{
  "abstract": "Cancer progression following treatment failure is an evolutionary process in which therapy acts as a selection pressure driving Darwinian selection on heritable variation to favor resistant clones. Heritable variation is a key determinant of how rapidly tumors can adapt to therapy. Heritable variation arises through various mechanisms such as genetic mutations, epigenetics, chromosomal rearrangements, and copy number variations, resulting in a lack of replicative fidelity. We consider evolvability as a property or trait of the cancer cells that influences the rate at which fitness-relevant heritable variation is generated. Using this as our definition, we present an evolutionary game-theoretic model to evaluate how evolvability shapes resistance dynamics under two treatment modalities: targeted therapy and chemotherapy. We first compare cancer populations with fixed evolvabilities: low or high. Targeted therapy imposes a steep selection gradient, enabling rapid resistance evolution, while chemotherapy imposes a flatter selection gradient but drives tumors toward more extreme resistance strategies. We show that targeted therapy works better in cancers with low evolvability, whereas chemotherapy better controls cancers with high evolvability. We then extend the model to incorporate \"plastic evolvability,\" in which cancer cells can adjust their evolvability in response to therapy-induced stress. In this case, cells fine-tune the trade-off between acquiring higher resistance and limiting the costs of resistance and evolvability. Plastic evolvability sustains a higher tumor burden than fixed-evolvability. To address the challenge of plastic evolvability, we develop and simulate an evolutionary double bind using sequential cycles of chemotherapy and targeted therapy. With an appropriate sequence and timing, the double bind can drive cancers with plastic evolvability to extinction. Our results highlight the importance of evolvability in shaping treatment response and underscore the need to incorporate evolutionary principles into therapy design.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Integrated Mathematical Oncology H. Lee Moffitt Cancer Center and Research Institute Tampa Florida USA.",
    "Department of Cancer Biology University of South Florida Tampa Florida USA.",
    "Department of Mathematics City St. George's, University of London London UK.",
    "Department of Radiology H. Lee Moffitt Cancer Center and Research Institute Tampa Florida USA.",
    "Cancer Biology and Evolution Program Moffitt Cancer Center and Research Institute Tampa Florida USA."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Ranjini Bhattacharya",
      "orcid": "0000-0002-5045-9333"
    },
    {
      "affs": [
        2
      ],
      "name": "Anuraag Bukkuri",
      "orcid": "0000-0002-3616-626X"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Robert A Gatenby",
      "orcid": "0000-0002-1621-1510"
    },
    {
      "affs": [
        0,
        4
      ],
      "name": "Joel S Brown",
      "orcid": "0000-0002-0958-9494"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dcgx9pg2r1wts.bib",
  "cited_by": 0,
  "doi": "10.1111/eva.70320",
  "fulltext_url": "https://europepmc.org/articles/PMC13711687",
  "id": "dcgx9pg2r1wts",
  "issns": [
    "1752-4571"
  ],
  "landing_url": "https://europepmc.org/article/MED/42948278",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Evolutionary Medicine"
    ],
    [
      "heading",
      "ORIGINAL ARTICLE"
    ],
    [
      "heading",
      "Original Article"
    ],
    [
      "openalex",
      "2611|Mathematical Biology Tumor Growth"
    ]
  ],
  "openalex": "W7223678087",
  "pdf_url": "https://europepmc.org/articles/PMC13711687?pdf=render",
  "pmcid": "PMC13711687",
  "pmid": "42948278",
  "published": "2026-10-25",
  "publisher": "Wiley",
  "ris": "https://research.draconitic.com/paper/dcgx9pg2r1wts.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:20:34Z",
      "native_id": "MED:42948278",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42948278"
    },
    {
      "first_seen": "2026-10-31T12:22:25Z",
      "native_id": "PMC13711687",
      "source": "pmc",
      "url": "https://doi.org/10.1111/eva.70320"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "journal-openalex:1311|Genetic diversity and population structure",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "Cancer Evolvability Determines Therapy Outcomes.",
  "title_text": "Cancer Evolvability Determines Therapy Outcomes.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T12:22:25Z",
  "url": "https://research.draconitic.com/paper/dcgx9pg2r1wts",
  "venue": "Evolutionary applications",
  "versions": []
}