ABSTRACT DATA DRIVEN APPROACHES to IDENTIFY DETERMINANTS of HEART DISEASES and CANCER RESISTANCE Avinash Das Sahu, Doctor Of
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ABSTRACT Title of dissertation: DATA DRIVEN APPROACHES TO IDENTIFY DETERMINANTS OF HEART DISEASES AND CANCER RESISTANCE Avinash Das Sahu, Doctor of Philosophy, 2016 Dissertation directed by: Professor Sridhar Hannenhalli Department of Computer Science Cancer and cardio-vascular diseases are the leading causes of death world-wide. Caused by systemic genetic and molecular disruptions in cells, these disorders are the manifestation of profound disturbance of normal cellular homeostasis. People suffering or at high risk for these disorders need early diagnosis and personalized therapeutic intervention. Successful implementation of such clinical measures can significantly improve global health. However, development of effective therapies is hindered by the challenges in identifying genetic and molecular determinants of the onset of diseases; and in cases where therapies already exist, the main challenge is to identify molecular determinants that drive resistance to the therapies. Due to the progress in sequencing technologies, the access to a large genome-wide biolog- ical data is now extended far beyond few experimental labs to the global research community. The unprecedented availability of the data has revolutionized the ca- pabilities of computational researchers, enabling them to collaboratively address the long standing problems from many different perspectives. Likewise, this thesis tackles the two main public health related challenges using data driven approaches. Numerous association studies have been proposed to identify genomic variants that determine disease. However, their clinical utility remains limited due to their inability to distinguish causal variants from associated variants. In the presented thesis, we first propose a simple scheme that improves association studies in su- pervised fashion and has shown its applicability in identifying genomic regulatory variants associated with hypertension. Next, we propose a coupled Bayesian re- gression approach { eQTeL, which leverages epigenetic data to estimate regulatory and gene interaction potential, and identifies combinations of regulatory genomic variants that explain the gene expression variance. On human heart data, eQTeL not only explains a significantly greater proportion of expression variance in sam- ples, but also predicts gene expression more accurately than other methods. We demonstrate that eQTeL accurately detects causal regulatory SNPs by simulation, particularly those with small effect sizes. Using various functional data, we show that SNPs detected by eQTeL are enriched for allele-specific protein binding and hi- stone modifications, which potentially disrupt binding of core cardiac transcription factors and are spatially proximal to their target. eQTeL SNPs capture a substantial proportion of genetic determinants of expression variance and we estimate that 58% of these SNPs are putatively causal. The challenge of identifying molecular determinants of cancer resistance so far could only be dealt with labor intensive and costly experimental studies, and in case of experimental drugs such studies are infeasible. Here we take a fundamentally different data driven approach to understand the evolving landscape of emerging re- sistance. We introduce a novel class of genetic interactions termed synthetic rescues (SR) in cancer, which denotes a functional interaction between two genes where a change in the activity of one vulnerable gene (which may be a target of a cancer drug) is lethal, but subsequently altered activity of its partner rescuer gene restores cell viability. Next we describe a comprehensive computational framework {termed INCISOR{ for identifying SR underlying cancer resistance. Applying INCISOR to mine The Cancer Genome Atlas (TCGA), a large collection of cancer patient data, we identified the first pan-cancer SR networks, composed of interactions common to many cancer types. We experimentally test and validate a subset of these in- teractions involving the master regulator gene mTOR. We find that rescuer genes become increasingly activated as breast cancer progresses, testifying to pervasive ongoing rescue processes. We show that SRs can be utilized to successfully predict patients' survival and response to the majority of current cancer drugs, and impor- tantly, for predicting the emergence of drug resistance from the initial tumor biopsy. Our analysis suggests a potential new strategy for enhancing the effectiveness of ex- isting cancer therapies by targeting their rescuer genes to counteract resistance. The thesis provides statistical frameworks that can harness ever increasing high throughput genomic data to address challenges in determining the molecular underpinnings of hypertension, cardiovascular disease and cancer resistance. We discover novel molecular mechanistic insights that will advance the progress in early disease prevention and personalized therapeutics. Our analyses sheds light on the fundamental biological understanding of gene regulation and interaction, and opens up exciting avenues of translational applications in risk prediction and therapeutics. DATA DRIVEN APPROACHES TO IDENTIFY DETERMINANTS OF HEART DISEASES & CANCER RESISTANCE by Avinash Das Sahu Dissertation submitted to the Faculty of the Graduate School of the University of Maryland, College Park in partial fulfillment of the requirements for the degree of Doctor of Philosophy 2016 Advisory Committee: Professor Sridhar Hannenhalli, Chair/Advisor Professor Eytan Ruppin, Co-Advisor Professor Laura Elnitski Professor Hector Corrada Bravo Professor Michael Cummings c Copyright by Avinash Das Sahu 2016 ii Contents iii 1 Introduction1 1.1 How does a cell function?........................1 1.2 How does a cell produce proteins?....................2 1.3 How can same DNA give rise to drastically different cells?......4 1.4 How do eukaryotes regulate genes in a cell-type specific manner?..6 1.5 Biological processes performed by genes.................8 1.6 Disruption of biological processes causes diseases...........9 1.6.1 Mutation.............................9 1.6.2 Coding mutation......................... 10 1.6.3 Non-coding mutation....................... 10 1.7 Heritable mutation disorder....................... 12 1.7.1 Expression quantitative trail loci (eQTL)............ 13 1.7.2 Genome wide association studies (GWAS)........... 14 1.7.3 Limitation of association studies................. 14 1.7.4 How to improve association studies?.............. 16 1.8 Somatic mutation disorder........................ 17 1.8.1 Hallmarks of cancer........................ 17 1.8.2 Cancer therapies......................... 20 1.8.3 Cancer resistance and molecular reprogramming........ 21 1.8.4 Genetic interactions in cancer.................. 22 1.9 Computation challenges......................... 23 1.10 Significance................................ 26 1.10.1 Cardio-vascular disease and hypertension............ 27 1.10.2 Cancer............................... 28 1.11 Organization of Thesis.......................... 29 1.12 Contribution................................ 30 I Cardio-vascular disease and hyper-tension 33 2 EPIGENOMIC MODEL OF CARDIAC ENHANCERS WITH AP- PLICATION TO GENOME WIDE ASSOCIATION STUDIES 35 2.1 Overview.................................. 35 iii 2.2 Background................................ 38 2.2.1 Expression quantitive trait loci................. 38 2.2.2 Genome wide association studies................ 40 2.2.3 Epigenetics and regulation.................... 41 2.2.4 Epigenetic Modifications..................... 42 2.2.5 Epigenetic Inheritance...................... 43 2.2.6 Support vector machines (SVM)................. 43 2.3 Methods.................................. 44 2.3.1 Correlating DNase Hypersensitivity and Gene Expression... 46 2.4 Results................................... 47 2.4.1 SVM model for cardiac enhancers................ 47 2.4.2 Identification of cardiac enhancers near SNPs associated with cardiac phenotypes........................ 51 2.4.3 Cardiac enhancers near cardiac GWAS SNPs are enriched for cardiac regulator motifs..................... 53 2.4.4 Cardiac enhancers near cardiac GWAS SNPs are likely to reg- ulate the nearby genes...................... 54 2.4.5 Genes near cardiac enhancers are enriched for cardiac function 56 2.5 Conclusion................................. 57 3 Bayesian integration of genetics and epigenetics detects causal regulatory SNPs underlying expression variability 59 3.1 Introduction................................ 59 3.2 Results................................... 61 3.3 Quantitative Trait enhancer Loci (eQTeL) model........... 61 3.4 eQTeL detects expression regulatory SNP in MAGNet......... 65 3.5 eQTeL detects causal SNPs in semi-synthetic data........... 68 3.6 eQTeL detects SNPs with small effect sizes............... 71 3.7 eeSNPs lie within protein-bound genomic regions........... 74 3.8 eeSNPs exhibit binding and regulatory allele specificity........ 75 3.9 eeSNPs are spatially proximal to their target gene........... 78 3.10 eeSNPs disrupt motifs of cardiac transcription factors......... 78 3.11 Proportion of eeSNPs that are causal.................. 80 3.12 Methods.................................. 82 3.12.1 Modeling regulatory-interaction potential:........... 82 3.12.2 Modeling Gene Expression:................... 82 3.12.3 Cardiac expression data (MAGNet):.............. 85 3.12.4 Selection of genes:......................... 86 3.12.5 Pre-procession of gene-expression:................ 86 3.12.6 Genotypes and imputation for cardiac