Abstract 4347: Medulloblastoma comprises four distinct diseases
Notice bibliographique
Résumé
Abstract Prior attempts to subgroup medulloblastoma (MB) using genomics have identified 4-6 distinct molecular subtypes, including two subgroups driven by activated Wnt and Shh signaling. We performed an integrative analysis on a cohort of 103 primary MBs using a combination of Affymetrix expression and SNP genotyping arrays to determine how many subgroups of the disease exist, how they differ, and the extent of overlap between subgroups. Both unsupervised hierarchical clustering and principal component analysis of expression data reveals very high confidence for the existence of four medulloblastoma subgroups: WNT, SHH, Group C, and Group D. Additional bioinformatic analyses using non-negative matrix factorization (NMF) and subclass mapping (SubMap) further strengthens the support for the four subgroups. These subgroups exhibit distinct demographics: SHH tumors occur in infants and adults, Group C tumors are restricted to children, and WNT and Group D tumors are found across all age groups. While the sex ratio for the entire cohort is ∼1.5:1 (M:F), the sex ratio for WNT group tumors was ∼1:3. We identified a number of previously uncharacterized, subgroup-specific regions of chromosomal abnormality including 9p, 3q, 20q, and 21q gain in SHH tumors, 1q gain and 5q, 16q, and 10q loss in Group C tumors, and near universal isochromosome 17q and frequent loss of the X chromosome in Group D tumors. These regions likely harbor subgroup-specific oncogenes and tumor suppressor genes that could be targets for rationale therapy. Our demographic, transcriptional, and genetic data support the non-overlapping character of these four subgroups of MB. We identified ‘signature’ genes over-expressed in each subgroup for which there are high quality commercial antibodies available. Staining two separate MB tissue microarrays containing 294 non-overlapping tumors for DKK1 (WNT), SFRP1 (SHH), NPR3 (Group C), and KCNA1 (Group D) demonstrated that 288/294 (98%) tumors stained positive for only a single marker. Analysis of the demographics in these patients validated the results observed in our discovery series studied at the RNA level. Leptomeningeal dissemination was highly over-represented in Group C (47%) followed by Group D (30%) patients. A multivariate analysis that included age, extent of resection, histology, M stage, and subgroup revealed that only LCA histology and Group C affiliation were prognostic. As M0 Group C tumors have a very poor prognosis, we suggest that Group C patients include many of the children with ‘average-risk’ MB who relapse after current therapies. Our data highly support the existence of four independent subtypes of MB that differ in their demographics, transcription, genetic events, rate of metastases, and clinical outcome. Our novel ‘4 antibody’ technique is capable of determining MB subgroup through immunohistochemistry on formalin-fixed, paraffin-embedded material suggesting that it will be broadly generalizable across the globe. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4347.
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) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».