Resisting Resistance
Resisting Resistance
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DOI：
10.1146/annurev-cancerbio-042716-094839
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发表时间：
2017-01-01
期刊：
影响因子：
--
通讯作者：
Nowak, Martin A.
中科院分区：
文献类型：
--
作者：
Bozic, Ivana;Nowak, Martin A.
靶向治疗、免疫治疗和改进的化疗正在开发中，以减少人类癌症带来的痛苦和死亡率。尽管这些方法，特别是它们的组合，预计最终会在很大程度上取得成功，但它们都面临着一个障碍：复制细胞的群体--通常是在高维空间--远离它们遇到的任何相反的选择压力。它们会进化出抵抗力。然而，有可能对这个问题发展一个精确的数学理解，并设计治疗策略，如果可能的话，防止耐药性，否则就管理耐药性。在这篇文章中，我们提出了描述耐药性演变的基本方程。我们提供了在治疗开始时耐药细胞存在的概率、耐药克隆的平均数量和大小以及成功联合治疗的概率的公式。我们还证明，开发只最大化癌细胞杀伤率的新疗法可能并不是最优的，相反，决定耐药细胞比例及其生长速度的参数对癌症的长期控制有更大的影响。这些数学工具为寻找旨在治愈癌症的最佳疗法的过程提供了信息。
Targeted therapies, immunotherapies, and improved chemotherapies are being developed to reduce the suffering and mortality that come from human cancer. Although these approaches, and in particular combinations of them, are expected to succeed eventually to a large degree, they all suffer one obstacle: Populations of replicating cells move away-typically in a high-dimensional space-from any opposing selection pressure they encounter. They evolve resistance. It is possible, however, to develop a precise mathematical understanding of the problem and to design treatment strategies that prevent resistance if possible or manage resistance otherwise. In this article, we present the fundamental equations that characterize the evolution of resistance. We provide formulas for the probability that resistant cells exist at the start of therapy, for the average number and sizes of resistant clones, and for the probability of successful combination treatment. We also demonstrate that developing new therapies that only maximize the killing rate of cancer cells may not be optimal, and that instead the parameters determining the fraction of resistant cells and their growth rate have a larger effect on the long-term control of cancer. These mathematical tools inform the search process for optimal therapies that aim to cure cancer.