Insufficiently treated hepatitis C virus (HCV) infection remains a major healthcare issue. Individual therapy responses vary considerably from spontaneous clearing of the virus to lethal conditions. Host genetics currently receives a major scientific and clinical interest as an important source of interindividual variability in treatment. Mainly the associations of interleukin 28B gene (IL28B) variants with decreased HCV clearance under standard therapy are considered as "state of the art" of hepatitis C pharmacogenetics. However, a search in PubMed identified 41 genes reportedly modulating the individual therapy response, e.g., genes coding for major histocompatibility complex (HLA), the tumor necrosis factor (TNF), interleukin 10 (IL10), other interferon coding genes than IL28B (e.g., IFNAR1, IFNAR2, IFNG), several components of downstream interferon signaling as well as genes modulating side effects of current anti-HCV therapeutics (e.g., SLC28A3, ITPA involved in ribavirin associated hemolytic effects or SLC6A4 and HTR1A involved in serotonin associated psychiatric side effects). Applying knowledge discovery methods from the area of data mining and machine-learning to this comprehensive set of HCV therapy modulating genes, relating the HCV genes to the world wide knowledge on genes given in the form of the Gene Ontology (GO) knowledge base, found that the relevant genes belong to the GO subcategories of "inflammatory response" and "immune response" and "response to virus". This complex approaches to the pharmacogenomics of HCV may serve to identify future candidates for a personalization of HCV therapy and structured approach to possible new therapeutic targets for the control of hepatitis C virus.
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