Exploring the impact of immune response on tumor heterogeneity through mathematical modeling

Abstract

Aim: This article presents an investigation into various mathematical models for cell population growth, including tumor cells, and their dynamics. Methods: We classify the models into five categories: exponential, logistic, time-tested, heterogeneous, and immunology. Mathematical modeling provides insights into the development of tumors over time and how their proliferation rate becomes more dangerous. To explore the impact of immune response on tumor heterogeneity, we develop a reaction-diffusion model of tumor growth that incorporates tumor-immune interactions and a mechanism for tumor mutation and clonal expansion. We use numerical simulations to investigate how variation in immune response affects tumor heterogeneity. Results: Our findings show that a stronger immune response leads to greater homogeneity in the tumor population, which suggests that enhancing immune response could reduce tumor heterogeneity and improve treatment outcomes. Conclusions: These results have important implications for the development of therapeutic strategies targeting the immune system to combat tumor heterogeneity.