Single-cell multi-omic profiling of glioblastoma-associated myeloid cells
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ABSTRACT:
PROVIDER: EGAS00001004871 | EGA |
REPOSITORIES: EGA
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Project description:Glioblastoma is an incurable brain cancer characterized by high genetic and pathological heterogeneity. Here we mapped active chromatin landscapes with gene expression, whole-exomes, copy number profiles, and DNA methylomes across 44 glioblastoma stem cell (GSCs) models, 50 primary glioblastomas, and 10 neural stem cells (NSCs) with the goal of identifying essential super enhancer (SE)-associated genes and the core transcription factors that establish them and glioblastoma identity. Glioblastomas segregate with two dominant enhancer profiles that coopt unique developmental transcription factor regulatory programs to enforce tumor identity. From group specific enhancer profiles, we inferred core transcription factors that define subgroup identity. These transcription factors show higher activity in glioblastomas versus normal neural stem cells, are associated with poor clinical outcomes, and are required for glioblastoma growth in vitro and in vivo. Given challenges with genetically-defined targeted therapies for glioblastoma, we propose targeting underlying transcriptional identity may serve as an important therapeutic strategy.
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Project description:Microarray analysis was used to determine the expression of 12,000 genes in a set of 50 gliomas, 28 glioblastomas and 22 anaplastic oligodendrogliomas. Supervised learning approaches were used to build a two-class prediction model based on a subset of 14 glioblastomas and 7 anaplastic oligodendrogliomas with classic histology. A 20-feature k-nearest neighbor model correctly classified 18 of the 21 classic cases in leave-one-out cross-validation when compared with pathological diagnoses. This model was then used to predict the classification of clinically common, histologically nonclassic samples. When tumors were classified according to pathology, the survival of patients with nonclassic glioblastoma and nonclassic anaplastic oligodendroglioma was not significantly different (P = 0.19). However, class distinctions according to the model were significantly associated with survival outcome (P = 0.05). This class prediction model was capable of classifying high-grade, nonclassic glial tumors objectively and reproducibly. Moreover, the model provided a more accurate predictor of prognosis in these nonclassic lesions than did pathological classification. These data suggest that class prediction models, based on defined molecular profiles, classify diagnostically challenging malignant gliomas in a manner that better correlates with clinical outcome than does standard pathology. louis-00379 Assay Type: Gene Expression Provider: Affymetrix Array Designs: HG_U95Av2 Organism: Homo sapiens (ncbitax) Material Types: total_RNA, synthetic_RNA, organism_part, whole_organism Disease States: Classic anaplastic oligodendroglioma, Non-classic glioblastoma, Classic glioblastoma, Non-classic anaplastic oligodendroglioma
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