The Importance of the Insulin-Like Growth Factor Signaling Pathway in Lung Cancer and the Potential of Its Components As Therapeutic Targets

The Importance of the Insulin-like Growth Factor Signaling Pathway in Lung Cancer and the Potential of its Components as Therapeutic Targets

Sarah Elizabeth Franks

A Thesis presented to The University of Guelph

In partial fulfilment of requirements for the degree of Doctor of Philosophy in Biomedical Science

Guelph, Ontario, Canada

ABSTRACT

THE IMPORTANCE OF THE INSULIN-LIKE GROWTH FACTOR SIGNALING PATHWAY IN LUNG CANCER AND THE POTENTIAL OF ITS COMPONENTS AS THERAPEUTIC

Sarah Elizabeth Franks Advisor: University of Guelph, 2015 Dr. Roger Moorehead

Lung cancer is the leading cause of cancer-related mortalities worldwide. Despite the introduction of new therapeutics, there has been very little improvement in survival for patients with advanced stage disease. The insulin-like growth factor (IGF) system is frequently activated in lung cancer and the type I insulin-like growth factor receptor (IGF-

IR) has emerged as a potential therapeutic target. However, clinical trials to date have had limited success and it is clear that a more thorough understanding of the IGF-system is required to improve clinical efficacy in targeting this pathway. Here we show that treatment with a dual inhibitor that targets the IGF-IR and the insulin receptor decreased proliferation and survival of lung cancer cells, which was mediated by inhibition of Akt signaling. Furthermore, use of the dual inhibitor enhanced the effects of low doses of platinum therapeutics in lung cancer cells. Akt is an important mediator of IGF-IR signaling and is currently under investigation as a therapeutic target in its own right. Akt exists as three isoforms (Akt1-3), which were previously thought to be largely redundant in activity; however, recent evidence indicates that they have unique functions. In order to investigate the specific roles of Akt isoforms in lung tumorigenesis our lab used a previously developed tissue-specific, inducible transgenic mouse model of lung cancer. In these mice, IGF-IR overexpression in type II alveolar cells is sufficient to induce the formation of nodular adenomas and adenocarcinomas. By combining these transgenic mice with isoform specific Akt knockout mice, we were able to determine if the lack of a single

Akt isoform was sufficient to disrupt lung tumorigenesis. Akt1 deficiency was sufficient to decrease IGF-IR-mediated lung tumor development resulting in fewer surface tumors and a lower tumor burden but having no influence in tumor histology. In contrast, Akt2 deficient mice had an increase in tumor burden as well as a change in tumor histology.

These mice had diffuse tumor tissue throughout their lungs as tumors did not maintain nodular growth. Therefore, Akt isoforms have unique roles in IGF-IR mediated lung tumorigenesis, which should be reflected in the design and implementation of Akt-targeting therapeutics.

Acknowledgements Primarily, I would like to thank my advisor Dr. Roger Moorehead for his patience, support and guidance. Under his supervision, I have had the opportunity to learn, work, and troubleshoot independently while always having help and advice close at hand. I have learned a great deal to give me an excellent start to a career in science. I would also like to thank the members of my advisory committee, Dr. Jim Petrik, Dr. Geoff Wood, and Dr. Allan King for their insight and suggestions along the way.

I am grateful to all the people I have worked with in the Moorehead and RHB labs (who are numerous) who have created a wonderful work environment. I would especially like to thank

Katrina Watson who has patiently answered my frequent questions, been incredibly helpful and a delight to work beside. I would also like to thank Megan Siwicky, who set the foundations for many of my experiments and guided me through several techniques when I was starting out, as well as Dr. Rob Jones for his help with my RNA-sequencing data analysis. Additional thanks to

Michelle Ross, Kata Osz, Helen Coates, Allison MacKay, Ed Reyes, Monica Antenos, and

Esther Semple for their technical support and hard work keeping the lab organized and running smoothly. I would also like to acknowledge Barb Mitchell and the staff at the isolation unit who cared for the mice.

Finally, I would like to thank my friends and family for all of their support. Particularly, thanks to my parents, Dianne and David, who always believed I was capable of anything and provided all kinds support in my chosen path, even sending money. And to my partner, Daniel

Pasula, whose love, encouragement, and humor have the power to dissolve the frustration of failed experiments and keep me headed for success. Without them, I am sure I would not have survived life as a student for so long.

Declaration of Work Performed I declare that, with the exception of the items listed below, all work contained within this thesis was performed by me.

The SPC-IGFIR transgenic mice were previously derived by Dr. Nicolle Linnerth-Petrik and the Akt1-/- and Akt2-/- FVB mice were generated by Katrina Watson. The Akt isoform specific knockout mice were crossed with the SPC-IGFIR mice to generate SPC-IGFIR-Akt1-/- and SP-IGFIR-Akt2-/- mice by Megan Siwicky.

Slide scanning was provided by Kathilyn Allewell at Western University. RNA-

Sequencing was performed by the McGill University and Genome Quebec Innovation Centre.

Some additional cluster analysis from RNA-sequencing data was performed by Dr. Rob Jones.

PCNA staining on lung tissue and tumors was performed by Katrina Watson.

Table of Contents Acknowledgements ...... iv Declaration of Work Performed...... v List of Figures ...... ix List of Tables ...... xi List of Abbreviations ...... xii Introduction: Review of the Literature and Rationale ...... 1 Lung Cancer ...... 1 Incidence, mortality, and risk factors ...... 1 Classification, pathology, and staging ...... 3 Diagnosis and treatment ...... 4 The IGF System ...... 9 Ligands ...... 9 Binding proteins ...... 10 Receptors...... 11 IGF-IR signal transduction and function ...... 13 Targeting the IGF-IR in Lung Cancer ...... 17 Rationale: defining a role for the IGF-IR in lung cancer ...... 17 Co-targeting the insulin receptor with the IGF-IR ...... 22 IGF-IR targeting agents ...... 25 Challenges for successful therapeutic use...... 32 Akt: An Important Mediator of IGF-IR Signaling and Beyond ...... 35 Akt as a therapeutic target...... 35 Akt isoforms and their specific functions ...... 40 Rationale ...... 45 Hypothesis...... 47 Objectives ...... 48 Chapter 1: Dual type I insulin-like growth factor receptor/insulin receptor inhibitor BMS-754807 enhances the effects of platinum chemotherapeutics in lung adenocarcinoma cells ...... 49 Background ...... 49 Materials and Methods ...... 52 Cell lines and Reagents ...... 52 Western Blotting ...... 53 Cell Viability WST-1 Assay ...... 53

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Analysis of Single and Combination Drug Response ...... 53 Immunofluorescence ...... 54 Wound Healing Assay ...... 54 Statistics ...... 55 Results ...... 55 IGF-IR is overexpressed in lung cancer cells ...... 55 IGF-IR activity is inhibited by BMS-754807 ...... 55 Treatment with BMS-754807 or platinum chemotherapeutics reduces cell survival ...... 56 BMS-754807 synergizes with platinum based chemotherapeutics ...... 56 BMS-754807 inhibits proliferation and induces apoptosis ...... 57 BMS-754807 impairs wound closure ...... 57 Discussion ...... 64 Chapter 2: Unique roles for Akt Isoforms in IGF-IR-mediated lung Tumorigenesis ...... 67 Background ...... 67 Materials and Methods ...... 69 Mice ...... 69 Histology and Immunohistochemistry ...... 71 Western blotting ...... 72 RNA isolation, qRT-PCR and RNA Sequencing ...... 73 Statistics ...... 74 Results ...... 74 Tumorigenesis is augmented in SPC-IGFIR-Akt2-/- mice and suppressed in SPC-IGFIR- Akt1-/- mice ...... 74 Loss of Akt2 does not alter Akt or ERK1/2 activation ...... 76 SPC-IGFIR and SPC-IGFIR-Akt2-/- tumors have differential gene expression ...... 77 Genes implicated in lung cancer are differentially expressed in Akt2 null tumors ...... 79 Proliferation and immune cell abundance not different in Akt2 null tumors ...... 80 Discussion ...... 96 General Discussion ...... 103 References ...... 111 Appendix I: Differentially expressed transcripts in tumors from SPC-IGFIR-Akt2 null mice compared to SPC-IGFIR mice ...... 159 Appendix II: KLF5 knockdown decreases proliferation of non-small cell lung cancer cells .... 171 Background ...... 171 Materials and Methods ...... 173

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Cell lines and Reagents ...... 173 Transient transfection with siRNA ...... 173 Generation of stable cell lines ...... 173 Western Blotting ...... 174 RNA isolation and qRT-PCR...... 174 Immunofluorescence ...... 175 Statistics ...... 175 Results ...... 176 Transient KLF5 knockdown decreased proliferation of NCI-H358 lung cancer cells ...... 176 Stable KLF5 knockdown decreased proliferation of NCI-H358 lung cancer cells ...... 176 Discussion ...... 180 Appendix III: Source of Materials ...... 183 Appendix IV: Recipes for Solutions ...... 186

List of Figures

Figure 1: A simplified diagram of IGF-IR signaling transduction that highlights key effectors and the corresponding biological effects in lung cancer...... 16

Figure 2: Akt isoform structure...... 36

Figure 3: Tissue-specific, inducible expression of the IGF-IR in SPC-IGFIR transgenic mice. 47

Figure 4: IGF-IR is overexpressed in lung cancer cells and is inhibited by treatment with BMS- 754807...... 58

Figure 5: Treatment with BMS-754807 or platinum chemotherapeutics reduces cell survival in a dose dependent manner...... 59

Figure 6: BMS-754807 synergizes with platinum based chemotherapeutics to enhance killing of lung cancer cells...... 60

Figure 7: BMS-754807 reduces proliferation in lung cancer cells...... 61

Figure 8: BMS-754807 induces apoptosis in lung cancer cells...... 62

Figure 9: BMS-754807 reduces wound closure of lung cancer cells...... 63

Figure 10: Overexpression of IGF-IR caused tumor formation in all mice regardless of the lack of one Akt isoform...... 81

Figure 11: Mice lacking Akt1 had reduced tumor burden, while those lacking Akt2 had increased tumor burden...... 82

Figure 12: Tumors from SPC-IGFIR mice and SPC-IGFIR-Akt2-/- had similar activation of IGF- IR signaling pathways...... 83

Figure 13: Differentially expressed genes and transcripts from tumors of SPC-IGFIR and SPC- IGFIR-Akt2-/- mice cluster together and separately from wild-type normal lung samples...... 84

Figure 14: Diseases and biological functions significantly altered between tumor tissues from SPC-IGFIR and SPC-IGFIR-Akt2-/- mice...... 87

Figure 15: Neoplasia of tumor cell lines is predicted to be downregulated in Akt2 null tumors. 90

Figure 16: Lack of Akt2 influences expression of transcripts functionally related to cell movement...... 91

Figure 17: Development of endothelial tissue and proliferation of epithelial cells is predicted to be upregulated in Akt2 null tumors...... 92

Figure 18: Lack of Akt2 influences expression of transcripts functionally related to inflammation...... 93

Figure 19: Immunohistochemistry for proliferation and immune cell markers in tumors...... 95

Figure 20: Transient KLF5 knockdown decreased proliferation of NCI H358 lung cancer cells...... 178

Figure 21: Stable KLF5 knockdown decreased proliferation of NCI H358 lung cancer cells. . 179

List of Tables

Table 1: Chemotherapeutic drugs used for the treatment of NSCLC...... 8

Table 2: IGF-IR targeting agents currently in clinical trials...... 27

Table 3: Clinical Trials of IGF-IR targeting agents specifically in the treatment of lung cancer...... 30

Table 4: Doses (µM) of BMS-754805, Cisplatin, and Carboplatin that elicit 50% growth inhibition (IC50) of lung cancer cell lines ...... 59

Table 5: Sequences of primers used to genotype transgenic and knockout mice...... 71

Table 6: Transcripts within the IGF-IR signaling pathway are differentially expressed in tumors from SPC-IGFIR-Akt2-/- null mice compared to SPC-IGFIR mice...... 83

Table 7: Top 25 upregulated transcripts in tumors from SPC-IGFIR-Akt2-/- mice compared to SPC-IGFIR mice...... 85

Table 8: Top 25 downregulated transcripts in tumors from SPC-IGFIR-Akt2-/- mice compared to SPC-IGFIR mice...... 86

Table 9: Molecular and cellular functions predicted to be significantly upregulated or downregulated in SPC-IGFIR-Akt2-/- tumors compared to SPC-IGFIR tumors...... 88

Table 10: Relative transcript expression of genes associated with lung cancer...... 94

Table 11: Differentially expressed transcripts in tumors from SPC-IGFIR-Akt2-/- mice compared to SPC-IGFIR mice...... 159

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List of Abbreviations

ACTH Adrenocorticotropic hormone ALK Anaplastic lymphoma kinase ALS Acid-labile subunit ATP Adenosine triphosphate BAD Bcl-2 associated death promoter Bcl-2 B-cell lymphoma/leukemia 2 BRET Bioluminescence resonance energy transfer BSA Bovine serum albumin COX-2 Cyclooxygenase-2 COPD Chronic obstructive pulmonary disease CREB Cyclic AMP response element binding protein CT Computed tomography DAPI 4’-6 diamino-2-phenylindole DMEM Dulbeco’s Modified Eagle Medium DMSO Dimethyl sulphoxide DNA Deoxyribonucleic acid DTT Dithiothreitol ECM Extracelluar matrix EDTA Ethylenediaminetetraacetic acid EGF Epidermal growth factor EGFR Epidermal growth factor receptor EMT Epithelial-mesenchymal transition ERBB2 Epidermal growth factor receptor 2 ERK1/2 Extracellular regulated kinase 1 and 2 FAK Focal adhesion kinase FBS Fetal bovine serum FGF Fibroblast growth factor GH Growth hormone GHR Growth hormone receptor GLUT1-4 Glucose transporter 1-4 GSK-3 Glycogen synthase kinase-3 H&E Hematoxylin and Eosin HIF-1α Hypoxia-inducible factor 1 alpha HRAS Harvey rat sarcoma viral oncogene homolog HRP Horseradish peroxidase IGF Insulin-like growth factor IGFBP Insulin-like growth factor binding protein IGF-IR Type I insulin-like growth factor receptor IGF-IIR Type II insulin-like growth factor receptor IR Insulin receptor IRS Insulin receptor substrate JAK Janus kinase kDa Kilodalton KLF5 Kruppel-like factor 5

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KRAS Kristin rat sarcoma viral oncogene homolog KSFM Keratinocyte serum-free media M6P Mannose-6-phosphate mAb Monoclonal antibody MAPK Mitogen activated protein kinase mRNA Messenger ribonucleic acid mTOR Mammalian target of rapamycin mTORC1/2 Mammalian target of rapamycin complex 1/2 NNK Nicotine-derived nitrosamine ketone NSCLC Non-small cell lung cancer P70S6K 70kDA ribosomal protein S6 kinase 1 PBS Phosphate buffered saline PCNA Proliferating cell nuclear antigen PCR Polymerase chain reaction PDGF Platelet derived growth factor PHLPP PH domain and leucine rich repeat protein phosphatases PI3K Phosphoinositide 3-kinase PKA Protein kinase A PKB Protein kinase B PKC Protein kinase C PTEN Phosphatase and tensin homolog qPCR Quantitative polymerase chain reaction qRT-PCR Quantitative reverse transcription polymerase chain reaction RNA ribonucleic acid RNAi ribonucleic acid interference RPMI Roswell Park Memorial Institute rtTA reverse tetracycline transactivator SCLC Small cell lung cancer SEM Standard error of the mean Shc Src homology and collagen domain protein shRNA Short hairpin RNA siRNA Small interfering RNA SPC Surfactant protein C STAT Signal transducers and activators of transcription TGF-β Transforming growth factor beta TKI Tyrosine kinase inhibitor TNM Tumor, node involvement, metastasis TRE Tetracycline response element VEGF Vascular endothelial growth factor

Introduction: Review of the Literature and Rationale

Lung Cancer

Incidence, mortality, and risk factors There were 1.6 million new cases of lung cancer diagnosed in 2008 throughout the world, which represented 13% of all new cancer cases (Jemal et al. 2011). Lung cancer is the leading cause of cancer-related mortalities worldwide, causing 1.4 million deaths in 2008 (Jemal et al.

2011) and 1.5 million deaths in 2010, which represented 19% of all cancer related mortalities

(Lozano et al. 2012). The Canadian Cancer Society estimated that in 2014 26,100 Canadians would be diagnosed with lung cancer, representing 14% of new cancer cases, and that 20,500

Canadians would die because of lung cancer, which will represent 27% of cancer-related deaths

(Canadian Cancer Society 2014). Lung cancer is generally asymptomatic in the early stages and is often not detected until advanced stages of the disease, contributing to the high mortality rate.

In fact, Morgensztern et al. (2010) reported that 65% of patients present with locally advanced or metastatic disease. In Canada, the five year survival rate for lung cancer is only 14% in men and

20% for women (Canadian Cancer Society 2014). This low survival rate, and the frequent late diagnosis, is due to a lack of effective screening programs and ineffective treatments for advanced stage disease. The survival rate for advanced stage lung cancer remains below 1%, despite advances in treatment and the incorporation of new drugs and targeted therapies (Carnio et al. 2014).

Tobacco smoke remains the largest contributor to lung cancer incidence and it has been estimated to account for 80% of lung cancers worldwide (Ezzati and Lopez 2003; Ezzati et al.

2005). However, lung cancer still occurs at high rates in never smokers (Rudin et al. 2009; Samet et al. 2009). Considering lung cancer incidence and mortality rates only in never smokers, they

remain similar to many other major cancer types and were responsible for an estimated 16,000-

24,000 deaths in the United States in 2008 (Rudin et al. 2009). There are several other risk factors that may contribute to the development of lung cancer in both smokers and never smokers including occupational and environmental exposure to: particulate matter in the air

(asbestos and crystalline silica) (Jemal et al. 2011; Järvholm and Aström 2014; Kachuri et al.

2014; Olsson et al. 2014); fuel exhaust, wood smoke or cooking fumes (polycyclic aromatic hydrocarbons) (Jemal et al. 2011; Jin et al. 2014; Kim et al. 2014c; Olsson et al. 2014; Tomioka et al. 2014); pesticides (chlorophenols) (Zendehdel et al. 2014); radiation (radon and plutonium)

(Marsh et al. 2014; Torres-Durán et al. 2014); heavy metals (arsenic and chromium) (Celik et al.

2008; Olsson et al. 2014); and second hand cigarette smoke (Jemal et al. 2011). Pre-existing lung disease, such as chronic obstructive pulmonary disease (COPD), chronic bronchitis and emphysema, are also risk factors (Denholm et al. 2014). There are gender specific differences in lung cancer incidence, with females being more frequent lung cancer patients among never- smokers (Gupta et al. 2014), and males among smokers (Yu et al. 2014b). This difference may be due to hormones, such as estrogens (Yano et al. 2011). Alternately, Jemal et al. (2011) suggest that high lung cancer rates in Chinese women may be due to indoor air pollution from unventilated coal-fueled stoves and high-temperature cooking fumes. Certain genetic mutations increase the risk of lung cancer development including TP53 (Hung et al. 2008), cyclin dependent kinase inhibitor 2A (CDKN2A) (Potrony et al. 2014), and BRCA2 (Wang et al. 2014a).

Although obesity is associated with increased risk of many types of cancer, recent studies have reported no association between body mass index (BMI) and lung cancer in never smokers

(Bhaskaran et al. 2014), and an inverse association in smokers (Bhaskaran et al. 2014; Everatt et al. 2014). Preventative measures can be made to reduce exposure to some known carcinogens in

order to reduce individual risk and overall incidence of lung cancer. Indeed, smoking cessation programs and good ventilation reducing indoor air pollution are effective measures (Jemal et al.

2011; Jin et al. 2014).

Classification, pathology, and staging Lung cancer can be broadly divided into two categories, small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) based on histologic and pathologic features of the tumor according to World Health Organization (WHO) guidelines (2004). NSCLC accounts for

80% of lung cancers (Devesa et al. 2005; Gridelli et al. 2010) and can be further subdivided into different subtypes, the most common of which are adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. Adenocarcinoma is the most common NSCLC, accounting for 38% of lung cancers, and is more common in never smokers, women and younger people (Travis 2011).

Squamous cell carcinoma is the second most common, accounting for 20% of lung cancers, and is associated with a history of smoking or exposure to environmental and occupational carcinogens (Travis 2011; Olsson et al. 2014). Adenocarcinoma generally originates peripherally in the alveoli, while squamous cell carcinoma is usually central and close to the bronchi (Travis

2011). Theses subtypes can be even further classified based on tumor histology and cytology.

Specific classification is becoming increasingly important as new treatments are being developed to which responses are variable and require a more personalized approach to treatment. This is reflected by the recent joint publication by the International Association for Study of Lung

Cancer, American Thoracic Society, and European Respiratory Society International, updating guidelines for the classification of lung adenocarcinoma and their recommendation that a diagnosis of NSCLC-NOS (not otherwise specified) is no longer acceptable (Travis et al. 2011b).

Detailed information on specific classification can be found within these guidelines and reviewed

by Travis et al. (2011b). With the advances in targeted therapies, molecular pathology and classification of NSCLC is becoming increasingly important and recommendations in the updated guidelines call for identification of genetic mutations.

NSCLC is further classified based on grade, which refers to the level of differentiation, and gives important information on prognosis since poorly differentiated tumors are generally faster growing and more likely to metastasize (Travis et al. 2011a). Grading is on a scale of 1-4, with grade 1 for well differentiated tumors and grade 4 for poorly differentiated tumors (Travis et al. 2011a). There are not different grades of SCLC.

Staging of lung cancer assesses disease progression and differs between SCLC and

NSCLC. SCLC is simply categorized as limited or extensive, where limited SCLC is only present in the lungs or surrounding lymph nodes on one side of the chest and extensive cancer has spread to the other side of the chest and beyond (Kalemkerian 2011). NSCLC staging is based on the TNM system, more specifically, the size of the tumor, lymph node involvement, and distant metastasis. Staging of NSCLC is complicated and a detailed description can be found by Goldstraw et al. (2011). Briefly, there are four stages (I-IV) progressing from early (stage I-

IIIa) to advanced (stage IIIb-IV): stage I requires the tumor is limited to the lung and no more than 3cm; stage II includes bronchial or hilar lymph node involvement; stage III includes mediastinal lymph node involvement; and stage IV involves metastasis to the pleura or distant organs.

Diagnosis and treatment Lung cancer is mainly detected using X-ray or CT imaging, which usually occurs when the patient becomes symptomatic or during unrelated testing (Reck et al. 2013). As previously discussed, this often means diagnosis at advanced disease stages where survival rates are low.

Despite this, there is not a satisfactory screening tool for lung cancer currently in widespread use and lung cancer screening remains a highly debated issue among professionals. After several trials reporting no benefit to using chest X-ray for screening (Kanne 2014), the large scale

National Lung Screening Trial (NLST) recently reported a benefit to screening with low-dose

CT in a high-risk population (Aberle et al. 2011; Church et al. 2013). This has led to recommendations from the American Cancer Society (Wender et al.), American College of Chest

Physicians and the American Society of Clinical Oncologists (Bach et al. 2012; Detterbeck et al.

2013) that screening be made available to high-risk individuals at specialized centers.

Implementation of this screening program has begun in the United States based on self-referral

(Eberth et al. 2014; Kanne 2014). However, the same benefit of low-dose CT screening was not confirmed by the smaller Danish trial (DANTE) (Infante et al. 2009). Criticisms and concerns over a large scale screening program include false positive detection of benign nodules leading to harmful and expensive over-diagnosis, over-radiation of patients, and the high cost to benefit ratio especially compared to the effectiveness of preventative strategies such as smoking cessation (Gill et al. 2013; Black et al. 2014; Kanne 2014; Patz et al. 2014). In addition to imaging techniques, diagnosis involves other tests including sputum cytology and biopsy

(Davidson et al. 2013). Biopsy is necessary for histologic and molecular analysis in order to give a complete diagnosis based on classification, stage and grade as discussed above. The treatment plan for lung cancer varies depending on these criteria.

SCLC is considered very aggressive and is rarely treated with surgical intervention.

Limited disease can be treated with chemotherapy and radiation and is curable in 20-25% of patients (Kalemkerian 2011). Extensive disease is usually incurable and treatment is generally

palliative, and chemotherapy may be used to improve quality of life or extend life (Kalemkerian

2011).

In the early stages, NSCLC may be treated by surgical resection with or without adjuvant chemotherapy and radiation (Howington et al. 2013); however, advanced stage disease is usually inoperable and is treated with chemotherapy and radiation (D’Addario et al. 2009; Goldstraw

2011; Reck et al. 2013). Combined chemotherapy and radiation are more effective than radiation alone, and simultaneous treatment is more effective than sequential (Marino et al. 1995; Furuse et al. 1999; Zatloukal et al. 2004). Platinum based chemotherapeutics remain the gold standard in lung cancer therapy (Mahalingam et al. 2009); however, non-platinum agents may be just as effective in NSCLC (Kosmidis 2002). Chemotherapeutics used to treat NSCLC are listed in

Table 1. Initial treatment is with a platinum based doublet, usually cisplatin combined with etoposide, vinorelbine, gemcitabine, paclitaxel, docetaxel, or pemetrexed (Goldstraw et al.

2011). The majority of patients with advanced NSCLC will experience disease progression after receiving first line treatment. At this point they may receive further combination therapy or a single agent second line treatment of docetaxel or pemetrexed (Mahalingam et al. 2009;

Goldstraw et al. 2011). Maintenance therapy can be used following first line treatment in order to extend the period before relapse, however, only certain agents are tolerated for extended treatment cycles. Single agents can be used for maintenance therapy, such as pemetrexed in non- squamous NSCLC (Johnson and Patel 2014).

The poor survival of lung cancer patients has led to a strong push for the development of targeted therapies (Pal et al. 2010; Sechler et al. 2013). Indeed, targeted therapies using monoclonal antibodies and tyrosine kinase inhibitors (TKIs) have been approved for use in first line therapies with platinum-based chemotherapy for select patients. These include: epidermal

growth factor receptor (EGFR) targeted agents, erlotinib and gefitinib, for patients with EGFR mutations or EGFR overexpression; vascular endothelial growth factor (VEGF) targeting agent, bevacizumab, in patients with non-squamous NSCLC; and anaplastic lymphoma kinase (ALK) targeting agent, crizotinib, in patients with ALK gene rearrangements (Jett and Carr 2013;

Carnio et al. 2014). Additionally, EGFR TKIs can be used as a single agent therapy in maintenance therapy or as a second or third line treatment option (Jett and Carr 2013; Jakopovic et al. 2014). There are many more targeted agents in clinical development against a wide variety of targets, which are thoroughly reviewed by Forde and Ettinger (Forde and Ettinger 2013).

Table 1: Chemotherapeutic drugs used for the treatment of NSCLC. First or Second Line Second or Combination Third Line Drug Name Drug Class Therapy Monotherapy Conditions of Use Bevacizumab VEGF targeted Yes No In combination with paclitaxel and agent carboplatin for treatment of unresectable, advanced, metastatic or recurrent non-squamous NSCLC

Carboplatin Platinum Yes No N/A Alkylating Agent

Cisplatin Platinum Yes No N/A Alkylating Agent

Crizotinib ALK targeted Yes No Presence of ALK gene rearrangement agent

Docetaxel Taxane Yes Yes N/A

Erlotinib EGFR targeted Yes Yes EGFR is mutation or overexpression, or agent when EGFR status is unknown and 2 previous chemotherapy treatments have failed

Etoposide Topoisomerase Yes No N/A Inhibitor

Gefitinib, EGFR targeted Yes Yes EGFR is mutation or overexpression agent

Gemcitabine Antimetabolite Yes Yes N/A

Paclixaxel Taxane Yes No N/A

Pemetrexed Antimetabolite Yes Yes Non-squamous NSCLC

Vinorelbine Vinca Akyloid Yes Yes N/A

N/A: Not applicable

The IGF System

Ligands There are three ligands belonging to the insulin-like growth factor (IGF) family: IGF-1;

IGF-II; and insulin. All three share structural similarities, although the IGFs are larger than insulin, 7.5kDa and 5.8kDa, respectively, and contain two additional domains (Rinderknecht and

Humbel 1978; Ciszak and Smith 1994; Torres et al. 1995). IGF-I and IGF-II primarily regulate post-natal and fetal growth, respectively, while insulin is a major regulator of metabolism and glucose homeostasis (Baker et al. 1993; Saltiel and Kahn 2001; Gluckman and Pinal 2003). IGF-

I is produced in the liver in response to growth hormone (GH) and released into circulation in order to regulate post-natal growth (Adamo et al. 1993; Clemmons 2007a). In addition to stimulating growth, IGF-I acts as a negative feedback regulator by inhibiting GH secretion at the level of the hypothalamus and pituitary (Giustina and Veldhuis 1998). Although the majority of

IGF-I is produced in the liver as part of the endocrine system, it is also produced in extra-hepatic tissues to function in an autocrine/paracrine fashion (Adamo et al. 1993; Frystyk et al. 1994;

Yakar and Adamo 2012). IGF-I expression can be influenced by reproductive hormones, stress hormones (ACTH), as well as diet and nutrition (Forbes et al. 1989; Frystyk et al. 1994; Dunn et al. 1997; Yu 2000). Local IGF-I can be produced in response to injury; more specifically it is induced by epidermal growth factor (EGF), fibroblast growth factor (FGF), and platelet-derived growth factor (PDGF) (Clemmons 2007b). IGF-II expression is not responsive to GH, but is regulated by genomic imprinting (Engström et al.). During fetal development, IGF-II is produced in a wide variety of somatic tissues. IGF-II is not produced in mice after weaning, however, in humans IGF-II production is continued into adulthood and is the prevalent IGF in circulation

(Holly and Perks 2012; Yakar and Adamo 2012; Bergman et al. 2013). Despite its lower concentration, IGF-I is still expected to be the primary functional IGF after birth, in part because

IGF-I has a three-fold higher binding affinity for the IGF-IR than IGF-II (Denley et al. 2005) and because IGF-I production fluctuates based on hormonal control, surging during puberty, while post-natal IGF-II levels are relatively stable (Yu 2000). Beyond development, the role of IGF-II in human physiology is currently not well understood. The roles of IGFs in normal physiology are reviewed in more detail by Yakar and Adamo (2012) and Holly and Perks (2012).

Binding proteins There are six high-affinity insulin-like growth factor binding proteins (IGFBP1-6) that mainly function to regulate IGF bioavailability (Scagliotti and Novello 2012; Hjortebjerg and

Frystyk 2013). IGFBPs can bind to ligands in circulation in order to stabilize them and prolong their half-lives while they are transported to tissues for action (Guler et al. 1989; Frystyk et al.

1999). IGFBP-3 is the most abundant binding protein in circulation with 70-80% of circulating

IGFs bound in complex with IGFBP-3 and the acid-labile subunit (ALS) (Baxter et al. 1989; Lee et al. 1997; Yu et al. 1999; Kodama et al. 2002; Clemmons 2007b). At these target sites, IGFBPs can bind to IGFs essentially sequestering them and preventing ligand binding and subsequent receptor activation (Yu and Berkel 1999). IGFs are released from the IGFBP complex by proteolysis of IGFBP by enzymes such as matrix metalloproteinases (Nakamura et al. 2005), by

IGFBP binding to components of the extracellular matrix (ECM), or by phosphorylation of

IGFBP (Firth and Baxter 2002). IGFBPs (1,2,3, and 5) are dual acting and can both potentiate or inhibit IGF action in a tissue and context specific manner (Yu 2000; Duan and Xu 2005).

Furthermore, there is evidence supporting IGF-independent activity of IGFBPs (1,2,3, and 5) regulating migration, apoptosis, proliferation and differentiation (Oh et al. 1993; Yamanaka et al.

1999; Duan and Xu 2005; Wang et al. 2006; Ammoun et al. 2012). The regulation of IGF function by IGFBPs is complex and is not fully understood; more detailed reviews can be found

elsewhere (Mazerbourg et al. 2004; Duan and Xu 2005; Beattie et al. 2006; Durai et al. 2006;

Bach et al. 2013; Hjortebjerg and Frystyk 2013).

Receptors There are three receptors in the IGF family: the type I insulin-like growth factor receptor

(IGF-IR); the type II insulin-like growth factor receptor (IGF-IIR); and the insulin receptor (IR).

The IGF ligands regulate cellular function primarily through binding the tyrosine kinase receptor, IGF-IR. The IGF-IR pro-receptor is cleaved by furin into α- and β-subunits, 135kDa and 95kDa, respectively (Massagué and Czech 1982; Ward et al. 2001). The α-subunit forms the extracellular ligand binding domain (Garrett et al. 1998), while the β-subunit contains the transmembrane and intracellular tyrosine kinase domains (Pautsch et al. 2001). The two subunits are joined by a disulfide bond and two of these monomers are covalently bonded by disulfide bonds to form a functional receptor (Adams et al. 2000). The IGF-IR responds to ligand stimulation by IGF-I and IGF-II, and although the IGF-IR can bind insulin, this low affinity interaction typically does not occur at physiologic concentrations (Pandini et al. 1999).

The IGF-IR and the IR share 60% structural similarity (Benyoucef et al. 2007). More specifically, they share 45-65% structural similarity in the ligand binding domain and 60-85% in the tyrosine kinase domain and substrate recruitment domain (Ullrich et al. 1986; Yip et al. 1991;

Mynarcik et al. 1997; Whittaker et al. 2001). There are major differences in the alpha subunits which result in differences in ligand binding (Lawrence et al. 2007). Alternative splicing of exon

11 produces two variants of the insulin receptor, IR-A (exon 11-) and IR-B (exon 11+) (Benecke et al. 1992). Exon 11 corresponds to a 12 amino acid sequence at the c-terminus of the α-chain; therefore, these isoforms will exhibit different ligand affinities. The IR-A and IR-B monomers can form homodimers or heterodimers containing both isoforms. While both IR isoforms have a

high affinity for insulin, IR-A has a relatively high affinity for IGF-II as well (Frasca et al. 1999;

Denley et al. 2006). Both IR isoforms express a low affinity for IGF-I (Frasca et al. 1999;

Sciacca et al. 2003). The difference in ligand affinities combined with differential expression of the isoforms indicate varying function, with the mitogenic IR-A predominantly expressed in fetal tissues (Frasca et al. 1999) and metabolic IR-B highly expressed in classically insulin-responsive adult tissues (Benecke et al. 1992; Sesti et al. 1994).

Since the insulin receptor shares a similar structure with the IGF-IR, either isoform can dimerize with an IGF-IR protein monomer to form a functional hybrid receptor (Pandini et al.

1999, 2002; Benyoucef et al. 2007). Ligand binding properties of hybrid receptors were evaluated by radio ligand competition using BRET and revealed that IGF-IR/IR hybrids containing either IR isoform had a relatively low affinity for insulin compared to IR homodimers and the presence of hybrid receptors decreased insulin stimulated IR activation. Alternately, the hybrid receptors bound IGF-I and IGF-II with a similar affinity to the IGF-IR irrespective of spice variant (Benyoucef et al. 2007). As expected these hybrid receptors were activated more strongly by IGF-I than insulin. Similar results were seen in purified hybrid receptors (Slaaby et al. 2006). In contrast, another study using semi-purified receptors reported differential ligand activation of hybrid receptors containing IR-A or IR-B (Pandini et al. 2002). This could be due to differences in hybrid receptor purification and unintended capture of IR homodimers.

The IGF-IIR is a membrane-bound truncated receptor with no kinase activity. It binds

IGF-II with high affinity, sequestering it and essentially acting as an IGF-II sink through receptor internalization and subsequent degradation (Scott and Firth 2004; El-Shewy and Luttrell 2009).

The IGF-IIR binds IGF-I with low affinity and does not bind to insulin (Lee et al. 1986; Ewton et al. 1987; Tong et al. 1988). The IGF-IIR will, however, bind proteins with a mannose-6-

phosphate (M6P) motif, such as transforming growth factor-beta (TGF-β), and is often referred to as the IGF-II/M6P receptor (El-Shewy and Luttrell 2009).

IGF-IR signal transduction and function The IGF-IR acts mainly through two signaling pathways, the PI3K/Akt pathway and the

RAS/MAPK pathway, to induce proliferation, cell survival, migration, differentiation and various metabolic effects (Siddle 2011) as depicted in Figure 1. These pathways are common to many growth factor receptors, including the IR, however slight differences in recruitment and activation of intracellular mediators allow for specific effects of each receptor (Frasca et al.

2008; Boucher et al. 2010). Ligand binding causes a conformational change resulting in trans- auto-phosphorylation at several sites on the tyrosine kinase domain of the β-subunit and exposure of the ATP binding site (Rubin et al. 1983; Hubbard 1997; Pautsch et al. 2001; Samani et al. 2007). This leads to the recruitment and activation of various docking and scaffold proteins, including insulin response substrates (IRS) 1-4 and Shc to Tyr950 of the IGF-IR, and initiation of the PI3K/Akt and RAS/MAPK signaling cascades, respectively. The IGF-IR can also signal through the JAK/STAT pathway (Zong et al. 2000; Yadav et al. 2005), however, this has not been reported in lung cancer cells (Song et al. 2003).

The IRS proteins, of which IRS-1 and IRS-2 are the predominant forms in humans (Kim

1998) activate phosphotidylinositol 3-kinase (PI3K) which then phosphorylates membrane bound phosphoinositols, converting PIP2 to PIP3 (Mora et al. 2004; Manning 2010). PTEN negatively regulates this pathway by converting PIP3 to PIP2. PIP3 recruits and activates PDK1 which in turn will phosphorylate Akt at Thr308; additional phosphorylation of Akt at Ser473 by mTORC2 fully activates Akt (Mora et al. 2004; Manning and Cantley 2007).

Another important component of this pathway is the mammalian target of rapamycin

(mTOR). Akt phosphorylates TSC2, which in complex with TSC1 relieves its inhibitory effect on the G-protein Rheb allowing activation of mTOR complex 1 (mTORC1). The mTORC1 increases protein synthesis and promotes cell growth and cell cycle progression primarily through the effectors p70S6K and 4E-BP1 (Dupont et al. 2003; Manning and Cantley 2007).

IGF-stimulated cell cycle progression from G1 to S phase is promoted by an increase in cyclin

D1 expression (Furlanetto et al. 1994; Diehl et al. 1998; Samani et al. 2007; Girnita et al. 2014).

IGF caused an increase in cyclin A and B as well as cyclin dependent kinases, which are involved in the G2 to M phase transition (Furlanetto et al. 1994). IGF-1 can decrease the activity of cyclin dependent kinase inhibitor p27(Kip1) in an Akt-dependent manner (Chakravarthy et al.

2000). IGF-1 induces an Akt-dependent upregulation of MDM2, which can be activated by phosphorylation by Akt and is an ubiquitin ligase responsible for degradation of the tumor suppressor p53 (Mayo and Donner 2001; Du et al. 2013). Akt is also involved in cell cycle progression through the modulation of DNA repair, and DNA repair checkpoints (Trojanek et al.

2003; Manning and Cantley 2007).

Akt is an important mediator of IGF-induced cell survival. Akt directly inhibits pro- apoptotic proteins Bcl-2 family member BAD, the transcription factors forkhead box proteins

(FOXO-1,3,4), and caspase 9 (Datta et al. 1997; Kulik et al. 1997; Schmidt et al. 2002; Manning and Cantley 2007). Furthermore, Akt signaling increases expression of anti-apoptotic proteins

Bcl-2, Bcl-XL (Minshall et al. 1997; Párrizas et al. 1997). Akt increases activation of the pro- survival transcription factors NF-κβ (through IKK phosphorylation) and CREB (Kulik et al.

1997; Girnita et al. 2014). IGF-1 also promotes cell survival via an mTOR/p70S6K- dependent increase in survivin (Vaira et al. 2007; Song et al. 2013).

In addition to regulating proliferation and survival, Akt regulates cell metabolism.

Glycogen synthase kinase-3 (GSK-3) inhibition by Akt leads to increased glycogen synthesis and glycolysis (Manning and Cantley 2007). Akt can phosphorylate AS160, leading to translocation of GLUT4 and increased glucose uptake (Mora et al. 2004). Although this is primarily through insulin and the IR, AS160 activation can be induced by IGF-1 (Geraghty et al. 2007).

In addition to the PI3K/Akt pathway, the other main IGF-IR signaling pathway is through

Ras/MAPK pathway. IRS can form a complex with the adaptor protein Grb2, Shc and son of sevenless (Sos) to recruit the GTP binding protein Ras to the cell surface for activation (Craparo et al. 1995). Ras activation leads to phosphorylation of the protein kinase Raf and subsequent activation of the MAPK kinase MEK1 (Rozakis-Adcock et al. 1992). MEK1 then phosphorylates

ERK1/2 causing nuclear translocation and activation of several transcription factors involved in proliferation, cell survival, and migration (Manning and Cantley 2007; Samani et al. 2007;

Belfiore et al. 2009; Girnita et al. 2014). The Ras pathway can be activated independently of

IRS-1 by direct phosphorylation of the docking protein Shc by tyrosine kinase receptors and complex formation with Grb2 (Baltensperger et al. 1993; Skolnik et al. 1993). Some targets of

ERK are shared with the Akt pathway including CREB and Cyclin D1 (Girnita et al. 2014), and there is cross-talk between the two pathways (Sinha et al. 2004).

IGFs promote migration and invasion through the above mentioned pathways by increasing the expression of proteins that will influence cell adhesion and the microenvironment, such as matrix metalloproteinases and VEGF (Zhang et al. 2003). The IGF-IR can influence migration beyond the classical signaling though cross-talk with integrins and focal adhesion kinases (FAKs), which can be mediated by IRS-1 or Gab1/Shp2 (Jones et al. 1996; Goel et al.

2004; Girnita et al. 2014).

Figure 1: A simplified diagram of IGF-IR signaling transduction that highlights key effectors and the corresponding biological effects in lung cancer.

Targeting the IGF-IR in Lung Cancer

Rationale: defining a role for the IGF-IR in lung cancer The IGF-IR has emerged as a potential therapeutic target in lung cancer, since numerous studies provide evidence that signaling through the IGF-axis can be a driving force of lung cancer development and progression. Several studies have demonstrated that disruption of IGF-

IR signaling in lung cancer cells will decrease proliferation, cell survival and migration in vitro and decrease tumor formation in xenograft models (Samani et al. 2004; Cosaceanu et al. 2007;

Dong et al. 2007; Ma et al. 2007; Qian et al. 2007). Both NSCLC cells (Reeve et al. 1992;

Favoni et al. 1994; Quinn et al. 1996) and tumors (Pavelić et al. 2002; Kim et al. 2009) express

IGF-IR and can produce both IGF-I and IGF-II in order to stimulate growth in an autocrine/paracrine fashion. However, IGF-II is more frequently expressed in lung cancer so it is likely to be the primary IGF in autocrine growth (Reeve et al. 1992; Quinn et al. 1996; Pavelić et al. 2002). Expression of IGF family members may vary by histological subtype. Recently, Kim et al. (2014d) reported that intra-tumoral IGF-I was higher in adenocarcinomas, while IGF-II was higher in squamous cell carcinomas. IGF-IR expression is frequently associated with squamous cell carcinomas (Cappuzzo et al. 2010; Dziadziuszko et al. 2010; Kim et al. 2012d; Nakagawa et al. 2012; Ludovini et al. 2013; Tsuta et al. 2013; Gately et al. 2014; Reinmuth et al. 2014), but other studies have found it is highly expressed in adenocarcinomas (Hurbin et al. 2011; Kikuchi et al. 2012; Nakagawa et al. 2012; Tsuta et al. 2013; Zhang et al. 2014c).

The importance of IGFs in regulating lung cancer growth is supported by elevated levels of circulating IGF-I and IGF-II in patients with NSCLC compared to healthy individuals (Izycki et al. 2004; Han et al. 2006; Zhang et al. 2014a) and increased amounts of IGF-I in NSCLC tissue compared to normal or hyperplastic bronchial epithelium (Kim et al. 2011b). High levels of IGF-I in serum (Masago et al. 2011; Fu et al. 2013) and within the tumor (Kim et al. 2014d)

were associated with poor survival, particularly in patients with adenocarcinoma (Kim et al.

2014d) or advanced disease (Fu et al. 2013). Loss of imprinting on the IGF-II gene increases the risk of developing lung cancer and was associated with advanced disease and lymph node metastasis (Zhang et al. 2014b). High levels of IGF-II found in NSCLC lymph node metastasis were associated with poor prognosis (Zhang et al. 2013). Low levels of intra-tumoral IGFBP-3 have been associated with decreased survival in early stage NSCLC and can be caused by promoter hypermethylation (Chang et al. 2002a, 2002b). Furthermore, high plasma IGFBP-3 was associated with improved prognosis in advanced stage NSCLC (Han et al. 2006; Vollebergh et al. 2010). Alternately, a few studies have reported no association between IGF-1 and IGFBP-3 and survival (Meek et al. 2010; Shersher et al. 2011) and one study reported that high circulating

IGF-I and IGF-II were found to be positive predictors of survival in patients with advanced

NSCLC (Han et al. 2006). It is unclear why the association of these IGF system regulators with lung cancer survival is not consistent; it may be confounded by the overall poor survival of lung cancer patients or it may depend on patient characteristics that remain to be elucidated. Overall, increases in IGFs and decreases in IGFBP-3 suggest that the IGF-axis can be activated in lung cancer to promote disease progression which is further supported by a correlation between IGF-I and IGFBP-3 and increased levels of activated IGF-IR (Kim et al. 2011b).

The IGF-IR itself is highly expressed in 53-79% SCLC patients (Chang et al. 2009;

Badzio et al. 2010; Ferté et al. 2013) and 30-84% of NSCLC patients (Gong et al. 2009;

Ludovini et al. 2009, 2013; Cappuzzo et al. 2010; Dziadziuszko et al. 2010; Fidler et al. 2012;

Kikuchi et al. 2012; Nakagawa et al. 2012; Tsuta et al. 2013; Vilmar et al. 2014; Zhang et al.

2014c; Gately et al. 2014; Reinmuth et al. 2014). An increase in IGF-IR gene copy number is relatively uncommon in lung cancer, reported at 8-27% in NSCLC, but did correlate with IGF-IR

protein expression (Dziadziuszko et al. 2010; Tsuta et al. 2013). Several of these studies investigated the prognostic significance of IGF-IR expression; however, the results were highly variable. Most of these studies did not find IGF-IR expression to be an independent predictor of survival (Lee et al. 2008a; Badzio et al. 2010; Cappuzzo et al. 2010; Fidler et al. 2012; Tsuta et al. 2013; Zhang et al. 2014c), others found that IGF-IR expression was associated with improved survival (Chang et al. 2009; Kikuchi et al. 2012; Reinmuth et al. 2014). One study reported a non-significant decrease in disease-free survival (Nakagawa et al. 2012); another reported that

IGF-IR expression determined by mRNA but not immunohistochemistry predicted a decrease in survival (Vilmar et al. 2014). Although there is not a clear relationship between IGF-IR expression and survival, IGF-IR expression in lung cancer has been associated with other factors which indicate a role in disease progression including: grade III disease (Cappuzzo et al. 2010); advanced stage disease (Dziadziuszko et al. 2010); progressive disease (Hurbin et al. 2011; Peled et al. 2013); increased proliferation (Ki67 staining) (Nakagawa et al. 2012); larger tumor size

(Ludovini et al. 2009; Tsuta et al. 2013); recurrence (Nakagawa et al. 2012); and brain metastasis

(Wu et al. 2013).

Studies using lung cancer cells and mouse xenografts corroborate the observations from lung cancer patients and further our understanding of the role of IGF-IR signaling in advanced disease and tumor invasion and metastasis. IGF-IR inhibition caused decreased expression of invasion related genes Mmp-2, Mmp-9, and α-PA in lung cancer cell lines (Ouban et al. 2003;

Qian et al. 2007). IGF-IR can interact with cell adhesion proteins in order to mediate migration and invasion: E-cadherin (Canonici et al. 2008); and integrins αV (Canonici et al. 2008) and β1

(Kiely et al. 2006; Sayeed et al. 2012). Lung carcinoma cells engineered to express only the extra-cellular domain of the IGF-IR had an IGF-1 neutralizing effect, and 90% reduction of

hepatic metastasis following intrasplenic/portal inoculation (Samani et al. 2004). IGF-IR signaling has been related to the level of differentiation of tumor cells, specifically in epithelial- to-mesenchymal transition (EMT) which is a phenotypic change in cancer cells enabling invasion and metastasis (Malaguarnera and Belfiore 2014). The disruption of IGF-IR signaling in lung cancer cells via dominant negative receptors affected the histology of tumors formed in a xenograft mouse model, producing tumors displaying glandular differentiation (Jiang et al.

1999). IGF-IR blockade prevented TGFβ-1-induced EMT in NSCLC cells (Vazquez-Martin et al. 2013). Furthermore, the tumor suppressor, fibulin-3, inhibits EMT through inactivation of

IGF-IR signaling (Kim et al. 2014b). IGF-IR expression was associated with the EMT marker, vimentin, in advanced stage NSCLC patients (Chen et al. 2013). In contrast, another study reported that low IGF-IR gene expression was associated with poorly differentiated adenocarcinomas; however, most patients enrolled in this study had early stage disease (Kikuchi et al. 2012). Therefore, IGF-IR signaling may not be driving EMT but still has a critical role in the process, and its role may differ depending on the stage of disease. IGF-II produced by cancer-associated fibroblasts inducing paracrine IGF-IR signaling in lung cancer cells induced

Nanog expression and was involved in de-differentiation and promoting stemness in the formation of cancer stem cells (Chen et al. 2014). IGF-IR expression was associated with cancer stem cell markers in human lung adenocarcinomas and co-expression of IGF-IR with β-catenin and POU5F1 was predictive of poor prognosis (Xu et al. 2013).

In addition to promoting disease progression, IGF-IR plays an important role in transformation of normal cells and lung tumorigenesis. IGF-IR overexpression in type II alveolar cells or Clara cells was sufficient to induce spontaneous tumor formation in mice (Linnerth et al.

2009). Similarly, IGF-II overexpressing transgenic mice develop lung tumors in 69% of mice

(Moorehead et al. 2003). IGF-IR signaling enhances tobacco carcinogen-mediated lung tumorigenesis in mice (Kim et al. 2009) and increases tumor size (Siwicky et al. 2010). Mice injected with a tobacco carcinogen developed tumors that overexpressed the IGF-IR (Siwicky et al. 2010). Furthermore, the IGF-IR is required for transformation of fibroblasts by virus SV40

(Sell et al. 1994) and Src oncogenes (Valentinis et al. 1997) in vitro.

Activation of the IGF-axis via the IGF-IR has been identified as a mechanism of resistance to current lung cancer therapies. Silencing IGFBP-3 by promoter methylation and increased activation of the IGF-IR were associated with cisplatin resistance in both NSCLC cells and patients (Ibanez de Caceres et al. 2010; Cortés-Sempere et al. 2013). Similar results were reported by Sun et al. (2012b), who further showed that expression of IGFBP-3 or knockdown of

IGF-IR re-sensitized cisplatin resistant lung cancer cells. IGF-IR signaling confers resistance to radiotherapy in NSCLC cells by inducing repair of double stranded DNA breaks and treatment with IGF-IR inhibitors radiosensitized the cells (Allen et al. 2007; Iwasa et al. 2009). IGF-IR- mediated resistance to EGFR targeted therapies has been reported in lung cancer cells and human tumors (Morgillo et al. 2007; Guix et al. 2008; Jameson et al. 2011; Cortot et al. 2013; Suda et al.

2014), which may be in part due to formation of IGF-IR/EGFR heterodimers (Morgillo et al.

2007). EGFR resistant NSCLC cells were found to have an increased activation of IGF-IR as a result of loss of IGFBP-3 (Cortot et al. 2013). IGF-IR has been associated with resistance to other targeted therapies including a Ras inhibitor, lonafarnib, (Oh et al. 2008), and an ALK inhibitor, crizotinib (Lovly et al. 2014). Hypoxia, which is associated with resistance to radiotherapy and chemotherapy (Brown 2007), induced IGF-I expression and increased activation of IGF-IR via HIF-1α and increased gefitinib resistance in NSCLC cell lines

(Murakami et al. 2014). Resistance to chemotherapy and radiation in NSCLC cells was provided

by hypoxia induced SM22α expression SM22α directly interacted with IGF-IRβ activating the

IGF-IR/PI3K/Akt signaling pathway, and this interaction was HIF-independent (Kim et al.

2012b). The lungs are generally an oxygen rich environment; however, large tumors may still have hypoxic regions (Graves et al. 2010). Furthermore hypoxia in the lungs can be caused by smoking, which leads to an increase in IGF-II expression (Bae et al. 1999; Feldser et al. 1999).

Thus, there is not only evidence to support a role for IGF signaling in lung cancer but that targeting this pathway could be beneficial in combination with current therapeutic strategies.

Co-targeting the insulin receptor with the IGF-IR Many inhibitors targeting the IGF-IR also inhibit the IR; however, the role of the IR in lung cancer is not as well understood. As previously discussed, the IR can contribute to IGF signaling through IGF-II binding to IR-A (Sciacca et al. 1999) as well as IGF-I and IGF-II binding to IGF-IR/IR hybrid receptors (Benyoucef et al. 2007). Therefore, co-targeting the IR with the IGF-IR may be necessary to completely inhibit the IGF-Axis. However, targeting the IR raises concerns about toxicity due to its important role in glucose homeostasis. There is evidence emerging indicating that the IR is involved in lung cancer, supporting the rationale for targeting both receptors. Indeed, NSCLC patients with high expression of both IGF-IR and IR had worse overall survival than those with low levels of either receptor, and IR expression was an independent prognostic marker for poor survival (Kim et al. 2012a). Furthermore, in lung cancer cells with high IR to IGF-IR ratio, co-targeting the IR and IGF-IR decreased cell proliferation more than targeting either alone (Vincent et al. 2013).

A role for the IR is better defined in other types of cancer; overexpressed IR contributes to several types of cancer including breast, colon, ovary, and thyroid (Frasca et al. 1999; Frittitta et al. 1999; Vella et al. 2002; Buck et al. 2010; Zhang et al. 2010a), which have preferential

expression of the mitogenic IR-A (Frasca et al. 1999; Sciacca et al. 1999; Kalli et al. 2002; Vella et al. 2002). Increased IR expression in NSCLC tumours has been detected (McDoniels-Silvers et al. 2002) and was associated with and increased risk of metastasis when expressed in stage I/II

NSCLC (Muller-Tidow et al. 2005). Looking at the specific isoforms, IR-A expression was increased in lung tumors compared to corresponding normal tissues but the difference was not significant due to highly variable expression between tumours (Frasca et al. 1999). Another study reported a decrease in IR-B expression in lung tumors compared to normal lung tissue, causing an increased IR-A to IR-B ratio in the tumors, however, this was associated with a low

EMT signature and improved survival (Jiang et al. 2014). Additionally, intra-tumoral insulin and its activated receptor have been observed in low grade NSCLC and in hyperplastic regions surrounding insulin negative tumours, which may indicate an independent role for insulin and its receptor in early lung tumorigenesis (Mattarocci et al. 2009).

While it has not been reported in lung cancer, IR has been identified as a mediator of intrinsic resistance to IGF-IR targeted therapies. In an IGF-II dependent transgenic mouse model of pancreatic neuroendocrine carcinogenesis, IR was up-regulated during multi-step tumor progression and ablation of IR expression sensitized the tumors to an IGF-IR inhibitor (Ulanet et al. 2010). Similarly, breast cancer cells expressing high IR to IGF-IR ratios were less sensitive to

IGF-IR inhibition (Ulanet et al. 2010). In breast cancer cells, IGF-IR down-regulation increased insulin sensitivity without changing the levels of IR expression, which may be due to increased

IR homodimer formation from IR monomers previously forming hybrid receptors (Zhang et al.

2007). Ewing Sarcoma cells up-regulated IGF-II and IR-A expression as a mechanism of acquired resistance to highly specific IGF-IR targeted therapies (Garofalo et al. 2011). This demonstrates that IR expression does not need to be high before treatment in order to contribute

to resistance. The roles of the IGF-IR and IR are not redundant in cancer signaling. For example,

IGF-II mediates metastasis in hepatocellular carcinoma cells, and in a mouse model IGF-IR but not IR is required for metastasis (Chen et al. 2009). Therefore, in the case of acquired resistance, the resistant cells may not have the same properties and tumorigenicity.

Insulin resistance is a key factor in many metabolic disorders, including type II diabetes mellitus, and is associated with compensatory hyperinsulinemia and hyperglycemia. Insulin resistance may play a role in cancer development through stimulation of mitogenic pathways by insulin, as insulin resistance is characterized by a disruption in the PI3K pathway while the

MAPK pathway remains functional (Cusi et al. 2000). Insulin resistance is associated with increased risk for many types of cancer, but its association with lung cancer is not as clear. The

National Health and Nutrition Examination Survey reported that insulin resistance was associated with a 41% increase in cancer mortality, however, they noted that this association became stronger when lung cancer was removed (Parekh et al. 2010). Another study reported that insulin resistance was associated with an increased risk of lung cancer, although when adjusted for serum adiponectin and leptin levels the association was no longer significant

(Petridou et al. 2011). In contrast, a diet induced mouse model of obesity and insulin resistance with hyperinsulinemia caused increased growth of lung cancer cells, which displayed increased activation of the IR, and these effects were attenuated by metformin treatment (Algire et al.

2011). These results are in agreement with an observed improvement in survival of NSCLC patients with type II diabetes being treated with metformin compared to insulin (Tan et al. 2011).

The link between insulin resistance and lung cancer progression is further complicated by concurrent hyperglycemia. Many cancers have increased glucose uptake and utilization (Vander

Heiden et al. 2009); however this can be independent of insulin. There are several reports of

increased expression of the glucose transporter GLUT-1 in lung tumours compared to normal lung tissue (Ito et al. 1998; Brown et al. 1999; Kurata et al. 1999; Chung et al. 2004; Mamede et al. 2005), but very few tumours have shown increased expression of the insulin-dependent

GLUT-4 (Ito et al. 1998; Brown et al. 1999). The National Health Insurance Corporation Study reported that individuals with high fasting serum glucose had an increased mortality rate for lung cancer (Park et al. 2006), so it is possible that the improved survival in patients treated with metformin (Tan et al. 2011) could be in part to do with improved glycemic control. Furthermore, metformin has more direct anti-cancer actions including inhibition of IGF-1 signaling in the lung

(Memmott et al. 2010; Salani et al. 2012).

IGF-IR targeting agents There is strong evidence that signaling through the IGF-axis, particularly via the IGF-IR, promotes lung cancer development, progression and therapeutic resistance. Therefore, disrupting

IGF signaling by targeting the IGF-IR is an appealing therapeutic strategy. Several IGF-IR targeting agents are in clinical development, which are reviewed well by Scagliotti and Novello

(2012) as well as Chen and Sharon (2013) and are summarized in Table 2. There are more IGF-

IR targeting agents not listed within this table that are in the preclinical stage of development.

The two most common types of drugs targeting the IGF-IR are monoclonal antibodies (mAb) and tyrosine kinase inhibitors (TKI), which work by different mechanisms and each has benefits and limitations. Monoclonal antibodies bind to the extracellular ligand domain of the receptor, which often induces receptor internalization and endosomal degradation, preventing activation of the receptor by ligand binding (Yee 2007). Monoclonal antibodies, as a drug class, have the ability to stimulate the immune system which may improve efficacy at the expense of increased hematological toxicity (Chames et al. 2009; Scagliotti and Novello 2012). Though TKIs may

have different mechanisms of action, most are ATP-competitive inhibitors and block phosphorylation in the tyrosine kinase domain and subsequent activation of signaling cascades

(Broekman et al. 2011; Takeuchi and Ito 2011). TKIs are usually less specific than monoclonal antibodies because the structure of the tyrosine kinase domain is generally better conserved between different tyrosine kinase receptors compared with the ligand binding domains (Yee

2007; Broekman et al. 2011). The ability of TKIs to inhibit multiple targets can be considered desirable since resistance is frequently developed by activation of an alternate tyrosine kinase receptor, however, it may increase toxicity due to off-target effects (Broekman et al. 2011). TKIs are small in size often resulting in improved tumor perfusion (Broekman et al. 2011).

Table 2: IGF-IR targeting agents previously or currently in clinical trials. All clinical trials registered on clinicaltrials.gov current as of March 2015 (Clinicaltrials.gov 2015). Drug Name Developer Class Target Stage of Clinical Development

Figitumumab Pfizer mAb IGF-IR Phase III; discontinued (CP-751,871)

Cixutumumab IM Clone Systems mAb IGF-IR Phase II (IMC-A12)

Ganitumab Amgen mAb IGF-IR Phase III (AMG-479)

BIIB022 Biogen Idec. mAb IGF-IR Phase I; discontinued

Dalotuzumab Merck mAb IGF-IR Phase III (MK-0646)

Robatumumab Shering-Plough mAb IGF-IR Phase I; discontinued (SCH -717454)

R1507 Roche mAb IGF-IR Phase II

AVE1642 ImmunoGen/Safoni mAb IGF-IR Phase I; discontinued

BMS-754807 Bristol Myers TKI IGF-IR and IRa Phase II Squibb

Linsitinib OSI Pharmaceuticals TKI IGF-IR and IRb Phase III (OSI-906)

XL228 Exelexis TKI IGF-IRc Phase I

Picropodophyllin Axelar TKI IGF-IR Phase II (AXL1717)

INSM-18 (NDGA) Insmed TKI IGF-IR and Phase II HER-2

Off target inhibition of: a TrkB, Met, TrkA, Aurora A; b Bcr-Abl, Src, Aurora A, LYN; c IRR;

Early results from phase I clinical trials reported some success of IGF-IR targeted therapies. A phase I clinical trial for the inhibitor piropodophyllin (AXL1717) including four patients with squamous NSCLC observed primary tumor necrosis and no further development of metastasis (Ekman et al. 2011). Similarly, the mAb AMG-479 caused stable disease in NSCLC patients (Murakami et al. 2012). Positive responses in NSCLC patients in phase I studies and strong preclinical evidence led to advancing clinical trials for several IGF-IR targeting agents; these recent trials, are summarized in Table 3. The IGF-IR mAb figitumumab advanced the farthest through clinical trials in NSCLC. In a phase I clinical trial, 14 of 35 patients with advanced NSCLC had an objective response to figitumumab combined with carboplatin and paclitaxel, which correlated with high free IGF-I to IGFBP-3 ratio (Karp et al. 2009b). The following phase II trial in NSCLC initially reported positive results with an objective response rate (ORR) of 54% vs 42%, respectively, for carboplatin and paclitaxel with figitumumab or alone, and an even more impressive ORR of 79% in squamous carcinomas within the figitumumab treatment arm (Karp et al. 2009a). However, this data was misreported and the phase II results were retracted and corrected results were released as an ORR of 37% and 27%, respectively, for carboplatin and paclitaxel with figitumumab or alone, which no longer represented a significant difference, and an ORR of 43% in squamous carcinomas within the figitumumab treatment arm (2012; Langer et al. 2014). The initial phase II results prompted a phase III clinical trial in non-adenocarcinoma NSCLC, which was terminated early because it was not expected that figitumumab would improve survival for patients and there were significant safety concerns (Langer et al. 2014). The overall survival (OS) for patients was 8.6 and 9.8 months, respectively, for carboplatin and paclitaxel with figitumumab or alone (HR 1.18 p=0.06) (Langer et al. 2014). Serious adverse events (SAE) were increased in the figitumumab

treatment arm, including deaths (Langer et al. 2014), a safety issue that did not arise in the phase

I or II trials (Haluska et al. 2007; Karp et al. 2009a, 2009b). The end result was discontinuation of figitumumab development by Pfizer. Recently, results have been released (Clinicaltrials.gov

2015) for a phase II clinical trial of the mAb cixutumumab combined with carboplatin and paclitaxel with or without cetuximumab (targets EGFR) which reported no significant effect on survival and increased fatal SAEs in patients receiving all four drugs. While other agents have not displayed similar safety concerns, other phase II trials have similarly reported no benefit to incorporating IGF-IR targeting agents with current treatments for unselected lung cancer patients

(Ramalingam et al. 2011; Weickhardt et al. 2012). Results remain to be published for several more trials, however, many ongoing trials have been terminated or have ceased enrollment of new patients and the future of IGF-IR targeted therapeutics seems uncertain (Yee 2012; Chen and Sharon 2013; King et al. 2014).

Table 3: Clinical Trials of IGF-IR targeting agents specifically in the treatment of lung cancer. Data as reported by trail sponsors on clinicaltrials.gov unless otherwise specified. All clinical trials registered on clinicaltrials.gov current as of March 2015 (Clinicaltrials.gov 2015). Drug Phase Diseases Drug Combinations Status/Outcome

Figitumumab Phase Advanced non- Carboplatin and Paclitaxel Terminated; N.S. effect on (CP-751, 871) III adenocarcinoma survival (Langer et al. 2014) NSCLC

Phase Refractory non- Erlotinib a Terminated; N.S. effect on III adenocarcinoma survival NSCLC

Phase II SCLC Cisplatin or Carboplatin Terminated with Etoposide

Phase Advanced NSCLC Carboplatin and Paclitaxel Completed; N.S. effect on I/II (untreated) survival (Karp et al. 2009a; 2012)

Phase I Advanced NSCLC Cisplatin and Gemcitabine Completed; N.S. effect on or Pemetrexed survival

Phase I Lung Neoplasms Pegvisomant b Terminated

Cixutumumab Phase II Advanced NSCLC Carboplatin Gemcitabine Completed (IMC-A12) (untreated) and Cetuximab b

Phase II Advanced non- Carboplatin, Paclitaxel and In progress; not recruiting squamous NSCLC Bevacizumab c

Phase II Extensive SCLC Cisplatin and Etoposide In progress; not recruiting

Phase Advanced NSCLC Erlotinib a Completed; No objective I/II responses (Weickhardt et al. 2012)

Phase II Advanced or Carboplatin and Paclitaxel Completed; N.S. effect on Recurrent NSCLC with or without Cetuximab a survival (untreated)

Phase II Lung Cancer Carboplatin and Pemetrexed In progress; not recruiting

Phase II Advanced NSCLC Cisplatin and Pemetrexed In progress; not recruiting

R1507 Phase II Advanced NSCLC Erlotinib a Terminated (recurrent after Erlotinb monotherapy)

Phase II Advanced NSCLC Erlotinib a Completed; N.S. effect on (recurrent) survival (Ramalingam et al. 2011) Targeted drugs against: a EGFR; b GHR; c VEGF; d mTOR

Table 3 Continued Drug Phase Diseases Drug Combinations Status/Outcome

Ganitumab Phase I/II Advanced squamous NSCLC Carboplatin and Terminated (AMG-479) Paclitaxel

Phase I/II Advanced NSCLC Everolimus d or Recruiting Panitumumab a

Phase I/II Advanced NSCLC AMG-655 a Terminated (refractory)

Phase I/II Extensive SCLC Carboplatin and Completed (untreated) Etoposide

BIIB022 Phase I Advanced NSCLC Carboplatin and Completed (untreated) Paclitaxel

Dalotuzumab Phase II Advanced, metastatic non- Cisplatin and In progress; not (MK-0646) squamous NSCLC Pemetrexed recruiting (untreated)

Phase II Recurrent NSCLC Erlotinib a Completed

Phase I Advanced NSCLC Erlotinib a Completed

Phase I/II Extensive SCLC Cisplatin and Completed Etoposide

Linsitinib Phase II Recurrent SCLC N/A In progress, not (OSI-906) recruiting

Phase II Advanced NSCLC Erlotinib a Completed

Phase II Advanced NSCLC Erlotinib a In progress; not (nonprogression after first (as maintenance recruiting line chemotherapy) therapy)

Picropodophyllin Phase II Advanced NSCLC Docetaxel Completed (AXL1717) Phase I Advanced NSCLC Carboplatin and Completed (untreated) Gemcitabine Targeted drugs against: a EGFR; b GHR; c VEGF; d mTOR

Challenges for successful therapeutic use In light of the disappointing results of completed clinical trials for IGF-IR targeting agents, researchers and clinicians are left puzzling over the discontinuity between positive preclinical results and the poor performance in patients. Moving forward in the development of

IGF-IR targeted therapies in lung cancer will require continued research to identify subsets of patients who would benefit from IGF-IR targeted therapy and identify drug combinations that will prove most effective. However, toxicity of IGF-IR targeting agents, as seen in the phase III trial of figitumumab (Langer et al. 2014), remains a concern. While many IGF-IR targeting agents are well tolerated on their own, they appear to enhance toxic effects when combined with other chemotherapeutic drugs. IGF-IR is a fairly ubiquitous receptor (Baserga 2005) and its broad expression in normal tissues creates the opportunity for greater toxicity upon inhibition

(Yee 2007).

An expected toxicity of IGF-IR inhibition is hyperglycemia, which is seen to some degree with all IGF-IR targeting agents and considered a class effect (Karp et al. 2009b; Neal and Sequist 2010; Scagliotti and Novello 2012; Langer et al. 2014). This was an issue of debate over the use of TKIs that also inhibit the IR since it was assumed it would be largely avoided with the more specific monoclonal antibodies, however, this proved untrue. Hyperglycemia in patients treated with IGF-IR mAbs may be caused by feedback loops with an increase in IGF-1 and GH as a response to blockade of receptors on hypothalamic-pituitary axis leading to insulin resistance in classically insulin-responsive tissues as seen in acromegaly (Pollak 2008; Goto et al. 2012; Guha 2013). Hyperglycemia is rarely reported to be severe and usually manageable with anti-diabetic drugs such as metformin (Pollak 2012; Langer et al. 2014). However, newer drugs are being designed to attempt to minimize metabolic side effects of IGF-axis disruption.

MedImmune’s IGF-I/II neutralizing antibody, MEDI-573, is currently in phase II trials in

estrogen receptor positive breast cancer and reports no hyperglycemia or hyperinsulinemia (Gao et al. 2011; Guha 2013). Boehringer Ingelheim’s TKI, BI-836845, targets IGF-IR and IR-A as an alternative to target the mitogenic effects of the IR without inhibiting the metabolic IR-B (Guha

2013).

There is not currently a clear biomarker to identify patients who would benefit form IGF-

IR targeted therapy. Some of the difficulty in identifying a marker of IGF-axis stimulation in cancer patients may stem from the complexity of the IGF system with its many components.

While many clinical trials have reported no benefit of IGF-IR targeted agents in unselected

NSCLC patients, several have reported trends toward better response in patients with high baseline serum IGF-I (Goto et al. 2012; Weickhardt et al. 2012; Langer et al. 2014). Serum IGF-I is easy to measure but it does not take into account locally produced IGF-I or IGF-II in the tumor environment. IGFBPs may further complicate the matter as demonstrated by a preclinical study which reported that despite both NSCLC cells and SCLC expressing IGF-IR, the SCLC cells failed to respond to stimulation by IGF-I or IGF-II due to increased presence of IGFBP-2 which bound to the IGF ligands preventing receptor activation (Reeve et al. 1993). Other preclinical data shows that lung cancer cell lines with high IGF-IR expression had a better response to mAb

R1507 where 4 of 5 lung cancer cell lines that were sensitive to IGF-IR targeting mAb R1507 expressed high levels of total IGF-IR, only 1 of 17 resistant cell lines had high IGIF-IR expression (Gong et al. 2009). These R1507-sensitive cell lines also had higher IGF-IR gene copy numbers, but activated IGFIR did not correlate with sensitivity (Gong et al. 2009). The severe toxicity seen in the phase III trial of figitumumab was more common in patients with low baseline serum IGF-I (Langer et al. 2014), which further highlights the importance of patient selection for the success of IGF-IR targeting agents.

Determining effective uses of IGF-IR targeting agents includes understanding their potential in combination with current treatments, particularly other targeted agents. Patients that meet eligibility for other targeted agents may represent a group that will benefit from IGF-axis inhibition. For example, patients with EGFR mutation or high EGFR expression may benefit from IGF-IR targeted therapy. Co-expression of IGF-IR and EGFR in NSCLC was associated with shorter disease-free survival (Ludovini et al. 2009, 2013; Gately et al. 2014). Preclinical studies provide further support for co-targeting the IGF-IR and EGFR. In EGFR mutant lung cancer cell lines, combining erlotinib and R1507 led to enhanced induction of apoptosis, mediated by Akt (Gong et al. 2009). Increased EGFR synthesis and signaling has been identified as a mechanism of resistance to the anti-IGF-IR antibody cixutumumab, and co-targeting the receptors produced synergistic anti-tumor effects both in vitro and in vivo (Shin et al. 2011).

Inhibitors are being developed that co-target the IGF-IR and EGFR/ERBB2 by Abbott

Laboratories (Hubbard et al. 2009). Additionally, in a study combining R1507 with erlotinib there was no significant difference in survival, but subgroup analysis revealed that patients with activating KRAS mutations who received treatment with R1507 had improved progression free survival at 12 weeks than those with erlotinib alone, 36% vs 0%, respectively (Ramalingam et al.

2011). KRAS mutant bronchial epithelial cells with a transformed phenotype overexpress IGF-I and IGF-II (Kim et al. 2009), which further supports KRAS as a possible biomarker for IGF-axis activation. Recently, Lovly et al. (2014) reported a therapeutic benefit of combined ALK and

IGF-IR inhibitors in a patient with ALK rearrangement, and noted an increase in IGF-IR and

IRS-1 in lung tumors of patients receiving the ALK inhibitor crizotinib.

Another avenue that may influence the efficacy of IGF-IR targeting agents in combination with other chemotherapeutics is the dosing schedule. This is more easily adjusted

with TKIs because of their relatively short half-lives compared to monoclonal antibodies, which can extend longer than the usual chemotherapy cycle (Yee 2007). As a stimulator of proliferation, pre-treatment with IGF-IR targeting agents could make many traditional chemotherapeutics less effective. On the other hand, disruption of pro-survival pathways mediated by IGF-IR signaling could enhance chemotherapeutics. Although this has not been addressed in clinical trials, preclinical evidence of sequence specific differences exists. Using the

IGF-IR mAb AVE1642 after paclitaxel was more effective than simultaneous treatment, and using AVE1642 before paclitaxel showed no benefit compared to treatment with paclitaxel alone on NSCLC cells (Spiliotaki et al. 2011). Further research is needed to investigate whether similar results would be found with different IGF-IR targeting agents and traditional chemotherapeutics.

Akt: An Important Mediator of IGF-IR Signaling and Beyond

Akt as a therapeutic target Targeting downstream pathways offers an alternative therapeutic strategy to targeting receptors, such as the IGF-IR, that are contributing to the progression of cancers. Akt, also known as protein kinase B (PKB), is an important mediator of IGF-IR signaling, as well as other tyrosine kinase receptors, G-protein coupled receptors, and cytokine receptors (Cheung and

Testa 2013; Cohen 2013); therefore, a benefit to the clinical development of Akt targeting agents includes possible efficacy in a broader range tumors. Akt exists as three isoforms (Akt1-3 or

PKBα/β/γ), which are encoded by separate genes located on different chromosomes (Cohen

2013; Romano 2013). However, the proteins have a high sequence similarity and share the same domain structure involving an N-terminal PH domain followed by a linker region, a kinase domain, and a short C-terminal hydrophobic motif (Hanada et al. 2004; Romano 2013). They

share similar phosphorylation sites for activation that vary slightly in location, a threonine residue in the kinase domain (Thr308/309/305) and a serine residue in the hydrophobic motif

(Ser473/474/472) as depicted in Figure 2 (Hanada et al. 2004; Romano 2013). Akt regulates a diverse set of cellular functions relevant in the growth and progression of cancer cells, discussed previously in this document as part of “IGF-IR signal transduction and function”. There are Akt targeting agents in various stages of clinical development, which are reviewed by Hers et al.

(2011). An allosteric inhibitor of Akt developed by Merck, MK2206 (Hirai et al. 2010), is currently in a phase II clinical trial for use with erlotinib in lung cancer patients

(Clinicaltrials.gov 2015). As such, there is strong evidence demonstrating that signaling through the Akt pathway, specifically, mediates lung cancer development and progression.

Figure 2: Akt isoform structure. Akt isoforms all share a pleckstrin homology (PH) domain, a kinase domain, and a hydrophobic motif (HM). The phosphorylation sites are similar, but differ slightly by amino acid residue location.

Activating mutations in Akt are rare, but when they do occur they may be oncogenic, since they have been associated with clinicopathologic features. Mutations in AKT1 are found in squamous cell carcinoma (Do et al. 2008; Malanga et al. 2008; Rekhtman et al. 2012) and adenosquamous lung cancer (Wang et al. 2014b), and were associated with activated Akt

(Malanga et al. 2008), poor survival (Giovannetti et al. 2010; Pu et al. 2011), resistance to cisplatin (Xu et al. 2012a), and brain metastasis (Li et al. 2013b). AKT2 mutations are found in adenocarcinomas, with concurrent EGFR and/or PIK3CA mutations (Soung et al. 2006; Sasaki et al. 2008; Kim et al. 2014a). The oncogenic potential of Akt is not limited to mutations since Akt activation can be stimulated by mutations or overexpression in upstream receptors and their ligands, and other upstream regulators such as PIK3CA mutations (PI3K) or loss of negative regulators such as PTEN or PHLPP (Cheung and Testa 2013).

In SCLC, increased activation of Akt was found to be frequent but not prognostic

(Blackhall et al. 2003). However, increased activation of Akt was observed in NSCLC tumors compared to surrounding tissue, and was associated with decreased survival (David et al. 2004;

Tsurutani et al. 2006; Liu et al. 2011). High levels of activated Akt was detected across all subtypes of NSCLC (Lee et al. 2002), and has been specifically associated with higher grade and stage (Scrima et al. 2012) as well as lymph node metastasis (Dobashi et al. 2012). Lack of activated Akt was an independent predictor of improved recurrence free survival in NSCLC (Shi et al. 2011). A meta-analysis of the literature reports that activated Akt within the tumor is predictive of poor prognosis (Qiu et al. 2013). A few studies have found that activation of Akt alone is not prognostic, but when combined with activation of downstream effectors, e1F4E

(Yoshizawa et al. 2010) or 4EBP1 (Trigka et al. 2013), it was associated with poor prognosis in

NSCLC. Similarly, NSCLC patients with high levels of activated Akt and PTEN loss had worse

survival than those with no activated Akt, and this was associated with poor tumor differentiation, late stage disease, and metastasis to lymph nodes and distant sites (Tang et al.

2006). In contrast, the presence of activated Akt was associated with good prognosis in early stage NSCLC, although it was also associated with high grade tumors and active Akt within the nucleus was associated with nodal metastasis and squamous cell histology (Shah et al. 2005).

Another study looking at early stage NSCLC found that activated Akt expression was not associated with survival, but with increased tumor size and squamous cell histology when present within the nucleus (Mayadagli et al. 2014). The combination of high nuclear phospho-

Akt and low cytoplasmic phospho-Akt was predictive of poor survival in early stage NSCLC

(Yip et al. 2014), highlighting the importance of subcellular localization to the contribution of disease progression. The prognostic significance of active Akt may be related to the disease stage, as demonstrated by Lim et al. (2007) who reported that patients with stage IV NSCLC who had high levels of activated Akt had worse survival, but the association was not seen in early stage disease.

Despite inconsistent results relating Akt to survival in early stage NSCLC, there is evidence supporting a role for Akt signaling in the early development of disease. Activated Akt is frequently detected in both early and late stage NSCLC, and is even found in metaplastic/dysplastic pre-lesions at higher levels than in normal/hyperplastic tissue (Balsara et al. 2004; Tichelaar et al. 2004). One study reported that active Akt was more frequently detected in bronchial dysplasia than in early stage NSCLC (88% vs 33%, respectively) (Tsao et al. 2003), while another study detected active Akt frequently in early stage NSCLC (73%) (Mukohara et al.

2004). Furthermore, there is evidence indicating that Akt is involved in lung tumorigenesis and disease progression stimulated by carcinogens. Increased activation of Akt was also seen in

metaplastic and dysplastic cells in women exposed to air pollution through cooking with biomass fuel (Roychoudhury et al. 2012). Additionally, chronic exposure to the components of cigarette smoke can cause EMT in NSCLC cells (Zhang et al. 2012), normal bronchial epithelial cells

(Zhao et al. 2013), and alveolar cells (Shen et al. 2014), which was linked to signaling via the

Rac1/Akt pathway (Shen et al. 2014). Exposure to the tobacco carcinogen NNK, specifically, leads to an increase in Akt activation in lung cancer cells (Tsurutani et al. 2005) as well as human airway epithelial cells which developed a partially transformed phenotype (West et al.

2003). Furthermore, high levels of activated Akt are found in lung tumors of smokers and NNK injected mice (West et al. 2003). A role for Akt in transformation is supported by the ability of constitutively active Akt1 to transform fibroblasts in vitro (Sun et al. 2001).

Akt is constitutively active in many NSCLC cell lines (Brognard et al. 2001). Akt inhibition in lung cancer cells is sufficient to inhibit proliferation (Chen et al. 2011), migration and invasion (Balsara et al. 2004; Lee et al. 2010; Park et al. 2010), and induce cell death (Chen et al. 2004; Cho et al. 2009). Inhibition of Akt enhanced the effects of irradiation and several chemotherapeutic agents in lung cancer cells including doxorubicin, camptothecin, gemcitabine,

5-florouracin, lapatinib, docetaxel, etoposide, paclitaxel, trastuzumab, cisplatin and carboplatin

(Brognard et al. 2001; Krystal et al. 2002; Lee et al. 2008b, 2011; Hirai et al. 2010; Lin et al.

2011). Akt mediates chemoresistance through activation of survival pathways (Hövelmann et al.

2004; West et al. 2004) and chemosensitization can be conferred by inhibition of a single isoform, Akt1 (Liu et al. 2007; Lee et al. 2008b, 2011; Sun et al. 2012a; Zheng et al. 2013).

Furthermore, chemoresistance via Akt signaling has been linked to cancer stem cell (CSC) populations (Sabisz and Skladanowski 2009). Signaling through the Akt pathway also provides a

mechanism for acquired resistance to EGFR therapies in NSCLC cells (Hirai et al. 2010; Iida et al. 2013).

Therefore, evidence from cell lines and human tumors suggests that targeting Akt would be an effective therapeutic strategy in lung cancer. However, Akt targeting agents may face challenges in clinical application. Similarly to IGF-IR inhibition, Akt inhibition can lead to insulin resistance and hyperglycemia (Hirai et al. 2010). Additionally, inhibition of the Akt pathway can alleviate negative feedback loops and cause activation of Akt independent pathways

(O’Reilly et al. 2006; Carracedo and Pandolfi 2008; Chandarlapaty et al. 2011). Therefore, Akt targeting agents may be more effective in combination with other therapeutic agents. Akt is structurally similar to several other kinases including PKA, PKC, p70S6K and p90RSK

(Romano 2013). This means the specificity of Akt targeting agents raises serious toxicity concerns, particularly considering the importance of PKA in cardiac muscle contraction (Hers et al. 2011). Extensive preclinical testing of Akt targeting agents will likely prove beneficial.

Akt isoforms and their specific functions While it was previously thought that the three Akt isoforms were largely redundant in activity, evidence has emerged indicating that they have independent functions. The earliest and most clear evidence comes from isoform specific knockout mice. Akt1 is critical for overall growth as demonstrated by Akt1 knockout mice with low birth weight and overall reduced size compared to their wildtype littermates (Cho et al. 2001b). Akt2 is the prominent isoform involved in metabolism since Akt2 knockout mice will develop insulin resistance and a type II diabetes-like phenotype characterized by hyperglyciemia, hyperinsulinemia, glucose intolerance, impaired muscle glucose uptake and age-dependent adipose loss (Cho et al. 2001a; Garofalo et al. 2003). Akt3 knockout mice display impaired brain development indicating that it is only

critical in neuronal development (Tschopp et al. 2005). The tissue distribution of the isoforms reflects their specific functions. In mice, Akt1 and Akt2 are widely expressed but Akt2 is the predominant isoform expressed in classically insulin-responsive tissues, such as liver, adipose, and skeletal muscle, and Akt3 is highly expressed in the brain and testes (Yang et al. 2003; Hay

2011; Hers et al. 2011). However, studies investigating isoform specific roles at the cellular level do not paint such a clear picture. While some studies have clearly demonstrated isoform specific differences, many results are conflicting. Therefore, it is likely that the factors influencing isoform specificity are complex.

Several studies have identified isoform specific effects due to differential activation of substrates. In untransformed myoblasts, Akt1 and Akt2 had opposing effects on proliferation;

Akt2 bound to and stabilized p21 in the nucleus to prevent cell cycle progression, while Akt1 phosphorylated and inhibited p21 to promote cell cycle progression (Héron-Milhavet et al.

2006). Furthermore, binding of p21 by Akt2 prevented phosphorylation by Akt1 (Héron-

Milhavet et al. 2006), which demonstrates a direct antagonistic relationship. Skp2, an E3 ubiquitin ligase that recognises the cell cycle inhibitor p27, has been identified as an Akt1 specific substrate in order to promote cell cycle progression (Gao et al. 2009). In lung cancer derived-disseminated tumor cells, Akt3 knockdown down-regulated both cyclin D1 and cyclin

D3 to decrease proliferation; however, in the same cells Akt1 knockdown down-regulated cyclin

D1 but not cyclin D3 and caused a smaller decrease in proliferation (Grabinski et al. 2011). In lung cancer cells, Akt1 and Akt2 were both important for cell survival although by different mechanisms; Akt1 knockdown was associated with decreased ERK1/2 activity, while Akt2 cleaved Bcl-2 family member MCL-1 to initiate the caspase cascade (Lee et al. 2011). Therefore,

even when isoforms do not differ in function, they may exert their effects through different substrates.

Akt isoform specific differences have been identified in the regulation of cell migration and invasion through a variety of mechanisms. PDGF-stimulated migration was impeded in mouse embryo fibroblasts lacking Akt1; however, it was enhanced in cells lacking Akt2 which had elevated Pak1 and Rac activity (Zhou et al. 2006). This study also demonstrated that Pak1 was only inhibited by Akt2 which indicates an inhibitory effect of Akt2 on Rac/Pak signaling

(Zhou et al. 2006). Another study using fibroblasts demonstrated that Akt1 activation led to translocation of Rac1 to membrane ruffles and stimulated Rac1/Pak signaling and lamellepodia formation to promote migration (Somanath and Byzova 2009). In contrast, the Akt isoforms appear to have the opposite effects on migration in breast cancer cells, with Akt1 having an inhibitory role and Akt2 stimulating migration (Chin and Toker 2010b). These effects were mediated through palladin, which only Akt1 could phosphorylate and inactivate, (Chin and

Toker 2010b) and was upregulated by Akt2 (Chin and Toker 2010a). The ability of Akt isoforms to phosphorylate palladin is due to differential regulation of the isoforms. It corresponds to a residue in the linker region of Akt1 which is phosphorylated by CK2 to increase the activity of

Akt1 (Di Maira et al. 2005, 2009), but this region on Akt2 cannot be phosphorylated by CK2 in vivo due to steric hindrance (Girardi et al. 2014). Another study similarly reported that Akt2 was the isoform driving invasion of breast and ovarian cancer cells, but attributed the difference to subcellular location, with Akt2, but not Akt1, localized at sites adjacent to the ECM during adhesion (Arboleda et al. 2003). Knockdown of Akt1 inhibited migration of prostate cancer cells but knockdown of Akt2 had the opposite effect; this difference was also associated with

localization with Akt1 and Akt2 within the cytoplasm and nucleus, respectively (Cariaga-

Martinez et al. 2013).

Differential recruitment to cellular locations is also involved in the isoform specific metabolic effects attributed to Akt2. In adipocytes, Akt2 is preferentially recruited to plasma membrane upon insulin stimulation to regulate GLUT4 translocation, which was due to a difference within the PH domain of Akt isoforms (Gonzalez and McGraw 2009). Akt1 and Akt2 are selectively dephosphorylated by PHLPP2 and PHLPP1, respectively (Brognard et al. 2007;

Nitsche et al. 2012). This difference in regulation also contributes to isoform specific metabolic effects because increased PHlPP1 is functionally associated with impaired Akt2 phosphorylation in response to insulin in muscle cells from patients with type II diabetes mellitus (Cozzone et al.

2008).

Differential subcellular location may not only represent functional differences between isoforms but context-specific functions of the same isoform. Santi and Lee (2010) investigated the subcellular localization of the Akt isoforms in transformed and untransformed breast and prostate cell lines and found that the isoforms were consistently localized with Akt1 in the cytoplasm, Akt2 the mitochondria, and Akt3 in the nucleus. However, HEK-293 cells had cytoplasmic Akt1, while HEK-293T had nuclear Akt1; therefore, transformation of these cells by

SV40 may cause nuclear translocation of Akt1 (Santi and Lee 2010). Akt1 location has been associated with cancer grade, with cytoplasmic Akt1 found in differentiated and benign thyroid lesions and nuclear Akt1 in anaplastic thyroid cancers (Krześlak et al. 2011). Furthermore, phospho-Akt co-localized with Akt1 in thyroid cancers, not Akt2 or Akt3 (Vasko et al. 2004). As previously discussed, subcellular location of Akt has prognostic significance in lung cancer (Yip et al. 2014). Therefore, an understanding of the relationship between function and subcellular

locations of Akt isoforms, which is easily determined with immunohistochemistry of tumor biopsies, could be clinically relevant.

Within the examples discussed there are discrepancies between the functional roles of the

Akt isoforms. These context-specific differences may vary depending on tissue type, differentiation, transformation status, or other unidentified factors. A tissue specific role for Akt isoforms is supported by a transgenic PTEN+/- mouse model of cancer in which Akt1 deficiency reduces prostate and endometrial tumors (Chen et al. 2006b), while Akt2 deficiency reduces thyroid tumors (Xu et al. 2012b). Furthermore, whether the lack of any Akt isoform can induce apoptosis is cell line specific (Koseoglu et al. 2007). The high variability and complexity of isoform specific functions makes extrapolation of results challenging and will require extensive investigation to provide a complete understanding.

RNA for all three isoforms has been detected in normal lung tissue as well as lung tumors

(Zinda et al. 2001) and several studies have demonstrated unique roles for Akt isoforms in lung cancer. In both mutant KRAS and NNK mediated mouse models of lung cancer, lack of Akt1 inhibited tumorigenesis, an effect that was not seen in the absence of Akt2 or Akt3 (Hollander et al. 2011). Lung cancer cells over-expressing Axl have increased cell migration which is due to

Akt1 stimulated Rac1 activity, and does not require Akt2 (Huang et al. 2013). Similarly, another study reported that Akt1, and not Akt2, was involved in lung cancer cell migration and colony formation, although both isoforms promoted cell survival (Lee et al. 2011). In NSCLC patients, high levels of intra-tumoral Akt2 were associated with increased survival but activated Akt was a negative prognosticator of survival (Al-Saad et al. 2009), indicating Akt2 may have an opposing, protective role. Evidence of a protective role for Akt2 was also seen in a virally induced mouse model of lung tumorigenesis, which was in contrast to the effects of Akt1 and Atk3 (Linnerth-

Petrik et al. 2014). In contrast, lack of Akt2 has been shown to impair the growth of lung cancer cells in vitro (Kim et al. 2011a) and in vivo (Sithanandam et al. 2012) and increased Akt activation in lung cancer cells is associated with low levels of PHLPP1 (Zhiqiang et al. 2012). In disseminated tumor cells derived from lung tumors, Akt2 was the primary isoform involved in migration, however only knock down of Akt1 or Akt3 decreased cell proliferation and survival, not Akt2 (Grabinski et al. 2011). Furthermore, Akt2, and not Akt1 is recruited to the FAK complex in NSCLC cells during TGF-β-induced EMT (Xue et al. 2014). Akt3 appears to have a different role in NSCLC patients where high level of Akt3 within the tumor stroma was indicative of good prognosis (Al-Saad et al. 2009). Our understanding of the Akt isoforms within lung cancer is limited. Many Akt inhibitors are being developed that do not discriminate between isoforms (Hers et al. 2011). Considering evidence that Akt1 and Akt2 can have opposing roles, it is clear more research is needed to ensure safe and effective implementation of Akt targeted agents as a therapeutic strategy.

Rationale

As discussed above, there is a growing body of evidence supporting a role for IGF signaling in the promotion of lung cancer. Preclinical studies demonstrated great promise in targeting the IGF-IR in lung cancer cells. However, the lack of efficacy of IGF-IR targeted agents in clinical trials indicates the complexity of the IGF system within human tumors. Further research is required to gain a more thorough understanding in order to improve therapeutic strategies targeting IGF signaling. In order to study IGF-IR signaling in lung cancer with a clinically relevant model, our lab generated a doxycycline-inducible, tissue specific transgenic mouse model of lung tumorigenesis (Linnerth et al. 2009). Using a surfactant protein C (SPC)

promoter, the IGF-IR is overexpressed in type II alveolar cells, which is sufficient to initiate the development of tumors in the lungs. This is made inducible using two gene inserts where the

SPC promoter drives expression of a reverse tetracycline trans-activator, which can bind to the tetracycline response element plasmid in the presence of doxycycline to drive human IGF-IR expression as illustrated in Figure 3. Mice are fed rodent-chow supplemented with doxycycline

(2g/kg) ad libitum starting at 21 days of age to induce IGF-IR overexpression once the lungs are fully developed. IGF-IR overexpressing cells are present at 2 months after treatment initiation and tumors are visible in all mice following 9 months of treatment with doxycycline. Linnerth et al. (2009) characterized the tumors that form as lung adenomas and adenocarcinomas which typically present as one or more discrete nodules on the surface of the lung. These tumors express high levels of activated Akt but did not have increased ERK activation, indicating that

IGF-IR signaling is primarily through Akt (Linnerth et al. 2009). These mice were further characterized by Siwicky et al. (2010), who found that Akt1 and Akt2 were expressed within these tumors but did not detect Akt3. However, the contribution of each isoform to lung tumorigenesis is not known. These transgenic mice represent a model of genetically induced lung cancer that resembles human tumor formation and is therefore relevant to human tumors in which IGF signaling is promoting growth and initiation. Furthermore, these mice provide a model to investigate how other factors could contribute to or prevent the formation and growth of lung tumors.

Figure 3: Tissue-specific, inducible expression of the IGF-IR in SPC-IGFIR transgenic mice. The tissue specific surfactant protein C (SPC) promoter drives expression of reverse tetracycline transactivator (rtTA) which, in the presence of doxycycline, binds to the tetracycline response element (TRE) to drive transcription of the IGF-IR.

Hypothesis Disruption of IGF signaling by directly targeting the IGF-IR will decrease proliferation and survival of lung cancer cells, and enhance the effects of chemotherapeutics. Furthermore, indirect disruption of IGF-IR signaling through downstream signaling adaptors, Akt1 and Akt2, will disrupt lung tumorigenesis.

Objectives 1) Disrupt IGF-IR signaling using an IGF-IR targeted agent in human lung

adenocarcinoma cell lines and evaluate its efficacy alone and in combination with

platinum chemotherapeutics.

2) Disrupt IGF-IR signaling downstream of the receptor through the elimination of Akt1

or Akt2 from our IGF-IR overexpressing transgenic mouse model of lung

tumorigenesis and determine if the absence of a single Akt isoform is sufficient to

influence tumor development.

Chapter 1: Dual type I insulin-like growth factor receptor/insulin receptor inhibitor BMS-754807 enhances the effects of platinum chemotherapeutics in lung adenocarcinoma cells

Background Lung cancer remains the leading cause of cancer-related deaths worldwide (Jemal et al.

2011), 80% of which are non-small cell lung cancer (NSCLC) (Devesa et al. 2005). It is often not diagnosed until later stages (Hansen 2002; Morgesztern et al. 2010) when it is no longer responsive to the standard treatment which is based on platinum chemotherapeutics and radiation

(Jemal et al. 2008; Carnio et al. 2014; Johnson and Patel 2014). Despite the development of a number of new targeted therapies against epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF), and anaplastic lymphoma kinase (ALK), which have been approved for use and have led to modest improvements in patient outcomes, overall survival rates remain poor (Mahalingam et al. 2009; Forde and Ettinger 2013; Carnio et al. 2014; Horn and Carbone 2014). There is a need to develop new treatment strategies including new drugs and an understanding the most effective way to incorporate them into current treatments. Several type I insulin-like growth factor receptor (IGF-IR) targeting agents have been in clinical development, including monoclonal antibodies and tyrosine kinase inhibitors, which are reviewed by Scagliotti and Novello (2012) and Chen and Sharon (2013). Figitumumab, a monoclonal antibody against IGF-IR, reached a phase III clinical trial in non-adenocarcinoma

NSCLC that was ended due to poor outcomes (Langer et al. 2014). This disappointing outcome leaves researchers asking if a better understanding of the role of IGF-IR in lung cancer would identify certain biomarkers, histological subtypes, and drug combinations or sequences that could improve the effectiveness of targeting the IGF-IR as part of lung cancer treatment.

There is strong evidence identifying a role for IGF-IR in lung cancer. Molecular studies using lung cancer cell lines have shown that disruption of IGF-IR signaling will decrease proliferation, cell survival and migration in several lung cancer cell lines (Samani et al. 2004;

Cosaceanu et al. 2007; Dong et al. 2007). IGF-IR is also involved in tumor initiation since overexpression of the IGF-IR in type II alveolar cells or Clara cells was sufficient to induce spontaneous tumor formation in mice (Linnerth et al. 2009). Several studies have found the IGF-

IR to be frequently over-expressed in NSCLC, ranging from 30-84% of patients (Gong et al.

2009; Ludovini et al. 2009; Cappuzzo et al. 2010; Dziadziuszko et al. 2010; Fidler et al. 2012;

Kikuchi et al. 2012; Nakagawa et al. 2012; Tsuta et al. 2013; Vilmar et al. 2014; Zhang et al.

2014c; Gately et al. 2014; Reinmuth et al. 2014). While many of these studies did not find high

IGF-IR expression to be associated with overall survival, it has been associated with progressive disease (Cappuzzo et al. 2010; Dziadziuszko et al. 2010; Hurbin et al. 2011; Peled et al. 2013), larger tumor size (Ludovini et al. 2009; Tsuta et al. 2013), recurrence (Nakagawa et al. 2012), and brain metastasis (Wu et al. 2013). Additionally, high IGF-1 and low IGFBP-3 have been associated with poor outcome (Chang et al. 2002a, 2002b; Unsal et al. 2005; Vollebergh et al.

2010; Masago et al. 2011; Fu et al. 2013; Kim et al. 2014d). This suggests increased activation of the IGF axis is contributing to lung cancer progression; therefore, targeting the receptor would be an effective blockade. Furthermore, activation of the IGF-axis via the IGF-IR has been identified as a mechanism of resistance to EGFR targeted therapies (Morgillo et al. 2007; Guix et al. 2008;

Jameson et al. 2011; Chen et al. 2013; Cortot et al. 2013; Suda et al. 2014) and to cisplatin (Sun et al. 2012b; Cortés-Sempere et al. 2013; Ferté et al. 2013).

The IGF-IR is a tyrosine kinase receptor which exists as a dimer (α2β2): two monomers consisting of a ligand binding extracellular alpha subunit and intracellular beta subunit, which

contains the kinase domain, joined by disulfide bonds (Ward et al. 2001). The IGF-IR signals through two main pathways, the PI3K/Akt pathway and the MAPK/ERK pathway (Frasca et al.

2008; Siddle 2011). The IGF-IR will form heterodimers with the closely related insulin receptor

(IR), which shares 60% structural similarity (Pandini et al. 1999, 2002; Benyoucef et al. 2007).

The IGF-IR responds to ligand stimulation by IGF-I, IGF-II, and insulin. While the IR is primarily activated by insulin, it will also bind to IGF-II and can bind both IGF-I and IGF-II when part of a heterodimer with IGF-IR (Denley et al. 2004, 2006; Pandini et al. 2004; Boucher et al. 2010; Kanou et al. 2011; Morcavallo et al. 2011). Since both IGF-I and IGF-II are reported to be important activators of IGF-IR signaling in lung cancer (Pavelić et al. 2002; Moorehead et al. 2003; Izycki et al. 2004; Kim et al. 2011b), there is a good rationale for targeting the IR as well as the IGF-IR in order to achieve complete inhibition of the IGF axis. Additionally, in breast cancer cells and Ewing sarcoma the IR was identified as a mechanism of resistance to IGF-IR specific monoclonal antibodies (Ulanet et al. 2010; Garofalo et al. 2011). There may be a therapeutic benefit to dual inhibition of both receptors over IGF-IR specific monoclonal antibodies. BMS-754807 is an ATP competitive inhibitor that targets both the IGF-IR and the IR

(Carboni et al. 2009; Wittman et al. 2009). This research evaluates the effects of inhibiting the

IGF axis in adenocarcinoma NSCLC cell lines alone and in combination with platinum chemotherapeutics. In order to inhibit the IGF-IR axis the dual inhibitor BMS-754807 will be used.

Materials and Methods

Cell lines and Reagents Lung cancer cell lines LA-4 (murine), A549 and NCI-H358 (human), and normal bronchial epithelial cell line, HBE-135, were purchased from American Type Culture Collection

(ATCC, Manassas, VA). Murine normal lung cell line RLE was kindly donated by Dr. Wooton

(University of Guelph). LA-4 cells were cultured in Ham’s F12K medium supplemented with

15% FBS and 1% antibiotic/antimycotic (v/v) (Life Technologies, Burlington, ON). A549 and

NCI-H358 cells were cultured in RPMI 1640 medium supplemented with 10% FBS and 1% antibiotic/antimycotic (v/v) (Life Technologies, Burlington, ON). HBE-135 cells were cultured in KSFM medium supplemented with 0.05ug/ml bovine pituitary extract, 5ng/ml recombinant human epidermal growth factor, and 1% antibiotic/antimycotic (v/v) (Life Technologies,

Burlington, ON). RLE cells were cultured in a 1:1 mixture of KSFM (as described above) and

NIH-3T3 conditioned DMEM. NIH-3T3 conditioned media was made by growing NIH-3T3 cells (ATCC, Manassas, VA) to confluencey in DMEM media supplemented with 10% FBS

(v/v) (Life Technologies, Burlington, ON), then changing media to DMEM media supplemeted with 2% FBS (v/v) (Life Technologies, Burlington, ON). After 3 days, conditioned media was collected and filter sterilized. All cells were maintained in incubators at 37°C and 5% carbon dioxide.

BMS-754807 was purchased from Sellek Chemicals (Houston, TX). Cisplatin and carboplatin were both purchased from Sigma-Aldrich (Oakvill, ON). BMS-754807 was dissolved in DMSO (Sigma-Aldrich, Oakville, ON) and stored at -20°C. Cisplatin was dissolved in saline. Carboplatin was dissolved in water. Cisplatin and carboplatin solutions were made fresh for each experiment, and stored at 4°C for the duration of the experiment.

Western Blotting Following treatment with BMS-745807, adherent cells were lysed, protein was isolated and western blots performed as previously described (Jones et al. 2007). Primary antibodies used were against IGF-IRβ(1:1000), phospho-IGF-IR/IR(1:500), phospho-Akt(1:1000), phospho-

ERK1/2(1:500), and β-actin(1:5000) (Cell Signaling Technology, Danvers, MA) overnight at

4°C. An anti-Rabbit secondary antibody(1:2000) (Cell Signaling Technology, Danvers, MA) for one hour at room temperature. Membranes were visualized using chemiluminescence substrate

(PerkinElmer, Inc., Waltham, MA) and FluoroChem 8800 gel documentation system (Alpha

Innotech – ProteinSimple, Toronto, ON).

Cell Viability WST-1 Assay Cells were plated in a 96 well plate at a density of 2500cells/well, and allowed to adhere for 4 hours. BMS-754807, cisplatin, or carboplatin were added either alone or in combination or a solvent only control was added (1% of total volume). Drugs and media were refreshed every 24 hours. At 72 hours, cell viability reagent WST-1 (Roche, Mississauga, ON) was added and absorbance was read using Universal Microplate Reader EL800 (Bio-Tek Insturments, Inc.,

Winooski, VT) after a 3 hour incubation.

Analysis of Single and Combination Drug Response Cell viability determined with WST-1 (Roche, Mississauga, ON) assay after single agent and combination treatments with BMS-754807, cisplatin, and carboplatin were used to dermine the IC50 and Combination Indices (CI) as calculated using Calcusyn software (Biosoft,

Cambridge, UK). For combination effects: a synergistic effect is considered for a CI<1; an additive effect is considered for a CI=1; and an inhibitory effect is considered for a CI>1.

Immunofluorescence Cells were plated on coverslips in 6-well dishes at a density of 0.5 -1x105cells/well, allowed to adhere overnight, then treated with two doses BMS-754807 (0.25uM and 0.5uM), or solvent only (to 0.1% v/v of total volume). Cells were fixed at 24 hours post-treatment in 10% buffered formalin (Fisher Scientific, Ottawa, ON) for 1 hour. Cells were washed in PBS, permeabilized in 0.5% Triton-X in PBS, and blocked in 5% BSA in PBS. Then the cells were incuabated in primary antibodies against proliferation marker Ki67 (1:400) (Abcam, Toronto,

ON) or apoptosis marker cleaved caspase 3 (1:200) (Millipore, Etobicoke, ON) overnight at 4°C.

Cells were incubated with fluorescent anti-Rabbit secondary antibody (1:200) (Alexaflour – Life

Technologies, Burlington, ON) for 1 hour at room temperature. Dapi nuclear stain was used as a counterstain, and coverslips were mounted using Prolong-gold (Life Technologies, Burlington,

ON). Images were captured using Olympus BX61 microscope and Metamorph software

(Molecular Devices, Sunnyvale, CA). Proliferation and apoptosis rates within cells were quantified by manual counts of the number of cells staining positive for Ki67 or cleaved caspase

3, respectively, and expressed as percentages relative to the number of cells present as revealed by DAPI staining.

Wound Healing Assay Cells were plated on 12 well plate at a density of 2.0x105 cells to reach 90-95% confluency next day. A wound was made through the centre of the well and cells were treated with two doses BMS-754807 (0.25uM or 0.5uM), or solvent only (to 0.1% v/v of total volume).

Drugs and media were refreshed and wound closure was measured every 24 hours. Images were captured using Olympus IX71 microscope and Q-Capture software (Q-Imaging, Surrey, BC).

Statistics All experiments were repeated in triplicate or quadruplicate. Statistical analysis was performed using Graphpad Prism 5 (Graphpad Software, Inc., La Jolla, CA). Means were compared using a one-way, repeated-measure ANOVA and post-hoc Dunnett’s test. Error is represented by Standard Error of the Mean (SEM). Statistical significance is noted as p<0.05.

Results

IGF-IR is overexpressed in lung cancer cells Basal levels of IGF-IR protein expression in normal growth meduim were assessed in two human NSCLC cell lines (A549 and NCI-H358), one murine lung cancer cell line (LA-4), one human normal bronchial epithelium cell line (HBE-135), and one normal murine lung cell line (RLE). The IGF-IR was more highly expressed in all three lung cancer cell lines compared to the normal lung epitheal cells (Fig 4a). Despite the high expression of IGF-IR, there was not an associated increase in activation of downstream signaling adaptors Akt and ERK1/2, with the exception of increased pAKT in A549 cells (Fig 4a)

IGF-IR activity is inhibited by BMS-754807 Lung cancer cells (A549, NCI-H358, and LA-4) were treated with dual IGF-IR/IR inhibitor BMS-754807 under regular growth conditions. When BMS-754807 was used at doses of 0.25uM and 0.5uM, the levels of activated IGF-IR/IR (pIGF-IRβ (Y1135)) were reduced within 4 hours, indicating BMS-754807 inhibited IGF-IR activation (Fig 4b). Furthermore, treatment with BMS-754807 caused a concurrent decrease in activated Akt in all three lung cancer cell lines (Fig 4b). However, there did not appear to be a change in activaion of the

MAPK/ERK pathway in any of the cell lines (Fig 4b). These changes were sustained over 24 hours (Fig 4c).

Treatment with BMS-754807 or platinum chemotherapeutics reduces cell survival Dose-response curves were generated to determine the sensitivity of each lung cancer cell line to BMS-754807, cisplatin, and carboplatin when they were used as single agent therapies.

Inhibition of IGF-IR/IR with BMS-754807 was sufficient to reduce cell viability in all lung cancer cells (Fig 5a) and this occurred in a dose dependant manner. All cell lines were also sensitve to growth inhibition by cisplatin and carboplatin (Fig 5b,c). The IC50 values were determined using Calcusyn software (Table 4). LA-4 cells were the most sensitive to all agents with IC50 values of 0.246µM (BMS-754807), 1.843µM (cisplatin), and 17.288µM (carboplatin).

NCI-H358 cells were more sensitve to BMS-754807 than A549 cells, with IC50 values of

0.761µM and 1.084uM, respectively. A549 cells were more sensitive to platinum chemotherapeutics than NCI-H358 cells, with IC50 values of 3.327µM and 4.834µM for cisplatin and 65.294µM and 84.058µM for carboplatin, respectively. All cell lines dsplayed higher sensitvity to cisplatin than to carboplatin, since all had lower IC50 values for cisplatin than carboplatin.

BMS-754807 synergizes with platinum based chemotherapeutics To determine if the addition of BMS-754807 could enhance the anticancer effects of platinum based chemotherapeutics at low doses, BMS-754807 was combined simultaneously with either cisplatin or carboplatin and the effects on cell viability were compared to those of each single agent. Low doses of BMS-754807 (0.01µM, 0.1µM, 0.25µM) were combined with low doses of cisplatin (0.5µM, 1µM, 2.5µM) or carboplatin (10µM, 25µM, 50µM), which based on the single agent doses were responsible for no more than approximately 25-30% decrease in cell viability (Fig 6). At all doses of cisplatin and carboplatin used, the inhibition of the IGF-

IR/IR enhanced the effects of cisplatin and carboplatin, leading to greater cell death. The combination indices all fall below 1, indicating synergy in the combinations used (Fig 6).

BMS-754807 inhibits proliferation and induces apoptosis Proliferation and apoptosis rates were determined in two NSCLC cell lines (A549 and

NCI-H358) folowing treatment with BMS-754807 by immunoflourescence detecting Ki67 or cleaved caspase 3, respectively. BMS-754807 effectively inhibitied proliferation in A549 cells at

0.25µM and 0.5µM doses (Fig 7b), a similar trend was seen in NCI-H358 cells and this was approacing significance (p=0.0890) (Fig 7c). Treatment with BMS-754807 significantly increased apoptosis in A549 and NCI H358 cells at 0.5µM, and a non-significant increase was seen at 0.25µM (Fig 8b,c).

BMS-754807 impairs wound closure To determine if inhibition of the IGF-IR/IR influences the motilitity of lung cancer cells, scratch wound assays were performed. There was a minor, but significant, impairment of wound closure in NCI-H358 and A549 cells treated with 0.25µM BMS-754807 at 48 hours (Fig 9b,c). A similar trend was seen with 0.5µM BMS-754807 but the differences were not significant.

Figure 4: IGF-IR is overexpressed in lung cancer cells and is inhibited by treatment with BMS-754807. Expression of the IGF-IR and basal levels of activated Akt and ERK1/2 were evaluated by western blot in untreated murine (LA-4) and human (A549, NCI-H358) lung cancer cell lines as well as a normal murine lung cell line (RLE) and a human bronchial epithelial cell line (NBE 135) (a). Lung cancer cells were treated with dual IGF-IR/IR inhibitor BMS-754807 for 4h (b) or 24h (c) and levels of activated IGF-IR, Akt and ERK1/2 were evaluated by western blot.

Figure 5: Treatment with BMS-754807 or platinum chemotherapeutics reduces cell survival in a dose dependent manner. Lung cancer cells were treated with various doses of BMS-754807 (a), cisplatin (b), and carboplatin (c). Media and drugs were refreshed every 24 hours for 72 hours, then cell viability was measured with a WST-1 assay. All values are relative to control and are mean(±SEM) (n=3). IC50 values were calculated using Calcusyn software (d).

Table 4: Doses (µM) of BMS-754805, Cisplatin, and Carboplatin that elicit 50% growth inhibition (IC50) of lung cancer cell lines IC50 values determined from cell viability measured with WST-1 assays and calculated using Calcusyn software. Cell line BMS-754807 Cisplatin Carboplatin A549 1.084 3.327 65.294 NCI-H358 0.761 4.834 84.058 LA-4 0.246 1.843 17.288

Figure 6: BMS-754807 synergizes with platinum based chemotherapeutics to enhance killing of lung cancer cells. Lung cancer cells were treated with a combination of dual IGF-IR/IR inhibitor BMS754807 and either cisplatin (a,b) or carboplatin (c,d). Media and drugs were refreshed every 24 hours for 72 hours, then cell viability was measured using a WST-1 assay. Data shown as mean(±SEM) (n=4) Combination indices were calcualted usin Calcusyn software (e,f).

Figure 7: BMS-754807 reduces proliferation in lung cancer cells. Cells were treated with dual IGF-IR/IR inhibitor BMS-754807 for 24 hours, then fixed and stained for proliferation maker, Ki67. Representative images are from A549 cells (a). Quantitative proliferation rates are relative to control and represent mean(±SEM) (n=4) (b,c). *p<0.05

Figure 8: BMS-754807 induces apoptosis in lung cancer cells. Cells were treated with dual IGF-IR/IR inhibitor BMS-754807 for 24 hours, then fixed and stained for apoptotic marker, cleaved caspase 3. Representative images are from A549 cells (a). Quantitative apoptosis rates are relative to control and represent mean(±SEM) (n=4) (b,c). *p<0.05

Figure 9: BMS-754807 reduces wound closure of lung cancer cells. Scratch wounds were made on near confluent plates of lung cancer cells. Cells were treated with dual IGF-IR/IR inhibitor BMS-754807 every 24 hours, and wound closure was measured over 72 hours. Representative images are from NCI-H358 cells at 48 hours (a). The relative scratch wound closure at 48 hours was measured as a percent of the scratch wound area at 0 hours, data represents mean(±SEM) (n=4) (c,d). *p<0.05

Discussion The IGF-IR is over expressed in all lung cancer cell lines compared to normal lung epithelial cell lines. However, this does not appear to result in an increased activation of downstream Akt or ERK, with the exception of increased Akt activation in the A549 cell line.

This could be due to activation of these pathways by different tyrosine kinase receptors that share these pathways in the normal cells in order to drive growth in culture. Treatment with

BMS-754807 effectively inhibits the IGF-IR (and/or IR because the antibody cross reacts) and decreases cell survival in a dose-dependent manner. Its effects are mediated via the Akt pathway, since treatment with BMS-754807 decreased activation of Akt and not ERK1/2. When Wittman et al. (2009) first reported the development of BMS-754807 they repported it to be more effective at MAPK inhibition than Akt with IC50 of 13nM and 22nM, respectively. However, subsequent reports in cell lines suggest the BMS-754807 is a more potent inhibitor of Akt that

ERK phosporylation (Carboni et al. 2009). Furthermore, BMS-754807 was also reported to preferentialy inhibit the Akt pathway in breast cancer (Hou et al. 2011) and prostate cancer cells

(Dayyani et al. 2012). An activating KRAS mutation present in the A549 and NCI-H358 cells may also contribute to the lack of inhibtion of the MAPK pathway, however this effect was also found in the LA-4 cells, which have not been reported to posses mutant KRAS. These findings are supported by a previous study that found KRAS mutant NSCLC cell lines to be sensitive to

IGF-IR inhibition through selective inhibition of the PI3K/Akt pathway, an effect that was not seen in KRAS wild-type NSCLC cells (Molina-Arcas et al. 2013). Effective inhibtion of Akt by

BMS-754807 makes it a good canditate for effective combination therapy with EGFR TKIs because the PI3K/Akt pathway as been reported to promote gefitinib resistance in KRAS mutant

NSCLC (Jeannot et al. 2014). Additionally, increased activation of Akt was associated with IGF-

IR signaling in cisplatin resitant NSCLC (Cortés-Sempere et al. 2013).

All cell lines, especially A549 and NCI-H358 cells were more sensitive to cisplatin than carboplatin. Mahalingham et al. (2009) reported that cisplatin was more effective than carboplatin in the treatment of NSCLC. The effects of cisplatin and carboplatin in the combination analysis were diminished compared to their activity as a single agent. This is likely due to the effects of DMSO, the solvent for BMS-754807, which has poor solubility in water or saline. Other studies have similarly reported an inhibitory effect of DMSO on the activity of platinum based agents (Fischer et al. 2008; Hall et al. 2014).

The effects of cisplatin or carboplatin were enhanced by the simultaneous addition of

BMS-754807 and the interaction is synergistic. Similarly, targting the IGF-IR using mAb NVP-

ADW742 sensitizes SCLC cell lines to the effects of etoposide and carboplatin (Warshamana-

Greene et al. 2005). BMS-754807 alone significantly reduced proliferation of the lung cancer cells, and this did not prevent the cell cycle dependent effects of cisplatin and carboplatin.

Although BMS-754807 only induced apoptosis at a high dose, it may have sensitized the cells to the effects of the platinum agents by blocking anti-apoptotic and survival pathways activated through IGF-IR and Akt signaling. In this way, an Akt/mTOR dependent increase in survivin was identified as the mechanism with which IGF-IR signalling confered resistance to the RAS inhibitor lonafarnib (Oh et al. 2008) and the EGFR inhibitor gefitinib (Morgillo et al. 2007).

IGF-IR knockdown has also sensitzed lung cancer cells to apoptosis induction by ethanol exposure associated with a decrease in Bcl-2 and increase in caspase 3 (Dong et al. 2007).

Alternately, the IGF-IR targeting antibody R1507 synergized with cisplatin and radiation via reduced nucleotide excision repair, which was further assocated with a reduction in Akt signaling

(Ferté et al. 2013).

BMS-754807 reduced cell motility in a wound healing assay, however, the effects were modest. Since wound closure was measured over a period of 48 hours, the effects of BMS-

754807 on proliferation could contribute to the delay in wound closure. There are several studies demonstrating an important role for IGF-IR in the motility and invasion of lung cancer cells

(Long et al. 1998; Ma et al. 2007; Qian et al. 2007; Liu et al. 2013). BMS-754807 preferentially inhibited the Akt pathway, which appears to have a small effect on motility in these cells, however the unaffected MAPK/ERK pathway may be sustaining cell motility. Therefore, a greater effect on cell motiltiy may be achieved with combined inhibition of the Akt and

MAPK/ERK pathways.

This research demonstrates there may be a therapeutic benefit from combining BMS-

754807, or other IGF-IR targeted therapies, with platinum based cytotoxic agents simultaneosly, particularly in patients with KRAS mutations and adenocarcinoma. However, in vivo studies are needed to confirm this advantage and to further investigate the role of IGF-IR inhibition on the metastatic potential of lung cancer cells.

Chapter 2: Unique roles for Akt Isoforms in IGF-IR-mediated lung Tumorigenesis

Background Lung cancer is the leading cause of cancer-related mortalities worldwide, (Jemal et al.

2011; Lozano et al. 2012). Lung cancer is often not detected until advanced stages of the disease

(Morgesztern et al. 2010) where the survival rate remains below 1% (Carnio et al. 2014), despite advances in treatment and the incorporation of new drugs and targeted therapies. Therefore, the development of new therapeutics for the treatment of lung cancer is critical for patients facing this illness. Akt, also known as protein kinase B (PKB), regulates a diverse set of cellular functions relevant in the growth and progression of lung cancer cells, including proliferation

(Chen et al. 2011), survival (Chen et al. 2004; Cho et al. 2009), migration and invasion (Balsara et al. 2004; Lee et al. 2010; Park et al. 2010). Furthermore, Akt is an important mediator of the

PI3K/Akt/mTOR pathway, which is a common signaling pathway used by several growth factors and cytokines that can be involved in promoting cancer growth and progression (Cheung and

Testa 2013; Cohen 2013). This makes targeting the PI3K/Akt/mTOR pathway an appealing therapeutic strategy in order to combat the clinical challenges of tumor heterogeneity and acquired resistance (West 2013). Indeed, Akt targeted drugs are currently in development to treat several types of cancer, including lung cancer (Pal and Mandal 2012; Fumarola et al. 2014) .

Increased activation of Akt is frequently observed in SCLC (Blackhall et al. 2003) and across all subtypes of NSCLC (Lee et al. 2002; David et al. 2004; Tsurutani et al. 2006; Liu et al.

2011). High levels of activated Akt in NSCLC is associated with decreased survival (David et al.

2004; Tsurutani et al. 2006; Liu et al. 2011; Shi et al. 2011; Qiu et al. 2013) and has been specifically associated with higher grade and stage (Shah et al. 2005; Scrima et al. 2012) as well

as lymph node metastasis (Dobashi et al. 2012). The prognostic significance of active Akt may be related to the disease stage, as demonstrated by Lim et al. (2007) who reported that patients with stage IV NSCLC who had high levels of activated Akt had worse survival, but the association was not seen in early stage disease. Other studies have similarly have reported that activated Akt is not prognostic (Mayadagli et al. 2014) or associated with favorable prognosis

(Shah et al. 2005) in early stage NSCLC; however, when activated Akt was localized to the nucleus it was associated with nodal metastasis (Shah et al. 2005) and increased tumor size

(Mayadagli et al. 2014). Despite this, activated Akt is frequently detected in early stage NSCLC

(Mukohara et al. 2004) and metaplastic/dysplastic pre-lesions (Tsao et al. 2003; Balsara et al.

2004; Tichelaar et al. 2004; Roychoudhury et al. 2012) and may be associated with tumorigenesis from exposure to air pollution (Roychoudhury et al. 2012) and tobacco carcinogens (West et al. 2003; Zhao et al. 2013; Shen et al. 2014).

Akt exists as three isoforms (Akt1-3 or PKBα/β/γ), which are encoded by separate genes located on different chromosomes (Cohen 2013; Romano 2013). However, the proteins have a high sequence similarity and share similar structure and activation sites involving an N-terminal

PH domain followed by a linker region, a kinase domain, and a short C-terminal hydrophobic motif (Hanada et al. 2004; Romano 2013). While it was previously thought that the three Akt isoforms were redundant in activity, evidence has emerged supporting isoform specific roles.

Isoform specific knockout mice demonstrate that Akt1 is primarily involved in somatic growth

(Cho et al. 2001b), Akt2 is the prominent isoform involved in glucose metabolism (Cho et al.

2001a; Garofalo et al. 2003), and Akt3 is only critical in neuronal development (Tschopp et al.

2005). Unique roles for Akt isoforms in tumorigenesis have been observed in mouse models of breast (Maroulakou et al. 2007) and lung (Hollander et al. 2011; Linnerth-Petrik et al. 2014)

cancers. There are several studies indicating isoform differences in lung cancer cell migration

(Lee et al. 2011; Huang et al. 2013), proliferation (Kim et al. 2011a), and epithelial- mesenchymal transition (Xue et al. 2014). Furthermore, high levels of intra-tumoral Akt2 in

NSCLC patients was associated with increased survival but activated Akt was a negative prognosticator of survival (Al-Saad et al. 2009), indicating Akt2 may have an opposing, protective role. Despite growing evidence of differing roles, there are inconsistencies in the literature and the roles of Akt isoforms within lung cancer remain poorly understood.

Our lab has generated a doxycycline-inducible, tissue-specific transgenic mouse model of lung cancer (Linnerth et al. 2009). Using a surfactant protein C (SPC) promoter, the type I insulin-like growth factor receptor (IGF-IR) is overexpressed in type II alveolar cells which initiates the development of tumors in the lungs. These lung adenomas and adenocarcinomas typically present as one or more discrete nodules on the surface of the lung and express high levels of activated Akt (Linnerth et al. 2009). Increased ERK activation was not observed in these tumors, indicating that IGF-IR signaling is primarily through Akt (Linnerth et al. 2009). To investigate the role of specific Akt isoforms in lung tumorigenesis, these SPC-IGFIR transgenic mice were crossed with Akt1 null or Akt2 null mice to generate SPC-IGFIR-Akt1-/- and SPC-

IGFIR-Akt2-/- mice. Using this transgenic mouse model we were able to evaluate whether the lack of a single Akt isoform was sufficient to impair IGF-IR-mediated lung tumorigenesis and identify and isoform specific roles within this process.

Materials and Methods

Mice SPC-IGFIR transgenic FVB mice were generated and characterized as previously described by Linnerth et al. (2009). In these mice, expression of the reverse tetracycline trans-

activator is driven by the SPC promoter and IGF-IR expression is driven by a tetracycline response element. Doxycycline treatment in these mice induces IGF-IR overexpression in type II alveolar cells. Akt1 and Akt2 knockout FVB mice were generated as previously described by

Watson and Moorehead (2013). SPC-IGFIR mice were mated with Akt1-/- or Akt2-/- mice producing SPC-IGFIR-Akt1-/- and SPC-IGFIR-Akt2-/- mice. The genotype of all mice was determined by PCR for each genetic alteration from a tissue sample collected between 7-10 days of age. Primers for genotyping are listed in Table 5. Mice were fed rodent-chow supplemented with doxycycline (2g/kg) ad libitum starting at 21 days of age to induce IGF-IR overexpression.

Mice were maintained and cared for following the Canadian Council for Animal Care guidelines and ethical approval was provided by the Animal Care Committee at the University of Guelph.

Mice were sacrificed following 8-9 months of treatment with doxycycline. At this time, visible tumors on the surface of the lungs were counted. A small amount of lung tumor tissue and normal lung tissue were isolated and flash frozen in liquid nitrogen then stored at -80°C. The remainder of the lung tissue, containing tumors, and other tissues including liver, kidney, spleen, and brain were fixed in 10% buffered formalin for 24 hours and embedded in paraffin.

Additionally, normal lung tissue from wild-type mice, which had been treated with doxycycline, was collected as a control.

Table 5: Sequences of primers used to genotype transgenic and knockout mice. Primer Target Primer Sequence

SPC AAAATCTTGCCAGCTTTCCCC

SPC GACACATATAAGACCCTGGTCA

IGFIR CTGATAGTCGTTGCGGATGT

IGFIR (TRE) CGCCTGGAGACGCCATC

AKT2 TGGACAATCTGTCTTCATGCCAG

AKT2 TACACTTCATTCTCAGTATTGTTTTGC

AKT2 ACCAACCCCCTTTCAGCACTTG

AKT1 ATAAGCACACCTTCAGGTCCC

AKT1 TGGATGTGGAATGTGTGCCA

AKT1 AGCTCTTCTTCCACCTGTCTC

Histology and Immunohistochemistry

Tissues were deparaffinized and reyhydrated, and were either stained with hematoxylin and eosin or continued with immunohistochemistry staining and blocked in 3% hydrogen peroxide for 10-15 minutes. Heat mediated antigen retrieval was performed using 10mM citrate buffer (pH 6.0) for 12 minutes or using TRIS-EDTA buffer (10mM Tris, 1mM EDTA, pH 9.0) for 30 minutes (for CD3 antibody only) and allowed to cool for 20 minutes. Tissues were blocked in 5% BSA in PBST (w/v) for 10 minutes. Tissues being stained for CD3 were additionally blocked in avidin (0.001% in PBS (w/v)) then biotin (0.001% in PBS (w/v)) (Sigma-

Aldrich, Oakville, ON) for 15 minutes each. Antibodies were diluted in 1% BSA in PBST and primary antibodies were used against human (transgene) IGF-IR(1:1000) (R&D Systems,

Minneapolis, MN), CD3(1:500), CD45R(1:2000), F4/80(1:500) (AbD Serotec, Raleigh, NC), and proliferating cell nuclear antigen (PCNA)(1:100) (Santa Cruz Biotechnology, Dallas, TX)

overnight at 4°C. Tissues were incubated in secondary anti-goat(1:200), anti-rat(1:500), or anti- rabbit(1:100) antibody (Sigma-Aldrich, Oakville, ON) then extravidin(1:50) (Sigma-Aldrich,

Oakville, ON) for one hour each at room temperature then treated with Sigma Fast 3,3′- diaminobenzidine tablets (Sigma-Aldrich, Oakville, ON). Stained tissues were then counterstained with hematoxylin, dehydrated and mounted with coverslips using Cytoseal XYL mounting media (Thermo Scientific, Waltham, MA).

Sections of lung tissue with immunohistochemistry staining for the transgenic IGF-IR were scanned with an Aperio Scanscope GL (Leica Biosystems, Concord, ON) slide scanner at

Western University. Images of all other slides were captured using Nikon E600 microscope and

Q-Capture software (Q-Imaging, Surrey, BC). Staining was quantified using Aperio ImageScope software (Leica Biosystems, Concord, ON). Transgenic IGF-IR was used as a marker for tumor burden, which was calculated for each mouse as the number of positive pixels per total area of lung tissue analyzed (mm2).

Western blotting Protein isolation and western blots were performed as previously described (Jones et al.

2007). Primary antibodies were used against IGF-IRβ(1:1000), phospho-IGF-IR/IR(1:500), phospho-Akt(1:1000), pan-Akt(1:1000), Akt1(1:1000), Akt2(1:500), phospho-ERK1/2(1:500),

ERK(1:1000), and β-actin(1:5000) (Cell Signaling Technology, Danvers, MA) and against human(transgenic) IGF-IR (1:1000) (R&D Systems, Minneapolis MN) overnight at 4°C. Either an anti-Rabbit(1:2000) (Cell Signaling Technology, Danvers, MA) or anti-goat(1:2000) (Santa

Cruz Biotechnology, Dallas, TX) secondary antibody for one hour at room temperature.

Membranes were visualized using chemiluminescence substrate (PerkinElmer, Waltham, MA)

and FluoroChem 8800 gel documentation system (Alpha Innotech - ProteinSimple, Toronto,

ON).

RNA isolation, qRT-PCR and RNA Sequencing RNA was isolated using the mirVana miRNA Isolation Kit (Ambion - Life Technologies,

Burlington, ON) according to manufacturer protocol. RNA was quantified and tested for quality using Nanodrop 200c Spectrophotometer (Thermo Scientific, Waltham, MA). RNA (250ng) was reveresed-transcribed using SuperScript II Reverse Transcriptase, 5x First-Strand buffer, DTT,

RNaseOUT Recombinant Ribonuclease Inhibitor, Oligo(dT)12-18, and dNTP (Life

Technologies, Burlington, ON). Gene expression determined using qPCR reaction with Platinum

SYBR Green qPCR SuperMix-UDG (Life Technologies, Burlington, ON) and performed on the

CFX Connect Real-time PCR Detection System (Bio-rad Laboratories, Mississauga, ON).

Primers for qPCR were used for Mmp2, CxCl12, Mdk, Ntrk2, Bpifa1, Actc1, Ptpr21, Scgb3a2,

Ccl5, Crym, Hprt (Bio-rad Laboratories, Mississauga, ON), and Actb (Origene Technologies,

Rockville, MD). Primer efficiencies were determined by qRT-PCR with a dilution series.

Relative quantification of gene expression using qPCR was determined using the ΔΔCq method normalizing with Hprt and Actb as reference genes using CFX-Manager 3.1 (Bio-rad

Laboratories, Missisauga, ON).

RNA sequencing and differential analysis was performed on tumor tissue from SPC-

IGFIR and SPC-IGFIR-Akt2-/- mice and normal lung tissue from wild-type mice by the McGill

University and Genome Quebec Innovation Centre using the Illumina Hiseq 2000/2500 sequencer. Additional unsupervised heirearchal clustering analysis was performed using Gene

Cluster 3.0 (de Hoon et al. 2004) and visualized with Java Treeview (Saldanha 2004) (Open

Source). RNA Sequencing data was further analyzed with Ingenuity Pathway Analysis software

(IPA – QIAGEN, Redwood City, CA) using transcripts with at least 1.5 fold difference in expression and a minium p-value (significance) and q-value (false discovery rate) of 0.05.

Statistics Statistical analysis was performed using Graphpad Prism 6 (Graphpad Software, Inc., La

Jolla, CA). When two means were compared, as in the tumor burden quantification ad Student’s

T-test was used. When 3 means were compared, as in quantification of PCNA and immune cell markers, a one-way ANOVA was performed followed by a post-hoc Tukey’s Test. Error is represented by standard error of the mean (SEM). Statistical significance is noted as p<0.05.

Results

Tumorigenesis is augmented in SPC-IGFIR-Akt2-/- mice and suppressed in SPC- IGFIR-Akt1-/- mice Transgenic mice with genotypes of SPC-IGFIR, SPC-IGFIR-Akt1-/-, and SPC-IGFIR-Akt

2-/-, were treated with rodent chow containing 2g/kg doxycycline to induce IGF-IR overexpression in type II alveolar cells. Initially, we anticipated treating mice with doxycycline for 9 months based on previous studies (Linnerth et al. 2009; Siwicky et al. 2010) in which treatment for this length of time led to the formation of at least one macroscopically visible lung tumor on each SPC-IGFIR mouse evaluated. However, some SPC-IGFIR-Akt2-/- mice displayed signs of morbidity by 8 months of treatment including ruffled coat, labored breathing and weight loss. Necroscopy of these mice revealed extensive tumor growth throughout all lung lobes (Fig

10c,f). Therefore all SPC-IGFIR-Akt2-/- mice were sacrificed for tumor collection following 8 months of IGF-IR overexpression as well as a subset of the SPC-IGFIR controls. The remaining

SPC-IGFIR and SPC-IGFIR-Akt1-/- mice were collected after 9 months of IGF-IR overexpression.

Overexpression of the IGF-IR was sufficient to cause lung tumor development in all mice. These lung tumors were visible macroscopically on the surface of the lungs as white, round nodules in SPC-IGFIR (Fig 1a) and SPC-IGFIR-Akt1-/- mice (Fig 10b), however, the white solid tumor tissue in SPC-IGFIR-Akt2-/- mice (Fig 10c) did not retain the nodular shape and appeared to have more extensive growth into the lung tissue. Microscopically, similar phenotypes were seen in H&E stained tissues (Fig 10d-i). While some small tumors in SPC-

IGFIR-Akt2-/- mice appear nodular, the majority of tumor growth lacks the defined boundaries of the nodular adenomas and adenocarcinomas observed in the SPC-IGFIR mice. Another characteristic unique to SPC-IGFIR-Akt2-/- mice was the presence of some fibrous tissue at the lung periphery adjacent to tumor tissue (Fig 10i)

Histological sections suggested that tumor burden was greatest in the SPC-IGFIR-Akt2-/- mice and lowest in the SPC-IGFIR-Akt1-/- mice. Since tumor burden was difficult to quantify in the histological sections we took advantage of the fact that the tumor cells expressed high levels of IGF-IR and performed IGF-IR immunohistochemistry. As shown in Fig 11a-f, tumor cells within the nodules and in the surrounding lung tissue stained strongly for IGF-IR. We then used this staining to quantify tumor burden. Immunohistochemistry for transgenic IGF-IR on whole lungs was quantified as the number of positive pixels per total area of the lung lobes (mm2).

SPC-IGFIR-Akt1-/- mice (n=9) had significantly reduced tumor burden compared to SPC-IGFIR mice (n=10) after 9 months of treatment with doxycycline to induce IGF-IR overexpression (Fig

11g). As previously discussed, SPC-IGFIR-Akt2-/- mice developed extensive lung tumors after only 8 months of doxycycline treatment and thus tumor burden was evaluated in SPC-IGFIR-

Akt2-/- mice and a subset of SPC-IGFIR mice following 8 months of IGF-IR overexpression, at

which point, SPC-IGFIR-Akt2-/- mice (n=12) had increased tumor burden compared to SPC-

IGFIR mice (n=12) (Fig 11h).

Additionally, SPC-IGFIR-Akt1-/- mice (n=10) had significantly fewer nodular tumors visible on the surface of the lungs than SPC-IGFIR mice (n=10) (Fig 11i). Due to the different tumor phenotypes, a similar comparison could not be made between SPC-IGFIR and SPC-

IGFIR-Akt2-/- mice. There was individual variation in tumor burden in all genotypes. SPC-

IGFIR-Akt2-/- mice with lower tumor burden display the same phenotype of dispersed tumor tissue which indicates that these tumors do not initially form as distinct nodules.

Histological evaluation was performed on sections from major tissues including the liver, kidneys, pancreas, and brain. There were no obvious metastatic lesions observed in any of these tissues from any of the genotypes.

Loss of Akt2 does not alter Akt or ERK1/2 activation Protein was isolated from lung tumors of SPC-IGFIR and SPC-IGFIR-Akt2-/- mice and analyzed by western blotting in order to determine the level of activation of the IGF-IR pathway

(Fig 12a). Due to the small size and quantity of tumors in the Akt1 null mice, there was insufficient tissue for protein isolation. While there appears to be higher expression of the transgenic IGF-IR and activated IGF-IR in tumors from mice lacking Akt2, there was no increase in activation of Akt (all isoforms) or ERK. Western blots for IGF-IR reveal three bands appearing at approximately 95kDa, 135kDa, and 250kDa, which represent the β-subunit, α- subunit and pro-receptor, respectively. Despite the absence of Akt2 protein in tumors from Akt2 null mice, there was no apparent change in the amount of total Akt or Akt1. Similarly there was no increase in levels of activated ERK1/2. Akt3 was not detectable in any tumors by western blotting (data not shown).

When components of the IGF-IR system were examined in the RNA sequencing data it was observed that IGF1 and IGFBP6 were upregulated in tumors from Akt2 null mice while

IGFBP3 and IGFIR were downregulated in these tumors (Table 6). Multiple Akt2 transcripts were found in the SPC-IGFIR-Akt2-/- tumors however one Akt2 transcript

(ENSMUST00000142365) was not expressed in any of the four SPC-IGFIR-Akt2-/- tumor samples evaluated. The RNA-sequencing data also indicated that neither Akt1 nor Akt3 mRNA expression was significantly elevated in the SPC-IGFIR-Akt2-/- tumors compared to SPC-IGFIR tumors. This finding was confirmed by qRT-PCR and qRT-PCR also showed that neither Akt2 nor Akt3 was significantly elevated in the SPC-IGFIR-Akt1-/- tumors compared to SPC-IGFIR tumors (Fig 12b,c,d). These results suggest that knockout of Akt1 or Akt2 was not compensated by an increase in expression of untargeted Akt isoforms.

SPC-IGFIR and SPC-IGFIR-Akt2-/- tumors have differential gene expression RNA sequencing of tumors from SPC-IGFIR and SPC-IGFIR-Akt2-/- mice was performed in order to investigate differences in gene and transcript expression that may be related to the difference in tumor histology. Normal lung tissue from wild-type mice was also sent for RNA sequencing. Clustering of samples based on transcript expression patterns was performed to determine the similarity between samples (Fig 13a). As expected, all tumor samples clustered together, likewise, the normal lung tissue samples also clustered together (Fig

13b) demonstrating a common expression profile within the tumor samples that is distinct from the normal tissue. Within the tumor samples, tumors from SPC-IGFIR mice clustered most closely together while three of four SPC-IGFIR-Akt2-/- samples clustered closely together, but separate from the SPC-IGFIR tumors (Fig 13b). The fourth SPC-IGFIR-Akt2-/- tumor sample’s expression profile was more similar to the SPC-IGFIR tumor samples than the other SPC-IGFIR-

Akt2-/- tumor samples, but also shared similarities with the other Akt2 null tumors. Tumor samples clustered based on gene expression data demonstrated the same patterns of similarity

(data not shown). The top 75 genes that are differentially expressed between the three groups are displayed as a heat map (Fig 13c). The top 25 upregulated transcripts in SPC-IGFR-Akt2-/- tumors compared to SPC-IGFIR tumors are listed in Table 7 and the top 25 downregulated transcripts in SPC-IGFR-Akt2-/- tumors compared to SPC-IGFIR tumors are listed in Table 8.

Pathway analysis of transcripts that were differentially expressed between tumors from

SPC-IGFIR and SPC-IGFIR-Akt2-/- mice identified several diseases and biological functions associated with these differentially expressed transcripts (Fig 14). The most significantly represented disease was cancer, and several molecular and cellular functions relating to cancer were identified including cell movement, cellular growth and proliferation, cell death and survival, cell cycle, and DNA replication, recombination and repair. Some more specific categories within these diseases and biological functions were significantly predicted to be up- or down-regulated, with Activation Z-scores <2 or >-2, respectively (Table 9). Neoplasia of tumor cell lines (p=2.28e-4, z-score -2.504) was predicted to be downregulated in Akt2 null tumors as well as colony formation of cells (p=7.57e-9, z-score -2.056) and tumor cell lines (p=9.78e-8, z- score -2.435). In line with this, apoptosis of tumor cell lines was predicted to be upregulated

(p=5.95e-5, z-score 2.199) and proliferation of tumor cells (p=2.15e-7, z-score -1.028) was significantly represented on the list of differentiated transcripts and tended towards downregulation, although the prediction z-score did not reach significance (Fig 15). Several transcripts related to cell movement (p=7.04e-9, z-score 0.163), migration (p=1.61e-5, z-score

1.61), and invasion (p=1.52e-7, z-score -0.707) of tumor cells were differentially expressed, but there was no consistent directional effect of the functions, although migration tended towards

upregulation (Fig 16). Additionally, development of cardiovascular tissue (p=1.50e-5, z-score

2.353), development of endothelial tissue (p=1.18e-5, z-score 2.353) and proliferation of endothelial cells (p=2.64e-5, z-score 2.461) are all predicted to be upregulated in Akt2 null tumors and many of the transcripts related to these functions overlap with angiogenesis

(p=2.76e-5, z-score 1.343) (Fig 17).

The pathway analysis revealed several differentially expressed transcripts were related to inflammatory response, infectious disease, inflammatory disease, immunological disease, and immune cell trafficking (Fig 14). While colitis (p=1.21e-5) was the only one of these pathways to be significantly predicted to be upregulated, others such as inflammatory response (p=6.52e-9, z-score 1.40) and quantity of macrophages (p=2.76e-5, z-score 1.976) indicate a general trend towards activation of these pathways in Akt2 null mice (Fig 18). Metabolic functions related to differentially expressed transcripts include protein synthesis, molecular transport, carbohydrate metabolism, lipid metabolism, energy production, drug metabolism, and vitamin and mineral metabolism (Fig 14).

Genes implicated in lung cancer are differentially expressed in Akt2 null tumors In order to confirm the RNA sequencing data, several genes were selected that were differentially expressed between SPC-IGFIR-Akt2-/- tumors and SPC-IGFIR tumors and were previously reported to be associated with lung cancer and expression levels were quantified using qRT-PCR. Additionally, the expression of these genes was determined in SPC-IGFIR-

Akt1-/- tumors. The genes of interest included Mmp2, Ccl5, Cxcl12, Ntrk2, Mdk, Bpifa1, Actc1,

Scgb3a2, Crym and Ptprz1 (Table 10). The differential expression of transcripts in the RNA sequencing data correlated well with the relative expression determined by qRT-PCR, with the exception of Ccl5, which had a fold change of 5.99 and 1.61 by each method, respectively. Of

the genes investigated, only Mdk, Crym, and Ccl5 had greater than twofold change (+/-) in Akt1 null tumors. In each case, the direction of the change, up- or downregulation, was the same as in Akt2 null tumors only smaller in magnitude. Expression of Ptprz1 could not be accurately detected by qRT-PCR due to low abundance of transcripts that were beyond the sensitivity of this method.

Proliferation and immune cell abundance not different in Akt2 null tumors To further investigate functional differences suggested by the pathway analysis, immunohistochemistry for markers of proliferation and various immune cells was performed

(Fig 19). Immunohistochemistry for proliferating cell nuclear antigen (PCNA) in tumors indicated that there was no significant difference in proliferation rates in tumors from the three genotypes (Fig 19m). There was no significant difference in the abundance of macrophages, B- lymphocytes or T-lymphocytes between SPC-IGFIR and SPC-IGFIR-Akt2-/- tumors (Fig 19n-p).

Although, there was a significant increase in marcrophages in tumors from SPC-IGFIR-Akt2-/- mice compared to SPC-IGFIR-Akt1-/- mice and an increase in B-lymphocytes in tumors from

SPC-IGFIR-Akt1-/- mice compared to SPC-IGFIR mice (Fig 19n,o). T-lymphocytes did not significantly differ in tumors from the three genotypes (Fig 19p).

Figure 10: Overexpression of IGF-IR caused tumor formation in all mice regardless of the lack of one Akt isoform. SPC-IGFIR (a,d,g,j), SPC-IGFIR-Akt1-/- (b,e,h,k), and SPC-IGFIR-Akt2-/- (c,f,i,k) transgenic mice were treated for 8-9 months with doxycycline to induce IGF-IR overexpression in type II alveolar cells. Macroscopic tumors were visible on the surface of lungs from transgenic mice, as indicated by arrows (a-c). Lung sections containing tumors were stained with hematoxylin and eosin and scanned with Aperio Scanscope digital scanner (d-f) or captured with a Nikon E600 microscope at 10x (g-i) and 20x (j-l) . An area of peripheral fibrosis is identified by an arrowhead (i)

Figure 11: Mice lacking Akt1 had reduced tumor burden, while those lacking Akt2 had increased tumor burden. Lungs from transgenic mice with SPC-IGFIR (a,b), SPC-IGFIR-Akt1-/- (c,d), and SPC-IGFIR-Akt2-/- (e,f) genotypes were stained using an anti-human IGF-IR antibody to determine transgenic IGF-IR expression. Images were obtained using ScanScope digital scanner and quantified using Aperio ImageScope software. Tumor burden was quantified using immunohistochemical analysis of transgenic IGF-IR expression in the lungs of SPC-IGFIR-Akt1-/- (n=9) and SPC-IGFIR (n=10) following 9 months of treatment with doxycycline (g) and in SPC-IGFIR-Akt2-/- (n=12) and SPC-IGFIR (n=12) mice following 8 months of treatment with doxycycline (h) and expressed as as the number of positive pixels per lung tissue area (mm2). The quantity of macroscopic surface tumors on the lungs of SPC-IGFIR-Akt1-/- (n=9) and SPC-IGFIR (n=10) following 9 months of treatment with doxycycline (i). Mean and SEM is depicted for each group. **p<0.01;***p<0.001.

Figure 12: Tumors from SPC-IGFIR mice and SPC-IGFIR-Akt2-/- had similar activation of IGF-IR signaling pathways. Western blot of protein isolated from tumors from SPC-IGFIR and SPC-IGFIR-Akt2-/- mice. Expression of IGF-IR transgene, phospho-IGF-IR, pan-Akt, phospho-Akt Akt1, Akt2, ERK1/2, and phospho-ERK1/2 were evaluated with β-actin was used as a loading control (a). Gene expression of Akt1 (b), Akt2 (b) and Akt3 (c) in tumors from SPC- IGFIR (n=4), SPC-IGFIR-Akt1-/- (n=4), and SPC-IGFIR-Akt2-/- (n=4). Gene expression was determined using qRT- PCR and is expressed as mean with SEM.

Table 6: Transcripts within the IGF-IR signaling pathway are differentially expressed in tumors from SPC-IGFIR- Akt2-/- null mice compared to SPC-IGFIR mice. Relative expression determined by RNA-Sequencing performed by McGill University and Genome Quebec Innovation Centre. Gene Symbol Gene Name Fold Change p-value q-value AKT2 Akt2 -4.865 2.00E-04 2.12E-02 IGF1 IGF-I 2.213 5.00E-05 7.14E-03 IGF1R IGFI-I receptor -1.998 3.00E-04 2.77E-02 IGFBP3 IGF binding protein 3 -2.105 5.00E-05 7.14E-03 IGFBP6 IGF binding protein 6 3.131 5.00E-05 7.14E-03 Abbreviations: insulin-like growth factor (IGF)

Figure 13: Differentially expressed genes and transcripts from tumors of SPC-IGFIR and SPC-IGFIR-Akt2-/- mice cluster together and separately from wild-type normal lung samples. RNA-Sequencing was performed by McGill University and Genome Quebec Innovation Centre on tumor samples from SPC-IGFIR and SPC-IGFIR-Akt2-/- tumors and wild-type lung tissue. Heat map of differentially expressed transcripts created with Gene Cluster 3.0 and Treeview (a). Clustering of samples based on total transcript expression created by McGill University and Genome Quebec Innovation Centre (b). Heat map of the top 75 differentially expressed genes between the three groups created by McGill University and Genome Quebec Innovation Centre (c).

Table 7: Top 25 upregulated transcripts in tumors from SPC-IGFIR-Akt2-/- mice compared to SPC-IGFIR mice. RNA-Sequencing was performed by McGill University and Genome Quebec Innovation Centre. Transcripts are ranked by q-value and fold change. Gene Symbol Gene Name Fold Change p-value q-value Ltf Lactotransferrin 25.31569 5.00E-05 0.007141 Cyp2a5 Cytochrome P450 family member 21.23408 5.00E-05 0.007141 Scgb3a1 Segretoglobin family member 13.67359 5.00E-05 0.007141 BC048546 Alpha-2 macroglobulin 12.80762 5.00E-05 0.007141 D6Mm5e Meiosis1 associated protein 11.20833 5.00E-05 0.007141 Dmkn Dermokine 10.91772 5.00E-05 0.007141 Adh7 Alcohol dehydrogenase 7 10.89066 5.00E-05 0.007141 Fam180a Family with sequence similarity member 8.455851 5.00E-05 0.007141 Muc5b Mucin 5b 8.342733 5.00E-05 0.007141 Actc1 Alpha-cardiac actin 7.839491 5.00E-05 0.007141 Klrg1 Killer cell lectin-like receptor subfamily member 7.498334 5.00E-05 0.007141 Aspg Asparaginase 7.396537 5.00E-05 0.007141 Cnn1 Calponin-1 6.612336 5.00E-05 0.007141 Igkv1-133 Immunoglobulin kappa variable cluster 6.322346 5.00E-05 0.007141 Ighv5-9 Immunoglobulin heavy variable cluster 6.242959 5.00E-05 0.007141 Gap43 Growth associated protein 43 6.204049 5.00E-05 0.007141 Ccdc153 Coiled-coil domain containing protein 153 6.055055 5.00E-05 0.007141 Ccl5 Beta-chemokine (RANTES) 5.987484 5.00E-05 0.007141 Tpm2 Tropomyosin beta 5.852325 5.00E-05 0.007141 Scgb3a2 Secretoglobin family member 5.793278 5.00E-05 0.007141 Tnfsf18 Tumor necrosis factor ligand superfamily member 5.764638 5.00E-05 0.007141 Cd177 CD177 antigen 5.362441 5.00E-05 0.007141 Gabrp Gamma-aminobutyric acid A receptor, Pi 5.332529 5.00E-05 0.007141 Snhg11 Small nuclear host gene 11 5.325806 5.00E-05 0.007141 Lyz1 Lysozyme 5.305321 5.00E-05 0.007141

Table 8: Top 25 downregulated transcripts in tumors from SPC-IGFIR-Akt2-/- mice compared to SPC-IGFIR mice. RNA-Sequencing was performed by McGill University and Genome Quebec Innovation Centre. Transcripts are ranked by q-value and fold change. Gene Symbol Gene Name Fold Change p-value q-value Ighg1 Immunoglobulin heavy locus -65.2877 5.00E-05 0.007141 Igkv3-10 Immunoglobulin kappa variable cluster -19.8416 5.00E-05 0.007141 Igkv4-68 Immunoglobulin kappa variable cluster -19.7408 5.00E-05 0.007141 Igkv15-103 Immunoglobulin kappa variable cluster -16.4725 5.00E-05 0.007141 H2-M2 Murin MHC class 1b -15.6221 5.00E-05 0.007141 Igkv6-32 Immunoglobulin kappa variable cluster -15.2075 5.00E-05 0.007141 Zfp419 Zinc finger protein 419 -14.8312 5.00E-05 0.007141 Ighv8-5 Immunoglobulin heavy variable cluster -14.1294 5.00E-05 0.007141 Ighv2-9-1 Immunoglobulin heavy variable cluster -12.7517 5.00E-05 0.007141 Igkv3-1 Immunoglobulin kappa variable cluster -12.638 5.00E-05 0.007141 Ighv8-8 Immunoglobulin heavy variable cluster -12.3116 5.00E-05 0.007141 Igkv2-112 Immunoglobulin kappa variable cluster -11.9603 5.00E-05 0.007141 Ighg2c Immunoglobulin heavy locus -11.8694 5.00E-05 0.007141 Ighg Immunoglobulin heavy locus -10.6908 5.00E-05 0.007141 Susd2 Sushi domain containing 2 -10.1881 5.00E-05 0.007141 Igkv3-7 Immunoglobulin kappa variable cluster -10.1341 5.00E-05 0.007141 Ighg3 Immunoglobulin heavy locus -10.1277 5.00E-05 0.007141 Igkv19-93 Immunoglobulin kappa variable cluster -9.52745 5.00E-05 0.007141 Alb Albumin -9.09826 5.00E-05 0.007141 Igkv12-44 Immunoglobulin kappa variable cluster -8.33181 5.00E-05 0.007141 Clstn3 Calsyntenin 3 -8.17637 5.00E-05 0.007141 Igkv3-4 Immunoglobulin kappa variable cluster -8.15288 5.00E-05 0.007141 Igkv6-25 Immunoglobulin kappa variable cluster -8.05352 5.00E-05 0.007141 Steap4 Six-transmembrane epithelial antigen of prostate 4 -7.97779 5.00E-05 0.007141 Igkv8-21 Immunoglobulin kappa variable cluster -7.78324 5.00E-05 0.007141

Figure 14: Diseases and biological functions significantly altered between tumor tissues from SPC-IGFIR and SPC- IGFIR-Akt2-/- mice. Pathway analysis of differentially expressed transcripts with IPA software revealed overrepresented pathways categorized into diseases and disorders (a), molecular and cellular functions (b), and physiological system development and functions (c). Threshold marks significance of p=0.05.

Table 9: Molecular and cellular functions predicted to be significantly upregulated or downregulated in SPC- IGFIR-Akt2-/- tumors compared to SPC-IGFIR tumors. Determined using IPA software, a Z-score of less than -2 indicates a predicted significant decrease and a Z-score greater than 2 indicates a predicted significant increase in that pathway. Predicted Number Differentially Expressed Transcripts Within Pathway Name P-value Effect Z-score of Genes Pathway Colony 7.57E-09 Decreased -2.056 33 ALDH1A1, BPIFA1, CCND1, CLCA1, formation of CLDN4, CSF2RB, CXCL12, DCN, EDN1, cells EPHA2, EREG, GJA1, GPC3, HPGD, IGF1, IGF1R, IGFBP3, IL1B, IL1RN, LOXL1, LUM, MDK, MMP2, NDN, NDRG1, NTRK2, NUPR1, PTGS2, RASD1, S100A10, SCN3B, TNFSF10, WIF1 Colony 9.78E-08 Decreased -2.435 21 ALDH1A1, CLCA1, CLDN4, EDN1, EPHA2, formation of GJA1, HPGD, IGF1R, IGFBP3, LOXL1, LUM, tumor cell lines MDK, NDN, NDRG1, NUPR1, PTGS2, RASD1, S100A10, SCN3B, TNFSF10, WIF1 Activation of 1.79E-06 Decreased -2.377 8 ALB, EDN1, IL1B, IL1RN, LCN2, MAPT, astrocytes PTGS2, SERPINE1 adhesion of 6.78E-05 Decreased -2.006 21 A2M, AKT2, ANGPT1, CCL5, CCND1, blood cells CHST4 ,CSF2RB, CXCL12, DCN, DCSTAMP, ICAM2, IGHM, IL1B, ITGA7, ITGB2, LTF, Ly6a (includes others), OLR1, PROCR, PTGS2, THBD Neoplasia of 2.28E-04 Decreased -2.504 16 AKT2, AREG, ATP2C2, CCND1, CNN1, tumor cell lines EPHA2, EREG, GJA1, IGF1, IGF1R, LOXL1, PROCR, PTGS2, PTPRZ1, THBD, ZFYVE21 Differentiation 3.35E-10 Increased 2.045 42 A2M, AKT2, AREG, Arxes1/Arxes2, CA2, of connective CCL5, CCND1, Clec2d (includes others), tissue cells CREB3L1, CTGF, CYTL1, DCSTAMP, EDN1, EPHA2, FHL2, GDF10, GJA1, GLIS3, GPC3, HSD11B1, IBSP, IGF1, IL1B, LRG1, LRP6, LRRC8C, LTF,Ly6a (includes others), MDK, PDE5A, PDK4, PLAC8, PPARD, PTGS2, RARRES2, Retnla, SLC2A4, STC1, TCF21, TNFSF10, WIF1, ZBTB16 Differentiation 1.80E-09 Increased 2.176 44 A2M, AKT2, AREG, Arxes1/Arxes2, CA2, of connective CCL5, CCND1, Clec2d (includes others), tissue CREB3L1, CSF2RB, CTGF, CYTL1, DCSTAMP, EDN1, EPHA2, FHL2, GDF10, GJA1, GLIS3, GPC3, HSD11B1, IBSP, IGF1, IL1B, LCN2, LRG1, LRP6, LRRC8C, LTF, Ly6a (includes others), MDK, PDE5A, PDK4, PLAC8, PPARD, PTGS2, RARRES2, Retnla, SLC2A4, STC1, TCF21, TNFSF10, WIF1, ZBTB16 Metabolism of 5.47E-07 Increased 2.419 19 AKT2, ANGPT1, C1QTNF2, CCL5, CTGF, polysaccharide CYTL1, EDN1, IGF1, IGF1R, IGFBP3, IL1B, IL1RN, LTF, MMP2, PPARD, SLC2A4, SULF1, TGFB3, THBD Synthesis of 6.13E-07 Increased 2.272 16 AKT2, ANGPT1, C1QTNF2, CCL5, CTGF, polysaccharide CYTL1, EDN1, IGF1, IGF1R, IGFBP3, IL1B, IL1RN, LTF, SLC2A4, TGFB3, THBD

Table 8 continued. Predicted Number Differentially Expressed Transcripts Within Pathway Name P-value Effect Z-score of Genes Pathway Development of 1.12E-05 Increased 2.102 28 ANGPT1, CA2, CCND1, CEACAM1, CTGF, epithelial tissue CXCL12, DDAH1, EDN1, EPHA2, FLNB, FOXJ1, GJA1, HBEGF, IGF1, IL1B, INHBB, KCNJ2, LRG1, MDK, MMP2, PROCR, PTGS2, SEMA5A, SERPINE1, SPRED1, STC1, SULF1, TNFSF10 Mitogenesis of 1.13E-05 Increased 2.000 4 EDN1, EDN3, IGF1, TGFB3 central nervous system cells Development of 1.18E-05 Increased 2.353 22 ANGPT1, CEACAM1, CXCL12, DDAH1, endothelial EDN1, EPHA2, GJA1, HBEGF, IGF1, IL1B, tissue KCNJ2, LRG1, MDK, MMP2, PROCR, PTGS2, SEMA5A, SERPINE1, SPRED1, STC1, SULF1, TNFSF10 Colitis 1.21E-05 Increased 2.328 17 AKT2, ALB, CALCB, CEACAM1, EDN1, EREG, HPGD, HSD11B1, IGHM, IL1B, IL1RN, LCN2, NFE2L2, PROCR, PTGS2, Retnla, SERPINE1 Development of 1.50E-05 Increased 2.353 24 ACTC1, ANGPT1, CEACAM1, CXCL12, cardiovascular DDAH1, EDN1, EPHA2, GJA1, HBEGF, tissue IGF1, IL1B, KCNJ2, LRG1, MDK, MMP2, PROCR, PTGS2, SEMA5A, SERPINE1, SPRED1, STC1, SULF1, TBX2, TNFSF10 Endothelial cell 2.06E-05 Increased 2.617 21 ANGPT1, CEACAM1, CXCL12, DDAH1, development EDN1, GJA1, HBEGF, IGF1, IL1B, KCNJ2, LRG1, MDK, MMP2, PROCR, PTGS2, SEMA5A, SERPINE1, SPRED1, STC1, SULF1, TNFSF10 Proliferation of 2.64E-05 Increased 2.461 19 ANGPT1, CEACAM1, CXCL12, DDAH1, endothelial cells EDN1, GJA1, HBEGF, IGF1, IL1B, KCNJ2, LRG1, MDK, PROCR, PTGS2, SEMA5A, SERPINE1, SPRED1, SULF1, TNFSF10 Apoptosis of 5.95E-05 Increased 2.199 49 AKT2, ALB, AREG, BPIFA1, CALCB, tumor cell lines CCND1, CEACAM1, CKAP5, CSF2RB, CTGF, CXCL12, DKK3, EDN1, EPHA2, EREG, FLNB, GJA1, GLIS3, GUCA2A, HBEGF, HK2, HLF, IGF1, IGF1R, IGFBP3, IGFBP6, IGHM, IL1B, ITPR2, LCN2, LMNB1, MAPT, NDN, NDRG1, NFE2L2, NTRK2, NUPR1, PPARD, PRKCDBP, PTGS2, RASD1, SCGB3A1, SCN3B, SERPINE1, SULF1, TNFSF10, TNFSF18, WIF1, ZBTB16 Metabolism of 9.54E-05 Increased 2.207 30 AKT2, ALDH1A1, ANGPT1, C1QTNF2, carbohydrate CCL5, CHST4, CTGF, CYTL1, DCN, EDN1, ENPP2, FBP1, FBP2, HBEGF, HK2, IGF1, IGF1R, IGFBP3, IL1B, IL1RN, ITGB2, LRP6, LTF, MMP2, PLA1A, PPARD, SLC2A4, SULF1, TGFB3, THBD Cell movement 1.65E-04 Increased 2.168 8 CCL5, CSF2RB, CXCL12, EDN1, FHL2, of lymphatic HBEGF, IGF1, Retnla system cells

Figure 15: Neoplasia of tumor cell lines is predicted to be downregulated in Akt2 null tumors. Differentially expressed transcripts that are upregulated (red) or down-regulated (green) in SPC-IGFIR-Akt2-/- tumors compared to SPC-IGFIR tumors and their predicted effect on neoplasia of tumor cell lines (p=2.28e-4, z- score -2.504), apoptosis of tumor cell lines (p=5.95e-5. z-score 2.199) and proliferation of tumor cells (p=2.15e-17, z-score -1.028) as determined using IPA software. Orange lines between the transcript and pathway node indicate that the observed change in transcript expression is predicted to increase that pathway, blue lines indicate a predicted decrease in that pathway, yellow lines indicate that the predicted effect is opposite to the overall predicted trend, and grey lines indicate that a functional prediction could not be made. Network was generated with IPA software.

Figure 16: Lack of Akt2 influences expression of transcripts functionally related to cell movement. Differentially expressed transcripts that are upregulated (red) or down-regulated (green) in SPC-IGFIR-Akt2-/- tumors compared to SPC-IGFIR tumors and their predicted effect on cell movement of tumor cell lines (p=7.04e-9, z-score 0.168), migration of tumor cells (p=1.61e-5, z-score 1.113), and invasion of tumor cells (p=1.52e-7, z-score -0.707) as determined using IPA software. Orange lines between the transcript and pathway node indicate that the observed change in transcript expression is predicted to increase that pathway, blue lines indicate a predicted decrease in that pathway, yellow lines indicate that the predicted effect is opposite to the overall predicted trend, and grey lines indicate that a functional prediction could not be made. Network was generated with IPA software.

Figure 17: Development of endothelial tissue and proliferation of epithelial cells is predicted to be upregulated in Akt2 null tumors. Differentially expressed transcripts that are upregulated (red) or down-regulated (green) in SPC-IGFIR-Akt2-/- tumors compared to SPC-IGFIR tumors and their predicted effect on development of endothelial tissue (p=1.18e-5, z-score 2.353), proliferation of endothelial cells (p=2.64e-5, z-score 2.461), and angiogenesis (p=1.16e-8, z-score 1.343) as determined using IPA software. Orange lines between the transcript and pathway node indicate that the observed change in transcript expression is predicted to increase that pathway, blue lines indicate a predicted decrease in that pathway, yellow lines indicate that the predicted effect is opposite to the overall predicted trend, and grey lines indicate that a functional prediction could not be made. Network was generated with IPA software.

Figure 18: Lack of Akt2 influences expression of transcripts functionally related to inflammation. Differentially expressed transcripts that are upregulated (red) or down-regulated (green) in SPC-IGFIR-Akt2-/- tumors compared to SPC-IGFIR tumors and their predicted effect on inflammatory response (p=6.52e-9, z-score 1.40), quantity of macrophages (p=2.76e-5, z-score 1.976), and colitis (p=1.21e-5, z-score 2.328) as determined using IPA software. Orange lines between the transcript and pathway node indicate that the observed change in transcript expression is predicted to increase that pathway, blue lines indicate a predicted decrease in that pathway, yellow lines indicate that the predicted effect is opposite to the overall predicted trend, and grey lines indicate that a functional prediction could not be made. Network was generated with IPA software.

Table 10: Relative transcript expression of genes associated with lung cancer. Fold change in tumors from SPC-IGFIR-Akt1-/- or SPC-IGFIR-Akt2-/- mice are relative to expression of transcripts in tumors from SPC-IGFIR mice, as determined by either qRT-PCR or RNA sequencing. Fold Fold Change in Change in Fold Akt1 Null Akt2 Null Change in Tumors Tumors Akt2 Null Gene (qRT- (qRT- Tumors p-value q-value Symbol Name PCR) PCR) (RNA-Seq) (RNA-Seq) (RNA-Seq) Actc1 Alpha-cardiac actin 1.02 7.37 7.84 5.00E-05 0.007141

Bpifa1 Lung-Specific Protein X -1.28 442.85 384.54 0.00015 0.017055

Ccl5 Beta-Chemokine RANTES 3.20 7.60 5.99 5.00E-05 0.007141

Crym Crystallin, Mu -2.04 -4.91 -6.67 5.00E-05 0.007141

CxCl12 Stromal Cell-Derived 1.14 2.03 2.30 2.00E-04 0.021165 Factor 1

Mdk Midkine 2.53 4.11 3.33 5.00E-05 0.007141

Mmp2 Matrix Metalloproteinase 2 -1.27 2.14 2.43 5.00E-05 0.007141

Ntrk2 Neurotrophic Tyrosine 1.05 4.77 3.98 5.00E-05 0.007141 Kinase Receptor Type 2

Ptprz1 Protein-Tyrosine NA* NA* 6.63 0.00015 0.017055 Phosphatase Receptor Type Z Polypeptide 1 Scgb3a2 Pneumo Secretory Protein 1 -1.14 5.40 5.79 5.00E-05 0.007141

*Expression could not be determined due to low abundance of transcript.

Figure 19: Immunohistochemistry for proliferation and immune cell markers in tumors. Lungs from transgenic mice with SPC-IGFIR (a-d), SPC-IGFIR-Akt1-/- (e-h), and SPC-IGFIR-Akt2-/- (i-l) genotypes were stained using antibodies specific for markers of proliferation (PCNA) (a,e,i), macrophage (F4/80) (b,f,j), B- lymphocytes (CD45R) (c,g,k), and T-lymphocytes (CD3) (d, h, l). Images were obtained at 20x magnification using a Nikon E600 microscope and quantified using Aperio ImageScope software. Data is expressed as a ratio of the number of positive staining pixels to total area of lung tissue in um2 (m-p).*p<0.05

Discussion Akt is frequently implicated as an important mediator in the growth and development of many types of cancer, including lung cancer (Balsara et al. 2004; Cheung and Testa 2013). Yet the specific roles of each of the three Akt isoforms in lung cancer are not well understood. Using a transgenic mouse model of IGF-IR driven lung tumorigenesis, we evaluated the importance of

Akt1 and Akt2 in the initiation and progression of lung cancer. Overexpression of the IGF-IR in type II alveolar cells was sufficient to induce lung tumor formation in mice lacking a single Akt isoform, either Akt1 or Akt2. Lack of only Akt1 was sufficient to decrease tumor burden in these mice. Alternately, mice lacking only Akt2 had increased tumor burden. Linnerth-Petrik et al.

(2014) reported the same opposing roles of Akt1 and Akt2 in a virally induced mouse model of lung tumorigenesis. Similarly, Hollander et al. (2011) reported a lack of only Akt1 was sufficient to decrease tumor burden and formation in KRAS and NNK driven mouse models of lung cancer; however, this study reported no difference in tumor burden in mice lacking only Akt2. A novel finding of our study is a difference in histology of tumors lacking Akt2. IGF-IR overexpressing tumors with all Akt isoforms or lacking only Akt1 formed as nodular adenomas, as previously reported in our model (Linnerth et al. 2009); however, those lacking only Akt2 had

IGF-IR overexpressing tumor tissue diffuse within the lung.

Despite a difference in tumor burden between genotypes in our model, there was no significant difference in proliferation between any groups based on immunohistochemistry for

PCNA. This is in contrast to the findings of Linnerth-Petrik et al. (2014), who found the opposing roles of Akt isoforms were related to changes in proliferation and apoptosis. This discrepancy could be related to the different models of lung cancer and upstream regulators of

Akt, or the high levels of transgenic IGF-IR expression seen in our model. Indeed, IGF-IR signaling was not diminished in Akt2 null tumors as no difference was seen in activation of Akt

or ERK1/2. Additionally, IGF-I was upregulated in Akt2 null tumors and the negative regulator

IGFBP-3 was downregulated, which correlates with the observed increase in IGF-IR activation in Akt2 null tumors. Although none of the other isoforms were upregulated to compensate for the lack of either Akt1 or Akt2, there was likely increased activation of alternate isoforms in order to maintain the same level of Akt activation. We were unable to detect Akt3 by western blotting which indicates that Akt3 is likely expressed at low levels; therefore, Akt1 is likely the isoform that is activated in Akt2 null tumors.

Using RNA-sequencing we identified several differentially expressed transcripts in Akt2 null tumors that have been found to be elevated in lung tumors including: the chemokine Mdk

(Garver et al. 1993); pro-inflammatory chemokines CxCl12 (Imai et al. 2010; Rodriguez-Lara et al. 2014) and Ccl5 (also known as RANTES) (Moran et al. 2002; Henriquet et al. 2007); matrix metalloproteinase Mmp2 (Falcone et al. 2013; Li et al. 2013a; Ma et al. 2014); neurotrophin tyrosine kinase receptor Ntrk2 (also known as TrkB) (Zhang et al. 2010b); lung specific secretory proteins Scgb3a2 (Tachihara-Yoshikawa et al. 2008; Davidson et al. 2012) and Bpifa1

(also known as LUNX) (Chong et al. 2006); cardiac cytoskeletal actin Actc1 (Huang et al. 2010;

Che et al. 2013); tyrosine phosphatase Ptprz1 (Makinoshima et al. 2012); and the thyroid hormone inhibitor Crym (Chong et al. 2006). In Akt2 null tumors, all but Crym had greater than two-fold increase in transcripts. Furthermore, expression of Ntrk2 (Okamura et al. 2012), Bpifa1

(Li et al. 2014a), and Ccl5 (Moran et al. 2002; Borczuk et al. 2008; Umekawa et al. 2013) have all been associated with poor prognosis in lung cancer. Of these, Mdk (Lv et al. 2013), Bpifa1

(Cheng et al. 2008; Lv et al. 2011; Tang and Xu 2013; Bao et al. 2014; Yu et al. 2014a; Karimi et al. 2015), and Ccl5 (Lee et al. 2012) have shown promise as possible biomarkers for lung cancer. Mdk (Hao et al. 2013), CxCl12 (Imai et al. 2010; Dai et al. 2013; Xie et al. 2014), Ntrk2

(Perez-Pinera et al. 2007; Tatematsu et al. 2014), and Bpifa1 (Zheng et al. 2015) have been reported to promote lung cancer cell proliferation, although we did not see increased proliferation rates in tumors. RNA-sequencing identified several differentially expressed transcripts that would indicate decreased proliferation and increased apoptosis within these tumors. This includes downregulation of cyclin D1 (Ccnd1) as well as EGFR ligands amphiregulin (Areg) and epiregulin (Ereg), and upregulation of decorin (Dcn) and Wnt inhibitors

(Dkk3 and Wif1). The net effect could then result in no functional change in proliferation within the tumor cells. Furthermore, up- or downregulation of these transcripts does not necessarily reflect the functional activation at the protein level.

Since there is no difference in proliferation rates in tumors lacking either Akt1 or Akt2, there must be another explanation for the difference in tumor burden. It is possible that the Akt isoforms have differing roles in tumor initiation. Hollander et al. (2011) reported a decrease in tumor multiplicity in Akt1 null mice within their lung cancer model, and further that Akt1 was required for transformation of fibroblasts by mutant KRAS and epidermal growth factor (EGF).

Nomura et al. (2003) reported that expression of a dominant negative Akt1 prevented transformation of epidermal cells without influencing proliferation, which is consistent with our findings. When the number of independent nodular tumors was quantified, there were fewer visible on the surface of the lungs of Akt1 null mice, which supports the hypothesis that the tumor burden could be influenced by fewer transformation events. Conversely, if there were more sites of tumor initiation in Akt2 null mice that could contribute to the diffuse appearance of tumor tissue in these mice as many tumors growing together. Unfortunately, this could not be quantified in Akt2 null tumors. There was individual variability in tumor burden within our model, and histological sections from Akt2 null mice that had lower tumor burden show diffuse

IGF-IR overexpressing tumor tissue that would be consistent with multiple sites of tumor initiation but also confirms that these tumors do not initially form as distinct nodules as in the

SPC-IGFIR mice. However, a role for Akt2 as a tumor suppressor has not been previously reported.

Several of the lung cancer-associated genes that were differentially regulated in Akt2 null tumors are specifically related to tumor properties associated with more advanced disease such as angiogenesis, migration and metastasis. There were as many as 106 differentially expressed transcripts in Akt2 null tumors related to cellular movement. While there is no consensus found in the pathway analysis as to whether cell movement and migration would be increased or decreased, some of the genes identified have been reported to promote the migration of lung cancer cells including, Mdk (Salama et al. 2006), Cxcl12 (Imai et al. 2010; Dai et al. 2013; Ma et al. 2014; Xie et al. 2014), Ntrk2 (Zhang et al. 2010b; Harada et al. 2011), and Ccl5 (Huang et al.

2009). Increased migration of tumor cells seems likely considering the difference in tumor histology in Akt2 null mice and the abundance of diffuse tumor tissue in Akt2 null mice compared to tumors in other SPC-IGFIR mice, which retain a uniform nodular structure even when they grow quite large. Among the differentially expressed transcripts, Cxcl12

(Economidou et al. 2010; Paratore et al. 2010; Yang et al. 2012; Cavallaro 2013), Mmp2 (Li et al. 2013a; Wang et al. 2013; Kuo et al. 2014), Ntrk2 (Zhang et al. 2010b; Sinkevicius et al.

2014), Bpifa1 (Katseli et al. 2013; Li et al. 2014a; Zheng et al. 2015), and Ccl5 (Borczuk et al.

2008) have been associated with lung cancer metastasis; however, in our model no evidence of metastasis was found in any of the common sites of metastasis for lung cancer. There was no change in many frequently used markers of epithelial-mesenchymal transition (EMT) such as E- cadherin, N-cadherin, vimentin, snail, or zeb, which accompany a phenotypic change in cancer

cells enabling invasion and metastasis (Malaguarnera and Belfiore 2014). Therefore, the Akt2 null tumors may represent only a locally invasive phenotype.

A number of differentially expressed transcripts mapped to vascular functions including development of endothelial tissue, proliferation of endothelial cells and angiogenesis. Among these are CxCl12, Ccl5, Mdk, Mmp2, Actc1, and Ptprz1 as well as the vasoactive compounds angiopoietin (Angpt1) and endothelin (Edn1 and Edn3), which were upregulated. CxCl12

(Franco et al. 2012) and Ntrk2 (Okamura et al. 2012) have been specifically linked to microvessel density and angiogenesis in lung cancer. Although lung tissue is rich in both oxygen and vasculature, some Akt2 null mice in our study had such extensive tumor growth that large portions of the lungs were filled with tumor tissue undoubtedly disrupting normal function and structure causing a need for angiogenesis and blood vessel remodeling for continued nourishment of the tumor cells. Indeed, microvessel density of lung tumors is prognostic of metastasis and poor survival and forms the rationale for the use of vascular endothelial growth factor (VEGF) targeted therapy in lung cancer (Hilbe et al.; Herbst et al. 2005).

In addition to angiogenesis, there are other ways the tumor microenvironment could contribute to the difference in tumor burden, particularly in the Akt2 null mice. Akt2 null mice are known to develop hyperglycemia (Cho et al. 2001a; Garofalo et al. 2003) which has been associated with poor prognosis of lung cancer (Park et al. 2006) and cancer in general (Parekh et al. 2010). However, an increased tumor burden in Akt2 null mice is not consistently seen in mouse models of lung (Hollander et al. 2011) and breast cancers (Watson and Moorehead 2013), which would be expected if hyperglycemia was the driving force. RNA-sequencing and pathway analysis indicated an increase in inflammation for Akt2 null tumors, with upregulation of pro- inflammatory chemokines such as Ccl5, Cxcl12 and Mdk. With increased expression of

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inflammatory mediator, we expected to see increased recruitment of inflammatory and immune cells; however, immunohistochemistry for immune cells in these tumors indicated no significant difference in abundance of macrophages, B-lymphocytes, or T-lymphocytes. Furthermore, we expected fewer B-lymphocytes to be present within the tumors due to the downregulated expression of a large number of immunoglobulins in the Akt2 null mice. However, there was a non-significant increase in B-lymphocyte abundance within these tumors. The marker used to detect B-lymphocytes is not specific for activated cells, which may explain our findings.

Alternately, immunoglobulins can be expressed by epithelial tumor cells (Hu et al. 2008). The peripheral fibrous tissue visible in lungs in Akt2 null mice would be consistent with inflammation and with the predicted increase in connective tissue differentiation identified in the pathway analysis. Cancer-related inflammation occurs with lung cancer (Candido and Hagemann

2013; Gomes et al. 2014) and can be intrinsic or extrinsic (Mantovani et al. 2008); although it is difficult to determine whether inflammation could be a precursor or consequence of tumor formation. Inflammation is associated with conditions that increase the risk of developing lung cancer, such as cigarette smoke, particulate irritation or in association with chronic lung disease like chronic obstructive pulmonary disease (COPD) (Caramori et al. 2011; Rooney and Sethi

2011). Alternately, cancer-related inflammation can be due to tumor cells stimulating local inflammation to promote migration, angiogenesis and epithelial-mesenchymal transition. In our model, inflammation could occur as a response to the heavy tumor burden and dispersion of tumor tissue throughout the lung tissue causing decreased lung function and a hypoxic environment – similar to what is seen with acute lung injury (Fröhlich et al. 2013). It is important to note that while some pro-inflammatory cytokines are upregulated in Akt2 null tumors, others, such as Il1b and Ptgs2 (aka COX-2), are downregulated.

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It is becoming increasingly clear that Akt isoforms are not redundant in activity and have independent and differing physiologic roles. Our results have demonstrated that Akt1 and Akt2 have unique functions in lung tumorigenesis. The reduced tumor burden in Akt1 null mice observed in our study supports Akt1 as a candidate for effective targeted therapies; however, if the lack of Akt1 is only influencing tumor initiation then inhibition of only Akt1 may not be effective on established tumors. Despite this, our results suggest inhibition of Akt2 could lead to intra-tumoral expression of genes promoting inflammation, migration and angiogenesis, which are critical steps in cancer progression. However, it is not clear whether the differential expression of genes related to these functions is indeed due to a decrease in Akt2 signaling and not a compensatory increase in Akt1 signaling. At minimum, the use of orally administered agents inhibiting Akt2 could cause adverse physiologic effects that would undermine the treatment. The roles of Akt isoforms in lung cancer initiation and progression appear to be complex, may vary depending on disease stage and upstream regulators; and require further study for clarification. Akt inhibitors that do not discriminate between isoforms are already in clinical trials (Hers et al. 2011); however, further research will prove useful to intelligently design trials and maximize therapeutic success.

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General Discussion The IGF-IR has emerged as a potential therapeutic target in several types of cancer, including lung cancer. The IGF-IR has been frequently reported as overexpressed in human

NSCLC (Gong et al. 2009; Ludovini et al. 2009; Cappuzzo et al. 2010; Dziadziuszko et al. 2010;

Fidler et al. 2012; Kikuchi et al. 2012; Nakagawa et al. 2012; Tsuta et al. 2013; Vilmar et al.

2014; Zhang et al. 2014c; Gately et al. 2014; Reinmuth et al. 2014). Furthermore, many studies have demonstrated that its inhibition is detrimental to the growth of lung cancer cells in vitro and in vivo xenograft models (Samani et al. 2004; Cosaceanu et al. 2007; Dong et al. 2007).

However, many IGF-IR targeting agents have underperformed in clinical trials (Ramalingam et al. 2011; Weickhardt et al. 2012; Langer et al. 2014) despite strong preclinical evidence indicating that the IGF-axis is active in promoting lung cancer development and progression.

Thus, there is a need to modify and improve the clinical strategies for targeting the IGF-system in lung cancer. The research within this thesis suggests there could be a therapeutic benefit to disrupting IGF-IR signaling directly or indirectly, through the downstream mediator Akt1.

Furthermore, we have demonstrated unique roles for Akt1 and Akt2 in lung tumorigenesis that indicate the inhibition of Akt2 could be detrimental to therapeutic success.

The identification of suitable drug combinations is essential in order to address therapeutic efficacy of IGF-IR inhibition. We used a dual IGF-IR/IR inhibitor (BMS-754807) to target the IGF-IR in KRAS mutant lung adenocarcinoma cells expressing high levels of the IGF-

IR and found that this decreased the proliferation and survival of the cells when used alone.

Furthermore, BMS-754807 enhanced the effects of low doses of platinum chemotherapeutics when they were used simultaneously. The effects of IGF-IR inhibition were mediated by a decrease in signaling through the Akt pathway while the MAPK/ERK pathway remained unaffected, which is not surprising in cells containing an activating KRAS mutation. Inhibition

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of Akt signaling may be critical for BMS-754807 to act synergistically with the platinum chemotherapeutics as Akt has been reported to confer chemoresistance through activation of pro- survival pathways (Cortés-Sempere et al. 2013; Jeannot et al. 2014).

Some studies have reported that KRAS mutant cancer cells are not sensitive to IGF inhibition (Kim et al. 2012c), and signaling through the ERK pathway was sufficient for maintained growth. However, sensitivity of KRAS mutant cells to IGF-IR inhibition has been reported in other studies which further reported that KRAS mutant bronchial epithelial cells displaying a transformed phenotype had increased IGF-I and IGF-II expression (Kim et al.

2009). Detailed molecular analysis of KRAS mutant lung tumors found that these tumors had increased activation of Akt as well as ERK1/2 (Kalari et al. 2012). Similarly, KRAS mutant colorectal cells were resistant to treatment with BMS-754807 with the exception of KRAS mutant cells that had high levels of IGF-IR expression or Akt activation (Huang et al. 2014).

Consideration of the molecular profiles of lung cancer cell lines used in pre-clinical studies may aid in identifying biomarkers of treatment response and improve translation into clinical application.

Further investigation into the efficacy of IGF-IR inhibition in lung cancer will require confirmation of these findings using an in vivo model. Monolayer cell culture is a simple and efficient model to investigate cancer treatments, but is not a close representation to human tumors. Cell culture lacks the cell-cell interaction found in a solid tumor as well as the complexities and contributions of the tumor microenvironment. It further lacks the influence of the physiology of a whole organism, which is important not only to model possible influences on the tumor but also allows for early identification of possible adverse effects of therapeutic regimens. Currently, there is not a well-established xenograft model for lung cancer that allows

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the lung cancer cells to grow within the lungs themselves. Instead, many studies use sub- cutaneous injection of lung cancer cells into the flank of a mouse. Furthermore, xenografts using human lung cells injected into mice require the mice to be immune deficient. Elimination of immune cell interaction with the tumor omits an aspect of the tumor environment that could be relevant to the growth of cancer cells within a human patient. Therefore, our transgenic mouse model of IGF-IR-mediated lung tumorigenesis is a valuable model for lung cancer research as it allows the study of lung cancer development in a whole organism with a fully functioning immune system in a way that more closely resembles tumor formation in humans.

Individual variability in tumor burden within in our model, while reflective of human cancers, represents a limitation. This variation also exists within treatment groups; therefore, a large number of animals was required in order to identify differences between groups.

Furthermore, the use of a designated duration of doxycycline treatment as the study end point allows us to evaluate the rate at which tumors are forming or growing; however, further investigation into the tumor characteristics requires the comparison of tumors that are not at the same stage in development. This could be remedied by the use of imaging techniques to monitor and measure disease progression; however, our facilities do not have imaging equipment that would enable this.

Our SPC-IGFIR transgenic mouse model of lung cancer is clinically relevant to patients that have tumor growth driven by IGF signaling through the IGF-IR. Through additional manipulation of this system, we can elucidate the importance of different components of the

IGF-IR signaling pathway in the process of lung tumorigenesis. In this way, with the use of isoform specific Akt knockout mice, we have demonstrated that Akt isoforms do not have redundant functions in IGF-IR-mediated lung tumorigenesis. Previous studies have found

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increased activation of Akt in early stage NSCLC and in metaplasic and dysplastic pre-lesions compared to normal and hyperplastic lung tissue (Balsara et al. 2004; Tichelaar et al. 2004;

Roychoudhury et al. 2012) indicating involvement of Akt in the early stages of human lung cancer development. However, a limited number of studies have addressed the involvement of specific Akt isoforms. Previous transgenic models of lung tumorigenesis with a variety of initiators have similarly found isoform specific roles (Hollander et al. 2011; Linnerth-Petrik et al.

2014).

Here we report that mice lacking Akt1 had decreased tumor formation and a lower total tumor burden, suggesting that Akt1 may promote transformation and tumor formation in IGF-IR- mediated tumorigenesis. Previous reports demonstrate a role for Akt1 in the transformation of fibroblasts in vitro (Sun et al. 2001). In contrast, we found there was an increase in tumor burden in Akt2 deficient mice, which may be due to an increased number of transformation events. We were unable to conclusively determine if the difference in tumor burden is due to a protective role for Akt2 or an increase in activation of Akt1 in compensation for the absent Akt2. It is not clear if Akt2 can act as a tumor suppressor; however, high levels of intra-tumoral Akt2 in

NSCLC were associated with increased survival (Al-Saad et al. 2009). In addition to affecting tumor burden, the Akt2 deficiency caused a difference in tumor histology. Akt2 null tumors were diffuse within the lung tissue, while tumors expressing all Akt isoforms or lacking only Akt1 consistently developed as nodular adenomas or adenocarcinomas. This finding is novel, as neither isoform has been previously reported to influence tumor histology.

In order to investigate the mechanisms behind the difference in tumor formation in the

Akt2 null mice, we used RNA-sequencing to look at whole gene expression profiles of SPC-

IGFIR and SPC-IGFIR-Akt2-/- tumors. RNA-sequencing provided a useful tool to get a large

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amount of information from a small amount of starting material. We were limited in the amount of tumor tissue we were able to collect and were, unfortunately, unable to collect sufficient tumor tissue from Akt1 null mice to perform RNA-sequencing or protein analysis by western blotting. From the RNA-sequencing we were able to identify 458 differentially expressed transcripts in the Akt2 null tumors, which are listed in Appendix I. From this list we used pathway analysis to map these transcripts to biological functions. There were many differentially regulated genes that are involved in metabolism, migration, angiogenesis and inflammation.

However, interpretation of these results is limited because up- or downregulated expression does not necessarily correlate to functional activation of the protein since protein expression and function can be regulated at many levels, including translation, post-translational modification and degradation.

We did not see any evidence of metastasis to any common sites in any of our mice, which is consistent with previous characterizations of our transgenic SPC-IGFIR mice (Linnerth et al.

2009; Siwicky et al. 2010). Increased migration of tumor cells would be consistent with the presence of diffuse tumor tissue within the lungs of Akt2 null mice. Increased migration of Atk2 null tumor cells is consistent with previous reports of Akt1 being the isoform primarily involved in lung cancer cell migration (Lee et al. 2011; Huang et al. 2013). However, any increase in tumor cell migration was in the absence of development of an invasive or metastatic phenotype, such as EMT. Akt2, but not Akt1, was required for TGF-β induced EMT of lung cancer cells

(Xue et al. 2014). The lack of metastasis in our transgenic model indicates that while IGF-IR overexpression is sufficient to induce tumor initiation it is not sufficient to induce metastasis.

This is consistent with the dogma that multiple mutations are required for metastatic progression of cancer (Hanahan and Weinberg 2011).

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The lack of metastasis in our model highlights its use as an excellent model to investigate tumorigenesis and early stage disease but not advanced stage disease. Unfortunately, advanced stage disease represents the greatest clinical challenge, since this is where survival rates are the lowest and the point at which lung cancer is most frequently detected (Carnio et al. 2014).

Furthermore, this is the disease stage when most clinical trials are conducted. Previous studies have suggested that the role of both IGF-IR and Akt signaling in lung cancer could vary depending on disease stage (Lim et al. 2007; Kikuchi et al. 2012; Chen et al. 2013). Therefore, future research investigating each Akt isoform using a model of advanced disease or lung cancer metastasis would be useful. The differential expression of transcripts related to migration, angiogenesis, and inflammation, which are properties frequently associated with cancer progression and metastasis (Hanahan and Weinberg 2011), indicate that signaling through Akt isoforms would be relevant to advanced disease.

Akt2 null mice are known to develop hyperglycemia and a diabetes like phenotype (Cho et al. 2001a; Garofalo et al. 2003). Hyperglycemia has been associated with poor prognosis of lung cancer (Park et al. 2006) and cancer in general (Parekh et al. 2010) and the influence on our model of lung cancer cannot be ruled out. However, an increased tumor burden in Akt2 null mice is not consistently seen in mouse models of lung (Hollander et al. 2011) and breast cancers

(Watson and Moorehead 2013). Furthermore, while there is a strong link between obesity, metabolic syndrome, and diabetes with many types of cancer, the association is not as clear with lung cancer (Bhaskaran et al. 2014; Everatt et al. 2014; Szablewski 2014). Nonetheless, hyperglycemia is relevant to human cancers in patients with pre-existing diabetes and as an adverse effect of both IGF-IR and Akt targeted therapeutics (Hirai et al. 2010; Scagliotti and

Novello 2012). Hyperglycemia can be clinically managed by the use of antidiabetic drugs, such

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as metformin; however, the use of metformin in our model would be confounding because it inhibits IGF signaling in lung tissue, particularly through Akt (Memmott et al. 2010; Salani et al.

2012). Similar experiments using tissue specific or inducible isoform specific Akt knockout mice could clarify the source of some physiologic and genetic effects on tumorigenesis and progression. A tissue specific knockout that did not influence Akt function in insulin-responsive tissue could prevent or minimize hyperglycemia in the mice.

Our results suggest Akt1 is the primary isoform involved in IGF-IR-mediated lung tumorigenesis. This is consistent with reports from other transgenic models of lung cancer

(Hollander et al. 2011; Linnerth-Petrik et al. 2014) and some lung cancer cells (Lee et al. 2011;

Huang et al. 2013). Therefore, Akt1 represents a potential therapeutic target. Targeting Akt1 in combination with current therapies may be beneficial as inhibition of Akt1 has been reported to sensitize lung cancer cells to various therapeutic agents (Liu et al. 2007; Lee et al. 2008b, 2011;

Sun et al. 2012a; Zheng et al. 2013). Akt1 inhibition may also target angiogenesis, which requires Akt1 expression in endothelial cells (Lee et al. 2014). Furthermore, targeting only Akt1 may prevent metabolic side effects, such as hyperglycemia because Akt2 is the primary isoform in glucose metabolism (Cho et al. 2001b; Garofalo et al. 2003). Although we found the absence of Akt1 decreased tumor burden, it was not sufficient to alter proliferation rates within the tumors. Therefore it is not clear if inhibiting Akt1 alone would be effective in the treatment of established lung tumors. However, the absence of Akt1 was sufficient to decrease proliferation and increase apoptosis in another transgenic model of lung cancer (Linnerth et al. 2005). There are several reports of Akt2 promoting proliferation (Kim et al. 2011a; Sithanandam et al. 2012) and EMT (Xue et al. 2014) in lung cancer cells, so it is possible that Akt2 continues to drive proliferation in SPC-IGFR-Akt1-/- tumors. Research investigating isoform specific Akt inhibition

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in already established tumors in vivo will be essential in order to fully evaluate the potential of either isoform as a therapeutic target. Furthermore, future experiments should investigate the efficacy of inhibition of isoforms alone or in combination.

The research contained within this thesis further supports the importance of IGF-IR signaling in lung cancer. Despite the clinical challenges discovered in targeting the IGF-IR in lung cancer patients, these trials have identified areas where additional research is needed.

Continuation of these research programs will require an approach of bench to bedside and back to the bench again. Advancement of lung cancer treatment will require a more personalized approach based on detailed tumor characteristics including molecular signatures. Targeting the

IGF-IR may provide a therapeutic benefit in combination with traditional treatments; however, this may only apply to a specific subset of patients. This work suggests that patients who would benefit from IGF-axis disruption may include those with KRAS mutations and high IGF-IR expression. Additionally, we report that Akt1 deficiency inhibits IGF-IR signaling and is sufficient to decrease tumor development. Therefore, alternate therapeutic targets exist to disrupt

IGF signaling downstream of the receptor. Furthermore, this research demonstrates that Akt isoforms are not redundant in IGF-mediated lung tumorigenesis. Future studies investigating the role of Akt in cancer and physiology should treat each isoform as a unique, although closely related, proteins. These results provide information that will be useful to guide future studies in the much-needed development of new therapeutic strategies for lung cancer.

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Appendix I: Differentially expressed transcripts in tumors from SPC-IGFIR-Akt2 null mice compared to SPC-IGFIR mice

Table 11: Differentially expressed transcripts in tumors from SPC-IGFIR-Akt2-/- mice compared to SPC-IGFIR mice. RNA-Sequencing was performed by McGill University and Genome Quebec Innovation Centre. Ratio represents transcript expression in SPC-IGFIR-Akt2-/- tumors relative to SPC-IGFIR tumors. Ensembl Transcript ID Gene Symbol Ratio p-value q-value ENSMUST00000029919 Clca3 Infinity 5.00E-05 0.007141 ENSMUST00000035077 Ltf 25.31569 5.00E-05 0.007141 ENSMUST00000005685 Cyp2a5 21.23408 5.00E-05 0.007141 ENSMUST00000043873 Scgb3a1 13.67359 5.00E-05 0.007141 ENSMUST00000060574 BC048546 12.80762 5.00E-05 0.007141 ENSMUST00000113980 D6Mm5e 11.20833 5.00E-05 0.007141 ENSMUST00000041703 Dmkn 10.91772 5.00E-05 0.007141 ENSMUST00000090171 Adh7 10.89066 5.00E-05 0.007141 ENSMUST00000051176 Fam180a 8.455851 5.00E-05 0.007141 ENSMUST00000084424 Muc5b 8.342733 5.00E-05 0.007141 ENSMUST00000090269 Actc1 7.839491 5.00E-05 0.007141 ENSMUST00000032207 Klrg1 7.498334 5.00E-05 0.007141 ENSMUST00000079400 Aspg 7.396537 5.00E-05 0.007141 ENSMUST00000001384 Cnn1 6.612336 5.00E-05 0.007141 ENSMUST00000103304 Igkv1-133 6.322346 5.00E-05 0.007141 ENSMUST00000103448 Ighv5-9 6.242959 5.00E-05 0.007141 ENSMUST00000102817 Gap43 6.204049 5.00E-05 0.007141 ENSMUST00000092426 Ccdc153 6.055055 5.00E-05 0.007141 ENSMUST00000035938 Ccl5 5.987484 5.00E-05 0.007141 ENSMUST00000107914 Tpm2 5.852325 5.00E-05 0.007141 ENSMUST00000115525 Scgb3a2 5.793278 5.00E-05 0.007141 ENSMUST00000086084 Tnfsf18 5.764638 5.00E-05 0.007141 ENSMUST00000063956 Cd177 5.362441 5.00E-05 0.007141 ENSMUST00000020366 Gabrp 5.332529 5.00E-05 0.007141 ENSMUST00000109488 Snhg11 5.325806 5.00E-05 0.007141 ENSMUST00000092162 Lyz1 5.305321 5.00E-05 0.007141 ENSMUST00000027529 Rab17 5.25456 5.00E-05 0.007141 ENSMUST00000020439 Wif1 5.088886 5.00E-05 0.007141 ENSMUST00000070518 Nkg7 4.82263 5.00E-05 0.007141 ENSMUST00000036215 Foxj1 4.653964 5.00E-05 0.007141 ENSMUST00000022337 Cdhr1 4.549159 5.00E-05 0.007141 ENSMUST00000070132 Col8a2 4.528835 5.00E-05 0.007141 ENSMUST00000067048 Dnahc5 4.268325 5.00E-05 0.007141

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Ensembl Transcript ID Gene Symbol Ratio p-value q-value ENSMUST00000082390 mt-Rnr2 4.207928 5.00E-05 0.007141 ENSMUST00000064547 Isoc2b 4.161461 5.00E-05 0.007141 ENSMUST00000109838 Ntrk2 3.975703 5.00E-05 0.007141 ENSMUST00000024107 Wfdc1 3.947447 5.00E-05 0.007141 ENSMUST00000017836 Rhbdl3 3.886119 5.00E-05 0.007141 ENSMUST00000168727 Gdf10 3.805379 5.00E-05 0.007141 ENSMUST00000038160 Lum 3.736688 5.00E-05 0.007141 ENSMUST00000019721 Pdk4 3.711623 5.00E-05 0.007141 ENSMUST00000051401 Cldn4 3.676543 5.00E-05 0.007141 ENSMUST00000092788 Tmem100 3.603103 5.00E-05 0.007141 ENSMUST00000002850 Abcc6 3.589518 5.00E-05 0.007141 ENSMUST00000031093 Cckar 3.571698 5.00E-05 0.007141 ENSMUST00000033152 2010110P09Rik 3.567022 5.00E-05 0.007141 ENSMUST00000111332 Pcp4l1 3.506156 5.00E-05 0.007141 ENSMUST00000043577 Cldn5 3.486284 5.00E-05 0.007141 ENSMUST00000034026 Hpgd 3.425062 5.00E-05 0.007141 ENSMUST00000057679 C1qtnf2 3.424539 5.00E-05 0.007141 ENSMUST00000096363 Tmem28 3.404422 5.00E-05 0.007141 ENSMUST00000004232 Adh1 3.404139 5.00E-05 0.007141 ENSMUST00000069423 Mdk 3.326604 5.00E-05 0.007141 ENSMUST00000087638 Aldh1a1 3.252908 5.00E-05 0.007141 ENSMUST00000045902 Fmo2 3.232947 5.00E-05 0.007141 ENSMUST00000106992 Mapt 3.212263 5.00E-05 0.007141 ENSMUST00000008280 Fhl2 3.153011 5.00E-05 0.007141 ENSMUST00000027035 Sox17 3.147006 5.00E-05 0.007141 ENSMUST00000023807 Igfbp6 3.13084 5.00E-05 0.007141 ENSMUST00000093852 Zbtb16 3.125225 5.00E-05 0.007141 ENSMUST00000131787 2410006H16Rik 3.053915 5.00E-05 0.007141 ENSMUST00000079038 Hhip 3.021615 5.00E-05 0.007141 ENSMUST00000027409 Des 3.010577 5.00E-05 0.007141 ENSMUST00000048187 Ppp1r14a 2.977579 5.00E-05 0.007141 ENSMUST00000015498 Pcolce2 2.975104 5.00E-05 0.007141 ENSMUST00000102486 Hspb7 2.92941 5.00E-05 0.007141 ENSMUST00000023660 Ripply3 2.907139 5.00E-05 0.007141 ENSMUST00000090746 Hmgcs2 2.896981 5.00E-05 0.007141 ENSMUST00000171760 2410066E13Rik 2.874617 5.00E-05 0.007141 ENSMUST00000029030 Edn3 2.871471 5.00E-05 0.007141 ENSMUST00000075884 Msln 2.858186 5.00E-05 0.007141 ENSMUST00000042792 Scn7a 2.85585 5.00E-05 0.007141 ENSMUST00000052622 1810011O10Rik 2.847883 5.00E-05 0.007141

160

Ensembl Transcript ID Gene Symbol Ratio p-value q-value ENSMUST00000028187 Lamc3 2.841869 5.00E-05 0.007141 ENSMUST00000023329 Retnla 2.839506 5.00E-05 0.007141 ENSMUST00000036069 Mamdc2 2.783114 5.00E-05 0.007141 ENSMUST00000091882 Rps2-ps10 2.771659 5.00E-05 0.007141 ENSMUST00000028612 Pamr1 2.767935 5.00E-05 0.007141 ENSMUST00000099270 Thbd 2.758454 5.00E-05 0.007141 ENSMUST00000001055 Icam2 2.733009 5.00E-05 0.007141 ENSMUST00000076354 Tspan7 2.728012 5.00E-05 0.007141 ENSMUST00000085939 Fxyd6 2.72752 5.00E-05 0.007141 ENSMUST00000102707 Cyp4b1 2.711047 5.00E-05 0.007141 ENSMUST00000024857 Lbh 2.674242 5.00E-05 0.007141 ENSMUST00000105287 Dcn 2.667337 5.00E-05 0.007141 ENSMUST00000090180 Sema3g 2.662959 5.00E-05 0.007141 ENSMUST00000035804 Cdo1 2.64972 5.00E-05 0.007141 ENSMUST00000033036 Dkk3 2.64319 5.00E-05 0.007141 ENSMUST00000102765 Col23a1 2.635908 5.00E-05 0.007141 ENSMUST00000161737 Hsd11b1 2.601836 5.00E-05 0.007141 ENSMUST00000046383 Tnfsf10 2.596413 5.00E-05 0.007141 ENSMUST00000026890 Clec3b 2.594704 5.00E-05 0.007141 ENSMUST00000148314 Gm13889 2.585888 5.00E-05 0.007141 ENSMUST00000094894 Gm12824 2.572266 5.00E-05 0.007141 ENSMUST00000029140 Procr 2.552852 5.00E-05 0.007141 ENSMUST00000062405 Rasd1 2.539087 5.00E-05 0.007141 ENSMUST00000051572 Sdpr 2.538419 5.00E-05 0.007141 ENSMUST00000069360 Gpc3 2.53376 5.00E-05 0.007141 ENSMUST00000027861 Dpt 2.532391 5.00E-05 0.007141 ENSMUST00000088552 Myl9 2.525607 5.00E-05 0.007141 ENSMUST00000023101 Slc38a4 2.525397 5.00E-05 0.007141 ENSMUST00000099307 Ism1 2.49545 5.00E-05 0.007141 ENSMUST00000045756 S100a10 2.452563 5.00E-05 0.007141 ENSMUST00000054445 Hilpda 2.447604 5.00E-05 0.007141 ENSMUST00000034187 Mmp2 2.429417 5.00E-05 0.007141 ENSMUST00000054351 A930038C07Rik 2.422657 5.00E-05 0.007141 ENSMUST00000043314 Cyp2s1 2.396253 5.00E-05 0.007141 ENSMUST00000085453 Rasl12 2.364164 5.00E-05 0.007141 ENSMUST00000065112 Adamts15 2.349738 5.00E-05 0.007141 ENSMUST00000021170 Mxra7 2.339889 5.00E-05 0.007141 ENSMUST00000133064 Ecscr 2.312895 5.00E-05 0.007141 ENSMUST00000115383 A430107O13Rik 2.279236 5.00E-05 0.007141 ENSMUST00000000095 Tbx2 2.260387 5.00E-05 0.007141

161

Ensembl Transcript ID Gene Symbol Ratio p-value q-value ENSMUST00000095360 Igf1 2.21347 5.00E-05 0.007141 ENSMUST00000029078 Car2 2.207265 5.00E-05 0.007141 ENSMUST00000032260 Clec2d 2.194725 5.00E-05 0.007141 ENSMUST00000105999 Tinagl1 2.174479 5.00E-05 0.007141 ENSMUST00000052068 Rbp1 2.171271 5.00E-05 0.007141 ENSMUST00000114174 Cyyr1 2.165695 5.00E-05 0.007141 ENSMUST00000053753 Gja4 2.159294 5.00E-05 0.007141 ENSMUST00000113453 Hopx 2.148173 5.00E-05 0.007141 ENSMUST00000020896 Tspan13 2.14786 5.00E-05 0.007141 ENSMUST00000106663 Galntl4 2.128549 5.00E-05 0.007141 ENSMUST00000046687 Spon1 2.126219 5.00E-05 0.007141 ENSMUST00000073926 Rps12 2.100321 5.00E-05 0.007141 ENSMUST00000048308 C130074G19Rik 2.075876 5.00E-05 0.007141 ENSMUST00000042844 Nbl1 2.044262 5.00E-05 0.007141 ENSMUST00000059080 Rps21 2.042364 5.00E-05 0.007141 ENSMUST00000051937 Rasl11b 1.990664 5.00E-05 0.007141 ENSMUST00000020171 Ctgf 1.925761 5.00E-05 0.007141 ENSMUST00000113211 Rpl36a 1.891087 5.00E-05 0.007141 ENSMUST00000052678 Flnb 0.546158 5.00E-05 0.007141 ENSMUST00000028829 Spred1 0.524731 5.00E-05 0.007141 ENSMUST00000147041 D630013G24Rik 0.508436 5.00E-05 0.007141 ENSMUST00000096356 Csf2rb2 0.497056 5.00E-05 0.007141 ENSMUST00000006614 Epha2 0.49656 5.00E-05 0.007141 ENSMUST00000034989 Me1 0.487603 5.00E-05 0.007141 ENSMUST00000025486 Lmnb1 0.484376 5.00E-05 0.007141 ENSMUST00000018918 Cd68 0.484135 5.00E-05 0.007141 ENSMUST00000019999 D10Bwg1379e 0.483984 5.00E-05 0.007141 ENSMUST00000112393 Pm20d1 0.482112 5.00E-05 0.007141 ENSMUST00000000299 Itgb2 0.478506 5.00E-05 0.007141 ENSMUST00000020702 Igfbp3 0.47501 5.00E-05 0.007141 ENSMUST00000021907 Fbp2 0.472855 5.00E-05 0.007141 ENSMUST00000102917 Col15a1 0.468984 5.00E-05 0.007141 ENSMUST00000041357 Lrg1 0.465389 5.00E-05 0.007141 ENSMUST00000005493 Slc1a3 0.46348 5.00E-05 0.007141 ENSMUST00000025363 Hbegf 0.462938 5.00E-05 0.007141 ENSMUST00000035065 Ptgs2 0.45213 5.00E-05 0.007141 ENSMUST00000074839 Ear2 0.451485 5.00E-05 0.007141 ENSMUST00000064762 Mtap1b 0.450341 5.00E-05 0.007141 ENSMUST00000100542 Ly6c2 0.448967 5.00E-05 0.007141 ENSMUST00000031325 Areg 0.445974 5.00E-05 0.007141

162

Ensembl Transcript ID Gene Symbol Ratio p-value q-value ENSMUST00000038765 Inhbb 0.439371 5.00E-05 0.007141 ENSMUST00000027943 Batf3 0.43902 5.00E-05 0.007141 ENSMUST00000087033 Igj 0.438072 5.00E-05 0.007141 ENSMUST00000099112 Itga7 0.434221 5.00E-05 0.007141 ENSMUST00000100691 Ear1 0.433466 5.00E-05 0.007141 ENSMUST00000001027 Aox1 0.433448 5.00E-05 0.007141 ENSMUST00000067924 Lrrc8c 0.429554 5.00E-05 0.007141 ENSMUST00000032573 Pglyrp1 0.421656 5.00E-05 0.007141 ENSMUST00000068581 Gja1 0.414599 5.00E-05 0.007141 ENSMUST00000166433 Itgax 0.413899 5.00E-05 0.007141 ENSMUST00000162022 Glis3 0.41261 5.00E-05 0.007141 ENSMUST00000032265 Olr1 0.406732 5.00E-05 0.007141 ENSMUST00000037370 Sorcs2 0.406718 5.00E-05 0.007141 ENSMUST00000028252 Grb14 0.404993 5.00E-05 0.007141 ENSMUST00000067680 Adam12 0.393646 5.00E-05 0.007141 ENSMUST00000148750 Slc4a4 0.378105 5.00E-05 0.007141 ENSMUST00000000642 Hk2 0.372352 5.00E-05 0.007141 ENSMUST00000039490 Tnfsf9 0.368976 5.00E-05 0.007141 ENSMUST00000031324 Ereg 0.365076 5.00E-05 0.007141 ENSMUST00000127305 Epn3 0.364639 5.00E-05 0.007141 ENSMUST00000048880 Macc1 0.361857 5.00E-05 0.007141 ENSMUST00000103410 Igkc 0.357598 5.00E-05 0.007141 ENSMUST00000080882 Atp1a3 0.355197 5.00E-05 0.007141 ENSMUST00000060419 Gjb4 0.354545 5.00E-05 0.007141 ENSMUST00000071750 Col12a1 0.33772 5.00E-05 0.007141 ENSMUST00000022913 Tm7sf4 0.336123 5.00E-05 0.007141 ENSMUST00000050785 Lcn2 0.33397 5.00E-05 0.007141 ENSMUST00000009236 Derl3 0.327011 5.00E-05 0.007141 ENSMUST00000080953 Lrp2 0.325712 5.00E-05 0.007141 ENSMUST00000025198 Btnl2 0.317399 5.00E-05 0.007141 ENSMUST00000060747 Bhlha15 0.315288 5.00E-05 0.007141 ENSMUST00000049027 Slc26a9 0.30126 5.00E-05 0.007141 ENSMUST00000052969 Ston2 0.298448 5.00E-05 0.007141 ENSMUST00000028102 Kif5c 0.29419 5.00E-05 0.007141 ENSMUST00000103316 Igkv9-120 0.278261 5.00E-05 0.007141 ENSMUST00000002395 Rec8 0.259756 5.00E-05 0.007141 ENSMUST00000172746 Gm20482 0.256173 5.00E-05 0.007141 ENSMUST00000092888 Fbp1 0.252032 5.00E-05 0.007141 ENSMUST00000100689 Ear-ps2 0.250088 5.00E-05 0.007141 ENSMUST00000103328 Igkv10-96 0.240344 5.00E-05 0.007141

163

Ensembl Transcript ID Gene Symbol Ratio p-value q-value ENSMUST00000103396 Igkv3-12 0.230427 5.00E-05 0.007141 ENSMUST00000022580 Slc25a30 0.223854 5.00E-05 0.007141 ENSMUST00000041388 Serpine1 0.223621 5.00E-05 0.007141 ENSMUST00000050325 H2-Eb2 0.221937 5.00E-05 0.007141 ENSMUST00000095262 Gm88 0.219536 5.00E-05 0.007141 ENSMUST00000045870 2310014L17Rik 0.209779 5.00E-05 0.007141 ENSMUST00000075406 Szt2 0.208411 5.00E-05 0.007141 ENSMUST00000103303 Igkv1-135 0.206202 5.00E-05 0.007141 ENSMUST00000030709 Smpdl3b 0.204907 5.00E-05 0.007141 ENSMUST00000103354 Igkv4-59 0.203384 5.00E-05 0.007141 ENSMUST00000103378 Igkv8-30 0.194312 5.00E-05 0.007141 ENSMUST00000103321 Igkv1-110 0.193608 5.00E-05 0.007141 ENSMUST00000173575 Gm3912 0.188737 5.00E-05 0.007141 ENSMUST00000103317 Igkv1-117 0.18166 5.00E-05 0.007141 ENSMUST00000029794 Them5 0.175646 5.00E-05 0.007141 ENSMUST00000103365 Igkv12-46 0.171137 5.00E-05 0.007141 ENSMUST00000103536 Ighv8-11 0.164953 5.00E-05 0.007141 ENSMUST00000103459 Ighv5-17 0.162442 5.00E-05 0.007141 ENSMUST00000103320 Igkv14-111 0.156192 5.00E-05 0.007141 ENSMUST00000103498 Ighv1-9 0.152991 5.00E-05 0.007141 ENSMUST00000033198 Crym 0.149997 5.00E-05 0.007141 ENSMUST00000109624 Ighv1-33 0.143051 5.00E-05 0.007141 ENSMUST00000103400 Igkv3-5 0.141797 5.00E-05 0.007141 ENSMUST00000103330 Igkv10-94 0.135935 5.00E-05 0.007141 ENSMUST00000103387 Igkv8-21 0.128481 5.00E-05 0.007141 ENSMUST00000115421 Steap4 0.125348 5.00E-05 0.007141 ENSMUST00000103383 Igkv6-25 0.124169 5.00E-05 0.007141 ENSMUST00000103401 Igkv3-4 0.122656 5.00E-05 0.007141 ENSMUST00000008297 Clstn3 0.122304 5.00E-05 0.007141 ENSMUST00000103367 Igkv12-44 0.120022 5.00E-05 0.007141 ENSMUST00000031314 Alb 0.109911 5.00E-05 0.007141 ENSMUST00000103331 Igkv19-93 0.10496 5.00E-05 0.007141 ENSMUST00000103423 Ighg3 0.098739 5.00E-05 0.007141 ENSMUST00000103399 Igkv3-7 0.098676 5.00E-05 0.007141 ENSMUST00000095541 Susd2 0.098153 5.00E-05 0.007141 ENSMUST00000103418 Ighg 0.093538 5.00E-05 0.007141 ENSMUST00000103416 Ighg2c 0.084251 5.00E-05 0.007141 ENSMUST00000103319 Igkv2-112 0.08361 5.00E-05 0.007141 ENSMUST00000103528 Ighv8-8 0.081224 5.00E-05 0.007141 ENSMUST00000103404 Igkv3-1 0.079126 5.00E-05 0.007141

164

Ensembl Transcript ID Gene Symbol Ratio p-value q-value ENSMUST00000103454 Ighv2-9-1 0.078421 5.00E-05 0.007141 ENSMUST00000103520 Ighv8-5 0.070774 5.00E-05 0.007141 ENSMUST00000173446 Zfp419 0.067426 5.00E-05 0.007141 ENSMUST00000103377 Igkv6-32 0.065757 5.00E-05 0.007141 ENSMUST00000171139 H2-M2 0.064012 5.00E-05 0.007141 ENSMUST00000103324 Igkv15-103 0.060707 5.00E-05 0.007141 ENSMUST00000103350 Igkv4-68 0.050656 5.00E-05 0.007141 ENSMUST00000103397 Igkv3-10 0.050399 5.00E-05 0.007141 ENSMUST00000103420 Ighg1 0.015317 5.00E-05 0.007141 ENSMUST00000103390 Igkv8-18 -Infinity 5.00E-05 0.007141 ENSMUST00000103457 Ighv5-15 -Infinity 5.00E-05 0.007141 ENSMUST00000103503 Ighv1-30 -Infinity 5.00E-05 0.007141 ENSMUST00000119005 Gm11222 -Infinity 5.00E-05 0.007141 ENSMUST00000064862 Ceacam2 14.16187 1.00E-04 0.012415 ENSMUST00000058077 Tmem212 5.040441 1.00E-04 0.012415 ENSMUST00000095774 Cdhr3 4.518175 1.00E-04 0.012415 ENSMUST00000075161 Actg2 3.157516 1.00E-04 0.012415 ENSMUST00000164666 Mcc 3.032778 1.00E-04 0.012415 ENSMUST00000071977 Mfap2 2.964729 1.00E-04 0.012415 ENSMUST00000105874 Slc30a2 2.872188 1.00E-04 0.012415 ENSMUST00000049941 Scn3b 2.778815 1.00E-04 0.012415 ENSMUST00000003569 Inmt 2.594183 1.00E-04 0.012415 ENSMUST00000009425 Rarres2 2.562266 1.00E-04 0.012415 ENSMUST00000062254 Clec14a 2.513295 1.00E-04 0.012415 ENSMUST00000021880 Ctla2a 2.283964 1.00E-04 0.012415 ENSMUST00000099349 Hspa12b 2.257021 1.00E-04 0.012415 ENSMUST00000029376 Tm4sf1 2.191897 1.00E-04 0.012415 ENSMUST00000029766 Bcar3 2.17225 1.00E-04 0.012415 ENSMUST00000031670 Gng11 2.169466 1.00E-04 0.012415 ENSMUST00000003687 Tgfb3 2.141824 1.00E-04 0.012415 ENSMUST00000034172 Ces1d 2.114769 1.00E-04 0.012415 ENSMUST00000062783 AY036118 2.064082 1.00E-04 0.012415 ENSMUST00000088585 Sulf1 1.990144 1.00E-04 0.012415 ENSMUST00000024755 Clic5 1.924348 1.00E-04 0.012415 ENSMUST00000042970 Kcnj2 0.526916 1.00E-04 0.012415 ENSMUST00000047622 Plxna4 0.525224 1.00E-04 0.012415 ENSMUST00000047206 Plekhh2 0.518746 1.00E-04 0.012415 ENSMUST00000096355 Csf2rb 0.513917 1.00E-04 0.012415 ENSMUST00000093962 Ccnd1 0.488967 1.00E-04 0.012415 ENSMUST00000169190 Bend4 0.473045 1.00E-04 0.012415

165

Ensembl Transcript ID Gene Symbol Ratio p-value q-value ENSMUST00000028663 Creb3l1 0.454273 1.00E-04 0.012415 ENSMUST00000047687 Entpd3 0.363324 1.00E-04 0.012415 ENSMUST00000028794 Siglec1 0.282744 1.00E-04 0.012415 ENSMUST00000006071 Otx1 0.249073 1.00E-04 0.012415 ENSMUST00000036426 Prss35 0.200769 1.00E-04 0.012415 ENSMUST00000103309 Igkv17-127 0.114158 1.00E-04 0.012415 ENSMUST00000103310 Igkv14-126 0.100113 1.00E-04 0.012415 ENSMUST00000173038 Gm3654 0.04522 1.00E-04 0.012415 ENSMUST00000032203 A2m 0.032619 1.00E-04 0.012415 ENSMUST00000103495 Ighv10-3 0 1.00E-04 0.012415 ENSMUST00000028985 Bpifa1 384.5374 0.00015 0.017055 ENSMUST00000057831 Cilp2 19.66012 0.00015 0.017055 ENSMUST00000090568 Ptprz1 6.629729 0.00015 0.017055 ENSMUST00000024015 Guca2a 6.040299 0.00015 0.017055 ENSMUST00000089295 Fam19a4 5.64843 0.00015 0.017055 ENSMUST00000058119 Arxes2 3.655275 0.00015 0.017055 ENSMUST00000056686 2210011C24Rik 3.463955 0.00015 0.017055 ENSMUST00000029845 Ddah1 3.306305 0.00015 0.017055 ENSMUST00000004051 Hlf 2.864593 0.00015 0.017055 ENSMUST00000002926 Pla1a 2.71863 0.00015 0.017055 ENSMUST00000031263 Slc10a6 2.598069 0.00015 0.017055 ENSMUST00000108493 Dact3 2.483302 0.00015 0.017055 ENSMUST00000020926 Fam84a 2.446552 0.00015 0.017055 ENSMUST00000025092 Tmem178 2.431877 0.00015 0.017055 ENSMUST00000128402 Kif26a 2.322373 0.00015 0.017055 ENSMUST00000167824 Rab3c 2.185374 0.00015 0.017055 ENSMUST00000022954 Khdrbs3 2.177285 0.00015 0.017055 ENSMUST00000061799 Loxl1 2.072985 0.00015 0.017055 ENSMUST00000047040 Prkcdbp 2.025924 0.00015 0.017055 ENSMUST00000100473 Kif13b 0.511794 0.00015 0.017055 ENSMUST00000071985 Slco4c1 0.501689 0.00015 0.017055 ENSMUST00000100330 Tanc2 0.483947 0.00015 0.017055 ENSMUST00000105902 Shank2 0.362328 0.00015 0.017055 ENSMUST00000111338 Ckap5 0.305152 0.00015 0.017055 ENSMUST00000103395 Igkv6-13 0.070172 0.00015 0.017055 ENSMUST00000169874 Gm4242 -Infinity 0.00015 0.017055 ENSMUST00000036136 Colec11 4.530813 2.00E-04 0.021165 ENSMUST00000030952 Tnfrsf4 4.354056 2.00E-04 0.021165 ENSMUST00000095171 Atp2c2 4.342091 2.00E-04 0.021165 ENSMUST00000065487 Prx 3.8862 2.00E-04 0.021165

166

Ensembl Transcript ID Gene Symbol Ratio p-value q-value ENSMUST00000046993 Dnahc3 3.485921 2.00E-04 0.021165 ENSMUST00000064433 Tmod2 3.317945 2.00E-04 0.021165 ENSMUST00000074262 Calcrl 2.561005 2.00E-04 0.021165 ENSMUST00000123325 Ankrd33b 2.305932 2.00E-04 0.021165 ENSMUST00000073043 Cxcl12 2.302482 2.00E-04 0.021165 ENSMUST00000031246 Ibsp 2.2295 2.00E-04 0.021165 ENSMUST00000102844 Rps27a 1.95706 2.00E-04 0.021165 ENSMUST00000066728 Pde5a 1.896802 2.00E-04 0.021165 ENSMUST00000070673 Rab31 1.893156 2.00E-04 0.021165 ENSMUST00000082388 mt-Rnr1 1.877254 2.00E-04 0.021165 ENSMUST00000075190 Cdh11 1.795822 2.00E-04 0.021165 ENSMUST00000072607 Ints1 0.559408 2.00E-04 0.021165 ENSMUST00000040021 Ptpn23 0.554055 2.00E-04 0.021165 ENSMUST00000080449 Hspa2 0.501715 2.00E-04 0.021165 ENSMUST00000098796 Fat1 0.400615 2.00E-04 0.021165 ENSMUST00000109416 R3hdml 0.307085 2.00E-04 0.021165 ENSMUST00000167435 Akt2 0.205547 2.00E-04 0.021165 ENSMUST00000103348 Igkv4-70 0.174986 2.00E-04 0.021165 ENSMUST00000103534 Ighv1-63 0.075119 2.00E-04 0.021165 ENSMUST00000113547 Tcte1 7.417176 0.00025 0.024197 ENSMUST00000073554 Cytl1 6.19056 0.00025 0.024197 ENSMUST00000032907 Calca 4.788952 0.00025 0.024197 ENSMUST00000058747 9030224M15Rik 4.393802 0.00025 0.024197 ENSMUST00000078170 Dynlrb2 3.817161 0.00025 0.024197 ENSMUST00000065957 Syt5 3.601655 0.00025 0.024197 ENSMUST00000023583 Ahsg 3.475475 0.00025 0.024197 ENSMUST00000046944 D630003M21Rik 3.426273 0.00025 0.024197 ENSMUST00000009329 Ccl8 3.348837 0.00025 0.024197 ENSMUST00000077248 Myh11 2.720383 0.00025 0.024197 ENSMUST00000034702 Lysmd2 2.653433 0.00025 0.024197 ENSMUST00000021796 Edn1 2.634976 0.00025 0.024197 ENSMUST00000049930 Tcf21 2.500853 0.00025 0.024197 ENSMUST00000032341 Art4 2.309099 0.00025 0.024197 ENSMUST00000041865 Nostrin 2.222849 0.00025 0.024197 ENSMUST00000022629 Dpysl2 1.976175 0.00025 0.024197 ENSMUST00000090726 Slc43a3 1.966628 0.00025 0.024197 ENSMUST00000074267 Rps7 1.815206 0.00025 0.024197 ENSMUST00000114474 1600029D21Rik 0.566684 0.00025 0.024197 ENSMUST00000038794 Dpp9 0.539451 0.00025 0.024197 ENSMUST00000161553 Parp4 0.536231 0.00025 0.024197

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Ensembl Transcript ID Gene Symbol Ratio p-value q-value ENSMUST00000040999 Aox3 0.474372 0.00025 0.024197 ENSMUST00000124191 Gm2115 0.463908 0.00025 0.024197 ENSMUST00000085786 Card11 0.45793 0.00025 0.024197 ENSMUST00000004201 Col5a3 0.419325 0.00025 0.024197 ENSMUST00000099513 Hcn2 0.381552 0.00025 0.024197 ENSMUST00000103302 Igkv2-137 0.326053 0.00025 0.024197 ENSMUST00000093811 Filip1 0.276465 0.00025 0.024197 ENSMUST00000103493 Ighv1-4 0.156856 0.00025 0.024197 ENSMUST00000103451 Ighv2-6 0.144561 0.00025 0.024197 ENSMUST00000103351 Igkv4-63 0.08535 0.00025 0.024197 ENSMUST00000100289 Itgbl1 2.927299 3.00E-04 0.027665 ENSMUST00000022460 Galntl2 2.438459 3.00E-04 0.027665 ENSMUST00000022921 Angpt1 2.297588 3.00E-04 0.027665 ENSMUST00000067458 Sema5a 2.284186 3.00E-04 0.027665 ENSMUST00000080630 Tspan8 2.08099 3.00E-04 0.027665 ENSMUST00000032237 Bms1 2.051018 3.00E-04 0.027665 ENSMUST00000116456 Cyth3 1.885466 3.00E-04 0.027665 ENSMUST00000005256 Ndrg1 1.876409 3.00E-04 0.027665 ENSMUST00000049020 Irgq 0.56901 3.00E-04 0.027665 ENSMUST00000150819 AI661453 0.546429 3.00E-04 0.027665 ENSMUST00000035667 Trim62 0.538368 3.00E-04 0.027665 ENSMUST00000113306 B3gnt7 0.528741 3.00E-04 0.027665 ENSMUST00000053273 Itpr2 0.518404 3.00E-04 0.027665 ENSMUST00000005671 Igf1r 0.500593 3.00E-04 0.027665 ENSMUST00000028016 Prrc2c 0.496884 3.00E-04 0.027665 ENSMUST00000027237 Il18rap 0.38488 3.00E-04 0.027665 ENSMUST00000103369 Igkv12-41 0.252141 3.00E-04 0.027665 ENSMUST00000103540 Ighv8-12 0.251723 3.00E-04 0.027665 ENSMUST00000101448 Ccdc164 4.265693 0.00035 0.03137 ENSMUST00000034227 Pllp 2.82044 0.00035 0.03137 ENSMUST00000066295 Kcnk3 2.552162 0.00035 0.03137 ENSMUST00000018113 Ptgis 2.379832 0.00035 0.03137 ENSMUST00000165902 Cyp2d22 2.36682 0.00035 0.03137 ENSMUST00000050715 Pcsk5 2.309947 0.00035 0.03137 ENSMUST00000101126 Lrig1 1.995839 0.00035 0.03137 ENSMUST00000037029 9430020K01Rik 1.908557 0.00035 0.03137 ENSMUST00000015664 Ctsk 1.858293 0.00035 0.03137 ENSMUST00000031637 Ndufa4 1.791751 0.00035 0.03137 ENSMUST00000028881 Il1b 0.405097 0.00035 0.03137 ENSMUST00000109222 Chst4 6.600887 4.00E-04 0.034786

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Ensembl Transcript ID Gene Symbol Ratio p-value q-value ENSMUST00000107073 Higd1b 3.373766 4.00E-04 0.034786 ENSMUST00000038775 Ndn 3.189499 4.00E-04 0.034786 ENSMUST00000030868 Sema3d 2.485369 4.00E-04 0.034786 ENSMUST00000056153 Fads6 2.476667 4.00E-04 0.034786 ENSMUST00000021714 Zfyve21 2.122861 4.00E-04 0.034786 ENSMUST00000029635 Gucy1b3 1.897386 4.00E-04 0.034786 ENSMUST00000032961 Nupr1 1.887694 4.00E-04 0.034786 ENSMUST00000040531 Samd4b 0.570718 4.00E-04 0.034786 ENSMUST00000014957 Stc1 0.526207 4.00E-04 0.034786 ENSMUST00000029700 Sema4a 0.497497 4.00E-04 0.034786 ENSMUST00000102868 Malt1 0.486975 4.00E-04 0.034786 ENSMUST00000040715 Mustn1 2.309339 0.00045 0.037914 ENSMUST00000169872 Ret 2.268093 0.00045 0.037914 ENSMUST00000019683 Rcn3 2.087144 0.00045 0.037914 ENSMUST00000023958 Leprel2 1.892214 0.00045 0.037914 ENSMUST00000080300 Rps25 1.797338 0.00045 0.037914 ENSMUST00000079716 Rpl17 1.712334 0.00045 0.037914 ENSMUST00000032322 Lrp6 0.564951 0.00045 0.037914 ENSMUST00000060989 Sorl1 0.546717 0.00045 0.037914 ENSMUST00000036328 Zfhx2 0.5049 0.00045 0.037914 ENSMUST00000017544 Stac2 0.365651 0.00045 0.037914 ENSMUST00000093812 Cd109 0.32432 0.00045 0.037914 ENSMUST00000031370 Rimbp2 0.243314 0.00045 0.037914 ENSMUST00000103496 Ighv1-7 0.139625 0.00045 0.037914 ENSMUST00000098496 BC007180 4.52416 5.00E-04 0.041334 ENSMUST00000041591 Enpp2 2.107788 5.00E-04 0.041334 ENSMUST00000094469 Selm 1.933384 5.00E-04 0.041334 ENSMUST00000039990 Leprel1 0.511598 5.00E-04 0.041334 ENSMUST00000168757 Atp6v0a1 0.445439 5.00E-04 0.041334 ENSMUST00000114482 Il1rn 0.390504 5.00E-04 0.041334 ENSMUST00000034554 Pou2af1 0.382028 5.00E-04 0.041334 ENSMUST00000094344 Wfdc10 0.35126 5.00E-04 0.041334 ENSMUST00000020878 Efcab10 7.253866 0.00055 0.044118 ENSMUST00000018710 Slc2a4 3.96585 0.00055 0.044118 ENSMUST00000010188 Zmynd10 3.659179 0.00055 0.044118 ENSMUST00000064257 Tchh 2.875235 0.00055 0.044118 ENSMUST00000028103 Lypd6b 2.569451 0.00055 0.044118 ENSMUST00000031264 Plac8 2.168429 0.00055 0.044118 ENSMUST00000056089 Tmem37 0.566284 0.00055 0.044118 ENSMUST00000002320 Ppard 0.542368 0.00055 0.044118

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Ensembl Transcript ID Gene Symbol Ratio p-value q-value ENSMUST00000079601 Etv5 0.478749 0.00055 0.044118 ENSMUST00000103505 Ighv1-19 0.231111 0.00055 0.044118 ENSMUST00000077523 Serpine1 0.090717 0.00055 0.044118 ENSMUST00000025636 Ms4a8a 0.089208 0.00055 0.044118 ENSMUST00000070209 Spock1 0.031456 0.00055 0.044118 ENSMUST00000148517 Mfap5 2.271681 6.00E-04 0.047055 ENSMUST00000090543 Nr1d2 1.805521 6.00E-04 0.047055 ENSMUST00000029440 Olfml3 1.761393 6.00E-04 0.047055 ENSMUST00000040069 Colec12 1.716009 6.00E-04 0.047055 ENSMUST00000027952 Plxna2 0.571164 6.00E-04 0.047055 ENSMUST00000008032 Crlf1 0.543436 6.00E-04 0.047055 ENSMUST00000052348 Slc22a18 0.535169 6.00E-04 0.047055 ENSMUST00000054599 Sprr1a 0.444043 6.00E-04 0.047055 ENSMUST00000016696 Foxred2 0.361293 6.00E-04 0.047055 ENSMUST00000081809 Ighv1-73 -Infinity 6.00E-04 0.047055 ENSMUST00000103553 Tcrg-V7 Infinity 0.00065 0.049863 ENSMUST00000094666 Tmem200b 3.70607 0.00065 0.049863 ENSMUST00000174459 Gypc 2.472396 0.00065 0.049863 ENSMUST00000027137 Slc40a1 2.366722 0.00065 0.049863 ENSMUST00000108110 Fxyd1 2.255817 0.00065 0.049863 ENSMUST00000044113 Eif2c2 0.585513 0.00065 0.049863 ENSMUST00000098548 Scn1b 0.567298 0.00065 0.049863 ENSMUST00000102672 Nfe2l2 0.517994 0.00065 0.049863 ENSMUST00000081945 Krt83 0.355421 0.00065 0.049863 ENSMUST00000103443 Ighv2-2 0.2795 0.00065 0.049863

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Appendix II: KLF5 knockdown decreases proliferation of non-small cell lung cancer cells

Background Over the past decade there has been very little improvement to the incredibly poor survival rate of patients with lung cancer despite the development and incorporation of new therapeutics (Horn and Carbone 2014; Jakopovic et al. 2014). Therefore, a better understanding of disease pathology as well as the discovery and evaluation of new therapeutic targets is needed for progress in lung cancer treatment. Our lab developed an inducible transgenic mouse model of lung cancer in which overexpression of the IGF-IR in Clara cells or type-II alveolar cells led to the development of lung tumors (Linnerth et al. 2009). These tumors expressed high levels of

Kruppel-like factor-5 (KLF5), which had not previously been reported in lung tumors. However,

KLF5 has been associated with other types of cancer including colon (Bateman et al. 2004), breast (Chen et al. 2002; Liu et al. 2009; Zhao et al. 2012) and prostate cancers (Chen et al.

2003b; Nakajima et al. 2011).

KLF5, also known as basic transcription element-binding protein-2 (BTEB2) or intestine enriched KLF (IKLF), is a transcription factor belonging to a large family with 20 structurally similar members characterized by three tandem zinc-finger domains at the c-terminus (Dong and

Chen 2009). KLF5 promotes proliferation during development in epithelial tissue (Ohnishi et al.

2000) and in adults is highly expressed in intestinal crypt cells (Shindo et al. 2002) where it is required for proliferation and maintenance of intestinal architecture (McConnell et al. 2011; Bell and Shroyer 2015; Nandan et al. 2015). In these intestinal epithelial stem cells, KLF5 is regulated by Wnt signaling (McConnell et al. 2011). KLF5 expression in transformed cells can be regulated by the MAPK pathway (Nandan et al. 2004; Liu et al. 2009) and HIF-1α (Mori et al.

171

2009). KLF5 is also involved in other cellular functions related to cancer including apoptosis

(Zhu et al. 2006; Suzuki et al. 2007), migration (Yang et al. 2008) and EMT (Shimamura et al.

2011). Furthermore, KLF5 is essential in HRAS-mediated transformation of fibroblasts (Nandan et al. 2004) and for intestinal tumorigenesis in a KRAS transgenic mouse model (Nandan et al.

2010). Additionally, KLF5 contributes to stemness where it is involved in the self-renewal of embryonic stem cells (Jiang et al. 2008; Parisi et al. 2008) and can replace KLF4 to induce pleuripotency with Oct4, Sox-2, and c-myc (Nakagawa et al. 2008). The regulation of KLF5 is complex and includes phosphorylation, acetylation, ubiquitination and sumoylation (Dong and

Chen 2009). KLF5 can switch from a transcriptional activator to repressor through acetylation by

TGF-β (Guo et al. 2009a, 2009b, 2009c), causing KLF5 to be pro-proliferative or anti- proliferative in a context-dependent manner. As such, KLF5 has been reported to promote proliferation in some cell lines (Bateman et al. 2004; Chen et al. 2006a; Yang et al. 2007;

Nandan et al. 2008) and inhibit proliferation in others (Chen et al. 2003a; Bateman et al. 2004;

Yang et al. 2005).

KLF5 is expressed in lung tissues (Sogawa et al. 1993; Conkright et al. 1999; Shi et al.

1999) and is essential for normal lung development and function (Wan et al. 2008). Recently, Li et al. (2014a) reported that KLF5 expression was higher in lung tumors compared to adjacent normal lung tissue. However, another study found that high expression KLF5 in NSCLC tumors was associated with better disease specific survival and KLF5 knockdown in lung cancer cells increased the anthracycline drug transporter ABCG2 which conferred doxorubicin resistance

(Meyer et al. 2010). KLF5 has been reported to increase proliferation in lung cancer cells in a few studies (Li et al. 2014b, 2014c), while another study reported it had no influence on lung cancer cell proliferation (Meyer et al. 2010). Therefore, the role of KLF5 in lung cancer remains

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unclear. Here we investigate if the downregulation of KLF5 in human lung cancer cell lines influences proliferation and survival.

Materials and Methods

Cell lines and Reagents Human lung cancer cell lines, A549 and NCI-H358, were purchased from American

Type Culture Collection (ATCC, Manassas, VA). A549 and NCI-H358 cells were cultured in

RPMI 1640 medium supplemented with 10% FBS and 1% antibiotic/antimycotic (v/v) (Life

Technologies, Burlington, ON). HBE-135 cells were cultured in KSFM medium supplemented with 0.05ug/ml bovine pituitary extract, 5ng/ml recombinant human epidermal growth factor, and 1% antibiotic/antimycotic (v/v) (Life Technologies, Burlington, ON).

Transient transfection with siRNA A549 and NCI-H358 cells were transfected with one of three siRNA constructs targeted to KLF5 or a GC control siRNA (Life Technologies, Burlington, ON) with lipofectamine 2000

(Life Technologies, Burlington, ON) according to mauracturer recommendations. Cells were plated the day before to reach an approximate confluency of 30% at the time of transfection.

Transfection reagents were removed and growth media refreshed 5 hours post-transfection, and cells were lysed or fixed for analysis at 24 hours post-transfection.

Generation of stable cell lines A549 and NCI-H358 cells were transduced with 1x105 infectious units of lentiviral particles containing shRNA for KLF5 or control shRNA (Santa Cruz Biotechnology, Dallas,

TX) according to manufacturer’s reccommendations. Cells were plated on 24 well tissue culture plate at a density of 1.25x104 cells/well and allowed to adhere for 24 hours before transduction.

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Infected cells were selected for using 2.5ug/ml of puromycin (Cell Signaling Technology,

Danvers, MA) in growth media as determined using a dose titration in both cell lines. Stable cells were maintained in selection media containing puromycin.

Western Blotting Adherent cells were lysed, protein was isolated, and western blots performed as previously described [Jones 2007]. For transfected cells, protein was collected at 24 hours post- transfection. Primary antibodies against KLF5 (1:500) (Millipore, Etobicoke, ON) and β-actin

(1:5000) (Cell Signaling Technology, Danvers, MA) were used overnight at 4°C. Cells were then exposed to an anti-Rabbit secondary antibody (1:2000) (Cell Signaling Technology, Danvers,

MA) for one hour at room temperature. Membranes were visualized using chemiluminescence substrate (PerkinElmer, Inc., Waltham, MA) and FluoroChem 8800 gel documentation system

(Alpha Innotech – ProteinSimple, Toronto, ON). Densitomitry was performed with FluoroChem

8800 software (Alpha Innotech – ProteinSimple, Toronto, ON).

RNA isolation and qRT-PCR RNA was isolated using the mirVana miRNA Isolation Kit (Ambion - Life Technologies,

Burlington, ON) according to manufacturer protocol. RNA was quantified and tested for quality using a Nanodrop 200c Spectrophotometer (Thermo Scientific, Waltham MA). RNA (1ug) was reveresed-transcribed using SuperScript II Reverse Transcriptase, 5x First-Strand buffer, DTT,

RNaseOUT Recombinant Ribonuclease Inhibitor, Oligo(dT)12-18, and dNTP (Life

Technologies, Burlington, ON). Gene expression determined using qPCR reaction with Platinum

SYBR Green qPCR SuperMix-UDG (Life Technologies, Burlington, ON) and performed on the

CFX Connect Real-time PCR Detection System (Bio-rad Laboratories, Mississauga, ON).

Primers for qPCR were used for KLF5 (Origene Technologies, Rockville, MD), and ACTB.

174

Relative quantification of gene expression using qPCR was determined using the ΔΔCq method normalizing to Actb as a reference gene using CFX-Manager 3.1 (Bio-rad Laboratories,

Missisauga, ON).

Immunofluorescence Stable cell lines were plated on coverslips in 6-well dishes at a density of 1x105cells/well and allowed to adhere for 24 hours. Alternately, cells were fixed at 24 hours post-transfection in

10% buffered formalin (Fisher Scientific, Ottawa, ON) for 1 hour. Cells were washed in PBS, permeabilized in 0.5% Triton-X in PBS, and blocked in 5% BSA in PBS. Then the cells were incuabated in primary antibodies against proliferation marker phospho-Histone H3 (1:2000)

(Abcam, Toronto, ON) or apoptosis marker cleaved caspase 3 (1:200) (Millipore, Etobicoke,

ON) overnight at 4°C. Cells were incubated with fluorescent anti-Rabbit secondary antibody

(1:200) (Alexaflour – Life Technologies, Burlington, ON) for 1 hour at room temperature. Dapi nuclear stain was used as a counterstain, and coverslips were mounted using Prolong-gold (Life

Technologies, Burlington, ON). Images were captured using Olympus BX61 microscope and

Metamorph software (Molecular Devices, Sunnyvale, CA).

Statistics All experiments were repeated in triplicate or quadruplicate. Statistical analysis was performed using Graphpad Prism 6 (Graphpad Software, Inc., La Jolla, CA). Means were compared using a Student’s T-test. Error is represented by standard error of the mean (SEM).

Statistical significance is noted as p<0.05.

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Results

Transient KLF5 knockdown decreased proliferation of NCI-H358 lung cancer cells KLF5 expression was decreased in two lung cancer cell lines, A549 and NCI-H358, by transfection with three different siRNA constructs that target KLF5. KLF5 expression was lower

24 hours post-transfection at the RNA level with siRNA construct #3 causing the greatest decrease (Fig 20a,b). Therefore RNA construct #3 was used for subsequent experiments.

Western blots indicated that there was a decrease in KLF5 protein (52kDa) in A549 cells transduced with KLF5 siRNA, however, non-specific bands appeared in all samples at 48kDa,

30kDa and 18kDa (Fig 20c). KLF5 downregulation had no influence on proliferation of A549 cells (Fig 20d), but decreased proliferation in NCI-H358 cells (Fig 20e). KLF5 downregulation caused a non-significant increase in apoptosis in both A549 and NCI-H358 (Fig 20f,g).

Stable KLF5 knockdown decreased proliferation of NCI-H358 lung cancer cells Lung cancer cell lines A549 and NCI-H358 were transduced with lentiviral particles containing shRNA targeting KLF5 in order to generate cell lines with stable KLF5 downregulation. KLF5 downregulation was confirmed in these cells using qRT-PCR (Fig 21a,b).

Western blotting on protein collected from cells two passages following puromycin selection revealed a band of approximately 52kDa, the expected size for KLF5, which was decreased by

61% and 59% in A549 and NCI-H358 KLF5 stable knock-down cells, respectively (Fig 21c).

Several additional bands appeared (50kDa, 48kDa, 30kDa and 18kDa), but did not differ between the KLF5 shRNA containing cells and their controls. However, when the western blot was repeated on protein collected from cells collected sixteen passages following puromycin selection (Fig 21f) there was a strong band at approximately 70kDa that appeared to be decreased in KLF5 stable knock-down cells. There were also four bands all between 52kDa and

45kDa in size as well as two at 30kDa and 18kDa. Bands at 52kDa and 50kDa appear to be

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decreased in A549 cells with KLF5 shRNA compared to control A549 cells; however no difference appears between the NCI-H358 transduced cell lines. Stable KLF5 downregulation had no influence on proliferation of A549 cells (Fig 21d), but decreased proliferation in NCI-

H358 cells (Fig 21e). However, stable KLF5 downregulation had no effect on apoptosis rates of either cell lines (Fig 21g,h).

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Figure 20: Transient KLF5 knockdown decreased proliferation of NCI H358 lung cancer cells. Lung cancer cell lines A549 and NCI-H358 were transfected with siRNA targeting KLF5. KLF5 expression 24 hours post-transfection was determined by qRT-PCR on A449 cells (a) and NCI-H358 cells (b) and by western blotting of A549 cells (c). Relative rates of proliferation (d,e) and apoptosis (f,g) were determined using immunofluorescence for phospho-Histone H3 and cleaved Caspase 3, respectively (n=3). *p<0.05.

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Figure 21: Stable KLF5 knockdown decreased proliferation of NCI H358 lung cancer cells. Lung cancer cell lines A549 and NCI-H358 were transduced with lentiviral particles containing shRNA targeting KLF5. KLF5 expression was determined using qRT-PCR (a,b) and western blotting at passages P2 (c) and P16 (f) following puromycin selection. Relative rates of proliferation (d,e) and apoptosis (f,g) were determined using immunofluorescence for phospho-Histone H3 and cleaved Caspase 3, respectively (n=3). *p<0.05.

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Discussion Recently, increased KLF5 expression has been found in human and mouse lung tumors

(Linnerth et al. 2009; Li et al. 2014b); however the function of KLF5 in lung cancer cells remains unknown. KLF5 has also been identified as an important mediator of RAS-induced intestinal tumorigenesis (Nandan et al. 2010). Here we induced transient and stable downregulation of KLF5 in two human KRAS mutant lung cancer cells lines. Downregulation of

KLF5 decreased proliferation in NCI-H358 lung cancer cells. These findings were consistent with both transient downregulation of KLF5 from transfection with siRNA and in cells stably transduced with lentiviral particles containing KLF5-targeting shRNA. However, downregulation of KLF5 did not influence proliferation in A549 lung cancer cells. Furthermore, downregulation of KLF5 did not significantly alter apoptosis in either cell line. The difference in effect of KLF5 downregulation on each cell line could be due to difference in expression of upstream regulators in each cell line, since the function of KLF5 is so extensively regulated and its activity can be context dependent (Dong and Chen 2009). The lack of significant effect of

KLF5 on apoptosis indicates a role primarily regulating proliferation, but could also be influenced by the low rate of apoptosis in these cell lines under normal growth conditions.

The current literature on KLF5 function in lung cancer cells is conflicting. Meyer et al.

(2010) reported that KLF5 expression had no influence on proliferation of several KRAS mutant lung cancer cell lines in monolayer cell culture, including A549 cells. In contrast, Li et al.

(2014a) reported that KLF5 downregulation in A549 cells decreased proliferation, while KLF5 overexpression promoted proliferation. It is possible that this discrepancy is due to experimental conditions. Mori et al. (2009) reported that KLF5 expression in pancreatic cancer cells grown in monolayer was influenced by cell density, with KLF5 expression increasing at confluence. Mori et al. (2009) also reported that KLF5 expression was increased during anchorage independent

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growth and in sphere culture. Meyer et al. (2010) demonstrated that KLF5 overexpression in lung cancer cells increased anchorage independent growth, and KLF5 downregulation decreased anchorage independent growth even though monolayer growth of these cells was not influenced by KLF5 expression. If cell contact is critical to the function of KLF5 as this suggests, it could explain the modest decrease in proliferation seen in our study since we measured proliferation during the exponential growth phase when cells were sub-confluent.

We created cell lines stably downregulating KLF5 with the intention of using them to investigate long term growth in sphere culture as well as in vivo using mouse xenografts.

However, because these cells had less than two-fold downregulation of KLF5 at the RNA level and we did not feel we could confidently confirm stable knockdown at the protein level, we did not deem these cells ideal for continued experiments. In our western blots, several bands appear close to the expected size of KLF5 and the banding pattern was not consistent in the stable cell lines over several passages. Furthermore, several other anti-KLF5 antibodies were tested and a

KLF5 knockdown could not be confirmed with them (data not shown). In a preliminary trial of sphere culture on our stable cell lines, there was no difference in sphere formation in A549 cells but NCI-H358 cells with KLF5 downregulated failed to form secondary spheres upon the first passage (data not shown).

Meyer et al. (2010) found that KLF5 was not required for lung tumorigenesis by KRAS mutant transgenic mouse model. In our transgenic mouse model of IGF-IR induced lung cancer, tumors expressing high levels of KLF5 had increased activation of Akt but not ERK1/2 (Linnerth et al. 2009). This indicated that KLF5 regulation in our mouse model could be independent of the

MAPK/ERK pathway and may have a different role in lung cancer cells that do not have a RAS mutation. Furthermore, KLF5 could promote proliferation of tumor cells without being required

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for tumor initiation. It is clear that KLF5 function is complex and that continued research is needed to understand its role in lung cancer. However, monolayer cell culture may not represent an ideal research model to achieve this and 3D and sphere culture as well as in vivo models will likely be more informative. At this point, research into the function of KLF5 is limited by the availability of suitable reagents. Furthermore, expression of KLF5 alone is likely not sufficient criteria to evaluate its utility as a therapeutic target and more information about the regulation of

KLF5 function is needed.

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Appendix III: Source of Materials

Acrylamide Bio-Rad Laboratories, Mississauga, ON Agarose Life Technologies, Burlington, ON AlexaFluor secondary antibodies Life Technologies, Burlington, ON Aluminum potassium sulfate Fisher Scientific, Ottawa, ON Ammonium persulfate Fisher Scientific, Ottawa, ON Anti-β-actin antibody Cell Signaling Technology, Danvers, MA Anti-Akt antibody Cell Signaling Technology, Danvers, MA Anti-Akt1 antibody Cell Signaling Technology, Danvers, MA Anti-Akt2 antibody Cell Signaling Technology, Danvers, MA Anti-Akt3 antibody Cell Signaling Technology, Danvers, MA Anti-CD3 antibody AbD Serotec, Raleigh, NC Anti-CD45R antibody AbD Serotec, Raleigh, NC Anti-cleaved caspase 3 antibody Millipore, Etobicoke, ON Anti-ERK1/2 antibody Cell Signaling Technology, Danvers, MA Anti-F4/80 antibody AbD Serotec, Raleigh, NC Anti-goat biotin conjugated antibody Sigma-Aldrich, Oakville, ON Anti-goat HRP conjugated antibody Santa Cruz Biotechnology, Dallas, TX Anti-IGF-IRβ antibody Cell Signaling Technology, Danvers, MA Anti-IGF-IR (human) antibody R&D Systems, Minneapolis, MN Anti-Ki67 antibody Abcam, Toronto, ON Anti-KLF5 antibody Millipore, Etobicoke, ON Anti-PCNA antibody Santa Cruz Biotechnology, Dallas, TX Anti-phospho-Akt antibody Cell Signaling Technology, Danvers, MA Anti-phospho-ERK1/2 antibody Cell Signaling Technology, Danvers, MA Anti-phospho-histone H3 antibody Millipore, Etobicoke, ON Anti-phospho-IGF-IR/IR antibody Cell Signaling Technology, Danvers, MA Anti-rabbit biotin conjugated antibody Sigma-Aldrich, Oakville, ON Anti-rabbit HRP conjugated antibody Cell Signaling Technology, Danvers, MA Anti-rat biotin conjugated antibody Sigma-Aldrich, Oakville, ON Antibiotic-antimycotic Life Technologies, Burlington, ON Aprotonin Sigma-Aldrich, Oakville, ON Avidin Sigma-Aldrich, Oakville, ON Biotin Sigma-Aldrich, Oakville, ON BMS-754807 Sellek Chemicals, Houston, TX Bovine pituitary extract Life Technologies, Burlington, ON Bovine serum albumin Life Technologies, Burlington, ON Bromophenol blue Sigma-Aldrich, Oakville, ON Carboplatin Sigma-Aldrich, Oakville, ON Cisplatin Sigma-Aldrich, Oakville, ON Citric acid Fisher Scientific, Ottawa, ON Chemiluminescence substrate Perkin Elmer, Waltham, MA Cell culture dishes Fisher Scientific, Ottawa, ON Coverslips Fisher Scientific, Ottawa, ON

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Cytoseal mounting media Fisher Scientific, Ottawa, ON DC protein assay Bio-Rad Laboratories Mississauga, ON Diaminobenzidine tetrahydrochloride Sigma-Aldrich, Oakville, ON Dihydrochloride hydrate (DAPI) Sigma-Aldrich, Oakville, ON Dimethyl sulfoxide (DMSO) Sigma-Aldrich, Oakville, ON Dithiothreitol (DTT) Life Technologies, Burlington, ON DMEM media Life Technologies, Burlington, ON dNTP Life Technologies, Burlington, ON dNTP New England Biolabs, Pickering, ON Doxycycline rodent chow Bioserv, Frenchtown, NJ EDTA Fisher Scientific, Ottawa, ON Eosin Fisher Scientific, Ottawa, ON Ethanol Greenfield, Brampton, ON Ethidium bromide Life Technologies, Burlington, ON Extravidin peroxidase Sigma-Aldrich, Oakville, ON Fetal bovine serum Life Technologies, Burlington, ON Formalin (buffered) Fisher Scientific, Ottawa, ON Glacial acetic acid Fisher Scientific, Ottawa, ON Glass slides Fisher Scientific, Ottawa, ON Glycerol Fisher Scientific, Ottawa, ON Glycine Fisher Scientific, Ottawa, ON Hematoxylin Fisher Scientific, Ottawa, ON Hydrochloric acid Fisher Scientific, Ottawa, ON Hydrogen peroxide Sigma-Aldrich, Oakville, ON Isopropanol Fisher Scientific, Ottawa, ON KSFM media Life Technologies, Burlington, ON Lentiviral particles Santa Cruz Biotechnologym Dallas, TX Leupeptin Sigma-Aldrich, Oakville, ON Lipofectamine 2000 Life Technologies, Burlington, ON Methanol Fisher Scientific, Ottawa, ON mirVana miRNA isolation kit Life Technologies, Burlington, ON Nitrocellulose membranes Amercham, Piscataway, NJ Oligo(dT)12-18 primers Life Technologies, Burlington, ON Opti-MEM media Life Technologies, ON Pepstatin A Sigma-Aldrich, Oakville, ON Phenylmethylsulfonyl Fluoride (PMSF) Fisher Scientific, Ottawa, ON Platinum SYBR green qPCR supermix-UDG Life Technologies, Burlington, ON Polybrene Santa Cruz Biotechnology, Dallas, TX Potassium chloride Fisher Scientific, Ottawa, ON Potassium phosphate Fisher Scientific, Ottawa, ON Primers (qPCR) Bio-Rad Laboratories, Mississauga, ON Primers (qPCR) Origene Technologies, Rockville, MD Prolong gold anifade reagent Life Technologies, Burlington, ON Propidium iodide Sigma-Aldrich, Oakville, ON Puromycin Cell Signaling Technology, Danvers, MA Recombinant EGF Life Technologies, Burlington, ON

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RNaseOUT recombinant ribonuclease inhibitor Life Technologies, Burlington, ON RPMI 1640 media Life Technologies, Burlington, ON Sigmafast 3,3’-diaminobenzidine tablets Sigma-Aldrich, Oakville, ON Sodium acetate Fisher Scientific, Ottawa, ON Sodium citrate Fisher Scientific, Ottawa, ON Sodium dodecyl sulfate Fisher Scientific, Ottawa, ON Sodium fluoride Fisher Scientific, Ottawa, ON Sodium hydroxide Fisher Scientific, Ottawa, ON Sodium orthovanadate Fisher Scientific, Ottawa, ON Sodium phosphate dibasic anhydrous Fisher Scientific, Ottawa, ON Stealth RNAi Life Technologies, Burlington, ON Sucrose Sigma-Aldrich, Oakville, ON Superscript II reverse transcriptase Life Technologies, Burlington, ON Taq DNA polymerase New England Biolabs, Pickering, ON TEMED Life Technologies, Burlington, ON Tris base Fisher Scientific, Ottawa, ON Triton X-100 Sigma-Aldrich, Oakville, ON Trypsin-EDTA Life Technologies, Burlington, ON Tween-20 Fisher Scientific, Ottawa, ON Trypan blue stain Life Technologies, Burlington, ON WST-1 Roche, Mississauga, ON Xylene Fisher Scientific, Ottawa, ON

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Appendix IV: Recipes for Solutions

Carazzi’s Hematoxylin Hematoxylin 0.25g Glycerol 50mL Aluminum potassium sulfate 12.5g Water to 200mL Potassium Iodide 0.05g

Citrate Buffer (10mM) Trisodium citrate (dehydrate) 2.94g Water to 1L pH adjusted to 6.0 Tween-20 0.5mL

DNA Loading Buffer Sucrose 4g Bromophenol blue 25mg 0.5M EDTA 2.4mL Water to 10mL

Digestion Buffer (for genotyping) 1M Tris-HCl (pH 8.0) .2mL 0.5M EDTA (pH8.0) .2mL 10% sodium dodecyl sulfate .5mL 20mg/mL Proteinase K .2mL Water to 10mL

EDTA Buffer (0.5M) Ethylenediaminetetraacetic acid 73.06g Water to 500mL pH adjusted to 8.0

Phosphate Buffered Saline (PBS) Sodium chloride 16.0g Potassium chloride 0.4g Sodium phosphate dibasic anhydrous 2.3g Potassium phosphate monobasic 0.4g pH adjusted to 7.4

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Protein Lysis Buffer Sodium Chloride 0.2922g Tetrasodium pyrophosphate 0.7976g 1M Tris buffer pH 7.6 1mL 0.5M EDTA 1mL Water to 100mL Triton X-100 1mL (Add protease inhibitors before use – per mL) 2.5mg/mL aprotonin 0.002mL 50mM PMSF in isopropanol 0.02mL 0.1mM sodium orthovanadate 0.002mL 50mM NaF 0.05mL 1mg/mL pepstatinA 0.001mL 2mg/mL leupeptin 0.001mL

Reducing Buffer 10% sodium dodecyl sulfate 2mL Glycerol 1mL 1M Tris-HCl 0.5mL Bromophenol blue 0.01g DTT 0.125M Water to 500mL

Resolving Gel (10% Polyacrylamide) Water 4mL 30% Acrylamide 3.3mL 1.5M Tris Buffer (pH 8.8) 2.5mL 10% sodium dodecyl sulfate 0.1mL 10% ammonium persulfate 0.1mL Tetramethylethylenediamine 0.004mL

Running Buffer Tris base 15.1g Glycine 72.1g 10% SDS 10mL Water to 1L

Stacking Gel Water 2.1mL 30% acrylamide 0.5mL 1.0M Tris buffer (pH 6.8) 0.38mL 10% sodium dodecyl sulfate 0.03mL 10% ammonium persulfate 0.03mL Tetramethylethylenediamine 0.003mL

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Transfer Buffer Tris base 7g Glycine 3.02g Methanol 200mL Water to 1L

Tris Buffer (1.0M) Tris base 12.12g Water to 100uL pH adjusted to 6.8

Tris Buffer (1.5M) Tris base 18.16g Water to 100mL pH adjusted to 8.8

Tris Acetate EDTA (TAE) Buffer (50x) Tris base 242g Glacial acetic acid 57.2mL 0.5M EDTA 100mL Water to 1L

Tris-EDTA Buffer Tris base 1.21g 0.5M EDTA 2mL Water to 1L pH adjusted to 9.0 Tween-20 0.5mL

Tris Buffered Saline with Tween-20 (TBST) Tris base 24.2g Sodium Chloride 80g Water to 1L pH adjusted to 7.4 Tween-20 1mL

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