Medicine, Faculty of
DSpace
UBCV
Stewart, Gregory
Doctor of Philosophy - PhD
University of British Columbia
Lung cancer remains the deadliest form of cancer, and less than half of lung adenocarcinoma (LUAD) patients harbour clinically actionable driver genes, emphasizing the need to explore alternative mechanisms of cancer gene deregulation. The advent of next generation sequencing has begun to reveal the functional importance of long non-coding RNAs (lncRNAs) in human cell biology, which can be exploited by tumours to drive the hallmarks of cancer. Due to their complex tertiary structure and unknown binding motifs there is a growing disparity between number of lncRNAs identified and those that have been functionally characterized. As such, lncRNAs deregulated in cancer may represent critical members of cancer pathways that could hold therapeutic applicability. The goal of this thesis is to identify lncRNAs important to LUAD biology, discover shared features and mechanisms used to regulate cancer driving protein coding genes, and evaluate the clinical relevance of these non-coding genes. We discover and investigate three major mechanisms harnesses by lncRNAs in LUAD: (i) cis-acting regulation of neighbouring genes, (ii) trans-acting regulation through sequence homology and (iii) regulation through shared miRNAs. This work uncovers evidence to suggest that alteration of lncRNAs is a major mechanism of cancer gene regulation in LUAD. Further characterization of these understudied gene regulatory mechanisms could lead to novel therapies that silence oncogenes or reactivate tumour suppressor genes.
ALTERATION OF NON-CODING RNAS AS A MECHANISM OF LUNG CANCER GENE DEREGULATION by GREGORY STEWART B.Sc, The University of Victoria, 2010 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 2020 © Gregory Stewart, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Alteration of non-coding RNAs as a mechanism of lung cancer gene deregulation submitted by Greg Stewart in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Interdisciplinary Oncology Examining Committee: Dr. Wan Lam (Interdisciplinary Oncology) Supervisor Dr. Calum MacAulay (Pathology and Laboratory Medicine) Supervisory Committee Member Dr. Isabella Tai (Experimental Medicine) University Examiner Dr. Decheng Yang (Pathology and Laboratory Medicine) University Examiner Additional Supervisory Committee Members: Dr. Stephen Lam (Medicine) Supervisory Committee Member iii Abstract Lung cancer remains the deadliest form of cancer, and less than half of lung adenocarcinoma (LUAD) patients harbour clinically actionable driver genes, emphasizing the need to explore alternative mechanisms of cancer gene deregulation. The advent of next generation sequencing has begun to reveal the functional importance of long non-coding RNAs (lncRNAs) in human cell biology, which can be exploited by tumours to drive the hallmarks of cancer. Due to their complex tertiary structure and unknown binding motifs there is a growing disparity between number of lncRNAs identified and those that have been functionally characterized. As such, lncRNAs deregulated in cancer may represent critical members of cancer pathways that could hold therapeutic applicability. The goal of this thesis is to identify lncRNAs important to LUAD biology, discover shared features and mechanisms used to regulate cancer driving protein coding genes, and evaluate the clinical relevance of these non-coding genes. We discover and investigate three major mechanisms harnesses by lncRNAs in LUAD: (i) cis-acting regulation of neighbouring genes, (ii) trans-acting regulation through sequence homology and (iii) regulation through shared miRNAs. This work uncovers evidence to suggest that alteration of lncRNAs is a major mechanism of cancer gene regulation in LUAD. Further characterization of these understudied gene regulatory mechanisms could lead to novel therapies that silence oncogenes or reactivate tumour suppressor genes. iv Lay Summary Lung cancer is a deadly disease with a 5-year survival rate of less than 15%. The biological mechanism of approximately 50% of lung cancer remains unknown and there is a need to identify new clinical targets. With recent advances in sequencing technology, we can now investigate previously unexplored areas of the tumour DNA, specifically the many overlooked genes that do not encode proteins called non-coding RNA genes. While several non-coding RNAs (ncRNAs) have been recently implicated in lung cancer development, the function of the vast majority of these ncRNAs remains unexplored. The research described here describes a methodology for identification and characterization of three different classes of ncRNA. Specifically, we find that these three classes of lncRNA are altered in lung cancer, where they regulate known cancer associated genes, and affect patient outcome. v Preface The UBC Ethics Board approval was obtained for the research presented in this thesis work. The ethics certificate numbers are as follows: EDRN H09-00008, CCSRI H09-00934, and W81XW-10-1-0634. The work in Chapter 3 has represents a manuscript in preparation, and has been presented at local, national, and international conferences during its preparation. The current author list for this manuscript in preparation is as follows: Greg L. Stewart, Adam P. Sage, Katey S.S. Enfield, Erin A. Marshall & Wan L. Lam (2019). As first author I was responsible for study design, and implementation, including primary data analysis, as well as in vitro analysis. Adam P. Sage assisted in data analysis, cell culture, construction of Figure 1.3 and editing. Katey S.S. Enfield and Erin A. Marshall provided invaluable advice for both in silico and in vitro analysis. This project was overseen and guided by Wan L. Lam. During its preparation Chapter 4 was presented at local, national, and international conferences and a version of Chapter 4 has been published in Frontiers in Genetics: Stewart GL, Enfield KSS, Sage AP, Martinez VD, Minatel BC, Pewarchuk ME, Marshall EA, Lam WL (2019). Aberrant expression of pseudogene-derived lncRNAs as an alternative mechanism of cancer gene regulation in lung adenocarcinoma. Front Genet. 6;10:138 As first author I was responsible for primary study design and implementation as well as the majority of all data analysis and writing. Katey S.S. Enfield provided vital feedback during design of the study, as well as advice for data analysis, and edited the manuscript. Adam P. Sage assisted in data analysis and figure design of Figure 2, in addition to editing. Victor D. Martinez assisted in processing of RAW RNA seq data files. Brenda C. Minatel assisted in analysis and vi generation of Figure 3. Michelle E Pewarchuk assisted with design of Figure 1. Erin A. Marshall provided vital feedback and editing. This project was guided and overseen by Wan L. Lam. During its preparation Chapter 5 was presented at local, national, and international conferences and a version of Chapter 5 has been published in PLOS One: Erin A. Marshall*, Greg L. Stewart*, Adam P. Sage, Wan L. Lam, Carolyn J. Brown (2019). Beyond sequence homology: Cellular biology limits the potential of XIST to act as a miRNA sponge. PLoS One. 14(8):e0221371. *Co-First Authorship As co-first author I was responsible for primary study design and implementation as well as the majority of all data analysis and writing. Erin A Marshall and I combined our respective expertise to design and implement this project, and both were heavily involved in data analysis, in vitro work, and writing. We were supported by Adam P. Sage who assisted in data analysis, figure design and manuscript editing. This project was overseen and guided by both Wan L Lam and Carolyn J. Brown. vii Table of Contents Abstract ......................................................................................................................................... iii Table of Contents ........................................................................................................................ vii List of Tables ............................................................................................................................... xii List of Figures ............................................................................................................................. xiii List of Abbreviations ...................................................................................................................xv Acknowledgements ......................................................................................................................xv Dedication ................................................................................................................................. xviii Chapter 1: Introduction ................................................................................................................1 1.1 Biology of the human lung ............................................................................................. 1 1.2 Lung cancer ..................................................................................................................... 2 1.3 Genetics of lung cancer ................................................................................................... 7 1.4 Molecular background and treatment ........................................................................... 11 1.5 Non-coding RNAs: a new frontier in gene regulation .................................................. 14 1.6 Small non-coding RNAs ............................................................................................... 15 1.6.1 What are small non-codings RNAs? ..................................................................... 15 1.6.2 microRNA function .............................................................................................. 17 1.6.3 microRNA biogenesis ........................................................................................... 19 1.6.4 Predictive tools...................................................................................................... 19 1.6.5 Small non-coding RNA in cancer and disease ...................................................... 20 1.6.6 small non-coding RNAs and drug resistance ........................................................ 22 1.6.7 small non-coding RNAs as clinical markers ......................................................... 25 viii 1.6.8 small non-coding RNAs as clinical targets ........................................................... 27 1.7 Long non-coding RNAs ................................................................................................ 28 1.7.1 An introduction to the wild world of long non-coding RNAs .............................. 28 1.7.2 Classes of long non-coding RNAs ........................................................................ 30 1.7.3 Long non-coding RNAs in cancer ........................................................................ 32 1.7.4 Long non-coding RNAs in the clinic .................................................................... 36 1.7.5 Challenges in long non-coding RNA characterization ......................................... 40 1.8 Thesis Rationale and objective ..................................................................................... 41 Chapter 2: Common methods .....................................................................................................42 2.1 Next generation sequencing of lung adenocarcinoma patient samples ........................ 42 2.1.1 Patient samples...................................................................................................... 42 2.1.2 Processing of RNA-sequencing data .................................................................... 42 2.2 Basic laboratory techniques .......................................................................................... 45 Chapter 3: A novel cis-acting long non-coding RNA controls HMGA1 expression in lung adenocarcinoma ...........................................................................................................................46 3.1 Introduction ................................................................................................................... 46 3.2 Materials and Methods .................................................................................................. 47 3.2.1 Sample collection and processing ......................................................................... 47 3.2.2 Gene Expression Analyses .................................................................................... 48 3.2.3 In vitro analyses .................................................................................................... 48 3.3 Results ........................................................................................................................... 50 3.3.1 cis-acting long non-coding RNAs are deregulated in LUAD ............................... 50 ix 3.3.2 Expression of HMGA1-lnc and HMGA1 are deregulated in lung adenocarcinoma……………………………......……………………………….52 3.3.3 HMGA1-lnc controls HMGA1 expression ............................................................ 55 3.4 Discussion ..................................................................................................................... 57 Chapter 4: Aberrant Expression of Pseudogene-derived lncRNAs as an Alternative Mechanism of Cancer Gene Regulation in Lung Adenocarcinoma. .......................................60 4.1 Introduction ................................................................................................................... 60 4.2 Methods......................................................................................................................... 61 4.2.1 Identification of long non-coding RNAs expressed from pseudogene loci and corresponding parent genes................................................................................................... 61 4.2.2 Statistical analysis ................................................................................................. 62 4.2.3 Clinical features .................................................................................................... 64 4.3 Results ........................................................................................................................... 67 4.3.1 Ѱ-lncRNA expression is deregulated in lung adenocarcinoma ............................ 67 4.3.2 Global patterns of Ѱ-lncRNA and parental gene expression ................................ 74 4.3.3 Ѱ-lncRNAs and their parent genes are associated with patient survival .............. 78 4.4 Discussion ..................................................................................................................... 84 4.5 Chapter Conclusions ..................................................................................................... 88 Chapter 5: An investigation of regulation of microRNA sponging, through the lens of XIST........................................................................................................................................................89 5.1 Introduction ................................................................................................................... 89 5.2 Methods............................................................................................................................... 92 5.2.1 Data processing ............................................................................................................ 92 x 5.2.2 Data analysis ................................................................................................................ 92 5.3 Identification of genes regulated by XIST through microRNA sponging .......................... 93 5.4 microRNAs targeting XIST exonic regions display stronger DMX relationships ............. 94 5.5 Chapter discussion ...................................................................................................... 100 Chapter 6: Conclusions .........................................................................................................103 6.1 Summary of thesis chapters ........................................................................................ 103 6.1.1 Overall summary of thesis findings .................................................................... 103 6.1.2 Summary of thesis Chapter 3 .............................................................................. 103 6.1.3 Summary of thesis Chapter 4 .............................................................................. 104 6.1.4 Summary of thesis Chapter 5 .............................................................................. 105 6.2 Strengths and limitations................................................................................................... 107 6.2.1 Strengths and limitations of Chapter 3................................................................ 107 6.2.1.1 Strengths ......................................................................................................... 107 6.2.1.1 Limitations ...................................................................................................... 108 6.2.2 Strengths and limitations of Chapter 4................................................................ 108 6.2.2.1 Strengths ......................................................................................................... 108 6.2.2.2 Limitations ...................................................................................................... 109 6.2.3 Strengths and limitations of Chapter 5................................................................ 110 6.2.3.1 Strengths ..................................................................................................... 110 6.2.3.2 Limitations ...................................................................................................... 111 6.3 Future directions ............................................................................................................... 111 6.3.1 Chapter 3 ............................................................................................................. 111 6.3.2 Chapter 4 ............................................................................................................. 112 xi 6.3.3 Chapter 5 ............................................................................................................. 113 6.3.4 Future directions for the non-coding field .......................................................... 115 References ...................................................................................................................................117 Appendices ..................................................................................................................................127 Appendix A Supplementary material...................................................................................... 127 A.1 Supplementary tables from Chapter 3................................................................. 127 Appendix B Description of published supplementary tables .................................................. 141 B.1 Description of published supplementary tables from Chapter 4 ......................... 141 B.2 Description of published supplementary tables from Chapter 5 ......................... 143 xii List of Tables Table 1.1 Histological classification of lung adenocarcinoma patient specimens ......................... 6 Table 1.2 Small non-coding RNA size and function ................................................................... 16 Table 2.1 Patient clinical characteristics of the discovery (BCCA), validation (TCGA), and survival analysis (KmPlotter) datasets. ......................................................................................... 44 Table 3.1 SiRNA sequences used to target HMGA1-lnc ............................................................. 49 Table 3.2 qRT-PCR probes used in Chapter 3 ............................................................................. 49 Table 3.3 LncRNAs deregulated in LUAD with cancer-associated neighbouring genes ............ 51 Table 4.1. Parent genes of deregulated Ѱ-lncRNA previously described in cancer literature ..... 75 Table 4.2 Associations between Ѱ-lncRNA, parent gene expression, and patient outcome ....... 80 Table A.1 Deregulated prospective cis-acting lncRNAs ........................................................... 127 xiii List of Figures Figure 1.1 Cellular anatomy of the human lungs .......................................................................... 1 Figure 1.2 Tumour stage in lung cancer patients at diagnosis. ...................................................... 3 Figure 1.2 Relative proportion of non-small cell lung cancer subtypes at diagnosis .................... 4 Figure 1.3 FBXW4 is deregulated in multiple genetic levels and is associated with ................. 10 patient survival. ............................................................................................................................. 10 Figure 1.4 Molecular subtypes lung adenocarcinoma defined by mutations and fusions of known driver genes. ...................................................................................................................... 13 Figure 1.5 Mechanism of inhibition for miRNAs and piRNAs. .................................................. 18 Figure 1.7 Changes in miRNA DNA copy number affect drug resistance in cancer cells. ......... 24 Figure 1.8 Addition of piRNAs improves risk stratification in 75 lung adenocarcinoma patients........................................................................................................................................................ 26 Figure 1.9 The many functions of long non-coding RNAs ......................................................... 29 Figure 1.10 Aberrantly expressed lncRNAs across human cancers. ........................................... 34 Figure 1.11 Function of Antisense Oligonucleotides (ASOs) ..................................................... 39 Figure 3.1 Expression of HMGA1 and HMGA1-lnc in lung adenocarcinoma ............................ 53 Figure 3.2 Expression of HMGA1 and HMGA1-lnc is associated with tumour stage ................. 54 Figure 3.3 Inhibition of HMGA1-lnc results in increases of HMGA1 expression ...................... 56 Figure 4.1 Summary of the regulatory mechanisms of Ѱ-lncRNAs and the analysis pipeline for the identification of their deregulation in lung adenocarcinoma. ................................................. 66 Figure 4.2 LncRNAs derived from pseudogene loci are significantly differentially expressed in lung adenocarcinoma compared to matched non-malignant lung tissue. ..................................... 70 xiv Figure 4.3 Genome-wide distribution of deregulated pseudogene-derived lncRNAs in lung adenocarcinoma. ........................................................................................................................... 73 Figure 4.4 Distribution of Spearman’s Correlation rho values for all Ѱ-lnc-parent-gene pairs in the TCGA dataset (n=391). ........................................................................................................... 77 Figure 4.5 Comparison of deregulated Ψ-lnc CTC-250I14.3 expression between Stage I tumors and tumors classified as Stage II and above using a Mann Whitney U-test (p-value ≤ 0.05). ..... 79 Figure 4.6 Associations of pseudogene-derived lncRNAs (upper row), their respective parent gene expression levels and their potential impact on patient outcome (middle row and bottom row). .............................................................................................................................................. 83 Figure 5.1 Number of published XIST-miRNA sponging manuscripts over time ...................... 91 Figure 5.2 Number of binding sites per sequence length of lncRNAs. ....................................... 95 Figure 5.3 Frequency of miRNA binding sites to specific intronic and exonic regions of XIST........................................................................................................................................................ 96 Figure 5.4 Comparison of miRNA exonic and intronic binding frequency. ............................... 97 Figure 5.5 Location of miRNA binding affects XIST-DMX relationships. ................................ 98 Figure 5.6 The number of target sequences each miRNA has on XIST. ..................................... 99 Figure 5.7 Number of shared miRNAs affects DMX and XIST expression correlations. ........ 100 xv List of Abbreviations 3’ UTR 3 prime untranslated region ALK Anaplastic Lymphoma Receptor Tyrosine Kinase ATCC American Type Culture Collection ATS American Thoracic Society B-H Benjimini-Hochberg BCCA British Columbia Cancer Agency (Renamed as BC Cancer in 2019) BP Base pair (nucleotides) BRAF B-Raf Proto-Oncogene CN Copy number DMX genes Defended from miRNA by XIST genes EGFR Epidermal Growth Factor Receptor ERS European Respiratory Society FC Fold change GISTIC Genomic Identification of Significant Targets in Cancer HER2 Tyrosine Kinase-Type Cell Surface Receptor HER2 HMGA1 High Mobility Group AT-Hook 1 IASLC International Association for the Study of Lung Cancer LNAs Locked nucleic acids lncRNA Long non-coding RNA LUAD Lung adenocarcinoma MAP2K1 Mitogen-Activated Protein Kinase Kinase 1 xvi MET MET Proto-Oncogene, Receptor Tyrosine Kinase miRNA MicroRNA mRNA Messenger RNA ncRNA Non-coding RNA NRAS Neuroblastoma RAS Viral Oncogene Homolog NSCLC Non small-cell lung cancer NTRK1 Neurotrophic Receptor Tyrosine Kinase 1 PIK3CA Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha piRNA PIWI-interacting RNA RET Ret Proto-Oncogene RISC RNA-induced silencing complex RNA Ribonucleic acid ROS1 ROS Proto-Oncogene 1, Receptor Tyrosine Kinase RPKM Reads per kilobase of exon model per million mapped reads RPM Reads per million mapped reads RT-qPCR Reverse Transcription Quantitative Polymerase Chain Reaction TCGA The Cancer Genome Atlas UTR Untranslated region XIST X Inactive Specific Transcript Ψ-lncRNA Pseudogene overlapping long non-coding RNA xvii Acknowledgements The many current and past members of the Lam lab were immensely helpful over the course of my program, providing multi-disciplinary expertise, advice, and friendship. This unique cast of characters were a treat to work with, and I thank them for their support over the years. Additionally, I am thankful and appreciative of the funding and scholarship support provided to me, and would like to acknowledge this support. The UBC Graduate entrance scholarship was a helpful start to my degree and the Interdisciplinary Oncology Program provided scholarship support multiple times over the course of my degree. I would also like to express my gratitude for The Canadian Institutes of Health Research Frederick Banting and Charles Best Canada Graduate Scholarship Doctoral Award and the UBC Four Year Fellowship Award. I would like to thank my supervisory committee, including Dr. Stephen Lam and Dr. Calum MacAulay for their expertise, helpful advice, and insight over the course of my degree. Additionally, this work would not have been possible if not for the immense support, encouragement, and guidance provided by my supervisor Dr. Wan Lam. xviii Dedication I would like to dedicate this thesis to my family, particularly my father Gary Stewart, who passed away from lung cancer before the start of my degree. His encouragement and genuine interest in the sciences instilled a curiosity in me that led me to become the person I am today. Dad, I hope this work would make you proud. Additionally, this wouldn’t have been possible without my mother and sister, who are major inspirational figures in my life, and remind me to work hard, eat well, and enjoy life. …Also my friends. They are pretty great too. 1 Chapter 1: Introduction 1.1 Biology of the human lung All cells in the human body require oxygen to function and therefore rely on the lungs for oxygen absorption into the bloodstream, and the removal of waste gasses, such as carbon dioxide. The lungs begin in the upper airways with the trachea (composed of columnar, squamous, goblet, basal and neuroendocrine cells) before dividing into the two main bronchi. Gas exchange takes place in the lower airways and lung periphery, where type I and II pneumocytes and club (Clara) cells make up the alveoli. The lungs are supported by an extensive vascular network to allow for large-scale transportation of oxygen rich cells to the heart, as well as the flow of unoxygenated blood back to the lungs. To accomplish this cellular gas exchange, the lungs have a massive surface area that is organized in a tree-like system of airways. Figure 1.1 Cellular anatomy of the human lungs The different anatomical regions of the lungs are composed of unique sets of cells. 2 1.2 Lung cancer Lung cancer is the most commonly diagnosed cancer for both men and women in Canada, and alarmingly, 78 Canadians are diagnosed with lung cancer every day. In 2017 alone, 28600 Canadians were diagnosed with the disease. In addition to being widespread, lung cancer is one of the deadliest forms of cancer, with 21100 Canadians succumbing to the disease in 2017 alone. Furthermore, lung cancer is also a very aggressive disease, as those diagnosed have an average 5-year survival rate of only 19% (http://www.cancer.ca/en/cancer-information/cancer-type/lung/statistics/?region=pe#ixzz5ijvFJcpl). Cancer is typically a disease of the elderly and lung cancer is no exception. Lung cancer cases under the age of 45 are rare and the typical age of diagnosis is over 60. Smoking is the major lung cancer risk factor and is preventable, but even if excluded, never smoker lung cancer is the 7th largest cause of cancer death, and therefore remains a huge health concern 1 2. Additionally, never smoker lung cancer is on the rise and former smokers, who make up a large portion of patients (35%), remain at increased risk of disease. The lungs lack pain receptors so many patients are asymptomatic until the tumour size begins to impact lung function and may present with shortness of breath or presence of blood when coughing. As such, the majority of lung cancer patients are diagnosed with late stage disease (Figure 1.1), where tumours are invasive or have metastasized to other areas of the body. Lung cancer typically metastasizes to the bones, liver, and brain, which are all challenging regions to perform surgery 3. Tumours that have metastasized are notoriously challenging to identify, resect, and effectively treat. Additionally, metastatic tumours may have developed new molecular drivers thus not respond to the same therapy as the primary tumour. Metastasis and complications arising from it are the most common cause of cancer associated death. 3 Figure 1.2 Tumour stage in lung cancer patients at diagnosis. Lung Cancer is typically discovered at more aggressive disease stages at the time of diagnosis. Stage III and IV disease are the most commonly diagnosed, when the disease has become invasive, and the patient presents with symptoms. Adapted from Canadian cancer society (http://www.cancer.ca/en/cancer-information/cancer-type/lung/statistics/?region=pe#ixzz5ijvFJcpl). 4 Figure 1.2 Relative proportion of non-small cell lung cancer subtypes at diagnosis Non-small cell lung cancer is composed of adenocarcinoma (LUAD), squamous cell carcinoma (SqCC), and large cell carcinoma. Small cell lung cancer (not shown) makes up roughly 15% of all diagnosed lung cancers. Adapted from Canadian cancer society (http://www.cancer.ca/en/cancer-information/cancer-type/lung/statistics/?region=pe#ixzz5ijvFJcpl). 5 There are two main types of lung cancer; small cell (15%), and non-small cell lung cancer (85%). Small cell lung cancer is almost entirely composed of patients with smoking history and is an incredibly aggressive disease, with a 5 year survival of under 10% 4,5. The most common type of lung cancer, Non-small cell lung cancer (NSCLC) is comprised of 3 subtypes; large cell carcinoma, squamous cell carcinoma (SCC), and lung adenocarcinoma (LUAD). SCC is located in the upper airways and may be composed of neuroendocrine cells 6 (Figure 1.2). LUAD is the most common type of lung cancer and is the sub-type focused on in this thesis work. LUAD is found in the lung periphery and is thought to arise from club cells (a.k.a Clara cells) or type II pneumocytes. In 2014 a new international multidisciplinary classification system (IASLC/ATS/ERS) revised the classifications for LUAD to include 11 sub-subtypes 7 (Table 1.1). The many histological subtypes of LUAD reflect the diverse genetic background of these tumours. 6 Table 1.1 Histological classification of lung adenocarcinoma patient specimens Adenocarcinoma in situ (≤3 cm formerly BAC) Nonmucinous Mucinous Mixed mucinous/nonmucinous Minimally invasive adenocarcinoma (≤3 cm lepidic predominant tumor with ≤5 mm invasion) Nonmucinous Mucinous Mixed mucinous/nonmucinous Invasive adenocarcinoma Lepidic predominant (formerly nonmucinous BAC pattern, with >5 mm invasion) Acinar predominant Papillary predominant Micropapillary predominant Solid predominant with mucin production Variants of invasive adenocarcinoma Invasive mucinous adenocarcinoma (formerly mucinous BAC) Colloid Fetal (low and high grade) 7 1.3 Genetics of lung cancer Cancer cell genomes can be drastically altered from those of normal cells, and lung cancer is no exception. Lung cancer tumours have widespread genetic alterations and while late stage diagnosis is one reason for the high mortality rate of lung cancer, another is due to the heterogenous molecular background of the disease, which makes these tumours difficult to treat. Genetic and epigenetic alterations are selected for in order to favour the activation of pro-growth genes, and the de-activation of regulatory genes. LUAD tumours can deploy many mechanisms to achieve this, such as mutations, DNA methylation, changes in DNA copy number, fusion genes, and deregulated gene expression. Lung cancer has one of the highest mutational burdens of any cancer, which can make it difficult to identify functional driver mutations. Nevertheless, many genetic drivers of LUAD have been identified, including activating mutations to oncogenes, such as EGFR, as well as de-activating mutations to tumour suppressor genes, such as TP53. Fusion genes are created from translocations of the genome that cause the merging of previously separated protein domains, and this process can result in new oncogenes. ALK, ROS1, and RET are all examples of oncogenic fusion genes present in NSCLC. DNA copy number alterations involve amplifications or deletions of regions of DNA. These alterations can range from small focal events to large scale events, effecting entire chromosome arms. Oncogenes frequently have gains (>2 copies) or amplifications (>4 copies) in copy number resulting in increased gene expression. In lung cancer, frequent recurrent gene amplifications are seen for oncogenes such as EGFR, MET, and ERBB2. Growth suppressive genes are also regularly lost (less than 2 copies) or deleted entirely. Many key growth control 8 genes must be fully deleted for a cell to become cancerous, as loss of one gene copy is not sufficient for tumourigenesis. In addition to genetic alterations, epigenetic alterations are also common. Epigenetic alterations are heritable features of cancer cells which can deregulate genes without changing the DNA sequence. DNA methylation is a common epigenetic alteration in many tumour types that functions through silencing genomic loci (hypermethylation) and can be reversed to release repression of a region (hypomethylation). In cancer, we typically see widespread hypomethylation of DNA, allowing for expression of many genes and regions that are usually silenced. Additionally, promoter specific hypermethylation of genes that may act as tumour suppressors is a common feature in many cancers. In NSCLC, frequent promoter hypermethylation is observed for the tumour suppressor genes CDKN2A (p16), MGMT, and RB1 8 9. Deregulated expression of genes can occur through a vast number of mechanisms and is not limited to DNA copy number alterations or changes in the methylome. For example, changes in transcription factor expression can lead to the deregulation of hundreds of genes. In general, tumours favor increased oncogene expression, such as EGFR, and MET and decreased expression of tumour suppressive genes, such as LKB1 in NSCLC 10. Some genes that are affected by multiple different genetic forms of alteration may not appear to be frequently altered if only one type of analysis is performed. By combining different dimensions of genetic data, we are able to identify genes that are recurrently altered by a variety of mechanisms. For example, in previously published work we performed a multiplatform analysis on the gene FBXW4. We analyzed FBXW4 DNA copy number, mutation, and expression in a number of cancers including lung cancer. While in any single dimension of data alteration of FBXW4 does not appear to be a 9 frequent event in NSCLC, using a multi-platform approach, we find that FBXW4 is mutated, lost and under-expressed in a variety of human cancer cell lines and clinical patient samples, and is associated with lung cancer patient survival (Figure 1.3) 11. This study highlights the utility of combining multidimensional data to identify a novel tumor suppressor gene that would not have met discovery thresholds by using one analysis method alone. Other studies have taken this approach to identify cancer driving genes in a number of other tumour types, and recently, many of these have been the subject of interest for the development of targeted therapies 12 13. 10 Figure 1.3 FBXW4 is deregulated in multiple genetic levels and is associated with patient survival. FBXW4 expression is significantly lower in tumours with FBXW4 copy number loss or deletion (A). Loss of FBXW4 expression is associated with poor survival (B). Adapted from our manuscript published in PLoS ONE “The novel ubiquitin ligase complex, SCF(Fbxw4), interacts with the COP9 signalosome in an F-box dependent manner, is mutated, lost and under-expressed in human cancers”11. A B11 1.4 Molecular background and treatment After sequencing the lung cancer genome, it was discovered that while many tumours may appear to be of similar histology, they can have vastly different molecular subtypes. While the high burden of genomic alterations in lung cancer complicates the separation of molecular drivers of the disease from passenger alterations, several recurrent cancer driving alterations have been identified. Traditional treatment has relied on non-specific targeting of fast growing cells, such as platinum therapy or demethylating agents. The identification of molecular drivers has lead to the development of targeted therapies that have greatly benefited subsets of patients who harbor these specific alterations. LUAD is defined by alterations that activate oncogenes such as EGFR, KRAS, ERBB4, KDR, FGFR4, and NTRK (Figure 1.4) 14 15. Additionally, growth suppressive genes such as RB1, TP53, KEAP1, NF1, STK11, and CDKN2A, are commonly de-activated or abrogated. Interestingly, many of these molecular drivers are associated with patient characteristics such as race and gender, and can be defined by smoking status. For example, EGFR is frequently mutated in Asian females with no smoking history, while patients who smoke are less likely to have tumours harbouring EGFR mutations and more likely to harbor mutations in KRAS 15. Additionally, smoking is associated with a significant increase in genome wide mutational burden in patients with LUAD 16 17. These molecular driver genes, and by association the up and downstream components of their signaling pathways, have become the focus for targeted drug development. For example, several successful targeted therapies include antibodies and tyrosine kinase inhibitors that were developed against mutant EGFR. In addition, fusion genes are attractive therapeutic targets due 12 to their cancer-specific expression, such as therapies developed to target the EML4-ALK fusion protein. While therapies targeting oncogenic protein coding genes have been successful for many patients, this type of approach may not work for all tumour types. Some driver mutations, such as KRAS, remain un-druggable despite numerous attempts. Additionally, only a fraction of patients harbor actionable druggable targets; targetable EGFR mutations are present in 15-20% of patients and EML4-ALK fusions are only present in 5-7%. Furthermore, there are currently no effective clinical strategies to turn back on silenced tumour suppressor genes. This highlights the need to explore alternative options, both to identify molecular drivers of the large subset of tumours with no known drivers, as well as to identify unknown members of known, un-targeted pathways that may serve as novel clinical targets. 13 Figure 1.4 Molecular subtypes lung adenocarcinoma defined by mutations and fusions of known driver genes. While lung adenocarcinoma is made up of many histological subtypes, the molecular alterations driving these tumours are equally as diverse. Several of these molecular driver genes are now targettable and used in the clinic, however the vast majority of tumours still remain un-druggable. Adapted from: 2018 Molecular Profiling of Lung Cancer, My Cancer Genome Website: https://www.mycancergenome.org/content/disease/lung-cancer. 18. 14 1.5 Non-coding RNAs: a new frontier in gene regulation The central dogma of genetics defines the flow of genetic information in the cell, beginning with DNA transcribed into messenger RNA, which in turn is translated into a functional protein product. Because of this core tenant of genetics, when RNA transcripts that did not code for protein were observed, they were generally regarded as non-functional “genomic junk.” Although recurring alterations were observed in regions that had no protein coding genes, which lead to the discovery of a handful of functional non-coding genes, these were thought to be the exception to this rule 19 20 21. The advent of next generation sequencing has led to the discovery of large-scale non-coding RNA transcription. It has become apparent that while up to 90% of the human genome is transcribed, only about 1.2% of the genome codes for proteins 22. Since the discovery of their prevalent transcription, these non-coding RNAs (ncRNAs) have been observed to play critical roles in all aspects of cell biology. Defined simply as transcribed RNA that does not code for protein, non-coding RNAs have revealed themselves to be involved in the regulation of DNA, proteins, and other RNA species. They are often tissue and cell type specific, and have been implicated in numerous cellular processes, including many involved in disease pathology. This includes cancer, where non-coding RNAs have been associated with multiple disease phenotypes, as well as appearing as promising biomarkers and potential therapeutic targets 22. These ncRNAs are split up into two main classes based entirely on sequence length, long, and short ncRNAs. 15 1.6 Small non-coding RNAs 1.6.1 What are small non-codings RNAs? Small non-coding RNAs (sncRNAs) are generally defined as any ncRNA under 200 nucleotides in length. There are many classes of sncRNAs, usually defined based on their length, expression, or folding patterns. These classes include micro RNAs (miRNAs), piwi-interacting RNAs (piRNAs), and small nucleolar and nuclear RNAs (snoRNAs and snRNAs, respectively). SnoRNAs and snRNAs are the least well studied class of sncRNAs. While small nucleolar have been studied much less than miRNAs, they are lowly expressed and have functions pertaining to RNA regulation and have been shown to play a role in the early stage of pre-mRNA processing. They can be 150 nucleotides long, are transcribed by pol II or III, and are named for their localization to cajal bodies and speckles within the cell nucleus. In contrast, snoRNAs are named after their expression in the nucleolus. Transcribed by RNA pol II, they contain sequences complementary to other RNAs and are thought to act as RNA guide sequences, leading to modification of other RNA species, including ribosome components. snoRNAs have been associated with carcinogenesis, but the mechanisms remain mostly unknown 23 24. PiRNAs are shorter than snoRNAs and snRNAs, having lengths 25–30 bp long 25. PiRNAs were discovered in germ cells, where they were thought to have functioned exclusively, but recently they have been observed in somatic tissues with conserved biological functions 26 27. PiRNAs function similarly to several other classes of small RNA, regulating gene expression through a small RNA guided mechanism that targets complementary DNA sequences. PiRNAs bind a class of proteins called PIWI proteins that are part of the RISC complex and guide this complex to complementary transcripts where the complex performs its canonical function to 16 inhibit target sequences 28. The function of piRNAs in germ cells was shown to be silencing of transposable elements and maintenance of genomic stability 29. Recent work has shown additional functions such as epigenetic activation and mRNA transcript silencing, as well as cellular responses to certain environmental agents 30 31 32 33. MiRNAs are the most well studied and widely expressed class of small ncRNA. First discovered by accident in the early 90’s, Scientists studying C. elegans, found a vital development gene called lin-4, and were surprised when they could not identify a protein product. Initially assumed to be an inhibitory protein coding gene, they discovered that it encoded two small non-coding genes. It wasn’t until 7 years later that the first human miRNA was discovered (let-7). Since then over 2500 miRNAs have been discovered in the human genome 34 35. Ranging from 19–24 nucleotides in length, they are shorter than piRNAs, snoRNAs, or snRNAs, and have a conserved hairpin structure. miRNAs also have a 7-8 base long “seed” sequence at the 5’ end of the miRNA transcript. MiRNAs are evenly spread across the genome with 50% in non-coding transcripts and 50% located in introns of protein coding regions 36. MiRNAs have become widespread in mammalian cell biology for their ability to regulate protein coding genes, and they will be the sncRNA most focused on in this thesis work 37. Table 1.2 Small non-coding RNA size and function TYPE SIZE (NT) KNOWN FUNCTION MIRNA 19-24 mRNA inhibitors PIRNA 25-30 DNA mediated gene regulation SNRNA ~150 Pre-mRNA processing SNORNA 60+ RNA guide sequences 17 1.6.2 microRNA function MiRNAs function to inhibit translation of protein coding mRNA targets. They do this by guiding a protein complex called RISC to specific mRNA target sequences. Each miRNA targets mRNAs with complementary sequences in their 3’ untranslated regions (UTR). However, full miRNA sequence complementarity is not required, as the miRNAs “seed” sequence sufficient to target mRNAs. As the seed sequence is only 7-8 bases long, the number of potential target genes for a particular miRNA in the genome is immense. In fact, each miRNA may be able to target hundreds of genes, and they have been described as master regulators of the genome because of their ability to target a wide variety of genes simultaneously 38. MiRNAs with identical sequences are expressed from multiple places in the genome simultaneously. In these cases, they are referred to as the same miRNA, but are given a number to denote their location in the genome. For example, miR-29b-1 is on chromosome 7, and miR-29b-2 is on chromosome 1, and both have the same sequence. MiRNAs can also be referred to as the same “family,” which is when they are derived from the same ancestor and have the same seed sequence. As such, families can target overlapping and similar sets of target genes 39. 18 Figure 1.5 Mechanism of inhibition for miRNAs and piRNAs. miRNAs join with the RISC complex and bind to mRNAs containing complementary sequences in the 3’UTR region. The mRNA is then either degraded or prevented from being translated into protein (below). PiRNAs join with the PIWI complex and bind to complementary DNA sequences to enact their function. This can result in changing the epigenomic state of the DNA, leading to effects like inhibition of translation through DNA methylation (above). Adapted from our manuscript “Deregulation of small non-coding RNAs at the DLK1-DIO3 imprinted locus predicts lung cancer patient outcome”, published in Oncotarget 40. 19 1.6.3 microRNA biogenesis MiRNA biogenesis has been studied extensively and is described in the following steps: 1. miRNA transcription: miRNAs are transcribed by polymerase 2 into 100+ nucleotide long primary transcripts with poly A tails known as pri-miRNAs. 2. pre-miRNA processing: pri-miRNAs are processed by a large protein RNaseIII complex called DGCR8-DROSHA into 70-120 nucleotide sequences known as pre-miRNAs. 3. Cytoplasmic export: pre-miRNAs are shuttled into the cytoplasm by Exportin 5 4. Cleavage into miRNA duplex: RNase III enzyme DICER cleaves pre-miRs into mature double stranded miRNAs. 5. Incorporation into RISC: 1 of 2 mature strands becomes a guide RNA for RISC complex and guides the complex to specific targets based on the miRNAs sequence 41. 6. Binding of RISC to target mRNA: Once bound, RISC causes the degradation of the mRNA transcript or prevents translation through steric hindrance. 1.6.4 Predictive tools The short length and conservation of miRNAs seven base target sequences make them ideal for computational prediction of mRNA binding targets, and since their discovery, there have been great advancements in the field of miRNA target prediction. Programs have been designed to search for targets by identifying 6-8mer sequences complementary to miRNAs seeds in the 3’UTRs of mRNAs. These in silico analyses consider sequence complementarity and conservation, as well as binding energy of complementary bases to predict which miRNAs will target which mRNAs. These tools have become vital for researchers studying miRNA-mRNA biology. More recent versions include increased stringency and can take experimentally 20 validated interactions into account 42. However, miRNAs are promiscuous, therefore it can be difficult to tell which of the many predicted interactions are prioritized in a complex biological system. 1.6.5 Small non-coding RNA in cancer and disease Because miRNAs are inhibitory molecules that target a vast number of genes in the genome, they can be harnessed by tumours to provide growth advantages. As such, they can be both oncogenic and tumour suppressive. MiRNAs that target tumour suppressor genes are often overexpressed in human cancers. Alternatively, miRNAs that target growth genes are often downregulated in tumours. MiRNAs can be affected by DNA level alterations similarly to protein coding genes, and tumours can take advantage of this. For example, the first tumour suppressor miRNA in humans was found by researchers searching for a canonical protein coding tumour suppressor. While searching for a tumour suppressor in the frequently deleted 13q14 region of B-cell leukemias, Calin and Croce discovered two tumour suppressive miRNAs, miR-15a and miR-16-1, instead of a protein coding gene 34 43. Shortly after this the first oncogenic miRNA was discovered in human lymphomas when a frequently overexpressed region was found to contain a cluster of miRNAs. This cluster of miRNAs, known as miR-17-92, is one of the best known examples of sncRNAs driving cancer 44. Since then miRNAs have been implicated in each of the hallmarks of cancer, and validated examples in a variety of cancer types are detailed below: Sustaining proliferative signaling: Unchecked cell growth is a key feature of cancer cells, and the oncogenes that drive this growth are often the most sought-after clinical targets. EGFR is one 21 of the most commonly deregulated oncogenes in LUAD and happens to be targeted by a number of miRNAs including miR-7, and miR-34 45 46. Activating invasion and metastasis: miRNAs have been shown to play a role in the spread of cancers from local invasion to systemic metastasis. Downregulation of the E-cadherin gene has been associated with the loss of cell adhesion leading to increased cell motility, and miR-200 has been shown to target this gene and be upregulated in lung cancer cells 47. Resisting cell-death: The caspase family of protease enzymes are a vital part of programmed cell death, and miRNAs have been shown to be utilized by tumours to circumvent this process. For example, miR-519a-3p targets Caspase-8 in breast cancer cells to avoid cell death induced by apoptotic stimuli and may also be associated with resistance to treatment 48. Genome instability: Compared to normal cells, cancer cells are saturated with genomic alterations and mutations, commonly a result of deficiency in the DNA damage response pathway (DDR). miRNAs have been both shown to be involved in the maintenance of genome integrity, as well as harnessed by cancers to downregulate genes involved in this process. In ovarian cancer miRNAs have been shown to both regulate DDR and predict the outcome of patients treated with DNA adduct causing platinum treatment 49. Evading growth suppressors: Another key tenant of cancer cells is the inactivation of cellular growth checkpoints. The well-known tumour suppressor gene TP53 is mutated, deleted, or downregulated frequently across many cancer types, including lung cancer. In lung cancer cells the miRNAs miR-641 and miR-660 have been shown to impact TP53 through downregulation of an upstream regulator, MDM2 50 51. Inducing angiogenesis: Increased vascularization is necessary in order to support the increased oxygen and nutrient requirements of rapidly growing tumours. The VEGF family of genes is 22 most commonly associated with this phenotype and miRNAs have been shown to be involved this process. For example, miR-494 downregulates the well-known tumour suppressor PTEN, which has anti-VEGF functions 52. Avoiding immune destruction: The complex interplay between tumour cells and the immune system has been recognized as a vital aspect of cancer biology and inspired a new generation of cancer drugs. Recently in lung cancer the PD-L1/PD-1 pathway has become a clinical target for certain immunogenic tumours. Interestingly, miRNAs have been recently shown to be involved in this pathway. For example, miR-34 targets the 3’UTR of PD-L1 and may suppress its translation into protein 53. Enabling replicative immortality: While normal cells have limited growth potential due to the shortening of telomeres after each replication, cancer cells find ways to skirt this limitation. hTERT, the catalytic subunit of telomerase has been shown to be regulated by miRNAs in several tumour types, such as miR-512-5p in head and neck cancer 54. Deregulation of cellular energetics: Fast growing and energy demanding cancer cells can reprogram metabolic pathways to bypass oxidative phosphorylation and favor glycolysis. One of the first steps in glycolysis revolves around the entry of glucose into the cells, which is primarily done by glucose transporter proteins known as GLUTs. miRNAs have been known to modulate the expression of these transporters in many cancer types. For example, in oral squamous carcinoma miR-340 targets GLUT1 and is frequently upregulated 55. 1.6.6 small non-coding RNAs and drug resistance A major issue in the treatment of cancer is a tumour becoming resistant to chemotherapeutic drugs. Small non-coding RNAs, including miRNAs, have been recently shown 23 to be associated with resistance or sensitivity to many cancers, including lung cancer. For example, miR-92a-2 has been proposed to be predictive of chemotherapy resistance in small cell lung cancer (SCLC) 56. While measuring pre-treatment expression of miRNAs is commonplace and can be used to predict outcome, drug resistance is hard to quantify within a patient. Post-treatment samples are infrequently taken as they are rarely of clinical benefit to the patient. As such cell models can be a useful tool to directly quantify molecular features that affect a cancer cells response to chemotherapeutics. In previously published work our laboratory has described the use of cell line data from multiple institutions to identify recurrent alterations to miRNA loci that were associated with resistance to chemotherapy. By integrating DNA CN and miRNA expression profiles we identified recurrent alterations in lung cancer cell lines that were either highly sensitive or highly resistant to 18 different chemotherapeutics. This lead to the discovery of several miRNAs that were frequently gained and overexpressed in chemotherapy resistant cell lines 57. For example, we identified miR-10b to display frequent DNA copy number gains, be overexpressed, as well as resistant to the chemotherapeutic proteasome inhibitor MG-132. Interestingly a predicted target of miR-10b, RAD1, is a component of the nucleotide excision repair complex, which is known to play an important role in chemotherapy response. We found that RAD1 is significantly downregulated in the same resistant cell lines where miR-10b is upregulated (Figure 1.7). Additionally, predicted targets of other resistance-associated miRNAs included genes in DNA replication and repair pathways, which are pathways known to be involved in chemotherapy resistance. This study emphasizes the potential impact of miRNAs in cellular response to therapies used in the clinic. Chemotherapy resistance-associated miRNAs could potentially be used as predictive biomarkers for selecting patients most likely to respond to specific therapies. 24 Figure 1.7 Changes in miRNA DNA copy number affect drug resistance in cancer cells. Copy number alterations in cell lines resistant to MG-132 (above) reveal miR-10b to be frequently gained. Similarly, miR-10b expression is significantly increased in resistant cell lines, while target gene RAD1 expression is significantly decreased, suggesting it may be inhibited by miR-10b (below). This figure is from our manuscript “MicroRNA Gene Dosage Alterations and Drug Response in Lung Cancer” published in the Journal of Biomedicine and Biotechnology 57. 25 1.6.7 small non-coding RNAs as clinical markers Biomarkers have become a vital tool in the clinic, as a way to both non-invasively predict disease severity as well as predict response to treatment. In addition to being present in primary tumours, sncRNAs have been found in the circulation, where they can be detected less invasively. They can be released through a variety of mechanisms, including exomes, apoptosis, and necrosis. MiRNAs have also been found to be more stable than protein coding genes and can remain stable in paraffin embedded tissue 58 59. As such, there are many examples of miRNAs being used as clinical markers. For example, miR-7 has been proposed as a prognostic biomarker in LUAD, where low levels of the tumour suppressive miRNA let-7 were shown to be associated with significantly shorter survival time 60. While expression of a single miRNA can be useful, one major limitation is sequence overlap between healthy and diseased tissue. High sensitivity and specificity are the most important criteria for clinical application of diagnostic or prognostic biomarkers, as without this there is an increased chance of false negative or false positive diagnosis, which could negatively affect patient care 61. Panels of miRNAs have been shown to be more effective biomarkers. For example, individual genes from the DLK1-DIO3 locus have been associated with LUAD outcome and a combined signature of three miRNAs has been shown to better predict overall survival after tumour resection 62. In a previous study our laboratory sought to determine whether consideration of other sncRNAs could improve predictive panels of miRNAs. We interrogated two independent datasets of LUAD and LUSC sequencing data for sncRNA expression at the DLK1-DIO3 locus. We found that seven of the 138 piRNAs encoded at the locus were expressed. When we incorporated four of the seven piRNAs into the existing 3-miRNA survival signature we discovered that it was able to better stratify patients into risk groups (Figure 1.8). While the three 26 miRNA signature could not stratify patients into risk groups in LUSC, we found the new seven sncRNA signature were able to effectively predict risk. These results highlight the clinical utility of incorporating piRNAs and other understudied sncRNA species into miRNA predictive panels. Figure 1.8 Addition of piRNAs improves risk stratification in 75 lung adenocarcinoma patients. Kaplan-Meier curves of (A) a miRNA miRNA only signature and (B) a signature with the addition of piRNAs. High risk is shown as red, intermediate risk as green, and low risk groups are shown as blue. Log-rank p-values of select survival comparisons are shown. (C) Patients re-classified by the addition of the miRNA+piRNA signature. Adapted from our manuscript “Deregulation of small non-coding RNAs at the DLK1-DIO3 imprinted locus predicts lung cancer patient outcome”, published in Oncotarget 40. 27 1.6.8 small non-coding RNAs as clinical targets In addition to their use as potential biomarkers and indicators of chemotherapy resistance, snRNAs have shown promise as direct clinical targets. As the most well studied class of sncRNAs, miRNAs have been the primary focus for therapeutic targets. Thus far, most miRNA based therapeutics fit into two categories: miRNA inhibitors and miRNA replacement therapy. MiRNAs inhibitors work based on the hypothesis that targeting oncogenic miRNAs for subsequent downregulation will either kill or slow down the growth of tumour cells. Some examples of types of miRNA inhibitors include: miRNA locked nucleic acids (LNAs), miRNA agonists, and miRNA specific antisense oligonucleotides. These therapies target miRNAs for degradation or inhibition of function. A therapeutic antagonist has been developed against miR-10b for use in patients in glioblastoma and is currently in pre-clinical development 63. MiRNA replacement therapy comprises of delivery of synthetic or exogenous tumour suppressive miRNAs into cancer cells. As these miRNAs have been downregulated in order to prevent inhibition of important oncogenes, these therapies attempt to re-introduce these miRNAs to downregulate oncogenes, and re-activate growth control signaling. Several of these miRNA replacement therapies have entered clinical trials. For example, a miR-34 mimic entered human clinical trials for patients with advanced or metastatic liver cancer, and let-7 mimics have been developed to treat a variety of solid cancers including LUAD 64 65. There are still problems to overcome with these novel therapies, such as delivery to cancer cells, accumulation in the liver, low bioavailability, and off-target effects. 28 1.7 Long non-coding RNAs 1.7.1 An introduction to the wild world of long non-coding RNAs The longer and more complex cousins of small ncRNAs are known as long non-coding RNAs (lncRNAs). They are defined as transcripts that do not encode for protein that exceed 200 base pairs in length. While a handful of lncRNAs were characterized before the human genome was decoded, such as the female specific X-chromosome silencing XIST, these were thought to be rare. Once sequencing studies revealed that only 2% of genes in the human genome were protein coding, the widespread expression of lncRNAs became clear. lncRNAs have many functions and are able to regulate proteins, DNA, and other RNA species. They have been found to be more tissue specific than protein coding genes, localize to different cellular compartments, and can contain multiple functional domains. lncRNAs have been shown to be involved in many vital cellular processes, including chromatin remodeling, splicing, protein complex formation, transport and localization, translation, miRNA sequestration, and regulation of transcription (Figure 1.9) 66 67. 29 Figure 1.9 The many functions of long non-coding RNAs Since their discovery lncRNAs have been observed to be involved in regulating DNA, proteins, and other RNAs. Several examples of lncRNA functions are seen above. Adapted from Gutschner et al. 2012 66. 30 1.7.2 Classes of long non-coding RNAs Due to their complexity, lncRNA classes are more fluid than small ncRNAs, and new functions are continually being discovered. However, some common functions are shared and can useful to classify lncRNAs. The two primary categories of lncRNAs focused on in this thesis are; cis-acting: those that enact function on their transcriptional loci or neighboring genes, and trans-acting: those that enact function on genome wide targets. The first cis-acting lncRNAs were a subgroup called natural antisense transcripts (NATs). Their function was thought to be derived from the protein coding gene they overlapped with. An example of this is the lncRNA Kcnq1ot1 which was discovered to silence overlapping protein coding Kcnq1 when transcribed 68. Later it was discovered that lncRNAs could function to not only regulate overlapping genes, but neighbouring genes and entire transcriptional loci, and that these lncRNAs are better defined as cis-acting. For example, the lncRNA XIST, originally thought of as a NAT, silences the entire duplicated X chromosome in females. To positively or negatively regulate their neighbors, cis-acting lncRNAs have been observed to use a handful of different mechanisms, such as head-to-head transcription blockage and recruitment of protein complexes, such as methylation and chromatin remodeling complexes. Head to head transcriptional interference occurs when the transcription of a lncRNA itself causes inhibition of overlapping genes. An example of this is the lncRNA AIRN; when AIRN recruits RNAPII, it reduces the ability of overlapping IGF2R to be transcribed 69. Interactions with chromatin remodeling complexes can result in selective activation or repression of specific gene loci. For example, the lncRNA ANRIL is responsible for recruiting the PRC2 chromatin remodeling complex to silence the neighboring CDKN2A/B locus 70. Another feature of cis-acting lncRNAs 31 is that they commonly localize to the nucleus and may not require many transcripts to cause large regulatory affects 71. In contrast, trans-acting lncRNAs regulate genes not associated with their transcriptional loci. This can refer to many possible types of gene regulation including: interacting with other RNAs, miRNA-based regulation, and recruiting protein complexes to distant transcriptional loci. Similarly, to lncRNAs acting in cis, the recruitment of protein complexes to transcriptional loci is a major method of regulation in trans. For example, one of the most widely studied lncRNAs, HOTAIR, is transcribed from the HOXC locus on chromosome 12 and travels to the HOXD locus on chromosome 2, where it recruits PRC2 to repress transcription 72. lncRNAs can also interact with other RNAs in order to improve or decrease stability of a transcript. For example, the lncRNA LAST stabilizes Cyclin D1 by recruiting the CNBP protein to the Cyclin D1 mRNA transcript 73. Another example is the lncRNA BACE1-AS, which binds to the BACE1 mRNA in the cytoplasm to enhance the stability of the protein coding gene, in part by blocking binding of miR-485-5p 74 75. Interactions between lncRNAs and miRNAs are thought to be a common mechanism of gene regulation. Numerous studies have demonstrated that a lncRNA can regulate an mRNA transcript by binding a shared miRNA, thereby releasing the mRNA from miRNA-mediated inhibition. This type of regulation is known as miRNA “sponging”. LncRNAs that may be the best candidates for miRNA sponging are lncRNAs expressed from pseudogene loci. Originally formed through duplication events or reverse transcription of an mRNA transcript, pseudogenes are relatives of protein coding genes littered throughout the human genome. Throughout the course of human evolution these redundant genes have lost the ability to code for protein due to mutations resulting in frameshifts and premature stop codons. However, these pseudogenes can 32 maintain a high degree of similarity to their protein coding relatives when they are expressed, and this degree of similarity means that they may contain several of the same miRNA binding sites as their parent genes. For example, the pseudogene PTENP1 shares many of the same miRNA binding sites as its parent protein coding gene PTEN. When expressed, PTENP1 positively regulates PTEN expression by acting as a decoy for these shared miRNAs. Pseudogene derived lncRNAs have also been observed to recruit protein complexes to the transcriptional loci of their parent gene. For example, the PTENP1-AS1 is able to recruit PRC2 to the PTEN locus transcriptional loci and silence the gene 76. Sponge-based regulation is an increasingly popular topic. Recently, researchers have speculated that all RNAs and miRNAs may regulate each other; this theory has been referred to as the competing endogenous RNAs (CERNA) hypothesis. According to CERNA, all miRNAs and their target genes have a reciprocal relationship. miRNAs are titrated by target binding sites, and that just as genes containing target sites are regulated by miRNAs, miRNAs are regulated by genes containing their target sites 77. However, this theory is controversial as the multi gene-miRNA-target site relationships predicted through CERNA largely lack sufficient validation in biological systems. 1.7.3 Long non-coding RNAs in cancer With such a wide variety of functions, it is perhaps unsurprising that lncRNAs are involved in many key processes in cancer cells. lncRNAs can function both as oncogenes, as well as tumour suppressor genes. For example, the oncogenic lncRNA ANRIL is overexpressed in prostate cancer tissue in order to silence the tumour suppressive INK4b/ARF/INK4a locus 70. In contrast, the lncRNA TARID acts as a tumour suppressor. TARID recruits GADD45A to 33 demethylate the tumour suppressive gene TCF21, and has been shown to be downregulated in a number of cancers, including lung cancer 78. In our previous work we performed the first pan-cancer transcriptional analysis of lncRNA expression. We re-mined serial analysis of gene expression (SAGE) data from 272 sequence libraries in order to interrogate the non-coding transcriptome of 19 different cancer types, as well as samples from 26 non-malignant tissues 79. We noted that the chromosomal expression patterns of lncRNAs across the genome did not match the patterns of expression seen for protein coding genes. Additionally, we found increased tissue-specific expression of lncRNAs compared to protein coding genes, and each cancer type had a significant number of tumour specific lncRNAs. For example, the majority of deregulated lncRNAs in each cancer type were specific, with moderate overlap between two tissues. Additionally, we found only 8 deregulated lncRNAs overlapped between our three largest cancer datasets, which were breast, brain and lung cancers (Figure 1.9). In addition to finding many novel cancer-associated lncRNAs, we confirmed the expression of known oncogenic lncRNAs, such as such as MALAT1, GAS5, and NEAT1. This study generated the first expression atlas of lncRNAs across multiple cancer types. Our results emphasized the widespread tissue-specific deregulation of lncRNAs in human cancers, which were later confirmed in subsequent sequencing studies 80. 34 Figure 1.10 Aberrantly expressed lncRNAs across human cancers. (A) Number of lncRNAs showing significant expression changes (>2 fold upregulated, BH p-value <0.05) across three major cancer types. Upregulated genes are indicated by solid bars while hatched bars indicate downregulated genes. (B) Venn diagram showing the overlap of these differentially expressed lncRNAs in the three major cancer types. Adapted from our manuscript “Human Cancer Long Non-Coding RNA Transcriptomes” published in PLoS One 79. 35 Since the discovery of their widespread deregulation in cancer, lnRNAs have been shown to play a role in each of the various hallmarks of cancer: Activating invasion and metastasis: one of the first lncRNAs found in the context of cancer was MALAT1, which stands for metastasis associated lung adenocarcinoma transcript 1. This highly expressed lncRNA was discovered in primary LUAD tumours that had a high risk of metastasis. MALAT1 interacts with the PRC2 complex to activate transcription of a number of cell growth related genes. Additionally, MALAT1 has been proposed to be involved in the organization of subnuclear components that participate in activation of pro-cell growth pathways 81 82. Sustaining proliferative signaling: PCAT-1 (prostate cancer associated transcript 1) is a transcriptional repressor for networks of genes associated with mitosis and cell cycle control. Resisting cell-death: lncRNA PANDA interacts with a transcription factor called NF-YA and results in decreased expression of genes associated with apoptotic functions in multiple cancer cell types. Genome instability: NORAD has been associated with breast cancer and loss of the lncRNA has been shown to promote chromosomal instability. Recent research has shown that NORAD is upregulated in breast cancer as part of the DNA damage response pathway and can promote nuclear assembly of the genome stability-associated NARC1 complex 83 84. Evading growth suppressors: ANRIL (antisense non-coding RNA in the INK4 locus) interacts with polycolm repression complex 2 (PRC2) and recruits the complex to condense active chromatin and silence the genes around its transcriptional loci, including the well-known tumour suppressor INK4B (p15). This occurs in multiple cancer types including lung cancer. 36 Inducing angiogenesis: HIF1a is a well-known critical regulator of angiogenesis and has a natural antisense transcript (aHIF). aHIF expression is associated with HIF1a mRNA loss of stability and degradation in multiple cancer tissues 85. Avoiding immune destruction: While researchers are just beginning to study the role that lncRNAs play in the immune system, the lncRNA THRIL is important for TNF-a and IL-6 secretion and is a vital part of innate immune response. THRIL has been associated with inflammatory disease and response to inflammation, which are major factors in multiple cancer types 86. Enabling replicative immortality: The RNA component of telomerase holoenzyme called TERC (Telomerase RNA component) is vital for telomerase function and had been found to be upregulated in several human cancers 87 88. Deregulation of cellular energetics: Just as miRNAs have been known to modulate the expression of glucose transporters to favour glycolysis in cancer cells, so have lncRNAs. For example, the lncRNA PCGEM1 promotes the expression of GLUT1 in prostate tumours 89. 1.7.4 Long non-coding RNAs in the clinic While not as well clinically studied as sncRNAs, lncRNAs have shown promise as disease biomarkers. In the context of blood based cancer diagnostic and prognostic biomarkers, lncRNA’s unique chemistry may provide stability advantages over protein coding genes. The complex folding and tertiary structure of lncRNAs results in high stability when circulating in bodily fluids. Furthermore, lncRNAs have been shown to be able to resist ribonuclease-based degradation and to have even greater stability when packaged in exosomes or apoptotic bodies 90 91. As such, lncRNAs may make excellent candidates for early diagnosis, detection of different 37 subtypes, monitoring of metastasis and progression, sensitivity to treatment, and disease recurrence. The attributes of a good biomarker are easily detectable (highly expressed), accessible (in the blood), as well as high sensitivity and specificity. An example of a lncRNA that fits these parameters is the lncRNA H19, which was one of the first lncRNAs discovered. H19 is a promising biomarker for early stage gastric cancer patients, where its sensitivity and specificity is higher of than that of conventional protein biomarkers such as CEA and CA199 92. The lncRNA MALAT1 has also been considered a biomarker in lung cancer 93. Just as panels of sncRNAs can improve specificity over single genes, so can the addition of lncRNAs to current biomarker panels. For example, a panel including the lncRNA GAS5 with CEA was more effective than CEA alone in diagnosing NSCLC 94. As lncRNAs have been increasingly implicated in major cancer pathways and phenotypes, they have become intriguing clinical targets. Currently there are three main methods to target a lncRNA: transcriptional inhibition, steric-hindrance of RNA-protein interactions, and post-transcriptional RNA degradation. Methods using post transcriptional RNA degradation have been the most well-studied. One type of therapy is particularly intriguing with regards to targeting lncRNAs for degradation: antisense oligonucleotides (ASO’s). ASO’s are a nucleic acid based therapy designed to bind to complementary target RNA, causing either degradation, splicing alterations, or steric hindrance of protein interaction. ASO’s are 15-20 nucleotides long and are quicker and cheaper to design than small molecule inhibitors for specific protein interaction domains 95. Additionally, ASO-based targeting has higher specificity and fewer off-target effects 96. These inhibitors have shown early promise in the clinic, with numerous ASO-based therapeutics entering clinical trials, and recent advances have added degradation resistance 38 phosporothioate backbone linkage modifications to improve stability 97. Intriguingly, ASO’s may be even better at targeting lncRNAs, as the RNA target is the final gene product rather than an intermediate product (Figure 1.10). Due to its overexpression in lung cancer, and association with metastasis, MALAT1 has become a potential target of interest. MALAT1 shRNA-mediated knockdown resulted in a significant reduction in both cell migration, and invasive ability, indicating that it may be an attractive target 98. ASO’s developed against MALAT1 have shown early promise in lung cancer xenografts models. Immune-deficient mice injected with human lung cancer cells show 70% less metastasis to the lungs when treated with MALAT1 ASO’s 99. MALAT1 ASO’s are also being tested for aerosol delivery to the lungs for the treatment of lung cancer, and ongoing pre-clinical studies on patient-derived tumour organoids are moving these treatments closer to the clinic. 39 Figure 1.11 Function of Antisense Oligonucleotides (ASOs) Antisense Oligonucleotides (ASO’s) function by binding to a target RNA and causing either steric hindrance or degradation via RNAse H. LncRNAs make attractive ASO targets as their final states are an RNA product, rather than an intermediate product before translation. 40 1.7.5 Challenges in long non-coding RNA characterization While lncRNAs have great clinical potential, there are significant challenges in the field. While there are several lncRNAs that have been well characterized, the vast majority remain undescribed. lncRNAs are notoriously difficult to characterize, and currently there are no reliable methodologies to predict lncRNA function. Unlike protein coding genes, lncRNAs do not have well characterized functional domains, and the techniques we use for protein coding gene functional characterization are not well suited to lncRNAs. For example, RNA-protein immunoprecipitation is commonly used to identify interaction partners. However, these assays have been found to be prone to nonspecific binding of RNAs, leading to false positive results 100. An example of this is the chromatin modifying PRC2 complex; PRC2 has been observed to physically interact with 20% of lncRNAs, making identification of meaningful interactions difficult 101. Additionally, the complex folding patterns and tertiary structure of lncRNAs complicates the identification of active sites. Furthermore, while highly conserved regions of a gene usually imply functional importance, lncRNAs evolved more recently in humans, so they are in general much less conserved. Lastly, unlike miRNAs, there are no “seed” sequences, and actives sites or important binding sequences remain unknown. Many studies have found lncRNAs to be associated with certain phenotypes, but it is more difficult to ascertain how these lncRNAs are functioning, and what their downstream targets may be. The number of lncRNAs discovered continues to be greatly outpaced by the number of lncRNAs characterized. There is a great need to better predict downstream targets of lncRNAs and this will be vital for this class of RNA to become viable clinical targets. 41 1.8 Thesis Rationale and objective Lung cancer is the deadliest form of cancer, and lung adenocarcinoma is the most common subtype of this disease. While targeted therapies have been of great benefit to those who carry these targetable alterations, there is a need to identify new driver genes, as well as find new clinical action points for known pathways. Next generation sequencing and following studies have shown the clear importance of ncRNAs in cancer, making them attractive candidates for investigation as novel targets. However, the field is being held back by a number of complicating features endemic to lncRNAs: complex folding patterns, lack of conserved domains, and unknown binding sequences make these genes difficult to characterize and complicates the identification of target genes. Current prediction methods primarily assume lncRNA function through physical interaction with miRNAs, and while these potential interactions are plentiful, it is unknown if they occur in a biologically relevant manner. While the number of lncRNAs named has risen significantly, the number characterized remains low, emphasizing the need for better ways to predict lncRNA function. Objective: To investigate the roles of non-coding RNAs in lung adenocarcinoma in order to identify alternate mechanisms of cancer gene deregulation. Hypothesis: Non-coding RNAs are deregulated on a global scale in the LUAD genome, and this affects the protein coding genes that these ncRNAs interact with, including those that drive LUAD. This thesis describes the discovery and investigation of three different mechanisms of lung cancer gene deregulation by non-coding RNAs. 42 Chapter 2: Common methods 2.1 Next generation sequencing of lung adenocarcinoma patient samples 2.1.1 Patient samples We performed Next Generation Illumina HiSeq RNA sequencing on a set of 36 micro-dissected LUAD tumours and matched adjacent non-malignant tissue (n=72). Our British Columbia Cancer Agency cohort (BCCA) was composed of fresh-frozen LUAD tumours and matched non-malignant lung parenchymal tissue collected from 36 patients at the Vancouver General Hospital with approval from the University of British Columbia-BCCA Research Ethics Board. Consent obtained from the tissue donors of this study was both informed and written. Matched non-malignant samples were collected from areas >2 cm away from the tumour. In order to reduce contaminating sequences derived from alternative cell types, tissue microdissection was guided by a pathologist. Samples used in this study contained >80% tumour cell or >80% non-malignant cell content. Total RNA was extracted using Trizol reagent and standard procedures. 2.1.2 Processing of RNA-sequencing data Total RNA was used for library construction at the Genome Sciences Center (GSC, Vancouver, Canada). Briefly, samples were first analyzed using Agilent Bioanalyzer RNA nanochip, and samples that passed quality check were arrayed into a 96-well plate. PolyA+ RNA was purified using the 96-well MultiMACS mRNA isolation kit on the MultiMACS 96 separator (Miltenyi Biotec, Germany) from 2 µg total RNA with on-column DNaseI-treatment as per the manufacturer's instructions. Double-stranded cDNA was synthesized from the purified polyA+-RNA using the Superscript Double-Stranded cDNA Synthesis kit (Life Technologies, USA) and random hexamer primers at a concentration of 5µM. The paired-end sequencing library was 43 prepared following the GSC paired-end library preparation protocol, which is strand specific. Sequencing was performed using the Illumina HiSeq 2000 platform. Raw sequencing reads were subject to a quality control process. Reads with a length < 50nt (under two thirds of maximum read length of 75 nt) and quality level (Phred) < 20 were discarded. High quality reads (.fastq files) were aligned to the NCBI GRCh37 reference human genome build using the STAR aligner (v 2.4.1d) under default parameters 102. Aligned reads (.bam files) were quantified using Ensembl Transcripts (Release 75) reference annotations103. Raw RNA sequencing reads from each patient (tumour and corresponding non-malignant tissue) were deposited at the at Bioproject http://www.ncbi.nlm.nih.gov/bioproject/516232 . Quantification was performed using the Partek Flow platform as reads per kilobase per million (RPKM). RNA sequencing (.bam files) and clinical data for a secondary set of LUAD tumours and matched non-malignant tissue (n=108) were downloaded from The Cancer Genome Atlas (TCGA) Data Portal for validation purposes (https://portal.gdc.cancer.gov/) (Table 2.1). Expression profiles from TCGA were processed as described above. Small RNAs: Small RNA sequencing data was downloaded for all LUAD tumours with available data from the TCGA (n= 586). Raw sequencing reads were then aligned to the human genome (hg 19), before being quantified using miRbase v22 (http://www.mirbase.org/ftp.shtml). 44 Table 2.1 Patient clinical characteristics of the discovery (BCCA), validation (TCGA), and survival analysis (KmPlotter) datasets. Characteristic BCCA TCGA KmPlotter Samples (pairs) 72 (36) 108 (54) 866 (n/a) Sex Male 10 24 344 Female 26 30 318 Average age 70 67 ̶ Stage I 20 (56%) 28 (52%) 370 (69%) II 11 (31%) 14 (30%) 136 (25%) III 3 (8%) 10 (19%) 24 (4%) IV 1 (3%) 2 (4%) 4 (1%) Ethnicity Caucasian 11 (31%) 51 (94%) ̶ Asian 14 (39%) ̶ ̶ Unknown 11 (31%) ̶ ̶ Black ̶̶ 3 (6%) ̶ Smoking Current 5 (14%) 7 (13%) 246 (Ever) a Former 6 (17%) 36 (67%) Never 25 (69%) 5 (9%) 246 a Ever smokers is a term that includes both current and former smokers. 45 2.2 Basic laboratory techniques Cell culture. Lung cancer, and immortalized lung epithelial cell lines maintained in supplier recommended media and may be supplemented with 10% fetal bovine extract (FBS) or bovine pituitary extract (BPE). Cells were grown in a humidified incubator at 37ᵒC and 5% CO2. Cell culture is performed in a ventilated type II biosafety hood. Trypsin is diluted and used to split cell lines prior to reaching confluence. RT-qPCR based transcript quantification: RNA was harvested from cell lines using published methods. In brief, the Trizol protocol was used, where the addition of Trizol disrupted cell structure, and precipitate cellular RNA while protecting from RNase activity. Isolated RNA was then resuspended in DEPC-treated water and converted into a cDNA template through use of reverse transcriptase. Appropriate cellular controls were used as appropriate depending on the RNA species being quantified (TaqMan - Applied Biosystems, Carlsbad, CA). Primers and controls are described in detail in each chapter. 46 Chapter 3: A novel cis-acting long non-coding RNA controls HMGA1 expression in lung adenocarcinoma 3.1 Introduction Long non-coding RNAs (lncRNAs) are a class of transcripts that function in gene regulation and have since been implicated in the onset of many cancer-associated phenotypes, such as progression, tumourigenesis, and metastasis. Since their discovery, one of the key challenges is effective identification of downstream target genes. In order to harness their potential for disease-specific markers and potential therapeutic targets, a better understanding of lncRNA transcription and mechanisms of action in disease is required. Recently, an emerging class of lncRNAs - cis-acting - has been shown to regulate the expression of neighbouring protein-coding genes, which frequently includes protein-coding genes with oncogenic or tumour-suppressive functions. Through a variety of mechanisms including the recruitment of protein complexes these non-coding transcripts can activate or repress transcription of neighbouring genes. For example, the lncRNA Tarid is able to recruit the protein complex GADD45A to actively de-methylate its neighbouring protein-coding gene TCF21, thereby increasing the expression of this tumour suppressive gene 78. Thus, cis-acting lncRNAs may represent novel mechanisms of cancer-gene regulation as well as potentially actionable intervention points in known cancer-driving pathways. Despite the prevalence of genetic and epigenetic deregulation events in lung adenocarcinoma (LUAD), the extent and consequences of aberrant cis-acting lncRNA expression on known cancer-driving genes is unknown. High mobility group A1 (HMGA1) chromatin remodeling protein is enriched in several aggressive cancer types, including non-small cell lung cancer (NSCLC), where its mRNA and protein expression are substantially increased 104. HMGA1 is part of a family of proteins involved 47 in chromatin architectural maintenance, which have all been implicated in tumourigenesis, particularly in breast cancer where they have been shown to facilitate every hallmark of cancer 105. Further, HMGA1 overexpression has been shown to be a key factor driving lung metastasis. The oncogenic role of HMGA proteins stems from their activation of cancer-driving genes such as E2F1, AP1, and CCNA1, as well as the repression of tumour suppressive genes such as TP53 106. In LUAD high HMGA1 gene expression has been associated with poor overall survival and chemotherapy resistance 104. While HMGA1 is deregulated in lung cancer, the mechanisms that mediate its expression are only beginning to emerge. Here I take a genome-wide large-scale approach to investigate putative cancer-relevant cis-acting lncRNAs and subsequently focus on an uncharacterized lncRNA that may represent an alternative mechanism of HMGA1 overexpression in LUAD patients. 3.2 Materials and Methods 3.2.1 Sample collection and processing Two separate cohorts of raw RNA sequencing reads from LUAD tumours with matched adjacent non-malignant tissues were used in this study: an in-house microdissected (80% tumour purity) cohort collected at the BC Cancer Research Centre (BCCA; n=36 pairs) and a publicly available cohort obtained from The Cancer Genome Atlas (TCGA; n=54 pairs). Collection and sequencing of both cohorts were performed in congruent manners as described in Chapter 2 and in a previously published manuscript 107. Briefly, after raw RNA sequencing reads were generated (Illumina HiSeq 2000), quality control analyses were performed (Phred>20; length>50nt), reads were aligned to the hg19 build of the human genome (STAR aligner), and expression was then 48 quantified as reads per kilobase per million (RPKM) 102,107 LncRNA genes were annotated according to release 75 of Ensembl. 3.2.2 Gene Expression Analyses LncRNAs close enough to enact transcriptional or epigenetic changes (within 1.5 Kb) to protein-coding genes were considered as putative cis-acting lncRNAs. Here, we performed a Wilcoxon signed-rank test on both lncRNA and protein-coding gene expression between tumour and matched non-malignant tissues. Genes significantly deregulated in both cohorts (BH-p<0.05; FC>1.5) were considered for further analyses. To assess potential cancer-relevant cis-acting lncRNAs, those neighbouring protein-coding genes with experimental evidence of associations with tumour biology were assessed. Significant associations between lncRNA and neighbouring protein-coding gene expression were determined using a Mann-Whitney U-Test between the upper and lower tertiles of samples based on lncRNA expression. 3.2.3 In vitro analyses The immortalized non-malignant epithelial lung cell line BEAS-2Bs was used to assess the effect of HMGA1-lnc inhibition on HMGA1 expression in vitro. Cells were cultured in serum-free medium: K-SFM supplemented with 30µg/mL bovine pituitary extract (BPE) and 0.0002ng/µL epidermal growth factor (EGF); maintained in an incubator at 37oC and 5% CO2. Once confluent, 2 mL of cell solution was seeded into each well of a 6x2 cm-well plate at a concentration of 50,000 cells/mL. DharmaFECT siRNAs were prepared for transfection as per manufacturer’s instructions in five conditions: i) untreated control; ii) a positive control siRNA targeting GAPDH (25 nM); iii) a non-targeting control siRNA (25 nM); iv) siRNA targeting HMGA1-lnc at a concentration of 12.5 nM; and v) siRNA targeting HMGA1-lnc at a concentration of 25 nM. Non-targetting control was designed to target no known human genes, and provide a baseline response for cellular 49 exposure to siRNAs (Dharmacon, D-001210-01-D001210-05). RNA was harvested after both 48 and 72 hours using the Quick-RNA™ MiniPrep Kit (Zymo Research, Catalog number R1055). Total RNA was converted to cDNA using the TaqMan Reverse Transcription kit. Gene expression was assessed using real-time quantitative PCR with custom primers specific to HMGA1-lnc generated by Thermo Fisher, as well as established primers for the 18S ribosomal RNA (endogenous control), GAPDH, and HMGA1. RT-qPCR reactions were performed in triplicate and relative expression was determined using the 2-ΔΔCt method. Table 3.1 SiRNA sequences used to target HMGA1-lnc Our ID ID Weight Sequence H1 ROWDG_000007 13400.9 GAGAGAAGACAGAGAGAAAUU H2 ROWDG_000009 13370.9 CAACAAAGGCAUUAAGAAAUU H3 ROWDG_000011 13370.9 GAGAAAUAUGUGAAGGAUAUU H4 ROWDG_000013 13461.0 GGAAGGAGCAGGAGGAGGAUU Table 3.2 qRT-PCR probes used in Chapter 3 Gene ID 18S Hs99999901_s1 GAPDH Hs03929097_g1 HMGA1-lnc Hs03921739_s1 HMGA1 Hs00852949_g1 50 3.3 Results 3.3.1 cis-acting long non-coding RNAs are deregulated in LUAD We first sought to examine lncRNA expression in LUAD at a global level to identify potentially biologically relevant cis-acting lncRNAs. LncRNAs that were (i) expressed in both datasets, (ii) significantly deregulated in both datasets (Fold Change, FC: 1.5), and (iii) were overlapping or closely neighbouring (within 1.5 Kb) protein coding genes were considered for further analyses. Using these parameters we found 84 lncRNAs overlapping protein-coding genes that were significantly overexpressed in tumours relative to matched non-malignant samples. Similarly we found 324 lncRNAs overlapping protein-coding genes that were significantly downregulated at least 1.5-fold in tumours (Table A1, found in the Appendix). We were interested whether the neighbouring protein coding genes were also deregulated in LUAD, and if they were deregulated in the same direction. We observed that the substantial proportion of lncRNA:protein-coding gene expression relationships were deregulated in the same direction (concordant; 79%), while only 21% displayed inverse deregulation patterns (discordant). A number of these deregulated lncRNAs overlap protein-coding genes that have been previously described to be involved in cancer biology (Table 3.3). For example, lncRNA OIP5-AS1 is downregulated in both datasets, and overlaps the protein-coding gene OIP5. OIP5 has been described to play a role in tumour progression and metastatic growth in multiple cancer types, and its elevated expression is associated with poor survival for lung cancer patients 108-110. To identify whether the expression of the deregulated lncRNAs of interest was associated with the expression of their neighbouring protein-coding genes, we compared groups of tumours with high levels of lncRNA expression to groups of tumours with low lncRNA expression levels. As concordant 51 expression relationships may be more affected by transcriptional noise as a product of genetic “passenger effects”, such as non-specific DNA copy number changes affecting multiple neighbouring genes, we focused on those with discordant relationships. Specifically, we decided to focus on a particular deregulated lncRNA, RP11.513I15.6, because of: (i) proximity to the known oncogene HMGA1, and (ii) the discordant expression relationship between the lncRNA and neighbouring protein-coding gene. For simplicity we refer to this lncRNA as HMGA1-lnc. Table 3.3 LncRNAs deregulated in LUAD with cancer-associated neighbouring genes lncRNA Deregulation Median FC cis-gene Deregulation Median FC cis-gene cancer PMID ID (BCCA) (BCCA) literature RN7SL5P Upregulated 7.83 PTPRD Downregulated 0.002 Lung Cancer 22245727 RP11-334E6.3 Upregulated 7.62 USP2 Downregulated 0.0291 Cancer 25687182 KRT18P12 Upregulated 86.68 PTPN14 Downregulated 0.208 Cancer 29017057 XXbac-BPG254F23.6 Upregulated 3.57E+04 HLA-DQB1 Downregulated 0.493 Lung Cancer 31114327 CTD-2033A16.3 Upregulated 3.09E+08 NQO1 Upregulated 2.54 Lung Cancer 30954648 RP11-276H19.1 Downregulated 0.051 GAS1 Downregulated 0.701 Cancer 26161998 AC012594.1 Downregulated 0.473 MYO3B Upregulated 7.49 Cancer 25500906 RP11-122M14.1 Downregulated 0.127 NEK2 Upregulated 16.8 Lung Cancer 25202351 OIP5-AS1 Downregulated 0.217 OIP5 Upregulated 1.25 Lung Cancer 22129094 RP11-513I15.6 Downregulated 0.234 HMGA1 Upregulated 1.76 Lung Cancer 25344216 52 3.3.2 Expression of HMGA1-lnc and HMGA1 are deregulated in lung adenocarcinoma We observed HMGA1-lnc to be significantly downregulated in tumours when compared to adjacent non-malignant tissues, which holds true in both datasets (Figure 3.1a). In contrast, the neighbouring protein-coding gene HMGA1 was found to be significantly overexpressed in both tumour datasets relative to matched non-malignant tissue (Figure 3.1b). To highlight this difference in expression, we compared the levels of HMGA1 between tertiles of tumours with the highest and lowest expression of HMGA1-lnc. Interestingly we found that HMGA1 and HMGA1-lnc were negatively correlated (p=0.0153), and levels of HMGA1 were significantly greater (p=0.0326) in the low lncRNA expressing tumours (Figure 3.1c,d). Thus, the expression of these two genes appears to be anti-correlated, possibly indicating that this lncRNA is involved in the inhibition of HMGA1 expression in normal lung contexts. 53 Figure 3.1 Expression of HMGA1 and HMGA1-lnc in lung adenocarcinoma Expression of HMGA is upregulated in LUAD compared to adjacent non-malignant tissue (p value) (A) while conversely, expression of HMGA1-lnc is downregulated in tumours (B). Additionally, tumours with high levels of HMGA1-lnc, have significantly lower levels of HMGA1, when compared to tumours with low levels of the lncRNA (C), and expression of HMGA1 and HMGA1-lnc are negatively correlated (D). 54 As expression of HMGA1 has been previously described to increase with tumour stage and cancer aggressiveness, we examined whether expression of HMGA1-lnc was inversely associated with stage 104. As the majority of our tumour samples were Stage I and II we performed a Mann-Whitney U-test between these two groups to identify significant associations (Figure 3.2). Interestingly, while HMGA1 was associated with increased tumour stage (p=0.0011), the opposite was true for HMGA1-lnc, where expression of the lncRNA is significantly decreased in more advanced tumours (p=0.0125). Figure 3.2 Expression of HMGA1 and HMGA1-lnc is associated with tumour stage Expression of HMGA1-lnc decreases with increasing tumour stage (A), whereas HMGA1 expression increases with higher grade tumours (B). 55 3.3.3 HMGA1-lnc controls HMGA1 expression To determine whether lost HMGA1-lnc expression is a mechanism of HMGA1 overexpression in the lung, we performed a siRNA-mediated knockdown of HMGA1-lnc in a non-malignant lung epithelial cell line (BEAS-2Bs) using a pool of siRNAs specific to HMGA-lnc. We then quantified expression changes using qRT-PCR as described in Methods, Chapter 2. From this, we observed a (3.42 fold reduction of the lncRNA) after (48 hours). Strikingly, in the cells with reduced HMGA1-lnc the mRNA expression levels of HMGA1 were increased by 1.57 fold compared with cells transfected with non-targeting control siRNAs (Figure 3.3). This observed increase in HMGA1 levels in the lncRNA-inhibited cell lines, suggests that HMGA1-lnc acts to inhibit the expression of HMGA1, and that downregulation of this lncRNA in LUAD is a mechanism for the overexpression of this well known cancer-driving gene. 56 Figure 3.3 Inhibition of HMGA1-lnc results in increases of HMGA1 expression SiRNA mediated inhibiton of HMGA1-lnc was performed in normal bronchial epithelial cells and resulted in significant reduction of the lncRNA conversely in cells where the lncRNA was inhibited significant increases in protein coding HMGA1 expression was observed. 57 3.4 Discussion The role of protein-coding genes in the onset and progression of LUAD is well-established; however, there remains a lack of treatment options for patients who do not harbor one of the few clinically-actionable driver-gene alterations. LncRNAs have been shown to have important roles in the regulation of cancer-associated genes, but complex folding patterns and unknown binding motifs make lncRNAs particularly difficult to functionally characterize. Here, we used an approach that considered the genomic location, as well as the known function of neighbouring protein coding genes, in order to identify candidate target genes of cis-acting lncRNAs deregulated in cancer. Using this approach, we identified 408 lncRNAs that are deregulated in two datasets of LUAD, which closely neighbour or overlap protein-coding genes. Further, many of the protein-coding neighbours of the deregulated lncRNAs are similarly deregulated in both cohorts. Many of these neighbouring protein-coding genes have been previously described in cancer, including well-known lung-cancer-associated genes such as NEK2, suggesting that there may be a selective pressure for the deregulation of these lncRNAs in order to release these cancer-promoting genes from negative regulation. Thus, alterations in lncRNA expression may consequently disrupt coding-gene expression as a means of promoting tumour development. The majority of the deregulated lncRNAs were positively correlated with the expression of their protein-coding neighbours. This may imply that this concordant regulation is favoured in LUAD, and previous studies have shown that many cis-acting lncRNAs have positive expression relationships with their neighbours in several tissue types 111. However, genes in the same vicinity are often subject to regulation that can affect whole genomic regions, such as silencing through chromatin condensation. In particular, tumours often have significantly elevated levels of these 58 broad genomic alterations to the DNA, which enables tumour-suppressor-gene silencing or oncogene activation. Genes neighbouring these oncogenes and tumour suppressors are often caught in these regions of alteration, and display concordant expression with these genes, a phenomenon known as the passenger effect 112 113. For example, frequent DNA amplification of the MET oncogene occurs in 5-20% of LUAD, leading the surrounding genes to display significantly increased DNA copy number 114,115. While it is difficult to separate cis-acting concordant regulatory relationships from oncogene passengers without further verifying direct interactions via in vitro expression modulation, genes displaying discordant expression relationships with their neighbours are less susceptible to this effect. Thus, we decided to focus on a deregulated lncRNA displaying a discordant expression pattern with its neighbouring oncogene, HMGA1-lnc. We found HMGA1-lnc to be significantly downregulated in LUAD, where its expression levels is decreased 18 fold in tumours, compared to HMGA1 which has expression levels 5 fold greater in tumours (TCGA). These observations in tandem with anti-correlated expression relationships within tumour samples led to our hypothesis that HMGA1-lnc acts to repress HMGA1 expression in non-malignant samples. Consequently, the finding that HMGA1-lnc was downregulated with increasing stage, while HMGA1 expression increased with more advanced stages strengthened this putative regulatory relationship. To verify that HMGA1-lnc was able to affect HMGA1 expression levels, we performed siRNA-mediated knockdown of the lncRNA in cells derived from normal lung epithelium (BEAS-2B cells). When the lncRNA was inhibited we noted significant increases in HMGA1 mRNA and protein levels, confirming that this lncRNA directly regulates the expression of HMGA1 in vitro. Previous studies modulating expression levels of HMGA1 in these same normal lung epithelial cells (BEAS-2B) have shown that increased 59 HMGA1 expression leads to transformed phenotypes and increases in anchorage-independent cell growth 116. These results suggest that downregulation of this previously-uncharacterized lncRNA may lead to HMGA1 upregulation, potentially driving the onset of these same cancer phenotypes in normal human lung epithelial cells. 60 Chapter 4: Aberrant Expression of Pseudogene-derived lncRNAs as an Alternative Mechanism of Cancer Gene Regulation in Lung Adenocarcinoma. 4.1 Introduction While lncRNAs have been observed to be important in cancer biology, functional prediction of newly-discovered lncRNAs remains a major challenge. In Chapter 3 we focused on one approach to identify lncRNAs that may act in cis to drive the many aspects of LUAD. In this chapter we focus on lncRNAs that may be acting in trans to regulate cancer driving genes in LUAD. Specifically, how many lncRNAs expressed from pseudogene loci have been shown to regulate genes with which they have sequence homology. Pseudogenes are DNA sequences that are defunct relatives of functional protein-coding genes (herein referred to as parent genes) and arise during either gene duplication events, or the reverse transcription of an mRNA transcript into a new genomic location. Through evolution these duplicated genes have acquired mutations such as premature stop codons and frameshifts, which results in the loss of protein coding ability, while still retaining a high degree of sequence homology with the original parent gene 117. Recently, pseudogene-derived lncRNAs have been shown to regulate their parent genes and this novel mechanism has been observed in many tumour types, including lung cancer 118,119. A prominent example is the tumour suppressor gene PTEN (chromosome 10), regulated both positively by PTENP1 (chromosome 9), a lncRNA transcribed from the sense strand of the pseudogene locus, and negatively by the lncRNA PTENP1-AS1, which is transcribed from the strand antisense to the parent gene 76. Pseudogenes have been continually omitted from large RNA-sequencing datasets due to the complexity of separating highly-similar pseudogene sequences from parent genes. However, 61 Millegan et al. recently generated an atlas of lncRNAs overlapping pseudogenes, which has provided a foundation for their analysis in RNA sequencing datasets 120. We hypothesize that the functions of pseudogene-derived lncRNAs are an under-explored mechanism of gene regulation that occurs more broadly than previously realized, and that these events contribute to the tumourigenesis of LUAD. We performed next generation RNA-sequencing on microdissected LUAD tumours and matched non-malignant tissue to identify deregulated lncRNAs expressed from pseudogene loci (herein referred to as Ѱ-lncs). We then explored the relationships of these Ѱ-lncs with their parent genes, and explored their significance in relation to patient clinical features in our discovery dataset as well as a validation dataset. 4.2 Methods 4.2.1 Identification of long non-coding RNAs expressed from pseudogene loci and corresponding parent genes Ѱ-lnc annotation: Millegan et al. recently published a global atlas of lncRNAs that have exonic overlap with positionally non-redundant (unique) pseudogenes from 3 major pseudogene databases 120. Using this resource we obtained a list of lncRNAs overlapping pseudogene loci (>1% intronic or exonic overlap) that we used as a foundation for our expression analysis (Supplemental table 1, Appendix A). Supplemental tables can be accessed in our published manuscript “Aberrant Expression of Pseudogene-Derived lncRNAs as an Alternative Mechanism of Cancer Gene Regulation in Lung Adenocarcinoma,” due to the large size of the supplementary tables in Chapter 4 (DOI: 10.3389/fgene.2019.00138) 107. Descriptions of all supplemental tables can be found in Appendix A. As the degree of sequence overlap required for a pseudogene-derived lncRNA to regulate its parent gene is unknown, we did not restrict our 62 analysis to full-length, expressed pseudogenes, and included lncRNAs with any exonic overlap, including sense and antisense transcripts in order to annotate the most comprehensive list of Ѱ-lncs (lncRNAs overlapping pseudogene loci). Parent gene annotation: The parent gene information was also extracted for all pseudogenes overlapping our list of Ѱ-lncs that have parent-gene annotations in the YaleHuman60 and Retroali5 databases (Supplemental Table 7, Appendix A). Manual literature search was performed for parent genes of deregulated Ѱ-lncs that were not contained in these databases. 4.2.2 Statistical analysis Identification of significantly deregulated Ѱ-lncs in paired LUAD and non-malignant lung tissue: Gene expression for protein-coding and non-coding genes was compared between tumours and non-malignant tissue and significantly deregulated genes were identified using a Wilcoxon signed-rank test (p<0.05) and subjected to a Benjamini-Hochberg (BH) FDR correction. Ѱ-lncs as identified previously were extracted, and those that were significantly deregulated between tumours and non-malignant tissue, in both our discovery (BCCA) and validation (TCGA) were selected for further analysis (n=104 deregulated lncRNAs) (Figure 4.1b). Ѱ-lncs and parent gene expression: Tumours were sorted by Ѱ-lnc expression for each Ѱ-lnc-parent gene pair, and grouped into top and bottom Ѱ-lnc expressing tertiles. Parent gene expression was then compared between the two groups using the Mann Whitney U-test (p-value ≤ 0.05). We performed a global expression analysis to determine whether Ѱ-lnc-parent pairs were more positive or negatively correlated than random chance. For all Ѱ-lncs with expression data in both datasets, Spearman’s correlation rho values were calculated for Ѱ-lnc-parent gene pairs (n=390) and compared to Ѱ-lnc-random gene pairs. Random genes in our expression 63 matrices were selected to pair with each Ѱ-lnc. Each gene was assigned a number and pairs were chosen by using a random number generator (https://www.random.org/). Spearman’s rho values were then plotted along a standard curve, and compared (Mann Whitney U-test, p-value ≤ 0.05). Rho value distribution was also compared between sense lnc-parent gene pairs (n=208) and antisense lnc-parent pairs (n=182). Literature searches: To determine if each gene of interest (Ѱ-lnc or parent gene) had been previously described in the context of tumours we searched Pubmed using the terms “gene + cancer” or “gene + lung cancer”. Hierarchical clustering and data visualization: Unsupervised hierarchical clustering was performed in order to visualize and examine the expression of the 104 deregulated Ѱ-lncs in individual samples (Figure 4.2a). Average Linkage was used as a cluster distance metric, while Pearson Correlation was used as a point distance metric. To visualize the expression patterns of the most highly expressed Ѱ-lncs, those with an average expression value of ≥ 10 RPKM in either tumour or non-malignant samples were included in the analysis. Distribution of deregulated Ѱ-lncs across genome: Locations of Ѱ-lncs and parent genes were compared to identify their genomic position (Supplemental Table 4, Appendix A). Circular plot visualization was performed using the R-package Circlize (Figure 4.3) 121. LUAD-specific regions of significant recurrent somatic copy number alterations had been previously identified by TCGA, and were used in this study to determine if the deregulated Ѱ-lncs overlapped with frequently altered regions 115. All genomic coordinates correspond to the NCBI GRCh37 reference human genome build. 64 4.2.3 Clinical features Survival analysis: A large public clinical database (Kaplan–Meier Plotter; http://kmplot.com/analysis/) comprised of 719 LUAD samples was used to determine the association between both protein-coding and non-coding gene expression with patient outcomes. Similar to the BCCA and TCGA cohorts, the patient samples in this 3rd dataset were mostly comprised of Stage I and Stage II tumours (Table 2.1). Of the 104 deregulated Ѱ-lncs, 19 were represented in this database, while 70% of parent genes (72 out of 103) were present. Default settings were used and a log-rank (Mantel–Cox) test was applied to compare survival between groups of tumours with high and low expression of each gene tested, where p < 0.05 was considered statistically significant. The optimal expression cut-off was selected for each gene. Association with tumour stage: The majority of tumours for BCCA and TCGA fell into the categories of Stage I and Stage II (Table 2.1). We compared expression of deregulated Ѱ-lncs between Stage I tumours and tumours classified as Stage II and above using a Mann Whitney U-test (p-value ≤ 0.05). 65 66 Figure 4.1 Summary of the regulatory mechanisms of Ѱ-lncRNAs and the analysis pipeline for the identification of their deregulation in lung adenocarcinoma. Summary of the regulatory mechanisms of pseudogene-derived lncRNAs (Ѱ-lncs) that retain sequence homology with the parent gene. Overall, lncRNAs have been shown to function through a variety of regulatory mechanisms, acting on the DNA, RNA, and protein levels (A). Flow diagram description of the analysis pipeline applied for the identification of deregulated Ѱ-lncs. Patient LUAD samples were collected and subjected to next generation sequencing to quantify RNA expression. Gene expression was then compared between tumours and matched non-malignant tissue to identify significantly deregulated transcripts. LncRNAs with exonic overlap to known pseudogenes were then identified and confirmed in a 2nd set of LUAD and matched non-malignant tissue. This lead to the identification of 104 deregulated Ѱ-lncs, which were then assayed to determine associations with clinical features (B). 67 4.3 Results 4.3.1 Ѱ-lncRNA expression is deregulated in lung adenocarcinoma Pseudogenes vary widely in terms of length, gene fraction, and identity to parent genes, and can be expressed as lncRNAs that are sense, antisense, partial overlapping, or internal to the parent. In light of this variation, Ѱ-lncs are observed to have vastly different regulatory effects on downstream target genes (Figure 4.1). In our curation of Ѱ-lncs in LUAD, we have included those that have exonic overlap with a pseudogene (partial or full length) and considered both sense and antisense transcripts (Supplemental Table 1, Appendix A). Ѱ-lncs were analyzed in an in-house discovery (BCCA, n=72) and external validation (TCGA, n=108) cohort of LUAD and paired non-malignant lung tissues (Table 2.1). We identified aberrantly expressed Ѱ-lncs that are significantly deregulated in both the discovery and validation datasets with the same direction of expression alteration (Ѱ-lncs upregulated or downregulated in tumours compared to matched non-malignant tissue). We found 104 lncRNAs expressed from 102 pseudogene loci to be significantly deregulated in LUAD (Supplemental Table 2, Appendix A). To our surprise, we found that the majority of these deregulated Ѱ-lncs were downregulated in tumours (Figure 4.2a and b). Most of these were unannotated lncRNAs, such as RP11-1007O24.3, which was downregulated in tumours, with only 24 of the total deregulated Ѱ-lncs having been previously described in scientific literature annotated in PubMed, albeit none in the field of pseudogene-mediated deregulation (Supplemental Table 3, Appendix A / Figure 4.2b). Twenty of these 24 have been described in the context of cancer, with only four in lung cancer. This includes DGCR5, a lncRNA we found to be overexpressed in tumours. DGCR5 has been reported to promote LUAD 68 progression by sequestering a variety of miRNAs involved in cell cycle regulation, although it has not been investigated with regard to its pseudogene-derived nature 122-124. 69 70 Figure 4.2 LncRNAs derived from pseudogene loci are significantly differentially expressed in lung adenocarcinoma compared to matched non-malignant lung tissue. Unsupervised hierarchical clustering of pseudogene-derived lncRNAs differentially expressed between lung adenocarcinoma (grey) and matched non-malignant tissue (purple). Average linkage was used as the cluster distance metric and Pearson Correlation was used as the point correlation metric. Expression values are stratified from low (blue) to high (red). Only pseudogene-derived lncRNAs with average expression values of greater than or equal to 10 RPKM were included in the clustering analysis. Clustering of samples highlights relative similarity in pseudogene-derived lncRNA expression between the two sample groups, while clustering of gene expression reveals a trend towards the widespread underexpression of these transcripts in lung adenocarcinoma (A). Highlighted examples of pseudogene-derived lncRNAs significantly deregulated between lung tumours and non-malignant tissues. Expression (RPKM) in tumours (grey) and normal tissues (purple) is represented on the Y-axis. Boxes represent the interquartile range and inner lines represent the median expression value (B). 71 We were interested in examining the genetic events that could impact pseudogene loci, and thus affect Ѱ-lnc expression. We mapped the chromosomal distribution of the deregulated Ѱ-lncs, finding them to be distributed throughout the genome and detected on most chromosomes, except for chromosomes 4 and Y (Figure 4.3). The locations of each of the parent genes of deregulated Ѱ-lncs are similarly distributed through the genome (Supplemental Table 4, Appendix A). We then determined the overlap of these genes with regions of recurrent chromosomal amplification and deletion as determined by The Cancer Genome Atlas (TCGA) for LUAD 115. While some Ѱ-lncs overlap with regions of recurrent deletion, the majority do not, indicating that they may be regulated by mechanisms other than copy number alteration (Figure 4.3). 72 Known regions of deletionKnown regions of amplificationDownregulated pseudo-lncsUpregulated pseudo-lncs73 Figure 4.3 Genome-wide distribution of deregulated pseudogene-derived lncRNAs in lung adenocarcinoma. Circular representation of the genomic distribution of the deregulated pseudogene-derived lncRNAs discovered in our study, as well as known regions of copy number alterations in lung adenocarcinoma as described by TCGA. The outer concentric circle represents the human karyotype from the genomic build hg19. The blue concentric circle contains known regions of copy number amplification (red boxes) and deletion (blue boxes) that have been previously published. The inner green circle represents the specific genomic location of our pseudogene-derived lncRNAs found to be either upregulated (green circles) or downregulated (purple circles) in lung adenocarcinoma. Finally, the inner connecting lines represent the interaction between the deregulated pseudogene-derived lncRNAs and the locations of their respective protein-coding parent genes. Chromosome 9 (magnified region) highlights that some of the downregulated pseudogene-derived lncRNAs overlap with genomic regions frequently deleted in lung adenocarcinoma. 74 4.3.2 Global patterns of Ѱ-lncRNA and parental gene expression As a first step to identify deregulated Ѱ-lncs that may function through regulation of their respective parent genes, we explored whether Ѱ-lncs with significantly deregulated expression were associated with altered parent gene transcript levels 76,119,125,126. We obtained parent gene information for the 95 deregulated Ѱ-lncs and determined that they shared 104 parent genes. Some pseudogenes contained multiple lncRNAs, and some lncRNAs overlapped multiple pseudogenes, constituting a total number of 116 Ѱ-lnc-parent gene pairings. For each Ѱ-lnc-parent pair we compared groups of tumours with high levels of Ѱ-lnc expression to those with low levels of Ѱ-lnc expression. We found that 33 Ѱ-lncs have a significant expression relationship with their parent gene in at least 1 dataset (Supplemental Table 5, Appendix A). This included 21 sense Ѱ-lnc-parent-gene pairs, and 13 antisense Ѱ-lnc-parent gene pairs. Having identified Ѱ-lncs with expression associated with parent gene expression, we investigated whether parent genes had known oncogenic or tumour suppressive roles. We performed a literature search to determine if any had been previously described in the context of cancer. Interestingly, we found that 65 of these parent genes had been previously described in cancer, and of those, 33 had been described in lung cancer (Table 4.1). Of the 34 significantly differentially expressed Ѱ-lnc-parent gene pairs, 25 parent genes were described in cancer. This includes lung cancer associated genes like CS, which affects tumour drug response, as well as RCN1, which is associated with poor prognosis and tumour progression in lung cancer 127,128. As the vast majority of the deregulated Ѱ-lncs that were correlated with their parent gene had positive associations, we were interested whether this was a global phenomenon or exclusive to 75 Table 4.1. Parent genes of deregulated Ѱ-lncRNA previously described in cancer literature Parent Gene Cancer a Lung a Cancer Ѱ-lnc Parent Gene Cancer a Lung a Cancer Ѱ-lncRNA ADC 8 - RP11-439L8.3 MT1 >1000 95 MMP24-AS1 AGAP1 12 - AGAP11 NEBL 17 - LINC00342 ARIH1 4 - RP11-1007O24.3 NUP210 9 2 H1FX-AS1 ATXN7L3 5 - RP11-56G10.2 PCBP2 26 - PCBP1-AS1 BCR >1000 >1000 AC008132.13 PKD1 238 5 RP11-1186N24.5 BMS1 4 - AGAP11 POTEF 4 - RP11-193H5.1 CDC42 1657 129 RP11-390F4.3 PPARGC1B 37 1 RP11-527N22.1 CECR7 3 - AP000525.9; PPY 91 5 CTD-2008P7.8 CTD-2314B22.3 PRKX 12 2 RP11-526I2.1 CELSR1 11 2 DGCR5 PTCHD3 2 - PTCHD3P1 CHRNB1 8 - RP11-650L12.2 PTMA 34 - LINC00987 CIC - 96 RP11-34P13.7 PZP 20 - LINC00987 CS - 10 LINC00883; RAB11FIP1 16 1 RP3-368A4.5 RP11-446H18.5 RAB40B 5 - LL0XNC01-250H12.3 CSPG4 62 5 DNM1P51 RCN1 17 1 TPT1-AS1 CTAGE1 49 - RP1-122P22.2 RNASEH1 7 - RP11-344E13.3 CYP4F2 29 - CYP4F35P RPL21 5 - AC005062.2 CYP4F3 10 1 FAM95B1 RPL23A 8 - HLA-F-AS1 CYP4F31P 10 1 CYP4F35P; RPSA 51 3 FTX FAM95B1 RPSAP58 - 3 LINC00466 DFFB 33 1 TOPORS-AS1 SEMA3A 163 25 SEMA3B DRD2 121 10 AP000438.2 SHQ1 10 1 RP11-50E11.3 EGLN1 158 5 RP11-182J1.1 SNAPC5 4 - LY86-AS1 FAM103A1 1 - RP11-324H6.5 SNX18 2 - RP11-435B5.5 GPR39 8 - RP11-399O19.9 SRSF9 5 - RP11-752G15.3 HMGB1 >1000 111 RP11-349A22.5; TACC3 122 21 LINC00667 ZBED3-AS1 TOMM40 10 2 CTD-2314B22.3 HMGN2P46 81 1 RAMP2-AS1 TPTE 69 7 TPTEP1 KRT8 94 7 RP5-1198O20.4 TUBB4B 5 - RP11-386G11.10 LINC00657 2 - CTA-14H9.5; TULP3 5 - LINC00359 HCG11 VENTX 8 1 RP11-81H3.2 LMNB2 3 1 RP11-161H23.9 VWF 733 52 TPTEP1 MARK4 25 3 CTD-2201G3.1 ZNF14 2 - CTD-2666L21.1; MICE 1 - HLA-F-AS1 ZNF833P MIPEPP3 4 1 C1QTNF9B-AS1 ZNF44 1 - CTD-2666L21.1 ZNF584 1 - CTD-2619J13.17 a denotes number of entries in PubMed 76 deregulated genes. We performed a Spearman’s correlation analysis on every Ѱ-lnc-parent-gene pair with expression data in our dataset irrespective of deregulation status (n=390 gene pairs). We plotted the distribution of Spearman’s rho (ρ) values for the Ѱ-lnc-parent-gene pairs and compared them to the rho values for Ѱ-lncs paired to randomly selected genes. We found that the Ѱ-lnc-parent-gene pairs have significantly more positive relationships than the random gene pairs in both the BCCA (Mann Whitney U-test, p<0.0001) and TCGA datasets (Mann Whitney U-test, p<0.0001) (Figure 4.4a). Studies have shown that lncRNAs transcribed from opposite strands can have different regulatory effects on target genes 76,111. To determine if transcriptional orientation has an effect on Ѱ-lnc-parent relationships we compared the Spearman’s rho values of sense Ѱ-lnc-parent pairs to antisense Ѱ-lnc-parent pairs. In both datasets we observed that the sense Ѱ-lnc-parent pairs to have significantly more positive relationships than the antisense Ѱ-lnc-parent pairs (Mann Whitney U-test, TCGA set (p<0.0001), and BCCA set (p<0.0025) (Figure 4.4b and Supplemental Table 6, Appendix A). Strongly positively correlated Ѱ-lnc-parent pairs include TPT1-AS1 / RCN1 and LINC00887 / CS (Figure 4.4c). 77 Figure 4.4 Distribution of Spearman’s Correlation rho values for all Ѱ-lnc-parent-gene pairs in the TCGA dataset (n=391). Distribution of Spearman's correlation coefficients between Ѱ-lnc-parent gene pairs (blue line, median R=0.088) and Ѱ-lnc-random gene pairs (pink dashed line, median R=0.019). Rho values of the groups were compared by Mann Whitney U-test (A). Distribution of Spearman's correlation coefficients between sense Ѱ-lnc-parent-gene pairs (turquoise line, median R=0.140) and antisense Ѱ-lnc-parent-gene pairs (orange line, median R=0.049) (B). Correlation scatter plots of Ѱ-lncs with positive expression correlations to cancer-associated parent genes (C). 78 4.3.3 Ѱ-lncRNAs and their parent genes are associated with patient survival If the aberrant expression of Ѱ-lncs is biologically relevant, it follows that they may be relevant in tumour aggressiveness, stage, and patient survival. We performed a two-group analysis using a Mann-Whitney U test between Stage I tumours and Stage II-IV tumours, as the majority of our tumours fell into these categories (Table 2.1). Of the deregulated Ѱ-lncs we found CTC-250I14.3 to be associated with Stage 1 disease and downregulated in both the BCCA and TCGA LUAD cohorts (Figure 4.5). Our discovery datasets were limited in sample size for survival association analysis; therefore, we examined a third cohort of 719 LUAD from the KM Plotter database (Kaplan–Meier Plotter; http://kmplot.com/analysis/) (Table 1.1). This cohort was limited to genes with probe coverage on microarray platforms. A total of 19 of the deregulated Ѱ-lncs were represented on this platform, yet, the majority of these Ѱ-lncs (16 of 19) were significantly associated with poor overall survival (log-rank p<0.05, Table 4.2). 79 Figure 4.5 Comparison of deregulated Ψ-lnc CTC-250I14.3 expression between Stage I tumors and tumors classified as Stage II and above using a Mann Whitney U-test (p-value ≤ 0.05). While we were not able to investigate the survival associations of all deregulated Ѱ-lncs as many were not covered by the microarray platforms, the majority (72 out of 103) of their parent genes were represented. We discovered that 67 of these parent genes were associated with patient survival. Twenty-eight of these survival associated parent genes were also significantly associated with the expression of their paired deregulated Ѱ-lnc. Furthermore, we found 11 pairs where both Ѱ-lnc and parent gene are associated with patient survival. For example RP11-1007O24.3, a Ѱ-lnc downregulated in tumours, is positively associated with expression of survival-associated parent gene ARIH1 in both the BCCA and TCGA datasets (Table 4.2, Figure 4.6a). 80 Table 4.2 Associations between Ѱ-lncRNA, parent gene expression, and patient outcome Ѱ-lncRNA Survival p (KmPlotter) Parent Gene Survival p (KmPlotter) Association with Parent (p, BCCA) Association with Parent (p, TCGA) AC008132.13 - BCR 0.00043 a 0.0173 - AGAP11 0.0099 b AGAP1 5.80E-10 b 0.0005 - AGAP11 - BMS1 0.00018 a 0.0121 - AP000525.9 - CECR7 3.20E-06 a - 0.0371 CTA-14H9.5 - LINC00657 6.00E-06 b - - CTD-2314B22.3 - CECR7 3.20E-06 a - 0.0402 CTD-2619J13.17 - ZNF584 1.70E-08 a - 0.0205 CTD-2666L21.1 - ZNF44 2.00E-15 b - 0.0013 CTD-2666L21.1 - ZNF14 1.90E-12 a - - DGCR5 4.00E-04 a CELSR1 2.70E-02 a - - FAM66C 3.00E-04 b DEFB130 - - - FAM95B1 - CYP4F3 2.00E-01 b - - HCG11 1.40E-08 b LINC00657 6.00E-06 b - 0.0046 LINC00342 1.80E-06 b NEBL 2.30E-05 b - - LINC00466 4.60E-06 a RPSAP58 - - - LINC00639 0.0087 b ZFP41 0.014 b - - LINC00667 4.50E-08 b TACC3 6.20E-09 a - - LINC00883 2.90E-03 b CS 0.00041 a 0.0449 - LINC00957 0.042 b RASA4B 0.035 a - - LINC00982 1.30E-06 b n/a c - - - LINC00987 - PZP 0.02 a - <0.0001 LY86-AS1 0.00025 b SNAPC5 5e-0.6 b - - MMP24-AS1 0.023 a MT1 1.20E-12 a - - PCBP1-AS1 - PCBP2 4.30E-13 b - - RAMP2-AS1 0.014 b HMGN2P46 - - - RP11-1007O24.3 - ARIH1 0.00093 b <0.0001 0.0030 RP11-1186N24.5 - PKD1 0.0013 b - <0.0001 RP11-182J1.1 - EGLN1 1.20E-08 b 0.0011 - RP11-344E13.3 - RNASEH1 1.00E-07 b 0.0007 - RP11-349A22.5 - HMGB1 7.50E-10 - - RP11-34P13.7 - CIC 1.20E-08 a - 0.0205 RP11-390F4.3 - CDC42 1.30E-05 0.0449 - RP11-439L8.3 - ADC 3.30E-09 0.0205 - RP11-446H18.5 - CS 0.00041 a - - RP11-93K22.13 - FAM86B3P 6.30E-05 b - 0.0129 TOPORS-AS1 - DFFB 9.10E-08 b - 0.0033 TPT1-AS1 0.00019 b RCN1 0.016 a 0.0242 - ZBED3-AS1 1.10E-10 b HMGB1 7.50E-10 b 0.0100 - ZNF833P - ZNF491 0.016 b - - ZNF833P - ZNF14 1.90E-12 a - 0.0079 a Denotes poor overall survival associated with high gene expression b Denotes poor overall survival associated with low gene expression c No information for the parent gene 81 Further examples also include Ѱ-lnc-parent pairs such as ZBED3-AS1 and HMGB1, which are positively correlated at the expression level, and both significantly associated with survival (Figure 4.6b). We also observe Ѱ-lnc-parent pairs that are associated with survival, but do not share an expression relationship such as LINC00667 and parent gene TACC3 (Figure 4.6c). Collectively, our discovery of the broad deregulation of Ѱ-lncs, many of which are survival-associated and associated with parent gene expression, may indicate that Ѱ-lncs impact LUAD biology through trans regulation of their cancer-associated parent genes. 82 83 Figure 4.6 Associations of pseudogene-derived lncRNAs (upper row), their respective parent gene expression levels and their potential impact on patient outcome (middle row and bottom row). Expression and survival associations as seen for: RP11-1007O24.3 and parent gene ARIH1 (A), ZBED3-AS1 and parent gene HMGB1 (B), and LINC00667 and parent gene TACC3 (C). Expression associations (upper row) for each pseudogene-derived lncRNA and parent gene pair were found by stratifying samples into tertiles by high (red) and low (black) expression of the pseudogene-derived lncRNA, and plotting the expression of the parent-gene (RPKM) on the y-axis. Survival associations were found for pseudogene-derived lncRNAs (middle row) and their respective parent genes (bottom row). Samples were stratified into tertiles with high (red) and low (black) expression of the gene-of-interest, and the significance of the associations were assessed using the logRank method through GraphPad Prism 8 software on data obtained from kmPlot (n=673). Survival information was not available for RP11-1007O24.3 and it was thus omitted from this analysis. 84 4.4 Discussion Here we expand upon the work done by Milligan et al 120, completing the first large-scale analysis of lncRNA expression from pseudogene loci in LUAD and paired non-malignant lung tissue. We discovered a broadly positive association between Ѱ-lncs and parent-gene expression, suggestive of an alternative mechanism of cancer gene regulation. While there have been singular examples of deregulated pseudogene-derived lncRNAs in cancer, we show that this phenomenon is widespread in LUAD. In addition to being correlated with Ѱ-lnc expression, we find that many of the parent genes of these deregulated lncRNAs are annotated cancer genes and are significantly associated with patient survival, highlighting how these previously-unappreciated non-coding genes may affect LUAD biology. While the identification of lncRNAs associated with cancer phenotypes is increasing, a great challenge in the field remains the accurate downstream prediction of lncRNA function. Unlike protein-coding genes or small non-coding RNAs, features like complex folding patterns and unknown binding motifs have contributed to the challenging functional characterization of lncRNAs. We utilized the sequence similarity found between lncRNAs expressed from pseudogene loci, and their parent genes to predict the function of this subset of lncRNAs in LUAD. We identified a set of 104 Ѱ-lncs deregulated in LUAD in two independent datasets. This greatly increases the number of deregulated lncRNAs known to be expressed from pseudogene loci in LUAD. Interestingly, the majority of these Ѱ-lncs were under-expressed in tumours compared to non-malignant tissues, suggesting that they may have tumour-suppressive roles, and that their downregulation is advantageous to LUAD tumourigenesis. Under-expression was not significantly associated with regions of recurrent copy number deletion in LUAD, although a subset of deregulated Ѱ-lnc loci were localized to these regions 85 (Figure 4.3). These observations suggest that they may be regulated by alternative molecular mechanisms, including broad chromosomal aberrations that affect whole chromosome arms, or epigenetic mechanisms. For example, endogenous retroviruses and repetitive elements often become aberrantly expressed in cancer due to deregulated methylation patterns 129. We did not observe enrichment of Ѱ-lncs or their parent genes on any chromosomes, despite the fact that pseudogenes are known to be overabundant on the human X chromosome 130. The direction of transcription often affects lncRNA function 111. For example, the PTEN pseudogene (PTENP1) expressed in the sense direction can function as a decoy for inhibitory miRNAs that would otherwise cause translational inhibition of the PTEN parent mRNA. Conversely, when the antisense lncRNA is expressed from the PTENP1 locus, the transcript is able to localize to the PTEN parent locus and recruit chromatin-remodeling machinery, which leads to the silencing of PTEN transcription 76,125. Both mechanisms have been coopted by cancer cells for their respective tumour suppressive and oncogenic roles 119. We found sense Ѱ-lnc-parent pairs, (which account for 208 out of 391 Ѱ-lncs examined) to be more positively correlated than antisense Ѱ-lnc-parent pairs in both cohorts (Figure 4.4b). This may imply that Ѱ-lncs are more likely to regulate their parent gene in a positive manner which may occur through mechanisms such as miRNA sponging or transcript stabilization when transcribed in the sense direction 119,131. The distribution of Spearman’s ρ-values for antisense-parent gene pairs suggests a more even split between positive and negative regulation. A limitation of this study is that we cannot discount the possibility that sequencing reads for sense overlapping Ѱ-lncs that have sequence homology with their parent gene are being mapped to the parent gene instead of the Ѱ-lnc. This potential issue warrants further investigation considering both the large number of annotated pseudogenes in the genome (n= 13,000) and the possibility of false interpretation of 86 sequencing data for both protein-coding and non-coding genes (https://www.genenames.org/cgi-bin/statistics). While these alignment errors could affect sense Ѱ-lnc-parent gene pairs, antisense Ѱ-lnc-parent gene pairs are not subjected to this same technical artefact. Recently, RNA-sequencing analysis strategies have emerged that begin to address this issue, and long read RNA-sequencing could be used to reduce errors in sequence alignment. When looking at the parent genes of these deregulated Ѱ-lncs we were interested to find that many had previously described roles in cancer (Table 4.1). This includes EGLN1, a well described cancer gene involved in regulation of tumour hypoxia, and CDC42, an oncogene involved in cell cycle control 132,133. Many of these deregulated Ѱ-lncs were also associated with clinical parameters such as patient survival and patient stage, in addition to the correlated expression between Ѱ-lncs and their parent genes (Table 4.2). For example, ZBED3-AS1 and HMGB1 were positively correlated at the expression level, and low expression of both genes was associated with poor patient survival (Figure 4.6b). We also observed Ѱ-lnc-parent pairs where both genes are associated with survival, but do not share an expression relationship. LINC00667 and parent gene TACC3, for example, are both survival-associated, but not correlated at the expression level. TACC3 is a component of the TACC3/ch-TOG/clathrin protein complex, and roles in complex assembly have been previously observed for lncRNAs 83 (Figure 4.6c). Thus, it is possible that LINC00667 is involved in a form of regulation that would not affect transcript levels, including protein-complex assembly. While we were unable to assess survival associations for many of the deregulated Ѱ-lncs, they may still impact patient survival through regulation of their parent genes. We found 28 out of 34 expression-associated Ѱ-lnc-parent gene pairs to have parent genes associated with patient survival. RP11-1007O24.3, for example, was positively correlated with survival-associated parent gene ARIH1 expression in both cohorts. 87 ARIH1 has been previously described to be a mediator of DNA-damage response and mitophagy in cancer cells (Figure 4.6a) 134,135. The potential regulatory impact of Ѱ-lncs on their clinically-relevant parent genes is considerable and may represent a novel avenue for targeted therapies. While this study focused on the broad effects of Ѱ-lnc deregulation, future studies utilizing in vitro and in vivo experiments will be necessary to determine the specific mechanisms of parent gene regulation. As Ѱ-lncs may represent an unexplored area of cancer-associated parent gene regulation, their therapeutic relevance should be further explored. LncRNAs make ideal targets for therapies that target RNA products such as Antisense Oligonucleotide (ASO) therapies, since RNA is their final functional state, rather than the intermediate product for protein-coding genes 136. In addition, ASOs are easier and less costly to develop than small molecule inhibitors, and are in development as aerosol sprays that may be ideal for lung cancer treatment 96,137. However, as ASOs target through complementary sequence pairing, they would have to be designed in such a way as to not interfere with the parent gene, especially in the case of Ѱ-lncs expressed from the sense strand. This strategy of identifying lncRNAs aberrantly expressed from pseudogene loci may be useful when applied to other cancer types. Indeed, we see that several of our deregulated Ѱ-lncs have been described in other tumour types, such as TPT1-AS1 in cervical cancer, and HGC11 in both prostate cancer and hepatocellular carcinoma 138-140. Additionally, as lncRNA expression is highly tissue specific, the application of this approach to other cancer types may yield novel disease-specific Ѱ-lnc-parent pairs, highlighting the clinical utility of examining these previously-underappreciated transcripts. Overall, Ѱ-lnc-cancer-parent-gene axes represent 88 alternative mechanisms of cancer gene regulation, and their identification is a critical step towards the functional characterization of lncRNAs. 4.5 Chapter conclusions and contribution to the field There is a growing need to functionally characterize lncRNAs. Pseudogene-derived lncRNAs have been shown to be involved in cancer and regulate the expression of their parent genes. We show here how pervasive this gene regulatory mechanism is in LUAD samples. We identify a large set of deregulated Ѱ-lncs, with aberrant expression observed in RNA-sequencing data from two LUAD cohorts of paired tumour and non-malignant lung tissue samples. We show that these deregulated Ѱ-lncs have clinical value and that the parent genes, many of which are correlated with Ѱ-lnc expression, have been implicated in cancer phenotypes and are associated with clinical outcome. Together, our results highlight the important roles of the non-coding transcriptome in cancer cellular biology. 89 Chapter 5: An investigation of regulation of microRNA sponging, through the lens of XIST 5.1 Introduction The previous chapters of this thesis have focused on understudied mechanisms used by lncRNAs to enact their function. In Chapter 5, we focused on the most widely cited mechanism of lncRNA function, which is when a lncRNA regulates another gene by acting as a miRNA sponge. As described earlier, miRNAs are small non-coding RNAs that enact their function by base-pairing to the 5’UTR of mRNAs that contain complementary target sequences. The mRNA is then either degraded or, more commonly, prevented from being translated into a functional protein product. Recently it was described that a lncRNA containing the same target “Seed” sequence as an mRNA can function as a sponge, or decoy for a particular miRNA. In this interaction, the lncRNA functions as a positive regulator of the target mRNA by decreasing the abundance of free miRNAs, thus preventing the inhibition of the mRNA by the shared targeting miRNA 141 125 77. Since first being described, miRNA sponging by a lncRNA has become a hugely popular topic. In fact, over 600 publications described this mechanism last year, and many more of these types of manuscripts are published each year. However, although lncRNA mediated regulation through a shared miRNA is simple in concept, there are many factors that complicate this interaction within a biological system. As miRNA’s target seed sequence can be as little as 6 nucleotides, the number of transcripts each miRNA is able to target can be in the hundreds. Furthermore, as lncRNAs are entirely untranslated, their whole sequence is theoretically available to be bound by miRNAs, thus these long transcripts may be bound by multiple miRNAs. 90 As such, there are a vast number of potential interactions for any lncRNA, miRNA, or mRNA target gene. As miRNA target prediction is based on complementary sequence homology, this complicates the identification of a biologically functional regulatory relationship. As the majority of recent manuscripts concerning cancer associated lncRNA now postulate they function as miRNA sponges, understanding the key features of this mechanism is increasingly important. This Chapter aims to evaluate the breadth of these predicted interactions and determine the likelihood that they hold true in a biologically relevant manner. The lncRNA XIST is one of the most commonly described miRNA sponges, perhaps due to its notoriety and long length. XIST was one of the first lncRNAs to be characterized, which was an important milestone in proving that non-coding RNAs have cellular functions. XIST expression is often deregulated in cancer, and several studies postulate that it may have key functions in lung cancer 142 143 144. Discovered in 1991 by Brown et al., this large (>27Kb) transcript was shown to be involved in the silencing of the inactive X chromosome in females. The second X chromosome is silenced through a mechanism similar to the cis-acting lncRNAs described in Chapter 3, thereby preventing drastic differences in gene expression in males and females 145 146 147. Since the sequencing of the human transcriptome revealed the widespread expression of lncRNAs, many researchers have revisited XIST when new lncRNA functions are observed. Since the discovery of miRNA sponging, there has been a drastic increase in the number of publications concerning XIST have been concerning miRNA sponging, rather than XIST’s canonical function, and this translates to XIST’s potential role in cancer (Figure 5.1). Recently XIST has now been described to function as an oncogene through sponging tumour suppressive miRNAs in many cancer types, including lung cancer 148, 149. However, these reports are 91 inconsistent, with XIST often associated with different phenotypes depending on the cancer being described. Furthermore, many of these studies describe XIST functioning through a single miRNA to protect a single mRNA, rather than a pool of miRNAs. Additionally, it is unknown whether certain regions of XIST are preferential or enriched for miRNA binding. Here we use the lncRNA XIST as a proof of principal to investigate the mechanism of miRNA sponging. We investigate the many shared miRNAs that target XIST, where and how they bind to the transcript, and how this affects the relationship of XIST with its putative sponge target genes. Figure 5.1 Number of published XIST-miRNA sponging manuscripts over time Proportion of XIST and XIST-miRNA papers in 2003, 2010, and 2017. The proportion of functional manuscripts describing XIST as a miRNA sponge has drastically increased since the mechanism was first described. 92 5.2 Methods 5.2.1 Data processing RNA sequencing data: Sequence data for all LUAD and non-malignant tissue samples available from the TCGA (n=304 female and 264 male) was obtained from CancerBrowser (Illumina HiSeq, https://genome-cancer.ucsc.edu/proj/site/hgHeatmap/). The raw sequence reads were then aligned to the human genome (hg19) and the Ensemble gene reference (Release 75) was used to quantify gene expression. Small RNA sequencing data: Sequence processing described as above in Chapter 2.2. miRNAs from the same LUAD sample set were considered for further analysis if they were expressed above 1 RPKM across 10% of all LUAD samples. 5.2.2 Data analysis Identification of genes that may be positively regulated through miRNA sponging by XIST (Defended from miRNA by XIST, DMX genes): Spearman’s correlations with XIST were performed for every gene annotated in Ensembl (Release 75). As XIST is expressed in females, only female LUAD samples were used in this initial analysis (n=274). Candidate DMX genes considered for further analysis if they had a significant correlation coefficient (Rho) >0.4 (n=543 genes), and multiple testing correction was performed using the Benjimini-Hochberg method (BH p<0.05). As miRNAs bind the 3’UTR of mRNAs, we obtained the sequence of 3’UTRs for all of the candidate DMX genes. The 3’UTRs were then analysed using the miRanda miRNA prediction algorithm. Predicted sequence homology and strength of binding energy was then determined for all miRNAs and the candidate DMX genes 150. Candidate DMX-miRNA pairs were considered for further analysis if they contained a binding score of at least 150, and a 93 binding energy of at least -20kCal/mol. The full sequence of XIST was then input into miRanda and miRNAs that were predicted under the same parameters were identified. miRNAs that were predicted to target at least one candidate DMX gene as well as XIST are considered our candidate “shared miRNAs”. 5.3 Identification of genes regulated by XIST through microRNA sponging In order to identify potential sponge targets of XIST we must first define what a sponge candidate will look like. We surmise that a mRNA target gene that is regulated by XIST mediated sponging must: 1) Be expressed in LUAD 2) Be positively correlated with XIST expression 3) Share at least 1 miRNA target sequence with XIST We define these candidate sponge target genes as target genes defended from miRNA by XIST, or DMX genes. To identify candidate DMX genes we first wanted to identify mRNAs with positive correlations to XIST. LUAD is an ideal sample to study XIST’s role as a miRNA sponge, as this cancer occurs in both males and females, and XIST has a wide range of expression in female lung samples 151. As males should have negligible XIST expression, we excluded them from this initial analysis. In our female LUAD samples (n = 304) we identified all expressed Ensemble annotated genes. We then performed a Spearman’s correlation to identify genes that were significantly positively correlated with XIST expression (Spearman’s Rho>0.4, BH p≤0.05). This provided us with a list of 543 candidate DMX genes. 94 We then sought to identify miRNAs that targeted both the candidate DMX genes, as well as XIST. To perform this analysis, we retrieved the 3’UTRs of the 543 candidate genes from the UCSC Genome Browser. Next, we used the miRanda binding algorithm to identify miRNAs that would target these genes. All annotated miRNAs were tested for binding potential using a stringent threshold of binding (ΔG≥-20kCal/mol, score>150). This led to the identification of 10,654 potential DMX-miRNA interactions, involving 124 candidate DMX genes, and 2052 unique miRNAs. Next, we wanted to determine which of these miRNAs also targeted XIST. We ran the full (unspliced) sequence of XIST in the miRanda binding algorithm using the same stringency thresholds (ΔG≥-20kCal/mol, score>150), which lead us to identify 864 unique miRNAs. Of these, 804 miRNAs were predicted to bind both XIST and at least one of the 124 candidate DMX genes. Lastly, as miRNAs have tissue specific expression, and are required to be expressed to function in a sponge-based regulation, we wanted to confirm the expression of these miRNAs in LUAD. All of the miRNAs identified were expressed in our LUAD samples, and therefore could potentially be sponged (Supplemental Table 3, Appendix B2). 5.4 microRNAs targeting XIST exonic regions display stronger DMX relationships Many questions remain about what features make for an effective miRNA sponge. With regards to how efficient a sponge can function, we were interested in the distribution and enrichment of miRNAs across XIST, and how multiple shared miRNAs may affect XIST’s relationship with candidate DMX genes. XIST is a very long transcript, and as a result contains many miRNA binding sites. We were interested in whether or not it contained more miRNA binding sites per sequence length than other well-known lncRNAs that are similarly considered 95 to have secondary function through miRNA sponging. When compared to other lncRNAs, including NEAT1 and MALAT1, XIST did not appear to be enriched for miRNA binding sites (Figure 5.2). Figure 5.2 Number of binding sites per sequence length of lncRNAs. Number of miRNA binding sites is displayed on the Y axis while lncRNA length is displayed on the X axis. Each dot represents a lncRNA. XIST is indicated in pink, while other well-known lncRNAs MALAT1 and NEAT1 are indicated by blue and green dots respectively. Next, to investigate the distribution of miRNA binding sites across XIST we obtained and mapped the binding sites of the 804 miRNAs to the XIST transcript. Interestingly we found that one small region of XIST, exon 5, which is only 163 nucleotides was enriched for binding sites compared to the rest of the transcript with 15 binding sites (Figure 5.3). In terms of distribution of shared miRNA binding sites, we did not observe significant enrichment in either exonic or intronic regions (Figure 5.4). 0 500000 1000000020004000length of lncRNA Number of binding sites• XIST• MALAT1• NEAT196 Figure 5.3 Frequency of miRNA binding sites to specific intronic and exonic regions of XIST. The number of predicted miRNA binding sites were mapped to exonic and intronic regions of XIST sequence. The number of binding sites per sequence length was then calculated to determine if certain regions of XIST were enriched for miRNA binding. Pink circles indicate exonic regions, and intronic regions are denoted by baby blue. 97 Figure 5.4 Comparison of miRNA exonic and intronic binding frequency. Number of binding sites per nucleotide for all exons and introns was compared using a Student’s t-test. This led us to investigate whether the location of shared miRNA binding may affect XIST’s ability to act as a sponge. We then separated candidate DMX genes into two groups, those targeted exclusively by miRNAs that bind to XIST’s exonic regions, and those targeted by miRNAs that bind to XIST’s intronic regions. We then compared the expression association of genes in these groups with XIST expression. Interestingly, we found that DMX genes that shared miRNAs that bound to exonic regions of XIST, exhibit stronger positive correlations with XIST, indicating exonic regions may be better sponge targets (Figure 5.5a). Furthermore, when we investigated the correlations between miRNAs and the candidate DMX genes, we found that a greater number reached significance when the miRNAs were predicted to bind exonic regions of XIST (Figure 5.5b). 98 Figure 5.5 Location of miRNA binding affects XIST-DMX relationships. MiRNAs bound to exonic regions of XIST, targeted DMX genes with significantly stronger positive expression associations with XIST (A). The number of significant correlations between miRNAs and the DMX genes also increased when miRNAs bound exonic regions of XIST. In theory, an effective miRNA sponge would be able to quench more than a single miRNA at a time, and artificial miRNA sponges are optimized to contain multiple target sites 141 152. We were interested in whether any of the shared miRNAs were able to bind XIST multiple times. Interestingly, we indeed found that many of the miRNAs targeted multiple regions of XIST’s sequence (Figure 5.6). Furthermore, when we compared DMX genes that shared multiple miRNAs with XIST, we found that they exhibited stronger correlations with XIST than DMX genes that shared single miRNAs. This suggests that efficient sponge regulation is mediated by a pool of shared miRNAs instead of a single shared miRNA (Figure 5.7a,b). Lastly, the expression level of miRNAs may be important for how readily a gene can be sponged. Interestingly, while 99 in general DMX-XIST correlations increase with shared miRNA expression, DMX genes that shared the highest expressed miRNAs show an unexpected decrease in correlation with XIST. Figure 5.6 The number of target sequences each miRNA has on XIST. While the majority of miRNAs target a single site on XIST, many target multiple sites along the XIST transcript. 100 Figure 5.7 Number of shared miRNAs affects DMX and XIST expression correlations. DMX genes that shared less than 3 predicted miRNAs with XIST are significantly less correlated with XIST expression than DMX genes that share the top percental of shared miRNAs (100+) (A). Percent rank of DMX gene correlation with XIST plotted against the number of shared miRNAs (B). 5.5 Chapter discussion This chapter aimed to identify the best candidates for XIST-mediated miRNA sponge regulation in LUAD, and to identify the factors that impact the efficiency of this mechanism. Importantly, we consider the biological sex of our samples, the full complement of miRNAs available to target XIST, and the expression of all mRNA targets and shared miRNAs. We identified a set of genes that are positively correlated with XIST, indicating a positive regulatory relationship consistent with miRNA sponging. We then used miRNA binding prediction 101 algorithms and discover that, of the miRNAs predicted to bind XIST (n=862), the vast majority (n=804) bind to these candidate DMX genes. We investigated how and where these shared miRNAs bind to XIST to determine if this had an effect on the candidate DMX genes. XIST has many splice isoforms and this may affect which binding sites are available for a miRNA to bind. While we did not observe enrichment of miRNA binding sites in the exons, we found that DMX genes that shared exonically targeting miRNAs exhibited significantly stronger correlations with XIST. This is interesting as the fully-spliced XIST transcript is the most abundant in lung tissue, and as a result these increased DMX correlations may be a result of the abundance of XISTs exonic target sites available for miRNA sponging 151. The number of miRNAs is also important for any sponge regulation. Studies on the optimization of synthetic miRNA sponges have determined that the optimum number of target sites is 4-10 per sponge to enact the greatest effect on target genes. We find that 10% of our shared miRNAs contain this many binding sites on XIST. We also observe that DMX genes that share multiple targeting miRNAs with XIST are more strongly positively correlated, indicating that this regulatory mechanism is more effective when a pool of shared miRNAs is involved. Lastly, when we tested the effect of miRNA expression upon DMX-XIST relationships, we observed that while general increased miRNA expression led to increased DMX-XIST correlations, the highest expressed miRNAs exhibited a reduction in association. While this may seem counter intuitive, it is possible that there is an upper limit to the number of free miRNAs XIST is able to bind; once this limit is reached, remaining miRNAs are free to target DMX genes once more, which reduces the association. 102 Overall, this chapter highlights the complexity of regulation by miRNA sponging. Any one lncRNA will have hundreds of genes that share targeting miRNAs, making identification of true regulatory targets difficult. We provide an in depth look at miRNA binding to XIST, and identify several features that may affect which target genes are preferentially protected from miRNA based degradation. These features will be critical to assist researchers studying this mechanism to identify better, more biologically relevant targets of this massively popular regulation type. 103 Chapter 6: Conclusions 6.1 Summary of thesis chapters 6.1.1 Overall summary of thesis findings Up to this point, research on the role of lncRNAs in cancer has been hindered by a lack of functional prediction. Each chapter in this thesis work highlights a different lncRNA mediated mechanism that could be harnessed by tumours to regulate oncogenes and tumour suppressor genes. This work describes methodology’s for identifying lncRNAs that function to deregulate cancer genes in cis, or in trans in LUAD, and additionally provides an in-depth analysis of the complex interactions involved in the mechanism of miRNA sponging. 6.1.2 Summary of thesis Chapter 3 With the goal of identifying mechanisms of lncRNA mediated deregulation of cancer genes, this chapter explored the role of lncRNAs that regulate protein coding genes in cis. In order to investigate the prevalence and landscape of cis-acting lncRNAs in lung adenocarcinoma, we harnessed two independent cohorts of RNA sequencing data from LUAD tumours, both with matched non malignant tissue. Combining sequence data with genomic location we were able to identify 408 deregulated lncRNAs that overlapped or closely neighboured protein coding genes. We performed literature review and discovered that many of these protein coding genes have been previously described in cancer. Additionally, several of these known cancer genes are associated with the expression of deregulated cis-acting lncRNAs, implying that these lncRNAs may be involved in regulating the expression of these genes. Furthermore, we surmise that these lncRNAs may be deregulated in order to modulate the expression of these cancer-associated genes. 104 To validate that this methodology can identify cis acting lncRNAs that function to regulate cancer driving genes, this chapter focused on a prospective cis-acting lncRNA that neighboured HMGA1, a known oncogene. We found HMGA1-lnc to be dowregulated, and inversely correlated with oncogene HMGA1. Furthermore, both genes were associated with tumour stage, with HMGA1 expression being associated with more aggressive disease, while HMGA1-lnc was associated with less aggressive disease. Lastly, we showed that HMGA1-lnc was able to control HMGA1 expression when we inhibited expression of HMGA1-lnc in vitro and we found significant increases in HMGA1 expression. This chapter demonstrates that deregulation of cis-acting lncRNAs is a frequent event in LUAD, and we observe that downregulation of HMGA1-lnc is an alternate mechanism of deregulation of the oncogene HMGA1. 6.1.3 Summary of thesis Chapter 4 Building upon work completed in Chapter 3, this chapter aimed to characterize the landscape of a type of lncRNA-based regulation previously never explored in lung cancer, the regulation of protein coding genes by pseudogene derived RNAs (Ѱ-lncs). lncRNAs expressed from pseudogene loci have been previously shown to be able to regulate their related protein coding parent genes, and we hypothesized that this form of regulation could be harnessed by cancer cells to deregulate cancer driving genes. The work here utilizes RNA-sequencing data in conjunction with sequence homology in two cohorts of lung adenocarcinoma, each of which contained matched non-malignant samples to identify 104 deregulated Ѱ-lncs in LUAD. Interestingly, upon investigation, we find that many of these deregulated lncRNAs were expressed from the loci of pseudogenes related to 105 known cancer genes. Furthermore, we identify many deregulated pseudogene-derived lncRNAs whose expression significantly correlated with these cancer associated protein coding genes, suggesting a regulatory relationship. We harnessed a large public clinical dataset to then determine if Ѱ-lncs, or their parent genes were associated with patient survival. While only 19 of the deregulated Ѱ-lncs were represented on the microarrays compiled in this platform, 16 Ѱ-lncs with deregulated expression were significantly associated with patient survival, implicating the clinical relevance of these genes. This work shows that deregulation of pseudogene-derived lncRNAs is a widespread phenomenon in LUAD and highlights trans-acting lncRNA regulation through sequence homology as an alternative mechanism of cancer gene deregulation in LUAD. A version of this chapter is published as “Aberrant Expression of Pseudogene-Derived lncRNAs as an Alternative Mechanism of Cancer Gene Regulation in Lung Adenocarcinoma” in Frontiers in Genetics (2019) 107. 6.1.4 Summary of thesis Chapter 5 While lncRNAs have been increasingly implicated in cancer, functional characterization has remained a major challenge, and there is a growing gap between lncRNAs named, and those characterized. Due to the ease of use and widespread availability of miRNA prediction software, by far the most published and frequently studied lncRNA function is when a lncRNA acts as a miRNA sponge. However, questions remain over how robust these types of analysis are when applied to complex multi-gene interaction such as miRNA-sponge networks, and it is unknown what features make a more efficient sponge. This chapter explores the mechanism of lncRNA based miRNA sponging by analyzing the lncRNA XIST and potential targets of shared miRNAs in LUAD. 106 To investigate the role of XIST acting as a miRNA sponge, we designed a pipeline to identify mRNAs that may be “sponged” by XIST that we define as or Defended from miRNAs by XIST or DMX genes. These genes are expected to display an expression pattern that correlates positively with that of XIST and must have shared miRNA binding sites. To find candidate DMX genes we scanned both the sequence of XIST and the 3’UTRs of DMX transcripts to find common microRNA binding sites and utilized RNA sequencing and miRNA sequencing data from hundreds of LUAD samples. While XIST is targeted by over 800 miRNAs, and positively correlated with over 500 genes, using this approach allowed us to narrow down our discovery set to 124 genes potentially regulated by XIST through the sequestration of miRNAs (DMX). We then set out to further explore the features important to miRNA-mediated gene regulation. We evaluated whether multiple miRNA binding sites or certain regions of a gene are better at sequestering miRNAs. This led us to find that microRNAs targeting XIST at exonic regions and DMX genes that share multiple miRNA binding sites with XIST display a stronger XIST-DMX relationship. Another main finding of this chapter is the potential for false positive sponge interactions. While there are hundreds of genes that may be predicted to be targets of sponge-based regulation, many of these predictions may not be biologically relevant. lncRNAs can be targeted by hundreds of miRNAs (XIST alone is predicted to be targeted by over 800 miRNAs), and each of these miRNAs can in turn target tens to hundreds of genes. As miRNA predictions are used as the primary method to assign function to a deregulated lncRNA, this work emphasizes the need for higher stringency when studying this mechanism. Considering basic cell biology, expression, and localization will lead to increased confidence in identifying targets with more clinical relevance. An expanded version of this Chapter is published in PLoS One as 107 “Beyond sequence homology: Cellular biology limits the potential of XIST to act as a miRNA sponge” (2019) 153. 6.2 Strengths and limitations 6.2.1 Strengths and limitations of Chapter 3 6.2.1.1 Strengths Identifying the function of lncRNAs has remained a major challenge in the field, and the mechanism of cis-acting lncRNAs is understudied. As over 30% of the genes in the human genome display overlap with other genes, this may be impacting a wide variety of genes globally 154 111. A major strength of this chapter is in its methodology to both generate and test hypotheses regarding lncRNA functions in cancer. This provides an alternate method to identify the candidate target genes of a lncRNA, as lab-based screens are both time consuming and expensive. Additionally, many of the screen based assays traditionally used to identify gene functions are intended for protein coding genes and are not well suited for lncRNAs. For example, pull downs on large protein complexes such as PRC2 reveal thousands of interacting RNAs, implying non-specific binding and complicating the identification of relevant gene interactions 155. The use of two datasets of LUAD with matched non malignant tissue is also an advantage of this chapter, as it reduces the sample bias of using a single dataset, and allows for increased confidence that the lncRNAs identified as deregulated are biologically relevant to lung cancer. We highlight the utility of this approach with the identification of a deregulated lncRNA, HMGA1-lnc, which controls the expression of HMGA1. HMGA1 is an important cancer gene and the discovery of a lncRNA that regulates it has implications for many tumour types and demonstrates the utility of using RNA sequencing data to interrogate genomic loci. We 108 confirmed the ability of HMGA1-lnc to regulate HMGA1 in BEAS-2B cells, a cell model where previous studies have shown HMGA1 expression to drive oncogenic phenotypes. This study shows the potential of this type of lncRNA mediated regulation and that there may be many other undiscovered non-coding members of cancer pathways in LUAD and other malignancies. 6.2.1.1 Limitations The many roles of lncRNAs in lung cancer have just begun to be uncovered, and while there remains considerable biology to discover, this methodology will be limited to capturing lncRNAs that are cis-acting. For example, this method will likely not identify trans-acting effects, as well as interactions with proteins and RNA that do not result in changes to neighbouring gene expression levels, including protein complex assembly, RNA splicing, and cellular localization. Additionally, while this methodology is hypothesis generating, there may be false positives and negatives as not all lncRNAs regulate their neighbouring genes, and these gene-pairs may be affected by other mechanisms that affect their transcript levels. This could include passenger effects such as DNA copy number alterations that by increasing or decreasing gene copies, can make cis-pairs appear to display concordant expression patterns. This highlights the importance of using cell models for additional experimental validation, and to confirm the regulatory ability of each prospective cis-acting lncRNA on their neighbouring genes of interest. 6.2.2 Strengths and limitations of Chapter 4 6.2.2.1 Strengths Chapter 4 is the first global look at pseudogene derived lncRNAs, shedding light upon an entire class of gene previously unexplored in LUAD. The analysis of expression from 109 pseudogene loci has previously been complicated by the sequence homology of pseudogenes to protein coding genes, and inconsistencies between pseudogene databases. Until recently, even the location and name of many pseudogenes could vary greatly based on the database used. This chapter is the first to utilize a non-redundant database of the three largest pseudogene databases to analyze sequence data in conjunction with RNA sequencing profiles and protein coding gene homology. We harnessed public databases containing hundreds of samples with clinical data to ascertain whether parent genes of these Ѱ-lncs are associated with survival 107. Similarly to the process used in Chapter 3, this methodology can be applicable to other cancer types, and may have a large impact on the cancer genome as there are estimated to be over 20, 000 pseudogenes in genome 156. This chapter raises awareness of the broad spectrum of Ѱ-lnc deregulation in LUAD, their expression relationships with known cancer genes, and associations with patient survival. Lastly, this chapter demonstrates the importance of this type of regulation and paves the way for further research on this understudied gene class, in LUAD, and other cancers. 6.2.2.2 Limitations In this chapter our goal was to identify high confidence Ѱ-lncs that may be involved in the regulation of their protein coding parent gene. To do this we focused on lncRNAs deregulated in 2 datasets of LUAD, but as this is a heterogenous disease we may have missed some Ѱ-lncs that are less frequently deregulated or expressed at low levels. While the sequence homology of these lncRNAs allows them to perform their functions, it is a double-edged sword when it comes to expression analysis. Sequencing reads that are identical between the lncRNA and the parent gene will likely be mapped to the protein coding gene, potentially biasing expression relationships to appear concordant. While this is largely an issue for lncRNAs 110 expressed from the sense strand of the pseudogene, and is less of an issue for lncRNAs expressed from the opposite strand, future studies will need to address this issue, in addition to pseudogene-based reads contaminating protein coding expression analysis. Further, the exact amount of sequence homology needed for regulation of a parent gene is unknown, and it may be different for different types of regulation. For example, the sequence necessary for a Ѱ-lnc to interact with DNA and recruit a protein complex may be much different in size than sequence homology to function as a decoy for shared miRNAs. Additionally, depending on said mechanism of regulation there may be certain locations where the sequence of the gene may be more important, for example 3’ UTRs. While the purpose of this study was to observe the global deregulation of these lncRNAs, individual Ѱ-lnc-parent interactions will need to be confirmed in cell models both to confirm their regulatory ability, as well as to assess their effect on cancer cell growth. 6.2.3 Strengths and limitations of Chapter 5 6.2.3.1 Strengths MiRNA sponging is an immensely popular topic, and currently the most popular method of predicting the function of a lncRNA. As we study this mechanism in depth, a strength of this chapter is how applicable this research will be to those currently using this method to base their studies on. While our study focused on XIST, researchers studying other miRNA sponges may be able to identify better sponge targets by taking into account the binding features discussed in this chapter. Another strength of this chapter is the novel approach taken to explore the topic of the mechanism of miRNA sponging. We are able to perform this analysis by taking advantage of large public repositories of sequencing data, combining datasets of miRNA sequencing and RNA sequencing to probe for XIST-miRNA-DMX relationships. 111 6.2.3.2 Limitations While this work has identified key features to consider while studying miRNA sponging, and finding several high confidence miRNA-XIST-DMX pairs in the process, confirming these complex interactions is still a complex and difficult process. Many things could interfere with any lncRNA-miRNA-mRNA interaction, including the number of total binding sites on the lncRNA, as well as the expression levels of a given miRNA. An even larger issue is that there are many other genes with varying numbers of miRNA binding sites that are competing for the binding of a miRNA. Additionally, the current standard method of confirming miRNA binding to a target mRNA in cell models usually involves the induced overexpression of a miRNA of interest. For example experimental validations such as luciferase assays or mutating binding sites do not mimic natural biological situations. This makes identification of biologically relevant sponging interactions difficult both in silico and in vivo as these conditions have artificially high miRNA levels and are not indicative of normal conditions within a cell. 6.3 Future directions 6.3.1 Chapter 3 The methodology used in Chapter 3 was able to identify candidate cis-acting lncRNAs that can regulate cancer driving genes like HMGA1. This approach may be useful in the study of other cancer types, where with different genetic backgrounds, other cis-acting lncRNAs may be regulating other known oncogenes or tumour suppressors specific to other cancer types. As HMGA1 is a known oncogene in other forms of malignancy, particularly breast cancer, it would be useful to determine if this lncRNA based mechanism is a common feature. If so this 112 interaction could represent a novel clinical intervention point in HMGA1-driven cancers. Additionally, future work into the exact nature of the HMGA-lnc mechanism may discover features that are applicable to a wide variety of cis-acting lncRNAs and could have clinical use. For example, lncRNAs that function through active methylation may be vulnerable to demethylating agents such as 5-azadeoxycytidine. Alternatively the discovery of a binding sequence or important secondary structure features used to recruit specific protein complexes would allow for a more targeted therapeutic approach. 6.3.2 Chapter 4 The Ѱ-lncs identified in this chapter are deregulated, as well as associated with the expression of their cancer associated parental genes survival. However there is much variability in the homology and sequence overlap of each pseudogene and parent. As such, the regulatory ability of each Ѱ-lnc-parent pair will need to be confirmed in cell models. Additionally, in vitro efforts concerning how the similarity of each Ѱ-lnc to its parental gene, affects the mechanism of action would be very useful in predicting functional interactions. Similar to the cis-acting lncRNAs identified in Chapter 3, efforts to deconvolute the mechanisms of these trans-acting Ѱ-lnc pairs will be important not only for understanding how this gene class functions, but for the potential therapeutic benefits of modulating expression of these genes. Applying this methodology to other cancer types may reveal many other Ѱ-lncs deregulated as an alternate mechanism of cancer gene deregulation. As these lncRNA-mRNA interactions are understudied there are many potential regulatory mechanisms at work here. For example, sense strand Ѱ-lncs may make for ideal miRNA sponge candidates, as they can contain many of the same miRNA binding sites as their protein coding parent gene. However this same 113 sense strand homology can be problematic when it comes to differentiating reads from highly similar segments of Ѱ-lncs and their parents. Current methods are better at differentiating regions of high dissimilarity, but have difficulty mapping similar reads. Future studies with higher depth sequencing will be able to better differentiate reads that map to regions with sparse, infrequent differences between each unique pseudogene and parent gene. Additionally, single cell sequencing will be a valuable asset to deconvolute Ѱ-lnc parent pairs, and reduce the transcriptomic noise caused by the heterogeneity found in bulk tumours. 6.3.3 Chapter 5 While this chapter details the first in depth analysis of miRNA sponging through XIST, many questions remain in regard to this mechanism. The approach taken in this chapter was to determine whether the binding patterns of miRNAs to XIST affect the sponge regulated genes. In our manuscript published on this topic we dive further into XIST’s role as a miRNA sponge, by further investigating our candidate DMX genes. While a similar approach using gene expression profiles and miRNA prediction can be used on other lncRNAs to investigate and identify high confidence sponge targets, XIST has unique expression patterns that allowed us to further test our candidate DMX genes. As XIST is only expressed in females, we can compare expression patterns in biological systems with, and without XIST expression by analysing the male and female LUAD samples. In our manuscript, we compare the correlation between miRNAs and the DMX genes in systems with (female n=304) and without (male n=264) XIST. Interestingly we find that a small subset of male samples have high XIST expression, and that the DMX genes are significantly more correlated with XIST than the males with no XIST expression. We expected that in the 114 systems with no sponge (males) the miRNA-DMX correlation of true sponged genes would be more negative as more free miRNAs would be available to perform their inhibitory action than those sequestered by the sponge. By comparing samples with, and without XIST, we discovered a high confidence set of 13 miRNA-DMX pairs with correlations that were significantly more negative in the LUAD samples without XIST (males). To further test the ability of these high confidence miRNAs to be sponged by XIST we then assessed the cellular location of the miRNAs. XIST is expressed exclusively in the nucleus, thus our candidate miRNAs must be present in the same compartment in order to interact with and be sponged by XIST. We tested five miRNAs for nuclear presence across 3 cells lines and found that all were present in the nucleus 153. Further work on the high confidence miRNAs, and their target genes may reveal if these genes could impact cancer. XIST mediated miRNA sponging is frequently brought up in cancer, so it would be interesting to see if these miRNAs affect lung cancer phenotypes through DMX gene interactions in female LUAD. Additionally, future studies analyzing single cell sequencing may provide a more in depth system to analyze the interaction of XIST with these miRNAs and their target genes. Currently, miRNA sponging is the most popular mechanism of lncRNA function, as in silico prediction of interactions with miRNAs are simpler to generate than many other possible interactions. However, future studies will require increased stringency to ensure that these interactions are important within the cell. Additionally, many questions remain and future studies will need to ascertain which binding sites are most relevant, and whether there are other features that determine which miRNAs are most likely to be used in sponging interactions. Further, certain lncRNAs may be better sponge candidates for certain genes. For example lncRNAs 115 expressed from pseudogene loci may share several of the same miRNA binding sites with their parent genes, and thus may function as superior sponges than lncRNAs sharing few miRNAs. 6.3.4 Future directions for the non-coding field The immune system has become a major focus in lung cancer research, resulting in new therapeutics that have benefited patients with tumours that evade the immune system. The role of non-coding RNAs within immune cells remains largely unknown, especially with regards to cancer. Recent work from our group suggests that there are immune cell type specific lncRNAs, and that these lncRNAs can be found in bulk lung tumours, which may have important implications for detection of immune cells in tumours or suggest an active role of lncRNAs in the tumour-immune response. Future studies will reveal if these cell type specific lncRNAs have clinical benefit. Lung cancer screening with low-dose computed tomography (CT), is currently in early studies to detect the disease early in high risk individuals, where the disease is less invasive and more treatable. It is estimated that by taking this approach lung cancer deaths can be reduced by 20% (www.bclungscreentrial.com). High risk individuals may be identified with blood based biomarkers and since miRNAs are stable in blood, these molecules could act as markers of disease. In particular, novel miRNAs have been shown to be extremely cell type specific and may have potential to better predict at-risk individuals 59 157 158. Recent studies from our group have shown tumour specific novel miRNA expression as well as associations with patient survival, which may hint at their clinical utility 159 160 161. Similar to how piRNAs were able to better separate survival curves for patients, adding these novel species of miRNAs to biomarker panels may also improve diagnostic and prognostic markers. 116 As focused on in Chapters 3 and 4, lncRNAs can act in cis and in trans as alternate mechanisms of cancer gene deregulation. As such, many of these non-coding genes may represent novel therapeutic intervention points. One promising method of targeting non-coding RNAs are Antisense Oligonucleotides (ASO’s). As ASO’s are cheaper to manufacture than small molecule inhibitors and specific to RNAs, they may be the most ideal way to target lncRNAs. Furthermore, they may make ideal drugs for lung cancer treatment, as they are currently being tested for dispersal by aerosol sprays to the lungs in vivo. 162. 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Cancers 11, doi:10.3390/cancers11020262 (2019). 127 Appendices Appendix A Supplementary material A.1 Supplementary tables from Chapter 3 Table A.1 Deregulated prospective cis-acting lncRNAs Tag Direction Median Fold Change (BCCA) BCCA BHC pValue cis-gene A2M-AS1 Downregulated 4.99278313 9.31E-07 A2M AC002066.1 Downregulated 8.4705882 4.39E-07 CAV1 AC002398.12 Downregulated 4.94318134 0.0000961 HSPB6 AC003102.3 Upregulated 6.72640629 0.00038918 RUNDC3A AC004490.1 Downregulated 6.12145001 1.49E-07 DOT1L AC004540.4 Downregulated 7.18341517 1.14E-07 SNX10 AC004540.5 Downregulated 5.74574039 0.00000033 SNX10 AC005264.2 Downregulated 4.24878293 0.00011162 GNA15 AC005740.6 Downregulated 5.04918033 4.87E-07 PCDH12 AC005789.11 Upregulated 2.375 0.00593123 SPRED3 AC006014.8 Downregulated 3.88757978 0.00062122 STAG3L1 AC007128.1 Upregulated 18282726.1 0.00036335 ICA1, NXPH1, GLCCI1 AC007277.3 Downregulated 4.67095866 0.00069463 MYO3B AC007405.4 Downregulated 9.00000001 0.00010601 ERICH2 AC007405.6 Downregulated 3.89614598 0.0000444 ERICH2 AC007743.1 Downregulated 28.6394546 1.14E-07 CCDC85A AC007750.5 Upregulated 2.91705041 0.00124182 FAP,GCG AC007970.1 Downregulated 29.1722945 0 LANCL1 AC009005.2 Upregulated 2.95873543 0.02763744 BSG AC010226.4 Downregulated 4.44841423 0.0000563 TMED7 AC010547.9 Downregulated 299.824543 0.0000321 ZNF19 AC010890.1 Upregulated 2 0.00298985 NCKAP5 AC011899.9 Downregulated 14.5490932 0 PTPRN2 AC012594.1 Downregulated 2.11530977 0.00303756 MYO3B AC013264.2 Downregulated 6.99999999 0.000027 ANKRD44 AC016683.6 Downregulated 4.1058999 0.00072576 PAX8 AC026703.1 Downregulated 4.96545096 5.15E-07 NPR3 128 AC066593.1 Upregulated 3 0.01522016 DPP10 AC079210.1 Downregulated 3.36386868 0.0000264 FAM20A AC083949.1 Downregulated 3.07382 0.0000647 EML4 AC090616.2 Downregulated 9.75046883 1.49E-07 RHOT1 AC093110.3 Downregulated 43.5289949 0 SPTBN1 AC093495.4 Downregulated 5.86308398 0.00000694 XPC AC096670.3 Downregulated 12 0.00000033 ACOXL AC097658.1 Downregulated 8.29400234 0.000002 GAB1 AC099850.1 Downregulated 9.73761196 0.00000345 SKA2 AC100830.3 Downregulated 3.5 0.0000621 OAZ2 AC124789.1 Downregulated 22.2039099 7.38E-08 ARHGAP23 ADAMTS9-AS1 Downregulated 42.3976211 0 ADAMTS9 ADAMTS9-AS2 Downregulated 21.9956398 0 ADAMTS10 ADIRF-AS1 Downregulated 14.5878435 0 ADIRF ADORA2A-AS1 Downregulated 2.48939696 0.00073329 ADORA2A AF131215.2 Downregulated 9.76271182 1.49E-07 XKR6 AFAP1-AS1 Upregulated 23.6712582 0 AFAP1, SORCS2 AGAP1-IT1 Downregulated 2.42857143 0.00320442 AGAP1 AL022476.2 Downregulated 2.87720175 0.00031687 TTLL1 AL049840.1 Downregulated 6.00663784 0.0001322 XRCC3 AL163636.6 Downregulated 8.25288088 0.0000013 ANG AL356356.1 Downregulated 4.48040053 0.00025988 ADAMTSL4 AL358113.1 Upregulated 506282.799 0.0000159 TJP2 AL591684.1 Downregulated 16.5069495 0.0000295 ANXA8 AL603965.1 Downregulated 83.3838014 6.55E-07 ANXA8L2 ALG1L13P Downregulated 7.23894278 3.07E-07 FAM86B3P AP000322.54 Downregulated 5.45454546 0.00000243 SMIM11 AP000330.8 Upregulated 5.99836919 0.00000175 RUNX1, RCAN1, CLIC6 AP000525.9 Upregulated 3.62479848 0.00257119 DUXAP8 AP000662.4 Downregulated 8.07528954 0.0000176 YPEL4 AP000695.4 Upregulated 2 0.01408789 CLDN14 AP001065.2 Upregulated 13.6065886 0.04932941 TRPM2 AP001189.4 Downregulated 13 0 LRRC32 AP003026.1 Downregulated 2.49896563 0.00774901 DLG2 ATP13A4-AS1 Downregulated 3.85996 0.0000884 ATP13A4 ATP5F1P5 Downregulated 9.9 4.39E-07 ATP5F1P6 129 BCRP3 Downregulated 6.72922396 0.00092449 BCRP4 C1orf170 Upregulated 13.8841333 1.49E-07 PLEKHN1 C21orf128 Downregulated 5.12195123 0.0000828 UMODL1 C5orf56 Downregulated 4.00357782 0.0000227 IRF1 C6orf165 Downregulated 32.4549793 0.00973151 SLC35A1 CASC9 Upregulated 16.4383 0.00706918 CASC9 CCDC13-AS1 Downregulated 3.22517461 0.0000824 CCDC13 CCL15-CCL14 Downregulated 17.3095032 0.0000133 CCL15 CEBPA-AS1 Downregulated 4.90251126 0.00093249 CEBPA CHIAP2 Downregulated 75.7752279 0 CHIAP2 CMAHP Downregulated 4.35784012 0.0000891 CMAHP COLCA1 Downregulated 15.3927404 0.00000281 COLCA2 CTA-134P22.2 Downregulated 16.4154455 0 CADM3 CTB-131B5.2 Downregulated 3.5 0.00016815 CYSTM1 CTB-134H23.3 Downregulated 41.7822538 0 RRN3P2 CTB-176F20.3 Downregulated 2.47368421 0.03855288 ZNF91 CTB-36H16.2 Downregulated 6.84415584 0.0000128 SNX2 CTB-50L17.14 Downregulated 4.46111796 0.00000163 LRG1 CTB-55O6.12 Downregulated 4.72625753 7.16E-07 LPHN1 CTB-55O6.4 Upregulated 2 0.00017365 RLN3 CTB-60B18.6 Downregulated 943396226 0.03046148 CGB CTC-250I14.3 Upregulated 2.51718028 0.00642486 NACC1, STX10 CTC-255N20.1 Upregulated 2 0.03276568 STK32A CTC-297N7.5 Downregulated 2.94728044 0.00000754 TMEM220 CTC-558O2.2 Downregulated 17.681967 1.85E-07 SLIT3 CTD-2033A16.3 Upregulated 309372122 0.0000766 NOB1, NFAT5, WWP2 CTD-2036P10.3 Downregulated 20746.888 0.00000425 TTBK2 CTD-2114J12.1 Upregulated 2.36189428 0.00623942 LRRC69 CTD-2207O23.10 Downregulated 218340.611 1.85E-07 PNPLA6 CTD-2207O23.3 Downregulated 310559006 1.85E-07 ARHGEF18 CTD-2314B22.3 Upregulated 2.06623421 0.00611609 DUXAP10 130 CTD-2319I12.2 Downregulated 2.11641332 0.00139631 HEATR6 CTD-2524L6.3 Downregulated 13.9993465 0.00012034 NR2E3 CTD-2527I21.4 Downregulated 74.9829957 0.00049933 FXYD1 CTD-2561B21.7 Upregulated 4929957469 0.00196435 CHMP6 CTD-2562J17.7 Downregulated 8.13401585 5.15E-07 ARRB1 CTD-2636A23.2 Downregulated 4.31053986 0.00012912 HMGCS1 CTD-3076O17.1 Upregulated 2.03031518 0.00986304 ADAMTS17 CTD-3105H18.14 Downregulated 2.53303703 0.00810136 ZNF799 CTD-3148I10.15 Downregulated 4524.82545 0.00165901 FLT3LG CTD-3187F8.14 Downregulated 2.82514319 0.02196737 SIGLEC7 CTD-3214H19.16 Downregulated 5756.25705 0 TRAPPC5 CYP1B1-AS1 Downregulated 3.42857142 0.00012807 CYP1B1 CYP4F29P Downregulated 2.38815909 0.00124182 CYP4F29P DGCR5 Upregulated 3.10672322 0.00068241 DGCR5 DGCR9 Upregulated 5.31922816 0.00327422 DGCR9 DIO3OS Downregulated 12.7827336 0 DIO3OS DNAJC9-AS1 Downregulated 3.16173368 0.00346898 DNAJC9 DNM1P46 Downregulated 105485.232 0.00000268 DNM1P47 EPB41L4A-AS2 Downregulated 7.87368419 5.56E-07 EPB41L4A EPHA1-AS1 Downregulated 5.57070425 0.00000473 EPHA1 ERICH2 Downregulated 2.18397626 0.00094987 ERICH2 FABP5P7 Downregulated 5.76451536 0.00000518 FABP5P7 FAM138A Upregulated 8645.65772 0.02739795 FAM138F FAM182A Downregulated 4.2590818 0.00041186 FAM182A FAM66C Downregulated 7.06605283 0.0000121 FAM66C FAM83A-AS1 Upregulated 5.96489641 0.00000425 FAM83A FAM85A Downregulated 7.74193546 7.38E-08 FAM85A FAM86JP Upregulated 3.74171985 0.01385637 ALG1L FEZF1-AS1 Upregulated 9.62390064 0.00000184 FEZF1 FGF14-AS2 Downregulated 7.05527637 7.38E-08 FGF14 FLG-AS1 Downregulated 2.17470686 0.00012265 FLG 131 FLI1-AS1 Downregulated 7.61531087 0.00000251 FLI1 FLJ00388 Upregulated 3.07E+14 0.04194006 TRABD2B FLJ20373 Downregulated 3.53830096 0.00038683 MAP4K4 FTO-IT1 Downregulated 7.46666669 5.27E-07 FTO FZD10-AS1 Downregulated 29.3273004 0.00038683 FZD10 GAS6-AS2 Downregulated 6.14634145 0.0000312 GAS6 GATA6-AS1 Downregulated 6.31568645 0.00000694 GATA6 GGT3P Downregulated 2.40649476 0.00888159 GGT3 GGTA1P Downregulated 6.9976291 0.00000425 GGTA1 GRTP1-AS1 Downregulated 5.09986955 0.00000209 GRTP1 GTF2IRD1P1 Upregulated 16.8368972 0.00218852 RABGEF1 GVINP1 Downregulated 11.0946627 0.00000384 GVINP2 HBZP1 Upregulated 1620.50547 0.00551042 HBM HERC2P3 Downregulated 231.857714 6.55E-07 HERC2P4 HHIP-AS1 Downregulated 11.6507031 0.00000694 HHIP HM13-AS1 Downregulated 2.19808047 0.00611609 HM13 HNRNPA1P33 Downregulated 8.47330887 0.00000177 HNRNPA1P34 HSPB2-C11orf52 Downregulated 23.2927601 1.49E-07 CRYAB IGHG4 Upregulated 3.08906608 0.00046879 IGHE IMPDH1P10 Downregulated 176.054075 0.00046879 CFLAR INMT-FAM188B Downregulated 584795.322 1.14E-07 FAM188B KM-PA-2 Upregulated 3.05904156 0.00014867 SCXB, MROH1 KRT18P12 Upregulated 86.684056 0.04164009 PTPN14 KRT8P9 Upregulated 3.33508045 0.0000304 LRRC49 LA16c-366D3.1 Downregulated 5.62915447 3.07E-07 LMF1 LBX1-AS1 Downregulated 3 0.00000215 LBX1 LIMD1-AS1 Downregulated 4.54678981 0.00000518 LIMD1 LINC00337 Upregulated 3.9994 0.00380086 ICMT LL0XNC01-250H12.3 Downregulated 6.78185687 0.0000159 RAB40A LL22NC03-86G7.1 Downregulated 4.18028034 6.55E-07 PPM1F LY6G6E Upregulated 2.78E+14 0.00011921 LY6G6E MAGI2-AS3 Downregulated 12.6956306 0 MAGI2 MAMDC2-AS1 Downregulated 4.36130862 0.00103839 MAMDC2 MBL1P Downregulated 4.21329572 0.00110557 MBL1P MBNL1-AS1 Downregulated 7.07364952 0.00000103 MBNL1 132 MEGT1 Upregulated 12.8600327 0.00000455 LY6G6F, LY6G6E MIR600HG Downregulated 5.73430147 0.0000133 STRBP NAV2-AS2 Downregulated 5.07240825 2.42E-07 NAV2 OIP5-AS1 Downregulated 4.61153189 0.000052 OIP5 OR2A20P Downregulated 5.6551903 0.0000348 OR2A1 OR7E14P Upregulated 2.12794267 0.00029623 OR7E14 PAN3-AS1 Downregulated 3.62068966 0.0000155 PAN3 PFN1P2 Downregulated 2.05551409 0.00698419 NBPF20 PGM5-AS1 Downregulated 6.59754999 0 PGM5 PPP1R14BP3 Upregulated 2.04527204 0.01942068 ELF2 PRKCQ-AS1 Downregulated 8.45562993 0.0000175 PRKCQ PSMD6-AS2 Downregulated 3.67241283 0.0000101 PSMD6 RAMP2-AS1 Downregulated 51.0819202 0 RAMP2 RBM26-AS1 Downregulated 4.87339664 0.0007718 RBM26 RBPMS-AS1 Downregulated 5.12034542 0.00000083 RBPMS RCC2P4 Downregulated 2 0.00326354 CHCHD6 RLIMP2 Downregulated 12422360.2 0.000041 LRIG2 RN7SL5P Upregulated 7.83343797 0.00218852 PTPRD RP1-170O19.22 Downregulated 688.471476 0.00033945 HOXA4 RP1-178F15.5 Downregulated 6.53739521 0.00000425 S100A1 RP1-18D14.7 Downregulated 14.4616734 0 TAL1 RP1-257A7.5 Downregulated 4.08391608 0.0000268 PHACTR1 RP1-310O13.7 Downregulated 9.81645516 0 CCM2L RP1-78O14.1 Downregulated 19 0 SYT1 RP1-85F18.5 Downregulated 5.99999999 0.0000014 EP300 RP11-1002K11.1 Downregulated 13.3 4.39E-07 NRG1 RP11-1079K10.4 Upregulated 1400.70571 0.00353288 PHB RP11-1090M7.1 Downregulated 6.202838 4.56E-07 ARHGAP44 RP11-122M14.1 Downregulated 7.875 0.00000083 NEK2 RP11-125B21.2 Downregulated 4.58149414 2.79E-07 VLDLR RP11-127I20.5 Upregulated 3.06870667 0.00074099 SMIM22 RP11-1293J14.1 Downregulated 3 0.00000274 ADI1 RP11-129B22.1 Downregulated 7.55065924 6.55E-07 PRICKLE2 133 RP11-140H17.1 Downregulated 17953321.4 0.00000469 CYB5B RP11-141J13.5 Downregulated 50.4950484 0 SMTNL2 RP11-146D12.2 Downregulated 8.66138157 0.00018456 ANKRD20A3 RP11-152P17.2 Upregulated 7.3165 0.0000368 ZFPM2 RP11-158M2.3 Downregulated 3.52149655 0.00000765 AKAP13 RP11-161H23.9 Downregulated 10.0206279 9.58E-07 PRPH RP11-164J13.1 Downregulated 7.36517061 0.0000111 CAPN3 RP11-16K12.1 Downregulated 6.24320786 0.00022627 RASGRF1 RP11-170N16.3 Downregulated 4.87179487 0.00013007 FGF2 RP11-175K6.1 Downregulated 5.00108674 2.42E-07 EBF1 RP11-178L8.9 Downregulated 2 0.01173613 FBX031 RP11-182J1.1 Downregulated 13.0180573 1.85E-07 SCAND2P RP11-192H23.6 Upregulated 58.6600343 0.03470539 SGK494 RP11-203J24.9 Downregulated 3.00952938 0.00041186 AK1 RP11-20I23.1 Downregulated 2.77490501 0.00012912 ATP6V0C RP11-215G15.5 Downregulated 13.6187563 0.00000614 ANKRD33B RP11-218M22.1 Downregulated 6.72450684 2.12E-07 NINJ2 RP11-23J9.4 Downregulated 8.08580141 4.39E-07 CCDC180 RP11-245J9.5 Downregulated 2.76 0.00049493 PSMD6 RP11-251M1.1 Downregulated 24.6262626 0 EGFL7 RP11-264F23.3 Downregulated 3 0.00020069 CCND2 RP11-275I14.4 Downregulated 5.74110583 0.000051 ACBD3 RP11-276H19.1 Downregulated 19.4871797 0 GAS1 RP11-283G6.4 Downregulated 2.22222222 0.00106582 SSPN RP11-284F21.10 Upregulated 2.50373766 0.00308919 BCAN RP11-284F21.7 Upregulated 2.11237062 0.00759476 BCAN 134 RP11-284F21.9 Upregulated 2 0.00552643 BCAN RP11-284N8.3 Downregulated 3.86266178 0.00020872 KCNA3 RP11-286E11.1 Downregulated 3.36751757 0.00013845 SGMS2 RP11-286H15.1 Downregulated 15.173709 0 MYO7B RP11-287D1.3 Downregulated 4.48080313 0.00053253 MTHFD2 RP11-290D2.6 Downregulated 333333.333 0.00000083 TPT1 RP11-295G20.2 Upregulated 2.00564593 0.00582219 DISC1, TSNAX RP11-2B6.2 Downregulated 5.0000327 0.00000222 MIRLET7-DHG RP11-302F12.1 Downregulated 2.625 0.03109525 SLC34A2 RP11-304L19.3 Upregulated 2.5 0.00567903 PKD1 RP11-304L19.8 Downregulated 1179.74751 0.00642486 PGP RP11-307C18.1 Downregulated 4 0.0000168 BAIAP2L1 RP11-309N17.4 Downregulated 19 0 HID1 RP11-314B1.2 Downregulated 4.42105264 0.0000751 NYAP2 RP11-317J10.2 Downregulated 2 0.00076564 CA3 RP11-318A15.7 Downregulated 428.733183 0.00479286 MFSD11 RP11-324E6.9 Upregulated 4.15318 0.01382385 HCAR1 RP11-325F22.5 Downregulated 9.23006133 0.0000438 LHFPL3 RP11-326F20.5 Downregulated 3.59251192 0.00000311 B4GALT1 RP11-328C8.4 Downregulated 10.5336627 0.000048 PRICKLE1 RP11-332H18.4 Downregulated 11.9068133 0 TBX2 RP11-334E6.3 Upregulated 7.61950936 0.00051008 USP2, RNF26, MFRP, C1QTNF5, THY1 RP11-343N15.5 Downregulated 4.44755718 0.0000297 SRGAP2B RP11-344E13.3 Downregulated 8.35397622 0.00000568 CCDC144NL RP11-345P4.10 Upregulated 542.71028 0.04763161 SLC35E2B, CDK11B RP11-350J20.12 Upregulated 2.06558949 0.01124402 LRP11 135 RP11-354E11.2 Downregulated 6.53850735 1.14E-07 C10ORF112 RP11-356K23.1 Downregulated 10.0038485 0.00000384 FOXN3 RP11-359E3.4 Downregulated 4.0261438 0.00016146 BMPR1A RP11-360L9.7 Upregulated 2.69410912 0.01165937 GINS4 RP11-366L5.1 Downregulated 5 9.58E-07 SSFA2 RP11-373N22.3 Downregulated 3.88390648 0.00049819 SPINK13, FBXO38 RP11-382D8.3 Downregulated 2 0.00703491 GGPS1 RP11-388P9.2 Downregulated 3.66864289 0.00000818 ANK3 RP11-389C8.2 Downregulated 11.2087912 7.38E-08 ZNF366 RP11-38L15.3 Downregulated 9.39408058 0 SYT15 RP11-38P22.2 Downregulated 8.17791526 0.00000103 P2RY1 RP11-390F4.3 Upregulated 2.02978481 0.00084593 KDM4C RP11-391L3.1 Downregulated 8.65180333 0.0299186 GAN RP11-391L3.5 Downregulated 2 0.02878929 CMIP RP11-391M1.4 Downregulated 3.40236687 0.0000593 RPL14 RP11-3K24.1 Downregulated 2 0.0180897 USP32 RP11-401P9.6 Downregulated 8 0.0000132 NKD1 RP11-403A21.1 Downregulated 8 1.14E-07 LAMA3 RP11-425I13.3 Downregulated 3.5 0.0000999 SCOC RP11-426C22.5 Downregulated 4.55667048 0.00000922 RRN3P2 RP11-426L16.3 Downregulated 2.03279882 0.0000345 MOV10 RP11-435I10.3 Upregulated 14.0306036 0.0017061 EIF3C RP11-449P15.1 Downregulated 4.83397474 0.0000827 GPR146 RP11-452C13.1 Downregulated 4 0.00000194 PTPRN2 RP11-455O6.2 Downregulated 9.00000001 1.14E-07 AZI1 RP11-465L10.10 Upregulated 3.17114259 0.00081961 MMP9 RP11-465N4.4 Upregulated 1.91719106 0.01942068 ELF3, RNPEP RP11-473M20.9 Downregulated 7.45359457 0.0000121 ZSCAN10 RP11-480G7.1 Downregulated 3 0.0000347 MYLK3 RP11-482H16.1 Downregulated 9.66134372 0.0000321 CCDC85A 136 RP11-486B10.4 Downregulated 2.49044586 0.01141125 HIVEP3 RP11-488C13.6 Downregulated 2 0.00935694 VASH1 RP11-489D6.2 Upregulated 2.29155448 0.00216376 RYR3 RP11-505K9.4 Downregulated 2 0.00552883 MLYCD, OSGIN1 RP11-507K2.3 Downregulated 4 6.89E-07 PTPN21 RP11-509E16.1 Downregulated 6.99817184 0.0002222 EFR3B RP11-510N19.5 Upregulated 2.04726226 0.00411536 ELF3 RP11-513I15.6 Downregulated 4.28406737 0.00000384 NUDT3, HMGA1 RP11-513M16.8 Downregulated 3.45476708 0.00027769 RPS6 RP11-529H20.5 Downregulated 2.61236238 0.00056669 ATXN3 RP11-531A24.5 Downregulated 4.20689655 0.00035946 SBSPON RP11-541N10.3 Downregulated 6.31479736 1.14E-07 OBFC1 RP11-546B15.2 Downregulated 3.08878732 0.00027769 GANC RP11-54F2.1 Downregulated 11 9.58E-07 ANKRD33B RP11-54O7.14 Upregulated 1.11E+15 0.00353288 AGRN RP11-558F24.4 Upregulated 2.30897436 0.00031986 PIK3CD RP11-566K11.4 Upregulated 30.450495 0.000052 MC1R, TUBB3 RP11-566K19.5 Downregulated 3.5927 0.00053598 NIPA1 RP11-582J16.4 Downregulated 5.22931214 2.12E-07 PPP3CC RP11-594N15.3 Downregulated 15.483871 7.38E-08 PKIA RP11-598F7.1 Upregulated 1.8E+16 0.0120053 FAM138D RP11-598F7.3 Downregulated 12.48 0 IQSEC3 RP11-598F7.4 Downregulated 4.73618215 5.56E-07 IQSEC3 RP11-613D13.5 Downregulated 4.02198091 0.00000226 HSD17B12 RP11-613D13.8 Downregulated 12.0031914 0 C11ORF96 RP11-624G17.3 Upregulated 58362344.7 0.0007718 RTN4RL2 137 RP11-627G18.1 Downregulated 9.45203938 0.00000117 GATA6 RP11-62H7.2 Downregulated 2.72727272 0.02408826 SNORA70 RP11-649E7.5 Upregulated 2.22321407 0.00231195 MGAT2 RP11-64D22.2 Downregulated 3.24368387 0.0000117 AADACL2 RP11-650L12.2 Upregulated 4.5876573 0.00168281 CHRNA5 RP11-666A8.9 Downregulated 8.41124784 1.85E-07 SNHG16 RP11-677M14.3 Downregulated 8.12769103 0.00000204 ESAM RP11-680F20.12 Downregulated 3.45559314 0.0000159 CDON RP11-680F8.3 Downregulated 6.38747552 0 TJP1 RP11-688G15.3 Upregulated 28.3487406 0.00000425 CCDC85C RP11-690D19.3 Downregulated 6.404253 0.00000342 DCUN1D5 RP11-6O2.3 Downregulated 5.23015832 0.0000111 TTC23 RP11-6O2.4 Downregulated 54.4404014 5.15E-07 SYNM RP11-70C1.1 Downregulated 3 4.56E-07 CCDC13 RP11-710C12.1 Downregulated 8 0 UNC5C RP11-714G18.1 Downregulated 4.16000001 0.0000154 LRP2BP RP11-71H17.7 Downregulated 5.58615003 0.0000348 KALRN RP11-720L2.4 Downregulated 4.90129202 0 COLEC12 RP11-723O4.6 Downregulated 7.93709408 0.00000425 ACAD9 RP11-724O16.1 Downregulated 2.3575346 0.01644789 BBS5 RP11-736K20.5 Downregulated 11.8309858 7.38E-08 PRSS23 RP11-74E22.5 Downregulated 8307.64885 0.00231195 PAFAH1B1 RP11-750H9.5 Downregulated 13.3749201 7.38E-08 SLC39A13 RP11-75C9.1 Downregulated 14.9967827 0.00000033 PTPRD RP11-77A13.1 Downregulated 30.1935485 0 MARCO RP11-783K16.5 Upregulated 6.19957 7.51E-07 PPP1R14B, VEGFB,FKBP2 RP11-787I22.3 Downregulated 7.37729591 0.00000518 SESN1 RP11-78A19.3 Upregulated 3.624538 0.04763161 CHMP1B, GNAL RP11-793H13.10 Downregulated 2.95661213 0.0000208 ATF7 RP11-793H13.3 Downregulated 8.16036801 0.000041 ATF7 138 RP11-797A18.6 Downregulated 4.16000001 0.0000517 TSPAN3 RP11-800A3.4 Downregulated 4.39125356 0.000029 P2RY2 RP11-800A3.7 Downregulated 7.13971409 6.55E-07 ARHGEF17 RP11-802O23.3 Downregulated 2.43498424 0.00335538 PDHB RP11-82L18.4 Downregulated 13.125 2.42E-07 SHC3 RP11-82L2.1 Downregulated 6.99999999 2.42E-07 SMC2 RP11-830F9.6 Downregulated 10 7.38E-08 CBFA2T3 RP11-845C23.3 Downregulated 1876172608 0.00000083 NEDD4L RP11-875O11.1 Downregulated 9.78884847 0 RHOBTB2 RP11-89B16.1 Downregulated 5.99999999 2.42E-07 FIP1L1 RP11-96C23.14 Downregulated 6.85892367 7.38E-08 ADIRF RP11-96C23.5 Downregulated 19.2875202 0 ADIRF RP11-986E7.7 Downregulated 15.000105 0.00000103 SERPINA3 RP11-98D18.15 Downregulated 4273504.27 0.00016013 MRPL9 RP13-514E23.2 Downregulated 3.02250968 0.00098133 ARHGAP24 RP13-580F15.2 Downregulated 1.9776915 0.02024785 SPNS3 RP13-638C3.4 Downregulated 424.390935 0.03738224 FOXK2 RP3-323P13.2 Downregulated 20.484355 2.12E-07 EYA4 RP3-340N1.5 Upregulated 2 0.04086601 UBXN10 RP3-395M20.8 Downregulated 2.35108159 0.03108627 TNFRSF14 RP3-497J21.1 Upregulated 2.021315 0.02020871 RPS6KA2 RP3-525N10.2 Downregulated 17.9908334 0 BAI3 RP4-539M6.21 Downregulated 2 0.00343067 SEC14L2 RP4-548D19.3 Upregulated 498.459208 0.02771379 SMARCD3, CHPF2 RP4-559A3.7 Downregulated 48780.4878 0.0039065 LEFTY1 RP4-569M23.2 Downregulated 2.16666666 0.04235529 ZMYND8 RP4-576H24.4 Downregulated 4.37016 0.00000346 SIRPB1 RP4-607J23.2 Downregulated 4.19756427 0.00000304 BAIAP2L1 RP4-639F20.1 Downregulated 6.40940696 0.0000175 CNN3 RP4-728D4.2 Downregulated 4.38729139 0.0000735 PSMB2 RP4-755D9.1 Downregulated 3.19476685 0.01165937 RHOXF1 139 RP5-1007M22.2 Downregulated 5.04377001 0.000013 LRRC8B RP5-1024N4.4 Downregulated 2.40861445 0.00547407 SLC5A9 RP5-1050D4.2 Downregulated 2.01357227 0.00922503 CAMTA2 RP5-1052I5.1 Downregulated 11.4521213 0.00000922 HS2ST1 RP5-1198O20.4 Downregulated 23.7845319 0 KLF17 RP5-940J5.9 Upregulated 164.16984 0.00000163 GAPDH RPL13AP17 Downregulated 39.9579786 0.00000204 MAGI2 RPL23AP1 Downregulated 10.1630053 0.00000033 HLA-F SEMA3B-AS1 Downregulated 4.44444444 0.00000211 SEMA3B SH3RF3-AS1 Downregulated 13.8333333 3.07E-07 SH3RF3 SLC2A1-AS1 Upregulated 3.64487871 0.00016968 SLC2A1 SLC2A3P1 Downregulated 1.8248E+13 0.00000117 PANK3 SNRK-AS1 Downregulated 9752.76735 0.00973151 SNRK SRGAP3-AS2 Downregulated 19.8917325 1.14E-07 SRGAP3 ST3GAL1P1 Downregulated 2 0.00882936 UBA6 ST7-AS1 Downregulated 3.42026631 0.00000765 ST7 STARD13-AS Downregulated 2 0.00000494 STARD13 TBX5-AS1 Downregulated 11.8753446 1.14E-07 TBX5 TCAM1P Upregulated 4 0.00039809 TCAM1P TFAP2A-AS1 Upregulated 2.66367 0.000345 TFAP2A TM4SF1-AS1 Upregulated 2.87412 0.01232631 TM4SF1 TMX2-CTNND1 Downregulated 5.2356E+15 1.49E-07 TMX2-CTNND1 TRHDE-AS1 Downregulated 11.6864 0.0000025 TRHDE TRIM53BP Downregulated 199.23335 0.00066796 TRIM53AP TSNAX-DISC1 Upregulated 18.8172043 0.0111517 TSNAX, SPRTN, EGLN1, EXOC8, GNPAT, C1orf131, TRIM67, FAM89A, ARV1, TTC13, C1orf198, CAPN9, COG2, AGT, PGBD5, GALNT2, SIPA1L2, MAP10, NTPCR, PCNXL2 TTLL10-AS1 Downregulated 11.8644001 0 TTLL10 TUBA3FP Downregulated 5.51918276 0.00000345 TUBA3F UBE2CP3 Downregulated 2 0.00110071 IGFBP7 UPK3BP1 Downregulated 46.6606555 0 UPK3B USP30-AS1 Downregulated 2.94339622 0.0000913 USP30 140 VIPR1-AS1 Downregulated 9.80397126 0 VIPR1 VNN3 Downregulated 26.4842425 0.00000345 VNN3 VWFP1 Downregulated 31.8310044 7.38E-08 TPTEP1 WASIR2 Upregulated 3.06987 0.00972207 WASIR2 WDFY3-AS2 Downregulated 5.85003364 0.00000576 WDFY3 WDR11-AS1 Downregulated 3.69277987 0.0000351 WDR11 WI2-1896O14.1 Downregulated 53.2327787 0.03108627 NBPF9 WWC2-AS2 Downregulated 5.85365348 3.07E-07 WWC2 WWTR1-AS1 Downregulated 6.08674639 0.0000013 WWTR1 XXbac-BPG254F23.6 Upregulated 35707.1629 0.00739268 HLA-DQB1 XXyac-YX155B6.6 Upregulated 193800421 0.00295904 NBPF8, NBPF24, NBPF11 ZEB2-AS1 Downregulated 16.0666264 0.0000469 ZEB2 ZFYVE9P1 Downregulated 8.9150628 0.000011 ZFYVE9 ZNF300P1 Downregulated 6.56657969 0.00000165 ZNF300 ZNF582-AS1 Downregulated 15.8323997 0.0000208 ZNF582 ZRANB2-AS1 Downregulated 2.78395765 0.000028 ZRANB2 141 Appendix B Description of published supplementary tables B.1 Description of published supplementary tables from Chapter 4 Supplemental tables can be accessed in our published manuscript “Aberrant Expression of Pseudogene-Derived lncRNAs as an Alternative Mechanism of Cancer Gene Regulation in Lung Adenocarcinoma” due to the large size of the supplementary tables in Chapter 4 (DOI: 10.3389/fgene.2019.00138) 107. Supplemental Table 1: Detailed information of all pseudogene-derived lncRNAs. This table includes a database of common gene names for the lncRNAs, stable ENSEMBLE IDs, pseudogene ID’s, direction of transcription (relative to parent gene), and exon-exon overlap. Supplemental Table 2: Statistical analysis of the deregulated lncRNAs in the BCCA and TCGA cohorts (BHC-corrected p-values). This table includes the pseudogene overlapping lncRNAs that were deregulated in the same direction in both of our matched LUAD datasets. Direction of deregulation, and BHC-corrected p-values are included. Supplemental Table 3: Number of PUBMED entries for lncRNA cancer-association. This table includes the number of published manuscripts in “cancer” or “lung cancer” for our deregulated pseudogene overlapping lncRNAs. Supplemental Table 4: Genomic locations of significant lncRNAs and their respective parent genes. This table includes the genomic location, and relative size of each deregulated lncRNA and parent gene pair. 142 Supplementary Table 5: Expression correlations between the lncRNAs and their respective parent genes in the BCCA and TCGA cohorts. This table includes the p-values for the expression association of the deregulated lncRNAs and their respective parent genes in both cohorts of LUAD. Supplementary Table 6: Spearman's correlation coefficients for global expression analysis of Ѱ-lnc-parent pairs. This table includes the Spearman’s correlation coefficients used to plot the global correlation patterns of Ѱ-lnc-parent pairs, and Ѱ-lnc-random gene pairs. Also included is the Spearman’s correlation coefficients used to plot the global correlation patterns of sense and antisense Ѱ-lnc-parent pairs. Supplementary Table 7: Parent gene information for pseudogenes contained in Retrolali5 and Yale60 databases. This table includes all matched parent genes for the pseudogenes in 2 major databases, Retrolali5 and Yale60. 143 B.2 Description of published supplementary tables from Chapter 5 Supplemental tables can be accessed in our published manuscript “Beyond sequence homology: Cellular biology limits the potential of XIST to act as a miRNA sponge” due to the large size of the supplementary tables in this Chapter (DOI: 10.1371/journal.pone.0221371) 153. Supplementary Table 3: miRNA binding prediction to DMX genes and XIST This table includes the binding energies for each of the miRNAs predicted to bind both the DMX genes, as well XIST. This table also includes the statistics for the as the Spearman’s correlation of DMX genes to XIST.
Thesis/Dissertation
eng
Interdisciplinary Oncology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Alteration of non-coding RNAs as a mechanism of lung cancer gene deregulation
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