Abstract
Abstract Purpose: Primary Central Nervous System Tumors (PCNST) are the most frequent solid neoplasias in childhood, in particular those derived from astrocytes, aracnoids and ependimum. The prognostic criteria is based on the anatomic localization, tumor size and surgical accesibility for resection. Nevertheless, this criteria is not enough to predict a therapeutic success in all patientes, either knows with exactitude the implied underlying biological process in paediatric cerebral tumors. The goals of this research are to identify gene expression profiles and integrate metabolic pathways potentialy related to each histological subtype. Methods: We included neoplastic tissue samples from pediatric patients with ependymomas, astrocytomas y medulloblastomas treated at the National Institute of Pediatics, Mexico. Diagnostic approach and treatment protocols were those stablished by the Pediatric Oncology Service considering only those patients with a signed consent. We purified total RNA from these samples using RNAeasy mini kit (Qiagen), considering those samples with 8 or more RIN (RNA Integrity Number) useful for microarray hybridization on the GeneChip® Human Gene 1.0 ST Array (Affymetrix). Statistic analysis was performed with Affymetrix Expression Console and Partek's Genomics Suite. We also used the Database for Annotation, Visualition and Intgrated Discovery (DAVID, Bioinformatics Resources v6.7) for data minning in order to identify metabolic pathways potentialy related to each histological subtype with the differential gene expression results. Results: We considered all microarrays that showed good correlation (index 0.8 to 1.0) for the statistical analysis. We found a list of genes differentially expressed according to the hystologic subtype (ependymomas, astrocytomas and medulloblastomas), and then we integrated the metabolic pathways apparently related to differentially expressed genes with DAVID. Details of these results will be presented at the meeting. Conclusion: These results show that gene expression profiles may clearly differentiate ependymomas, astrocytomas and medulloblastomas according to their histological subtype. Differential gene expression profiles in these PCNST are being analyzed as a diagnostic tool in order to provide a more accurate allocation criteria for antineoplastic treatments based not only on their own genomic profiles but in the neoplastic behavior potentially related to the metabolic pathways results. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr B97.
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