Molecular Subtypes Of Glioma Research Articles

A mesenchymal/stem cell predictor of survival of patients with malignant gliomas.

2049 Background: Identification of the molecular subtypes of diffuse gliomas will guide therapy and risk stratification. Attempts to dissect the biologic diversity of these tumors using array data have been limited to small cohort sizes or to a single histologic subtype. We proposed to overcome these limitations by unifying data from multiple series. Methods: Public and unpublished Affymetrix arrays data were compiled and samples with raw intensity files and clinical annotation were included. Unsupervised clustering was performed to identify major subgroups. Data from 517 tumors were randomly divided into training and validation sets for predictive modeling. Candidate models were evaluated by cross-validation. An independent verification cohort (n = 328) was used for further validation. Stem cell array data were obtained from published sources. Uni/multivariate correlations and survival analyses were performed to published radiation-associated and oncogene-pathway signatures. Results: Two primary molecular subtypes of gliomas, each with distinct survival outcomes, were observed by unsupervised analyses. Genes in the subtype whose expression correlated with poorer survival were associated mesenchymal ontology and characterized the majority of, in contrast to the lower grade tumors. The signature correlated with a glioma stem cells (GSC) profile. Predictive modeling identified a robust prognostic model in both training and primary validation sets (accuracy > 85%) and was validated on an independent data set, including cases from the Cancer Genome Atlas. The mesenchymal signature was correlated with radiation and ras and src oncogene pathway signatures (p < 0.0001) and was an independent predictors of survival. Conclusions: Using the largest reported unified glioma expression profiling dataset we identified a mesenchymal signature associated with poor survival and which correlated to a GSC-specific signature. We used this dataset to develop a robust prognostic marker of patient survival which outperforms existing clinico-pathologic factors, such as age and grade, and which was validated on an independent dataset. Based on its radiation/oncogene association, this predictor may be useful in guiding treatment. Author Disclosure Employment or Leadership Position Consultant or Advisory Role Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration Castle Biosciences, Inc.

Abstract 3135: Segregation of non-R132H mutations in IDH1 in distinct molecular subtypes of glioma

Abstract Mutations in the gene encoding the isocitrate dehydrogenase 1 gene (IDH1) occur at a high frequency (up to 80%) in many different subtypes of glioma. In this study, we have screened for IDH1 mutations in a cohort of 496 gliomas. IDH1 mutations were most frequently observed in low grade gliomas with 395G&amp;gt;A (R132H) representing &amp;gt;90% of all IDH1 mutations. Interestingly, non-R132H mutations segregate in distinct histological and molecular subtypes of glioma. Histologically, they occur sporadically in classic oligodendrogliomas and at significantly higher frequency in other grade II and III gliomas. Genetically, non-R132H mutations occur in tumors with TP53 mutation, are virtually absent in tumors with loss of heterozygosity on 1p and 19q and accumulate in distinct (gene-expression profiling based) intrinsic molecular subtypes. The IDH1 mutation type does not affect patient survival. Our results were validated on an independent sample cohort, indicating that the IDH1 mutation spectrum may aid glioma subtype classification. Functional differences between R132H and non-R132H mutated IDH1 may explain the segregation in distinct glioma subtypes. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3135.

Abstract 808: Intrinsic gene expression profiles of gliomas are a better predictor of survival than histology

Abstract Gliomas are the most common primary brain tumors with heterogeneous morphology and variable prognosis. Treatment decisions in patients rely mainly on histological classification and clinical parameters. However, differences between histological subclasses and grades are subtle, and classifying gliomas is subject to a large inter-observer variability. To improve current classification standards, we have performed gene expression profiling on a large cohort of glioma samples of all histological subtypes and grades. We identified seven distinct molecular subgroups that correlate with survival. These include two favorable prognostic subgroups (median survival &amp;gt;4.7 years), two with intermediate prognosis (median survival 1-4 years), two with poor prognosis (median survival &amp;lt;1 year), and one control group. The intrinsic molecular subtypes of glioma are different from histological subgroups and correlate better to patient survival. The prognostic value of molecular subgroups was validated on five independent sample cohorts (TCGA, REMBRANDT, GSE12907, GSE4271, and Li et al.). The power of intrinsic subtyping is demonstrated by its ability to identify a subset of prognostically favorable tumors within an external dataset that contains only histologically confirmed glioblastomas. Specific genetic changes (EGFR amplification, IDH1 mutation, 1p/19q LOH) segregate in distinct molecular subgroups. We identified a subgroup with molecular features associated with secondary glioblastoma, suggesting that different genetic changes drive gene expression profiles. Finally, we assessed response to treatment in molecular subgroups. Our data provides compelling evidence that expression profiling is a more accurate and objective method to classify gliomas than histological classification. Molecular classification therefore may aid diagnosis and can guide clinical decision making. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 808.

Segregation of non-p.R132H mutations inIDH1in distinct molecular subtypes of glioma

Mutations in the gene encoding the isocitrate dehydrogenase 1 gene (IDH1) occur at a high frequency (up to 80%) in many different subtypes of glioma. In this study, we have screened for IDH1 mutations in a cohort of 496 gliomas. IDH1 mutations were most frequently observed in low grade gliomas with c.395G>A (p.R132H) representing >90% of all IDH1 mutations. Interestingly, non-p.R132H mutations segregate in distinct histological and molecular subtypes of glioma. Histologically, they occur sporadically in classic oligodendrogliomas and at significantly higher frequency in other grade II and III gliomas. Genetically, non-p.R132H mutations occur in tumors with TP53 mutation, are virtually absent in tumors with loss of heterozygosity on 1p and 19q and accumulate in distinct (gene-expression profiling based) intrinsic molecular subtypes. The IDH1 mutation type does not affect patient survival. Our results were validated on an independent sample cohort, indicating that the IDH1 mutation spectrum may aid glioma subtype classification. Functional differences between p.R132H and non-p.R132H mutated IDH1 may explain the segregation in distinct glioma subtypes.

Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

Gliomas are the most common primary brain tumors with heterogeneous morphology and variable prognosis. Treatment decisions in patients rely mainly on histologic classification and clinical parameters. However, differences between histologic subclasses and grades are subtle, and classifying gliomas is subject to a large interobserver variability. To improve current classification standards, we have performed gene expression profiling on a large cohort of glioma samples of all histologic subtypes and grades. We identified seven distinct molecular subgroups that correlate with survival. These include two favorable prognostic subgroups (median survival, >4.7 years), two with intermediate prognosis (median survival, 1-4 years), two with poor prognosis (median survival, <1 year), and one control group. The intrinsic molecular subtypes of glioma are different from histologic subgroups and correlate better to patient survival. The prognostic value of molecular subgroups was validated on five independent sample cohorts (The Cancer Genome Atlas, Repository for Molecular Brain Neoplasia Data, GSE12907, GSE4271, and Li and colleagues). The power of intrinsic subtyping is shown by its ability to identify a subset of prognostically favorable tumors within an external data set that contains only histologically confirmed glioblastomas (GBM). Specific genetic changes (epidermal growth factor receptor amplification, IDH1 mutation, and 1p/19q loss of heterozygosity) segregate in distinct molecular subgroups. We identified a subgroup with molecular features associated with secondary GBM, suggesting that different genetic changes drive gene expression profiles. Finally, we assessed response to treatment in molecular subgroups. Our data provide compelling evidence that expression profiling is a more accurate and objective method to classify gliomas than histologic classification. Molecular classification therefore may aid diagnosis and can guide clinical decision making.