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Multicenter Study
, 379 (15), 1403-1415

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

CRyPTIC Consortium and the 100,000 Genomes ProjectCaroline Allix-BéguecIrena ArandjelovicLijun BiPatrick BeckertMaryline BonnetPhelim BradleyAndrea M CabibbeIrving Cancino-MuñozMark J CaulfieldAngkana ChaiprasertDaniela M CirilloDavid A CliftonIñaki ComasDerrick W CrookMaria R De FilippoHan de NeelingRoland DielFrancis A DrobniewskiKiatichai FaksriMaha R FarhatJoy FlemingPhilip FowlerTom A FowlerQian GaoJennifer GardyDeborah Gascoyne-BinziAna-Luiza Gibertoni-CruzAna Gil-BrusolaTanya GolubchikXimena GonzaloLouis GrandjeanGuangxue HeJennifer L GuthrieSarah HoosdallyMartin HuntZamin IqbalNazir IsmailJames JohnstonFaisal M KhanzadaChiea C KhorThomas A KohlClare KongSam LipworthQingyun LiuGugu MaphalalaElena MartinezVanessa MathysMatthias MerkerPaolo MiottoNerges MistryDavid A J MooreMegan MurrayStefan NiemannShaheed V OmarRick T-H OngTim E A PetoJames E PoseyTherdsak PrammanananAlexander PymCamilla RodriguesMabel RodriguesTimothy RodwellGian M RossoliniElisabeth Sánchez PadillaMarco SchitoXin ShenJay ShendureVitali SintchenkoAlex SloutskyE Grace SmithMatthew SnyderKarine SoetaertAngela M StarksPhilip SupplyPrapat SuriyapolSabira TahseenPatrick TangYik-Ying TeoThuong N T ThuongGuy ThwaitesEnrico TortoliDick van SoolingenA Sarah WalkerTimothy M WalkerMark WilcoxDaniel J WilsonDavid WyllieYang YangHongtai ZhangYanlin ZhaoBaoli Zhu
Multicenter Study

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

CRyPTIC Consortium and the 100,000 Genomes Project et al. N Engl J Med.

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.).

Figures

Figure 1
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.

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