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
Released Data Link
Team
Principal Investigator
Co-Investigator(s)
Team Members
Related Publications
Proteogenomic Characterization of Ovarian HGSC Implicates Mitotic Kinases, Replication Stress in Observed Chromosomal Instability
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
Residual tissue repositories as a resource for population-based cancer proteomic studies
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
Deep-Dive Targeted Quantification for Ultrasensitive Analysis of Proteins in Nondepleted Human Blood Plasma/Serum and Tissues
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
Quantitative proteomics identifies altered O‐GlcNAcylation of structural, synaptic and memory‐associated proteins in Alzheimer's disease
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
Targeted proteomic assays for quantitation of proteins identified by proteogenomic analysis of ovarian cancer
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
Role of CTGF in Sensitivity to Hyperthermia in Ovarian and Uterine Cancers
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
Recommendations for the Generation, Quantification, Storage, and Handling of Peptides Used for Mass Spectrometry–Based Assays
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
Proteogenomics connects somatic mutations to signalling in breast cancer
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.
Advances in targeted proteomics and applications to biomedical research
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
Reproducibility of Differential Proteomic Technologies in CPTAC Fractionated Xenografts
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
An Analysis of the Sensitivity of Proteogenomic Mapping of Somatic Mutations and Novel Splicing Events in Cancer
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
Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer
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
Using the CPTAC Assay Portal to Identify and Implement Highly Characterized Targeted Proteomics Assays
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
Contributions of immunoaffinity chromatography to deep proteome profiling of human biofluids
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
An Optimized Informatics Pipeline for Mass Spectrometry-Based Peptidomics
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]
Proteomics of ovarian cancer: functional insights and clinical applications
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