Ponatinib Inhibits STAT3 Activity and Reduces Colorectal Cancer Growth

Fiona Hue Yee Tan

Submitted to The University of Melbourne in total fulfilment of the requirements of the degree of Doctor of Philosophy

November 2017

The Royal Melbourne Hospital The Department of Surgery The University of Melbourne

ABSTRACT

Colorectal cancer is the 4th most common cancer globally and the 2nd most common cancer in Australia. Constitutive activation of Signal Transducer and Activator of Transcription 3 (STAT3) has been observed in over 50% of human colorectal carcinomas and its role in tumour progression has been confirmed in numerous mouse models and clinically in human samples. Previous data suggests the hyper-activation of upstream molecules notably, Epidermal Growth Factor Receptor (EGFR) and the Interleukin (IL)-6-gp130 family of cytokines, contributes to enhanced STAT3 activation and tumourigenesis [1]. With the aim of overcoming STAT3-driven tumourigenicity, we evaluated a panel of 1167 FDA approved agents for their ability to inhibit STAT3 activity. In this initial drug screen, human colorectal cancer cell lines were assessed by the adenoviral STAT3 luciferase reporter assay. In the presence of inhibitors, cells were also stimulated with EGF and IL-6 allowing enhanced STAT3 activity at 10µM. We identified 51 FDA approved agents to have reduced STAT3 activity by ≥50%. Our secondary drug screen further evaluated inhibitors by EGF and IL-6 mediated STAT3 activity by western blot and resulted with 9 inhibitors to have shown successive reduction at 1µM. It has been recently shown that IL-11, a closely related IL-6 family member has a more prominent role than IL-6 during the progression of gastrointestinal cancers, including colorectal tumours [5], and therefore in our tertiary screen we further evaluated agents that could inhibit IL-11, IL-6 and EGF mediated STAT3 phosphorylation by western blot analyses. As a result, Ponatinib (AP24534) markedly reduced EGF, IL-6 and IL-11 driven STAT3 activation. Ponatinib is a multi-targeted tyrosine kinase inhibitor and is currently approved for the treatment of chronic myeloid leukaemia and Philadelphia chromosome-positive acute lymphoblastic leukaemia. With further in-vitro and in-vivo analyses performed, Ponatinib also reduced transcriptional gene expression of STAT3 regulated genes (SOCS3), cell viability, migration and tumour growth. In addition, Ponatinib was also observed to reduce LIF (another member of the IL-6 family of cytokines) driven STAT3 activity. This study demonstrated Ponatinib’s preferentially targets the IL-11 driven STAT3 pathway in CRC cell lines when compared to IL-11 driven AKT and ERK1/2 signalling pathways. We therefore, further explored the possibility of Ponatinib to directly target IL-

11R/gp130 and speculate possible interaction however, further analysis is required to determine this theory. While the effectiveness of Ponatinib requires additional investigation, our findings both in-vitro and in-vivo offer proof-of-principle evidence for the potential use of Ponatinib for the treatment of STAT3 driven colorectal cancers.

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DECLARATION

This declaration is to certify that:

i. The thesis comprises only their original work towards the Ph.D. except where

indicated in the preface.

ii. Due acknowledgement has been made in the text to all other material used. iii. The thesis is fewer than 100,000 words in length, exclusive of tables, maps,

bibliographies and appendices.

Fiona Hue Yee Tan

The Royal Melbourne Hospital

The Department of Surgery

The University of Melbourne

PREFACE

The experimental data described in this thesis comprises only my original work, except for the following results, obtained in collaboration:

• The Ad-APRE-luc promoter was developed by the Zhu laboratory from the Department of Surgery at the University of Melbourne, The Royal Melbourne Hospital. • The L-gp130 construct was obtained from the Putoczki laboratory at the Walter and Elisa Hall Institute of Medical Research. • The plasmid preparation of IL-11R, L-gp130 and STAT3C was performed by our Research Assistant, Lucy Paradiso.

ACKNOWLEDGEMENTS

I would first like to thank my primary supervisor, Dr. Rodney Luwor for all his commitment, time and patience throughout my Ph.D. candidature. I remember in the earlier years, I would knock on your door at least twice a day and I thank you for having great patience with all my endless questions. Over the past 3.5 years, I have grown into a more confident scientist and I cannot thank you enough for your continual support and guidance. I am truly grateful to have a supervisor who is always available when I’m in a pickle or if I need clarification. I would not have been able to complete my Ph.D. without all your help and wisdom. Thank you!

I would also like to thank my co-supervisor Dr. Stanley Stylli for your joyful nature and your willingness to help me throughout these years. I am sincerely grateful for all the discussions we’ve had and for your endless encouragement throughout this journey of mine. I sincerely value the time you have sacrificed out of your day to see how I am going with experiments and thesis writing. Thank you for spreading joy in the lab with your ‘dad’ jokes and endless humour, never change. To my other co- supervisor, Dr. Tracy Putoczki, I sincerely thank you for all your knowledge and ongoing support throughout the years. Your drive and passion is evident in the several studies and manuscripts published and I am truly grateful to have you as my co- supervisor. I am also thankful for my Ph.D. committee members, Dr. Hong-Jian Zhu and Dr. Robert O’Donoghue for all their advice and support throughout my Ph.D. journey. Thank you for taking the time to suggest possible experiments that could help with my project. Your knowledge and encouragement has truly been invaluable.

I would like to thank my fellow lab members, past and present, for all their added support and numerous enjoyable conversations. In particularly, Lucy Paradiso for all her endless help with the ins and outs of the lab and for teaching me how to perform RNA extractions and for assisting with plasmid preparations. My gratitude also extends out to the members of the Zhu lab and Hovens lab. It has been a pleasure coming in everyday to have lovely corridor conversations with you all!

I acknowledge the various facilities involved with my project, especially to the members of the BRF facility for taking care of my mice and for all their assistance with

animal training. Additionally, I would like to also thank the MIPS facility in particularly, Dr. Cameron Nowell and Sandy Fung for all their assistance with FACS sorting.

I would also like to thank my family: my parents, my brother and sister for their endless support and encouragement throughout this journey of mine. Especially to my parents and their restaurant, Double Happiness for funding me and my belly throughout my Ph.D. life. I would also like to personally thank my friends, Jessica Ventura and Sonia Aithal who have been of great support. Lastly, I would like to thank my partner, for his endless support, patience and endless Gong Chas during my Ph.D. Your constant encouragement has been so important in allowing myself to always push through and to never give up – thank you!

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TABLE OF CONTENTS

ABSTRACT ...... ii

DECLARATION ...... iv

PREFACE ...... v

ACKNOWLEDGEMENTS ...... vi

TABLE OF CONTENTS ...... viii

LIST OF ABBREVIATIONS ...... xii

LIST OF TABLES ...... xiv

LIST OF FIGURES ...... xvi

1. CHAPTER 1 - INTRODUCTION ...... 2 1.1 Introduction to Colorectal Cancer ...... 2 Colorectal Cancer Epidemiology ...... 2 Polyp to cancer (Pathogenesis) ...... 5 Associated gene mutations ...... 6 Current treatments for Colorectal Cancer ...... 8 Current targeted therapies and prognosis ...... 9 Resistance against current treatments ...... 11 1.2 Signal transduction, STAT3 and CRC ...... 13 Signal transduction and oncogenesis ...... 13 STAT3 and family of proteins ...... 13 STAT3 activation and regulation ...... 14 Role of IL-11 and STAT3 in cancer ...... 16 STAT3: The road to oncogenesis ...... 20 Role of STAT3 in mediating resistance to molecular targeted therapy ...... 24 1.3 Targeting STAT3 with novel inhibitors ...... 32 Current upstream STAT3 inhibitors ...... 32 Current direct inhibitors targeting STAT3 ...... 39 1.4 Concluding remarks and project significance ...... 44

2. CHAPTER 2 – MATERIALS AND METHODS ...... 47 2.1 Cell Culture and Reagents ...... 47

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2.2 Generation of the Ad-APRE-luc Adenovirus ...... 47 2.3 Luciferase Reporter Assays ...... 47 2.4 Cell Viability Assays ...... 48 2.5 Antibodies and Western Blotting ...... 49 2.6 In-Vitro Wound Healing Assay ...... 52 2.7 RNA Extraction and Real-Time PCR Analysis ...... 52 2.8 Human Phospho-Receptor Tyrosine Kinase Array ...... 54 2.9 Subcutaneous Xenograft Mouse Model ...... 54 2.10 Plasmids and Stable Transfection ...... 55 IL-11R ...... 55 L-gp130 ...... 55 STAT3C-GFP ...... 55 2.11 Survexpress data mining ...... 56 2.12 Statistical analysis ...... 56

3. CHAPTER 3 – IDENTIFYING INHIBITORS TO ANTAGONISE STAT3 DRIVEN SIGNALLING ...... 58 3.1 Introduction ...... 58 Colorectal cancer and STAT3 related signalling ...... 58 Drug screens and role in cancer therapy ...... 59 Rationale and Aims ...... 59 3.2 Results ...... 61 Ligand and cytokine mediated STAT3 expression in human CRC cells ...... 61 Initial drug screen: 1167 FDA agents and the effect of STAT3 activity ...... 61 Secondary drug screen: Cell Viability and Western blot analysis ...... 69 Secondary drug screen: Cell Viability ...... 69 Secondary drug screen: Western blot ...... 69 Secondary drug screen: 9 FDA approved candidates ...... 70 Tertiary drug screen: Ponatinib inhibits IL-11 mediated STAT3 activity...... 78 3.3 Discussion ...... 87 EGF and IL-6 mediated STAT3 activity in CRC cell lines ...... 87 Underlying anti-STAT3 activity in FDA approved inhibitors ...... 88 Role of IL-11 driven STAT3 activity and cancer ...... 89 Broadening Ponatinib’s known inhibitory effects ...... 90 The novelty of our STAT3 luciferase reporter drug screen ...... 90 3.4 Conclusion ...... 92

4. CHAPTER 4 – EXPLORING THE EFFICACY OF PONATINIB IN-VITRO AND IN-VIVO ...... 94 4.1 Introduction ...... 94 Structural features of Ponatinib ...... 94 Pre-clinical evaluation of Ponatinib efficacy ...... 94 Clinical use of Ponatinib ...... 95 Ponatinib and known molecular targets ...... 97 Rationale and Aims ...... 98 4.2 Results ...... 101 Ponatinib reduces LIF mediated STAT3 activity ...... 101 Ponatinib significantly reduces IL-11 mediated STAT3 signalling and SOCS3 gene expression ...... 103 Ponatinib has greater efficacy in reducing STAT3 activity and cell viability compared to current JAK and Src inhibitors...... 106 Ponatinib reduces cell viability ...... 109 Ponatinib reduces cell migration ...... 111 Ponatinib inhibits tumour growth in subcutaneous xenograft models ...... 115 4.3 Discussion ...... 118 Ponatinib inhibits STAT3 activating pathways ...... 118 Ponatinib exhibits potent STAT3 activity compared to FDA approved JAK and Src inhibitors ...... 119 Ponatinib suppresses cell viability and cell migration ...... 121 Ponatinib and pre-clinical efficacy in CRC ...... 122 4.4 Conclusion ...... 123

5. CHAPTER 5 – UNDERSTANDING POSSIBLE MECHANISMS ASSOCIATED WITH PONATINIB AND IL-11 MEDIATED SIGNALLING ...... 125 5.1 Introduction ...... 125 Importance of pre-clinical validation ...... 125 Tools for STAT3 activation ...... 125 Understanding compensatory mechanisms ...... 126 Rationale and Aims ...... 127 5.2 Results ...... 129 The effect on Ponatinib and IL-11 mediated pathways ...... 129 Enhanced IL-11Rα results in elevated STAT3 activity and Ponatinib reduces viability to a lesser extent compared to parental lines...... 132

Overexpression of gp130 leads to elevated STAT3 activity and reduced Ponatinib’s inhibitory effect...... 136 Ponatinib is less efficacious in the presence of constitutively active STAT3 ...... 140 Ponatinib and receptor tyrosine kinase array ...... 143 5.3 Discussion ...... 148 The effect of Ponatinib and the components of the IL-11 mediated STAT3 pathway ...... 148 Ponatinib and IL-11 mediated cross-talk events ...... 150 5.4 Conclusion ...... 152

6. CHAPTER 6 - DISCUSSION ...... 154 6.1 General Discussion ...... 154 6.2 Concluding remarks ...... 163

7. REFERENCES ...... 164

8. SUPPLEMENTARY TABLES AND FIGURES ...... 196

LIST OF ABBREVIATIONS

Abbreviation Full name Abl Abelson proto-oncogene Ad-APRE-Luc Adenoviral STAT3 reporter ALL Acute lymphocytic leukaemia APC Adenomatous polyposis coli BRAF v-Raf murine sarcoma viral oncogene homolog B CCND1 Cyclin D1 CIMP Cpg island methylator phenotype CIN Chromosomal instability CML Chronic myeloid leukaemia CNTF Ciliary neurotrophic factor CRC Colorectal cancer DBD DNA binding domain EGF Epidermal growth factor EGFR Epidermal growth factor receptor ErbB Erythroblastic leukaemia viral oncogene ERK Extracellular signal regulated kinases FAP Familial adenomatous polyposis FDA Food and drug administration FGFR Fibroblast growth factor receptor FOBT Faecal occult blood test FOLFIRI Folinic acid (FOL), 5-Fluorouracil (F) and Irinotecan (IRI) FOLFOX Folinic acid (FOL), 5-Fluorouracil (F) and Oxaliplatin (OX) G418 Geneticin GF Growth factor GFP Green fluorescent protein Gp130 Glycoprotein 130 HER2/3 Human epidermal growth factor receptor 2/3 IL-11 Interleukin 11 IL-11Rα Interleukin 11 Receptor alpha IL-6 Interleukin 6

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JAK Janus tyrosine kinase LIF Leukaemia inhibitory factor MAPK Mitogen-activated protein kinase mCRC Metastatic colorectal cancer MMR Mismatch repair MSI Microsatellite instability OSM Oncostatin M P-STAT3 Phosphorylated signal transducer and activator of transcription 3 PCR Polymerase chain reaction PDGF Platelet-derived growth factor Ph+ Philadelphia chromosome positive PI3K/AKT PI3-kinase/AKT signalling PTPRD Protein tyrosine phosphatase receptor D RTK Receptor tyrosine kinase S727 Serine 727 SH2 SRC-homology 2 SOCS3 Suppressor of cytokine signalling 3 Src Sarcoma-family kinases STAT3 Signal transducer and activator of transcription 3 TGF-α/β Transforming growth factor-alpha/beta TKI Tyrosine kinase inhibitor U-STAT3 Unphosphorylated signal transducer and activator of transcription 3 VEGF Vascular endothelial growth factor Wnt Wingless-related integration site Y705 Tyrosine 705

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LIST OF TABLES

Table 1-1: Oncogenesis and STAT3 activation ...... 22

Table 1-2: Current FDA approved monotherapy inhibitors undergoing clinical evaluation in other malignancies ...... 25

Table 1-3: Pre-clinical studies in current combination chemotherapy analysing anti- EGFR and anti-STAT3 inhibitors ...... 30

Table 1-4: Current clinical trials for STAT3 related combinational targeted therapeutics ...... 31

Table 1-5: Current multi-tyrosine kinase FDA approved inhibitors undergoing clinical evaluation in other malignancies ...... 37

Table 1-6: Indirect STAT3 inhibitors undergoing clinical evaluation ...... 38

Table 1-7: Direct STAT3 inhibitors undergoing pre-clinical and clinical evaluation .... 42

Table 2-1: CRC cell lines ...... 48

Table 2-2: Primary and Secondary antibodies used for immunoblotting ...... 50

Table 2-3: PCR primers for amplifying SOCS3 and GAPDH ...... 53

Table 3-1: The initial FDA drug screen ...... 63

Table 3-2: Summary of 51 candidates selected from the initial drug screen with reduced STAT3 activity by greater than 50% ...... 66

Table 3-3: The secondary FDA drug screen – Summary of 51 agents and the effect on cell viability ...... 71

Table 3-4: The secondary drug screen: Summary of 9 selected FDA candidates ...... 77

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Table 4-1: Ponatinib’s pre-clinical trials for CML and Ph+ ALL patients ...... 95

Table 4-2: Ponatinib’s clinical trials in other cancer types ...... 97

Table 4-3: Ponatinib’s molecular targets in cancer ...... 98

LIST OF FIGURES

Figure 1-1: Incidence and mortality of colorectal cancer ...... 4

Figure 1-2: From polyp to cancer ...... 6

Figure 1-3: The multiple signalling pathways that contribute to the activation of signal transducer activator of transcription 3 (STAT3)...... 18

Figure 3-1: The effect of EGF and IL-6 induced STAT3 activity in 15 colorectal cancer cell lines ...... 62

Figure 3-2: FDA inhibitors from the first drug screen found to decrease STAT3 activity by ≥50% ...... 64

Figure 3-3: A representation of the effect of an analysed inhibitor from the inital FDA drug screen proportional to STAT3 luciferase activity ...... 68

Figure 3-4: The secondary drug screen: Summary of cell viability and western blot analysis on 51 candidates ...... 73

Figure 3-5: The secondary drug screen: Effects on cell viability by the selected 9 FDA inhibitors ...... 74

Figure 3-6: The secondary drug screen: Western Blot analysis and the effects on the selected 9 FDA inhibitors ...... 75

Figure 3-7: High IL-11-IL-11Rα expression is greatly associated to poor survival than high IL-6-IL-6R expression ...... 80

Figure 3-8: The IL-11-STAT3 pathway is highly expressed in CRC cancer patients and correlates with poor survival ...... 81

Figure 3-9: The effect of IL-11 and p-STAT3 activity in 15 colorectal cancer cell lines ...... 83

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Figure 3-10: The tertiary drug screen: Western Blot analysis and the effects on the selected 9 FDA inhibitors ...... 85

Figure 4-1: Chemical structure of Ponatinib ...... 94

Figure 4-2: Schematic diagram of members of the IL-6 family of cytokines ...... 101

Figure 4-3: Ponatinib reduces LIF mediated STAT3 activity, another IL-6 family member ...... 102

Figure 4-4: Ponatinib significantly reduces IL-11 mediated p-STAT3 activity by western blot analysis and SOCS3 gene expression ...... 104

Figure 4-5: Ponatinib is a more potent STAT3 activation inhibitor compared to current FDA approved JAK and Src agents ...... 107

Figure 4-6: Cell viability is significantly reduced by <50% in the presence of Ponatinib treatment ...... 110

Figure 4-7: Cell migration is significantly reduced during Ponatinib treatment ...... 112

Figure 4-8: Ponatinib inhibits tumour growth in subcutaneous mice models ...... 116

Figure 5-1: Ponatinib reduces IL-11 mediated STAT3 activity more prominently compared to ERK1/2 and AKT pathways ...... 130

Figure 5-2: Enhanced IL-11Rα increases STAT3 activity and reduced Ponatinib’s inhibitory effect ...... 133

Figure 5-3: Elevated gp130 results with increased STAT3 activity and Ponatinib was less efficacious ...... 137

Figure 5-4: STAT3C enhances STAT3 activity and reduces Ponatinib’s efficacy ...... 141

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Figure 5-5: The evaluation of 49 Phospho-RTK array in the presence of IL-11 and Ponatinib treatment ...... 144

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CHAPTER 1: Introduction

1. CHAPTER 1 - INTRODUCTION

1.1 Introduction to Colorectal Cancer

Colorectal Cancer Epidemiology Colorectal cancer (CRC) is one of the leading causes of cancer mortality worldwide [6]. CRC is a growing global health concern with nearly 1.4 million new cases diagnosed in 2012, affecting approximately 746,300 men and 614,300 women [7, 8]. Strikingly, countries reported with high incidence rates are predominately found in more industrialized and developed regions, including Australia, the United States of America, Europe and New Zealand, representing nearly 55% of all CRC cases reported in 2012, Figure 1-1 [9, 10]. Currently in Australia, CRC is the 2nd largest cause of cancer deaths in both men and women. In 2010, 14,860 CRC cases were reported in Australia and by 2020 an estimated 19,960 cases are expected to be diagnosed [11].

The risk of CRC development rises sharply at the age of 50 in both male and females [7]. Although uncommon, individuals under 50 years of age can also development CRC, accounting for approximately 7% of all CRC cases [12]. It has been well documented that environmental and genetic factors can significantly increase the possibility for CRC progression [13]. Although, inherited susceptibility has been associated with increased risk (~5-15%), the majority of CRC cases are epigenetic rather than inherited, accounting for approximately 75% of cases [14, 15]. Other factors contributing to increased CRC cases include low socioeconomic status where individuals have approximately 30% greater risk of developing CRC compared to individuals with high socioeconomic status [16, 17]. In these studies, potential modifiable behaviours such as physical inactivity, smoking, obesity and poor dietary practices are believed to account for a considerable portion of this socioeconomic group [16, 18, 19]. Individuals living in rural areas and those with limited access to health care are also at higher risk for CRC development [20]. In addition, individuals that are uneducated in understanding potential risks associated with the development of CRC and available screening methods can also contribute to the increase in CRC mortality [21]. In addition to individuals with low socioeconomic backgrounds,

universally, high risk factors also include genetic variations amongst the population, excessive consumption of alcohol and processed red meats [9, 22, 23].

Figure 1-1: Incidence and mortality of colorectal cancer

CRC is one of the most common cancers in the world and is highly prevalent in developed regions including, Australia, New Zealand, Europe and United States of America. Representation depicting CRC incidence rates in males per 100,000 individuals recorded in 2012 (A.). Estimated age-standardized rates of incidence (blue and mortality (red) for CRC in both males and females in 2012 (B.). (Image source: http://globocan.iarc.fr/Pages/fact_sheets_cancer.aspx?cancer=colorectal4 )

The mortality rate for individuals with CRC has declined progressively since the mid-1980’s in Australia and other developed countries [7]. This significant improvement can be credited to the development of early screening methods to detect the onset of cancerous polyps, removal of malignant polyps and greater improvement for standard of care therapies [8, 24, 25]. In 2006, the Australian government implemented the National Bowel Cancer Screening Program, which provides free faecal occult blood test (FOBT) to asymptomatic individuals over the age of 50 [26]. Studies have shown, the use of FOBT enhances the early detection of CRC and lowers the rate of CRC mortality [27, 28]. In contrary, the mortality rates continually increase in other countries with poorer health infrastructure and limited resources for early detection [10, 29].

Polyp to cancer (Pathogenesis) Colorectal cancers involve the development of tumours within the gastrointestinal tract, in particularly the large intestine (colon) and the rectum. CRC tumours are first developed on the inner lining (the mucosa) of the colon or rectum, known as a polyp, Figure 1-2. The formation of polyps involves the accumulation and continual growth of immature and abnormal cells. Adenomatous polyps (adenomas) and Hyperplastic polyps are the 2 most commonly found polyps in the colon or rectum. Hyperplastic polyps are more common, though are generally not pre- cancerous. In contrast, Adenomatous polyps have the ability to transform into cancerous polyps and are therefore defined as a pre-cancerous condition. These adenomas eventually grow into the wall of the colon or rectum and over time, cancer cells disseminate into blood vessels and nearby lymph nodes most often lodging in the liver [30]. However, CRC has been reported to metastasize to other organs including lung, peritoneum, brain and bone [31-34]. The gradual development of CRC is known as the polyp-carcinoma sequence, whereby this transformation occurs between 8 – 12 years [35].

Figure 1-2: From polyp to cancer

Schematic representation of the colon depicting the differences between a normal colon and colon cancer (A.). Colon cancer progresses as small benign adenomatous polyps and without surgical removal, these polyps could potentially develop into the malignant stages of adenocarcinoma and ultimately cancer (B.). This process can also occur in the rectum. Adapted from John Hopkins Medicine

Colorectal Cancer [2].

Associated gene mutations Both sporadic and genomic mutations are commonly observed in CRC cases and are strong driving forces for carcinogenesis, whereby several genetic alterations are required for the progression of tumours. CRC often arise to the progression of at least 3 distinct pathways of genomic instability, namely the chromosomal instability (CIN), microsatellite instability (MSI), and CpG island methylator phenotype (CIMP) pathways [36-38].

An estimated 70 – 85% of CRC cases are caused via the CIN pathway [39]. This pathway involves alterations in the Adenomatous Polyposis Coli (APC) gene and mutation of the KRAS oncogene, which is associated with the important tumour suppressor gene TP53 [40]. Mutations in the APC gene hinder the binding of APC

protein to b-catenin, which are responsible for the suppression of the Wnt-signalling pathway [41, 42]. The Wnt-signalling pathway regulates cellular differentiation, apoptosis and growth [43]. Familial adenomatous polyposis (FAP) is associated by inherited changes in the APC gene [44]. Studies have shown the development of CRC is essentially present in all FAP cases [45]. Another important gene within the CIN pathway is the KRAS oncogene. KRAS encodes a GTP-binding protein, however when mutated, this prevents GTPase activity and therefore results in constitutively active signalling through the RAS-RAK-MEK-ERK pathways [46]. Mutations found in the KRAS gene are associated with 35-42% of CRCs [46]. Additionally, the impairment of the TP53 gene, responsible for regulating the cell cycle and DNA repair, either via mutation or loss of heterozygosity is accountable for 50-75% of CRCs [46].

MSI is another pathway involved in genomic instability that is often found in CRCs. Microsatellites are short repetitive genetic sequences that are present within the tumour genome and are highly prone to mutations involving mismatch repair (MMR) dysfunction during the copying of short repeat sequences by DNA polymerase [47]. Proteins composed of the MMR system include, MLH1 and MSH2 are DNA repair enzyme genes and is commonly observed in Lynch syndrome or Hereditary non-polyposis colon cancer (HNPCC) [48]. In population-based studies, MSI accounts for an estimated 15% of CRCs [49]. There are several mutated genes carried within MSI tumours, such as BAX, a family member of BCL2 gene, is responsible for programmed cell death, however when mutated cell death is prevented allowing for the presence of immortalized tumourigenetic cells [50]. Other MSI genetic mutations implicated in CRCs also include TGFbRII, IGFIIR, and PTEN [51- 53].

Furthermore, the CIMP pathway is the second most common pathway to sporadic colorectal tumours responsible for approximately 15% of sporadic CRC cases [36, 54]. This pathway involves a subset of colorectal tumours developed by an epigenetic instability pathway which contain great hypermethylation of promoter CpG island sites, and therefore leading to the inactivation of multiple tumour suppressor genes, including CDKN2A, responsible for encoding the tumour suppressor p16 [36, 55]. Other mutations are also observed in CIMP CRCs, Shen et.al, have shown CIMP

tumours are often MSI tumours (80%) and strongly correlate with BRAF mutations (53%) and KRAS mutations (92%) [56]. Coinciding with these findings, Ang and colleagues also observed frequent mutations of KRAS and BRAF genes in CIMP- high tumours compared to CIMP-low tumours [57]. Gathered, mutations in both the genome and epigenome occur frequently in colorectal tumourigenesis and are key drivers for continual tumour progression.

The continual activation of key signalling pathways such as Signal Transducer and Activator of Transcription 3 (STAT3), wingless-related integration site (Wnt), bone morphogenic protein (BMP), Notch and Hedgehog (HH) are commonly known to also cause tumour burden in CRC patients [58-60]. In particularly, the STAT3 pathway is greatly known to drive cancer types, including CRC and is driven by several growth factor and cytokine related pathways. These include the notoriously known Epidermal Growth Factor Receptor (EGFR), which is found to be highly expressed in CRC patients and is associated with tumour stage [61, 62]. Moreover, other mediators known to drive STAT3 activity include, Interleukin (IL-) 6, 11, Leukaemia Inhibitory Factor (LIF) and Vascular Endothelial Growth Factor (VEGF) [63-66].

Current treatments for Colorectal Cancer Once patients are diagnosed with CRC, treatment options are dependent on the stage and progression of the disease. Commonly, the primary treatment for most CRC patients is the surgical removal of tumour, involving local excision and bowel resection. This is frequently observed in Stage I CRCs [67]. Secondary treatments involve chemotherapy or adjuvant radiotherapy and is often utilised after surgery. This procedure is commonly observed in patients with stage II and III CRC, with no sign of metastasis [68]. Chemotherapy drugs for stage II and III patients are either provided alone or in combination with other agents and/or radiation [69, 70]. The most commonly used chemotherapy drugs for the treatment of CRC are FOLFOX and FOLFIRI [71-73]. The FOLFOX chemotherapy regimen encompasses Folinic acid (FOL), 5-Fluorouracil (F) and Oxaliplatin (OX) and the FOLFIRI regimen consists of Folinic acid (FOL), 5-Fluorouracil (F) and Irinotecan (IRI) [74]. The regimen for 5- FU plus Oxaliplatin was based on the results conducted in the Multicentre International Study of chemotherapy drugs namely, Oxaliplatin/5-FU/Leucovorin in the adjuvant treatment of CRC, also known as the MOSAIC clinical trial in 2004 [75].

This study observed the addition of Oxaliplatin to a treatment regimen of 5-FU in combination with Leucovorin (FL) and demonstrated a significant improvement in 3- year disease-free survival, 78.2% FL plus Oxaliplatin compared to 72.9% FL alone. In correlation, the National Surgical Adjuvant Breast and Bowel Project conducted in 2007 also revealed similar outcomes [76]. These combinational chemotherapy regimens involving 5-FU plus other drugs including, Irinotecan, Oxaliplatin or Leucovorin can achieve a response rate of 50% and a median survival of over 20 months [77-79].

In contrast, patients with metastatic CRC (mCRC), also known as stage IV CRC are usually treated with different regimes after tumour resection. Most patients with stage IV CRC are often treated with combinational chemotherapy regimens and targeted therapies. Common chemotherapy drugs used for the treatment for mCRC include 5-FU, Irinotecan, Oxaliplatin, in combination with the anti-EGFR monoclonal antibodies Cetuximab (ErbituxÒ) and Panitumumab (VectibixÒ) [80, 81]. Similar to 5-FU, Capecitabine (XelodaÒ) was also FDA approved as an oral prodrug of 5- Flurouracil (5-FU) for the treatment of Stage III and metastatic CRC patients [82]. This was based on the X-ACT study and demonstrated the disease-free survival for patients treated with Capecitabine was at least equivalent to the treatment of 5-FU. Capecitabine also resulted with a greater response rate and improved safety profile when compare to intravenous 5-FU [83].

Often, these drugs are used in combination, such as the previously mentioned FOLFOX and FOLFIRI regimen and also the XELOX regimen (Oxaliplatin and Capecitabine), in the presence or absence of a monoclonal antibody agent, and have been shown to improve the outcome for mCRC patients [77, 84, 85]. For instance, the FOLFOX regimen in combination with Bevacizumab, a monoclonal antibody that primarily targets Vascular Endothelial Growth Factor (VEGF), has been linked to improve survival of patients given FOLFOX + Bevacizumab compared to FOLFOX + placebo (20.3 vs. 15.6 months respectively) [84, 86, 87].

Current targeted therapies and prognosis Targeted therapies involve the use of agents or small molecules to specifically inhibit certain molecular pathways responsible for tumour progression. One important receptor found in nearly 80% of CRC cases is the highly expressed epidermal growth

factor receptor (EGFR) and is associated with poor prognosis [88-90]. Once activated, EGFR is responsible for the initiation of several cancer promoting pathways, including Signal transducer and activator of transcription 3 (STAT3), Phosphatidylinositol 3-kinase/Akt, Ras/Raf/mitogen-activated protein kinase (MAPK) and Src kinases [91, 92]. Clinical studies have shown a strong correlation between highly expressed EGFR and CRC metastasis and progression. For instance, Spano et.al examined tissue sections from 150 grade IV CRC patients and discovered 97% of samples were positive for EGFR and 80% expressed high levels [90]. Moreover, the phase III Crystal study observed FOLFIRI plus Cetuximab greatly improved overall survival in 845 patients with KRAS-WT tumours when compared with chemotherapy alone [93]. These studies endorsed the use of anti-EGFR monoclonal antibodies in targeted therapies. These include the FDA approved monoclonal antibodies, Cetuximab and Panitumumab for the treatment of metastasised CRC harbouring KRAS wild-type (WT) in combination with FOLFIRI [94-97]. These agents act by competitively binding to the EGFR, inhibiting ligand binding and subsequent downstream signalling responsible for cancer progression, including tumour cell proliferation, apoptosis angiogenesis, invasion and metastasis [94, 98]. Prior to ant-EGFR treatment, CRC biopsies are screened for RAS mutations, and those that are positive for RAS mutations are now excluded from anti-EGFR based therapies. This is due to a series of retrospective studies showing that patients with KRAS mutations do not respond to Cetuximab compared to patients harbouring tumours with WT KRAS expression [99-103].

Importantly, not all patients with WT KRAS will respond to anti-EGFR antibodies, highlighting the presence of other mutations hindering the efficacy of these anti-EGFR treatments or the lack of EGFR expression in some of these patients. These mutations include other downstream effector genes, namely BRAF, NRAS, PTEN and PIK3CA expression, which have also been proposed as biomarkers with lack of response to anti-EGFR inhibitors [81, 104-106]. Despite these findings, other studies have shown conflicting results. For instance, BRAF mutants may contribute to resistance against anti-EGFR treatments, but have failed to establish BRAF as a predictive biomarker [107]. Notably, the prevalence of BRAF and NRAS mutations is seemingly low (5-7% and 10% of CRC cases). Some studies have suggested patients with the ‘triple-wild-type’ (KRAS/NRAS/BRAF) selected for anti-EGFR therapy may

improve patient overall survival, response rate and outcome [108]. Importantly, further validation is suggested for these studies due to the low prevalence of BRAF and NRAS mutations.

In summary, the availability of these agents, including Capecitabine, Oxaliplatin and Irinotecan has significantly improved median patient overall survival in patients with mCRC from 8-12 months to 18-21 months [79]. Additionally, the advances in targeted therapy have further improved patient overall survival to 21-24 months, highlighting the potential for targeted treatments [80, 109, 110].

Resistance against current treatments Despite significant improvements and advances in the treatment for CRC, in particularly patients with advanced disease and their complex heterogeneous tumour environment, fail to respond to treatments [111]. Importantly, acquired resistance to therapy is seen in approximately 90% of mCRC patients [112]. Moreover, malignant diseases can also have intrinsic resistance in conjunction with acquired resistance and has been shown to be of importance prior to distributing treatment options to patients. For instance, resistance against EGFR treatments was initially not well understood in which 10-20% of mCRC only responded to treatment, with others often developed resistance within 3-12 months of treatment [96, 113]. Further studies clarified, patients with RAS mutations do not respond to EGFR antagonists, and thus alternative treatment was required [81, 114]. Resistance to targeted therapies are often observed by mechanisms involving the mutation, upregulation or continual activation of downstream signalling proteins responsible for driving tumour-related pathways; elevated cross-talk between corresponding pathways; or more importantly pathway bypass mechanisms [112].

Pathway bypass mechanisms are also defined as compensatory changes in the signalling pathways of treated cancer cells that have the ability to avoid specific kinase inhibitors. These pathways are capable to restore the inhibited signalling pathway through other receptors and molecules, which elicit the same phenotypic consequences as the originally targeted pathway. In fact, recent studies have implicated dysregulated cytokine expression leading to compensatory signalling as a critical involvement in drug resistance [115, 116]. For instance, lung adenocarcinoma cells have the capability to bypass Gefitinib-mediated ErbB3 blockade through the

process of autocrine upregulation of Hepatocyte Growth Factor (HGF) in lung cancer cells with EGFR-activating mutations [117, 118]. This study demonstrated resistant cells to exhibit restored PI3-kinase/AKT signalling via the phosphorylation of Met and occurred independently of EGFR. In relation, HGF driven ERK and AKT pathway activation was also identified to confer resistance to the RAF inhibitor, PLX4720 (an analogue of the FDA approved Vemurafenib, used in melanoma patients with BRAF V600E mutations, and has been documented to exhibit high resistance in patients) [119, 120]. Moreover, breast cancer patients who are positive for HER2 (also known as ErbB2) are often treated with Trastuzumab however, these patients eventually develop Trastuzumab resistance [121]. This resistance was later discovered to be driven by high serum levels of amphiregulin (one of the ligands of EGFR), which allowed the activation of AKT and ERK1/2 signalling to bypass targeted treatment and continual tumour growth [122]. In this particular scenario, these observations demonstrate a drug resistant mechanism involving the activation of downstream signalling pathways that are common to the therapeutic target (HER2) and the cognate receptor (EGFR). Taken together, these drug resistance mechanisms successfully bypass targeted therapies and remain an ongoing issue during patient treatment.

Identifying inhibitors with anti-tumour activity could prove to relieve resistance towards current therapies. For instance, Regorafenib, a multi-tyrosine kinase inhibitor in combination with Cetuximab, has been documented to be a promising strategy to over-come resistance to EGFR therapy in mCRC patients [123]. Regorafenib, targets several tumorigenic pathways involving the inhibition of cell growth via KIT, RET and BRAF, reduces tumour-induced angiogenesis through the inhibition of VEGFR and TIE2, moreover this inhibitor also targets the tumour microenvironment involving the reduction of PDGFR and FGFR [124, 125]. However, it is unsure whether Regorafenib efficacy is due to the blockade of compensatory signalling pathways driven through the inhibition of the EGFR with Cetuximab.

One molecule that has been constantly shown to contribute to resistance to chemotherapy and targeted therapies is Signal Transducer and Activator of Transcription 3 (STAT3). In fact, elevated STAT3 activity, which leads to an increase

in resistance to treatments, has been widely observed in various cancers, including CRC [1, 126, 127].

1.2 Signal transduction, STAT3 and CRC Signal transduction and oncogenesis Cell division, growth and proliferation are normally tightly regulated. However, tumour cells often exhibit loss of cellular maintenance that is responsible for tightly regulating normal cellular function, allowing for uncontrolled cell division in the absence of extracellular stimuli [128]. Uncontrolled regulation of key cancer signalling pathways, lead to a complex, multi-step process in which normal cells are transformed into cancer cells, known as oncogenesis or carcinogenesis. As a result, cancer cells exhibit specific hallmarks or biological characteristics that are attained during oncogenesis. These hallmarks of cancer commonly include bypassing apoptotic events, resisting cell death, sustained angiogenesis, self-sufficiency in growth signals, evading growth suppressors and continual tissue invasion and metastasis [128].

CRC tumour cells exhibit hyper-activation of key oncogenic proteins responsible for malignant phenotypes. Common signal transducing pathways observed in CRC include, continual activation of Wnt/wingless signalling resulting with functional loss of the tumour suppressor Adenomatous Polyposis Coli (APC) and CTNNB1 (β-catenin) [45, 129, 130]. The Epidermal Growth Factor (EGF) pathway is also commonly involved with the development of CRC and includes drivers of KRAS and BRAF mutations, with a significant 40% of all CRC cases with KRAS mutants and 5-10% in BRAF mutations [108]. Other malignant transformation is activated by additional mutations present in the TP53, PIK3CA and the TGF-β pathway [108, 131, 132]. The Janus tyrosine kinase (JAK) - Signal Transducer and Activator of Transcription (STAT) pathway is a definitive example of a highly regulated signalling cascade in normal cells that becomes a key driver for oncogenesis [133].

STAT3 and family of proteins STAT3, originally known as acute phase response factor, is a DNA-binding transcription factor and is a member of the mammalian STAT family of transcription factors comprising of 7 proteins, namely, STAT 1, 2, 3, 4, 5a, 5b and 6 [134, 135]. These members were first discovered in the 1990’s as important proteins for cytokine

signalling and expression, involving upstream mediators such as growth factors, cytokines, intrinsic receptor tyrosine kinases (RTK) and non RTK [133]. Structurally, STAT3 involves an amino-terminal domain (N), coiled-coil domain, DNA-binding domain, SRC-homology 2 (SH2) domain and the transactivation domain comprising of tyrosine phosphorylation and serine phosphorylation [136].

STAT3 activation and regulation STAT3 exists as unphosphorylated (U-STAT3) and phosphorylated (p- STAT3) dimers, large complexes of 200-400kDa (“statosome I”) or those larger (“statosome II”) at >1MDa [137-140]. Phosphorylation of STAT3 can occur via several growth factors, including Epidermal Growth Factor (EGF) [141, 142], Platelet-Derived Growth Factor (PDGF) [143], Transforming Growth Factor alpha (TGF-α) and Hepatocyte Growth Factor/Scatter Factor (HGF/SF)/c-MET [135] mediated pathways. gp130 co-receptors also initiate STAT3 activity through interaction with cytokines such as interleukin-6 (IL-6). Wegenka and colleagues first discovered enhanced levels of STAT3 activity upon IL-6 activation in both rat liver in-vivo and human hepatoma cells in-vitro [144]. Moreover, the gp130 co-receptors and other IL-6 family members are also known to enhance STAT3 signalling, including Leukaemia Inhibitory Factor (LIF) [145-147], IL-11 [148-150], Ciliary Neurotrophic Factor (CNTF) [151] and Oncostatin M (OSM) [152].

Oncogenic proteins including Src [153] or Ras pathways and the Janus family kinases (JAK) [154, 155] are also known to drive STAT3 activation. Upon activation, STAT3 is predominantly phosphorylated at the unique tyrosine 705 (Y705) residue [156] which leads to a conformational change of the unphosphorylated dimer, coupled with differential DNA binding to that seen with U-STAT3 and ultimately the transcription of numerous pro-cancerous target genes involving, a variety of cellular functions, including proliferation, cell differentiation, metastasis, angiogenesis, apoptosis and immune responses, Figure 1-3 [157-161]. It should be noted however, that the bulk of p-STAT3 can remain cytoplasmic and may play a number of roles in non-transcriptional changes including effects on microtubules [162, 163], focal adhesions [164], sequestering endosomes [139] and mitochondrial function [165, 166]. Whilst phosphorylation typically occurs at Y705 residue, studies suggest S727 phosphorylation is required for maximal Y705 phosphorylated STAT3, however the activity of S727 remains controversial [167, 168]. There are many target genes of

STAT3, including anti-apoptotic genes such as B-cell lymphoma 2 (Bcl-2), cell cycle regulators such as Cyclin D1 (CCND1) and proto-oncogenes such as c-Myc [169]. Moreover, Suppressor of Cytokine Signalling 3 (SOCS3) is also strongly expressed upon STAT3 activation, and is responsible for the negative regulation of STAT3 activity [170].

In addition to SOCS3 gene expression other STAT3 regulators are also involved, namely, PIAS3 and Protein Tyrosine Phosphatase Receptor D (PTPRD) [171-173]. These tightly regulated proteins inhibit STAT3 DNA-binding activity and STAT3-mediated gene expression, maintaining homeostasis and preventing the development of tumourigenic characteristics. For instance, SOCS3 is highly induced by STAT3 activity and strongly regulates IL-6 induced signalling, preventing excessive activation of p-STAT3 [174]. However, even in the continual presence of SOCS3, further studies have identified the possibility of STAT3 re-activation through mechanisms that bypasses SOCS3, either through the inhibition between the interaction of SOCS3 and IL-6 or potential ‘cross-talking’ between receptors, allowing continual STAT3 activation [175]. For example, Wang et.al demonstrates prolonged STAT3 activation occurs through the binding of IL-6R to EGFR, cross-talk that is not negatively regulated by SOCS3 [175]. These events, enhances sustained STAT3 activity and ultimately the development of malignant tumours.

Importantly, U-STAT3 can also provide transcriptional activity, in response to cytokines, though these are discrete from pathways activating p-STAT3 [176]. These occur through the help of transporting proteins such as importin-a3 and -b1 proteins and nuclear factor kB (NFkB), which assist with U-STAT3 entry into the nucleus, [177-179]. Once U-STAT3 has been translocated into the nucleus, it binds to the DNA binding site of interferon g (gamma)-activated sequence (GAS) and initiates the expression of target genes [180]. After transcription, U-STAT3 is exported out of the nucleus and into the cytoplasm via exportin-7 proteins and the cycle continues.

The correlation between overexpression of cytokines and poor prognosis in CRC patients is commonly observed. Notably, studies have previously suggested IL-6 mediated STAT3 activation is of greater dominance than its other family members [181-183]; however in a recent study, Putoczki and colleagues demonstrated IL-11 has a stronger correlation with enhanced STAT3 activation in human gastrointestinal

cancers [5]. Similarly, other studies have shown enhanced levels of IL-11 in gastric and endometrial cancers [5, 184]. Additionally, LIF, another cytokine belonging to the family of IL-6, is generally known as a negative regulator of p53 [185]. Once active, this cytokine initiates downstream signalling activity involving selective pathways including the JAK/STAT3 pathway [186]. Importantly, elevated LIF induced JAK/STAT3 activity is associated with poor prognosis in CRC patients [65]. These studies further highlight the complexity and importance of STAT3 activation in CRC development and progression.

Role of IL-11 and STAT3 in cancer In recent times, interleukin (IL)-11, belonging to the family of IL-6 has emerged to be of great importance in the role of STAT3 activity in cancers. Activation occurs when IL-11 interacts with the IL-11Rα chain, a specific non- catalytic transmembrane receptor [187]. Moreover, a key characteristic shared within this family is the use of the ubiquitously expressed transmembrane protein glycoprotein-130 beta subunit (gp130 or also commonly referred to as CD130 or IL6ST) whereby the IL-11/IL-11Rα dimeric complex interacts with gp130 [187]. In turn, the formation of this larger tetrameric complex instigates signalling through the JAK family of tyrosine kinases, which are associated within the intracellular domain of gp130 [149]. This initiates the activation of several signalling pathways including, JAK/STAT3, RAS/RAF/ERK1/2 and PI3K/AKT [188-190]. Interestingly, genetic evidence suggests that in response to the initiation of gp130-family of cytokines, STAT3 activation is the most critical event to occur [191].

It is not until recent times that IL-11 emerged as the more dominant cytokine to drive tumour growth, compared to the previously thought IL-6. For instance, this was observed in a gastric cancer mouse model that is dependent on STAT3 activation for driving tumour growth and demonstrated IL-11 mediated STAT3 activity promoted tumour growth in comparison to IL-6 [5, 192]. Clinically, elevated IL-11 and IL-11Rα has been shown to correlate with poor survival in patients with CRC [193, 194]. Moreover, IL-11 expression increases with tumour grade as observed in gastric adenocarcinomas and endometrial cancers [66, 195].

Despite, the identification of the tumour enhancing activities caused by IL-11, there is currently no FDA approved anti-IL-11 inhibitor for cancer therapy. To date,

there are few published studies that have documented therapeutic targeting against IL- 11 signalling in pre-clinical cancer models. For instance, the human IL-11 mutein (mIL-11), an antagonistic form of IL-11 reduced gastric tumour burden in mice and reduced tumour size in a mouse model of chemically induced sporadic CRC [5]. In addition, other studies have shown the blockade of IL-11 signalling using anti-human IL-11Rα antibodies [196-199]. Although the development of IL-11 inhibitors for clinical trials has not yet occurred, the use of anti-cytokine and anti-receptor antibodies targeting the IL-6 family have been approved for treating patients with inflammatory diseases include the anti-IL-6R, Tocilizumab, Siltuximab and the recently approved Sarilumab for the treatment of rheumatoid arthritis [200-203]. Nevertheless, clinical trials using these existing inhibitors are currently underway for the treatment of cancers. For instance, a phase I study is recruiting patients with metastatic HER2 positive breast cancer for the treatment with Tocilizumab in combination with standard of care therapies (NCT03135171). Likewise, Infliximab is currently undergoing phase II clinical analysis for its potential use in patients with pancreatic cancers (NCT00060502). Taken together, the possibility for inhibiting the IL-11 pathway is possible, involving the targeting of the IL-11 cytokine or related components of the receptor complex, such as IL-11Rα, gp130 or downstream STAT3.

Figure 1-3: The multiple signalling pathways that contribute to the activation of signal transducer activator of transcription 3 (STAT3).

The STAT3 signalling pathway is activated via interaction of secreted ligands with cell surface growth factor (GF) receptors or cytokine receptors. This leads to the phosphorylation of STAT3 (p-STAT3) and translocation to the nucleus for the regulation of gene expression. STAT3 Phosphorylation can occur on tyrosine 705 or serine 727 residues. An example of endogenous inhibition of the STAT3 signalling pathway is the prevention of the phosphorylation of STAT3 and thus, translocation into the nucleus indirectly by suppressors of cytokine signalling 3 (SOCS3), protein-tyrosine phosphatase receptor-type D (PTPRD) and the protein inhibitor of activated STATs (PIAS). Importantly, un-phosphorylated STAT3 (U-STAT3) is able to shuttle between the cytoplasm and nucleus when associated with importin-α3, importin-β1 and exportin-7 proteins. Furthermore, U-STAT3 can also interact with Nuclear Factor κB (NFκB) as a vesicle to transport to the nucleus (not shown). U- STAT3 can also be transcriptionally active, through the binding of target genes discrete from p-STAT3. U-STAT3 and p-STAT3 are shown as monomers for simplicity; however they naturally occur as dimers.

Figure originated from: Tan, F. H., Putoczki, T. L., Stylli, S. S., & Luwor, R. B. (2014). The role of STAT3 signalling in mediating tumor resistance to cancer therapy. Current Drug Targets, 15(14), 1341-1353.

STAT3: The road to oncogenesis Tyrosine kinases are amongst the most frequently activated proteins linked to oncogenesis in cancer cells. It is therefore, perhaps unsurprising that the overactivation of STAT3 signalling has been observed in many tumour types, including breast, liver, prostate colorectal, head and neck, pancreatic, glioblastoma, prostate and non-small cell lung cancers, Table 1-1 [204-210]. Up to 70% of human tumours are linked to persistent STAT3 activity, suggesting that STAT3 is a promising potential anti-cancer target [211]. Importantly, in CRC over 50% of cases were found to contain enhanced STAT3 activity [8, 212]. In other cancers, clinical observations have identified that greater than 70% of prostate and breast cancer cells contain active STAT3 [213], while one report found that 80-100% of head and neck tumours also overexpress STAT3 activity [214]. Specifically in CRC, several pre-clinical studies have associated elevated STAT3 activity with continual CRC characteristics in-vitro and in-vivo. For instance, studies have identified the necessity for STAT3 activity for driving proliferation and survival in CRC cells [215]. Another study, conducted by Corvinus et.al also demonstrated constitutive active STAT3 present in CRC cell lines and xenograft models [212]. In line with these findings, other studies have also discovered the blockade of JAK2/STAT3 pathway induces CRC cellular apoptosis by regulating the Bcl-2 gene and promoting caspase activity as seen in xenograft tumours [216]. Similarly, Wei et.al explored the role of JAK/STAT3 pathway by assessing a sophisticated oncolytic adenoviral vector, AdCN305, which encompasses the STAT3 driven, SOCS3 gene to treat CRC cells. This study further confirmed the presence of constitutively active STAT3 in CRC cells and is efficiently suppressed when treated with AdCN305, in both in-vitro and in-vivo studies [63]. Moreover, another study demonstrated that stable knockdown of STAT3 led to reduced angiogenesis in the HT29 CRC cell line [217]. Taken together, these studies highlight the strong association between elevated STAT3 levels and tumourigenesis.

STAT3 activation has been shown as a prognostic marker in several tumour types. Kim et.al showed that total STAT3 is a marker of poor prognosis in 100 patients with gastric cancer from Korea using immunohistochemistry. Positive STAT3 activity correlated with a lower survival rate compared to the STAT3 negative group (p=0.001) [218]. Similarly, Yakata et.al investigated phosphorylated STAT3 20

expression in human gastric carcinomas and discovered patients with positive phospho-STAT3 expression had an unfavourable prognosis outcome when compared to those with negative phospho-STAT3 expression (p<0.05) [219]. Moreover, Ma and colleagues analysed 45 CRC tissues and correlated the overexpression of STAT3 activity is associated with advanced CRC (p=0.026) [60]. In line with these findings, elevated STAT3 activity is observed in other malignancies. For instance, in 125 cervical squamous-cell carcinoma specimens, STAT3 was identified as a poor prognostic marker [220]. Phosphorylated STAT3 expression was also highly expressed in advanced cancers, compared to ‘early’ cancers, further supporting its utility as a poor prognostic marker. Surprisingly, STAT3 activation has been shown to also correlate with a favourable clinical prognosis in colorectal cancer [221] and node-negative breast cancer [222] through tissue microarray and immunohistochemistry analysis. The recently dissected role of STAT3 in immune, stromal and epithelial compartments identifies diverging duties for this transcription factor, highlighting the need for a clear description of the STAT3 “high” cell populations analysed in these studies. Nonetheless, as mentioned previously, a number of studies have shown an essential role for STAT3 in cancer progression and the regulation of cell survival and proliferation [223, 224]. Gathered, due the association between STAT3 overexpression and patient survival, STAT3 has become a potential therapeutic target in the fight to combat tumourigenesis.

Table 1-1: Oncogenesis and STAT3 activation

TUMOUR TYPE SAMPLE TYPE OBSERVATION REFERENCE Human IL-6/IL-6R/GP130, JAK, Src contributes to enhanced STAT3 HT29, SW480, Caco-2, HCT116, DLD-1 [212, 225] cell lines activation Sporadic intestinal tumourigenesis requires IL-11 signalling; Mouse Sporadic mouse model and Gp130f/f mouse model has increased levels of IL-6 and IL-11. [5] models Gp130f/f Mice lacking one allele of STAT3 or IL-11Ra1 had consistently reduced tumour buden compared to control Tumour biopsies (grade 1, n=1; grade 2, n=28; Elevated STAT3 activity by IHC and quantitative PCR [226] COLORECTAL grade 3, n=3) CANCER Tumour biopsies (grade 1, n=0; grade 2, n=29; Elevated STAT3 activity by IHC [227] grade 3, n=2; grade 4, n=1) Patient tissue IL-6 and IL-11 mRNA expression were elevated in tumour tissue Primary CRC samples, n=14 [5] compared to unaffected GI tissues from the same patients

Tissue sections (grade 1, n=3; grade 2, n=16; Elevated EGFR and STAT3 was observed by IHC [61] grade 3, n=95; grade 4, n=34) U251, T98G, A172, U87-MG, D54; TATE, IL-6/IL-6R/GP130 induces the constitutive activation of STAT3 Human CGNH; IL-6 enhances STAT3 activation [228-230] cell lines LN18, LN229 OSM and LIF enhances STAT3 activation GLIOBLASTOMA Mouse GBM PDXs Constitutive activation of STAT3 observed by IHC [231] models Patient Tissue sections (grade 4, n=73) Elevated EGFR via EGFRvIII mutatant observed by IHC [232] tissue Human EGF/EGFR, IL-6/IL-6R/GP130, HGF, Src are shown to A549, H1299, H386, H460, H441, H23, H322 [233] cell lines contribute to elevated STAT3 expresion Tissue sections (grade 1, n=72; grade 2, n=94; Correlation between p-EGFR expression and p-STAT3 expression [234] LUNG CANCER grade 3, n=10) by IHC Patient Patient (n=100 adenocarcinomas and 27 tissue Increased p-STAT3 expression was observed in patient tissue squamous cell carcinomas) vs. normal tissue [235] compared to normal tissue via IHC (n=56) MDA-MB-468, SK-BR-3, Hs578T, MDA-MB- Human EGF/EGFR, IL-6/IL-6R/GP130, Src, JAK kinase activities 435, DU4475, MCF-7, T-47D, MDA-MB-231, [236-238] BREAST cell lines contributes to constitutive STAT3 MDA-MB-361, MDA-MB-453 CANCER Patient Patient sections (grade 3, n-45) vs normal tissue STAT3 and Src signalling were significantly expressed in IHC [239] tissue (n-45) MULTIPLE Human KMM1, OPM2, U266, KMS5, KMS11, KMS12, IL-6/IL-6R/GP130 contributes to continual STAT3 expression [240, 241] MYELOMA cell lines KMS18, KMS20

MULTIPLE Patient Patient sections (grade 1, n=39; grade 2, n=9) Constiutively active STAT3 was detected in patient tissue by IHC [240] MYELOMA tissue Human HN4, HN6, HN8, HN12, HN13, HN17, HN19, STAT3 activity was demonstrated at basal levels [242, 243] HEAD AND cell lines HN22, HN30, and HN31, HaCaT NECK CANCER Patient Patient sections (grade 1, n=11; grade 2, n=25; Enhanced p-STAT3 expression correlated with poor survival by [244] tissue grade 3, n=1; grade 4, n=14) IHC PROSTATE Human LNCaP, DU145, PC3, and TSU, NRP-152, NRP- JAK1/2 activity and IL-6 signalling pathways was shown to [245-247] CANCER cell lines 154 contribute to constiutively actived STAT3 MDA-MB-468, SK-BR-3, Hs578T, MDA-MB- Human EGF/EGFR, IL-6/IL-6R/GP130, Src, JAK kinase activities 435, DU4475, MCF-7, T-47D, MDA-MB-231, [236-238] BREAST cell lines contributes to constitutive STAT3 MDA-MB-361, MDA-MB-453 CANCER Patient Patient sections (grade 3, n-45) vs normal tissue STAT3 and Src signalling were significantly expressed in IHC [239] tissue (n-45)

Role of STAT3 in mediating resistance to molecular targeted therapy Many strategies to block growth factor receptor kinases, cytokine receptor kinases and intracellular non-receptor kinases have been developed over the past several decades in an attempt to inhibit tumour growth and metastasis. The presence of pre-existing intrinsic resistance mechanisms and the ability of tumours to develop or acquire resistance to these inhibitors is common and occurs through several proposed mechanisms.

One pathway strongly associated with STAT3 driven activity is the erbB family. The erbB family consisting of EGFR, ErbB2/Her2, ErbB3/Her3 and ErbB4/Her4 is one of the most intensely studied, and targeted, receptor tyrosine kinase families. Indeed, several therapeutic agents directed against this receptor family have successfully entered clinical trials and have been FDA-approved for numerous cancer types, summarised in Table 1-2. Despite FDA approval, therapy based around Cetuximab, Panitumumab, Gefitinib or Erlotinib (which all target the EGFR) only result in small increases in overall survival of cancer patients. These refractory outcomes have been attributed to several molecular mechanisms of resistance including point-mutations in the EGFR catalytic domain [248, 249], increased ubiquitination and reduced EGFR levels, increased activity of alternative receptor tyrosine kinases such as Her2, Her3, c-Met and IGF-R1 and AXL [250-254], mutations in the K-RAS gene (present in 30-40% of mCRC) [255-262], and alterations or mutations in B-Raf, PTEN, PI3-K H-Ras, N-Ras, cortactin and SphK1 [263-269]. In addition, several recent studies have demonstrated that STAT3 also plays a role in promoting resistance to EGFR therapeutics. Dobi and colleagues evaluated STAT3 phosphorylation levels in tumour tissue from patients with metastatic colorectal carcinoma by immunohistochemistry and correlated this expression to patient outcome [270]. Importantly, they demonstrated that only 13% (3 out of 23) of patients with positive phospho-STAT3 staining displayed objective responses to Cetuximab and chemotherapy in second line treatment or beyond versus 41% (29 out of 71) of patients with negative phospho-STAT3 staining. In addition, the lack of phospho- STAT3 staining correlated significantly to time of progression and overall survival of these patients [270].

Table 1-2: Current FDA approved monotherapy inhibitors undergoing clinical evaluation in other malignancies

FDA APPROVAL CURRENT AND PAST CLINICAL TRIALS FOR OTHER CANCERS UPSTREAM KINASE Targets Disease Tumour type Phase Remarks References INHIBITORS Awaiting results for the assessment of [95, 271, 272] PANITUMUMAB EGFR Colorectal cancer Pancreatic cancer II Chemoradiation with Gemcitabine in NCT01175733 (VECTIBIX™) Combination with Panitumumab Metastatic and non-metastatic Squamous Cell Inconclusive for the assessment of Cetuximab CETUXIMAB colorectal cancer, metastatic and [94, 110, 273] EGFR Carcinoma of head II plus valproic acid in patients with Squamous (ERBITUX®) non-metastatic head and neck NCT00240682 and neck Cell Carcinoma of head and neck cancer Awaiting results in patients with metastatic or [274] ERLOTINIB locally advanced, unresectable pancreatic EGFR Non-Small Cell Lung Cancer Pancreatic cancer II NCT00497224 (TARCEVA®) cancer who have received up to one line of

gemcitabine based chemotherapy GEFITINIB Metastatic Non-Small Cell Lung Hepatocellular [275, 276] EGFR II Unpublished results (IRESSA®) cancer carcinoma NCT00071994 Completed: Bosutinib monotherapy does not [277] appear to be effective in recurrent BOSUTINIB Glioblastoma; II; NCT01331291; Src/Abl Ph+ CML glioblastoma: (BOSULIF®) CML II NCT02906696 Recruiting patients with Chronic Myeloid

Leukaemia for dose-optimization studies DASATINIB Completed: Dasatinib monotherapy was well- [278, 279] Src Ph+ CML and Ph+ ALL Myeloma II (SPRYCEL®) tolerated NCT00429949 IBRUTINIB Chronic Lymphocytic Leukaemia Recruiting patients with localized prostate [280, 281] Src Prostate cancer I/II (IMBRUVICA®) and Small Lymphocytic Leukaemia cancer undergoing radical prostatectomy NCT02643667 Metastatic Gastric or TRASTUZUMAB gastroesophageal junction Esophageal Recruiting patients with resectable [282, 283] HER2 I/II (HERCEPTIN®) adenocarcinoma, HER2- Carcinoma HER2+Esophageal Carcinoma NCT02120911 overexpressing breast cancer SILTUXIMAB IL-6 Metastatic renal Stabilised disease in >50% of progressive [284] Multicentric Castleman’s Disease I/II (CNTO 328) cytokine cancer metastatic Renal cell carcinoma patients NCT00265135 RUXOLITINIB Chronic Myeloid Recruiting patients with Chronic Myeloid JAK 1/2 Myelofibrosis I/II NCT01751425 (JAKAVI®) Leukaemia Leukaemia 25

An explanation for STAT3 mediated resistance against targeted therapy could possibly be due to compensatory signalling from uninhibited pathways related to STAT3 signalling. For instance, in human lung cancer cell lines that were largely in- sensitive to Erlotinib inhibition displayed reduced, EGFR, AKT and ERK1/2 activity but maintained STAT3 activity upon Erlotinib treatment, suggesting that refractory responses of these cells may be due to unabated STAT3 signalling [209]. Similar results were reported by Kim and colleagues who found that Gefitinib had little effect on either STAT3 activity or in vitro and in vivo growth of the H1975 lung cancer cell line, while Lapatinib (a dual inhibitor of EGFR and Her2) could reduce STAT3 activity and cell growth in both cell culture and animal xenograft experiments [285]. The combination of Lapatinib and Cetuximab further reduced STAT3 activity and tumor growth compared to single agent treatment [285]. Interestingly, combination of both Cetuximab and Erlotinib led to reduced STAT3 activity in colon cancer cells compared to single treatments [286]. Similarly, inhibition of PDGFRα with the tyrosine kinase inhibitor (TKI) Sunitinib resulted in reduced AKT, ERK1/2 and S6 kinase activity but had no effect on STAT3 phosphorylation in the Sunitinib-resistant NSCLC cell line NCI-H1703, suggesting that STAT3 activity may continue to drive cell proliferation in these cells and thus provide refractory outcomes when challenged with PDGFRα inhibition [209]. Moreover, in our laboratory we generated AG1478 (an EGFR inhibitor) resistant HN5 (HN5-AG) cells through long-term culturing in the presence of AG1478 and found that the HN5-AG resistant cells displayed more STAT3 activity after AG1478 treatment suggesting a possible alternate or compensatory pathway that maintains STAT3 activity when EGFR is de-activated [287].

Additional cell line models for acquired resistance to anti-EGFR therapy have identified increases in the activity of upstream molecules known to regulate STAT3 activation (EGFR, c-MET, IGF-1R, IL-6 and c-Src) and increased expression and activity in molecules known to be regulated by STAT3 (Cyclin D1, COX-2 and VEGF) in refractory versus sensitive cells [251, 252, 288-295]. These studies did not evaluate if STAT3 activity was elevated in the resistant versus sensitive cells, however they do provide “circumstantial evidence” that STAT3 activity may assist in the refractory phenotypes of these cells to anti-EGFR therapy. Taken together, these studies indicate that enhanced or unabated STAT3 activity may protect cells from

anti-EGFR therapy and may be important in driving tumour recurrence and acquired resistance. In support of this theory, Colomiere and colleagues demonstrated the cross talk of signals between IL-6R and EGFR via JAK2/STAT3 mediated be epithelial- mesenchymal transition in ovarian cancer cell lines, OCVA 433 and SKOV3 [296]. Both cell lines exhibit IL-6R and in the presence of EGF for 1 h resulted with a several fold increase of IL-6 mRNA expression. Additionally, enhanced IL-6 activity was observed in serum-free medium after EGF exposure. Their findings suggest the activation of STAT3 may occur through the direct activation of EGFR or IL-6R or indirect activation through IL-6 signalling and provides a key insight to the complex pathways involved.

In relation, others have shown a role of Src-STAT3 signalling in resistance to anti-HER2 inhibitors, including the anti-HER2 monoclonal antibody Trastuzumab, which is FDA-approved for the adjuvant treatment of HER2-positive breast cancer. Another study observed, chronic exposure of the gastric cancer cell line SNU216 to Lapatinib in culture resulted in acquired Lapatinib resistance. This cell line displayed epithelial-mesenchymal transition phenotype and sustained the activation of protein- kinase signalling pathways, including STAT3 in the presence of Lapatinib, further explaining the acquired resistance [297]. Interestingly, sub-clones of tumour cells with acquired resistance to Cetuximab (generated by long-term culturing in the presence of Cetuximab) also displayed greater Src activity [292, 298]. Although, STAT3 activity was not directly examined in these reports, one can hypothesise that Src-mediated resistance may be at least partly due to enhanced STAT3 activity especially as previous reports have shown that Src-mediated cell transformation requires STAT3 activity [299] and that EGFR and HER2 activates STAT3 often via a Src dependent signalling mechanism [298, 300]. Moreover, other reports indicate that sustained inhibition of Src activity with Dasatinib (an anti-Src inhibitor) results in STAT3 re-activation and cells overcoming Dasatinib’s inhibitory effects via the upregulation of JAK1/2 signalling in both head and neck squamous cell carcinoma and NSCLC, further highlighting the possibility for compensatory signalling [301, 302].

Similar findings linking STAT3 with tumour resistance to molecular targeted therapy have been seen in other receptor and intracellular signalling systems. Enhanced c-MET activity and subsequent JAK1/2 and STAT3 signalling was shown to be a critical mediator of resistance to MEK inhibitors including the clinically

relevant Selumetinib (AZD6244) in K-RAS mutated colorectal carcinoma [303]. Likewise, enhanced IL-6 secretion and increased STAT3 signalling can circumvent the effect of Selumetinib in low-grade childhood astrocytoma xenografts and NSCLC cell lines despite successfully reducing MEK activity, leading to Selumetinib resistance [304, 305].

Combinational strategies have also been implemented due to the concept of ‘cross-talking’ between receptors related to pathways known to drive carcinogenesis including STAT3. Due to the clinical approval of several anti-EGFR inhibitors, studies demonstrate pre-clinical combinational strategies with anti-STAT3 related inhibitors, summarised in Table 1-3. For example, Cetuximab, an EGFR inhibitor and IL-6ab, an IL-6 agent has demonstrated reduced cell viability in pharyngeal cancer cell lines, when compared to alone treatments [306]. In relation, the combination of Gefitinib and Erlotinib (EGFR antagonists) with Metaformin (IL- 6/STAT3 inhibitor) has observed anti-proliferative, anti-migratory and induced apoptosis in NSCLC cell lines [307]. Combinational chemo-treatments have also shown significant reduction in in-vitro and in-vivo studies with current EGFR inhibitors and STAT3 inhibitors, namely Gefitinib (targets EGFR) and S3I-201 (directly targets STAT3) in soft tissue sarcoma and also Cetuximab (inhibits EGFR) and STAT3 decoy antisense (STAT3 inhibitor) in head and neck and bladder cancer lines [308, 309]. Moreover, several combination treatments are undergoing clinical trials, Table 1-4. For instance, patients with Ph+ ALL have been associated with activated JAK/STAT, Src/Abl and/or PI3K pathways and poor clinical prognosis [310]. In an aim to combat these pathways, a phase I trial is currently recruiting Ph+ ALL patients to evaluate the combination of the JAK inhibitor, Ruxolitinib and the Src inhibitor, Dasatinib (NCT02494882). Similarly, patients with NSCLC are commonly associated with overexpressed EGFR and VEGFR and thus several pre- clinical studies have suggested both receptors could potentially suppress tumour growth in NSCLC and are currently recruiting patients in a phase I trial evaluating the effects of Gefitinib (EGFR inhibitor) in combination with Apatinib (VEGFR antagonist) in NSCLC patients (NCT03050411) [311, 312].

Combinational treatments of 2 mono target agents could potentially relieve tumour burden however, due to the complex tumour environment targeting 2 molecules amongst several other tumour driving pathways may not be effective. The

presence of compensatory signalling of uninhibited molecules, also known as ‘cross- talk’, is often seen to continually drive tumour growth and is evidently clear that many signalling pathways converge on STAT3 activation. Therefore, it is suggested that enhanced STAT3 activity plays a key role in mediating refractory outcomes to current molecular targeted therapies. Taken together, it would be most effective to target multi-pathways simultaneously in combination with current of care treatment, in an attempt to reduce the possibility of compensatory signalling pathways. Moreover, STAT3 activation also appears critical for tumour recurrence after first- line therapy and is important in subsequent acquired resistance when recurrent tumours are re-challenged.

Table 1-3: Pre-clinical studies in current combination chemotherapy analysing anti-EGFR and anti-STAT3 inhibitors

AGENT #1 AGENT #2 PRE-CLINICAL STUDIES

NAME TARGETS NAME TARGETS TUMOUR TYPE MODEL REFERENCE

CETUXIMAB EGFR IL-6ab IL-6 Pharyngeal In vitro cell viability [306] (ERBITUX®) GEFITINIB (IRESSA®), In vitro cell viability, migration, invasion EGFR Metformin IL-6-STAT3 NSCLC [307] ERLOTINIB and apoptosis, in vivo xenografts (TARCEVA®) GEFITINIB In vitro cell viability, apoptosis and in vivo EGFR S3I-201 STAT3 Soft tissue sarcoma [309] (IRESSA®) xenografts CETUXIMAB STAT3 decoy Head and neck, EGFR STAT3 In vitro cell viability and in vivo xenografts [308] (ERBITUX®) antisense Bladder AG1478 EGFR AG490 JAK2 Cervical In vitro cell viability [313] ERLOTINIB In vitro cell proliferation, colony formation, EGFR TG101348 JAK2 NSCLC [314] (TARCEVA®) apoptosis and in vivo xenografts

Table 1-4: Current clinical trials for STAT3 related combinational targeted therapeutics

AGENT #1 AGENT #2 CURRENT CLINICAL TRIALS

NAME TARGETS NAME TARGETS TUMOUR TYPE PHASE REFERENCE

GEFITINIB EGFR Apatinib VEGFR NSCLC I NCT03050411 (IRESSA®) GEFITINIB EGFR Osimertinib EGFR T790M mutation NSCLC I NCT03122717 (IRESSA®) RUXOLITINIB JAK Dasatinib Src Ph+ ALL I NCT02494882 (JAKAVI®) RUXOLITINIB JAK Nilotinib BCR-Abl, c-kit, PDGFR Ph+ CML and Ph+ ALL I/II NCT01914484 (JAKAVI®) BEVACIZUMAB VEGFR-1, VEGFR-2, Advanced Renal Cell VEGFR Sunitinib I/II NCT02919371 (AVASTIN®) FLT3, c-kit Carcinoma AXITINIB VEGFR Bosutinib Src/Abl Ph+ CML and Ph+ ALL I/II NCT02782403 (INLYTA®)

1.3 Targeting STAT3 with novel inhibitors The development of anti-STAT3 inhibitors represents a novel mechanism of tumour inhibition and show great promise in improving patient survival outcomes. STAT3 inhibitors are generally classified into two classes including, I.) Indirectly inhibiting STAT3 through upstream tyrosine kinase molecules known to drive STAT3 activity, and II.) Directly inhibiting STAT3 via known targetable STAT3 domains.

Current upstream STAT3 inhibitors Inhibitors targeting upstream molecules of STAT3 have been widely developed in the treatment of numerous diseases, including carcinomas. Primarily, these inhibitors include upstream targets of EGFR, IL-6 family of cytokines, Src and JAK family of kinases, as these are commonly overexpressed/overactivated in cancers leading to continual STAT3 activation [236, 315], summarised in Table 1-2. For instance, Cetuximab gained FDA approval for the treatment of mCRC patients with WT-KRAS and EGFR overexpression on the basis of the clinical trials (CRYSTAL and OPUS studies), which observed a significant median overall survival of patients treated with Cetuximab plus FOLFIRI compared to FOLFIRI alone (23.5 vs. 19.5months) [93]. Other EGFR inhibitors have also been FDA approved for other malignancies, including the FDA approval of Panitumumab (Vectibix™, Amgen Inc.) for the treatment of CRC, Erlotinib (Tarceva®, Astellas Pharma Inc.) and Gefitinib (Iressa®, AstraZeneca) for patients with Non-Small Cell Lung cancer [95, 271, 275, 316]. Notably, other upstream molecules known to drive STAT3 have also been targeted including the FDA approved Src inhibitors: Bosutinib (Bosulif®, Pfizer Inc.) and Dasatinib (Sprycel®, Bristol-Myers Squibb) currently used in the treatment of patients with Philadelphia chromosome positive (Ph+) chronic myeloid leukaemia (CML) and acute lymphoblastic leukaemia (ALL) [277, 278]. Alongside Src inhibitors, anti-JAK agents have also been FDA approved including Ruxolitinib (Jakavi®, Novartis) for the treatment of myelofibrosis and Tofacitinib (Xelgianz®, Pfizer Inc) for the management of rheumatoid arthritis [317, 318]. Moreover, RAF kinase inhibitors including Sorafenib, approved for the treatment of patients with unresectable hepatocellular carcinoma and advanced renal cell carcinoma and have been show to inhibit STAT3 [319-321].

The IL-6 family comprises of several cytokines known to also initiate STAT3 activity notably, IL-6, IL-11 and LIF [65, 184, 322]. Therefore, targeting these family members could also reduce the consequences of continued STAT3 activity however, there is limited success with clinical development of inhibitors against the IL-6 family of cytokines. In fact, Siltuximab (CNTO-328) is the only FDA approved anti-IL-6 receptor inhibitor for the treatment of patients with Multicentric Castleman’s Disease [323]. More importantly, IL-11, another IL-6 family member has been uncovered to demonstrate a greater dominance in activating STAT3 activity compared to its more studied counterpart IL-6 [5, 192, 193]. Despite these findings, there is currently no FDA approved anti-IL-11 inhibitor used in cancer therapy. Similarly, there is also no current FDA approved LIF inhibitor in clinical application.

Due to the FDA approval of these agents, several strategies are currently being implemented to treat other malignancies and diseases. This strategy allows pharmaceutical companies and research facilities to bypass lengthy procedures for approval as observed in newly developed inhibitors and drugs. For instance, the widely used EGFR tyrosine kinase inhibitors, Erlotinib and Gefitinib are FDA approved for the treatment of NSCLC and is currently undergoing further analysis, in phase II trials for the treatment of pancreatic and hepatocellular carcinomas (NCT00497224, NCT00071994). Additionally, the only FDA approved IL-6 inhibitor Siltuximab, underwent further evaluation in Phase I/II trials for the treatment of metastatic renal cancer and resulted with >50% of patients with stabilized disease (NCT00265135) [284]. Moreover, FDA approved Src inhibitors have also been evaluated for the treatment in other malignancies, including Dasatinib, currently provided to patients with Ph+CML and ALL and has completed phase II trials for relapsed or plateau phase multiple myeloma patients (NCT00429949) [278]. This study demonstrated Dasatinib to be well tolerated; however this inhibitor had minimal single agent activity as a monotherapy treatment [279, 324]. Moreover, Bosutinib, another Src inhibitor, approved for patients with Ph+CML and ALL has completed phase II trials for the treatment of recurrent glioblastoma, though their findings suggest limited efficacy as a monotherapy treatment option (NCT01331291) [277]. Despite modest results, the reason behind limited efficacy, as a monotherapy option was not thoroughly examined.

A possible reason for the lack of efficacy is that singular treatment of these JAK or Src inhibitors may not account for other uninhibited compensatory pathways, which further drive tumour-causing characteristics. It is unsure as to whether Src inhibitors can block JAK/STAT3 signalling and vice versa, leading to continued STAT3 signalling driven through as alternative upstream pathway. Due to the independent nature of signalling pathways involved in tumourigenesis the targeting of several molecules involved in these compensatory pathways would be most beneficial for cancer treatment.

Multi-tyrosine kinase inhibitors are agents that target several kinases or pathways simultaneously, and have been widely developed for several diseases. In particularly, these inhibitors have been further evaluated to demonstrate anti-tumour effects in other cancers, Table 1-5. For instance, Niclosamide, is currently FDA- approved as an anthelmintic drug, and has been shown to target pathways including Wnt/β-catenin, MTORC1, NF-κB and The Notch signalling pathways [325]. Ren and colleagues identified Niclosamide to also inhibit the STAT3 signalling pathway [326]. This study utilised prostate cancer cells with constitutively active STAT3 to potently inhibit STAT3 activation and transcription, which ultimately lead to, induced apoptosis, cell growth, and cell cycle arrest. A similar effect was also observed in another study conducted by You et.al, which also demonstrated reduced STAT3 activity in-vitro and in-vivo in various human lung cancer cells and colon cancer cells [327, 328]. Due to these findings, Niclosamide is undergoing phase I clinical trials and is currently recruiting patients with resectable colon cancer and patients with castration-resistant metastatic prostate cancer (NCT02687009, NCT02532114) [325]. In addition, Imatinib, another multi-tyrosine kinase inhibitor that predominately targets Bcl-Abl, c-kit and PDGFR is currently approved for the treatment of patients with Ph+ CML [329]. Moreover, further clinical studies have been implemented for the treatment of other malignancies, including a current phase III trial that is recruiting patients with advanced gastrointestinal stromal tumours (NCT02260505) [330]. Furthermore, Ponatinib, another multi-tyrosine kinase inhibitor, which targets Src, Abl, FGFR, PDGFR and VEGFR, is currently FDA approved for patients with Ph+CML and Ph+ALL [331]. Notably, in pre-clinical studies Ponatinib has also demonstrated potent anti-tumourigeneic properties in other cancers, including Lung cancers and Glioblastoma and is currently recruiting patients in phase II trials for

patients harbouring these tumours (NCT02478164; NCT01935336) [332, 333]. Other multi-kinase inhibitors have also been FDA approved for certain malignancies and is currently undergoing further analysis in clinical trials for other tumour types, including Afatinib, Sunitinib and Lapatinib (NCT02423525; NCT00606008; NCT00096447 respectively).

Whilst there are several FDA approved inhibitors that target molecules upstream of STAT3, there are also several inhibitors, which are not FDA, approved and are currently undergoing clinical trials for the treatment of a variety of cancers, Table 1-6. Prior to clinical evaluation, these inhibitors showed significant potency in reducing STAT3 activity in in-vitro and in-vivo studies. For instance, Saracatinib (AZD0530) is a Src inhibitor and has demonstrated anti-migratory, anti-invasive and anti-proliferative properties in both in-vitro and in a murine model of bladder cancer [334]. Moreover, similar results were also observed in lung cancer cells, prostate cancer, melanoma and CML and Ph+ALL cancers, inhibiting invasion and inducing apoptotic signals [335-338]. Saracatinib has completed phase II clinical trials in patients with metastatic melanoma. Unfortunately, minimal clinical activity as a single agent was observed, although this inhibitor was generally well tolerated with few adverse effects. Nonetheless, Src family of kinases are highly expressed in the brain and thus, Saracatinib has been evaluated as a treatment for Alzheimer’s disease and is currently evaluating patients in a phase IIa trial (NCT02167256) [339]. Another example is the tyrosine kinase inhibitor Pacritinib (SB1518), a JAK2 specific agent which demonstrates potent anti-STAT3 activity in AML cells and murine models and is currently recruiting patients in phase I trials (NCT02323607) [340]. Therapeutic efficacy of Pacritinib however has yet to be determined in the clinical setting. Gathered, the development of upstream kinase inhibitors has not only shown promising results but also as a potential therapeutic option for other malignancies with clinical trials underway.

Indeed there are great advances for the development of STAT3 related inhibitors however, the limitations of most STAT3 inhibitors have been observed to have great adverse effects leading to discontinued clinical development. For example, a Phase II study of an oral JAK2 and FLT3 inhibitor, Lestaurtinib (CEP-701) demonstrated modest efficacy in patients with myelofibrosis but induced frequent gastrointestinal toxicity and resulted with discontinued clinical analysis

(NCT00494585) [341]. In line with this study, in a Phase I trial, another JAK1/2 inhibitor, AZD1480, caused neurological adverse events in patients with solid tumours and was also discontinued (NCT01112397) [342]. Gathered, the need to limit toxicity of newly found inhibitors is critical and plays an important role during the developmental stages. Thus, identifying FDA approved inhibitors for the treatment of other malignancies, including CRC with an aim to target the STAT3 pathway allows for a greater fast tracked approach.

Table 1-5: Current multi-tyrosine kinase FDA approved inhibitors undergoing clinical evaluation in other malignancies

FDA APPROVAL CURRENT AND PAST CLINICAL TRIALS FOR OTHER CANCERS UPSTREAM KINASE Targets Disease Tumour type Phase Remarks References INHIBITORS Recruiting patients with stage Philadelphia chromosome positive III-IV lung cancer [331] PONATINIB Abl, Src, FGFR, (Ph+) chronic myelogenous Lung cancer; II; Recruiting patients for recurrent NCT01935336 (ICLUSIG®) PDGFR, VEGFR leukaemia and acute lymphoblastic Glioblastoma II glioblastoma that has not NCT02478164 leukaemia responded to Bevacizumab Wnt/b-catenin, Recruiting patients with STAT3, MTORC1, [325] NICLOSAMIDE resectable colon cancer and NF-kB and The Anthelmintic agent Colon cancer I NCT02687009 (NICLOCIDE®) patients with castration-resistant Notch signalling NCT02532114 metastatic prostate cancer pathway Unresectable hepatocellular SORAFENIB VEGFR, PDGFR, Recruiting patients with [343] carcinoma and advanced renal cell Thyroid cancer II (NEXAVAR®) Raf Advanced Thyroid Cancer NCT02084732 carcinoma Philadelphia chromosome positive Recruiting patients with Chronic DASATINIB Chronic Myeloid Src, Bcl-Abl, c-kit (Ph+) chronic myelogenous IV Myeloid Leukaemia for a dose- NCT02689440 (SPRYCEL®) Leukaemia leukaemia (CML) optimization studies Philadelphia chromosome positive Advanced [330] IMATINIB Bcl-Abl, c-kit, Recruiting patients with (Ph+) chronic myelogenous gastrointestinal Stromal III NCT02260505 (GLIVEC®) PDGFR gastrointestinal stromal tumours leukaemia tumours Pancreatic neuroendocrine tumours, Completed: Sunitinib was well SUNITINIB VEGFR-1, VEGFR- [344] kidney cancer and gastrointestinal Glioblastoma II tolerated however, there was (SUTENT®) 2, FLT3, c-kit NCT00606008 stromal tumours insufficient anti-tumour activity Completed: Lapatinib had [345] LAPATINIB Metastatic breast cancer with HER1/2 and EGFR Endometrial cancer II limited activity in patients with NCT00096447 (TYKERB®) overexpressed HER2 persistent endometrial cancer Recruiting patients with AFATINIB Metastatic non-small cell lung Glioblastoma I; Glioblastoma; NCT02423525 EGFR, HER2/3 (GILOTRIF®) cancer Metastatic; Chordoma II Recruiting patients with NCT03083678 Metastatic Chordoma

Table 1-6: Indirect STAT3 inhibitors undergoing clinical evaluation

CURRENT AND PAST CLINICAL TRIALS

UPSTREAM KINASE Targets Tumour type Phase Remarks References INHIBITORS SARACATINIB Metastatic Completed: Saracatanib had minimal clinical [338] Src II (AZD0530) Melanoma activity as a single agent in this population NCT00669019 Completed: KX2-391 has a favourable pharmacokinetic profile, is well tolerated, [346] KX2-391 Src AML I demonstrates preliminary evidence of biologic activity, and warrants further evaluation in Phase NCT01397799 II trials. Advanced INCB052793 JAK I/I Currently recruiting participants NCT02265510 malignancies PACRITINIB [340] (SB1518) JAK2 AML I Currently recruiting participants NCT02323607

Current direct inhibitors targeting STAT3 The other strategy pursued for the inhibition of STAT3 activity includes the development of molecules that specifically target STAT3-DNA interactions. The generation of these inhibitors are largely produced by several techniques, involving large high-throughput compound library screens, computer virtual screening with the crystallisation of the structure of STAT3 and the use of multiple ligand simultaneous docking (MLSD) [347-349]. The inhibitors identified include oligonucleotides, peptides and small molecules, all of which have the ability to inhibit STAT3 by targeting the DNA binding domain (DBD), STAT3-SH2 domain or the N-terminal domain [350]. These regions are essential for STAT3 function, including the ability to form STAT3 dimers and are therefore a possible target to combat STAT3 activity. Current agents that directly target STAT3 are summarised in Table 1-7.

Firstly, oligonucleotide decoy inhibitors are agents that mimic the STAT3 DNA binding site on the c-fos promoter, which specifically targets the STAT3 DBD [351]. The first clinical trial evaluating a STAT3 decoy double stranded oligonucleotide was published in 2012 [352]. This study described a phase 0 trial predominantly testing the toxicity and pharmacodynamics of the STAT3 decoy in patients with head and neck squamous cell carcinoma. Importantly, a single dose of the STAT3 decoy (ranging from 0.25 - 1g/injection) resulted in reduced STAT3 target genes, cyclin D1 and Bcl-XL in tumour biopsies post-treatment compared to expression in tumours from patients treated with saline. However, the STAT3 decoy formulation used in this study required intra-tumoural delivery due to its lack of stability if injected systemically. To overcome this issue, cyclic STAT3 decoy oligonucleotides have been designed and show greater stability and efficacious results in pre-clinical evaluation when administered systemically [353]. A phase I/II open- label, dose-escalation, dose-expansion study evaluating a STAT3 antisense oligonucleotide for the treatment of patients with advanced cancers is currently ongoing [354]. It is anticipated that further cyclic STAT3 decoys will now enter clinical trials evaluating systemic administration for the treatment of patients with cancers of varying tumour types. However, these inhibitors will require considerable lengthy evaluation and regulatory processes before the possibility of approval.

Peptide inhibitors have been developed to mimic STAT3 binding sequences to antagonize protein-protein interactions. A common target is the N-terminal domain,

responsible for protein-protein interactions and also mediates tetramerization of two Y705-phosphorylated STAT3 dimers for the transcription of STAT3 driven genes [355]. Moreover, this domain is essential for successful nuclear transportation of unphosphorylated and phosphorylated STAT3 monomers [356]. Synthetic peptide agents are also developed from large drug libraries, for instance ST3-H2A2 is a synthetic peptide inhibitor which specifically binds to the STAT3 N-terminal domain and has been shown in pre-clinical studies to alter the regulation of STAT3 target genes [357]. Peptide inhibitors have also been developed with the aim to target the SH2 domain. This domain is essential for the recognition of an Y705 phosphorylated STAT3 monomer with the SH2 domain of another Y705-phosphorylated STAT3 monomer, resulting in the formation of a homo-dimer [358]. STAT3 inhibitors targeting the SH2 domain are generally composed of synthetic peptides, responsible for the prevention of dimerization and therefore antagonizing the function and activation of STAT3 [359]. Several SH2 domain-binding peptides have been observed to inhibit STAT3 dimerization, reduce tumour growth and induce apoptosis in pre- clinical studies, notably the synthetic agents of APT STAT3-9R and LLL-12 [360-362]. In addition, Hayakawa et.al, identified a novel STAT3 inhibitor, OPB-31121 which strongly inhibits STAT3 and STAT5 activation in leukaemia, Burkitt lymphoma and multiple myeloma [363]. Interestingly, this study demonstrated that this inhibitor targets STAT3/STAT5 specifically and without affecting upstream kinase inhibition. Studies have identified the mechanistic action of OPB-31121 and demonstrated high affinity interaction with the SH2 domain [364, 365]. Unfortunately the use of OPB- 31121 is limited due to unfavourable pharmacokinetic profiles in patients with advanced solid tumours and patients with leukaemia causing a halt in further investigations (NCT01029509) [366]. However, these early phase clinical trials using STAT3 inhibitors offer some encouragement that STAT3 is indeed “druggable” and additional inhibitors require continued support to generate a clinically successful anti- STAT3 therapeutic agent. Furthermore, small molecules have also been developed to target the SH2 domain, such as Stattic. This compound was established based on a fluorescent polarization assay screen, which has been shown to reduce tumour growth in xenografts, and induces chemo- and radio-sensitivity in nasopharyngeal carcinoma [367, 368]. STAT3 SH2 domain inhibitors have been the most developed type of agents to directly target STAT3 however, similarly to other STAT3 inhibitors

described above, approval and routine use of these agents in the clinical setting requires further development and evaluation.

Despite advances in developing direct inhibitors of STAT3, there are several limitations. For example, there is currently no clinically approved inhibitor of DNA oligonucleotides against the DNA binding domain (DBD) of STAT3, primarily due to the poor pharmacokinetics, short half-lives and bioavailability observed in preclinical studies [369]. Moreover, the delivery of oligonucleotides has also been problematic with poor membrane permeability and unsuccessful tissue specific delivery. Nonetheless, studies are modifying oligonucleotides to combat these limitations, such as altering the phosphate backbone to improve stability and reduce degradation in vivo [369]. In addition, researchers and pharmaceutical companies have regarded STAT3 to be an ‘undruggable target’ with limited selectivity and thus are unwilling to pursue this target. A study conducted by Huang et.al, utilised an in-silico approach to inhibit the DBD of STAT3, and as a result this study developed a small-molecule STAT3 inhibitor, inS3-54 [370]. This inhibitor had anti-proliferative characteristics (IC50 OF 3.2-5.4µM) demonstrated in several cancer cell lines through the inhibition of STAT3. Despite promising in-vitro results, inS3-54 performed poorly when evaluated in in-vivo models and demonstrated poor pharmacokinetics and was not readily absorbed. Nonetheless, this study provides an insight to further therapeutic developments in the target of DBD of STAT3 and may not be as ‘undruggable’ as previously thought.

The use of peptide therapeutics are observed to be highly potent and specific however, these inhibitors suffer from instability through rapid degradation and some have poor membrane permeability, though significant improvements in peptide pharmacodynamics and pharmacokinetic profiles are currently underway [371]. In contrast, small molecules have the ability to efficiently cross the membrane; however the three domains of STAT3 contain large planar surfaces, providing great difficulty for small molecules to specifically bind and prevent large proteins from interacting. Moreover, the structure of several small molecule inhibitors has also shown non- specific binding, reducing the efficacy of these agents. For instance, several small molecule STAT3 inhibitors such as Stattic, contains a ketone group, known to form non-specific bonds with nucleophilic groups of peptides, resulting with lower interactions between inhibitor and STAT3 [372].

Table 1-7: Direct STAT3 inhibitors undergoing pre-clinical and clinical evaluation

INHIBITORS: DIRECT TARGETS TUMOUR TYPE MODEL PHASE CLINICAL OUTCOME REFERENCES TARGETS OF STAT3 In-vitro: cell Discontinued: Due to Leukaemia, Myeloma, [363] STAT3 SH2 proliferation assay adverse effects involving OPB-31121 Hepatocellular I/II NCT01029509 domain In-vivo: Subcutaneous peripheral nervous- carcinoma NCT01406574 xenograft system-related toxicity In-vitro: cell Advanced solid Discontinued: Due to [373] STAT3 SH2 proliferation assay OPB-51602 tumours, particularly I adverse effects involving NCT01184807 domain In-vivo: Subcutaneous in NSCLC lactic acidosis xenograft In-vitro: cell Colon, Head and proliferation assay Targeting 3’ Currently recruiting for Neck, NSCLC, In-vivo: Subcutaneous [374] AZD9150 untranslated part II Colon, NSCLC and Pancreatic, xenograft NCT02983578 of STAT3 Pancreatic patients Neuroblastoma Phase I: tolerable responses In-vitro: inhibits cell Glioma cell and acute viability, proliferation, STAT3 INHIBITOR III, STAT3 SH2 Currently recruiting for myelogenous induces apoptosis I NCT01904123 WP1066 domain Brain cancer patients leukaemia cells In-vivo: xenograft tumour models In-vitro: inhibits STAT3 SH2 nuclear translocation STATTIC Breast cancer N/A N/A [372] domain of STAT3 and induces apoptosis In-vitro: inhibits cell STAT3 SH2 Colon and Liver viability, colony LY5 N/A N/A [375] domain cancer formation and migration In-vitro: cell proliferation, invasion Pancreatic and Breast FLLL31 AND FLLL32 JAK2 and SH2 and luciferase assay N/A N/A [376] cancer In-vivo: Subcutaneous xenograft

In-vitro: inhibits cell viability, colony Breast, Pancreatic, STAT3 SH2 formation induced LLL-12 and Glioblastoma N/A N/A [377, 378] domain apoptosis cancer cell lines In-vivo: reduced tumour growth In-vitro: inhibits cell viability, proliferation STAT3 SH2 APT -9R Melanoma cell lines In-vivo: xenograft and N/A N/A [379] STAT3 domain allograft tumour models In-vitro: inhibits cell GALIELLALACTONE DNA binding viability, proliferation Prostate N/A N/A [380] domain In-vivo: xenograft tumour models In-vitro: inhibits cell CUCURBITACIN I DNA binding Osteosarcoma, Lung viability, proliferation N/A N/A [381, 382] domain and Breast cancer In-vivo: xenograft tumour models In-vitro: inhibits cell growth and induces S3I-201 DNA binding Breast cancer apoptosis N/A N/A [361] (NSC 74859) domain In-vivo: xenograft tumour models STAT3 SH2 In-vitro: inhibits cell 5,15-DPP Breast cancer N/A N/A [383] domain viability, proliferation

1.4 Concluding remarks and project significance The link between STAT3 and oncogenesis is strongly referenced in the literature and clinical setting. In fact, aberrant STAT3 activity driven by growth factors and cytokines (i.e. EGF, IL-6, IL-11, and LIF) is commonly observed to enhance cell survival, proliferation, migration and resistance to therapies. These tumourigenic characteristics have been shown to exist in many cancer types, including CRC however, very little success has been made for the development and clinical approval of such anti-STAT3 agents. Moreover, the IL-11 cytokine has recently been known to strongly drive tumourigenesis compared to the initially thought IL-6 family member and unfortunately in the clinical setting, there is currently no FDA approved anti-IL-11 inhibitor. Current targeted treatments have demonstrated modest results however; tumour recurrence to these agents is common. This occurrence is potentially due to compensatory signalling or ‘cross-talk’ of uninhibited pathways, allowing for continual cancer growth. Several pathways drive STAT3 activity and thus specifically targeting one of these pathways may not result in effective treatment due to other, alternative upstream mediators activating STAT3.

The road from drug discovery to clinical approval is a lengthy and tedious procedure. Therefore, to bypass such hurdles, large pharmaceutical companies commonly utilize the screening of pre-existing FDA approved agents in an attempt to ‘re-purpose’ current agents to treat other diseases in particularly cancer. Despite ‘fast- tracked’ findings, these large high-throughput screens lack the ability to specifically analyse protein specific activity.

The aim of this study was to analyse a panel of FDA approved agents in an attempt to identify inhibitors with underlying anti-STAT3 characteristics mediated by several pathways. Our drug screen is the first to specifically identify these FDA agents through a luciferase based STAT3 reporter assay. Once classified, further assessment was performed to analyse potential anti-IL-11-STAT3 activity in CRC.

This study identified Ponatinib as a lead compound and further explores Ponatinib as an alternative treatment strategy for patients with elevated STAT3 activity mediated by these STAT3 related pathways. Specifically these aims are:

1. To identify agents that inhibits STAT3 activity in CRC cells from a large panel of FDA-approved inhibitors. 2. To determine the efficacy of Ponatinib in functional assays (in-vitro) and in animal models. 3. To understand possible mechanisms associated with Ponatinib and IL-11 mediated signalling.

CHAPTER 2: Materials and Methods

2. CHAPTER 2 – MATERIALS AND METHODS

2.1 Cell Culture and Reagents The Ludwig Institute provided all 15 human CRC cell lines for Cancer Research (Parkville and Melbourne-Austin Branches), Table 2-1. All cells were maintained in Dulbecco’s modified Eagle’s Medium (Life Technologies, CA, U.S.A) and contained 10% fetal bovine serum (FBS) (DKSH, VIC, Australia), 2 mM glutamine, 100U/mL penicillin and 100µg/ml streptomycin (Invitrogen, CA, U.S.A).

Cells were incubated in a humidified atmosphere of 10% CO2 and at 37 °C.

Human IL-6 and human EGF were acquired from Life Technologies (catalogue number: PHC0066, PHG0311L respectively) and recombinant IL-11 was generated at WEHI, Melbourne, Victoria, Australia. The FDA drug screen was obtained from Selleck Chemicals LLC, TX, U.S.A (catalogue number: L1300) and the Luciferase Reporter Assay reagents were purchased from Promega, WI, U.S.A.

2.2 Generation of the Ad-APRE-luc Adenovirus Prior to the beginning of this project the Ad-APRE-luc adenovirus was generated in our department at the Royal Melbourne Hospital by the Zhu laboratory [1]. The pAPRE-luc DNA construct was digested with SacI and Bgl-II yielding an APRE-luc insert that was subsequently cloned into an in-house pENTR 1A-CAGA construct.68 LR recombination was then performed with the pAd/PL-DEST destination vector (Invitrogen) to generate the pAPRE-luc Adenoviral Expression plasmid. The Expression plasmid was digested with Pac I to expose the ITRs and then transfected into 293A cell line using Lipofectamine LTX transfection reagent (Invitrogen). Cells were harvested approximately 2 weeks after transfection when lysis was observed in the majority of cells. The adenovirus was amplified and used to detect STAT3 transcriptional activity in cultured cell lines.

2.3 Luciferase Reporter Assays Cells were seeded into 96 welled plates and infected with the adenoviral STAT3 reporter (Ad-APRE-luc) and were allowed to adhere overnight. After 24h, cells were exposed to EGF (50ng/mL), IL-6 (50ng/mL), DMSO or serum free media in the presence of ± inhibitor, where indicated for a further 24h. Post 24h, cells were

lysed and assessed for STAT3 luciferase activity with the use of the Luciferase Reporter Assay Kit (Promega) following the manufacturer’s instructions.

Table 2-1: CRC cell lines

2.4 Cell Viability Assays Indicated cells were seeded into 96 welled plates (2500 cells/well) and allowed to adhere overnight. Post 24h, cells were treated with varying concentrations of inhibitors, as indicated in Chapter 3 and 4, for 72h. After this incubation period, the CellTiter-Glo Luminescent Cell Viability assay (Promega) was used following manufacturer’s instructions, to determine cell viability in response to drug treatment. CellTiter-Glo lysis buffer reagent (50µL) was added to each well and incubated for 20 minutes at room temperature. An aliquot (40µL) was transferred to a fluroblok (Microlon®) 96 well plate and was read on the Glomax 96-well luminometer (Promega) to assess the levels of ATP used as an indicator for cell viability.

2.5 Antibodies and Western Blotting Cells grown under the mentioned conditions were lysed for 20 min (on an orbital shaker at 4°C) with lysis buffer (50 mM Tris [pH 7.4], 150 mM NaCl, 1%

Triton-X-100, 50 mM NaF, 2 mM MgCl2, 1 mM Na3VO4, protease inhibitor cocktail [cOmplete mini, Roche] and phosphatase inhibitor [PhosStop, Roche]) and clarified by centrifugation (13000 g for 20 min at 4 °C). Protein quantitation was performed using the Pierce ™ BCA Protein Assay Kit (Thermo Fisher Scientific) as per the manufacturer’s instructions, to equilibrate protein levels across all samples to a final protein concentration of 1 µg/µL. Whole cell lysates were prepared in boiling sample reducing agent and sample buffer both purchased from Invitrogen.

Proteins (20µg) were then separated by 10% Sodium Dodecyl Sulfate– Polyacrylamide Gel Electrophoresis (SDS-PAGE) (Invitrogen) at 120V for 120 min, blotted onto nitro-cellulose membrane as a wet-transfer at 30V for 100 min and probed with the indicated primary antibodies for overnight on an orbital shaker platform at 4 °C, Table 2-2.

After overnight incubation, membranes were washed in Tris-Buffered Saline and Tween 20 (TBST) (Aniara, OH, U.S.A) for 5 minutes for a total of 3 washes. Secondary antibodies Goat Anti-Rabbit / Mouse Antibody Conjugated to Horseradish Peroxidase (Bio-Rad, CA, U.S.A) were diluted to 1:5000 with 1.5% Bovine Serum Albumin and incubated for 1h at room temperature on a rocker platform. The signal was visualized using the ECL chemilluminescence detection kit (GE Healthcare, NSW, Australia) and medical x-ray film (Fujifilm, TYO, Japan). All results were quantified by densitometry (Image J software, MD, USA).

Table 2-2: Primary and Secondary antibodies used for immunoblotting

PRIMARY ANTIBODIES Catalogue ANTIBODIES Dilution Source # ANTI-GAPDH Cell Signalling 1:1000 14C10 RABBIT POLYCLONAL Technologies ANTI-PHOSPHO-STAT3 (TYR705) Cell Signalling 1:1000 9145S MOUSE POLYCLONAL Technologies ANTI-STAT3 Santa Cruz 1:1000 sc-482 RABBIT POLYCLONAL Biotechnology ANTI-PHOSPHO-EGFR (TYR1068) Cell Signalling 1:1000 3777S RABBIT POLYCLONAL Technologies ANTI-EGFR Cell Signalling 1:1000 4267S RABBIT POLYCLONAL Technologies ANTI-PHOSPHO-ERBB2 Cell Signalling 1:1000 2243S (TYR1221/1222) RABBIT POLYCLONAL Technologies ANTI-ERBB2 Cell Signalling 1:1000 4290S RABBIT POLYCLONAL Technologies ANTI-PHOSPHO-ERBB3 (TYR1289) Cell Signalling 1:1000 2842S RABBIT POLYCLONAL Technologies ANTI-ERBB3 Cell Signalling 1:1000 12708S RABBIT POLYCLONAL Technologies ANTI-PHOSPHO-ERBB4 (TYR1284) Cell Signalling 1:1000 4767S RABBIT POLYCLONAL Technologies ANTI-ERBB4 Cell Signalling 1:1000 4795S RABBIT POLYCLONAL Technologies ANTI-PHOSPHO-AXL Cell Signalling 1:1000 5724S RABBIT POLYCLONAL Technologies ANTI-AXL Cell Signalling 1:1000 8661S RABBIT POLYCLONAL Technologies ANTI-PHOSPHO-C-MET Cell Signalling 1:1000 3077S (TYR1234/1235) RABBIT POLYCLONAL Technologies

ANTI-C-MET Cell Signalling 1:1000 8198S RABBIT POLYCLONAL Technologies

ANTI-PHOSPHO-P44/42 MAPK (ERK1/2) Santa Cruz (THR202/TYR204) 1:1000 4370S Biotechnology RABBIT POLYCLONAL ANTI-ERK1/2 Santa Cruz 1:1000 sc-93 RABBIT POLYCLONAL Biotechnology

ANTI-PHOSPHO-AKT (SER473) Cell Signalling 1:1000 9271S RABBIT POLYCLONAL Technologies ANTI-AKT Cell Signalling 1:1000 9272S RABBIT POLYCLONAL Technologies ANTI-GFP Cell Signalling 1:1000 2555S RABBIT POLYCLONAL Technologies SECONDARY ANTIBODIES MOUSE ANTI-GOAT IGG HRP 1:5000 170-6516 Bio-Rad CONJUGATE RABBIT ANTI-GOAT IGG HRP 1:5000 170-6515 Bio-Rad CONJUGATE

2.6 In-Vitro Wound Healing Assay Cells (DLD-1, SW48, LIM1215, LIM2405, Caco-2 and HCA-7) were seeded into 12-well plates (50,000 cells per well) and were cultured until 100% confluent. After which a wound was created with a p200 pipette tip. Media was replaced with Opti-MEM (Thermo Fisher Scientific, MA, U.S.A) for overnight incubation. Following serum starvation, cells were then treated with Ponatinib (0, 0.1, 0.5, 1µM) in cultured medium, whereby phase-contrast images were acquired at 0, 24 and 48h (and in the case of SW48, 72h) post-scratch. An inverted microscope (IX50, Olympus) and the Leica Application Suite (LAS v4.5) were used to process and capture images. ImageJ was utilised to quantify wound closure.

2.7 RNA Extraction and Real-Time PCR Analysis CRC cell lines of DIFI, DLD-1, SW48, LIM1215, HCA-7, Caco-2 and LIM2405 were seeded in 6 well plates (200,000 cells/well) and were allowed to adhere overnight. The cells were then starved in serum free, Opti-MEM (Thermo Fisher Scientific) for 24h. Following serum starvation, all cells were treated with indicated cytokine/ligand and varying doses of Ponatinib for 8h in serum free media (Opti-MEM), at 37ºC.

Total RNA was extracted with the RNeasy Mini Kit (Qiagen, Hilden, Germany) following the manufacturer’s instructions. The BioPhotometer (Eppendorf, Hamburg, Germany) was used to measure the quantity of the RNA samples at the absorption ratio of 260/280 nm. Reverse transcription was performed using the High Capacity RNA-to-cDNA Kit (Applied Biosystems, Waltham, MA, U.S.A) following the protocol provided from the manufacturer. Reverse Transcription-PCR was performed using the GeneAmp PCR System 2400 (Perkin Elmer, Waltham, MA, U.S.A) under the conditions of 37ºC for 60 minutes and 95ºC for 5 minutes at a reaction volume of 20µM. In order to quantify the transcripts of the genes of interest, real-time PCR was performed using the ViiA 7 Real-Time PCR system (Applied Biosystems). Each reaction mixture contained 2µL cDNA, 18µL of Taqman Fast Advanced Master Mix (Applied Biosystems) and 1µL of the primers shown in Table 2-3. PCR conditions included an initial holding period at 50ºC for 2 minutes followed by a 95ºC hold period for 20 seconds and subsequently 40 cycles of denaturing at 95ºC for 1 second and annealing at 60ºC for 20 seconds. GAPDH was used as a

reference gene. The relative expression from amplified RNA samples was calculated using the 2-∆∆CT method [384].

Table 2-3: PCR primers for amplifying SOCS3 and GAPDH

TARGET GENE ORIGIN COMPANY DETAILS TaqMan Gene Expression Assay (Applied SOCS3 Homo sapiens Biosystems) (Hs02330328_s1) TaqMan Gene Expression Assay (Applied GAPDH Homo sapiens Biosystems) (Hs02758991_g1)

2.8 Human Phospho-Receptor Tyrosine Kinase Array DLD-1 and DIFI CRC lines were seeded at 1x106 and 1.5x106 respectively in

10cm Petri dishes and were allowed to adhere overnight at 10% CO2, 37°C. After which, the cells were serum-starved in serum free media for 24h. Cells were then treated with and without ligand (DLD-1 and IL-11, DIFI and EGF) and Ponatinib (1µM) for 1h at 37°C. Cells were suspended in lysis buffer (Section 2.5) for 20 minutes at 4°C on a rocking platform. After incubation, cell debris was removed by centrifugation at 13000g for 20 min at 4°C. Protein concentrations were performed using the Pierce BCA Protein Assay Kit (Thermofisher, MA, U.S.A) following the instructions provided. Cell lysates were prepared at a concentration of 300µg and were analysed using the Phospho-Receptor Tyrosine Kinase Array Kit (R&D Systems, MN, U.S.A), following the manufacturer’s instructions; https://resources.rndsystems.com/pdfs/datasheets/ary001.pdf. Briefly, samples were incubated overnight at 4°C on a rocking platform and were incubated with secondary antibodies (obtained from kit) for 1h at room temperature on a rocking platform. After which chemiluminescent detection (obtained from kit) was performed and developed on X-ray film (Fujifilm). The pixel density for each spot of the array were analysed on Image J and graphed on GraphPad Prism (GraphPad Software, CA, U.S.A).

2.9 Subcutaneous Xenograft Mouse Model Subcutaneous xenograft models were performed to demonstrate Ponatinib's anti-tumour effect. CRC cell lines of DLD-1 (2.5 x 106) and SW48 (5 x 106) in 100µL of culture media were inoculated subcutaneously into both ventral flanks, anterior to the hind leg of 6-10 week old BALB/c nude mice (Animal Research Centre). Tumour volumes were recorded manually with electronic callipers on a daily basis. This volume was measured using the formula (length x width2)/2, where the length was the longest axis and the width was measured at right angles to the length. Tumours were allowed to reach approximately 100mm3 and were then separated into 3 groups of 5 mice. The groups received daily doses of orally delivered Ponatinib at doses of 0, 10 and 30mg/kg for 10 days with daily-recorded tumour volumes. After which, tumours were further measured for an additional 3 days post Ponatinib treatment. Ponatinib was formulated in aqueous 25mmol/L citrate buffer (pH = 2.75) [385]. Mice were

sacrificed when deemed unethically acceptable whereby tumours were collected and weighed.

2.10 Plasmids and Stable Transfection IL-11R To determine whether over-expression of IL-11R plays a role in Ponatinib’s inhibitory effect, DLD-1, SW48 and LIM1215 CRC cell lines were stably transfected with 10µg of human IL-11R (R&D) plasmid using FuGENE HD Transfection reagent (Promega). Similarly, to L-gp130 transfection, single cell-clones were derived with stable expression of L-gp130 by seeding 1x106 transfected cultured cells into 10cm tissue culture dishes. G418 (Sigma) at 1.0µg/ml in standard cultured media was added to adhered cells every 2 days over 1 month. Over this period, single-cell clones were picked with a 200µL pipette, which was then transferred to 12 well plates for expansion and further characterization.

L-gp130 To mimic activated IL-11/IL-6 signalling, DLD-1 and LIM1215 cell lines were stably transfected with the constitutively active L-gp130 construct at 10µg (supplied by Putoczki Laboratory, WEHI, Australia) using FuGENE HD Transfection reagent (Promega). Single cell-clones were derived with stable expression of L-gp130 by seeding 1x106 transfected cultured cells into 10cm tissue culture dishes. G418 (Sigma Aldrich, MO, USA) at 1.0µg/ml in standard cultured media was added to adhered cells every 2 days over 1 month. Over this period, single-cell clones were selected with a 200µL pipette, which was then transferred to 12 well plates for expansion and further characterization.

STAT3C-GFP To mimic activated STAT3, DLD-1 colon cancer cell line was stably transfected with constitutively active STAT3C-GFP plasmid (10µg) with a neomycin resistance system and with the aid of FuGENE (Promega). Cells were selected with G418 (Sigma Aldrich) at 1.5µg/ml in cultured media for several weeks. Notably, this was achieved with the help our research assistant; Lucy Paradiso who developed the STAT3C-GFP construct. The transfection efficiency was visualized over several days by green fluorescent protein (GFP) expression (IX50 inverted fluorescence microscope, Olympus, VIC, Australia). After which, the cells were prepared for

Fluorescence-activated cell sorting (FACS) with Sandy Fung at The Monash Institute of Pharmaceutical Sciences. Briefly, this process sorted the mixed population into GFP-tagged cells and non-GFP tagged cells. Both populations were further cultured and were allowed for expansion in the presence of selection media (G418) and were used for further analyses with Ponatinib.

2.11 Survexpress data mining SurvExpress (http://bioinformatica.mty.itesm.mx/SurvExpress) was used to analyse gene expression levels of STAT3-related regulators within colorectal cancer tissue. SurvExpress was developed by Aguirre-Gamboa and colleagues, consisting of over 20,000 cancer samples within 130 datasets with censored clinical information [4]. Data sourced from SurvExpress was used for this study in the differential expression analyses of STAT3-related regulators including IL-6, IL-6R, IL-11, IL- 11R, gp130 and SOCS3, comparing colorectal cancer patients with low and high expression vs. survival.

2.12 Statistical analysis All statistical analyses were performed using an unpaired, two-tail Student’s t test. All data sets, including IC50 values were generated using the program GraphPad Prism6 (Prism 6.04, CA, USA), representing mean ± SD. A probability value (p- value) if less than 0.05 was considered statistically significant and indicated using asterisks as follows: *p<0.05, **p<0.01, ***p<0.001.

CHAPTER 3: Identifying inhibitors to antagonise STAT3 driven signalling

3. CHAPTER 3 – IDENTIFYING INHIBITORS TO ANTAGONISE STAT3 DRIVEN SIGNALLING

3.1 Introduction Colorectal cancer and STAT3 related signalling Colorectal cancer (CRC) is a significant cause of mortality and morbidity globally, in particularly for Australians, with a predicted 1 in 12 individuals being diagnosed with CRC by the age of 85 [386]. For over two decades 5-Fluorouracil based chemotherapy has been the backbone treatment alongside Oxaliplatin and Irinotecan. Moreover, targeted therapy has been emerging as a treatment option for CRC patients. Despite significant advances, tumour recurrence is commonly observed amongst patients and often requires alternative treatment strategies.

Various studies have also shown the correlation between high STAT3 activity and cancer progression amongst several tumour types, including CRC, breast, NSCLC and prostate, as detailed in Chapter 1 [212, 387-389]. Considerable evidence has demonstrated an essential role for STAT3 in the regulation of genes participating in tumourigenesis, including proliferation, apoptosis, invasion/migration and resistance to therapies [390]. STAT3 is activated through phosphorylation in response to several cytokines (IL6, IL-11, TNF-a, OSM, LIF) and growth factors (EGF, PDGF, HGF). Research strongly suggests that the overexpression and/or hyperactivation of upstream drivers such as IL-6R and EGFR are responsible for constitutively active STAT3 and therefore contributes to enhanced tumourigenesis [183, 236, 391, 392].

Common enhanced upstream mediators of STAT3 observed in CRC include, EGF, IL-6 and IL-11. For instance, overexpression of EGFR is observed in 25-77% of CRC cases and is commonly linked with advanced tumour stage and poor prognosis [393, 394]. Additionally, at basal levels, studies have shown EGFR overexpression in DIFI cell lines, as a result of high level amplification of the EGFR gene locus [395] and subsequent enhanced levels of STAT3 activity upon EGF stimulation. Moreover, our laboratory has also demonstrated the overexpression of EGFR contributes to continual activation of STAT3 and increased tumourigenicity [1]. The IL-6-gp130 pathway is also known to mediate STAT3 activity in CRCs and has also been associated with poor survival [322, 396]. Research has also shown in certain CRC

lines, like DLD-1, with recombinant IL-6, elevating STAT3 activity and tumourigenesis [397-400].

Drug screens and role in cancer therapy Drug screens have become a well-established screening technique used to identify potential small molecules and/or agents in the treatment for several diseases, including cancer. The advantage for a high-throughput screen is to quickly and cost effectively identify compounds that can target cancer-driven signalling molecules, as potential agents to reduce tumourigenicity and ultimately, be used in the clinic. Drug screens can be performed in-silico however in-vitro screens with mammalian cells are most common. These screens include, cytotoxic assays, gene expression analysis with microarrays and ELISAs. High-throughput drug screens often assess novel compounds that require further analysis when potential ‘lead’ compounds are found. These candidates go through large scale pre-clinical and clinical testing before approval, which often require years to complete. Furthermore, many of these lead candidates do not make it passed pre-clinical and clinical testing due to several hurdles including safety, tolerability, toxicity and efficacy issues. Ultimately, these methods are tedious, time consuming and require large amount of resources to gain possible approval for use in the clinics.

Rationale and Aims While the role of STAT3 related pathways and tumour progression are well known in CRC [60, 63, 227, 401], identifying inhibitors, which target multiple STAT3 driven pathways for CRC treatment, is limited. Despite significant advances in drug screening and development, the FDA approval of such agents require lengthy procedures and is expensive. In our study, we bypass many of these regulatory procedures by utilising 1167 FDA agents that have all been previously FDA approved for patient treatment. This panel analysed consists of various approved agents used in the clinical field for the treatment of diseases including, cancer, cardiovascular disease, immunology, metabolic disease, endocrinology and neurological diseases. This chapter focuses on the evaluation of a large panel of 1167 FDA approved drugs (Selleck) as potential candidates with underlying anti-STAT3 properties for reducing STAT3 activity driven by EGF, IL-6 and IL-11, all known to contribute with the progression of CRC [5, 61, 322, 402]. Specifically, the aim of this chapter was to:

• Identify agents that inhibit STAT3 activity driven by EGF, IL-6 and IL-11 in colon cancer cells from a large panel of FDA-approved inhibitors. For the validation of potential STAT3 reducing agents, our FDA drug screen was segregated into 3 sections: initial, secondary and tertiary drug screen. In our initial drug screen we utilized a STAT3 luciferase reporter assay, which allowed for the measurement of quantified STAT3 activity, which is proportional to luciferin light intensity [1]. Our secondary and tertiary drug screens analysed the protein expression (by western blot analysis) of potential candidates for reducing EGF, IL-6 and IL-11 driven STAT3 properties. The cell viability for these candidates was also further assessed.

3.2 Results Ligand and cytokine mediated STAT3 expression in human CRC cells To determine which cell lines were most suitable for our drug screen, we first examined the EGF and IL-6 mediated STAT3 activity in 15 CRC cell lines by STAT3 luciferase reporter assay. Stimulation of EGF and IL-6 resulted in elevated STAT3 activity across all cell lines, with some cell lines exhibiting greater activity than others, compared to control (Figure 3-1, A.). Outstandingly, STAT3 was greatly enhanced with EGF treatment of DIFI lines (fold change with EGF vs. control: 602.0 vs. 1) and IL-6 stimulation of DLD-1 lines (fold change with IL-6 vs. control: 225.6 vs. 1) (Figure 3-1, B.). For this reason, DIFI and DLD-1 cells were chosen for further experiments outlined in this chapter.

Initial drug screen: 1167 FDA agents and the effect of STAT3 activity The ability of 1167 FDA approved agents to inhibit STAT3 activity in EGF- induced DIFI and IL-6-induced DLD-1 CRC cells were initially performed utilising a STAT3 luciferase reporter assay. As expected, both EGF and IL-6 mediated significant increases of STAT3 activity, compared to basal levels Supplementary Table 3-1. Importantly 89 agents reduced EGF mediated STAT3 activity, while 92 inhibitors reduced IL-6 induced STAT3 activity by greater than 50% respectively (Table 3-1). A list of the STAT3 luciferase activity results for all FDA approved inhibitors is found in Supplementary Table 3-1. Importantly, 51 agents were found to exhibit greater than 50% STAT3 inhibition in both EGF and IL-6 mediated pathways (Figure 3-2 and Table 3-2). In particular, one tested agent that was able to reduce EGF and IL-6-induced activation of STAT3 and thus of interest was the tyrosine kinase inhibitor, Ponatinib (Iclusig™, Takeda Pharmaceutical Company Limited and ARIAD Pharmaceuticals) (Figure 3-3).

A. 1000 *** Control (DMSO) EGF 500 IL-6

***

10 * **

(fold (fold change) * * ** * * 5 ** ** *

STAT3 Luciferase activity ** * * *

DIFI SW48 HT29 KM12 DLD-1 HCA-7Caco-2 SW480 LOVO LIM1215 LIM2405 LIM2550LIM2537 LIM2099 COLO205

Human CRC cell lines

Figure 3-1: The effect of EGF and IL-6 induced STAT3 activity in 15 colorectal cancer cell lines

The STAT3 luciferase reporter assay was performed to analyse the effect on EGF, IL-6 and IL-11 induced STAT3 activity across a panel of 15 CRC cell lines (A.). Increased STAT3 activity is representative of a greater fold change. Cells were infected with Ad-APRE-luc virus and were allowed to adhere in a 96 well plate for 24 h. Following overnight incubation, EGF (50ng/mL) and IL-6 (50ng/mL) were added for an additional 24 h. After, cells were lysed and assessed for STAT3 luciferase activity using the Luciferase Reporter Assay Kit (Promega). Data is representative of at least 3 individual experiments each performed in triplicates. Summary of CRC cell lines with significantly enhanced levels of EGF and IL-6 and IL-11 induced STAT3 activity (B.). Data represents mean ± SD of the averages of at least 3 individual experiments, *p<0.05, **p<0.01, ***p<0.001, two-tailed t-test.

DIFI DLD-1 STAT3 activity (%) EGF IL-6 ≥100 626 437 75 - <100 386 527 50 - <75 70 111 25 - <50 36 42 0 - <25 53 50 TOTAL 1167 1167

Table 3-1: The initial FDA drug screen

The effect of 1167 FDA-approved agents on EGF and IL-6 mediated STAT3 activity in DIFI and DLD-1 CRC cell lines. STAT3 transcriptional activity was determined using a bioluminometer, 48 h after infection with the Ad-APRE-luc adenovirus and 24 h post drug and ligand treatment. Data shown represents the averages of at least 3 individual experiments.

Figure 3-2: FDA inhibitors from the first drug screen found to decrease STAT3 activity by ≥50%

From the initial drug screen, 89 inhibitors were found to reduce EGF mediated STAT3 activity and 92 agents in IL-6 mediated STAT3 activity (A.). Interestingly, 51 FDA agents were shown to inhibit both EGF and IL-6 mediated STAT3 pathways. (B.) Identifies the 51 agents and relative STAT3 activity by the luciferase reporter assay. Numerical results for these inhibitors are shown in Supplementary Table 3-2. Data presented represents at least 3 independent experiments in triplicates. ***p<0.001, two-tailed t-test.

EGF IL-6 n = 51 n = 38 n = 41

Table 3-2: Summary of 51 candidates selected from the initial drug screen with reduced STAT3 activity by greater than 50%

Summary of 51 successful candidates selected from the STAT3 reporter luciferase drug screen with reduced STAT3 activity by ≥50% compared to ligand only control and DMSO control. Data shown represents the average of 3 independent experiments, each with 3 experimental replicates denoted here as mean ± SD.

DIFI (EGF) DLD-1 (IL-6) Number FDA Inhibitors STAT3 activity (%) SD STAT3 activity (%) SD 0 Control (DMSO) 0 0 0 0 0 Control (EGF/IL-6) 100 4 100 5 1 Ponatinib 3.1 0 3.8 1 2 Sorafenib 12.6 1 32.8 2 3 Diethylstilbestrol 43.2 4 22.2 5 4 Miconazole nitrate 37.5 4 16.1 3 5 Dronedarone HCl 19.1 3 38.3 4 6 Pyrithione zinc 0.1 0 0 0 7 Closantel 0.1 0 0 0 8 Triclabendazole 34.1 3 13.1 4 9 Closantel Sodium 12.4 3 0.2 0 10 Erlotinib HCl 0.1 0 40.7 4 11 Regorafenib 37.7 7 41.6 3 12 OSI-420 0.2 0 22.1 3 13 Vandetanib 0 0 47.6 2 14 Niclosamide 25.3 2 4.4 1 15 Bosutinib 3.4 1 40.5 5 16 Dasatinib 0.1 0 44.3 9 17 Sunitinib Malate 48.1 3 28.3 2 18 Irinotecan 38.9 1 37.9 2 19 Doxorubicin 2 2 31.8 6 20 Epirubicin HCl 47.5 4 17.1 2 21 Topotecan HCl 10.6 2 1 0 22 Bortezomib 8.3 1 3.2 4 23 Carfilzomib 2.9 1 7.1 2 24 Ivermectin 2 0 0.7 0 25 Flunarizine 2HCl 30 3 36.5 4 26 Azelnidipine 5.5 1 19.5 3 27 Fludarabine Phosphate 39.9 3 4.5 2 28 Azacitidine 20.8 3 35.1 2 29 Vorinostat 10.2 1 22.6 3 30 Mycophenolate mofetil 36.1 5 21 4 31 Cyclosporine 36.3 3 30 5 32 Elvitegravir 30.3 2 21.7 4 33 Rimonabant 47.6 3 6.8 1 34 Daunorubicin HCl 0.1 0 0.4 0 35 Ouabain 1 0 1.7 1 36 Benzbromarone 14.1 4 4.7 1 37 Bexarotene 22.2 6 23.8 4 38 Mitoxantrone HCl 14 3 16.5 4 39 Myc ophenolic 23 5 9.9 2 40 Sertaconazole nitrate 39.4 5 9.7 3 41 Penfluridol 48.9 5 49.2 4 42 Cetylpyridinium Chloride 0.1 0 0 0 43 Tiratricol 33.9 3 5.3 2 44 Domiphen Bromide 0.1 0 0 0 45 Chlorquinaldol 4.3 1 17.2 5 46 Montelukast Sodium 6.3 1 2 0 47 Alexidine HCl 0.1 0 0 0 48 Emetine 3.6 0 0 0 49 Thonzonium Bromide 6.3 2 24.1 3 50 Broxyquinoline 17.8 5 23.4 4 51 Cetrimonium Bromide 0.1 0 0 0 67

A. 150 100 50 10 Luciferase 5 *** STAT3 activity (%) 0 EGF (50ng/mL) - + + Ponatinib (10µM) - - +

B. 150

100 Ponatinib

50 Control (EGF) Control (DMSO) 10

Luciferase 5 *** STAT3 activity (%) 0 IL-6 (50ng/mL) - + + Ponatinib (10µM) - - +

Figure 3-3: A representation of the effect of an analysed inhibitor from the inital

FDA drug screen proportional to STAT3 luciferase activity The effect of Ponatinib and STAT3 luciferase activity in (A.) DIFI cell lines with Ponatinib EGF stimulation and (B.) DLD-1 cell linesControl with (IL-6) IL-6 stimulation for 24 h. Data Control (DMSO) represented here are expressed as relative luciferase activity relative to control, mean ± SD, ***p<0.001, two-tailed t-test.

Secondary drug screen: Cell Viability and Western blot analysis Next, a secondary screen was performed to further validate the 51 agents selected from the initial drug screen that could successfully inhibit both EGF and IL-6 mediated STAT3 luciferase activity. For this secondary screen, western blot analysis and cell viability assays were performed.

Secondary drug screen: Cell Viability Cell viability was determined in all 51 candidates amongst DIFI and DLD-1 cell lines, analysing the number of viable cells based on ATP quantification that resembles metabolically active cells at both 1µM and 10µM (Table 3-3). As a result, 38/51 candidates reduced cell viability at 1µM in DIFI lines and 34/51 in DLD-1 cell lines by greater than 50%. Additionally, 49/51 agents at 10µM reduced viability (>50%) in DIFI lines and 50/51 inhibitors in DLD-1 cell lines. In total, 48/51 candidates at 10µM, demonstrated reduced viability by greater than 50% in both cell lines and 30/51 inhibitors at 1µM (Figure 3-4, A.)

Secondary drug screen: Western blot In conjunction with cell viability, all 51 agents were next evaluated to determine whether they could inhibit EGF and IL-6 induced phosphorylation of STAT3 at both 1µM and 10µM by western blot (Supplementary Figure 3-1.) At 10µM, there were 19/51 agents that reduced EGF mediated p-STAT3 and 26/51 candidates in the IL-6 induced p-STAT3 pathway to have shown a significant reduction in p-STAT3 activity by greater than 50% when evaluated by densitometry (Image J). Furthermore, at 1µM 8/51 agents decreased EGF stimulated p-STAT3 and 10/51 inhibitors decreased IL-6 mediated p-STAT3 activity by greater than 50%, when compared to ligand only. In total, 13/51 inhibitors at 10 µM and 4/51 candidates at 1µM significantly reduced the activity (>50%) in both EGF and IL-6 induced p- STAT3 pathways (Figure 3-4, B.)

Secondary drug screen: 9 FDA approved candidates Importantly, to allow further assessment for each agent, drugs selected that fulfilled the criteria of being able to significantly reduce cell viability by >50% at 10µM in both DIFI and DLD-1 cell lines and effectively reduce p-STAT3 at 10µM via western blot in both EGF and IL-6 mediated pathways, compared to controls. As shown in Figure 3-5 and Figure 3-6, 9/51 inhibitors successfully passed these requirements and were further evaluated in the tertiary screen. The details of these 9 FDA agents are presented in Table 3-4.

Table 3-3: The secondary FDA drug screen – Summary of 51 agents and the effect on cell viability

DIFI and DLD-1 cells were seeded in 96-well plates and were allowed to adhere overnight. Inhibitors were added at concentrations of 1µM and 10µM for 72 h. Cells were lysed and cell viability was determined using the commercially available Cell Titer-Glo kit (Promega) and was read on a bioluminometer. Data shown represents the mean ± SD of 3 independent experiments, each with 3 experimental replicates, *p<0.05, **p<0.01, ***p<0.001, two- tailed t-test. ns: non-significant.

DIFI DLD-1 1uM 10uM 1uM 10uM # FDA Inhibitors % SD p-value % SD p-value % SD p-value % SD p-value 0 Control (DMSO) 100 3 - 100 4 - 100 5 - 100 1 - 1 Ponatinib 7.0 1.7 *** 1.0 0.0 *** 30.4 4.7 *** 9.6 3.0 *** 2 Sorafenib 78.7 4.7 ** 24.0 2.2 *** 74.1 6.3 ** 6.5 0.9 *** 3 Diethylstilbestrol 99.1 7.4 ns 22.5 2.8 *** 73.8 7.4 ** 15.5 1.5 *** 4 Miconazole nitrate 27.2 2.3 *** 0.6 0.0 *** 95.4 8.3 ns 7.4 2.0 *** 5 Dronedarone HCl 38.2 3.2 *** 0.6 0.1 *** 37.2 3.6 *** 1.2 0.1 *** 6 Pyrithione zinc 0.0 0.0 *** 0.0 0.0 *** 0.8 0.0 *** 0.0 0.0 *** 7 Closantel 0.0 0.0 *** 0.0 0.0 *** 1.4 0.0 *** 0.1 0.0 *** 8 Triclabendazole 40.3 3.5 *** 0.9 0.1 *** 34.4 2.7 *** 0.8 0.0 *** 9 Closantel Sodium 28.5 2.6 *** 0.6 0.1 *** 11.7 1.3 *** 0.4 0.1 *** 10 Erlotinib HCl 1.8 0.0 *** 0.2 0.0 *** 48.4 5.4 ** 33.3 2.5 *** 11 Regorafenib 103.9 5.4 ns 28.1 1.7 *** 65.7 4.7 ** 23.0 1.9 *** 12 OSI-420 2.0 0.1 *** 0.8 0.0 *** 90.2 7.3 ns 80.9 6.3 ** 13 Vandetanib 48.7 2.3 *** 2.4 0.1 *** 66.6 3.8 ** 31.3 2.6 *** 14 Niclosamide 20.1 1.2 *** 0.9 0.1 *** 22.3 2.6 *** 16.6 3.0 *** 15 Bosutinib 67.8 3.5 ** 3.8 1.0 *** 62.7 4.9 ** 6.8 1.5 *** 16 Dasatinib 76.1 4.0 ** 5.4 1.4 *** 65.7 3.8 ** 14.8 3.0 *** 17 Sunitinib Malate 98.9 5.3 ns 6.9 1.9 *** 59.3 5.9 ** 24.0 3.6 *** 18 Irinotecan 62.3 2.3 *** 35.8 3.9 *** 51.3 3.0 *** 15.4 1.9 *** 19 Doxorubicin 53.1 3.7 *** 12.2 2.3 *** 36.5 2.8 *** 14.6 1.4 *** 20 Epirubicin HCl 31.0 1.8 *** 4.7 1.9 *** 38.8 3.7 *** 32.1 3.0 *** 21 Topotecan HCl 45.5 2.6 *** 8.0 2.9 *** 36.8 4.5 *** 27.6 1.0 *** 22 Bortezomib 8.8 2.0 *** 1.1 0.1 *** 3.7 0.0 *** 0.3 0.1 *** 23 Carfilzomib 0.6 0.1 *** 0.1 0.0 *** 1.3 0.0 *** 0.8 0.1 *** 24 Ivermectin 47.8 3.0 *** 1.5 0.6 *** 58.0 5.9 ** 22.1 1.7 *** 25 Flunarizine 2HCl 83.7 3.9 ** 2.7 0.1 *** 41.5 3.2 *** 9.0 1.7 *** 26 Azelnidipine 61.8 3.3 *** 39.4 2.8 *** 48.5 3.0 *** 44.6 3.9 *** 27 Fludarabine Phosphate 100.9 5.2 ns 93.8 3.6 ns 51.4 5.3 ** 25.7 1.9 *** 28 Azacitidine 38.8 1.7 *** 14.4 1.7 *** 109.6 5.4 ns 39.8 7.4 *** 29 Vorinostat 19.1 1.2 *** 0.8 0.1 *** 48.9 4.3 *** 4.0 0.6 *** 30 Mycophenolate mofetil 29.4 1.8 *** 23.0 2.0 *** 54.0 5.7 ** 50.3 2.6 *** 31 Cyclosporine 81.6 3.0 ** 4.8 0.3 *** 109.7 6.4 ns 20.3 4.6 *** 32 Elvitegravir 19.5 1.9 *** 1.9 0.1 *** 37.3 2.9 *** 10.9 0.9 *** 33 Rimonabant 24.5 2.8 *** 0.6 0.0 *** 49.5 4.6 ** 3.7 0.2 *** 34 Daunorubicin HCl 9.5 2.0 *** 1.2 0.1 *** 22.8 3.0 *** 15.0 2.0 *** 35 Ouabain 0.3 0.1 *** 0.2 0.0 *** 13.2 1.0 *** 10.7 2.5 *** 36 Benzbromarone 15.0 1.8 *** 0.3 0.0 *** 22.9 3.6 *** 1.6 0.1 *** 37 Bexarotene 35.8 1.8 *** 0.9 0.1 *** 126.3 8.5 ns 3.1 0.9 *** 38 Mitoxantrone HCl 33.3 2.3 *** 0.7 0.0 *** 50.5 3.7 ** 28.7 2.0 *** 39 Myc ophenolic 48.0 1.9 *** 47.5 3.7 *** 44.8 3.7 *** 41.3 1.4 *** 40 Sertaconazole nitrate 48.8 3.9 *** 1.4 0.0 *** 52.8 2.9 *** 1.1 0.9 *** 41 Penfluridol 0.1 0.0 *** 0.0 0.0 *** 15.2 1.4 *** 0.3 0.0 *** Cetylpyridinium 42 1.2 0.0 *** 0.0 0.0 *** 19.0 2.7 *** 0.3 0.0 *** Chloride 43 Tiratricol 90.5 2.9 * 55.1 2.7 *** 70.2 5.9 ** 17.4 3.7 *** 44 Domiphen Bromide 0.0 0.0 *** 0.0 0.0 *** 31.6 3.0 *** 0.6 0.1 *** 45 Chlorquinaldol 0.3 0.0 *** 0.2 0.0 *** 4.8 1.0 *** 4.1 1.3 *** 46 Montelukast Sodium 0.0 0.0 *** 0.0 0.0 *** 13.6 2.6 *** 0.3 0.0 *** 47 Alexidine HCl 0.0 0.0 *** 0.0 0.0 *** 3.7 0.6 *** 0.1 0.0 *** 48 Emetine 0.3 0.0 *** 0.2 0.0 *** 14.0 2.0 *** 3.4 0.1 *** 49 Thonzonium Bromide 0.2 0.0 *** 0.0 0.0 *** 19.1 2.6 *** 0.4 0.0 *** 50 Broxyquinoline 20.2 1.9 *** 6.0 1.9 *** 40.8 3.6 *** 25.5 2.6 *** 51 Cetrimonium Bromide 1.5 0.1 *** 0.0 0.0 *** 14.0 1.6 *** 0.2 0.0 ***

A. FDA candidates at 10µM FDA candidates at 1µM

DIFI: DLD-1: DIFI: DLD-1: n=48 n=30 n=1 n=2 n=8 n=4

B. FDA candidates at 10µM FDA candidates at 1µM

DIFI DLD-1 DIFI DLD-1 +EGF: n=9 +IL-6: + EGF: n=4 + IL-6: n=16 n=23 n=8 n=10

Figure 3-4: The secondary drug screen: Summary of cell viability and western blot analysis on 51 candidates

The summary of candidates and the effect on cell viability by greater than 50% (A.). Cell lines were subjected to 72 h of drug exposure, after which viability was measured with the Cell Titre Glo following manufacturer’s instructions. Data shown is representative of 3 individual experiments performed in 3 replicates. (B.) The summary of candidates and the effect on EGF and IL-6 mediated p-STAT3 activity via western blot analysis. Cells were

incubated with growth factor/cytokine and inhibitor for 1 h at 37ºC, 10% CO2. Data shown is representative of 3 individual experiments.

Figure 3-5: The secondary drug screen: Effects on cell viability by the selected 9 FDA inhibitors

The cell viability results from the secondary FDA drug screen, showing 9/51 agents at concentrations of 1µM and 10µM in DIFI (A.) and DLD-1 (B.) cell lines. Cells were seeded into 96-well plates in triplicate and following overnight incubation treated with inhibitors stated for 72 h. The commercially available Cell Titer-Glo kit (Promega) was used to perform this assay and the samples were read on the Glomax bioluminometer (Promega). Data shown represents the mean of 3 independent experiments (% viability) compared to untreated cells ± SD, *p<0.05; **p<0.01; ***p<0.001, two-tailed t-test.

Figure 3-6: The secondary drug screen: Western Blot analysis and the effects on the selected 9 FDA inhibitors

Data shown represents western blot analysis from the secondary FDA drug screen, highlighting the effect of EGF and IL-6 mediated STAT3 in 9/51 candidates at concentrations of 1µM and 10µM. Cells were seeded into 6 well plates and were allowed to adhere for 24 h (A.). Cells were then serum starved for another 24 h and treated for 1 h with 1µM and 10µM of drug and ligand. Densitometry analysis on represented bands (B.). Data shown is representative of at least 3 independent experiments.

A. DIFI DLD-1 P-STAT3

STAT3 Ponatinib GAPDH

P-STAT3 STAT3 Sorafenib GAPDH

P-STAT3

STAT3 Diethystilbestrol GAPDH

P-STAT3 STAT3 Miconazole nitrate GAPDH

P-STAT3 STAT3 Dronedarone HCl GAPDH

P-STAT3 STAT3 Closantel sodium GAPDH

P-STAT3 STAT3 Triclabendazole GAPDH

P-STAT3

STAT3 Pyrithione zinc GAPDH

P-STAT3

STAT3 Closantel GAPDH

EGF (50ng/mL) - + + + - - - -

IL-6 (50ng/mL) - - - - - + + +

Inhibitor (μM) - - 1 10 - - 1 10

Densitometry for p-STAT3 relative to control (%) B. DIFI DLD-1 EGF (50ng/mL) - + + + - - - - IL-6 (50ng/mL) - - - - - + + + Concentration of drugs (µM) 0 0 1 10 0 0 1 10 Ponatinib 1.1 86.6 1.2 1.6 1.2 93.7 1.7 1.6 Sorafenib 0.0 89.4 7.3 2.3 0.0 150.2 36.4 0.0 Diethylstilbestrol 16.0 91.7 83.0 9.7 2.1 96.4 46.5 10.2 Miconazole nitrate 0.0 97.5 75.3 3.2 2.0 96.4 95.5 20.5 Dronedarone HCl 0.0 97.5 0.0 0.0 0.0 96.4 95.5 0.0 Closantel sodium 16.2 100.3 14.4 9.5 0.0 101.0 97.8 25.2 Triclabendazole 3.5 102.3 3.2 3.1 0.0 98.9 76.3 15.3 Pyrithione zinc 4.6 110.3 3.3 3.1 0.0 98.3 70.4 0.0 Closantel 1.6 7688.6 0.0 0.0 6.0 84.9 73.9 23.4

APPROVED # AGENTS TARGET(S) REFERENCE FOR

1 Ponatinib Abl, PDGFRa, VEGFR2, FGFR1, Src Cancer [331]

2 Sorafenib VEGFR, PDGFR, Raf Cancer [403] 3 Diethylstilbestrol Synthetic non-steroidal estrogen Cancer [404] 4 Miconazole nitrate Anti-fungal Infection [405] Neurological 5 Dronedarone HCl Multichannel blocker [406] Disease 6 Closantel Sodium Anti-bacterial Vermifuge [407] 7 Triclabendazole Tubulin Vermifuge [408] 8 Pyrithione zinc Proton pump Infection [409] 9 Closantel Anti-bacterial Vermifuge [407]

Table 3-4: The secondary drug screen: Summary of 9 selected FDA candidates

Summary of the selected 9 FDA approved candidates from the secondary drug screen. All of which successfully inhibited both EGF and IL-6 induced STAT3 pathways via western blot and demonstrated significantly reduced cell viability by greater than 50% in both DIFI and DLD-1 cell lines.

Tertiary drug screen: Ponatinib inhibits IL-11 mediated STAT3 activity. Overexpression of upstream signalling pathways has been known to upregulate STAT3 activity and therefore drives tumourigenesis [233, 410, 411]. Thus, the need to identify targeted therapies against several pathways simultaneously would counteract the overactivation or re-activation of STAT3 from un-inhibited pathways leading to compensatory STAT3 mediated signalling. In recent studies, the IL-11- STAT3 pathway has shown to provide a greater dominance to enhance STAT3 activity compared to the previously thought IL-6 family member in gastrointestinal tumourigenesis [5]. Clinical data retrieved from SurvExpress, an online database for cancer gene expression that uses survival analysis suggests similar results, whereby high IL-11-IL-11Ra expression correlates to poorer survival in CRC patients compared to those with high IL-6-IL-6R expression (Figure 3-7) [4]. Moreover, patients with high expression of IL-11, IL-11Ra, gp130 and the STAT3 related gene, SOCS3 resulted with poorer survival when compared to those with low expression (Figure 3-8). Interestingly, no correlation was observed when a single gene was assessed, further signifying the importance for developing mechanisms against all mediators involved in the IL-11-STAT3 pathway, in an attempt to alleviate patient outcome. Therefore, in this tertiary drug screen we aimed to further validate the selected 9 inhibitors and their potential role in hindering the IL-11 pathway in conjunction with the previously validated inhibition of IL-6 and EGF mediated STAT3 activity.

Prior to the commencement of the tertiary screen, the ability of IL-11 to mediate STAT3 phosphorylation in 15 CRC lines was analysed by western blot (Figure 3-9). As a result, 10/15 CRC lines exhibited significant increase in p-STAT3 levels in the presence of IL-11, namely DLD-1, DIFI, SW48, LIM1215, HCA-7, Caco-2, LIM2405, HT29 and SW480. Due to this observation, DLD-1, LIM1215 and SW48 were chosen for the tertiary screen.

Western blot analysis was performed in this final drug screen with candidates at 1µM and 10µM; amongst 3 CRC cell lines (DLD-1, LIM1215 and SW48) with IL- 11 (100ng/mL) mediated STAT3 activity. All inhibitors significantly decreased IL-11 mediated p-STAT3 at 10µM in all 3 cell lines tested (Figure 3-10). Ponatinib was the only inhibitor out of the 9 agents to show significantly reduced p-STAT3 in all cell

lines in the presence of EGF, IL-6 and IL-11 when compared to the ligand only treatment.

TCGA-COAD Study

A. IL-6/IL-6R/SOCS3 B. IL-11/IL-11Rα/SOCS3

Low expression

High expression

Cumulative survival p=0.069 p=0.011

Days (survival) Days (survival)

Figure 3-7: High IL-11-IL-11Rα expression is greatly associated to poor survival than high IL-6-IL-6R expression

The relationship between high and low IL-6-IL-6R-SOCS3 (A.) and IL-11-IL-11Rα-SOCS3 (B.) in relation to patient survival were evaluated using the SurvExpress database. Kaplain-Meier survival curves were evaluated in two studies sourced from TCGA, n=351. The retrieval of data was followed as previously described by Aguirre-Gamboa and colleagues [4].

Figure 3-8: The IL-11-STAT3 pathway is highly expressed in CRC cancer patients and correlates with poor survival

The relationship between IL-11-STAT3 pathway and clinical outcome of patients with CRC was examined using the Survexpress database. Kaplain-Meier survival curves were evaluated in two studies sourced from TCGA (Study 1) and GSE12945-Staub (Study 2), n=351 and 944, respectively. Genes were evaluated in the following analyses: 1. IL-11 and

SOCS3, 2. IL-11, IL-Ra and SOCS3 and 3. IL-11, IL-11Ra, gp130 (IL-6ST) and SOCS3. The retrieval of data was followed as previously described by Aguirre-Gamboa and colleagues [4].

Low expression

High expression

Figure 3-9: The effect of IL-11 and p-STAT3 activity in 15 colorectal cancer cell lines

Western blot analysis was performed to determine the effect of IL-11 induced STAT3 activity amongst 15 CRC cell lines. Cells were allowed to adhere for 24h and were subjected to 1 h of IL-11 (100ng/mL) treatment. Following incubation, cells were lysed and Western blot analysis was performed with the evaluation of p-STAT3 (Y705), STAT3 and GAPDH antibodies (A.). Densitometry analysis is shown relative to GAPDH (B.). Data shown here shown is representative of at least 3 independent experiments, mean ± SD, *p<0.05, **p<0.01, ***p<0.001, two-tailed t-test.

B. The effect p-STAT3 activity in the presence of IL-11 across 15 CRC cell lines 1.5 IL-11 (-) IL-11 (+) *** *** *** 1.0 * ** ** ** * Ratio 0.5 * ns ns ns ns ns ns 0.0 (p-STAT3/GAPDH)

DIFI SW48 HT-29 LOVO KM12 DLD-1 HCA-7Caco-2 SW480 LIM1215 LIM2405 LIM2550LIM2537 LIM2099 COLO205

Human CRC cell lines 84

Figure 3-10: The tertiary drug screen: Western Blot analysis and the effects on the selected 9 FDA inhibitors

The effect on IL-11 induced p-STAT3 with 9 FDA candidates (1µM and 10µM) by western blot in DLD-1, LIM1215 and SW48 CRC cell lines (A.). Notably, Ponatinib was the only inhibitor tested in this screen to observe a significant reduction in p-STAT3 expression at 1µM in all cell lines tested. Densitometry analysis was performed relative to GAPDH (B.). Data shown is representative of at least 3 independent experiments.

A. DLD-1 LIM1215 SW48

p-STAT3 STAT3 Ponatinib GAPDH

p-STAT3

STAT3 Sorafenib GAPDH

p-STAT3 STAT3 Diethystilbestrol GAPDH

p-STAT3 STAT3 Miconazole nitrate GAPDH

p-STAT3 STAT3 Dronedarone HCl GAPDH

p-STAT3 Closantel Sodium STAT3 GAPDH

p-STAT3 STAT3 Triclabendazole GAPDH

p-STAT3 STAT3 Pyrithione zinc GAPDH

p-STAT3 STAT3 Closantel GAPDH

Inhibitor (µM) - - 1 10 - - 1 10 - - 1 10

IL-11 (100ng/mL) - + + + - + + + - + + +

B. Ratio (p-STAT3:GAPDH) DLD-1 LIM1215 SW48 IL-11 (100ng/mL) - + + + - + + + - + + + Concentration of drugs (µM) 0 0 1 10 0 0 1 10 0 0 1 10 Ponatinib 0.00 0.96 0.00 0.00 0.00 1.61 0.00 0.00 0.00 1.05 0.00 0.00 Sorafenib 0.00 0.98 0.35 0.00 0.03 1.02 0.74 0.00 0.00 1.34 1.05 0.00 Diethylstilbestrol 0.04 0.93 0.87 0.35 0.00 0.85 0.82 0.00 0.00 1.08 1.07 0.18 Miconazole nitrate 0.00 0.82 0.79 0.14 0.00 0.98 0.94 0.00 0.00 1.24 1.17 0.04 Dronedarone HCl 0.00 0.93 0.92 0.00 0.00 0.93 0.60 0.00 0.00 0.98 0.97 0.15 Closantel sodium 0.00 0.95 0.89 0.05 0.00 1.00 0.29 0.00 0.00 0.81 0.17 0.00 Triclabendazole 0.00 0.99 0.96 0.39 0.00 1.13 0.35 0.10 0.12 1.13 1.10 0.07 Pyrithione zinc 0.00 1.02 0.55 0.00 0.00 0.55 0.00 0.00 0.00 1.16 0.18 0.00 Closantel 0.00 0.98 0.89 0.05 0.10 1.04 0.86 0.00 0.00 1.05 0.12 0.00

3.3 Discussion There is an overwhelming need to identify novel inhibitors for cancer patients due to the current lack of success with most treatment strategies in CRC. Due to this concern, pre-existing drugs or newly discovered agents have been developed which can specifically target important kinases responsible for driving tumourigenesis. Moreover, the FDA must approve the use of any inhibitor prior to the use in patients. The drug screen presented in this chapter is unique and specific to analysing inhibitors that target STAT3, a method not seen in the literature. Furthermore, our screen proves to be advantageous when compared to conventional drug screens, since only FDA approved inhibitors are comprised in our method, bypassing lengthy procedures that are known during commercial drug screen and development. Firstly, in this chapter, I provide evidence to suggest possible underlying inhibitory targets evaluated in our initial and secondary FDA drug screens against EGF and IL-6 mediated STAT3 activity in CRC cell lines. The tertiary drug screen evaluating IL-11 driven STAT3 signalling is later discussed in section 3.3.3.

EGF and IL-6 mediated STAT3 activity in CRC cell lines To best evaluate candidates with potential anti-STAT3 properties, the need to identify CRC lines that exhibit significant ligand/cytokine mediated STAT3 activity was of importance. Therefore, the assessment of EGF and IL-6 induced STAT3 activity was initially performed amongst 15 colorectal cancer cell lines by a luciferase STAT3 driven reporter assay (Table 3-1). As a result, EGF induced STAT3 in DIFI and IL-6 induced STAT3 in DLD-1 cell lines demonstrated significant levels of ligand/cytokine mediated STAT3 activity with an average fold change of 602.0 and 225.6 respectively when compared to control (DMSO) at 1 fold change. It is important to note that IL-11 induced STAT3 activity was difficult to assess due to possible weak binding interactions between APRE and IL-11 induced STAT3 activity and thus was incomparable to untreated. These weak interactions are possibly due to the activation of different promoter regions that is not specific to APRE, although this has not been thoroughly explored in the literature and is not a major theme of this thesis. Nonetheless, a study conducted by Yanagisawa et.al has observed strong interaction between GFAP (Glial Fibrillary Acidic Protein) and IL-11 mediated STAT3 by luciferase, in fetal neuroepithelial cells, highlighting the presence of other preferred promoters [412]. Due to this observation and initial attempts with inhibitors,

the IL-11 assessment was not performed in the 1167 FDA drug screen and was later assessed in other functional assays throughout this project.

Underlying anti-STAT3 activity in FDA approved inhibitors

Overexpression or enhanced activation of many cytokine and growth factor receptor systems, ultimately leads to elevated STAT3 activity and prolonged tumourigenesis thus, targeting several upstream pathways simultaneously could provide reduced tumour burden. Therefore, from this initial drug screen, we discovered 51/1167 compounds to have >50% STAT3 blockade in both EGF and IL-6 mediated pathways (Table 3-1 and Figure 3-2). Amongst these potential candidates were tyrosine kinase inhibitors, namely Erlotinib (targets EGFR), Regorafenib (targets VEGFR), multi-tyrosine kinase inhibitors, including Ponatinib (targets Bcl- Abl, Src, VEGFR), Bosutinib (Src, Abl) and Sorafenib (VEGFR, PDGFR, c-Kit).

The 51 candidates were further validated in our secondary screen via cell viability and western blot analysis. Successful inhibitors were required to pass the criteria of reduced cell viability by >50% and must exhibit reduced EGF and IL-6 mediated STAT3 activity by western blot. It is important to note that 4/51 candidates were significantly potent as observed in uneven GAPDH loading and viability; these were discarded from the screen. Nonetheless, 9/47 candidates sufficed these requirements and were allowed to proceed into the tertiary screen. Notably, these 9 agents consisted of multi-tyrosine kinase inhibitors, Ponatinib and Sorafenib and others including agents against infections namely Pyrithione zinc and Miconazole nitrate. Besides their primary role for treatment, our study has shown critical preliminary data, suggesting that the selected candidates also have secondary roles in targeting STAT3 in CRC cells. For instance, Sorafenib is a multi-tyrosine kinase inhibitor currently approved for the treatment of hepatocellular carcinoma, advanced renal cell carcinoma and recently radioactive iodine resistant advanced thyroid carcinoma [413-415]. This inhibitor is primarily known to target several key kinases, including VEGFR, PDGFR and Raf family kinases [416]. In addition to the known primary targets, our FDA drug screen has shown the inhibitory effect of STAT3 when treated with Sorafenib. Our finding coincides with other research studies, including Yang et.al, who reported that Sorafenib could exhibit anti-tumour effects in neuroblastoma cell lines, inhibiting cell proliferation and IL-6 mediated STAT3

activity [417]. Furthermore, research has also shown Sorafenib’s anti-tumour effect in targeting MEK/ERK/STAT3 and PI3K/Akt/STAT3 pathways in hepatocellular carcinoma [321]. From a STAT3 drug resistant angle, Chen et.al has demonstrated Sorafenib’s inhibitory effect by sensitizing resistant TRAIL/Apo2L hepatocellular carcinoma cells, through the blockade of STAT3 [320]. In conjunction with our findings, these studies further highlight the potential and importance for identifying novel inhibitors against STAT3 and related pathways for the improvement of current therapies.

Additionally, supporting our claim for finding pre-existing FDA approved inhibitors with secondary interests against STAT3 and related pathways; Pyrithione zinc was also observed to inhibit STAT3 in our tertiary drug screen. Pyrithione zinc is an antibacterial and antifungal agent found commonly to treat dandruff and related scalp conditions [418], nonetheless it has recently been reported to have anti-cancer activities. Srivastava et.al, discovered in a quantitative high throughput assay, pyrithione zinc was the most effective cytotoxic agent for the inhibition of cell proliferation and induced apoptosis in oral squamous cell carcinoma cells [419]. Importantly, this study further identified the signalling pathways of PI3K/Akt/mTOR and WNT/β-catenin to be significantly inhibited. In addition, Tailler et.al performed an imaging-based compound screen and discovered pyrithione zinc with anti- leukemic properties and was seen to efficiently induce apoptotic chromatin condensation and inhibit nuclear factor-κB survival pathways [420]. Despite these recent discoveries, there are currently no clinical trials pursued for cancer therapy. Nonetheless, in correlation with our preliminary results, these studies further support potential uses or repurposing current pre-existing agents with secondary interests, expanding current drugs for treatment amongst a range of diseases, in particularly cancer and ultimately targeting several tumourigenic pathways.

Role of IL-11 driven STAT3 activity and cancer

Studies have shown recent interest and re-evaluation of the cytokine IL-11 and its role in driving STAT3 and ultimately resulting with cancer. IL-11, belonging to the IL-6 cytokine family and has been known to drive tumourigenesis in several cancers, including CRC, osteosarcoma and glioblastoma [421-423]. Importantly, studies have further confirmed the tumour drivers related to the IL-11-STAT3 pathway contribute

to cancer progression, in particularly colorectal tumours. Putoczki et.al, established the essential link between IL-11Rα and STAT3, whereby genetic loss of IL-11Rα contributed to reduced STAT3 activation and also tumour cell proliferation [5]. Likewise, Ernst et.al, in a gastric cancer mouse model, which is strongly dependent on STAT3 activation to drive tumour growth, demonstrated that partial inhibition of IL-11 signalling lead to substantial reduction in tumour burden [192]. Together, these studies signify the essential role IL-11 plays in promoting tumourigenesis via the STAT3 pathway, thus in our tertiary screen, we pursued potential STAT3 inhibitory effects in the presence of IL-11 in the 9 candidates. The levels of IL-11 mediated STAT3 activity was demonstrated at basal levels, across 15 CRC lines by western blot and resulted with enhanced p-STAT3 in several lines, namely DLD-1, SW48 and LIM1215 (Table 3-9), which were chosen to complete the tertiary screen.

Broadening Ponatinib’s known inhibitory effects As a result of the tertiary screen, all 9 agents significantly reduced p-STAT3 activity in the presence of IL-11, in all CRC cell lines analysed. However, Ponatinib (previously known as AP24534) was the only agent out of the 9 analysed to significantly reduce all ligand and cytokine (EGF, IL-6 and IL-11) mediated STAT3 activity amongst all cell lines at 1µM (Figure 3-6 and Figure 3-10). Ponatinib is an FDA approved agent and is clinically used for the treatment of CML and Ph+ALL, which specifically targets relative mutants of Abl (IC50≤ 2nM) [424]. Furthermore, Ponatinib is a multi-tyrosine kinase inhibitor, primarily known to target kinases including Ret, Kit and members of the FGFR, PDGFR and VEGFR families of kinases (IC50 ≤ 2nM) [331]. Although, our study is the first to demonstrate significant blockade amongst the EGF, IL-6 and IL-11 mediated STAT3 pathways, other studies have shown similar inhibitory effects amongst other STAT3 activating pathways. For instance, Li et.al, has also implicated Ponatinib’s inhibitory properties with suppression of STAT3 through direct target of wild-type and mutated FGFR4 in Rhabdomyosarcoma [425]. Despite only analysing Ponatinib concentrations at 1µM and 10µM, our preliminary findings implicate potential inhibitory properties against the pathways of EGF, IL-6 and IL-11 driven STAT3 activity.

The novelty of our STAT3 luciferase reporter drug screen Overall, our luciferase based STAT3 reporter assay is a novel FDA drug screen approach, with no other methods found in the current literature. However,

there are several other approaches available for performing kinase drug screens, although none are STAT3 specific. Ryall et.al developed a Kinase Addiction Ranker (KAR) algorithm that incorporates comprehensive kinase inhibition data, high- throughput drug screening data and gene expression profiles to identify kinase dependency in cancer cells and validated FGFR1 and MTOR dependence in lung cancer cell lines [426]. Additionally, Cheung et.al, developed a bioluminescence compound screen with D-luciferin-firefly luciferase and identified 36/3000 inhibitors to strongly inhibit Mutlidrug resistance protein 4 (MRP4/ABCC4), which is reportedly highly expressed in several tumours [427]. Similarly, Zhang et.al established a bioluminescent imaging based high-throughput assay to screen for ABCG2 inhibitors, due to its role in resistance to several chemotherapeutic agents and identified 47/3273 compounds (>5 fold) [428]. These studies highlight the importance of drug screens and recent developments to identify novel inhibitors against specific kinases or proteins responsible for tumourigenesis.

Moreover, majority of currently used FDA inhibitors were initially discovered through drug screens. Since its discovery, the STAT3 pathway has been regarded as an important target for targeted therapy. Several existing compounds, with no previous studies on inhibiting STAT3, have been identified to exhibit anti-STAT3 properties in in-vivo and in-vitro assays. For instance, Niclosamide an anti-helminthic molecule has been demonstrated in NSCLC to overcome resistance to Erlotinib, through the suppression of STAT3 [429]. Additionally, JAK2 is a common kinase that can enhance STAT3 activity. In ovarian cancer cells, Ahmad et.al, has identified in combination of JAK2-specific inhibitor CYT387 and current of care treatment, Paclitaxel provided blockade of the JAK2/STAT3 pathway and reduced tumour burden when compared to Paclitaxel alone [430]. Additionally, Judd et.al has demonstrated in gastric cancer, WP1066, a caffeic acid derivative inhibits STAT3 dependent growth in-vitro and in-vivo [431]. Both studies further highlight the importance for developing potential candidates against STAT3 pathways. Unfortunately, there are currently no known FDA approved STAT3 inhibitors. Nonetheless, other modalities have been reported to indirectly inhibit STAT3 activity including FDA approved EGFR inhibitors, including Lapatinib and Trastuzumab against breast cancer [432] and Gefitinib for NSCLC [433] and JAK kinase inhibitors, namely Ruxolitinib for the treatment against myelofibrosis [434]. We demonstrate

here, for the first time, the potential anti-STAT3 inhibitory characteristics observed in Ponatinib and we further evaluate Ponatinib’s efficacy in our next chapter.

3.4 Conclusion In summary, we report the identification of the FDA approved inhibitor, Ponatinib and 8 other FDA approved agents with potent in vitro EGF, IL-6 and IL-11 mediated STAT3 inhibition using the specific STAT3 luciferase reporter assay, expression of STAT3 protein via western blot and cell viability assays. Taken together, the identification of novel inhibitors against mediated STAT3 activity could potentially enhance the effectiveness for targeted therapies. Although, these are preliminary findings, our data suggests novel targeted inhibitory properties amongst current FDA agents. Therefore, by broadening the uses of current FDA agents in the field of targeted therapies, enhances the possibility for improved treatment and alternative therapies for patients with limited options.

CHAPTER 4: Exploring the efficacy of Ponatinib in-vitro and in-vivo

4. CHAPTER 4 – EXPLORING THE EFFICACY OF PONATINIB IN-VITRO AND IN-VIVO

4.1 Introduction Structural features of Ponatinib

Ponatinib, also known as Iclusig™ or AP24534 (ARIAD Pharmaceuticals, Cambridge, MA, USA) is a potent, orally active multi-tyrosine kinase inhibitor. This inhibitor was structurally based on previously established BCR-Abl inhibitors such as Dasatinib. Ponatinib’s chemical name is 3-(imidazo[1,2-b] pyridazin-3-ylethynyl)-4- methyl-N-[1]benzamide hydrochloride, Figure 4-1 [424]. Ponatinib consists of a carbon-carbon triple bond linkage to target the T315I (tyrosine kinase inhibitor- refractory threonine-to-isoleucine mutation at position 315) point mutation within the kinase domain of BCR-ABL [424].

Figure 4-1: Chemical structure of Ponatinib

A representation of the structure of Ponatinib. The yellow box highlights the

carbon-carbon triple bond responsible for the target of the T315I point mutation

of the BCR-ABL molecule. Image sourced from O’Hare et.al, 2009.

Pre-clinical evaluation of Ponatinib efficacy

Several reports have evaluated the effects of Ponatinib in pre-clinical studies. One of Ponatinib’s first evaluation studies was conducted by O’Hare and colleagues and demonstrated strong inhibitory effects against the kinase activity of native BCR-

ABL ([IC50] 0.37nmol/L) and the mutant BCR-ABL T315I ([IC50] 2.0nmol/L) in

Ba/F3 transfectants (expressing native/T315I mutant BCR-ABL kinase) [331]. Moreover, other studies have further evaluated Ponatinib in several CML models in in-vitro and in-vivo functional assays and also discovered potent anti-tumour activity against BCR-ABL [425, 435]. The positive outcomes observed in these studies led to a phase I clinical trial demonstrating significant anti-leukemic activity, resulting in a major cytogenetic response of 72% in 81 CML and Ph+ ALL patients (Table 4-1) [436]. Furthermore, the phase II PACE trial demonstrated long lasting responses to Ponatinib after 4 years in heavily pre-treated CML and Ph+ALL patients harbouring BCR-ABL with T315I mutants, this study is currently ongoing (NCT01207440) [437, 438].

Table 4-1: Ponatinib’s pre-clinical trials for CML and Ph+ ALL patients

CLINICAL PATIENT PHASE STATUS OUTCOME REFERENCES TRIAL STUDY CRITERIA • Significant and durable responses Safety study of were observed in AP24534 to treat heavily pre-treated Resistant/refractory chronic CP-CML patients hematologic myelogenous Completed regardless of mutation [436]; I malignancies leukaemia (CML) status, and Ponatinib NCT00660920 patients were and other was generally well enrolled (n=81) haematological tolerated. malignancies • 72% of patients resulted with a major cytogenic response Chronic-phase CML, accelerated- • Ponatinib had phase CML, blast- Ponatinib Ph- significant anti- phase CML, or Ph- positive acute leukemic activity positive ALL with Results lymphoblastic across categories of resistance to or obtained [437, 438]; II leukaemia [ALL] disease stage and unacceptable side and NCT01207440 and CML mutation status effects from ongoing Evaluation • 83% of patients Dasatinib or (PACE) trial resulted with a major Nilotinib or with cytogenic response the T315I mutation (n=449)

Clinical use of Ponatinib On the 14th of December 2012, the FDA approved the use of Ponatinib as a third line treatment option for adult patients with T315I-postive CML or those with T315I-positive Ph+ALL [439]. As a third line therapy option, Ponatinib treatment is provided at 45mg daily, to patients that are intolerant or resistant to 2 or more prior

TKI therapies, including the use of Dasatinib, Nilotinib and/or Imatinib, Notably, current clinical trials are re-evaluating safety doses of Ponatinib, ranging from 15, 30 and 45mg, due to known adverse effects including vascular occlusions and heart failure (1-10% of cases; NCT02467270) [436]. Moreover, comparative clinical studies are currently underway, evaluating the efficacy of Ponatinib and currently used TKI’s (e.g. Nilotinib, Dasatinib) in CML patients (NCT02627677, NCT01667133). Ponatinib is also undergoing several clinical trials in other malignancies (Table 4-2). These include evaluating the efficacy of Ponatinib in thyroid cancers, gastrointestinal stromal tumours and glioblastoma (NCT01838642, NCT03171389, and NCT02478164 respectively). Moreover, based on pre-clinical studies Ponatinib’s potent effect against FGFR2 mutations, RET mutations and KIT mutations were observed in several tumours including billary cancer, solid cancers and advanced NSCLC and have now progressed into clinical trials (NCT02265341, NCT02272998 and NCT01813734) [332, 333, 440]. Importantly, there are currently no clinical trials conducted for evaluating Ponatinib in patients with CRC.

Table 4-2: Ponatinib’s clinical trials in other cancer types

PHASE CLINICAL TRIAL STUDY PATIENT CRITERIA STATUS OUTCOME A phase I/II study of Ponatinib CML patients in any phase Ongoing in Japanese patients with or Ph+ALL and was study and are I/II chronic myeloid leukaemia previously treated with and NCT01667133 not recruiting and Ph+ acute lymphoblastic resistant or intolerant to participants leukaemia (ALL) either Dasatinib or Nilotinib Ponatinib in patients with resistant chronic phase chronic Chronic phase-CML and are myeloid leukaemia (CML) to Recruiting II resistant to at least two prior NCT02467270 characterize the efficacy and patients TKIs safety of a range of doses (OPTIC) Unresectable, locally Recruiting Ponatinib for advanced advanced or metastatic II halted NCT01838642 medullary thyroid cancer medullary thyroid cancer prematurely

POETIG trial – Ponatinib after NCT03171389 GIST patients with failure or Recruiting II rEsisTance to Imatinib in intolerance to Imatinib patients NCT02478164 GIST (POETIG) Ongoing Trial of Ponatinib in patients Patients with glioblastoma study and are II with Bevacizumab-Refractory who are resistant to NCT02478164 not recruiting glioblastoma Bevacizumab participants Advanced biliary cancer that Ponatinib Hydrochloride in Ongoing is refractory or intolerant to treating patients with advanced study and are II gemcitabine or NCT02265341 biliary cancer with FGFR2 not recruiting fluoropyrimidine based fusions participants therapy Ponatinib for patients whose Refractory metastatic solid advanced solid tumour cancer tumour with activating Recruiting II has activating mutations NCT02272998 genomic alterations in patients involving FGFR1-4, RET and FGFR, RET and/or KIT KIT II Ponatinib in advanced NSCLC Advanced NSCLC patients Recruiting NCT01813734 with RET translocations with RET mutation patients A study comparing Ponatinib Chronic phase-CML and are and Nilotinib in patients with Recruiting III resistant to first-line Imatinib NCT02627677 chronic myeloid leukaemia patients treatment (OPTIC 2L)

Ponatinib and known molecular targets

Ponatinib has not only been discovered to target BCR-ABL but has also been identified to target several other receptors and molecules responsible for driving tumourigenesis in other cancer types (Table 4-3). For instance, O’Hare and colleagues has demonstrated varying degrees of inhibitory activity to other kinases in

haematologic cells including FLT3 ([IC50] 0.3-2nmol/L) and c-Kit ([IC50] 8- 20nmol/L) and is also effective against members of the tyrosine kinase families

including FGFR ([IC50] 2.2nmol/L), VEGFR ([IC50] 1.5nmol/L), PDGFR ([IC50]

1.1nmol/L), and c-SRC ([IC50] 5.4nmol/L) [331, 425, 435, 441]. Other studies also highlight potent inhibitory effect against the family members of FGFR, including gastrointestinal stromal tumour cells, rhabdomyosarcoma cancer cells, endometrial cancer cells and bladder cancer cells [385, 442, 443]. Additionally, Ponatinib’s broadened inhibitory targets also include kinases of RET, c-Src and c-Kit as demonstrated in several cancer types, including haematologic cells, thyroid cancer cells and gastrointestinal stromal tumour cells [331, 385, 444]. Importantly, there are no studies assessing Ponatinib’s efficacy against STAT3 driven pathways, which are activated most commonly be EGF, IL-6 and IL-11 in cancer.

Table 4-3: Ponatinib’s molecular targets in cancer

KNOWN MOLECULAR IC50 VALUES OBSERVED IN REFERENCES TARGETS (nM) Haematologic cells 1.5 [331] FGFR1 Non-small cell lung cancer 40.0 [332] Endometrial cancer cells 0.5 [442] FGFR2 Gastrointestinal stromal tumour cells 20.0 [385] Breast cancer cells 6.0 [385] FGFR3 Bladder cancer cells 40 [385] FGFR4 Rhabdomyosarcoma cells 72.2 [443] PDGFRα Haematologic cells 1.1 [331] VEGFR2 Haematologic cells 1.5 [331] c Src Haematologic cells 5.4 [331] Haematologic cells 12.5 [331] c Kit Gastrointestinal stromal tumour cells 11.0 [444] FLT3 Hematologic cells 4.0 [435] RET Thyroid cancer cells 25.8 [440]

Rationale and Aims As reported in several studies, Ponatinib has been observed to widely target several tyrosine kinases amongst a wide range of cancer types. Nonetheless, no previous studies have investigated the role of Ponatinib and possible inhibitory targets

against STAT3 driven pathways, namely the emerging IL-11 cytokine, which to-date has no FDA approved anti-IL-11 inhibitor in the clinics. Similarly, no published study has researched the role of Ponatinib and CRC in in-vitro and in-vivo assays. Moreover, the concept of compensatory signalling from uninhibited pathways, commonly leads to tumour recurrence in mono-targeted therapies and has been a challenging area in the research and clinical field [296, 445, 446]. The aim of the research described in this chapter were to further assess Ponatinib’s potential underlying anti-STAT3 activity driven by IL-6 family members in CRC cells, alongside with evaluating possible tumour reduction in CRC xenograft animal models and to compare Ponatinib’s effectiveness in reducing multiple STAT3 mediated pathways against current FDA approved JAK and Src agents. Specifically, the aims of this chapter were to:

• Determine the efficacy of Ponatinib against IL-11 and LIF driven STAT3 activity in functional assays (in-vitro). To evaluate Ponatinib’s role against IL-11 and LIF driven STAT3 activity in various CRC cell lines by assessing the protein expression of p- STAT3. Moreover, qPCR was utilised to analyse the effect of SOCS3 (a known STAT3 driven gene) in the presence of IL-11.

• Determine Ponatinib’s effect on cell viability, cell migration and tumour growth in-vivo. In a dose dependent manner, Ponatinib was further assessed as a cytotoxic agent via cell viability assays alongside with the evaluation of possible underlying anti-migratory properties utilising the wound healing assay in several CRC cell lines. Moreover, daily oral dosage of Ponatinib was administered to subcutaneously injected (DLD-1 and SW48 cell lines) nude mice. The tumour volume was measured daily.

• Evaluate the effectiveness of Ponatinib as an anti-STAT3 inhibitor in comparison to current FDA approved JAK and Src inhibitors. Several functional assays including protein expression analysis and luciferase STAT3 reporter assay were utilised to assess Ponatinib’s potent

effect against EGF, IL-6 and IL-11 driven STAT3 activity and were compared to current FDA approved JAK and Src inhibitors. Cell viability was also analysed amongst several JAK and Src agents and Ponatinib.

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4.2 Results Ponatinib reduces LIF mediated STAT3 activity In addition to IL-6 and IL-11, the IL-6 family of cytokines consist of other members that are known to drive tumourigenesis, including Leukaemia inhibitory factor (LIF), Ciliary neurotrophic factor (CNTF) and Oncostatin M (OSM), Figure 4- 2 [447-449]. In order to determine if the effect of Ponatinib on STAT3 activation was unique to IL-6 and IL-11, or was a shared feature of IL-6 family cytokines we next analysed the effect of Ponatinib on the LIF mediated STAT3 activity. LIF initiates downstream activation of the JAK/STAT3 pathway through a similar mechanism to that of other IL-6 family members, by binding its receptor (LIFR) and initiating interaction with the common co-receptor gp130 [450, 451].

Figure 4-2: Schematic diagram of members of the IL-6 family of cytokines

Members of the IL-6 family of cytokines and their receptor-cytokine complexes that

includes the gp130 receptor chain. These complexes (gp130/gp130; gp130/OSM;

gp130/LIFR) allow the activation of pathways involved with tumourigenesis

including, JAK/STAT3, PI3K/AKT, MAPK/ERK.

Modified from Richards, 2013 [3].

Ponatinib significantly reduced LIF mediated STAT3 activity in all 3 cell lines tested, in a dose dependent manner (0, 0.1, 0.5, 1µM) (Figure 4-3). All cell lines tested demonstrated a significant reduction at 1µM, with DLD-1 showing the most reduction at lower concentrations. Inhibition of LIF mediated STAT3 activity in the presence of Ponatinib, was demonstrated in all 3 cell lines tested, further identifying Ponatinib as an inhibitor of several upstream targets that induce STAT3 activity.

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Figure 4-3: Ponatinib reduces LIF mediated STAT3 activity, another IL-6 family member

DIFI, DLD-1 and SW48 cell lines were treated with LIF (50ng/mL) and Ponatinib (0, 0.1, 0.5, 1µM) for 1 h. Cells were then lysed and phosphorylated and total STAT3 levels were determined by western blot analysis. GAPDH expression was also determined as a loading control (A.). Respective densitometry was analysed by Image J (B.). Western blot data is representative of at least 3 independent experiments. **p<0.01, ***p<0.001, two-tailed t-test.

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Ponatinib significantly reduces IL-11 mediated STAT3 signalling and SOCS3 gene expression In order to validate that Ponatinib indeed reduced IL-11 mediated STAT3 activation (Figure 3-10), its activity in 7 CRC cell lines was next tested. The 7 CRC lines were selected based on earlier IL-11 results at a basal level in 15 CRC lines (Figure 3-9, A.). Ponatinib was found to successfully reduce IL-11 driven p-STAT3 activity in all 7 CRC cell lines tested in a dose dependent manner (Figure 4-4, A-B.). All 7 CRC cell lines treated, observed significant reduction of >50% p-STAT3 at 1µM. Notably, DLD-1, DIFI, LIM1215 and SW48 lines demonstrated reduction of p-STAT3 activity at lower concentrations. Furthermore, in this signalling cascade, the activation of STAT3 leads to the activation of several STAT3 related target genes, notably SOCS3. Therefore, to determine whether Ponatinib could inhibit STAT3 mediated gene transcription we examined the expression of the STAT3 specific driven gene, SOCS3 following IL-11 and Ponatinib treatment in 7 CRC cell lines (Figure 4-4, C.). As expected, IL-11 stimulation enhanced SOCS3 gene expression. Notably, LIM1215, SW48 and HCA-7 demonstrated the greatest fold change in the presence of IL-11. More importantly, Ponatinib treatment reduced SOCS3 expression in all cell lines relative to the control in a dose dependent manner, providing additional evidence that Ponatinib is effective against IL-11 mediated STAT3 activity, alongside EGF, IL-6 and LIF mediated pathways.

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Figure 4-4: Ponatinib significantly reduces IL-11 mediated p-STAT3 activity by western blot analysis and SOCS3 gene expression

DLD-1, DIFI, LIM1215, SW48, LIM2405, HCA-7 and Caco-2 cell lines were treated with IL-11 (100ng/mL) and Ponatinib (0, 0.01, 0.1 and 1µM) for 1 h. Cells were then lysed and phosphorylated and total STAT3 levels were determined by western blot analysis. GAPDH expression was determined as a loading control. Western blot data is representative of at least 3 independent experiments (A.). Respective densitometry was analysed by Image J (B.). The STAT3 specific gene, SOCS3 expression was analysed by quantitative real-time PCR in all 7 cell lines after 8 h treatment with IL-11 (100ng/mL) and Ponatinib (0, 0.1, 0.5 and 1µM) (C.). All results presented here are expressed as SOCS3/GAPDH. Values are the means ± SD from three independent experiments. *p<0.05, **p<0.01, ***p<0.001, two-tailed t-test.

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Ponatinib has greater efficacy in reducing STAT3 activity and cell viability compared to current JAK and Src inhibitors.

The initial screen in Chapter 3, demonstrated that several known FDA- approved JAK and Src inhibitors did not inhibit STAT3 activity by greater than 50% in both EGF and IL-6 mediated cell systems. This was surprising, as several key receptors have been known to activate JAK and Src kinase, which is essential for the recruitment and activation of STAT3 and therefore, tumourigenesis [452]. For this reason, we re-evaluated these FDA approved JAK and Src inhibitors to potentially inhibit all 3 mediated STAT3 pathways (EGF, IL-6 and IL-11) in DLD-1 and DIFI CRC cell lines and compared their effect to that of Ponatinib (Figure 4-5, A.). As a result, Ponatinib was the only inhibitor tested to demonstrate significant reduction in the EGF, IL-6 and IL-11 mediated STAT3 activation, as validated by the STAT3 reporter assay and western blot analysis (Figure 4-5, B. and C, i., ii.). All other JAK and Src inhibitors tested demonstrated reduction in STAT3 activity in one mediated pathway but not the other, and was consistent in both assays performed. Specifically, the tested JAK inhibitors included Ruxolitinib (reduced IL-6 and IL-11) and Tofacitinib (reduced IL-6 and IL-11). The tested Src inhibitors included Dasatinib (reduced EGF and IL-11), Bosutinib and Ibrutinib both reduced EGF only. Moreover, Ponatinib was also the only inhibitor to significantly reduce cell viability by >50% in both cell lines when compared to the same concentration as the other JAK and Src inhibitors (Figure 4-5, D.).

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Figure 4-5: Ponatinib is a more potent STAT3 activation inhibitor compared to current FDA approved JAK and Src agents

Table of FDA approved JAK and Src inhibitors available in the acquired drug screen (Selleck Chemicals LCC). (A.) DIFI and DLD-1 cell lines were treated with Ponatinib and JAK and Src inhibitors in the presence of EGF (DIFI), IL-6 (DLD-1) and IL-11 (DLD-1 and levels of STAT3 phosphorylation and transcriptional activity were determined by western blot (B.) and STAT3 reporter assay (C.). Cell viability for each cell line was measured following treatment with each inhibitor for 72 h (D.i). Raw STAT3 luciferase activity (%) and cell viability values (%) are represented in (E.). Western blot data is representative of at least 3 independent experiments. Luciferase and cell viability results are representative of at least 3 independent assays, in triplicate. **p<0.01, ***p<0.001, two-tailed t-test.

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A. Inhibitors Targets B.

Control N/A p-STAT3

Ponatinib Abl, Src, FGFR, PDGFR T-STAT3 EGF Ruxolitinib JAK GAPDH Dasatinib Src, Abl, c-kit p-STAT3 Bosutinib Src, Abl 6 - Ibrutinib Src T-STAT3 IL Tofacitinib JAK GAPDH

250 EGF(DIFI) p-STAT3

C. 11 -

125 IL T-STAT3 10 GAPDH

Luciferase *** 5 *** GF/Cytokine - + + + + + + + STAT3 activity (%) 0 150 250 IL-11 (DLD-1) IL-6 (DLD-1) Ibrutinib DasatinibBosutinib 125Control Control(-) (+)PonatinibRuxolitinib Tofacitinib 100 Ibrutinib Ponatinib Dasatinib Bosutinib Tofacitinib 10 Ruxolitinib

Luciferase 505 ***

Luciferase *** STAT3 activity (%) *** 0 (control) DMSO

STAT3 activity (%) activity STAT3 0 150 IL-11 (DLD-1) Ibrutinib Control (-) Ponatinib DasatinibBosutinib D. Control (+) Ruxolitinib Tofacitinib 100

Ibrutinib DIFI DLD-1 DasatinibBosutinib Control Control(-) (+)PonatinibRuxolitinib *** Tofacitinib 150 150 50 *** *** ***

Luciferase *** 100 100 STAT3 activity (%) activity STAT3 0 *** ** ** 50 50 GF/Cytokine - + + + + + + + *** *** *** *** Ibrutinib *** DasatinibBosutinib Control Control(-) (+)PonatinibRuxolitinib Tofacitinib 0 0 Cell viability (%) viability Cell Cell viability (%) viability Cell

Control Control Ibrutinib Ibrutinib Ibrutinib Ponatinib DasatinibBosutinib Ponatinib DasatinibBosutinib Dasatinib Ponatinib Tofacitinib Bosutinib Ruxolitinib Tofacitinib Ruxolitinib Tofacitinib Ruxolitinib DMSO (control) DMSO E. DIFI DLD-1 STAT3 Luciferase STAT3 Luciferase STAT3 Luciferase Cell viability (%) Cell viability (%) activity (%) activity (%) activity (%) EGF IL-6 IL-11 Mean SD Mean SD Mean SD Mean SD Mean SD Control (DMSO) 0.0 0.0 100 7.6 0.0 0.0 29.9 8.5 100.0 4.9 Control (GF/Cytokine) 100.0 1.3 n/a 100.0 1.3 100.0 0.0 n/a Ponatinib 3.1 0.2 7.2 2.6 3.1 0.2 1.1 0.2 33.0 2.9 Ruxolitinib 162.3 19.6 111.4 17.6 162.3 19.6 53.3 10.6 94.0 13.4 Dasatinib 0.1 0.0 14.5 3.8 0.1 0.0 27.4 10.2 51.1 7.6 Bosutinib 3.4 1.4 3.5 0.8 3.4 1.4 35.2 12.5 53.9 6.8 Ibrutinib 0.1 0.0 8.7 1.3 0.1 0.0 42.4 9.3 91.6 8.1 Tofacitinib 195.2 7.1 78.2 4.6 195.2 7.1 35.7 15.1 94.8 7.6

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Ponatinib reduces cell viability Following several reports confirming Ponatinib’s inhibitory effect against cell viability and cell migration in in-vitro studies assessing gastro-intestinal stromal tumours, glioblastoma and endometrial cancers [333, 442, 444], the pre-clinical efficacy on cell viability was further explored in human CRC cells. As a result, all 7 CRC cell lines tested, demonstrated <50% viability in the presence of 1µM Ponatinib after 72 h treatment compared to control (Figure 4-6.). Despite, all cell lines exhibiting <50% viability when treated with Ponatinib at 1µM , the % of surviving cells differed amongst CRC cell lines tested and may possibly be due to the varying mutations present within each cell line. For instance, DLD-1 cell lines harbour several mutations related to continual cell survival, namely in KRAS/BRAF, PIK3CA/PTEN and TP53 pathways [453, 454] and as such, in this assay, DLD-1 cell lines resulted with a greater number of cells surviving (%) in the presence of Ponatinib at 1µM. In comparison to DLD-1 cell lines, LIM2405 had lower cell viability (%) in the presence of Ponatinib, despite harbouring several mutations known to drive cell survival, including KRAS/BRAF, APC and EGFR mutations [453]. On the other hand, cell lines, such as SW48 that do not harbour these mutations [455] were more sensitive to Ponatinib treatment (% viability). Nonetheless, despite varying mutations present within these cell lines, I evidently observed Ponatinib’s potent cytotoxic effect in all 7 CRC cell lines observed with reduced cell viability (<50%) at 1µM.

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Figure 4-6: Cell viability is significantly reduced by <50% in the presence of Ponatinib treatment

DLD-1, DIFI, LIM1215, SW48, LIM2405, HCA-7 and Caco-2 cell lines were treated with Ponatinib (0, 0.1, 1µM) for 72 h and cell viability (Cell-Titer Glo) was performed to quantify the viability of cells. Results are normalised to untreated control. Data points represent mean ± SD of at least 3 independent experiments, each with 3 experimental replicates. *p<0.05, relative to control, two-tailed t-test.

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Ponatinib reduces cell migration Uncontrolled cell migratory properties can often lead to the initiation of metastasis in advanced cancers [30, 456, 457]. Therefore, I next evaluated Ponatinib’s possible inhibitory effect against cell migration by utilising the wound-healing assay (also known as scratch assay). When wounded, cell monolayers respond to the cell- cell disruption and results with the initiation of cellular characteristics involved with cell migration including an increased concentration of growth factors at the wound site and ultimately allows for the closure of the wound [458, 459]. The wound-healing assay was performed across 6 CRC cell lines, DLD-1 (Figure 4-7, A.), LIM1215 (B.), SW48 (C.), HCA-7 (D.), LIM2405 (E.) and Caco-2 (F.). The DIFI cell line was not included in this set of experiments, as DIFI’s were not able to form confluent monolayer cultures. Images were taken to assess the closure of the wound at 0, 24 and 48 h (and in the case of the slow growing SW48; 72 h) of incubation. Wound healing analysis was performed on Image J. Notably, Ponatinib at 0.5 and 1µM demonstrated significant reduction in cell migration in all cell lines tested after 24 and 48 h and for SW48 at 72 h of incubation when compared to control.

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Figure 4-7: Cell migration is significantly reduced during Ponatinib treatment

The effect of Ponatinib (0, 0.1, 0.5 and 1µM) on cell migration in (A.) DLD-1, (B.) LIM1215, (C.) SW48, (D.) LIM2405, (E.) HCA-7, (F. ) Caco-2 at 0, 24, 48 and 72 h (in the case of SW48). Results are normalised to untreated control. Data points represent mean ± SD of at least 3 independent experiments, each with 3 experimental replicates. *p<0.05; **p<0.01; ***p<0.001, two-tailed t-test.

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Ponatinib inhibits tumour growth in subcutaneous xenograft models To determine whether Ponatinib could inhibit tumour growth in-vivo, we next performed subcutaneous xenograft experiments. CRC cell lines DLD-1 (2.5 x 106) (Figure 4-8, A.) and SW48 (5 x 106) (B.) were inoculated subcutaneously into both ventral flanks, anterior to the hind leg of 6-10-week-old female BALB/c nude mice (Animal Research Centre). Ponatinib was orally administered at doses of 0, 10 and 30mg/kg in aqueous 25mmol/L citrate buffer (pH = 2.75) for 10 days and was allowed for further measurement for an additional 3 days’ post treatment [385]. Ponatinib significantly reduced tumour growth at 30mg/kg in both xenograft models (I). Notably, Ponatinib also significantly reduced DLD-1 tumours at 10mg/kg. Moreover, at the end of experimentation, all tumours were weighed and recorded. (II). Ponatinib did not produce any significant weight loss over the course of the treatment compared to vehicle control treated mice (III). These results further support Ponatinib’s inhibitory effect on tumour growth.

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Figure 4-8: Ponatinib inhibits tumour growth in subcutaneous mice models

The effect of Ponatinib in a subcutaneous xenograft model was assessed on two colorectal cancer cell lines, DLD-1 (A.) and SW48 (B.). Cells were subcutaneously injected into both ventral flanks, anterior to the hind leg of the recipient mice. On day 14 the mice were divided into 3 groups, all bearing tumours of approximately 100mm3. Mice were then treated with Ponatinib at doses of 0, 10 or 30mg/kg via oral gavage daily for 10 days between days 14 – 23 post inoculation represented as ‘ ’ (I). Data shown represents mean ± SEM (n=6, 5, 5 respectively in each group for DLD-1 and n=6 in all groups for SW48). At the end of the experiment mass of all tumours were recorded and is shown as a representation of tumours according to dose treatment (II). The weight of mice was recorded throughout the treatment groups (III). The significant difference is shown as **p<0.01 and ***p<0.001 relative to the untreated group, two-tailed t-test.

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4.3 Discussion Ponatinib is a multi-tyrosine kinase inhibitor and has been known to target several receptor pathways including VEGFR, PDGFR, FGFR, FLT3, c- Src and c- Kit) [331, 425, 435, 441]. Despite, potent inhibitory effects against these pathways, the literature have not shown Ponatinib’s effect against STAT3 driven pathways or its effect in CRC cell lines. In this chapter, I provide evidence to suggest Ponatinib’s underlying role against STAT3 driven pathways in CRC cell lines and potent cytotoxic properties resulting with abrogating tumour growth.

Ponatinib inhibits STAT3 activating pathways Various growth factors (EGF, PDGF, TGF-α) and cytokines (IL-6, IL-11, LIF, OSM) are known to mediate STAT3 activity, resulting with an increased expression of tumour related characteristics as observed in several cancer types, including CRC [61, 182, 184, 193, 460-462]. In our previous chapter we have identified Ponatinib’s underlying ability to inhibit EGF, IL-6 and IL-11 driven STAT3 pathways (Figure 3- 6 and Figure 3-10). In relation, Leukaemia inhibitory factor (LIF) is another member of the IL-6 family of cytokines, and has also been implicated to be overexpressed in several cancers including, CRC, breast cancer and nasopharyngeal carcinoma [65, 448, 462]. This chapter demonstrated Ponatinib to significantly reduce STAT3 activity in the presence of LIF, in a dose dependent manner in all 3 CRC cell lines tested. These findings further suggest Ponatinib’s broadened inhibitory ability to potentially target the IL-6 family of cytokines as demonstrated through the reduction of IL-6, IL-11 and LIF driven STAT3 activity. Despite our findings demonstrating that Ponatinib could inhibit EGF, IL-6 and LIF mediated STAT3, our major focus for the remainder of this chapter was IL-11 due to studies discovering that IL-11 is of greater dominance than its predecessor IL- 6, during tumourigenesis, as observed in mouse models of sporadic CRC, gastrointestinal models and clinical based studies mentioned earlier (Figure 3.7) [5]. Moreover, IL-11 gene expression and elevated STAT3 activity has been associated with poor survival in CRC patients. In a study conducted by Sumida et.al, discovered the expression of IL-11 was increased in tumour tissues when compared to the surrounding normal tissue area from the same patient [463]. Additionally, p-STAT3 staining as a marker for IHC confirmed positive staining in the same region of

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samples, highlighting the important role of IL-11 mediated STAT3 plays in driving tumourigenesis, particularly in CRC. In agreeance, the study by Nakayama and colleagues, also demonstrated similar findings in 73 cases of surgically resected human gastric adenocarcinomas with 72.6% of patients observed positive staining for IL-11 and IL-11Ra proteins by IHC [193]. IL-11 expression is not only observed in CRC but also in other malignancies, such as lung adenocarcinomas, endometrial cancer, glioblastoma, and breast cancers [184, 422, 460, 464]. Despite, several findings related to driving carcinogenesis, there are currently no FDA approved inhibitors developed that simultaneously target multiple key STAT3 drivers, namely mediators of the EGF, IL-6 or IL-11 family. Currently, there is also no FDA approved IL-11 inhibitors. Our study, further confirms the potency of Ponatinib in reducing not only EGF, IL-6 and LIF as observed previously, but also IL-11 mediated STAT3 activity in 7 CRC cell lines that were derived from primary tumours (DLD-1, DIFI, SW48, Caco-2, LIM2405 and HCA-7) and one metastatic omental cell line (LIM1215). These cell lines were chosen based on the strong STAT3 signal observed when induced with IL-11, as shown in (Figure 3-9). All cell lines exhibited anti- STAT3 activity at 1µM. Similarly, our findings also demonstrated potent inhibition in the STAT3 related, SOCS3 gene that is a common gene product as a result of STAT3 activation. All 7 CRC lines tested for SOCS3 gene expression resulted with a dose dependent response in the presence of IL-11 and Ponatinib, further supporting potential inhibitory characteristics against the IL-11-STAT3 driven pathway. Correspondingly, SOCS3 gene expression was further evaluated in the presence of EGF and IL-6 mediated pathways and resulted with similar inhibitory effects. Taken together, these functional assays highlight the newly discovered potent characteristics of Ponatinib as a possible anti-STAT3 inhibitor observed to inhibit 4 signalling pathways related to STAT3.

Ponatinib exhibits potent STAT3 activity compared to FDA approved JAK and Src inhibitors In addition to upstream receptors, JAK and Src kinases are also commonly observed to assist with downstream activation upon growth factor/cytokine-receptor interaction [465, 466]. Clinically, there are several FDA approved JAK and Src inhibitors utilised for cancer treatment, including Dasatinib, Bosutinib and Ibrutinib

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[467-469]. Despite these clinical successes, mono-targeted therapies fail to inhibit compensatory activation of other pathways, leading to continual tumour growth. A novel, yet potentially improved strategy to overcome compensatory signalling is to identify inhibitors that block multiple upstream molecules that are known to drive STAT3 activity in CRC. Our lead compound, Ponatinib demonstrated enhanced anti-STAT3 inhibitory effects when compared to other FDA inhibitors, including those that target JAK and Src kinases. The intricate pathways involved in the activation of STAT3 not only involve cytokine and growth factor mediation but also the activation of JAK and Src kinases. In fact, some growth factor/cytokine bound receptors do not have intrinsic tyrosine kinase activity and thus require the recruitment of JAK and/or Src to initiate STAT3 recruitment and activity [452, 465, 470]. Thus, a common drug discovery approach has focused on developing inhibitors targeting JAK and Src. Several of these agents that target JAK are FDA approved including Ruxolitinib and Tofacitinib for the use in patients with Myelofibrosis and Rheumatoid arthritis, respectively [317, 471, 472]. However, to date, there are currently no clinically approved JAK inhibitors for cancer treatment. In contrary, Src inhibitors have been FDA approved for the treatment of cancers, including Dasatinib, Bosutinib and Ibrutinib for patients with CML [467, 468, 473]. Despite advances in current TKI’s, patients can become non- responsive, a result observed from possible compensatory signalling mechanisms including pathway cross-talk, feedback inhibition and non-specific binding [474, 475]. It is unclear whether agents targeting JAK can also inhibit Src driven STAT3 and vice versa. Therefore the lack of response to JAK inhibitors may be due to continued Src activity and likewise, lack of response to Src inhibition may be due to continued JAK driven STAT3 mediated upstream by multiple growth factors or cytokine receptors. Therefore, the need to target STAT3 activity driven by several upstream mediators including JAK and Src would ultimately increase the effectiveness of tumour inhibition and also reduce the likelihood of compensatory signals via other uninhibited signalling pathways. Thus, in our study, we evaluated Ponatinib with currently available FDA approved JAK and Src inhibitors. Interestingly, Ponatinib was not only able to significantly reduce all 4 (EGF, IL-6, LIF and IL-11) mediated STAT3 pathways, but in comparison to available FDA JAK and Src inhibitors, Ponatinib was also observed to be the only inhibitor to exhibit significant potent inhibitory effects against both EGF, IL-6 and IL-11 mediated 120

STAT3 activity. These results also correlated with cytotoxic effects observed by cell viability assays. We demonstrate in this chapter, for the first time, that Ponatinib is a prospective STAT3 inhibitor in CRC that appears to have greater dominance in reducing STAT3 mediated pathways than current FDA approved JAK and Src agents, and thus further evaluating the efficacy of Ponatinib in CRC and STAT3 related pathways was of importance in this project.

Ponatinib suppresses cell viability and cell migration Cell viability and cell migration has been linked with driving properties involved with cancer survival and metastatic progression in CRC and other cancer types [30, 476, 477]. Several studies have highlighted the importance to target such pathways in order to reduce cell survival and possible dissemination of cancer cells into neighbouring organs [84, 478-481]. I therefore, evaluated Ponatinib and its effect on cell viability and cell migration on CRC cell lines. As a result, Ponatinib successfully suppressed cell viability by >50% in all CRC lines tested at 1µM (Figure 4-5). Ponatinib also reduced the rate of migration over a 3-day period in a dose dependent manner (Figure 4-6). This reduction in cell migration (wound closure) was evident at Ponatinib doses that did not have any effect on cell viability, ruling out the possibility that reduced migration was due to cell death. Despite, no other reports highlighting Ponatinib’s effect on cell viability and cell migration in CRC cell lines, other studies have demonstrated similar effects in other cancer types. For instance, a study conducted by Zhang and colleagues, assessed Ponatinib’s inhibitory effect against a common glioblastoma cell line U87MG in cells in-vitro and in-vivo [333]. Ponatinib was observed to inhibit cell viability and induced apoptotic events with elevated sub-G1 DNA content, a commonly used apoptotic marker in a dose dependent manner (0.78-200nM). Moreover, this study observed inhibition of cell migration and cell invasion with Ponatinib treatment at doses between 1.25-20nM. Furthermore, Ponatinib was also demonstrated to reduce neuroblastoma cells in-vitro and in-vivo. Whittle et.al, exposed Ponatinib to a range of neuroblastoma cell lines (CHP-134, CHp-212, NGP, LAN-5, SH-EP, SK-N-AS, SK- N-BE(2) and SK-N-SH) and was observed to reduce cell viability (<20µM) and cell migration at 1µM [482]. Taken together, this project and mentioned studies highlight Ponatinib’s potential role against cell viability and cell migration in a range of cancers.

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Nonetheless, another study utilised similar functional assays to evaluate Ponatinib and its effect on glioblastoma in-vitro [333]. Although this study did not focus on signalling pathways known to drive glioblastoma, they demonstrated reduced cell viability, induced apoptosis, suppressed migration and invasion effectively in U87MG glioblastoma cell lines. Additionally, a recent study by Whittle et.al also observed decreased cell migration and cell viability in neuroblastoma cells [482]. Despite these findings, this study did not further investigate potential pathways involved in Ponatinib’s inhibitory effect.

Ponatinib and pre-clinical efficacy in CRC In coincide with our in-vitro findings, our pre-clinical study also demonstrated Ponatinib’s anti-tumour effect in subcutaneous xenograft models at 30mg/kg in both DLD-1 and SW48 CRC cell lines when compared to the vehicle only group (Figure 4-7). Tumour reduction was observed after 10 days of Ponatinib treatment by oral gavage. Ponatinib also had an anti-tumour effect at a lower dose of 10mg/kg in mice inoculated with DLD-1 CRC cells. Gathered, our finding presented here, demonstrates tumour reduction in subcutaneous xenograft models as a mono- therapeutic agent. In relation to clinical doses, we speculate Ponatinib’s potential use as a combinational regime with other chemotherapy agents and therefore a lower clinical dose would be required for further evaluation, a scope beyond this thesis. Our findings are consistent with other pre-clinical studies evaluating the effect of Ponatinib and subcutaneous tumour growth in other cancer types. For instance, upon daily oral dosing of Ponatinib (10-30mg/kg), Gozjit and colleagues also observed reduced tumour growth in mice bearing AN3CA xenografts, an endometrial cancer line after 11 days of treatment [385]. In conjunction, this study also evaluated Ponatinib’s anti-tumour effect in a bladder cancer model with mice bearing UMUC14 xenografts and in a gastric tumour model with mice bearing SNU16 xenografts, which also showed reduced tumour growth at 30mg/kg. Another study conducted by Whittle et.al, also demonstrated inhibition of neuroblastoma xenograft tumour growth with mice inoculated with SK-N-AS and SK-N-SH tumour cells [482]. Taken together our results and these pre-clinical studies demonstrate potential anti-tumour characteristics in several cancer types including CRC as found in this thesis. In the clinical setting, patients presenting with CRC range from stage I-IV, which often harbour unique genetic mutations, including KRAS, B-Raf, NRAS, 122

PIK3CA mutants [107, 108, 483]. Patients with CRC harbouring these mutations generally have reduced treatment options. Data presented in this chapter, observed Ponatinib’s potent inhibitory effect against varying stages of CRC cell lines each harbouring several genetic mutations. For instance, our panel of human CRC cell lines consisted of KRAS mutant cell line, DLD-1 and B-Raf mutant cell line, LIM2405. Although the aim of this thesis did not focus on Ponatinib’s effect on specific CRC stages or mutant types, rather we investigated Ponatinib’s effect on a range of CRC cell lines and we highlight the potential of Ponatinib against these sub- populations of patients, further broadening Ponatinib’s targets and clinical relevance for the treatment of CRC patients.

4.4 Conclusion Taken together, our data is the first in the literature to not only demonstrate Ponatinib as a potent inhibitor for CRC but also as a potential anti-STAT3 inhibitor, with its ability to target multiple known STAT3 drivers including, EGF, IL-6 and IL- 11. We observed here significant reduction in IL-11 mediated STAT3 activity in several CRC lines and SOCS3 gene expression, data that has not been previously shown in the literature. Additionally, Ponatinib was observed to significantly reduce tumour growth in both CRC lines tested, further highlighting Ponatinib’s anti-tumour effect in CRC. Despite several studies evaluating Ponatinib’s effect in various cancers and pathways, none have shown a direct link between Ponatinib and drivers of STAT3 in particularly in CRC. Although our study thus far observed Ponatinib’s efficacy in reducing STAT3 and related drivers in CRC, in our next chapter (Chapter 5) we further evaluate, the possible mechanisms behind Ponatinib’s inhibitory effect in relation to the STAT3 pathway and key upstream drivers.

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CHAPTER 5: Understanding possible mechanisms associated with Ponatinib and IL-11 mediated signalling

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5. CHAPTER 5 – UNDERSTANDING POSSIBLE MECHANISMS ASSOCIATED WITH PONATINIB AND IL-11 MEDIATED SIGNALLING

5.1 Introduction Importance of pre-clinical validation It is of great importance to uncover the mechanism of a therapeutic agent for cancer therapy. These findings will assist with specializing targeted treatment regimens for patients with hyperactivated pathways contributing to tumour growth. In addition, the mechanism of a therapeutic agent is often required before clinical application, even with the re-purposing of FDA approved agents such as Ponatinib.

Due to the importance of the emerging IL-11 cytokine and related pathways, in this chapter, we further explore Ponatinb’s underlying inhibitory effect against elevated IL-11Ra and the overexpression of the ubiquitous gp130 receptor and STAT3 in CRC cell lines.

Tools for STAT3 activation In an attempt to study gp130 models in in-vitro and in-vivo, Stuhlmann-Laeisz and colleagues developed L-gp130, a constitutively activated gp130 [484]. This modified receptor consisted of the replacement of the entire extracellular portion of gp130 with the c-jun leucine zipper region in the chimeric receptor protein L-gp130, resulting in forced dimerization leading to constitutive STAT3 activation. Moreover, Putoczki and colleagues have further modified L-gp130 to include a tyrosine 757 (Y757) mutation which cannot interact with SOCS3, the negative regulator of gp130, allowing for continual STAT3 activation, despite still regulating SOCS3 gene expression [5]. In this chapter, we also explore the effect of Ponatinib in CRC lines transfected with constitutively active gp130 (L-gp130); further unravelling Ponatinib’s mechanistic action.

As expected, enhanced gp130 expression leads to elevated STAT3 activity and is commonly seen in several cancers, including CRC [212, 401, 485]. However, similarly to IL-11Rα and gp130 receptors, there are also no current clinically approved STAT3 inhibitors despite promising pre-clinical results in in-vitro and in- vivo models [375, 486]. Researchers commonly attempt to mimic constitutively active

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STAT3 by utilising the STAT3C construct. STAT3C consist of the substitutions of alanine to cysteine and asparagine to cysteine, allowing for spontaneous dimerization in the absence of tyrosine phosphorylation [487, 488]. We further assess the effect of Ponatinib when exogenous STAT3C is present, mimicking hyperactivity STAT3 that is commonly observed trait in CRC [60, 63].

Indeed, several studies commonly overexpress the molecule of interest to further examine the mechanism behind drug inhibition. For instance, Liu et.al, overexpressed STAT3 in MDA-MB-468 breast cancer cells and observed antiapoptotic events induced by 2 Sorafenib analogues, SC-1 and SC-43 [489]. Additionally, Yang and colleagues analysed the effect of Sunitinb and the overexpression of STAT3 and AKT in VC312 medulloblastoma cells and demonstrated partially reduced drug efficacy in these tumour cell lines [490]. Furthermore, Wu and colleagues identified Bazedoxifene to inhibit STAT3 phosphorylation and STAT3 DNA binding, induced apoptosis and suppressed tumour growth in pancreatic cancer cells exhibiting persistent IL-6/gp130/STAT3 signalling [491]. Our study, seeks to uncover the mechanistic mode of action of Ponatinib and the IL-11 mediated STAT3 pathway.

Understanding compensatory mechanisms As mentioned previously, STAT3 activity and subsequent pro-tumourigenic properties are initiated by many cytokine and growth factors [60, 237]. As such, compensatory signalling from uninhibited pathways allows for continued STAT3 activity and overall refractory outcomes to specific targeted therapeutic agents, a trait commonly seen in tumourigenesis. Studies have shown certain cytokine-induced activation of compensatory pathways is, in some cases, dependent on cross-talk with other receptor tyrosine kinases. For instance, Qiu et.al, demonstrated in prostate carcinoma cells that IL-6 traditionally induces IL-6R and gp130 receptors but was also shown to induce the EGFR family members of ErbB-2 and ErbB-3, resulting in MAPK activation and cell proliferation [492]. Coincide, similar cross-talk of signals were observed in ovarian carcinomas between EGFR and IL-6R via JAK/STAT3 pathway [296]. Gathered, these studies highlight the importance of cross-talk behaviour contributing to continual tumour growth via uninhibited pathways. Therefore, it is critical to target several STAT3 driven pathways to potentially reduce tumour growth. Despite these studies demonstrating cross-talk behaviour between IL-

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6R and family members of EGFR, no study has observed cross-talk behaviour between IL-11 and other receptor tyrosine kinases (RTK).

Rationale and Aims Despite the establishment of Ponatinib’s broad inhibitory targets [331, 385, 435, 444], the potential mechanism behind Ponatinib’s effect against the proteins or molecules responsible for IL-11 driven STAT3 activation, specifically IL-11R and gp130 has not been previously investigated. Likewise, there has been no research on whether Ponatinib has a role in targeting other IL-11 mediated pathways including IL- 11-ERK1/2 and IL-11-AKT. Additionally, there is also little evidence in the literature, which highlights possible cross-talk events with IL-11 and other receptor systems. Indeed, it has previously been observed that cross-talk events occur between IL-6R and other receptors/molecules including EGFR, VEGFR TGFβ1 and insulin receptor substrate-1 [182, 445, 493, 494]. In the case of cross-talk events between IL-6R and EGFR and IL-6R and VEGFR, several studies observed sustained STAT3 activity, further promoting tumour growth in several cancer types [175, 182, 492, 495].

An objective of this study was to investigate Ponatinib’s effect on enhanced IL-11R, gp130 and STAT3 pathways and to further evaluate Ponatinib’s inhibitory ability to abrogate potential cross-talk behaviour between IL-11R and other RTKs. Specifically, the aims of this chapter were to:

• Examine Ponatinib’s role against the IL-11 driven ERK1/2 and IL-11 driven AKT pathways. To explore whether Ponatinib has the ability to also target other IL- 11 mediated pathways including IL-11 driven ERK1/2 and AKT pathways by assessing the protein expression of p-STAT3 in several CRC cell lines.

• Determine whether elevated IL-11R and gp130 enhances STAT3 activity and the effect of these clones in the presence of Ponatinib. To generate stable clones with enhanced levels of IL-11R, gp130 and STAT3 expression as a means to investigate the presence of elevated STAT3 activity and the effect of cell viability in the presence of Ponatinib treatment.

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• Establish possible cross-talk events between IL-11 and other RTK’s and whether Ponatinib has the ability to abrogate such interactions. To analyse possible cross-talk between IL-11 and other RTKs in DLD-1 and DIFI CRC cell lines by utilising a commercially available Phospho-RTK human array. In addition, the presence of Ponatinib and IL- 11 stimulation was also performed to establish possible inhibitory effects against IL-11 mediated cross-talk events.

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5.2 Results The effect on Ponatinib and IL-11 mediated pathways It has been known that the major pathways activated upon IL-11 stimulation are JAK-STAT3, PI3K-AKT and Ras-MAPK-ERK1/2 [190, 496] (Figure 5-1, A.) To help determine whether Ponatinib can inhibit IL-11 mediated signalling pathways other than STAT3, we examined Ponatinib’s effect on IL-11 mediated ERK1/2 and AKT activity (B.).

Firstly, unlike p-STAT3, IL-11 alone did not consistently increase p-ERK1/2 or p-AKT levels in all 7 cell lines tested, therefore in the presence of Ponatinib the effect of Ponatinib and IL-11 driven p-AKT and p-ERK1/2 pathways were also inconsistent across all cell lines tested and did not provide conclusive results. In comparison to our previous results, obtained from Chapter 4, Figure 4-3, A., Ponatinib was successful in reducing p-STAT3 activity mediated by IL-11 when compared to the AKT and ERK1/2 pathways, whereby, a significant reduction of p- STAT3 expression was observed across all cell lines tested, markedly at 1µM (C.). This suggests a lack of IL-11 mediated in the case of p-AKT and in some cases, p- ERK1/2 in cell lines analysed, whereby a higher dosage of IL-11 may have been needed to demonstrate potential effect compared to p-STAT3. In addition, elevated ERK1/2 and AKT protein expression was observed in some cell lines but not all (as observed with untreated cell lines), suggesting the presence of constitutive ERK1/2 and AKT activation as a result of their harboured mutations. For instance, cancer cells harbouring KRAS and BRAF mutations (e.g. SW48 and Caco-2 respectively) exhibit elevated p-ERK1/2 and p-AKT expression, further emphasizing the likelihood of increased expression at basal levels as seen in our results [455, 497]. Taken together, this current data suggests that Ponatinib may potentially inhibit IL-11 mediated STAT3 activity more specifically than IL-11 induced ERK1/2 or AKT activity.

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Figure 5-1: Ponatinib reduces IL-11 mediated STAT3 activity more prominently compared to ERK1/2 and AKT pathways

The assessment of IL-11 mediated pathways and the effect of Ponatinib: A schematic diagram of IL-11Rα-gp130 complex activates STAT3, ERK1/2 and AKT pathways (A.). Western blot analysis was performed on 7 CRC cells to determine the effect of Ponatinib on several key proteins phosphorylated and total ERK1/2, AKT (B.). Cells were allowed to adhere overnight and were serum starved for 24 h and were treated with Ponatinib at concentrations of 0, 0.1 and 1.0µM with the addition of ± IL-11

(100ng/mL) for 1 h at 37°C, 10% CO2. The effect of Ponatinib on IL-11 mediated STAT3 was obtained from Chapter 4, Figure 4-3, A. as a comparison between IL-11 driven pathways (C.). Data shown represents at least 3 individual experiments.

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Enhanced IL-11Rα results in elevated STAT3 activity and Ponatinib reduces viability to a lesser extent compared to parental lines.

Over the past decade, studies have highlighted the vital role of IL-11 and components of its receptor (IL-11R) to contribute to increased STAT3 activation in several cancer types [389, 498, 499]. Whilst there has been clinical success on the closely related family member IL-6 (Tocilizumab; FDA approved for Rheumatoid arthritis) [500], there is currently no clinically approved anti-IL-11 agent. We have demonstrated that Ponatinib inhibits IL-11 mediated STAT3. Thus, we hypothesize that forced, exogenous overexpression of IL-11Rα and subsequent hyperactivation of STAT3 would result in reduced Ponatinib efficacy compared to normal endogenous levels of IL-11Rα and STAT3 signalling.

To test this hypothesis, we stably transfected IL-11Rα into DLD-1 (Figure 5- 2, I), LIM1215 (II) and SW48 (III) cell lines. To validate successful stable transfection, IL-11Rα gene expression was determined in potential positive clones and control cells, (A.). Further validation was performed to assess whether positive clones displayed enhanced STAT3 phosphorylation by western blot anaylsis (B.i, ii.) transcriptional activity (C.) and increased gene regulation of SOCS3 (D.). Gathered, these functional assays provide supportive evidence with obtaining positive IL-11Rα clones. In addition, to assess the importance of the IL-11Rα-STAT3 pathway in Ponatinib’s mode of action, positive clones were treated with Ponatinib in a dose dependent manner by cell viability for 72 h (E.). As a result, in the presence of Ponatinib, overexpressed IL-11Rα clones had a greater resistance to Ponatinib compared to parental cell lines, with IC50 values ranging from 0.4 - 0.6µM in stable clones compared to parental cell lines (IC50 between 0.1 – 0.3µM (F.). These findings indicate that the presence of elevated IL-11Rα allows the enhancement of STAT3 activity and as a result, decreases Ponatinib’s efficacy.

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Figure 5-2: Enhanced IL-11Rα increases STAT3 activity and reduced Ponatinib’s inhibitory effect

IL-11Rα was stably transfected into (I.) DLD-1, (II.) LIM1215 and (III.) SW48 cell lines. IL-11Rα positive cells are designated as DLD-1-11R-1/2, LIM1215-11R-1/2 and SW48- 11R-1/2. Functional assays were performed on IL-11Rα transfected cells in comparison to sensitive lines. Initial assays were performed on basal levels to confirm successful transfection of IL-11Rα: IL-11Rα gene expression was determined by qPCR (A.). Western blots were performed to assess the levels of p-STAT3 (Y-705) (B.i.) and were analysed by densitometry (B.ii.). The basal levels of STAT3 activity were measured via the Luciferase fire-fly assay (C.). Furthermore, levels of SOCS3 gene expression were evaluated by qPCR, (D.). After which Cell viability assay (Cell titre glo) was performed to measure a dose dependent response with Ponatinib at 0, 0.25, 0.50, 0.75 and 1.0µM, (E.). The half- maximal inhibitory concentration (IC50) is indicated in µM (F. ). All data points represent mean ± SD of at least 3 independent experiments, each with at least 3 experimental replicates. *p<0.05, **p<0.01, p***p<0.001 relative to control, two-tailed t-test.

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Overexpression of gp130 leads to elevated STAT3 activity and reduced Ponatinib’s inhibitory effect. Glycoprotein 130 (gp130) is a ubiquitous co-receptor and is an essential component involved with the downstream activation of the family of IL-6 cytokine signalling, including IL-11 driven pathways [5, 149, 501]. Alongside with overexpressed IL-11R associated with elevated tumourigeneic properties, enhanced gp130 receptor has also been observed to play a role in enhancing carcinogenic characteristics [191, 502, 503]. Therefore, I analysed stably transfected CRC cells with constitutively active gp130 (L-gp130) and hypothesized subsequent hyperactivation of STAT3 should result in a reduced sensitivity to Ponatinib. Once more, to test this hypothesis, DLD-1, LIM1215 and SW48 were all stably transfected with the L-gp130 construct.

As a result, DLD-1 (Figure 5-3, I.), LIM1215 (II.) and SW48 (III.) positive clones designated as -gp1, gp2, gp3, were shown to have increased phosphorylated STAT3 activity at basal levels as verified by western blot analysis (A.), STAT3- luciferase reporter (B.), and SOCS3 gene expression (C.). Moreover, in the presence of Ponatinib in a dose dependent manner, positive clones demonstrated an increase in viability compared to parental cell lines, (D.). The IC50 values ranged between 0.2 – 0.8µM in stable clones and 0.1 – 0.4µM in parental cell lines (E.). Notably, this effect was observed in all cell lines tested. These results signify in the case of higher levels of STAT3 activity as demonstrated by the constitutively active gp130 receptor, Ponatinib reduced cell viability to a lesser extent compared to parental lines.

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Figure 5-3: Elevated gp130 results with increased STAT3 activity and Ponatinib was less efficacious

L-gp130 was stably transfected into (I) DLD-1, (II) LIM1215 and (III) SW48 cell lines. L- gp130 positive cells are designated as DLD-1-gp1/2, LIM1215 gp1/2/3 and SW48-gp1/2. Functional assays were performed on L-gp130 transfected cells in comparison to sensitive lines. Initial assays were performed on basal levels to confirm successful transfection of L-gp130: The basal levels of STAT3 activity were measured via western blots were performed to assess the levels of p-STAT3 (Y705) with densitometry, (A.) the Luciferase fire-fly assay, (B.) and the levels of SOCS3 gene expression were evaluated by qPCR (C.). After which Cell viability assay (Cell titre glo) was performed to measure a dose dependent response with Ponatinib at 0, 0.25, 0.50, 0.75 and 1.0µM, (D.). The half- maximal inhibitory concentration (IC50) is indicated in µM (E.). All Data points represent mean ± SD of at least 3 independent experiments, each with at least 3 experimental replicates. *p<0.05, **p<0.01, p<***0.001 relative to control, two-tailed t-test.

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Ponatinib is less efficacious in the presence of constitutively active STAT3

Continual STAT3 activity has been associated with enhanced properties associated with tumour development and resistance to therapies [164, 504-509]. Therefore to provide further evidence to suggest elevated STAT3 activity enhances the resistance to Ponatinib, DLD-1 cell lines were stably transfected with constitutively active STAT3C-GFP plasmid. Cells were FACS sorted into GFP- tagged cells and non-GFP tagged cells and several functional assays were performed to confirm the presence of STAT3C. As shown in Figure 5-4, cells with STAT3C demonstrated an increase in luciferase STAT3 activity (A.) and observed increased p- STAT3 and expression of STAT3C by western blot analysis (B.) when compared to the parental cell line and the transfected negative control. Once confirmed positive, cells were subjected to Ponatinib treatment. Cell viability was performed to determine the effect of STAT3C transfected cells with Ponatinib in a dose dependent manner (C.). As a result, transfected cells with STAT3C resulted in a reduced sensitivity to Ponatinib treatment compared to parental cells and the empty vector (DLD-1 negative GFP). This finding provides an insight to Ponatinib’s reduced efficacy in the presence of constitutively active STAT3.

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Figure 5-4: STAT3C enhances STAT3 activity and reduces Ponatinib’s efficacy

DLD-1 cell lines were stably transfected with STAT3C to assess Ponatinib’s inhibitory abilities. Functional assays were performed on STAT3C transfected cells in comparison to DLD-1 lines and an empty vector (DLD-1 Negative GFP). Initial assays were performed on basal levels to confirm successful transfection of STAT3C: The basal levels of STAT3 activity were measured via the Luciferase fire-fly assay, (A.). western blots were performed to assess the levels of p-STAT3 (Y705), (B.). After which, cell viability (Cell Titre glo) was performed to measure a dose dependent response with Ponatinib at 0, 0.25, 0.50, 0.75 and 1.0µM, (C.). All Data points represent mean ± SD of at least 3 independent experiments, each with at least 3 experimental replicates. *p<0.05, **p<0.01 relative to control, two-tailed t-test.

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Ponatinib and receptor tyrosine kinase array Cross talk between receptor systems is well documented, and has shown that IL-6R interacts and activates several receptor tyrosine kinases [296]. Due to the similar nature between IL-6 and IL-11, we therefore next determined whether IL-11 stimulation led to increased phosphorylation of other RTK’s and whether Ponatinib could potentially inhibit this IL-11 mediated activity. To perform this, we employed a commercially available RTK array (R&D systems) containing 49 different phosphorylated human RTK’s. DLD-1 and DIFI cells (Figure 5-5, A and B.) were treated with ± IL-11 (100ng/mL) or IL-11 + Ponatinib (1µM) for 1 h.

As a result, in both cell lines analysed, phosphorylation of the RTK ErbB2 (HER2) was significantly reduced after Ponatinib and IL-11 treatment compared to IL-11 alone. Additionally, DIFI cell lines exhibited reduction in ErbB3 (HER3) in the presence of Ponatinib and IL-11. A target map is shown in (C.). To further verify these findings, protein expression was analysed by western blot was performed on the following RTK proteins: p-EGFR, p-ErbB2, p-ErbB3, p-ErbB4, p-AXL, p-STAT3 and p-HGFR (D.). DLD-1, DIFI and LIM1215 cell lines were subjected to Ponatinib (1µM) and IL-11 (100ng/mL) treatment for 1 h. As a result, a significant reduction was observed in p-ErbB2, p-ErbB3, p-ErbB4, p-STAT3 and p-HGFR. More importantly, a significant reduction was observed in p-ErbB3 and p-STAT3 between Ponatinib and IL-11 when compared to IL-11 alone in all 3 cell lines tested. From these results we can speculate possible cross talk between IL-11-gp130 complex and the EGFR family of receptor, and that Ponatinib may be able to inhibit this interaction. However, we cannot make any clear conclusions as to whether IL-11 promotes IL-11R and EGFR association and trans-activation from our findings. Taken together, further studies are required to confirm whether the possible cross-talk interaction occurs between IL-11 and EGFR.

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Figure 5-5: The evaluation of 49 Phospho-RTK array in the presence of IL-11 and Ponatinib treatment

To determine Ponatinib’s mode of action, DLD-1 and DIFI cell lines were screened with the R&D Human Phospho-RTK Array KIT (Chemiluminescent Readout). DLD-1 (A.) and DIFI

(B.) cells were treated with IL-11 (100ng/mL) ± 1µM Ponatinib for 1 h at 37°C, 10% CO2. All cells were serum starved for 24 h before treatment. A total of 300µg of cell lysate was applied. The chemiluminescent film image (i.) and the quantification of the blots (ii.) are shown for each cell line. All data shown is represented as an average pixel density of duplicated spots, normalized with the reference points. The target map for this array is also provided (C.). As a result, a few antibodies, including EGFR, ErbB2/3/4, Axl and HGFR were chosen based on protein reduction observed in treated vs. untreated cells. Further western blots were performed on DLD-1 (D, ii.), DIFI (D, iii.), and LIM1215 (D, iv.) cell lines with IL-11 ± 1µM Ponatinib for 1 h and densitometry was performed via ImageJ. Each western blot is representative of 3 independent experiments, mean ± SD. *p<0.05, **p<0.01, ***p<0.001 relative to control, two-tailed t-test.

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Receptor Receptor Coordinates RTK/Control Coordinates RTK/Control Family Family Reference A1, A2 - D1, D2 Tie Tie-2 Spots Reference A23, A24 - D3, D4 NGFR TrkA Spots

B1, B2 EGFR EGFR D5, D6 NGFR TrkB

B3, B4 EGFR ErbB2 D7, D8 NGFR TrkC

B5, B6 EGFR ErbB3 D9, D10 VEGFR VEGFR1

B7, B8 EGFR ErbB4 D11, D12 VEGFR VEGFR2

B9, B10 FGFR FGFR1 D13, D14 VEGFR VEGFR3

B11, B12 FGFR FGFR2α D15, D16 MuSK MuSK

B13, B14 FGFR FGFR3 D17, D18 EphR EphA1

B15, B16 FGFR FGFR4 D19, D20 EphR EphA2

B17, B18 Insulin R Insulin R D21, D22 EphR EphA3

B19, B20 Insulin R IGF-I R D23, D24 EphR EphA4

B21, B22 Axl Axl E1, E2 EphR EphA6

B23, B24 Axl Dtk E3, E4 EphR EphA7

C1, C2 Axl Mer E5, E6 EphR EphB1

C3, C4 HGFR HGFR E7, E8 EphR EphB2

C5, C6 HGFR MSPR E9, E10 EphR EphB4

C7, C8 PDGFR PDGFRα E11, E12 EphR EphB6

C9, C10 PDGFR PDGFRβ E13, E14 Control (-) Mouse IgG1

C11, C12 PDGFR SCF-R E15, E16 Control (-) Mouse IgG2a

C13, C14 PDGFR Flt-3 E17, E18 Control (-) Mouse IgG2b

C15, C16 PDGFR M-CSF R E19, E20 Control (-) Goat IgG

C17, C18 RET c-Ret E21, E22 Control (-) PBS

Reference C19, C20 ROR ROR1 F1, F2 - Spots Reference C21, C22 ROR ROR2 F23, F24 - Spots C23, C24 Tie Tie-1

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D.i. ii. 5 DLD-1, untreated 4 DLD-1, IL-11 DLD-1, IL-11 3 + 1µM Ponatinib

Ratio 2 *** 1 ** 0 (RTK activity/GAPDH) (RTK

p-AXL p-EGFRp-ErbB2p-ErbB3p-ErbB4 p-STAT3p-HGFR iii.

15 DIFI, untreated DIFI, IL-11 DIFI, IL-11 + 1µM Ponatinib 10

Ratio * 5 * *** ** 0 (RTK activity/GAPDH) (RTK

p-AXL p-EGFRp-ErbB2p-ErbB3p-ErbB4 p-STAT3p-HGFR iv.

1.5 LIM1215, untreated LIM1215, IL-11 LIM1215, IL-11 + 1µM Ponatinib *** 1.0 **

Ratio 0.5

0.0 (RTK activity/GAPDH) (RTK

p-AXL p-EGFRp-ErbB2p-ErbB3p-ErbB4 p-STAT3p-HGFR

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5.3 Discussion In the earlier chapters, we have discussed Ponatinib’s underlying inhibitory effects against several ligand and cytokine (EGF, IL-6, IL-11 and LIF) driven STAT3 pathways in CRC cell lines. Due to the novelty of the IL-11 mediated STAT3 pathway and components associated with such activation (i.e. gp130 receptor) and the commonality of overactive STAT3 observed in CRC (and other cancers), this chapter focuses on evaluating Ponatinib’s underlying mechanisms, with an aim to further broaden our knowledge of Ponatinib’s inhibitory potential.

The effect of Ponatinib and the components of the IL-11 mediated STAT3 pathway As mentioned previously, IL-11 has been reported to not only drive STAT3 activity but also activates AKT and ERK1/2 pathways [188, 496, 510, 511], although only STAT3 was constitutively activated upon IL-11 stimulation in the 7 CRC cell lines we tested (Figure 5-1). Moreover, there are currently no documented studies that observe Ponatinib’s effect on IL-11 driven AKT and ERK1/2 pathways. In our findings, we compared Ponatinib’s potency against the IL-11 mediated STAT3, AKT and ERK1/2 pathways and observed significant reduction in IL-11 mediated STAT3 pathway in all CRC lines tested, when compared to IL-11 driven AKT and ERK1/2. Inconsistent inhibition was observed in the IL-11 driven AKT and ERK1/2 pathways in all cell lines tested. This could possibly be due to the various genetic differences amongst the physiological nature of these cell lines tested when compared to the prominent IL-11 mediated STAT3 activity, though this conclusion has not been previously reported. Importantly, these results further support Ponatinib’s ability to potently inhibit the IL-11 mediated STAT3 pathway.

Due to the importance of the IL-11 driven STAT3 pathway, as stated previously, our chapter continues to focus on the underlying IL-11-STAT3 pathway, further expanding Ponatinib’s mode of action. As previously stated, in cancer, the IL- 11-STAT3 pathway is often hyperactive which ultimately results in continual tumourigenesis and increased migratory/invasive potential [193, 499]. In the literature, IL-11 and IL-11Ra is now emerging to be of greater importance than the previously thought IL-6 cytokine, as shown in various in-vitro and in-vivo gastric mouse models [5]. Moreover, elevated expression levels of IL-11Rα have been shown to correlate with cell invasion, proliferation, metastatic potential and poor prognosis 148

[193, 499]. In addition, there are currently no clinically approved anti-IL-11 inhibitors available for clinical use. Gathered, we further evaluated potential anti-IL-11-STAT3 properties Ponatinib may also exhibit as a multi-tyrosine kinase inhibitor. Therefore, we mimicked CRC cells to develop enhanced IL-11Rα and resulted with elevated IL- 11Rα gene expression, STAT3 activity and SOCS3 gene expression (Figure 5-2, A- D.). More importantly, positive IL-11Rα clones demonstrated increased resistance to Ponatinib, reducing its efficacy when compared to parental lines. It is important to note that the IL-11-STAT3 pathway may be one of many pathways to be inhibited by Ponatinib and our findings further broadens Ponatinib’s mode of action. Nonetheless, Ponatinib was still able to reduce cell viability of these clones to below 50%. This finding further broadens our knowledge of Ponatinib’s mode of action, to not only target other STAT3 mediated pathways, like EGF, IL-6, LIF and IL-11, as demonstrated in previous chapters but also inhibits enhanced IL-11Rα activity.

Gp130 is essential for the continual activation of downstream signalling pathways mediated by several cytokine-receptor complexes, including IL-11-IL- 11Ra, IL-6-IL-6R and LIF-LIFR [512]. Moreover, like-wise with elevated IL-11 and IL-11Rα, it is not surprising that overexpressed gp130 is associated with the development of tumourigenic properties [223, 513]. In addition, there is currently no gp130 inhibitor approved for clinical use. We therefore assessed Ponatinib’s potency in constitutively active gp130 (L-gp130) CRC cell lines. As a result, I demonstrated L-gp130 positive clones in DLD-1, LIM1215 and SW48 CRC cell lines with constitutively active STAT3 and SOCS3 gene expression through cytokine- independent activation of gp130 (Figure 5-3, A-C.). Similarly, to our IL-11Rα positive clones, Ponatinib was also shown to be less efficacious in positive L-gp130 clones when compared to parental lines, as shown by cell viability assays. Nonetheless, Ponatinib was still able to significantly reduce cell viability (>50%) in constitutively active gp130 clones when treated with Ponatinib in a dose dependent manner (Figure 5-3, D.). Our result demonstrated Ponatinib’s potential potency against gp130, a unique receptor shared amongst other family of IL-6 cytokines (i.e. IL-6, IL-11, LIF, OSM, and CNTF), potentially broadening Ponatinib’s inhibitory effect against these cytokines through the inhibition of gp130, which is required for further downstream activation.

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Highly active STAT3 has been widely known to contribute to cancer related characteristics including cell migration, cell invasion and cell survival that ultimately lead to resistance to therapies [60, 514, 515]. Therefore, to further support whether Ponatinib’s mode of action also abrogates STAT3, I generated STAT3C clones. STAT3C was previously developed to replace alanine-cysteine and asparagine- cysteine and as a result this allows for ligand and phosphorylated-independent dimerization [487, 488]. Furthermore, as observed in cell viability assays of enhanced IL-11Rα clones and overexpressed gp130 clones, elevated STAT3 activity by STAT3C, resulted with reduced inhibitory effect in the presence of Ponatinib, in comparison to parental lines (Figure 5-4). Nevertheless, Ponatinib was still able to significantly reduce cell viability of STAT3C clones by >50%. Overall, our results showing that cells containing exogenous expression of IL-11R, L-gp130 or STAT3C are more resistant to Ponatinib compared to control of parental cell lines. However, this does not necessary indicate as to whether Ponatinib specifically binds or inhibits IL-11R or gp130 directly. Together with our ERK1/2 and AKT data, where Ponatinib displayed inconsistent inhibition to IL-11 mediated ERK1/2 and AKT suggests that Ponatinib may block IL-11 driven STAT3 through the inhibition of an intermediate substrate downstream from the receptor.

Ponatinib and IL-11 mediated cross-talk events Cross talk between receptor systems is well documented, and despite limited studies that specifically report IL-11 and potential interaction with other RTKs, several studies have documented kinase interactions between the family member IL- 6R and other RTKs [296, 445, 494, 516]. For instance, in a study conducted by Qiu et.al, observed the recruitment of ErbB2 for signalling in the presence of IL-6 in prostate carcinoma cells [492]. Furthermore, Badache and colleagues have also demonstrated similar findings with IL-6 and EGFR in breast cancer cells [493]. Despite, these studies highlighting synergistic events in the presence of IL-6, we speculate here, similar events with IL-11. The human phospho-tyrosine RTK array allowed our study to highlight possible cross-talk behaviour between IL-11 and other RTKs. However, we did not identify significant increased phosphorylation of any of the 49 RTK’s assessed after IL-11 stimulation. This is either due to IL-11R not interacting with these RTK’s or more likely detection of increased phosphorylation was under the detectable threshold of this array as evident by approximately 80% of

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RTK’s showing no detectable phosphorylation. Interestingly however Ponatinib reduced the phosphorylation of ErbB3 in all 3 cell lines tested (Figure 5-5, D.). It is unsure whether this reduction is due to Ponatinib inhibiting IL-11R interacting with ErbB3 or as we expect Ponatinib may simply block ErbB3 activity independent of IL- 11R, further studies are required to confirm this. Nonetheless, if this speculation was proven, Ponatinib could be further analysed in cancers harbouring elevated ErbB3 expression, including patients with breast and ovarian cancers.

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5.4 Conclusion Overall, we highlight the underlying mechanism of Ponatinib to not only abrogate the EGF and IL-6 mediated STAT3 pathways but to also exhibit potent inhibitory effect on the novel IL-11-STAT3 pathway in CRC cell lines, with our study to first demonstrate this novel mode of action. We observe a reduction in the presence of Ponatinib against enhanced IL-11Rα, and overexpressed gp130 and STAT3 pathways, broadening Ponatinib’s multi-targeted effect. Furthermore, Ponatinib was observed to preferentially inhibit IL-11 mediated STAT3 activity in comparison to IL- 11 driven AKT and ERK1/2 pathways. In this chapter, we also explored the event of cross talk between activated IL-11Ra and other RTKs. We uncover significant reduction of ErbB3 and ErbB4 phosphorylation in the presence of Ponatinib, further highlighting Ponatinib’s multi-targeted properties. Taken together, we suggest Ponatinib’s mode of action has not been fully uncovered in the literature, with our study being the first to evaluate the effect of Ponatinib and STAT3 mediated pathways, in particularly driven by IL-11 in CRC. We reveal great potential in the multi-tyrosine kinase inhibitor, Ponatinib to also act upon and abrogate the novel IL- 11 driven STAT3 pathway.

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CHAPTER 6: Discussion

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6. CHAPTER 6 - DISCUSSION

6.1 General Discussion The relationship between STAT3 activity and CRC has been widely investigated. Studies identify STAT3 as a prominent tumour driver in CRC and is therefore observed as a potential target. Several anti-STAT3 agents, acting on upstream mediating molecules of STAT3 or directly on STAT3 have been developed. However, the transition between pre-clinical development and clinical approval is a lengthy procedure, with very few agents reaching clinical approval (i.e. Cetuximab and Panitumumab as anti-EGFR inhibitors and Dasatinib and Bosutinib as anti-Src inhibitors) and in the case of direct STAT3 inhibitors, no agent is currently clinically approved. This thesis investigated 1167 FDA approved agents to assess the possibility of underlying anti-STAT3 characteristics with the goal to repurpose successful candidates as STAT3 inhibitors. Growth factors (EGF, PDGF, TGF-a and HGF) and cytokines (IL-6, IL-11, LIF, OSM and CNTF) are all widely known to initiate STAT3 activity [65, 181, 192, 461, 517, 518]. Due to the presence of most, if not all of these ligands in the tumour environment and their redundancy in activating downstream molecules, current targeted therapeutic agents may effectively inhibit only one target, resulting in compensatory signalling from uninhibited pathways. Targeted agents against diseases including cancer are commonly identified through drug screens. Indeed, there are several methods adopted to identify inhibitors targeting specific protein-protein and protein-DNA interactions [519-521]. However, several challenging obstacles can arise from pursuing such methods including, identifying a handful of agents from a large library of drugs followed by lengthy procedures for clinical approval. In this thesis, we bypassed these hurdles by utilising a drug panel of 1167 FDA approved agents. Moreover, the method applied to our FDA drug screen specifically assesses STAT3 activity mediated by EGF and IL-6 in-vitro, which allowed for the identification of potential candidates with underlying anti-STAT3 characteristics. To date, the luciferase based STAT3 reporter assay applied in our drug screen, is the first in the literature to specifically assess STAT3 under in-vitro conditions. However, there are several similar drug screens utilised in an aim to specifically target certain molecules known to drive cancer, including MRP4 (Multi drug resistance protein 4),

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ABCG2 (ATP-binding cassette sub-family G member 2) and SMAD3 (Mothers against decapentaplegic homolog 3) [427, 428, 522-524]. This thesis evaluated a series of drug screening procedures, which included several functional assays to validate potential agents with anti-STAT3 properties. Our FDA drug screen (Chapter 3) allowed the identification of existing inhibitors with anti-STAT3 properties against EGF and IL-6 driven STAT3 pathways. More importantly, we uncovered the multi- tyrosine kinase inhibitor, Ponatinib to be our lead compound, which demonstrated a high degree of efficacy for STAT3 over other agents in our panel. Ponatinib is currently used as a third line treatment for patients with Ph+ALL and CML and is known to strongly inhibit Abl, PDGFR, FGFR, c-met and Src family of kinases [525-527]. In this study, we broaden the known targets/pathways of Ponatinib to include EGF, IL-6 and IL-11 driven STAT3 activity. In recent times, IL- 11 has emerged as an important cytokine in relation to cancer development [5, 192- 194], though no IL-11 inhibitor is currently clinical approved. We further report in this thesis, Ponatinib’s underlying anti-IL-11-STAT3 characteristics (Chapter 4). Ponatinib was observed to reduce IL-11 mediated STAT3 protein expression in all 7 CRC cell lines tested. Moreover, in the presence of IL-11 driven STAT3 activity, SOCS3 (a STAT3 specific driven gene), was also significantly reduced with the treatment of Ponatinib in a dose dependent manner and was consistent in all 7 CRC cell lines evaluated (Chapter 4, Figure 4-3). Ponatinib also reduced LIF mediated STAT3 activity, another member of the IL-6 family of cytokines (Chapter 4, Figure 4-2). From these results we observe Ponatinib’s underlying targets against IL-6, IL-11 and LIF driven STAT3 activity, which are commonly observed to be upregulated in several cancers, including gastric, colorectal, head and neck, chondrosarcoma, nasopharyngeal and breast cancers [199, 392, 402, 448, 496]. Moreover, these cytokines all signal through the ubiquitous gp130 receptor, which is also elevated in cancers [502, 528, 529]. Gathered, our results suggest Ponatinib’s multi-kinase inhibitory effect to target not only EGF driven STAT3 activity but also IL-6, IL-11 and LIF mediated STAT3 pathways, with the possibility to also target gp130 (although additional studies are required), further broadening Ponatinib’s potential as an anti-STAT3 inhibitor for cancer patients harbouring these elevated pathways. As mentioned previously, STAT3 activity can be mediated by several growth factors and cytokine related pathways, therefore the need to inhibit multiple signalling pathways simultaneously would greatly reduce continual STAT3 activity. In an aim to 155

identify an inhibitor, which has the capability to block multiple STAT3 related pathways, we uncovered Ponatinib’s ability to consistently inhibit STAT3 activity mediated by several upstream receptors which was not observed when compared to 5 FDA-approved JAK or Src inhibitors (Chapter 4, Figure 4-1). For instance, Bosutinib, a Src inhibitor, successfully abrogated EGF mediated STAT3 activity. However, Bosutinib could not inhibit IL-6 driven STAT3 activity. In contrast, the JAK inhibitor Tofacitinib, effectively reduced IL-6 and IL-11 driven STAT3 activity but, did not inhibit EGF driven STAT3 activity. As these growth factors/cytokines are most likely present simultaneously in the tumour micro-environment, we can speculate that these JAK/Src inhibitors may only be effective in blocking STAT3 driven activity from one growth factor/cytokine and not multiple. Importantly, these findings may also elucidate a possible mechanism of tumour resistance to these inhibitors, whereby STAT3 continues to be activated or is reactivated by other upstream kinases not inhibited by these agents. Although not examined here (where cells were treated for only 1 h) we could postulate that longer treatment times could have seen the re-emergence of activated STAT3 through other compensatory pathways, when treated with the JAK and Src inhibitors but not when treated with Ponatinib. Based on these findings we also hypothesize that an agent that can abrogate multiple pathways simultaneously to effectively reduce STAT3 activity (such as Ponatinib) would be superior in anti-tumour efficacy than the other tested JAK/Src inhibitors used in the clinic. Moreover, if this was the case, tumour cells may less likely develop acquired resistance to Ponatinib. To further support our findings, future experiments could also evaluate Ponatinib’s inhibitory effect against other JAK and Src inhibitors in in-vivo tumour xenograft models. In relation to cross-talk, the blockade of EGFR has been demonstrated in the literature to result with enhanced IL-6 secretion and therefore, continued STAT3 activity. For instance, Fletcher et.al, demonstrated that clinical EGFR inhibitors such as Erlotinib, Cetuximab, Panitumumab and Lapatinib induced the secretion of several pro-inflammatory cytokines such as IL-1, IL-4, IL-6, TNFα in head and neck cancer cell lines [530]. They further examined Erlotinib and its ability to block EGFR but also induce IL-6 secretion, resulting with continual tumour growth. Coincide, Ishiguro and colleagues demonstrated EGFR-TKI treatment increased expression levels of IL- 6 protein and mRNA, IL-6 secretion and transcriptional activity in tongue cancer and lung cancer cell lines [446]. Another study conducted by Gao and colleagues 156

observed the inhibition of EGFR by a mono-targeted agent (ZD) could activate the IL-6/gp130/STAT3 pathway, allowing for continual tumour growth in primary human lung adenocarcinoma cell lines [495]. Furthermore, this study demonstrated the treatment of either an EGFR inhibitor (ZD) or an Src kinase inhibitor (Dasatinib) had minimal effect on p-STAT3 levels in primary human lung adenocarcinoma cell lines compared to a pan-JAK inhibitor (p6), implicating the use of a pan-JAK inhibitor to be of great importance, compared to an inhibitor specific for only a single JAK, as multiple JAKs from different STAT3 related pathways could be involved with driving STAT3 activity. Indeed, several studies have demonstrated that EGFR blockade leads to the reactivation of STAT3 through increased IL-6 expression and overall poor response to these agents. Whether Ponatinib would enhance these anti-tumour effects should be explored in future studies. Taken together, in the clinical setting, these mono-targeted inhibitors are potentially less efficacious towards inhibiting tumour related characteristics, particularly in patients who do not respond to such treatments or those who become resistant to therapies. In fact, persistent activation of STAT3 activity has been suggested in conferring resistance to some anticancer drugs [1, 509, 531] and has been further demonstrated in in-vitro assays in several cancers including renal cell carcinoma and head and neck squamous cell carcinoma lines [532, 533]. These studies further highlight the presence of continual STAT3 activity from uninhibited pathways related to further drive STAT3 activity however, very few studies assess the effect of targeted inhibitors against multiple growth factor or cytokine stimulated STAT3 pathways. For instance, Sen et.al observed significant reduction of IL-6 mediated STAT3 expression when treated with AZD1480, a JAK inhibitor in head and neck squamous cell carcinoma cell lines both in-vitro and in- vivo [534]. Although these are positive findings, this study only analysed one out of several STAT3 mediated pathways and therefore did not explore the effectiveness of this agent on other cell lines/tumours that may be driven through other RTK-STAT3 signalling. In comparison, our study evaluated Ponatinib’s inhibitory effect amongst several key pathways known to initiate STAT3 activity (EGF, IL-6, IL-11 and LIF) and was shown to reduce all ligand mediated STAT3 activity. Prior to this study, no data identifying Ponatinib as a potential IL-11 inhibitor had been published. It was therefore the aim of this study to elucidate the mechanisms behind Ponatinib’s inhibitory effect against the IL-11 pathway. Data presented within this thesis utilise several overexpressed receptors and molecules that are commonly 157

associated for driving cancer including IL-11Rα, L-gp130 and STAT3C. In fact, the overexpression of cancer causing receptors and molecules are often key culprits for patients not responding to targeted treatments, resistance against current therapies and continued tumour burden [60, 61, 131, 247, 448, 502]. Enhanced STAT3 activity is notoriously known as a key tumour driver in CRC, which is commonly shown in several studies and plays a central role in the development and progression of CRC in-vitro and in-vivo [60, 63]. Other cancers that are also shown to exhibit a strong correlation between elevated STAT3 activity and tumour development include prostate cancer, glioblastoma, non-small cell carcinoma, gastric carcinomas, myeloma and breast cancer [229, 233, 236, 245, 485, 514]. Moreover, several studies often link elevated STAT3 activity with poor patient outcome and reduced overall survival [505, 535]. In addition, gp130 is also consistently activated in several cancer types and subtypes commonly due to the following reasons. I.) The overexpression of components related to the gp130 signalling pathways, including IL-6, IL-11, LIF and STAT3 pathways [501, 513, 536]. II.) Gain of function mutations, which ultimately lead to cytokine-independent signalling activation [175, 512, 537]. III.) Impairment of negative feedback molecules and therefore failed attempts to terminate signalling activation [461, 529]. Moreover, gp130 is significantly upregulated in several cancers including CRC, glioblastoma, renal cancer and lymphoma [183, 502, 538-540]. In relation, IL-11 and IL-11Rα have recently been identified to have a more prominent role in the development of tumorigenic characteristics when compared to its other family member, IL-6. This was observed in a study conducted by Putoczki and colleagues which showed a greater correlation with elevated STAT3 activation in comparison to IL-6, during the progression and development of human gastrointestinal cancers [5]. In addition, enhanced IL-11 activity has also been implicated in breast cancers, glioblastoma and endometrial cancers [184, 422, 541]. Gathered, due to the commonly overexpressed receptors and pro-tumour causing molecules like STAT3, it is therefore not surprising for researchers to mimic such environments in in-vitro and in-vivo studies and therefore determine the efficacy and effectiveness of agents against these pathways [491, 542]. In our study we examined Ponatinib’s potential against the novel IL-11, gp130 and STAT3 enhanced pathways. Firstly, as expected, our data confirmed that elevated exogenous expression of IL-11Rα and gp130 resulted in increased STAT3 activity and SOCS3 gene 158

expression (Chapter 5, Figure 5-2 and 5-3). Furthermore, overexpressed IL-11Rα and L-gp130 positive clones were slightly more resistant to Ponatinib’s inhibitory effect when compared to parental lines. However, from this set of experiments, our results were difficult to interpret and we could not conclusively suggest Ponatinib’s prominent role in IL-11 signalling nonetheless, our results could be interpreted in two ways. Firstly, elevated STAT3 activity through IL-11 signalling provides marginal resistance to Ponatinib treatment, signifying that the overexpression of these receptors can slightly affect the sensitivity to Ponatinib in cultured cell lines. However, it is important to note that, the cell viability of these positive clones were <50% in the presence of Ponatinib and could suggest IL-11 mediated STAT3 signalling is one of Ponatinib’s inhibitory targets, although our current data presented here requires further evaluation to truly determine this theory. To provide an insight to Ponatinib’s possible relation with the IL-11 driven STAT3 pathway, future in-vitro experiments could be applied to support this theory, including the use of Ba/F3 cells. Ba/F3 cells are pro-B cell lines, which lack the presence of receptors and are commonly used as a model system for assessing downstream signalling of kinases related to oncogenesis, and therefore provides an insight to identifying small-molecule kinase inhibitors to block such activity [543]. More importantly, these Ba/F3 cells are often engineered to study a certain pathway related to cancer. For instance, in relation to our study, we could potentially utilise Ba/F3 cells with genetically engineered gp130 and/or IL-11Rα to provide evidence to whether Ponatinib has anti-gp130 and/or anti- IL-11Rα properties when compared to parental Ba/F3 cells. Moreover, to further assess Ponatinib’s involvement with gp130, the gp130 (Y757F/Y757F) gastric cancer mouse model may also be an avenue worth pursuing. This model, allows the spontaneous formation of gastric hyperplasia, with histological features reminiscent of those observed in humans [537]. More importantly, the molecular consequence of the Y757 substitution mutation, simultaneously inhibits the binding of SOCS3 to gp130, resulting with continual STAT3 activation [537]. In the presence of Ponatinib, this model would provide an insight to whether Ponatinib has an effect on constitutively active gp130/STAT3 analysed in the organ of interest. Despite, demonstrating inconclusive results, we still observe Ponatinib to be a novel candidate for targeting STAT3 signalling in CRC and possibly other cancers.

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In this study, we demonstrated Ponatinib’s anti-tumour characteristics in DLD-1 and SW48 subcutaneous xenografts with significantly reduced tumour size and minimal toxicity of mice during the course of Ponatinib treatment (Chapter 4, Figure 4-7). Despite successful tumour reduction in our subcutaneous mouse models with Ponatinib, it would be beneficial to examine Ponatinib’s inhibitory effect in metastatic mice models. In patients with advanced CRC, tumour cells commonly metastasise to the liver however, it has also been documented to spread to the lungs and bones [30, 476, 544, 545]. Therefore, to mimic such environments animal models of metastasis have been documented including orthotopic xenografts of CRC cell lines, which involves the injection of differentiated CRC cells to the serosa of the intestine and results with successful liver metastasis [546, 547]. Moreover, patient- derived orthotopic xenografts with surgical specimens of CRC liver metastases to the caecum of immune-deficient mice have also been documented to generate models of metastatic CRC [548, 549]. To utilise animal models related to metastasis would provide a valuable insight to Ponatinib’s effect in patients with advanced CRC and liver metastasis. In relation, it would be beneficial to assess Ponatinib’s mechanism in certain mutated pathways commonly observed during CRC progression in patients. For instance, CRC initially develop with intestinal epithelial cells that harbour loss of function of the Adenomatous polyposis coli (Apc) pathway, which is associated with the Wnt signalling pathway [550]. More importantly, patients who harbour familial adenomatous polyposis (FAP) are often observed with a germline mutation in one Apc allele and result with the development of multiple polyps within the large intestine and are at high risk for developing CRC [551, 552]. To mimic these events, researchers utilise Apc mutant mice, which develop adenomas similar to those observed in FAP patients. This is due to the fact that they are partially non- immunogenic and develop in immunocompetent mice [552]. Therefore, the Apc (Min/+) mouse model could also be utilised to assess Ponatinib’s effectiveness and efficacy in a model that mimics tumourigenic characteristics in CRC patients. The Apc (Min/+) model is recognised as a recessive tumour suppressor gene and has been known to develop multiple intestinal neoplasias in intestinal tracts of mice, within several weeks of birth [553]. It should be noted, the development of intestinal tumours in Apc mutant mice arise in the small intestine, compared to the majority of human bowel cancers which are commonly located in the colon [553]. Nonetheless, this 160

mouse model would be beneficial to examine Ponatinib’s inhibitory effect on polyps within this commonly found CRC mutation.

There are currently no studies documenting signalling interactions between IL-11 or IL-11Rα with other RTKs, although the closely related family member, IL-6 has been observed in several studies to interact with EGFR, ErbB family of kinases, IL-1 and insulin receptor [296, 492, 494, 516]. The human phospho-RTK array was utilised to determine if IL-11Rα associated with other RTKs in the presence of IL-11 and whether Ponatinib could block this interaction. Our data indicated that IL-11 stimulation did not significantly increase phosphorylation of any RTKs in the panel (Chapter 5, Figure 5-6). However, we did not examine possible association that was triggered by IL-11 stimulation for longer than an hour and thus cannot rule out that IL-11Rα can interact with other RTKs if stimulated with IL-11 for a longer time frame or in different cellular systems. Nonetheless, Ponatinib reduced p-ErbB2/3 significantly and thus we therefore speculate Ponatinib could in fact directly target ErbB2 and ErbB3 independent of IL-11 stimulation, although further investigation is required. If our speculation was validated, Ponatinib could possibly be an anti- ErbB2/3 inhibitor for patients with known cancers that harbour enhanced ErbB2 and ErbB3 (also known as HER2/3) such as breast and ovarian carcinomas [554-557], further utilising Ponatinib as an anti-cancer drug for not only CML and Ph+ALL patients but to also potentially target several upregulated pathways known to drive multiple cancer types. Furthermore, the evaluation of IL-11 mediated ERK1/2 and AKT pathways were analysed in the presence of Ponatinib and the results obtained were inconsistent across all 7 CRC cell lines tested when compared to IL-11 driven STAT3 activity (Chapter 5, Figure 5-1). Nonetheless, in the presence of Ponatinib, we observed possible inhibition of IL-11 stimulated p-ERK1/2 and a slight reduction in IL-11 driven p-AKT expression in a few CRC cell lines tested. We can therefore speculate, Ponatinib does not only target the IL-11 driven STAT3 pathway but can also potentially target IL-11 mediated ERK1/2 pathway. If this was the case, in relation to cancer patients, Ras mutations (which is associated with the Ras-Raf-MEK-ERK pathway) are commonly observed in up to 30% of all cancers in particularly in pancreatic (90%), CRC (50%), thyroid (50%), lung (30%) and melanomas (25%) [558-560]. With further investigation, Ponatinib may target the IL-11 driven ERK1/2 161

pathway and could also possibly be an anti-ERK1/2 inhibitor to treat cancer patients who harbour these mutations. In this study, we pursued the possibility for cross-talk events between IL-11 and other phospho-RTKs, including ErbB2 and ErbB3 (Chapter 5, Figure 5-6) however, our results presented, were inconclusive and we were not able to confidently determine true cross-talk events between these pathways and thus in this study we can only speculate Ponatinib may have an effect amongst these receptors independent of IL-11 treatment. In future studies, we could utilise the phospho-RTK array and analyse the effect of Ponatinib alone in comparison to control, IL-11 alone and Ponatinib and IL-11 combined. This would provide an insight as to whether Ponatinib inhibits other RTKs independently of IL-11. Moreover, to determine protein-protein interactions between IL-11 and other RTKs, the glutathione S-transferase (GST) fusion protein pull-down assay could be utilised to determine possible cross-talk events as it has the ability to identify interactions between a probe protein and unknown targets and confirm speculated interactions between a probe protein and a known protein [561, 562].

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6.2 Concluding remarks Our findings within this thesis have highlighted several FDA approved agents with underlying anti-STAT3 activity in CRC cell lines. Based on our data and observations, it is possible that current FDA approved agents may have secondary purposes for the treatment of CRC cancer and should be further investigated. In addition, these results should be extended into other malignancies potentially providing a basis for STAT3 as potential target in cancer therapy. We observed our lead compound, Ponatinib to have potential anti-EGF, IL-6, LIF and IL-11 mediated STAT3 activity in CRC cell lines as demonstrated in several functional assays. Moreover, we observed significant tumour reduction in CRC xenograft mice models when treated with Ponatinib. Taken together, these results provide an insight into Ponatinib’s broad inhibitory targets, which have not been documented previously. In a clinical setting, based on our findings, Ponatinib may have great potential for the future treatment of patients with CRC harbouring persistent STAT3 activity driven by related growth factors and cytokines. Additionally, in the clinical setting, targeted therapies are often paired with standard of care chemotherapy agents for the treatment of CRC such as 5FU, Oxaliplatin, Irinotecan, Leucovorin. Therefore, it would be of great interest to analyse the effect of combinational therapy with Ponatinib and these current agents. Moreover, these results could be evaluated further in other cancer types known to exhibit elevated STAT3 activity, including glioblastoma, lung cancers and head and neck squamous cell carcinoma [242, 243, 495, 563, 564]. In this thesis, we further investigated Ponatinib’s mode of action as a potential anti-IL-11-STAT3 inhibitor in CRC. Our results speculate possible inhibitory action against overexpressed IL-11Rα, gp130 and STAT3 molecules, although further investigation is required to support our current findings. Moreover, based on our human phospho-RTK array, we speculate possible Ponatinib inhibition against the ErbB family of kinases, notably ErbB2 and ErbB3 independent of IL-11 mediated activity and could be further investigated in breast cancer and ovarian cancers, which are known to be hyper activated in these malignancies. Taken together, this body of work provides an insight to Ponatinib’s inhibitory effect against STAT3 related pathways in CRC and could ultimately lead to the repurposing of Ponatinib as a treatment option for patients with CRC.

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8. SUPPLEMENTARY TABLES AND FIGURES

Supplementary Table 1: The effect on EGF and IL-6 mediated STAT3 activity in DIFI and DLD-1 colorectal cancer cells (respectively) with 1167 FDA agents (10µM)

Shaded boxes (highlighted in purple) indicate inhibitors with reduced STAT3 activity by >50% observed in both EGF and IL-6 pathways. Data shown represents the averages of triplicates amongst at least 3 individual experiments.

196

DIFI (EGF) DLD-1 (IL-6) # FDA inhibitors STAT3 (%) SD STAT3 (%) SD 0 Negative control (DMSO) 0 0 0 0 0 Positive control (EGF or IL-6 only) 100.0 3.8 100.0 4.9 1 Axitinib 184.5 1.2 122.8 4.5 2 Afatinib (BIBW2992) 0.0 0.0 54.5 9.9 3 Bortezomib (Velcade) 8.3 1.2 3.2 3.6 4 Bosutinib (SKI-606) 3.4 1.4 40.5 5.4 5 Dasatinib (BMS-354825) 0.1 0.0 44.3 8.8 6 Erlotinib HCl 0.1 0.0 40.7 4.2 7 Gefitinib (Iressa) 0.1 0.0 59.5 2.5 8 Imatinib Mesylate 4.7 1.4 52.3 2.6 9 Lapatinib Ditosylate (Tykerb) 1.4 1.3 92.0 4.7 10 Lenalidomide 10.3 1.7 140.4 8.6 11 Nilotinib (AMN-107) 23.4 1.4 61.4 4.5 12 Pazopanib HCl 71.3 0.1 75.1 2.9 13 Rapamycin (Sirolimus) 45.3 7.6 69.1 10.2 14 Sorafenib (Nexavar) 12.6 1.4 32.8 1.8 15 Sunitinib Malate (Sutent) 48.1 3.4 28.3 1.6 16 Temsirolimus (Torisel) 80.7 13.5 47.5 1.7 17 Vandetanib (Zactima) 0.0 0.0 47.6 1.8 18 Vorinostat (SAHA) 10.2 0.9 22.6 2.5 19 Masitinib (AB1010) 37.6 1.4 98.8 8.4 20 Crizotinib (PF-02341066) 51.6 3.5 45.2 2.5 21 Vismodegib (GDC-0449) 84.9 1.5 101.7 5.1 22 XL-184 (Cabozantinib) 35.2 2.3 84.2 3.9 23 Everolimus (RAD001) 53.1 1.7 70.4 4.4 24 Abiraterone (CB-7598) 69.3 2.2 67.5 2.4 25 Pemetrexed 109.5 7.5 153.9 8.0 26 Ivacaftor (VX-770) 28.9 1.7 69.1 3.1 27 Docetaxel (Taxotere) 52.5 1.8 80.4 5.4 28 Gemcitabine HCl (Gemzar) 49.8 1.9 84.7 2.6 29 Paclitaxel (Taxol) 86.8 1.7 77.6 5.3 30 Capecitabine (Xeloda) 94.4 2.6 84.5 4.9 31 Cisplatin 96.5 3.0 89.8 4.0 32 Valproic acid sodium salt (Sodium valproate) 106.0 2.6 121.6 6.5 33 Regorafenib (BAY 73-4506) 37.7 6.9 41.6 3.2 34 Ritonavir 67.9 9.6 133.9 8.8 35 Anastrozole 98.9 1.6 128.0 7.6 36 Aprepitant (MK-0869) 66.1 3.5 91.6 4.1 37 Bicalutamide (Casodex) 71.0 2.7 96.6 5.1 38 Thalidomide 100.0 2.5 69.4 1.9 39 Exemestane 136.5 5.6 130.3 5.3

197

40 Finasteride 104.9 1.9 118.9 6.9 41 Irinotecan 38.9 1.4 37.9 2.4 42 Cladribine 81.5 3.6 139.6 10.2 43 Decitabine 48.3 1.9 86.9 4.8 44 Dutasteride 88.9 8.5 91.7 4.5 45 Melatonin 108.8 19.0 115.9 6.5 46 Bisoprolol 99.0 3.8 87.0 4.9 47 Doxorubicin (Adriamycin) 2.0 1.5 31.8 5.6 48 Adrucil (Fluorouracil) 82.7 3.3 184.3 10.6 49 Abitrexate (Methotrexate) 85.5 5.3 86.4 4.7 50 Imiquimod 105.4 3.8 115.1 5.7 51 Bendamustine HCL 109.5 3.1 106.9 5.7 52 Nelarabine (Arranon) 113.9 2.8 111.8 12.5 53 Bleomycin sulfate 96.8 2.9 111.5 5.9 54 Carboplatin 88.4 4.1 85.4 4.6 55 Clofarabine 71.2 3.8 78.4 2.1 56 Dacarbazine (DTIC-Dome) 120.9 4.5 137.5 13.5 57 Dexrazoxane Hydrochloride 111.9 2.9 122.3 17.4 58 Epirubicin Hydrochloride 47.5 3.7 17.1 1.7 59 Oxaliplatin (Eloxatin) 70.8 3.8 84.0 1.5 60 Etoposide (VP-16) 112.9 2.5 107.1 8.7 61 Evista (Raloxifene Hydrochloride) 58.7 7.4 128.6 5.7 62 Idarubicin HCl 6.8 1.1 51.6 4.2 63 Fludarabine Phosphate (Fludara) 39.9 2.6 4.5 1.5 64 Topotecan HCl 10.6 1.9 1.0 0.0 65 2-Methoxyestradiol 121.4 5.3 99.0 4.7 66 Letrozole 119.3 4.8 131.9 6.5 67 Leucovorin Calcium 121.9 4.1 134.9 5.5 68 Methazolastone 109.1 3.0 133.4 7.9 69 Vincristine 88.5 3.8 50.7 1.4 70 Agomelatine 114.4 2.9 74.4 1.5 71 Leflunomide 160.1 3.9 211.5 1.8 72 Vinblastine 107.2 3.2 169.4 1.6 73 MDV3100 (Enzalutamide) 103.7 3.5 152.5 9.5 74 Dienogest 122.7 3.5 163.0 9.1 75 Entecavir hydrate 149.3 5.8 140.2 6.4 76 Nepafenac 113.8 3.7 140.8 6.5 77 Rufinamide (Banzel) 112.7 3.6 116.9 6.7 78 Posaconazole 120.9 4.8 85.1 4.2 79 Prasugrel (Effient) 152.9 3.3 189.3 13.4 80 Ramelteon (TAK-375) 115.1 3.1 165.0 10.2 81 AMG-073 HCl (Cinacalcet hydrochloride) 21.3 2.9 153.4 9.1 82 Celecoxib 59.2 2.7 130.3 7.2 83 Vemurafenib (PLX4032) 185.2 5.3 37.5 3.4 84 Acarbose 119.3 2.8 127.4 18.4 198

85 Adapalene 124.7 2.7 66.2 5.2 86 Altretamine (Hexalen) 126.8 1.2 8.5 2.4 87 Amisulpride 86.5 6.5 113.3 8.9 88 Aniracetam 95.5 5.8 101.8 1.4 89 Artemisinin 106.4 4.6 113.4 8.9 90 Asenapine 101.5 2.4 108.6 7.0 91 Benazepril hydrochloride 84.6 8.6 90.3 4.6 92 Biperiden HCl 95.4 3.5 90.9 3.2 93 Budesonide 98.7 2.4 89.7 4.6 94 Bumetanide 96.9 1.2 83.4 2.4 95 Camptothecin 69.5 7.6 0.7 0.1 96 Carmofur 82.0 3.2 156.4 11.7 97 Cetirizine Dihydrochloride 95.8 7.8 106.8 10.2 98 Cilnidipine 102.0 9.4 101.2 9.4 99 Cilostazol 115.2 2.5 103.7 9.1 100 Floxuridine 106.3 6.7 139.3 2.7 101 Ftorafur 106.6 8.2 93.4 2.4 102 Ifosfamide 88.6 4.5 91.6 9.2 103 Megestrol Acetate 97.5 5.2 109.4 1.6 104 Mercaptopurine 98.3 1.4 109.0 15.4 105 Pamidronate Disodium 96.5 3.6 105.3 12.4 106 Streptozotocin (Zanosar) 96.6 3.9 97.7 2.4 107 Zoledronic Acid (Zoledronate) 79.9 5.3 86.7 1.2 108 Dexamethasone 109.0 9.6 92.9 2.0 109 Doxazosin mesylate 98.3 8.4 79.0 7.9 110 Edaravone (MCI-186) 102.5 1.4 83.5 5.0 111 Ellagic acid 121.0 1.6 115.5 9.7 112 Etodolac (Lodine) 104.8 8.4 106.8 8.4 113 Etomidate 107.2 7.3 107.1 8.9 114 Felbamate 101.0 2.6 104.7 4.2 115 Fluconazole 101.5 4.7 119.3 1.7 116 Flumazenil 104.9 5.9 89.8 4.7 117 Fluoxetine HCl 88.0 3.8 100.8 7.8 118 Fluvoxamine maleate 93.1 7.3 94.1 5.3 119 Gabapentin Hydrochloride 101.1 1.2 114.2 1.8 120 Gatifloxacin 103.9 1.9 112.9 2.7 121 Genistein 147.0 3.6 155.6 1.7 122 Glimepiride 104.3 7.2 106.4 5.9 123 Granisetron HCl 94.3 6.3 99.3 8.2 124 Ivermectin 2.0 0.2 0.7 0.1 125 Ketoconazole 87.1 6.4 88.3 5.8 126 Lansoprazole 156.4 8.9 119.8 2.7 127 Levetiracetam 96.1 5.2 115.3 4.9 128 Lidocaine (Alphacaine) 86.3 1.8 109.6 3.9 129 Loratadine 96.9 9.4 112.6 1.6 199

130 Losartan potassium 85.7 2.7 99.6 8.4 131 Acitretin 68.7 3.7 77.8 6.2 132 Biapenem 89.4 4.2 95.2 4.2 133 Cefoselis sulfate 94.8 5.3 94.7 4.6 134 Daptomycin 100.2 12.4 93.3 3.6 135 Doripenem Hydrate 100.5 10.6 116.0 14.7 136 Dorzolamide HCL 91.2 6.9 95.6 2.5 137 Gestodene 83.8 4.8 116.1 17.4 138 Drospirenone 98.0 5.2 112.2 1.8 139 Ruxolitinib (INCB018424) 162.3 19.6 2.1 0.5 140 Isotretinoin 76.4 3.7 85.4 1.6 141 Lopinavir (ABT-378) 81.0 3.2 84.0 3.8 142 Meropenem 104.4 7.7 88.4 7.4 143 Mianserin hydrochloride 114.3 6.2 142.0 20.5 144 Minoxidil 95.5 1.5 120.6 13.7 145 Mizoribine (Bredinin) 93.9 4.6 119.5 13.2 146 Mosapride citrate 98.8 2.5 110.9 12.7 147 Nafamostat mesylate 104.6 7.9 106.5 4.2 148 Omeprazole (Prilosec) 107.5 9.4 95.1 5.8 149 Ondansetron hydrochloride (Zofran) 94.9 4.7 104.1 2.7 150 Oxcarbazepine 109.9 9.3 108.6 2.5 151 Pizotifen malate 109.9 10.7 141.5 9.0 152 Resveratrol 127.4 10.4 156.8 5.2 153 Rocuronium bromide 95.1 2.6 113.6 4.6 154 Stavudine 86.5 5.8 118.5 7.2 155 Teicoplanin 92.5 3.5 99.9 3.8 156 Tenofovir Disoproxil Fumarate 124.9 10.3 116.5 17.4 157 Tenofovir (Viread) 85.7 8.2 93.0 6.2 158 Tigecycline 105.3 9.3 112.0 10.2 159 Trilostane 113.7 4.6 122.9 12.4 160 Vecuronium Bromide 105.2 6.8 114.4 15.4 161 Bimatoprost 111.4 17.6 113.4 16.4 162 Linezolid (Zyvox) 112.9 14.2 104.5 9.4 163 Alfuzosin hydrochloride (Uroxatral) 105.3 8.3 99.1 8.3 164 Clopidogrel (Plavix) 107.1 5.2 96.0 6.3 165 Prazosin HCl 113.2 7.4 103.3 3.6 166 Ranolazine dihydrochloride 107.1 9.3 105.0 6.7 167 Repaglinide 127.9 9.2 106.3 8.9 168 Risedronate sodium 112.3 10.2 116.7 9.3 169 Rolipram 108.9 8.2 107.3 9.0 170 Sildenafil citrate 110.6 3.1 100.5 7.4 171 Sumatriptan succinate 104.0 5.6 95.8 8.4 172 Tianeptine sodium 104.5 8.9 94.0 6.4 173 Tizanidine HCl 109.0 9.7 90.3 7.4 174 Topiramate 105.8 5.2 94.0 3.5 200

175 Tranilast (SB 252218) 194.2 15.7 117.3 7.9 176 Varenicline tartrate 97.1 7.4 113.4 10.2 177 Venlafaxine 96.9 4.7 121.9 12.6 178 Voriconazole 87.0 7.8 117.2 14.2 179 Zileuton 93.1 5.8 110.6 9.6 180 Ziprasidone hydrochloride 73.7 3.8 102.1 7.0 181 Zonisamide 86.8 6.8 100.6 5.9 182 Atazanavir sulfate 78.9 7.9 87.7 8.4 183 Ofloxacin (Floxin) 102.4 8.3 114.1 9.5 184 Marbofloxacin 102.7 6.3 128.4 7.3 185 Moxifloxacin hydrochloride 114.0 9.3 129.5 5.2 186 Calcitriol (Rocaltrol) 147.8 7.3 98.9 6.2 187 Doxercalciferol (Hectorol) 150.6 4.6 115.4 9.4 188 Alfacalcidol 121.1 4.7 117.2 8.9 189 Calcifediol 156.6 8.9 89.6 4.7 190 Iloperidone (Fanapt) 73.7 4.6 112.1 8.8 191 Naratriptan HCl 95.0 5.9 111.8 4.7 192 Ponatinib (AP24534) 3.1 0.2 3.8 0.7 193 Fludarabine (Fludara) 10.5 2.5 54.7 2.5 194 Pralatrexate (Folotyn) 106.7 6.4 95.1 7.4 195 Cefaclor (Ceclor) 126.3 6.8 109.9 10.3 196 Betamethasone (Celestone) 99.6 4.9 107.0 7.4 197 Mycophenolate mofetil (CellCept) 36.1 5.4 21.0 3.6 198 Cephalexin (Cefalexin) 81.4 3.5 95.8 6.3 199 Dyphylline (Dilor) 103.6 11.2 111.7 10.3 200 Aztreonam (Azactam, Cayston) 105.1 10.3 120.0 9.3 201 Perindopril Erbumine (Aceon) 88.3 4.2 114.3 7.4 202 Irbesartan (Avapro) 115.7 11.7 114.5 8.3 203 Alprostadil (Caverject) 123.4 8.4 107.8 7.9 204 Norfloxacin (Norxacin) 93.7 8 107.1 5.8 205 Tadalafil (Cialis) 84.3 5.7 101.1 6.8 206 Cyclosporine (Neoral) 36.3 3.2 30.0 4.6 207 Cidofovir (Vistide) 104.5 8.4 109.7 8.4 208 Natamycin (Pimaricin) 106.5 7.8 119.0 10.6 209 Ibuprofen Lysine (NeoProfen) 93.4 6.3 112.2 12.3 210 Telaprevir (VX-950) 83.8 6.2 93.3 4.2 211 Saxagliptin (BMS-477118,Onglyza) 86.0 3.6 100.7 7.3 212 Febuxostat (Uloric) 219.1 18.5 73.4 2.6 213 Nebivolol (Bystolic) 58.2 6.3 110.2 19.4 214 Pimobendan (Vetmedin) 127.5 13.5 97.1 4.6 215 Pomalidomide 109.0 11.3 116.2 6.4 216 Tazarotene (Avage) 97.9 6.2 130.0 8.5 217 Sulfasalazine (Azulfidine) 112.1 5.8 124.0 3.7 218 Candesartan (Atacand) 91.2 6.9 112.6 6.5 219 Ubenimex (Bestatin) 91.3 6.2 99.9 3.8 201

220 Apixaban 97.8 7.8 107.8 8.3 221 Reserpine 113.8 10.2 108.0 6.1 222 Furosemide (Lasix) 90.5 8.4 98.7 5.9 223 Olmesartan medoxomil (Benicar) 100.0 5.6 111.6 4.8 224 Cefdinir (Omnicef) 100.9 4.2 113.9 8.3 225 Clotrimazole (Canesten) 78.2 7.9 78.2 6.4 226 Rizatriptan Benzoate (Maxalt) 85.7 6.2 102.4 4.8 227 Pyridostigmine Bromide (Mestinon) 80.3 4.8 105.5 9.6 228 Methimazole (Tapazole, Northyx) 89.6 5.2 102.1 7.3 229 Metolazone (Zaroxolyn) 72.3 7.2 98.0 6.8 230 Cefoperazone (Cefobid) 84.7 3.8 95.9 6.1 231 Silodosin (Rapaflo) 99.8 5.3 112.9 9.3 232 Riluzole (Rilutek) 134.2 9.9 142.7 8.4 233 Risperidone (Risperdal) 86.7 3.6 107.2 12.5 234 Sulfapyridine (Dagenan) 85.7 7.4 110.9 6.9 235 Sulfameter (Bayrena) 81.4 2.7 112.2 7.8 236 Prilocaine 78.0 8.5 105.2 8.3 237 Darunavir Ethanolate (Prezista) 88.1 8.2 100.3 6.2 238 Prednisone (Adasone) 88.8 3.7 91.1 8.5 239 Acetylcysteine 96.2 6.4 122.1 12.5 240 Alendronate (Fosamax) 93.5 2.7 117.3 7.3 241 Ethinyl Estradiol 80.6 7.4 102.9 5.8 242 Naproxen (Aleve) 88.7 4.7 108.2 5.3 243 Nitazoxanide (Alinia, Annita) 178.6 4.9 81.7 6.7 244 Triamcinolone Acetonide 88.2 3.2 105.4 10.2 245 Orlistat (Alli, Xenical) 46.5 5.8 51.5 5.7 246 Allopurinol (Zyloprim) 81.7 5.3 99.1 4.7 247 Zafirlukast (Accolate) 171.4 8.4 134.3 12.3 248 Erythromycin (E-Mycin) 99.7 6.7 120.3 18.4 249 Amphotericin B (Abelcet) 98.7 8.9 129.6 13.2 250 Docosanol (Abreva) 82.6 5.2 109.4 4.5 251 Ibuprofen (Advil) 89.8 3.7 115.1 8.9 252 Amprenavir (Agenerase) 89.1 5.8 111.2 4.8 253 Albendazole (Albenza) 69.5 4.2 106.6 5.0 254 Chlorothiazide 93.1 3.8 106.4 4.2 255 Methyldopa (Aldomet) 125.2 10.6 132.6 5.3 256 Ursodiol (Actigal Urso) 146.4 11.4 146.6 9.7 257 Nitrofurazone (Nitrofural) 113.3 9.4 120.3 10.2 258 Ketoprofen (Actron) 99.4 7.5 113.4 6.4 259 Ketorolac (Toradol) 109.9 6.5 114.2 3.7 260 Adenosine (Adenocard) 55.6 3.4 100.9 8.9 261 Cytarabine 343.8 25.2 128.8 4.5 262 Zolmitriptan (Zomig) 79.0 6.4 97.8 7.8 263 Telbivudine (Sebivo, Tyzeka) 86.6 9.3 110.9 9 264 Monobenzone (Benoquin) 146.8 12.4 244.1 20.3 202

265 Tretinoin (Aberela) 132.2 10.2 120.6 15.6 266 Phenylbutazone (Butazolidin, Butatron) 78.0 4.7 96.9 8.0 267 Ezetimibe (Zetia) 81.7 5.2 94.2 6.8 268 Enalaprilat dihydrate 79.9 6.3 102.4 6.2 269 Dofetilide (Tikosyn) 95.4 4.2 100.5 3.7 270 Isradipine (Dynacirc) 79.3 7.5 76.2 8.9 271 Estrone 103.4 7.5 115.7 5.8 272 Flucytosine (Ancobon) 102.5 7.9 123.6 10.2 273 Trichlormethiazide (Achletin) 81.1 5.2 112.5 12.6 274 Loteprednol etabonate 86.6 6.8 106.8 8.9 275 Aminocaproic acid (Amicar) 85.6 4.6 111.1 8.9 276 Aminoglutethimide (Cytadren) 83.4 2.2 111.4 4.7 277 Aminophylline (Truphylline) 82.0 3.6 93.6 8.3 278 Amorolfine Hydrochloride 93.2 4.7 49.2 6.4 279 Chloramphenicol (Chloromycetin) 102.9 2.8 114.8 10.2 280 Flurbiprofen (Ansaid) 119.8 10.3 128.3 14.7 281 Disulfiram (Antabuse) 113.8 11.4 160.9 17.5 282 Mesalamine (Lialda) 133.9 12.6 116.7 8.2 283 Ipratropium bromide 84.7 7.4 112.3 6.4 284 Sulfanilamide 79.5 3.8 111.1 3.7 285 Betamethasone Dipropionate (Diprolene) 118.2 5.7 111.0 5.7 286 Betapar (Meprednisone) 94.3 5.7 101.3 4.9 287 Betamethasone valerate (Betnovate) 125.3 6.9 123.3 7.8 288 Praziquantel (Biltricide) 107.4 5.9 128.3 5.8 289 Busulfan (Myleran, Busulfex) 98.5 6.2 105.6 6.3 290 Carbamazepine (Carbatrol) 91.6 4.8 111.4 8.5 291 Hydrocortisone (Cortisol) 84.7 4.6 113.9 10.7 292 Torsemide (Demadex) 85.9 3.8 114.9 12.5 293 Desonide 89.3 6.9 106.3 11.2 294 Didanosine (Videx) 94.4 7.4 102.8 9.7 295 Divalproex sodium 106.8 8.2 111.6 7.8 296 Emtricitabine (Emtriva) 104.3 6.3 123.3 9.3 297 Progesterone (Prometrium) 102.7 8.5 110.1 3.7 298 Lamivudine (Epivir) 99.0 3.7 119.1 5.8 299 Eplerenone 81.2 5.9 111.2 2.6 300 Hydrochlorothiazide 85.0 6.2 106.4 8.4 301 Estradiol 88.1 3.8 96.9 6.3 302 Deferasirox (Exjade) 151.9 5.9 49.2 2.3 303 Piroxicam (Feldene) 114.9 6.4 121.3 12.5 304 Gemcitabine (Gemzar) 72.8 9.6 73.1 5.8 305 Glipizide (Glucotrol) 92.9 3.7 150.7 12.8 306 Glyburide (Diabeta) 92.0 8.5 120.5 18.5 307 Adefovir Dipivoxil (Preveon, Hepsera) 68.2 3.8 33.2 2.8 308 Zalcitabine 22.2 5.8 117.8 10.2 309 Azathioprine (Azasan, Imuran) 81.0 6 97.9 6.8 203

310 Indomethacin (Indocid, Indocin) 81.7 3.8 89.2 6.9 311 Paliperidone (Invega) 116.8 9.5 114.3 5.8 312 Terbinafine (Lamisil, Terbinex) 87.5 5.8 92.5 4.8 313 Levodopa (Sinemet) 74.9 5.2 133.1 5.9 314 Levonorgestrel (Levonelle) 72.5 3.7 118.3 4.8 315 Gemfibrozil (Lopid) 74.4 5.8 106.3 6.3 316 Indapamide (Lozol) 78.8 9.5 91.0 4.9 317 Mitotane (Lysodren) 71.5 5.9 75.4 5.2 318 Methylprednisolone 92.4 6.4 99.9 8.9 319 Meloxicam (Mobic) 107.5 9.5 128.4 6.9 320 Mesna (Uromitexan, Mesnex) 98.6 7.4 109.8 6.3 321 Methocarbamol (Robaxin) 72.1 5.9 109.7 8.3 322 Prednisolone (Hydroretrocortine) 81.8 6.8 104.6 9.5 323 Telmisartan (Micardis) 64.4 6.9 88.3 7.4 324 Thiabendazole 116.5 6.2 118.0 10.2 325 Guaifenesin (Guaiphenesin) 81.6 3.8 95.6 8.4 326 Rifabutin (Mycobutin) 84.2 7.5 89.2 4.6 327 Nevirapine (Viramune) 100.5 10.5 101.8 6.4 328 Esomeprazole magnesium (Nexium) 129.1 16.7 122.7 5.3 329 Niacin (Nicotinic acid) 91.1 6.3 102.0 6.3 330 Nimodipine (Nimotop) 86.3 4.9 96.4 6.4 331 Nisoldipine (Sular) 77.4 4.2 37.3 7.4 332 L-Glutamine 79.3 7.9 100.2 2.6 333 Gadodiamide (Omniscan) 80.7 4.9 96.6 5.2 334 Oxybutynin (Ditropan) 89.0 3 113.2 5.7 335 Enoxacin (Penetrex) 133.1 16.5 104.0 4.2 336 Pitavastatin calcium (Livalo) 75.5 5.3 87.2 5.7 337 Rifapentine (Priftin) 78.1 7.3 46.6 4.2 338 Suprofen (Profenal) 106.9 8.4 104.7 4.6 339 Pyrazinamide (Pyrazinoic acid amide) 85.0 7.5 99.6 5.7 340 Quetiapine fumarate (Seroquel) 97.8 2.5 102.6 5.2 341 Rifampin (Rifadin, Rimactane) 88.1 7.5 88.5 5.8 342 Beta Carotene 170.5 20.5 95.1 6.8 343 Cefditoren pivoxil 155.4 17.3 129.9 9.5 344 Sulfadiazine 113.3 10.3 126.2 10.2 345 Chlorprothixene 99.4 5.7 130.6 16.4 346 Oxytetracycline (Terramycin) 120.4 7.4 101.5 5.3 347 Thioguanine 74.7 3.8 33.4 1.4 348 Toremifene Citrate (Fareston, Acapodene) 75.9 5.8 90.8 6.4 349 Ethionamide 81.1 5.9 93.0 7.8 350 Trifluridine (Viroptic) 118.0 4.7 95.5 4.2 351 Azacitidine (Vidaza) 20.8 3.2 35.1 2.4 352 Vidarabine (Vira-A) 139.6 8.4 99.7 5.7 353 Verteporfin (Visudyne) 51.0 2.8 48.3 9.1 354 Teniposide (Vumon) 55.8 5.8 26.1 3.6 204

355 Rifaximin (Xifaxan) 59.9 6.9 81.4 4.7 356 Bacitracin zinc 95.7 6.3 91.5 5.0 357 Simvastatin (Zocor) 62.6 7.9 73.4 3.8 358 Ramipril (Altace) 77.3 4.9 76.8 6.3 359 Fenofibrate (Tricor, Trilipix) 87.8 7.3 63.8 8.6 360 Ranolazine (Ranexa) 82.3 6.8 89.2 4.6 361 Ranitidine (Zantac) 79.8 7.4 95.2 3.5 362 Acadesine 76.0 3.8 89.7 1.2 363 Acetylcholine chloride 90.3 5.9 92.5 2.5 364 Acipimox 102.1 9.7 89.5 3.1 365 Acyclovir (Aciclovir) 93.9 6.4 88.9 3.6 366 Nifedipine (Adalat) 257.8 15.7 48.9 4.1 367 Amiloride hydrochloride (Midamor) 105.6 8.4 98.9 2.6 368 Amlodipine besylate (Norvasc) 1.0 0.1 55.1 4.7 369 Chlorpheniramine Maleate 88.5 4.2 97.4 5.7 370 Clofibrate (Atromid-S) 106.8 7.5 91.7 3.5 371 Fenoprofen calcium 78.3 2.8 89.8 4.6 372 Erdosteine 83.7 5.9 88.9 4.6 373 Betaxolol hydrochloride (Betoptic) 87.5 4.7 93.1 7.5 374 Proparacaine HCl 83.8 5.9 84.2 3.5 375 Pranlukast 123.4 14.8 103.3 6.4 376 Oxfendazole 83.9 4.9 72.1 2.7 377 Carvedilol 86.8 6 93.8 5.3 378 Atracurium besylate 155.1 4.2 69.9 7.5 379 Butoconazole nitrate 52.9 6.4 60.6 7.4 380 Azithromycin (Zithromax) 81.9 7.9 82.1 3.4 381 Albendazole Oxide (Ricobendazole) 94.7 8.3 75.7 4.6 382 Flubendazole (Flutelmium) 109.2 7.3 79.1 3.4 383 Chloroxine 40.2 3.4 50.5 6.2 384 Lomustine (CeeNU) 221.0 7.4 115.1 2.3 385 Chenodeoxycholic acid 141.5 14.7 106.0 3.5 386 Cimetidine (Tagamet) 80.5 6.8 89.8 4.6 387 Clemastine Fumarate 58.3 3.1 102.5 1.2 388 Curcumin 83.7 2.8 71.7 4.2 389 Daidzein 345.8 25.7 119.7 6.0 390 Oxibendazole 65.4 4.8 89.8 4.7 391 Penicillamine (Cuprimine) 108.1 3.8 99.1 4.2 392 Bifonazole 31.3 2.6 62.1 2.5 393 Pefloxacin mesylate 90.4 5.9 93.8 3.0 394 Metoprolol tartrate 93.2 6.9 90.4 4.2 395 Etidronate (Didronel) 81.8 3.1 87.2 4.6 396 Diethylstilbestrol (Stilbestrol) 43.2 4.3 22.2 4.7 397 Diltiazem HCl (Tiazac) 103.1 9.5 81.6 3.5 398 Diphenhydramine HCl (Benadryl) 102.3 7.6 84.6 3.2 399 Dapoxetine hydrochloride (Priligy) 84.0 5.2 103.3 6.4 205

400 Risedronic acid (Actonel) 101.3 5.8 105.1 2.6 401 Tranexamic acid (Transamin) 90.6 5.2 92.6 4.7 402 Valaciclovir HCl 89.6 8.9 88.1 3.5 403 Ganciclovir 84.9 5.8 89.4 1.5 404 Roxatidine acetate HCl 81.3 5.2 86.2 3.5 405 Protionamide (Prothionamide) 87.8 8.2 84.0 2.5 406 Idoxuridine 89.4 6.2 83.1 4.1 407 Sparfloxacin 141.6 5.8 110.9 3.6 408 Felodipine (Plendil) 99.6 5.9 63.0 3.8 409 Deflazacort (Calcort) 92.8 6.3 91.8 5.2 410 Nizatidine 89.4 7.8 90.4 7.8 411 Carbidopa 92.6 4.8 88.8 4.2 412 Valsartan (Diovan) 86.7 5.2 87.4 6.4 413 Dipyridamole (Persantine) 128.2 7.4 80.9 1.2 414 Hydroxyurea (Cytodrox) 124.3 8.4 85.1 5.3 415 Potassium iodide 130.6 10.2 103.8 5.6 416 Tropisetron 118.7 6.3 103.5 2.4 417 Nicotinamide (Niacinamide) 101.6 8.6 94.9 5.6 418 Talc 93.2 4.7 89.9 3.2 419 Vitamin B12 76.9 5.8 87.2 5.2 420 Diclofenac 134.5 10.6 60.7 5.1 421 Avobenzone (Parsol 1789) 209.6 13.8 82.0 4.6 422 Amlodipine (Norvasc) 48.8 4.8 79.5 4.1 423 Metronidazole (Flagyl) 119.9 7.8 104.2 7.3 424 Flutamide (Eulexin) 135.4 5.7 89.6 5.0 425 Fluvastatin sodium (Lescol) 92.7 3.7 90.2 5.7 426 Tioconazole 60.0 5.8 78.7 4.2 427 Disodium Cromoglycate 96.8 5.9 91.5 4.6 428 Tropicamide 87.8 4.7 85.1 4.7 429 Pregnenolone 104.7 2.3 105.6 5.8 430 Sulfamethoxazole 105.6 10.3 87.0 5.7 431 Sulfisoxazole 115.6 11.8 103.9 3.6 432 Crystal violet 0.0 0 0.0 0.0 433 Haloperidol (Haldol) 76.1 12.6 107.0 7.5 434 Phenindione (Rectadione) 125.6 9.2 128.2 3.6 435 Alibendol 90.8 7.3 91.7 5.3 436 Irsogladine 83.8 4.8 89.4 1.2 437 Triamcinolone (Aristocort) 95.2 4.9 95.5 2.4 438 Nystatin (Mycostatin) 138.9 5.9 98.9 2.3 439 Isoniazid (Tubizid) 111.4 10.4 80.9 3.1 440 Levamisole Hydrochloride (Ergamisol) 101.0 11.8 96.8 7.4 441 Levofloxacin (Levaquin) 101.1 8.2 107.7 3.1 442 Enalapril maleate (Vasotec) 91.1 7.2 88.7 3.6 443 Menadione 156.1 12.8 24.7 4.2 444 Metformin hydrochloride (Glucophage) 100.2 7.2 89.7 1.2 206

445 Methoxsalen (Oxsoralen) 108.2 6.3 83.7 5.3 446 Miconazole nitrate 37.5 3.9 16.1 2.6 447 Sulfamethizole (Proklar) 113.9 9.3 103.1 2.4 448 Sulbactam 110.8 7.4 98.0 3.1 449 Tolfenamic acid 131.4 6.9 50.3 2.6 450 Pranoprofen 126.1 4.8 101.5 4.2 451 Sulphadimethoxine 97.2 6.4 94.7 3.1 452 Rimantadine (Flumadine) 96.1 9.6 100.8 6.2 453 Primidone (Mysoline) 94.0 3.7 91.2 1.4 454 Nefiracetam (Translon) 94.1 4.8 80.2 5.7 455 Nicorandil (Ikorel) 135.4 10.4 96.4 4.6 456 Tamoxifen Citrate (Nolvadex) 51.7 9.5 10.0 2.1 457 Meglumine 107.3 5.8 103.8 4.0 458 Aripiprazole (Abilify) 119.4 3.6 85.6 3.5 459 Sarafloxacin HCl 97.2 8.4 100.4 2.6 460 Methscopolamine (Pamine) 102.1 5.8 93.1 5.6 461 Amiodarone HCl 108.8 9.2 96.6 3.5 462 Adenine 88.2 7.2 77.6 6.4 463 Adenine sulfate 105.5 7.9 89.1 3.7 464 Adenine hydrochloride 87.9 5.7 103.7 5.4 465 Ticlopidine HCl 121.8 8.3 103.7 2.5 466 ATP (Adenosine-Triphosphate) 87.4 6.2 86.9 3.5 467 Meclizine 2HCl 62.6 7.4 53.8 3.2 468 Mometasone furoate 104.1 4.8 91.8 5.3 469 Propylthiouracil 107.5 6.9 90.6 3.2 470 Fluticasone propionate (Flonase, Veramyst) 109.8 5.7 74.3 5.2 471 Lacidipine (Lacipil, Motens) 34.2 3.9 53.0 8.5 472 Procarbazine HCl (Matulane) 122.0 16.8 105.5 3.2 473 Ondansetron (Zofran) 110.7 11.4 103.1 5.3 474 Liranaftate 96.4 6.3 37.1 4.6 475 D-Cycloserine 97.7 8.3 96.7 2.4 476 Sodium butyrate 110.4 8.5 95.2 4.2 477 Sodium orthovanadate 98.3 6.2 94.7 3.5 478 Elvitegravir (GS-9137) 30.3 2.4 21.7 3.7 479 Maraviroc 124.4 7.3 94.8 3.5 480 Raltegravir (MK-0518) 130.1 8.9 97.5 4.7 481 Pyrimethamine 139.7 10.4 136.4 5.2 482 Sulindac (Clinoril) 128.4 16.3 95.4 5.7 483 Taurine 94.2 5.7 91.1 5.2 484 Pramipexole dihydrochloride monohydrate 99.0 3.8 101.9 8.6 485 Suplatast tosylate 92.8 8.2 80.6 4.1 486 Mirtazapine (Remeron, Avanza) 113.3 9.5 85.9 2.4 487 Benidipine hydrochloride 76.1 7.3 39.8 2.1 488 Formoterol hemifumarate 152.5 5.2 100.9 4.1 489 Chlormezanone (Trancopal) 110.2 7.2 100.8 2.4 207

490 Ketotifen fumarate (Zaditor) 113.3 6.3 100.1 6.1 491 Urapidil HCl 106.7 8.4 98.7 2.4 492 Ginkgolide A 108.0 7.5 93.7 3.6 493 Ciprofloxacin (Cipro) 110.5 11.4 90.4 4.1 494 Diclazuril 81.0 7.3 27.5 2.5 495 Uridine 153.8 12.7 108.9 2.6 496 Flunarizine 2HCl 30.0 2.8 36.5 4.3 497 Fenticonazole nitrate 49.6 5.9 56.4 8.2 498 Rebamipide 92.5 7.3 102.5 3.6 499 Epalrestat 309.2 20.6 148.4 10.5 500 Aspartame 99.4 6.3 93.5 6.4 501 Candesartan cilexetil (Atacand) 50.2 5.4 59.1 9.1 502 Phentolamine mesilate 133.2 13.6 94.5 3.5 503 Nimesulide 156.5 16.4 92.4 6.5 504 Dyclonine HCl 183.3 17.3 80.5 3.6 505 Memantine HCl (Namenda) 113.2 7.9 103.4 3.5 506 Cyproheptadine HCl (Periactin) 97.8 5.8 101.1 7.8 507 Doxifluridine 131.1 10.5 115.3 4.1 508 Pioglitazone hydrochloride (Actos) 100.8 9.6 97.0 3.5 509 Lornoxicam (Xefo) 107.9 7.5 86.9 2.6 510 Clindamycin phosphate 122.5 8.6 85.5 4.1 511 Strontium ranelate (Protelos) 194.4 10.7 109.4 2.8 512 Captopril (Capoten) 132.8 12.5 116.8 5.7 513 Oxytetracycline dihydrate 169.4 19.7 103.4 4.6 514 Cytidine 137.6 14.2 102.9 3.7 515 Orphenadrine citrate (Norflex) 121.3 15.3 111.9 4.2 516 Gimeracil 112.6 12.7 97.6 5.7 517 Cyclophosphamide monohydrate 122.9 11.7 104.6 8.6 518 Tolnaftate 181.0 17.4 89.9 4.7 519 Terazosin HCl (Hytrin) 184.5 18.5 115.6 2.7 520 Bromhexine HCl 146.1 14.3 76.1 5.2 521 Lovastatin (Mevacor) 123.1 11.3 95.2 4.8 522 Tiopronin (Thiola) 115.7 10.4 99.2 6.3 523 Balofloxacin 97.7 7.4 98.5 7.5 524 Lafutidine 120.1 9.5 98.1 4.3 525 Moxonidine 119.6 5.3 94.2 7.8 526 Ozagrel HCl 113.4 8.9 85.4 5.2 527 Argatroban 81.0 5.8 113.1 6.5 528 Mitiglinide calcium 86.4 2.7 117.2 4.2 529 Mecarbinate 112.1 4.6 102.3 1.2 530 Rosiglitazone HCl 80.0 4.7 87.7 2.5 531 Lisinopril (Zestril) 73.6 3.7 106.8 4.2 532 Atorvastatin calcium (Lipitor) 94.3 4.1 127.5 3.5 533 Famotidine (Pepcid) 68.1 4.8 102.1 3.1 534 Moexipril HCl 72.1 5.8 97.1 3.5 208

535 Cleviprex (Clevidipine) 131.9 10.2 113.8 8.5 536 Cilazapril monohydrate (Inhibace) 89.5 3.6 119.2 3.7 537 Adiphenine HCl 84.7 4.7 125.7 5.7 538 Duloxetine HCl (Cymbalta) 51.7 7.2 93.3 3.1 539 Trimebutine 97.3 4.6 106.7 6.9 540 Ivabradine HCl (Procoralan) 73.7 3.1 109.5 6.2 541 Rivastigmine tartrate (Exelon) 84.2 2.8 106.0 5.3 542 Dexmedetomidine HCl (Precedex) 78.6 5.3 97.2 2.8 543 Betaxolol (Betoptic) 120.3 9.5 122.8 5.4 544 Detomidine HCl 100.3 7.4 110.8 7.5 545 Fosinopril sodium (Monopril) 91.0 5.2 115.4 5.7 546 Almotriptan malate (Axert) 98.9 4.2 118.5 2.4 547 Ambrisentan 93.4 5.3 106.6 3.8 548 Bexarotene 22.2 5.7 23.8 3.8 549 Temocapril HCl 88.8 4.8 101.5 4.7 550 Gabexate mesylate 88.6 5.7 109.5 7.3 551 Rasagiline mesylate 107.5 6.3 121.9 4.6 552 Naltrexone HCl 100.7 7.4 119.9 5.3 553 Levosulpiride (Levogastrol) 99.9 3.8 118.0 2.6 554 Azasetron HCl (Y-25130) 99.3 4.2 115.9 4.6 555 Mizolastine (Mizollen) 116.6 5.8 114.4 3.7 556 Flunixin meglumin 120.3 10.2 70.3 3.6 557 Imidapril (Tanatril) HCl 92.7 5.7 100.6 5.2 558 Vinpocetine (Cavinton) 116.8 4.6 105.8 6.4 559 Lapatinib 0.5 0.06 63.7 3.6 560 Blonanserin (Lonasen) 108.7 5.7 114.1 3.6 561 Cisatracurium besylate (Nimbex) 213.7 8.6 94.7 4.6 562 Dronedarone HCl (Multaq) 19.1 3.4 38.3 3.7 563 Conivaptan HCl (Vaprisol) 89.3 7.4 87.2 2.5 564 Ibutilide fumarate 79.5 4.8 99.6 4.6 565 Probucol 79.8 3.7 98.4 3.1 566 Arbidol HCl 10.2 5.3 85.8 2.5 567 Licofelone 142.2 6.3 13.8 4.6 568 Dextrose (D-glucose) 109.5 5.8 114.8 2.5 569 Xylose 97.4 3.8 110.7 4.6 570 Mestranol 104.9 6.8 120.3 4.7 571 Naftopidil (Flivas) 59.8 2.8 75.3 2.5 572 S-(+)-Rolipram 89.9 4.2 104.1 4.7 573 Bazedoxifene HCl 38.7 1.7 55.7 1.3 574 Fudosteine 102.3 5.9 98.4 4.6 575 Atropine 127.8 5.4 103.4 4.2 576 Roflumilast (Daxas) 95.7 3.7 106.1 7.5 577 Gabapentin (Neurontin) 92.5 4.9 109.5 2.6 578 Sitafloxacin hydrate 98.0 7.4 101.4 4.7 579 BIBR-1048 (Dabigatran) 120.2 10.2 87.8 5.3 209

580 Tebipenem pivoxil (L-084) 115.1 8.5 72.3 4.7 581 Rosuvastatin calcium (Crestor) 81.6 4.8 85.4 1.2 582 Dichlorphenamide (Diclofenamide) 99.7 6.4 97.4 4.5 583 BIBR 953 (Dabigatran etexilate, Pradaxa) 149.0 13.6 120.7 11.2 584 Aliskiren hemifumarate 142.5 12.6 123.4 10.2 585 OSI-420 (Desmethyl Erlotinib) 0.2 0 22.1 2.5 586 DAPT (GSI-IX) 104.7 8.5 99.5 4.2 587 Irinotecan HCl Trihydrate (Campto) 100.4 4 60.2 2.5 588 Apatinib (YN968D1) 257.6 14.8 46.9 1.4 589 TAME 104.2 5.8 102.4 4.3 590 Eltrombopag (SB-497115-GR) 117.4 5.7 51.4 3.2 591 Esomeprazole sodium (Nexium) 179.1 6.8 121.1 6.4 592 Fesoterodine fumarate (Toviaz) 110.3 4.8 123.6 7.0 593 Abiraterone Acetate (CB7630) 109.2 6.8 71.8 4.6 594 Artemether (SM-224) 91.5 5.8 94.2 4.2 595 DL-Carnitine hydrochloride 105.8 4.7 103.3 5.7 596 Nalidixic acid (NegGram) 83.0 5.2 98.0 7.5 597 Ammonium Glycyrrhizinate (AMGZ) 137.9 5.9 119.0 5.0 598 D-Mannitol (Osmitrol) 109.0 5.7 100.0 3.7 599 L-carnitine (Levocarnitine) 131.4 7.4 133.4 4.7 600 Sorbitol (Glucitol) 112.8 5.3 132.5 6.2 601 Cephalomannine 87.0 5.2 68.7 4.6 602 10-DAB (10-Deacetylbaccatin) 90.0 4.8 109.6 5.2 603 Paeoniflorin 104.0 6.8 112.2 4.6 604 Geniposide 98.7 6.2 110.5 4.1 605 Genipin 115.6 8.5 112.2 2.6 606 Geniposidic acid 114.9 8.9 106.9 5.3 607 Tolbutamide 113.7 6.9 129.7 4.3 608 Levosimendan 129.2 4.7 49.4 2.5 609 Amantadine hydrochloride (Symmetrel) 95.5 7.3 122.4 3.2 610 Amfebutamone (Bupropion) 84.9 3.8 114.4 4.6 611 Benserazide 108.7 5.8 107.1 4.2 612 Bupivacaine hydrochloride (Marcain) 80.0 3.7 106.8 2.6 613 Bethanechol chloride 102.8 7.4 112.9 5.3 614 Chlorpromazine (Sonazine) 108.5 6.2 98.0 2.5 615 Clindamycin hydrochloride (Dalacin) 104.1 5.9 100.0 4.1 616 Clonidine hydrochloride (Catapres) 112.8 4.7 107.6 2.6 617 Clozapine (Clozaril) 105.6 7.3 113.6 5.2 618 Pramipexole (Mirapex) 94.8 6.9 107.5 3.6 619 Domperidone (Motilium) 117.0 4.2 103.4 5.3 620 Donepezil HCl (Aricept) 79.6 5.9 93.1 2.4 621 Estriol 85.1 3.7 98.0 4.8 622 Famciclovir (Famvir) 82.1 4.1 87.9 5.2 623 Fenbendazole (Panacur) 98.8 2.7 101.5 4.5 624 Fleroxacin (Quinodis) 113.4 5.9 105.8 6.2 210

625 Fluocinolone acetonide (Flucort-N) 116.0 6.2 100.3 5.8 626 Gallamine triethiodide (Flaxedil) 114.7 4.9 102.7 6.3 627 Imatinib (Gleevec) 136.4 7.4 96.5 6.8 628 Itraconazole (Sporanox) 89.6 6.9 89.8 3.5 629 Lincomycin hydrochloride (Lincocin) 91.3 6.3 101.1 3.7 630 Loperamide hydrochloride 71.5 4.8 100.0 5.1 631 Manidipine (Manyper) 65.5 4.9 52.2 2.5 632 Milrinone (Primacor) 132.5 3.3 103.9 4.8 633 Mitoxantrone Hydrochloride 14.0 2.6 16.5 3.9 634 Moroxydine 121.2 7.2 98.0 5.8 635 Mycophenolic (Mycophenolate) 23.0 5.4 9.9 2.1 636 Nateglinide (Starlix) 107.3 3.6 95.5 4.6 637 Neostigmine bromide (Prostigmin) 99.9 5.2 99.1 3.5 638 Nitrendipine 98.0 4.2 71.7 4.7 639 Novobiocin sodium (Albamycin) 125.3 2.4 87.5 5.2 640 Olanzapine (Zyprexa) 134.2 5.3 109.2 5.7 641 Olopatadine hydrochloride (Opatanol) 115.2 6.4 103.4 5.2 642 Oxymetazoline hydrochloride 121.2 6.8 98.5 6.8 643 Ozagrel 106.6 6.2 89.9 5.2 644 Pancuronium (Pavulon) 107.1 1.5 90.1 5.7 645 Pantothenic acid (pantothenate) 98.5 3.7 94.1 5.2 646 Phenoxybenzamine HCl 98.8 5.3 83.3 2.4 647 Propafenone (Rytmonorm) 98.4 2.3 112.1 4.7 648 Quinine hydrochloride dihydrate 120.4 5.3 108.0 5.2 649 Racecadotril (Acetorphan) 119.1 1.5 99.4 4.7 650 Ribavirin (Copegus) 81.1 3.6 87.6 5.2 651 Rosiglitazone maleate 111.7 4.3 84.1 6 652 Roxithromycin (Roxl-150) 103.3 2.7 86.0 3.5 653 Salbutamol sulfate (Albuterol) 98.5 5.3 93.7 3.7 654 Scopolamine hydrobromide 91.1 2.4 79.6 5.2 655 Sotalol (Betapace) 113.1 6.4 96.3 4.6 656 Spectinomycin hydrochloride 114.5 3.6 101.5 5.7 657 Sulfadoxine (Sulphadoxine) 112.3 4.7 100.8 5.2 658 Tenoxicam (Mobiflex) 111.7 5.2 96.8 4.8 659 Tobramycin 103.3 5.3 84.8 6.3 660 Vardenafil (Vivanza) 116.9 2.4 90.7 5.2 661 Xylazine HCl 91.5 6.3 90.0 2.5 662 Maprotiline hydrochloride 94.0 3.2 93.6 3.8 663 Naphazoline hydrochloride (Naphcon) 121.6 7.5 101.9 6.2 664 Epinephrine bitartrate (Adrenalinium) 117.6 3.5 105.7 4.6 665 L-Adrenaline (Epinephrine) 118.4 2.4 98.7 4.1 666 DL-Adrenaline 121.4 3.7 95.5 3.7 667 Phenytoin sodium (Dilantin) 95.0 5.3 95.7 5.3 668 Phenytoin (Lepitoin) 95.4 2.4 92.1 5.8 669 Methacycline hydrochloride (Physiomycine) 103.7 5.3 69.7 4.1 211

670 Ciclopirox (Penlac) 146.5 8.5 36.4 2.6 671 Dopamine hydrochloride (Inotropin) 137.6 10.2 97.3 4.1 672 Ritodrine hydrochloride (Yutopar) 140.6 7.4 103.7 6.2 673 Isoconazole nitrate (Travogen) 62.2 1.2 55.2 1.5 674 Econazole nitrate (Spectazole) 77.7 5.2 63.4 3.5 675 Miconazole (Monistat) 70.3 2.1 69.9 2.1 676 Secnidazole (Flagentyl) 106.0 6.2 96.7 3.6 677 Acetanilide (Antifebrin) 104.8 1.5 100.4 3.1 678 Lomefloxacin hydrochloride (Maxaquin) 107.8 4.1 93.6 4.7 679 Riboflavin (Vitamin B2) 182.2 13.2 104.7 4.2 680 Clomipramine hydrochloride (Anafranil) 124.0 6.3 127.7 6.2 681 Phenformin hydrochloride 132.6 9.3 97.2 4.7 682 Ceftiofur hydrochloride 119.2 5.3 99.3 4.1 683 Cefprozil hydrate (Cefzil) 110.2 4.6 93.3 2.5 684 Scopine 106.7 6 97.0 2.7 685 Tiotropium Bromide hydrate 111.2 3.4 98.4 5.3 686 Trospium chloride (Sanctura) 105.1 5.2 96.5 6.3 687 Tolterodine tartrate (Detrol LA) 141.1 2.6 137.0 7.8 688 Sulbactam sodium (Unasyn) 135.4 5.3 117.7 4.8 689 Azelastine hydrochloride (Astelin) 103.7 2.6 130.8 6.2 690 5-Aminolevulinic acid hydrochloride 114.5 3.1 102.1 4.2 691 Clarithromycin (Biaxin, Klacid) 118.8 6.4 95.1 5.4 692 Rosiglitazone (Avandia) 120.5 3.7 89.7 3.6 693 Terbinafine hydrochloride (Lamisil) 110.2 5.2 77.8 4.6 694 Cortisone acetate (Cortone) 102.1 5.3 99.5 4.7 695 Amiloride hydrochloride dihydrate 140.7 7.8 114.6 5.2 696 Clomifene citrate (Serophene) 75.5 4.1 91.6 4.7 697 Hydralazine hydrochloride 185.5 2.6 141.5 6.8 698 Oxacillin sodium monohydrate 110.3 4.2 106.7 6.2 699 Cloxacillin sodium (Cloxacap) 98.9 5.3 103.1 5.7 700 Amoxicillin sodium (Amox) 100.4 5.2 101.3 8.6 701 Isoprenaline hydrochloride 103.3 4.7 104.4 3.8 702 Medroxyprogesterone acetate 97.7 4.1 90.3 3.2 703 Neomycin sulfate 122.6 2.6 120.4 4 704 Phenylephrine HCl 131.3 5.3 113.7 2.3 705 Prednisolone acetate (Omnipred) 130.8 4.7 104.2 6.2 706 Streptomycin sulfate 125.6 5.1 105.5 3.1 707 Tetracaine hydrochloride (Pontocaine) 116.1 2.5 119.8 2.5 708 Tetracycline HCl 119.9 6.4 86.1 4.7 709 Vancomycin HCl (Vancocin) 115.7 8.6 99.6 5.2 710 Xylometazoline HCl 119.4 3.6 98.1 3.7 711 Phenacetin 129.3 5.2 119.0 6.4 712 Zidovudine (Retrovir) 215.5 15.6 126.8 5.8 713 Quinapril HCl (Accupril) 152.6 4.3 102.9 5.3 714 Trazodone hydrochloride (Desyrel) 132.7 10.2 91.2 6.8 212

715 Thiamphenicol (Thiophenicol) 128.4 6.3 95.0 6.3 716 Clobetasol propionate 139.9 5.7 101.5 5.2 717 Brompheniramine 118.5 3.6 99.5 7.5 718 Dimethyl Fumarate 161.1 5.2 94.5 4.7 719 Calcium levofolinate (Calcium Folinate) 141.0 4.8 108.6 6.5 720 Miglitol (Glyset) 132.8 6.3 103.1 4.7 721 Pioglitazone (Actos) 136.2 5.8 105.2 5.1 722 Tolvaptan (OPC-41061) 137.3 6.3 91.4 2.6 723 Pramiracetam 128.5 2.8 98.0 6.8 724 Clindamycin palmitate HCl 113.6 7.5 82.1 5.3 725 Oseltamivir phosphate (Tamiflu) 117.6 3.5 92.3 6.2 726 L-Thyroxine 108.7 2.4 77.1 4.9 727 Gliclazide (Diamicron) 139.9 3.5 114.2 7.3 728 Acemetacin (Emflex) 136.6 5.4 104.9 3 729 Tioxolone 397.2 18.5 120.6 1.2 730 Dehydroepiandrosterone (DHEA) 138.7 3.7 102.9 4.2 731 Idebenone 228.8 5.3 63.0 5.1 732 Mifepristone (Mifeprex) 107.7 7.6 69.0 2.7 733 Buflomedil HCl 122.9 8.6 98.0 4.1 734 Fluocinonide (Vanos) 122.2 4.8 82.8 2.5 735 Lonidamine 216.0 7.3 120.9 3.1 736 Ethisterone 154.1 2.6 107.4 4.6 737 Clorsulon 148.4 6.8 97.7 4.2 738 Arecoline 198.6 11.7 90.5 2.4 739 Noradrenaline bitartrate monohydrate (Levophed) 138.4 8.5 90.4 5.2 740 PCI-32765 (Ibrutinib) 0.1 0 57.7 4.1 741 Peramivir Trihydrate 139.4 7.9 89.1 3.7 742 Nilvadipine (ARC029) 144.0 6.3 70.9 1.2 743 Dabrafenib (GSK2118436) 84.4 3 109.3 2.4 744 Clindamycin 149.3 10.4 106.5 3.6 745 Carfilzomib (PR-171) 2.9 0.7 7.1 2.4 746 Cobicistat (GS-9350) 117.3 6.8 86.6 4.6 747 Hygromycin B 121.0 6.1 63.8 4.2 748 Carbazochrome sodium sulfonate 132.6 2 87.4 4.6 749 Rivaroxaban (Xarelto) 133.2 6.8 87.6 1.3 750 Paroxetine HCl 72.7 1.2 69.4 3.1 751 Zanamivir (Relenza) 155.4 4.8 111.1 2.9 752 Zaltoprofen 177.4 6.2 106.5 5.1 753 Pazopanib 140.8 5.2 30.0 4.1 754 Amoxicillin (Amoxycillin) 136.8 4.7 92.0 4.8 755 Aspirin (Acetylsalicylic acid) 123.7 6.3 90.6 5.1 756 Niflumic acid 163.6 2.1 98.9 2.6 757 Ciclopirox ethanolamine 189.0 10.6 53.6 4.7 758 Rimonabant (SR141716) 47.6 2.5 6.8 1.3 759 Cabazitaxel (Jevtana) 123.8 7.6 82.7 3.1 213

760 Bufexamac 143.4 10.6 118.7 3.6 761 Lamotrigine 142.6 6.3 99.3 4.1 762 PMSF (Phenylmethylsulfonyl Fluoride) 133.0 4.8 96.7 2.7 763 Fenoprofen calcium hydrate 135.1 5.7 93.5 6.4 764 Niclosamide (Niclocide) 25.3 2.4 4.4 1.2 765 Linagliptin (BI-1356) 147.3 4.7 98.7 5.2 766 Bindarit 124.1 6.3 88.3 4.7 767 Vildagliptin (LAF-237) 119.7 3.8 94.6 5.1 768 Daunorubicin HCl (Daunomycin HCl) 0.1 0 0.4 0.1 769 Pravastatin sodium 121.0 6.3 101.7 5.7 770 Bepotastine Besilate 124.4 5.7 94.8 4.2 771 Fosaprepitant dimeglumine 99.5 1.5 67.7 3.8 772 Droxidopa (L-DOPS) 118.8 4.8 91.1 6.3 773 Rofecoxib (Vioxx) 147.4 6.2 89.8 6.4 774 Lurasidone HCl 88.9 5.2 78.0 1.6 775 Cinepazide maleate 140.0 5.9 103.8 5.3 776 Azilsartan (TAK-536) 181.8 4.2 106.0 6.7 777 Otilonium Bromide 37.3 0.8 50.3 3.2 778 Solifenacin succinate 105.4 4.7 106.3 6.8 779 Palonosetron HCl 120.8 6.9 75.4 6.2 780 Azelnidipine 5.5 1.3 19.5 3.2 781 Alverine Citrate 102.7 5.1 97.9 5.2 782 Besifloxacin HCl (Besivance) 131.3 5.8 90.4 4.8 783 Azilsartan Medoxomil (TAK-491) 243.2 12.8 104.7 6.2 784 Danofloxacin Mesylate 190.6 16.3 91.1 3.6 785 Enrofloxacin 140.7 5.8 96.9 4.6 786 Medetomidine HCl 107.8 6.2 94.4 4 787 Diclofenac Potassium 174.0 7.5 58.4 2.3 788 Diclofenac Diethylamine 169.2 4.8 62.7 2.5 789 Amikacin sulfate 123.5 6.5 98.5 3.1 790 Naloxone HCl 123.4 5.5 96.1 6.3 791 (R)-baclofen 111.4 2.7 96.7 5.8 792 Caspofungin acetate 113.6 6.4 94.0 6.4 793 Dexmedetomidine 105.7 5.2 94.9 3.2 794 Beclomethasone dipropionate 130.8 3 105.6 7.8 795 Atovaquone (Atavaquone) 92.3 2.5 10.1 0.7 796 Etravirine (TMC125) 113.1 3.2 64.0 2.5 797 Ulipristal 114.4 2.6 72.0 4.1 798 Indacaterol Maleate 71.5 3.8 79.3 3.5 799 2-Thiouracil 116.0 6.2 94.3 2.6 800 Creatinine 102.4 5.3 88.4 4.2 801 Moguisteine 109.2 4.8 95.7 4.6 802 Nadifloxacin 128.5 6.3 96.7 1.2 803 Pidotimod 99.5 5.3 82.1 4.1 804 Pyridoxine hydrochloride 104.4 6.8 83.9 3.6 214

805 Vitamin C (Ascorbic acid) 101.8 5.2 79.6 4.7 806 Sulfathiazole 100.0 6 77.4 4.1 807 Oxybutynin chloride 101.3 4.1 94.0 2.5 808 Ornidazole 109.0 4.6 88.4 3.7 809 Amikacin hydrate 116.6 5.8 89.6 5.2 810 Dexamethasone acetate 107.1 7.5 90.4 6.4 811 Trimethoprim 97.1 2.6 86.7 2.4 812 Biotin (Vitamin B7) 100.7 4.2 78.2 1.2 813 Sulfamerazine 97.8 4 81.8 5.4 814 Sulfamethazine 93.1 2.1 72.9 1.9 815 Sodium salicylate 122.3 8.6 96.6 6.1 816 Methylthiouracil 106.2 4.6 94.1 1.7 817 Methenamine (Mandelamine) 110.3 3.2 85.4 4.6 818 Milnacipran HCl 101.3 6.4 90.3 3 819 Darifenacin HBr 57.8 1.5 94.9 2.5 820 Tripelennamine HCl 116.7 6.8 83.9 3.7 821 Entacapone 130.2 5.3 84.6 5.2 822 Ibandronate sodium 103.7 2.7 76.4 5.2 823 Estradiol valerate 112.1 5 52.1 3.5 824 Articaine HCl 98.8 1.6 92.5 2.3 825 Gliquidone 133.7 7.4 62.6 2.9 826 Butenafine HCl 100.3 3.2 33.4 1.8 827 Mepivacaine HCl 87.9 4.2 81.6 4.1 828 Naftifine HCl 85.2 3.7 49.0 3.5 829 Ethynodiol diacetate 96.8 2.3 49.3 2.4 830 Sertaconazole nitrate 39.4 4.7 9.7 2.5 831 Tylosin tartrate 117.2 4.6 82.6 5.1 832 Benztropine mesylate 75.6 1.4 97.5 6.1 833 Abacavir sulfate 102.4 3.7 85.2 2.6 834 Altrenogest 142.8 5.3 81.3 3.5 835 Ampicillin sodium 90.5 2.5 76.3 2.7 836 Anagrelide HCl 99.7 4.2 73.4 4.2 837 Antipyrine 90.4 4.7 75.4 5.2 838 L-Arginine HCl 91.1 5.1 75.4 1.2 839 Atomoxetine HCl 137.4 5.9 110.3 4 840 Betahistine 2HCl 114.5 6.2 90.3 3.5 841 Brinzolamide 112.2 5.7 98.0 6.2 842 Carbenicillin disodium 103.7 5.2 90.8 4.7 843 Flumequine 89.7 4.1 81.2 5.2 844 Amitriptyline HCl 87.7 2.8 87.5 3.6 845 Adrenalone HCl 101.7 1.9 87.3 4.2 846 Azatadine dimaleate 86.1 4.1 76.7 5.7 847 (+,-)-Octopamine HCl 116.8 3.8 97.9 5.1 848 Ropinirole HCl 107.3 7.5 86.9 2.6 849 Azlocillin sodium salt 114.6 3.6 90.8 3.2 215

850 Azacyclonol 84.7 1.2 86.2 4.5 851 Reboxetine mesylate 86.7 3.1 88.2 2.6 852 Triflusal 89.6 2.7 85.3 4.2 853 Trifluoperazine 2HCl 36.5 0.7 97.3 5.3 854 Catharanthine 166.5 3.2 78.1 4.2 855 Meptazinol HCl 125.4 5.7 88.1 3.7 856 Fexofenadine HCl 107.0 5.1 94.1 5.2 857 Amidopyrine 104.5 2.6 92.2 4.7 858 Thiamine HCl (Vitamin B1) 97.9 4.2 90.6 5.3 859 Moclobemide 106.9 4.1 98.0 3.6 860 Pergolide mesylate 99.3 4.9 90.5 5.4 861 Lithocholic acid 139.5 5.1 49.7 7.2 862 Ethambutol HCl 92.2 2.4 80.7 5.8 863 Doxycycline HCl 133.3 5.2 77.9 5.3 864 Clinafloxacin (PD127391) 111.9 1.7 96.3 6.4 865 Pentamidine 84.1 5.2 78.4 3.5 866 Pemirolast (BMY 26517) potassium 94.7 4.7 90.7 3.8 867 Mirabegron (YM178) 97.2 1.2 93.8 4.6 868 Acebutolol HCl 98.6 2.4 82.7 2.5 869 Ampiroxicam 101.0 4.6 85.8 3.9 870 Desloratadine 99.6 4.1 77.3 7.4 871 Sodium Monofluorophosphate 120.0 2.8 99.3 2.7 872 Hyoscyamine (Daturine) 107.8 6.3 94.9 5.3 873 Cyclamic acid 112.3 4.8 96.0 3.6 874 Ouabain 1.0 0.2 1.7 0.8 875 Allylthiourea 96.7 5.1 87.7 4.6 876 Avanafil 113.9 9.7 71.2 2.5 877 Sodium Picosulfate 102.3 1.6 88.3 3.7 878 Tolcapone 101.8 4.2 21.0 2.3 879 Probenecid (Benemid) 102.3 1.5 95.8 5.6 880 Procaine (Novocaine) HCl 92.0 8.6 91.6 2.5 881 Homatropine Bromide 86.5 4.2 85.5 4.7 882 Hydroxyzine 2HCl 41.8 1.9 81.8 5.3 883 Flavoxate HCl 76.2 6.3 85.1 4.5 884 Colistin Sulfate 77.3 2.3 82.9 2.3 885 Aclidinium Bromide 76.0 2.9 79.3 4.1 886 Bismuth Subsalicylate 92.7 5 79.7 3.7 887 Diphemanil methylsulfate 113.9 4.2 95.6 5.4 888 Vitamin D2 119.6 8.7 21.5 3.8 889 Doxapram HCl 97.2 5.2 83.5 5.9 890 Dibucaine HCL 71.3 8.5 92.2 4.2 891 Methazolamide 95.4 3.7 85.1 4.6 892 norethindrone 95.9 1.4 91.3 5.3 893 olsalazine sodium 98.8 4.4 77.7 2.5 894 nafcillin sodium monohydrate 98.9 3.7 80.1 4.7 216

895 tetrahydrozoline hydrochloride 128.1 5.2 97.0 4.1 896 toltrazuril 98.9 5 61.9 2.5 897 Pheniramine Maleate 109.5 6.8 86.2 2.5 898 Bisacodyl 129.8 5.3 76.4 4.6 899 Carbimazole 102.5 5.3 82.4 1.4 900 Bextra (valdecoxib) 113.7 2.6 71.6 2.7 901 valganciclovir hydrochloride 123.0 4.1 72.8 5.2 902 Nabumetone 201.8 10.6 118.5 4.7 903 Netilmicin Sulfate 120.1 4.2 106.4 5.1 904 Sertraline HCl 20.2 0.7 53.4 5.9 905 Spironolactone 122.9 5.2 59.7 1.5 906 Retapamulin 115.4 3.8 107.8 7.5 907 Methyclothiazide 82.9 1.6 85.8 3.8 908 Ropivacaine HCl 99.7 2.5 86.3 6.3 909 Sodium Nitroprusside 102.1 2.7 82.5 2.7 910 Erythromycin Ethylsuccinate 125.8 3.1 83.5 5.9 911 Ronidazole 121.5 2.7 102.9 7.4 912 Vitamin D3 (Cholecalciferol) 123.1 5.1 57.5 3.8 913 Escitalopram oxalate 72.9 2.7 91.9 6.3 914 Guanabenz acetate 90.2 5.1 89.1 6.7 915 Dequalinium chloride 83.0 2.6 51.5 2.5 916 Deferiprone 101.2 3.6 85.6 3.6 917 tinidazole 107.6 4.1 80.4 5.7 918 Hexamethonium bromide 132.2 2.7 84.5 4.1 919 Guanidine HCl 126.2 5.3 102.0 4.6 920 Decamethonium bromide 110.3 0.6 89.4 3.1 921 Aminosalicylate sodium 114.9 4.1 81.3 2.8 922 Sodium nitrite 103.4 0.7 82.7 6.2 923 Pyrithione zinc 0.5 0.1 0.0 0 924 Propranolol HCl 102.4 8.7 80.2 5.1 925 Mequinol 115.6 4.5 90.2 5.7 926 Mefenamic acid 134.7 3.6 74.4 6.2 927 Ticagrelor 167.3 4.1 98.0 5.2 928 triamterene 290.0 15.3 97.1 4.9 929 sulfacetamide sodium 102.5 6.4 83.4 7.2 930 Spiramycin 105.8 5.2 77.9 5.1 931 Lomerizine HCl 66.9 2.1 65.8 2.5 932 Levobetaxolol HCl 99.9 5.3 83.0 3.2 933 Loxapine Succinate 117.6 6.4 93.0 5.6 934 Oxymetholone 225.3 18.6 83.7 3.2 935 Flumethasone 133.8 4.7 105.0 4.8 936 Halobetasol Propionate 126.5 5.3 102.4 6.3 937 Fenspiride HCl 82.3 2.7 89.1 2.3 938 Pramoxine HCl 98.8 4.1 97.0 6.4 939 Bismuth Subcitrate Potassium 92.5 2.6 80.6 7.5 217

940 Tetramisole HCl 100.8 4.2 83.6 2.1 941 Difluprednate 137.0 1.5 84.3 2.3 942 Droperidol 136.0 2.6 78.6 3.6 943 Dydrogesterone 125.8 4.1 104.3 4.2 944 Dexlansoprazole 239.8 10.5 127.6 1.6 945 Esmolol HCl 107.5 4.3 88.0 4.2 946 Voglibose 103.5 2.6 83.3 5.1 947 Eprosartan Mesylate 109.5 5.1 83.5 1.2 948 Diminazene Aceturate 86.3 2.5 83.6 1.8 949 Closantel Sodium 12.4 2.9 0.2 0 950 Closantel 0.1 0 0.0 0 951 Clofazimine 123.4 4.2 51.0 1.7 952 Clodronate Disodium 114.0 1.8 95.5 2.1 953 Dicloxacillin Sodium 102.2 2.5 96.9 4 954 Triclabendazole 39.3 2.7 13.8 3.6 955 Isovaleramide 91.1 5.2 81.9 4.7 956 Histamine Phosphate 98.0 4.1 92.9 4.1 957 Sulconazole Nitrate 83.0 2.1 46.7 2.6 958 Tilmicosin 102.3 1.8 84.2 4.1 959 Troxipide 129.7 5 112.5 2.6 960 Sevelamer HCl 111.2 2.6 98.5 4.2 961 Deoxyarbutin 132.9 4.7 99.1 1.6 962 Clorprenaline HCL 104.2 5.1 86.0 4.1 963 Carprofen 108.0 2.7 92.3 2.7 964 Eprazinone 2HCl 91.7 5.2 82.5 5.2 965 Dropropizine 87.9 4.1 78.1 4.6 966 Amprolium HCl 107.1 2.5 79.3 4.1 967 Decoquinate 105.1 4.1 104.0 1.7 968 Bacitracin 87.6 1.2 88.7 5.3 969 Azithromycin Dihydrate 76.3 1.8 82.6 2.6 970 Ampicillin Trihydrate 77.3 5.7 78.4 5.8 971 Amfenac Sodium (monohydrate) 67.5 4.1 79.7 6.2 972 Orbifloxacin 67.7 4 75.0 5.1 973 Penfluridol 48.9 4.7 49.2 4.1 974 Ethamsylate 74.4 0.9 75.7 4.7 975 D-Phenylalanine 117.2 5.1 96.3 5.1 976 Chlorzoxazone 99.0 4.7 83.0 2.7 977 Chlortetracycline HCl 135.1 9.4 33.5 2.4 978 Chloroquine Phosphate 75.3 2.7 88.5 4.6 979 Bezafibrate 80.4 5.3 81.1 4.2 980 Benzylpenicillin sodium 79.8 3.2 77.4 1.2 981 Benzoic acid 70.5 1.5 72.4 6.4 982 Benzethonium chloride 0.1 0 54.9 2.7 983 Doxofylline 131.1 4.6 98.8 5.1 984 Benzydamine Hydrochloride 106.5 4.2 100.0 2.7 218

985 Chlorpropamide 102.7 4.2 74.4 5.2 986 Cyromazine 92.7 1.5 75.5 5.7 987 Teriflunomide 121.1 4.3 78.1 4.2 988 Coumarin 89.3 5.3 73.9 1.6 989 Choline Chloride 77.4 2.5 67.2 5 990 Cetylpyridinium Chloride 0.1 0 0.0 0 991 1-Hexadecanol 138.2 5.8 99.1 3.1 992 Sodium Gluconate 103.4 6.2 84.2 3 993 Sulfaguanidine 96.7 4.1 72.2 4.1 994 Trometamol 82.8 2.6 76.0 2.5 995 Uracil 75.4 5.3 74.1 3.6 996 Climbazole 65.8 2.5 62.7 1.2 997 Mezlocillin Sodium 88.2 1.2 76.0 0.7 998 Nefopam HCl 95.7 2.1 69.6 4 999 Nicardipine HCl 118.4 5.3 98.4 1.7 1000 Nifuroxazide 171.1 4.8 87.6 5 1001 Paromomycin Sulfate 102.4 6.3 77.0 4.2 1002 Penciclovir 95.5 3.2 86.6 1.7 1003 Tiratricol 33.9 3.2 5.3 1.6 1004 Domiphen Bromide 0.1 0 0.0 0 1005 Salicylanilide 95.1 2.8 88.8 4.6 1006 Sasapyrine 88.9 3.2 78.1 3.1 1007 Cyclandelate 161.0 5.4 112.4 2.6 1008 Cinchophen 117.3 4.2 93.5 4.1 1009 Betamipron 96.8 3.6 79.1 2.5 1010 Chlorquinaldol 3.4 1.1 17.2 4.5 1011 Azaguanine-8 58.6 1.7 67.3 4.6 1012 Broxyquinoline 17.8 4.6 23.4 3.5 1013 Ethacridine lactate monohydrate 44.8 0.7 92.5 3.1 1014 Bemegride 101.1 4.5 73.8 3.1 1015 Aminothiazole 132.5 2.5 108.5 6.4 1016 Antazoline HCl 105.4 3.6 93.1 3.2 1017 Tolperisone HCl 82.2 5.2 82.3 4.2 1018 Florfenicol 77.7 4.6 73.1 5.2 1019 Furaltadone HCl 69.7 2.6 72.7 4.1 1020 Isosorbide 100.0 4.2 70.3 3.6 1021 Dibenzothiophene 83.6 3.2 66.8 4.2 1022 Cysteamine HCl 109.6 1.5 71.0 1.3 1023 Clofibric acid 113.6 4.3 103.1 5.3 1024 Chromocarb 111.8 2.7 84.8 3.7 1025 Chlorocresol 95.7 1.5 76.7 5.3 1026 Benzocaine 77.4 4.2 74.6 2.5 1027 Montelukast Sodium 6.3 1.4 2.0 0.3 1028 Dirithromycin 91.5 2.5 60.6 4.6 1029 Sucralose 74.3 4.2 65.1 2.3 219

1030 Valnemulin HCl 117.4 4.7 53.3 5.1 1031 Liothyronine Sodium 141.2 5.1 71.9 2.4 1032 Amoxapine 103.5 2.4 115.4 7.5 1033 Azaperone 88.6 1.8 89.2 4.2 1034 Benzbromarone 14.1 3.7 4.7 1.2 1035 Piperacillin Sodium 71.1 3.2 65.4 2.4 1036 Mevastatin 71.3 2.1 68.7 2.6 1037 Mexiletine HCl 80.3 5.4 69.5 1.5 1038 Minocycline HCl 97.8 3.2 43.9 3.2 1039 Fidaxomicin 115.9 2.7 46.7 5.3 1040 Fluorometholone Acetate 117.2 4.2 96.9 2.6 1041 Oxybuprocaine HCl 98.0 3.5 88.6 5.2 1042 Oxaprozin 78.2 1.6 80.3 6.4 1043 Pilocarpine HCl 75.6 5.3 76.6 3 1044 Nithiamide 84.5 4.7 71.1 2.6 1045 Zoxazolamine 75.7 1.5 73.2 4.2 1046 Phenazopyridine HCl 146.1 7.5 116.4 5 1047 Primaquine Diphosphate 142.6 6.9 97.7 6.4 1048 Cepharanthine 380.0 19.4 113.7 2.5 1049 Bergapten 93.0 3.6 86.0 5.3 1050 Doxylamine Succinate 70.7 4.1 84.0 3.5 1051 Cetrimonium Bromide 0.1 0 0.0 0 1052 Deoxycorticosterone acetate 76.0 2.6 76.8 1.5 1053 Serotonin HCl 70.6 4.1 71.2 2.1 1054 Sodium ascorbate 76.7 2.8 80.7 3 1055 Benfotiamine 119.8 4.2 115.4 5.2 1056 (R)-(+)-Atenolol 101.5 4.1 105.6 6.4 1057 Alexidine HCl 0.2 0 0.0 0 1058 9-Aminoacridine 8.1 2.7 77.8 3.1 1059 Anisindione 215.8 14.6 124.2 4.6 1060 Anisotropine Methylbromide 102.9 4.6 92.0 4.7 1061 Benzthiazide 109.8 5.8 85.8 2.5 1062 Bromocriptine Mesylate 192.7 7.5 72.6 4.6 1063 Butacaine 103.0 8.6 102.4 3.1 1064 Calcium Gluceptate 121.6 8.3 88.7 4.3 1065 Carbadox 130.0 4.3 87.1 6.5 1066 Ceftazidime Pentahydrate 131.1 2.6 89.0 2.4 1067 Clinafoxacin HCl 121.9 4.3 75.4 5.6 1068 Clopamide 124.3 7.6 87.2 4.2 1069 Clorgyline HCl 143.1 5.4 101.4 4.7 1070 Colistimethate Sodium 141.1 7.2 116.5 5.2 1071 Dibenzepine HCl 127.5 4.6 113.9 4.2 1072 Dimaprit 2HCl 176.5 4.3 99.4 3.5 1073 Diphenylpyraline HCl 99.6 1.6 91.6 3.1 1074 Disopyramide Phosphate 120.0 4.2 90.5 2.3 220

1075 Emetine 3.6 0.2 0.0 0 1076 Physostigmine Hemisulfate Salt 130.1 2.5 86.6 4.1 1077 Ethoxzolamide 156.8 4.3 113.2 2.6 1078 Famprofazone 119.1 6.4 114.9 5.2 1079 Guanethidine Sulfate 142.0 2.5 104.6 4.6 1080 Hemicholinium Bromide 126.8 4.7 93.3 5.3 1081 Isoetharine Mesylate 145.0 5.3 80.6 4.2 1082 Meclocycline Sulfosalicylate 135.0 7.5 39.8 1.5 1083 Medrysone 156.6 4.3 83.8 3.5 1084 Mepiroxol 120.3 5.3 79.8 2.4 1085 Mesoridazine Besylate 118.5 3.6 91.3 3.5 1086 Metaproterenol Sulfate 158.3 4.1 119.9 3.5 1087 Metaraminol Bitartrate 140.4 2.5 107.8 3.6 1088 Methapyrilene HCl 141.4 4.2 101.1 4.3 1089 Methoxamine HCl 108.1 3.7 84.1 2.6 1090 Meticrane 121.0 5.1 85.2 5.2 1091 Moxalactam Disodium 117.2 3.5 85.0 3.5 1092 Nalmefene HCl 130.5 5.3 89.3 4.6 1093 Nialamide 117.6 6.7 79.6 4.2 1094 Oxethazaine 85.5 1.3 58.5 4.3 1095 Oxprenolol HCl 130.8 2.6 113.8 6.4 1096 Pentoxifylline 123.6 4.2 89.0 2.4 1097 Physostigmine Salicylate 123.8 5.7 88.4 1.3 1098 Pipenzolate Bromide 111.0 6.4 78.9 5.2 1099 Piromidic Acid 123.8 8.6 81.8 3.5 1100 Procyclidine HCl 141.6 4.6 87.4 4.2 1101 R-(-)-Apomorphine HCl Hemihydrate 110.7 3.7 72.0 3.6 1102 Ractopamine HCl 183.8 6.3 132.8 4.6 1103 Rolitetracycline 153.6 4.6 111.4 4.1 1104 Terfenadine 21.2 0.6 61.1 2.5 1105 Thiostrepton 110.6 4.2 5.0 0.35 1106 Thonzonium Bromide 6.3 2.3 24.1 3.4 1107 Tolazamide 123.4 3.5 82.9 3.6 1108 Tacrine HCl 127.9 4.7 86.5 4.1 1109 Pimozide 84.3 3.5 63.3 6.5 1110 Carbachol 238.9 15.2 118.8 6.7 1111 Tolmetin Sodium 125.1 5.3 105.5 4.6 1112 Cinoxacin 120.4 6.5 83.5 1.2 1113 Glafenine HCl 104.7 4.7 83.4 4.2 1114 Noscapine HCl 108.0 5.1 73.5 3.1 1115 Phenothrin 91.5 4.2 87.1 3.6 1116 Phthalylsulfacetamide 98.6 4.8 83.4 5.3 1117 Pinacidil 104.6 6.2 81.1 4.3 1118 Suxibuzone 151.4 4.8 104.5 2.7 1119 Carbenoxolone Sodium 128.5 7.3 89.6 5.3 221

1120 Hydrastinine HCl 123.3 4.2 93.3 3.7 1121 Nicotine Ditartrate 113.7 3.7 90.0 6.4 1122 Pridinol Methanesulfonate 125.8 5.1 97.4 3.4 1123 Triflupromazine HCl 76.4 2.5 92.7 6.1 1124 Dicyclomine HCl 132.0 3.7 94.3 3.5 1125 Thioridazine HCl 91.6 1.2 106.3 6.3 1126 Mepenzolate Bromide 134.9 5.2 114.6 5.3 1127 Aceclidine HCl 124.6 3 101.8 2.5 1128 Imipramine HCl 127.2 7.4 91.2 6.4 1129 Methylhydantoin-5-(D) 107.8 3.6 83.7 3.5 1130 Metrizamide 94.8 1.5 80.3 4.2 1131 Nomifensine Maleate 109.5 2.4 77.6 3.7 1132 Proadifen HCl 103.6 4.2 82.3 5.4 1133 Pyrilamine Maleate 127.0 4.7 85.0 3.7 1134 Bekanamycin 129.3 5.2 101.8 3.7 1135 Difloxacin HCl 119.0 4.7 95.9 4.6 1136 Fosfomycin Tromethamine 99.8 3.4 83.9 4.2 1137 Acetarsone 90.3 2.6 75.1 3.5 1138 Bendroflumethiazide 87.9 1.7 83.2 2.7 1139 Bentiromide 97.0 4.2 81.7 5.3 1140 Bephenium Hydroxynaphthoate 83.8 3.7 79.2 2.6 1141 Brucine 79.5 5.1 71.7 1.1 1142 Camylofin Chlorhydrate 79.4 2.5 129.7 5.4 1143 Potassium Canrenoate 105.3 3.6 165.1 7.5 1144 Cephapirin Sodium 197.6 7.5 177.6 10.7 1145 Clofoctol 73.9 2.1 18.6 0.6 1146 Dichlorisone Acetate 113.7 4.9 119.5 14.2 1147 Diperodon HCl 78.8 3.1 154.5 13.6 1148 Isoxicam 131.1 2.7 135.2 10.4 1149 Lithium Citrate 228.3 8.8 151.5 15.3 1150 Lofexidine HCl 133.5 4.7 251.2 14.2 1151 Nifenazone 166.3 5.1 152.0 9.4 1152 Oxeladin Citrate 47.8 0.5 122.3 2.7 1153 Pasiniazid 173.3 5.8 150.8 5.2 1154 Picrotoxinin 157.8 6.9 145.6 4.7 1155 Pindolol 71.5 2.1 170.0 5.8 1156 Prochlorperazine Dimaleate 75.1 1.8 82.9 1.4 1157 Procodazole 243.4 8.6 150.0 13.5 1158 Quipazine Maleate 107.5 4.1 228.3 16.4 1159 Tetraethylenepentamine 5HCl 196.9 6 176.7 4.7 1160 Phosphatidylcholine 160.9 7.6 137.8 9.2 1161 Nitarsone 109.5 4.8 103.4 5.7 1162 Sodium 4-aminohippurate Hydrate 115.7 7.2 115.8 4.1 1163 Trimipramine Maleate 186.0 5.9 194.0 7.9 1164 Tofacitinib citrate (CP-690550 citrate) 195.2 7.1 1.7 0.05 222

1165 FK-506 (Tacrolimus) 87.2 2.5 168.3 16.4 1166 Pimecrolimus 54.1 2.1 64.1 2.6 1167 Plerixafor (AMD3100) 118.0 4.8 111.0 4.2

223

Supplementary Figure 1: The secondary FDA drug screen

Western blot analysis on the summary of 51 candidates and the effect of p-STAT3 activity in the presence of EGF and IL-6. Data represents one example of at least 3 individual assays.

224

Densitometry for P-STAT3 relative to control (%) DIFI DLD-1 I.D Treatment (-/+ ligand) 0 Control (-) 0.0 0.2 0 Control (+) 100 100

Concentration of drugs 1µM 10µM 1µM 10µM 1 Erlotinib HCl 0.6 0.4 98.2 81.1 2 Regorafenib 99.3 91.4 104.6 30.8 3 Vandetanib 0.3 0.2 100.6 0.2 4 Bosutinib 0.1 0.3 99.2 94.0 5 Dasatinib 22.1 0.6 95.5 94.3 6 Sorafenib 95.6 90.8 96.1 12.2 7 Sunitinib Malate 96.1 83.8 90.0 45.8 8 Irinotecan 104.5 101.8 95.0 93.9 9 Vorinostat 101.8 96.4 96.3 93.9 10 Bortezomib 101.6 81.5 75.8 75.4 11 Doxorubicin 98.3 87.5 44.8 31.1

225

Densitometry for P-STAT3 relative to control (%) DIFI DLD-1 I.D Treatment (-/+ ligand) 0 Control (-) 0.6 0.0 0 Control (+) 100 100

Concentration of drugs 1µM 10µM 1µM 10µM 12 Epirubicin HCl 98.6 99.7 83.2 77.3 13 Topotecan HCl 91.0 102.1 62.8 50.6 14 Fludarabine Phosphate 99.5 99.8 54.3 34.3 15 Ivermectin 99.1 99.5 47.2 17.1 16 Ponatinib 18.9 0.7 0.2 0.2 17 Mycophenolate mofetil 51.0 34.1 82.2 78.2 18 Cyclosporine 99.0 98.8 76.1 35.5 19 Azacitidine 99.5 99.0 40.3 38.4 20 Diethylstilbestrol 99.0 0.7 31.8 16.1 21 Flunarizine 2HCl 49.1 46.4 28.8 26.8 22 Elvitegravir 37.1 5.9 31.5 28.2

226

Densitometry for P-STAT3 relative to control (%) DIFI DLD-1 I.D Treatment (-/+ ligand) 0 Control (-) 0.1 0.4 0 Control (+) 100 100

Concentration of drugs 1µM 10µM 1µM 10µM 23 Miconazole nitrate 84.4 40.4 97.9 21.4 24 OSI-420 0.1 0.1 98.5 96.9 25 Bexarotene 97.7 97.5 97.0 25.1 26 Dronedarone HCl 91.3 0.1 98.8 26.4 27 Mitoxantrone HCl 100.2 98.8 97.6 98.1 28 Mycophenolic 98.9 99.4 99.1 99.7 29 Niclosamide 86.4 83.7 96.4 97.2 30 Azelnidipine 99.9 100.1 98.7 97.0 31 Carfilzomib 98.3 97.9 99.5 99.0 32 Rimonabant 100.4 98.8 99.3 99.2 33 Daunorubicin HCl 100.2 100.5 98.8 99.9

227

Densitometry for P-STAT3 relative to control (%) DIFI DLD-1 I.D Treatment (-/+ ligand) 0 Control (-) 1.3 1.1 0 Control (+) 100 100

Concentration of drugs 1µM 10µM 1µM 10µM 34 Sertaconazole nitrate 78.9 56.3 87.3 44.0 35 Ouabain 91.9 93.8 82.4 75.8 36 Pyrithione zinc 44.1 32.8 56.9 5.9 37 Closantel 37.6 27.7 21.1 14.8 38 Closantel Sodium 54.4 22.1 30.9 3.0 39 Triclabendazole 54.7 6.9 76.1 42.2 40 Penfluridol 63.2 53.7 84.8 35.1 41 Broxyquinoline 69.4 64.3 79.7 69.6 42 Cetrimonium Bromide 60.4 4.5 70.7 5.0 43 Benzbromarone 52.9 41.8 66.8 65.8 44 Cetylpyridinium Chloride 52.8 8.5 69.8 0.5

228

Densitometry for P-STAT3 relative to control (%) DIFI DLD-1 I.D Treatment (-/+ ligand) 0 Control (-) 3.7 4.9 0 Control (+) 100 100

Concentration of drugs 1µM 10µM 1µM 10µM 45 Tiratricol (+) 92.8 90.0 102.6 81.0 46 Domiphen Bromide (+) 88.9 26.0 100.4 88.0 47 Chlorquinaldol (+) 82.2 55.5 85.4 44.8 48 Montelukast Sodium (+) 99.6 94.4 66.3 59.2 49 Alexidine (+) 28.9 10.7 47.5 0.3 50 Emetine (+) 93.8 98.4 69.7 68.5 51 Thonzonium Bromide (+) 84.3 79.9 71.9 73.4

229

Minerva Access is the Institutional Repository of The University of Melbourne

Author/s: Tan, Fiona

Title: Ponatinib inhibits STAT3 activity and reduces colorectal cancer growth

Date: 2017

Persistent Link: http://hdl.handle.net/11343/208894

File Description: PhD complete thesis

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