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Using spatial statistics to infer game-theoretic interactions in an agent-based model of cancer cells
Sydney Leither1,2, Maximilian A R Strobl3, Jacob G Scott3,4,5
1Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, United States.
Biorxiv : the Preprint Server for Biology
|August 12, 2025
Summary
Spatial patterns in cancer cell images can reveal how drug-sensitive and drug-resistant cells interact. This finding may lead to new cancer therapies that use ecological dynamics to combat drug resistance.
Area of Science:
- Cancer Research
- Evolutionary Biology
- Computational Biology
Background:
- Drug resistance in cancer is a complex problem influenced by evolutionary and ecological factors.
- Interactions between drug-sensitive and drug-resistant tumor subpopulations can promote resistance, even without treatment.
- Current therapies often fail due to these complex interactions, necessitating novel approaches like drug holidays.
Purpose of the Study:
- To investigate if spatial patterns in cell populations can identify underlying game theoretic interactions between sensitive and resistant cancer cells.
- To explore the potential of using spatial statistics from single time-point images to infer these interactions.
- To develop a computational framework for understanding eco-evolutionary dynamics in tumors.
Main Methods:
- Developed an agent-based model simulating cell reproduction governed by local game-theoretic interactions.
- Computed a suite of spatial statistics on images generated by the agent-based model under various game scenarios.
- Employed a machine learning model to classify game types based on computed spatial statistics.
Main Results:
- Spatial patterns in cell population images contain significant information about underlying game theoretic interactions.
- A machine learning model successfully classified different types of cell-cell interactions based on spatial statistics.
- The informativeness of various spatial statistics for inferring ecological interactions was quantified.
Conclusions:
- Tumor spatial structure holds sufficient information to infer ecological interactions between cancer cell subpopulations.
- This research is a step towards developing clinically applicable tools for identifying cell-cell interactions in tumors.
- Findings support the development of ecologically informed cancer therapies to overcome drug resistance.
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