摘要

全基因组测序（WGS）有可能加速药物敏感性试验（DST），为耐药结核病（TB）设计合适的方案。最近开发的一些自动化软件工具承诺对WGS数据的分析和解释进行标准化。我们评估了关于DST和系统发育谱系分类的五种工具（CASTB，KvarQ，Mykrobe Predictor TB，PhyResSE和TBProfiler），我们将其与表型DST，Sanger测序和91种菌株的传统分型结果进行比较。这些工具的谱系分类通常只在结果的分辨率上有所不同。然而，一些菌株根本无法分类，一种菌株被错误分类。这些工具的异烟肼和利福平耐药性的敏感性和特异性很高，而乙胺丁醇，吡嗪酰胺和链霉素耐药的结果更加多变。假敏感的DST结果主要是由于在各自的工具用于数据解释的抗性目录中缺失突变。值得注意的是，由于将多态性错误归类为抗性突变，我们还发现了假抗性病例。总之，目前用于DST的WGS分析工具的性能差异很大。需要可持续的商业模式和共同的高质量抗性突变目录来确保这些工具的临床实用性。

Whole-genome sequencing (WGS) has the potential to accelerate drug-susceptibility testing (DST) to design appropriate regimens for drug-resistant tuberculosis (TB). Several recently developed automated software tools promise to standardize the analysis and interpretation of WGS data. We assessed five tools (CASTB, KvarQ, Mykrobe Predictor TB, PhyResSE, and TBProfiler) with regards to DST and phylogenetic lineage classification, which we compared with phenotypic DST, Sanger sequencing, and traditional typing results for a collection of 91 strains. The lineage classifications by the tools generally only differed in the resolution of the results. However, some strains could not be classified at all and one strain was misclassified. The sensitivities and specificities for isoniazid and rifampicin resistance of the tools were high, whereas the results for ethambutol, pyrazinamide, and streptomycin resistance were more variable. False-susceptible DST results were mainly due to missing mutations in the resistance catalogues that the respective tools employed for data interpretation. Notably, we also found cases of false-resistance because of the misclassification of polymorphisms as resistance mutations. In conclusion, the performance of current WGS analysis tools for DST is highly variable. Sustainable business models and a shared, high-quality catalogue of resistance mutations are needed to ensure the clinical utility of these tools.

https://www.nature.com/articles/srep46327