Abstract 1184: Clonal evolution of diffuse intrinsic pontine glioma
Notice bibliographique
Résumé
Abstract Diffuse intrinsic pontine gliomas (DIPG) are devastating tumours arising in the pons of children. Despite collaborative efforts, patient prognosis remains dismal with a median survival of 10 months and a 2+ year survival at ~5%. Previous efforts have identified the genetic drivers of DIPG revealing recurrent K27M mutations in histone H3 HIST1H3B or variant H3F3A which have been shown to dysregulate global lysine K27 methylation patterns of the tumour. Additional genetic hits including those in P53 and ACVR1 have also been identified. Previously, we showed that these driver mutations were conserved across all sites of DIPG dissemination, while secondary genetic hits showed subclonal enrichment. Here, we aim to investigate how DIPGs evolve with respect to both genetic (SNVs, CNVs) and non-genetic (epigenetics and transcriptomic) factors. We hypothesized that the genetic evolution of the disease will interplay with both the tumour's epigenetic changes and RNA expression to better explain DIPG development. To date, we have collected a total of 43 samples (range 3-12) from both the primary and disseminated site of 7 DIPG samples with matched normal brain. All samples have been subjected to whole exome sequencing, whole transcriptome sequencing (RNAseq) and MethylationEPIC analysis. The mutation burden across the samples ranged from 1.8-4.7 SNVs/Mb, with the exclusion of 4 sample locations that had acquired a hypermutant phenotype (range: 7.3-36.4 SNVs/Mb). Histone H3 mutations were the most frequent, being detected across all sampling locations in 6 of the 7 patients (5 in H3F3A and 1 in HIST1H3B). P53 mutations or LOH were detected in 4 patients, all of which also harboured a H3F3A mutation. No ACVR1 mutations were detected in this dataset. The final patient harboured MYCN, MYC-PVT1, and ID2 amplifications consistent with the MYCN subtype of DIPG. Clonal evolution analysis revealed distinct tumour heterogeneity in 6/7 samples, with the MYCN driven tumour appearing homogenous throughout all disseminated sites. In the remaining 6 samples, an average of 5 clones (range 4-11) were identified. H3F3A, HIST1H3B and P53 mutations were universally observed in the truncal clone of tumours and maintained throughout all sampling sites. Events driving subclones included PDGFRA amplification, MET amplification, PTEN loss, PIK3R1 mutations and hypermutant driving POLE and POLH mutations. Our preliminary work here provides insight into the genetic evolution of DIPGs. This work suggests that DIPG are heterogeneous in their development, but maintain homogeneity of the key tumour driver events throughout dissemination. However, due to the strong therapeutic potential of subclonal events described here, it remains important that the tumour's genetic complexity is not underestimated. Future goals looks to integrate both epigenetic and transcriptomic data into the evolution of DIPG to provide a clear and concise roadmap of how these tumours develop. Citation Format: Scott T. Ryall, Robert Siddaway, Arun Ramani, Andrei Turinsky, Michael Brudno, Cynthia Hawkins. Clonal evolution of diffuse intrinsic pontine glioma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1184.
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 | 0,000 |
| 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) | 0,002 | 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 ».