Applying whole genome sequencing to predict phenotypic drug resistance in Mycobacterium tuberculosis : leveraging 20 years of nationwide data from Denmark

Abstract

ABSTRACT Infection with Mycobacterium tuberculosis remains one of the biggest causes of death from a single microorganism worldwide, and the continuous emergence of drug resistance aggravates our ability to cure the disease. New improved resistance detection methods are needed to provide adequate treatment, such as whole genome sequencing (WGS), which has been used increasingly to identify resistance-conferring mutations over the last decade. The steadily increasing knowledge of resistance-conferring mutations increases our ability to predict resistance based on genomic data alone. This study evaluates the performance of WGS to predict M. tuberculosis complex resistance. It compares WGS predictions with the phenotypic (culture-based) drug susceptibility results based on 20 years of nationwide Danish data. Analyzing 6,230 WGS-sequenced samples, the sensitivities for isoniazid, rifampicin, ethambutol, and pyrazinamide were 82.5% [78.0%–86.5%, 95% confidence interval (CI)], 97.3% (90.6%–99.7%, 95% CI), 58.0% (43.2%–71.8%, 95% CI), and 60.5% (49.0%–71.2%, 95% CI), respectively, and specificities were 99.8% (99.7%–99.9%, 95% CI), 99.8% (99.7%–99.9%, 95% CI), 99.4% (99.2%–99.6%, 95% CI), and 99.9% (99.7%–99.9%, 95% CI), respectively. A broader range of both sensitivities and specificities was observed for second-line drugs. The results conform with previously reported values and indicate that WGS is reliable for routine resistance detection in resource-rich tuberculosis low-incidence and low-resistance settings such as Denmark.