Diagnostic impact of DNA methylation classification in adult and pediatric CNS tumors

Abstract

Abstract Over the past decade, neuropathological diagnosis has undergone significant changes, integrating morphological features with molecular biomarkers. The molecular era has successfully refined neuropathological diagnostic accuracy; however, a substantial number of CNS tumor diagnoses remain challenging, particularly in children. DNA methylation classification has emerged as a powerful machine learning approach for clinical decision-making in CNS tumors. The aim of this study is to share our experience using DNA methylation classification in daily routine practice, illustrated through clinical cases. We employed a classification system to evaluate discrepancies between histo-molecular and DNA methylation diagnoses, with a specific focus on adult versus pediatric CNS tumors. In our study, we observed that 40% of cases fell into Class I, 47% into Class II, and 13% into Class III among the “matched cases” (≥ 0.84). In other words, DNA methylation classification confirmed morphological diagnoses in 63% of adult and 23% of pediatric cases. Refinement of diagnosis was particularly evident in the pediatric population (65% vs. 21% for the adult population, p = 0.006). Additionally, we discussed cases classified with low calibrated scores. In conclusion, our study confirms that DNA methylation classification provides significant added-value for CNS tumors diagnosis, particularly in pediatric cases.

Keywords

• DNA methylation
• Neuropathology
• CNS tumor
• Pediatric CNS tumors
• Next generation sequencing
• DKFZ classifier