Recent studies have shown that a strategy aiming for containment, not elimination, can control tumour burden more effectively in vitro, in mouse models and in the clinic. These outcomes are consistent with the hypothesis that emergence of resistance to cancer therapy may be prevented or delayed by exploiting competitive ecological interactions between drug-sensitive and drug-resistant tumour cell subpopulations. However, although various mathematical and computational models have been proposed to explain the superiority of particular containment strategies, this evolutionary approach to cancer therapy lacks a rigorous theoretical foundation. Here we combine extensive mathematical analysis and numerical simulations to establish general conditions under which a containment strategy is expected to control tumour burden more effectively than applying the maximum tolerated dose. We show that containment may substantially outperform more aggressive treatment strategies even if resistance incurs no cellular fitness cost. We further provide formulas for predicting the clinical benefits attributable to containment strategies in a wide range of scenarios and compare the outcomes of theoretically optimal treatments with those of more practical protocols. Our results strengthen the rationale for clinical trials of evolutionarily informed cancer therapy, while also clarifying conditions under which containment might fail to outperform standard of care.

最近的研究表明，旨在遏制而不是消除的策略可以在体外、小鼠模型和临床中更有效地控制肿瘤负荷。这些结果与以下假设一致：通过利用药物敏感和耐药肿瘤细胞亚群之间的竞争性生态相互作用，可以预防或延迟癌症治疗耐药性的出现。然而，尽管已经提出了各种数学和计算模型来解释特定遏制策略的优越性，但这种癌症治疗的进化方法缺乏严格的理论基础。在这里，我们结合广泛的数学分析和数值模拟来建立一般条件，在该条件下，遏制策略预计比应用最大耐受剂量更有效地控制肿瘤负荷。我们表明，即使抵抗不会导致细胞健康成本，遏制措施也可能大大优于更积极的治疗策略。我们进一步提供了预测各种情况下遏制策略带来的临床效益的公式，并将理论上最佳治疗的结果与更实用的方案的结果进行比较。我们的结果强化了基于进化的癌症治疗临床试验的基本原理，同时也澄清了遏制措施可能无法超越护理标准的条件。