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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Resource-Related Research Projects--Cooperative Agreements (U24)
Project #
5U24CA160019-03
Application #
8521192
Study Section
Special Emphasis Panel (ZCA1-SRLB-R (J1))
Program Officer
Boja, Emily L
Project Start
2011-08-26
Project End
2016-07-31
Budget Start
2013-08-01
Budget End
2014-07-31
Support Year
3
Fiscal Year
2013
Total Cost
$2,748,603
Indirect Cost
$1,228,113
Name
Battelle Pacific Northwest Laboratories
Department
Type
DUNS #
032987476
City
Richland
State
WA
Country
United States
Zip Code
99352
Rodland, Karin D; Piehowski, Paul; Smith, Richard D (2018) Moonshot Objectives: Catalyze New Scientific Breakthroughs-Proteogenomics. Cancer J 24:121-125
Piehowski, Paul D; Petyuk, Vladislav A; Sontag, Ryan L et al. (2018) Residual tissue repositories as a resource for population-based cancer proteomic studies. Clin Proteomics 15:26
Liu, Tao; Rodland, Karin D; Smith, Richard D (2018) Characterization of the Ovarian Tumor Peptidome. Vitam Horm 107:515-531
Garabedian, Alyssa; Benigni, Paolo; Ramirez, Cesar E et al. (2018) Towards Discovery and Targeted Peptide Biomarker Detection Using nanoESI-TIMS-TOF MS. J Am Soc Mass Spectrom 29:817-826
Shi, Tujin; Gaffrey, Matthew J; Fillmore, Thomas L et al. (2018) Facile carrier-assisted targeted mass spectrometric approach for proteomic analysis of low numbers of mammalian cells. Commun Biol 1:103
Duhen, Thomas; Duhen, Rebekka; Montler, Ryan et al. (2018) Co-expression of CD39 and CD103 identifies tumor-reactive CD8 T cells in human solid tumors. Nat Commun 9:2724
Wang, Sheng; Yang, Feng; Petyuk, Vladislav A et al. (2017) Quantitative proteomics identifies altered O-GlcNAcylation of structural, synaptic and memory-associated proteins in Alzheimer's disease. J Pathol 243:78-88
Wang, Jing; Ma, Zihao; Carr, Steven A et al. (2017) Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction. Mol Cell Proteomics 16:121-134
Nie, Song; Shi, Tujin; Fillmore, Thomas L et al. (2017) Deep-Dive Targeted Quantification for Ultrasensitive Analysis of Proteins in Nondepleted Human Blood Plasma/Serum and Tissues. Anal Chem 89:9139-9146
Park, Jungkap; Piehowski, Paul D; Wilkins, Christopher et al. (2017) Informed-Proteomics: open-source software package for top-down proteomics. Nat Methods 14:909-914

Showing the most recent 10 out of 57 publications