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. 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. Serum 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. The 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. These 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.

中文翻译：

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在DAA治疗的患者中检测低水平HCV变异：在三种不同的NGS数据分析方案之间进行比较。

尽管直接作用抗病毒药（DAA）用于治疗慢性感染的丙型肝炎病毒（HCV）患者，但仍存在与治疗失败有关的耐药相关替代药物（RAS）的出现。桑格测序仍是用于检测RAS的参考技术，它可以检测到高达15％的病毒变体，这意味着使用该技术无法检测到少数变体。迄今为止，许多研究都集中在使用下一代测序（NGS）技术分析HCV低变异体的影响上，但是这些少数变异体的重要性仍在争论中，而且重要的是，仍然没有定义通用的数据分析方法。在基线和衰竭时收集了四名DAA治疗失败的患者的血清样本，并扩增了NS3，对每个样品进行NS5A和NS5B基因。使用Sanger和NGS Illumina测序对扩增的基因进行测序，并使用不同的方法分析产生的数据。三种不同的NGS数据分析方法，两种自制的in silico管道和一种可商购获得认证的用户友好软件被用来检测低水平的变异。NGS方法可以推断出非常低水平的病毒变体。而且，数据处理允许生成高精度数据，这导致每个单序列多态性的错误率降低。结果改善了分析患者的HCV准种中低水平病毒变异的检测，并且在一名患者中鉴定出与治疗失败相关的低水平RAS。重要的，从三种数据分析策略中只有两种获得的结果在RAS的检测和频率方面完全一致。这些结果强调需要找到一种可靠的NGS数据分析方法来标准化NGS结果，以便更好地理解低水平HCV变异体的临床作用。基于数据分析方法对于湿数据解释的极端重要性，对使用的管线进行详细描述以及计算机分析的进一步标准化可能会增加诊断实验室网络，以释放NGS的真正潜力。这些结果强调需要找到一种可靠的NGS数据分析方法来标准化NGS结果，以便更好地理解低水平HCV变异体的临床作用。基于数据分析方法对于湿数据解释的极端重要性，对使用的管线进行详细描述以及计算机分析的进一步标准化可能会增加诊断实验室网络，以释放NGS的真正潜力。这些结果强调需要找到一种可靠的NGS数据分析方法来标准化NGS结果，以便更好地理解低水平HCV变异体的临床作用。基于数据分析方法对于湿数据解释的极端重要性，对使用的管线进行详细描述以及计算机分析的进一步标准化可能会增加诊断实验室网络，以释放NGS的真正潜力。