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

Area of Science:

• Computational biology
• Cancer research
• Immunology

Background:

• Tumor-immune interactions are critical for cancer progression and treatment success.
• Understanding cellular heterogeneity and spatial dynamics is key to modeling tumor microenvironments.

Purpose of the Study:

• To develop a stochastic agent-based model simulating tumor growth and immune response.
• To evaluate the efficacy of various therapeutic interventions, including combination therapies.
• To analyze determinants of therapeutic efficacy and identify response saturation effects.

Main Methods:

• Developed a 2D agent-based model integrating cellular heterogeneity, spatial interactions, and drug resistance.
• Simulated tumor cells, cytotoxic T lymphocytes, helper T cells, and regulatory T cells.
• Incorporated proliferation, apoptosis, migration, and immune regulation processes.

Main Results:

• Simulations reproduced immune privilege and spatial immune exclusion phenomena.
• All simulated therapies suppressed tumor growth, with combination therapies being most effective.
• Short-term efficacy depends on drug sensitivity, not resistance rates; immunotherapy shows saturation effects.

Conclusions:

• Agent-based models are valuable for understanding complex tumor-immune dynamics.
• The model provides a platform for optimizing cancer treatment strategies.
• Findings highlight the importance of combination therapies and reveal immunotherapy-specific response limitations.