Correlative Analysis of Gene Expression Profile and prognosis in patients with Gliomatosis Cerebri

Background In modern clinical neuro-oncology the pathological diagnoses are very challenging, creating significant clinical confusion and affecting therapeutic decisions and prognosis. Methods TP53 and PTEN gene sequences were analysed and microarray expression profiling was also performed. We investigated whether gene expression profiling, coupled with class prediction methodology, could be used to assist prognosis of gliomatosis cerebri in a more consistent manner than standard pathology. Results We report the results of a molecular study in fifty-nine cases of gliomatosis cerebri, correlating these results with prognosis. The well-known prognostic factors of gliomas, i.e., age, KPS, histology (Grade 2 vs. 3), or contrast enhancement, was predictive of response or outcome only in a percentage of patients but not in all patients. We identified a 23 gene signature able to predict patient prognosis with microarray gene expression profiling. With the aim of producing a prognosis tool useful in clinical investigation we studied the expression of this 23 gene signature by RTi-qPCR. Real time expression values relative to these 23 gene features were used to built a prediction method able to distinguish patients with good prognosis (more likely to be responsive to therapy) from patients with a poor prognosis (less likely to be responsive to therapy). Conclusions These results demonstrated not only a strong association of gene expression patterns with the survival of the patients but also a robust replicability of these gene expression– based predictors