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<p>Accurate detection of drug-resistant <i>Mycobacterium tuberculosis</i> (MTB) is essential for tuberculosis control, particularly with increasing multidrug-resistant strains. Whole-genome sequencing (WGS), as a supplement to phenotypic drug susceptibility testing (DST), holds great potential for resistance detection, but comparative analyses of multiple WGS tools remain limited. This study assessed four WGS-based tools—SAM-TB, TB-Profiler, Mykrobe predictor, and PhyResSE—for predicting resistance to four first-line drugs (isoniazid, rifampicin, ethambutol, and streptomycin) using 113 clinical MTB isolates from Ganzhou, China, with phenotypic DST as reference. All tools showed similar sensitivity (85.04–88.19%) and high specificity (95.69–96.31%). Several known resistance-associated mutations were identified, along with seven novel mutations absent from the WHO catalogue and thirteen of uncertain clinical significance. Discrepancies between WGS and DST occurred across all drugs, potentially due to disputed mutations, outdated databases, or limitations of phenotypic methods. These results highlight the complementary roles of WGS and phenotypic DST in drug resistance surveillance and emphasize the need for continuously updated mutation databases, including the WHO catalogue, and standardized interpretation criteria to enhance the clinical utility of WGS.</p>

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Accuracy of SAM-TB Platform as a Diagnostic Assay for Detection of Mycobacterium tuberculosis Drug Resistance Versus TB-Proflier, PhyResSE, and Mykrobe: A Head-to-Head Comparison

  • 期刊论文

  • 发表日期：

    2025年10月11日

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  • Juexin Wang,
  • Xiaoling Lai,
  • Chuchu Wu,
  • Jielian Wang,
  • Yong Liao,
  • Qiong Lei,
  • Zhenbo Xu

摘要

Accurate detection of drug-resistant Mycobacterium tuberculosis (MTB) is essential for tuberculosis control, particularly with increasing multidrug-resistant strains. Whole-genome sequencing (WGS), as a supplement to phenotypic drug susceptibility testing (DST), holds great potential for resistance detection, but comparative analyses of multiple WGS tools remain limited. This study assessed four WGS-based tools—SAM-TB, TB-Profiler, Mykrobe predictor, and PhyResSE—for predicting resistance to four first-line drugs (isoniazid, rifampicin, ethambutol, and streptomycin) using 113 clinical MTB isolates from Ganzhou, China, with phenotypic DST as reference. All tools showed similar sensitivity (85.04–88.19%) and high specificity (95.69–96.31%). Several known resistance-associated mutations were identified, along with seven novel mutations absent from the WHO catalogue and thirteen of uncertain clinical significance. Discrepancies between WGS and DST occurred across all drugs, potentially due to disputed mutations, outdated databases, or limitations of phenotypic methods. These results highlight the complementary roles of WGS and phenotypic DST in drug resistance surveillance and emphasize the need for continuously updated mutation databases, including the WHO catalogue, and standardized interpretation criteria to enhance the clinical utility of WGS.