Inverse game theory characterizes frequency-dependent selection driven by karyotypic diversity in triple negative breast cancer

Thomas Veith, Richard J Beck, Joel S Brown and Noemi Andor

PLOS Computational Biology, 2026, vol. 22, issue 3, 1-18

Abstract: Chromosomal instability, characterized by pervasive copy number alterations (CNAs), significantly contributes to cancer progression and therapeutic resistance. CNAs drive intratumoral genetic heterogeneity, creating distinct subpopulations whose interactions shape tumor evolution through frequency-dependent selection. Here, we introduce, ECO-K (Ecological-Karyotypes), an inverse game theory framework that quantifies frequency-dependent interaction coefficients among karyotypically defined subpopulations under the assumption that their fitness is frequency-dependent. Applying this approach to serially-passaged, triple-negative breast cancer cell lines and patient-derived xenografts (PDXs), we estimated interaction matrices consistent with the observed time-series dynamics. In one PDX lineage, the inferred matrices consistently assigned large interaction coefficients to a subpopulation characterized by chromosome 1 loss and chromosome 14p gain, suggesting it may act as an ecological hub within the frequency-dependent model. Our framework provides testable predictions of intratumoral ecological dynamics, highlighting opportunities to strategically target key subpopulations to disrupt tumor evolution.Author summary: Cancer evolves rapidly because tumor cells continuously change their genetic makeup, particularly through alterations in chromosome numbers via a process called chromosomal instability. This genetic variation allows multiple distinct cell populations to emerge within a tumor, each competing or cooperating with others. Understanding these interactions could reveal new ways to disrupt tumor growth. We introduce a computational approach called ECO-K, inspired by evolutionary game theory. ECO-K analyzes genetic data from single cancer cells collected over time to infer how these cell populations interact. We applied this method to breast cancer cells grown in the lab and tumors grown in mouse models. Our results identified specific cancer cell populations acting as critical “hubs,” influencing the survival and proliferation of neighboring cells. By pinpointing these influential populations, ECO-K reveals potential vulnerabilities in the tumor’s internal ecological network.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1013897

DOI: 10.1371/journal.pcbi.1013897

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