Optimization of Immune Checkpoint Blockade via a Multiscale Model System

Cancer progresses when cancer cells selectively bind to inhibitory receptors on a T cell surface, downregulating tumor immune response. One standard-of-care strategy to combat this process is immune checkpoint blockade. Immune checkpoint blockade occurs when a therapeutic agent binds to, and inhibits, inhibitory receptors on a T cell surface, such that immune stimulation is favored when T cells and cancer cells interact. However, many cancers fail to respond to immune checkpoint blockade treatments. Here we explore a whole-tumor and an individual cell-focused model system to test expected outcomes of blockade perturbations in tumor-immune interactions. We first observe a transition point at which patients become more likely to reach "remission" or "stable disease" as a terminal state, and a "progressive disease" state is less likely. We propose a physical, agent-based framework for testing blockade strategies at the cellular level. This offers valuable guidance for blockade efficacy optimization in future development and design of therapeutic antibodies.