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

TL;DR

This study introduces a stochastic agent-based model simulating tumor-immune interactions, evaluating various therapies, and identifying optimal treatment combinations and dosing thresholds.

Contribution

The paper presents a novel, biologically interpretable agent-based model that captures complex tumor-immune dynamics and assesses multiple therapeutic strategies.

Findings

Combination therapies, especially targeted therapy with immunotherapy, most effectively control tumor growth.

Simulations reproduce phenomena like immune privilege and spatial immune exclusion.

Sensitivity analysis reveals nonlinear effects of treatment intensity on efficacy.

Abstract

Tumor-immune interactions are central to cancer progression and treatment outcomes. In this study, we present a stochastic agent-based model that integrates cellular heterogeneity, spatial cell-cell interactions, and drug resistance evolution to simulate tumor growth and immune response in a two-dimensional microenvironment. The model captures dynamic behaviors of four major cell types--tumor cells, cytotoxic T lymphocytes, helper T cells, and regulatory T cells--and incorporates key biological processes such as 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 show that all therapies suppress tumor growth to varying degrees and reshape the tumor microenvironment. Notably, combination therapies--especially targeted therapy with immunotherapy--achieve the most effective tumor control and delay the emergence of resistance. Additionally, sensitivity analyses reveal a nonlinear relationship between treatment intensity and therapeutic efficacy, highlighting the existence of optimal dosing thresholds. This work demonstrates the utility of agent-based modeling 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.