Reinforcement Learning-Driven Evolutionary Stackelberg Game Model for Adaptive Breast Cancer Therapy

In this paper, we present an integrative framework based on Evolutionary Stackelberg Game Theory to model the strategic interaction between a physician, acting as a rational leader, and a heterogeneous population of treatment-sensitive and treatment-resistant breast cancer cells. The model incorporates ecological competition, evolutionary adaptation, and spatial heterogeneity, enabling prediction of tumor progression under clinically relevant treatment protocols. Using tumor volume data obtained from breast cancer-bearing mice treated with Capecitabine and Gemcitabine, we estimated treatment and subject-specific parameters via the GEKKO optimization package in Python. Benchmarking against classical tumor growth models (Exponential, Logistic, and Gompertz) showed that while classical models capture monotonic growth, they fail to reproduce complex, non-monotonic behaviors such as treatment-induced regression, rebound, and phenotypic switching. The game-theoretic approach achieved superior alignment with experimental data across Maximum Tolerated Dose, Dose-Modulation Adaptive Therapy, and Intermittent Adaptive Therapy protocols. To enhance adaptability, we integrated reinforcement learning (RL) for both single-agent and combination chemotherapy. The RL agent learned dosing policies that maximized tumor regression while minimizing cumulative drug exposure and resistance, with combination therapy exploiting dose diversification to improve control without exceeding total dose budgets. Incorporating reaction diffusion equations allowed the model to capture spatial dispersal of sensitive (cooperative) and resistant (defector) phenotypes, revealing that spatially aware adaptive strategies more effectively suppress resistant clones than non-spatial approaches. These results demonstrate that evolutionarily informed, spatially explicit, and computationally optimized strategies can outperform conventional fixed-dose regimens in reducing resistance, lowering toxicity, and improving efficacy. This framework offers a biologically interpretable tool for guiding evolution-aware, patient-tailored cancer therapies toward improved long-term outcomes.