Abstract SY25-02: Recent advances in the genomics and genetics of the medulloblastomas: The impact of intertumoral and intratumoral heterogeneity
Notice bibliographique
Résumé
Abstract ‘Medulloblastoma’ is the most common malignant pediatric brain tumor, and a major source of morbidity and mortality in the pediatric oncology population. Current treatment regimens that include maximal safe surgery, craniospinal radiation, and high dose chemotherapy result in five-year progression free survivals in excess of 70%. Unfortunately, survivors are often left with considerable physical, neurological, and neuropsychological deficits secondary to both the disease and its therapy. Recent publications have suggested that the histological entity is in fact comprised of at least four distinct variants, each with their own separate epidemiology, transcriptomics, somatic genetic events, and clinical outcomes. Methods for subgroup assignment in the setting of clinical trials are under development around the world. Similarly, laboratories around the globe are working on developing more accurate mouse models of medulloblastoma that correspond to the various subgroups of the disease. To further highlight the extent of intertumoral heterogeneity, several of these ‘subgroups’ of medulloblastoma can likely be further subdivided into clinically and genetically distinct ‘subtypes’, the diagnoses of which have critical clinical implications. We will review the results of recent large-scale efforts to study the genetics and genomics of the various medulloblastoma subgroups and their subtypes. Intratumoral heterogeneity through space and time also likely pose a significant challenge to understanding, and eventually curing medulloblastoma. Recent evidence from mouse models and human medulloblastoma specimens demonstrates that primary medulloblastoma has very different somatic genetic events and transcriptomics as compared to leptomeningeal metastases from the same mouse/child. This supports a bicompartmental model of medulloblastoma, and suggests that in some circumstances the primary tumor and its metastases will have to be targeted separately for therapy. Similarly, it has been assumed that recurrent medulloblastoma is highly similar to its untreated forerunner. We now present evidence demonstrating that recurrent primary is highly divergent from the untreated primary tumor, and that targets for rationale therapy discovered in the untreated tumor may not be present in the recurrence. This underlines a potential fatal flaw in current method of novel drug development and therapy in which targets are identified from untreated primary tumor, and then taken to clinical trial to be tested on children with highly treated recurrent disease. The discovery, quantification, and classification of intertumoral and intratumoral heterogeneity among the medulloblastomas will allow us to design better experiments, and to select more homogeneous populations for inclusion in clinical trials. The extent of inter/intratumoral heterogeneity also highlights the need for large cohorts of tumors and children that will require effective international collaborations. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr SY25-02. doi:1538-7445.AM2012-SY25-02
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 | 0,002 | 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 | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».