Histone H3K27M Mutation Overrides Histological Grading in Pediatric Gliomas

Abstract Pediatric high-grade gliomas (HGG) are rare aggressive tumors that present a prognostic and therapeutic challenge. Diffuse midline glioma, H3K27M–mutant is a new entity introduced to HGG in the latest WHO classification. In this study we evaluated the presence of H3K27M mutation in 105 tumo...

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Autores principales: Amal Mosaab, Moatasem El-Ayadi, Eman N. Khorshed, Nada Amer, Amal Refaat, Mohamed El-Beltagy, Zeinab Hassan, Sameh H. Soror, Mohamed Saad Zaghloul, Shahenda El-Naggar
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2020
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Acceso en línea:https://doaj.org/article/8a04d8c9e4a441e288eb794de5590171
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Sumario:Abstract Pediatric high-grade gliomas (HGG) are rare aggressive tumors that present a prognostic and therapeutic challenge. Diffuse midline glioma, H3K27M–mutant is a new entity introduced to HGG in the latest WHO classification. In this study we evaluated the presence of H3K27M mutation in 105 tumor samples histologically classified into low-grade gliomas (LGG) (n = 45), and HGG (n = 60). Samples were screened for the mutation in histone H3.3 and H3.1 variants to examine its prevalence, prognostic impact, and assess its potential clinical value in limited resource settings. H3K27M mutation was detected in 28 of 105 (26.7%) samples, and its distribution was significantly associated with midline locations (p-value < 0.0001) and HGG (p-value = 0.003). Overall and event- free survival (OS and EFS, respectively) of patients with mutant tumors did not differ significantly, neither according to histologic grade (OS p-value = 0.736, EFS p-value = 0.75) nor across anatomical sites (OS p-value = 0.068, EFS p-value = 0.153). Detection of H3K27M mutation in pediatric gliomas provides more precise risk stratification compared to traditional histopathological techniques. Hence, mutation detection should be pursued in all pediatric gliomas. Meanwhile, focusing on midline LGG can be an alternative in lower-middle-income countries to maximally optimize patients’ treatment options.