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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\u2019s 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.<\/jats:p>","DOI":"10.1007\/s13235-024-00609-z","type":"journal-article","created":{"date-parts":[[2024,12,14]],"date-time":"2024-12-14T02:04:42Z","timestamp":1734141882000},"page":"1750-1769","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Stackelberg Evolutionary Games of Cancer Treatment: What Treatment Strategy to Choose if Cancer Can be Stabilized?"],"prefix":"10.1007","volume":"15","author":[{"given":"Monica","family":"Salvioli","sequence":"first","affiliation":[]},{"given":"Hasti","family":"Garjani","sequence":"additional","affiliation":[]},{"given":"Mohammadreza","family":"Satouri","sequence":"additional","affiliation":[]},{"given":"Mark","family":"Broom","sequence":"additional","affiliation":[]},{"given":"Yannick","family":"Viossat","sequence":"additional","affiliation":[]},{"given":"Joel S.","family":"Brown","sequence":"additional","affiliation":[]},{"given":"Johan","family":"Dubbeldam","sequence":"additional","affiliation":[]},{"given":"Kate\u0159ina","family":"Sta\u0148kov\u00e1","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,14]]},"reference":[{"issue":"9","key":"609_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2024.110699","volume":"27","author":"G Aguad\u00e9-Gorgori\u00f3","year":"2024","unstructured":"Aguad\u00e9-Gorgori\u00f3 G, Anderson AR, Sol\u00e9 R (2024) Modeling tumors as complex ecosystems. iScience 27(9):110699. https:\/\/doi.org\/10.1016\/j.isci.2024.110699","journal-title":"iScience"},{"issue":"4","key":"609_CR2","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s00285-024-02062-3","volume":"88","author":"FE Alvarez","year":"2024","unstructured":"Alvarez FE, Viossat Y (2024) Tumor containment: a more general mathematical analysis. 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