Speaker
Description
Tumors grow and metastasize through intricate interactions among the diverse signals and cell types composing the tumor microenvironment (TME). Cancer systems immunology combines mathematical modeling with analysis of high-dimensional multi-modal data and machine learning to gain insight into the complex ecosystems created by the immune system in and around a tumor. Immunotherapies such as checkpoint inhibition can provide durable responses in a subset of metastatic breast cancer patients, yet the mechanisms governing intrinsic resistance and response remain poorly understood. Here, via cancer systems immunology methods we integrate single-cell genomics and spatial transcriptomics with mathematical modeling to dissect metastatic breast cancer TMEs. We developed methods to identify the cell circuits mediating responses with treatment in specific TMEs. In the mouse lung TME, using cell circuits we discovered that treatment with entinostat (a histone deacetylase inhibitor) modulates chemokine and adhesive signaling pathways. We revealed that the benefits of this treatment with checkpoint inhibitors are mediated by activating B cells and decreasing immunosuppressive myeloid cell---T cell interactions. We validated these predictions using mathematical modeling and analysis of clinical samples from a phase 1b trial. We further developed mathematical models of the tumor-immune dynamics of tumors at different sites of metastasis and fit them to RECIST clinical response data via Bayesian parameter inference. In doing so, we derived mechanistic explanations for differential patient outcomes and generated testable predictions for therapeutic interventions. Together, this work offers a generalizable framework for decomposing complex TMEs, inferring multiscale interaction networks, and modeling their dynamics to guide therapeutic strategy.
