Integrating evolutionary dynamics into cancer therapy
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.
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Mutations enabling rapid proliferation are selected positively in cancers because they accelerate the emergence of aggressive phenotypes through somatic evolution.
“Mutations enabling rapid proliferation are selected positively in cancers because they accelerate the emergence of aggressive phenotypes through somatic evolution.1,2”
From Dynamic IL-6R/STAT3 signaling leads to heterogeneity of metabolic phenotype in pancreatic ductal adenocarcinoma cells (2024) · cites this paper as [1]
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References 105 · Cited by 227
- Authors
- Gatenby, R. A., & Brown, J. S.
- Affiliations
- Moffitt Cancer Center (US)
- Corresponding author
- Gatenby, Robert A.
- Venue
- Nature Reviews Clinical Oncology
- Published
- 2020-07-22
- Volume
- 17
- Issue
- 11
- Pages
- 675-686
- ISSN
- 1759-4774 (ISSN-L)
- 1759-4782
- OpenAlex
- W3045230440
- Cited by
- 227
- References
- 105
- Citations by year
- 2026: 39 · 2025: 41 · 2024: 34 · 2023: 43 · 2022: 27 · 2021: 35 · 2020: 7 · 2012: 1
- FWCI
- 1.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Reviews Clinical Oncology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Cancer Institute (R01CA187532, U54CA143970, U01CA232382, R01CA170595)
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › Mathematical Biology Tumor Growth
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Keywords
- tumor evolution, treatment resistance, eco-evolutionary dynamics, acquired drug resistance, metastatic cancer, resistance selection, dose optimization
- MeSH terms
- Antineoplastic Agents, Humans, Neoplasm Metastasis, Neoplasm Staging, Neoplasms, Drug Resistance, Neoplasm, Evolution, Molecular, Genetic Fitness
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