Core pathway mutations induce de-differentiation of murine astrocytes into glioblastoma stem cells that are sensitive to radiation but resistant to temozolomide

Background

Glioma stem cells (GSCs) from human glioblastomas (GBMs) are resistant to radiation and chemotherapy and may drive recurrence. Treatment efficacy may depend on GSCs, expression of DNA repair enzymes such as methylguanine methyltransferase (MGMT), or transcriptome subtype.

Methods

To model genetic alterations in human GBM core signaling pathways, we induced Rb knockout, Kras activation, and Pten deletion mutations in cortical murine astrocytes. Neurosphere culture, differentiation, and orthotopic transplantation assays were used to assess whether these mutations induced de-differentiation into GSCs. Genome-wide chromatin landscape alterations and expression profiles were examined by formaldehyde-assisted isolation of regulatory elements (FAIRE) seq and RNA-seq. Radiation and temozolomide efficacy were examined in vitro and in an allograft model in vivo. Effects of radiation on transcriptome subtype were examined by microarray expression profiling.

Results

Cultured triple mutant astrocytes gained unlimited self-renewal and multilineage differentiation capacity. These cells harbored significantly altered chromatin landscapes that were associated with downregulation of astrocyte- and upregulation of stem cell-associated genes, particularly the Hoxa locus of embryonic transcription factors. Triple-mutant astrocytes formed serially transplantable glioblastoma allografts that were sensitive to radiation but expressed MGMT and were resistant to temozolomide. Radiation induced a shift in transcriptome subtype of GBM allografts from proneural to mesenchymal.

Conclusion

A defined set of core signaling pathway mutations induces de-differentiation of cortical murine astrocytes into GSCs with altered chromatin landscapes and transcriptomes. This non-germline genetically engineered mouse model mimics human proneural GBM on histopathological, molecular, and treatment response levels. It may be useful for dissecting the mechanisms of treatment resistance and developing more effective therapies.

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