title: A novel approach to identify candidate prognostic factors for hepatitis C treatment response integrating clinical and viral genetic data.
abstract: The combined therapy of pegylated interferon (IFN) plus ribavirin (RBV) has been for a long time the standard treatment for patients infected with hepatitis C virus (HCV). In the case of genotype 1, only 38%-48% of patients have a positive response to the combined treatment. In previous studies, viral genetic information has been occasionally included as a predictor. Here, we consider viral genetic variation in addition to 11 clinical and 19 viral populations and evolutionary parameters to identify candidate baseline prognostic factors that could be involved in the treatment outcome. We obtained potential prognostic models for HCV subtypes la and lb in combination as well as separately. We also found that viral genetic information is relevant for the combined treatment assessment of patients, as the potential prognostic model of joint subtypes includes 9 viral-related variables out of 11. Our proposed methodology fully characterizes viral genetic information and finds a combination of positions that modulate inter-patient variability.
authors.affiliation: Instituto Cavanilles de Biodiversidad y Biología Evolutiva, Universidad de Valencia. ; Computational Genomics Department, Centro de Investigatión Príncipe Felipe.
authors.forename: Alicia
authors.initials: A
authors.lastname: Amadoz
authors.affiliation: Unidad Mixta Infectión y Salud Pública FISABIO-Universidad de Valencia, Instituto Cavanilles de Biodiversidad y Biología Evolutiva, Valencia, Spain. ; CIBER en Epidemiología y Salud Pública, Spain.
authors.forename: Fernando
authors.initials: F
authors.lastname: González-Candelas
keywords: data integration; evolutionary genetics; genetic variability; hepatitis C virus; prognostic model; treatment response