Project Summary Cancer proteogenomics encompasses methods that integrate mass spectrometry (MS)-based measurements of protein abundance and post-translational modifications (PTMs) with genomic, epigenomic, and transcriptomic data from preclinical cancer models and tumor samples. The multidisciplinary Proteogenomic Characterization Center we propose will employ a range of state-of-the-art MS-based proteomic and metabolomic technologies to systematically generate and integrate high quality, comprehensive and quantitative proteomic and metabolomic data with genomic data. Our overarching goals are to leverage the integrated data to identify signatures of cancer drivers, detect signaling network adaptations and provide information on PTMs that affect cellular signaling, molecular complex formation, and protein location, translation and stability in human biospecimens and relevant models of cancer. Peptidomes of the class I and II human leukocyte antigens (HLA) of the tumors will also be analyzed to shed light on tumor-immune escape mechanisms and antigen processing in cancer, improve algorithms for prediction of antigen display and immunogenicity and inform development of personalized cancer vaccines. We hypothesize that integrating deep, high quality, quantitative proteomic and, especially, PTM-omic, HLA-peptidomic and metabolomic data with genomic and transcriptomic data will provide novel insights into the pathophysiology of cancer and help to identify new, actionable targets for drug development and treatment. Data will be rapidly distributed to the cancer biology and clinical communities, as we have done for the past 15 years in the NCI-CPTAC program. The resulting datasets will enable a broad range of investigation by many teams, accelerating molecularly-oriented cancer research toward biological and clinical impact. We will also systematically develop and apply high sensitivity targeted MS assays to peptide/protein targets identified in the Discovery Arm, with an emphasis on posttranslationally-modified peptides in signaling cascades, oncogenic pathway regulators and effectors, and druggable proteins. Assays will use stable isotope- labeled standards for unambiguous identification and quantification and follow Tier 2 guidelines developed from the community-based effort led by the Broad proteomics team. Existing technologies will be further developed and automated to enable comprehensive analysis of rare tumor cell populations, to evaluate tumor heterogeneity, to increase depth and breadth of post-translational modification analysis, and to improve depth, reliability and repeatability of peptide identification and quantification in general by intelligent data acquisition.