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
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
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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
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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
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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
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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
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