Which evolutionary game-theoretic model best captures non-small cell lung cancer dynamics?

Understanding and predicting the eco-evolutionary dynamics of cancer requires identifying mathematical models that best capture tumor growth and treatment response. In this study, we fit a family of two-population models to in-vitro data from non-small cell lung cancer (NSCLC), tracking drug-sensitive and drug-resistant cells under varying environmental conditions. The dataset, originally presented by Kaznatcheev et al., includes conditions with and without the drug Alectinib and cancer-associated fibroblasts (CAFs). We compare combinations of growth models (logistic, Gompertz, von Bertalanffy) and drug efficacy terms (Norton-Simon, linear, ratio-dependent) to identify which best explains the observed dynamics. Our models incorporate density dependence, frequency-dependent competition, and drug response, enabling mechanistic interpretation of tumor cell interactions. The logistic model with ratio-dependent drug efficacy best fits monoculture data. Using growth parameters from monocultures, we infer inter-type competition coefficients in co-cultures. We find that growth rate and carrying capacity are stable across CAF conditions, while competition and drug efficacy parameters shift, altering interaction dynamics. Notably, CAFs promote coexistence between resistant and sensitive cells, whereas Alectinib induces competitive exclusion. Our results underscore the need to evaluate both model fit and biological plausibility to guide therapeutic modeling of cancer. Author summaryHow cancer cells grow, compete, and respond to treatment depends not only on the drug, but also on their ecological context, including interactions with other cells and components of the tumor microenvironment. In this study, we explore how different mathematical models capture the behavior of non-small cell lung cancer (NSCLC) cells under various conditions. We focus on two cell populations: one sensitive to treatment, and one resistant. Using in-vitro data, we compare growth models and drug response types to identify which model best explains the observed population dynamics. We also investigate how different factors, such as the drug Alectinib and cancer-associated fibroblasts (CAFs), change the way cancer cells interact. Our game-theoretic approach allows us to quantify how these external conditions affect competition between cell types, revealing when resistant and sensitive cells can coexist. These findings contribute to a deeper understanding of tumor ecology and may support the development of adaptive cancer therapies that anticipate evolutionary responses to treatment.