CALIFORNIA STATE UNIVERSITY, NORTHRIDGE

The Function and Mechanism of Alpha 1

in the Pathogenesis of Pancreatic

A thesis submitted in partial fulfillment of the requirements For the degree of Master of Science in Biology

By Armen Edik Gharibi

August 2016 The thesis of Armen Edik Gharibi is approved by:

______Dr. Ray Hong Date

______Dr. Sean Murray Date

______Dr. Jonathan Kelber, Chair Date

California State University, Northridge

ii

Acknowledgments

I would like to express my appreciation to the outstanding faculty members within the Department of Biology at California State University, Northridge for allowing this campus to be my second home. Specifically, my committee members Dr. Ray Hong, Dr.

Sean Murray and my principle investigator, Dr. Jonathan Kelber for providing me the necessary training to obtain a master’s of science degree. Furthermore, I would like to acknowledge the graduate coordinator of the biology department, Dr. Tim Karels, for addressing questions in an extremely prompt manner and keeping me on track to complete my requirements for graduation. I would also like to thank the Department of

Biology, the College of Math and Science, Graduate Studies and Associated Students for providing financial support throughout my time at the university. Special thanks are required for my project funders, which include, CSUPERB, Medtronic/MiniMed and the

NIH.

I would like to recognize the contributions from members of administration within the Department of Biology that assisted me with administrative issues. These members include: Cherie Hawthorne, Sarah Cohen, Vickie Everhart and Linda

Gharakhanian. I would like to thank Mark Harris, Karen Moore, Marc Felix and William

Krohmer for their dedication in aiding with technical support.

Additionally, I would like to articulate my gratitude towards the members of Dr.

Kelber’s Developmental Laboratory: Yvess Adamian, Sa La Kim, Malachia

Hoover, Justin Molnar, Preston Shisgal, Erika Duell and Julie Hong. The connections we have built with each other are special and I am forever grateful for having the opportunity to work in an environment where stress was minimal. Moreover, I would like to thank a

iii former member of this lab, Megan Agajanian, who set a high standard for current and future graduate students for our lab. Most importantly, special thanks are needed towards

Yvess for training me with all of the techniques I have learned since the first day.

Furthermore, I am grateful for Yvess continuously being by my side as my life partner.

Lastly, I would like to acknowledge my mentor, Dr. Jonathan Kelber. Without him, earning a master’s degree may not have been possible. Words cannot explain the appreciation I have for his help. With his guidance, Dr. Kelber allowed me to seek my inner-self and I discovered many qualities about myself that I did not know existed. Not only has Dr. Kelber developed me into a great researcher, he has also made me a better person by shaping my character. The amount of time he took to insure that I am on the right track and his dedication to my success is deeply appreciated.

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Dedication

This work is dedicated to my family who has always believed in and supported me with every decision I have made. I am thankful for the mental and financial support my parents have provided for me, especially to my father who has been patiently waiting for me to stand on my own two feet. To my father, I apologize for taking this long in deciding my career path as I understand you could have used these years towards your retirement. To my mother, I apologize for continuously taking my stress out on you. You are the bravest woman I know and I am privileged to be the only man who can call you mother. To my one and only sister, you are just as brave as our mother. Your inner- strength has inspired me to become the man I am today. I apologize that my research was not on Type I Diabetes; however, believe me that I would not have wanted anything more but to find the cure for this disease. Nonetheless, I hope you are still proud of me for my efforts and contributions towards pancreatic cancer.

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Table of Contents

Signature Page ii

Acknowledgements iii

Dedication v

List of Tables vii

List of Figures viii

Abstract ix

Chapter 1: Introduction - Cellular and Molecular Aspects of Pancreatic Cancer 1

Pancreatic Cancer Statistics (1975-present) 1

Factors Contributing to Poor Survival 4

Common Genetic Mutations or Expression Alterations 13

Oncogene Addiction, Synthetic Lethality, Stem Cells and

Stroma in PDAC 25

Identifying New Diagnostic Biomarkers and Therapeutic Targets 30

Concluding remarks 37

Implications 37

Chapter 2: Materials and Methods 39

Chapter 3: Results 43

Chapter 4: Discussion 62

References 66

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List of Tables

Table 1.1 23

Table 1.2 36

vii

List of Figures

Figure 1.1 7

Figure 3.1 45

Figure 3.2 46

Figure 3.3 49

Figure 3.4 52

Figure 3.5 54

Figure 3.6 57

Figure 3.7 60

Figure 3.8 61

Figure 4.1 65

viii

Abstract

The Function and Mechanism of

in the Pathogenesis of Pancreatic Cancer

By

Armen Edik Gharibi

Master of Science in Biology

Pancreatic ductal adenocarcinoma (PDAC) is a deadly malignancy that affects nearly

50,000 patients each year. The overall 5-year survival rate for this malignancy remains the lowest of any cancer at around 7% due to limited diagnostic methods, disease aggressiveness and a lack of targeted therapeutic interventions. Therefore, there is a dire need for novel biomarkers and therapeutic targets for patients diagnosed with PDAC.

Since most cancer-related deaths are a result of metastatic tumors, we reasoned that our previously published proteomic signature of the cell pseudopodium (PD) may be a good pool from which to identify novel biomarkers or therapeutic targets. Here, we cross-referenced PD with Oncomine and identified 37 that are upregulated in PDAC. The function of integrin alpha 1 (ITGA1) – a cell surface for collagen, had not been previously characterized in the context of PDAC. We discovered that collagen induces rapid ITGA1-dependent PDAC cell spreading. Epithelial-mesenchymal transition (EMT) is a well-characterized process by which pancreatic cancer cells become more invasive and gain metastatic potential. Analysis of EMT markers in comparison to

ITGA1 in PDAC cell lines and patient samples suggests that ITGA1 may be a regulator

ix of EMT. Since transforming beta (TGF) is a potent inducer of EMT, we tested the function of ITGA1 on TGF-induced EMT. Our results indicate that ITGA1 is upregulated by TGF during EMT, and required for complete EMT in the presence of collagen. In an effort to identify the mechanism by which ITGA1 is regulating TGF- induced EMT, we searched for the top ITGA1 co-expressors using RNA-Seq data from the Cancer Bio Portal. Interestingly, PEAK1 kinase (a protein that we’ve previously shown to be required for PDAC progression) co-expresses with ITGA1 (Pearson R-value

= 0.70). Taken together, these results suggest that developing detection methods and/or therapies against ITGA1 may help diagnose PDAC earlier or improve patient responses to already existing chemotherapies.

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Chapter 1: Introduction - Cellular and Molecular Aspects of Pancreatic Cancer

Pancreatic Cancer Statistics (1975-present)

History

It was believed for a long time that cancer of the pancreas was not existent, and it was not until 1882 when a solid tumor was successfully excised for the first time from this tissue. The patient, however, died within hours of being discharged [1]. Since then, many surgical procedures have been proposed for resection of malignant pancreatic tissue. Walter Kausch and Allan Whipple started performing pancreatic surgery around

1910 and developed a method known as pancreaticoduodenectomy (Kausch-Whipple procedure). Early on, this difficult surgical procedure led to high morbidity and mortality and a poor long-term outcome, which prompted many to wonder if the surgery should be performed at all. Nonetheless, significant advances have been made to date and pancreaticoduodenectomy is currently the standard operative procedure in patients diagnosed with pancreatic cancer [2].

Despite the advances, surgical resection still provides the patient with the highest probability for survival, but is usually only performed when the tumor is localized to the pancreas [3]. Currently, patients’ median survival is 14-20 months with up to a 25% 5- year survival rate following surgical resection [2, 4]. Although substantial progress has been made in understanding this disease, mortality rates in this malignancy have been progressively rising, while the incidence and mortality rates of other common have been declining [3].

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Incidence

Pancreatic ductal adenocarcinoma (PDAC) is currently the fourth leading cause of cancer-related deaths in the United States and its incident rate has been steadily rising. In the current year, 48,960 new cases of PDAC are expected to arise in the United States and of these cases, 51% and 49% are projected to affect males and females, respectively.

The estimated number of deaths is 20,710 in males and 19,850 in females. 83% of all

PDAC diagnoses will result in patient death [5]. While the probability of developing

PDAC is 30% greater in males, age and racial/ethnic backgrounds play important roles in the incidence rates. Of all diagnosed cases, about 27% are diagnosed between ages of 75 and 84, whereas about 9% are diagnosed between the ages of 45 and 54. African-

Americans (15.7 out of 100,000) have the highest incidence rate of developing PDAC amongst the different racial/ethnic groups while Asian-Americans/Pacific Islanders (9.8 out of 100,000) have the lowest [6]. Nonetheless, this malignancy is deadly irrespective of gender, age or race.

Survival

Detecting the tumor early and when it is confined to the pancreas only (localized) is the most promising means by which to significantly increase patient survival; however, only

9% of PDAC diagnoses are made at this early stage [6]. For other common carcinomas, identifying the tumor at the localized stage results in 5-year survival rates of 99% (breast and prostate), 92% (kidney), 90% (colon/rectum), 83% (oral cavity/pharynx), 64%

(stomach) and 54% (lung/bronchus). Still the 5-year survival rate for PDAC that is detected at this early stage is still only 26% [2, 6]. Ultimately, researchers and clinicians

2 alike are striving to discover ways to detect PDAC as early as possible and improve treatment responses.

As PDAC progresses into the regional stage, in which the malignant cancer invades surrounding organs/tissues and the lymph nodes, the 5-year survival rate for patients decreases to only 10%. Detection of the tumor, however, is more likely (28%).

As with all cancers, once the primary tumor metastasizes to distant organs via the circulatory system, the probability of survival diminishes significantly. In the case of

PDAC, the 5-year survival rate for this disease once it has reached distant organs is only

2%. Detection at this stage is 53% [6], but unfortunately few patients respond to the available interventions.

Over the past 4 decades, 5-year survival rates have increased across several different cancers. The 5-year survival rate for increased from 68% in

1975 to >99% in 2010. Patients diagnosed with leukemia between 2004 and 2010 had a

5-year survival rate of 60%, whereas between 1975 and 1977, it was only 34%.

Pancreatic cancer, on the other hand, has not seen such drastic improvements. Between

1975 and 1977, the 5-year survival rate was 3%. It increased to 4% between 1987 and

1989 and currently, the 5-year survival rate is 7% [5]. Although there is a statistical significance (p<0.05) between 1975 and the present, the 4% difference over the last forty years is not impressive. We need to better understand the reasons for low survival and develop methods for identifying potential therapeutic targets to create more sufficient drugs against this deadly disease. This review focuses on the advances that have been made over the last few decades in understanding the biology of the low survival rates in

3

PDAC patients and to introduce potential novel biomarkers and therapeutic targets in

PDAC.

Factors Contributing to Poor Survival

There are several reasons why PDAC patients have such low survival. First, there are few viable methods for detecting this malignancy early. Second, PDAC is a highly aggressive cancer and can metastasize early. Finally, few targeted therapies exist for treating PDAC, and those therapies that are used remain relatively ineffective.

Limited diagnostic methods - symptoms and biomarkers

Pancreatic cancer remains one of the most difficult cancers to detect during its pre-invasive stages. Once it has metastasized, only 6% of patients survive beyond 5 years and thus it is virtually incurable. Thus, detecting the tumor while it is still localized to the pancreas provides the best possibility for survival. However, because patients rarely have physical symptoms during these pre-invasive stages - the pancreas is a visceral organ and imaging modalities are costly - it will require validation of diagnostic biomarkers before this cancer can be detected during these early stages. Currently, the most reliable and frequently used biomarker of PDAC is carbohydrate antigen 19-9 (CA19-9), also known as sialylated Lewis (a) antigen [7, 8]. CA19-9’s sensitivity ranges from 69% to 98% and its specificity from 46% to 98% [8]. Consequently, CA19-9 is non-PDAC specific and can provide false positive results as it is found in both benign and malignant gastrointestinal tumors. Moreover, in 10% of the population, CA19-9 can be deficient due to persons who genotypically cannot produce the protein [8]. Several recent efforts have been made to identify novel serum biomarkers of PDAC, but to date, no clinical

4 benefit of these studies has been realized. Thus, it is crucial for future efforts to identify novel PDAC-specific biomarkers that could be used to diagnose this cancer earlier.

PDAC is an aggressive malignancy

As a consequence of PDAC developing in a moderately symptom-free manner, has usually already occurred in the majority of the cases at the time of diagnosis. [9, 10].

Development of PDAC

The development of malignant pancreatic tumors from normal pancreatic tissue occurs through non-invasive precursor lesions known as pancreatic intraepithelial neoplasms (PanINs), intraductal papillary mucinous neoplasms (IPMN) or mucinous cystic neoplasms (MCN) [7]. Invasive pancreatic ductal cancer cells most commonly arise from PanINs while acquiring specific gene mutations as they progress from PanIN-1 to PanIN-3 [3]. PanINs differ from one another based on the degree of cytological and tissue architecture atypia [7]. Genetic mutations occur as early as PanIN-1, where the activation of the KRas oncogene is present in 95% of PDAC cases. As these early neoplasms transition to PanIN-2 and PanIN-3, tumor suppressor genes such as CDKN2A

(/Ink4A), TP53 (), and Smad4 (DPC4) are often times inactivated or deleted [3].

Figure 1 illustrates PanIN-to-PDAC progression along with genetic alterations in specific genes as the disease progresses.

IPMNs are mucin-producing neoplasms that arise within the pancreatic duct or one of its branches [7]. They have many genetic mutations as seen in PanINs with one major difference: IPMNs rarely show Smad4 deletion [3, 7]. Alternatively, MCNs typically arise in females and contain an ovarian-like stromal cells and proteins. Unlike

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IPMNs, MCNs do not communicate with large pancreatic ducts. Interestingly, IPMNs and MCNs are found in patients who are on average 3-5 years younger than those with

PanIN-derived invasive lesions [7].

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Figure 1.1: Top – schematic of the morphological changes that occur at the cellular and tissue levels during PanIN  PDAC progression. PanIN-1s are characterized by hyperproliferative cells with some papillary structures. PanIN-IIs are characterized by loss of basal localization of the and nuclear atypia. PanIN-IIIs are characterized by more pronounced nuclear atypia and intraluminal (in situ) cell clusters. PDAC is characterized by loss of , local cellular invasion and systemic metastasis. Bottom – most commonly associated genetic alterations associated with the various stages of PanIN  PDAC progression. Red indicates loss-of-function changes. Green indicates gain-of-function changes. Additional genetic alterations are discussed in the text and in Table 1.1 [11].

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Invasive carcinomas

From the appearance of the tumor tissue, it is evident that pancreatic cancers are extremely infiltrative neoplasms. Surgically resected tumors contain vascular and perineural invasions, which leads to metastasis to regional lymph nodes, the liver and other distant sites [7].

Early vs. late dissemination and metastasis

Two recent reports have addressed the timing of metastasis in PDAC using unique, but complementary methods [4, 12]. Nonetheless, on the surface these reports may seem to contradict, but a careful analysis reveals that they point to distinct mechanisms and timing of local and systemic PDAC dissemination/metastasis. Rhim et al. demonstrated that epithelial-to-mesenchymal transition (EMT) and dissemination of pancreatic cancer cells can occur early – even before a malignant tumor can be detected.

Using Pdx1-Cre mouse models, they generated PDA-specific mutations in the KRas and p53 genes (See Section II.B1 and Section III for more details). They fluorescently tagged epithelial pancreatic cancer cells with RosaYFP and tracked them and monitored both epithelial (E-) and mesenchymal markers (Zeb1 and Fsp1). They noticed that in in mice harboring PDA-causing mutations, E-cadherin expression decreased while Zeb1 and Fsp1 expression significantly increased in the pre-invasive PanIN lesions. This suggested that EMT was being induced in these tissues and could lead to local invasion and dissemination of these cells at early stages during disease progression. Thus, they hypothesized that mesenchymal PanIN cells may have invasive properties. In support of this, they noticed YFP+ cells had crossed the basement membrane and dissociated from any apparent pancreatic epithelial structure. This raised the question of whether

8 circulating pancreatic cells (CPCs) from PanINs seeded distant organs. Usage of YFP lineage labeling, 4/11 8- to 10-week-old mice revealed YFP+ cells seeded within the liver, suggesting that prior to tumor formation, cells found in PanIN 2 and 3 lesions are present in the circulation and are able to seed distant organs. These data taken together propose that metastasis is an early event, precedes primary tumor formation in PDAC and may lead to cells lying dormant in distant tissues – only detectable after identification of the primary tumor [12].

On the other hand, work from Yachida et al. suggested that pancreatic cancer can remain localized at the primary tumor for over a decade suggesting that early diagnosis may drastically improve the overall patient survival. They generated data by sequencing the genomes from human patient pancreatic cancer tissue (n=7). In all patients, metastasis was found in two or more sites, most commonly in the liver, lung and peritoneum.

Comparing the genomes from each patient’s tissue, they were able to identify mutations in two categories. ‘Founder’ mutations referred to mutations that were found in all samples (pancreas, liver, lung and peritoneum) from a given patient whereas ‘progressor’ mutations were all other mutations that were not present in each sample from a given patient. ‘Founder’ mutations had a mean frequency across patients of 64% while

‘progressor’ mutations had a mean frequency of 36%. This suggested that these mutations arose prior to metastatic lesion development. These data also aided the discovery of one parental clone per patient. Using single nucleotide polymorphism (SNP) chip data and the sequencing data, it was found that the majority of the homozygous mutations (89%) were present in the parental clones and they carried the typical mutations found in PDAC (KRas, TP52 and Smad4). Each parental clone gave rise to

9 many subclones, which eventually led to metastasis. Using their mathematical model, they conservatively predict that the time it takes for an initiated tumor cell to give rise to a parental clone is 11.7 ± 3.1 years. And the time a parental clone gives rise to subclones with metastatic capacity takes an additional 6.8 ± 3.4 years. Finally, they suggest the time it takes between metastasis and patient death to be 2.7 ± 1.2 years. Taken together, these data propose that the progression of pancreatic cancer from initiation to metastasis could be well over a decade, thus implying a much longer time period for early detection [4].

Highly resistant to current available therapies

Desmoplasia and vascularity

Desmoplasia, often referred to as deposition of stromal cells and (ECM), plays a major role in aiding tumor progression by inducing therapy resistance [13, 14]. The accumulation of ECM components such as collagen, fibronectin, proteoglycans, and non-tumor cells distorts the normal arrangement of pancreatic tissue; therefore, causing compression on blood vessels, which ultimately leads to failed drug delivery [13]. Additionally, stromal cells can be co-opted by the tumor cells to promote the progression of the disease. Depletion of the desmoplastic stroma may be a therapeutic option in PDAC patients in the near future. Section IV-4 below discusses this tumor compartment in more detail as it relates to cancer progression.

Because accumulation of ECM components and stromal cells compresses against blood vessels, the vasculature within pancreatic tissue is also affected. Deficiency in vasculature explains the poor delivery of drugs to tumor tissue [15]. This can also be problematic to tumor cells for growth. With limited access to vasculature, pancreatic cancer cells must rely on their ability to reprogram metabolic pathways to provide the

10 necessary ingredients to survive and proliferate [16]. This presents two problems: 1) drug delivery is inefficient due to limited access to the vasculature and 2) tumor cells are able to survive and proliferate despite nutrient and resource deprivation.

Current anti-cancer therapies

Gemcitabine

Currently, there are two FDA approved drugs normally administered together to

PDAC patients. However, there is minimal increase in survival. Gemcitabine is administered to PDAC patients as chemotherapy at all stages of the disease and it inhibits

DNA replication and repair [17]. Pancreatic cancer cells build resistance to gemcitabine.

One mechanism of resistance is autophagy in response to the drug, which prevents the cells from entering the apoptotic pathway [18].

Erlotinib

Unlike gemcitabine, erlotinib is a targeted therapy and kinase inhibitor designed to block the epidermal growth factor receptor (EGFR) and is commonly given to PDAC patients along with gemcitabine. Erlotinib can also potentiate gemcitabine- induced apoptosis [19]. Since not all PDAC patients carry the EGFR mutation, erlotinib is helpful for a small percentage of PDAC patients and even then, the cancer cells do not respond to the drug. Even with erlotinib and gemcitabine administered together, the overall survival is prolonged very minimally. Details regarding resistance mechanisms are explained in section 4.1 (Oncogene addiction) and 4.2 (Synthetic lethality). Thus, it is of great importance to discover novel therapeutic targets to fight against this disease.

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Radiation Therapy

Radiation therapy (RT) is a common treatment option for cancer patients where high-energy particles or waves directly target proliferation tumor cells and destroys them.

In this regard, RT is often times given to patients with localized tumors; however, RT can be administered for locally advanced tumors as well. For PDAC patients, RT may be a treatment option for the small population of those diagnosed at the earliest stage. Using

RT for PDAC patients has been rather controversial as results have not shown much improvement in overall survival [20].Patients with resectable tumors can receive RT before (neoadjuvant) or after (adjuvant) surgical resection. Neoadjuvant RT is the preferred route as physicians would like to see shrinkage of the tumor before surgically removing it. Often times, an invasive surgery, such as the Whipple procedure, may lead to increased mortality and morbidity rates; therefore, post-operative complications may be greater with the addition of RT. For that reason, neoadjuvant RT can minimize the degree of resection, risk of survival and rarely, eliminate the tumor. On the contrary, patients may not respond to RT and this can postpone surgery. By delaying surgical resection, this provides an opportunity for the localized tumor cells to spread. Adjuvant

RT bypasses this risk because the localized tumor has been surgically removed. Adjuvant

RT is applied mainly to prevent recurrence, an event where >80% of surgically curable

PDAC patients suffer from within the first year after surgery [20]. Recurrence is the main reason for the low 5-year survival rate of patients who have had their tumor surgically removed from their pancreas [21]. More efforts should focus on neoadjuvant RT to minimize tumor size in surgically curable PDAC patients.

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Common Genetic Mutations or Alterations

Genetically Engineered Mouse Models (GEMMs)

Genetically engineered mouse models (GEMMs) for PDAC research were created to recapitulate both genetic and morphologic alterations that often lead to the development of the disease. Experiments conducted in mouse models have shown identical results to human PDAC. Over the past decade, pancreatic cancer GEMMs have garnered significant attention since they show promise for assisting in the development of new diagnostic and therapeutic approaches. Here, we discuss genes alterations that have been genetically engineered into mice and summarize what we have learned from these experiments.

KRas

Kirsten rat sarcoma (KRas) is the most commonly mutated gene in PDAC (95%)

[3, 7, 22]; therefore, GEMMs carrying the KRas mutation are heavily studied models.

Although KRas would be an ideal gene to therapeutically target, research over the last 30 years has shown disappointing results in targeting KRas, and the gene has been deemed

“undruggable.” The activating point-mutation of KRas is predominately found on codon

G12 (98%), and less commonly found on codons G13 and Q61 [23]. KRas mutations (or

KRasG12D) impair essential GTPase activity of the KRas protein by blocking its interaction with GTPase-activating proteins (GAPs) needed to hydrolyze GTP. Thus,

KRas remains constitutively active and stimulates downstream pathways to induce proliferation, metabolic reprogramming, anti-apoptosis, invasion, migration, metastasis, etc, all hallmarks of cancer progression [23]. Therefore, studying KRasG12D in GEMMs is crucial for the overall understanding of tumor development and progression of PDAC. It

13 has been shown that oncogenic KRas not only initiates PDAC, but is also necessary for tumor progression and maintenance [24-26] as well as growth of metastatic lesions [27].

As a result, it has been shown that GEMMS containing activating mutations in KRas undergo tumor initiation and progression, as seen in humans.

p53

p53 is a tumor suppressing which is inactivated in 75% of

PDAC cases [7, 22]. p53 can promote arrest by regulating the G1/S checkpoint, maintain G2/M arrest and initiate apoptosis [7]. Therefore, loss of function of this key cell cycle regulator can lead to uncontrollable cell division and tumor progression.

Hingorani et al. demonstrated in GEMMs that initiated pre-invasive disease by KRasG12D develops into an invasive and metastatic disease by the additional inactivating point- mutation in TP53, Trp53R172H, which mirrors human PDAC [28]. Wagner et al. created a model in which mice spontaneously develop pancreatic cancer by the over-expression of

TGFα [29]. When the p53 mutant is introduced to these mice, tumor development is accelerated and mice develop invasive carcinomas [30]. Mouse models carrying the p53 mutation have illustrated the importance of its loss of function in PDAC progression such that upon mutation of the gene, the disease becomes invasive and metastatic.

p16

CDKN2A (p16 or Ink4) is the most commonly inactivated (via mutation) tumor suppressor in PDAC and normally arises during PanIN-2 [7, 22]. Like p53, p16 has important functions in regulating the cell cycle as it inhibits cell cycle progression through the G1-S checkpoint [7]. Loss of function of this gene is found in over 90% of

PDAC cases [7, 22]; therefore, generating a mouse model carrying this mutation was

14 important in the overall understanding of pancreatic cancer progression. Aguirre et al. generated the mouse model Ink4a/Arflox to sustain Cre-mediated removal of exons 2 and

3 to exclude p16INK4A and p19ARF at the protein level. They noticed that when Ink4a inactivation was combined with KRasG12D, there was a rapid progression in local invasion and growth; whereas when either gene was mutated alone, this phenotype was not observed [31]. They also demonstrated similarities between murine pancreatic tumors to human pancreatic adenocarcinoma on the tissue level; thus, suggesting a parallel progression model between murine models and human disease.

Smad4 and Tgfbr2

Smad4 (DPC4), another tumor suppressor and transcription factor, mediates

TGFβ signaling, which has important growth-inhibitory effects by regulating specific target genes [32]; thus, loss of this signaling pathway may cause cells to grow inappropriately due to deregulation [33]. Smad4 is deleted in 55% of PDAC cases normally during late PanIN-3 lesions [7, 22]. Smad4 deletion has also been suggested to not occur until PDAC [34]. Thus, Smad4-dependent TGFβ signaling can provide tumor cells an advantage in growth in the absence of Smad4. Smad4 deletion alone does not impair normal pancreatic growth, development or function. It has also been shown that

Smad4 deletion alone does not initiate pancreatic cancer [32]. Because we know

KRasG12D initiates PanIN formation, and development of PDAC requires additional mutations, Smad4 deletion has been shown to contribute to pancreatic cancer progression when combined with activating KRas mutations in the murine pancreas. Bardeesy et al. demonstrated that Pdx1-Cre Smad4lox/lox along with Pdx1-Cre LSL-KRasG12D rapidly developed into intraductal papillary mucinous neoplasias (IPMN), identical to IPMNs

15 found in humans. They also showed that PDAC development was accelerated upon

Smad4 deletion in KRasG12D INK4A/ARF heterozygous mice. At the same time, they observed that cells from these animals responded to TGFβ treatment by increasing their proliferation [32]. In related work, deletion of the TGFβ Type-II receptor (TβRII) – a mutation that is found in a subset of PDAC patients - promotes tumor progression in

GEMMs. Ptf1a-Cre; Tgfbr2flox/flox mice demonstrated that blockage of TGFβ-signaling was insufficient for pancreatic development and did not initiate pancreatic tumor formation. However, when combined with the KRas-activating mutation there was a rapid progression to PDAC that resulted with a median survival of 59 days. Symptoms such as weight loss, bloody ascites and jaundice were observed in these mice, which recapitulate symptoms found in human PDAC patients [33]. We have learned that the canonical TGFβ-signaling pathway is essential for normal pancreatic tissue growth regulation such that deletion of either Smad4 or Tgfbr2 contributes significantly to PDAC progression.

MUC1

MUC1 is a expressed on the apical surfaces of normal glandular epithelia and functions to bind pathogens and is involved in cell signaling. In PDAC,

MUC1 is unusually glycosylated and is found to be overexpressed in over 60% of PDAC cases, normally in high-grade PanINs [35]. Therefore, MUC1 can play an important role in tumor progression. Using a KRasG12D mouse model to express MUC1 in levels similar to that in human tumors of the pancreas, Tinder and colleagues observed PDAC progression at accelerated rates relative to KRas and MUC1 mutations alone. The weight of the tumor, levels of metastatic lesions and cell proliferation were greater in the double

16 mutant mice, suggesting that overexpression of MUC1 in the context of KRasG12D leads to accelerated tumor progression [35]. In another study conducted by this same group, it was shown that MUC1 overexpression induced EMT, which led to an increase in invasion and metastasis. Genetic deletion of MUC1 also significantly reduced EMT [36].

They also demonstrated that MUC1-null mice had slower tumor progression and metastatic rates along with less tumorigenic capacity [37]. MUC1 has shown clear implications of its oncogenic capabilities in mouse models. Mechanistically, more recent work has implicated MUC1 in the regulation metabolism and hypoxic response via its regulation of HIF1 expression/stability and activity. Notably, this role for MUC1 connects the cellular microenvironment to metabolism within the epithelial tumor cell compartment [38].

Lkb1

Germ-line mutation in the Lkb1 gene have been shown to induce hereditary intestinal polyposis syndrome, more commonly known as Peutz-Jeghers syndrome, which increases the risk of developing pancreatic cancer by more than 100-fold [39, 40].

Lkb1 can regulate cell growth and apoptosis and can activate adenosine monophosphate- activated protein kinase (AMPK) to negatively regulate rapamycin kinase [41].

Therefore, generating a mouse model for this was essential for our general understanding of PDAC. Hezel et al. demonstrated that Pdx1-Cre; Lkb1flox/flox mice developed pancreatic serous cystadenomas. Also, mice lacking Lkb1 had impaired acinar cell structure that often led to acinar-ductal metaplasia (ADM). Although PDAC was never observed in these models, precursor lesions were seen with high frequency

[42]. In the context of KRas-activating mutations, the additional mutation of Lkb1 (Pdx1-

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Cre; KRasG12D; Lkb1flox/+) resulted in a median survival rate that was dramatically reduced relative to the respective single-mutants. Additionally, the number of PanIN lesions were significantly greater in the double-mutants. It was also shown that homozygous loss of Lkb1 (Pdx1-Cre; Lkb1flox/flox) led to pancreatic tumor development in 100% of the mice and further decreased median survival. Although homozygous loss of Lkb1 led to PDAC, it was demonstrated that loss of a single allele was sufficient in

PDAC progression [43]. To conclude, Lkb1 is a critical tumor suppressor gene such that a single deletion of an allele can lead to PDAC precursor lesions and eventually to PDAC in the background of activated KRas.

Src

The tyrosine kinase Src has been fundamentally linked with cellular transformation and changes in its activity can influence proliferation, invasion and migration [44]. Although Src is usually not mutated in PDAC, its overexpression is seen in 70% of the cases [45]. Therefore, Src’s activity due to constantly being active suggests that its regulation is disrupted during tumor progression. One way that Src can be activated is through reduced abundance or activity of C-terminal Src Kinase (CSK).

CSK’s of Src at Y527 enables Src to be continuously active [46]. Shields et al. generated a mouse model with depleted CSK in the context of mutated KRas: Pdx1-

Cre; LSL-KRasG12D; CSKf/f. Precursor lesions were detectable as early as 3 weeks in these mice and PDAC was seen in 5-8 weeks with a median survival rate of just 6 weeks.

Also, mice harboring deletion of CSK alone did not develop precursor lesions. Thus, the data suggests that Src accelerates pancreatic tumor progression in a KRas-dependent manner and that activation of Src alone is not sufficient to initiate pancreatic cancer [47].

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EGFR

The epidermal growth factor receptor (EGFR) family contains four transmembrane receptors that have specific roles in the cell. EGFR (ERBB1 or HER1) can either homodimerize or heterodimerize with other ERBB receptors when it binds one of its seven ligands, most commonly epidermal growth factor (EGF) or TGFα. Upon binding and dimerization, many signaling pathways are activated intracellularly

[48]. Currently, the EGFR antagonist erlotinib is the only FDA approved targeted therapy for pancreatic cancer but has shown nominal effects on increasing patient survival because of acquired resistance to the drug. Although EGFR mutations are found in less than 3% of PDAC cases [7, 49], its presence has been demonstrated to be extremely crucial for pancreatic cancer progression. IHC staining revealed no staining of EGFR in normal pancreatic tissue in mice; however, there was significantly more staining in metaplasia, pancreatitis, PanINs [50] and PDAC [49]. Navas et al. showed that EGFR signaling is required for acinar-ductal metaplasia (ADM) in the presence of KRas activating mutations. In Elas-KRasG12V; Egfrlox/lox mice, PanIN lesions or PDAC were not observed; in contrast, mice harboring the KRas-activating mutation with a wild-type

EGFR or a single depleted allele displayed many PanIN lesions that eventually led to

PDAC. To further understand the necessity of EGFR, it was also shown that additional mutation of p16, which is a commonly mutated tumor suppressor in PDAC, did not progress in PanIN formation in KRas-activating and EGFR depleted mice. However, this effect was not reproduced when p53 was inactivated demonstrating that EGFR functions independent of p53 in most pancreatic cancers [49].

19

In vitro and in vivo xenograft studies

ErbB2

EGFR can also dimerize with ErbB2 (HER2). This heterodimerization leads to tumor cell proliferation, adhesion, migration [51] and suppress apoptosis [52]; therefore, if not regulated properly, ERBB2 can lead to uncontrollable cell growth and metastasis in pancreatic cancer. Unlike other malignancies in which ERBB2 is known to contribute to cancer initiation and progression, it is amplified in only 2% of PDAC patients [52]. Yet, its expression is upregulated in over 50% of patients suggesting that this receptor may be an important target for therapeutic intervention in PDAC [48]. Like EGFR, ERBB2 has shown increased staining in PanIN lesions compared to normal pancreatic tissue [50].

Since drugs targeting both EGFR (e.g., erlotinib) and ERBB2 (e.g., lapatinib and trastuzumab) already exist, the idea of dual inhibition of these EGFR kinases was investigated. Studies have shown promise of dual inhibition of EGFR and ERBB2, yet the extent of this benefit and how this regimen may be combined with gemcitabine-based therapy is not clear [53, 54]. Interestingly, our group previously reported on the effect of

ERBB2 knockdown in PDAC cell growth and metastasis in vivo. Surprisingly, we discovered that when ERBB2 was silenced or suppressed with the drug trastuzumab, cellular levels of the novel kinase PEAK1 and Src activity were increased and the cells were more aggressive – decreasing the time to pre-morbidity by almost three weeks [55].

Notably, this effect could be reversed by blocking PEAK1 kinase.

PEAK1

Pseudopodium-enriched atypical kinase one (PEAK1), or SKG269, has been recently identified as a potential biomarker and therapeutic target for PDAC [56]. This

20 catalytically active non- can control cell migration by localizing to integrin-mediated focal adhesions and stress fibers by regulating the and altering cell shape [55]. PEAK1 can also receive signals via growth factors, which can promote proliferation, survival and motility, all important characteristics of tumor progression. Of importance, PEAK1 is seen to be overexpressed in PDAC, along with other malignancies, and its upregulation has been shown to be driven by oncogenic KRas via Src-dependent transcriptional regulation [55]. In vivo, mice carrying tumor cells with

PEAK1 knocked down (shPEAK1) displayed smaller tumors, less metastasis to many distant organs and longer survival relative to parental and shControl mice [55]. Since targeting KRas has shown little-to-no success, targeting downstream effectors may be the next option. Since PEAK1 upregulation is driven by oncogenic KRas, PEAK1 can be a therapeutic target since its expression has been shown to be necessary for pancreatic tumor progression. In this regard, we’ve also recently reported that PEAK1 translation is dependent upon the post-translational hypusination of eIF5A. Furthermore, targeting the hypusination enzymes DOHH and DHPS with CPX or GC7, respectively, can block

PEAK1 protein production and tumorigenic properties in pancreatic cancer cells [57].

HIF1α

Hypoxia-inducible factor 1-α (HIF1α) has been shown to be overexpressed in pancreatic cancer and functions to promote tumor progression [58]. Once cells are deficient of oxygen, HIF1α induces transcription of important genes involved in angiogenesis and erythropoiesis. This increases oxygen delivery in areas deprived of oxygen [59]. Thus, HIF1α is necessary for tumor cells to proliferate and survive by increasing oxygen delivery and is therefore overexpressed in many types of cancers. At

21 the same time, overexpression of HIF1α also increases drug resistance. With that said, targeting HIF1α for therapy can be promising. Zhao et al. showed that inhibiting HIF1α and with simultaneous gemcitabine treatment significantly enhanced anti-tumor effects in

PDAC both in vitro and in vivo [60, 61]. Table 1 summarizes the most commonly studied genes in PanIN-to-PDAC progression.

22

Gene Genetic Alteration Role in PDAC

KRas Activating mutation An oncogene that promotes proliferation, metabolic reprogramming, anti-apoptosis, invasion, migration [23]. p53 Inactivation Deregulation of the G1/S checkpoint/G2/M arrest and apoptosis because of its inactivation[7]. p16 Inactivation Deregulation of the G1/S checkpoint because of its inactivation[7].

SMAD4/ Deletion/Deletion Growth-inhibitory effects are deregulated because of TRII the absence of either gene[32, 33, 62].

BRCA2/ Inactivation/Inactivation Repair of damaged DNA and disposal of irreparable BRCA1 DNA is deregulated due to the inactivation of either gene[7].

Notch2/ Overexpression/Deletion Notch2 regulates PanIN progression by increasing Notch1 signaling[63]. Notch1 depletion increases cell proliferation[64].

- Activating mutation Promotes SPN formation[65] by mediating Wnt- targeted gene expression[66].

MUC1 Overexpression Increases tumor weight, induces proliferation, metastatic rates and EMT[37].

RAC1 Overexpression Induces ADM formation and PDAC progression[67].

LKB1 Inactivation Deregulation of cell growth and apoptosis which leads to ADM formation and PDAC progression [41, 42, 50].

Ikk2/ Upregulation/Deactivation Ikk2 regulates PanIN progression by inducing the PTEN Notch and NF-B pathways [54, 58, 68, 69]. PTEN depletion activates the NF-B and PI3K/AKT pathways[70].

SRC Overexpression Induces proliferation, invasion and migration that leads to PanIN formation and PDAC progression[44, 47, 59].

EGFR Overexpression Activates the PI3K/AKT pathway[71] and induces ADM and PanIN formation[72].

23

ERBB2 Amplification/Upregulation Heterodimerization with EGFR increases proliferation, adhesion, migration[51] and suppresses apoptosis [52].

PEAK1 Overexpression Increases cell migration by regulating the cytoskeleton and alters cell shape. Increases proliferation, survival and motility [55].

HIF1 Overexpression Induces angiogenesis and erythropoiesis by regulating target genes to increase oxygen delivery, leads to increased proliferation and survival [59].

Table 1.1: Common genetic alterations and their roles in PDAC [11].

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Oncogene Addiction, Synthetic Lethality, Stem Cells and Stroma in PDAC

Oncogene addiction

Cancer cells often times depend on specific for their growth and survival. This concept is known as “oncogene addiction” [71]. Targeting a single oncogene that drives tumor cell survival would be the most ideal approach for defeating malignancies; however, there has been little success with this approach. Because these tumor cells are exceedingly reliant on specific oncogenes, it has been predicted that these genes or cells carry second-site mutations prior to drug treatment that ultimately lead to drug resistance [72]. Erlotinib, a tyrosine kinase inhibitor, targets oncogenic epidermal growth factor receptor (EGFR) and is commonly administered with gemcitabine to

PDAC patients. Overall, survival minimally increases as patients acquire resistance. One potential mechanism for this acquired resistance may be the “addiction” pancreatic cancer cells have for EGFR such that a second-site mutation substitutes for EGFR’s oncogenic functions. In the case of lung cancer, a second-site mutation of T790M in

EGFR was found in 63% of patients with acquired resistance [73]. It has also been shown that patients with colorectal cancer acquire resistance towards cetuximab, a common

EGFR inhibitor for metastatic colorectal cancers patients, due to an acquired S492R mutation on the extracellular domain of EGFR [74]. Alternatively, oncogene addiction can also lead to activation of specific tumorigenic pathways, which ultimately lead to tumor cell survival and drug resistance. EGFR can activate the PI3K/AKT pathway, which induces proliferation and inhibits apoptosis [48]. It has been shown that this pathway remains active despite EGFR being inhibited. Guix et al. illustrated in squamous cell cancer that the insulin-like growth factor 1 receptor (IFG1R) pathway had increased

25 activity and was responsible for the increased PI3K/Akt activity during EGFR inhibition.

Inhibiting both EGFR with gefitinib and IFG1R with an antibody in a mouse model inhibited PI3K/Akt signaling, reduced cell growth and tumor size [75]. It is evident that tumor cells acquire oncogene addiction and developing multi-targeted therapies may be required for treatment.

Synthetic lethality

Synthetic lethality refers to cell death when two or more genes are mutated at the same time; however, if either gene is mutated alone, the cells remain viable [76]. Because of oncogene addiction, we have learned that pancreatic cancer cells depend on EGFR function for growth and survival such that upon inhibition of this oncogene using erlotinib, second site mutations arise and pathways regulated by EGFR remain active.

Therefore, targeting EGFR and downstream effectors simultaneously may be required to induce synthetic lethality. Combination therapy of gemcitabine and erlotinib has been well studied and is currently the most common therapeutic intervention for pancreatic cancer. Although the prolonged survival is statistically significant compared to gemcitabine alone, combination therapy of gemcitabine and erlotinib increases the survival rate minimally. Miyabayashi et al. demonstrated that gemcitabine induces

MAPK signaling and this activity increases expression of EGFR in PDAC cells grown in a mouse model harboring KRasG12D and Tgfbr2KO mutations. When introducing erlotinib in this context, EGFR activity was inhibited; however, gemcitabine continued to induce

MAPK signaling which led to elevated EGFR levels. Therefore, a MAPK kinase (MEK) inhibitor was added to inhibit MAPK activity and gemcitabine-induced MAPK signaling and EGFR expression was reduced [77]. Thus, targeting downstream effectors, such as

26

MAPK, along with targeting EGFR may improve overall survival. Similarly, Diep et al. showed that combination treatment of erlotinib and two MEK inhibitors displayed significant synergistic effects and increased antitumorigenic activity [78]. Another potential gene that is synergistically lethal with EGFR inhibition is the S6 kinase ribosomal protein S6 kinase 2 (RPS6KA2). Activated RPS6KA2 has been observed to be downstream of EGFR/RAS/MAPK/ERK signaling and has been shown to be activated by

EGF, independent of KRas mutations. Knockdown of RPS6KA2 led to apoptosis only in the presence of erlotinib and increased erlotinib’s effect on tumor cell survival [79].

Identifying additional genes for targeted therapy along with erlotinib is essential for increasing overall survival of PDAC patients. By inducing synthetic lethality, patient survival is likely to increase.

Stem cells

Cancer stem cells (CSCs) have recently been reported as potential therapeutic targets; however, they are difficult to target because of the small percentage of the tumor that they occupy. Typically, CSCs inhabit less than 5% of total tumor cells, innately have enormous proliferative potential [80] and can be resistant to standard therapies. Such drugs given to induce cell death can temporarily result in tumor reduction; however, this approach can easily neglect the limited number of CSCs – consequently, the tumor builds resistance against these drugs and can relapse [81]. As a result, CSCs survive and can regenerate the tumor in true stem cell fashion. In an immunocompromised mouse model, human pancreatic cancer cells were xenografted and a highly tumorigenic subpopulation of pancreatic CSCs were identified using stem cell markers CD44, CD24 and epithelial- specific antigen (ESA). CD44+CD24+ESA+ cells displayed a 100-fold increased

27 tumorigenic potential compared to nontumorigenic cells, stem cell properties such as self- renewal and differentiation, as well as increased expression of the developmental signaling molecule sonic hedgehog, which is commonly upregulated in pancreatic cancer.

Interestingly, mice injected with as little as 100 CD44+CD24+ESA+ cells led to pancreatic cancers indistinguishable from those observed in humans [80]. Similarly, Hermann et al. demonstrated that CD133+ pancreatic CSCs displayed highly tumorigenic characteristics in xenografted mouse models. As few as 500 CD133+ CSCs injected into mice were able to induce orthotopic tumor formation, whereas 1 million CD133- CSCs did not display tumor presence. Importantly, the group was able to show that CSCs do not represent a homogeneous population of tumor-initiating cells; rather, specific migrating CSCs were critically involved in metastasis. Of these cells, CXCR4 was seen to be expressed and depletion of this gene abolished metastatic activity of pancreatic cancer cells [82].

Because CSCs are limited in number, perhaps targeting stem cell regulators may be more beneficial. Nodal and Activin, members of the TGFβ family, are essential regulators of human embryonic stem cell fate [83]. Inhibiting/targeting their receptors ALK4/7 reversed CSC self-renewal and tumorigenic capacity. However, targeting this receptor is challenging because of the extensive stroma that is often present in pancreatic cancer, as well as the pleiotropic effects that these receptors play in other tissues. As mentioned above, the sonic hedgehog pathway has been observed to be upregulated in pancreatic cancer, especially in stromal cells. Therefore, inhibiting stroma-specific molecules can increase drug delivery to tumor cells. With that said, inhibition of both the hedgehog pathway and ALK4/7 has been suggested as a therapeutic strategy for targeting pancreatic CSCs [84].

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Stroma

Inhibiting stroma-specific molecules; however, remains an area of intense interest. The surrounding stroma of PDAC tumors constitutes the majority of the tumor mass and PDAC is one of the most stroma-rich cancers. PDAC stroma consists of many extracellular matrix components, fibroblasts, myofibroblasts, pancreatic stellate cells, immune cells, blood vessels, cytokines and growth factors [13]. In theory, targeting

PDAC stromal components should reduce tumor growth and potentially aid in drug delivery. Olive et al. showed that inhibiting hedgehog signaling enhanced gemcitabine delivery in a mouse model that led to increased survival. They inhibited a downstream hedgehog effector Smoothened (Smo), using IPI-926, and found that mice responded better to gemcitabine leading to increased survival reduced metastatic burden in the liver

[85]. On the contrary, targeting stromal components does not always lead to tumor suppression. Ozdemir et al. demonstrated that depletion of myofibroblasts in GEMMs with activated KRas and deleted Tgfbr2 led to increased tumor invasion and decreased overall survival. Myofibroblast depletion also illustrated suppression of angiogenesis, enhanced tumor hypoxia, induction of EMT, CSC-like phenotypes and an overall alteration in the ECM. Mice with depleted myofibroblasts did not show increased survival upon treatment with gemcitabine relative to control mice; rather, there were no statistical differences. The group suggested that gemcitabine may target myofibroblasts, which leads to tumor progression as previously described, and may explain a potential reason for drug resistance in PDAC patients [86]. Finally, recent evidence suggests that both the tumor epithelial and stromal compartments are enriched for metabolic activity, emphasizing that regulators of metabolism maybe new and novel targets to

29 simultaneously reduce both tumor and stromal cell contributions to PDAC [38]. In agreement with this, a recent report has demonstrated that vitamin C can target mutant

KRas cancer cells by GLUT1-/ROS-mediated inactivation of GAPDH [87]. This offers promise in PDAC, as most tumors are positive for activating mutations in KRas. It is evident that stromal targeting is a promising approach for PDAC treatment, but identifying exactly what to target requires more research.

Identifying New Diagnostic Biomarkers and Therapeutic Targets

Proteomics

Over the past decade, proteomics has quickly found its place as a useful technique for identifying potential biomarkers or therapeutic targets since this method can detect the protein and post-translationally modified protein levels [88]. Identification of such biomolecules may help in predict disease prognosis and identification of new targets; thus, in the case of PDAC, proteomics is very practical. One application of this method is to compare normal and tumor tissue from PDAC patients for global protein level differences. An increase or decrease in a protein’s level in the tumor may suggest whether a particular protein is involved in tumor progression or suppression. A potential hurdle, however, arises when isolating PDAC tissue from its microenvironment. Because

PDAC is highly desmoplastic, containing all types of cells, it becomes difficult to dissect the tumor cell versus stromal cell proteomes [89, 90]. Another similar application is to compare global protein levels between primary and metastatic PDAC tumors in order to identify the proteins that are involved in the process of metastasis [91]. McKinney et al. examined protein levels from cells derived from primary pancreatic tumors and compared

30 them to the protein levels from cells derived from metastatic sites. By using standard proteomic methods, they analyzed their data through bioinformatics and were able to identify 540 specific proteins uniquely expressed in the primary tumor cells and 487 in metastatic cells. Of these proteins, the differential levels of 134 of them were statistically significant between the two populations [92]. If we further look into these 134 proteins, we may be able to better understand which proteins are responsible for metastasis and potentially develop drugs targeting these proteins. For a disease like PDAC, which has a dire need for novel biomarkers and therapeutic targets, identifying proteins via proteomics holds great promise for addressing many unanswered questions.

Secretomics

A branch of proteomics is secretomics, which analyzes all of the secreted proteins from cells. Secreted proteins play important roles in cell signaling and are pivotal for priming tumor cells to disseminate during metastasis [93]. Analysis of secretome samples from pancreatic cancer cells or pancreatic juice are most accessible; therefore, studies should be conducted using these samples. One quantitative method for identifying proteins via mass spectrometry is known as stable isotope labeling with amino acids in cell culture (SILAC). Gronborg et al. used this technique to identify 145 differentially secreted proteins with greater than a 1.5 fold change from pancreatic cancer-derived cells compared to normal pancreatic epithelial cells. Of these proteins, many were indicated to have altered expression prior to their work; however, new proteins were discovered with altered expression [94]. These novel proteins could serve as biomarkers as they are secreted at different levels in PDAC cells. Studying these proteins, among others, could provide valuable information at the different steps of PDAC progression. Currently, the

31 best available biomarker of pancreatic cancer is CA 19-9, a secreted protein that is used to screen for in pancreatic cancer in patients [93, 94]. Another approach in identifying proteins from the secretome is by investigating exosomes from tumor tissue. Exosomes are membrane-enclosed vesicles that carry proteins and nucleic acids and are secreted into the circulation by all cells [95]. Analyzing the contents within cancer-specific exosomes can provide insight on proteins involved in metastasis. At the same time, these proteins may also serve as biomarkers for early detection of cancer. Using mass spectrometry analyses, glypican-1 (GPC1) was recently discovered to be an enriched cell- surface protein on pancreatic cancer cell-derived exosomes. GPC1+ circulating exosomes were significantly higher in patients with PDAC relative to healthy individuals.

Interestingly, wild-type KRas was present in both GPC1+ and GPC1- circulating exosomes whereas mutant KRas was detected only in GPC1+ circulating exosomes; thus, further validating the specificity of GPC1+ circulating exosomes to PDAC. When comparing the specificity and sensitivity of the standard PDAC biomarker CA 19-9 to

GPC1+ circulating exosomes, it was shown that CA 19-9 levels were high in the sera of patients with benign pancreatic disease and patients with PDAC relative to healthy individuals. On the other hand, GPC1+ circulating exosomes were elevated in patients with PDAC only and that GPC1+ circulating exosome levels were similar in healthy individuals compared to patients with benign pancreatic disease. At the same time, CA

19-9 expression was indistinguishable between sera from healthy individuals and patients with pancreatic cancer precursor lesions while GPC1+ circulating exosomes were significantly higher in patients with precursor lesions relative to healthy donors [96].

Thus, GPC1+ circulating exosomes provides more specificity and sensitivity as a

32 biomarker for patients with PDAC relative to CA 19-9. It is evident that the secretome can offer useful information in regards to identifying novel proteins that may serve as biomarkers or therapeutic targets and further research should focus in this area.

Sequencing

Determining the order of nucleotide bases across the genome or exome is known as sequencing. Sequencing can be done on DNA or RNA. Similar to proteomics, these methods may be used to compare genomic or transcriptomic differences between PDAC tissue and normal pancreatic tissue. These methods enable researchers to identify gene mutations and isoform preferences, in addition to overall expression patterns. If an oncogenic mutation is identified, drugs may be developed to target the site of mutation.

Since not all of the cells in the organism will carry this mutation, the specificity of the drug to target the tumor cells will increase while limiting off-target effects. Like proteomics, sequencing becomes difficult when isolating PDAC tissue from its microenvironment because of the vast amounts of stromal cells and inflammatory cells

[89, 90]. In a recent publication, Witkiewicz et al. used microdissection techniques to separate pancreatic tumor cells from its microenvironment in 109 surgically resected pancreatic cancer patients. These cells were subjected to whole-exome sequencing and new mutations were discovered using multiple sequencing techniques. Mutation

Significance of covariance (MutSigCV) is a technique used to identify mutations from patient tumor issue [97]. MutSigCV analysis of these tumor cells resulted in 24 significantly mutated genes that occurred in >3.5% of the cases. Some of these mutations have been previously described, such as KRas, TP53, CDKN2A and Smad4; however, novel genes such as BCLAF1, IRF6, FLG, AXIN1, GLI3 and PIK3CA were identified to

33 be significantly mutated using this sequencing approach. Interestingly, the group was able to discover the amplification of MYC, which correlated with poor outcome [98].

Sequencing techniques can provide valuable information for discovering novel therapeutic targets.

Circulating Tumor Cells

Circulating tumor cells (CTCs) are cells derived from a primary tumor that have entered the vasculature and circulate within the blood stream looking to seed in distant organs [99]. CTCs appear in extremely low frequencies in the blood; about 1-10 CTCs are found within 1 mL of blood [100]. From the total amount of CTCs that escape the primary tumor, only 0.01% develops into metastases [101]. This minute amount of CTCs found in the blood makes it difficult to detect their presence; however, if identified, CTCs can be essential biomarkers for cancer initiation and progression. A common method used for detecting CTCs is by using antibodies against antigens found on the cell surface of these cells [102, 103]. Epithelial molecule (EpCAM) is the marker that is used to detect CTCs as expression of EpCAM in blood cells is absent. CellSearch

(Veridex) is an FDA approved method that uses ferrofluids enriched with EpCAM antibody-coated magnetic beads to capture CTCs. Once fixed, the cells are visualized by staining for antibodies for epithelial cytokeratins 8, 18 and 19. Staining for CD45 is also required as it is a marker for leukocytes and is used as a negative control. Cells that positively stain for the cytokeratins and do not stain for CD45 are classified as CTCs

[102, 103]. A study to detect CTCs in multiple cancers was conducted using this method.

CTCs found in prostate and breast cancers were significantly greater than those found in pancreatic cancer. Albeit CTCs from pancreatic cancer were detected, their levels were

34 comparable to CTCs found in nonmalignant diseases [104]. Thus, a drawback using this method for CTC detection in pancreatic cancer is its low sensitivity and it is EpCAM dependent. Aside from detecting CTCs, fluorescently tagging these cells and tracking them in a mouse model could provide valuable information on their role in cancer progression. Rhim et al. traced CTCs in mice by tagging yellow fluorescent protein

(YFP) to PanIN and PDAC cells. They also tagged EMT markers and concluded that

CTCs from PanIN and PDAC animals were phenotypically similar and largely maintained a mesenchymal phenotype. This was confirmed by qPCR from isolated YFP+ cells [12]. Although the group was able to track CTCs in a mouse model, the issue arises in clinical settings. If we can improve antigen-specificity to PDAC, we can use antibodies specific to the CTCs to detect them; therefore, newer technologies are required for better detection of the limited amount of CTCs present in the blood with higher specificity to pancreatic cancer. Table 2 summarizes the advantages, disadvantages and potential therapeutic options for the methods of identifying novel diagnostic markers and/or therapeutic targets.

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Method Advantages Disadvantages Potential Uses Proteomics Insight on proteins required for Isolating cells from -Biomarkers tumor progression/metastasis microenvironment is -Therapeutic [90, 91, 101]. difficult[88, 89]. targets

Secretomics -Samples are very accessible. Most secretome Biomarkers [92] studies are performed -Proteins enriched in secreted in vitro. [94] exosomes may provide insight for metastasis. [95] Sequencing -Identify specific mutations on Isolating cells from Therapeutic the genomic and transcriptomic microenvironment is Targets levels. difficult [88, 89]. -Increased specificity for targeting to minimize off-target effects. Circulating Provides useful information for -EpCAM Biomarkers Tumor Cells metastasis[12]. dependent[103]. (CTCs) -Appear in extremely low frequencies[99, 100] Table 1.2: Methods for identification of new diagnostic biomarkers and therapeutic targets.

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Concluding remarks

Research on pancreatic cancer has vastly increased over the past few decades, yet the survival rate has remained rather low. Advancements in technologies have provided us a “blue-print” of PDAC initiation, development and progression. This has given us the opportunity to identify potential biomarkers and therapeutic targets for this malignancy; however, more studies are required for definitive treatment options. Due to the lack of noticeable symptoms while the tumor is localized to the pancreas, a “trace-back- footsteps” approach ought to be implemented such that future studies should focus on metastatic mechanisms that come with pancreatic cancer since the majority of PDAC patients are diagnosed at this stage. By identifying genes required for metastasis, working backwards towards regulators of these genes can explain what triggers cells to metastasize from the lymph nodes. Once this is accomplished, we will have a better understanding of what genes are required for tumor cells to spread from the pancreas.

And this “moving-backwards” method may eventually identify the cell-of-origin for this lethal disease.

Implications

Pancreatic ductal adenocarcinoma (PDAC) is an extremely invasive and migratory form of cancer. The aggressive behavior of the tumor cells within the ductal epithelia lead to dissemination and metastasis, which results in poor overall survival in patients diagnosed with this disease. Because metastasis is the leading cause of death in most cancers, it is imperative to discover novel regulators of this phenomenon to prolong overall survival, specifically in PDAC. Therefore, we reasoned to study proteins enriched in the pseudopodial structures of cells as these regions are necessary for cell migration, which translates to metastasis in the context of cancer. Furthermore, we analyzed the 819

37 pseudopodial enriched proteins (PDE) as previously described by Wang et al [105].

Through various criteria, we were able to identify integrin alpha 1 (ITGA1) as a potential biomarker and/or therapeutic target in PDAC. We further characterized its role and function in this lethal malignancy. We identified that ITGA1 is required for collagen- induced cell-spreading as well as TGF-induced EMT. Furthermore, we report for the first time that collagen and TGF cooperatively induce EMT in PDAC. Because ITGA1 is enriched in the pseudopodia, it is upregulated in pancreatic cancer, the role it plays in mediating cell-spreading and EMT, we believe ITGA1 may be a viable biomarker and therapeutic target in pancreatic cancer.

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Chapter 2: Materials and Methods

Cell Culture: BxPC3, FG, PANC1 and MPC2 cells were cultured in

DMEM/High-Glucose growth media supplemented with 10% FBS, pen/strep and gentamicin. Cells were maintained in an incubator at 37 degrees Celsius and 5% CO2.

Quantitative PCR: RNA was purified from whole cells using the Thermo

Scientific GeneJet RNA purification following the instructions provided. NanoDrop was used to determine the respective RNA concentrations as well as purity. The Thermo

Scientific cDNA synthesis kit was used to synthesize cDNA using 100ng of template,

Maxima Enzyme Mix and 5X Reaction Mix. The samples were run through a thermocycler according to the kit specifications. NanoDrop was used to determine the respective cDNA concentrations as well as purity. The cDNA samples were diluted to

22.5ng/mL to perform qPCR. Primers were purchased from Integrated DNA technologies and used at a concentration of 10nmol/mL. 8.75L of nuclease-free water was mixed with 2.5L of diluted cDNA, 1.25L of gene-specific primer and 12.5L of Thermo

Scientific Maxima SYBR Green. Samples were ran on an ABI7300 instrument.

Western Blot: Cells were lysed with Stringent RIPA Buffer and rotated at 4 degrees Celsius for ~3-4hours. Lysates were precleared by centrifugation and the protein concentration was determined via a Bradford Assay. 4-12% Bis-Tris gels were ran and transferred to nitrocellulose membranes. The membranes were probed at 4 degrees

Celsius overnight with indicated antibodies with the following dilutions: ITGA1 (abcam

1:1000), E-cadherin (cell signaling 1:1000), -Actin (1:1000). Secondary antibodies were used at 1:10,000 dilutions.

39

Lentiviral Transduction: FG and PANC1 cells were plated at 1.6x10^4 cells/well into a 96-well plate and left to attach overnight. Cells were then treated with 10uL of viral particles containing a puromycin resistant pKLO.1 vector with a scramble shRNA,

ITGA1-specific shRNA (3’-UTR targeting) in 110L of complete media and left to incubate for 18 hours, after which the media was changed. The following day, media was changed and supplemented with 10g/mL puromycin. Media was changed with puromycin supplemented media every 3 days until resistance was definite. Cells were then maintained in media containing puromycin (10g/mL).

Cell Morphology and Spreading Assay: 6-well plates were either uncoated

(plastic) or coated with collagen (5g/mL). PANC1-transduced cells were plated at

5x10^4 cells/mL in 2mL per well. Cells were imaged at six different positions per well at

10X magnification using phase-contrast microscopy 24 hours post-plating. All cells from each position were analyzed and were classified as spread or unspread. Percent spreading was calculated from the number of cells spread relative to the total number of cells per position. Student t-test analysis was used to determine statistical significance.

Cell Attachment Assay: 96-well plates were either uncoated (plastic) or coated with collagen (5g/mL) or fibronectin (5g/mL). PANC1-transduced cells pretreated for

48 hours with 2.5ng/mL TGFbeta or 0.1% BSA were plated at 1.0x10^4/well in 200mL serum-free media per well. Wells were aspirated, washed and refilled at the time points of

10min, 30min, 60min, 6hrs and 24hrs. After the wells were refilled, 40L of AQueous

One reagent was added to each well and incubated at 37C for 3 hours. Absorbance readings were measured at 490nm. Student t-test analysis was used to determine statistical significance.

40

Chorioallantoic Membrane Assay: White Rock Hatching Eggs were purchased from Meyer Hatchery and were incubated at 38 degrees Celsius at 60% humidity for 10 days. On the 10th day, a small perforation was made on the blunt end of the egg and a mild vacuum was applied to this area to drop the CAM. A 2x3cm window was made and a sterile ring was placed onto the CAM in an area of dense vasculature. 1x10^7 cells suspended in 20uL of matrigel were xenografted within the sterile ring. Sterile surgical tape was applied to the windowed area as well as the blunt end and the eggs were placed back into the incubator at the same conditions for 7 days. On the 7th day, the CAM was analyzed for a primary tumor, and if present, was weighed and collected. The chick was sacrificed via decapitation and liver and lung tissues were collected and either flash frozen or formalin fixed. gDNA was extracted using the Thermo Scientific GeneJET

Genomic DNA Purification Kit. Concentrations were determined by NanoDrop. Samples of gDNA were diluted to 22.5ng/mL to perform qPCR. Primers were purchased from

Integrated DNA technologies and used at a concentration of 10nmol/mL. 8.75uL of nuclease-free water was mixed with 2.5uL of diluted gDNA, 1.25uL of gene-specific primer and 12.5uL of Thermo Scientific Maxima SYBR Green. If gDNA concentrations were less than 22.5ng/mL, calculated volumes of the sample was added and less water was added to each well for compensation. Samples were ran on an ABI7300 instrument.

Bioinformatics: Human Protein Atlas was used to search for

Immunohistochemistry staining for the indicated proteins. Protein expression indicated as moderate or highly expressed were classified as ‘high’ and protein expression indicated as undetectable or low were classified as ‘low.’ Cancer BioPortal was used to look for

41

Pearson R values for indicated genes. A threshold of >0.6 was used as our cutoff point when cross-referencing ITGA1 co-expressors with TBRS genes.

Statistical Analyses: Statistical differences were determined using Student’s t-test.

*, **, and *** indicate values of <0.05, 0.01 and 0.001, respectively.

42

Chapter 3: Results

Identification of proteins involved in cell migration, motility and adhesion may provide insight about regulators of metastasis. Therefore, we investigated the proteins enriched in the pseudopodial regions as previously described by Wang et al. [105]. From the 819 pseudopodial enriched proteins (PDE), we sorted these genes through various criteria and thresholds using patient gene expression data via Oncomine. We determined the top 100 PDE genes that are overexpressed in pancreatic cancer and other malignancies (Figure 3.1A). Next, we analyzed these genes to determine the pancreatic cancer overexpression frequency (PCOF) and identified 37 genes to be overexpressed in pancreatic cancer (Figure 3.1B). We categorized these genes based on their biological processes and molecular functions (Figure 3.1C). Our focus was narrowed down to integrin-mediated signaling pathways and small GTPase mediated as these molecular functions encompass the biological processes the remaining genes are involved in. From the 37 PCOF genes, integrin alpha 1 (ITGA1) was selected as our gene of interest for three reasons: 1) ITGA1 is a cell-surface protein; therefore, targeting it would be easier than small GTPases (which are found within the cell), 2) ITGA1 has not been characterized in pancreatic cancer, 3) the pancreatic tumor microenvironment primary consists of stromal cells, of which collagen type 4 is most abundant, and it so happens to be the receptor for ITGA1. An interactome was generated with the known interactors of ITGA1 (Figure 3.1D). The steps of identifying ITGA1 are summarized in

Figure 3.1A.

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Furthermore, we used data from the Human Protein Atlas to compare protein expression of ITGA1 in normal pancreatic tissue and tissue from PDAC patients [106].

Immunohistochemical (IHC) staining confirmed ITGA1 is overexpressed within pancreatic ductal tissue in multiple patients and is further upregulated as the tumor progresses relative to normal pancreatic ductal tissue (Figure 3.2A). Additionally, we used Oncomine to compare the gene expression of ITGA1 in normal pancreatic tissue and tissue from PDAC patients. The results from Oncomine reveal ITGA1 to be expressed >3-fold in PDAC relative to normal pancreatic tissue (Figure 3.2B). Because of the method used to identify ITGA1 and its upregulation in PDAC, we decided to begin focusing our experiments on this protein and characterize its role in PDAC.

Therefore, we began our studies by first detecting our gene of interest and its expression levels within the cell line models available in our lab. We performed qPCR for ITGA1 expression on the mRNA level and performed a Western Blot for ITGA1 expression on the protein level. ITGA1 expression is highest in BxPC3 and FG cells, whereas it is lowest in MPC2 cells on the mRNA level. The mRNA levels within our cell lines correlate with the protein expression (Figure 3.2C).

Because proteins carryout functions, our studies focused on FG and PANC1 cells as they have highest expression of ITGA1 in KRas-mutant cells. Although BxPC3 cells have higher levels of ITGA1 on the protein level in comparison to PANC1, we figured that these cells may not be the best representation of pancreatic cancer because they lack mutant KRas.

Integrins are transmembrane receptors that transmit intracellular signals when they bind to their respective extracellular matrix (ECM) proteins. are

44 heterodimeric, in which a complete integrin consists of an alpha and beta subunit. They can only bind to the ECM when the subunits form a heterodimeric complex; therefore,

Figure 3.1: Identification of ITGA1 as a novel biomarker and/or therapeutic target for PDAC. (A) Flow chart illustrating the steps taken to identify ITGA1 as a biomarker in PDAC. (B) 37 pancreatic cancer over-expression frequency (PCOF) genes identified to be upregulated in PDAC and other malignancies. (C). Pie-chart categorizing the molecular functions and biological process the 37 PCOR genes shown in 2.1B fall under. (D) Interactome demonstrating the known interactors of ITGA1.

45

Figure 3.2: ITGA1 is upregulated in PDAC. (A) staining for ITGA1 in normal pancreatic tissue and pancreatic tumor tissue from a Human Protein Atlas Portal. (B) Tukey plot of relative ITGA1 mRNA levels of human tissue samples from Oncomine. (C) qPCR and Western Blot of ITGA1 in PDAC cells. POLR2A was used as the house-keeping gene for qPCR.

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integrin-mediated intracellular signaling primarily happens when the alpha and beta subunit heterodimerize and bind to an ECM protein [107].

Integrin alpha 1 (ITGA1) heterodimerizes only with (ITGB1); however, ITGB1 can heterodimerize with several alpha subunits. ITGA1 is primarily a collagen receptor, specifically, collagen types 1, 4 and 6 [107]; however, it can also bind to . Other alpha subunits, such as (ITGA2), may also bind to collagen. However, ITGA2 does not bind to collagen type 4, but ITGA1 does [108].

We began by looking for the frequency of expression in different collagen subtypes in pancreatic cancer tissue from Human Protein Atlas. We first looked for collagen type 1 (COL1) staining in PDAC patients. From the available data, we found

IHC staining from 19 different pancreatic cancer patients in which COL1 was stained for.

Of these 19 samples, 10 (53%) showed relatively high levels of COL1 (Figure 3.3A). We used this same method to look for collagen type 4 (COL4) staining from PDAC patient tissue. We came across 31 tissue samples in which COL4 was stained for. Of these 31 samples, 25 (81%) showed relatively high levels of COL4 (Figure 3.3A). This suggests that COL4 is found to be upregulated in pancreatic cancer more frequently than COL1.

We next wanted to see if any of these patients had both COL1 and COL4 stained on their tissue and found 10 samples where there was staining performed on both collagen subtypes. Of these 10 samples, none (0%) had low levels of both COL1 and COL4

(Figure 3.3B). 2 patient samples (20%) had high levels of COL1, but low levels of COL4.

4 patient samples (40%) had high levels of COL4, but low levels of COL1. Lastly, 4 patients (40%) had high levels for both COL1 and COL4 (Figure 3.3B). Confirming our

47 previous analysis, this suggests that more often than not, COL4 is upregulated more frequently than COL1. This further supported our decision to focus our studies on ITGA1 rather than ITGA2 because ITGA1 binds to both COL1 and COL4 whereas ITGA2 binds to COL1 only.

We next wanted to know what role ITGA1 plays in PDAC. Therefore, we knocked-down ITGA1 using shRNA in FG and PANC1 cells. We chose these cell lines because they had the highest levels of ITGA1 on the protein level in KRas-mutant models. We confirmed that ITGA1 was knocked down in FG and PANC1 cells on the mRNA level using qPCR and validated the knockdown on the protein level using a

Western Blot in PANC1 cells (Figure 3.4A). Because ITGA1 is a cell-surface receptor for collagen, we reasoned that plating PDAC cells on collagen would give us insight on what ITGA1 is doing in the context of cancer progression. We noticed in both FG and

PANC1 cells plated on collagen, that there was an increase in cell spreading relative to when these cells were plated on plastic (Figure 3.4B). We next asked whether the cell spreading effect we noticed was ITGA1-dependent; therefore, we repeated this experiment with PANC1-transduced lines. We observed collagen-induced cell spreading was diminished in the ITGA1-knockdown lines (Figure 3.4C). We quantified the data from Figure 3.4C by counting cells considered to be spread relative to the total number of cells per position (Figure 3.4D). A significant decrease was seen in PANC1-shITGA1(2) relative to PANC1-shScramble when plated on plastic. Most importantly, when comparing the results from these cells that were plated on collagen, we see an increase in percent spreading in PANC1-shScramble cells; however, the increase in percent

48 spreading in PANC1-shITGA1(2) and PANC1-shITGA1(4) cells remained relatively the same as they were on plastic.

Figure 3.3: COL4 is more abundant in PDAC than COL1. (A) Immunohistochemistry staining for COL1 and COL4 in pancreatic tumor tissue samples from a Human Protein Atlas Portal. (B) Quantification of panel 3.1A by cross-referencing all available tissue staining for both COL1 and COL4.

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This suggests that collagen induces cell spreading in an ITGA1-dependent matter. Lastly, we performed a cell attachment assay using the AQueous One reagent in PANC1- transduced cells plated on plastic, collagen and fibronectin at various time points. Cell attachment significantly increased in PANC1-shScramble lines relative to the knockdown lines when plated on collagen (Figure 3.4E). This suggests that collagen induces cell attachment in an ITGA1-dependent manner.

We next asked whether a decrease in cell spreading due to ITGA1 depletion would have an effect in an in vivo model. Therefore, we used a chick embryo model via the chorioallantoic membrane (CAM) assay to test this. We xenografted PANC1- transduced cells onto the CAM and allowed the cells 7 days to grow while incubating the eggs. After 7 days, we analyzed the eggs for a primary tumor, and if present, was collected and weighed. We next harvested the liver and lung for downstream gDNA extraction and qPCR for human ALU repeats relative to chicken GAPDH. We first wanted to know whether a primary tumor could form on the CAM with the cell lines we used. Our results indicate that primary tumors can form in all three PANC1-transduced lines (Figure 3.5A). Our data also suggests that ITGA1 depletion does not affect primary tumor mass or tumor take (Figure 3.5B). qPCR analysis revealed that ITGA1 does not promote metastasis in the CAM model (Figure 3.5C). We next asked whether blocking

ITGA1 using an immunoneutralizing ITGA1-specific antibody would yield different results. However, our data suggests that blocking ITGA1 does not affect the metastatic potential in these cells in the CAM model (Figure 3.5D). Nonetheless, we found it

50 interesting that ITGA1 is able to mediate epithelial-mesenchymal transition (EMT)-like phenotypes. Therefore, we wanted to know whether ITGA1 may be an indicator of EMT in PDAC.

Epithelial-mesenchymal transition (EMT) is a well characterized phenomenon and is a key component of metastasis. Epithelial cells, such as pancreatic ductal cells, carry characteristics of epithelial origin and express epithelial markers. Some of these characteristics include tight cell-cell junctions, they grow in clusters and they attach to a basement membrane. Epithelial cells express high levels of E-cadherin (CDH1), MUC1 and other epithelial markers. Mesenchymal cells lack these characteristics and they like to be more spread out. They do not attach to a basement membrane and develop protrusions (or pseudopodia) and are more invasive and migratory. They express mesenchymal markers such as N-cadherin (CDH2), ZEB1, FN1 and other mesenchymal markers. The process of EMT is when epithelial cells lose their epithelial characteristics and transition into a mesenchymal phenotype. There have been many characterized inducers of EMT, which include Twist, Wnt/-catenin, Snail, TGF and extracellular matrix (ECM) proteins [109, 110]. The transition into a mesenchymal phenotype in cancer allows cells to become more invasive and migratory; therefore, EMT predicts a more aggressive form of cancer. EMT has also been classified as a prerequisite to metastasis in some cancers. With that said, finding regulators of EMT may prevent metastasis in patients and increase overall survival.

Because we noticed an ITGA1-dependent cell-spreading effect in PDAC cells when plated on collagen, we asked whether EMT was being induced, and if so, what factors are involved in this mechanism. Therefore, we used the Human Protein Atlas to

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Figure 3.4: Collagen induces cell spreading and attachment in an ITGA1-dependent manner. (A) (Left) qPCR results in FG and PANC1-transduced cell lines confirming ITGA1 knockdown on the mRNA level. HPRT1 was used as the house-keeping gene for qPCR. (Right) Western Blot in PANC1-transduced cells confirming ITGA1 knockdown on the protein level. (B) Phase-contrast images of FG and PANC1 cells plated on plastic or collagen 1 or 7 days post-plating. (C) Phase-contrast images of PANC1-transduced cells plated on plastic or collagen 1 day post-plating. (D) Quantification of panel C. Six positions were quantified per cell-line per substrate. (E) Cell attachment assay using the AQueous One reagent at the indicated time points. Student t-test analysis was used to determine statistical significance.

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Figure 3.5: ITGA1 does not mediate metastasis in the CAM model. (A) Images of primary tumors in PANC1-transduced lines 7 days after xenografting. (B) (Left) Primary tumor weight 7 days after xenografting from PANC1-transduced lines. (Right) Tumor take percentage 7 days after xenografting from PANC1-transduced lines. (C) Relative metastasis 7 days after xenografting from PANC1-transduced lines of the liver and lung. (D) Immunoneutralizing ITGA1-specific antibody was used to block ITGA1 in parental PANC1 cells. IgG was used as a control. Human ALU repeats were used to detect human cells in the liver and lung relative to chicken GAPDH.

54 look for well-known EMT markers in ITGA1 high and low pancreatic cancer tissue samples. Our findings suggest that ITGA1 may be an indicator of EMT. At the same time, we took into consideration that since a majority of pancreatic cancer patients have a deletion of Smad4 [7, 22], a key tumor suppressor gene and mediator of TGF signaling, to focus on the effect TGF may have in pancreatic cancer progression.

We began looking for expression patterns of EMT markers in patient tissue samples from a Human Protein Atlas Portal relative to ITGA1 expression. We wanted to know whether there was a correlation between ITGA1 expression and expression of EMT markers. We noticed that in a patient with high levels of ITGA1, they had low levels of

E-cadherin (CDH1), a well known epithelial marker. In another patient, whose ITGA1 expression is relatively low, they had high levels of CDH1 (Figure 3.6A). This suggests that high levels of ITGA1 correlate with a more mesenchymal phenotype, and low levels of ITGA1 correlate with a more epithelial phenotype. We also looked at mesenchymal

EMT markers within these same patients. SERPINE1 and Vimentin (VIM) staining appear to be darker in the ITGA1 high patient relative to the ITGA1 low patient (Figure

3.6A). Alpha smooth muscle actin 2 (ACTA2), a mesenchymal marker, staining is clearly higher in the ITGA1 high patient relative to the ITGA1 low patient (Figure 3.6A). Our results indicate that ITGA1 may be an indicator of EMT in PDAC.

We next used cBioPortal to look for the top ITGA1 co-expressors in PDAC. We took this list and cross-referenced it with TGF response signature (TBRS) genes. We identified four genes to be ITGA1 co-expressors and also are TBRS genes (Figure 3.6B and 3.6C). Interestingly, PEAK1, a gene that our lab has previously reported on, is one of the four genes within this list.

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Epithelial-mesenchymal transition (EMT) can be induced by transforming growth factor beta (TGF) or extracellular matrix (ECM) proteins. Because we noticed an

ITGA1-dependent cell-spreading effect in PDAC cells when plated on collagen and that

ITGA1 is an indicator of EMT since it correlates with TGF response signature (TBRS) genes, we wanted to see the effect TGF would have on PDAC cells when plated on collagen. At the same time, we wanted to know if EMT was indeed being induced and how ITGA1 expression was effected.

We first wanted to know what effect TGF would have on our PDAC cells.

Therefore, we plated FG and PANC1 cells on collagen and treated with TGF or control.

TGF caused an EMT-like morphology shift in both FG and PANC1 cells where epithelial characteristics were lost and cell-spreading was increased (Figure 3.7A). We next wanted to see how ITGA1 expression was effected upon TGF treatment within our cell lines. Therefore, we ran qPCR for ITGA1 expression in FG cells from lysates collected 1 day and 7 days post-TGF treatment and in PANC1 cells from lysates collected 1 day and 4 days post-TGF treatment. ITGA1 expression was elevated in FG cells by day 1, and in PANC1 cells on day 1 and day 4 (Figure 3.7B). Next, we confirmed

TGF-induced EMT by performing a Western Blot for E-cadherin (CDH1). CDH1 expression significantly decreased in TGF treated cells (Figure 3.7C). We next tested whether TGF-induced EMT was ITGA1 dependent; therefore, we repeated this experiment with FG and PANC1-transduced cell lines. Although there was a shift in morphology in the ITGA1-depeleted cell lines, the change in phenotype was not as

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Figure 3.6: ITGA1 is an indicator of EMT in PDAC. (A) Immunohistochemistry staining for EMT markers in ITGA1 high (top row) and ITGA1 low (bottom row) PDAC tissue samples from a Human Protein Atlas Portal. (B) Flow chart illustrating the steps taken to identify ITGA1 co-expressors and TBRS genes. (C) ITGA1 Pearson R values in relation to the identified genes from panel B using cBioPortal.

57 drastic as it was in FG- and PANC1-shScramble lines (Figure 3.7D). Therefore, this suggests that ITGA1 is required for complete TGF-induced EMT on collagen.

We next looked at the effect TGF had on cell attachment within PANC1- transduced cells. A cell attachment assay was performed using the AQueous One reagent at various time points. TGF significantly increased cell attachment in PANC1- shScramble cells; however, this effect was not seen in either PANC1-shITGA1 lines

(Figure 3.7E). This suggests that ITGA1 is required for TGF-induced cell attachment on collagen. Lastly, we looked for gene expression changes of EMT markers in PANC1- transduced cells when treated with TGF. Fibronectin (FN1), a mesenchymal marker, increased in the three transduced lines; however, the increase in the ITGA1-depleted cells was not as high as it was in the scramble line. At the same time, MUC1, an epithelial marker, significantly decreased in PANC1-shScramble cells as expected; however, there was no significant decrease in PANC1-shITGA1 (2). Although there was a significant decrease in MUC1 expression in PANC1-shITGA1 (4), the decrease was not as much as it was in PANC1-shScramble (Figure 3.7F). Unexpectedly, ZEB1 expression was not induced in PANC1-shScramble cells when treated with TGF. ZEB1 is a well known

TBRS gene and it is unclear why its expression level was not elevated. Just as surprising,

ZEB1 expression did significantly increase in PANC1-shITGA1 cells upon TGF treatment (Figure 3.7F). Further studies are needed to explain why ZEB1 expression is not correlating with our prediction.

We next wanted to repeat the previously mentioned experiments on plastic to see whether the results we saw were collagen-dependent. TGF caused an EMT-like morphology shift in both FG and PANC1 cells (Figure 3.8A); however, induction of

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ITGA1 expression was only seen in FG cells 1 day post-TGFtreatment (Figure 3.8B). A

Western Blot confirmed EMT was induced by TGFb; however, EMT was modest in comparison to this experiment done on collagen (Figure 3.8C). ITGA1 was required for

TGF-induced EMT-like morphology shifts in both FG- and PANC1-transduced cells

(Figure 3.8D). However, TGF did not induced cell attachment within PANC1- transduced cells in the presence or absence of ITGA1 (Figure 3.8E). Lastly, TGF induced expression of FN1 in all three PANC1-transduced lines; however, the increase in

FN1 expression was not as high in PANC1-shITGA1 lines relative to PANC1- shScramble. Interestingly, MUC1 expression was not affected in any of the cell lines; however, there was a significant increase in ZEB1 expression in all three lines (Figure

3.8F). We conclude that TGF still induces EMT in PDAC cells plated on plastic; however, the effects are modest compared the effects seen on collagen.

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Figure 3.7: ITGA1 mediates TGF/collagen-induced EMT and cell attachment. (A) Phase-contrast images of FG and PANC1 cells plated on collagen 1 or 7 days post-TGF treatment (FG) and 1 or 4 days post-TGF treatment (PANC1). (B) qPCR for ITGA1 expression in FG and PANC1 cells 1- and 7-days post-TGF treatment (FG) and 1- and 4-days post-TGF treatment (PANC1) POLR2A was used as the house-keeping gene. (C) Western Blot confirming EMT induction. (D) Phase-contrast images of FG- and PANC1- transduced cells with or without TGF treatment at the end-points. (E) Cell attachment assay using the AQueous One reagent at the indicated time points with or without TGF treatment. (F) qPCR for EMT markers in PANC1-transduced cells from lysates harvested 4 days post-TGFb treatment. POLR2A was used as the house-keeping gene. Student t-test analysis was used to determine statistical significance.

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Figure 3.8: ITGA1 mediates TGF-induced EMT on plastic. (A) Phase-contrast images of FG and PANC1 cells plated on plastic 1 or 7 days post-TGF treatment (FG) and 1 or 4 days post-TGF treatment (PANC1). (B) qPCR for ITGA1 expression in FG and PANC1 cells 1- and 7-days post-TGF treatment (FG) and 1- and 4-days post-TGF treatment (PANC1) POLR2A was used as the house-keeping gene. (C) Western Blot confirming EMT induction. (D) Phase-contrast images of FG- and PANC1-transduced cells with or without TGF treatment at the end-points. (E) Cell attachment assay using the AQueous One reagent at the indicated time points with or without TGF treatment. (F) qPCR for EMT markers in PANC1-transduced cells from lysates harvested 4 days post-TGFb treatment. POLR2A was used as the house-keeping gene. Student t-test analysis was used to determine statistical significance.

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Chapter 4: Discussion

We began our studies on the basis of the urgent need for novel biomarkers and/or therapeutic targets for pancreatic ductal adenocarcinoma (PDAC), one of the deadliest forms of invasive cancers. We used previously published data of pseudopodia-enriched

(PDE) proteins to identify a protein to study in the context of PDAC progression. We discovered integrin alpha 1 (ITGA1) to be upregulated in PDAC and began identifying its role in pancreatic cancer.

We found it logical to study ITGA1 because of the abundance of collagen type 4

(COL4) in the tumor microenvironment in pancreatic cancer patients. ITGA1is the only integrin that has been shown to bind to COL4. Because PDAC is an invasive and highly metastatic disease, ITGA1 is overexpressed in PDAC patients and extracellular matrix proteins have been shown to promote morphological changes, we hypothesized that

ITGA1 mediates pancreatic cancer progression by promoting epithelial-mesenchymal transition (EMT) phenotypes.

We found that collagen induces cell-spreading in an ITGA1-dependent manner.

We reasoned that because ITGA1 is required for cell-spreading, an in vivo model should yield a decreased metastatic rate of cells depleted of ITGA1 relative to when ITGA1 is present. However, our data did not lead to the predicted results. A possible explanation for this may be the mismatched composition of integrins to ECM proteins within the

CAM. There is little research done on the ECM composition of the CAM such that it is unclear which ECM proteins are abundant within this membrane. Perhaps the CAM does not have enough collagen to induce a cell-spreading effect that we saw within our

PANC1-transduced cell lines (specifically, plated on collagen) and this could have

62 yielded in no significant difference in metastasis of ITGA1-depleted cells relative to the control. Plating these cells on collagen to induce an ITGA1-dependent cell-spreading effect prior to xenografting onto the CAM may yield in better results. At the same time, a better representative model would be to orthotopically xenograft these cells into the pancreas using a mouse model to mimic the pancreatic microenvironment and have a more accurate understanding of ITGA1’s role in PDAC metastasis.

We next wanted to know how ITGA1 is involved in EMT-like phenotypes mechanistically. Therefore, we looked for EMT markers in ITGA1 high and ITGA1 low

PDAC tissue samples. The data showed that ITGA1 may be an indicator of EMT. Next, we looked for ITGA1 co-expressors in PDAC and cross-referenced this list with known

TGF response signature (TBRS) genes. We looked for TBRS genes because TGF is a well characterized EMT inducer and the fact that a key mediator of its signaling pathway,

Smad4, is deleted in the majority of PDAC patients. We identified four genes to be

ITGA1 co-expressors as well as TBRS genes, one of which was PEAK1, a gene our lab has previously reported on in the context of TGF signaling.

Therefore, we wanted to see the effects TGF would have on our cell line models.

We conclude that TGF induces EMT on collagen and plastic; however, the effects on plastic are modest. We also show that TGF induces ITGA1 expression on collagen, but not on plastic. Lastly, we show that TGF-induced EMT is ITGA1-dependent.

Unexpectedly, ZEB1 expression did not increase in PANC1-shScramble cells on collagen when stimulated by TGF relative to the control; however, ZEB1 expression did significantly increases in both ITGA1-knock down lines after TGF treatment. ZEB1 co- expresses with ITGA1 in PDAC and is also a TGF response gene; therefore, we are

63 unclear as to why its expression did not increase in PANC1-shScramble cells on collagen, but did increase ITGA1-depeleted cells on collagen when treated with TGF. RNA- sequencing may reveal changes in expression patterns of certain genes when ITGA1 is knocked down and this may help explain the effects we saw in ZEB1.

The aforementioned indicates that ITGA1 may be a novel biomarker and/or therapeutic target for PDAC patients. Because ITGA1 is required for cell-spreading and mediates TGF-induced EMT, targeting this protein may prevent cells from becoming more invasive and less metastatic. This may lead to an increase in overall survival in

PDAC patients. We propose a model indicating a collagen-mediated, TGF-induced

EMT and ITGA1 expression to promote pancreatic cancer (Figure 4.1). This model can be supported by inhibiting key players with RNAi, small molecules or antibodies as shown in Figure 4.1.

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Figure 4.1: ITGA1/collagen mediated TGF signaling drives tumor-promoting ITGA1 functions. (1) ITGA1/Collagen triggers activation of Src kinase[111]. (2) TGF binds to its Type 2 receptor (TRII) [109]. (3) Src phosphorylates TRII, in which Grb2 is recruited [112]. (3) Grb2 mediates MAPK activation [112]. (4) Phosphorylated MAPK activates the linker region of Smad3 [113]. (4) Activated MAPK, Smad3 linker as well as TGF induce Myc expression [113, 114]. (5) Increased Myc expression leads to an induction of ITGA1 expression [115]. (6) Induction of ITGA1 mediates EMT and tumor progression.

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