Linked Life Data

21381069

Source:http://linkedlifedata.com/resource/pubmed/id/21381069

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2011-5-27
pubmed:abstractText
Chronic hepatitis C (CHC) infection is a leading cause of endstage liver disease. Current standard-of-care (SOC) interferon-based therapy results in sustained virological response (SVR) in only one-half of patients, and is associated with significant side effects. Accurate host predictors of virologic response are needed to individualize treatment regimens. We applied a label-free liquid chromatography mass spectrometry (LC-MS)-based proteomics discovery platform to pretreatment sera from a well-characterized and matched training cohort of 55 CHC patients, and an independent validation set of 41 CHC genotype 1 patients with characterized IL28B genotype. Accurate mass and retention time methods aligned samples to generate quantitative peptide data, with predictive modeling using Bayesian sparse latent factor regression. We identified 105 proteins of interest with two or more peptides, and a total of 3,768 peptides. Regression modeling selected three identified metaproteins, vitamin D binding protein, alpha 2 HS glycoprotein, and Complement C5, with a high predictive area under the receiver operator characteristic curve (AUROC) of 0.90 for SVR in the training cohort. A model averaging approach for identified peptides resulted in an AUROC of 0.86 in the validation cohort, and correctly identified virologic response in 71% of patients without the favorable IL28B "responder" genotype. CONCLUSION: Our preliminary data indicate that a serum-based protein signature can accurately predict treatment response to current SOC in most CHC patients.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1527-3350
pubmed:author
pubmed:copyrightInfo
Copyright © 2011 American Association for the Study of Liver Diseases.
pubmed:issnType
Electronic
pubmed:volume
53
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1809-18
pubmed:dateRevised
2011-11-17
pubmed:meshHeading
pubmed-meshheading:21381069-Antiviral Agents, pubmed-meshheading:21381069-Blood Proteins, pubmed-meshheading:21381069-Cohort Studies, pubmed-meshheading:21381069-Complement C5, pubmed-meshheading:21381069-Genotype, pubmed-meshheading:21381069-Hepatitis C, Chronic, pubmed-meshheading:21381069-Humans, pubmed-meshheading:21381069-Interferons, pubmed-meshheading:21381069-Interleukins, pubmed-meshheading:21381069-Models, Statistical, pubmed-meshheading:21381069-Protein Array Analysis, pubmed-meshheading:21381069-Proteomics, pubmed-meshheading:21381069-ROC Curve, pubmed-meshheading:21381069-Regression Analysis, pubmed-meshheading:21381069-Sensitivity and Specificity, pubmed-meshheading:21381069-Treatment Outcome, pubmed-meshheading:21381069-Vitamin D-Binding Protein, pubmed-meshheading:21381069-alpha-2-HS-Glycoprotein
pubmed:year
2011
pubmed:articleTitle
High predictive accuracy of an unbiased proteomic profile for sustained virologic response in chronic hepatitis C patients.
pubmed:affiliation
Duke Clinical Research Institute, Duke University, Durham, NC 27715, USA. keyur.patel@duke.edu
pubmed:publicationType
Journal Article, Comparative Study, Evaluation Studies