"Game theory to design evolutionary therapy in non-small cell lung cancer"Stankova, KaterinaStandard of care in metastatic cancer typically involves applying the maximum tolerable dose of treatment either continuously or in repeated identical cycles, with the goal of killing as many cancer cells as fast as possible. This strategy often promotes treatment-induced resistance. Recent theoretical and clinical studies have shown that evolutionary therapy, i.e., therapy that anticipates and forestalls treatment-induced resistance, may be a better strategy than the standard of care, as it has the potential to prolong the time to progression and improve patients' quality of life. Here, we focus on designing evolutionary therapy for metastatic non-small cell lung cancer (NSCLC) treated with tyrosine kinase inhibitors. First, we demonstrate how data-driven mathematical modeling, especially evolutionary game theory, can help us model this cancer's response to the standard of care. Second, we demonstrate how existing evolutionary therapies and therapies obtained through optimizing time to progression in our models lead to a higher expected time to progression. While we concentrate on NSCLC, our results contribute to our general understanding of cancer eco-evolutionary dynamics and the designing of anti-cancer treatments that improve patients' quality and quantity of life. |
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