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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.

|June 1, 2026

Summary

This summary is machine-generated.

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.

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Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

Published on: September 19, 2019

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.