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.
Omics integration; Omics science; Clinical application; Risk prediction; Proteomics; Metabolomics; Genomics
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.
Gene Expression; Cancer Proteomics; Protein Phosphorylation; Mass Spectrometry; Cancer Genome Atlas
The successful application of MRM in biological specimens raises the exciting possibility that assays can be configured to measure all human proteins, resulting in an assay resource that would promote advances in biomedical research. We report the results of a pilot study designed to test the feasibility of a large-scale, international effort in MRM assay generation. We have configured, validated across three laboratories, and made publicly available as a resource to the community 645 novel MRM assays representing 319 proteins expressed in human breast cancer. Assays were multiplexed in groups of >150 peptides and deployed to quantify endogenous analyte in a panel of breast cancer-related cell lines. Median assay precision was 5.4%, with high inter-laboratory correlation (R2 >0.96). Peptide measurements in breast cancer cell lines were able to discriminate amongst molecular subtypes and identify genome-driven changes in the cancer proteome. These results establish the feasibility of a scaled, international effort.
Despite substantial evidence of the benefit of frequent self-monitoring of blood glucose (SMBG) in type 1 diabetes, certain insurers limit the number of test strips that they will provide. The large database of the T1D Exchange clinic registry provided an opportunity to evaluate the relationship between the number of SMBG measurements per day and HbA1c levels across a wide age range of children and adults.
RESEARCH DESIGN AND METHODS
The analysis included 20,555 participants in the T1D Exchange clinic registry with type 1 diabetes ≥1 year and not using a continuous glucose monitor (11,641 younger than age 18 years and 8,914 18 years old or older). General linear models were used to assess the association between the number of SMBG measurements and HbA1c levels after adjusting for potential confounding variables.
A higher number of SMBG measurements per day were associated with non-Hispanic white race, insurance coverage, higher household income, and use of an insulin pump for insulin delivery (P < 0.001 for each factor). After adjusting for these factors, a higher number of SMBG measurements per day was strongly associated with a lower HbA1c level (adjusted P < 0.001), with the association being present in all age-groups and in both insulin pump and injection users.
There is a strong association between higher SMBG frequency and lower HbA1c levels. It is important for insurers to consider that reducing restrictions on the number of test strips provided per month may lead to improved glycemic control for some patients with type 1 diabetes.
During the past several decades, the understanding of cancer at the molecular level has been primarily focused on mechanisms on how signaling molecules transform homeostatically balanced cells into malignant ones within an individual pathway. However, it is becoming more apparent that pathways are dynamic and crosstalk at different control points of the signaling cascades, making the traditional linear signaling models inadequate to interpret complex biological systems. Recent technological advances in high throughput, deep sequencing for the human genomes and proteomic technologies to comprehensively characterize the human proteomes in conjunction with multiplexed targeted proteomic assays to measure panels of proteins involved in biologically relevant pathways have made significant progress in understanding cancer at the molecular level. It is undeniable that proteomic profiling of differentially expressed proteins under many perturbation conditions, or between normal and “diseased” states is important to capture a first glance at the overall proteomic landscape, which has been a main focus of proteomics research during the past 15-20 years. However, the research community is gradually shifting its heavy focus from that initial discovery step to protein target verification using multiplexed quantitative proteomic assays, capable of measuring changes in proteins and their interacting partners, isoforms, and post-translational modifications (PTMs) in response to stimuli in the context of signaling pathways and protein networks. With a critical link to genotypes (i.e., high throughput genomics and transcriptomics data), new and complementary information can be gleaned from multi-dimensional omics data to (1) assess the effect of genomic and transcriptomic aberrations on such complex molecular machinery in the context of cell signaling architectures associated with pathological diseases such as cancer (i.e., from genotype to proteotype to phenotype); and (2) target pathway- and network-driven changes and map the fluctuations of these functional units (proteins) responsible for cellular activities in response to perturbation in a spatiotemporal fashion to better understand cancer biology as a whole system.
Protein identification and quantitation; Post-translational modification; Targeted proteomics; Proteogenomics; Protein networks; Signaling pathways; Mathematical and computational modeling; Omics integration; Systems biology
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.
Statistical Experiment Design; Biomarker; Proteomics; Unbiasedness; Power Calculation
Biomarkers; clinical proteomics; quantification; biomarker verification (analytical validation); biomarker qualification (clinical validation); MRM-MS
To review the evidence about the impact of hypoglycemia on patients with diabetes that has become available since the past reviews of this subject by the American Diabetes Association and The Endocrine Society and to provide guidance about how this new information should be incorporated into clinical practice.
Five members of the American Diabetes Association and five members of The Endocrine Society with expertise in different aspects of hypoglycemia were invited by the Chair, who is a member of both, to participate in a planning conference call and a 2-day meeting that was also attended by staff from both organizations. Subsequent communications took place via e-mail and phone calls. The writing group consisted of those invitees who participated in the writing of the manuscript. The workgroup meeting was supported by educational grants to the American Diabetes Association from Lilly USA, LLC and Novo Nordisk and sponsorship to the American Diabetes Association from Sanofi. The sponsors had no input into the development of or content of the report.
The writing group considered data from recent clinical trials and other studies to update the prior workgroup report. Unpublished data were not used. Expert opinion was used to develop some conclusions.
Consensus was achieved by group discussion during conference calls and face-to-face meetings, as well as by iterative revisions of the written document. The document was reviewed and approved by the American Diabetes Association’s Professional Practice Committee in October 2012 and approved by the Executive Committee of the Board of Directors in November 2012 and was reviewed and approved by The Endocrine Society’s Clinical Affairs Core Committee in October 2012 and by Council in November 2012.
The workgroup reconfirmed the previous definitions of hypoglycemia in diabetes, reviewed the implications of hypoglycemia on both short- and long-term outcomes, considered the implications of hypoglycemia on treatment outcomes, presented strategies to prevent hypoglycemia, and identified knowledge gaps that should be addressed by future research. In addition, tools for patients to report hypoglycemia at each visit and for clinicians to document counseling are provided.
Underutilization of glucose data and lack of easy and standardized glucose data collection, analysis, visualization, and guided clinical decision making are key contributors to poor glycemic control among individuals with type 1 diabetes mellitus. An expert panel of diabetes specialists, facilitated by the International Diabetes Center and sponsored by the Helmsley Charitable Trust, met in 2012 to discuss recommendations for standardizing the analysis and presentation of glucose monitoring data, with the initial focus on data derived from continuous glucose monitoring systems. The panel members were introduced to a universal software report, the Ambulatory Glucose Profile, and asked to provide feedback on its content and functionality, both as a research tool and in clinical settings. This article provides a summary of the topics and issues discussed during the meeting and presents recommendations from the expert panel regarding the need to standardize glucose profile summary metrics and the value of a uniform glucose report to aid clinicians, researchers, and patients.
ambulatory glucose profile; continuous glucose monitoring; insulin; type 1 diabetes mellitus
Bacterial adherence to the acquired dental pellicle, important in dental caries (caries), is mediated by receptor-adhesins such as salivary agglutinin binding to Streptococcus mutans antigen I/II (I/II). Ten selected I/II epitopes were chosen to determine their reactivity to human salivary IgA. Previous studies suggested that a specific HLA biomarker group (HLA-DRB1*04) may have differential influence of immune responses to I/II. However, it was not known whether secretory IgA (SIgA) responses to the selected epitopes from HLA-DRB1*04 positive subjects were different compared to controls, or across other caries-related factors such as total IgA (TIgA). Thirty-two total subjects were matched according to HLA type, gender, ethnicity and age. HLA genotyping, oral bacterial, immunoglobulin and antibody analyses were performed. A large observed difference emerged with regard to the natural immune reservoir of TIgA in HLA-DRB1*04 positive subjects, specifically, a 27.6% reduction compared to controls. In contrast to all other epitopes studied, HLA-DRB1*04 positive subjects also exhibited reduced reactivity to I/II epitope 834–853. HLA-DRB1*04 positive subjects exhibited lower specific SIgA activity/TIgA to 834–853 and also a lower specific reactivity to 834–853/whole cell S. mutans UA159. Furthermore, HLA-DRB1*04 positive subjects exhibited lower responses to I/II in its entirety. The large observed difference in TIgA and the 834–853 reactivity pattern across multiple measures suggest potentially important connections pertaining to the link between HLA-DRB1*04 and caries.
Dental Caries; Streptococcus mutans; I/II; IgA; Immunomodulation; Immunogenetics; HLA-II; DRB1; DRB1*04
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.
The aim of this study was to evaluate HbA1c as an alternative criterion for impaired glucose tolerance (IGT) or type 1 diabetes (T1D) in high-risk subjects <21 years of age.
RESEARCH DESIGN AND METHODS
Subjects <21 years of age who participated in the prospective DPT-1, TEDDY, TRIGR, and Type 1 Diabetes TrialNet Natural History (TrialNet) studies and had an HbA1c within 90 days of an OGTT with a 2-h plasma glucose (2-hPG) measure were included. An OGTT of 140–199 mg/dL defined IGT, and an OGTT with 2-hPG ≥200 mg/dL or fasting plasma glucose ≥126 mg/dL defined diabetes. HbA1c ≥5.7% defined IGT, and HbA1c ≥ 6.5% defined diabetes. Receiver-operating characteristic curve analysis was used to assess diagnostic accuracy of HbA1c compared with OGTT.
There were 587 subjects from DPT-1, 884 from TrialNet, 91 from TEDDY, and 420 from TRIGR. As an indicator for IGT, HbA1c sensitivity was very low across the studies (8–42%), and specificity was variable (64–95%). With HbA1c ≥6.5% threshold used for T1D diagnosis, the sensitivity was very low and specificity was high (sensitivity and specificity: DPT-1 24 and 98%, TrialNet 28 and 99%, TEDDY 34 and 98%, and TRIGR 33 and 99%, respectively). The positive predictive value of HbA1c ≥6.5% for the development of T1D was variable (50–94%) across the four studies.
HbA1c ≥6.5% is a specific but not sensitive early indicator for T1D in high-risk subjects <21 years of age diagnosed by OGTT or asymptomatic hyperglycemia. Redefining the HbA1c threshold is recommended if used as an alternative criterion in diagnosing T1D.
Type 1 diabetes (T1DM) is an autoimmune disease leading to destruction of insulin producing beta cells and life-long requirement for insulin therapy. Glutamic acid decarboxylase (GAD) is a major target of this immune response. Studies in animal models of autoimmunity have shown that treatment with a target antigen can modulate aggressive autoimmunity. We evaluated immunization with GAD formulated in aluminum hydroxide (alum) as an adjuvant in recent onset T1DM.
In this multicentre, double-masked, randomised controlled trial, 145 subjects (ages 3-45) with T1DM for less than 3 months received 3 injections of 20 μg GAD-alum (48 subjects), 2 injections of GAD-alum and one of alum alone (49 subjects) or 3 injections of alum (48 subjects) subcutaneously at baseline, 4 weeks later and 8 weeks after the second injection. Primary outcome was baseline-adjusted geometric mean 2-hour area under the curve (AUC) serum C-peptide following a mixed meal tolerance test at one year. Secondary outcomes included changes in HbA1c and insulin dose, and safety. This trial is registered in ClinicalTrials.gov (NCT00529399).
The ratio (experimental to control) of the adjusted population mean of C-peptide for the GAD-alum ×3 and GAD-alum ×2/alum ×1 groups is 0.998 (95% CI: [0.779, 1.22], p = 0.98) and 0.926 (95% CI: [0.720, 1.13], p = 0.50), respectively. HbA1c and insulin use did not differ between groups. There was no difference in rate or severity of adverse events between groups.
Antigen-based immunotherapy therapy using GAD-alum given subcutaneously in two or three doses over 4 to 12 weeks does not alter the course of loss of insulin secretion over one year in subjects with recently diagnosed T1DM. While antigen-based therapy is a highly desireable treatment and is effective in animal models, translation to human autoimmune disease remains a challenge.
National Institutes of Health.
glutamic acid decarboxylase; type 1 diabetes; antigen specific therapy; immune modulation; children
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.
selected reaction monitoring; bioinformatics; data quality; metrics; open access; Amsterdam Principles; standards
The immunopathogenesis of type 1 diabetes mellitus (T1DM) is associated with T-lymphocyte autoimmunity. To be fully active, immune T-lymphocytes require a co-stimulatory signal in addition to the main antigen driven signal. Abatacept modulates co-stimulation andprevents full T-lymphocyte activation. We evaluated the effect of abatacept in recent-onset T1DM.
In this multicentre, double-masked, randomised controlled trial, 112 subjects (ages 6–36) recently diagnosed with T1DM; 77 received abatacept (10 mg/kg, maximum 1000 mg/dose) and 35 received placebo infusions intravenously on days 1, 14, 28, and monthly for a total of 27 infusions over two years. Primary outcome was baseline-adjusted geometric mean 2-hour area under the curve (AUC) serum C-peptide following a mixed meal tolerance test at two years. Secondary outcomes include difference between groups in incidence of loss of peak C-peptide to < 0·2 pmol/ml, slope of C-peptide over time, changes in HbA1c and insulin dose, and safety. This trial is registered in ClinicalTrials.gov (NCT00505375).
Adjusted C-peptide AUC was 59% (95% CI: 6·1%, 112%) higher at two years with abatacept (0·378 pmol/ml) versus placebo (0·238 pmol/ml) (p=0·0029). The difference between groups was present throughout the trial, with an estimated 9·6 months’ delay in decline with abatacept. There was lower HbA1c (p<0·002) but similar insulin use. There were few, clinically not significant infusion related adverse events and minimal overall adverse events. There was no increase in infections or neutropenia.
Co-stimulation modulation with abatacept slowed decline of beta cell function over two years. The beneficial effect suggests that T-lymphocyte activation still occurs around the time of clinical diagnosis of T1DM. Yet, despite continued administration of abatacept over 24 months, the decline in beta cell function with abatacept was parallel to that with placebo after six months of treatment, causing us to speculate that T-lymphocyte activation may lessen with time. Further observation will determine whether the beneficial effect continues after cessation of abatacept infusions.
National Institutes of Health.
abatacept; type 1 diabetes; CTLA4-Ig; co-stimulation; T-lymphocyte; children
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’.
biomarker; multiple reaction monitoring mass spectrometry; proteomics; verification
This trial tested whether mycophenolate mofetil (MMF) alone or with daclizumab (DZB) could arrest the loss of insulin-producing β-cells in subjects with new-onset type 1 diabetes.
RESEARCH DESIGN AND METHODS
A multi-center, randomized, placebo-controlled, double-masked trial was initiated by Type 1 Diabetes TrialNet at 13 sites in North America and Europe. Subjects diagnosed with type 1 diabetes and with sufficient C-peptide within 3 months of diagnosis were randomized to either MMF alone, MMF plus DZB, or placebo, and then followed for 2 years. The primary outcome was the geometric mean area under the curve (AUC) C-peptide from the 2-h mixed meal tolerance test.
One hundred and twenty-six subjects were randomized and treated during the trial. The geometric mean C-peptide AUC at 2 years was unaffected by MMF alone or MMF plus DZB versus placebo. Adverse events were more frequent in the active therapy groups relative to the control group, but not significantly.
Neither MMF alone nor MMF in combination with DZB had an effect on the loss of C-peptide in subjects with new-onset type 1 diabetes. Higher doses or more targeted immunotherapies may be needed to affect the autoimmune process.
Better biomarkers are urgently needed to cancer detection, diagnosis, and prognosis. While the genomics community is making significant advances in understanding the molecular basis of disease, proteomics will delineate the functional units of a cell, proteins and their intricate interaction network and signaling pathways for the underlying disease. Great progress has been made to characterize thousands of proteins qualitatively and quantitatively in complex biological systems by utilizing multi-dimensional sample fractionation strategies, mass spectrometry and protein microarrays. Comparative/quantitative analysis of high-quality clinical biospecimen (e.g., tissue and biofluids) of human cancer proteome landscape has the potential to reveal protein/peptide biomarkers responsible for this disease by means of their altered levels of expression, post-translational modifications as well as different forms of protein variants. Despite technological advances in proteomics, major hurdles still exist in every step of the biomarker development pipeline. The National Cancer Institute's Clinical Proteomic Technologies for Cancer initiative (NCI-CPTC) has taken a critical step to close the gap between biomarker discovery and qualification by introducing a pre-clinical "verification" stage in the pipeline, partnering with clinical laboratory organizations to develop and implement common standards, and developing regulatory science documents with the US Food and Drug Administration to educate the proteomics community on analytical evaluation requirements for multiplex assays in order to ensure the safety and effectiveness of these tests for their intended use.
Quantitative proteomics; Biomarker; Multiplex protein assays; MRM-MS; Immunoassays
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.
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.
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.
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.
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.
Genomic DNA of Nostoc commune (Cyanobacteria) became covalently modified during decades of desiccation. Amplification of gene loci from desiccated cells required pretreatment of DNA with N-phenacylthiazolium bromide, a reagent that cleaves DNA- and protein-linked advanced glycosylation end-products. DNA from 13 year desiccated cells did not show any higher levels of the commonly studied oxidatively modified DNA damage biomarkers 8-hydroxyguanine, 8-hydroxyadenine and 5-hydroxyuracil, compared to commercially available calf thymus DNA. Different patterns of amplification products were obtained with DNA from desiccated/rehydrating cells and a liquid culture derived from the dried material, using the same set of primers. In contrast, a reproducible fingerprint was obtained, irrespective of time of rehydration of the DNA, using a primer (5′-GWCWATCGCC-3′) based upon a highly iterated palindromic repeat sequence present in the genome. In vitro, the desiccation of cccDNA led to loss of supercoiling, aggregation, loss of resolution during agarose gel electrophoresis and loss of transformation and transfection efficiency. These changes were minimized when DNA was desiccated and stored in the presence of trehalose, a non-reducing disaccharide present in Nostoc colonies. The response of the N.commune genome to desiccation is different from the response of the genomes of cyanobacteria and Deinococcus radiodurans to ionizing radiation.