ArticleBrain pathology (Zurich, Switzerland)2025
Validation of target-enriched enzymatic methylation sequencing for brain tumor classification from formalin-fixed paraffin embedded-derived DNA.
Article in Brain pathology (Zurich, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- DNA methylation as a biomarker of prenatal exposures: current challenges and opportunities.Environmental epigenetics · 2026Review
- Validation of target-enriched enzymatic methylation sequencing for brain tumor classification from formalin-fixed paraffin embedded-derived DNA.Brain pathology (Zurich, Switzerland) · 2025Article
- Robust molecular subgrouping and reference-free aneuploidy detection in medulloblastoma using low-depth whole genome bisulfite sequencing.Acta neuropathologica communications · 2025Article
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Authors and funding
7 authors.
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Abstract
DNA methylation profiling by Illumina methylation array-based methods has revolutionized the molecular classification and diagnosis of brain tumors. A significant barrier to adopting these methods in a clinical environment is the requirement for specialized scanners, which results in high additional costs and a larger laboratory footprint. DNA sequencing-based alternatives are attractive because most clinical molecular pathology laboratories already use sequencers for other molecular assays. This study aimed to compare the utility of the newly developed sequencing-based enzymatic methyl sequencing (EM-seq) method paired with the Twist Human Methylome panel for brain tumor classification with standard Infinium Methylation BeadChip-based methods. We used DNA from fresh-frozen or formalin-fixed, paraffin-embedded (FFPE) brain cancer samples from 19 patients and 1 control sample to construct DNA libraries covering 3.98 million CpG sites. We developed and validated a bioinformatics pipeline to analyze target-enriched EM-seq (TEEM-seq) data in comparison with standard array-based methods for tumor classification and copy number profiling. We found high concordance between TEEM-seq and traditional methods, with high correlation coefficients (>0.98) between FFPE replicates. We successfully classified tumor samples into the expected molecular classes with robust prediction scores (>0.82). We observed that FFPE samples required a sequencing depth of at least 35x to achieve consistently high and reliable prediction scores. The TEEM-seq method has the potential to complement existing tumor classification methods and lower the barriers for the adoption of methylation profiling in routine clinical use.
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Registered trials
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