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

Publication ,  Journal Article
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 in: Proteomics Clin Appl
May 2019

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.

Duke Scholars

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Published In

Proteomics Clin Appl

DOI

EISSN

1862-8354

Publication Date

May 2019

Volume

13

Issue

3

Start / End Page

e1800006

Location

Germany

Related Subject Headings

  • Treatment Outcome
  • Ribavirin
  • Proteomics
  • Polymorphism, Genetic
  • Pharmacogenetics
  • Middle Aged
  • Male
  • Interferons
  • Humans
  • Hepatitis C
 

Citation

APA
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ICMJE
MLA
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Naggie, S., Clement, M., Lusk, S., Osinusi, A., Himmel, T., Lucas, J. E., … Patel, K. (2019). Using Stepwise Pharmacogenomics and Proteomics to Predict Hepatitis C Treatment Response in Difficult to Treat Patient Populations. Proteomics Clin Appl, 13(3), e1800006. https://doi.org/10.1002/prca.201800006
Naggie, Susanna, Meredith Clement, Sam Lusk, Anu Osinusi, Tiffany Himmel, Joseph E. Lucas, Will J. Thompson, et al. “Using Stepwise Pharmacogenomics and Proteomics to Predict Hepatitis C Treatment Response in Difficult to Treat Patient Populations.Proteomics Clin Appl 13, no. 3 (May 2019): e1800006. https://doi.org/10.1002/prca.201800006.
Naggie S, Clement M, Lusk S, Osinusi A, Himmel T, Lucas JE, et al. Using Stepwise Pharmacogenomics and Proteomics to Predict Hepatitis C Treatment Response in Difficult to Treat Patient Populations. Proteomics Clin Appl. 2019 May;13(3):e1800006.
Naggie, Susanna, et al. “Using Stepwise Pharmacogenomics and Proteomics to Predict Hepatitis C Treatment Response in Difficult to Treat Patient Populations.Proteomics Clin Appl, vol. 13, no. 3, May 2019, p. e1800006. Pubmed, doi:10.1002/prca.201800006.
Naggie S, Clement M, Lusk S, Osinusi A, Himmel T, Lucas JE, Thompson WJ, Dubois L, Moseley MA, Clark PJ, Kottilil S, Patel K. Using Stepwise Pharmacogenomics and Proteomics to Predict Hepatitis C Treatment Response in Difficult to Treat Patient Populations. Proteomics Clin Appl. 2019 May;13(3):e1800006.
Journal cover image

Published In

Proteomics Clin Appl

DOI

EISSN

1862-8354

Publication Date

May 2019

Volume

13

Issue

3

Start / End Page

e1800006

Location

Germany

Related Subject Headings

  • Treatment Outcome
  • Ribavirin
  • Proteomics
  • Polymorphism, Genetic
  • Pharmacogenetics
  • Middle Aged
  • Male
  • Interferons
  • Humans
  • Hepatitis C