Expression profiling in ependymoma reveals differences between benign and anaplastic ependymoma

Abstract

Ependymomas arise from the ependymal cells lining the ventricular system of the CNS and account for approximately 10% of paediatric brain tumours. Approximately 70% of ependymomas are histologically benign and correspond to WHO grade II, whilst the remainder are anaplastic (WHO grade III). The 5-year survival rates in children are 34–45%, with local recurrence being the major source of therapeutic failure. Anaplasia does not appear to be associated with worse prognosis, and at present there are no molecular or genetic markers which can be used as predictors of outcome. Indeed, the genetic events that contribute to the pathogenesis of ependymoma are essentially unknown. We have used human oligonucleotide arrays to generate gene expression profiles in 10 ependymoma samples from patients with different histopathological/clinical parameters in order to identify new prognostic markers. Our sample group is composed of 7 ependymoma (WHO grade II) and 3 anaplastic ependymoma (WHO grade III). Three patients were <3 years of age at first presentation. Five tumours have chromosomal aberrations identified by comparative genomic hybridisation (CGH). Our preliminary data show that overexpression of specific functional categories of genes is dependant on histology when compared to normal controls. Cell cycle and adhesion related genes, oncogenes, and genes involved in apoptosis were mainly overexpressed in anaplastic tumours. Benign tumours, however, overexpressed mainly growth factor related genes. Some common candidates emerged for all tumours; Wee1+, a cell cycle related gene that regulates entry into mitosis, was up to six fold overexpressed in tumours. The oncogene c-myc, which maps to an amplicon at 8q24 detected by CGH in a subset of ependymomas, was also overexpressed in some tumours and may be an interesting candidate in their development. To our knowledge, none of these genes have been associated previously with this class of brain tumours. Further analysis of differential gene expression profiles using large series of tumours will help in the identification of molecular markers. This information, when linked to clinical and pathology data, could also help in the classification of these tumours and the choice of therapy.