The role of whole genome sequencing in antimicrobial susceptibility prediction of bacteria: 2025 update from the European Committee on Antimicrobial Susceptibility Testing Subcommittee
25 affiliations
- 1Norwegian Centre for Detection of Antimicrobial Resistance, Department of Microbiology and Infection Control, University Hospital of North Norway, Tromsø, Norway; Department of Pharmacy, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway. Electronic address: [email protected]
- 2Servicio de Microbiología, Hospital Son Espases, IdISBa, Palma de Mallorca, Spain; CIBER de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain
- 3Research Group for Genomic Epidemiology, National Food Institute, Technical University of Denmark, Lyngby, Denmark
- 4Department of Bacteria, Parasites and Fungi, Statens Serum Institut, Copenhagen, Denmark
- 5Wageningen Bioveterinary Research, Part of Wageningen University and Research, Lelystad, The Netherlands; Institute for Risk Assessment Sciences (IRAS), Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
- 6CIBER de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain; Servicio de Microbiología, Hospital Universitario Ramón y Cajal and Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain
- 7Institute of Medical Microbiology, University of Zurich, Zurich, Switzerland
- 8Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA
- 9Institute of Medical Microbiology and Virology, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
- 10Department of Molecular Bacteriology, Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany; Institute of Molecular Bacteriology, TWINCORE Centre for Experimental and Clinical Infection Research, a Joint Venture of the Hannover Medical School (MHH) and the HZI, Hannover, Germany; Department of Clinical Microbiology, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
- 11Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, London, UK; Department of Infectious Diseases, School of Translational Medicine, Monash University, Melbourne, Victoria, Australia
- 12Antimicrobial Resistance and Healthcare Associated Infections (AMRHAI) Reference Unit. 2, AMR & HCAI Division, UK Health Security Agency, London, UK
- 13Laboratoire Chrono-environnement (UMR 6249), University Marie and Louis Pasteur, Laboratoire de bactériologie CHU Besançon, CNRS, Besançon, France
- 14EUCAST Development Laboratory, Central Hospital, Växjö, Sweden
- 15Department of Genetics, University of Cambridge, Cambridge, UK; Department of Biomedical and Clinical Sciences, Division of Infection and Inflammation, Linköping University, Linköping, Sweden
- 16Division of Infectious Diseases and Department of Pathology, School of Medicine, University of Virginia, Charlottesville, VA
- 17Team 'Resist', UMR1184, INSERM, Faculty of Medicine, Université Paris-Saclay, CEA, Le Kremlin-Bicêtre, France; Department of Bacteriology-Hygiene, Bicêtre Hospital, AP-HP Paris-Saclay, Le Kremlin-Bicêtre, France; French National Reference Center for Antibiotic Resistance: Carbapenemase-producing Enterobacterales, Le Kremlin-Bicêtre, France
- 18Laboratory of Clinical Microbiology, Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
- 19Université Paris Cité and Université Sorbonne Paris Nord, Inserm, IAME and AP-HP, Hôpital Bichat-Claude Bernard, Laboratoire de Bactériologie, Paris, France
- 20Department of Biomedical and Clinical Sciences, Division of Infection and Inflammation, Linköping University, Linköping, Sweden; Department of Infectious Diseases, Kalmar County Hospital, Linköping University, Kalmar, Sweden; Department of Infectious Diseases, Linköping University Hospital, Linköping, Sweden
- 21Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
- 22Division of Nosocomial Pathogens and Antibiotic Resistances, Department of Infectious Diseases, Robert Koch Institute, Wernigerode Branch, Germany; National Reference Centre for Staphylococci and Enterococci, Wernigerode Branch, 38855 Wernigerode, Germany
- 23M.G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
- 24Division of Clinical Microbiology, Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Microbiology, Karolinska University Hospital, Stockholm, Sweden
- 25Servicio de Microbiología, Hospital Son Espases, IdISBa, Palma de Mallorca, Spain; CIBER de Enfermedades Infecciosas (CIBERINFEC), Instituto de Salud Carlos III, Madrid, Spain. Electronic address: [email protected]
Abstract
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.
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BibTeX
@article{samuelsen2026role,
title = {{The role of whole genome sequencing in antimicrobial susceptibility prediction of bacteria: 2025 update from the European Committee on Antimicrobial Susceptibility Testing Subcommittee}},
author = {Ørjan Samuelsen and Carla López-Causapé and Frank M. Aarestrup and Valeria Bortolaia and Michael S. M. Brouwer and Rafael Cantón and Adrian Egli and Yonatan H. Grad and Axel G. Hamprecht and Susanne Häußler and Kathryn E. Holt and Katie L. Hopkins and Benjamin P. Howden and Katy Jeannot and Gunnar Kahlmeter and Claudio Umberto Köser and Amy J. Mathers and Thierry Naas and Spyros Pournaras and Étienne Ruppé and Thomas Schön and Nicole Stoesser and John D. Turnidge and Guido Werner and Gerard D. Wright and Christian G. Giske and Antonio Oliver},
journal = {Clinical Microbiology and Infection},
year = {2026},
month = may,
doi = {10.1016/j.cmi.2026.05.012},
url = {https://doi.org/10.1016/j.cmi.2026.05.012},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - The role of whole genome sequencing in antimicrobial susceptibility prediction of bacteria: 2025 update from the European Committee on Antimicrobial Susceptibility Testing Subcommittee
AU - Ørjan Samuelsen
AU - Carla López-Causapé
AU - Frank M. Aarestrup
AU - Valeria Bortolaia
AU - Michael S. M. Brouwer
AU - Rafael Cantón
AU - Adrian Egli
AU - Yonatan H. Grad
AU - Axel G. Hamprecht
AU - Susanne Häußler
AU - Kathryn E. Holt
AU - Katie L. Hopkins
AU - Benjamin P. Howden
AU - Katy Jeannot
AU - Gunnar Kahlmeter
AU - Claudio Umberto Köser
AU - Amy J. Mathers
AU - Thierry Naas
AU - Spyros Pournaras
AU - Étienne Ruppé
AU - Thomas Schön
AU - Nicole Stoesser
AU - John D. Turnidge
AU - Guido Werner
AU - Gerard D. Wright
AU - Christian G. Giske
AU - Antonio Oliver
PY - 2026
DA - 2026/05/16
JO - Clinical Microbiology and Infection
DO - 10.1016/j.cmi.2026.05.012
UR - https://doi.org/10.1016/j.cmi.2026.05.012
AB - 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.
SN - 1198-743X
SN - 1469-0691
ER -