Using Stepwise Pharmacogenomics and Proteomics to Predict Hepatitis C Treatment Response in Difficult to Treat Patient Populations.

Published

Journal Article

PURPOSE: In the interferon era of hepatitis C virus (HCV) therapies, genotype/subtype, cirrhosis, prior treatment failure, sex, and race predicted relapse. Our objective is to validate a targeted proteomics platform of 17 peptides to predict sustained virologic response (SVR). EXPERIMENTAL DESIGN: Stored plasma from three, open-label, trials of HIV/HCV-coinfected subjects receiving interferon-containing regimens is identified. LC-MS/MS is used to quantitate the peptides directly from plasma, and IL28B genotyping is completed using stored peripheral blood mononuclear cells (PBMC). A logistic regression model is built to analyze the probability of SVR using responders and nonresponders to interferon-based regimens. RESULTS: The cohort (N = 35) is predominantly black (51.4%), male (86%), and with median age 48 years. Most patients achieve SVR (54%). Using multivariable models, it is verified that three human corticosteroid binding globulin (CBG) peptides are predictive of SVR in patients with the unfavorable IL28B genotypes (CT/TT). The model performs better than IL28B alone, with an area under the curve of 0.870. CONCLUSIONS AND CLINICAL RELEVANCE: In HIV/HCV-coinfected patients, three human CBG peptides that accurately predict treatment response with interferon-based therapy are identified. This study suggests that a stepwise approach combining a genetic predictor followed by targeted proteomics can improve the accuracy of clinical decision-making.

Full Text

Duke Authors

Cited Authors

  • Naggie, S; Clement, M; Lusk, S; Osinusi, A; Himmel, T; Lucas, JE; Thompson, WJ; Dubois, L; Moseley, MA; Clark, PJ; Kottilil, S; Patel, K

Published Date

  • May 2019

Published In

Volume / Issue

  • 13 / 3

Start / End Page

  • e1800006 -

PubMed ID

  • 30058111

Pubmed Central ID

  • 30058111

Electronic International Standard Serial Number (EISSN)

  • 1862-8354

Digital Object Identifier (DOI)

  • 10.1002/prca.201800006

Language

  • eng

Conference Location

  • Germany