Belkhir Sophia, Thomas Frederic, Roche Benjamin
CREEC/MIVEGEC, Université de Montpellier, CNRS, IRD, 34394 Montpellier, France.
École Normale Supérieure de Lyon, Département de Biologie, Lyon CEDEX 07, 69342 Lyon, France.
Cancers (Basel). 2021 Sep 3;13(17):4448. doi: 10.3390/cancers13174448.
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.
传统抗癌治疗的主要问题之一是它们会导致产生抗治疗细胞，从而导致治疗失败。为了避免或延缓这种现象，考虑肿瘤的生态进化动力学是有意义的。设计基于进化的治疗策略可能有助于克服耐药性问题。特别是，一种很有前景的方法是适应性疗法，这是一种控制策略，它根据肿瘤的进化来调整治疗周期，以便控制抗治疗细胞的数量。数学建模是理解癌症在治疗反应中的动力学以及预测这些治疗结果的关键工具。在本综述中，我们强调了计算机建模在设计适应性治疗策略以及评估它们是否能有效改善治疗结果方面的益处。具体而言，我们回顾了两种主要类型的模型（即基于洛特卡 - 沃尔泰拉方程的数学模型和基于主体的模型）如何被用于模拟肿瘤对适应性治疗的动力学。我们给出它们在适应性治疗领域所取得进展的例子，并讨论这些模型如何能够整合到实验方法和临床试验设计中。
