Evolutionary dynamics of non-genetic mechanisms of drug resistance in cancer

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Although cancer is traditionally viewed as a genetic disease, it is becoming increasingly recognized that non-genetic sources of heterogeneity amongst tumor cell populations play an important role in disease progression and drug resistance leading to treatment failure. For example, common sources of non-genetic heterogeneity such as epigenetic phenomena and environmental noise have been shown to induce transient or reversible drug-resistant states in tumor cells. Since these phenotypic switches often operate at significantly faster time scales than genetic evolution, they can exert large influences on the course of tumor evolution and response to therapy. These observations have generated tremendous interest in the clinical potential of novel drugs, such as epigenetic therapies, that target the cellular machinery controlling these phenotypic switches; however, the success of these therapies has been limited so far. One obstacle to these efforts has been an inadequate mechanistic understanding of how stochastic phenotypic transitions at the level of individual cells arise and influence tumor population-level responses to therapy. This project aims to elucidate these questions through the development of mathematical theories that link the mechanisms of phenotypic transitions at the subcellular level to population-level tumor evolutionary dynamics, within the context of a changing tumor microenvironment. This framework will be applied to understand the development of therapeutic resistance in leukemias and colorectal cancers, and to explore novel treatment strategies for preventing treatment failure. Since analogous phenomena occur in the context of bacterial populations treated with antibiotics, such models can also be leveraged to provide insights into antimicrobial therapy resistance.In the first part of the project, the PI will develop and analyze a continuous-time Markov process model of drug resistance driven by reversible phenotypic transitions in response to stromal cell-secreted factors in the tumor microenvironment. In the second part of the project, the PI will develop a novel multiscale modeling framework using branching random walks to link stochastic subcellular epigenetic dynamics to population-level evolutionary dynamics, and use this model to explore the impact of epigenetic processes on the evolution of drug resistance in cancer. These models will be analyzed to elucidate how the underlying processes driving non-genetic heterogeneity in a population influence overall evolutionary shifts towards drug resistance and tumor recurrence. This work will involve the analysis of state-dependent branching random walks on nonstandard geometries as well as branching process approximations of continuous time Markov processes. In particular, analyses of the limiting behaviors of these models, stochastic hitting times (e.g. recurrence time, gene silencing/activation times), and extinction probabilities will be conducted. The PI will work closely with experimental collaborators to apply these models and results to understand specific cancer systems in which drug resistance is driven by non-genetic mechanisms.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.