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Accès ouvert déclaré 2026 article

Interlaboratory performance testing on EPIC v2.0 CNS tumor profiling demonstrates high reproducibility of tumor classification but reveals the need for harmonized copy number variation reporting

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52Institutions déclarées
11Pays d’affiliation déclarés

Résumé fourni par la source

BeadChip array-based DNA methylation profiling has been recognized by the World Health Organization (WHO) as a key diagnostic tool for brain tumor classification. While its diagnostic utility has been well established, data on technical reproducibility, interlaboratory comparability, and data interpretation under real-world diagnostic conditions remain limited. Bridging this gap, we here report the results of an international proficiency test using the Infinium MethylationEPIC v2.0 platform and the corresponding Brain Tumor Classifier version 12.8. Tissue slides of eight FFPE brain tumor samples, covering a representative range of CNS tumor entities, were distributed among 24 laboratories in 10 different countries. Participants were asked to report methylation classes and copy number variation (CNV) profiles. Pre-array workflows were left to local procedures and results had to be submitted within 15 working days. Technical data reproducibility was high with a median pairwise beta-value correlation of 0.99 (range 0.93-1.0). In general, participating centers generated high-quality data, reflected by consistently low detection p-values (<0.01). Eighteen of the 24 participating centers (75%) successfully passed the test. Of the six centers that failed the test, two laboratories experienced technical issues that led to misclassification of individual cases and contributed to incorrect CNV reporting. Four additional centers showed substantial discrepancies in the interpretation of diagnostically highly relevant CNVs, whereas methylation classification was not impaired. While accurate DNA quantification proved to be an important pre-array step, the use of the DNA restoration kit had only minor influence on overall results. Taken together, our interlaboratory performance testing on EPIC v2.0 CNS tumor profiling confirms high reproducibility of tumor classification but reveals the need for harmonized CNV reporting.

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Contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé, mais le titre doit être comparé manuellement.

Titre Crossref
Interlaboratory performance testing on <scp>EPIC</scp> v2.0 <scp>CNS</scp> tumor profiling demonstrates high reproducibility of tumor classification but reveals the need for harmonized copy number variation reporting
Date Crossref
15/07/2026
Éditeur
Wiley
Type
journal-article

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude et ne compte pas comme une seconde source scientifique indépendante.

Institutions déclarées

University Hospital RegensburgHumboldt-Universität zu BerlinGerman Agency for Quality in MedicineUniversität HamburgUniversity Medical Center Hamburg-EppendorfGiessen School of TheologyLeipzig UniversityCentre Hospitalier Universitaire de GrenobleCentre National de la Recherche ScientifiqueInsermCentre Hospitalier Universitaire de NiceLaboratoire de PhysioMédecine MoléculaireInstitut de Recherche sur le Cancer et le Vieillissement de NiceTechnical University of MunichUniversity of RegensburgUniversity of AugsburgUniversity Hospital AugsburgUniversity of FreiburgJohannes Gutenberg University MainzUniversity Medical Center of the Johannes Gutenberg University MainzLaboratoire National de SantéUniversity Hospital BonnLife & Brain (Germany)German Cancer Research CenterHeidelberg UniversityLMU KlinikumGerman Centre for Cardiovascular ResearchLudwig-Maximilians-Universität MünchenChongqing Emergency Medical CenterChongqing Medical UniversityParacelsus Medical UniversityRoyal Prince Alfred HospitalTechnische Hochschule AugsburgCentre Léon BérardRigshospitaletUniversity of LuxembourgArbed (Luxembourg)Oslo University HospitalUniversity Hospital ZurichSt Olav's University HospitalNorwegian University of Science and TechnologyCenter for Human GeneticsKU LeuvenUniversity of BernGoethe University FrankfurtUniversity Hospital FrankfurtFrankfurt Cancer InstituteMedical University of ViennaUniversity of AntwerpAntwerp University HospitalUniversity Hospital HeidelbergGerman Cancer Society

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

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