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Extracellular vesicle miRNAs are regulators of lung adenocarcinoma tumor progression and are important… Lawson, James 2018

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 Extracellular vesicle miRNAs are regulators of lung adenocarcinoma tumor progression and are important tumor-stromal communicators by  James Lawson  B.Sc., The University of Guelph, 2013  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Interdisciplinary Oncology)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  April 2018  © James Lawson, 2018 ii  Abstract Lung adenocarcinoma (LAC) is the most common subtype of non-small cell lung cancer and is the leading cause of cancer death worldwide. Five-year survival rates of LAC remain dismally low at ~17%, due to the late stage of diagnosis. MicroRNAs (miRNAs) are small RNAs 17-22 nucleotides long and are stable within the serum and when present within extracellular vesicles (EVs), additionally EV miRNAs contribute to tumor-stromal communication. Determining non-invasive novel biomarkers for early disease detection and understanding EV miRNA tumor-stromal communication may aid in improving overall survival.  In this thesis, I identify miRNAs that are differentially detected within the serum of patients with LAC using miRNA profiling of patient serum samples and demographically matched controls. I additionally characterize the function of LAC secreted miRNA within EVs when entering normal fibroblast and endothelial cells. My hypotheses are that miRNAs within the serum of LAC patients will show a unique signature that will be distinguishable from non-cancer high risk individuals, and that miRNAs selectively released from LAC cells within EVs will promote tumorigenesis in endothelial cells through stimulating angiogenesis and in fibroblasts through promoting the cancer associated fibroblast phenotype. A unique signature of miRNAs within the serum of LAC patients is found and miRNAs within patient serum is dependent on sex. Several miRNAs within LAC EVs are then functionally characterized in the role they play when signaling to normal stromal cells.  Together, this thesis provides a comprehensive analysis of LAC miRNAs and the roles they play as biomarkers within the serum and as tumor-stromal signals when within EVs.  iii  Lay Summary  Lung cancer is the leading cause of cancer death worldwide. A better understanding of the biological mechanisms that drive this disease are required to improve lung cancer survival. MicroRNAs are a group of small molecules that regulate processes within cells. When deregulated, they can play a role in driving cancer growth and development. Cells have been shown to selectively package and release specific microRNAs into small cellular containers called extracellular vesicles. The released vesicles can then affect the surrounding environment, including transforming neighboring cells into become more cancer-like. In my thesis, I examine whether cancer cell secreted microRNAs can be used to detect lung cancer early in development within the blood of patients. In addition, I determine the function of specific microRNAs within extracellular vesicles when they enter neighboring normal cells and how this mechanism contributes to transforming those cells into become more cancer-like.  iv  Preface  The research conducted within this thesis has been approved by the University of British Columbia Research Ethics Board (Certificate Number: H10-02846)  Chapters 3 and 4 were co-authored for publication. The full author lists are as follows.   A version of Chapter 3 has been published as:  Lawson, J., Dickman, C., MacLellan, S., Towle, R., Jabalee, J., Lam, S., & Garnis, C. (2017). Selective secretion of microRNAs from lung cancer cells via extracellular vesicles promotes CAMK1D-mediated tube formation in endothelial cells. Oncotarget, 8:83913-83924.  I am first author of this work. I conducted experiments, wrote manuscript, analyzed data, interpreted the results and constructed figures. Dickman, C., Maclellan, S., Towle, R., Jabalee, J. and Garnis C. were critical in conceptualizing the project.  A version of Chapter 4 is currently submitted: Lawson, J., Dickman, C., Towle, R., Jabalee, J., & Garnis, C. (2017). Secretion of miR-142-3p from lung adenocarcinoma cells through extracellular vesicles promotes tumor associated changes in the stroma.  I am first author of this work. I conducted 95% of experiments, wrote manuscript, analyzed data, interpreted the results and constructed figures. Dickman, C. conducted the experiment for Figure 4.5. Towle, R., Jabalee, J. and Garnis C. were critical in conceptualizing the project.  v  Table of Contents  Abstract .......................................................................................................................................... ii Lay Summary ............................................................................................................................... iii Preface ........................................................................................................................................... iv Table of Contents ...........................................................................................................................v List of Tables ..................................................................................................................................x List of Figures .............................................................................................................................. xii List of Abbreviations ...................................................................................................................xv Acknowledgements ................................................................................................................... xvii Dedication ................................................................................................................................. xviii Chapter 1: Introduction ................................................................................................................1 1.1 Introduction to Lung Cancer ........................................................................................... 1 1.2 Lung Cancer Subtypes .................................................................................................... 4 1.2.1 Small cell lung cancer ................................................................................................. 4 1.2.2 Non-small cell lung cancer ......................................................................................... 5 1.3 Staging of Lung Adenocarcinoma .................................................................................. 7 1.4 miRNA Biogenesis and Function ................................................................................... 7 1.5 Extracellular Vesicle Biogenesis and Packaging .......................................................... 10 1.5.1 Isolation of EVs ........................................................................................................ 16 1.5.2 EV markers and contents .......................................................................................... 17 1.5.3 miRNA incorporation into exosomes ....................................................................... 19 1.6 Detection and Diagnosis of Lung Cancer ..................................................................... 19 vi  1.6.1 Early detection of lung cancer .................................................................................. 20 1.6.2 Sputum as an early detector of lung cancer .............................................................. 21 1.6.3 Circulating miRNAs as biomarkers .......................................................................... 21 1.6.4 Prognostic miRNA biomarkers in NSCLC ............................................................... 26 1.7 Tumor Microenvironment ............................................................................................. 26 1.7.1 Lung EV miRNAs in tumor development ................................................................ 27 1.7.2 Extracellular communication to stromal cells in cancer ........................................... 28 1.8 Thesis Themes, Rationale, Objectives and Hypothesis ................................................ 30 1.9 Specific Aims and Thesis Outline................................................................................. 31 Chapter 2: Identification of serum miRNA biomarkers for early detection of lung adenocarcinoma ...........................................................................................................................32 2.1 Introduction ................................................................................................................... 32 2.2 Materials and Methods .................................................................................................. 36 2.2.1 Sample collection ...................................................................................................... 36 2.2.2 Blood processing ....................................................................................................... 38 2.2.3 RNA isolation ........................................................................................................... 38 2.2.4 qRT-PCR of serum miRNAs .................................................................................... 38 2.2.5 Data analysis ............................................................................................................. 39 2.3 Results ........................................................................................................................... 41 2.3.1 Identification of a circulating miRNA for use as a normalizer ................................. 41 2.3.2 Validation of endogenous miRNA normalizers ........................................................ 46 2.3.3 Identification of miRNAs differentially detected in lung adenocarcinoma and healthy controls ..................................................................................................................... 48 vii  2.3.4 Identification of miRNAs able to differentially distinguish between LAC and non-cancer.. .................................................................................................................................. 52 2.3.5 Validation of serum miRNA signatures in separate serum sample cohort ............... 56 2.3.6 Serum miRNAs are differentially detected in male and female samples ................. 57 2.3.7 Validation of sex-specific serum miRNA signatures in separate serum sample cohort.. .................................................................................................................................. 60 2.4 Discussion ..................................................................................................................... 68 Chapter 3: Selective secretion of microRNAs from lung cancer cells via extracellular vesicles promotes CAMK1D-mediated tube formation in endothelial cells ...........................72 3.1 Introduction ................................................................................................................... 72 3.2 Materials and Methods .................................................................................................. 73 3.2.1 Cell culture ................................................................................................................ 73 3.2.2 EV collection ............................................................................................................ 74 3.2.3 qRT-PCR................................................................................................................... 75 3.2.4 Vectors ...................................................................................................................... 75 3.2.5 Serum analysis .......................................................................................................... 76 3.2.6 EV incubation ........................................................................................................... 77 3.2.7 Tube formation assays .............................................................................................. 78 3.2.8 Western blot .............................................................................................................. 78 3.3 Results ........................................................................................................................... 79 3.3.1 Identification of miRNAs enriched in the EVs of LAC cells ................................... 79 3.3.2 Inhibition of EV release increases the levels of candidate miRNAs within LAC cells…. .................................................................................................................................. 86 viii  3.3.3 MiR-143-3p and miR-145-5p are enriched within serum draining directly from LAC tumors. .................................................................................................................................. 88 3.3.4 MiR-145-5p and miR-143-3p promote tube formation in endothelial cells ............. 90 3.4 Discussion ..................................................................................................................... 96 Chapter 4: Extracellular vesicle secretion of miR-142-3p from lung adenocarcinoma cells promote tumorigenesis through cell-cell communication with the stroma ..........................100 4.1 Introduction ................................................................................................................. 100 4.2 Materials and Methods ................................................................................................ 101 4.2.1 Cell lines ................................................................................................................. 101 4.2.2 Vectors .................................................................................................................... 101 4.2.3 EV isolation ............................................................................................................ 102 4.2.4 Quantitative PCR .................................................................................................... 103 4.2.5 Proliferation assay ................................................................................................... 103 4.2.6 Tube formation assay .............................................................................................. 103 4.2.7 Fibroblast activation................................................................................................ 104 4.2.8 Wound healing assay .............................................................................................. 105 4.2.9 Western blotting ...................................................................................................... 106 4.3 Results ......................................................................................................................... 106 4.3.1 MiR-142-3p is transferred from LAC cells to endothelial cells and fibroblasts through EVs ........................................................................................................................ 106 4.3.2 Secreted miR-142-3p targets TGFβR1 in HMEC-1 cells ....................................... 112 4.3.3 MiR-142-3p containing EVs promote CAF phenotype .......................................... 117 4.3.4 Secreted miR-142-3p promotes cancer-associated fibroblast transformation ........ 120 ix  4.4 Discussion ................................................................................................................... 124 Chapter 5: Discussion and Conclusion ....................................................................................131 5.1 Discussing Rationale and Thesis Concepts................................................................. 131 5.1.1 Tumor released factors promote tumorigenesis ...................................................... 131 5.1.2 Tumor released miRNAs contribute to cancer development .................................. 132 5.2 Summary of Findings .................................................................................................. 133 5.2.1 Serum miRNAs as biomarkers in the detection of LAC......................................... 133 5.2.2 Function of LAC secreted miRNAs ........................................................................ 136 5.3 Conclusions Regarding Hypothesis ............................................................................ 138 5.4 Overall Significance.................................................................................................... 139 Bibliography ...............................................................................................................................141  x  List of Tables  Table 1.1 TMN staging of NSCLC. Table adapted from Detterback et al. (2017). ....................... 3 Table 1.2 miRNAs published as potential circulating lung cancer biomarkers ............................ 24 Table 2.1 Patient demographics .................................................................................................... 37 Table 2.2 MiRNAs affected by hemolysis as published by MacLellan et al. (2014)139 ............... 40 Table 2.3 miRNAs detected in every sample and not affected by hemolysis ............................... 42 Table 2.4 geNorm analysis of 11 candidate miRNAs for serum normalization ........................... 44 Table 2.5 NormFinder analysis of 11 candidate miRNAs for serum normalization. ................... 44 Table 2.6 geNorm analysis of normalization candidates for validation data ................................ 48 Table 2.7 NormFinder analysis of normalization candidates for validation data ......................... 48 Table 2.8 List of miRNAs not affected by hemolysis and detected in more than 50% of all samples .......................................................................................................................................... 49 Table 2.9 miRNAs significantly enriched within lung adenocarcinoma serum compared to non-cancer control serum as analyzed by Mann Whitney U test with a Benjamini-Hochberg correction ...................................................................................................................................... 51 Table 2.10 miRNAs from forward stepwise logit regression for entrance into best subset analysis....................................................................................................................................................... 54 Table 2.11 MiRNAs included in logit regression and their associated AUC score ...................... 54 Table 2.12 Formulas for discrimination of patient serum ............................................................. 55 Table 2.13 Predicted cases using independent validation data normalized to miR-23a ............... 56 Table 2.14 Misclassified tumour stages in validation cohort ....................................................... 57 xi  Table 2.15 miRNAs significantly enriched within male lung adenocarcinoma serum compared to male non-cancer control serum as analyzed by Mann Whitney U test with a Benjamini-Hochberg correction. ..................................................................................................................................... 58 Table 2.16 : Table of sex specific discriminate score analysis for identification of LAC serum. 60 Table 2.17 Female predicted cases using independent validation data normalized to miR-23a .. 61 Table 2.18 Male predicted cases using independent validation data normalized to miR-23a ...... 61 Table 2.19 Male sex-specific formula for prediction of LAC normalized to miR-23a ................ 65 Table 2.20 Prediction of lung adenocarcinoma or non-cancer in validation samples normalized to miR-23a......................................................................................................................................... 65 Table 2.21 Tumour stages misclassified by male discriminative score in male validation patient cohort ............................................................................................................................................ 66 Table 3.1 Patient demographics .................................................................................................... 77 Table 3.2 Tumour stages, mutation profile and characteristics of early LAC cell lines according to ATCC ........................................................................................................................................ 79 Table 3.3 MiRNAs enriched in cell fraction compared to EVs by at least 4-fold ........................ 82 Table 3.4 MiRNAs enriched in LAC cell line EVs compared to cell fraction by at least 4-fold.. 83 Table 3.5 Profiling fold change of miRNAs enriched by 4-fold in the EVs of at least 3 lung adenocarcinoma cell lines using microRNA qRT-PCR................................................................ 84 Table 3.6 Mean fold change of miRNAs enriched in at least 5 of the 10 tumor draining samples....................................................................................................................................................... 89 Table 4.1 Tumour stage of LAC cell lines, mutation profiles and characteristics of A549, H23 and H838 according to ATCC .................................................................................................... 108  xii  List of Figures  Figure 1.1 MiRNA biogenesis ........................................................................................................ 9 Figure 1.2 Exosome biogenesis. ................................................................................................... 11 Figure 2.1 Box plot of training data raw Ct values ....................................................................... 45 Figure 2.2 Box plot of candidate miRNA normalizers between lung adenocarcinoma and non-cancer in validation sample sets .................................................................................................... 47 Figure 2.3 ROC curve of LAC miRNA classifiers normalized to miR-23a ................................. 55 Figure 2.4 ROC curve of A) miR-23a normalized male serum data for derived miRNA signature B) miR-23a normalized female serum data for derived miRNA signature .................................. 59 Figure 2.5 Box plots of male miRNA candidates normalized to miR-23a ................................... 62 Figure 2.6 Box plots of female miRNA candidates normalized to miR-23a ................................ 63 Figure 2.7 Delta Ct values of examined miRNAs between identical serum samples analyzed using SYBR (Training) green and TaqMan (Validation). ............................................................ 64 Figure 2.8 ROC curve of miR-23a normalized male serum data without miR-142-3p ................ 66 Figure 3.1 EV identification ......................................................................................................... 80 Figure 3.2 Fold Change of EV candidate miRNAs in LAC cell lines H1395, H1437, H2073, H2228, H2347 and normal lung line HPL1D in triplicate using TaqMan qRT-PCR for miRNAs....................................................................................................................................................... 85 Figure 3.3 Fold change of LAC cell lines intracellular candidate miRNAs compared to HPL1D intracellular miRNAs normalized to U6 ....................................................................................... 86 Figure 3.4 Analysis of EV inhibition using SMPD3 .................................................................... 87 xiii  Figure 3.5 Relative fold change within the EVs of LAC miRNA overexpression cell lines H1437 and H2073 compared to miR-Scramble EVs ................................................................................ 91 Figure 3.6 Lung adenocarcinoma cell extracellular vesicle miRNAs are taken up by HMEC-1 cells. Intracellular fold change analysis of miR-143-3p, miR-145-5p and miR-346 in HMEC-1 cells incubated with EVs from H1437 cells over expressing miR-143, miR-145 and miR-Scramble, compared to HMEC-1 cells receiving no extracellular vesicle incubation. ................ 92 Figure 3.7 Impact of miR-143 and miR-145 on tube formation ................................................... 93 Figure 3.8 Analysis of CAMK1D targeting by miR-143 and miR-145 ....................................... 95 Figure 4.1 MTT of direct overexpression of miR-C or miR-142 in A) H1437 B) H2073 ......... 109 Figure 4.2 Lung adenocarcinoma cell extracellular vesicle miRNAs are taken up by HMEC-1, WI-38 and IMR90 cells .............................................................................................................. 111 Figure 4.3 Analysis of TGFβR1 protein expression by western blot in HMEC-1 cells after direct over expression of miR-142 and miR-C within HMEC-1 cells and after incubation with EVs isolated from H1437 and H2073 over expressing miR-142, miR-C and untreated cell. ............ 113 Figure 4.4 Tube formation assay of HMEC-1 cells run in triplicate .......................................... 115 Figure 4.5 Rescue of HMEC-1 cells using TGFβR1 ORF ......................................................... 116 Figure 4.6 Western blot analysis of WI-38 and IMR90 cells incubated with EVs from H1437 and H2073 cell lines .......................................................................................................................... 118 Figure 4.7 Western blot of WI-38 and IMR90 cells incubated with EVs or TGFβ for PDGFR-β, αSMA, pSMAD2/3 and H3 ........................................................................................................ 119    xiv  Figure 4.8 Wound healing assays of A) WI-38 and B) IMR90 with extracellular vesicle and  TGFβ treatment ........................................................................................................................... 122 Figure 4.9 Graphs of wound healing assay for A) WI-38 and B) IMR90 cell lines. .................. 123 Figure 4.10 Summary of the role of miR-142-3p in LAC. ......................................................... 130  xv  List of Abbreviations  ARHGAP  Rho GTPase activating proteins CAF   Cancer associated fibroblast CAMK1D  Calcium/calmodulin-dependent protein kinase ID CT Scan  Computed tomography scan DNA   Deoxyribonucleic acid dFBS   Depleted fetal bovine serum EGFR   Epidermal growth factor receptor ESCRT  Endosomal sorting complex required for transport EV   Extracellular vesicle FBS    Fetal bovine serum hnRNPs  Heterogeneous nuclear riboproteins HMEC-1  Human microvascular endothelial cell line-1 HRS   Hepatocyte growth factor-regulated tyrosine kinase substrate IGF1R   Insulin-like growth factor 1 receptor ILV   Intraluminal vesicle LAC    Lung adenocarcinoma LNA   Locked nucleic acid MDM2  Mouse double minute 2 homolog miRNA/miR  MicroRNA MUC4   Mucin 4 MVB   Multivesicular bodies xvi  NKG2D  Natural-killer group 2, member D NSCLC   Non-small cell lung cancer NTA   Nanoparticle tracking analysis PCR   Polymerase chain reaction PET Scan  Positron electron tomography scan PNEC   Pulmonary neuroendocrine cells qRT-PCR  Quantitative real time polymerase chain reaction RAB14  Ras-related protein Rab-14 RNA    Ribonucleic acid ROC Curve  Receiver operating characteristic curve SCLC   Small cell lung cancer SMAD2  SMAD family member 2 SMAD3   SMAD family member 3 SqCC   Squamous cell carcinoma TGF-β   Transforming growth factor beta TGFβR  Transforming growth factor beta receptor TSG101  Tumor suppressor gene 101 VEGF   Vascular endothelial growth factor    xvii  Acknowledgements I am so grateful to all of those who have supported me throughout my PhD, I could not have done this without you.   I would especially like to thank Dr. Cathie Garnis, who gave me a wonderful opportunity and fostered my passion for science and medicine providing support and guidance along the way. I would also like to thank my committee Dr. Martial Guillaud, Dr. Kevin Bennewith and Dr. Will Lockwood for their words of wisdom, scientific criticisms and invaluable support.   I would like to acknowledge the funding support for my research, the Terry Fox Research Institute. Additionally, I would like to acknowledge my scholarship support from the Interdisciplinary Oncology Program.   I want to thank Cathie Garnis lab members for their support and collaborations over the last seven years. Additionally, I would like to thank my peers in the Integrative Oncology Department at the BC Cancer Research Centre who provided advice and support.  xviii  Dedication  To my father whose life motivated me to fight against cancer. To my mother who taught me strength and resilience. To my step-father for teaching me passion and commitment. To my sister for teaching me to have fun along the way. To my wonderful girlfriend for her amazing support throughout all of this and for the amazing adventure that we are embarking upon. 1  Chapter 1: Introduction  1.1 Introduction to Lung Cancer The lung is an anatomically and histologically complex organ and can generally be divided into two main regions; the conducting airways and the respiratory compartment. The conducting airways consist of two large bronchi entering the lung, which then branch into secondary bronchi and further again into tertiary bronchi. The tertiary bronchioles finally branch into bronchioles and these bronchioles connect with the respiratory compartment, which consists of alveolar ducts and alveolar which are the site of gas exchange. The large bronchial airways are lined with basal, ciliated, goblet and serous cells which are organized into a pseudostratified epithelium, pulmonary neuroendocrine cells (PNEC) are also embedded intermittently within this region1. Within the conducting airways goblet cells are replaced by Clara cells and a more structured columnar epithelium becomes present. A transition point appears between the tertiary bronchi and the respiratory compartment, at this point ciliated cuboidal epithelium merge with the squamous epithelium of the alveoli. The alveoli contain type 1 pneumocytes responsible for the transfer of gas between the lungs and blood, and type 2 pneumocytes responsible for surfactant production. Being a large complex organ critical in the transport of oxygen to the body, it is not surprising that lung cancer has a very low survival rate. In Canada lung cancer is the leading cause of cancer death, responsible for more deaths than the next three major cancer types (breast, prostate and colorectal) combined2. It is estimated that in 2017 lung cancer will cause 21,100 deaths in Canada alone2. While lung cancer incidence has decreased over the last 40 years, it remains the highest contributor to cancer death in males and females3. Lung cancer globally is the leading contributor to new cancer cases, accounting for 2  an estimated 12.4% or 1,350,000 new cancer cases4. It is the largest contributing cancer to cancer deaths, accounting for around 17.6% of total cancer deaths or 1,180,000 deaths. Although targeted therapies have been developed to treat lung cancer, five-year survival rates remain low at ~15%5.   A major factor contributing to lung cancer survival rates is the late stage of diagnosis. Table 1.1 summarizes lung cancer TMN stages. Stage I lung cancer is localized to the lung with no regional metastasis and is less than 5cm large while stage II lung cancer is either larger than 5cm or contains regional lymph metastasis6. Stage III lung cancer is a tumor of any size with local invasion and regional lymph metastasis6. Finally stage IV lung cancer is any tumor that has distant metastases and is known to have the worst survival rate6. Lung cancer is generally asymptomatic until the disease progresses to a late stage, usually Stage III or IV, manifesting as coughing and shortness of breath which are also common symptoms of heavy smokers7,8. This makes it difficult to detect lung cancer early in development as patients are unlikely to seek medical attention for minor symptoms. Considering that diagnosis at earlier stages greatly improves survival it is important that lung cancer is detected early.          3  Stage TMN Classification Description Ia1 T1a(mi)N0M0 Tumor ≤ 1cm in diameter No regional lymph node metastasis or regional metastasis T1aN0M0 Ia2 T1bN0M0 Tumor ≤ 2cm in diameter No regional lymph node metastasis or regional metastasis Ia3 T1cN0M0 Tumor ≤ 3cm in diameter No regional lymph node metastasis or regional metastasis Ib T2aN0M0 Tumor > 3cm but ≤ 5cm in diameter No regional lymph metastasis or regional metastasis IIa  T1aN1M0 Tumor > 5cm but ≤ 7cm in diameter or metastasis in an ipsilateral peribronchial and/or ipsilateral hilar lymph node and intrapulmonary nodes No distant metastasis T1bN1M0 T2bN0M0 IIb T1a-cN1M0 Tumor > 7cm or metastasis in an ipsilateral peribronchial and/or ipsilateral hilar lymph node and intrapulmonary nodes No distant metastasis T2aN2M0 T2bN1M0 T3N0M0 IIIa T1a-cN2M0 Tumor < 7cm with metastasis in ipsilateral mediastinal and/or subcarinal lymph nodes; or tumor > 7cm with metastasis in either an ipsilateral peribronchial and/or ipsilateral hilar lymph node and intrapulmonary nodes or metastasis in the ipsilateral mediastinal and/or subcarinal lymph nodes; or any size tumor that has invaded: the mediastinum, heart, great vessels, trachea, recurrent laryngeal nerve, oesophagus, vertebral body or carina or separate tumor nodule in a different ipsilateral lobe with or without metastasis in ipsilateral peribronchial and/or ipsilateral hilar lymph node and intrapulmonary nodes No distant metastasis T2a-bN2M0 T3N1M0 T4N0M0 T4N1M0 IIIb T1a-cN3M0 Tumor of any size that has invaded: the mediastinum, heart, great vessels, trachea, recurrent laryngeal nerve, oesophagus, vertebral body or carina or separate tumor nodule in a different ipsilateral lobe and has metastasis in the ipsilateral mediastinal and/or subcarinal lymph nodes; or any size tumor with metastasis in the contralateral mediastinal, contralateral hilar, ipsilateral or contralateral scalene or supraclavicular lymph nodes  T4N2M0  T1N3M0  T2N3M0  T3N2M0 IIIc T3N3M0 Tumor > 7cm with metastasis in contralateral mediastinal, contralateral hilar, ipsilateral or contralateral scalene or supraclavicular lymph node (s).  No distant metastasis T4N3M0 IVa Any T, Any N, M1a or M1b Any size tumor with separate nodule in contralateral lobe or single extrathoracic metastasis IVb Any T, Any N M1c Any size tumor with multiple extrathoracic metastases in one or more organs.  Table 1.1 TMN staging of NSCLC. Table adapted from Detterback et al. (2017).  4   The most common causes of lung cancer are smoking tobacco, second-hand smoke, radon exposure and asbestos4. Tobacco smoke is the highest contributing factor of lung cancer and is estimated to account for approximately 85% of lung cancer cases in Canada9. This includes smoking from pipes, cigars, herbal cigarettes, hookahs, and cigarettes. The risks of developing cigarette smoking related lung cancer increase with an increase in smoking duration, the age at which smoking started and the number of cigarettes each day10,11.    1.2 Lung Cancer Subtypes The lung is a heterogeneous organ with a highly complex collection of cell types. Lung cancer is a collection of phenotypically diverse cancers that appear in distinct regions of the lung. Lung cancer is divided into two major subtypes based on clinical and histological criteria: small cell lung cancer (SCLC) and non-small lung cancer (NSCLC).  1.2.1 Small cell lung cancer SCLC is the most aggressive form of lung cancer and accounts for approximately 15% of all lung cancer cases. SCLC tumors are characterized by their rapid doubling time and the early onset of invasion12. The tumors are known to invade the bronchial walls and the parenchyma, given these features, dissemination via lymphatic and blood vessels are common, making SCLC patients generally poor candidates for surgical resection12. While chemotherapy treatments are generally successful with a 70-90% objective response rate, resistance and recurrence generally occur rapidly after treatment13. Given these features of SCLC, it has the worst 5-year survival of all lung cancers ranging from 31% at stage I to 2% at stage IV. While overall 5-year survival rates have increased over the past three decades from 4.9% (1983-1992) to 5.9% (1993-2002) to 5  6.4% (2003-2012), the median survival has remained at 7 months for each decade14. With general improvement in treatment over the decades survival rates have largely remained unchanged, it is evident that more effective treatment strategies are needed. In addition, early detection may be the best form of SCLC prevention due to the increase in survival if caught early.  The central airways are typically the site of SCLC development, occurring mainly in the secondary bronchi. The cell of origin for SCLC is currently unclear showing both neuroendocrine markers and features of NSCLC 15.  PNECs are proposed as the likely source of SCLC however no identifiable precursory lesion has been identified16. Light microscopy is a common tool to identify SCLC as these tumors have distinct histopathological features including, small cells with fine granular nuclear chromatin and an absent nucleoli17. SCLC cancers demonstrate features of NSCLC indicating a potential “common” cell of origin, however they generally express a variety of neuroendocrine markers and transcription factors indicating that neuroendocrine cells may be the cell of origin for SCLC15.  1.2.2 Non-small cell lung cancer The most common form of lung cancer is NSCLC accounting for 85% of all lung cancer cases. NSCLC is divided into three subtypes, the two most common being lung adenocarcinoma (LAC) and squamous cell carcinoma (SqCC) and the rarer subtype large cell carcinoma18. LAC is the most common subtype of lung cancer accounting for 40% of all lung cancer cases. NSCLC treatment is dependent on stage and metastasis, most Stage I and II NSCLC cancers can be treated by surgical resection. 6  Patients receiving surgical resection of NSCLC tumors are generally prescribed adjuvant chemotherapy, which can reduce recurrence19. For Stage III NSCLC, chemotherapy is typically recommended along with high-dose radiation treatments. In Stage IV NSCLC chemotherapy is the primary treatment and radiation is used only for palliative care. The standard chemotherapy regimen includes either cisplatin or carboplatin, combined with docetaxel, gemcitabine, paclitaxel, vinorelbine or pemetrexed20. If NSCLC cancer appear with specific markers targeted therapies can be given; in EGFR mutated NSCLC cancer Erlotinib, Afatinib or Gefitinib is recommended dependent on EGFR mutation type21. Other therapies currently under investigation include utilizing bevacizumab, an anti-angiogenic therapeutic22.  LAC develops from uncontrolled growth of epithelial cells generally in the periphery of the lung23. LAC is thought to arise from a variety of cell types, in K-Ras driven LAC, alveolar type II cells are the predominant cell of origin but LAC can also arise from club cells, bronchioalveolar stem cells and progenitors of these cells24. Tracheal basal cell progenitors are speculated to be the cell of origin in SqCC15. Large-cell lung carcinoma is the final subtype of NSCLC and represents around 10% of NSCLC cases, large-cell lung carcinoma generally originates from epithelial cells of the lung15.   We limited our research to LAC as NSCLC cancers have distinct genomic subtypes and have differing levels of incidence, with LAC being the most common25. Additionally, LAC incidence is on the rise and is highly associated with smoking, so this subtype of lung cancer may be the most clinically relevant subtype of lung cancer for designing biomarkers and finding potential therapeutic targets26.   7  1.3 Staging of Lung Adenocarcinoma Using multiple factors LAC can be staged into five stages 0-IV, each with their own variations of the specific stage (Table 1.1). The factors dictating staging are tumor size (T), regional lymph node involvement (N) and distant metastatic sites (M). Current staging guidelines are being replaced by newly constructed guides based on a 90 000-patient collection of data27.  The newly designed guidelines place less emphasis on tumor size and will cause reclassification of T3 tumors with N2 disease being moved from stage IIIA to stage IIIB and those with N3 disease being moved from stage IIIB to stage IIIC which directly affects treatment and survivability27. Reclassification of LAC tumors can result in new staging for tumors and therefore change treatment options. Changing LAC staging based on new patient data can have a positive impact on the effectiveness of treatment for LAC and has the potential to modify treatment options around the world.  1.4 miRNA Biogenesis and Function MiRNAs are small non-coding RNAs that are on average 17-22 nucleotides long that function in post-transcriptional gene regulation. The biogenesis of miRNAs within humans occurs in a stepwise manner, first miRNAs are transcribed from genomic DNA into pri-miRNAs which are then processed both within the nucleus and the cytoplasm to form mature miRNAs. Beginning in the nucleus miRNAs are transcribed by RNA polymerase II or polymerase III, which results in the production of pri-miRNAs (Figure 1.1)28,29. The pri-miRNAs within the nucleus are then processed by the protein RNase III endonuclease Drosha and double-stranded RNA binding protein DiGreorge syndrome crucial region gene 8 (DGCR8) (Figure 1.1)30,31. Drosha with the aid of DGCR8 processes the pri-miRNA cutting 60-70 base pairs (bp) and 8  generating a precursor miRNA (pre-miRNA) (Figure 1.1)32. However, miRNAs that are encoded within protein coding genes skip processing by Drosha and are released from their genes through splicing33. Pre-miRNAs exit the nucleus using Exportin-5 and enter the cytoplasm where they are processed by RNase II endonuclease Dicer (Dicer) into mature miRNAs approximately 22 nucleotides in length. The RNA induced silencing complex (RISC) forms around the mature miRNAs removing the opposing strand leaving the guide strand within the RISC complex (Figure 1.1). The RISC complex contains a variety of proteins including Argonaute (Ago), the protein into which the single RNA strand is integrated into34. Based on the miRNA sequence the RISC complex binds to target mRNAs resulting in: translational repression, mRNA destabilization or mRNA cleavage. Generally binding and targeting of mRNAs is located within the 3’ untranslated region (3’UTR) of transcript, however it is noted that some can target beyond the 3’UTR35,36. Pre-miRNAs and mature miRNAs have been found within extracellular vesicles, a type of secreted vesicle released from cells, and have been shown to enter the extracellular environment to exert their function within other cells37.  9   Figure 1.1 MiRNA biogenesis 10  1.5 Extracellular Vesicle Biogenesis and Packaging Extracellular vesicles (EVs) are small membrane bound vesicles that are released from virtually all cell types. First discovered in the 1980’s as “exosomes” the vesicles were found to be exfoliates from the cellular membranes38. However, further research has reclassified, the “exosomes” based on their size and origin.  EVs are secreted vesicles that include exosomes, microvesicles, oncosomes and apoptotic bodies. Exosomes originate from the endocytic pathway and are formed by budding into an early endosome forming a multivesicular body (MVBs) (Figure 1.2). The endocytic pathway is a highly studied process functioning in internalizing fluids, solutes, macromolecules and plasma membrane components through invagination of the plasma membrane39. Endocytosis is regulated by many different endocytotic pathways depending on the type of cargo being taken up. These pathways are divided into two main pathways: clathrin-dependent and clathrin-independent. Both pathways share a common first step, an inward curvature of the plasma membrane which changes the shape of the cells plasma membrane40. As the curvature increases, the invagination begins to take different shapes depending on the endocytotic pathway, where spherical, ellipsoid or tubular structures form and a narrow membrane neck is created. The narrow neck undergoes scission, which releases the vesicular structure into the intracellular milieu and enters the endosome.  11   Figure 1.2 Exosome biogenesis.      12   An endosome is a persistent membranous structure which acts as the primary location for entering vesicles from the endocytic pathways. The early endosome is composed of regions of tubular extensions and large vesicles containing membrane invaginations giving rise to a multi-vesicular appearance41. It is thought that the different regions of the early endosome function as domains that have different functions, for instance, proteins targeted for recycling may cluster within primary tubular membranes42. Vesicles arising from different regions of the early endosome contain different acidic properties, changing from a pH of 6.2 to ~5.5 in the lumen of the multivesicular bodies42,43. While many ligands are degraded within the early endosome many receptors are recycled back to the surface of the cell44. The endosome is critical for recycling and degrading membrane components. In addition, the endosome can mature into lysosomes or into multivesicular bodies, which have differing functions. The fate of the endosome is dependent on markers on the endosome (Figure 1.2).   Ras-associated binding (Rab) proteins are small GTP-binding proteins that localize to intracellular membranes where they are able to interact and recruit Rab effector proteins45. Rab5 and Rab4 are primarily localized to the early endosome and regulate the early endocytotic events46-49. Other Rabs known to be localized in the early endosome include Rab10, Rab14, Rab21 and Rab22: however, their role is less characterized. Rab5 has been extensively researched due to its function in regulating the entry of cargo from the plasma membrane into the early endosome, in addition Rab5 recruits factors which contributes to specialized trafficking and sorting including regulating homotypic fusion50. Homotypic fusion increases the size of the early endosome, which is essential for endosomes to generate enough membrane surface to generate intraluminal vesicles (ILVs) and Rab5 mediated homotypic fusion is required for endosomal maturation51,52. Homotypic fusion of the early endosome is a fusion of two early endosomes 13  together; this is mediated by VAMP8, which acts as a R-SNARE to fuse the membranes together53. Rab proteins are not only important for early endosome function but they are also very important to regulate the traffic of vesicles within the early endosome (Figure 1.2). The early endosome is responsible for sorting incoming vesicles to destinations (Figure 1.2). Sorting events occurring at this location determine the fate of internalized proteins and lipids marking them for recycling to the plasma membrane, degradation in the lysosome or delivery to the trans-Golgi network43.  As the early endosome begins to mature it develops into a late endosome and becomes characterized by the formation of ILVs. These ILVs form inside the lumen of the late endosome and are formed by the inward budding of the endosomal membrane (Figure 1.2). The transformation of the early endosome into the late endosome is marked by a change of shape and location. Early endosome has a tube-like shape while the late endosome has a spherical shape, the endosome also moves from the periphery of the cell to the center of the cell towards the nucleus54. The late endosome containing ILVs is now considered a multivesicular body (MVB). The fate of the MVB can vary, it can be targeted for fusion to a lysosome as marked by a conversion of Rab5 to Rab755. As an alternative to degradation the MVBs can fuse with the plasma membrane causing the release of the ILVs into the extracellular environment. The released ILVs are termed exosomes. There are several key Rabs that regulate the secretion of exosomes including: Rab27a, Rab27b, Rab35 and Rab115,56,57. ILVs are formed through invagination and budding from the limiting membrane of the late endosome. This forms vesicles within the lumen of the late endosome containing cytosol and expose the extracellular domain of transferrin receptors at their surface. The most well described methods of ILV formation in late endosomes is driven by the endosomal sorting complex 14  required for transport (ESCRT) which is composed of approximately 30 proteins58. These proteins assemble into four complexes (ESCRT 0, -I, -II, -III) that act in succession to form ILVs. First ESCRT-0 recognizes and sequesters ubiquitinated proteins in the endosomal membrane, this is followed by membrane deformation by ESCRT-I and –II complexes causing buds with sequestered cargo; finally, ESCRT-III components drive vesicle scission59,60. ESCRT-0 is comprised of hepatocyte growth factor-regulated tyrosine kinase substrate that recognizes the mono-ubiquitylated cargo proteins and associates with STAM, Eps15 and clathrin to form a complex61. Hepatocyte growth factor-regulated tyrosine kinase substrate (HRS) recruits tumor suppressor gene 101 (TSG101) of the ESCRT-I complex which then recruits ESCRT-III, by ESCRT-II or ALIX (an ESCRT-accessory protein)58. Dissociation of ESCRT occurs through interactions with the AAA-ATPase Vps458.  ESCRT-III has recently been shown to be critical for ILV membrane scission. The ESCRT-III subunits assemble into large filamentous structures62-64. Snf7 an ESCRT-III subunit was shown to cause the formation of curved polymeric spirals on membranes in vivo when over expressed65. Snf7 spirals are able to induce a negative curvature to the underlying membrane, which gives rise to membrane tubules that extrude away from the cytoplasm. These ESCRT-III subunits all localize to the bud neck where they are able to cleave the bud to form ILVs66.      15  Depleting ESCRT subunits does not completely impair ILV formation within late endosomes, which suggests that there are other methods for ILV formation67. ILV formation can occur using a type II sphingomyelinase that hydrolyses sphingomyelin to ceramide68. This pathway is dependent on raft-based microdomains for the lateral segregation of cargo within the endosomal membrane. The hydrolysis of sphingomyelin is conducted by the protein neutral sphingomyelinase (nSMase) and the product ceramide has a cone-shape structure which may induce spontaneous negative curvature68. Once ILVs are produced the MVB is shuttled to the cell membrane where the ILVs are released and are now considered exosomes (Figure 1.2).  The biogenesis of microvesicles (MVs) is not as well understood to that of exosomes. In general, MVs are formed through outward budding and fission of the cellular membrane. A combination of factors have been shown to induce formation of MVs. For example, redistribution of phosphatidylserine to the outer leaflet in addition to shrinking of actin-myosin machinery will cause ERK to be recruited to the membrane resulting in activation of myosin light chain kinase (MLCK) triggering the releases of MVs69. Another study has shown that recruitment of ESCRT-I subunit TSG101 to the plasma membrane within the Arrestin 1 domain-containing protein 1 (ARRDC1), causes the release of MVs containing TSG10170. MVs are unique compared to exosomes as the content of MVs generally reflects the process of its biogenesis. Oncosomes are large vesicles typically 1-10 µm in diameter and are produced through non-apoptotic blebbing from the plasma membrane71.   16  1.5.1 Isolation of EVs There has recently been a large surge of research into the function of miRNAs within EVs in communication; however, in LAC there is little information on precise mechanisms by which EVs function as communicators. Numerous methods can be used in the isolation of EVs and within certain protocols, many different fractions and purities of EVs can be isolated. Differential ultracentrifugation yields many different types of EVs including exosomes, oncosomes and microvesicles and in some cases has yielded protein aggregates72. To purify certain subtypes of EV or to purify EVs further, more steps or alternate methods are required. Inserting a sucrose gradient step into the centrifugation process has been shown to eliminate protein aggregate contamination from the EVs; however, high density lipoproteins have also been isolated using this method73. Another gradient used to separate EVs from apoptotic bodies and virus is iodixanol74.            17   Additional methods of EV isolation include using microbeads, normally magnetic beads coated with an antibody recognizing EV surface markers. This technology is important for isolating specific subsets of EVs, however in in vivo settings different cell types can create EVs with overlapping surface marker types75. Due to the surface area of the beads, saturation of bindings can occur and a large population of EVs may be left out of the sample, resulting in low EVs and sometimes unusable yields. Other bead types for EV isolation include surfactant-free latex beads, that can be either hydrophilic or hydrophobic and covalently bind to EVs regardless of size or surface markers76. Size exclusion chromatography is another method of EV isolation that first removes large objects such as organelles through low-speed centrifugation, which is followed by filtration and finally size exclusion chromatography77.  These EV isolation methods allow for the selection of a specific subset of EVs, however with many unknown EV subsets as well as overlapping markers it is difficult to confirm that one type of EV is isolated from a specific isolation technique.  1.5.2 EV markers and contents EVs contain many of the same proteins that cellular compartments contain, thus there is no single marker that can separately distinguish EVs78. In addition, there are no currently accepted markers that can uniquely differentiate subsets of EVs from each other. Identification of EVs within supernatants is best performed using multiple techniques including determining size of EVs through nanoparticle tracking analysis (NTA) or electron microscopy and western blots for EV proteins. NTA allows visualization of particles in liquids in addition to quantification of particles79. The International Society of Extracellular Vesicles (ISEV) put forward a statement indicating the minimum requirements for the definition of extracellular vesicles and required 18  three markers to be present in EV preparations with an optional fourth80. The first marker suggests the presence of a membrane (CD63 or CD81), the second suggests enrichment of EVs (TSG101) and the third must be under-represented in the isolate due to its association with other compartments (H3)80. The fourth marker can be demonstrated as a membrane binding protein although association of these markers with EVs has been found to be variable. Characterization of EVs is necessary to indicate proper isolation of vesicles as contamination of lipoprotein complexes can lead to incorrect conclusions81.   EVs contain a plethora of contents including proteins, RNAs and DNA. Sequencing of total RNA from serum derived EVs suggest that miRNAs and tRNAs  make up approximately 15% of EV RNA82. Cancer EVs have also been shown to contain more total RNA compared to EVs from normal cells83. DNA within EVs can range in size from 100 base pairs to 2.5 kilobase pairs and can span across all chromosomes of genomic DNA84,85. The protein content of EVs varies depending on the method of EV biogenesis and cell type. Exosomes that arise from the late endosome compartment are generally more enriched with tetraspanins such as CD63 and CD8186,87. Microvesicle production through membrane budding involves the protein arrestin containing protein 1 (AARDC1) and contains both AARDC1 and TSG10170. The contents of EVs and microvesicles can overlap considerably with many of the same products found within them. However, there are some differences including microvesicles containing different repertoires of proteins compared to exosomes, such as the proteins glycoprotein Ib (GPIb) and P-selectin due to their plasma membrane origins87. The contents of EVs have recently been shown to be functional including miRNAs, proteins and mRNAs, even when transferred to other cells. In addition, processing of miRNAs within EVs has been shown to be independent of the cell and occurs with the necessary machinery inside the EVs88. 19   1.5.3 miRNA incorporation into exosomes MiRNAs have been demonstrated to have a higher proportion within exosomes when compared to the population of RNA in parental cells, and as profiling studies have shown, miRNAs are not randomly incorporated into exosomes89. For example, certain miRNAs such as miR-451a, miR-150-5p and miR-142-3p were demonstrated to be enriched within EVs from a variety of cell lines compared to their cellular levels90. Currently, miRNA sorting into exosomes is not well understood, however some methods of miRNA packaging have been elucidated. Proteins such as heterogeneous nuclear riboproteins (hnRNPs) hnRNPA2B1 and hnRNPA1 recognize specific miRNA binding motifs and were shown to selectively package miRNAs within exosomes91. Other methods of miRNAs packaging have been found at the ILV formation step of exosome biogenesis, including components of the ESCRT pathway which resulted in impaired miRNA activity within the EVs92. Another proposed mechanism utilizes the RISC complex which, captures miRNAs and transports them to early endosomes through the ESCRT pathway where the miRNAs are directed into ILVs88. Although this research demonstrates the enrichment of select miRNAs within exosomes, the mechanism as to how other miRNAs are selectively incorporated into exosomes remains unclear.   1.6 Detection and Diagnosis of Lung Cancer As previously stated, lung cancer is generally asymptomatic before diagnosis. Many patients are unaware of their cancer until a symptom arises such as a cough, dyspnea, chest pain, bloody sputum or metastatic symptoms93. Lung cancer is diagnosed using numerous investigative methods, where a patient presenting with suspected lung cancer will first be taken 20  for chest imaging94. Generally, an X-ray, computed tomography (CT) scan, a positron emission tomography (PET) scan or magnetic resonance imaging is performed. The chest scans are then analyzed for unusual lesions or potentially cancerous masses and if warranted biopsies are taken. Biopsies are removed from sites in the lungs suspected of harboring the cancer, which can be guided by an ultrasound and/or endoscopy. Once obtained, the biopsies are taken to a pathologist for subtype and stage diagnosis, tests for this can include immunohistochemistry and genotyping94,95.  Lung cancer is generally diagnosed in late stages of disease development, there are a number of research studies suggesting early detection of lung cancer improves overall survival outcomes and demonstrates the benefit of screening high risk patients96,97.  1.6.1 Early detection of lung cancer There are currently recommendations for screening of lung cancer in heavy smokers, although there are no current provincial or territorial screening programs in place97. However, it is recommended that heavy smokers are screened for lung cancer and pilot programs for early screening have started in some areas. The National Lung Screening Trail (NLST) showed that using low dose CT scanning for early detection of lung cancer could be achieved and would result in reduced lung cancer mortality98. The PanCan Early Detection of Lung Cancer taking place across Canada uses bronchoscopy screening, which has a better potential to detect early central lung cancers96. These methods may help in reducing lung cancer mortality through the use of active screening, however, due to time and limited hospital resources, other non-invasive and less time-consuming methods may need to be developed to accommodate high throughput screening.   21  1.6.2 Sputum as an early detector of lung cancer Sputum is liquid that originates from your respiratory tract that contains mucus, bacteria and cells. The collection of sputum can be informative when diagnosing infections in the respiratory tract and has come under interest for use as an early indicator of lung cancer. Sputum cytology examines sputum microscopically to detect the presence of abnormal cells within the sputum however, this method was found to be neither additive in diagnostic value or in improving lung cancer survival99. However, more positive results have been found recently due to detection of mutations, DNA hypermethylation markers, miRNAs and tumor related proteins within the sputum, which have shown the potential for screening purposes99.   Endogenous miRNAs were stably detected within sputum samples with miR-21 and miR-155 identified100.  The overexpression of miR-21 within the sputum was significantly associated with NSCLC compared to cancer-free patients100.  However, tumor stage was not associated with miRNA expression, which may suggest that miRNA signatures may only be used for detection. In another set of patients’ sputum samples, miR-21, miR-31 and miR-210 were used as biomarkers to distinguish between lung cancer and solitary pulmonary nodules101. These markers can assist in indicating the presence of lung cancer through a non-invasive screening method.   1.6.3 Circulating miRNAs as biomarkers There is significant interest in miRNAs circulating within the serum of patients as biomarkers for cancer detection102. Currently, there are a number of circulating miRNAs that are reported for non-invasive cancer detection, including the qRT-PCR analysis of miR-155 in the serum of breast cancer patients103-105. Numerous studies have investigated circulating miRNAs as biomarkers for early disease detection (Table 1.2)106-137. These studies applied numerous 22  methods, including miRNA sequencing, qRT-PCR and miRNA microarrays. Unfortunately, there was minimal overlap among their results in terms of identifying circulating miRNA biomarkers. While some miRNAs were consistently found elevated in NSCLC serum such as miR-21-5p, which was found elevated in six profiling studies, many of the other studies show no difference in miR-21-5p levels109,128,131,134,135,138. In addition, miR-155 was found to be elevated in NSCLC serum in four profiling studies; however, due to its common enrichment in breast cancer, this may not be suitable as a marker in women as it may generate false positives109,128,133,137. There are several factors that can explain the differences in miRNA profiling studies such as detection methods, sample preparation, sample size and demographics. Additionally, many studies do not consider the impact of hemolysis, which has consistently been found to alter miRNA levels106,109,113,116-122,126,128-130,133-140. While some studies do control for hemolysis through removing samples that contain high hemoglobin levels, the threshold for hemolysis is set at a high level and miRNA changes below this threshold can occur141. Finally, an important factor in determining biomarkers for screening of lung cancer is utilizing proper and clinically relevant controls. Many studies searching for circulating miRNA biomarkers utilize healthy individuals as controls rather than high risk heavy smokers, whom would more often be at risk for developing lung cancer and would undergo screening124,125,127-129,132-134. By utilizing healthy individuals who have never smoked, the differences in circulating miRNAs may be caused by smoking itself rather than the presence of cancer. The discrepancies between studies demonstrate the need for more strict guidelines and standardized methods for circulating miRNA biomarker discovery.    23  Lung Cancer Subtype Altered miRNA in Circulation (↑ = increase in cancer, ↓ = decreased in cancer) Method Reference NSCLC Diagnosis ↓miR-20b (plasma) TaqMan miRNA Assay  Silva et al., 2011119 NSCLC Diagnosis ↓let-7c, miR-152 (plasma) qRT-PCR  Dou et al., 2015116 NSCLC Diagnosis ↓miR-16-5p, miR-17b-5p, miR-19-3p, miR-20a-5p, miR-92-3p, ↑miR-15b-5p (serum) TaqMan miRNA Assay  Fan et al., 2015117 NSCLC Prognosis ↓miR-486, miR-30d, ↑miR-1, miR-499 (plasma) Solexa Sequencing  Hu et al., 2010118 Asymptomatic NSCLC Diagnosis  34 miRNA Signature (serum) TaqMan miRNA Assay  Bianchi et al., 2011115 NSCLC Diagnosis ↑ miR-125a-5p, miR-145-5p, miR-146a (Serum) qRT-PCR  Wang et al. 2015142 NSCLC Diagnosis 24 miRNA Signature (plasma) TaqMan miRNA Assay  Wozniak et al., 2015107 NSCLC Diagnosis  8 miRNA Signature (exosomes) qRT-PCR Giallombardo et al., 2016123 NSCLC Diagnosis  ↑miR-429, miR-205, miR-200b, miR-203, miR-125b, miR-34b (serum) TaqMan miRNA Assay  Halvorsen et al., 2016108 NSCLC Diagnosis ↑miR-1254,  miR-574-5p (serum) qRT-PCR  Foss et al. 2011122 NSCLC Diagnosis  ↑miR-20a, miR-24, miR-25, miR-145,  miR-152, miR-199a-5p, miR-221, miR-222, miR-223, miR-230 (serum) TaqMan miRNA Assay  Chen et al., 2011121 NSCLC Diagnosis miR-15b, miR-27b Signature (serum) TaqMan miRNA Assay  Hennessey et al., 2012124 NSCLC Diagnostic and Prognostic ↑miR-125b (serum) qRT-PCR  Yuxia et al., 2012127 NSCLC Diagnostic and Prognostic ↑miR-210 (serum) qRT-PCR  Li et al., 2013125 24  Lung Cancer Diagnosis ↓miR-125a-5p, miR-25, miR-126 (serum) TaqMan miRNA Assay  Wang et al., 2015143 NSCLC Diagnosis  ↑miR-483-5p , miR-193a-3p, miR-25, miR-214, miR-7 (serum) TLDA RT-qPCR Assay Wang et al., 2015120 NSCLC Diagnosis ↑miR-141 (plasma) qRT-PCR  Arab et al., 2017129 NSCLC Diagnosis  ↑miR-141, miR-200b, miR-193b, miR-301 (serum) TaqMan miRNA Assay  Nadal et al., 2015106 Lung Cancer Diagnosis ↑miR-21, miR-210 ↓miR-486-5p (plasma) qRT-PCR  Shen et al., 2011134 Lung Cancer Diagnosis ↑miR-197, miR-155 (serum) miRNA Microarray  Zheng et al., 2011137 LA Diagnosis ↓miR-339-5p ↑miR-21 (plasma) miRNA microarray  Sun et al., 2017138 NSCLC Diagnosis ↑pri-miR-944,  pri-miR-3662 TaqMan miRNA Assay  Powrózek et al., 2017132 NSCLC Prognosis  ↑miR-150 (serum) qRT-PCR  Zhang et al., 2017136 NSCLC Prognosis ↑miR-2, miR-24,  miR-34a (plasma) TaqMan miRNA Assay Franchina et al., 2014130 NSCLC Prognosis ↑miR-23b-3p,  miR-10b-5p, miR-21-5p (exosomes) qRT-PCR  Liu et al., 2017131 Lung Cancer Diagnosis ↑miR-10b, miR-141, miR-155 (serum) TaqMan miRNA Assay  Roth et al., 2011133 Lung Cancer Diagnosis ↑miR-21, miR-155, miR-182, miR-197 (serum) miRNA Microarray  Abd-El-Fattah et al., 2013128 NSCLC Diagnosis ↑miR-21 ↓miR-152, miR-148a, miR-148b (serum) TaqMan miRNA Assay Yang et al., 2015135 Lung Cancer  ↑miR-21, miR-155 ↓miR-145 (plasma) TaqMan miRNA Assay Tang et al., 2013109 NSCLC Prognosis ↑miR-1290 TaqMan miRNA Assay Mo et al., 2015144  Table 1.2 miRNAs published as potential circulating lung cancer biomarkers  25   Currently, new approaches for determining circulating miRNA biomarkers for NSCLC diagnosis are being developed including utilizing tumor-derived exosomal miRNAs145. In 2012 a study showed that the majority of miRNAs within the serum are concentrated within exosomes, which prompted many researchers to only examine exosomes as within the serum for miRNA markers for disease detection146.  In LAC miR-181-5p, miR-30a-3p, miR-30e-3p and miR-361-5p were observed as verified diagnostic markers and are promising candidate in early detection of NSCLC145. With more selective methods of isolating tumor secreted miRNAs, effects from hemolysis may be eliminated and allow for lung cancer biomarkers to become more robust and selective.               26  1.6.4 Prognostic miRNA biomarkers in NSCLC Producing tools to increase prognostic accuracy facilitates physicians in choosing treatment options. A number of studies have sought to identify markers that aid in predicting patient response to treatment and prognosis in NSCLC (Table 1.2). High levels of miR-1290 were found in NSCLC tissue and serum, which correlated with poor prognosis, lymph node metastasis and TMN staging144. In another study, elevation of serum miR-146a and a decrease in serum miR-19b predicated survival and response to chemotherapy in NSCLC147. Recently, examination of exosomal specific miRNAs has begun due to an increase in the number of tumor secreted exosomes present within the serum. High levels of exosomal miR-23b-3p, miR-10b-5p and miR-21-5p were associated with poor survival in the plasma of NSCLC patients131. Another lung exosomal miRNA study showed that miR-21 and miR-155 were upregulated in the serum of nude mice with recurrent H1299 cell line tumors compared to that of primary tumor bearing mice and non-tumor mice148. Taken together prognostic exosomal miRNAs biomarkers for NSCLC is still in its infancy, however with continued work and consistent, established protocols biomarkers may become clinically relevant and reproducible  1.7 Tumor Microenvironment The tumor microenvironment consists of both cellular and non-cellular components, including the extracellular matrix, myofibroblasts, fibroblasts, neuroendocrine cells, adipose tissue, immune inflammatory cells, endothelial cells and the lymphatic system149. Non-malignant cells can make up more than 50% of the primary tumor as well as at metastatic sites150. The integration and communication of malignant cells and the supporting tumor microenvironment are critical to the development of tumors. For example, myofibroblasts can secrete factors that 27  can remodel the extracellular matrix causing increases in metastasis, angiogenesis and inflammation151. The tumor microenvironment has a large impact on the growth of cancer, and recently therapeutic strategies have been developed in order to combat target cells within the tumor microenvironment. Considering the important role the microenvironment plays in cancer development, communication to and from the tumor to the stroma is critical in remodeling the microenvironment and making it more favorable for tumor growth and development.  1.7.1 Lung EV miRNAs in tumor development Tumor released EVs are mediators of intercellular communication between tumor cells and their surrounding environment, playing a role in both tumor growth and transforming the microenvironment152. In LAC, EVs are capable of increasing the metastatic potential of otherwise poorly metastatic cells. For examples in lung tissue and lymph nodes the transfer of EV miR-494-3p and miR-542-3p can enhance the metastatic potential of the stroma, while miR-21-5p may be capable of increasing resistance to cisplatin by transfer from resistant to non-resistant cells153,154. Additionally, LAC derived EV associated miR-21-5p and miR-29a-5p have been demonstrated to cause pro-inflammatory changes in immune cells through the toll-like receptor, TLR8155. This complements the work completed in other cancer types, where the tumor to stroma communication via miRNA cargo within EVs was examined and shown to promote angiogenesis, as well as activate the growth promoting properties of fibroblasts3,156-159.   28  1.7.2 Extracellular communication to stromal cells in cancer LAC cells are supported by a variety of non-cancer cells comprising the tumor stroma, including: fibroblasts which may provide growth factors, macrophages which may suppress an immune response, and endothelial cells which provide blood supply by creating new blood vessels through angiogenesis160-162. The two most abundant stromal cell types within the LAC tumor microenvironment are endothelial cells and fibroblasts. Endothelial cells are critical for tumor growth, such that tumors cannot exceed 2-3 mm in diameter without sufficient formation of new blood vessels to support continued growth163. Angiogenesis establishes new blood vessels within the tumor, which then supplies nutrients and oxygen to the tumor promoting growth and metastasis164,165. Stimulation of angiogenesis is triggered by cancer cells through cell-to-cell communication occurring through soluble factors such as VEGF, cytokines, extracellular matrix modifications and secreted extracellular vesicles166. EVs are important mediators of angiogenesis as transfer of miR-126, miR-214, miR-296, miR-125a, miR-31 and miR-150 through extracellular vesicles have all been shown to stimulate angiogenesis167-173. Other mediators within EVs that can stimulate angiogenesis are proteins such as VEGF, FGF-2, PDGF and metalloproteases174-178. Promotion of angiogenesis leads to increased vascular formation within the tumor and allows continued growth an invasion.  The transformation of normal fibroblast to cancer-associated fibroblasts (CAFs) is highly affected by extracellular vesicle communication from the tumor179-181. Prostate cancer exosomes have previously been shown to induce TGFβ1 dependent fibroblast differentiation into the myofibroblast phenotype, through transfer of TGFβ1 within exosomes182. However, activation of fibroblasts through soluble TGFβ1 resulted in myofibroblasts that were not pro-tumorigenic or tumor-promoting182. Other cancers have implicated specific miRNAs in CAF transformation 29  including miR-155 in pancreatic cancer, where EVs targeting TP53INP1 in normal fibroblasts induced CAF formation183. In triple negative breast cancer miR-9 when transferred through EVs induces CAF formation and can enhance cell motility within the fibroblasts158. Activation of myofibroblast is critical in forming the tumor stroma and evidence suggests that EVs play a prominent role in promoting the CAF phenotype.                  30  1.8 Thesis Themes, Rationale, Objectives and Hypothesis The central theme of my thesis is the functional characterization of LAC secreted miRNAs. The majority of research effort has been focused on identifying miRNAs as biomarkers however, many have failed to address important selection factors, and little is known about the function of early LAC secreted miRNAs. LAC has a higher survival rate at earlier stages of disease therefore it is important to identify novel ways to diagnose this disease early and to better understand the mechanisms that drive LAC. A comprehensive analysis of circulating LAC miRNAs at early disease stages will allow for the development of LAC biomarkers to predict the presence of early LAC in a clinically relevant cohort of patients. Further, analyzing LAC secreted miRNAs contained in EVs will aid in understanding the mechanisms by which LAC communicates via miRNAs to its surrounding stroma, which may provide novel strategies for targeting early tumor development.   My overall objectives are to determine a miRNA signature in the serum of LAC patients to identify LAC in high risk patients as well as to characterize the function of LAC secreted EV miRNAs within the tumor stromal.  The hypotheses for the work presented are: 1. A miRNA signature in the serum of LAC patients will allow for identification of LAC in high risk patients. 2. LAC EV miRNAs communicate with stromal cells to modify the tumor microenvironment and promote tumorigenesis  31  1.9 Specific Aims and Thesis Outline Aim 1: Profile LAC and non-cancer serum samples to identify a miRNA signature that can differentiate between tumor and normal cases  Chapter 2 describes the analysis of patient serum samples profiled for serum miRNAs. The chapter describes the methods taken to determine a set of miRNAs that can distinguish LAC from non-cancer.  Aim 2: Characterize early LAC EVs to determine specific miRNAs that are released from early LAC cell lines  Chapter 3 focuses on determining whether early LAC cell lines selectively package a select set of miRNAs for release from the cell. miRNA profiling is performed on both cell and EVs of five early LAC cell lines. Eight miRNAs are found to be selectively released from early LAC cell lines, which provides a basis for miRNAs to investigate in communication function. I correlate this finding with serum samples taken directly from LAC tumor vasculature beds during surgical resection to find miRNAs that are secreted from the tumor.  Aim 3: Determine the function of specific LAC EV miRNAs when transferred to normal stromal cells  Chapter 3 and 4 determine the function of miR-143-3p, miR-145-5p and miR-142-3p when transferred through EVs into endothelial cells and fibroblasts. EV incubation, western blots, wound healing assays and tube formation assays are performed on stromal cell lines. Targets are determined for miR-143-3p and miR-145-5p when transferred to endothelial cells. Chapter 4 further analyzes the function of miR-142-3p in fibroblasts cells and the role it plays in promoting the cancer associated fibroblast phenotype.   32  Chapter 2: Identification of serum miRNA biomarkers for early detection of lung adenocarcinoma  2.1  Introduction Many patients with LAC remain without symptoms until later stage of disease progression. This makes it difficult to detect LAC at early stages as patients often have no reason to undergo lung cancer screening. Screening is generally conducted through sputum cytology, computed tomography (CT) scan, magnetic resonance imaging (MRI) scan and positron emission tomography (PET) scan. These tests show images of the lung and can return false positive results due to lung nodules in abundance within heavy smokers and are present in smaller amounts in virtually all people beyond young adulthood184. There are many advances coming forward in identifying benign lung nodules to remove them from analysis, however it remains a problem185. The national lung cancer screening trial in the United States demonstrated that 96.4% of positive results in low-dose CT screening were false positives while 94.5% of positive radiography screening results were false positives98. Patients who received treatment or biopsies for these false positives were put at risk of complications98. Once screened, patients suspected of lung cancer are sent for diagnosing either by; sputum cytology, thoracentesis, needle biopsy or bronchoscopy. The sampling of tissues through these methods is done to confirm diagnosis and the least invasive method is generally used to take a biopsy186. However, many of these methods are highly invasive and require surgery which can result in complications. In CT-guided transthoracic lung biopsy, pneumothorax occurs in 25.3% of patients and major complications (including hemothorax, air embolism, needle tract seeding and 33  death) occur in 5.7% of patients187. These methods are also performed on suspected lesions which can result in unnecessary invasive testing for benign lesions in the lung, as well false positives that can even lead to overtreatment. From these indications it is evident that a non-invasive screening and diagnostic method is required to reduce complications and reduce false positives.   Circulating biomarkers are a potential addition to the lung cancer screening process, as they may be used to reduce false positives in the screening process, limit the use of biopsies or be directly involved in diagnostics. One promising circulating biomarker, are miRNAs, due to their stability within the blood and during sample collection including storage188. All cell types, including tumor cells, secrete circulating miRNAs into the blood, which can be packaged within EVs or RNA-binding proteins, these packaging methods provide protection from RNases3,92,189-191. The secreted miRNAs are found within both the serum and the plasma of blood samples, however the plasma contains miRNAs associated with platelets, which can interfere with miRNA readings126. These aspects of serum miRNAs allow them to be robust non-invasive biomarkers.         34  Serum miRNAs were first discovered in 2007 in the serum of lymphoma patients, and shortly after they became useful as non-invasive biomarkers for detection of cancer102,192,193. Research has shown that circulating miRNAs can be used to not only detect the presence of cancer but other diseases as well including Alzheimer’s, autism, schizophrenia, epilepsy and cardiovascular disease194-198. Utilizing serum miRNAs as diagnostic biomarkers can help provide non-invasive methods to diagnose disease early in development. miRNAs are comparably much more stable than mRNAs making them less prone to differences between samples and allowing for more consistent measurements146. Circulating serum miRNAs are found freely circulating, bound to Agronaut-2 protein and enclosed within EVs199,200.  Exosomes are a subset of EVs produced through formation of intraluminal vesicles within endosomes, which are packaged with miRNAs in a selective manner201. Exosomes provide a protection for the miRNAs from degradation due to their lipid bilayer which separates the lumen of the exosomes from the serum191. Due the stability of miRNAs and the enrichment that exosomes provide for analysis, serum exosome miRNAs seem to be a promising biomarker for early cancer detection202,203. Research showed that the majority of miRNAs within the serum are located within exosomes, a subset of extracellular vesicles (EVs) 70-100nm in diameter146. However, contradicting studies showed that miRNAs are mainly bound to proteins within the serum and are free-floating199,200. Due to these contradicting studies we performed whole serum miRNA profiling to examine all potential sources for miRNA biomarkers.  Numerous studies have investigated circulating miRNAs as biomarkers for detection of NSCLC however, the results of these studies have largely been inconclusive and contradicting. The first use of a miRNA signature for NSCLC diagnosis was first conducted using Solexa sequencing and identified 91 miRNAs within the serum of healthy Chinese patients114. Two 35  NSCLC serum miRNAs (miR-25 and miR-223) were identified as upregulated in the serum and were validated in an independent trial of 75 healthy and 152 cancer patients using qRT-PCR114. This study resulted in numerous other researchers looking for unique miRNA signatures in cancer patient serum. The same researchers later used the same panel of 91 miRNAs across 400 NSCLC cases and 220 healthy control cases and found a panel of 10 miRNAs upregulated in NSCLC serum (miR-145-5p, miR-152, miR-199a-5p, miR-20a, miR-221, miR-222, miR-223, miR-24, miR-25 and miR-320)121. The healthy donors in both these cases however, were not heavy smokers and therefore were not likely mimicking the conditions of high risk individuals that clinical testing would examine114,121. Other miRNA panels for NSCLC detection have also been conducted, however the candidate miRNAs in each case vary significantly (Table 2.1). This may be caused by numerous issues including factors relating to sample collection, method of miRNA detection and type of analysis. Our research eliminates the effects of hemolysis through removal of miRNAs related to hemolysis and utilizes heavy smoker patients at high risk of developing LAC as controls. This will allow for us to create a clinically relevant reproducible circulating miRNA biomarker for discrimination between LAC and non-cancer. In this chapter I examine circulating serum miRNAs in LAC patients compared to high risk non-cancer controls, to determine a subset of miRNAs that are able to identify LAC in high-risk patient serum based on the detection of miRNAs.    36  2.2  Materials and Methods 2.2.1 Sample collection To examine the utility of differentially expressed miRNAs as NSCLC biomarkers, we collected serum samples from patients with early LAC and heavy smoker non-cancer volunteers using a peripheral blood draw. 50 samples of early LAC serum samples were collected and compared to 51 non-cancer samples collected from demographically matched, high risk smokers without cancer. Our results were validated in an independent set of samples consisting of 35 patients with LAC and 28 demographically matched high risk non-cancer controls. All patient samples are listed in Table 2.1. (Ethics Number)               37   Lung Adenocarcinoma Patients Non-cancer Controls P-value (Adenocarcinoma vs Control) Validation Lung Adenocarcinoma Samples Validation Non-Cancer Samples Total patients 50 51  35 28 Mean age 67 63 0.0129b 70 65 Age range 48-83 50-75  44-85 53-78 # of males 26 29 0.46c 13 9 Former Smokers 26 30  0.14c 24 13 Current Smokers 24 21   10 15 Never Smokers 0 0  0 0 Mean pack years 43.6 46.5 0.88b 49.7 46.8 Pack year range 16.5-82 31-80  10-150 17-66         Stage (%) Stage (%)  1A 8 (30.7)   9 (25.7)  1B 5 (19.2)   4 (11.4)  2A 3 (11.5)   5 (14.3)  2B 2 (7.6)   3 (8.6)  3A 6 (23)   11 (31.4)  Unknown 2 (7.6)   3 (8.6)      Caucasian 26 43    a Data missing data for some cases b Student’s t-test c Fisher’s exact test  Table 2.1 Patient demographics    38  2.2.2 Blood processing Samples were collected in SST vacutainer tubes and allowed to clot at room temperature for 30 minutes. After clotting samples were centrifuged for 15 minutes at room temperature at 1500g. Aliquots of serum samples were collected and frozen at -80oC for storage.  2.2.3 RNA isolation RNA was extracted by miRNeasy Mini Kit (Qiagen) from 200 µL of serum according to instructions from manufacturer. MS2, an RNA carrier (Roche Applied Science), was added to QIAzol Lysis Reagent prior to isolation as it improves RNA purification, MS2 was added at 1.25 µL per 200 µL of serum204. Once isolated, RNA was re-suspended in 50 µL of nuclease-free water and immediately frozen at -80oC.   2.2.4 qRT-PCR of serum miRNAs Serum miRNAs were unable to be reliably measured due to low yields, so fixed volumes of eluted RNA was utilized for profiling205,206. Reverse transcription was conducted using miRCURY LNA Universal RT miRNA PCR, Polyadenylation and cDNA synthesis kit (Exiqon Inc.). Quantitative real-time polymerase chain reaction (qRT-PCR) was conducted on cDNA using miRNA Read-to-Use PCR, Human panel I and panel II with miRCURY LNA Universal RT miRNA PCR, SYBR Green master mix. ROX reference dye normalization was conducted using Viia7 software (Applied Biosystems). miRNAs measured having more than 1 melting curve were excluded from analysis along with miRNAs within 5 Ct’s of the negative control (Ct > 35).  39   Validation set qRT-PCR was conducted using Thermo Fisher TaqMan custom pre-spotted plates with each miRNA run in triplicates. Validation samples were profiled for miR-197-3p, miR-23a, miR-23b, miR-142-3p, miR-582-5p, miR-92b-3p, miR-200a-3p and miR-497-5p. RNA was run utilizing TaqMan miRNA reverse transcription kit and TaqMan Master Mix II, no UNG was used for qRT-PCR, according to manufacturer’s recommendations.  2.2.5 Data analysis Raw serum miRNA profiles from human panel I+II were entered into GenEx (MultiD) for compilation and interplate calibration (n=101). Once calibrated, raw miRNA qRT-PCR data was exported for analysis. MiRNAs that were detected at less than 35 Ct’s in fewer than 50% of samples were eliminated from further analysis to ensure reliable detection in the majority of patients. MiRNAs affected by hemolysis, which have previously been determined by MacLellan et al. (2014), were then further removed from analysis (Table 2.2)139. Remaining miRNAs were normalized to the best miRNA normalizer as determined by geNorm and NormFinder. Normalized data was then loaded into Statistica for further analysis. Mann Whitney U tests were conducted using Statistica Software requiring a p-value < 0.05 for significance, data was then entered into an excel file for a Benjamini-Hochberg correction to account for multiple testing207. For best subset analysis, Logit model was utilized, and a forward stepwise model was built for each normalized data set, p-values to enter and to be removed were set at 0.05. miRNAs with variable status “In” were selected for use in the Logit model best subset analysis. Using a best subset analysis, we selected the smallest number of miRNAs that were able to maintain an AUC above 0.8000, to reduce the odds of overfitting of models. Equations for LAC and non-cancer identification, were formed using parameter estimates with p-values < 0.05. 40  MiRNA MiRNA MiRNA MiRNA hsa-let-7a hsa-miR-16-2* hsa-miR-26a hsa-miR-454 hsa-let-7a* hsa-miR-17 hsa-miR-26b hsa-miR-484 hsa-let-7b hsa-miR-17* hsa-miR-27a hsa-miR-486-3p hsa-let-7c hsa-miR-181a hsa-miR-296-5p hsa-miR-486-5p hsa-let-7d hsa-miR-181b hsa-miR-29a hsa-miR-496 hsa-let-7d* hsa-miR-181d hsa-miR-29a* hsa-miR-500a hsa-let-7f-1* hsa-miR-182 hsa-miR-29b hsa-miR-501-3p hsa-let-7f-2* hsa-miR-183 hsa-miR-29b-2* hsa-miR-501-5p hsa-let-7g hsa-miR-185 hsa-miR-29c hsa-miR-502-3p hsa-let-7i hsa-miR-186 hsa-miR-301a hsa-miR-502-5p hsa-miR-101 hsa-miR-188-5p hsa-miR-30b hsa-miR-503 hsa-miR-103 hsa-miR-18a hsa-miR-30c hsa-miR-532-3p hsa-miR-103-2* hsa-miR-18b hsa-miR-30d hsa-miR-532-5p hsa-miR-106a hsa-miR-190 hsa-miR-30e hsa-miR-545 hsa-miR-106b hsa-miR-1908 hsa-miR-30e* hsa-miR-550a hsa-miR-107 hsa-miR-191 hsa-miR-32 hsa-miR-574-3p hsa-miR-126 hsa-miR-192 hsa-miR-320a hsa-miR-576-3p hsa-miR-126* hsa-miR-194 hsa-miR-320b hsa-miR-576-5p hsa-miR-130a hsa-miR-195 hsa-miR-324-3p hsa-miR-584 hsa-miR-130b hsa-miR-196b hsa-miR-324-5p hsa-miR-590-5p hsa-miR-132 hsa-miR-1974 hsa-miR-328 hsa-miR-598 hsa-miR-133b hsa-miR-1979 hsa-miR-331-3p hsa-miR-624* hsa-miR-135a hsa-miR-199a-3p hsa-miR-335 hsa-miR-625* hsa-miR-140-3p hsa-miR-19a hsa-miR-338-3p hsa-miR-627 hsa-miR-140-5p hsa-miR-19b hsa-miR-33b hsa-miR-628-3p hsa-miR-142-5p hsa-miR-20a hsa-miR-342-5p hsa-miR-629 hsa-miR-143 hsa-miR-20a* hsa-miR-345 hsa-miR-636 hsa-miR-144 hsa-miR-20b hsa-miR-361-3p hsa-miR-651 hsa-miR-144* hsa-miR-21 hsa-miR-361-5p hsa-miR-652 hsa-miR-146a hsa-miR-210 hsa-miR-363 hsa-miR-660 hsa-miR-146b-5p hsa-miR-2110 hsa-miR-374a hsa-miR-7 hsa-miR-148a hsa-miR-215 hsa-miR-374b hsa-miR-886-5p hsa-miR-148b hsa-miR-22 hsa-miR-376c hsa-miR-92a hsa-miR-150 hsa-miR-221 hsa-miR-378 hsa-miR-93 hsa-miR-151-3p hsa-miR-222 hsa-miR-378* hsa-miR-93* hsa-miR-151-5p hsa-miR-223 hsa-miR-421 hsa-miR-941 hsa-miR-152 hsa-miR-223* hsa-miR-423-3p hsa-miR-942 hsa-miR-15a hsa-miR-24 hsa-miR-423-5p hsa-miR-96 hsa-miR-15b hsa-miR-25 hsa-miR-424 hsa-miR-98 hsa-miR-15b* hsa-miR-25* hsa-miR-425  hsa-miR-15b* hsa-miR-26a hsa-miR-425*  hsa-miR-16 hsa-miR-26b hsa-miR-451   Table 2.2 MiRNAs affected by hemolysis as published by MacLellan et al. (2014)139 41  2.3 Results 2.3.1 Identification of a circulating miRNA for use as a normalizer Normalization of serum miRNAs aims to remove differences due to sample collection and quality of profiled miRNA. MiRNAs normally used as housekeeping and endogenous controls in tissue are generally unsuitable as circulating miRNA normalizers due to their abundance within red blood cells, and so appropriate miRNA normalizers within the serum are difficult to determine. For example, miR-16-5p is the most expressed miRNA in red blood cells, and hemolysis leads to substantial increase of that miRNA in serum concentration141,208. Therefore, lysis of red blood cells will cause contamination of serum miRNA content resulting in different standardization based on amount of hemolysis. Another example, is RNU6B (U6) a common miRNA used for tissue normalization, however in the serum it has seen high variability even in healthy patients indicating its unsuitability209. Currently, no miRNA has been shown to be a consistent normalizer in the serum of patients, although if detecting more than 100 miRNAs global normalization is often referred to as the gold standard210. However, we were only able to detect 11 miRNAs not affected by hemolysis and detected in each sample, so global mean could not be an effective method (Table 2.3). To achieve normalization within our sample set, we sought to determine an endogenous miRNA within our serum samples that could be utilized as a normalizer. To select miRNAs candidates to act as normalizers in our serum samples, I eliminated any miRNAs detected above 35 Ct’s in every sample. After elimination 69 miRNAs remained, miRNAs affected by hemolysis (Table 2.2) were then removed leaving 11 miRNA candidates remaining (Table 2.3)139. Remaining miRNAs for normalizations were analyzed for their potential as normalizers using both geNorm and NormFinder software211,212.  42  miRNAs  miR-23a miR-23b miR-197 miR-145 miR-505 miR-99a miR-125a-5p miR-342-3p miR-125b miR-142-3p miR-122 Table 2.3 miRNAs detected in every sample and not affected by hemolysis               43  NormFinder estimates the standard deviation (SD) of each miRNA, and then compares it against the expression of the other genes. The miRNA which has the lowest intra- and inter-group variation is considered the most stable miRNA212. The program geNorm analyses the stability of each miRNAs and associates an expression stability value (M-value) for each miRNA and identifies the best miRNA as a reference gene. The M-value is based on mean pairwise variation between genes across all samples, the miRNA with the lowest M-value is considered to be the most stable211. Scores for each miRNA candidate and analysis are reported in Table 2.4 and Table 2.5. Three miRNAs showed consistently strong scores for miRNAs as normalizers miR-197, miR-23a and miR-23b (Figure 2.1). We selected three miRNA normalizers to use, to be highly robust in our selection of miRNAs and to ensure that validation of at least one miRNA normalizer would occur. MiR-197 and miR-23a have both previously been reported as useful miRNAs for normalization, however this is the first time miR-23b has been demonstrated as a miRNA normalizer for serum miRNAs. We had previously reported that miR-122-5p was a suitable normalizer for serum samples, however within our LAC serum analysis miR-122-5p had the highest variability between samples139. This may be caused by differing platforms as the previously published data was utilizing an older platform with differing products and for certain miRNAs primer design was altered.      44   Gene Name M-Value miR-23a 0.501929434 miR-23b 0.501929434 miR-197 0.583430471 miR-145 0.62397506 miR-505 0.684115404 miR-99a 0.724214161 miR-125a-5p 0.75853554 miR-342-3p 0.791727679 miR-125b 0.812377637 miR-142-3p 0.837827795 miR-122 0.952339571 *Average expression stability (M-Value) Table 2.4 geNorm analysis of 11 candidate miRNAs for serum normalization  Gene Name SD Acc. SD miR-197 0.0113 0.0113 miR-23a 0.0113 0.008 miR-23b 0.0134 0.007 miR-125b 0.0148 0.0058 miR-99a 0.0148 0.0055 miR-505 0.0158 0.0052 miR-145 0.0165 0.005 miR-342-3p 0.0174 0.0048 miR-125a-5p 0.0194 0.0048 miR-142-3p 0.0209 0.0047 miR-122 0.0272 0.0049 *Acc. SD (accumulated standard deviation) *SD (Standard Deviation)  Table 2.5 NormFinder analysis of 11 candidate miRNAs for serum normalization.       45                                   Figure 2.1 Box plot of training data raw Ct values of A) miR-197, B) miR-23a and C) miR-23b from both LAC serum and non-cancer controls. (n = 50 LAC, n=51 Non-Cancer)   Median   25%-75%   Non-Outlier Range   Outliers        Raw Training Data0 1242526272829303132Raw miR-23b Ct    LAC   Non-Cancer  Raw Training Data1 02223242526272829Raw miR-23a Ct    LAC   Non-Cancer  Raw Training Data0 126.527.027.528.028.529.029.530.030.531.031.532.0Raw miR-197 Ct    LAC   Non-Cancer  46  2.3.2 Validation of endogenous miRNA normalizers To confirm that our miRNAs would be normalizers across platforms we utilized individual TaqMan qRT-PCR primers on a separate subset of samples to determine if expression of normalizers was consistent (Figure 2.2). Using raw qRT-PCR data we entered the data into both geNorm and NormFinder and found that miR-197 showed a much higher standard deviation between LAC and non-cancer indicating that a shift had occurred between platforms and that miR-197 was no longer suitable for use as a normalizer (Table 2.6). Indicated by NormFinder analysis, miR-23a and miR-23b remained suitable as normalizers due to low M-values < 0.15 (Table 2.7). However, miR-23b had a higher standard deviation in geNorm analysis compared to miR-23a which indicated miR-23a is the more suitable normalizer for LAC and non-cancer serum (Table 2.4 and Table 2.5).  Thus miR-197 and miR-23b have been removed as normalizers for analysis.            47                 Figure 2.2 Box plot of candidate miRNA normalizers between lung adenocarcinoma and non-cancer in validation sample sets A) miR-197 B) miR-23a C) miR-23b. (n = 35 LAC, n=28 Non-Cancer)   Median   25%-75%   Non-Outlier Range   Outliers          Raw Validation Data1 025262728293031Raw miR-23b CtRaw Validation Data1 022232425262728Raw miR-23a Ct    LAC   Non-Cancer     LAC   Non-Cancer Raw Validation Data1 027.027.528.028.529.029.530.030.531.031.532.032.5Raw miR-197 Ct    LAC   Non-Cancer A) B) C) 48  Gene Name SD Acc. SD miR-23a 0.1086 0.1086 miR-23b 0.1253 0.0829 miR-197 0.3384 0.1256 *Acc. SD (accumulated standard deviation) *SD (Standard Deviation)  Table 2.6 geNorm analysis of normalization candidates for validation data  Gene Name M-Value miR-197 0.281448704 miR-23a 0.127979299 miR-23b 0.127979299 *Average expression stability (M-Value) Table 2.7 NormFinder analysis of normalization candidates for validation data   2.3.3 Identification of miRNAs differentially detected in lung adenocarcinoma and healthy controls To identify potential microRNA candidates as biomarkers for early LAC detection, microRNAs in LAC sera and non-cancer control sera had to meet three criteria: (1) Ct values <35 to ensure reliable consistent detection, (2) microRNAs must not be affected by hemolysis to ensure serum microRNAs cannot be affected by blood drawing technique and storage and (3) microRNAs must be present in >50% of all samples. These criteria were utilized to ensure that the miRNAs indicated as biomarkers are reliably detected, not affected by hemolysis and would be useful as indicators of LAC in a majority of patients. A total of 86 microRNAs met these criteria (Table 2.8).    49  miRNAs Detected <35Ct’s and Not Affected by Hemolysis hsa-let-7b* hsa-miR-28-3p  hsa-miR-95 hsa-let-7e hsa-miR-28-5p  hsa-miR-99a hsa-miR-1 hsa-miR-29b-1*  hsa-miR-99b hsa-miR-106b* hsa-miR-301b   hsa-miR-10a hsa-miR-30a   hsa-miR-10b hsa-miR-30a*   hsa-miR-122 hsa-miR-326   hsa-miR-1249 hsa-miR-339-3p   hsa-miR-125a-5p hsa-miR-339-5p   hsa-miR-125b hsa-miR-33a   hsa-miR-1266 hsa-miR-342-3p   hsa-miR-127-3p hsa-miR-34a   hsa-miR-128 hsa-miR-365   hsa-miR-130b* hsa-miR-373*   hsa-miR-133a hsa-miR-375   hsa-miR-134 hsa-miR-376a   hsa-miR-136 hsa-miR-382   hsa-miR-139-5p hsa-miR-409-3p   hsa-miR-141 hsa-miR-490-3p   hsa-miR-142-3p hsa-miR-491-5p   hsa-miR-145 hsa-miR-495   hsa-miR-1537 hsa-miR-497   hsa-miR-1538 hsa-miR-543   hsa-miR-154 hsa-miR-550a*   hsa-miR-181c* hsa-miR-551a   hsa-miR-1909 hsa-miR-551b   hsa-miR-1913 hsa-miR-582-5p   hsa-miR-193a-5p hsa-miR-632   hsa-miR-193b hsa-miR-638   hsa-miR-197 hsa-miR-654-3p   hsa-miR-199a-5p hsa-miR-663   hsa-miR-199b-5p hsa-miR-664   hsa-miR-200a hsa-miR-671-5p   hsa-miR-200c hsa-miR-744   hsa-miR-205 hsa-miR-766   hsa-miR-22* hsa-miR-769-5p   hsa-miR-23a hsa-miR-885-5p   hsa-miR-23b hsa-miR-888   hsa-miR-24-2* hsa-miR-92b   hsa-miR-26b* hsa-miR-934   hsa-miR-27b hsa-miR-940   Table 2.8 List of miRNAs not affected by hemolysis and detected in more than 50% of all samples 50   A Mann-Whitney U test was conducted on miRNA detected in >50% of all serum samples and normalized to miR-23a. After correcting for multiple testing using the Benjamini-Hochberg correction 22 miRNAs were significantly enriched in LAC serum while one miRNA was enriched in non-cancer serum (Table 2.9). MiR-142-3p was the most significantly different miRNA, being enriched in LAC serum compared to non-cancer. MiR-142-3p when enriched in the serum of LAC patients has previously been associated with, patients suffering from recurrence within 24 months of tumor excision213.                 51  miRNAs (miR-23a Normalized) Uncorrected p-value Benjamini-Hochberg Significance (FDR 0.10) Benjamini-Hochberg p-value Enriched in LAC Serum hsa-miR-142-3p 0.000102 significant 0.00548686 hsa-miR-551b 0.000187 significant 0.00548686 hsa-miR-199a-5p 0.000191 significant 0.00548686 hsa-miR-27b 0.000487 significant 0.010471255 hsa-miR-766 0.000927 significant 0.015942803 hsa-miR-33a 0.001522 significant 0.018709302 hsa-miR-497 0.001853 significant 0.018709302 hsa-miR-23b 0.002429 significant 0.018709302 hsa-miR-139-5p 0.002573 significant 0.018709302 hsa-miR-28-5p 0.002622 significant 0.018709302 hsa-miR-127-3p 0.002828 significant 0.018709302 hsa-miR-376a 0.002828 significant 0.018709302 hsa-miR-136 0.004654 significant 0.02816332 hsa-miR-24-2* 0.004912 significant 0.02816332 hsa-miR-495 0.005665 significant 0.030448303 hsa-let-7e 0.015876 significant 0.080314886 hsa-miR-339-5p 0.017312 significant 0.082713469 hsa-miR-22* 0.021492 significant 0.095267448 hsa-miR-154 0.022155 significant 0.095267448 hsa-miR-1 0.024991 significant 0.096273199 hsa-miR-34a 0.025747 significant 0.096273199 Enriched in Non-Cancer Serum hsa-miR-582-5p 0.001946 significant 0.018709302  Table 2.9 miRNAs significantly enriched within lung adenocarcinoma serum compared to non-cancer control serum as analyzed by Mann Whitney U test with a Benjamini-Hochberg correction         52  2.3.4 Identification of miRNAs able to differentially distinguish between LAC and non-cancer  In order to identify miRNAs that could be utilized to differentiate between LAC and non-cancer we conducted a logistic regression model. We began by setting minimums for selecting miRNAs first an AUC of 0.8000 must be met to ensure specificity and sensitivity and the minimum number of miRNAs that fit above 0.8000 AUC would be used, to prevent overfitting.  Next miRNAs were entered into Statistica for a forward stepwise logit regression to identify miRNAs that were suitable for inclusion in best subset analysis. Certain miRNAs were removed from analysis due to recent findings, including miR-720 which was discovered to be a fragment of tRNA214. MiR-155 was also removed from analysis due to its correlation with smoking-associated diseases including coronary artery disease and type 2 diabetes215-217. This miRNA was therefore removed, as coronary artery disease and type 2 diabetes were not recorded in patient data and may lead to discriminating of smoking-associated diseases rather than LAC218. miRNAs that were found to have a variable status of “In” in the forward stepwise analysis were then placed into the logit regression to determine best subsets for discriminating between LAC and non-cancer.        53  Using miR-23a normalized serum data ten miRNAs were selected for inclusion in the model (miR-142-3p, miR-497, miR-582-5p, miR-27b, miR-92b, miR-200c, miR-638, miR-29b-1-3p, miR-1909 and miR-940) (Table 2.10). Included miRNAs were then entered into a logit regression best subset analysis which examines which miRNAs are best at discriminating between LAC and non-cancer in combination. The ROC curve for all ten miRNAs was found to have an AUC of 0.8877, while a four-miRNA signature of miR-582-5p, miR-497, miR-27b and miR-92b was found to have an AUC of 0.8045 (Table 2.11) (Figure 2.3). To prevent overfitting, we utilized the four-miRNA signature in discriminating between LAC and non-cancer. A discriminate score equation was derived from the miRNA signature utilizing parameter estimates (Table 2.12).               54  Logit Modeled Probability miR-23a Normalized LAC vs Non-Cancer Serum  Effect  Degree of Freedom   Wald Stat.  Wald p   Var. Status  hsa-miR-142-3p 1 5.217141 0.022365 In hsa-miR-582-5p 1 8.734898 0.003122 In hsa-miR-638 1 9.545789 0.002004 In hsa-miR-497 1 9.226806 0.002385 In hsa-miR-29b-1* 1 4.750941 0.029282 In hsa-miR-92b 1 4.379165 0.036381 In hsa-miR-27b 1 5.628781 0.017668 In hsa-miR-200c 1 6.202137 0.012760 In hsa-miR-940 1 4.694019 0.030268 In hsa-miR-1909 1 5.047135 0.024667 In  Table 2.10 miRNAs from forward stepwise logit regression for entrance into best subset analysis   Number of miRNAs Included in Logit Regression AUC Score 10 0.8877 9 0.8564 8 0.8440 7 0.8194 6 0.8156 5 0.8127 4 0.8045 3 0.7694 2 0.7549  Table 2.11 MiRNAs included in logit regression and their associated AUC score 55  ROC CurveArea: 0.8045-0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.21-Specificity-0.20.00.20.40.60.81.01.2Sensitivity Figure 2.3 ROC curve of LAC miRNA classifiers normalized to miR-23a        Formula (Score if score < 0, LAC Predicted)  Discriminate Score = −3.7122 − 1.3276(𝐶𝑇 𝑚𝑖𝑅 − 27𝑏) –  0.6026(𝐶𝑇 𝑚𝑖𝑅 − 497) +  0.5841(𝐶𝑇 𝑚𝑖𝑅 − 582 − 5𝑝) +  0.8318(𝐶𝑇 𝑚𝑖𝑅 − 92𝑏)  Table 2.12 Formulas for discrimination of patient serum  56  2.3.5 Validation of serum miRNA signatures in separate serum sample cohort To validate miRNA linear regression models in predicting LAC presence in serum, 64 new patient serum samples were run on a custom TaqMan designed panel of miRNAs including miR-23a, miR-92b, miR-582-5p, miR-27b and miR-497. Samples were normalized to miR-23a and entered the linear regression model equation described in Table 2.12. A score of less than 0 indicated LAC prediction while a score more than 0 indicated non-cancer prediction. In a collection of 63 validation samples LAC was correctly predicted 68.6% of the time and non-cancer 67.9% of the time (Table 2.13). Patient samples that were misclassified ranged from stage I to stage III and were evenly distributed between the groups, which may suggest that the discriminate score is not biased towards the tumor stage (Table 2.14). Given the changes in sensitivity and specificity between our training and validation data we sought to improve our accuracy by looking for demographic differences between training and validation. Our validation samples had a large difference between male and female samples (~35% of validation samples, compared to 50% of training samples), so we sought to determine if sex may play a role in the detection of serum miRNAs (Table 2.1).    Predicted LAC Predicted Non-Cancer Percent Correct Observed LAC 24 11 0.686 Observed Non-Cancer 9 19 0.679  Table 2.13 Predicted cases using independent validation data normalized to miR-23a   57  Tumour Stages  Misclassified Cases/Total Cases I 3/9 II 4/13 III 5/14  Table 2.14 Misclassified tumour stages in validation cohort   2.3.6 Serum miRNAs are differentially detected in male and female samples Previously researchers have shown sex associated differences in male and female serum miRNAs126,219,220. These differences in miRNAs by sex may impact miRNAs as biomarkers and to account for this effect we separated LAC and non-cancer samples based on sex. We conducted a Mann Whitney U test on male patient samples between LAC and non-cancer serum, and on female patient samples between LAC and non-cancer serum. In males nineteen miRNAs were significantly differentially enriched between LAC and non-cancer serum after a Benjamini-Hochberg correction when normalized to miR-23a (Table 2.15). In females only six miRNAs were found to be differentially enriched between LAC and non-cancer serum, but no miRNAs remained significant after correction for multiple testing. These results indicate that LAC serum miRNAs may have a higher probability for detecting the presence of LAC in males compared to females.       58   miRNAs (miR-23a Normalized) Uncorrected p-value Benjamini-Hochberg Significance (FDR 0.10) Benjamini-Hochberg p-value Enriched in LAC Serum hsa-miR-142-3p 0.000065 significant 0.005978015 hsa-miR-139-5p 0.000325 significant 0.014946457 hsa-miR-27b 0.001024 significant 0.02753425 hsa-miR-551b 0.001628 significant 0.029035395 hsa-miR-199a-5p 0.001894 significant 0.029035395 hsa-miR-28-5p 0.003243 significant 0.042622181 hsa-miR-136 0.005177 significant 0.059538833 hsa-miR-339-5p 0.007097 significant 0.064363034 hsa-miR-376a 0.007418 significant 0.064363034 hsa-miR-23b 0.008462 significant 0.064363034 hsa-miR-495 0.008462 significant 0.064363034 hsa-let-7f 0.010494 significant 0.064363034 hsa-miR-33a 0.010494 significant 0.064363034 hsa-miR-155 0.015267 significant 0.087785674 hsa-miR-766 0.016893 significant 0.088102476 hsa-let-7e 0.017237 significant 0.088102476 Enriched Non-Cancer Serum hsa-miR-582-5p 0.001197 significant 0.02753425 hsa-miR-505 0.010494 significant 0.064363034  Table 2.15 miRNAs significantly enriched within male lung adenocarcinoma serum compared to male non-cancer control serum as analyzed by Mann Whitney U test with a Benjamini-Hochberg correction.   Due to the differences caused by sex in miRNAs significantly enriched within LAC serum and non-cancer controls, I further examined the best subset of miRNAs for classification of LAC by sex. We performed linear regression best subset analysis on training data separated by sex. In males a two-miRNA signature was derived for normalization to miR-23a using miR-142-3p and miR-582-5p as classifiers with an AUC of 0.8309 (Figure 2.4A) (Table 2.16). In females a two-miRNA signature of miR-497 and miR-200a was derived for miR-23a normalized data and with an AUC of 0.7903 (Figure 2.4B) (Table 2.16).   59  ROC CurveArea: 0.8309-0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.21-Specificity-0.20.00.20.40.60.81.01.2Sensitivity ROC CurveArea: 0.7903-0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.21-Specificity-0.20.00.20.40.60.81.01.2Sensitivity Figure 2.4 ROC curve of A) miR-23a normalized male serum data for derived miRNA signature B) miR-23a normalized female serum data for derived miRNA signature   A) B) 60     Formula (Score if score < 0, LAC Predicted)  Male Discrimination Score =  −3.9161–  1.3184(𝐶𝑡 𝑚𝑖𝑅 − 142 − 3𝑝) +  0.7065(𝐶𝑡 𝑚𝑖𝑅 − 582 − 5𝑝)   Female Discrimination Score =  1.1027 –  1.0421(𝐶𝑡 𝑚𝑖𝑅 − 497)–  0.7623(𝐶𝑡 𝑚𝑖𝑅 − 200𝑎)   Table 2.16 : Table of sex specific discriminate score analysis for identification of LAC serum.   2.3.7 Validation of sex-specific serum miRNA signatures in separate serum sample cohort We next sought to validate our sex specific miRNA signatures on a separate sample cohort. Utilizing the same validation samples to profile miR-142-3p, miR-200a, miR-497, miR-582-5p and miR-92b using TaqMan primers and the serum discrimination equations derived previously in table 2.11, it was found that female specific discrimination equations poorly predicted LAC serum with around a 35% accuracy, while predicting non-cancer serum accurately around 75% of the time (Table 2.17). In female discrimination equations we found that non-cancer tended to be predicted. In males LAC was predicted in all but one sample, suggesting that the derived formula tends to predict LAC (Table 2.18). We sought to determine if a specific miRNA may be causing these tendencies by examining normalized Ct values for each sex specific miRNAs between validation and training (Figure 2.5). In figure 2.5 and figure 2.6 we see that most miRNA normalized Ct values show the same trend between training and validation, except for miR-142-3p which shows a deviation in trend from differentially expressed in training data to non-differentially expressed in validation data. In addition, we examined identical serum samples run on the different platforms to ensure the trends we were seeing were 61  validated. We found a significant difference between training miR-142-3p and validation miR-142-3p in identical samples using a T-test (Figure 2.7). We sought to remove miR-142-3p from male analysis to determine if a different subset of miRNAs could be used to differentiate between LAC and non-cancer, using only previously used miRNAs on validation panels.     Predicted LAC Predicted Non-Cancer Percent Correct Observed LAC 8 15 0.348 Observed Non-Cancer 4 14 0.778  Table 2.17 Female predicted cases using independent validation data normalized to miR-23a       Predicted LAC Predicted Non-Cancer Percent Correct Observed LAC 12 0 1.000 Observed Non-Cancer 8 1 0.111  Table 2.18 Male predicted cases using independent validation data normalized to miR-23a   62                                        Figure 2.5 Box plots of male miRNA candidates normalized to miR-23a a) miR-142-3p training data LAC vs Non-Cancer b) miR-142-3p validation data lung adenocarcinoma vs Non-Cancer c) miR-582-5p training data LAC vs Non-Cancer d) miR-582-5p validation data lung adenocarcinoma vs Non-Cancer. (Training Male: n = 26 LAC, n= 29 Non-Cancer, Validation Male: n = 13 LAC, n = 9 Non-Cancer)   Median   25%-75%   Non-Outlier Range  ✵ Extremes  Outliers    Male Training1 0-2-101234miR-142-3p Normalized Ct    LAC     Non-Cancer Male Training1 0456789101112miR-582-5p Normalized Ct    LAC     Non-Cancer Male Validation0 1-0.50.00.51.01.52.02.53.03.54.0miR-142-3p Normalized Ct    LAC     Non-Cancer Male Validation0 16.06.57.07.58.08.59.09.510.0miR-582-5p Normalized Ct    LAC     Non-Cancer A) B) C) D) 63                                    Figure 2.6 Box plots of female miRNA candidates normalized to miR-23a a) miR-200a training data LAC vs Non-Cancer b) miR-200a validation data LAC vs Non-Cancer c) miR-497 training data LAC vs Non-Cancer d) miR-497 validation data LAC vs Non-Cancer. (Training Female: n = 24 LAC, n= 22 Non-Cancer, Validation Male: n = 23 LAC, n = 19 Non-Cancer)   Median   25%-75%   Non-Outlier Range  Outliers     Female Training1 056789101112miR-200a Normalized CtNon-Cancer LACFemale Validation0 1567891011121314miR-200a Normalized CtLACNon-Cancer    LAC   Non-Cancer     LAC     Non-Cancer A) B) Female Training1 0567891011miR-497 Normalized CtNon-Cancer LAC    LAC     Non-Cancer Female Validation0 1567891011miR-497 Normalized CtNon-Cancer LAC    LAC    Non-Cancer C) D) 64    m iR -1 4 2 -3 p  T r a in in gm iR -1 4 2 -3 p  Va lid a t io n-10123m iR -1 4 2 -3 p  N o rm a liz e dDelta Ctm iR -5 8 2 -5 pT r a in in gm iR -5 8 2 -5 p  Va lid a t io n024681 0m iR -5 8 2 -5 p  N o rm a liz e dDelat Ct m iR -4 9 7 -5 p  T r a in in gm iR -4 9 7 -5 p  Va lid a t io n024681 0m iR -4 9 7 -5 p  N o rm a liz e dDelta  Ctm iR -2 0 0 a -3 p  T r a in in gm iR -2 0 0 a -3 p  Va lid a t io n051 01 5m iR -2 0 0 a -3 p  N o rm a liz e dDelta  Ct Figure 2.7 Delta Ct values of examined miRNAs between identical serum samples analyzed using SYBR (Training) green and TaqMan (Validation). 65  After removal of miR-142-3p from male training data analysis, and using miRNAs previously validated on panels in our best subset analysis, we found a three-miRNA signature able to differentiate LAC from non-cancer in males (miR-27b, miR-582-5p and miR-92b), it had an ROC curve of 0.8464 (Figure 2.8) (Table 2.16). Using the male sex specific formula lacking miR-142-3p we found that LAC was correctly predicted 83% of the time and non-cancer was correctly predicted 67% of the time (Table 2.17). This male sex specific formula shows the highest accuracy of all derived formulas and shows the greatest promise for use as a clinical biomarker panel. The two tumor samples misclassified by the discriminate score were stage III LAC patients, which could indicate that the discriminate score is more useful for discriminating between stage I and II LAC patients than later stages, however further validation with a larger sample size would be required (Table 2.21).       Formula (Score if score < 0, LAC Predicted)  Male Discrimination Formula =  −7.1458–  3.0974(𝐶𝑡 𝑚𝑖𝑅 − 27𝑏) +  0.8773(𝐶𝑡 𝑚𝑖𝑅 − 582 − 5𝑝)+  0.9141(𝐶𝑡 𝑚𝑖𝑅 − 92𝑏)  Table 2.19 Male sex-specific formula for prediction of LAC normalized to miR-23a    Predicted LAC Predicted Non-Cancer Percent Correct Observed LAC 10 2 0.833 Observed Non-Cancer 3 6 0.666  Table 2.20 Prediction of lung adenocarcinoma or non-cancer in validation samples normalized to miR-23a   66    ROC CurveArea: 0.8464-0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.21-Specificity-0.20.00.20.40.60.81.01.2Sensitivity Figure 2.8 ROC curve of miR-23a normalized male serum data without miR-142-3p   Tumour Stages  Misclassified Cases/Total Cases I 0/3 II 0/3 III 2/7  Table 2.21 Tumour stages misclassified by male discriminative score in male validation patient cohort   67  Taken together the data demonstrates that discriminating between LAC serum and non-cancer serum may be more easily distinguishable through analyzing male and female serum separately. The ability of miRNAs to distinguish between LAC and non-cancer serum was increased when female patient serum was eliminated. However, with an inability to classify female serum successfully we may want to expand the search for miRNAs in female patients either through profiling for an increased number of miRNAs or increasing the number of samples profiled. In male samples however normalizing to miR-23a and examining differentially expressed miR-27b, miR-582-5p and miR-92b presents itself as a promising biomarker for use in LAC detection in high risk patients.             68  2.4 Discussion Our results demonstrate that while there is an abundance of miRNAs differentially enriched in LAC serum compared to non-cancer controls, the construction of a sensitive and specific panel of microRNA biomarkers may be possible in male patients. While attempting to validate the results of the Human Panel I +II SYBR Green product, we changed platforms to reflect the more accurate and specific TaqMan assays. We took six samples from our training set and ran them on the TaqMan assays (Figure 2.7). The result of changing platforms resulted in changes within our miRNA sequences and the efficiency of their detection which was demonstrated in Figure 2.7. We chose to continue with our analysis and validate our serum candidate miRNAs using TaqMan as it is considered to have a higher specificity for miRNA analysis221. One difference to note is our normalization candidate miR-197 which, can most likely be attributed to the change in platform (Figure 2.1 and Figure 2.2). In the case of miR-197 the change in trend from being evenly expressed in all serum samples to having higher expression in LAC, indicates that changing of platforms caused a shift in miR-197 detection through either detection method or primer sequence. This aspect of our research demonstrates the need for a consistent platform that researchers can utilize to conduct analysis, and for a normalization method to be found which can be utilized across platforms.  NSCLC has been extensively profiled for miRNA biomarkers in both the sputum and the serum of patients to predict the presence of NSCLC in patients. Profiling of NSCLC sputum showed that detection of miR-21 was significantly higher in patients with NSCLC100. An important caveat to this study was that samples have a significant change in average pack years between NSCLC and non-cancer controls100. In further sputum analysis for miRNA biomarkers a panel of miRNAs including miR-21, miR-486-5p, miR-375 and miR-200b, was capable of 69  distinguishing LAC patients from normal subjects, this research showed no significant differences in pack years between non-cancer and LAC sputum222. Sputum miRNA biomarkers are able to avoid the issues of hemolysis in serum studies, which may make them a more robust model in determining miRNA biomarkers as you are not limited by miRNAs affected by hemolysis. For example, in lung cancer plasma miR-21 and miR-210 were shown to be elevated while miR-486-5p had lower enrichment in lung cancer serum134. However, miR-21 is associated with hemolysis and therefor proper controls will be required if miR-21 is to be utilized as a biomarker within the serum139. Within our analysis we excluded miR-21 as a potential biomarker due to its significant effects from hemolysis. This may indicate that miR-21 is acceptable as a diagnostic sputum biomarker but not as a serum, again indicating that sputum biomarkers may be more robust due to not relying on non-hemolysis affected miRNAs. However, for NSCLC located in the periphery of the lung, well away from the major airways, sputum miRNAs may not be relevant as secreted miRNAs may not be able to enter the sputum efficiently for detection. For LAC and lung cancers located at distant sites in the lung periphery serum miRNAs may become a more accurate method for detection. In two meta-analyses conducted by He et al. (2015) and Wang et al. (2015) it was found that panels consisting of multiple miRNAs offered a higher sensitivity and specificity for disease detection compared to single miRNAs113,223. Both studies showed that panels consisting of several miRNAs had a much higher diagnostic potential as biomarkers for NSCLC. In trials using multiple miRNAs as biomarkers positive results have been found. One group of investigators utilized a 13 miRNAs signature on 1115 participants all heavy smokers and above 50 years old participating in the COSMOS lung cancer screening trial, they found a sensitivity and specificity of 79.2% and 75.5% respectively113. The reported miRNA signature may be 70  useful as a first-line screening tool in high-risk heavy smoker individuals. While the miRNAs are useful in diagnosis, they may also have use in discriminating between benign lesions and NSCLC. A four-miRNA signature (miR-378a, miR-379, miR-139-5p and miR-200b-5p) was identified as being able to discriminate between LAC patients and granuloma patients within a cohort of healthy former smokers224. These studies confirm that serum miRNAs are useful in identifying and diagnosing lung cancers, however due to their distinct signature compared to our derived signatures obvious differences are found. The method used within their study does utilize some miRNAs we found affected by hemolysis (miR-378a), as well as only former smokers as controls.  The usage of clinically relevant controls is essential when determining biomarkers for LAC detection. Within many lung cancer miRNA biomarker studies, healthy non-smokers are utilized to differentiate between smoking-related LAC and non-cancer124,125,127-129,132-134. However, in the clinic high-risk individuals who are likely to be screened for LAC presence would be individuals with heavy smoking backgrounds. Our non-cancer samples mirror clinical patients that would be actively screened for LAC presence and these types of non-cancer patient samples are rare, thus our study offers a unique investigation into LAC circulating miRNA biomarkers. These results demonstrate the need for defined guidelines for both miRNA exclusions and proper controls for miRNA biomarker discovery.  Circulating miRNAs as biomarkers are still in its infancy, despite being highly researched for many diseases no current clinical trials are being conducted. There are many factors that can affect serum miRNA levels including amount of hemolysis, presence of virus/infection, age, sex and ethnicity219,225-227. With the number of factors influencing the amount of miRNAs within the serum and specific levels of certain miRNAs, a more stringent selection process may need to be 71  conducted with patients grouped into age, sex and ethnic cohorts for analysis. Through grouping of samples into more specific categories for analysis we may be able to determine clinical markers for detection of disease. Another factor contributing to challenges in using miRNAs as biomarkers is the lack of a standard endogenous control. In our research using the SYBR platform miR-197 was found to be a strong endogenous control, however when switching platforms miR-197 became an unusable endogenous miRNA (Figure 2.2 and Figure 2.3). This can also be seen in meningioma serum as miR-197 has been shown to decrease by 2-fold in the serum, suggesting its use as a endogenous control is situational and platform specific220. It is critical that a miRNA standard for normalization of serum biomarkers is created as common miRNAs used as endogenous controls like U6 and miR-16, which are highly influenced by the level of hemolysis within the serum sample, are unsuitable139,228. By creating a highly reproducible cross-platform normalizer for serum analysis it will accelerate and allow for comparative analysis of serum miRNAs as biomarkers.  72  Chapter 3: Selective secretion of microRNAs from lung cancer cells via extracellular vesicles promotes CAMK1D-mediated tube formation in endothelial cells  3.1 Introduction Lung cancer remains the leading cause of cancer death worldwide, and LAC, the most common histological subtype, has a dismal 5-year survival rate of ~16%229,230.  While advances have been made in treatments, survival rates remain poor231. With a greater understanding of the molecular mechanisms driving LAC, development of innovative treatment options to better combat this disease may become possible. Recently, there has been increased interest in the role of extracellular vesicles (EVs) in tumorigenesis. EVs are a heterogeneous group of secreted small membrane bound vesicles. They are known to play a variety of roles in cancers, including establishing a pre-metastatic niche, conferring drug resistance, and promoting angiogenesis through cell-cell communication 232,233. EVs contain a variety of cargo that determine EV function, such as mRNAs, miRNAs, and proteins234. MiRNAs are small non-coding RNAs that post-transcriptionally regulate expression of protein coding genes235,236. MiRNAs are known to be involved in numerous biological processes, in disease development, and are highly deregulated in cancer237. In breast cancer, EVs containing miR-181c promote metastases to the brain through destruction of the blood brain barrier238. Other roles for EV miRNA include promoting chemotherapy resistance.  For example, in breast cancer, the transfer of EVs containing miR-100, miR-122 and miR-30a from resistant MCF-7 breast cancer cells are known to promote chemotherapy resistance to MCF-7 drugs 73  sensitive cells239. Finally, the role of tumor derived EVs in promoting angiogenesis has been well established, Liu et al. found that EVs containing miR-21 was able to increase VEGF levels in recipient cells through targeting of STAT3240-242. These roles of EVs play are critical to the developing tumor, allowing for communication to surrounding cells and for the tumor to influence the stroma to support its growth. The loading of miRNAs into EVs is not a passive process and can differ depending on cell type and disease state237. Certain miRNAs are selected for EV loading and exclusion from cells, while others are selectively retained by cells, suggesting a biological role for these miRNAs in cancer243. Sorting of miRNAs into EVs has been previously reported through hnRNPA2B1, a RNA binding protein regulated by SUMOylation and able to bind the RNA motif GGAG, however it does not fully explain all miRNA sorting into EVs91. The ability of cells to selectively secrete specific miRNAs within EVs to use as communicators is of great interest as they transform the cells they enter and can even be used as potential biomarkers. Herein, we report on miRNAs that are selectively enriched within EVs of LAC cells and show that EVs enriched for specific miRNAs enter endothelial cells and promote blood vessel formation by altering the activity of CAMK1D, an anti-angiogenic kinase.   3.2 Materials and Methods 3.2.1 Cell culture All LAC cell lines and 293T cells were obtained from ATCC. LAC cell lines H1395, H1437, H2073, H2228 and H2347 were cultured in RPMI 1640 media supplemented with 10% fetal bovine serum (FBS). HMEC-1 cells were generously donated by the Aly Karson Lab and, HPL1D cells were donated by the Will Lockwood lab. Cell lines HMEC-1 and 293T were 74  cultured in DMEM supplemented with 10% FBS. For EV collection, cells were grown in media supplemented with 1% depleted FBS (dFBS), which is FBS that has been depleted of EVs through overnight centrifugation at 110,000g and at 4oC. Cell counting was performed using a hemocytometer.   3.2.2 EV collection To collect EVs, “donor” LAC cells were grown in ten plates (15 cm), under normal conditions. Forty-eight hours prior to reaching 85% confluence, media was replaced with RPMI 1640 containing 1% dFBS. Cells were allowed to secrete EVs into dFBS media for 48 hours, after which the conditioned media was collected. Our EV isolation protocol is based on a differential ultracentrifugation protocol described by Rani et al. 244. Conditioned media was centrifuged at 300g for 10 min, then at 2,000g’s for 20 min and at 10,000g’s for 30 min at 4oC. Supernatant was collected at each stage of centrifugation and transferred to fresh 50 mL tubes for continued centrifugation. When completed, conditioned media was placed into open top ultracentrifuge tubes (Seton) and, using an ultracentrifuge set at 4oC, media was spun for 70 min at 110,000g. Supernatant was discarded, and the EV pellet was rinsed with PBS to reduce protein contamination before a second round of ultracentrifugation. The EV pellet was either processed to collect RNA (using a miRCURY RNA Isolation Kit [Exiqon]) or protein (using RIPA buffer) or re-suspended in 0.02 µm-filtered PBS to perform NanoSight analysis or re-suspended in un-supplemented DMEM media to perform EV transfer assays.  75  3.2.3 qRT-PCR Cell line RNA and EV RNA was collected using miRCURY RNA Isolation Kit (Exiqon). Seven hundred and forty-two miRNAs were measured using microRNA Ready-to-Use PCR, Human panel I+II (Exiqon) on a Viia 7 Real-Time PCR System (Thermo Fisher). Cell line and EV profiles were normalized to global mean and fold-change analysis was used to compare matched cellular and EV miRNA profiles245. We subsequently validated EV miRNA candidates using a TaqMan Assay normalized to input and cel-miR-39-3p miRNA spike in to account for PCR efficiencies.  3.2.4 Vectors Over-expression (OE) cell lines were created using the FIV lenti-vectors HmiR0085-MR01 for miR-145, HmiR0084-MR01 for miR-143, or CmiR0001-MR01 (miR-Scramble) for a scramble sequence control (GeneCopoeia), vectors express both 3p and 5p miRNAs. Vectors were packaged using 293T cells and a Lenti-Pac FIV Expression Packaging Kit (GeneCopoeia). For knockdown assays, we used SMPD3 shRNA vectors TRCN0000048944, TRCN0000048945, TRCN000004896 and TRCN0000048947 (GE Dharmacon). Empty vector pLKO.1 was used as a control. The shRNA vectors were packaged using 293T cells and the plasmids VSVG and d8.91 using TransIT-LT1 transfection reagent (Mirus). Cells were infected over a 24-hour period, after which miRNA over-expression lines were selected using G418 for ten days, and shRNA lines were selected using puromycin for four days.  76  3.2.5 Serum analysis Serum samples were collected from patients undergoing LAC resection with curative intent (Table 3.1). All blood was collected in SST vacutainer tubes. For each patient, blood draws were taken from pulmonary venous effluent draining the tumor vascular bed, and from the systemic arterial blood of the same patient during surgery. Serum was isolated by centrifugation at 1500g for 15 min, and then frozen and stored at -80°C. RNA was isolated from 200 μl of serum using a Qiagen miRNeasy kit. MiRNAs were profiled for 742 miRNAs as described above. We removed miRNAs that are known to be affected by hemolysis by a fold change of greater than four139. Samples were normalized to miR-122-5p, which we have previously reported as an appropriate miRNA for normalizing patient matched samples on the SYBR Green Human Panel I+II, as it is not affected by hemolysis and shows little inter-patient variability139.             77  Table 3.1: Patient demographics     3.2.6 EV incubation Previously collected EVs were suspended in 100 μl DMEM media with no added supplements. The media was added to 50% confluent HMEC-1 cells in a 96 well plate and incubated for 24 hours, after which protein and RNA were collected.   Patient Demographics:  Patients:    Variable: Patients: 10    Age (Years):    Mean: 71.55555556  Median: 73  Range: 62 - 79 Sex:    Females: 5  Male: 4  N/A: 1 Smoking History:    Current/Former: 7  Never: 1  N/A: 2 Stage:    I: 4  II: 2  III: 0  IV: 0  NA: 4  Table 3.1 Patient demographics 78  3.2.7 Tube formation assays In triplicate for each treatment, 1.2x105 HMEC-1 cells were added onto 24-well plates containing 200 μl of 10mg/ml Matrigel (Corning). EVs suspended in un-supplemented DMEM were incubated with the HMEC-1 cells for 16 hours. DIC images were taken using an Axiovert S1000 microscope under the 10X objective, with an image being taken at the center of each well. Tube length was quantified using ImageJ with the ‘Angiogenesis Analyzer’ plugin, and triplicates were averaged.  3.2.8 Western blot 1:100 diluted phosphatase inhibitor cocktails I&II (Millipore Sigma) and 1:100 protease inhibitors (Thermo Fisher) were added to RIPA buffer to decrease sample degradation. Protein was quantified using a Pierce BCA kit and 10 μg of each sample was separated with a 12% SDS-PAGE gel and transferred onto PVDF membranes. Membranes were blocked in 5% BSA, 1X TBS and 0.1% Tween-20 for 1 hour. Membranes were incubated overnight at 4°C with primary antibodies diluted in blocking buffer. We used anti-CD63 (1:1000 dilution, EXOAB-Cd63A-1, System Biosciences), anti-TSG101 (1:1000 dilution, ab83, Abcam), anti-CAMK1D (1:800 dilution, #3365, Cell Signaling) and loading control anti-Histone H3 (1:4000, # 4499 Cell Signaling) followed by HRP conjugated anti-rabbit (1:2000, #7074, Cell Signaling). Detection was done using an Amersham ECL kit and hyperfilm (General Electric). Western images were quantified using the ‘gel analysis’ function in ImageJ (http://imagej.nih.gov).  79  3.3 Results 3.3.1 Identification of miRNAs enriched in the EVs of LAC cells  EVs from early stage LAC cell lines H1395, H1437, H2073, H2228, and H2347 were collected by differential ultracentrifugation (Table 3.2)244. Analysis by nanoparticle tracking analysis (NTA) revealed the majority of EVs were ~110 nm in size (Figure 3.1A-F). This size range is consistent with the size of exosomes, a subset of EVs. Furthermore, EV presence was confirmed by western blot for common EV markers CD63 and TSG101 (Figure 3.1H). RNA from both donor cells and isolated EVs was extracted using the miRCURY™ RNA Isolation (Exiqon) kit and profiled for miRNA expression using microRNA Ready-to-Use PCR, Human panel I+II, V4.M (Exiqon).    Cell Line Stage Mutations and Features H1395 II 15 pack years, BRAF mutation c.1406G>C H1437 I 70 pack years, TP53 mutation c.800G>C, CDKN2A mutation c.1_471del471, MET mutation c.970R>C246 H2073 IIIa 30 pack years H2228 - Non-smoker, RB1 mutation c.610delG, CDKN2A mutation c.1_471del471, TP53 mutation c.991C>T, EML-ALK fusion247  H2347 I Non-smoker, NRAS mutation c.182A>G  Table 3.2 Tumour stages, mutation profile and characteristics of early LAC cell lines according to ATCC248.     80   Figure 3.1 EV identification. A-C) Nanoparticle Tracking Analysis of EVs, quantifying size and concentration using NanoSight: A) H1395 B) H1437 C) H2073 D) H2228 E) H2347 F) HPL1D cell line G) Comparison of number of EVs produced per cell over 48 hours F) Western blot confirmation of EV associated proteins CD63 and TSG101. Error bars represent standard deviation. Red shaded area is standard deviation between triplicates for each vesicle size. 81  Of the 742 miRNAs examined, an average of 264 miRNAs were detected in the EV fractions and 258 miRNAs in the donor cell fractions. For each donor cell-EV pair on average, four miRNAs were uniquely detected in the donor cell fraction and 12 miRNAs were uniquely detected in the EV fraction. We used a 4-fold detection cut-off between the EV and donor cell miRNA profiles to identify miRNAs that were selectively enriched in EVs. A 4-fold cut off was utilized to be highly stringent on candidate miRNA selection and allow for high reproducibility. On average 11 miRNAs were selectively enriched within EVs per cell line (Table 3.3).  On average, 14 miRNAs were selectively retained in each donor cell line as compared with associated EVs (Table 3.4). Nine miRNAs were recurrently enriched in the EVs of ≥3 of the five cell lines profiled (Table 3.5). However, none of these donor cell-enriched miRNAs were commonly enriched across the entire panel of cell lines. Recurrent miRNAs were validated in the matched donor cell and EV RNA samples using individual qRT-PCR reactions. A 4-fold difference was observed for all candidate miRNAs, except for miR-144-3p, which was eliminated from further analysis. To determine whether the EV-enriched miRNAs were tumor cell specific, we isolated EVs from a normal epithelial lung cell line (HPL1D). RNA was extracted from donor cells and EVs were isolated as above. Using qRT-PCR, we determined the expression of our eight candidate miRNAs in the two RNA isolates. We found four of our eight miRNA candidates were also enriched within the EVs of HPL1Ds (miR-142-3p, miR-150-5p, miR-223-3p, and miR-451a); however, the extent of the enrichment observed in the tumor EV fraction was at least double that observed within the HPL1D EVs (Figure 3.2). This difference cannot be attributed to differences in miRNA expression in donor cells, as cellular expression of these miRNAs is similar between HPL1Ds and the tumor cell lines (Figure 3.3). Additionally, NTA analysis 82  revealed that EV production from HPL1D cells was less than 25% of what was observed in LAC cells: while HPL1D cells produced ~92 EVs per cell over 48 hours, H1395, H1437, H2073, H2228 and H2347 cells produced ~972, ~425, ~540, ~396 and ~972 EVs per cell over 48 hours, respectively (Figure 3.1G). In addition, while some miRNAs are commonly selected for EV export between tumor and normal cells, the enrichment in the tumor EVs is far greater (Figure 3.2) and EV production from the tumor cells is on average ten times higher than normal lung cell production (Figure 3.1G).   miRNAs Enriched in LAC Cell Lines    H1395 H1437 H2073 H2228 H2347 hsa-let-7a hsa-miR-1908 hsa-miR-1248 hsa-let-7a hsa-let-7a-2* hsa-let-7b hsa-miR-1974 hsa-miR-181a* hsa-miR-149 hsa-miR-1201 hsa-let-7c hsa-miR-326 hsa-miR-193b* hsa-miR-181d hsa-miR-128 hsa-let-7d hsa-miR-502-5p hsa-miR-1974 hsa-miR-1974 hsa-miR-193b* hsa-let-7e hsa-miR-671-5p hsa-miR-24-1* hsa-miR-24-1* hsa-miR-551b hsa-miR-106b*  hsa-miR-490-3p hsa-miR-299-5p hsa-miR-671-5p hsa-miR-1201  hsa-miR-671-3p hsa-miR-342-3p hsa-miR-766 hsa-miR-128   hsa-miR-423-3p  hsa-miR-181b   hsa-miR-489  hsa-miR-181d   hsa-miR-500a  hsa-miR-18a*   hsa-miR-501-3p  hsa-miR-1908   hsa-miR-502-5p  hsa-miR-196a   hsa-miR-542-5p  hsa-miR-197   hsa-miR-93*  hsa-miR-29b-2*     hsa-miR-324-3p     hsa-miR-331-3p     hsa-miR-423-3p      Table 3.3 MiRNAs enriched in cell fraction compared to EVs by at least 4-fold     83  miRNAs Enriched in Exosomes    H1395 H1437 H2073 H2228 H2347 hsa-miR-126 hsa-miR-103-2* hsa-let-7d* hsa-miR-142-3p hsa-miR-142-3p hsa-miR-142-3p hsa-miR-126* hsa-miR-142-3p hsa-miR-143 hsa-miR-144 hsa-miR-144 hsa-miR-1260 hsa-miR-143 hsa-miR-144 hsa-miR-145 hsa-miR-145 hsa-miR-128 hsa-miR-144 hsa-miR-150 hsa-miR-150 hsa-miR-150 hsa-miR-142-3p hsa-miR-145 hsa-miR-223 hsa-miR-20a* hsa-miR-223 hsa-miR-143 hsa-miR-150 hsa-miR-451 hsa-miR-223 hsa-miR-31* hsa-miR-144 hsa-miR-223 hsa-miR-486-5p hsa-miR-451 hsa-miR-32 hsa-miR-145 hsa-miR-30a* hsa-miR-502-3p hsa-miR-502-5p hsa-miR-451 hsa-miR-150 hsa-miR-330-5p hsa-miR-605 hsa-miR-605 hsa-miR-486-5p hsa-miR-199a-3p hsa-miR-451 hsa-miR-638 hsa-miR-99b* hsa-miR-720 hsa-miR-199a-5p hsa-miR-486-5p hsa-miR-720  hsa-miR-886-3p hsa-miR-223 hsa-miR-501-3p   hsa-miR-888 hsa-miR-451 hsa-miR-502-5p    hsa-miR-486-5p hsa-miR-605    hsa-miR-542-5p hsa-miR-720    hsa-miR-545 hsa-miR-766    hsa-miR-605     hsa-miR-616*     hsa-miR-663     hsa-miR-720     hsa-miR-886-3p     Table 3.4 MiRNAs enriched in LAC cell line EVs compared to cell fraction by at least 4-fold      84   *: indicates EV miRNA fold change where no miRNA was detected intracellularly, and intracellular Ct levels was set to threshold cut off, of 35 Cts for fold change analysis  miRNAs Number of Cells Lines with miRNA Enriched in EVs >4-Fold (n=5) H1395 Fold Change H1437 Fold Change H2073 Fold Change H2228 Fold Change H2347 Fold Change miR-451a 5 273.22* 214.29* 50.42* 458.11* 83.26* miR-142-3p 5 30.93 71.08* 17.70* 95.84* 18.68 miR-223-3p 5 179.01* 41.20* 12.10* 55.89* 20.46* miR-144-3p 5 51.05* 32.55* 6.38* 59.16* 8.49* miR-605-5p 4 0.92 17.11* 5.20* 54.51* 8.90* miR-150-5p 4 79.45* 5.35 2.93* 21.15* 8.73* miR-145-5p 4 31.25* 9.19* 7.39* 3.10* 4.15 miR-486-5p 3 38.42 9.62* 3.07* 6.88* 3.52* miR-143-3p 3 20.07* 13.13* 1.76* 11.24* 1.23 Table 3.5 Profiling fold change of miRNAs enriched by 4-fold in the EVs of at least 3 lung adenocarcinoma cell lines using microRNA qRT-PCR 85   Figure 3.2 Fold Change of EV candidate miRNAs in LAC cell lines H1395, H1437, H2073, H2228, H2347 and normal lung line HPL1D in triplicate using TaqMan qRT-PCR for miRNAs: A) miR-142-3p B) miR-143-3p C) miR-145-5p D) miR-150-5p E) miR-223-3p F) miR-451a G) miR-486-5p H) miR-605-5p. Error bars represent standard deviation.   86    Figure 3.3 Fold change of LAC cell lines intracellular candidate miRNAs compared to HPL1D intracellular miRNAs normalized to U6. Error bars represent standard deviation.   3.3.2 Inhibition of EV release increases the levels of candidate miRNAs within LAC cells To confirm that the detected candidate miRNAs were isolated from EVs, as opposed to co-precipitating factors, we inhibited the release of EVs by silencing SMPD3, a critical protein in ceramide-dependent exosome biogenesis156. We would expect the miRNA cargo within the EVs to remain in the cell and therefore show increased cellular expression. SMPD3 was inhibited using shRNAs and a ~75% knockdown was observed in both H2073 and H1437 cell lines (Figure 3.4A). When SMPD3 was inhibited in H1437 cells EV production decreased 83%, in H2073 cells we observed a ~60% decrease in EV production (Figure 3.4B) and a cellular increase in all candidate miRNAs was noted in both cell lines (Figure 3.4C).  87   Figure 3.4 Analysis of EV inhibition using SMPD3. A) Cellular inhibition of SMPD3 in H1437 and H2073 cell lines using shRNA against SMPD3 confirmed using qRT-PCR B) EV secretion analysis measuring EVs produced per cell over 48 hours of H1437 and H2073 cells lines using SMPD3 shRNA inhibition using NanoSight C) Fold change analysis of candidate miRNAs from H1437 and H2073 intracellular RNA with SMPD3 shRNA inhibition. Error bars represent standard deviation. 88  3.3.3 MiR-143-3p and miR-145-5p are enriched within serum draining directly from LAC tumors We next undertook qRT-PCR profiling of miRNAs in serum samples obtained from the pulmonary venous effluent draining directly from the tumor vascular bed. These serum samples were collected during surgical resection of LAC, as were matched samples from peripheral blood vessels. MiRNAs known to be influenced by hemolysis were removed from analysis139. On average, 169 miRNAs were detected in each sample, 52 miRNAs were expressed in all samples, 72 miRNAs were enriched in each tumor effluent sample by at least 4-fold, and only 12 miRNAs were enriched in peripheral samples by at least 4-fold. Twenty-two miRNAs were consistently enriched by at least 4-fold in the tumor effluent samples in at least half of the ten paired samples, while no miRNAs were recurrently down-regulated in tumor effluent samples (Table 3.6). Five of our eight EV miRNA candidates (miR-142-3p, miR-150-5p, miR-223-3p, miR-451a, miR-486-5p) were shown to be increased by hemolysis and were thus excluded from analysis. Of the remaining EV miRNA candidates, miR-143-3p and miR-145-5p were enriched in tumor draining effluent samples. These miRNAs both exhibited effluent sample enrichment in six of ten cases and exhibited 9.1-fold and 13.8-fold average over-expression in tumor effluent samples as compared to peripheral samples. Given their increased EV expression in LAC cell lines and increased serum abundance in LAC patient serum samples, we reasoned that EV-mediated miR-145-5p and miR-143-3p activity might promote in lung tumorigenesis.     89  miRNA Mean Fold Change Up Regulated by 4-Fold in Tumor Draining Serum (n =10) hsa-miR-375 18.16 9 hsa-miR-200c-3p 36.86 9 hsa-miR-125b-5p 18.76 9 hsa-miR-200b-3p 20.97 8 hsa-miR-141-3p 64.67 8 hsa-miR-10a-5p 9.88 8 hsa-miR-34a-5p 12.95 7 hsa-miR-204-5p 9.30 7 hsa-miR-99a-5p 8.63 6 hsa-miR-95-5p 5.30 6 hsa-miR-497-5p 10.57 6 hsa-miR-30a-3p 8.69 6 hsa-miR-145-5p 9.14 6 hsa-miR-143-3p 13.78 6 hsa-miR-99b-5p 7.16 5 hsa-miR-99a-3p 8.73 5 hsa-miR-365a-3p 5.35 5 hsa-miR-30a-5p 11.71 5 hsa-miR-199b-5p 7.78 5 hsa-miR-193b-3p 7.88 5 hsa-miR-18a-3p 4.32 5 hsa-miR-133a-5p 6.01 5  Table 3.6 Mean fold change of miRNAs enriched in at least 5 of the 10 tumor draining samples         90  3.3.4 MiR-145-5p and miR-143-3p promote tube formation in endothelial cells Recent work in LAC demonstrated that miR-145-5p and miR-143-3p can promote neoangiogenesis by direct targeting of CAMK1D in endothelial cells, and that loss of the miR-145/miR-143 cluster in these cells led to decreases in neoangiogenesis via CAMK1D249. It was suggested that miR-143/145 was not expressed by the tumor cells, but rather was produced by a subset of endothelial cells driving neoangiogenesis. We show that miR-145-5p and miR-143-3p are in fact produced by LAC cells; however, no expression is observed in the tumor cells, as these miRNAs are efficiently packaged into EVs and exported from the cell.   To interrogate the impact of EV-associated miR-143-3p and miR-145-5p, the levels of these miRNAs within EVs must be manipulated. We over-expressed miR-143, miR-145 and a miR-Scramble (SCR/C) individually in H1437 cells and H2073 cells which resulted in >90-fold over-expression of these miRNAs in the EVs derived from these cells (Figure 3.5). EVs from cells over-expressing miR-143 and miR-145, as well as miR-Scramble EVs (using a miRNA scramble, but still expressing normal levels of miR-143-3p and miR-145-5p in the EVs) were collected and incubated with HMEC-1 cells for 24 hours, along with HMEC-1 cells receiving no EVs. Following this, EVs were removed, the cells were washed and cellular RNA from HMEC-1 cells was extracted. MiR-145-5p and miR-143-3p increased by ~14-fold and ~6-fold in HMEC-1 cells incubated with EVs over-expressing miR-143 and miR-145 respectively when compared to HMEC-1 cells that were not incubated with EVs (Figure 3.6). HMEC-1 cells incubated with miR-Scramble over-expressing EVs (with normal miR-145-5p and miR-143-3p levels within EVs) showed an increase in miR-145-5p >5 fold and a slight increase in mR-143-3p by >2 fold (Figure 3.6). On the other hand, HMEC-1 cells showed no significant changes in intracellular levels of a miRNA (miR-346) that was not found in EVs from H1437 cells (Figure 3.6). 91    Figure 3.5 Relative fold change within the EVs of LAC miRNA overexpression cell lines H1437 and H2073 compared to miR-Scramble EVs for A) miR-143 OE EVs and B) miR-145 OE EVs. Error bars represent standard deviation m iR -1 4 3 -3 p  05 01 0 01 5 02 0 0Relative EV Fold ChangeH 1 4 3 7  m iR -1 4 3  O E  E V sH 2 0 7 3  m iR -1 4 3  O E  E V sAm iR -1 4 5 -5 p  05 01 0 01 5 0Relative EV Fold ChangeH 1 4 3 7  m iR -1 4 5  O E  E V sH 2 0 7 3  m iR -1 4 5  O E  E V sB92   Figure 3.6 Lung adenocarcinoma cell extracellular vesicle miRNAs are taken up by HMEC-1 cells. Intracellular fold change analysis of miR-143-3p, miR-145-5p and miR-346 in HMEC-1 cells incubated with EVs from H1437 cells over expressing miR-143, miR-145 and miR-Scramble, compared to HMEC-1 cells receiving no extracellular vesicle incubation. Error bars represent standard deviation.   We next sought to determine if the transfer of EVs, and in particular miR-143-3p and miR-145-5p, within the EVs is capable of inducing tube formation changes in endothelial cells. HMEC-1 cells were grown without EVs or in the presence of EVs enriched with miR-143, EVs enriched with miR-145 or control EVs (endogenous EV levels of miR-143 and miR-145). All HMEC-1 cells exposed to EV incubation treatments formed tubes after 16 hours (Figure 3.7B). The addition of EVs from H1437 miR-Scramble over-expressing cells (with normal miR-145-5p and miR-143-3p levels within EVs) significantly increased the ability of HMEC-1 cells to form tubes compared to the HMEC-1 cells receiving no EVs (p<0.01) (Figure 3.6B). The addition of EVs over-expressing miR-145-5p or miR-143-3p further increased the length of tubes formed compared to miR-Scramble over-expressing EVs (Figure 3.7B). Significance was reached for miR-145 (p<0.05) but not for miR-143 (p>0.05) (Figure 3.7B). m iR -1 4 3 -3 pm iR -1 4 5 -5 pm iR -3 4 6051 01 52 0Fold ChangeH M E C -1  In cu b a te d  w ith  H 1 4 3 7  E V s  m iR -1 4 3  O EH M E C -1  In cu b a te d  w ith  H 1 4 3 7  E V s  m iR -1 4 5  O EH M E C -1  In c u b a te d  w ith  H 1 4 3 7  E V s  m iR -S c ra m b leH M E C -1  C e lls  N o  E V  In cu b a tion93     * = p-value < 0.05 ** = p-value < 0.01  Figure 3.7 Impact of miR-143 and miR-145 on tube formation. A) Tube formation photos of HMEC-1 cells at 10x magnification with i) no vector ii) control vector iii) miR-143-3p over-expression vector or iv) miR-145-5p over-expression vector v) graph of tube formation on HMEC-1 endothelial cells with no direct over expression (-) and direct over expression of miR-Scramble (C), miR-143 (143)  and miR-145 (145) B) Tube formation photos of HMEC-1 cells at 10x magnification incubated with EVs from H1437 cells i) No extracellular vesicles, ii) miR-Scramble over-expressing extracellular vesicles iii) miR-143-3p over-expressing extracellular vesicles or iv) miR-145-5p over-expressing extracellular vesicles v) graph of  tube formation for HMEC-1 cells incubated with no EVs (-), H1437 miR-Scramble overexpressing extracellular vesicles (C EVs), H1437 miR-143 overexpressing extracellular vesicles (143) and H1437 miR-145 overexpressing extracellular vesicles (145). Error bars represent standard deviation.   94  To determine if the EV-transferred miRNAs target CAMK1D in HMEC-1 cells, protein lysates from HMEC-1 cells were collected after incubation with EVs or with the negative control (incubation with no EVs). All HMEC-1 cells that received EVs containing miR-143-3p or miR-145-5p showed a decrease in CAMK1D protein by western blot (Figure 3.8A). The addition of miR-Scramble over-expressing EVs from H1437 cell lines caused a decrease in the cellular quantity of CAMK1D by 53% compared to the negative control (Figure 3.8A). A greater decrease was observed in HMEC-1 cells incubated with EVs over-expressing miR-145 or miR-143, showing a 79% and 83% decrease respectively compared to HMEC-1 cells receiving no EVs (Figure 3.8A).  To confirm that the observed increases in tube formation were at least in part caused by the presence of miR-145-5p or miR-143-3p, we examined the tube forming ability of HMEC-1 cells that directly over-expressed miR-145 and miR-143 (as opposed to being over-expressed via EVs). Figure 3.7A demonstrates that direct over-expression was capable of increasing tube formation by 36% and 34% for miR-145 and miR-143, respectively (p<0.05 for both). No changes in tube formation were noted in cells transfected with a miR-Scramble vector or in cells that were not exposed to infection. HMEC-1 cells over-expressing miR-143 and miR-145 had decreased CAMK1D protein levels (87% and 96%, respectively) compared to HMEC-1 cells over-expressing the miR-Scramble (Figure 3.7A,B).      95   Figure 3.8 Analysis of CAMK1D targeting by miR-143 and miR-145. A) Western blot of CAMK1D and Histone 3 in HMEC-1 cells after direct over expression of miR-143, miR-145 and miR-Scramble within HMEC-1 cells and after incubation with EVs isolated from H1437 over expressing miR-143, miR-145, miR-Scramble and unmodified cell B) Densitometry analysis of CAMK1D western blot in Figure 3.8A.   96   We identified a set of miRNAs that are selectively packaged into EVs and released from LAC cell lines. Cross referencing with miRNA profiles obtained from serum collected from blood directly draining from the tumor suggests a biological role for miR-143-3p and miR-145-5p in lung tumorigenesis. These miRNAs were shown to be produced by LAC cells and packaged into EVs. Once packaged the EVs are capable of shuttling miRNA cargo to endothelial cells in vitro where EVs containing miR-143-3p and miR-145-5p down-regulate CAMK1D, promoting angiogenesis.   3.4 Discussion In the present study, to determine early events in EV miRNA communication, we used early stage LAC cell lines and identified several miRNAs (miR-142-3p, miR-143-3p, miR-145-5p, miR-150-5p, miR-223-3p, miR-451a, miR-486-5p, and miR-605-5p) that are selectively packaged into EVs and released into the culture media. The function of tumor-derived EVs is dictated by EV cargo and it is now appreciated that EV cargo differs between cell types and disease state. Therefore, it is imperative to evaluate the function of EVs in LAC in order to understand their role in this disease. Profiling of a panel of LAC cell line EVs revealed that eight miRNAs were frequently enriched within EVs and absent within the donor cells that gave rise to these EVs. These results suggest the enriched miRNA candidates are sorted and packaged efficiently into EVs, so much so that their intracellular detection in donor cells is difficult with standard molecular techniques. The mechanisms by which miRNAs are packaged into EVs are not well understood. Previous studies have proposed miRNA uptake into EVs occurs through specific miRNA sequences and hydrophobic modifications such as methylation91,243. These mechanisms may explain some 97  miRNA selection, but they do not fully account for the specific cohort of candidate miRNAs that we observe here. For example, miR-451a (which we observe to be enriched in the EVs) also contains a CL (cellular) motif, which has been suggested to mediate exclusion from EVs91. Four of our miRNA candidates (miR-142-3p, miR-150-5p, miR-451a, and miR-486-5p) are found within EVs of many cell types including HEK 293T cells, breast cancer cells, and oral cancer cells90,250. These miRNAs are known tumor suppressors that alter activity of many genes involved in tumorigenesis including TGFβR1, MUC4, ARHGAP, and RAB14251-254. Selection of these miRNAs for export would then promote tumor growth by eliminating intracellular tumor suppressive activity of the miRNAs251-254.  Our remaining miRNA candidates (miR-143-3p, miR-145-5p, miR-223-3p, and miR-605-5p) appear to be more specific to LAC. These miRNAs are generally considered tumor suppressive miRNAs in many cancer types. MiR-143-3p has been shown to target KRAS and IGF1R in colorectal cancer255,256. MiR-145-5p is also known to target IGF1R in colorectal cancer, as well as EGFR in LAC256,257. Over-expression of miR-223 in ovarian cancer cell lines reduces proliferation and colony formation, also by targeting of IGF1R258. MiR-605-5p has not been widely studied; however, it may function as a tumor suppressor given some indication that it can enhance transactivation of p53 by inhibiting MDM2 in lung and breast cancer cell lines259.  There is limited information on the function of these candidate miRNAs in cells of the tumor microenvironment. Continued research into stromal function of EV miRNAs will allow for a greater understanding of tumor-stromal EV signaling in cancer. To the best of our knowledge, this is the first time miR-143-3p and miR-145-5p are reported as enriched within EVs released directly from LAC cells.  MiR-143-3p and miR-145-5p are important tumor suppressors in many cancer types including lung, bladder, colon, and breast260-263.  98  The data presented suggests the function of selective packaging and release of certain miRNAs is two-fold. The tumor cells selectively package tumor suppressive miRNAs within EVs to eliminate their function intracellularly and, in doing so, end up transporting miRNAs to endothelial cells (and possibly other stromal cells), which creates a more favorable tumor microenvironment.  Herein, we also demonstrated that miR-143-3p and miR-145-5p derived from LAC cell EVs can target the endothelial cell function of CAMK1D, a negative regulator of angiogenesis. Dimitrova et al. (2016) first showed that stromal expression of miR-143/miR-145 promotes neoangiogenesis by targeting CAMK1D249. They also reported that expression of miR-143/miR-145 occurs in a small population of endothelial cells, but not lung epithelial cells249. Our results show that miR-143-3p and miR-145-5p are in fact expressed by LAC cells; however, they are packaged into EVs so efficiently that expression of these miRNAs in the donor cells is typically not detected. Once in the EVs, miR-143/miR-145 can enter endothelial cells and down-regulate CAMK1D protein levels. The depletion of CAMK1D mediated by LAC EVs results in increased tube formation in the endothelial cells. These two results are compatible with Dimitrova et al. (2016) findings and may be happening in combination, which may further promote neoangiogenesis.  It is evident that miRNAs within EVs play a significant role in tumorigenesis. We report that 8 miRNAs are frequently enriched within EVs released from LAC cells. MiR-143-3p and miR-145-5p, two miRNAs enriched within LAC EVs, were also found enriched within serum obtained from blood draining directly from LAC tumor bearing lungs which suggests that LAC tumors secreted miR-143-3p and miR-145-5p into the serum. Both miRNAs have been previously implicated as mediators of neo-angiogenesis and our analysis shows that when 99  transferred through tumor derived EVs miR-143-3p and miR-145-5p promote tube formation through down-regulation of CAMK1D in endothelial cells. Our work highlights how certain miRNAs considered to be down-regulated (or absent) in LAC are efficiently selectively packaged and released from the tumour cells via EVs and play a role in altering the tumor microenvironment. Further research into the role of additional miRNAs that are selectively packaged into EVs is required to better understand cell-cell communication between the tumor and its microenvironment. 100  Chapter 4: Extracellular vesicle secretion of miR-142-3p from lung adenocarcinoma cells promote tumorigenesis through cell-cell communication with the stroma  4.1 Introduction Extracellular communication between tumor cells and cells within the adjacent stroma is a strong promoter in creating a favorable tumor microenvironment264. EVs represent a group of secreted vesicles released by virtually all cell types, the EVs along with their content can be transferred from one cell to another, allowing cancer cells to communicate with cells in their microenvironment156-159. This type of communication has been shown to promote angiogenesis, and activate growth promoting properties of fibroblasts in several tumor types3,156-159. EV cargo is dependent on the type of donor cell and its disease state, with differences in miRNA profiles being used to differentiate between cancerous and non-cancerous cells265-267. Certain miRNAs are enriched in EVs compared to the donor cell from which they originated, indicating that a subset of miRNAs are selectively packaged into EVs268. In Chapter 3, I described how I was able to determine miRNAs enriched within LAC derived EVs. We found a number of miRNAs enriched within LAC EVs with miR-142-3p being the most abundant (Table 3.5). It was also found enriched in normal HPL1D lung cell line EVs, however not to the extent of LAC line enrichment. MiR-142-3p has previously been found to be enriched within oral cancer cell line EVs and when transferred to endothelial cells promoted angiogenesis through repression of TGFβR1269. MiR-142-3p has also been found enriched in the exosomes of patients during cardiac allograft rejection, which compromised the endothelial barrier and resulted in increased 101  vascular endothelial permeability270. These studies indicate the importance of EV associated miRNAs and that their contents can be used to signal adjacent cells into transforming and altering the microenvironment.  Within LAC tissue miR-142-3p is reported to be down regulated by 2-fold in 52% of LAC cases271. Re-expression of miR-142 has been shown to silence TGFβR1251,272. TGFβR1 increase in expression is associated with invasion and metastasis in tumor development273. In addition, overexpression of miR-142-3p was shown to induce apoptosis in select LAC cell lines272. Cellular elimination of miR-142-3p via EVs may be a mechanism to eliminate tumor suppressive miRNAs but may also be promoting tumorigenesis through cell-cell communication via EVs as we have previously shown in oral cancer269. Herein we examine the impact of LAC EV secreted miR-142-3p on both endothelial cells and fibroblasts and demonstrate the pro-tumorigenic influence on the tumor stroma.    4.2 Materials and Methods 4.2.1 Cell lines Cell lines were obtained from ATCC, the LAC cell lines H1437 and H2073 were cultured in RPMI 1640 media with 10% FBS, according to the supplier’s guidelines. HMEC-1 and 293T cells were cultured in DMEM media supplemented with 10% FBS.  4.2.2 Vectors Over-expression (OE) of miR-142-3p was achieved using the lenti vector: miR-142 OE (HmiR02082-MR01), a miR-Scramble OE (miR-C) sequence was used as a control (CmiR0001-MR01). Virus was produced in 293T cells using the Lenti-Pac FIV Expression Packaging Kit 102  (FPK-LvTR, GeneCopoeia) according to the supplied protocol. Infected cells were selected using G418 at 200 μg/mL. TGFβR1 open reading frame (ORF) clone (TRCN0000488036) within a pLX_TRC317 vector as well as an empty vector control were purchased from Sigma Aldrich (hereafter TGFβR1 ORF and Empty ORF) and used for rescue experiments. ORF vectors were packaged in 293T cells using the packaging vectors VSVG and d8.91 using the transfection reagent TransIT-LT1. Cells were selected using puromycin over 4 days.  4.2.3 EV isolation EVs were isolated as described previously 244. Briefly, cells were seeded onto 10, 15 cm plates. 48 hours prior to reaching 90% confluency standard media was replaced with RPMI media, supplemented with 1% FBS depleted of bovine EVs by ultracentrifugation. Upon cells reaching 90% confluency EV containing media was removed. To remove non-EV debris, at 4°C EV containing media was centrifuged at 200g for 10 minutes, 2000g for 20 minutes and 10,000g for 60 minutes and then at 110,000g for 90 minutes to pellet EVs. The pellet was rinsed and re-suspended with PBS and then pelleted again at 110,000g for 90 minutes, in order to separate out possible impurities.       103  4.2.4 Quantitative PCR RNA was extracted using the miRCURY RNA Isolation Kit (Exiqon) according to the manufacturer’s protocol. RT-PCR was performed on 100ng of RNA using TaqMan miRNA reverse transcription kits and TaqMan Universal Master Mix II and MicroRNA assays (Life Technologies). All assays were performed according to the manufacturer’s protocol in triplicate. U6 RNA was used as a reference gene when examining cellular miRNA, EV miRNA was normalized to input and cel-miR-39 spike-in (Qiagen) as there is yet no established endogenous controls for EVs.  4.2.5 Proliferation assay H2073 and H1437 cell lines stably overexpressing miR-142 or miR-C were plated in 6 wells of five 96 well plates at a density of 1000 cells per well. Cell viability was measured over five days using Colorimetric thiazolyl blue tetrazolium bromide (Sigma-Aldrich) as described previously, with two outliers (highest and lowest) values from each treatment removed from analysis. Statistical significance was determined using a student T-test. A p-value cut off < 0.05 being used for significance.   4.2.6 Tube formation assay Coated plates were created by the addition of 300μl of growth factor reduced Matrigel (Corning) to 12 well plates. HMEC-1 cells grown to 80% confluency were seeded at 2.0x105 cells per well. EVs diluted in DMEM were collected from 1, 15cm plate of either miR-C or miR-142 OE H1437 cells and incubated with the HMEC-1 cells before seeding on Matrigel plates. No EV incubation controls that had the addition of unaltered media was also included. This was 104  performed in triplicate. To isolate the effect of miR-142-3p HMEC-1 cells with miR-142 or miR-C OE were also analyzed. After 16 hours the center of each well was imaged at 10x objective magnification using differential interference contrast Axiovert S1000 microscope. The ImageJ macro ‘Angiogenesis Analyzer’ (http://imagej.nih.gov) was used to quantify the average tube length.  4.2.7 Fibroblast activation WI-38 and IMR90 cells were seeded in 96 well plates after reaching 30% confluence. EVs isolated from one 15cm plate of H1437 miR-C OE and H2073 miR-C OE cells and miR-142 OE H1437 and H2073 lines were added to the culture media per well. The media was replaced with EV supplemented media at 24 hours, and again at 48 hours after the first EV incubation. Protein and RNA were extracted 72 hours post initial EV incubation. To determine whether EV signaling activated the TGFβ signaling pathway, WI-38 and IMR90 cells were incubated in DMEM with 10% dFBS with a treatment consisting of either: No Treatment, H1437 miR-C EVs, H1437 miR-142 EVs, H2073 miR-C EVs, H2073 miR-142 EV or 10ng/mL TGFβ. Protein was harvested at 0 hour, 24 hours, 48 hours, 72 hours and 96 hours.      105  4.2.8 Wound healing assay WI-38 and IMR90 cells were seeded in 12 well plates, upon reaching 80% confluency, a 1mm scratch from a P1000 pipette tip was made. The cells were rinsed with DMEM containing 10% FBS twice, and then the treatment was added. Treatments were conducted within DMEM with 10% dFBS and were: H1437 miR-C OE EVs, H1437 miR-142 OE EVs, H2073 miR-C OE EVs, H2073 miR-142 OE EVs, TGFβ 10ng/mL and no treatment. Experiment was conducted in triplicate and photos were taken at HR0, HR18, HR24 and H48. Analysis of wound healing assay was conducted on tScratch (https://github.com/cselab/TScratch.git)274. Statistical analysis of wound closure was conducted using a one-way ANOVA followed by post hoc analysis.               106  4.2.9 Western blotting RIPA buffer with 1:100 phosphatase inhibitor cocktail I & II (Sigma-Aldrich) and protease inhibitor (Thermo Fisher Scientific) was used to lyse EVs and cells. Protein was quantified using a Pierce BCA kit (Thermo Fisher Scientific). 10μg of protein was run on a NuPage 4-12% Bis-Tris Gels (Thermo Fisher Scientific) and then transferred to PVDF membranes (Millipore). Membranes were blocked in 5% BSA, 1x TBS, and 0.1% Tween-20 for 1 hour at room temperature. Primary antibodies 1:1000 anti-TGFβR1 (AF3025, R&D Systems), 1:1000 anti-PDGFRβ (SC-432, Santa Cruz), 1:2000 anti-Desmin, 1:1000 anti-αSMA (14968, Cell Signaling Tehcnology), 1:4000 anit-H3 (C1H2, Cell Signaling Technology), 1:1000 pSMAD2/3 (8828, Cell Signaling Technology) and 1:8000 anti-GAPDH (2118, Cell Signaling Technology) were applied for 16 hours at 4°C diluted in blocking buffer. Horseradish peroxides conjugated secondary antibodies 1:2000 anti-mouse (NXA931, GE Healthcare) or 1:2000 anti-rabbit (7074, Cell Signaling Technology) were applied for 1 hour at room temperature. Quantification was performed with normalization of TGFβR1 to GAPDH using the ImageJ ‘gel analysis’ function.   4.3 Results 4.3.1 MiR-142-3p is transferred from LAC cells to endothelial cells and fibroblasts through EVs Previously we have demonstrated that miR-142-3p is selectively enriched by > 4-fold in the EVs derived from LAC cells compared to the donor cell275.  Three of the five cell lines analyzed (H1437, H2073, H2228) did not have detectable quantities of miR-142-3p in the cellular RNA fraction, indicating a very efficient EV packaging mechanism.  MiR-142-3p was 107  detectable in the remaining intracellular fraction of cell lines (H1395 and H2347), but still showed a >18-fold increase in the corresponding EVs275. In order to generate EVs that secrete miR-142-3p at an elevated level, we constructed LAC cell lines over expressing either a miRNA scramble (miR-C) or miR-142. The miR-142 over-expressing cell lines showed an increase in miR-142-3p within the EVs by 3.4-fold in H1437 cells and a 5.1-fold in H2073 cells compared to miR-C paired lines. Levels of miR-142-3p within miR-C cell line EVs showed no significant changes compared to non-transformed cell lines. Thus, we created LAC EVs with an enrichment of miR-142-3p within them.  I sought to determine if overexpression of miR-142 within LAC cell lines affected proliferation. Considering its previously established role in targeting of TGFβR1 in LAC, we expected to see a decrease in proliferation due to silencing of TGFβR1251. Instead, we found that miR-142 when overexpressed did not affect proliferation of H1437 or H2073 cell lines (Figure 4.1). This is a little surprising as A549, H23 and H838 cell lines have previously been shown to have decreased proliferation when miR-142 is overexpressed272,276. However, the effect of miR-142-3p expression has yet to be determined in both of the cell lines used here (H1437 and H2073). This difference may be due to the variable origins and mutational status of each of these lines compared to the early LAC cell lines we utilize (Table 3.2 and Table 4.1).        108   Cell Line Stage  Mutations and Features A549 - Hypotriploid in 24% of cells, der(6)t(1;6) (q11;q27); ?del(6) (p23); del(11) (q21), del(2) (q11), M4 and M5, Y chromosome lost in 40% of cells, produce lecithin, KRAS mutation c.34G>A, CDKN2A mutation c.1_471del471 H23 - Derived prior to therapy, TP53 mutation c.738G>C, KRAS mutation c.34G>T H838 3B 80 pack years, CDKN2A mutation c.1_471del471, TP53 mutation c.184G>T  Table 4.1 Tumour stage of LAC cell lines, mutation profiles and characteristics of A549, H23 and H838 according to ATCC248          109    Figure 4.1 MTT of direct overexpression of miR-C or miR-142 in A) H1437 B) H2073. Error bars represent standard deviation.     Day 1Day 2Day 3Day 4Day 50 .00 .40 .81 .2H 1 4 3 7  m iR -1 4 2  O E  M T TODH 1 4 3 7  m iR -1 4 2  O EH 1 4 3 7  m iR -C  O EDay 1Day 2Day 3Day 4Day 50 .00 .20 .40 .60 .8H 2 0 7 3  m iR -1 4 2  O EODH 2 0 7 3  m iR -1 4 2  O EH 2 0 7 3  m iR -C  O EA B 110  To determine if miR-142-3p in EVs from LAC cells can communicate with cells in the tumor microenvironment we asked whether miR-142-3p can be transferred to endothelial and/or fibroblast cells. EVs from LAC cell lines H1437 and H2073 over-expressing miR-C or miR-142 were collected and incubated with either endothelial HMEC-1 cells or lung fibroblast WI-38 and IMR90 cells. After 48 hours of incubation the cells were washed to eliminate remaining EVs. Intracellular RNA was collected, and miR-142-3p was quantified by qRT-PCR. HMEC-1 cells receiving miR-C EVs (expressing endogenous miR-142-3p levels within the EVs) had a 2.4 (H1437) and 2.3 (H2073) fold greater level of intracellular miR-142-3p then those receiving none (Figure 4.2). We observed a 2.5 and a 2.0-fold increase in intracellular miR-142-3p levels in WI-38 cells and a 1.9 and 2.1fold increase in IMR90 cells incubated with miR-C H1437 EVs and H2073 EVs respectively (which contain endogenous levels of miR-142-3p in their EVs) (Figure 4.2). To ensure that this effect was caused by miRNA transfer and not an induction of local miRNA expression by way of some other EV contained factor, this experiment was also performed using EVs collected from miR-142 OE H1437 and H2073 cells. The addition of miR-142 OE EVs increased concentration of miR-142-3p in HMEC-1 receiver cells by 8.7 and 9.5-fold for EVs from H1437 and H2073 respectively (Figure 4.2). While an 11.1-fold and an 8.4-fold increase in intracellular miR-142-3p levels was seen in WI-38 cells and 7.9-fold and 8.2-fold in IMR90 cells when incubated with miR-142 OE EVs from H1437 and H2073 cell lines respectively compared to WI-38 and IMR90 cells receiving no EVs. Indicating that both H1437 and H2073 EVs enter HMEC-1 cells, WI-38 and IMR90 cells and increase intracellular miRNAs, which we have previously demonstrated275.  111   Figure 4.2 Lung adenocarcinoma cell extracellular vesicle miRNAs are taken up by HMEC-1, WI-38 and IMR90 cells. Intracellular fold change analysis of miR-142-3p in HMEC-1, WI-38 and IMR90 cells run in triplicate incubated with extracellular vesicles from H1437 and H2073 cells over expressing miR-142 and miR-C, compared to HMEC-1 cells receiving no extracellular vesicle incubation. Error bars represent standard error mean. 112  4.3.2 Secreted miR-142-3p targets TGFβR1 in HMEC-1 cells TGFβR1 has been shown to be a target of miR-142-3p in LAC cells and has also been shown to be a target in HMEC-1 endothelial cells251,277. To determine if the TGFβR1 pathway is involved in attenuation of angiogenesis via LAC secreted miR-142-3p, EVs from H1437 cells or H2073 cells that OE miR-142 or miR-C were incubated with HMEC-1 cells. 48 hours after EV incubation, protein was extracted from the cells and compared to HMEC-1 cells grown without the addition of EVs. There was an observed decrease of TGFβR1 in HMEC-1 cells receiving miR-C EVs (containing endogenous levels of miR-142) from H1437 cells of 66% and a decrease of 37% with miR-C EVs from H2073 cells (Figure 4.3). The decrease in TGFβR1 was enhanced when HMEC-1 cells were incubated with miR-142 OE EVs from donor cells. A decrease of 83% in TGFβR1 was observed when HMEC-1 cells were incubated with H1437 miR-142 OE EVs and 55% when incubated with H2073 miR-142 OE EVs compared to HMEC-1 cells incubated with no EVs (Figure 4.3). To confirm that TGFβR1 was inhibited by miR-142-3p, HMEC-1 cells were directly infected with stable OE of miR-142 and a 91% decrease in TGFβR1 was observed (Figure 4.3).          113   Figure 4.3 Analysis of TGFβR1 protein expression by western blot in HMEC-1 cells after direct over expression of miR-142 and miR-C within HMEC-1 cells and after incubation with EVs isolated from H1437 and H2073 over expressing miR-142, miR-C and untreated cell.           114  To determine if LAC secreted miR-142-3p can promote angiogenesis we performed a tube formation assay on HMEC1 endothelial cells. Endothelial cells were in three treatment groups, those that did not receive EVs, those receiving EVs from miR-C cells (containing endogenous levels of miR-142-3p) and those receiving EVs from miR-142 OE cells (resulting in an increase of miR-142-3p in the EVs). This was performed with EVs derived from both H1437 and H2073 cells. All treatments were capable of forming tubes (Figure 4.4 A-F, I). The addition of miR-C EVs increased tube formation by 9% (p<0.05, H1437) and 23% (p<0.05, H0273). The addition of miR-142-3p OE EVs increased tube formation by 37%, (p<0.05 H1437) and 40% (p<0.05 H2073) when compared to the HMEC-1 cells with no EVs added. To confirm alterations in tube formation were a result of miR-142-3p, the tube formation assays were repeated with direct OE of miR-142 (Figure 4.4 G-I). This caused an increase in tube formation by 40% (p<0.05) compared to HMEC-1 cells with miR-C OE.  To address the possibility that the interaction between miR-142-3p and TGFβR1 is incidental to the finding of an alteration in tube formation we performed a phenotypic rescue experiment in HMEC-1 cells. HMEC-1 cells were infected with a TGFβR1 ORF clone, an open reading frame (ORF) is a construct expressing the mRNA of the target gene, in this case TGFβR1 that lacks the binding site for miR-142-3p and becomes insensitive to inhibition from miR-142-3p (Figure 4.5 A). This experiment showed that tube formation was decreased upon OE of TGFβR1 even with the addition of miR-142 OE EVs, from H1437 or H2073. This caused a decrease in tube formation of 34 and 35% respectively (Figure 4.5 B-E).    115                     Figure 4.4 Tube formation assay of HMEC-1 cells run in triplicate A) no extracellular vesicle incubation B) incubated with H1437 miR-C extracellular vesicles C) incubated with H1437 miR-142 EVs D) no extracellular vesicle incubation E) incubated with H2073 miR-C extracellular vesicles F) incubated with H2073 miR-142 extracellular vesicles G) direct overexpression of miR-C H) direct overexpression of miR-142 I) quantification of tube formation assays. Error bars represent standard deviation. 116      * = p-value < 0.05  Figure 4.5 Rescue of HMEC-1 cells using TGFβR1 ORF A) Western blot of TGFβR1 and GAPDH in HMEC-1 cells with either a control ORF or TGFβR1 ORF incubated with miR-142 OE extracellular vesicles from H1437 or H2073 cell lines B i-iv)) tube formation images of TGFβR1 ORF rescue in HMEC-1 C) quantification of rescue tube formation (B i-iv). Error bars represent standard deviation. 117  4.3.3 MiR-142-3p containing EVs promote CAF phenotype  Fibroblasts are the most abundant tumour microenvironment cell type in solid tumours, and mainly reside in a CAF state due to reprogramming by the adjacent tumour278. To determine if secreted miR-142-3p also had an impact on lung fibroblast cells, we incubated WI-38 lung fibroblast cells with miR-C and miR-142 OE EVs for 72 hours from H1437 and H2073 cells. Protein was extracted and western blots were performed for known CAF markers α-SMA, and PDGFR-β (Figure 4.6A), which are positively correlated with fibroblast activation and CAF transformation279. An increase in the cytoskeleton protein α-SMA and the growth factor receptor PDGFR-β were observed. In contrast to endothelial cells no significant changes were observed for the protein TGFβR1 in fibroblast cells upon addition of the LAC EVs and subsequent increase in miR-142-3p (Figure 4.6B). We next aimed to determine whether the CAF phenotype as seen by an increase in CAF markers, was caused by an increase in TGFβ signaling, a common pathway for CAF phenotype activation280. To investigation activation of TGFβ signaling we examined the levels of phosphorylation (p) on the proteins SMAD Family Member 2 & 3 (SMAD2/3) (Figure 4.7). When TGFβ signaling is activated both SMAD 2 and 3 are phosphorylated on serine residues and act as transducers for TGFβ signaling. These proteins associate with SMAD4 which, causes translocation to the nucleus where it binds to promoters and recruits proteins in other areas to act as repressors281. We saw typical induction of CAF markers PDGFR-β and αSMA in both WI-38 and IMR90 cells when treated with EVs and TGFβ (Figure 4.7). WI-38 and IMR90 cells that were treated with TGFβ showed increased pSMAD2/3 which, indicates signaling by TGFβ (Figure 4.7). We did not see SMAD2/3 phosphorylation in WI-38 and IMR90 cells incubated with any EV treatment, indicating the mechanism by which lung fibroblast cells are promoted to the CAF phenotype is independent of TGFβ signaling.  118   Figure 4.6 Western blot analysis of WI-38 and IMR90 cells incubated with EVs from H1437 and H2073 cell lines A) Western blot of PDGF-β, α-SMA and H3 in WI-38 and IMR90 after EV incubation B) Western blot of TGFβ and GAPDH in WI-38 and IMR90 cells after EV incubation   119   Figure 4.7 Western blot of WI-38 and IMR90 cells incubated with EVs or TGFβ for PDGFR-β, αSMA, pSMAD2/3 and H3     120  4.3.4 Secreted miR-142-3p promotes cancer associated fibroblast transformation The results above suggest that EVs containing enriched miR-142-3p contributes to fibroblast activation through a pathway independent of TGFβ. This is expected as a large decrease in TGFβR1 would be expected to inhibit CAF formation as TGFβ signaling is a known activator of the CAF phenotype282,283. We next sought to determine whether the miR-142-3p containing EVs were able to promote migration of fibroblast cells, another characteristic of the CAF phenotype. Using a scratch assay, a scratch 1mm in diameter was made in a 24 well plate using a p1000 pipette tip, and either EVs, TFGβ or no treatment was incubated with the cells for 48 hours. WI-38 and IMR90 cells that were incubated with EVs showed higher wound closure ability compared to cells that received no treatment (Figure 4.8 and Figure 4.9). MiR-142 EVs showed significantly higher wound healing ability than miR-C EVs in WI-38 cells at 18 hours for both H1437 and H2073 EVs (p<0.05). At 24 hours H1437 miR-142 OE EVs remained significantly better at wound healing than H1437 miR-C EVs (~60% wound closure and ~40% wound closure respectively p<0.05), but H2073 miR-142 OE EVs no longer remained significantly better at wound healing compared to miR-C EVs. At 48 hours both H1437 and H2073 miR-142 OE EVs were no longer significantly different from miR-C EVs (Figure 4.8 and Figure 4.9). In IMR90 cells H2073 miR-142 OE EVs were significantly better at wound healing than H2073 miR-C EVs starting at 18 hours (H2073 18 hours: miR-142 OE EVs 30% wound closure, miR-C EVs 24% wound closure, p-value<0.05, 24 hours: miR-142 OE EVs ~48% wound closure miR-C EVs 41% wound closure, p-value<0.05, 48 hours: miR-142 OE EVs 88% wound closure, miR-C EVs 75% wound closure, p-value<0.05).  H1437 miR-142 OE EVs however, were only significantly better at wound healing than H1437 miR-C EVs at 48 hours (18 hours: miR-142 OE 121  EVs 28% wound closure miR-C EVs 24% wound closure, p-value>0.50, 24 hours: miR-142 OE EVs 45% wound closure miR-C EVs 39% wound closure, p-value>0.50). At 48 hours H1437 miR-142 OE EVs had 85% wound closure while miR-C EVs had 75% wound closure (p-value>0.05), this could suggest that H1437 EVs are not as strong inducers of IMR90 migration, or that miR-142-3p within H1437 EVs is not an important early inducer of migration in IMR90 cells and that miR-142-3p is acting secondary to another inducer until 48 hours (Figure 4.8 and Figure 4.9). For both WI-38 and IMR90 cells, miR-142 OE EVs showed significantly higher wound closure compared to standard TGFβ treatment at all time points (p-values>0.05) (Figure 4.8 and Figure 4.9). This suggests that the EVs that have a higher enrichment of miR-142 from the LAC lines are better able to promote fibroblast migration compared to TGFβ alone. While miR-142 OE EVs were strong inducers of fibroblast migration, miR-C EVs showed higher variability in response. In IMR90 cells, H2073 and H1437 miR-C EVs had a significantly higher wound closure than TGFβ treatment at only 18 hours (24%, 24% and 16% wound closure respectively, p-value<0.001). At longer time points (24 and 48 hours), these miR-C EVs became non-significant. In WI-38 cells, H1437 miR-C EVs had a significant difference at only 18 hours compared to TGFβ (26% vs 23% wound closure respectively p-value<0.05) however, in H2073 miR-C EVs a significant difference was seen only at 48 hours compared to TGFβ treatment (100% vs 60% wound closure, p-value <0.001). These results suggest that miR-C EVs, which contains endogenous levels of miRNAs within their EVs, are still strong inducers of fibroblast migration and are comparable to a known strong inducer of the CAF phenotype TGFβ. Together, these results demonstrate the ability for LAC derived EVs to promote the wound healing ability of CAFs, and that LAC derived EVs containing enrichment of miR-142-3p are regulators of the CAF phenotype in vitro.  122    Figure 4.8 Wound healing assays of A) WI-38 and B) IMR90 with extracellular vesicle and TGFβ treatment A B 123  * = p-value < 0.05 ** = p-value < 0.01     Figure 4.9 Graphs of wound healing assay for A) WI-38 and B) IMR90 cell lines. Error bars represent standard deviation. 124  4.4 Discussion MiR-142-3p is highly deregulated in many tumor tissues including colon, breast, lung, osteosarcoma, pancreatic and cervical cancers where it shows decreased tumor tissue expression compared to normal tissue284-287. In these tumor types miR-142-3p has been classified as a tumor suppressor, with several known mRNA targets in addition to TGFβR1 such as WASL, TGAV, cytoskeleton-associated molecules, CD133, Lgr5, ABCG2 and Rac1, that can impact tumorigenesis286-288. miR-142-3p overexpression has also been shown to decrease HIF-1α expression in the tumor microenvironment leading to reduced invasion of cells and proliferation285.  However, miR-142-3p has also been classified as an oncomiR in several tumor types such as renal cell carcinoma, T-cell acute lymphoblastic leukemia, nasopharyngeal carcinoma and gastric cancers where it is shown to be upregulated289-292. In renal cell carcinoma tissue miR-142-3p was found to be elevated compared to adjacent normal tissue and functioned as an oncogenic miRNA, as when miR-142-3p was inhibited proliferation and migration were reduced and apoptosis was promoted291. In nasopharyngeal cancer miR-142-3p inhibition led to a decrease in proliferation and cell cycle progression in vitro and suppressed tumor growth in mouse models289. In T-Cell acute lymphoblastic leukemia, miR-142-3p at high levels reduced cyclic adenosine monophosphate (cAMP) and protein kinase A (PKA) levels and relieved the inhibitory effect of PKA on proliferation290. Its oncogenic role is further supported by its tumor promoting activity in gastric cancer through targeting of FOXO4292. This suggests that miR-142-3p can serve as both tumor suppressor and oncogene depending on the cell type in which it is present.   125  In LAC miR-142-3p has been reported as a tumor suppressor and is frequently decreased in tumor compared to normal tissue272. TGFβR1 and HMGB1, two well-known oncogenes, are validated targets of miR-142-3p in NSCLC251,272. Inhibition of HMGB1 by miR-142-3p resulted in increased apoptosis and decreased growth, and upregulation of TGFβR1 has been shown to promote NSCLC tumorigenesis272,273. In our model system miR-142-3p was not detectable within the cellular fraction by qRT-PCR in three of the cell lines used (H1437, H2073, H2347), however enrichment of this miRNA was observed in the EVs derived from these lines indicating an efficient packaging mechanism for miR-142-3p into EVs. This efficient packaging may serve several functions, one to eliminate suppressive miRNAs from the tumor cells and two to serve as cell-cell communicators thereby creating a more favorable tumor microenvironment. EVs are cell-cell communicators and are known to be released from tumors and enter the serum of the patient, which has led to the interest of circulating miRNAs as serum biomarkers. The majority of miRNAs in circulation are packaged within EVs which grants them stability and protects them from RNase digestion114. miR-142-3p has been predominantly found within 120 nm particles (associated with microvesicles and exosomes) and found at elevated levels in the serum of LAC patients213. This was associated with higher risk of relapse in patients with adjuvant therapy213. Furthermore, an increase of miR-142-3p in the plasma was associated with aggressive NSCLC disease293. Considering miR-142-3p is elevated within LAC cell line EVs and is found elevated in the serum of LAC and NSCLC patients suggests that miR-142-3p is secreted from tumors and enter the serum, to be used as communicators for distant signaling or local signaling.  126  MiRNAs within EVs are important regulators of tumor physiology, playing roles in immune regulation, modifying tumor microenvironment to facilitate tumor progression, angiogenesis, metastasis and chemotherapy resistance. Angiogenesis is essential for tumor development as establishing blood flow to the tumor is important for tumor growth and metastasis, for example in leukemia exosomal miR-92a was found to be transferred into endothelial cells and causing increased migration as well as tube formation157. In regulating the immune system, tumor derived EVs have been shown to both activate and suppress the immune response to tumors. In glioma, treatment of dendritic cells with tumor derived EVs were able to induce glioma-specific CD8(+) cytotoxic T-lymphocyte response against autologous glioma cells in vitro294. This data along with other publications demonstrate the tumor suppressive role of EVs, however anti-T Cell and anti-Natural Killer cell effects have been found as a result of tumor EV transfer295,296. Changes in monocyte differentiation were found to be caused by circulating microvesicles released from the tumor, which promoted the generation of myeloid suppressive cell subsets297. MiR-21 containing exosomes were shown to promote recurrence and distant metastasis in esophageal cancer through targeting and repression of programmed cell death 4 (PDCD4)298. Chemotherapy resistance has been found to be mediated by the transfer of miRNA EVs. In breast cancer the transfer of EVs from docetaxel resistant cells to non-resistant cells resulted in an increase of chemoresistance to docetaxel. Taken together EVs have been shown to be critical in promoting many hallmarks of cancer and their communication mechanism must be further studied to determine potential therapeutic interventions.  EVs are known as potent cell to cell communicators transferring mRNA, proteins and miRNAs. One important aspect of miRNA function is that one single miRNA can have many targets in a single cell299. This feature of miRNAs allows for it to have different targets 127  depending on the cell type the miRNA is present within. For example, in certain cell types a miRNA may act to target a tumor suppressor however, in another cell with minimal expression of the tumor suppressor gene that miRNA may effect different targets as the tumor suppressor gene is not abundant enough. Due to this unique feature we sought to investigate the function of miR-142-3p and its targets in endothelial cells and fibroblasts.                   128  Currently the role of miR-142-3p is not well established within fibroblasts and endothelial cells. In endothelial cells we reported TGFβR1 is inhibited by miR-142-3p containing EVs. When TGFβR1 was inhibited, angiogenesis was promoted, showing miR-142-3p is a promoter of angiogenesis in endothelial cells in vitro. We report here that miR-142-3p containing EVs produced by early LAC cell lines promote the CAF phenotype, as the protein levels of α-SMA and PDGFR-β were elevated (Figure 4.6). Additionally, fibroblast migration, another phenotype associated with myofibroblasts, was significantly increased by incubation with miR-142 OE EVs. It was most evident compared to WI-38 and IMR90 cells incubated with TGFβ, where significant increases in migration were seen across all time points. This could indicate that miR-142-3p when elevated within EVs is a strong inducer of fibroblast migration and may be a driving force for the CAF phenotype. While miR-C EVs did not impact migration as much as miR-142 OE EVs, they still showed significant increases in migration compared to TGFβ incubation in IMR90 fibroblasts at early time points coming from both H1437 and H2073 cell lines (Figure 4.9). This may indicate that EVs from LAC cells are important initiators of fibroblast migration and can accelerate the early processes of cellular migration in IMR90 cells but are eclipsed by other factors. In WI-38 cells, we found that miR-C EVs from H2073 cells were strong inducers of migration but only after 48 hours. These results may indicate that specific fibroblast cell lines will respond differently to EVs and that the cell line of origin for the EVs is equally as important.  In our fibroblast cell lines, we found that TGFβR1 was not the target of miR-142-3p, and that the mechanism by which miR-142-3p induced the CAF phenotype was independent of TGFβ signaling as was shown by a lack of phospho-SMAD2/3. The role of SMAD2/3 in fibroblast activation was previously established, as constitutively active TGFβR1 mutant resulted 129  in increased phospho-SMAD2/3 and caused an increase in myofibroblast markers300. In addition, sustained phospho-SMAD2/3 has been found in fibroblasts of models for fibrosis, which again indicates the importance of transducing the TGFβ signal to induce myofibroblast changes301. We did not see activation of the TGFβ signaling mechanism after incubation with miR-142-3p containing EVs, thus the CAF phenotype is being promoted through a different mechanism. Altogether these results demonstrate that the function of miR-142-3p is dependent on the cell type it is within and that miR-142-3p packaging within LAC cell line EVs may be a method of promoting pro-tumorigenic effects to both endothelial and fibroblast cells.  With the role that miRNAs play within EVs and cancer it is logical that they may be an avenue for therapeutics. One proposed method of therapeutic intervention consists of  EV removal from the circulatory system using an affinity plasmapheresis platform302. This removes tumor produced EVs from circulation, which is expected to reduce metastasis and tumor intercellular signaling. Clinical trials have currently begun by Aethlon Medical on this platform. Other methods of EV reduction such as by targeting EV production genes like neutral sphingomyelinase 2 (nSMase2) and RAB27B with siRNAs in breast cancer has suppressed invasion through the blood brain barrier238. We demonstrate that LAC EVs are important in the tumor microenvironment, so targeting of EV production either by removal or inhibition may be a useful mechanism to treat LAC.      130  In summary the role of miR-142-3p in LAC is complex (Figure 4.10). We provide evidence that EVs from LAC cell lines can enter endothelial and fibroblast cell lines to promote angiogenesis and the CAF phenotype. This is mediated by TGFβR1 targeting through miR-142-3p in endothelial cells leading to promotion of angiogenesis but is not mediated by TGFβR1 targeting in fibroblasts to cause features of the CAF phenotype. In conclusion miR-142-3p when secreted in LAC EVs can promote tumorigenesis through signaling to the surrounding stromal cells and may represent a mechanism to interrupt to reduce cancer development.               Figure 4.10 Summary of the role of miR-142-3p in LAC. 131  Chapter 5: Discussion and Conclusion  5.1 Discussing Rationale and Thesis Concepts LAC is the most common subtype of lung cancer worldwide and its dismal 5-year survival rate of ~15% is generally caused by its late stage of diagnosis. The development of LAC is mainly caused by tobacco smoke and develops in the periphery of the lung where it can remain with no symptoms for a long period of time.  By the time diagnostic procedures are conducted a metastasized tumor is generally found and there are few options for treatment. Determining a method for early detection of the disease and understanding early tumor development will allow for us to improve survival in LAC patients. Thus, an in-depth analysis of tumor released miRNAs may provide information on the function of EVs in LAC development and be a useful non-invasive biomarker for early disease detection. The work within this thesis presents serum miRNA profiling of LAC patients and high-risk non-cancer controls, in addition to the functional characterization of EV associated miR-143-3p, miR-145-5p and miR-142-3p in the surrounding tumor stroma.   5.1.1 Tumor released factors promote tumorigenesis  Tumor cells are potent communicators, sending growth factors, immunosuppressive factors, invasion promoters and metastasis promoters to surrounding and distant cells to promote their own growth. Many of these factors end up in the tumor microenvironment, which is a collection of non-tumor cells surrounding and integrated within the cancer. The factors can have a variety of functions. To facilitate angiogenesis within the tumor, cancer cells can secrete VEGF to stimulate endothelial cells to form new blood vessels303. In breast cancer the secretion of 132  exosomes containing TGF-β has been shown to cause immunosuppression by suppressing the proliferation of T cells303-307. In oral, breast, head and neck cancers, the secretion of the proteins MMP-9 to the tumor microenvironment is implicated in degrading extracellular matrices which contributes to invasion and metastasis304-306,308. MMP-9 functions by degrading type IV collagen, which is one of the initial steps in tumor invasion. Together tumor secreted factors play many roles in promoting tumorigenesis and facilitating the growth of the tumor.  5.1.2 Tumor released miRNAs contribute to cancer development One type of tumor secreted factor are miRNAs. As previously discussed in the introduction, miRNAs are released from tumor cells mainly enclosed within EVs. These EVs can then be transferred to stromal cells locally or distant cells through the blood. The miRNAs can have a variety of actions once transferred including promoting angiogenesis, immunosuppression, promoting invasion and metastasis. miR-1245 has recently been implicated as an immunosuppressive miRNA due to its targeting of natural-killer group 2, member D (NKG2D) in natural killer cells, which reduced their efficacy309. Other secreted miRNAs like miR-21 and miR-29a have been shown to bind to toll-like receptors (TLR) and trigger a TLR mediated pro-metastatic inflammatory response, and thus contributes to the cancers growth and/or metastasis155. Our results add to this understanding of tumor released miRNAs in LAC, we demonstrate in Chapter 3 and 4 the ability of miR-142-3p, miR-143-3p and miR-145-5p to contribute to tumorigenesis through promotion of angiogenesis, and the CAF phenotype. Considering the roles in miRNAs within the tumor it is not surprising that expression of miRNAs within tumors are deregulated. 133   Tumors have differing expression of miRNAs compared to their normal counter parts. In NSCLC some miRNAs have been shown to be down regulated within the tumor cells. MiR-145-5p was shown to be reduced in the NSCLC cells which mirrored results in colon cancers310,311.The reduction of miR-145-5p within the cells may not be an indication of the levels of miR-145-5p total expression. In our results miR-145-5p was found almost entirely within LAC EVs, which indicted that this miRNA may only be found within the EVs due to efficient shuttling of this miRNA. Other miRNAs such as miR-21-5p was shown to be upregulated in NSCLC in addition to others310. This deregulation of miRNAs contributes to the cancer development by reducing the expression of tumor suppressing miRNAs and increasing the expression of oncogenic miRNAs312. Considering that tumor miRNAs are deregulated we would expect to see differences in secreted miRNAs and therefore differences in the miRNA serum profiles of cancer patients compared to non-cancer patients.   5.2 Summary of Findings 5.2.1 Serum miRNAs as biomarkers in the detection of LAC One of the goals our research was to determine whether differences in serum miRNAs could be detected in the serum profiles of LAC patients compared to high-risk non-cancer patients. In chapter 2, I provide a comprehensive profiling of serum miRNAs in early stage LAC patients and high-risk non-cancer controls. Within the chapter I initially profile for 742 different miRNAs and find a set of miRNAs that are able to distinguish LAC from non-cancer. This method of biomarker detection has a number of strengths associated with it. First, the profiling of serum miRNAs by qRT-PCR is highly sensitive and with our stringent selection of candidates by eliminating any miRNA affected by hemolysis offers a robust selection of miRNAs for use as 134  candidates. These factors of selection are clinically relevant data, since hemolysis contamination is a common problem in blood draws. In addition, we validated across platforms suggesting that these miRNAs are highly sensitive and could be used in a clinic regardless of platform. One of the more unique aspects of my study is that we utilize early stage LAC serum samples which are difficult to obtain but offer the potential to find early indications of LAC development. In general, these samples would show smaller changes in miRNAs as on average they have lower cell numbers that are able to secrete miRNA containing EVs. Additionally, early stage LAC tumors would have limited access to both the lymph and circulatory systems to secrete EVs into for detection compared to later stage disseminated tumors. However, they are more relevant to the clinic considering it is easier to treat early stages of LAC and early stages of LAC are generally asymptomatic. These early stages of LAC offer a high yield reward if we can detect LAC presence early. Finally, the use of high-risk non-cancer serum samples as our comparator offers a highly clinically relevant examination of high-risk patients who are more likely to be screened. These samples mimic what would occur in the clinic and therefore more relevant that many previous studies which use healthy patient controls. This type of patient data is not commonly used as a comparison, and therefor is quite rare.  There are however some limitations to our data and analysis, first, in patient serum data there is a significant difference in age between patients in our training data and considering age is a factor in serum miRNAs this may create a bias based on age between LAC and non-cancer227. Although there is a difference in age it is mainly seen in serum that is greater than 10 years of age apart, and therefore may not present a large factor227.    135  Another limitation within our profiling of LAC serum miRNAs, is that we changed qRT-PCR platforms for validation of our miRNA candidates. Doing so, we saw that miR-197 and miR-142-3p did not show similar values in our validation samples compared to our training data. This suggests that the platform significantly affected the levels of miRNAs detected. To become more confident in our biomarker selection, initial profiling of serum miRNAs on the TaqMan platform, would have given us a stronger miRNA signature with a higher chance of validating as expected. However, given this limitation once miRNAs that failed to validate were eliminated the discriminate equations were reproducible across two platforms.                136  The results of this biomarker examination show specific miRNAs including miR-27b, miR-582-5p, miR-497, miR-92b could be used as indicators of LAC presence in both sexes using the SYBR Green platform. However, when we attempted to validate using the TaqMan platform on a separate set of samples the accuracy dropped considerably. After finding that sex played a role in serum miRNA levels, I separated the samples based on sex to construct sex-specific miRNA signatures. I found two unique signatures that separated LAC from non-cancer in each sex. The signature in females had low sensitivity and specificity compared to males and when validated on the TaqMan panels tended to call LAC over non-cancer. In males we saw a similar trend which additionally favored calling LAC, however after examining paired samples we found that miR-142-3p showed significantly different detection in paired samples and was thus removed from analysis. A new male sex specific signature was determined and validated which had the highest sensitivity and specificity. This male specific miRNA signature may offer a non-invasive method of screening for LAC in high risk patients. Further validation must be conducted on a larger sample size to confirm these results. In the future, examining these patient miRNA profiles with tumor grading changed according to the new guidelines and follow up survival may allow for us to determine if the miRNA signatures are associated with stage or survival, and further allow for us to stratify the patient miRNA profiles.   5.2.2 Function of LAC secreted miRNAs The second goal of this thesis was to determine the function of specific miRNAs within LAC EVs in the tumor stroma. In chapter 3 we profiled LAC cell line EVs to determine what miRNAs were enriched within them. This does raise a limitation to our research as the early LAC cell lines utilized are stage I – IIIa but are well established as coming from patients with 137  advanced disease, thus our cell lines may not properly represent early LAC tumors. However, there are very few staged early LAC cell lines and we utilized the best cell lines available for our in vitro research. After determining the miRNAs frequently enriched we selected miR-143-3p and miR-145-5p for further functional validation as they were additionally found to be enriched in serum draining directly from LAC tumor beds. This represented a major strength of this thesis chapter as these samples are extremely rare and offer the highest concentration of tumor secreted miRNAs and allow for us to more accurately determine miRNAs that are secreted from LAC tumors. Although this data does not utilize miR-Seq, the data in this thesis represents a comprehensive interrogation of tumor secreted miRNAs, as it analyzes 742 of the most abundant human miRNAs and presents a highly sensitive analysis of these miRNAs. The detection of miR-143-3p and miR-145-5p within these rare serum samples lead us to follow up on the function that these miRNAs may have when released from LAC cells. A further investigation of these miRNAs demonstrated that the miRNAs promoted angiogenesis in vivo in endothelial cells when directly overexpressed and when transferred through EVs, which was mediated by the targeting of CAMK1D. Going forward deletion of these miRNAs within LAC cell lines may present a robust method for examining the exact role miR-143-3p and miR-145-5p play in tumor development. By utilizing CRISPR, miR-143-3p and miR-145-5p could be deleted in LAC cell lines and then injected into mice for in vivo analysis. Once injected and allowed to grow, examination of angiogenesis in an in vivo model will allow for a strong conclusion about the role that these miRNAs play in tumorigenesis.   In chapter 4 we investigate miR-142-3p due to its common enrichment in oral cancer cell EVs and due to its abundance in LAC EVs. I investigated its function which revealed that miR-142-3p targets TGFβR1 in endothelial cells and promotes angiogenesis through this effect. When 138  transferred through EVs these miRNAs with the additional EVs contents can induce angiogenesis. However, in fibroblast cells miR-142-3p did not target TGFβR1 but promoted the CAF phenotype through an unidentified mechanism which increased CAF markers αSMA and PDGFR. This research presented itself with a limitation due to the inability to identify the target of miR-142-3p in the fibroblast cells. In the future, overexpressing miR-142-3p within fibroblast cells and conducting proteomic analysis on overexpression and control cell lines may offer a narrowed list of miR-142-3p targets in fibroblasts. Once narrowed, western blots could be conducted for validation followed by luciferase assays for target confirmation. If a target is confirmed to be inhibited by miR-142-3p then further examination of the role the target plays in CAF formation could be examined and a more defined role for miR-142-3p could be given.    5.3 Conclusions Regarding Hypothesis For this thesis the hypotheses were: 1) A miRNA signature in the serum of LAC patients will allow for discrimination between LAC and non-cancer 2) LAC EV miRNAs promote tumorigenic changes in stromal cells to promote tumorigenesis.  Hypothesis 1 was examined in Chapter 2 of this thesis where I identified a unique miRNA signature that could reasonably separate LAC patient serum from high-risk non-cancer controls. This signature allows for discrimination of LAC and non-cancer confirming my hypothesis.  Hypothesis 2 was examined in Chapter 3 and 4, where the results demonstrated that miR-142-3p, miR-143-3p and miR-145-5p were able to be transferred from LAC cell lines within EVs to stromal cells where they promoted cancer associated features. In Chapter 3 specifically the role of miR-143-3p and miR-145-5p were outlined as promoting cancer associated features 139  through inducing angiogenesis and therefore supporting the hypothesis. Chapter 4 additionally supported this hypothesis as we examined the role miR-142-3p played on both endothelial cells and fibroblasts where it was able to promote angiogenesis and features of the CAF phenotype, both of which promote tumorigenesis.  5.4 Overall Significance The goal of this research was to identify tumor secreted miRNAs from LAC and determine whether they could be used as biomarkers in the serum and to determine their function when transferred to the surrounding stroma. In our investigation we present the detailed analysis of secreted miRNAs from LAC.   Chapter 2 presents the analysis of miRNAs within patient serum samples taken from patients with LAC and high-risk non-cancer controls. We first establish a method for normalizing serum miRNAs using miR-23a. Throughout the analysis we find a miRNA signature that was able to distinguish LAC from non-cancer 68% of the time, while this is a relatively decent detection rate, we were able to find a stronger signature in male patients only. This male specific signature had the highest sensitivity and specificity compared to the other signatures, making the signature a strong candidate for use in the clinic. Further follow up and larger validation cohorts are required to validate the determined LAC classifiers to determine their use in the clinic.   The analysis of EV-associated miRNAs in LAC is the first to show that specific miRNAs are selectively packaged into EVs in LAC. To date many studies on LAC EVs utilizes the cell line A549 cells which, is a highly mutated model of LAC or utilize late stage LAC cell lines (Table 4.1). This is extremely important as stromal changes and microenvironment 140  transformation occur at all stages of cancer development to support the growing tumor. Utilizing early LAC cell lines allow for the identification of miRNAs that are important early in development of the cancer to potentially intervene at a stage before metastasis. In our research of early LAC cell lines, we demonstrate that EVs from LAC are able to enter into endothelial cells and fibroblast cells where they are able to affect the function of these cells. In Chapter 3 we demonstrate that miR-143-3p and miR-145-5p increase tube formation through inhibition of CAMK1D, this was the first time LAC EVs were shown to increase angiogenesis within endothelial cells. This data demonstrated that LAC EVs are functional in vitro and increase the tumor associated changes in the endothelial and fibroblast cells.   We also present the function of miR-142-3p in LAC EVs. We identify the target of miR-142-3p in endothelial cells as TGFβR1, and which when targeted increases tube formation. 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