Improving mathematical models of cancer by including resistance to therapy: a study in non-small cell lung cancer

Abstract

AbstractIn this paper, a large dataset of 590 Non-Small Cell Lung Patients treated with either chemotherapy or immunotherapy was used to determine whether a game-theoretic model including both evolution of therapy resistance and cost of resistance provides a better fit than classical mathematical models of population growth (exponential, logistic, classic Bertalanffy, general Bertalanffy, Gompertz, general Gompertz). This is the first time a large clinical patient cohort (as opposed to only in-vitro data) has been used to apply a game-theoretic cancer model. The game-theoretic model provides a better fit to the tumor dynamics of the 590 Non-Small Cell Lung Cancer patients than any of the non-evolutionary population growth models. This is not simply due to having more parameters in the game-theoretic model. The game-theoretic model is able to fit accurately patients whose tumor burden exhibit a U-shaped trajectory over time. We then demonstrate how this game-theoretic model provides predictions of tumor growth based on just a few initial measurements. Assuming that treatment-specific parameters define the treatment impact completely, we then explore alternative treatment protocols and their impact on the tumor growth. As such, the model can be used to suggest patient-specific optimal treatment regimens with the goal of minimizing final tumor burden. Therapeutic protocols based on game-theoretic modeling can predict tumor growth, and improve patient outcome. The model invites evolutionary therapies that anticipate and steer the evolution of therapy resistance.