SUMMARY – PROJECT 3. Mechanisms of immunotherapy action. The goal of this project is to collect data and construct computational models that provide a systems-level understanding of the myriad interactions that contribute to immune surveillance of cancer, thereby improving our ability to manipulate these interactions for cancer therapy. We will study the effects of perturbations (genetic and drug-induced) on metabolic and signaling pathways and on anti-tumor T cell function in mouse models. This is expected to significantly increase our understanding of anti-tumor responses by T cells and to generate pre-clinical data needed to design new combination therapies for possible translation into the clinic. We aim for computational models that predict the consequences of therapeutic intervention based on assays of pre-treatment state. Immune-tumor interactions are dependent on the intracellular states of cells, which we will measure at the levels of gene expression, signal transduction and cellular metabolism. Metabolic state affects the ability of immune cells to function in tumor cell killing and immune checkpoint inhibitors alter T cell metabolism. Metabolic enzymes thus represent an emerging class of targets for therapeutics that aim to augment anti-tumor immune responses by blocking or mitigating the effects of T-cell exhaustion. Since cell-non-autonomous mechanisms play a major role in ICI, computational models will focus on interactions among cells, in which data on cell state is modeled as influencing the strength of these interactions. We hypothesize that such models will reveal new ways to enhance the efficacy of immunotherapy by combining ICIs, targeted therapies and drugs that modulate the activity of metabolic enzymes. Aim 6.1 Will define cellular and metabolic interactions among immune checkpoint receptors by exposing syngeneic mouse tumor models to antibodies against immune checkpoint receptors individually and in combination, and then measuring the effects on tumor and immune cell states using multiple profiling technologies at single cell resolution. We hope to identify “exhaustion targets” that might be drugged to increase the efficacy of immune checkpoint blockade. Aim 6.2 Will study immune signaling networks known to be important in T-cell biology and ICI function and link the activities of these networks to the metabolic states of both tumor and immune cells. Extensive evidence shows that metabolic and signaling states of immune cells are important in tumor surveillance but relatively few parallel studies have been performed linking activity of immune and tumor signaling to metabolism. Aim 6.3 Will investigate the cellular and molecular events underlying successful combination immunotherapy using syngeneic and genetically engineered mouse (GEM) models in which a combination of ICIs, cytokines and a lymph node-targeted vaccine results in regression of well-established tumors. We will also assess whether efficacious responses can be detected in circulating immune cells in the blood, a first step towards developing a convenient response bioassay for use in humans.