Abstract
Conventional molecular tests for detecting Mycobacterium tuberculosis complex (MTBC) drug resistance on clinical samples cover a limited set of mutations. Whole genome sequencing (WGS) typically requires culture. Here, we evaluated the Deeplex Myc-TB targeted deep sequencing assay for prediction of resistance to 13 anti-tuberculous drugs/drug classes, directly applicable on sputum. With MTBC DNA tests, the limit of detection was 100–1000 genome copies for fixed resistance mutations. Deeplex Myc-TB captured in silico 97.1–99.3% of resistance phenotypes correctly predicted by WGS from 3651 MTBC genomes. On 429 isolates, the assay predicted 92.2% of 2369 first- and second-line phenotypes, with a sensitivity of 95.3% and specificity of 97.4%. Fifty-six of 69 (81.2%) residual discrepancies with phenotypic results involved pyrazinamide, ethambutol, and ethionamide, and low-level rifampicin- or isoniazid-resistance mutations, all notoriously prone to phenotypic testing variability. Only 2 of 91 (2.2%) resistance phenotypes undetected by Deeplex Myc-TB had known resistance-associated mutations by WGS analysis outside Deeplex Myc-TB targets. Phenotype predictions from Deeplex Myc-TB analysis directly on 109 sputa from a Djibouti survey matched those of MTBSeq/PhyResSE/Mykrobe, fed with WGS data from subsequent cultures, with a sensitivity of 93.5/98.5/93.1% and specificity of 98.5/97.2/95.3%. Most residual discordances involved gene deletions/indels and 3–12% heteroresistant calls undetected by WGS analysis, or natural pyrazinamide resistance of globally rare “M. canettii” strains then unreported by Deeplex Myc-TB. On 1494 arduous sputa from a Democratic Republic of the Congo survey, 14 902 of 19 422 (76.7%) possible susceptible or resistance phenotypes could be predicted culture-free. Deeplex Myc-TB may enable fast, tailored tuberculosis treatment.
Footnotes
This manuscript has recently been accepted for publication in the European Respiratory Journal. It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article.
Conflict of interest: Dr. Jouet is an employee of Genoscreen.
Conflict of interest: Dr. Gaudin is an employee of Genoscreen.
Conflict of interest: Dr. Badalato is an employee of Genoscreen.
Conflict of interest: Dr. Allix-Béguec is an employee of Genoscreen.
Conflict of interest: Dr. Duthoy is an employee of Genoscreen.
Conflict of interest: Dr. Ferré is an employee of Genoscreen.
Conflict of interest: Diels has nothing to disclose.
Conflict of interest: Dr. Laurent is an employee of Genoscreen.
Conflict of interest: Dr. Contreras is an employee of Genoscreen.
Conflict of interest: Dr. Feuerriegel has nothing to disclose.
Conflict of interest: Dr. Niemann reports grants from German Center for Infection Research, grants from Excellenz Cluster Precision Medicine in Chronic Inflammation EXC 2167, grants from Leibniz Science Campus Evolutionary Medicine of the LUNG (EvoLUNG), grants from ECDC public tender: OJ/2017/OCS/7766
Conflict of interest: Dr. André has nothing to disclose.
Conflict of interest: Dr. Kaswa has nothing to disclose.
Conflict of interest: Dr. Tagliani has nothing to disclose.
Conflict of interest: Dr. Cabibbe has nothing to disclose.
Conflict of interest: Dr. Mathys has nothing to disclose.
Conflict of interest: Dr. cirillo has nothing to disclose.
Conflict of interest: Dr. de Jong has nothing to disclose.
Conflict of interest: Dr. Rigouts has nothing to disclose.
Conflict of interest: Dr. Supply reports personal fees from Genoscreen, grants from European Union PathoNGen-Trace project (FP7- 278864), during the conduct of the study.
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- Received June 15, 2020.
- Accepted September 3, 2020.
- Copyright ©ERS 2020
