Hepatitis C treatment gets personal: predicting drug response

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Around 3% of the world’s population is infected with hepatitis C virus (HCV), which affects the liver. The current standard-of-care therapy is not effective in the majority of genotype 1 viral cases and more serious, chronic diseases of the liver can result. Recently published in Genome Medicine, David Booth and colleagues from the Universities of Sydney and Melbourne use a powerful sequencing approach to identify DNA variants that can predict failure to respond to hepatitis C therapy. Their findings could help to optimize treatment options for many hepatitis C patients.    

The recommended treatment for HCV infection is a 48-week course of  pegylated interferon alpha and ribavirin, which clears the infection in less than 50% of genotype 1 cases. Over the past few years, researchers have performed genome-wide association studies (GWAS) to identify genetic factors underlying the lack of viral clearance in most patients. These analyses revealed that single nucleotide changes, or polymorphisms, in the IL28B gene region can predict non-response to treatment. In their latest study, David Booth and colleagues used a high-throughput “massively parallel sequencing” approach to identify new, highly sensitive genetic predictors of drug response. DNA samples from responders or non-responders were pooled, so that many patients could be screened simultaneously and cost-effectively for common mutations. Compared with previous results, the genetic variants identified through this analysis were shown to predict failure to respond with high sensitivity and specificity.

By predicting which patients are unlikely to respond to the standard treatment, clinicians would be able to make an informed choice about which patients should be offered newly emerging therapies. These results therefore hold great promise for the clinical management of hepatitis C.  

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