Modeling Tumor Metabolic Heterogeneity through Evolutionary Game Theory: Insights into Therapy Design

Tumor growth is driven not only by genetic mutations but also by ecological interactions among heterogeneous cell populations within the tumor microenvironment. In this study, we apply evolutionary game theory (EGT) to model competition between glycolytic (G) and oxidative (O) tumor cells under distinct environmental scenarios. Using a payoff matrix to encode fitness interactions, we implement replicator dynamics to simulate changes in cell population fractions over time. Three numerical scenarios are considered: a baseline balanced competition, an acidic microenvironment, and a pH-buffered therapeutic intervention. Our simulations reveal that environmental acidity strongly favors glycolytic dominance, consistent with aggressive tumor phenotypes, while pH-buffered interventions can restore oxidative prevalence, potentially enhancing susceptibility to conventional therapies. These results provide mechanistic insight into how microenvironmental conditions shape tumor composition and highlight the potential of evolutionary-informed strategies—such as adaptive and ecological therapies—to steer tumor evolution toward less aggressive states. Overall, this work demonstrates the utility of EGT as a quantitative framework for understanding tumor heterogeneity and guiding personalized treatment planning.