%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Belkhir, S.
%A Thomas, F.
%A Roche, Benjamin
%T Darwinian approaches for cancer treatment : benefits of mathematical modeling
%D 2021
%L fdi:010082790
%G ENG
%J Cancers
%K adaptive therapy ; cancer evolution ; Lotka-Volterra models ; agent-based models
%M ISI:000695557800001
%N 17
%P 4448 [19 ]
%R 10.3390/cancers13174448
%U https://www.documentation.ird.fr/hor/fdi:010082790
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2021-10/010082790.pdf
%V 13
%W Horizon (IRD)
%X Simple Summary Many cancers develop resistance and become unresponsive to traditional treatment strategies. In this review we highlight how mathematical models can aid the implementation of alternative treatment strategies that take into account the ecology and evolution of tumors in order to circumvent the emergence of resistance. We review some of the mathematical models that can be used and that have contributed to showing that Darwinian approaches for cancer treatment, like adaptive therapy, are promising anti-cancer treatment strategies. One of the major problems of traditional anti-cancer treatments is that they lead to the emergence of treatment-resistant cells, which results in treatment failure. To avoid or delay this phenomenon, it is relevant to take into account the eco-evolutionary dynamics of tumors. Designing evolution-based treatment strategies may help overcoming the problem of drug resistance. In particular, a promising candidate is adaptive therapy, a containment strategy which adjusts treatment cycles to the evolution of the tumors in order to keep the population of treatment-resistant cells under control. Mathematical modeling is a crucial tool to understand the dynamics of cancer in response to treatments, and to make predictions about the outcomes of these treatments. In this review, we highlight the benefits of in silico modeling to design adaptive therapy strategies, and to assess whether they could effectively improve treatment outcomes. Specifically, we review how two main types of models (i.e., mathematical models based on Lotka-Volterra equations and agent-based models) have been used to model tumor dynamics in response to adaptive therapy. We give examples of the advances they permitted in the field of adaptive therapy and discuss about how these models can be integrated in experimental approaches and clinical trial design.
%$ 050 ; 020