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

Abstract

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 Methods: Public and unpublished Affymetrix arrays data were compiled and samples with raw intensity files and clinical annotation were included.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.