Whole-genome sequencing for prediction of Mycobacterium tuberculosis drug susceptibility and resistance: A retrospective cohort study

Modernizing Medical Microbiology (MMM) Informatics Group

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Abstract

Background: Diagnosing drug-resistance remains an obstacle to the elimination of tuberculosis. Phenotypic drug-susceptibility testing is slow and expensive, and commercial genotypic assays screen only common resistance-determining mutations. We used whole-genome sequencing to characterise common and rare mutations predicting drug resistance, or consistency with susceptibility, for all first-line and second-line drugs for tuberculosis. Methods: Between Sept 1, 2010, and Dec 1, 2013, we sequenced a training set of 2099 Mycobacterium tuberculosis genomes. For 23 candidate genes identified from the drug-resistance scientific literature, we algorithmically characterised genetic mutations as not conferring resistance (benign), resistance determinants, or uncharacterised. We then assessed the ability of these characterisations to predict phenotypic drug-susceptibility testing for an independent validation set of 1552 genomes. We sought mutations under similar selection pressure to those characterised as resistance determinants outside candidate genes to account for residual phenotypic resistance. Findings: We characterised 120 training-set mutations as resistance determining, and 772 as benign. With these mutations, we could predict 89·2% of the validation-set phenotypes with a mean 92·3% sensitivity (95% CI 90·7-93·7) and 98·4% specificity (98·1-98·7). 10·8% of validation-set phenotypes could not be predicted because uncharacterised mutations were present. With an in-silico comparison, characterised resistance determinants had higher sensitivity than the mutations from three line-probe assays (85·1% vs 81·6%). No additional resistance determinants were identified among mutations under selection pressure in non-candidate genes. Interpretation: A broad catalogue of genetic mutations enable data from whole-genome sequencing to be used clinically to predict drug resistance, drug susceptibility, or to identify drug phenotypes that cannot yet be genetically predicted. This approach could be integrated into routine diagnostic workflows, phasing out phenotypic drug-susceptibility testing while reporting drug resistance early. Funding: Wellcome Trust, National Institute of Health Research, Medical Research Council, and the European Union.

Original languageEnglish
Article number68
Pages (from-to)1193-1202
Number of pages10
JournalThe Lancet Infectious Diseases
Volume15
Issue number10
DOIs
Publication statusPublished - 1 Oct 2015

Bibliographical note

Funding Information:
This study was funded by the UK Clinical Research Collaboration ( Wellcome Trust [grant 087646/Z/08/Z] , Medical Research Council, National Institute for Health Research [NIHR grant G0800778] ); NIHR Oxford Biomedical Research Centre, NIHR Oxford Health Protection Research Unit on Healthcare Associated Infection and Antimicrobial Resistance ( grant HPRU-2012-10041 ), Health Innovation Challenge Fund (UK Department of Health and the Wellcome Trust [grant T5-358] ), Wellcome Trust Sanger Institute core funding (grant 098051) , EU FP7 Patho-Ngen-Trace (grant FP7-278864-2) . We thank the High-Throughput Genomics Group at the Wellcome Trust Centre for Human Genetics (funded by Wellcome Trust grant 090532/Z/09/Z) for the generation of the sequencing data. All sequences are available in NCBI or ENA (or both), phenotypes and archive accession numbers are in the supplementary tables ( appendix 2 ). This report is independent research by the NIHR. The views expressed in this publication are those of the authors and not necessarily those of the UK National Health Service, the NIHR, or the Department of Health.

Funding Information:
PS is a consultant for Genoscreen. CG is, and CA-B was, an employee of Genoscreen. JP has received support for conference travel and accommodation from Illumina. DAC is funded by a research fellowship from the Royal Academy of Engineering and Balliol College, Oxford. TMW is an MRC Research Training Fellow. PB has a Genomic Medicine and Statistics Wellcome Trust DPhil studentship. ZI and DJW are Wellcome Trust and Royal Society Sir Henry Dale Fellows. KEN is a Rhodes Scholar, Rhodes Trust and part of the RCUK Digital Economy Programme grant number EP/G03861/1 (centre for Doctoral Training in Healthcare Innovation). DWC and TEAP are NIHR senior investigators. All other authors declare no competing interests.

Publisher Copyright:
© 2015 Walker et al. Open Access article distributed under the terms of CC-BY.

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