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新型高通量自动化测序检测丙型肝炎病毒基因分型和耐药相关变异的临床评估:与线性探针检测方法的比较

Clinical evaluation of a newly developed automated massively parallel sequencing assay for hepatitis C virus genotyping and detection of resistance-association variants. Comparison with a line probe assay.

机构信息

Department of Clinical Pathology, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand.

Virology Laboratory and Center for Medical Genomics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand.

出版信息

J Virol Methods. 2017 Nov;249:31-37. doi: 10.1016/j.jviromet.2017.08.017. Epub 2017 Aug 26.

Abstract

Hepatitis C virus (HCV) infection is a leading cause of chronic liver disease, cirrhosis and hepatocellular carcinoma. Recently, HCV was classified into 6 major genotypes (GTs) and 67 subtypes (STs). Efficient genotyping has become an essential tool for prognosis and indicating suitable treatment, prior to starting therapy in all HCV-infected individuals. The widely used genotyping assays have limitation with regard to genotype accuracy. This study was a comparative evaluation of exact HCV genotyping in a newly developed automated-massively parallel sequencing (MPS) system, versus the established Line probe assay 2.0 (LiPA), substantiated by Sanger sequencing, using 120 previously identified-HCV RNA positive specimens. LiPA gave identical genotypes in the majority of samples tested with MPS. However, as much as 25% of LiPA did not identify subtypes, whereas MPS did, and 0.83% of results were incompatible. Interestingly, only MPS could identify mixed infections in the remaining cases (1.67%). In addition, MPS could detect Resistance-Associated Variants (RAVs) simultaneously in GT1 in 56.82% of the specimens, which were known to affect drug resistance in the HCV NS3/NS4A and NS5A genomic regions. MPS can thus be deemed beneficial for guiding decisions on HCV therapy and saving costs in the long term when compared to traditional methods.

摘要

丙型肝炎病毒(HCV)感染是慢性肝病、肝硬化和肝细胞癌的主要原因。最近,HCV 被分为 6 种主要基因型(GTs)和 67 种亚型(STs)。在所有 HCV 感染者开始治疗之前,高效的基因分型已成为预后和确定合适治疗方法的必要工具。广泛使用的基因分型检测方法在基因型准确性方面存在局限性。本研究比较了新开发的自动化大规模平行测序(MPS)系统与经测序证实的既定线性探针分析 2.0(LiPA)对 120 份先前鉴定为 HCV RNA 阳性的标本进行精确 HCV 基因分型的效果。LiPA 与 MPS 检测的大多数样本的基因型相同。然而,高达 25%的 LiPA 无法识别亚型,而 MPS 可以,0.83%的结果不兼容。有趣的是,只有 MPS 可以在其余病例(1.67%)中识别混合感染。此外,MPS 可以同时检测到 GT1 中 56.82%的标本中的耐药相关变异(RAVs),这些变异已知会影响 HCV NS3/NS4A 和 NS5A 基因组区域的耐药性。因此,与传统方法相比,MPS 可被视为对 HCV 治疗决策有指导意义,并可长期节省成本。

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