Comparative assessment of line probe assays and targeted next-generation sequencing in drug-resistant tuberculosis diagnosis

BACKGROUND: Rapid and accurate detection of drug-resistant tuberculosis (DR-TB) is crucial for ensuring effective treatment, halting transmission and preventing the amplification of resistance. Comparative evaluations of molecular diagnostic assays in high-burden settings are essential for informing clinical decision-making for DR-TB treatment. METHODS: The Seq&Treat clinical study previously evaluated the performance of two targeted next-generation sequencing (tNGS) workflows, GenoScreen Deeplex Myc-TB and Oxford Nanopore Technologies Tuberculosis Drug Resistance Test, on direct sediment samples from persons at risk for DR-TB. Hain Line Probe Assay (LPAs-MTBDRplus and MTBDRsl) were run as a comparator test using an aliquot of the same sediment samples. Diagnostic performance of the LPAs and previously established tNGS performance were compared, including sensitivity and specificity, for rifampicin, isoniazid, fluoroquinolones (moxifloxacin, levofloxacin), and amikacin, using a composite reference standard of phenotypic drug susceptibility testing and whole-genome sequencing. FINDINGS: Among 720 clinical samples tested, MTBDRplus LPA sensitivity for rifampicin and isoniazid was 92.3% (95% CI 88.9-94.8) and 91.9% (88.4-94.4), each significantly lower than ≥95% achieved by both tNGS workflows (p < 0.01). For fluoroquinolones (moxifloxacin and levofloxacin), the MTBDRsl LPA and ONT had similar sensitivities (94.3% and 92.7%, and 94.8% and 93.9%, respectively), while GenoScreen outperformed both (97.3% and 96.6%). GenoScreen also demonstrated the highest sensitivity for amikacin resistance (94.6%) compared to LPAs (88.7%) and ONT (88.3%). Complete assay failure rates were low for LPAs (4.9%) and ONT (5.0%) and moderately higher for GenoScreen (8.6%), with differences in single-target failures across all assays. INTERPRETATION: LPAs demonstrated lower sensitivity and more limited drug resistance detection compared to tNGS workflows, underscoring the advantages of tNGS for improving DR-TB diagnostic algorithms. These findings provide critical evidence to guide updates in DR-TB diagnostic programs. FUNDING: Support for the Seq&Treat project was provided through funding from Unitaid (2019-32-FIND MDR).