This is a renewal application for our T32 Proteogenomics of Cancer Training Program (PCTP) at the University of Michigan. The National Cancer Institute has made substantial investments in new technology platforms for cancer proteomics, most recently through the Clinical Proteomic Tumor Analysis Consortium (CPTAC). The proteome is critical to understanding functional genomics and systems biology of cancers and to discovery and validation of biomarker candidates and molecular targets for therapy and prevention. Sophisticated analysis of proteomes requires advanced informatics to deal with the complexity of specimens, the extreme dynamic range of protein concentrations, post-translational modifications, alternative splice isoforms, responses to all sorts of perturbations, and differences in databases and data formats. Furthermore, integrative proteogenomics analysis of data generated using multiple omics technologies (proteomics, genomics, transcriptomics, metabolomics, etc.) has emerged as a powerful approach for reconstructing targetable pathways in cancer. The main goal of PCTP is to address the current scarcity of scientists able to effectively generate and bioinformaticly analyze their own proteomics data, to take advantage of CPTAC and other publicly available large-scale proteomics datasets, and to perform multi-level proteogenomics data integration. PCTP is a truly multi-disciplinary Training Program that is also very unique, with the U of M and nationally. Anchored in the university-wide Center for Computational Medicine and Bioinformatics, it provides an opportunity to students from diverse backgrounds to become well skilled in all PCTP focus areas - cancer biology, proteomics, bioinformatics, and proteogenomics. PCTP is requesting 7 slots/year to supports pre-doctoral trainees for 1-2 years. We have a robust community of cancer researchers, bioinformaticians, data scientists, statisticians, and chemists. Our faculty and students are in the leadership of Human Proteome Organization initiatives, development and global deployment of data repositories and data analysis systems, and creation of new algorithms for proteome bioinformatics and multi-omics data integration. Our trainees come from larger Graduate Programs within the U of M (including Bioinformatics, Pathology, Cancer Biology, Chemistry, and Biomedical Engineering) and receive training in cancer biology, bioinformatics, and proteomics through courses, seminars, and special workshops. PCTP strongly encourages dual mentorship of each trainee by a cancer researcher and a computational scientist. We will further strengthen our Training Program with an addition of a special Annual Workshop for our T32 trainees on Proteogenomics Data Analysis, taking advantage of our Faculty?s engagement in the efforts of the CPTAC consortium. In summary, our ongoing NCI training program in Proteogenomics of Cancer trains a new generation of scientists well prepared for an independent career in interdisciplinary biomedical research, enhances faculty research, and supports NCI goals.

Public Health Relevance

Very large, complex datasets are being generated by cancer researchers using new proteomics technologies that allow them to study thousands of proteins simultaneously. This avalanche of data requires scientists well-trained in the specialized and multidisciplinary field of proteomics. Insufficient numbers of scientists are being trained in this rapidly growing field and this T32 proposes to address this issue. !

National Institute of Health (NIH)
National Cancer Institute (NCI)
Institutional National Research Service Award (T32)
Project #
Application #
Study Section
Subcommittee I - Transistion to Independence (NCI)
Program Officer
Schmidt, Michael K
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Michigan Ann Arbor
Biostatistics & Other Math Sci
Schools of Medicine
Ann Arbor
United States
Zip Code
Hoesli, Rebecca; Birkeland, Andrew C; Rosko, Andrew J et al. (2018) Proportion of CD4 and CD8 tumor infiltrating lymphocytes predicts survival in persistent/recurrent laryngeal squamous cell carcinoma. Oral Oncol 77:83-89
Serio, J; Ropa, J; Chen, W et al. (2018) The PAF complex regulation of Prmt5 facilitates the progression and maintenance of MLL fusion leukemia. Oncogene 37:450-460
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
Wu, Jiansheng; Zhang, Qiuming; Wu, Weijian et al. (2018) WDL-RF: predicting bioactivities of ligand molecules acting with G protein-coupled receptors by combining weighted deep learning and random forest. Bioinformatics 34:2271-2282
Grimley, Edward; Dressler, Gregory R (2018) Are Pax proteins potential therapeutic targets in kidney disease and cancer? Kidney Int 94:259-267
Tamura, Shuzo; Wang, Yin; Veeneman, Brendan et al. (2018) Molecular Correlates of In Vitro Responses to Dacomitinib and Afatinib in Bladder Cancer. Bladder Cancer 4:77-90
Anwar, Talha; Arellano-Garcia, Caroline; Ropa, James et al. (2018) p38-mediated phosphorylation at T367 induces EZH2 cytoplasmic localization to promote breast cancer metastasis. Nat Commun 9:2801
Mady, Ahmed S A; Liao, Chenzhong; Bajwa, Naval et al. (2018) Discovery of Mcl-1 inhibitors from integrated high throughput and virtual screening. Sci Rep 8:10210
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
Song, James M; Menon, Arya; Mitchell, Dylan C et al. (2017) High-Throughput Chemical Probing of Full-Length Protein-Protein Interactions. ACS Comb Sci 19:763-769

Showing the most recent 10 out of 41 publications