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High predictive accuracy of an unbiased proteomic profile for sustained virologic response in chronic hepatitis C patients.

Publication ,  Journal Article
Patel, K; Lucas, JE; Thompson, JW; Dubois, LG; Tillmann, HL; Thompson, AJ; Uzarski, D; Califf, RM; Moseley, MA; Ginsburg, GS; McHutchison, JG ...
Published in: Hepatology
June 2011

UNLABELLED: 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.

Duke Scholars

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Published In

Hepatology

DOI

EISSN

1527-3350

Publication Date

June 2011

Volume

53

Issue

6

Start / End Page

1809 / 1818

Location

United States

Related Subject Headings

  • alpha-2-HS-Glycoprotein
  • Vitamin D-Binding Protein
  • Treatment Outcome
  • Sensitivity and Specificity
  • Regression Analysis
  • ROC Curve
  • Proteomics
  • Protein Array Analysis
  • Models, Statistical
  • Interleukins
 

Citation

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Patel, K., Lucas, J. E., Thompson, J. W., Dubois, L. G., Tillmann, H. L., Thompson, A. J., … MURDOCK Horizon 1 Study Team, . (2011). High predictive accuracy of an unbiased proteomic profile for sustained virologic response in chronic hepatitis C patients. Hepatology, 53(6), 1809–1818. https://doi.org/10.1002/hep.24284
Patel, Keyur, Joseph E. Lucas, J Will Thompson, Laura G. Dubois, Hans L. Tillmann, Alexander J. Thompson, Diane Uzarski, et al. “High predictive accuracy of an unbiased proteomic profile for sustained virologic response in chronic hepatitis C patients.Hepatology 53, no. 6 (June 2011): 1809–18. https://doi.org/10.1002/hep.24284.
Patel K, Lucas JE, Thompson JW, Dubois LG, Tillmann HL, Thompson AJ, et al. High predictive accuracy of an unbiased proteomic profile for sustained virologic response in chronic hepatitis C patients. Hepatology. 2011 Jun;53(6):1809–18.
Patel, Keyur, et al. “High predictive accuracy of an unbiased proteomic profile for sustained virologic response in chronic hepatitis C patients.Hepatology, vol. 53, no. 6, June 2011, pp. 1809–18. Pubmed, doi:10.1002/hep.24284.
Patel K, Lucas JE, Thompson JW, Dubois LG, Tillmann HL, Thompson AJ, Uzarski D, Califf RM, Moseley MA, Ginsburg GS, McHutchison JG, McCarthy JJ, MURDOCK Horizon 1 Study Team. High predictive accuracy of an unbiased proteomic profile for sustained virologic response in chronic hepatitis C patients. Hepatology. 2011 Jun;53(6):1809–1818.
Journal cover image

Published In

Hepatology

DOI

EISSN

1527-3350

Publication Date

June 2011

Volume

53

Issue

6

Start / End Page

1809 / 1818

Location

United States

Related Subject Headings

  • alpha-2-HS-Glycoprotein
  • Vitamin D-Binding Protein
  • Treatment Outcome
  • Sensitivity and Specificity
  • Regression Analysis
  • ROC Curve
  • Proteomics
  • Protein Array Analysis
  • Models, Statistical
  • Interleukins