Agent-based modeling of tumor-immune interactions reveals determinants of final tumor states

Abstract

Interactions between tumor and immune cells in the tumor microenvironment (TME) influence tumor growth and the tumors response to treatment. Excitingly, this complex landscape of tumor-immune interactions can be studied using computational modeling. Mathematical oncology can provide quantitative insights into the TME, serving as a framework for understanding tumor dynamics. Here, we use an agent-based model to simulate the interactions among cancer cells, macrophages (naive, M1, and M2), and T cells (active CD8+ and inactive) in a 2D representation of the TME. Key diffusible factors, IL-4 and IFN-{gamma}, are also incorporated. We apply the model to predict how cell-specific properties influence tumor progression. The model predictions and analyses revealed the relationships between different cell populations and highlighted the importance of macrophages and T cells in shaping the TME. Thus, we quantify how components of the TME influence the final tumor state and the effects of macrophage-based therapies. The findings emphasize the significant role of computational models in unraveling the intricate dynamics of tumor-immune interactions and their potential for guiding the development of tailored immunotherapeutic strategies. This study provides a foundation for future investigations aiming to refine and expand the model, validate predictions experimentally, and pave the way for improved cancer treatments.