Date of Award

Fall 1-1-2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computational Biology and Bioinformatics

First Advisor

Townsend, Jeffrey

Abstract

Cancers are predominantly understood as diseases in which somatic tissue lineagesacquire genetic or epigenetic alterations that confer traits which lead to aberrant cell growth. Such phenotypes include maintenance of proliferative signaling, resistance of apoptotic signaling, and immune suppression. In a microcosm of classical evolution, which progresses via random genetic drift and natural selection, somatic cells accumulate mutations throughout the life course. The small subset of mutations (or other heritable alterations) that provide proliferative advantages to the cells in which they occur tend to gain prevalence in subsequent generations, which may eventually lead to clonal evolution of cancer cell traits. While this theory of oncogenesis has achieved widespread acceptance since its proposal by Peter Nowell in 1976, the study of how cancers initiate and progress has, for the most part, not made use of the underlying evolutionary theory. In this dissertation, methods are presented for estimating various types of somatic mutation rates and quantifying somatic selection. Software implementations of these methods, which enable their use by the broader research community, are described, and selected biological findings are presented.

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