The Role of Whole Genome Sequencing in Antimicrobial Susceptibility Prediction of Bacteria: 2025 Update from the European Committee on Antimicrobial Susceptibility Testing Subcommittee.

SCOPE:The 2017 European Committee on Antimicrobial Susceptibility Testing (EUCAST) subcommittee report on the role of whole genome sequencing (WGS) in antimicrobial susceptibility testing (AST) concluded that WGS antimicrobial susceptibility prediction (WGS-ASP) was not a sufficiently robust alternative to AST to guide clinical decision making at that stage and that more evidence was required [1]. Since then, the use of WGS, bioinformatic tools, machine learning (ML)/artificial intelligence (AI), databases, and prediction approaches has greatly expanded, along with an increased knowledge of resistance mechanisms and their contribution to antimicrobial susceptibility. In response, a new EUCAST ad hoc subcommittee was established in 2024 to review the literature, with the aim of assessing the current potential and limitations of WGS-ASP. METHODS:As in the previous report, the subcommittee reviewed the literature on a 'by organism' basis but expanded the list to also include enterococci, Haemophilus influenzae, and Bacteroides fragilis in addition to those already included in the first version: Enterobacterales, Pseudomonas aeruginosa, Acinetobacter baumannii, Neisseria gonorrhoeae, Staphylococcus aureus, Streptococcus pneumoniae, Clostridioides difficile, and Mycobacterium tuberculosis. Additional sections were included to cover advances in metagenomics, other omics technologies and ML/AI. The full report was compiled and reviewed by all subcommittee members before public consultation in November 2025. CONCLUSIONS AND RECOMMENDATIONS:Significant progress has been achieved in WGS-ASP, with growing evidence supporting its ability to distinguish wild-type from non-wild-type isolates and, consequently, susceptible from resistant strains, particularly for M. tuberculosis and when clinical breakpoints align with the epidemiological cut-off (ECOFF). Despite these advances, important challenges remain before WGS-ASP can be adopted as a clinical decision-making tool. Addressing these gaps will require integrated phenotypic and genotypic surveillance to strengthen the evidence base for complex resistance mechanisms and newer antimicrobial agents, alongside comparative assessments that consider both ECOFF and clinical breakpoints. The analyses will require reference method phenotypic AST and high-quality genomic data. It is critical to ensure that datasets reflect the target populations and encompass the full spectrum of antimicrobial susceptibility, while developing unified interpretation frameworks and harmonized bioinformatics tools to standardize outputs. Robust external quality assessment schemes will be essential for clinical validation, and emerging technologies such as AI and ML offer promising avenues to enhance predictive accuracy. Finally, improvements in cost and turnaround time, coupled with evaluations of setting-specific cost-effectiveness, will be key to enabling practical implementation of WGS-ASP.