Primer ID ultra-deep sequencing reveals dynamics of drug resistance-associated variants in breakthrough hepatitis C viruses: Relevance to treatment outcome and resistance screening

Background: Use of direct-acting antiviral drugs (DAAs) that target the Hepatitis C virus may be hampered by the rapid selection of viral strains that harbour drug resistance-associated variants (RAVs). These RAVs are often associated with a fitness cost and tend to occur on low frequency strains within treatment naïve subjects. To address the clinical relevance of low frequency RAVs in the setting of DAAs, this study utilised a Primer ID ultra-deep sequencing approach to mitigate PCR errors and bias to accurately quantify viral sequences in subjects that failed DAA treatment. Methods: Subjects were enrolled in the follow-up study P05063 of previous treatment with boceprevir and all had detectable RAVs at virological failure (VF) based on Sanger-based population sequencing. Twelve subjects had three time-points available: baseline, VF, and follow-up (median 830.5 days). Viral RNA was amplified using unique primer identifiers (primer IDs) and sequenced using 454 ultra-deep sequencing. Results: The sequencing strategy used in this study improved the detection of clinically relevant low frequency strains bearing RAVs compared to population sequencing and showed that these strains can persist for up to two years post-treatment failure. Strains carrying multiple RAVs were common in breakthrough viruses. Putative compensatory mutations were identified. Conclusions: The Primer ID ultra-deep sequencing approach identifies RAVs that can reduce drug sensitivity at levels below the detection threshold for population sequencing. The approach also removes PCR errors and biases, suggesting this sequencing strategy should become the standard approach by which to perform temporal quasispecies studies and resistance screening