LAUSR

Many effective drugs for metastatic and/or advanced-stage cancers have been developed over the past decade, although the evolution of resistance remains the major barrier to disease control or cure. In large, diverse populations such as the cells that compose metastatic cancers, the emergence of cells that are resistant or that can quickly develop resistance is virtually inevitable and most likely cannot be prevented. However, clinically significant resistance occurs only when the pre-existing resistant phenotypes are able to proliferate extensively, a process governed by eco-evolutionary dynamics. Attempts to disrupt the molecular mechanisms of resistance have generally been unsuccessful in clinical practice. In this Review, we focus on the Darwinian processes driving the eco-evolutionary dynamics of treatment-resistant cancer populations. We describe a variety of evolutionarily informed strategies designed to increase the probability of disease control or cure by anticipating and steering the evolutionary dynamics of acquired resistance. Most systemic cancer therapies are administered at doses at or close to the maximum tolerated dose until disease progression. However, this approach usually leads to treatment resistance and fails to take into account several potentially relevant evolutionary principles. In this Review, the authors describe how existing approaches to cancer therapy might be optimized by incorporating an understanding of evolutionary dynamics into cancer therapy. Despite the increasing numbers of effective agents for treating metastatic cancers, evolution of resistance remains the fundamental barrier to cure and long-term disease control. Efforts to overcome resistance by blocking the molecular machinery of resistance have had little clinical success, probably because many alternative evolutionary resistance pathways are accessible to cancer cells. Resistant cells are almost inevitably present in the large heterogeneous populations of metastatic cancers, and the subsequent fates of those cells (proliferation, quiescence or death) are governed by evolutionary forces. Adaptive therapy cycles the application of treatment to synchronize with patient-specific intratumoural evolutionary dynamics to suppress proliferation of resistant cells and prolong response to treatment. Extinction therapy involves the aggressive application of new perturbations (second strikes) to the resistant cells following the initial (first strike) therapy to exploit the vulnerabilities of small populations with the explicit goal of cure. Clinical trial designs based on evolutionary mathematical models enable the detailed evaluation of outcomes in individual patients, thus promoting an understanding of why the trial failed or succeeded and providing guidance for new strategies designed to optimize patient outcomes.