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

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.

Optimization of radiation dosing schedules for proneural glioblastoma.

Glioblastomas are the most aggressive primary brain tumor. Despite treatment with surgery, radiation and chemotherapy, these tumors remain uncurable and few significant increases in survival have been observed over the last half-century. We recently employed a combined theoretical and experimental approach to predict the effectiveness of radiation administration schedules, identifying two schedules that led to superior survival in a mouse model of the disease (Leder et al., Cell 156(3):603-616, 2014). Here we extended this approach to consider fractionated schedules to best minimize toxicity arising in early- and late-responding tissues. To this end, we decomposed the problem into two separate solvable optimization tasks: (i) optimization of the amount of radiation per dose, and (ii) optimization of the amount of time that passes between radiation doses. To ensure clinical applicability, we then considered the impact of clinical operating hours by incorporating time constraints consistent with operational schedules of the radiology clinic. We found that there was no significant loss incurred by restricting dosage to an 8:00 a.m. to 5:00 p.m. window. Our flexible approach is also applicable to other tumor types treated with radiotherapy.