iPGDAC, An Integrative Proteogenomic Data Analysis Center for CPTAC

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Project Summary By combining mass spectrometry (MS)-based proteomics with genomics, epigenomics, and transcriptomics, proteogenomics holds great potential to better illuminate cancer complexities than individual ‘omes. During the past 10 years, the Clinical Proteomics Tumor Analysis Consortium (CPTAC) has performed comprehensive proteogenomic characterization of >1,500 tumors across 10 cancer types. These studies not only yield novel biological and clinical insights into different cancer types but also produce valuable datasets and computational tools that can be further used by the broad scientific community. The next phase of the CPTAC program seeks to expand the current success to more cancer types and translational research focusing on clinically relevant questions. Our integrative proteogenomic data analysis center (iPGDAC) is one of the current CPTAC funded PGDACs. We have participated in the studies of all CPTAC cancer types and have played a leading role in data analysis for several cancer types. This application seeks to continue and enhance our contribution to the next phase of the CPTAC program. The overarching goal of our PGDAC is to accelerate the translation of cancer proteogenomic data into better understanding of cancer biology and improved cancer treatment. We will continue developing and improving our computing tools, workflows, and web portals that have already been successfully used in the CPTAC studies for sequence-based and pathway/network-based proteogenomic data integration. In addition, we will address unmet needs in post-translational modification (PTM)-related analyses by using protein sequence and natural language-based deep learning techniques to improve PTM peptide identification, to predict genomic variant impact on PTMs, and to connect PTM sites to existing knowledge. Using unique tools from our team and cutting edge statistical inference and machine learning algorithms, we will perform integrated analysis on proteogenomic data from the CPTAC studies to: 1) create a comprehensive molecular and cellular portrait for each patient’s tumor; 2) identify and characterize molecular and tumor microenvironment/immune subtypes; 3) prioritize functional genomic aberrations using proteogenomic data; 4) reveal molecular mechanisms of cancer phenotypes; and 5) develop predictive models for patient prognosis and treatment response. Our PGDAC brings to the CPTAC network a fully integrated, completely established program with expertise in all the critical areas specified by the RFA. We have a proven track record of leadership in computational proteogenomics and successful collaboration in the CPTAC network, and we expect to broadly advance the field through this project.