title: Baseline prediction of combination therapy outcome in hepatitis C virus 1b infected patients by discriminant analysis using viral and host factors.
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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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
  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.
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.
authors.forename: Verónica
authors.initials: V
authors.lastname: Saludes
authors.forename: Maria Alma
authors.initials: MA
authors.lastname: Bracho
authors.forename: Oliver
authors.initials: O
authors.lastname: Valero
authors.forename: Mercè
authors.initials: M
authors.lastname: Ardèvol
authors.forename: Ramón
authors.initials: R
authors.lastname: Planas
authors.forename: Fernando
authors.initials: F
authors.lastname: González-Candelas
authors.forename: Vicente
authors.initials: V
authors.lastname: Ausina
authors.forename: Elisa
authors.initials: E
authors.lastname: Martró
mesh_terms: D000328:Adult; D000998:Antiviral Agents / Q000627:therapeutic use; D016002:Discriminant Analysis; D004359:Drug Therapy, Combination; D005260:Female; D014644:Genetic Variation; D016174:Hepacivirus / Q000145:classification / Q000187:drug effects* / Q000235:genetics; D006526:Hepatitis C / Q000188:drug therapy* / Q000821:virology; D006801:Humans; D000077190:Interferon alpha-2; D016898:Interferon-alpha / Q000737:chemistry / Q000627:therapeutic use*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011092:Polyethylene Glycols / Q000737:chemistry; D011379:Prognosis; D011994:Recombinant Proteins; D012189:Retrospective Studies; D012254:Ribavirin / Q000627:therapeutic use*; D016896:Treatment Outcome; D019562:Viral Load
chemical_list: D000998:Antiviral Agents; D000077190:Interferon alpha-2; D016898:Interferon-alpha; D011994:Recombinant Proteins; D011092:Polyethylene Glycols; D012254:Ribavirin