title: Relevance of baseline viral genetic heterogeneity and host factors for treatment outcome prediction in hepatitis C virus 1b-infected patients.
abstract: BACKGROUND
  Only about 50% of patients chronically infected with HCV genotype 1 (HCV-1) respond to treatment with pegylated interferon-alfa and ribavirin (dual therapy), and protease inhibitors have to be administered together with these drugs increasing costs and side-effects. We aimed to develop a predictive model of treatment response based on a combination of baseline clinical and viral parameters.
  
  
  METHODS
  Seventy-four patients chronically infected with HCV-1b and treated with dual therapy were studied (53 retrospectively -training group-, and 21 prospectively -validation group-). Host and viral-related factors (viral load, and genetic variability in the E1-E2, core and Interferon Sensitivity Determining Region) were assessed. Multivariate discriminant analysis and decision tree analysis were used to develop predictive models on the training group, which were then validated in the validation group.
  
  
  RESULTS
  A multivariate discriminant predictive model was generated including the following variables in decreasing order of significance: the number of viral variants in the E1-E2 region, an amino acid substitution pattern in the viral core region, the IL28B polymorphism, serum GGT and ALT levels, and viral load. Using this model treatment outcome was accurately predicted in the training group (AUROC = 0.9444; 96.3% specificity, 94.7% PPV, 75% sensitivity, 81% NPV), and the accuracy remained high in the validation group (AUROC = 0.8148, 88.9% specificity, 90.0% PPV, 75.0% sensitivity, 72.7% NPV). A second model was obtained by a decision tree analysis and showed a similarly high accuracy in the training group but a worse reproducibility in the validation group (AUROC = 0.9072 vs. 0.7361, respectively).
  
  
  CONCLUSIONS
  The baseline predictive models obtained including both host and viral variables had a high positive predictive value in our population of Spanish HCV-1b treatment naïve patients. Accurately identifying those patients that would respond to the dual therapy could help reducing implementation costs and additional side effects of new treatment regimens.
authors.affiliation: Microbiology Service, Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol, Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona, Badalona, Spain ; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain.
authors.forename: Verónica
authors.initials: V
authors.lastname: Saludes
authors.forename: Elisabet
authors.initials: E
authors.lastname: Bascuñana
authors.forename: Elena
authors.initials: E
authors.lastname: Jordana-Lluch
authors.forename: Sònia
authors.initials: S
authors.lastname: Casanovas
authors.forename: Mercè
authors.initials: M
authors.lastname: Ardèvol
authors.forename: Esther
authors.initials: E
authors.lastname: Soler
authors.forename: Ramón
authors.initials: R
authors.lastname: Planas
authors.forename: Vicente
authors.initials: V
authors.lastname: Ausina
authors.forename: Elisa
authors.initials: E
authors.lastname: Martró
mesh_terms: D000328:Adult; D005260:Female; D005838:Genotype; D016174:Hepacivirus / Q000235:genetics*; D019698:Hepatitis C, Chronic / Q000097:blood / Q000235:genetics / Q000628:therapy; D006801:Humans; D007372:Interferons; D007378:Interleukins / Q000097:blood / Q000235:genetics*; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D011110:Polymorphism, Genetic; D011446:Prospective Studies; D012189:Retrospective Studies; D019562:Viral Load; D000095022:Interferon Lambda
chemical_list: D007372:Interferons; D007378:Interleukins; C000624353:interferon-lambda, human; D000095022:Interferon Lambda