Diagnostic impact of DNA methylation classification in adult and pediatric CNS tumors
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 ch...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-025-87079-4 |
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| author | Laetitia Lebrun Nathalie Gilis Manon Dausort Chloé Gillard Stefan Rusu Karim Slimani Olivier De Witte Fabienne Escande Florence Lefranc Nicky D’Haene Claude Alain Maurage Isabelle Salmon |
| author_facet | Laetitia Lebrun Nathalie Gilis Manon Dausort Chloé Gillard Stefan Rusu Karim Slimani Olivier De Witte Fabienne Escande Florence Lefranc Nicky D’Haene Claude Alain Maurage Isabelle Salmon |
| author_sort | Laetitia Lebrun |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4def4ad9aa7549f598bb82420abc6083 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4def4ad9aa7549f598bb82420abc60832025-01-26T12:27:15ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-025-87079-4Diagnostic impact of DNA methylation classification in adult and pediatric CNS tumorsLaetitia Lebrun0Nathalie Gilis1Manon Dausort2Chloé Gillard3Stefan Rusu4Karim Slimani5Olivier De Witte6Fabienne Escande7Florence Lefranc8Nicky D’Haene9Claude Alain Maurage10Isabelle Salmon11Department of Pathology, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), CUB Hôpital Erasme, Erasme University HospitalDepartment of Neurosurgery, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), CUB Hôpital Erasme, Erasme University HospitalInstitute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvainDIAPath, Center for Microscopy and Molecular Imaging (CMMI), Université Libre de Bruxelles (ULB)Department of Pathology, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), CUB Hôpital Erasme, Erasme University HospitalDepartment of Pathology, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), CUB Hôpital Erasme, Erasme University HospitalDepartment of Neurosurgery, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), CUB Hôpital Erasme, Erasme University HospitalService de Biochimie et Biologie Moléculaire, Pole Pathologie Biologie, CHU LilleDepartment of Neurosurgery, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), CUB Hôpital Erasme, Erasme University HospitalDepartment of Pathology, Université Libre de Bruxelles (ULB), Hôpital Universitaire de Bruxelles (HUB), CUB Hôpital Erasme, Erasme University HospitalUFR3S - Laboratoire d’Histologie, Univ. LilleDIAPath, Center for Microscopy and Molecular Imaging (CMMI), Université Libre de Bruxelles (ULB)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.https://doi.org/10.1038/s41598-025-87079-4DNA methylationNeuropathologyCNS tumorPediatric CNS tumorsNext generation sequencingDKFZ classifier |
| spellingShingle | Laetitia Lebrun Nathalie Gilis Manon Dausort Chloé Gillard Stefan Rusu Karim Slimani Olivier De Witte Fabienne Escande Florence Lefranc Nicky D’Haene Claude Alain Maurage Isabelle Salmon Diagnostic impact of DNA methylation classification in adult and pediatric CNS tumors Scientific Reports DNA methylation Neuropathology CNS tumor Pediatric CNS tumors Next generation sequencing DKFZ classifier |
| title | Diagnostic impact of DNA methylation classification in adult and pediatric CNS tumors |
| title_full | Diagnostic impact of DNA methylation classification in adult and pediatric CNS tumors |
| title_fullStr | Diagnostic impact of DNA methylation classification in adult and pediatric CNS tumors |
| title_full_unstemmed | Diagnostic impact of DNA methylation classification in adult and pediatric CNS tumors |
| title_short | Diagnostic impact of DNA methylation classification in adult and pediatric CNS tumors |
| title_sort | diagnostic impact of dna methylation classification in adult and pediatric cns tumors |
| topic | DNA methylation Neuropathology CNS tumor Pediatric CNS tumors Next generation sequencing DKFZ classifier |
| url | https://doi.org/10.1038/s41598-025-87079-4 |
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