DATA DRIVEN APPROACHES TO IDENTIFY DETERMINANTS OF HEART DISEASES AND CANCER RESISTANCE

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 biological data is now extended far beyond few experimental labs to the global research community. The unprecedented availability of the data has revolutionized the capabilities 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 supervised fashion and has shown its applicability in identifying genomic regulatory variants associated with hypertension. Next, we propose a coupled Bayesian regression 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 samples, 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 histone 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 resistance. 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 interactions 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 importantly, for predicting the emergence of drug resistance from the initial tumor biopsy. Our analysis suggests a potential new strategy for enhancing the effectiveness of existing 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.

The relationship between health literacy and indicators of informed decision making for colorectal cancer screening among African Americans

African Americans are disproportionately affected by colorectal cancer (CRC) incidence and mortality. CRC early detection leads to better treatment outcomes and, depending on the screening test, can prevent the development of CRC. African Americans, however, are screened less often than Whites. Aspects of decision making (e.g., decisional conflict, decision self-efficacy) can impact decision making outcomes and may be influenced by social determinants of health, including health literacy. However the relationship between social determinants of health and indicators of decision making in this population is not fully understood. Additionally, individuals have a choice between different CRC screening tests and an individual’s desire to use a particular screening test may be associated with social determinants of health such as health literacy. This study aimed to examine the relationship between social determinants of health and indicators of decision making for CRC screening among African Americans. A total of 111 participants completed a baseline and 14-month follow-up survey assessing decisional conflict, decision self-efficacy, decisional preference (shared versus informed decision making), and CRC test preference. Health literacy was negatively associated with decisional conflict and positively associated with decision self-efficacy (ps < .05). Individuals who were unemployed or working part-time had significantly greater decisional conflict than individuals working full-time (ps < .05). Individuals with a first-degree family history of CRC had significantly lower decision self-efficacy than individuals without a family history (p < .05). Women were significantly more likely to prefer making a shared decision rather than an informed decision compared to men (p < .05). Lastly, previous CRC screening behavior was significantly associated with CRC test preference (e.g., individuals previously screened using colonoscopy were significantly more likely to prefer colonoscopy for their next screening test; ps < .05). These findings begin to identify social determinants of health (e.g., health literacy, employment) that are related to indicators of decision making for CRC among African Americans. Furthermore, these findings suggest further research is needed to better understand these relationships to help with the future development and improvement of interventions targeting decision making outcomes for CRC screening in this population.