Abstract 2789: Investigating the role of DNA methylation in pediatric choroid plexus tumors
Notice bibliographique
Résumé
Abstract Choroid plexus tumors (CPTs) are rare neoplasms of the central nervous system most commonly found in the pediatric population. CPTs represent 1- 4% of all childhood brain tumors, with 10- 20% occurring during the first year of life. Within this family of tumors, choroid plexus carcinoma (CPC) is the malignant neoplasm which is categorized as a grade III tumor by the WHO. Choroid plexus papilloma (CPP) is a benign form classified as a grade I tumor, and atypical choroid plexus papilloma (aCPP) as a grade II tumor. Distinction between these tumor subtypes is essential for treatment stratification. Previous studies performed in our laboratory suggest that CPTs are highly unstable and harbor unique patterns of chromosome-wide gains and losses. To better understand the complexities of tumor biology of CPTs as well as to identify better molecular biomarkers to distinguish between aggressive and benign forms of CPTs we performed a genome-wide DNA methylation study using Illumina Human Methylation450 BeadChip. We analyzed genome-wide DNA methylation profiles from 34 CPT (14 CPCs, 5 aCPPs and 15 CPPs) samples. Differential DNA methylation analysis did not identify significant differences between aCPPs and CPPs, therefore we explored CPC-specific DNA methylation signature in comparison to CPPs. Using a median beta value difference of 0.3 or greater and an FDR adjusted p-value<0.05, we identified 3361 CpGs that showed significant difference in methylation between CPCs and CPPs or aCPPs. Two-way clustering performed using Pearson's correlation and average linkage for both the sample tree and the gene tree revealed segregation between the majority of CPCs and CPPs or aCPPs. Two main clusters were discovered within CPCs that were due to differences in TP53 mutation status. Pathway analysis on a 1328 gene set overlapping the 3361 CpGs using the IPA software revealed nine canonical pathways with GABA receptor on top of the list and several biofunction categories associated with cellular growth and proliferation that were significantly enriched in CPCs in comparison with CPPs or aCPPs. To identify minimal CPC specific signature, we applied a difference in DNA methylation of 40% and a p-value of 0.001. This increased statistical stringency led to the identification of 59 CpG sites encompassing 33 candidate genes. Of them, 3 genes were validated by pyrosequencing in the initial CPT cohort (n = 34) and a new replication cohort of n = 23 CPT samples. Next, we tested the sensitivity of the CPC specific DNA methylation signature against DNA methylation profiles of several brain tumor datasets extracted from GEO database and found that CPC-DNA methylation signature was highly specific. Our data suggest that dysregulation of epigenetic mechanisms contribute to the molecular events leading to tumor development and progression in CPC and that our DNA methylation based biomarker signature can have prognostic value for this disease and enable better treatment strategies. Citation Format: Malgorzata Pienkowska, Sanaa Choufani, Andrei Turinsky, Diana Merino, Ana Novokmet, Michael Brudno, Rosanna Weksberg, Adam Shlien, Cynthia Hawkins, Eric Bouffet, Uri Tabori, Richard Gilbertson, David Malkin. Investigating the role of DNA methylation in pediatric choroid plexus tumors. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2789.
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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 ».