How Evolution Shapes the Future of Cancer Therapies

Cancer can be viewed as an eco-evolutionary system, where genetic mutations generate a diverse population of tumor cells that compete, cooperate, and adapt within a constantly evolving microenvironment. To understand and control these complex dynamics, we propose a unified framework consisting of two key components: a tumor population model based on Darwinian dynamics that captures mutation, selection, resource-based interactions (competition and mutualism), and environmental feedback; and a control layer based on multi-objective optimization to design dynamic treatment strategies aimed at reducing tumor burden and resistance, while minimizing therapy-induced toxicity. Achieving effective tumor growth control is particularly challenging because tumor cells are remarkably resilient, sometimes appearing almost “smart” in their ability to adapt and survive. This apparent intelligence is, indeed, the outcome of evolutionary pressures that drive the selection of the most therapy-resistant clones. Viewing treatment as a strategic interaction between the physician and the evolving tumor, we frame the control problem as a Stackelberg evolutionary game: the physician acts as a rational leader, while tumor cells adapt as evolving followers. Furthermore, to reflect the crucial role of the immune system, which can alternatively support treatment and promote tumor survival, we extend this game-theoretic framework to include this third agent, modeled as a passive modulator. The modulator dynamically shifts its influence, altering the interaction between the physician and the tumor. This results in a three-agent game, which may guide the development of combination therapies, such as chemotherapy coupled with anti-PD1 immunotherapy, tailored to the immune state. The proposed framework enables the design of adaptive, evolution-informed treatments that can more effectively manage tumor progression and improve patient outcomes