In this section
@article{Leither:2025:10.1101/2025.07.09.664005,
author = {Leither, S and Strobl, MAR and Scott, JG and Dolson, E},
doi = {10.1101/2025.07.09.664005},
journal = {bioRxiv},
title = {Using spatial statistics to infer game-theoretic interactions in an agent-based model of cancer cells.},
url = {http://dx.doi.org/10.1101/2025.07.09.664005},
year = {2025}
}
TY - JOUR
AB - Drug resistance in cancer is shaped not only by evolutionary processes but also by eco-evolutionary interactions between tumor subpopulations. These interactions can support the persistence of resistant cells even in the absence of treatment, undermining standard aggressive therapies and motivating drug holiday-based approaches that leverage ecological dynamics. A key challenge in implementing such strategies is efficiently identifying interaction between drug-sensitive and drug-resistant subpopulations. Evolutionary game theory provides a framework for characterizing these interactions. We investigate whether spatial patterns in single time-point images of cell populations can reveal the underlying game theoretic interactions between sensitive and resistant cells. To achieve this goal, we develop an agent-based model in which cell reproduction is governed by local game-theoretic interactions. We compute a suite of spatial statistics on single time-point images from the agent-based model under a range of games being played between cells. We quantify the informativeness of each spatial statistic and demonstrate that a simple machine learning model can classify the type of game being played. Our findings suggest that spatial structure contains sufficient information to infer ecological interactions. This work represents a step toward clinically viable tools for identifying cell-cell interactions in tumors, supporting the development of ecologically informed cancer therapies.
AU - Leither,S
AU - Strobl,MAR
AU - Scott,JG
AU - Dolson,E
DO - 10.1101/2025.07.09.664005
PY - 2025///
TI - Using spatial statistics to infer game-theoretic interactions in an agent-based model of cancer cells.
T2 - bioRxiv
UR - http://dx.doi.org/10.1101/2025.07.09.664005
UR - https://www.ncbi.nlm.nih.gov/pubmed/40791473
ER -
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