Abstract 3089: (Epi)genetic profiling enables molecular re-classification of CNS-primitive neuroectodermal tumors
Notice bibliographique
Résumé
Abstract According to the current WHO classification of CNS tumors, childhood CNS primitive neuro-ectodermal tumors (CNS-PNETs; WHO °IV) are poorly differentiated embryonal tumors with early onset and aggressive clinical behavior. Histological diagnosis can be complicated by morphological heterogeneity and divergent differentiation. Recent studies suggest the existence of molecular subgroups of CNS-PNETs sharing biological characteristics with other childhood CNS tumors. Here, we aimed at a comprehensive molecular characterization of CNS-PNETs and compared our results to profiles of other brain tumor classes in order to define the biological nature of tumors diagnosed as CNS-PNETs. A collective of 197 fresh-frozen or paraffin-embedded tumor samples with an institutional diagnosis “CNS-PNET” was profiled for genome-wide DNA methylation patterns and copy-number alterations, complemented by transcriptomic profiling of a subset (n=63). (Epi-)genetic profiles of CNS-PNETs were compared to those of >1.000 other childhood brain tumors including embryonal, astrocytic, and ependymal entities, and their respective molecular subgroups. We screened selected groups of tumors for recurrent mutations and expression of established molecular markers. Five experienced neuropathologists independently revisited the histology of 48 CNS-PNETs. Bioinformatic analysis of DNA methylation and gene expression profiles using clustering methods and class prediction algorithms resulted in a clear segregation of pediatric brain tumors by histological entities and molecular subgroups. Exceptionally, profiles of tumors classified as “CNS-PNET” suggested significant overlap with various well-defined entities, including AT/RT, ETMR, high-grade glioma, medulloblastoma, and ependymoma. When screening CNS-PNETs with DNA methylation profiles highly resembling other entities, hallmark genetic alterations of these, such as amplification of 19q13.42, mutations in IDH1 or H3F3A, or mutations/deletions of the SMARCB1 locus, were frequently detected. Molecularly distinct tumor subsets were associated with differential protein expression patterns of previously established subgroup markers INI-1, LIN28A, and OLIG2. Blinded histopathological evaluation resulted in a large proportion of CNS-PNETs being re-classified in line with affiliations suggested by molecular diagnostic tools. The correct classification of CNS-PNET remains challenging. Based on the detection of recurrent genetic aberrations, many cases can be reliably re-classified, indicating that a significant proportion of CNS-PNETs may comprise a variety of other tumor subtypes. These findings suggest that the use of established and novel subgroups markers is needed in order to assist the histopathological evaluation of these tumors. The molecular biology underlying distinct subsets of CNS-PNETs only remains to be elucidated. Citation Format: Dominik Sturm, Paul A. Northcott, David T.W. Jones, Andrey Korshunov, Daniel Picard, Peter Lichter, Annie Huang, Stefan M. Pfister, Marcel Kool. (Epi)genetic profiling enables molecular re-classification of CNS-primitive neuroectodermal tumors. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3089. doi:10.1158/1538-7445.AM2014-3089
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».