{ "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. Author summary: Cancer cells often evolve resistance to treatments during therapy by acquiring specific mutations, leading to eventual relapse and treatment failure. While using multiple treatments in combination can often increase the therapy success rate, carefully timing individual therapies may provide a way to avoid resistance and increase the likelihood of success. One method of understanding the effects of treatment timing on therapy success rate is to model of how cancer cells proliferate, mutate, and respond to therapy. In this study, we use such evolutionary simulations to show that, when applying two treatments in sequence to a population of cancer cells, the timing of switching from the first treatment to the second is an important factor in the ability of the treatments to wipe out the cancer cell population. We also show that this timing approach can lead to better outcomes than applying both drugs at the same time when cancer cells are able to acquire resistance to both treatments in a single mutation. Our work indicates that modeling the evolution of resistance in cancer may enhance the success of existing therapies and reduce the likelihood of relapse through more controlled treatment timing.", "abstract_shown": true, "affiliations": [ "Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, United States of America", "Department of Cancer Biology and Evolution, Moffitt Cancer Center, Tampa, Florida, United States of America", "Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida, United States of America", "Diagnostic Imaging Department, Moffitt Cancer Center, Tampa, Florida, United States of America", "Department of Biology, University of North Carolina, Chapel Hill, North Carolina, United States of America", "Integrative Program for Biological and Genome Sciences, University of North Carolina, Chapel Hill, North Carolina, United States of America", "UNC Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina, United States of America" ], "article_type": "Research Article", "arxiv": null, "author_total": 5, "authors": [ { "affs": [ 0 ], "name": "Amjad Dabi", "orcid": "0000-0002-7259-4608" }, { "affs": [ 1, 2 ], "name": "Joel S. Brown" }, { "affs": [ 1, 2, 3 ], "name": "Robert A. Gatenby" }, { "affs": [ 4, 5, 6 ], "name": "Corbin D. Jones", "orcid": "0000-0001-7281-7130" }, { "affs": [ 0, 6 ], "name": "Daniel R. Schrider", "orcid": "0000-0001-5249-4151" } ], "bibtex": "https://research.draconitic.com/paper/ba0cph56gwxf2.bib", "cited_by": 0, "doi": "10.1371/journal.pcbi.1014803", "fulltext_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014803", "id": "ba0cph56gwxf2", "issns": [ "1553-7358", "1553-734X" ], "landing_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014803", "language": "en", "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "article", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "heading", "Biology and Life Sciences" ], [ "heading", "Cancer Treatment" ], [ "heading", "Cancers and Neoplasms" ], [ "heading", "Carcinogenesis" ], [ "heading", "Drug Therapy" ], [ "heading", "Evolutionary Biology" ], [ "heading", "Evolutionary Genetics" ], [ "heading", "Genetics" ], [ "heading", "Heredity" ], [ "heading", "Medicine and Health Sciences" ], [ "heading", "Oncology" ], [ "heading", "Pharmaceutics" ], [ "heading", "Phenotypes" ], [ "heading", "Population Biology" ], [ "heading", "Population Metrics" ], [ "heading", "Population Size" ], [ "heading", "Quantitative Traits" ], [ "heading", "Research Article" ], [ "heading", "Research and Analysis Methods" ], [ "heading", "Simulation and Modeling" ], [ "openalex", "2611|Mathematical Biology Tumor Growth" ], [ "plos", "/Biology and life sciences/Evolutionary biology/Evolutionary genetics" ], [ "plos", "/Biology and life sciences/Genetics/Heredity/Quantitative traits" ], [ "plos", "/Biology and life sciences/Genetics/Phenotypes/Quantitative traits" ], [ "plos", "/Biology and life sciences/Population biology/Population metrics/Population size" ], [ "plos", "/Medicine and health sciences/Oncology/Cancer treatment" ], [ "plos", "/Medicine and health sciences/Oncology/Cancers and neoplasms" ], [ "plos", "/Medicine and health sciences/Oncology/Carcinogenesis" ], [ "plos", "/Medicine and health sciences/Pharmaceutics/Drug therapy" ], [ "plos", "/Research and analysis methods/Simulation and modeling" ] ], "openalex": "W7213561205", "pdf_url": "https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1014803&type=printable", "pmcid": "PMC13722315", "pmid": "42758795", "published": "2026-09-18", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/ba0cph56gwxf2.ris", "sources": [ { "first_seen": "2026-10-30T12:17:49Z", "native_id": "10.1371/journal.pcbi.1014803", "source": "plos", "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014803" }, { "first_seen": "2026-11-03T12:22:51Z", "native_id": "PMC13722315", "source": "pmc", "url": "https://doi.org/10.1371/journal.pcbi.1014803" } ], "subjects": [ { "field": "biology", "origin": "heading:Evolutionary Biology", "subfield": "evolutionary-biology" }, { "field": "biology", "origin": "plos:genetics", "subfield": "genetics-genomics" }, { "field": "medicine", "origin": "plos:oncology", "subfield": "oncology" } ], "title": "Evolutionary rescue model informs strategies for driving cancer cell populations to extinction", "title_text": "Evolutionary rescue model informs strategies for driving cancer cell populations to extinction", "type": "peer-reviewed", "updated_at": "2026-11-03T12:22:51Z", "url": "https://research.draconitic.com/paper/ba0cph56gwxf2", "venue": "PLOS Computational Biology", "versions": [] }