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Multivariable regression models improve accuracy and sensitive grading of antibiotic resistance mutations in Mycobacterium tuberculosis

Author

Listed:
  • Sanjana G. Kulkarni

    (Harvard Medical School)

  • Sacha Laurent

    (Foundation for Innovative New Diagnostics (FIND))

  • Paolo Miotto

    (IRCCS San Raffaele Scientific Institute)

  • Timothy M. Walker

    (University of Oxford
    Oxford University Clinical Research Unit)

  • Leonid Chindelevitch

    (Imperial College London)

  • Carl-Michael Nathanson

    (World Health Organization (WHO))

  • Nazir Ismail

    (World Health Organization (WHO)
    University of the Witwatersrand)

  • Timothy C. Rodwell

    (Foundation for Innovative New Diagnostics (FIND)
    University of California)

  • Maha R. Farhat

    (Harvard Medical School
    Massachusetts General Hospital)

Abstract

Rapid genotype-based drug susceptibility testing for the Mycobacterium tuberculosis complex (MTBC) relies on a comprehensive knowledgebase of the genetic determinants of resistance. Here we present a catalogue of resistance-associated mutations using a regression-based approach and benchmark it against the 2nd edition of the World Health Organisation (WHO) mutation catalogue. We train multivariate logistic regression models on over 52,000 MTBC isolates to associate binary resistance phenotypes for 15 antitubercular drugs with variants extracted from candidate resistance genes. Regression detects 450/457 (98%) resistance-associated variants identified using the existing method (a.k.a, SOLO method) and grades 221 (29%) more total variants than SOLO. The regression-based catalogue achieves higher sensitivity on average (+3.2 percentage points, pp) than SOLO with smaller average decreases in specificity (−1.0 pp) and positive predictive value (−1.6 pp). Sensitivity gains are highest for ethambutol, clofazimine, streptomycin, and ethionamide as regression graded considerably more resistance-associated variants than SOLO for these drugs. There is no difference between SOLO and regression with regards to meeting the target product profiles set by the WHO for genetic drug susceptibility testing, except for rifampicin, for which regression specificity is below the threshold of 98% at 97%. The regression pipeline also detects isoniazid resistance compensatory mutations in ahpC and variants linked to bedaquiline and aminoglycoside hypersusceptibility. These results inform the continued development of targeted next generation sequencing, whole genome sequencing, and other commercial molecular assays for diagnosing resistance in the MTBC.

Suggested Citation

  • Sanjana G. Kulkarni & Sacha Laurent & Paolo Miotto & Timothy M. Walker & Leonid Chindelevitch & Carl-Michael Nathanson & Nazir Ismail & Timothy C. Rodwell & Maha R. Farhat, 2025. "Multivariable regression models improve accuracy and sensitive grading of antibiotic resistance mutations in Mycobacterium tuberculosis," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-57174-1
    DOI: 10.1038/s41467-025-57174-1
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    References listed on IDEAS

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    1. Luca Freschi & Roger Vargas & Ashaque Husain & S. M. Mostofa Kamal & Alena Skrahina & Sabira Tahseen & Nazir Ismail & Anna Barbova & Stefan Niemann & Daniela Maria Cirillo & Anna S. Dean & Matteo Zign, 2021. "Population structure, biogeography and transmissibility of Mycobacterium tuberculosis," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    2. Anna G. Green & Chang Ho Yoon & Michael L. Chen & Yasha Ektefaie & Mack Fina & Luca Freschi & Matthias I. Gröschel & Isaac Kohane & Andrew Beam & Maha Farhat, 2022. "A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    3. Maha R. Farhat & Luca Freschi & Roger Calderon & Thomas Ioerger & Matthew Snyder & Conor J. Meehan & Bouke de Jong & Leen Rigouts & Alex Sloutsky & Devinder Kaur & Shamil Sunyaev & Dick van Soolingen , 2019. "GWAS for quantitative resistance phenotypes in Mycobacterium tuberculosis reveals resistance genes and regulatory regions," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    4. Francesc Coll & Ruth McNerney & José Afonso Guerra-Assunção & Judith R. Glynn & João Perdigão & Miguel Viveiros & Isabel Portugal & Arnab Pain & Nigel Martin & Taane G. Clark, 2014. "A robust SNP barcode for typing Mycobacterium tuberculosis complex strains," Nature Communications, Nature, vol. 5(1), pages 1-5, December.
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