Abstract

Evolutionary game theory formally characterizes and analyzes biological systems where one’s propensity to survive and proliferate (fitness) may be dependent not only on one’s own phenotype (strategy) but also on the phenotypes displayed by all others. Applied to cancer modeling, this allows us to delineate how cell type frequencies change over time. Given the existence of a favorable long-term outcome, one may steer the tumor towards it. But if the long-term result is not beneficial, the aim switches to maximally delaying progression. Evolutionary game theory equips us with a mathematical basis to understand and design treatment regimens via the construction of cancerspecific models which naturally expose well-defined parameters that can be fitted via experiments. The fundamental idea is to exploit natural interactions within the tumor and to foresee the effects of changes in cell type frequencies. An intuitive example is the idea of maintaining a treatment-sensitive cell type at maximal frequency in order to inhibit proliferation of resistant cells due to competition for resources and space. One class of such models uses fitnessgenerating functions which allow us to track both density and frequency-dependent selection within tumors. Allowing EGT models to inspire novel therapies holds the premise to increase time to progression and to reduce cumulative drug dose. This approach is particularly promising, because modern oncological diagnosis methods have the potential to calibrate several EGT models.

Keywords: Evolutionary game theory; Cancer treatment; Competitive release; Drug holiday; Resistance; Stackelberg evolutionary game; Game theory