Glioblastoma is the most malignant form of astrocytic brain tumor; patients have a mean survival time of one year after diagnosis. Extensive genetic analyses have recognized few genetic alterations and have not yielded any predictive factors. A more comprehensive approach includes analysis of differential gene expression, reflecting regulatory differences. We seek to identify differentially expressed genes relevant for tumor development or possessing predictive value, in order to gain insights into the molecular mechanisms of tumor progression and uncover new therapeutic targets. Using complementary DNA macroarrays (Clontech) we have constructed gene expression profiles in glioblastoma biopsies from a homogenous group of patients enrolled in a pilot clinical trial (concomitant and adjuvant temozolomide and irradiation for newly diagnosed glioblastoma). We subjected the expression profiles to cluster analysis to shed light on the mechanistic aspects of tumor development in association with data on gene alterations, such as p53 mutations and deletion of the p16/p14ARF locus, which are hallmarks of previously defined genetic pathways. Our preliminary results with eight tumors confirm upregulation of some genes already known to be important in the development of glioblastoma and the exclusivity of some genetic pathways (p53 mutations versus EGFR overexpression). We have created expression profiles of 120 differentially expressed genes using the Cluster program from Stanford University, and we have grouped tumors according to genetic pathways. We will link patterns of differential gene expression to clinical outcomes to identify subtypes of tumors with distinct clinical behavior.
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