GNMT LPA RGN SULT1A1 KANK4 ALDOB C14orf68 CYP4F3 GLYAT LEAP2 RBP4 SULT1A4 LIME1 ALPL C20orf56 CYP8B1 GLYATL1 LONP2 RDH16 SULT2A1 NAT2 AMBP C21orf34 CYB5D2 GYS2 MARCO RNASE4 SOCS2 NDRG2 AHSG C8orf4 CPEB3 GLTPD2 MST1 SARDH THBS1 NPC1L1 AZGP1 CLRN3 DEFA1 GADD45G MASP1 SPP2 THRSP RSPO3 A2M F9 DNASE1L3 GHR MBL2 SDS TFR2 RAB17 AMDHD1 F7 DCXR GAMT MEG3 SHMT1 TRPM8 RAB26 AASS F11 DPYS HP MT1F SERPINA10 TPR RANBP3L AGL F12 DAK HRSP12 MT1G SERPINA11 TMPRSS6 RCL1 APCS COLEC10 DCDC5 HBA1, HBA2 MT1H SERPINA3 TMEM27 RUNDC3B ANG COLEC11 DUSP10 HPX MT1E SERPINA5 TMEM82 RND1 ANGPTL3 CSF3R ETFDH HLF MT1X SERPINC1 TMEM86B RND3 ANGPTL4 C1S ENO3 HEPACAM MAT1A SERPINE1 TTR SEC14L2 ANXA10 C4BPA ECHDC2 HAMP MUT SERPINF2 TDO2 SMOC1 APOA1 C5 EHHADH HPN MTTP SERPING1 TPPP2 TBX15 APOA5 C6 EPHX2 HAL MPDZ CYP4F12 TUBE1 TIMD4 APOC3 C8A EPB41L4B HRG NNMT SIGIRR TAT

Discussion: chapter 1

The most aggressive tumors are typically differentiation deranged including hepatocellular carcinoma121. While differentiation derangement is sometimes not obvious by light microscopy e.g., ‘well-differentiated HCC’ by histologic grade

(Grade 1), it is evident by gene expression analyses - hundreds of specialized liver differentiation genes are substantially suppressed in Grade 1 HCC compared to normal liver (Figure 13).

Selection pressure for differentiation gene suppression as a means for neoplastic Figure 14. Key transcription factor drivers of hepatocyte transformation is terminal differentiation are suppressed in HCC with GATA4 alteration and/or HCC with SWI/SNF alterations. A) Using presumably because microarray data from Singapore HCC cases terminal differentiation gene expression was highest in normal liver n=46 for all genes terminal differentiation analyzed. Expression was decreased in HCC without GATA4/coactivator mutation. Expression was lowest in HCC with is the routine control these alterations B) These analysis was observed also by RNA on exponential tissue sequence analysis in data downloaded from TCGA. precursor growth. The mechanisms underlying such differentiation gene suppression, however, are generally unknown. Only a few of the hundreds of transcription factors expressed in cells are master commanders of lineage differentiation, illustrated most strikingly by lineage-conversion experiments122.

Notably, one such master transcription factor, GATA4 that drives hepatocyte lineage-fate18,109, is located in a minimal deleted segment of chromosome 8p.

The minimal deleted segment of 8p identified in our series was within the minimal commonly deleted regions described in other HCC series including that of the

TCGA95,96,99. Moreover, we found 2 HCC cases with a very rare germ-line missense mutation of GATA4 (GATA4 V267M) that abrogated its interactions with mediator (MED12 etc.), a central protein complex in the machinery of transcription activation123,124. The HCC cases with this loss-of-function mutation had atypical clinico-pathologic features compared to other HCC, displaying none of the usual risk factors for HCC and minimal structural genetic alterations, but were similar to each other. In other words, the only identified risk factor for their epidemiologically and genetically atypical HCC seemed to be the germ-line mutation in GATA4.

Deletion or mutation of one GATA4 allele without alteration of the remaining allele in HCC suggests that haploinsufficiency is enough to contribute to growth advantage. Accordingly, conditional deletion of one allele of Gata4 from murine liver created an enlarged, proliferative, fatty phenotype, with a gene expression profile of impaired differentiation that mimicked that of HCC. Since these mice did not develop HCC, this imply that inactivation of GATA4, impairs terminal differentiation but preserves precursor growth. Therefore secondary alterations are needed illustrating the multi-hit nature of HCC-genesis. Current literature modeling similar alterations support this idea (e.g., Runx1 haploinsufficient mice have defects in hematopoietic differentiation but do not get leukemia, even though RUNX1 haploinsufficiency is the most frequently identified cause of familial acute myeloid leukemia)125. Another major growth control,

57 engaged when cells are stressed or damaged, is apoptosis, coordinated by the master transcription factor p53. As such, p53-system disruption is essentially universal in HCC, if not through deletions and mutations to TP53 itself, then through inactivating mutations to other key p53-system genes (e.g., CDKN2A,

ATM, MDM2 ). These proteins also mediate cell cycle exits in senescence126. We are currently attempting to model conditional Trp53 deletion to attenuate apoptosis (and senescence)-mediated growth control combined with Gata4 deletion to attenuate differentiation-mediated growth control. In addition to serving as a master transcription factor driver of differentiation, GATA4 has also been implicated as a mediator of senescence-associated secretory phenotype, a state in which senescent cells secrete inflammatory cytokines and growth factors that can promote tumorigenesis126. That is, in the senescence context, GATA4 gain-of-function is expected to be pro-tumorigenic. Here we have modeled Gata4 loss-of-function, since this is the genetic context of HCC. The suppression of key hepatocyte differentiation genes and proliferative phenotype observed even in young Gata4wt/∆ mice (3 months), and the effect of GATA4 introduction into HCC cells of upregulating hundreds of hepatocyte differentiation genes, downregulating MYC, upregulating p27/CDKN1B, and terminating proliferation of p53-null HCC cells, support that the pathway mediating the consequences of

GATA4 haploinsufficiency is loss of terminal-differentiation. Genetic defects in

TP53 and in the GATA4 pathway in HCC do have treatment implications. Most oncotherapeutics engage apoptosis, by applying stress upstream of p53 with the goal of activating it. Understandably therefore, physical absence of functional p53

58 by its mutation and deletion has been shown to explain treatment resistance and poor outcomes32,33.

HCC, the 2nd-most common cause of cancer death world-wide, has documentable p53-system defects in almost all cases. Thus, treatments that do not depend on p53 to mediate cell cycle exits would be a useful addition to the current therapy. Cell cycle exits by differentiation do not require p53127. There are no treatments for HCC intending cell cycle exits by differentiation, however, contrasting with the innumerable treatments that have been evaluated intending cell cycle exits by p53-dependent apoptosis. One reason for this therapeutic imbalance is that we did not understand whether or how loss of differentiation- related growth control operated in HCC. Master transcription factors use coactivator protein complexes to activate genes128,129. Accordingly, coactivators were abundantly represented in the GATA4 protein interactome. These included

SWI/SNF family members that use the energy from hydrolysis of ATP to reposition nucleosomes, a decisive act in the epigenetics of gene regulation.

Crucially, genetic disruption of SWI/SNF (Smarcb1) has been shown to potently suppress terminal hepatocyte differentiation and increase liver cell proliferation120. It was thus notable that several SWI/SNF coactivators

(SMARCAD1, ARID1A, ARID2, SMARCA4) in the GATA4 interactome are recurrently inactivated by mutation and deletion in HCC (>30% of cases).

Altogether, this suggests that mutation/deletion of key GATA4 cofactors is another way in which GATA4-driven differentiation may be disrupted in HCC.

These observations also underscore that downstream of genetic defects, the

59 repression of terminal differentiation genes is epigenetic, and hence in principle reversible. This has been demonstrated in actual practice for other cancers that like HCC contain genetic defects in master differentiation-driving transcription factor circuits (myeloid malignancies)85,88,130. That is, genetic dose-reduction of coactivators can potentially be redressed by pharmacologic dose-reduction of orthogonally-opposed corepressors88.

Another master transcription factor besides GATA4 and TP53 is recurrently altered in HCC - chromosome 8q gain, centered on the MYC locus, is the most (Singapore series) or second-most (TCGA series) frequent genetic gain event in HCC102,111. MYC is the master transcription factor coordinator of cell growth and division. Inactivation of Myc in a murine model of HCC terminated proliferation via differentiation into hepatocytes and biliary epithelial cells102.

Thus, a reasonable thought is that MYC gain-of-function might itself inhibit progressive differentiation. Other experimental data here and elsewhere, however, shows that terminal differentiation factors, e.g., HNF4A, dominantly antagonize MYC function even in cancer cells with MYC amplification131,132. A need to directly suppress differentiation genes for transformation could explain why GATA4 and SWI/SNF deletions/mutations are more frequent than, and co- occur with, MYC amplifications in HCC.

Cancers of the lung, colon, breast, prostate, head, neck, bladder, ovary, prostate, and brain also very frequently contain 8p deletions. The reported minimal commonly deleted regions incorporate GATA4133-137. In three patients with glioblastoma, non-recurrent loss-of-function insertions in the remaining

GATA4 allele were also reported133. It is notable then, that GATA4 is not only a master transcription factor driver of hepatocyte differentiation (endoderm germ layer), but also has key differentiation-driving roles in tissues originating from other germ-layers138,139. Thus, it is conceivable that GATA4 deletion is the means by which intrinsic differentiation-imposed growth control is enfeebled in these other cancers also - this possibility has been evaluated experimentally only in glioblastoma - GATA4 re-introduction into glioblastoma cell lines promoted differentiation and decreased proliferation133. Interestingly, in a genetic reengineering model of 8p loss in breast cancer, there was marked alteration in lipid metabolism of the cells140. The data here suggests that GATA4 loss might have contributed to this phenotype.

Missing from our understanding of HCC-genesis was the genes and pathways driving selection for chromosome 8p deletion. In this chapter we showed that GATA4 is a key tumor suppressor gene on this chromosome arm, and that GATA4 deficiency and inactivation of GATA4 cofactors explains in large part how HCC cells suppress hundreds of hepatocyte differentiation genes, to thereby deter intrinsic, maturation-related growth control of replicative precursors.

Chapter 1: Summary and Significance

The most recurrent genetic deletion in hepatocellular carcinoma (HCC) is of chromosome 8p. We examined minimal commonly deleted segments of 8p in

HCC, complemented with gene expression analyses. GATA4, the master transcription factor GATA4 of hepatocyte lineage commitment, was identified as the 8p tumor suppressor gene on this chromosome arm. The pathway/phenotype

61 consequences of GATA4 deficiency were therefore evaluated by conditional deletion of Gata4 in mice. Gata4+/- liver was enlarged, proliferative and fatty, with a gene expression profile that resembled that of HCC, with suppression of hundreds of hepatocyte differentiati on/maturati on genes compared Figure 15. Chapter 1: Graphical summary: Several genetic alterations in HCC target the GATA4 transaction pathway Normal liver cells have intact to normal master transcription factors and coactivators that activate downstream target liver. genes. In HCC cells GATA4 is frequently deleted while GATA4 co-activators (ARID1A, ARID2, SMARCA4 etc) are inactivated by mutation/deletion to impair GATA4 activation of hepatocyte target differentiation genes. introduction into GATA4-deficient, TP53-mutated HCC cells activated these genes including MYC-antagonists (e.g., HNF4A, CEBPD), downregulated MYC, upregulated p27/CDKN1B, and terminated proliferation. In sum, GATA4 deletion/mutation in HCC suppresses the hepatocyte maturation that naturally restricts proliferation. The co-partner proteins on the hepatocyte enhanceosome include other lineage factors e.g., FOXA1/2 and various transcriptional coactivators that coordinator hepatocyte gene activation programs. While other hepatocyte transcription factors were not frequently altered, multiple co-activators were frequently inactivated also in HCC with or without GATA4 alterations, where

ARID1A was the most frequently mutated coactivator. Together, alterations of

62 this pathway implicated epigenetic alterations of downstream terminal differentiation genes. In HCC cells with wild-type GATA4 and mutation of a coactivator ARID1A, reintroduction of ARID1A profoundly induced phenotypic epithelial differentiation. Differentiation routinely restrains the exponential growth of committed tissue precursors. As such, differentiation derangement is emblematic of HCC - even HCC histologically classified as ‘well-differentiated’ by light microscopy - displays lower expression of hundreds of specialized liver differentiation genes compared to non-malignant liver. Suppression of so many differentiation genes suggests disruption to the upstream core master transcription factor circuit – accordingly, deletion/mutation of GATA4, a master transcription factor driver of hepatocyte differentiation, explained differentiation- impairment in most HCC while loss of function of its key co-activators e.g.,

SWI/SNF proteins, appeared to be a cause in the rest. Thus, genetic alterations in the GATA4 transactivation pathway contribute to the HCC phenotype of deranged differentiation and its corollary persistent proliferation.

These findings have important translational significance. The current literature in carcinogenesis of HCC and other cancers have traditionally focused on the mechanisms by which cancer cells avoid pathways of cell cycle exits by apoptosis. Expectedly, most FDA approved first line and second line of HCC therapy attempts to induce apoptosis in the cancer. The key drivers of this pathway are however inactivated in the cancer but not in normal liver cells.

Therefore, these form of therapeutic approach is cytotoxic to normal cells and can be resisted by cancer cells.

We provide a new approach in understanding oncogenesis also by evaluating the major method by which normal cell exit the cell cycle “Terminal differentiation”. While some master transcription factors in this pathway are inactivated other master transcription factors are wild-type. These wild-type transcription factors fail to induce differentiation of lineage committed HCC cells.

Therefore, an understanding of why such transcription factors are unable to promote differentiation can yield novel approaches of therapy that attempts to induce differentiation instead of apoptosis. These mechanistic insights are the focus of chapter 2.

Methods: Chapter 1

Primary HCC and paired non-malignant liver tissue

Fresh primary HCC tumors were surgically removed (therapeutic segmentectomy or hemihepatectomy) at the National Cancer Center Singapore between 2008 and 2011. Written informed consent was obtained from each patient in accordance with the Declaration of Helsinki and approved by the Institutional

Review Board. Resected liver tissue was analyzed by frozen section to identify neoplastic and non-neoplastic areas. These were macrodissected for analyses as HCC and paired non-malignant liver tissue.

Liver-conditional knockout of Gata4

All animal studies were approved by the Cleveland Clinic Institutional Animal

Care and Use Committees (IACUC). Gata4 double floxed mice were purchased from Jackson Laboratory (Stock number 008194). These mice were crossed to

64 albumin cre mice also from Jackson laboratory (Stock number 003574) to generate mice harboring Gata4 haploinsufficiency in hepatocyte cells only. Both double floxed and Gata4 happloinssufficient mice were monitored daily and animals demonstrating signs of distress were euthanized by IUCAC approved protocol. Genotyping primer sequences were provided from Jackson lab. DNA was isolated from mouse tails using DNA purification kit (Promega catalog

#A1020). PCR genotyping was performed using standard protocols by using tail vein and Liver tissue gDNA isolates.

RNA and DNA extraction

Snap frozen specimens were equilibrated with a buffer (RNAlater-ICE, Ambion,

Grand Island, New York) that preserves RNA integrity. Frozen tissue specimens, no larger than 0.5 cm in the largest dimension, were added to RNAlater-ICE that was first cooled to -80oC in polypropylene tubes that were large enough that the tissue specimen could move freely. After inverting several times to mix, the tube containing the tissue specimen was equilibrated with the RNAlater-ICE at -20oC overnight (for at least 16 hours). The specimen was then moved to a Petri dish on a cold freezer block for cutting into separate samples for (a) tissue homogenization and RNA extraction, and (b) Proteinase K digestion and DNA extraction: (a) The fragment for RNA extraction was homogenized using a tissue homogenizer. The mirVana kit (Ambion) was used per manufacturer’s protocol for extraction of total RNA. (b) The fragment for DNA extraction was minced with a blade into fragments approximately 2mm in dimension. These fragments were placed in a 1.5cc Eppendorf tube with cold PBS and left on ice for 5-10 minutes

65 to leach out the RNAlater-ICE before digestion in proteinase K for DNA extraction. After pipetting off the PBS, lysis buffer was added followed by proteinase K digestion as per the DNeasy Blood and Tissue Kit (Qiagen,

Valencia, CA). DNA quantity and A260/280 ratio were obtained from a spectrophotometer NanoDrop-1000 (Thermo-Fisher Scientific, Wilmington, DE).

Cell culture and transfection

Human HCC cell lines (Sk-HepG2 and PLC) were cultured in Roswell Park

Memorial Institute (RPMI) media with 10% fetal bovine serum (FBS), 100U ml-1 penicillin and 100 μg ml-1 streptomycin (Mediatech, Herndon, VA). Cells were

o incubated at 37 C in a 5% CO2 atmosphere. DNA was isolated from both cell lines for SNP array analysis. PLC cells have chromosome 8p deletion that removes one copy of GATA4 (Cancer Cell Line Encyclopedia). PLC cells were transfected with GATA4 WT and GATA4 V267M vector and ~ 60x106 cell pellets were harvested at 72 hours. Harvested cells were re-suspended in

1xPBS+PI+PMSF ( PBSW buffer PH7-9).The pellet was centrifuged at 3000RPM for 5 minutes. Cells were washed 3 times in PBSW buffer.

GATA4 expression vector construction

WT GATA4 cDNA was cloned into p-Flag-CMV4 (OriGene Technologies,

Rockville, MD) using Clonetech infusion cloning (Clonetech Laboratories Inc CA).

The primers used for infusion cloning were designed to insert WT GATA4 cDNA at the C Terminal end of the flag tag in the pFlagCMV4 vector (OriGene

Technologies, Rockville, MD). Transient transfection of GATA4 WT or empty vector into HCC cell lines was performed using xfect transfection (clonetech,

Mountain view, CA) following the manufactures’ guidelines. Cell pellets were isolated at 0, 48 and 96 hours after transfection for downstream analysis.

In vitro site directed mutagenesis

In vitro site directed mutagenesis was performed using Stratagene quick change site directed mutagenesis kit (catalog # 200519). Mutagenic oligonucleotide primers to generate the point mutation 11607635G>A in GATA4 exon 4 were designed and ordered from Intergrated DNA Technologies (ITD). Both the forward and the reverse primer were designed to anneal to the same sequence on opposite strands of the plasmid expressing WT GATA4 cDNA. The mutant strand synthesis reaction was performed with the recommended PCR conditions from stratagene kit and using PfuTurbo DNA polymerase (stratagene catalog #

200519). After the thermocycler reaction, the PCR product was treated with Dpn I endonuclease reagent to digest the WT parentral DNA template. The Dpn I treated PCR product was purified and used to transform XL1 Blue supercompetent cells. The cells containing the mutant cDNA were used to transform E coli at 37°C overnight. Colonies were selected and mini-prep reaction was carried out using QIAGEN mini-prep kit (catalog # 27106). The isolated bacteria DNA was sequenced to confirm the presence of the mutant

GATA4 cDNA by both sanger sequencing and target deep sequencing using primers designed to amplify WT GATA4 cDNA (data not shown). Colonies with

Mutant GATA4 were maxi-prepared using promega Pure Yield Plasmid kit

(Catalog # A2492) and used for transfection experiments.

Quantitative real-time polymerase chain reaction (RT-PCR) using SYBR

Green

RNA was isolated using the RNeasy method (Qiagen, Valencia, CA) and cDNA was prepared using iScript cDNA synthesis kit (BioRad Laboratories Inc, CA).

Quantitative real-time qPCR was done using an ABI Prism 7500 Sequence

Detection System (Applied Biosystems, Foster City, CA) and SYBR Premix Ex

TaqTM II (TakaRa, Tokyo, Japan). Real-time PCR primers were designed with

PrimerQuest (www.idtdna.com). The relative number of copies of mRNA (RQ) was calculated based on the average Ct values using the housekeeping gene

GAPDH as internal control and baseline controls for relative expression. Results are shown as mean±SD of three independent experiments.

Targeted Deep sequencing

Detection of allelic frequencies and SNPs was performed by applying deep sequencing to target exon of GATA4 (Exon4), and all coding exons for other genes that are frequently reported to be mutated in HCC including HNF1A,

ARID1A, SMARCA4, ARID2, CTNNB1, and TP53. Primers used are in. Each target exon was amplified by PCR using ≥ 100ng/µL of primary DNA. The PCR amplification protocol included initial denaturation at 94°C (5 minutes),

[denaturation 94°C (30 seconds, at 25% Ramp cooling temperature), Annealing and Extension at 60°C (3 minutes at 40% ramp cooling)]x19. After PCR amplification subsequent purification and sequencing library preparation were generated according to Illumina pair-end library protocol. Briefly DNA was purified using magnetic beads. The purified PCR Products were end-repaired to

68 introduce sticky ends using end repair enzyme (NEB Catalog# E6050S). Pair end adapters were then ligated using T4 DNA Ligase (NEB Catalog# M0202S) to the amplified PCR fragments of about 250bp in length. Nick fill reaction was performed using Bst DNA polymerase (NEB Catalog MO374S). Library fragments and all PCR amplification was performed using HotStart ABM Taq

DNA polymerase (ABM Catalog# G011). The Library was subjected to deep sequencing on illumina genome analyzer IIx also known as HiSeq 2000 using standard protocol. Sequences were aligned to the reference genome using novoalign and were analyzed using Intergrated Genome Viewer (IGV) software.

Detected mutations were validated by using targeted Sanger sequencing.

Sanger sequencing

Genomic sequencing was performed on all coding region exons of GATA4 using gDNA from paired primary HCC and non-malignant liver tissue. Bidirectional sequencing was performed by standard techniques using an ABI 3730×I DNA analyzer (Applied Biosystems, Foster City, CA). Genetic alterations were scored as pathogenic on the basis that they were non-synonymous and inactivating

(Insertion/deletions). Emphasis of mutation calling was on somatically acquired variations observed in gDNA isolated from tumor but not non-malignant liver.

Primers were designed using PrimerQuest (www.idtdna.com).

Gene expression analysis by microarray

The HumanHT-12 v3 gene expression microarray (Illumina) was used to analyze

RNA from paired HCC and non-malignant liver. The array evaluated >25,000 annotated genes with >48,000 probes designed using the RefSeq (Build 36.2,

Rel 22) and UniGene (Build 199). Microarray probe intensity values were subject to average normalization by GenomeStudio software to minimize the effects of variation from non-biological factors and to calculate expression measures from the raw data. Expression measures of probe sets covering specific genes of interest were exported as a spreadsheet to the SAS System V9.2 (SAS Institute

Inc., Cary, NC) for further statistical analysis. Only probe intensity data with detection p-values <0.05 (a statistical calculation that provides the probability that the signal from a given probe is greater than the average signal from the negative controls) were used in analyses of differences between groups of samples. Heat maps were generated by ArrayStarv3 (DNAStar, Madison, WI,

USA).

High resolution molecular karyotyping by single nucleotide polymorphism

(SNP) array

The Human660W-Quad v1.0 DNA Analysis Bead Chip and kit were used for high resolution molecular karyotyping of DNA isolated from primary HCC specimens and a control non-malignant DNA. The Bead Chip analyzed >660,000 individual loci. Genome Studio (Illumina), Karyostudio (Illumina) and Integrative Genomics

Viewer (IGV, Broad Institute)(Robinson et al., 2011) software were used to document large chromosome aberrations (e.g., >75kb), to score these aberrations as loss, gain or uniparental disomy (UPD), and for crossmatching these aberrations with information from public databases.

Cell fractionation and nuclear protein extraction

Cells were re-suspended in 500uL of PBSW followed by addition of 10% NP-40

(1:20uL). This was incubated on ice for 1 minute followed by centrifugation at

3000rpm for 10 minutes. Supernatant was transferred to a tube labeled cytoplasmic fraction. Nuclear pellets were washed in PBSW buffer and centrifuged at 3000rpm for 10 minutes. 1uL of benzonase was added and this was incubated on ice for 90minutes and vortexed every 30 minutes. 250uL of nuclear extraction buffer E1 (250µL PBSW + 2%NP40+500mM [5µL] + 5MNaCl

[25µL]) and incubated on ice for 10 minutes; mixture was vortexed every 5 minutes. Sample was centrifuged at full speed for 10 minutes. Supernatant was saved in a tube labeled nuclear protein extract. 5µL of 10% SDS was added to the remaining Nuclear Pellet. Nuclear protein was extracted again by adding

250uL of nuclear extraction buffer E2 (250µL PBSW + 1%NP40+500mM [2.5µL] to the remaining pellet. Sample was incubated on ice for 10 minutes, Vortex every 5 minutes as above. This was followed by centrifugation at full speed for 15 minutes. Supernatant was added to the nuclear protein extraction tube. 200uL of nuclear extraction buffer was added to the tube containing the remaining pellet.

This was incubated on ice for 10 minutes with vortexing every 5 minutes. The sample was centrifuged at full speed for 15 minutes and supernatant was added to the nuclear pellet tube. Concentration of the total protein extracted was measured using using BCA.

Covalent binding of antibody to protein G beads

25mg (200µL) of Protein G-sepharose was washed twice with 1xPBS followed by incubation with 200uL of flag antibody for 1hr at RTP. Antibody bound Protein G

71 were incubated in 1% Chicken egg Albumin for 1hr. This was washed twice with

1xPBS. 25mg of dimethylpimelimidate were added to1 mL of 300mM NEM followed by swirling for 30 minutes at RTP. This was repeated twice. Glycine-HCl

(PH3), was added followed spin down. This was washed 3X using 1xPBS. This was followed by 2X was using nuclear extraction buffer.

Immunoprecipitation

200µL of nuclear protein lysate was pre-cleared using G-Sepharose (50% slury).

This was incubated at 4°C for ~60minutes Spun for 10 minutes at 4°C.

Supernatant was transferred to fresh tubes. 30mg of nuclear protein extracts

(pre-cleared lysate) was transferred to tubes with antibody bound protein G beads and rocked gently at 4°C overnight. This was washed 5 times with 1xPBS containing 1%NP-40. Samples were dried using a Spin-Dry vacuum centrifugation at -100 on Speed Vac vapor trap. Immunoprecipitation products were extracted from the protein G beads using Laemmli sample buffer.

Western blot analysis

Western blot was by standard methods for protein analysis experiments.

Antibodies used were: GATA4 (Abcam Ab cat# Ab124265), Anti-Flag (Sigma

Cat# F7425-2MG) c-MYC (Cell signaling Ab cat# 5605), p27/CDKN1B (Cell signaling Ab cat# 3833) and β-actin (Sigma, #a3854), Histone 3 (Cell Signaling

Cat# 9715S) MED12 (Cell Signaling cat#4529S,) SMARCA5, (Cell Signaling cat#13543s).

Protein identification by LC-MS/MS

Anti-flag and isotype antibody immunoprecipitation products were separated by molecular weight on SDS-polyacrylamide gel electrophoresis and stained with colloidal Coomassie Blue (Gel Code Blue, Pierce Chemical). Separated proteins on the Gel slices were excised based on the corresponding molecular weights; proteins were reduced with dithiothreitol (Sigma-Aldrich, D0632, 10mM), alkylated with iodoacetamide (Sigma-Aldrich, I1149, 55mM), and digested in situ with trypsin. Peptides were extracted from gel pieces 3 times using 60% acetonitrile and 0.1% formic acid/water. The dried tryptic peptide mixture was redissolved in 20 μL of 1% formic acid for mass spectrometric analysis. Tryptic peptide mixtures were analyzed by on-line LC-coupled tandem mass spectrometry (LC-MS/MS) on an Orbitrap mass spectrometer (Theomo Fisher

Scientific).

Database Search and Data Validation

Mascot Daemon software (version 2.3.2; Matrix Science, London, UK) was used to perform database searches, using the Extract_msn.exe macro provided with

Xcalibur (version 2.0 SR2; Thermo Fisher Scientific) to generate peaklists. The following parameters were set for creation of the peaklists: parent ions in the mass range 400–4500, no grouping of MS/MS scans, and threshold at 1000. A peaklist was created for each analyzed fraction (i.e., gel slice), and individual

Mascot (version 2.3.01) searches were performed for each fraction. The data were searched against Homo sapiens entries in Uniprot protein database (May

2015 release, 151,569 total sequences). Carbamidomethylation of cysteines was

73 set as a fixed modification, and oxidation of methionine was set as a variable modification. Specificity of trypsin digestion was set for cleavage after Lys or Arg, and two missed trypsin cleavage sites were allowed. The mass tolerances in MS and MS/MS were set to 10 ppm and 0.6 Da, respectively, and the instrument setting was specified as “ESI-Trap.” To calculate the false discovery rate (FDR), the search was performed using the “decoy” option in Mascot. The spectral FDR and protein FDR are 0.35±0.17 % and 4.28±1.99 % respectively. A minimum

Mascot ion score of 25 and peptide rank 1 was used for automatically accepting all peptide MS/MS spectra.

Label free relative protein quantitation (LFQ)

Relative protein quantification was performed using spectral count-based LFQ.

For each biological sample, data from the individual gel slices were combined.

Statistical analysis was performed on all proteins identified with average spectral counts of ≥2 of at least one of the three experiments. The spectral count data was normalized by total spectral counts of the bait protein (GATA4) in each sample to adjust for differences in overall protein levels among samples. Proteins were considered to have a significant difference in abundance if there was a difference of twofold or greater in normalized spectral counts between experiments and a p value ≤ 0.01 using a two-tailed t test. Spectral counts for all proteins and peptides identified are provided in supplementary material.

Bioinformatic and statistical analysis

Proteins identified by label-free LC-MS/MS were analyzed by the Ingenuity

Pathway Analysis Tool (IPA, Ingenuity Systems, Redwood City, CA). The “core analysis” function included in IPA (Ingenuity System Inc.) was used to interpret the data in the context of biological processes, pathways, and networks. Right- tailed Fisher's exact test was used to determine a p-value indicating that the probability of biological functions, canonical pathways and diseases associated with the networks is because of chance alone. Networks were generated using data sets containing protein identifiers. Each protein identifier was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base. Networks of these proteins were then algorithmically generated based on their connectivity.

Graphical representations of the molecular relationships between proteins were generated. Proteins are represented as nodes, and the biological relationship between two nodes is represented as an edge (line). All edges are supported by at least one reference from the literature or from canonical information stored in the Ingenuity Pathways Knowledge Base.

For ontology (GOTERM BP1) and tissue expression (UNIGENE EST

QUARTILE) mapping of genes that are differentially expressed between HCC and paired non-malignant liver tissue, gene lists were uploaded into

DAVID(Huang da et al., 2009). These analyses entailed matching of the gene lists to terms in functional ontologies, providing a ranked representation of ontologies that are most saturated or "enriched" with the input gene lists.

For classification of hepatocyte precursor and hepatocyte genes, genes expressed during murine liver development were downloaded from Gene expression omnibus (GEO) dataset GSE13149 (Li et al., 2009 C57/B6) that evaluated gene expression patterns sequentially from early to late stages of liver development. Liver precursor genes were defined as genes significantly higher expressed at intermediate developmental stages versus terminal hepatocyte differentiation stages (Figure 9).

SDs for each set of measurements were calculated and represented as y- axis error bars on each graph. JMPR Pro 10.0 or SAS statistical software was used to perform statistical analysis (SAS Institute Inc, http://www.jmp.com) including correlation analyses.

GATA4 DNA Binding Analysis

A GATA probe (ATTACTGATAATGGTGX3) and negative control probe

(ATTACTCCCCATGGTGX3) were designed and ordered from ITD. The probes were labeled with biotinulated with biotin labeling kit following the manufacture’s guidelines (ThermoFisher Scientific catalog# K0651). GATA4 WT and GATA4

V267M vector were transfected in HCC cell PLC, cell pellets from 6.0x106 cells were isolated at 48 hours, and nuclear fractions were extracted using nuclear extraction buffer (250 µL 1xPBS+PI+PMSF ( PBSW buffer PH7-9) +(

2%NP40+500mM [5µL] + 5MNaCl [25µL]). Isolated nuclear fractions were incubated with the GATA Probe and the scramble control probe at 4°C overnight.

Incubated fractions were pulled down using streptavidin beads for 1hr at RT. Flag western blot was performed as described.

Chapter 2

Transcriptional Co-repressors are Logical Molecular Targets for Differentiation Therapy in Hepatocellular Carcinoma

Enane, F1, Quteba E1, Gu X1, Gromovsky, A2, Ferguson, D2, Brown J M2, Saunthararajah, Y1

1Translational Hematology Oncology Research, Taussig Cancer Institute, Cleveland OH

2Department of Cellular and Molecular Medicine, Learner Research Institute, Cleveland OH

Abstract

The notion underlying this work is that proliferative HCC cells are hepatocyte precursors that have attained aberrant exponential growth driven by MYC, but have failed commitment programs of terminal hepatocyte lineage. In chapter 1 we discussed how alterations on the hepatocyte enhanceosome targeted the

GATA4 transcactivation pathway to subsequently epigenetically suppress hundreds of terminal hepatocyte MYC antagonists in HCC. Here we hypothesize that GATA4 co-transcription factors of the hepatocyte lineage such as FOXA1/2 that are wild-type in HCC fail to drive terminal hepatocyte differentiation through interactions with transcriptional co-repressors. We investigated FOXA1/2 interacting partners using immunoprecipitation tandem mass spectroscopy in

HCC cells with/without GATA4 alterations. In Sk-HepG2 cells that are GATA4 intact and defined as well differentiated HCC by light microscopy, FOXA1/2 interactions favored coactivators than corepressors. In PLC cells that are

GATA4 null and defined as poorly differentiated HCC by light microscopy,

FOXA1/2 interactions favored corepressor molecules. GATA4 re-introduction into

PLC cells switched FOXA1 interaction from corepressors to coactivators illustrating that in presence of wild-type GATA4, FOXA1/2 could mediate hepatocyte differentiation programs. Furthermore, FOXA1/2 were identified to strongly bind to regulatory regions of hepatocyte terminal gene HNF4A using studies of chromatin immunoprecipitation with sequencing and data from encyclopedia of DNA elements (ENCODE). The major corepressor identified active in HCC was DNA methyl transferase 1 (DNMT1) that strongly pulled down

79 with FOXA1 and FOXA2. We thus evaluated inhibition of DNMT1 using clinically available DNMT1 inhibitor decitabine (Dec) that targets DNMT1 for proteasome degradation using non-cytotoxic concentrations. Using in vitro and in vivo studies, we demonstrated that inhibition of DNMT1 by this method induced global switch of FOXA1 and FOXA2 interactions from corepressor molecules to coactivator molecules. This switch from corepressors to coactivators induced hepatocyte terminal differentiation programs in-vitro. Furthermore, using murine model of HCC non-cytotoxic DNMT1 inhibition induced tumor regression without evidence of cytotoxcicty. Therefore, inhibition of active transcriptional corepressors provides opportunities for non-cytotoxic p53 independent mechanism of therapy in hepatocellular carcinoma.

Introduction and Rationale

The scientific evidence presented in chapter 1 demonstrated that alterations of hepatocyte master transcription factor GATA4 and its key coactivators (ARID1A, ARID2, SMARCA4 and SMARCAD1) altered pathways of terminal differentiation through epigenetic suppression of hundreds of hepatocyte differentiation genes. From a biological perspective, epigenetic suppression of terminal hepatocyte, genes provide opportunities for actionable molecular targets as novel p53 independent epigenetic therapy in HCC if underlying molecules that induce suppression are known. By definition, an enhanceosome is a high order multi-protein complex that assembles at gene regulatory regions to modulate

80 expression of target genes141. The master transcription factors on the hepatic enhanceosome that Figure 16. Curable versus incurable disseminated solid tumors. Clinical reality of curable versus incurable cooperate to turn on target disseminated adult solid tumor malignancies succinctly illustrates the lethal effects of TP53 system alterations. genes are known and Testicular cancer is the only adult disseminated adult solid tumor malignancy that is reliably cured by chemotherapy, include GATA4 and and is characterized by essentially a 0% TP53 mutation rate, FOXA1/2 family of except in the cases which relapse. Meanwhile, notoriously treatment refractory cancers have very high rates of TP53 transcription factors mutation.

(Figure iii)18,110. While GATA4 alone can promote hepatocyte differentiation, a combination of 2-3 other master transcription factors (FOXA1/2, HNF1A and

GATA6) can also mediate hepatocyte differentiation gene programs18,110.

Hepatocellular carcinoma cells present a unique paradox; they express high levels of wild-type transcription factors e.g., FOXA1 and FOXA2 but are differentiation impaired142,143. Our data in chapter 1 contributed to a better understanding of this paradox: inactivating alterations of GATA4 or coactivator

(e.g., ARID1A) disrupted the integrity of the hepatic enhanceosome turning off target genes. The SWI/SNF chromatin remodeling genes reported here such as

ARID1A, ARID2 and SMARCA4 are frequently inactivated in multiple other cancers also14 but lack of post-translational modification has impeded the ability to develop scientific rationale to define why cancer cells select to mutate these factors in the first place. Evidence revealed here together with conventional literature imply that alteration of these chromatin remodeling factors favor a

81 heterochromatin state that impedes binding of transcription factors to target DNA sequences through recruitment of co-repressors counterparts144. In studies of protein-protein interactions, it has been demonstrated that master transcription machinery rely on ATP-dependent SWI/SNF factors to open chromatin and provide DNA binding sequences for activation of target genes145. Thus, a combinatorial inactivation of both upstream transcription factors such as GATA4 and their related co-activator genes (ARID1A, ARID2, and SMARCA4) impair hepatic enhanceosome by shifting the balance to co-repressor counterparts to non-mutated hepatocyte master transcription factors (i.e. FOXA1/2), thus impairing hepatocyte differentiation pathways.

Traditional therapies of HCC and other cancers have focused on targeting inactivated tumor suppressor genes such as TP53 and CDKN2A to promote cell death of cancer cells. Inactivating mutations in these genes however alter apoptotic pathways, thereby making it irrational to use apoptosis induction therapy in cancer/HCC59,60,64,146,147. Clinical reality of curable versus incurable disseminated adult solid tumor malignancies succinctly illustrates the lethal effects of TP53 system alterations (Figure 16). Testicular cancer is the only adult disseminated solid tumor malignancy that is reliably cured by chemotherapy, and is characterized by essentially a 0% TP53 mutation rate, except in the cases that relapse. Meanwhile, notoriously treatment refractory cancers have very high rates of TP53 mutation148 (Figure 16).

To our knowledge, minimal studies have attempted to define the underlying molecular mechanism used by HCC cells to avoid cell cycle exits by

82 differentiation. Here we reveal that a single genetic alteration of a liver lineage transcription factor GATA4 leads to aberrant epigenetic suppression of liver differentiation genes through recruitment of co-repressor proteins on intact master transcription factors. Various studies have also demonstrated that a single genetic event on a particular enhanceosome could disrupt the overall biological role of the enhanceosome85,88,149-151. Therefore by suppressing cell cycle exit by differentiation, abnormalities such as GATA4 and GATA4 cofactor

(HNF1A, ARID1A, ARID2 and SMARCA4 ) mutation/deletion in HCC likely complement abnormalities such as TP53 mutation/deletion that suppress cell cycle exit by apoptosis. Importantly, however, the GATA4 target genes (e.g.,

HNF4A, CEBPD) that antagonize MYC function to terminate proliferation are genetically intact and interact with their master transcription factors such as

FOXA1/2. Unlike key mediators of cell cycle exit by apoptosis, (e.g., TP53, p16/CDKN2A) that are physically unavailable to mediate therapeutic effect of therapy, hepatocyte terminal differentiation genes can be reactivated through inhibition of active corepressor molecules on their master transcription factor.

This form therapy can provide a refinement therapeutic intervention that engages differentiation instead of apoptosis as a means of therapy in HCC. This therapeutic approach has been demonstrated to be p53 independent and non- cytotoxic in various cancers84,87.

In this chapter, we demonstrate how induction of differentiation in HCC provides opportunities for non-cytotoxic therapy. We provide a mechanistic description of how master transcription factors of hepatocyte lineage interact with

83 transcriptional corepressors to turn off target genes. We also provide in-vitro and in-vivo evidence that inhibition of transcriptional corepressors induces tumor cell differentiation by p53 independent methods.

Figure 17. Model 2 Copressors aberrantly recruited to FOXA1/2 are

logical targets for pharmacologic inhibition.

Key observations

 Master transcription factors (TFs) (e.g., GATA4/FOXA1/2) cooperate to recruit coactivators  Transcription factor – coactivator complex induce expression of MYC antagonists  Alterations of GATA4 and coactivators promote aberrant corepressor activity to partner TFs  Active corepressors epigenetically suppress target genes and fail to antagonize MYC physiologically

Results

Wild-type hepatocyte transcription factors bind to gene regulatory regions of hepatocyte terminal differentiation genes.

To regulate gene expression, interacting proteins of a given enhanceosome assemble at the regulatory regions of target genes, a process dictated by master transcription factors. We investigated the binding affinity for hepatocyte master transcription factors GATA4, FOXA1 and FOXA2 to HNF4A using data from encyclopedia of DNA elements (ENCODE). In these studies of chromatin immunoprecipitation using antibodies against FOXA1 and FOXA2 complemented with sequencing demonstrated strong interaction to the enhancer of HNF4A

(Figure 18A). Figure 18. FOXA1 and FOXA2 bind to HNF4A Enhancer in HCC. A) Using Histone encyclopedia of DNA elements (ENCODE) database, we evaluated FOXA1 and FOXA2 to binding to regulatory regions of hepatocyte terminal promoter differentiation gene HNF4A in HCC cell line HepG2. Strong signals were seen for both FOXA1, FOXA2 and enhancer marker H3K27ac at HNF4A gene. marks Polymerase 2 (Pol2) was used as positive control for both enhancer and promoter. H3K27me3 is marker for repressed genes. B) Chromatin of HCC H3K4me1 and cell line PLC and HepG2 cells. C) qRT PCR analysis of FOXA1, FOXA2 and enhancer GATA4 ChIP was used to validate ENCODE data marks H3K27ac were used to determine regulatory regions. Polymerase 1, which

86 bind to both promoters and enhancers to mediate gene activation was used as a positive control.

Primers annealing to this region were generated and Figure 19. Copy number loss of GATA4 by 8p deletion in PLC. confirmatory DNA was isolated from HCC cell line PLC and HepG2 and analyzed studies were by SNPA-array A) SNP array PLC cells. B) SNP array HepG2 Cells evaluated using chromatin immunoprecipitation complimented by qPCR with antibodies against

GATA4, FOXA1 and FOXA2 (Figure18C). FOXA1 and FOXA2 binding to the regulatory regions of hepatocyte genes was however not translated into gene activation as multiple hepatocyte terminal differentiation genes including bonafide terminal hepatocyte transcription factors e.g., HNF4A, HLF, NRIH4, NR2F1 were epigenetically suppressed in HCC relative to normal liver as comprehensively described in chapter 1.

We thus developed the hypothesis that alterations of either GATA4 or

GATA4 co-activator partner also reported in chapter 1 impaired the gene

87 activation by epigenetic methods through efficient recruitment of co-repressors to wild type hepatocyte master transcription factors (i.e, FOXA1 and FOXA2). This mode of interaction between co-repressor and hepatocyte lineage specifying transcription factors favor proliferation by impeding hepatocyte master transcription factors from activating MYC antagonist genes in HCC cells. Such alterations impair active transcription of target hepatocyte terminal differentiation genes by epigenetic methods driven by active corepressors. This model was evaluated comprehensively using studies of protein-protein interaction by

LCMS/MS and confirmatory western blots.

GATA4 deficiency promotes co-repressors interaction to FOXA1 and

FOXA2

We evaluated the rationale that alteration of one master transcription factor on the hepatocyte enhanceosome favors recruitment of co-repressors to non-mutated hepatocyte master transcription in HCC. This mechanistic alteration thereby enhance proliferation but impede commitment programs by turning off hepatocyte MYC antagonist gene networks. The active co-repressors on wild- type TFs epigenetically suppress target genes activated by the transcription factor. Targeted deep sequencing of HCC cell line HepG2 on all exons of GATA4 demonstrated that GATA4 was wild-type. Furthermore, SNPA array analysis demonstrated that PLC cells, another HCC cell line was deficient in GATA4 by loss of chromosome 8p: SNPA of HepG2 showed intact 8p (Figure 19A-B). PLC cells are also p53 null by mutation119. These two cell lines provided a unique model to evaluate effects of GATA4 pathway alterations on the hepatocyte

88 enhanceosome. Using biochemical studies of immunoprecipitation complimented with mass spectrometry analysis; we performed immnuoprecipitation of FOXA2 in HepG2 cells that are GATA4 wild type and PLC cells that are GATA4 deficient by 8p loss. Interestingly, FOXA2 interacted strongly with co-activators

(SMARCA4 and ARID1A) in HepG2 cells compared to PLC cells (Figure 20B-C).

In PLC cells, FOXA2 interactome included a heavy recruitment of co-repressor molecules (e.g., DNMT1). We further demonstrated that irrespective of GATA4 status, the interactions of transcription factors FOXA1 and FOXA2 was unaltered

(Figure 25).

Figure 20. Wild-type GATA4 promotes stronger FOXA2-coactivator interaction in HCC. Immunoprecipitation with antibody against GATA4 and FOXA2 was carried out in HCC with wild-type GATA4 (HepG2) and HCC with GATA4 haploinsufficiency (PLC) A) GATA4 western blot. B) coactivator ARID1A western blot C) coactivator SMARCA4 western blot

GATA4 reintroduction promotes co-activator interaction to FOXA1 in HCC

If GATA4 alterations impaired coactivator recruitment, does GATA4 rescue promote coactivator recruitment to FOXA1/2? This hypothesis was evaluated by re-introduction of wild-type GATA4 in PLC cells that are GATA4

89 deficient by 8p loss. In these studies, GATA4 rescue promoted a stronger recruitment of co- Figure 21. Wild-type GATA4 enhance FOXA1-coactivator interaction in HCC. Flag-tagged wild-type GATA4 was transfected into GATA4 activator haploinsufficient HCC cells PLC followed by immunoprecipitation using FOXA1 antibody. A) evaluation of transfected GATA4 in cytoplasmic and molecules nuclear fractions. Flag antibody detects transfected GATA4, GATA4 (ARID1A and antibody detects endogenous and transfected GATA4. B) FOXA1 immunoprecipitation to evaluate interact partners. GATA4 expressing SMARCA4) to cells had decreased DNMT1 levels, ARID1A levels increased with GATA4 expression. FOXA1 (Figure

20). As expected, co-repressor DNMT1 inefficiently interacted with FOXA1 in

GATA4 wild-type expressing PLC cells (Figure 21). Together these analyses revealed that inactivation of GATA4 in HCC promotes recruitment of co-repressor molecules to wild-type hepatocyte transcription factors. This form of recruitment engages HCC cells to have a non-oncogene addiction to co-repressor molecules, which favor proliferation by epigenetic suppression of hepatocyte MYC antagonists in HCC cells.

Re-introduction of co-activators in HCC cells promote cell cycle exit by differentiation

To clarify the effect of co-activator and co-repressors, we further sort to evaluate why HCC cells inactivate co-activators. In chapter 1, we observed that chromatin modifying enzymes such as SMARCA4, ARID1A, ARID2, and

SMARCAD1 were frequently mutated and deleted in primary HCC

(Figure 12). The most frequently inactivated genes were ARID1A (44% n = 51) and SMARCA4

(24% n = 44). We also sequenced HCC cell lines and identified similar inactivating mutations in

HepG2 cells, where Figure 22. Re-introduction of coactivators in HCC promote GATA4 was wild type. cell cycle exit by differentiation. HCC cell line HepG2 were transfected with ARID1A and SMARCA4. A) Cell counts by Expression of wild type automated cell counters B) Changes in p27, MYC in SMARCA4 SMARCA4 and ARID1A in transfected cells. C) Changes in p27, MYC in ARID1A expressing cells. D) Morphological changes by SMARCA4 and ARID1A these cells induced transfection. E) Quantification of morphological changes by flow analysis of Forward scatter and side scatter. differentiation of HCC and attenuated exponential proliferation of HepG2 cells compared to control vector

(Figure 22A-C). Differentiation was evaluated by morphological analysis at 24- hour interval time points. Cells expressing WT SMARCA4 and ARID1A had increased ratio of cytoplasm to nucleus at 96 hours, a profound illustration of

91 differentiation induction (Figure 22D). Furthermore, morphological evaluation using flow cytometry demonstrated a forward scatter shift (change in cell size) and side scatter shift

(change in granularity) of cells transfected with ARID1A and Figure 23. Inhibition of DNMT1 induce cell cycle exit by differentiation in P53 mutant GATA4 haploinsufficient HCC. FOXA2 strongly SMARCA4 interacted with corepressor DNMT1 in GATA4 happloinsuficient TP53 (Figure 22E). mutant HCC cells PLC A) GATA4 FOXA2 immunoprecipitation (IP), FOXA2 western blot. B) DNMT1 western blot. C) DNMT1 depletion using Even more non-cytotoxic concentration of Decitabine (Dec) increased p27 and evident, decreased MYC by western blot D) Cell counts by automated cell counter. F) Expression of HNF4A and CEBPD by qRT-PCR western blot analysis demonstrated increasing protein levels of the marker of terminal differentiation p27/CDKN2. Collectively this demonstrated that mutations of coactivators contribute to epigenetic suppression of terminal differentiation pathways. This knowledge also substantiated the idea that HCC cells have a non-oncogene addiction to co-repressor molecules such as DNMT1. Such corepressors though not amplified by genetic methods contribute to inactivation of terminal hepatocyte genes by epigenetic methods, and they are therefore logical molecular targets for pharmacologic inhibition. We evaluated this rationale as

92 novel approach in the treatment of hepatocellular carcinoma using mechanism based studies of protein-protein interaction and small molecule inhibition of candidate corepressor DNMT1 by clinically available methods of non-cytotoxic treatment with DNMT1 inhibitor decitabine (Dec).

Non-cytotoxic inhibition of DNMT1 shifts hepatocyte enhanceosome interactions from co-repressors to co-activators in vitro

Transcription factors are difficult to drug. In contrast, the chromatin modifying enzymes that mediate the actions of transcription factors are eminently drugable.

Tens of very specific chromatin regulator inhibitors are in development as potential oncotherapeutics, a dramatic expansion from the two categories of chromatin regulator inhibitors currently in the clinic (histone deacetylase and

DNA methyltransferase inhibitors). There is very limited rationale, however, to guide precision application of these drugs: which of the hundreds of chromatin regulators expressed in cells are logical targets for therapy? Our genetic, gene expression and epigenetic studies, suggested a model/hypothesis in which coactivator/corepressor imbalance at differentiation gene loci (e.g., HNF4A,

CEBPD) is created by GATA4 mutation or deletion, such that corepressors instead of coactivators are recruited to partner TF (e.g., FOXA1/2) (Figure 16).

This results in epigenetic repression of MYC-antagonist differentiation genes.

Inhibiting the specific corepressors recruited to FOXA1/2 could be a method to

93 restore expression of these endogenous

MYC-antagonists and trigger p53- independent cell cycle exits. As a first step towards such translational goal we documented corepressor/coactivat or exchange, including cataloguing of the specific Figure 24. Inhibition of DNMT1 shifts balance of corepressor towards corepressors and coactivators in HCC. Immunoprecipitation tandem LCMS/MS was used to evaluate FOXA1 interactome at baseline and after DNMT1 depletion A) coactivators that Baseline DNMT1 interactome includes a heavy recruitment of corepressors (RED) and some coactivators (Green) B) Depletion of DNMT1 shifted interact with FOXA1/2 interaction towards coactivators. to successfully influence epigenetic control of hepatocyte gene expression. One major corepressor that was identified using affinity-purification-tandem mass spectrometry

(LC/MSMS), with confirmatory Western blot analyses, was DNA methyl transferase 1 (DNMT1). DNMT1 is a viable and logical molecular target for pharmacologic targeting in various liquid tumors152. In fact, one potent inhibitor of

DNMT1 decitabine (Dec), is FDA approved for treatment of mylodysplastic

94 syndrome (MDS). Our laboratory recently demonstrated that low dosage of decitabine can be used in treatment of MDS by engaging terminal differentiation pathways instead of apoptosis genes87. We thus decided to use non-cytotoxic concentration of Dec, to evaluate effects of DNMT1 degradation in HCC.

Mechanistically, DNMT1 inhibition by this method restored the interaction of hepatocyte master transcription factors FOXA1/2 from co-repressors to coactivators (Figure 24). Interestingly, while Dec treatment altered interaction with co-repressors, it did not alter protein localization from their nuclear compartments and did not impair transcriptional interaction between FOXA1, FOXA2 and

GATA4 (Figure 25). Such a heavy shift from co-repressor complexes to coactivator complexes at the hepatic enhanceosome suggested a shift from inactive to active gene expression environment.

Inhibition of DNMT1 activates expression of hepatocyte MYC antagonist genes to promote HCC cell cycle exit by differentiation

HCC cell lines PLC and HepG2 were treated with non-toxic concentrations (0.2-

0.5µM) of decitabine. Gene expression of bonafide hepatocyte MYC antagonists

HNF4A and CEBPD was analyzed by qRT PCR studies. Relative expression of these genes was upregulated in Dec treated cells relative to vehicle control

(Figure 23). Moreover, the protein levels of the marker of terminal differentiation p-27/ CDKN1B was upregulated with treatment while protein levels of MYC was down-regulated (Figure 23). These analysis together with the proteomic studies that demonstrated shift from co-repressor interaction to co-activator at the hepatic enhanceosome of FOXA1/2 provided a mechanistic evidence of how

95 decitabine treatment contributes to elevated expression of hepatocyte terminal differentiation genes to induce cell cycle exit by Figure 25. Inhibition of DNMT1 does not impair transcription factor interactions in HCC. PLC cells were treated with non-cytotoxic differentiation. concentrations of decibine A) DNMT1 depletion does not alter FOXA1 and FOXA2 levels. Western blot B) DNMT1 depletion does not dislocalize FOXA1 Since PLC and FOXA2 from the nucleus. C) DNMT1 depletion does not impair cells are p53 interactions between FOXA1, FOXA2, and GATA4. IP=immunoprecipitation null, our studies demonstrate that cell cycle exit by differentiation can occur independent of p5387,153,154.

Inhibition of DNMT1 promote HCC regression in vivo

Pre-clinical in vivo proof of efficacy of above studies was evaluated using obesity induced murine model of HCC mice treated with 1 dose of the carcinogen DMBA followed by a high fat diet feeding. In this model, 100% of the male mice developed spontaneous tumors in the liver with a small number 5% developing lung metastasis and skin cancers. In pilot experiments pharmacodynamic measurements of intended pharmacodynamic effect were used to determine dosages and schedules for efficacy determinations. End-points included disease latency, blood counts (tail-vein phlebotomy every 4 weeks), tumor size and weight, weight of affected organs, overall weight, appearance and behavior of animals. Pharmacodynamic epigenetic effects were evaluated by Western blot of tumor specimens and bone marrow cells. MYC and p27 immunohistochemistry were used to measure cell cycle exit by terminal differentiation (per established

96 methods38,42,44,45). Promising clinical translation was a non-cytotoxic chromatin modifying enzyme inhibitor that targets the DNMT1 aberrantly recruited to

FOXA1 and FOXA2 in GATA4-deficient HCC cells (Figure 23). The marketed formulation of decitabine to inhibit DNMT1 is essentially inactive in the liver because of rapid destruction by cytidine deaminase. Using novel agent oral THU- decitabine that combines decitabine with THU, an inhibitor of cytidine deaminase

HCC mice were treated with 0.2mg/Kg of Dec and 10mg/Kg of THU. This combination agent is significantly active in the liver without toxicity to more sensitive tissues such as the bone marrow38. This treatment promoted tumor regression in vivo after 6 week treatment schedule (n=5, p<0.03).

Figure 26. Inhibition of DNMT1 Induces HCC tumor regression in vivo. HCC mouse model was generated by feeding mice with high fat diet (HFD) and treatment with carcinogen DMBA. A) Schematic mouse model and treatment approach B) Liver images in Dec treated mice versus PBS controls. C) Total

body and liver weight. D) Quantified tumor burden.

Discussion: chapter 2

Avoiding pathways of terminal differentiation is essential in the genesis of hepatocellular carcinoma, but limited literature has attempted to evaluate the therapeutic benefits of this pathway. An interesting paradox was revealed by our studies: HCC cells express high levels of hepatocyte master transcription factors

(FOXA1 and FOXA2) but are differentiation deranged by unknown mechanisms.

While investigating these mechanisms, we demonstrated in chapter 1 that differentiation derangement could partly be explained by genetic dose reduction changes in partner transcription factor, GATA4 (>60% CNV-Loss in HCC) and inactivating mutations of the coactivators of this pathway (e.g., ARID1A, ARID2,

SMARCAD1, KMDT2 e.t.c.,). Master transcription machinery rely on ATP- dependent SWI/SNF factors to open chromatin providing DNA binding sequences for activation target genes 145. While our findings expand upon the knowledge of the relationship between master transcription factors (GATA4,

FOXA1 and FOXA2), and their co-activator partners (e.g., ARID1A) in modulating gene activation of the hepatocyte lineage69-71,143, it was insufficient to clarify why

FOXA1 and FOXA2 are unable to turn on hepatocyte differentiation programs in

HCC. Since HCC cells with or without GATA4 deletion/mutation express high levels of these key transcription factors (FOXA1/2) necessary for hepatocyte commitment and differentiation, we sought to determine the molecular mechanisms impairing their transcriptional activity in HCC/cancer.

First, hundreds of hepatocyte terminal differentiation genes were epigenetically suppressed in HCC cells. From a translational perspective, the

98 most important hepatocyte maturation genes are those that antagonize MYC function and terminate proliferation (e.g., HNF4A, CEBPD) 80,105-107,117,118.

Transcriptional regulation by master transcription factors e.g., GATA4, FOXA1/2, is a coordinated process involving multiple cofactors that signify on/off signals at target genes120,123,124,150. Transcription factors of a given lineage interact with very specific co-activators to turn on target genes. We demonstrated by comprehensive gene expression studies in chapter 1 that terminal hepatocyte commitment genes were suppressed in HCC relative to normal liver. This suppression was linked to the genetic alterations of GATA4 and co-activators that included SWI/SNF, mediators and MLL factors. Since FOXA1 or FOXA2 were not mutated in HCC, we developed the hypothesis that alterations of

GATA4 and coactivators shifted the balance on FOXA1/2 from coactivators to corepressor molecules. Therefore, by interacting with corepressors, FOXA1/2 are transcriptionally inactive and unable to promote activation of hepatocyte terminal differentiation gene programs.

The findings in chapter 2 supported this idea. First, we catalogued that

FOXA1 and FOXA2 as well as downstream terminal differentiation genes were never mutated in HCC. Lack of mutation however did not translate into expression of FOXA1 and FOXA2 target genes. We evaluated FOXA1 and

FOXA2 transcriptional interactions in HCC with GATA4 wild-type versus GATA4 haploinsufficient HCC. Interestingly, well differentiated HCC cells by light microscopy (Sk-HepG2 ATCC –HB8065) were GATA4, FOXA1 and FOXA2 intact with some mutations in co-activators. In these cells, FOXA1 and FOXA2

99 interacted strongly with transcriptional coactivators such as ARID1A and

SMARCA4 compared to HCC with GATA4 heterozygous deletion that are poorly differentiated by light microscopy deletion (PLC cells CRL ATCC - 8040). In PLC cells, FOXA1 and FOXA2 strongly interacted with co-repressors e.g., DNA methyl transferase 1 (DNMT1). Re-introduction of wild-type GATA4 in PLC cells shifted FOXA1/2 interaction from co-repressors to co-activators. This illustrated that a single genetic alteration on the enhanceosome involving GATA4, FOXA1 and FOXA2 could impair the transcriptional activity by recruiting corepressors on non-altered transcription factors.

Second, if FOXA1 and FOXA2 are never mutated/deleted in HCC, and therefore transcriptionally inactive by a stronger interaction with corepressor than coactivators, could this provide opportunities for differentiation promoting therapy? Several studies have demonstrated that active corepressor molecules in cancer can be targeted for therapeutic purposes87,154,155. For instance, RUNX1 deficiency in AML alters PU.1 interactome from coactivators to corepressors and these corepressors have been reported as candidate targets to promote AML differentiation88,149. This mode of interaction is similar to that of HCC where the expression of terminal hepatocyte genes is limited by FOXA1/2 interaction with co-repressor molecules. In other words, active corepressors mimic genetic dose reduction of either GATA4 and/or frameshift mutation of co-activators as described in chapter 1. However, unlike genetic mutations/deletions, genetic dose reduction by corepressors has vital translational relevance: i) In HCC cells with mutation/deletion of GATA4, removal/reduction of active corepressors on

100

FOXA1/2 could form basis for epigenetic therapy. ii) This form of therapeutic approach will engage differentiation gene network to promote HCC cell cycle exit by differentiation. iii) Differentiation induction therapy would be non-cytotoxic by terminating cancer cell proliferation by p53 independent methods. Therefore by understanding the key co-repressor molecules interacting with FOXA1/2 in HCC, new molecular targets can be generated for precision medicine therapy that engages differentiation machinery and terminates exponential growth of HCC by p53 independent methods127,130,156.

The data here demonstrated that HCC cells have a non-oncogene addiction to chromatin remodeling factors that maintain a heterochromatin state

(co-repressors activity), and this impedes FOXA1/2 binding to hepatocyte genes to promote expression. Therefore FOXA1 and FOXA2 interaction with corepressor molecules emphasize that downstream of genetic defects of GATA4 transactivation pathway described in chapter 1, the repression of hepatocyte terminal differentiation genes is by epigenetic methods, and hence in principle reversible.

Finally, what are the therapeutic gains of targeting transcriptional corepressors in HCC? We evaluated the ability to use inhibitors of chromatin modifying enzymes in HCC by studying the mechanistic effect of inhibition of

DNMT1, a key co-repressor identified in the FOXA1 and FOXA2 interactome.

DNMT1 inhibitor decitabine (Dec) is clinical available method of therapy in cancer and has been demonstrated to extend overall survival by our group and others by p53 independent methods87,157. In this study, we demonstrated that

101 inhibition of DNMT1 using Dec shifted the interactome of FOXA1 and FOXA2 from transcriptional co-repressors to co-activators in HCC cells. Treatment of

HCC cells with decitabine induced the expression of hepatocyte MYC antagonists e.g., HNF4A and CEBPD compared to vehicle control and induced epithelial differentiation. This suggests that at the center of transcriptional corepressor complexes, DNMT1 is a key enzyme driving epigenetic suppression of terminal hepatocyte genes in HCC.

Moreover, we evaluated the therapeutic potential of DNMT1 inhibition in

HCC murine model of HCC. We treated obesity induced HCC mice with non- cytotoxic concentrations of decitabine (0.2mg/Kg) 3 times/week for 19 weeks.

Mice treated with decitabine had a striking tumor regression compared to PBS treated vehicle controls (n = 5 mice per group, p<0.03) While these findings provide encouraging evidence of the therapeutic efficacy of DNMT1inhbition in

HCC as means of therapy, this illustration could be substantiated by a better mouse model. Therefore we are currently working on generating a stronger mouse model of Gata4 and Trp53 liver conditional haploinsuffiency, where we hypothesize that aggressive tumors will be observed as early as three month to provide longer treatment times satisfactory to evaluate overall survival.

Moreover the most important element in cancer therapy is resistance and toxicity. While p53 independent approach can solve the problem of toxicity i.e. therapy does not induce apoptosis in normal or tumor cells ), potential mechanisms of resistance will need to be explained. Our group is consistently evaluating these mechanisms. Currently, drugs available for the purpose DNMT1

102 inhibition have pharmacologic limitations, that are barriers to immediate clinical translation to treat HCC155. For example, decitabine and 5-azacytidine that deplete DNMT1 and which are effective in the treatment of differentiation- impaired p53-null myeloid malignancies have negligible distribution into the liver, because this tissue highly expresses an enzyme that rapidly inactivates these drugs (cytidine deaminase)155. To address this pharmacologic limitation, decitabine has been combined with an inhibitor of cytidine deaminase, a clinical development effort that may enable treatment of HCC155,158 (Ebrahem et al.,

2012; Lavelle et al., 2012). Targeting both the rate limiting enzyme of pyrimidine metabolism - cytidine deaminase – using tetrahydrouridine (THU), and DNMT1 using Decitabine, is therefore currently under investigation to further identify overall survival of HCC mice treated by these methods.

In sum, the present work reveal a clinically relevant discovery that inactivation of GATA4, by genetic mutation/deletion, modulate gene activation by favoring recruitment of co-repressors to partner transcription factors FOXA1/2 to turn off target genes and prevent cell cycle exits via differentiation. Therefore, by suppressing cell cycle exit by differentiation, abnormalities such as GATA4 and

GATA4 cofactor (HNF1A, ARID1A, ARID2 and SMARCA4) mutation/deletion in

HCC likely complement abnormalities such as TP53 mutation/deletion that suppress cell cycle exit by apoptosis. Importantly, however, the hepatocyte terminal differentiation genes (e.g., HNF4A, CEBPD) that antagonize MYC function to terminate proliferation are genetically intact, and therefore, unlike key mediators of cell cycle exit by apoptosis (e.g., TP53, p16/CDKN2A), are available

103 for activation, and could form a refinement of therapeutic intervention in HCC that is p53-independent. Therefore, evaluation of the use of co-repressor intervention therapy in human HCC is of considerable interest, since such therapies would promote also the differentiation/expansion of normal stem cells preventing the risk of adverse effects observed from current p53 dependent therapies.

Chapter 2: Summary and Significance

GATA4 and

GATA4

Transcriptio nal coactivator genes

(chromatin modifying Figure 27. Chapter 2 Model summary: Normal hepatocytes have intact master enzymes transcription factors that coordinate interaction between co-repressors and coactivators to favor recruitment co-activator partners to turn on hepatocyte terminal that make differentiation target genes. In hepatocellular carcinoma genetic alterations of DNA more master transcription factors and inactivating mutations in their co-activator partners shifts the balance towards co-repressors that interact with wild-type available hepatocyte master transcription factors to turn-off hepatocyte terminal differentiation genes. The novelty in this hypothesis is that if the active co- for repressors are known, they can serve as logical molecular targets for activation) pharmacological inhibition yielding novel p53-independent differentiation promoting therapies in HCC. that cooperate with transcription factors are frequently genetically inactivated in human HCC. Such inactivation contributed to genetic dose reduction of

104 transactivation activity but enhance transcriptional repression activity of wild-type transcription factors by recruitment of transcriptional co-repressors (chromatin modifying enzymes that package DNA into nucleosome and prevent gene activation). Chromatin modifying enzymes lack post-translational modification and this has impeded the ability to develop scientific rationale to define why cancer cells select to mutate these factors. While current literature has focused on defining how HCC and other cancers avoid cell cycle exit by apoptosis, we illustrated here that inactivation of either GATA4 or SWI/SNF coactivator altered the differentiation pathways, providing new opportunities for differentiation promoting therapy. To our knowledge, the mechanistic link accounting for these alterations has not been previously described despite such high frequency of alterations.

We detailed that in HCC FOXA1 and FOXA2; hepatocyte master transcription factors that have been shown to co-operatively mediate differentiation pathways in this lineage are rarely mutated. Luck of mutations and the data generated suggested that FOXA1/2 actively interact with transcriptional co-repressor enzymes, that efficiently turn off target genes. Studies of protein- protein interactions of FOXA1 and FOXA2 were evaluated in HCC cells with intact GATA4 and those with 8p deletion to mimic human HCC. These master transcription factors interacted strongly with coactivators when GATA4 was absent in HCC. The interaction with corepressors was more pronounced in HCC with GATA4 deficiency suggesting that these co-repressors could be targeted for pharmacologic inhibition.

105

We thus contributed new knowledge of the mechanisms linking epigenetic suppression of terminal hepatocyte genes in HCC and provided molecular pharmacologic targets necessary for clinical translation into novel therapies: i.e. a profound suppression of multiple target genes through recruitment of corepressor proteins e.g,. ( DNMT1, SMARCA5, EED, EZH2, WDR4, BAZ1B/A) to non-mutated liver differentiation transcription factors. Therefore, genetic dose- reduction in HCC is achieved through mutations in GATA4, the hepatic master transcription factor and co-activators and also through non-oncogene addiction to co-repressors. We demonstrated that in HCC cells where co-activator SMARCA4 and ARID1A were found inactivated; introduction of wild-type ARID1A and

SMARCA4 induced terminal hepatocyte differentiation.

We conclude here that active transcriptional corepressors can be targeted for inhibition as a method to engage terminal hepatocyte differentiation gene networks to terminate exponential HCC growth by p53 independent methods.

Methods chapter 2

Analysis of ENCODE data

Data was downloaded from encode and peak calling was analyzed using intergrated genome view (IGV). Two HNF4A enhancer sequences were downloaded from roadmap epigenetic software available from Washington State

University. Primers corresponding the identified enhancers were designed using

Intergrated DND technologies.

106

Chromatin immunoprecipitation

Chromatin was isolated from 100 million HepG2 and PLC cells by crossing linking DNA to Chromatin using 37% formaldehyde per milliliter of cell culture (in growth medium in tissue culture flasks) while slowly shaking the cells for 10 min at room temperature. The cross-linking was quenched by adding 1.375 M glycine per milliliter of culture. The growth media was removed and cells were washed three times using ice cold 1x PBS. Cells were scraped of the plate using sterile cell scrappers in a 15mL conical tubes. Cell pellets were generated by spinning the cells in 1xPBS at 1500 rpm for 10 min at 4°C. Cells were re-suspended in 10 mL of cell lysis buffer for ChIP and incubated for 10 minutes on ice. Nuclear lyste was generated by spinning the cells at 1000 rpm on a benchtop centrifuge for 10 minutes at 4°C. Supernatant was removed. The cell nuclei was re-suspended in

500 μL of MNase digestion buffer and 5µL of MNase was added. Cells were incubated for 1 h on ice. The MNase digestion was terminated by adding EDTA to a final concentration of 50 mM. The nuclear pellets were resuspended in 1 mL of nuclei lysis buffer for ChIP supplemented with protease inhibitors and incubated for 10 min on ice. Purified nuclear lysates were sonicated with 8 10- sec pulses followed by 30-sec rest periods for 90 minutes. Cross linking was reversed by heating 1% of the samples at 65 for 1 hour and chromatin was evaluated by running samples on 1% agarose gel.

107

Preclearing Chromatin and Immunoprecipitation

Samples were centrifuged at full speed in a microcentrifuge for 15 min at 4°C to pellet precipitated SDS. Supernatant was transferred to a fresh microcentrifuge tube. Small 100uL volumes of the successfully sheared chromatin (DNA ranging from 100bp-500bp) were aliquoted and stored at −80°C. DNA concentration was measured by spectrophotometer. 100 μg of total chromatin used used for immunoprecipitation. The chromatin was diluted to a final volume of 300 μL with dilution buffer containing protease inhibitors. Preclearing was achieved using 50

μL of protein A/G-agarose beads with IgG corresponding to primary antibody species using the chromatin and incubated at 4°C for 1 hour. Samples were centrifuged at 3000 rpm for 5 minutes at 4°C. Supernatants was transferred to a fresh microcentrifuge tubes. 10 μg of primary antibody for GATA4, FOXA1 and

FOXA2 were was for immunoprecipitation overnight overnight at 4°C with agitation. 50 μL of protein A/G agarose beads were added to the chromatin samples on ice. Samples were incubated for 2 hours at 4°C and centrifuged at

3000 rpm for 2 min at 4°C. 5% of input was isolated from each sample and stored on ice. 1 mL of high-salt (5M NaCL) was added to ChIP wash buffer for all samples and incubation was done on a rotor for 10 min at room temperature.

Samples were centrifuged at 3000 rpm for 2 min at room temperature.

Supernatant was transferred to fresh tubes and this step was repeated twice.

Supernatants were washed twice with TE buffer. Input samples were resusped in

300 μL of elution buffer (TE buffer) for ChIP supplemented with 1 μL of proteinase K (20 μg/μL), and samples were incubated for 2 h at 55°C. Reverse

108 cross-linking was achieved by incubating samples at 65°C overnight. DNA was isolated by spinning samples at full speed for 10 minutes. Down-stream analysis was done by qRT-PCR (qRT-PCR methods are described in chapter 1 of this dissertation)

Cell culture and DNA transfection

Human HCC cell lines (Sk-HepG2 and PLC) were cultured in Roswell Park

Memorial Institute (RPMI) media with 10% fetal bovine serum (FBS), 100U ml-1 penicillin and 100 μg ml-1 streptomycin (Mediatech, Herndon, VA). Cells were

o incubated at 37 C in a 5% CO2 atmosphere. DNA was isolated from both cell lines for SNP array analysis. HepG2 cells were transfected with ARID1A WT and

SMARCA4 WT vector and ~ 60x106 cell pellets were harvested at 72 hours.

Harvested cells were re-suspended in 1xPBS+PI+PMSF ( PBSW buffer PH7-

9).The pellet was centrifuged at 3000RPM for 5 minutes. Cells were washed 3 times in PBSW buffer.

RNA and DNA extraction

Snap frozen specimens were equilibrated with a buffer (RNAlater-ICE, Ambion,

109 homogenization and RNA extraction, and (b) Proteinase K digestion and DNA extraction: (a) The fragment for RNA extraction was homogenized using a tissue homogenizer. The mirVana kit (Ambion) was used per manufacturer’s protocol for extraction of total RNA. (b) The fragment for DNA extraction was minced with a blade into fragments approximately 2mm in dimension. These fragments were placed in a 1.5cc Eppendorf tube with cold PBS and left on ice for 5-10 minutes to leach out the RNAlater-ICE before digestion in proteinase K for DNA extraction. After pipetting off the PBS, lysis buffer was added followed by proteinase K digestion as per the DNeasy Blood and Tissue Kit (Qiagen,

Valencia, CA). DNA quantity and A260/280 ratio were obtained from a spectrophotometer NanoDrop-1000 (Thermo-Fisher Scientific, Wilmington, DE).

Western blot analysis

Western blot was by standard methods for protein analysis experiments.

Antibodies used were: GATA4 (Abcam Ab cat# Ab124265), Anti-Flag (Sigma

Cat# F7425-2MG) c-MYC (Cell signaling Ab cat# 5605), p27/CDKN1B (Cell signaling Ab cat# 3833) and β-actin (Sigma, #a3854), Histone 3 (Cell Signaling

Cat# 9715S) SMARCA4 (ABCAM,) ARID1A (cell signaling) SMARCA5, (Cell

Signaling cat#13543s) FOXA1 (Santa cruz) FOXA2 (santa cruz).

Immunoprecipitation

200µL of nuclear protein lysate was pre-cleared using G-Sepharose (50% slury).

This was incubated at 4°C for ~60minutes Spun for 10 minutes at 4°C.

Supernatant was transferred to fresh tubes. 30mg of nuclear protein extracts

(pre-cleared lysate) was transferred to tubes with antibody bound protein G

110 beads and rocked gently at 4°C overnight. This was washed 5 times with 1xPBS containing 1%NP-40. Samples were dried using a Spin-Dry vacuum centrifugation at -100 on Speed Vac vapor trap. Immunoprecipitation products were extracted from the protein G beads using Laemmli sample buffer.

DNA Vector expression

GATA4, SMARCA4, and ARID1A DNA vectors were Transient transfected into

HCC cell lines following xfect transfection (clonetech, Mountain view, CA) manufactures’ guidelines. Cell pellets were isolated at 0, 48 and 96 hours after transfection for downstream analysis.

In vitro and In vivo decitabine treatment

Pre-clinical in vitro and in vivo proof principle for DNMT1 inhibition was performed in HCC cell lines and murine HCC mouse models. Non cytotoxic concentrations of decitabine (0.25-0.5µM) were used for treatment of HCC cell lines at day 1 and day 3. Cell pellets were isolated for downstream experiments evaluating treatment effects starting at day 0, day 1, day 3 and day 4.

The mouse model used here was kindly generated and provided by the laboratory of Dr. Mark Brown of department of cellular and molecular medicine of learner research institute Cleveland clinic Ohio. Briefly mice were treated with 1 dose of carcinogen DBMA and subsequently fed with a high fat diet. At week 30 mice 100% of male mice developed spontaneous tumors in the liver. These mice were treated with 0.2mg/Kg of decitabine and 10mg/Kg of tetrahydrourine 3 times a week for 19 weeks. Treatment was initiated at week 11 and mice were sacrificed at week 30. Number of visible tumors were counted and quantified by

111 two independent investigators. Moreover, tumors were quantified using image analysis software image J to quantified total number of tumor cells per region of interest.

Statistical analysis

Statistics were evaluating using Prism and JMPr softwares. P values were evaluated by Wilcoxon sum rank test or Analysis of variance as per experimental conditions.

Future Studies

The work presented here was an attempt to define how solid tumor cancer models avoid pathways of terminal differentiation by using HCC as the cancer model. The results provided are a promising first step towards our greater understanding of how cancer cells are differentiation deranged and also the mechanisms promoting aberrant proliferation phenotype.

Normal physiology has two pathways regulating cell cycle exits: Apoptosis and terminal differentiation. In HCC both these pathways are frequently altered and multiple studies have linked alterations of the apoptosis pathway to TP53 system alterations. In this dissertation we illustrated that structural alterations of chromosome 8p in HCC target the GATA4 gene to alter cell cycle exits by differentiation. Conditional heterozygous loss of Gata4 generated mice with liver phenotype and systemic obesity. In the future, mice harboring a combinatory haploinsufficiency of Gata4 and Trp53 (mouse form of TP53), could provide a stronger model that closely replicates the human disease as these pathways are

112 altered at equal frequency in human HCC. As part of our ongoing investigation, we have developed conditional liver haploinssuficiency mice where both Gata4 and Trp53 have been targeted. Initial phenotypic evaluations will be characterized in a timely fashion starting at three month of age. Moreover, the frequency of alteration of transcriptional coactivators warrants further investigation in vivo. Since ARID1A was the most frequently inactivated coactivator in HCC, we have thus generated Arid1a liver conditional haploinsufficient mice to characterize phenotypic consequence of loss of Arid1a in the liver. If necessary, development of mice with conditional deletion of Gata4,

Trp53 and Arid1a may be generated for further studies since these genes are mutated/deleted with highest frequency in HCC.

We also demonstrated here by in vitro and in vivo preclinical proof of principle that the mechanisms leading to differentiation derangement in HCC can provide opportunities for novel therapy. We will further clarify this methods of therapy by evaluating overall survival of murine HCC models treated by these methods. More important we plan to generate clinical trials using this novel approach of targeting the co-repressor complexes in human HCC by first illustrating the potential effects of using DNMT1-inhibitors that are already available for clinical use in various other cancers. The goal of these studies are to define non-cytotoxic therapies in HCC.

Multiple other co-repressors such as SMARCA5, WDR1-5, EZH2,

SMYD2, BAZ1B/A and EED were also found to be active in HCC by interacting with hepatocyte master transcription factors FOXA1 and FOXA2. There are small

113 molecule inhibitors for some of these corepressor molecules that can be investigated as future mechanism of therapy in HCC. We anticipate future studies to provide specific rationale and mechanisms linking activity of these corepressor to epigenetic suppression of terminal differentiation genes to further provide opportunities for additional molecular targets for pharmacologic inhibition in primary HCC that engage differentiation genes.

From another perspective, differentiation derangement of multiple solid tumor models (prostate cancer, renal cancer, pancreatic cancer, lung cancer, bladder cancer, breast cancer, and ovarian cancer,) has not yet been defined.

We have initiated these studies in some of these models. For example, we have recently characterized how PBRM1, the most frequently mutated coactivator in renal cell carcinoma (RCC) impairs differentiation pathways, this work is currently under review for publication. It is interesting also that GATA4 alteration by chromosome 8p deletion is frequently observed in carcinomas of endodermal origin such as ovarian cancer, pancreatic cancer and lung cancer. We plan to look into the mechanistic link of chromosome 8p deletion and effects of impaired differentiation in these tumors. In pancreatic cancer for example, the frequency of alterations in SWI/SNF co-activators and GATA4 deletion by 8p loss is just as frequent as in HCC perhaps due to the similar endodermal cell origin. These two organs are also so closely linked in physiological processes and it is probable that the mechanisms of suppression of terminal differentiation genes are strikingly similar in hepatocytes as in pancreatic cells. We will flesh out these

114 mechanisms for novel therapies of solid tumor cancers that are non-toxic and engage differentiation pathways.

115

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