New Genetic Test May Help Predict Response to Hepatitis C Treatment

By Liz Highleyman

Given the side effects, inconvenience, and cost of treatment for chronic hepatitis C virus (HCV) infection using pegylated interferon plus ribavirin, researchers have explored various factors that predict in advance which patients will achieve a sustained response, so the rest can be spared futile therapy.

Overall, about half of patients treated with standard-of-care therapy achieve a sustained response. Known predictive factors include infection with HCV genotypes 2 or 3 (vs genotypes 1 or 4), low pre-treatment HCV viral load, adequate drug doses and duration, and rapid virological response (RVR) at week 4 of therapy. Now, researchers have also shown that specific HCV genetic patterns may also predict response.

As reported in the December 22, 2008 advance online edition of the Journal of Clinical Investigation, researchers at Saint Louis University sought to determine whether specific genomic sequences of HCV isolates -- which differ by approximately 10% -- are associated with better or worse treatment response.

The investigators used a mathematical algorithm to analyze amino acid covariance within the full viral coding region of pre-treatment HCV RNA sequences from 94 participants in the Viral Resistance to Antiviral Therapy of Chronic Hepatitis C (Virahep-C) trial.

Results

• Covarying amino acid positions were common and linked together into networks that differed by response to therapy.

• There were 3 times more hydrophobic amino acid pairs in HCV from non-responders compared with responders.

• Using this analysis to detect patterns within the networks, treatment outcomes could be predicted with greater than 95% coverage and 100% accuracy.

These results, the authors wrote, "[raise] the possibility of a prognostic test to reduce therapeutic failures." Furthermore, they added, "the hub positions in the networks are attractive antiviral targets because of their genetic linkage with many other positions that we predict would suppress evolution of resistant variants."

The investigators suggested that hydrophobic amino acid interactions might contribute to treatment failure by stabilizing viral protein complexes. This type of covariance network analysis, they concluded, "could be applicable to any virus with sufficient genetic variation, including most human RNA viruses" -- potentially including HIV.

Department of Molecular Microbiology and Immunology, Department of Biochemistry and Molecular Biology, and Saint Louis University Liver Center, Saint Louis University School of Medicine, St. Louis, Missouri.

1/06/09

Reference
R Aurora, MJ Donlin, NA, Cannon, and JE Tavis. Genome-wide hepatitis C virus amino acid covariance networks can predict response to antiviral therapy in humans. Journal of Clinical Investigation. December 22, 2008 [Epub ahead of print]. Full text.