Contact PD/PI: Kummar, Shivaani PROJECT SUMMARY/ABSTRACT Immunotherapy, specifically checkpoint inhibitor (CKI) therapy, has resulted in dramatic and sustained tumor responses in patients with a variety of advanced cancers, such as melanoma, non-small cell lung cancer, head and neck cancer and others. However, only a subset of patients with advanced solid tumors derive clinical benefit, and of those a substantial portion progress over time. The ability to better select patients likely to respond to CKI therapy would optimize delivery; and reduce the number of patients exposed to agents not likely to work on their disease. There are multiple ongoing efforts focusing on blood or tissue based biomarkers; and number of immune cells, especially T cells, in the tumor lesions has correlated with tumor response to CKI treatment. However, it remains difficult and risky to biopsy solid tumor lesions repeatedly in a given patient. Since imaging can be safely performed at multiple time points, and multiple disease sites can be assessed simultaneously, it presents an attractive strategy for patient selection. [18F]F-AraG, a 18F-labeled analog of arabinofuranosylguanine (AraG), has been shown to be selectively taken up by T cells in laboratory models and in initial human studies. We hypothesize that [18F]F-AraG signal will increase in patients who develop T-cell immune responses in tumor following CKI therapy, and this will correlate with subsequent clinical benefit. In aim 1, we will correlate changes in [18F]F-AraG signal to number of T cells in tumor biopsies obtained pre and post-treatment with CKI. In our second aim, we will correlate the change in [18F]F-AraG signal following treatment to observed clinical benefit, defined as either tumor stabilization or shrinkage. In the third aim, correlate change in [18F]-AraG signal in lung and GI tract with the subsequent occurrence of higher grade immune related adverse events. Data generated from the proposed study will inform development of a novel imaging biomarker of response to immunotherapies, optimizing patient selection and outcome. In addition, our ability to predict which patients will go on to develop more severe toxicities will inform institution of therapies earlier to mitigate these side effects, as well as personalize management of adverse events.