Methylation profiling in CNS tumor diagnostics: a single-centre real-world experience from Central Europe
In brief
DNA methylation profiling gives definitive diagnosis in 70% of CNS tumors
In a prospective series of 291 brain tumors, high-confidence methylation matches were achieved in 70% of cases, and the test altered tumor grade in nearly 40% of lesions that were histologically ambiguous. Over half of lower-confidence results were still clinically useful when combined with pathology, while only 2% were misleading, underscoring the need for integrated interpretation.
- Journal
- Virchows Archiv : an international journal of pathology (Q1)
- Published
- 8 September 2026
- Study design
- Prospective / inception cohort
- Evidence level
- Level 2, Moderate (CEBM 2b)
- Authors
- Marián Švajdler, Tomáš Vaněček, Petr Martínek, Veronika Hájková, Jiří Soukup, Tomáš Jirásek, et al.
- PMID
- 42709194
- DOI
- 10.1007/s00428-026-04712-x
Why clinicians should know about it
- Picked for Pathology and Forensic Medicine (paper of the day, 9 September 2026): Methylation profiling improves CNS tumor diagnosis
Abstract
Genome-wide DNA methylation profiling has transformed neuro-oncology by providing an objective, machine learning-based taxonomy that mitigates interobserver variability and refines the histo-molecular criteria of the current WHO classification. We evaluate the real-world diagnostic performance and clinical utility of this modality in a prospective, consecutively accrued three-year cohort of 291 central nervous system (CNS) tumors across a mixed adult-pediatric population. Successful profiling was completed in 95.9% of cases. Using the Epignostix classifier, a high-confidence diagnostic match (calibrated score [CS] ≥ 0.84) was achieved in 70.3% of analyzable samples, while 26.5% returned lower-confidence scores (≥ 0.3 to < 0.84) and only 3.2% remained completely unclassifiable (CS < 0.3). When integrated into a comprehensive diagnostic framework, methylation profiling provided clinically useful results in 81.1% of cases, establishing diagnoses in 70 cases submitted for molecular subclassification and resolving diagnostic uncertainty or prompting major revisions in 149 histologically challenging tumors. Within truly ambiguous lesions, integration of methylome data dictated tumor grade modifications in 38.8% of cases (upgrading in 29.4% and downgrading in 9.4%), shifting patient risk stratification. Crucially, over half (52.7%) of the lower-confidence cases yielded meaningful clinical integration when supported by histomorphology and ancillary genetic or immunohistochemical markers, demonstrating that rigid score cutoffs should not dictate assay failure. Discrepant or misleading classifications occurred in 1.9%. Updating bioinformatic pipelines from version 11b4 to 12.8 rescued multiple ambiguous entries, increasing overall clinical utility to 84.1%. These findings demonstrate that integrating computational epigenomics with classical neuropathology enhances diagnostic precision, while highlighting the ongoing need for careful clinical-pathological correlation.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.