Jacob Sesate

Thesis Advisor: Dr. Lisette de Pillis (Harvey Mudd College - Department of Mathematics)

Second Reader: Dr. TBD  ()

Contact: jsesate@hmc.edu

Abstract

As tumors evolve, they can develop the ability to evade the immune system by expressing proteins that tell immune cells to stay away. Immune checkpoint inhibitor (ICI) therapy neutralizes these proteins, allowing the immune system to bypass tumor evasion and fight back. ICI therapy is approved for dozens of cancer types and continues to broaden its impact with over 3000 active clinical trials. As it stands, ICI therapy has been shown to produce long-term survival benefits in patients with late-stage, metastatic cancers. Despite its success, validated mathematical models that represent the underlying tumor-immune dynamics are either restricted to single therapies, contain many unidentifiable parameters, or lack robust experimental validation. This project addressed each of these gaps by creating a simple model that requires fitting to relatively few parameters. The resulting model is simple and highly interpretable. Fit to datasets from three independent laboratories, our model successfully captures the tumor-immune dynamics that proceed from both single and combination ICI therapies. We are currently preparing this baseline model for publication while extending the framework to incorporate organ-specific lymphocyte infiltration and inflammatory markers associated with immune-related adverse events.