Agent-based Modeling of Dysregulated Cell Signaling and the Tumor-Immune Landscape Predicts New Possibilities for Combination Therapy

Dr. Trachette Jackson
Professor of Mathematics and Associate Vice President for Research – Strategic Partnerships and Inclusive Excellence
University of Michigan

Mathematical models, specifically agent-based models (ABMs), have shown recent successes in uncovering the multiscale dynamics that shape the trajectory of cancer. They have enabled the optimization of treatment methods and the identification of novel therapeutic strategies. To assess the combined effects on tumor growth and the immune response of monoclonal antibodies that boost the immune system (immunotherapy) and small molecule inhibitors (SMI) that counteract the effect of driver mutations, we build and analyze an ABM that captures key facets of tumor heterogeneity and immune cell dynamics, their spatial interactions, and their response to therapeutic pressures. Our model predicts that under certain conditions, immunotherapy alone is optimal; in others, immunotherapy followed by mutation-targeted therapy is best. These results suggest that optimal treatment depends on the strength of cellular signaling pathways and highlight the need to quantify mutation-dependent cell signaling and the fitness advantage conferred on cancer cells harboring these mutations.