12488 Exome Sequencing Unravels New Susceptibility Genes For Pheochromocytomas And Paragangliomas
Notice bibliographique
Résumé
Abstract Disclosure: G.F. Fagundes: None. F.F. Castro: None. L.S. Santana: None. A.F. Afonso: None. A.W. Maciel: None. F.L. Ledesma: None. C.A. Pereira: None. I.C. Soares: None. D.M. Lourenço Junior: None. M.A. Pereira: None. V. Srougi: None. F.Y. Tanno: None. J.L. Chambo: None. M.C. Fragoso: None. A.O. Hoff: None. B.B. Mendonca: None. A. Latronico: None. M.Q. Almeida: None. Background: Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors with a high association with hereditary disease, affecting 40% of the cases. PPGL susceptibility genes are categorized into three clusters based on their pathophysiological and metabolomic mechanisms. Somatic oncogenic variants are identified in 30% of the tumors. Most of the germline and somatic drivers are mutually exclusive in PPGLs. Thus, 30% of the PPGLs remain without known genetic causes. Aim: To investigate new susceptibility genes for PPGLs using whole exome sequencing. Methods: We included 145 index patients with PPGLs (93 female and 52 male) with 91 pheochromocytomas (PHEO) and 60 paragangliomas, of which 43 (29.65%) were metastatic. A target next-generation sequencing panel for susceptibility genes was initially performed. Whole exome sequencing was performed in 56 patients with PPGLs (38 were paired with tumor DNA). Results:SDHB was the most frequently affected gene in 29 of 145 patients (20%). Germline SDHB exon 1 deletion was identified in 14 patients (48.27%). Germline variants were identified as follow: RET 22 (15.17%), VHL 13 (8.96%), SDHA 6 (4.14%), SDHD 5 (3.45%), NF1 5 (3.45%), FH 2 (1.38%), MAX 2 (1.38%), DLST 1 (0.69%), TMEM127 1 (0.69%), SDHC 1 (0.69%) and H3F3A 1 (0.69%) case. Somatic variants were identified in 21 tumors: NF1 6 (4.14%), HRAS 5 (3.45%), VHL 2 (1.38%), FGFR1 2 (1.38%), and RET 1 (0.69%). Six variants (five germline and one somatic) were identified in 3 new candidate genes: 1) Three very rare germline CHEK2 likely pathogenic or pathogenic variants (c.475T>C/ p.Tyr159His; c.362G>A/ p.Cys121Tyr; c.319+2T>A) in one metastatic PHEO and two PGLs (one metastatic). The metastatic PGL did not harbor any somatic oncogenic variant, while exome sequencing of a metastatic lesion from the PHEO had a somatic likely oncogenic HRAS variant; 2) Two very rare germline BRCA2 pathogenic variants (c.3680_3681delTG/ p.Leu1227fs; c.7806-2A>C) in a 33-year-old man with non-metastatic PHEO and in a 25-year-old man with metastatic PGL. All germline variants have not been reported in the Brazilian genomic variant repository. Exome sequencing did not reveal any somatic oncogenic driver in both tumors; 3) The somatic GNAS likely oncogenic variant c.601C>T/ p.Arg201Cys in a pheochromocytoma. The prevalence of CHEK2 and BRCA2 pathogenic or likely pathogenic variants in our cohort were significantly higher compared with those in the gnomAD population database (p < 0.0001 and p = 0.0004, respectively). Co-occurrence of germline and somatic drivers were found in 3 cases: FH and FGFR1, DLST and NF1, and CHEK2 and HRAS. In summary, germline and somatic diagnosis were reached in 63.45% and 15.18% of the PPGLs in our cohort, respectively. Conclusion: Our findings support CHEK2, BRCA2 and GNAS as novel susceptibility genes for PPGLs. Support: Sao Paulo Research Foundation (FAPESP) grant 2019/15873-6. Presentation: 6/2/2024
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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 | 0,001 | 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) | 0,004 | 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 ».