Cross-Platform Concordance in DNA Methylation-Based Classification of Central Nervous System (CNS) Tumors

DNA methylation profiling enables precise classification of pediatric central nervous system (CNS) tumors. Oxford Nanopore Technologies (ONT) offers same-day, single-sample methylation readouts, but its concordance with Illumina EPIC arrays in routine diagnostic tasks remains incompletely defined. We profiled 23 pediatric tumors (18 CNS and 5 non-CNS) by EPIC arrays and ONT. Methylation profiles from both platforms were classified with crossNN (brain model or pan-cancer model); ONT data were additionally classified with Rapid-CNS² and Sturgeon. We compared (1) classifier agreement with integrated histology (without next-generation sequencing [NGS]) at family/class levels, (2) pass rate above platform-specific score cutoffs, (3) cross-platform concordance of copy number variation, and (4) MGMT promoter methylation status. In CNS cases, ONT and EPIC methylation profiles demonstrated a strong correlation, except for a single outlier (P2), which was excluded from further analysis. Comparative assessment of the 2 platforms showed that (1) molecular classification of CNS tumors using the crossNN classifier was consistent with histology (without NGS) at the family level in all cases, (2) copy number profiles indicated high concordance between platforms, and (3) MGMT promoter methylation status matched in 94% (16/17) of cases. Comparing ONT-specific analysis pipelines using ONT data, the Rapid-CNS² pipeline yielded the most reliable class-level assignments with 94% (16/17) concordance with the histopathological diagnosis, which marginally exceeded the crossNN and Sturgeon classifiers. In non-CNS tumors, the pan-cancer model produced low-confidence outputs with poor agreement with histology (without NGS) (only 1/5 concordant), indicating limited readiness for these entities. In conclusion, ONT enables same-day, clinically reliable family-level CNS tumor classification with high concordance to arrays, whereas EPIC retains a modest class-level edge. A key limitation of ONT is its reliance on fresh-frozen DNA and on classifiers originally built around array-derived CpG sites rather than on models developed natively from ONT data.