Opportunities and limitations of genomics for diagnosing bedaquiline-resistant tuberculosis: an individual isolate meta-analysis

Background Clinical bedaquiline resistance predominantly involves mutations in mmpR5 (Rv0678). However, mmpR5 resistance-associated variants (RAVs) have a variable relationship with phenotypic M. tuberculosis resistance. We performed a systematic review to (1) assess the maximal sensitivity of sequencing bedaquiline resistance-associated genes and (2) evaluate the association between RAVs and phenotypic resistance, using traditional and machine-based learning techniques. Methods We screened public databases for articles published until October 2022. Eligible studies performed sequencing of at least mmpR5 and atpE on clinically-sourced M. tuberculosis isolates and measured bedaquiline minimum inhibitory concentrations (MICs). We performed genetic analysis for identification of phenotypic resistance and determined the association of RAVs with resistance. Machine-based learning methods were employed to define test characteristics of optimised sets of RAVs, and mmpR5 mutations were mapped to the protein structure to highlight mechanisms of resistance. Results Eighteen eligible studies were identified, comprising 975 M. tuberculosis isolates containing >=1 potential RAV (mutation in mmpR5, atpE, atpB or pepQ), with 201 (20.6%) demonstrating phenotypic bedaquiline resistance. 84/285 (29.5%) resistant isolates had no candidate gene mutation. Sensitivity and positive predictive value of taking an "any mutation" approach was 69% and 14% respectively. Thirteen mutations, all in mmpR5, had a significant association with a resistant MIC (adjusted p<0.05). Gradient-boosted machine classifier models for predicting intermediate/resistant and resistant phenotypes both had receiver operator characteristic c-statistics of 0.73. Frameshift mutations clustered in the alpha 1 helix DNA binding domain, and substitutions in the alpha 2 and 3 helix hinge region and in the alpha 4 helix binding domain. Discussion Sequencing candidate genes is insufficiently sensitive to diagnose clinical bedaquiline resistance, but where identified a limited number of mutations should be assumed to be associated with resistance. Genomic tools are most likely to be effective in combination with rapid phenotypic diagnostics. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by the Francis Crick Institute (CN) which receives its core funding from Cancer Research UK (CC2169), the UK Medical Research Council (CC2169), and the Wellcome Trust (CC2169), the Academy of Medical Sciences (CN, SGL027\1072) and NIH/NIAID through grants R01AI124413 (ML, MO), R01AI114900 (MO) and K23AI163364 (MJC). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data analysed in the manuscript are available online at referenced sources.