A standardised method for interpreting the association between mutations and phenotypic drug resistance inMycobacterium tuberculosis

Abstract

A clear understanding of the genetic basis of antibiotic resistance in Mycobacterium tuberculosis is required to accelerate the development of rapid drug susceptibility testing methods based on genetic sequence.Raw genotype–phenotype correlation data were extracted as part of a comprehensive systematic review to develop a standardised analytical approach for interpreting resistance associated mutations for rifampicin, isoniazid, ofloxacin/levofloxacin, moxifloxacin, amikacin, kanamycin, capreomycin, streptomycin, ethionamide/prothionamide and pyrazinamide. Mutation frequencies in resistant and susceptible isolates were calculated, together with novel statistical measures to classify mutations as high, moderate, minimal or indeterminate confidence for predicting resistance.We identified 286 confidence-graded mutations associated with resistance. Compared to phenotypic methods, sensitivity (95% CI) for rifampicin was 90.3% (89.6–90.9%), while for isoniazid it was 78.2% (77.4–79.0%) and their specificities were 96.3% (95.7–96.8%) and 94.4% (93.1–95.5%), respectively. For second-line drugs, sensitivity varied from 67.4% (64.1–70.6%) for capreomycin to 88.2% (85.1–90.9%) for moxifloxacin, with specificity ranging from 90.0% (87.1–92.5%) for moxifloxacin to 99.5% (99.0–99.8%) for amikacin.This study provides a standardised and comprehensive approach for the interpretation of mutations as predictors of M. tuberculosis drug-resistant phenotypes. These data have implications for the clinical interpretation of molecular diagnostics and next-generation sequencing as well as efficient individualised therapy for patients with drug-resistant tuberculosis.

Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: An inter-laboratory study

“…In addition to this, although the identification of the presence of genes is performed in a systematic way, the prediction of resistance is still performed in an ad hoc manner by scientists and therefore subject to user error given the same set of genes. Once again M. tuberculosis is providing the first example of the need for a defined decision tree when working from the presence of genes to the prediction of phenotypic drug resistance [41]. Interpretation and reporting of this genotypic data will need to be subjected to the same level of scrutiny as current tests if it is to form part of an accredited laboratory service within the healthcare service.…”

Drug resistance mechanisms and drug susceptibility testing for tuberculosis

“…It is clear that an internationally recognized database for standardized genotype–phenotype correlation interpretation for clinicians and microbiologists is needed. At this end, a global public–private partnership has been established to develop a Relational Sequencing TB Data Platform (ReSeqTB) to simplify and standardize both WGS data analysis and interpretation of antimicrobial drug susceptibility . This represents the first attempt to have an international repository for both WGS data for MTB and evidence‐based standard interpretation of mutations for drug resistance prediction …”

“…172,173 This represents the first attempt to have an international repository for both WGS data for MTB and evidence-based standard interpretation of mutations for drug resistance prediction. 173…”

Perspectives for personalized therapy for patients with multidrug‐resistant tuberculosis

“… Where available, MIC ranges corresponding to selected commonly detected high‐confidence resistance mutations for fluoroquinolones (FQs), second line injectable drugs (SLIDs), rifamycins and isoniazid (INH) are listed. The suggested clinical interpretation and genotype‐phenotype correlations from the limited data at hand are based on the SIR‐system with the following definitions: S = Isolate with a high likelihood for therapeutic success at standard dosing.…”

“…High confidence resistance conferring mutations are based on published likelihood ratios, and supplemented with other well-investigated mutations with sufficient evidence to be associated with resistant phenotypes. Table modified from Miotto et al [55]. Drugs are classified according to recent WHO guidelines [10].…”