Abstract
BackgroundNotwithstanding the efforts of direct-acting antivirals (DAAs) for the treatment of chronically infected hepatitis C virus (HCV) patients, concerns exist regarding the emergence of resistance-associated substitutions (RAS) related to therapy failure. Sanger sequencing is still the reference technique used for the detection of RAS and it detects viral variants present up to 15%, meaning that minority variants are undetectable, using this technique. To date, many studies are focused on the analysis of the impact of HCV low variants using next-generation sequencing (NGS) techniques, but the importance of these minority variants is still debated, and importantly, a common data analysis method is still not defined.MethodsSerum samples from four patients failing DAAs therapy were collected at baseline and failure, and amplification of NS3, NS5A and NS5B genes was performed on each sample. The genes amplified were sequenced using Sanger and NGS Illumina sequencing and the data generated were analyzed with different approaches. Three different NGS data analysis methods, two homemade in silico pipeline and one commercially available certified user-friendly software, were used to detect low-level variants.ResultsThe NGS approach allowed to infer also very-low level virus variants. Moreover, data processing allowed to generate high accuracy data which results in reduction in the error rates for each single sequence polymorphism. The results improved the detection of low-level viral variants in the HCV quasispecies of the analyzed patients, and in one patient a low-level RAS related to treatment failure was identified. Importantly, the results obtained from only two out of the three data analysis strategies were in complete agreement in terms of both detection and frequency of RAS.ConclusionsThese results highlight the need to find a robust NGS data analysis method to standardize NGS results for a better comprehension of the clinical role of low-level HCV variants. Based on the extreme importance of data analysis approaches for wet-data interpretation, a detailed description of the used pipelines and further standardization of the in silico analysis could allow increasing diagnostic laboratory networking to unleash true potentials of NGS.
Highlights
It is estimated that seventy-one million people are infected by hepatitis C virus (HCV), but only 20% of them are aware of their infectious status
Samples extraction and amplification Samples were collected at two time-points: before the beginning of the therapy and after therapy failure
Results of the failure revealed that Pt.1 lost all mutations detected at his baseline on NS5B, while maintained the same mutations detected at his baseline on NS3 and acquired two mutations on NS5A: the L31V associated with resistance to daclatasvir and Y93H associated with failure to all inhibitors except for pibrentasvir
Summary
It is estimated that seventy-one million people are infected by hepatitis C virus (HCV), but only 20% of them are aware of their infectious status. Direct-acting antivirals (DAAs), targeting three different viral proteins encoded by NS3, NS5A and NS5B genes, have been used in combination since 2013. DAAs treatment can achieve a cure rate of over 95%, meaning that 5% of patients fail the first-round treatment because the selection of resistance-associated substitutions (RAS) which can confer resistance or reduced susceptibility to a certain DAA [2, 3]. Notwithstanding the efforts of direct-acting antivirals (DAAs) for the treatment of chronically infected hepatitis C virus (HCV) patients, concerns exist regarding the emergence of resistance-associated substitutions (RAS) related to therapy failure. Sanger sequencing is still the reference technique used for the detection of RAS and it detects viral variants present up to 15%, meaning that minority variants are undetectable, using this technique. Many studies are focused on the analysis of the impact of HCV low variants using next-generation sequencing (NGS) techniques, but the importance of these minority variants is still debated, and importantly, a common data analysis method is still not defined
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