PROJECT SUMMARY Research Question to be Addressed: The application of immunotherapies, such as immune checkpoint inhibitors (ICIs), in the treatment of cancer has led to significant improvements in long-term patient outcomes. However, existing therapies are not effective in all patients, and our knowledge of the immunological factors that contribute to positive treatment responses and resistance remains limited. This demands targeted research efforts to more comprehensively characterize immune profiles in patients receiving immunotherapies, as a means to uncover biological mechanisms of action and identify more effective biomarkers of treatment response or resistance. Novel tumor- infiltrating lymphocyte (TIL) therapies hold great promise in the clinic for cancer patients that have proven refractory to other treatment strategies. This proposal will take a multi-omic and data-driven approach to deeply characterize immunological signatures in blood and tumor tissues obtained from melanoma patients participating in a clinical trial testing the efficacy of a novel TIL therapy. Our goal is to identify specific biomarkers that associate with treatment outcomes. This effort will result in unique and rich immunological datasets and novel tools and models for clinical outcome prediction and will drive the design of next-generation immunotherapies. Impact of Proposed Team Science: To effectively address the research questions posed, we have assembled a team of researchers with diverse expertise and skill sets. Our team builds from our parent COBRE, drawing on the extensive expertise of MPIs Chesney and Yan, who are world leaders in cancer immunology and clinical research. Both will make critical contributions through administrative oversight. Our research team includes three independent investigators with established research programs in cancer immunology (Yaddanapudi; Aim 1), genomics and immunogenetics (Watson; Aim 2), and bioinformatics and data science (Park; Aim 3). Studies proposed in Aims 1 and 2 are focused on the deep characterization of immunological profiles of melanoma patient tumors, TILs, and blood collected pre- and post-treatment using multi-omics approaches. These aims will delineate immunological variables that vary between tissues and patients in the context of TIL therapy. Given the complex nature of the high-dimensional datasets to be generated in Aims 1 and 2, the activities planned for Aim 3 will be critical to advancing the overall objectives of the proposal. Specifically, studies proposed in Aim 3 will focus on building more streamlined pipelines and tools for processing and analysis of data generated in Aims 1 and 2. In addition, the second major activity of Aim 3 will include the construction of novel machine learning models for predicting therapy outcomes, via the integration of datasets (from Aims 1 and 2) with the rich de-identified clinical data that will be collected as part of the trial. Critically, the completion of the ...