Discerning Drivers of Cancer: Computational Approaches to Somatic Exome Sequencing Data
Date of Award
Doctor of Philosophy (PhD)
Paired tumor-normal sequencing of thousands of patient’s exomes has revealed millions of somatic mutations, but functional characterization and clinical decision making are stymied because biologically neutral ‘passenger’ mutations greatly outnumber pathogenic ‘driver’ mutations. Since most mutations will return negative results if tested, conventional resource-intensive experiments are reserved for mutations which are observed in multiple patients or rarer mutations found in well-established cancer genes. Most mutations are therefore never tested, diminishing the potential to discover new mechanisms of cancer development and treatment opportunities. Computational methods that reliably prioritize mutations for testing would greatly increase the translation of sequencing results to clinical care. The goal of this thesis is to develop new approaches that use datasets of protein-coding somatic mutations to identify putative cancer-causing genes and mutations, and to validate these predictions in silico and experimentally. This effort will be split among several inter-related efforts, which taken together will help experimental biologists and clinicians focus on hypotheses that can yield novel insights into cancer biology, development, and treatment.
Chair and Committee
Daniel Link, Li Ding, Don Conrad, Joshua Swamidass,
Kumar, Runjun, "Discerning Drivers of Cancer: Computational Approaches to Somatic Exome Sequencing Data" (2018). Arts & Sciences Electronic Theses and Dissertations. 1552.