Modeling of Cellular Competition and Drug Resistance in Cancer via Evolutionary Game Theory: A Review

Conventional cancer treatments based on maximum tolerated dose (MTD) often fail due to the emergence of drug resistance and competitive release. Today, cancer is recognized not merely as a genetic disease, but as a complex eco-evolutionary system. The purpose of this review article is to investigate and model the competition between drug-sensitive and drug-resistant cells using evolutionary game theory (EGT) frameworks and adaptive therapy scheduling. In the present study, we analyze genetic and epigenetic mechanisms, generalized Lotka-Volterra (GLV) equations, multilayer spatial models, the role of the host immune system (macrophages), and multi-drug cycles. Furthermore, the integration of Stackelberg games (leader-follower) with reinforcement learning (AI) for personalizing drug dosing is explored, and the clinical challenges facing these novel approaches are evaluated.

کلیدواژه ها:

Evolutionary Game Theory (EGT) ، Cancer Drug Resistance ، Adaptive Therapy ، Eco-evolutionary Dynamics ، Reinforcement Learning