Gene expression profiling identifies molecular subtypes of gliomas

Abstract

Identification of distinct molecular subtypes is a critical challenge for cancer biology. In this study, we used Affymetrix high-density oligonucleotide arrays to identify the global gene expression signatures associated with gliomas of different types and grades. Here, we show that the global transcriptional profiles of gliomas of different types and grades are distinct from each other and from the normal brain. To determine whether our data could be used to uncover molecular subtypes without prior knowledge of pathologic type and grade, we performed K-means clustering analysis and found evidence for three clusters with the aid of multidimensional scaling plots. These clusters corresponded to glioblastomas, lower grade astrocytomas and oligodendrogliomas (P<0.00001). A predictor constructed from the 170 genes that are most differentially expressed between the subsets correctly identified the type and grade of all samples, indicating that a relatively small number of genes can be used to distinguish between these molecular subtypes. These results further define molecular subsets of gliomas which may potentially be used for patient stratification, and suggest potential targets for treatment.