Summary In normal brain, the side population (SP) phenotype is generated by ABC transporter activity and identifies stem cell and endothelial cell sub-populations by dye exclusion. By drug efflux, the ABCG2 transporter provides chemoresistance in stem cells and contributes to the blood brain barrier (BBB) when active in endothelial cells. We investigated the SP phenotype of mouse and human gliomas. In glioma endothelial cells, ABC transporter function is impaired, corresponding to disruption of the BBB in these tumors. By contrast, the SP phenotype is increased in non-endothelial cells that form neurospheres and are highly tumorigenic. In this cell population, Akt, but not its downstream target mTOR, regulates ABCG2 activity, and loss of PTEN increases the SP. This Akt-induced ABCG2 activation results from its transport to the plasma membrane. Temozolomide, the standard treatment of gliomas, although not an ABCG2 substrate, increases the SP in glioma cells, especially in cells missing PTEN.

Primary brain tumors, including gliomas and medulloblastomas, often represent the most devastating and difficult-to-treat tumors, and are thought to arise from glial cells and/or their precursors or the external granule cell layer, respectively. The majority of genetic alterations characteristic of the human brain tumors are thought to occur in genes encoding proteins involved in signal transduction or cell cycle regulation. Accurate recapitulation of these genetic alterations using genetically engineered mouse models allows for in vivo modeling of brain tumors with similar histopathology, etiology, and biology. These mouse models, in turn, increase our understanding of brain tumor initiation, formation, progression, and metastasis, providing an experimental system to discover novel therapeutic targets and test various therapeutic agents.

Gene rearrangement in the form of an intragenic deletion is the primary mechanism of oncogenic mutation of the epidermal growth factor receptor (EGFR) gene in gliomas. However, the incidence of platelet-derived growth factor receptor-a (PDGFRA) gene rearrangement in these tumors is unknown. We investigated the PDGFRA locus in PDGFRA-amplified gliomas and identified two rearrangements, including the first case of a gene fusion between kinase insert domain receptor (KDR) (VEGFRII) and the PDGFRA gene, and six cases of PDGFRA D8, 9 , an intragenic deletion rearrangement. The PDGFRA D8, 9 mutant was common, being present in 40% of the glioblastoma multiformes (GBMs) with PDGFRA amplification. Tumors with these two types of PDGFRA rearrangement displayed histologic features of oligodendroglioma, and the gene products of both rearrangements showed constitutively elevated tyrosine kinase activity and transforming potential that was reversed by PDGFR blockade. These results suggest the possibility that these PDGFRA mutants behave as oncogenes in this subset of gliomas, and that the prevalence of such rearrangements may have been considerably underestimated.[Keywords: Copy number alteration; glioma; PDGFRA gene rearrangement; receptor tyrosine kinase] Supplemental material is available at http://www.genesdev.org.

Gli signaling is critical for central nervous system development and is implicated in tumorigenesis. To monitor Gli signaling in gliomas in vivo, we created platelet-derived growth factor-induced gliomas in a Gli-luciferase reporter mouse. We find that Gli activation is found in gliomas and correlates with grade. In addition, we find that sonic hedgehog (SHH) is expressed in these tumors and also correlates with grade. We identify microvascular proliferation and pseudopalisades, elements that define high-grade gliomas as SHH-producing microenvironments. We describe two populations of SHH-producing stromal cells that reside in perivascular niche (PVN), namely low-cycling astrocytes and endothelial cells. Using the Ptc-LacZ knock-in mouse as a second Gli responsive reporter, we show B-galactosidase activity in the PVN and in some tumors diffusely throughout the tumor. Lastly, we observe that SHH is similarly expressed in human gliomas and note that an intact tumor microenvironment or neurosphere conditions in vitro are required for Gli activity. [Cancer Res 2008;68(7):2241-9]

OBJECTIVERecent large-cohort sequencing studies have investigated the genomic landscape of meningiomas, identifying somatic coding alterations in NF2, SMARCB1, SMARCE1, TRAF7, KLF4, POLR2A, BAP1, and members of the PI3K and Hedgehog signaling pathways. Initial associations between clinical features and genomic subgroups have been described, including location, grade, and histology. However, further investigation using an expanded collection of samples is needed to confirm previous findings, as well as elucidate relationships not evident in smaller discovery cohorts.METHODSTargeted sequencing of established meningioma driver genes was performed on a multiinstitution cohort of 3016 meningiomas for classification into mutually exclusive subgroups. Relevant clinical information was collected for all available cases and correlated with genomic subgroup. Nominal variables were analyzed using Fisher’s exact tests, while ordinal and continuous variables were assessed using Kruskal-Wallis and 1-way ANOVA tests, respectively. Machine-learning approaches were used to predict genomic subgroup based on noninvasive clinical features.RESULTSGenomic subgroups were strongly associated with tumor locations, including correlation of HH tumors with midline location, and non-NF2 tumors in anterior skull base regions. NF2 meningiomas were significantly enriched in male patients, while KLF4 and POLR2A mutations were associated with female sex. Among histologies, the results confirmed previously identified relationships, and observed enrichment of microcystic features among “mutation unknown” samples. Additionally, KLF4-mutant meningiomas were associated with larger peritumoral brain edema, while SMARCB1 cases exhibited elevated Ki-67 index. Machine-learning methods revealed that observable, noninvasive patient features were largely predictive of each tumor’s underlying driver mutation.CONCLUSIONSUsing a rigorous and comprehensive approach, this study expands previously described correlations between genomic drivers and clinical features, enhancing our understanding of meningioma pathogenesis, and laying further groundwork for the use of targeted therapies. Importantly, the authors found that noninvasive patient variables exhibited a moderate predictive value of underlying genomic subgroup, which could improve with additional training data. With continued development, this framework may enable selection of appropriate precision medications without the need for invasive sampling procedures.