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Baseline Prediction of Combination Therapy Outcome in Hepatitis C Virus 1b Infected Patients by Discriminant Analysis Using Viral and Host Factors

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  • Verónica Saludes
  • Maria Alma Bracho
  • Oliver Valero
  • Mercè Ardèvol
  • Ramón Planas
  • Fernando González-Candelas
  • Vicente Ausina
  • Elisa Martró

Abstract

Background: Current treatment of chronic hepatitis C virus (HCV) infection has limited efficacy −especially among genotype 1 infected patients−, is costly, and involves severe side effects. Thus, predicting non-response is of major interest for both patient wellbeing and health care expense. At present, treatment cannot be individualized on the basis of any baseline predictor of response. We aimed to identify pre-treatment clinical and virological parameters associated with treatment failure, as well as to assess whether therapy outcome could be predicted at baseline. Methodology: Forty-three HCV subtype 1b (HCV-1b) chronically infected patients treated with pegylated-interferon alpha plus ribavirin were retrospectively studied (21 responders and 22 non-responders). Host (gender, age, weight, transaminase levels, fibrosis stage, and source of infection) and viral-related factors (viral load, and genetic variability in the E1–E2 and Core regions) were assessed. Logistic regression and discriminant analyses were used to develop predictive models. A “leave-one-out” cross-validation method was used to assess the reliability of the discriminant models. Principal Findings: Lower alanine transaminase levels (ALT, p = 0.009), a higher number of quasispecies variants in the E1–E2 region (number of haplotypes, nHap_E1–E2) (p = 0.003), and the absence of both amino acid arginine at position 70 and leucine at position 91 in the Core region (p = 0.039) were significantly associated with treatment failure. Therapy outcome was most accurately predicted by discriminant analysis (90.5% sensitivity and 95.5% specificity, 85.7% sensitivity and 81.8% specificity after cross-validation); the most significant variables included in the predictive model were the Core amino acid pattern, the nHap_E1–E2, and gamma-glutamyl transferase and ALT levels. Conclusions and Significance: Discriminant analysis has been shown as a useful tool to predict treatment outcome using baseline HCV genetic variability and host characteristics. The discriminant models obtained in this study led to accurate predictions in our population of Spanish HCV-1b treatment naïve patients.

Suggested Citation

  • Verónica Saludes & Maria Alma Bracho & Oliver Valero & Mercè Ardèvol & Ramón Planas & Fernando González-Candelas & Vicente Ausina & Elisa Martró, 2010. "Baseline Prediction of Combination Therapy Outcome in Hepatitis C Virus 1b Infected Patients by Discriminant Analysis Using Viral and Host Factors," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-8, November.
  • Handle: RePEc:plo:pone00:0014132
    DOI: 10.1371/journal.pone.0014132
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    1. Dongliang Ge & Jacques Fellay & Alexander J. Thompson & Jason S. Simon & Kevin V. Shianna & Thomas J. Urban & Erin L. Heinzen & Ping Qiu & Arthur H. Bertelsen & Andrew J. Muir & Mark Sulkowski & John , 2009. "Genetic variation in IL28B predicts hepatitis C treatment-induced viral clearance," Nature, Nature, vol. 461(7262), pages 399-401, September.
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    1. Verónica Saludes & Elisabet Bascuñana & Elena Jordana-Lluch & Sònia Casanovas & Mercè Ardèvol & Esther Soler & Ramón Planas & Vicente Ausina & Elisa Martró, 2013. "Relevance of Baseline Viral Genetic Heterogeneity and Host Factors for Treatment Outcome Prediction in Hepatitis C Virus 1b-Infected Patients," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.

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