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1.  Integration of omics sciences to advance biology and medicine 
Clinical Proteomics  2014;11(1):45.
In the past two decades, our ability to study cellular and molecular systems has been transformed through the development of omics sciences. While unlimited potential lies within massive omics datasets, the success of omics sciences to further our understanding of human disease and/or translating these findings to clinical utility remains elusive due to a number of factors. A significant limiting factor is the integration of different omics datasets (i.e., integromics) for extraction of biological and clinical insights. To this end, the National Cancer Institute (NCI) and the National Heart, Lung and Blood Institute (NHLBI) organized a joint workshop in June 2012 with the focus on integration issues related to multi-omics technologies that needed to be resolved in order to realize the full utility of integrating omics datasets by providing a glimpse into the disease as an integrated “system”. The overarching goals were to (1) identify challenges and roadblocks in omics integration, and (2) facilitate the full maturation of ‘integromics’ in biology and medicine. Participants reached a consensus on the most significant barriers for integrating omics sciences and provided recommendations on viable approaches to overcome each of these barriers within the areas of technology, bioinformatics and clinical medicine.
PMCID: PMC4274684
Omics integration; Omics science; Clinical application; Risk prediction; Proteomics; Metabolomics; Genomics
2.  NCI Think Tank Concerning the Identifiability of Biospecimens and “-Omic” Data 
On June 11 and 12, 2012, the National Cancer Institute (NCI) hosted a think tank concerning the identifiability of biospecimens and “omic” Data in order to explore challenges surrounding this complex and multifaceted topic. The think tank brought together forty-six leaders from several fields, including cancer genomics, bioinformatics, human subject protection, patient advocacy, and commercial genetics. The first day involved presentations regarding the state of the science of re-identification; current and proposed regulatory frameworks for assessing identifiability; developments in law, industry and biotechnology; and the expectations of patients and research participants. The second day was spent by think tank participants in small break-out groups designed to address specific sub-topics under the umbrella issue of identifiability, including considerations for the development of best practices for data sharing and consent, and targeted opportunities for further empirical research. We describe the outcomes of this two day meeting, including two complimentary themes that emerged from moderated discussions following the presentations on Day 1, and ideas presented for further empirical research to discern the preferences and concerns of research participants about data sharing and individual identifiability.
PMCID: PMC4097316  PMID: 23579437
3.  Connecting Genomic Alterations to Cancer Biology with Proteomics: The NCI Clinical Proteomic Tumor Analysis Consortium 
Cancer discovery  2013;3(10):1108-1112.
The National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium (CPTAC) is applying latest generation of proteomic technologies to genomically annotated tumors from The Cancer Genome Atlas (TCGA) program, a joint initiative of the NCI and National Human Genome Research Institute. By providing a fully integrated accounting of DNA, RNA and protein abnormalities in individual tumors, these datasets will illuminate the complex relationship between genomic abnormalities and cancer phenotypes, thus producing biological insights as well as a wave of novel candidate biomarkers and therapeutic targets amenable to verification using targeted mass spectrometry methods.
PMCID: PMC3800055  PMID: 24124232
Gene Expression; Cancer Proteomics; Protein Phosphorylation; Mass Spectrometry; Cancer Genome Atlas
4.  Statistical Design for Biospecimen Cohort Size in Proteomics-based Biomarker Discovery and Verification Studies 
Journal of proteome research  2013;12(12):5383-5394.
Protein biomarkers are needed to deepen our understanding of cancer biology and to improve our ability to diagnose, monitor and treat cancers. Important analytical and clinical hurdles must be overcome to allow the most promising protein biomarker candidates to advance into clinical validation studies. Although contemporary proteomics technologies support the measurement of large numbers of proteins in individual clinical specimens, sample throughput remains comparatively low. This problem is amplified in typical clinical proteomics research studies, which routinely suffer from a lack of proper experimental design, resulting in analysis of too few biospecimens to achieve adequate statistical power at each stage of a biomarker pipeline. To address this critical shortcoming, a joint workshop was held by the National Cancer Institute (NCI), National Heart, Lung and Blood Institute (NHLBI), and American Association for Clinical Chemistry (AACC), with participation from the U.S. Food and Drug Administration (FDA). An important output from the workshop was a statistical framework for the design of biomarker discovery and verification studies. Herein, we describe the use of quantitative clinical judgments to set statistical criteria for clinical relevance, and the development of an approach to calculate biospecimen sample size for proteomic studies in discovery and verification stages prior to clinical validation stage. This represents a first step towards building a consensus on quantitative criteria for statistical design of proteomics biomarker discovery and verification research.
PMCID: PMC4039197  PMID: 24063748
Statistical Experiment Design; Biomarker; Proteomics; Unbiasedness; Power Calculation
5.  Reconstructing the Pipeline by Introducing Multiplexed Multiple Reaction Monitoring Mass Spectrometry for Cancer Biomarker Verification: An NCI-CPTC Initiative Perspective 
Proteomics. Clinical applications  2010;4(12):904-914.
PMCID: PMC4035121  PMID: 21137031
Biomarkers; clinical proteomics; quantification; biomarker verification (analytical validation); biomarker qualification (clinical validation); MRM-MS
6.  Targeted Peptide Measurements in Biology and Medicine: Best Practices for Mass Spectrometry-based Assay Development Using a Fit-for-Purpose Approach* 
Adoption of targeted mass spectrometry (MS) approaches such as multiple reaction monitoring (MRM) to study biological and biomedical questions is well underway in the proteomics community. Successful application depends on the ability to generate reliable assays that uniquely and confidently identify target peptides in a sample. Unfortunately, there is a wide range of criteria being applied to say that an assay has been successfully developed. There is no consensus on what criteria are acceptable and little understanding of the impact of variable criteria on the quality of the results generated. Publications describing targeted MS assays for peptides frequently do not contain sufficient information for readers to establish confidence that the tests work as intended or to be able to apply the tests described in their own labs. Guidance must be developed so that targeted MS assays with established performance can be made widely distributed and applied by many labs worldwide. To begin to address the problems and their solutions, a workshop was held at the National Institutes of Health with representatives from the multiple communities developing and employing targeted MS assays. Participants discussed the analytical goals of their experiments and the experimental evidence needed to establish that the assays they develop work as intended and are achieving the required levels of performance. Using this “fit-for-purpose” approach, the group defined three tiers of assays distinguished by their performance and extent of analytical characterization. Computational and statistical tools useful for the analysis of targeted MS results were described. Participants also detailed the information that authors need to provide in their manuscripts to enable reviewers and readers to clearly understand what procedures were performed and to evaluate the reliability of the peptide or protein quantification measurements reported. This paper presents a summary of the meeting and recommendations.
PMCID: PMC3945918  PMID: 24443746
7.  Recommendations for Mass Spectrometry Data Quality Metrics for Open Access Data (Corollary to the Amsterdam Principles) 
Journal of Proteome Research  2011;11(2):1412-1419.
Policies supporting the rapid and open sharing of proteomic data are being implemented by the leading journals in the field. The proteomics community is taking steps to ensure that data are made publicly accessible and are of high quality, a challenging task that requires the development and deployment of methods for measuring and documenting data quality metrics. On September 18, 2010, the U.S. National Cancer Institute (NCI) convened the “International Workshop on Proteomic Data Quality Metrics” in Sydney, Australia, to identify and address issues facing the development and use of such methods for open access proteomics data. The stakeholders at the workshop enumerated the key principles underlying a framework for data quality assessment in mass spectrometry data that will meet the needs of the research community, journals, funding agencies, and data repositories. Attendees discussed and agreed up on two primary needs for the wide use of quality metrics: (1) an evolving list of comprehensive quality metrics and (2) standards accompanied by software analytics. Attendees stressed the importance of increased education and training programs to promote reliable protocols in proteomics. This workshop report explores the historic precedents, key discussions, and necessary next steps to enhance the quality of open access data.
By agreement, this article is published simultaneously in the Journal of Proteome Research, Molecular and Cellular Proteomics, Proteomics, and Proteomics Clinical Applications as a public service to the research community. The peer review process was a coordinated effort conducted by a panel of referees selected by the journals.
PMCID: PMC3272102  PMID: 22053864
selected reaction monitoring; bioinformatics; data quality; metrics; open access; Amsterdam Principles; standards
8.  Restructuring proteomics through verification 
Biomarkers in medicine  2010;4(6):799-803.
Proteomics technologies have revolutionized cell biology and biochemistry by providing powerful new tools to characterize complex proteomes, multiprotein complexes and post-translational modifications. Although proteomics technologies could address important problems in clinical and translational cancer research, attempts to use proteomics approaches to discover cancer biomarkers in biofluids and tissues have been largely unsuccessful and have given rise to considerable skepticism. The National Cancer Institute has taken a leading role in facilitating the translation of proteomics from research to clinical application, through its Clinical Proteomic Technologies for Cancer. This article highlights the building of a more reliable and efficient protein biomarker development pipeline that incorporates three steps: discovery, verification and qualification. In addition, we discuss the merits of multiple reaction monitoring mass spectrometry, a multiplex targeted proteomics platform, which has emerged as a potentially promising, high-throughput protein biomarker measurements technology for preclinical ‘verification’.
PMCID: PMC3041639  PMID: 21133699
biomarker; multiple reaction monitoring mass spectrometry; proteomics; verification
9.  Repeatability and Reproducibility in Proteomic Identifications by Liquid Chromatography—Tandem Mass Spectrometry 
The complexity of proteomic instrumentation for LC-MS/MS introduces many possible sources of variability. Data-dependent sampling of peptides constitutes a stochastic element at the heart of discovery proteomics. Although this variation impacts the identification of peptides, proteomic identifications are far from completely random. In this study, we analyzed interlaboratory data sets from the NCI Clinical Proteomic Technology Assessment for Cancer to examine repeatability and reproducibility in peptide and protein identifications. Included data spanned 144 LC-MS/MS experiments on four Thermo LTQ and four Orbitrap instruments. Samples included yeast lysate, the NCI-20 defined dynamic range protein mix, and the Sigma UPS 1 defined equimolar protein mix. Some of our findings reinforced conventional wisdom, such as repeatability and reproducibility being higher for proteins than for peptides. Most lessons from the data, however, were more subtle. Orbitraps proved capable of higher repeatability and reproducibility, but aberrant performance occasionally erased these gains. Even the simplest protein digestions yielded more peptide ions than LC-MS/MS could identify during a single experiment. We observed that peptide lists from pairs of technical replicates overlapped by 35–60%, giving a range for peptide-level repeatability in these experiments. Sample complexity did not appear to affect peptide identification repeatability, even as numbers of identified spectra changed by an order of magnitude. Statistical analysis of protein spectral counts revealed greater stability across technical replicates for Orbitraps, making them superior to LTQ instruments for biomarker candidate discovery. The most repeatable peptides were those corresponding to conventional tryptic cleavage sites, those that produced intense MS signals, and those that resulted from proteins generating many distinct peptides. Reproducibility among different instruments of the same type lagged behind repeatability of technical replicates on a single instrument by several percent. These findings reinforce the importance of evaluating repeatability as a fundamental characteristic of analytical technologies.
PMCID: PMC2818771  PMID: 19921851
10.  Recommendations from the 2008 International Summit on Proteomics Data Release and Sharing Policy - The Amsterdam Principles 
Journal of proteome research  2009;8(7):3689-3692.
Policies supporting the rapid and open sharing of genomic data have directly fueled the accelerated pace of discovery in large-scale genomics research. The proteomics community is starting to implement analogous policies and infrastructure for making large-scale proteomics data widely available on a pre-competitive basis. On August 14, 2008, the National Cancer Institute (NCI) convened the “International Summit on Proteomics Data Release and Sharing Policy” in Amsterdam, the Netherlands, to identify and address potential roadblocks to rapid and open access to data.
The six principles agreed upon by key stakeholders at the summit addressed issues surrounding 1) timing, 2) comprehensiveness, 3) format, 4) deposition to repositories, 5) quality metrics, and 6) responsibility for proteomics data release. This summit report explores various approaches to develop a framework of data release and sharing principles that will most effectively fulfill the needs of the funding agencies and the research community.
PMCID: PMC2742685  PMID: 19344107
11.  Multi-site assessment of the precision and reproducibility of multiple reaction monitoring–based measurements of proteins in plasma 
Nature biotechnology  2009;27(7):633-641.
Verification of candidate biomarkers relies upon specific, quantitative assays optimized for selective detection of target proteins, and is increasingly viewed as a critical step in the discovery pipeline that bridges unbiased biomarker discovery to preclinical validation. Although individual laboratories have demonstrated that multiple reaction monitoring (MRM) coupled with isotope dilution mass spectrometry can quantify candidate protein biomarkers in plasma, reproducibility and transferability of these assays between laboratories have not been demonstrated. We describe a multilaboratory study to assess reproducibility, recovery, linear dynamic range and limits of detection and quantification of multiplexed, MRM-based assays, conducted by NCI-CPTAC. Using common materials and standardized protocols, we demonstrate that these assays can be highly reproducible within and across laboratories and instrument platforms, and are sensitive to low µg/ml protein concentrations in unfractionated plasma. We provide data and benchmarks against which individual laboratories can compare their performance and evaluate new technologies for biomarker verification in plasma.
PMCID: PMC2855883  PMID: 19561596
12.  Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project 
Nature biotechnology  2008;26(8):889-896.
The Minimum Information for Biological and Biomedical Investigations (MIBBI) project provides a resource for those exploring the range of extant minimum information checklists and fosters coordinated development of such checklists.
PMCID: PMC2771753  PMID: 18688244
13.  Performance Metrics for Liquid Chromatography-Tandem Mass Spectrometry Systems in Proteomics Analyses* 
A major unmet need in LC-MS/MS-based proteomics analyses is a set of tools for quantitative assessment of system performance and evaluation of technical variability. Here we describe 46 system performance metrics for monitoring chromatographic performance, electrospray source stability, MS1 and MS2 signals, dynamic sampling of ions for MS/MS, and peptide identification. Applied to data sets from replicate LC-MS/MS analyses, these metrics displayed consistent, reasonable responses to controlled perturbations. The metrics typically displayed variations less than 10% and thus can reveal even subtle differences in performance of system components. Analyses of data from interlaboratory studies conducted under a common standard operating procedure identified outlier data and provided clues to specific causes. Moreover, interlaboratory variation reflected by the metrics indicates which system components vary the most between laboratories. Application of these metrics enables rational, quantitative quality assessment for proteomics and other LC-MS/MS analytical applications.
PMCID: PMC2830836  PMID: 19837981

Results 1-13 (13)