Using spatial statistics to infer game-theoretic interactions in an agent-based model of cancer cells
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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.
Author summary
Drug resistance is a major challenge in cancer treatment, often leading to relapse despite initially successful therapy. While mutations are a key driver, ecological interactions between drug-sensitive and drug-resistant cells also play a critical role. These interactions are complex and dynamic, and few molecular biomarkers exist, making them difficult to study and account for in treatment planning. We use evolutionary game theory, a framework for quantifying interactions between cells, to investigate whether it is possible to infer these interactions using just a single time-point image of the cells. We develop an agent-based model where cells reproduce based on local interactions and quantify the resulting patterns in how cells are distributed across space using a suite of spatial statistics. We find that specific interaction types produce distinct spatial patterns that are evident in these metrics, and we train a simple machine learning model to classify the interaction type based on the metrics. Our results suggest that spatial data alone can offer valuable insights into tumor dynamics, potentially enabling more informed and adaptable cancer treatments based on eco-evolutionary principles.
