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. 2018 Jun;18(6):675-683.
doi: 10.1016/S1473-3099(18)30073-2. Epub 2018 Mar 21.

Genetic sequencing for surveillance of drug resistance in tuberculosis in highly endemic countries: a multi-country population-based surveillance study

Matteo Zignol  1 , Andrea Maurizio Cabibbe  2 , Anna S Dean  3 , Philippe Glaziou  3 , Natavan Alikhanova  4 , Cecilia Ama  5 , Sönke Andres  6 , Anna Barbova  7 , Angeli Borbe-Reyes  5 , Daniel P Chin  8 , Daniela Maria Cirillo  9 , Charlotte Colvin  10 , Andrei Dadu  11 , Andries Dreyer  12 , Michèle Driesen  13 , Christopher Gilpin  3 , Rumina Hasan  14 , Zahra Hasan  14 , Sven Hoffner  15 , Alamdar Hussain  16 , Nazir Ismail  17 , S M Mostofa Kamal  18 , Faisal Masood Khanzada  16 , Michael Kimerling  19 , Thomas Andreas Kohl  20 , Mikael Mansjö  21 , Paolo Miotto  9 , Ya Diul Mukadi  10 , Lindiwe Mvusi  22 , Stefan Niemann  20 , Shaheed V Omar  12 , Leen Rigouts  23 , Marco Schito  24 , Ivita Sela  25 , Mehriban Seyfaddinova  4 , Girts Skenders  25 , Alena Skrahina  26 , Sabira Tahseen  16 , William A Wells  10 , Alexander Zhurilo  27 , Karin Weyer  3 , Katherine Floyd  3 , Mario C Raviglione  3
Affiliations

Genetic sequencing for surveillance of drug resistance in tuberculosis in highly endemic countries: a multi-country population-based surveillance study

Matteo Zignol et al. Lancet Infect Dis. 2018 Jun.

Erratum in

  • Corrections.
    [No authors listed] [No authors listed] Lancet Infect Dis. 2018 May;18(5):492. doi: 10.1016/S1473-3099(18)30227-5. Epub 2018 Mar 27. Lancet Infect Dis. 2018. PMID: 29602752 Free PMC article. No abstract available.

Abstract

Background: In many countries, regular monitoring of the emergence of resistance to anti-tuberculosis drugs is hampered by the limitations of phenotypic testing for drug susceptibility. We therefore evaluated the use of genetic sequencing for surveillance of drug resistance in tuberculosis.

Methods: Population-level surveys were done in hospitals and clinics in seven countries (Azerbaijan, Bangladesh, Belarus, Pakistan, Philippines, South Africa, and Ukraine) to evaluate the use of genetic sequencing to estimate the resistance of Mycobacterium tuberculosis isolates to rifampicin, isoniazid, ofloxacin, moxifloxacin, pyrazinamide, kanamycin, amikacin, and capreomycin. For each drug, we assessed the accuracy of genetic sequencing by a comparison of the adjusted prevalence of resistance, measured by genetic sequencing, with the true prevalence of resistance, determined by phenotypic testing.

Findings: Isolates were taken from 7094 patients with tuberculosis who were enrolled in the study between November, 2009, and May, 2014. In all tuberculosis cases, the overall pooled sensitivity values for predicting resistance by genetic sequencing were 91% (95% CI 87-94) for rpoB (rifampicin resistance), 86% (74-93) for katG, inhA, and fabG promoter combined (isoniazid resistance), 54% (39-68) for pncA (pyrazinamide resistance), 85% (77-91) for gyrA and gyrB combined (ofloxacin resistance), and 88% (81-92) for gyrA and gyrB combined (moxifloxacin resistance). For nearly all drugs and in most settings, there was a large overlap in the estimated prevalence of drug resistance by genetic sequencing and the estimated prevalence by phenotypic testing.

Interpretation: Genetic sequencing can be a valuable tool for surveillance of drug resistance, providing new opportunities to monitor drug resistance in tuberculosis in resource-poor countries. Before its widespread adoption for surveillance purposes, there is a need to standardise DNA extraction methods, recording and reporting nomenclature, and data interpretation.

Funding: Bill & Melinda Gates Foundation, United States Agency for International Development, Global Alliance for Tuberculosis Drug Development.

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Figures

Figure 1
Figure 1
Prevalence of rifampicin resistance, estimated through genetic sequencing compared with phenotypic testing Data are the adjusted prevalence of rifampicin resistance from genetic sequencing compared with the true prevalence of rifampicin resistance from phenotypic testing, shown for all tuberculosis cases. Absolute numbers are shown in the appendix.
Figure 2
Figure 2
Prevalence of isoniazid resistance, estimated through genetic sequencing compared with phenotypic testing Data are the adjusted prevalence of isoniazid resistance from genetic sequencing compared with the true prevalence of isoniazid resistance from phenotypic testing, stratified by rifampicin-resistant and rifampicin-susceptible cases. Absolute numbers are shown in the appendix.
Figure 3
Figure 3
Prevalence of fluoroquinolone resistance, estimated through genetic sequencing compared with phenotypic testing Data are the adjusted prevalence of fluoroquinolone resistance from genetic sequencing compared with the true prevalence of fluoroquinolone resistance from phenotypic testing, stratified by (A) ofloxacin and (B) moxifloxacin resistance and rifampicin-resistant and rifampicin-susceptible cases. Absolute numbers are shown in the appendix.
Figure 4
Figure 4
Prevalence of pyrazinamide resistance, estimated through genetic sequencing compared with phenotypic testing Data are the adjusted prevalence of pyrazinamide resistance from genetic sequencing compared with the true prevalence of pyrazinamide resistance from phenotypic testing, stratified by rifampicin-resistant and rifampicin-susceptible cases. Absolute numbers are shown in the appendix.
Figure 5
Figure 5
Prevalence of resistance to injectable kanamycin, amikacin, and capreomycin, estimated through genetic sequencing compared with phenotypic testing Data are the adjusted prevalence of resistance to injectable drugs, determined from genetic sequencing, compared with the true prevalence of resistance to injectable drugs from phenotypic testing, stratified into (A) kanamycin, (B) amikacin, and (C) capreomycin resistance, and are shown in rifampicin-resistant cases. Absolute numbers are shown in the appendix.
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References

    1. WHO Antimicrobial resistance: global report on surveillance 2014. April, 2014. http://www.who.int/drugresistance/documents/surveillancereport/en/ (accessed Dec 29, 2017).
    1. WHO Global tuberculosis report 2017. 2017. http://www.who.int/tb/publications/global_report/en/ (accessed Dec 29, 2017).
    1. Zignol M, Dean AS, Falzon D. Twenty years of global surveillance of antituberculosis-drug resistance. N Engl J Med. 2016;375:1081–1089. - PubMed
    1. Uplekar M, Weil D, Lonnroth K. WHO's new end TB strategy. Lancet. 2015;385:1799–1801. - PubMed
    1. Denkinger CM, Dolinger D, Schito M. Target product profile of a molecular drug-susceptibility test for use in microscopy centers. J Infect Dis. 2015;211(suppl 2):S39–S49. - PMC - PubMed

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