Stone, Thomas J;
Mankad, Kshitij;
Tan, AI Peng;
Jan, Wajanat;
Pickles, Jessica C;
Gogou, Maria;
Chalker, Jane;
... Jacques, Thomas S; + view all
(2023)
DNA methylation-based classification of glioneuronal tumours synergises with histology and radiology to refine accurate molecular stratification.
Neuropathology and Applied Neurobiology
, 49
(2)
, Article e12894. 10.1111/nan.12894.
Preview |
Text
Neuropathology Appl Neurobio 2023 Stone DNA.pdf.pdf - Published Version Download (2MB) | Preview |
Abstract
AIMS: Glioneuronal tumours (GNTs) are poorly distinguished by their histology and lack robust diagnostic indicators. Previously, we showed that common GNTs comprise two molecularly distinct groups, correlating poorly with histology. To refine diagnosis, we constructed a methylation-based model for GNT classification, subsequently evaluating standards for molecular stratification by methylation, histology and radiology. METHODS: We comprehensively analysed methylation, radiology and histology for 83 GNT samples: a training cohort of 49, previously classified into molecularly defined groups by genomic profiles, plus a validation cohort of 34. We identified histological and radiological correlates to molecular classification and constructed a methylation-based support vector machine (SVM) model for prediction. Subsequently, we contrasted methylation, radiological and histological classifications in validation GNTs. RESULTS: By methylation clustering, all training and 23/34 validation GNTs segregated into two groups, the remaining 11 clustering alongside control cortex. Histological review identified prominent astrocytic/oligodendrocyte-like components, dysplastic neurons, and a specific glioneuronal element as discriminators between groups. However, these were present in only a subset of tumours. Radiological review identified location, margin definition, enhancement, and T2 FLAIR-rim sign as discriminators. When validation GNTs were classified by SVM, 22/23 classified correctly, comparing favourably against histology and radiology which resolved 17/22 and 15/21 respectively where data were available for comparison. CONCLUSIONS: Diagnostic criteria inadequately reflect glioneuronal tumour biology, leaving a proportion unresolvable. In the largest cohort of molecularly defined glioneuronal tumours, we develop molecular, histological, and radiological approaches for biologically meaningful classification and demonstrate almost all cases are resolvable, emphasising the importance of an integrated diagnostic approach.
| Type: | Article |
|---|---|
| Title: | DNA methylation-based classification of glioneuronal tumours synergises with histology and radiology to refine accurate molecular stratification |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1111/nan.12894 |
| Publisher version: | https://doi.org/10.1111/nan.12894 |
| Language: | English |
| Additional information: | © 2023 The Authors. Neuropathology and Applied Neurobiology published by John Wiley & Sons Ltd on behalf of British Neuropathological Society. This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | dysembryoplastic neuroepithelial tumour, ganglioglioma, glioneuronal tumour, machine learning, molecular pathology |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Developmental Biology and Cancer Dept UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Genetics and Genomic Medicine Dept |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10166586 |
Archive Staff Only
 |
View Item |
