Graduation Year

2025

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Molecular Biosciences

Major Professor

David Basanta, Ph.D.

Co-Major Professor

Andriy Marusyk, Ph.D.

Committee Member

Noemi Andor, Ph.D.

Committee Member

Philipp Altrock, Ph.D.

Committee Member

Joel Brown, Ph.D.

Keywords

Cancer evolution, Evolvability, Mutation, adaptation, Treatment Resistance

Abstract

Genetic diversification, the process by which genetic variation arises in a population, is fundamental to evolution by natural selection. Mutation, a central diversification process, fuels adaptation across species, including in cancer. Yet most new mutations are neutral or deleterious on cell fitness, raising the question of how mutation-driven adaptation persists. To explore this paradox, I developed a spatial agent-based model (ABM) in which population fitness emerges from individual cells acquiring mutations at varied rates and with diverse fitness effects. I evaluated model behavior across adaptive states, mutation rates, and distributions of fitness effects. The results show that high mutation rates benefit populations near extinction but are neutral or harmful in well-adapted populations, with these effects intensifying at higher mutation rates. These findings are qualitatively supported by in vitro data from lung cancer cells exposed to targeted therapy and a chemical mutagen. While elevated mutation rates promote population survival at the edge of extinction, an increased mutation rate can harm individual cells since it implies increased odds of acquiring deleterious mutations. This questions the ability of high mutation rates to be selected. To address this, I extend the model to understand the effects of frequency-dependence, adaptive state, and the population size on the selection dynamics in a population of individuals with a baseline and elevated mutation rates. The results show that high mutation rates can be transiently favored during adaptation but are later selected against at the fitness peak. Then, I analyzed the selection for high mutation rate relative to the Stress-Induced Mutagenesis, i.e., a transient, regulated increase in the mutation rate. These findings suggest that while selection for high mutation rates can promote survival and accelerate adaptation, regulating a mutation rate by SIM is essential to maintain high fitness post-adaptation. To contrast mutation with another diversification process, I investigated the adaptive effects of extrachromosomal DNA (ecDNA): a potent driver of tumor evolution. Experimental evidence suggests that the selective benefit of ecDNA might have limits, yet these limitations remain poorly understood. Using a non-spatial ABM calibrated with experimental data, I show that the adaptive state of the population influences the selective benefit of ecDNA, and that its fitness advantage diminishes at the adaptive peak. Overall, this thesis emphasizes the context-dependent impact of genetic diversification and the necessity of specifying ecological and evolutionary factors to predict the fate of evolving populations, especially for the benefit of cancer patients.

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