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Center for Application of Advanced Clinical Proteomic Technologies for Cancer (NCI U24; 59963)


EMSL Project ID
48666

Abstract

The overall objective of the PNNL Clinical Proteome Characterization Center (PCPCC) is to facilitate cancer biomarker development by linking the cancer genotype to the cancer phenotype using detailed comprehensive and quantitative characterization of cancer proteomes to complement the extensive genome-level characterization provided by The Cancer Genome Atlas (TCGA). The PCPCC will contribute to the success of the planned network of Protein Characterization Centers (PPCs) by utilizing robust and quantitative proteomics technologies and workflows for systematic discovery and verification of protein biomarkers that can be qualified in clinical studies, using the cancer specimens and associated data provided by the CPTAC. The Discovery Unit will make measurements providing a comprehensive and quantitative characterization of the cancer proteomes that provides information including protein abundances, splicing variants, mutations, and posttranslational modifications to complement the genomic characterization for CPTAC-supplied biospecimens. The extensive database of genomic information on these samples will be integrated with the quantitative proteomic measurements made by the PCPCC, other available proteomics information (e.g., from other PCC's), and a systems-level analysis of tumor-specific pathways to produce a prioritized list of highly credentialed candidates based on a weighted integration of multiple sources of information, including clinical oncology and cancer biology. The Verification Unit will systemically develop and apply multiplexed verification assays directed at specific protein targets as identified and selected by the Biomarker Candidate Selection Subcommittee. The PCPCC will develop sensitive, selective, quantitative assays for up to 100 protein targets per year and apply ultra-sensitive assays to biomarker verification with a throughput of up to 200 clinical samples per year. Additionally, as in the Discovery efforts, measurements with the best available validated platform will be used to provide quantitative measurements for low-abundance otherwise undetectable candidates. As part of a consortium of PCC's, the PCPCC will also work to advance the efforts of others based upon e.g. the cancer tumor proteomics data generated, as well as subsequent biomarker clinical qualification and validation efforts.

Project Details

Start Date
2014-11-03
End Date
2017-09-30
Status
Closed

Team

Principal Investigator

Richard Smith
Institution
Pacific Northwest National Laboratory

Co-Investigator(s)

Karin Rodland
Institution
Pacific Northwest National Laboratory

Team Members

Sarah Williams
Institution
Environmental Molecular Sciences Laboratory

Samuel Payne
Institution
Pacific Northwest National Laboratory

Thomas Fillmore
Institution
Environmental Molecular Sciences Laboratory

Tujin Shi
Institution
Pacific Northwest National Laboratory

Jason McDermott
Institution
Pacific Northwest National Laboratory

Matthew Monroe
Institution
Pacific Northwest National Laboratory

Ronald Moore
Institution
Pacific Northwest National Laboratory

Vladislav Petyuk
Institution
Pacific Northwest National Laboratory

Marina Gritsenko
Institution
Pacific Northwest National Laboratory

Tao Liu
Institution
Pacific Northwest National Laboratory

Weijun Qian
Institution
Pacific Northwest National Laboratory

Rui Zhao
Institution
Environmental Molecular Sciences Laboratory

David Camp
Institution
Pacific Northwest National Laboratory

Anil Shukla
Institution
Pacific Northwest National Laboratory

Related Publications

Elzek MA, and KD Rodland. 2015. "Proteomics of ovarian cancer: functional insights and clinical applications." Cancer and Metastasis Reviews 34(1):83-96. doi:10.1007/s10555-014-9547-8
Gritsenko MA, Z Xu, T Liu, and RD Smith. 2016. "Large-Scale and Deep Quantitative Proteome Profiling Using Isobaric Labeling Coupled with Two-Dimensional LC-MS/MS." In Quantitative Proteomics by Mass Spectrometry, 2nd edition. Series: Methods in Molecular Biology, vol. 1410, ed. S Sechi, pp. 237-247. Springer, New York, NY.
Hatakeyama H, SY Wu, YA Lyons, S Pradeep, W Wang, Q Huang, KA Court, T Liu, S Nie, C Rodriguez-Aguayo, F Shen, Y Huang, T Hisamatsu, T Mitamura, N Jennings, J Shim, PL Dornialk, LS Mangala, M Petrillo, VA Petyuk, AA Schepmoes, AK Shukla, M Torres-Lugo, JS Lee, KD Rodland, A Fagotti, G Lopez-Bernstein, C Li, and AK Sood. 2016. "Role of CTGF in Sensitivity to Hyperthermia in Ovarian and Uterine Cancers." Cell Reports 17(6):1621-1631. doi:10.1016/j.celrep.2016.10.020
Hoofnagle AN, JR Whiteaker, SA Carr, E Kuhn, T Liu, SA Massoni, SN Thomas, R Townsend, LJ Zimmerman, E Boja, J Chen, DL Crimmins, S Davies, Y Gao, TR Hiltke, K Ketchum, C Kinsinger, M Mesri, MR Meyer, W Qian, RM Schoenherr, M Scott, T Shi, G Whiteley, J Wrobel, C Wu, BL Ackermann, R Aebersold, DR Barnidge, DM Bunk, N Clarke, JB Fishman, RP Grant, U Kusebauch, MM Kushnir, MS Lowenthal, R Moritz , H Neubert, SD Patterson, AL Rockwood, J Rogers, RJ Singh, J Van Eyk , SH Wong, S Zhang, DW Chan, X Chen, MJ Ellis, D Liebler, KD Rodland, H Rodriguez, RD Smith, Z Zhang, H Zhang, and AG Paulovich. 2016. "Recommendations for the generation, quantification, storage and handling of peptides used for mass spectrometry-based assays." Clinical Chemistry 62(1):48-69. doi:10. 1373/clinchem. 2015. 250563
McDermott J.E., O.A. Arshad, V.A. Petyuk, Y. Fu, M.A. Gritsenko, T.R. Clauss, and R.J. Moore, et al. 2020. "Proteogenomic characterization of ovarian HGSC implicates mitotic kinases, replication stress in observed chromosomal instability." Cell Reports Medicine 1, no. 1:Article No. 100004. PNNL-SA-145681. doi:10.1016/j.xcrm.2020.100004
Mertins P, Mani DR, Ruggles KV, Gillette MA, Clauser KR, Wang P, Wang X, Qiao JW, Cao S, Petralia F, Kawaler E, Mundt F, Krug K, Tu Z, Lei JT, Gatza ML, Wilkerson M, Perou CM, Yellapantula V, Huang KL, Lin C, McLellan MD, Yan P, Davies SR, Townsend RR, Skates SJ, Wang J, Zhang B, Kinsinger CR, Mesri M, Rodriguez H, Ding L, Paulovich AG, Fenyö D, Ellis MJ, Carr SA; NCI CPTAC. Proteogenomics connects somatic mutations to signalling in breast cancer. Nature. 2016 May 25;534(7605):55-62. doi: 10.1038/nature18003.
Nie S, T Shi, TL Fillmore, AA Schepmoes, HM Brewer, Y Gao, E Song, H Wang, KD Rodland, W Qian, RD Smith, and T Liu. 2017. "Deep-dive Targeted Quantification for Ultrasensitive Analysis of Proteins in Nondepleted Human Blood Plasma/Serum and Tissues." Analytical Chemistry 89(17):9139-9146. doi:10.1021/acs.analchem.7b01878
Piehowski P.D., V.A. Petyuk, R.L. Sontag, M.A. Gritsenko, K.K. Weitz, T.L. Fillmore, and H. Makhlouf, et al. 2018. "Residual Tissue Repositories as a Resource for Population-based Cancer Proteomic Studies." Clinical Proteomics 15. PNNL-SA-130920. doi:10.1186/s12014-018-9202-4
Ruggles K, Z Tang, X Wang, H Grover, M Askenazi, J Teubl, S Cao, M McLennan, K Clauser, DL Tabb, P Mertins, R Slebos, P Erdmann-Gilmore, S Li, H Gunawardena, L Xie, T Liu, JY Zhou, S Sun, KA Hoadley, CM Perou, X Chen, S Davies, CA Maher, C Kinsinger, KD Rodland, H Zhang, Z Zhang, L Ding, R Townsend, H Rodriguez, DW Chan, RD Smith, D Liebler, SA Carr, SH Payne, M Ellis, and D Fenyo. 2016. "An Analysis of the Sensitivity of Proteogenomic Mapping of Somatic Mutations and Novel Splicing Events in Cancer." Molecular and Cellular Proteomics 15(3):1060-1071. doi:10. 1074/mcp. M115. 056226
Shi T, E Song, S Nie, KD Rodland, T Liu, WJ Qian, and RD Smith. 2016. "Advances in targeted proteomics and applications to biomedical research." Biomedical Research 16(15-16):2160-2182. doi:10. 1002/pmic. 201500449
Song E, Y Gao, C Wu, T Shi, S Nie, TL Fillmore, AA Schepmoes, MA Gritsenko, W Qian, RD Smith, KD Rodland, and T Liu. 2017. "Targeted proteomic assays for quantitation of proteins identified by proteogenomic analysis of ovarian cancer." Scientific Data 4:170091. doi:10.1038/sdata.2017.91
Tabb DL, X Wang, SA Carr, K Clauser, P Mertins, MC Chambers, JD Holman, J Wang, B Zhang, LJ Zimmerman, X Chen, H Gunawardena, S Davies, M Ellis, S Li, R Townsend, E Boja, K Ketchum, C Kinsinger, M Mesri, H Rodriguez, T Liu, S Kim, JE McDermott, SH Payne, VA Petyuk, KD Rodland, RD Smith, F Yang, DW Chan, B Zhang, H Zhang, Z Zhang, JY Zhou, and D Liebler. 2016. "Reproducibility of differential proteomic technologies in CPTAC fractionated xenografts." Journal of Proteome Research. doi:10. 1021/acs. jproteome. 5b00859
Wang J, Z Ma, SA Carr, P Mertins, H Zhang, Z Zhang, DW Chan, MJ Ellis, R Townsend, RD Smith, JE McDermott, X Chen, AG Paulovich, E Boja, M Mesri, C Kinsinger, H Rodriguez, KD Rodland, D Liebler, and B Zhang. 2017. "Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction." PNNL-SA-124098, Pacific Northwest National Laboratory, Richland, WA. [Unpublished]
Wang S, F Yang, VA Petyuk, AK Shukla, ME Monroe, MA Gritsenko, KD Rodland, RD Smith, W Qian, CX Gong, and T Liu. 2017. "Quantitative proteomics identifies altered O-GlcNAcylation of structural, synaptic and memory-associated proteins in Alzheimer's disease." The Journal of Pathology 243(1):78-88. doi:10.1002/path.4929
Whiteaker JR, G Halusa, AN Hoofnagle, V Sharma, B MacLean, P Yan, J Wrobel, J Kennedy, DR Mani, LJ Zimmerman, MR Meyer, M Mesri, E Boja, SA Carr, DW Chan, X Chen, J Chen, S Davies, M Ellis, D Fenyo, T Hiltket, K Ketchum, C Kinsinger, E Kuhn, D Liebler, T Liu, M Loss, M MacCoss, W Qian, R Rivers, KD Rodland, K Ruggles, M Scott, RD Smith, SN Thomas, R Townsend, G Whiteley, C Wu, H Zhang, Z Zhang, H Rodriguez, and AG Paulovich. 2016. "Using the CPTAC Assay Portal to identify and implement highly characterized targeted proteomics assays ." In Quantitative Proteomics by Mass Spectrometry, 2nd edition. Series: Methods in Molecular Biology, vol. 1410, ed. S Sechi, pp. 223-263. Springer, New York, NY. doi:10. 1007/978-1-4939-3524-6_13
Wu C, J Duan, T Liu, RD Smith, and W Qian. 2016. "Contributions of Immunoaffinity Chromatography to Deep Proteome Profiling of Human Biofluids." Journal of Chromatography B. doi:10.1016/j.jchromb.2016.01.015
Wu C, ME Monroe, Z Xu, GW Slysz, SH Payne, KD Rodland, T Liu, and RD Smith. 2015. "An Optimized Informatics Pipeline for Mass Spectrometry-Based Peptidomics." Journal of the American Society for Mass Spectrometry . doi:10.1007/s13361-015-1169-z [In Press]
Zhang H, T Liu, Z Zhang, SH Payne, B Zhang, JE McDermott, JY Zhou, VA Petyuk, L Chen, D Ray, S Sun, F Yang, L Chen, J Wang, P Shah, SW Cha, P Aiyetan, S Woo, Y Tian, MA Gritsenko, TRW Clauss, C Choi, ME Monroe, SN Thomas, S Nie, C Wu, RJ Moore, KH Yu, DL Tabb, D Fenyo, V Bafna, Y Wang, H Rodriguez, E Boja, T Hiltket, R Rivers, LJ Sokoll, H Zhu, IM Shih, L Cope, A Pandey, B Zhang, M Snyder, D Levine, RD Smith, DW Chan, and KD Rodland. 2016. "Integrated proteogenomic characterization of human high grade serous ovarian cancer." Cell 166(3):755-765. doi:10. 1016/j. cell. 2016. 05. 069