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Resistance-Associated Variants in HCV Genotype 1 Populations

Resistance-Associated Variants in HCV Genotype 1 Populations content piece image
HCV treatment with directly acting antiviral drugs (DAAs) has a short duration very effective with few adverse effects. However, not all patients can be treated with DAAs alone, as pegylated interferon and/or ribavirin are needed for some genotypes. Accurate genotyping therefore remains one of the pillars of treatment selection. Given the increasing number of DAAs coming to market the detection of resistance-associated variants (RAVs) is becoming increasingly important to further refine and optimize drug therapy. In this study we specifically investigated RAVs emerging in the globally prevalent HCV genotype (GT1). 110 EDTA-plasma and serum samples from Asian patients with chronic HCV GT1a (n=56) or GT1b (n=54) infection were included in this study. We used a novel automated Next Generation Sequencing (NGS)-based integrated workflow, comprised of a robotic platform, RNA extraction and library preparation kits (Sentosa SQ HCV Genotyping Assay), Ion Torrent deep sequencing and software. The system generates an automated report based on proprietary software. The sequencing data analysis includes 136 known RAVs in the NS3, NS5A and NS5B genes. 52.7%(58/110) of HCV GT1a and GT1b strains were carrying 1 or multiple RAVs in 23 positions across all target genes. An unequal distribution of 4 mutations in the GT1 subtypes was observed. The frequency of the Q80K mutation (NS3) was 25%(14/56) in GT1a and 1.9%(1/54) in GT1b while mutations Q54H and Y93H (NS5A) were prevalent in GT1b at 42.6%(23/54) and 18.5%(10/54) respectively. Y93H was detected in GT1a at 1.8%(1/56). Mutation V499A in NS5B was only found in GT1b at 25.9%(14/54), but not in GT1a. In conclusion, beyond the crucial role of accurate HCV genotyping detection of RAVs by NGS across drug target genes is becoming increasingly important for fine-tuning of HCV treatment. A combined approach by a newly developed NGS-based system can help to streamline generation of relevant pre-treatment information.