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Integrating evolutionary dynamics into cancer therapy

Nature Reviews Clinical Oncology volume 17, pages 675–686 (2020)Cite this article

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Abstract

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.

Key points

  • 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.

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Fig. 1: Evolutionary strategy for tumour control.
Fig. 2: Evolutionary strategies for cure, based on the dynamics of anthropogenic extinctions.

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Acknowledgements

The authors thank D. Lemanne, Oregon Integrative Oncology, for providing a steady stream of good ideas for framing and applying extinction therapy. This work was supported by the European Commission Horizon 2020 research and innovation programme (grant agreement no. 690817), a James S. McDonnell Foundation grant (“Cancer Therapy: Perturbing a Complex Adaptive System”), a V Foundation grant, NIH/National Cancer Institute (NCI) grants R01CA170595 (“Application of Evolutionary Principles to Maintain Cancer Control (PQ21)”), U54CA143970-05 (Physical Science Oncology Network; “Cancer as a Complex Adaptive System”) and R01CA187532-01 (“Imaging Habitats in Sarcoma”), the National Paediatric Cancer Foundation and the Jacobson Foundation (“Integrating Evolution into Cancer Therapy”).

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Authors and Affiliations

  1. Cancer Biology and Evolution Program, Moffitt Cancer Center, Tampa, FL, USA

    Robert A. Gatenby & Joel S. Brown

  2. Integrated Mathematical Oncology Department, Moffitt Cancer Center, Tampa, FL, USA

    Robert A. Gatenby & Joel S. Brown

  3. Diagnostic Imaging Department, Moffitt Cancer Center, Tampa, FL, USA

    Robert A. Gatenby

  4. Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL, USA

    Joel S. Brown

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Gatenby, R.A., Brown, J.S. Integrating evolutionary dynamics into cancer therapy. Nat Rev Clin Oncol 17, 675–686 (2020). https://doi.org/10.1038/s41571-020-0411-1

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