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Growth rate-driven modelling suggests that phenotypic adaptation drives drug resistance in BRAFV600E-mutant melanoma

Abstract:
Phenotypic adaptation, the ability of cells to change phenotype in response to external pressures, has been identified as a driver of drug resistance in cancer. To quantify phenotypic adaptation in BRAFV600E-mutant melanoma, we develop a theoretical model informed by growth-rate data of WM239A-BRAFV600E cells challenged with the BRAF-inhibitor encorafenib. We use an individual-based model (IBM) in which each cell is described by one of multiple discrete and plastic phenotype states that are directly linked to drug-dependent net growth rates and, by extension, drug resistance. To describe how cells transition between phenotype states, we explore a gamut of candidate models common in the mathematical biology literature. Comparing these on their ability to reproduce in vitro growth curves, data-matched simulations suggest that phenotypic adaptation is directed towards states of high net growth rates, enabling the evasion of drug-effects. The model subsequently provides an explanation for when and why intermittent treatments outperform continuous treatments in the studied system, and demonstrates the benefits of not only targeting, but also leveraging, phenotypic adaptation in treatment protocols. Building on the IBM, we present a flexible mathematical methodology based on ordinary differential equations to compare responses to continuous and intermittent treatments through long-term effective net growth rates.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1038/s42003-026-09760-2

Authors

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Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author
More by this author
Role:
Author
ORCID:
0000-0001-9103-7092
More by this author
Role:
Author
ORCID:
0000-0003-3127-0532
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author


Publisher:
Nature Research
Journal:
Communications Biology More from this journal
Publication date:
2026-02-26
Acceptance date:
2026-02-17
DOI:
EISSN:
2399-3642
ISSN:
2399-3642


Language:
English
Keywords:
Pubs id:
2384655
Local pid:
pubs:2384655
Source identifiers:
W7131667515
Deposit date:
2026-03-06
ARK identifier:
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