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

2016-05-28 | E-GEOD-82009 | biostudies-arrayexpress

d3795c8c-0bca-46c2-89be-7a77f37824eb - samples

Project description:Oligodendroglioma (WHO gr. 2

| EGAD00001008299 | EGA

75912ee5-be7f-4fd1-bf40-727823badd41 - samples

Project description:Oligodendroglioma (WHO gr. 2)

| EGAD00001008306 | EGA

3c473c4b-6c3e-4398-8d9d-14cae94a9b31 - samples

Project description:Oligodendroglioma (WHO gr. 2

| EGAD00001008300 | EGA

56a49001-405f-4a3f-931a-71d72a62ab8d - samples

Project description:Oligodendroglioma (WHO gr. 2)

| EGAD00001008308 | EGA

86740494-4dfc-4ce0-aa45-c80ff4c32898 - samples

Project description:Oligodendroglioma (WHO gr. 2)

| EGAD00001008297 | EGA

Histone Modification Analysis of Oligodendroglioma and Astrocytoma Brain Cancer Samples

Project description:WHO classification for tumors of the central nervous system strongly endorses molecular tests for the precise diagnosis of diffuse gliomas. While alterations in the DNA methylation status of gliomas are already well documented and used in specialised clinical centers to distinguish between brain tumor entities, changes to the epigenetic layer at the level of histone modifications are only poorly characterised. Here, we applied a recently developed data-independent acquisition (DIA) - mass spectrometry method to generate a comprehensive histone epi-proteomic map that documents the abundance of almost all characterized and many uncharacterized histone modifications to a series of IDH-mutant oligodendroglioma and astrocytoma samples. Our analysis documented significant abundance differences in almost one-third of the 144 quantified histone peptides. Among them are lower abundance levels of the polycomb repressive mark H3K27me3 in oligodendroglioma samples compared to astrocytomas. We validated this finding by immunohistochemistry using the C36B11 antibody. Surprisingly, we observed inconsistencies with another widely applied H3K27me3 antibody (07-449), providing a warning flag for immunohistochemistry of brain cancers. An unbiased unsupervised clustering analysis of the proteomic dataset separated the two IDH-mutant glioma subtypes in full accordance to the EPIC DNA methylation classifier and the 1p/19q status. The clustering also revealed at least two histone epi-proteomic subgroups of oligodendroglioma, a feature not observable in the DNA methylation dataset. Our results indicate that histone epi-proteomic profiling at the depth of the current method has the capacity to identify clinically-relevant glioma sub-groups. In addition to being of use for diagnostic purposes, this could also provide novel insights in glioma biology and may identify new therapeutic targets.

2020-01-17 | PXD015151 | Pride

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