Cited by 500Tuberculosis Research and EpidemiologyInfectious Diseases and TuberculosisAntibiotic Resistance in Bacteria

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Prediction of Susceptibility to First-Line Tuberculosis Drugs by DNA Sequencing

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The CRyPTIC Consortium and the 100,000 Genomes Project·2018·New England Journal of Medicine

DOI 10.1056/nejmoa1800474PMC PMC6121966

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Abstract

BACKGROUND: The World Health Organization recommends drug-susceptibility testing of Mycobacterium tuberculosis complex for all patients with tuberculosis to guide treatment decisions and improve outcomes. Whether DNA sequencing can be used to accurately predict profiles of susceptibility to first-line antituberculosis drugs has not been clear. METHODS: We obtained whole-genome sequences and associated phenotypes of resistance or susceptibility to the first-line antituberculosis drugs isoniazid, rifampin, ethambutol, and pyrazinamide for isolates from 16 countries across six continents. For each isolate, mutations associated with drug resistance and drug susceptibility were identified across nine genes, and individual phenotypes were predicted unless mutations of unknown association were also present. To identify how whole-genome sequencing might direct first-line drug therapy, complete susceptibility profiles were predicted. These profiles were predicted to be susceptible to all four drugs (i.e., pansusceptible) if they were predicted to be susceptible to isoniazid and to the other drugs or if they contained mutations of unknown association in genes that affect susceptibility to the other drugs. We simulated the way in which the negative predictive value changed with the prevalence of drug resistance. RESULTS: A total of 10,209 isolates were analyzed. The largest proportion of phenotypes was predicted for rifampin (9660 [95.4%] of 10,130) and the smallest was predicted for ethambutol (8794 [89.8%] of 9794). Resistance to isoniazid, rifampin, ethambutol, and pyrazinamide was correctly predicted with 97.1%, 97.5%, 94.6%, and 91.3% sensitivity, respectively, and susceptibility to these drugs was correctly predicted with 99.0%, 98.8%, 93.6%, and 96.8% specificity. Of the 7516 isolates with complete phenotypic drug-susceptibility profiles, 5865 (78.0%) had complete genotypic predictions, among which 5250 profiles (89.5%) were correctly predicted. Among the 4037 phenotypic profiles that were predicted to be pansusceptible, 3952 (97.9%) were correctly predicted. CONCLUSIONS: Genotypic predictions of the susceptibility of M. tuberculosis to first-line drugs were found to be correlated with phenotypic susceptibility to these drugs. (Funded by the Bill and Melinda Gates Foundation and others.).

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Figure 1 Simulated negative predictive values for individual drugs and complete drug profiles Negative predictive vales shown for individual drugs and complete drug profiles, according to simulated prevalence of resistance to each drug, or within each drug profile (‘any resistance’). For each percentage prevalence between 10% and 90%, 1,000 isolates were randomly selected, 1,000 times. Lines indicate the median with shaded areas showing the 95% confidence intervals.CC BY · PubMed Central

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Authors: The CRyPTIC Consortium and the 100, 0. G. P.
Venue: New England Journal of Medicine
Publisher: Massachusetts Medical Society
Published: 2018-10-11
Volume: 379
Issue: 15
Pages: 1403-1415
ISSN: 0028-4793 (ISSN-L)
1533-4406
DOI: 10.1056/nejmoa1800474
PMC: PMC6121966
OpenAlex: W2902271181
Cited by: 500
References: 22
Citations by year: 2026: 25 · 2025: 41 · 2024: 58 · 2023: 61 · 2022: 78 · 2021: 65 · 2020: 89 · 2019: 72 · 2018: 9
FWCI: 44.44
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
www.nejmgroup.org/legal/terms-of-use.htm (vor)
Locations: New England Journal of Medicine (publishedVersion, cc-by)
PubMed (publishedVersion)
White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
Europe PMC (PubMed Central) (acceptedVersion, cc-by)
PubMed Central (acceptedVersion, cc-by)
LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
HAL (submittedVersion)
DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion)
eScholarship (California Digital Library) (submittedVersion, cc-by)
Florence Research (University of Florence) (submittedVersion)
LillOA (Université de Lille (University Of Lille)) (submittedVersion, other-oa)
Spiral (Imperial College London) (submittedVersion)
Funders: Seventh Framework Programme (278864)
European Research Council (638553-TB-ACCELERATE)
Wellcome Trust and the Royal Society Sir Henry Dale Fellowships (101237/Z/13/Z, 102541/A/13/Z)
National University of Singapore Yong Loo Lin School of Medicine Aspiration Fund (NUHSRO/2014/069/AF-New Idea/04)
Ministerio de Economía y Competitividad (SAF2016-77346-R)
National Science and Technology Key Program of China (2014ZX10003002)
National Basic Research program of China (2014CB744403)
Health Services and Delivery Research Programme
Bill and Melinda Gates Foundation (OPP1133541)
Wellcome Trust (200205/Z/15/Z)
Topics: Health Sciences › Medicine › Infectious Diseases › Tuberculosis Research and Epidemiology
Health Sciences › Medicine › Infectious Diseases › Infectious Diseases and Tuberculosis
Health Sciences › Medicine › Microbiology (medical) › Antibiotic Resistance in Bacteria
Keywords: Mycobacterium tuberculosis complex, first-line antituberculosis drugs, isoniazid resistance, rifampicin resistance, ethambutol resistance, pyrazinamide resistance, drug resistance mutations, whole genome sequencing, genotype-phenotype correlation, negative predictive value
MeSH terms: Whole Genome Sequencing, Antitubercular Agents, Ethambutol, Genotype, Humans, Isoniazid, Microbial Sensitivity Tests, Mutation, Mycobacterium tuberculosis, Phenotype, Pyrazinamide, Rifampin, Tuberculosis, Genome, Bacterial, Drug Resistance, Bacterial

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