This is an application to establish the University of Michigan Proteogenomic Data Analysis Center (UM- PGDAC). The National Cancer Institute (NCI) has made significant investments in new technology platforms for cancer proteomics through the Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative. Proteomics provides complementary information not apparent from the analysis of genomic and transcriptomic data alone. First, it is critical to identify which of the thousands of novel or previously poorly characterized transcripts or sequence variants discovered using genomic and transcriptomic approaches are expressed at the protein level, prioritizing such variants for subsequent validation studies. Second, integration of quantitative information across multiple data types has emerged as a powerful strategy for reconstructing targetable pathways in cancer and for nomination of potential drug targets. At the same time, sophisticated, integrative analyses across genome and proteome data require advanced bioinformatics tools and stringent quality control measures. UM-PGDAC is uniquely positioned to implement advanced bioinformatics infrastructure to address these challenges and apply it across CPTAC data. It brings together a multi-disciplinary team of scientists who are leading experts in the areas of computational proteomics, transcriptomics, genomics, cancer systems biology and precision oncology. The team is anchored at the Michigan Center for Translational Pathology (MCTP), which has a long history of successful collaborations between the individual investigators. UM-PGDAC builds upon more than a decade of highly relevant work that resulted in the development of a comprehensive infrastructure required for proteogenomics and multi- omics data integration research. UM-PGDAC investigators will work to further improve the speed and accuracy of proteogenomics analyses. The integrated genome/transcriptome/proteome pipelines used by the UM-PDGAC will be enhanced with automated data visualization capabilities and report generation tools for presenting the findings to cancer biologists in a transparent and easy to interpret manner. UM-PGDAC will work collaboratively with other members of the CPTAC to ensure minimal duplication of efforts, efficient exchange of data and bioinformatics methods and tools and interoperability via the use of common file formats and data standards. Building upon its extensive experience in the area of biomarker discovery and precision oncology, further enhanced through participation of UM-PGDAC investigators in the EDRN, SPORE, and other NIH funded initiatives, UM-PGDAC will engage in a second round of prioritization work to select candidate cancer-specific proteins and peptides for subsequent targeted validation using multiplex proteomic assays. Finally, UM-PGDAC will take advantage of a unique opportunity ? in the form of the NCI funded T32 Proteome Informatics Training Program at the University of Michigan ? to create a unique environment for training the new generation of cancer researchers versed in proteomics technology.
This is an application to establish the University of Michigan Proteogenomic Data Analysis Center (UM- PGDAC) in support of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative. UM-PGDAC, working in collaboration with other PGDACs, will develop advanced computational infrastructure for comprehensive and global characterization of genomics (DNA), transcriptomics (RNA), and proteomics (protein) data collected from several cohorts of human tumors using NCI-provided biospecimens. UM- PGDAC will conduct integrative analyses of data across these different data types with a goal to identify potentially cancer-related molecular alterations and to elucidate how distinct changes at the protein level are related to abnormalities in cancer genomes. A second priority of UM-PGDAC is to select cancer-specific protein candidates for subsequent functional studies in order to evaluate their potential for clinical utility.
|Hawkins, Allegra G; Basrur, Venkatesha; da Veiga Leprevost, Felipe et al. (2018) The Ewing Sarcoma Secretome and Its Response to Activation of Wnt/beta-catenin Signaling. Mol Cell Proteomics 17:901-912|
|Omenn, Gilbert S; Lane, Lydie; Overall, Christopher M et al. (2018) Progress on Identifying and Characterizing the Human Proteome: 2018 Metrics from the HUPO Human Proteome Project. J Proteome Res :|
|Shao, Wenguang; Pedrioli, Patrick G A; Wolski, Witold et al. (2018) The SysteMHC Atlas project. Nucleic Acids Res 46:D1237-D1247|
|Siddiqui, Omer; Zhang, Hongjiu; Guan, Yuanfang et al. (2018) Chromosome 17 Missing Proteins: Recent Progress and Future Directions as Part of the neXt-MP50 Challenge. J Proteome Res :|
|Zhang, Chengxin; Wei, Xiaoqiong; Omenn, Gilbert S et al. (2018) Structure and Protein Interaction-based Gene Ontology Annotations Reveal Likely Functions of Uncharacterized Proteins on Human Chromosome 17. J Proteome Res :|
|Ropa, James; Saha, Nirmalya; Chen, Zhiling et al. (2018) PAF1 complex interactions with SETDB1 mediate promoter H3K9 methylation and transcriptional repression of Hoxa9 and Meis1 in acute myeloid leukemia. Oncotarget 9:22123-22136|
|Avtonomov, Dmitry M; Polasky, Daniel A; Ruotolo, Brandon T et al. (2018) IMTBX and Grppr: Software for Top-Down Proteomics Utilizing Ion Mobility-Mass Spectrometry. Anal Chem 90:2369-2375|
|Xu, Tao; Park, Sung-Soo; Giaimo, Benedetto Daniele et al. (2017) RBPJ/CBF1 interacts with L3MBTL3/MBT1 to promote repression of Notch signaling via histone demethylase KDM1A/LSD1. EMBO J 36:3232-3249|
|Paik, Young-Ki; Overall, Christopher M; Deutsch, Eric W et al. (2017) Progress and Future Direction of Chromosome-Centric Human Proteome Project. J Proteome Res 16:4253-4258|
|Kong, Andy T; Leprevost, Felipe V; Avtonomov, Dmitry M et al. (2017) MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics. Nat Methods 14:513-520|
Showing the most recent 10 out of 17 publications