Optimizing adaptive cancer therapy: dynamic programming and evolutionary game theory

1. Background

Intratumoural heterogeneity is increasingly recognized as a cause of metastasis, progression and resistance to therapy [1]. While genetic instability, a hallmark of malignancy [2], can result in this heterogeneity, it is being increasingly understood that eco-evolutionary factors, like selection and clonal interference, can also drive and maintain it [3,4].

While sequencing technologies have enabled increasingly in-depth quantitative understanding of the genetic heterogeneity, relatively little experimental work has sought to directly quantify the eco-evolutionary interactions involved. As more studies come to light showing the efficacy of treatments based on eco-evolutionary trial designs, this lack of quantification is coming into focus.

In line with standard, cell-autonomous growth-based theories, conventional chemotherapy is given to patients at the maximum tolerated doses (MTD): the highest doses that most patients can safely tolerate. Although the MTD-based therapy offers advantages in survival compared to no therapy, cures remain elusive, and side effects can be severe.

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