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

Area of Science:

• Genomics
• Evolutionary Biology
• Computational Biology

Background:

• Chromosomal instability, marked by copy number alterations (CNAs), fuels cancer progression and treatment resistance.
• CNAs generate intratumoral genetic heterogeneity, leading to distinct subpopulations that evolve via frequency-dependent selection.

Purpose of the Study:

• Introduce ECO-K (Ecological-Karyotypes), an inverse game theory framework.
• Quantify frequency-dependent interaction coefficients among karyotypically defined subpopulations.
• Model tumor evolution dynamics and identify key subpopulations for targeted therapies.

Main Methods:

• Applied inverse game theory to model ecological interactions between cancer subpopulations.
• Utilized ECO-K to estimate interaction matrices from time-series data of triple-negative breast cancer cell lines and patient-derived xenografts (PDXs).

Main Results:

• Inferred interaction matrices accurately reflected observed time-series dynamics in cancer models.
• Identified a subpopulation with chromosome 1 loss and 14p gain as a potential ecological hub in one PDX lineage due to large interaction coefficients.

Conclusions:

• ECO-K provides a novel framework for analyzing intratumoral ecological dynamics.
• The model offers testable predictions for disrupting tumor evolution by targeting specific subpopulations.
• Highlights potential therapeutic strategies by targeting key ecological hubs within tumors.