Abstract 4371: Integrated molecular characterization of pheochromocytoma and paraganglioma including a novel, recurrent and prognostic fusion gene
Notice bibliographique
Résumé
Abstract Pheochromocytomas (PCC) and paragangliomas (PGL) are tumors of the autonomic nervous system; 25% are metastatic or locally aggressive. Characterization of the inherited basis of disease has identified a variety of underlying germline mutations; however, understanding of somatic alterations remains limited. As part of The Cancer Genome Atlas, we performed the most comprehensive genomic characterization of PCC/PGL to date, by applying eight genomic profiling assays to 173 patients. Despite having a low overall mutation rate per tumor, we observed remarkable diversity in genomic alterations. 27% of patients had a pathogenic germline mutation among eight known familial PCC/PGL susceptibility genes, thus making PCC/PGL the tumor type with the greatest rate of germline mutations in The Cancer Genome Atlas. 38% of patients possessed a somatic driver mutation across 12 genes. RET, NF1 and VHL were affected by both germline and somatic mutation, albeit with different mutation site tendencies. We identified a new somatic driver gene, CSDE1, which had coordinated intron splicing defects, DNA copy number loss, and RNA under-expression, suggesting a loss of function consequence. Most notably, we discovered the first fusion genes in PCC/PGL from RNA and DNA sequencing (7% of patients), demonstrating for the first time that inter-chromosomal translocation and gene fusion is a method of molecular pathogenesis in this disease. Recurrent, novel MAML3 fusion genes spanned three isoforms and were activating based on over-expression of MAML3 and on fusion transcript exonic expression. MAML3 fusion positive tumors had concomitant dual focal DNA amplification of the fusion gene partners and a significantly divergent methylation profile. Another novel driver gene in PCC/PGL, BRAF, was affected by a hotspot somatic mutation and by an activating fusion gene. Through integrated platform analysis, four statistically significant molecular subtypes of PCC/PGL were detected and found to represent divergent molecular etiology – the kinase signaling subtype, the pseudohypoxia subtype, the Wnt-altered subtype, and the cortical admixture subtype. In particular, MAML3 fusions and CSDE1 mutations defined the new Wnt-altered expression subtype of PCC. Adding to the limited set of prognostic markers in PCC/PGL, three molecular markers were positively associated with clinically aggressive disease: germline mutations in SDHB, somatic mutations in ATRX and fusions involving MAML3. Nearly all somatic driver mutations, germline driver mutations and fusion genes were mutually exclusive across the cohort and covered a large portion of the cohort (69%). Our study provides important novel insights into PCC/PGL biology and identifies potential markers for aggressive disease and therapeutic intervention. Citation Format: Lauren Fishbein, Ignaty Leshchiner, Vonn Walter, Ludmila Danilova, A Gordon Robertson, Amy Johnson, Tara Lichtenberg, Bradley A. Murray, Hanse K. Ghayee, Tobias Else, Shiyun Ling, Stuart R. Jefferys, Aguirre A. de Cubas, Brandon Wenz, Esther Korpershoek, Antonio L. Amelio, Liza Makowski, W Kimryn Rathmell, Anne-Paule Gimenez-Roqueplo, Thomas J. Giordano, Sylvia L. Asa, Arthur S. Tischler, The Cancer Genome Atlas Pheochromocytoma and Paraganglioma Analysis Working Group, Karel Pacak, Katherine L. Nathanson, Matthew D. Wilkerson. Integrated molecular characterization of pheochromocytoma and paraganglioma including a novel, recurrent and prognostic fusion gene. [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 4371.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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 | 0,000 | 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) | 0,000 | 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 tête enseignante, 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 ».