Genomic Prediction of Antimicrobial Resistance Moves Toward Clinical Laboratory Adoption
This Clinical Chemistry review describes how next-generation sequencing is transitioning from research tool to clinical resistance predictor, using M. tuberculosis, S. aureus, and N. gonorrhoeae as exemplars. While genomic approaches have already added value in specific clinical scenarios, further advances in turnaround time, cost reduction, and bioinformatics are needed for routine diagnostic adoption.
The original study
Genomic Prediction of Antimicrobial Resistance: Ready or Not, Here It Comes!
- Authors
- Ransom EM, Potter RF, Dantas G, Burnham CD
- Journal
- Clinical chemistry
- Type
- Journal Article, Review
- PMID
- 32918462
Original abstract
BACKGROUND: Next-generation sequencing (NGS) technologies are being used to predict antimicrobial resistance. The field is evolving rapidly and transitioning out of the research setting into clinical use. Clinical laboratories are evaluating the accuracy and utility of genomic resistance prediction, including methods for NGS, downstream bioinformatic pipeline components, and the clinical settings in which this type of testing should be offered. CONTENT: We describe genomic sequencing as it pertains to predicting antimicrobial resistance in clinical isolates and samples. We elaborate on current methodologies and workflows to perform this testing and summarize the current state of genomic resistance prediction in clinical settings. To highlight this aspect, we include 3 medically relevant microorganism exemplars: Mycobacterium tuberculosis, Staphylococcus aureus, and Neisseria gonorrhoeae. Last, we discuss the future of genomic-based resistance detection in clinical microbiology laboratories. SUMMARY: Antimicrobial resistance prediction by genomic approaches is in its infancy for routine patient care. Genomic approaches have already added value to the current diagnostic testing landscape in specific circumstances and will play an increasingly important role in diagnostic microbiology. Future advancements will shorten turnaround time, reduce costs, and improve our analysis and interpretation of clinically actionable results.