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

What is it about?

Although sequencing and molecular methods for characterizing Mycobacterium tuberculosis complex (MTBC) have become increasingly accurate, reproducible and cost-effective, defining a clear-cut “one-size-fits-all” criterion for the definition of resistance associated mutations has been challenging. Several attempts have been made in the past to compile databases for the determination of such correlations. However, many of these databases are not actively curated, and most lack significant clinical metadata. In addition, they focus mainly on collecting published data in terms of frequency of mutations in susceptible and resistant isolates, leaving the final interpretation concerning the genotype-phenotype correlation to the user. At present, no standardized, internationally recognized rules exist for the unambiguous clinical interpretation of genetic changes predicting phenotypic resistance to anti-tuberculosis drugs. A PubMed search was performed to identify publications up to Dec 30, 2015 that studied the genetic basis of phenotypic resistance of MTBC to the following antibiotics: rifampicin, isoniazid, moxifloxacin, ofloxacin, levofloxacin, amikacin, kanamycin, capreomycin, streptomycin, ethionamide/prothionamide, and pyrazinamide. Data from 1748 papers were collected. A standardized analytical approach for interpreting drug resistance-associated mutations was developed by consensus and applied to the comprehensive systematic review of MTBC drug resistance. The accuracy of any given mutation to predict phenotypic resistance was calculated. The results showed that the use of graded mutations can solve ambiguous interpretation of genotypic tests for some drugs that yield different results on phenotypic testing in liquid or solid media (e.g. the ‘disputed’ rpoB mutations). In addition, the use of graded mutations as markers of resistance leads to an increase in specificity, with a relatively small loss in terms of sensitivity when compared to the use of all mutations in resistance genes.

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Why is it important?

The scoring system developed in this study improves the clinical interpretation of resistance associated mutations in MTBC. This approach will be utilized for the ReSeqTB data sharing platform, a prospective, curated database for MTBC genomic sequencing data.