Prior to the current Clinical Proteomic Tumor Analysis Consortium (CPTAC), previously funded initiatives associated with clinical proteomics research included:
Clinical Proteomic Tumor Analysis Consortium (CPTAC 2.0)
Clinical Proteomic Technologies for Cancer Initiative (CPTC)
-Biospecimen Samples available
Mouse Proteomic Technologies Initiative
Clinical Proteomic Tumor Analysis Consortium 2.0
The Clinical Proteomic Tumor Analysis Consortium (CPTAC) program began in 2006 as a part of the Clinical Proteomic Technologies for Cancer initiative (CPTC) at the NCI (see below), the purpose of which was to develop standardized proteomic assays and workflows to ensure analytical reproducibility of proteomic measurements (Rigor & Reproducibility), in order to complement genomic and transcriptomic analyses. To begin to apply these technical outputs, CPTAC was launched in 2011 (RFA-CA-10-016; commonly referred to as CPTAC 2.0) as a pilot program to utilize the developed state-of-the-art standardized proteomic workflows on genomically-characterized tumors (such as those from The Cancer Genome Atlas - TCGA) to add an additional layer of functional biology to cancer. The goal was to determine if additional biological insights would be identified – biology that is difficult or impossible to obtain solely through genomics approaches.
The CPTAC 2.0 program was composed of five Proteome Characterization Centers (PCCs) with expertise in proteomics, genomics, cancer biology, oncology and clinical chemistry that perform coordinated research projects to comprehensively characterize and analyze cancer specimens selected for study. CPTAC’s “proteogenomics” approach (comprehensive proteomics combined with genomics), successfully demonstrated the scientific benefits of integrating proteomics with genomics to produce a more unified understanding of cancer biology and possibly therapeutic interventions for patients, while creating resources that are widely used by the global cancer community.
Key Research Outputs include:
- Understanding tumor preanalytical variables to help refine tumor collection protocols optimized for proteomic analyses by minimizing preanalytical variables such as ischemic time, etc. (Mol Cell Proteomics 2014; PMID 24719451)
- Development of a Comparative Reference Material (CompRef) for proteomics labs to benchmark analytical drift over the course of an analysis (J Proteome Res. 2016, PMID 26653538)
- Flagship proteogenomic cancer studies:
- Colorectal cancer study – Nature 2014, PMID 25043054. The colorectal study produced several key findings: First, measurements of messenger RNA abundance did not reliably predict protein abundance. The investigators were not surprised by this discordance, because many regulatory controls lie between RNA and protein expression. Second, most of the focal amplifications (increased amounts of certain chromosome segments) observed in the earlier genomic analyses of the same tumors did not result in corresponding elevations in protein level. Proteomic analyses identified a few amplifications that had dramatic effects on protein levels and may represent potentially important targets for diagnosis or therapeutic intervention. Third, proteomics identified five colon cancer subtypes, including classifications that could not be derived from genomic data. Protein expression signatures for one of the subtypes indicated molecular characteristics associated with highly aggressive tumors with poor clinical outcome.
- Breast cancer study - Nature 2016, PMID 27251275. The effort produced a broad overview of the landscape of the proteome (all the detectable proteins found in a cell) and the phosphoproteome (the sites at which proteins are tagged by phosphorylation, a chemical modification that drives communication in the cell) across a set of breast cancer tumors that had been genomically characterized in the TCGA project. Although the TCGA produced an extensive catalog of somatic mutations found in cancer, the effects of many of those mutations on cellular functions or patients’ outcomes are unknown. In addition, not all mutated genes are true “drivers” of cancer - some are merely “passenger” mutations that have little functional consequence. And some mutations are found within very large DNA regions that are deleted or present in extra copies, so winnowing the list of candidate genes by studying the activity of their protein products can help identify therapeutic targets. This analysis uncovered new protein markers and signaling pathways for breast cancer subtypes and tumors carrying frequent mutations such as PIK3CA and TP53 mutations. The study also correlated copy number alterations (extra or missing DNA) in some genes with protein levels, identifying 10 new candidate regulators. Two of these candidate genes, SKP1 and CETN3, are connected to the oncogene EGFR, which is a marker for a particularly aggressive breast cancer subtype, known as “basal-like” tumors.
- Ovarian cancer study – Cell 2016, PMID 27372738. Deep proteomic characterization of TCGA ovarian tumors yielded a number of insights, such as how different copy-number alternations influence the proteome, the proteins associated with chromosomal instability, the sets of signaling pathways that diverse genome rearrangements converge on, and the ones most associated with short overall survival. Specific protein acetylations associated with homologous recombination deficiency suggested a potential means for stratifying patients for therapy. In addition to providing a valuable resource, these findings provided a view of how the somatic genome drives the cancer proteome and associations between protein and PTM levels (phosphorylation) and clinical outcomes in high-grade serous carcinomas.
CPTAC’s proteogenomic approach to science created open community resources that are now some of the most widely used by the global cancer community. Raw data files are released to the public before CPTAC’s flagship publications, while targeted protein assays are developed using a fit-for-purpose framework established in coordination with the Food and Drug Administration (FDA) and American Association for Clinical Chemistry (AACC). Community resources include:
- Data Portal
- Assay Portal
- Antibody Portal
Clinical Proteomic Technologies for Cancer Initiative
The Clinical Proteomic Technologies for Cancer Initiative (RFA-CA-07-012 and RFA-CA-07-005), launched in 2006, was developed to address the pre-analytical and analytical variability issues that are major barriers to the field of proteomics. These barriers were: (1) experimental design; (2) technological and technical aspects of protein identification; (3) variability related to biospecimens collection; (4) the processes of data acquisition, analysis, and reporting; (5) the lack of reproducible proteomic technologies; and (6) the lack of highly characterized and standardized reagents. The initiative was composed of three integrated programs that worked together to overcome these barriers:
- Clinical Proteomic Technology Assessment for Cancer network: Network of 5 multidisciplinary teams that collaborated on research projects to increase the understanding of experimental and analytical sources of error for existing technologies.
- Advanced Proteomic Platforms and Computational Sciences: Team of individual investigators that developed new analytical tools, technology, and software to improve the accuracy and reproducibility of proteomic measurement.
- Proteomic Reagents and Resources component: Provided high quality, well-characterized reagents (antibodies), data, and standard reference materials for the research community.
Key Research Outputs include:
The program has showed the effectiveness of a multidisciplinary team approach to address the issues of variability in proteomic technologies. Program achievement highlights include:
- Standardization of mass spectrometry (MS) methodologies for untargeted protein analyses (discovery proteomics - Rigor & Reproducibility)
- Standardization of multiple reaction monitoring (MRM) mass spectrometry in targeted protein analyses (targeted proteomics - (Reproducibility & Transferability)
- Open-source computational tool (Skyline) for designing targeted mass spec assays
- Adoption of a MRM assay for thyroglobulin by clinical reference laboratories, development of an open-source computational tool (Skyline) for designing MRM assays that is supported by major instrument vendors
- Development of mock 510(k) device clearance documents using targeted proteomic platforms in coordination with the Food and Drug Administration (FDA) and the American Association for Clinical Chemistry (AACC)
- Development of open data sharing policies in proteomics that are supported by peer-reviewed journals (Amsterdam Principles). Documents 1 and 2.
Additional outputs from this initiative include:
- Reference Materials: In collaboration with the National Institute of Standards and Technology (NIST), a soluble yeast protein extract reference material and software metrics tool (to monitor the performance of liquid chromatography-mass spectrometry systems) were developed. This, along with its publicly available reference datasets, provides a foundation for laboratories to benchmark their own performance, improve upon current methods, and evaluate new for technologies.
- Biospecimen Samples: The CPTAC network collected clinical plasma samples were collected from approximately 1,700 patients undergoing breast biopsies. Collection consists of one sample per patient across four collection sites, using a common collection protocol. Additionally, there is a second collection of approximately 200 breast cancer patients with multiples time points per patient collected after completion of treatment. These samples were all collected using similar blood collection and plasma processing conditions. Accompanying each sample is a set of clinical patient data covering the demographic, medical history, pathology report, and sample processing variables that can be downloaded (click here). In addition, SOPs for the collection, processing and storage of plasma samples were developed and available upon request.
- Community Resources: Well-characterized antibody reagents with data from standardized processes were made publicly available to the research community online at the Antibody Portal. Another community resource are plasma samples. Here, clinical plasma samples were collected from approximately 1,700 patients undergoing breast biopsies.
More information related to the reconstructed biomarker pipeline and other key research outputs are available in the 2009 CPTC Annual Report, to download click here.
Mouse Proteomic Technologies Initiative
Mouse models of human cancer offer many opportunities to optimize procedures for profiling major human cancers. The Mouse Proteomic Technologies Initiative, designed to use these animal models to develop and standardize technologies to help improve the accurate measurement of proteins and peptides linked to cancer processes. Launched in 2004 with funding to two consortia, the program was designed as a multidisciplinary and collaborative team science approach towards the development of standard tools and resources needed to accelerate protein biomarker discovery. The goals of the initiative were to use mouse models to: 1) standardize methods for protein and peptide detection and analysis; 2) develop metrics for benchmarking performance and supporting reagents for promising technologies; 3) identify or characterize new biomarkers associated with cancer processes in mouse models of human cancer; 4) enhance current technologies for the analysis of proteins and peptides in biological fluids; 5) refine and standardize methods of specimen preparation and develop specimen reference standards; 6) design common data elements and algorithms to facilitate data sharing amongst different laboratories; and lastly 7) improve detection capabilities associated with current technologies, including sensitivity and resolution.
Key Research Outputs include:
The Mouse Model consortium produced several advances in the field of proteomics including a number of research tools available to the cancer research community. These tools include: Computational Proteomics Analysis System (CPAS): An open-source, web-based proteomics data management software suite that combines laboratory information management systems and informatics modules for high-throughput liquid chromatography/tandem mass spectrometry (LC/MS/MS) experiments. CPAS enables the cancer proteomics community to store, analyze, and share clinical proteomics data.
Program Structure
The Initiative funded two consortia of laboratories: the "Eastern Consortium" based at the University of Michigan (Ann Arbor, MI) and the "Western Consortium" based at the Fred Hutchinson Cancer Research Center (Seattle, WA), to develop and standardize technologies used to identify proteins and peptides in complex mixtures.
The "Eastern Consortium" based at the University of Michigan (Ann Arbor, MI) included:
- Dana-Farber Cancer Institute (Boston, MA)
- Fred Hutchinson Cancer Research Center (Seattle, WA)
- Harvard-Partners Center for Genetics and Genomics (Cambridge, MA)
- Massachusetts Institute of Technology (Cambridge, MA)
- Memorial Sloan-Kettering Cancer Center (New York, NY)
- Van Andel Research Institute (Grand Rapids, MI)
The "Western Consortium" based at the Fred Hutchinson Cancer Research Center (Seattle, WA) included:
- Institute for Systems Biology (Seattle, WA)
- Pacific Northwest National Laboratory (Richland, WA)
- Plasma Proteome Institute (Washington, DC)