Which evolutionary game-theoretic model best captures NSCLC dynamics?
Hasti Garjani1, Johan Dubbeldam1, Kateřina Staňková2
1Delft Institute of Applied Mathematics, Delft University of Technology, Delft, The Netherlands.
Plos One
|June 1, 2026
Summary
Mathematical models predict cancer dynamics by tracking drug-sensitive and resistant non-small cell lung cancer cells. Cancer-associated fibroblasts promote cell coexistence, while Alectinib drives competitive exclusion.
Area of Science:
- Oncology
- Mathematical Biology
- Cancer Research
Background:
- Predicting tumor growth and treatment response necessitates accurate mathematical models.
- Eco-evolutionary dynamics in cancer involve interactions between sensitive and resistant cell populations.
Purpose of the Study:
- To identify the best mathematical models for capturing non-small cell lung cancer (NSCLC) in-vitro dynamics.
- To evaluate how environmental factors like Alectinib and cancer-associated fibroblasts (CAFs) influence tumor cell interactions.
Main Methods:
- Fitting a family of two-population models to in-vitro NSCLC data under varying conditions (with/without Alectinib and CAFs).
- Comparing logistic, Gompertz, and von Bertalanffy growth models with Norton-Simon, linear, and ratio-dependent drug efficacy terms.
- Incorporating density dependence, frequency-dependent competition, and drug response into models for mechanistic interpretation.
Main Results:
- The logistic growth model with ratio-dependent drug efficacy provided the best fit for monoculture data.
- Growth rate and carrying capacity remained stable across different CAF conditions.
- CAFs promoted coexistence of drug-sensitive and resistant cells, while Alectinib led to competitive exclusion.
Conclusions:
- Model selection requires evaluating both statistical fit and biological plausibility for therapeutic applications.
- Environmental factors significantly alter competitive dynamics and drug response in NSCLC.
- Understanding eco-evolutionary dynamics is crucial for developing effective cancer therapies.
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