Project description:Migrated from 1.6 id: 1015897590491013 GEDP id: 760 In current clinical practice, histology-based grading of diffuse infiltrative gliomas is the best predictor of patient survival time. Yet histology provides little insight into the underlying biology of gliomas and is limited in its ability to identify and guide new molecularly targeted therapies. We have performed large-scale gene expression analysis using the Affymetrix HG U133 oligonucleotide arrays on 85 diffuse infiltrating gliomas of all histologic types to assess whether a gene expression-based, histology-independent classifier is predictive of survival and to determine whether gene expression signatures provide insight into the biology of gliomas. We found that gene expression-based grouping of tumors is a more powerful survival predictor than histologic grade or age. The poor prognosis samples could be grouped into three different poor prognosis groups, each with distinct molecular signatures. We further describe a list of 44 genes whose expression patterns reliably classify gliomas into previously unrecognized biological and prognostic groups: these genes are outstanding candidates for use in histology-independent classification of high-grade gliomas. The ability of the large scale and 44 gene set expression signatures to group tumors into strong survival groups was validated with an additional external and independent data set from another institution composed of 50 additional gliomas. This demonstrates that large-scale gene expression analysis and subset analysis of gliomas reveals unrecognized heterogeneity of tumors and is efficient at selecting prognosis-related gene expression differences which are able to be applied across institutions. nelso-00262 Assay Type: Gene Expression Provider: Affymetrix Array Designs: HG-U133A, HG-U133B Organism: Homo sapiens (ncbitax) Material Types: total RNA, synthetic_RNA, organism_part, whole_organism Disease States: Glioma, Glioblastoma, Oligodendroglial Tumor, astrocytomas

Project description:Diffuse infiltrating gliomas are the most common primary brain malignancy found in adults, and Glioblastoma multiforme, the highest grade glioma, is associated with a median survival of 7 months. Transcriptional profiling has been applied to 85 gliomas from 74 patients to elucidate glioma biology, prognosticate survival, and define tumor sub-classes. These studies reveal that transcriptional profiling of gliomas is more accurate at predicting survival than traditional pathologic grading, and that gliomas characteristically express coordinately regulated genes of one of four molecular signatures: neurogenesis, synaptic transmission, mitotic, or extra-cellular matrix. Elucidation of these survival associated molecular signatures will aid in tumor prognostication and define targets for future directed therapy. Evaluate a large number of diffuse infiltrating gliomas through transcriptional profiling. Glioma tumor sub-classes may be identified through large scale gene expression studies. All patients undergoing surgical treatment at the University of California, Los Angeles for primary brain cancers between 1996 and 2003 were invited to participate in this Institutional Review Board approved study. 74 of the patients participating in this broad protocol were analyzed as part of this study if their initial tumor was diagnosed as a grade III (n=24) or IV (n=50) glioma of any histologic type on initial surgical treatment and fresh frozen material was obtained. Only grade III and IV gliomas were included in this study as the distinction between these grades is subtle and prone to misclassification. The time in days elapsed from resection to the day of death, or if the patient has remained alive, to the current day was recorded for all samples studied. Patient ages at diagnosis varied from 18 to 82 years. There were 46 females and 28 males. Probes were prepared using standard Affymetrix protocols, and hybridized to Affymetrix HG-U133A and HG-U133B arrays.

Project description:Diffuse infiltrating gliomas are the most common primary brain malignancy found in adults, and Glioblastoma multiforme, the highest grade glioma, is associated with a median survival of 7 months. Transcriptional profiling has been applied to 85 gliomas from 74 patients to elucidate glioma biology, prognosticate survival, and define tumor sub-classes. These studies reveal that transcriptional profiling of gliomas is more accurate at predicting survival than traditional pathologic grading, and that gliomas characteristically express coordinately regulated genes of one of four molecular signatures: neurogenesis, synaptic transmission, mitotic, or extra-cellular matrix. Elucidation of these survival associated molecular signatures will aid in tumor prognostication and define targets for future directed therapy. Evaluate a large number of diffuse infiltrating gliomas through transcriptional profiling. Glioma tumor sub-classes may be identified through large scale gene expression studies. All patients undergoing surgical treatment at the University of California, Los Angeles for primary brain cancers between 1996 and 2003 were invited to participate in this Institutional Review Board approved study. 74 of the patients participating in this broad protocol were analyzed as part of this study if their initial tumor was diagnosed as a grade III (n=24) or IV (n=50) glioma of any histologic type on initial surgical treatment and fresh frozen material was obtained. Only grade III and IV gliomas were included in this study as the distinction between these grades is subtle and prone to misclassification. The time in days elapsed from resection to the day of death, or if the patient has remained alive, to the current day was recorded for all samples studied. Patient ages at diagnosis varied from 18 to 82 years. There were 46 females and 28 males. Probes were prepared using standard Affymetrix protocols, and hybridized to Affymetrix HG-U133A and HG-U133B arrays. Keywords: disease state analysis

Project description:The outcome of patients with anaplastic gliomas varies considerably depending on histology and single molecular markers such as codeletion of 1p/19q and mutations of the isocitrate dehydrogenase (IDH) gene. Whether a molecularly-based classification of anaplastic gliomas based on large scale genomic or epigenomic analyses is superior to histopathology, may reflect distinct biological subtypes, predict outcome and guide therapy decisions had yet to be determined. Epigenome-wide DNA methylation analysis, which also allows for the detection of copy-number aberrations, was performed in a cohort of 228 patients with anaplastic gliomas (astrocytomas, oligoastrocytomas and oligodendrogliomas), including 115 patients of the NOA-04 trial. We further compared these tumors with a group of 55 glioblastomas. Unsupervised clustering demonstrated two main groups based on IDH mutation status: CpG island methylator phenotype (CIMP) positive (77.5%) or negative (22.5%). CIMP+ (IDH mutant) tumors showed a further separation based on copy-number status of chromosome arms 1p and 19q, but not based on histopathology. CIMP- (IDH wild type) tumors on the other hand showed hallmark copy-number alterations of glioblastomas. These tumors clustered together with CIMP- glioblastomas without forming separate groups based on WHO grade. There was no Tumor classification based on CIMP and 1p/19q status was significantly associated with survival allowing a better prediction of outcome than the current histopathological classification alone: Patients with CIMP+ tumors with 1p/19q codeletion had the best prognosis, followed by patients with CIMP+ but intact 1p/19q status. Patients with CIMP- anaplastic gliomas had the worst prognosis. Collectively, our data suggest that anaplastic gliomas can be grouped into three molecular subtypes with clear association to underlying biology and clinical outcome based on IDH and 1p/19q status. The data do not provide a molecular basis for the diagnosis of anaplastic oligoastrocytoma. We investigated a subset of 228 anaplastic gliomas using the Illumina 450k methylation array.

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CaArray_nelso-00262: Gene expression profiling of gliomas strongly predicts survival

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