My laboratory is focused on understanding how cancer acquires treatment resistance to both conventional therapies and to immunotherapies, and how resistance can be overcome. In order to better understand the basis for this, we utilize data-driven genomics approaches towards both experimental and translational research goals. Hypothesis generation and testing relies on integrating various data types from animal models, molecular biology, genome-wide profiling, immune profiling, clinical data mining, and statistical modeling.  Using these methods we have identified regulatory networks and signaling pathways that not only predict but also promote treatment resistance. We have discovered that signaling pathways that are normally associated with an anti-viral response are associated with treatment resistance, suggesting an intriguing overlap between the anti-viral response and ways that cancer can evade the cytotoxic effects of therapy and/or the immune system. We are currently investigating this overlap by studying the regulation by the tumor microenvironment and how these anti-viral responses influence response to therapy. We are also investigating how tumor response to immune checkpoint blockade can be enhanced with ablative tumor radiation, how resistance to this combination therapy can develop, and how resistance can be therapeutically reversed. Studies using mouse models have been performed in parallel with analogous clinical trials in order to corroborate pre-clinical results with human patients. In this way, laboratory findings can be used to inform the design of next generation clinical trials.