Genetic driver mutations define the expression signature and microenvironmental composition of high-grade gliomas. Academic Article uri icon

Overview

abstract

  • 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.

publication date

  • August 24, 2017

Research

keywords

  • Brain Neoplasms
  • Gene Expression Regulation, Neoplastic
  • Glioma
  • Mutation
  • Proto-Oncogene Proteins c-sis

Identity

PubMed Central ID

  • PMC5988206

Scopus Document Identifier

  • 85028008286

Digital Object Identifier (DOI)

  • 10.1002/glia.23203

PubMed ID

  • 28836293

Additional Document Info

volume

  • 65

issue

  • 12