Evolutionary rescue model informs strategies for driving cancer cell populations to extinction

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Publication Details

Journal
PLoS Computational Biology
Published
2026-09-18
DOI
https://doi.org/10.1371/journal.pcbi.1014803
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
0.00

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article

Evolutionary rescue model informs strategies for driving cancer cell populations to extinction

Robert A. Gatenby, Amjad Dabi, Daniel R. Schrider, Corbin D. Jones et al.
PLoS Computational Biology
Cancer Genomics and Diagnostics
article

Evolutionary rescue model informs strategies for driving cancer cell populations to extinction

Robert A. Gatenby, Amjad Dabi, Daniel R. Schrider, Corbin D. Jones, Joel S. Brown
article en

Abstract

Cancers exhibit a remarkable ability to develop resistance to a range of treatments, often resulting in relapse following first-line therapies and significantly worse outcomes for subsequent treatments. While our understanding of the mechanisms and dynamics of the emergence of resistance during cancer therapy continues to advance, questions remain about how to minimize the probability that resistance will evolve, thereby improving long-term patient outcomes. Here, we present an evolutionary simulation model of a clonal population of cells that can acquire resistance mutations to one or more treatments. We leverage this model to examine the efficacy of a two-strike "extinction therapy" protocol, in which two treatments are applied sequentially to first contract the population to a vulnerable state and then push it to extinction, and compare it to a combination therapy protocol. We investigate how factors such as the timing of the switch between the two strikes, the rate of emergence of resistant mutations, the dose effects of the applied drugs, the presence of cross-resistance, and whether resistance is a discrete or a quantitative trait affect the outcome. Our results show that the timing of switching to the second strike has a marked effect on the likelihood of driving the cancer to extinction, and that extinction therapy outperforms combination therapy when cross-resistance is present. We conduct an in silico trial that reveals when and why a second strike will succeed or fail. Finally, we demonstrate that our conclusions hold whether we model resistance as a discrete trait or as a quantitative, multi-locus trait.

PLoS Computational BiologyVol. 22(9)
Moffitt Cancer Center (US), UNC Lineberger Comprehensive Cancer Center
National Institute of General Medical Sciences, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill
Openalex Percentile: Top 15%
Cancer Genomics and Diagnostics
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