Genetic driver mutations define the expression signature and microenvironmental composition of high-grade gliomas.
Glia
; 65(12): 1914-1926, 2017 12.
Article
em En
| MEDLINE
| ID: mdl-28836293
High-grade gliomas (HGG), including glioblastomas, are characterized by invasive growth, resistance to therapy, and high inter- and intra-tumoral heterogeneity. The key histological hallmarks of glioblastoma are pseudopalisading necrosis and microvascular proliferation, which allow pathologists to distinguish glioblastoma from lower-grade gliomas. In addition to being genetically and molecularly heterogeneous, HGG are also heterogeneous with respect to the composition of their microenvironment. The question of whether this microenvironmental heterogeneity is driven by the molecular identity of the tumor remains controversial. However, this question is of utmost importance since microenvironmental, non-neoplastic cells are key components of the most radiotherapy- and chemotherapy-resistant niches of the tumor. Our work demonstrates a versatile, reliable, and reproducible adult HGG mouse model with NF1-silencing as a driver mutation. This model shows significant differences in tumor microenvironment, expression of subtype-specific markers, and response to standard therapy when compared to our established PDGFB-overexpressing HGG mouse model. PDGFB-overexpressing and NF1-silenced murine tumors closely cluster with human proneural and mesenchymal subtypes, as well as PDGFRA-amplified and NF1-deleted/mutant human tumors, respectively, at both the RNA and protein expression levels. These models can be generated in fully immunocompetent mixed or C57BL/6 genetic background mice, and therefore can easily be incorporated into preclinical studies for cancer cell-specific or immune cell-targeting drug discovery studies.
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Base de dados:
MEDLINE
Assunto principal:
Neoplasias Encefálicas
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Regulação Neoplásica da Expressão Gênica
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Proteínas Proto-Oncogênicas c-sis
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Glioma
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Mutação
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article