Stackelberg Evolutionary Games of Cancer Treatment

What Treatment Strategy to Choose if Cancer Can be Stabilized?

Journal Article (2024)
Author(s)

M. Salvioli (Maastricht University, TU Delft - Transport and Logistics)

Hasti Garjani (TU Delft - Mathematical Physics)

M. Satouri (TU Delft - Systems Decision Methods)

Mark Broom (City University London)

Yannick Viossat (Université Paris-Dauphine)

Joel S. Brown (Lee Moffitt Cancer Center and Research Institute)

Johan L.A. Dubbeldam (TU Delft - Mathematical Physics)

Kateřina Stankova (TU Delft - Systems Decision Methods)

Research Group
Mathematical Physics
DOI related publication
https://doi.org/10.1007/s13235-024-00609-z
More Info
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Publication Year
2024
Language
English
Research Group
Mathematical Physics
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Abstract

We present a game-theoretic model of a polymorphic cancer cell population where the treatment-induced resistance is a quantitative evolving trait. When stabilization of the tumor burden is possible, we expand the model into a Stackelberg evolutionary game, where the physician is the leader and the cancer cells are followers. The physician chooses a treatment dose to maximize an objective function that is a proxy of the patient’s quality of life. In response, the cancer cells evolve a resistance level that maximizes their proliferation and survival. Assuming that cancer is in its ecological equilibrium, we compare the outcomes of three different treatment strategies: giving the maximum tolerable dose throughout, corresponding to the standard of care for most metastatic cancers, an ecologically enlightened therapy, where the physician anticipates the short-run, ecological response of cancer cells to their treatment, but not the evolution of resistance to treatment, and an evolutionarily enlightened therapy, where the physician anticipates both ecological and evolutionary consequences of the treatment. Of the three therapeutic strategies, the evolutionarily enlightened therapy leads to the highest values of the objective function, the lowest treatment dose, and the lowest treatment-induced resistance. Conversely, in our model, the maximum tolerable dose leads to the worst values of the objective function, the highest treatment dose, and the highest treatment-induced resistance.