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, including simultaneous application of state-of-the-art validated platforms and advanced developmental platforms, 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 CPTC. 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 CPTC-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 a minimum of 100 protein targets per year and apply ultra-sensitive assays to biometric verification with a throughput of at least 500 plasma (or serum) samples per year, for a total of at least 2500 samples. Additionally, as in the Discovery efforts, measurements with the best available validated platform will be augmented by measurements with a developmental high performance platform for the same samples (for an overall total of at least 1000 samples per year, and >5000 total) 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.
Despite recent declines in the cancer death rate, cancer remains a significant source of mortality: 25% of all deaths In the US are attributed to cancer. There Is a real clinical need for earlier diagnosis, more accurate prognosis, and more effective therapeutic targeting to improve the outcome for patients with cancer. The PCPCC is dedicated to using the best state-of-the-art and advanced developmental MS platforms and methods, systems biology approaches, and clinical collaborations, to advance the discovery and verification of cancer biomarkers.
|Wu, Chaochao; Duan, Jicheng; Liu, Tao et al. (2016) Contributions of immunoaffinity chromatography to deep proteome profiling of human biofluids. J Chromatogr B Analyt Technol Biomed Life Sci 1021:57-68|
|Whiteaker, Jeffrey R; Halusa, Goran N; Hoofnagle, Andrew N et al. (2016) Using the CPTAC Assay Portal to Identify and Implement Highly Characterized Targeted Proteomics Assays. Methods Mol Biol 1410:223-36|
|Hoofnagle, Andrew N; Whiteaker, Jeffrey R; Carr, Steven A et al. (2016) Recommendations for the Generation, Quantification, Storage, and Handling of Peptides Used for Mass Spectrometry-Based Assays. Clin Chem 62:48-69|
|Wang, Hui; Shi, Tujin; Qian, Wei-Jun et al. (2016) The clinical impact of recent advances in LC-MS for cancer biomarker discovery and verification. Expert Rev Proteomics 13:99-114|
|Ruggles, Kelly V; Tang, Zuojian; Wang, Xuya et al. (2016) An Analysis of the Sensitivity of Proteogenomic Mapping of Somatic Mutations and Novel Splicing Events in Cancer. Mol Cell Proteomics 15:1060-71|
|Tabb, David L; Wang, Xia; Carr, Steven A et al. (2016) Reproducibility of Differential Proteomic Technologies in CPTAC Fractionated Xenografts. J Proteome Res 15:691-706|
|Mertins, Philipp; Mani, D R; Ruggles, Kelly V et al. (2016) Proteogenomics connects somatic mutations to signalling in breast cancer. Nature 534:55-62|
|Gritsenko, Marina A; Xu, Zhe; Liu, Tao et al. (2016) Large-Scale and Deep Quantitative Proteome Profiling Using Isobaric Labeling Coupled with Two-Dimensional LC-MS/MS. Methods Mol Biol 1410:237-47|
|Duan, Jicheng; Kodali, Vamsi K; Gaffrey, Matthew J et al. (2016) Quantitative Profiling of Protein S-Glutathionylation Reveals Redox-Dependent Regulation of Macrophage Function during Nanoparticle-Induced Oxidative Stress. ACS Nano 10:524-38|
|Zhang, Hui; Liu, Tao; Zhang, Zhen et al. (2016) Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer. Cell 166:755-65|
Showing the most recent 10 out of 46 publications