Graduation Year
2023
Document Type
Dissertation
Degree
Ph.D.
Degree Name
Doctor of Philosophy (Ph.D.)
Degree Granting Department
Biomedical Engineering
Major Professor
Joel S. Brown, Ph.D.
Co-Major Professor
Richard Heller, Ph.D.
Committee Member
Jill A. Gallaher, Ph.D.
Committee Member
Tomar Ghansah, Ph.D.
Committee Member
Dmitry Goldgof, Ph.D.
Keywords
Evolution, Drug Resistance, Adaptive Therapy, Population Dynamics, Strategy Dynamics
Abstract
Adaptive responses in cancer promote disease initiation, progression, and resistance to treatment. Understanding the underlying evolutionary dynamics behind these adaptive responses is crucial for developing effective therapeutic strategies. Evolutionary game theory (EGT) has proven a useful tool in analyzing how different cellular behaviors and evolutionary forces shape the dynamics of tumor growth, response to therapy, and the emergence of resistance. In this dissertation, we utilize EGT to understand the adaptive responses of cancer through the lens of ecology and evolution. By employing fitness-generating models and exploring various ecological contexts, we aimed to unveil critical factors influencing aggressive cell phenotypes, treatment efficacy, and the evolution of resistance.
We begin, by investigating the constitutive expression of hypoxia-inducible factors (HIFs). To do this we develop a mathematical model that predicts how fluctuating oxygenation affects HIF stabilization and impacts net cell proliferation. We find that cells constitutively expressing HIF may be at a selective advantage when the cost of expression is low. Next, we utilize G-functions to examine the impact of evolutionary speed on the efficacy of adaptive therapy (AT) compared to maximum tolerable dose (MTD) in a monomorphic and polymorphic population, where the cost of resistance is reflected in cell division or resource availability. Moving forward we also explore the therapeutic potential of a double bind treatment strategy. In both cases, AT and double bind therapy, the speed of evolution played a significant role in treatment outcome. We find that a polymorphic population would benefit most from AT when evolution is rapid. In the double bind context, the speed of evolution determined drug order and application. Lastly, we investigate an AT strategy in a non-small cell lung cancer (NSCLC) mouse model. In this model, AT was less beneficial than MTD. We subsequently utilized a math model to showcase that lack of density dependence and slow evolvability to sensitive states limit the effectiveness of AT. Collectively, our findings underscore the central role of evolutionary speed and strategy costs in shaping adaptive responses in cancer. These insights provide valuable guidance for the development of more effective treatment strategies aimed at exploiting evolutionary dynamics to the patient's advantage.
Scholar Commons Citation
Pressley, Mariyah, "Understanding How Cancer Adapts to Stress and Therapy Using Evolutionary Game Theory" (2023). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/10129
