Paper

Improving Mathematical Models of Cancer through Game-Theoretic Modelling: A Study in Non-Small Cell Lung Cancer

Thuijsman, Frank, Narmin Ghaffari Laleh, Stankova, Katerina, Cavill, Rachel, Martinez, Virginia Ardévol, Brown, Joel S, Salvioli, Monica, Kather, Jakob Nikolas

2022

Overview

In this paper, a large dataset of 590 Non-Small Cell Lung Patients treated with either chemotherapy or immunotherapy was used to determine whether a game-theoretic model including both evolution of therapy resistance and cost of resistance provides a better fit than classical mathematical models of population growth (exponential, logistic, classic Bertalanffy, general Bertalanffy, Gompertz, general Gompertz). This is the first time a large clinical patient cohort (as opposed to only in-vitro data) has been used to apply a game-theoretic cancer model. The game-theoretic model provides a better fit to the tumor dynamics of the 590 Non-Small Cell Lung Cancer patients than any of the non-evolutionary population growth models. This is not simply due to having more parameters in the game-theoretic model. The game-theoretic model is able to fit accurately patients whose tumor burden exhibit a U-shaped trajectory over time. We then demonstrate how this game-theoretic model provides predictions of tumor growth based on just a few initial measurements. Assuming that treatment-specific parameters define the treatment impact completely, we then explore alternative treatment protocols and their impact on the tumor growth. As such, the model can be used to suggest patient-specific optimal treatment regimens with the goal of minimizing final tumor burden. Therapeutic protocols based on game-theoretic modeling can predict tumor growth, and improve patient outcome. The model invites evolutionary therapies that anticipate and steer the evolution of therapy resistance. Competing Interest Statement JNK declares consulting services for Owkin, France and Panakeia, UK. No other potential conflicts of interest are reported by any of the authors. Footnotes * - small adjustments to title - small adjustments to text - few relevant references added * https://github.com/yo9299/NSCLC

• Publisher: Cold Spring Harbor Laboratory Press,Cold Spring Harbor Laboratory
• Subject: Cancer Biology / Cancer therapies / Chemotherapy / Evolution / Growth models / Immunotherapy / Lung cancer / Mathematical models / Non-small cell lung carcinoma / Patients / Population growth / Small cell lung carcinoma / Tumors