A stochastic agent-based model for simulating tumor-immune dynamics and evaluating therapeutic strategies

Tumor-immune interactions are central to understanding cancer progression and treatment outcomes. In this study, we present a stochastic agent-based model that integrates cellular heterogeneity, spatial cell-cell interactions, and the evolution of drug resistance to simulate tumor growth and the immune response in a two-dimensional microenvironment. This model captures the dynamic behaviors of four major cell types, specifically tumor cells, cytotoxic T lymphocytes, helper T cells, and regulatory T cells, and incorporates key biological processes, including proliferation, apoptosis, migration, and immune regulation. Using this framework, we simulate tumor progression under different therapeutic interventions, including radiotherapy, targeted therapy, and immune checkpoint blockade. Our simulations reproduce emergent phenomena such as immune privilege and spatial immune exclusion. Quantitative analyses demonstrate that all therapies suppress tumor growth to varying degrees and reshape the tumor microenvironment. Notably, combination therapies, particularly targeted therapy with immunotherapy, achieve the most effective tumor control. Crucially, sensitivity analyses reveal a distinct hierarchy among therapeutic determinants: Short-term efficacy is predominantly governed by intrinsic drug sensitivity thresholds rather than drug resistance update rates. Furthermore, we identify a response saturation effect that is specific to immunotherapy, where efficacy plateaus beyond a certain sensitivity threshold due to the spatial limits of immune infiltration. This work demonstrates the utility of agent-based models in capturing complex tumor-immune dynamics and provides a computational platform for optimizing cancer treatment strategies. The model is extensible, biologically interpretable, and well-suited for future integration with experimental or clinical data.