Project description:Background: Meningiomas account for about 27% of primary brain tumors, making them one of the most common brain tumor. They are most common in people between the ages of 40 and 70 and are more common in women than in men. Most meningiomas (90%) are categorized as benign tumors, with the remaining 10% being atypical or malignant. Multiple classifications exist today, but the most commonly used is the World Health Organization’s (WHO) which classifies meningiomas into three histological grades: grade I (benign), grade II (atypical), and grade III (anaplastic) in accordance with the clinical prognosis. Most of these subtypes behave similarly, however anaplastics are the most aggressive. The ability to distinguish benign from atypical and anaplastic tumors is important because of its impact on treatment decisions. A molecular based classification system has the likelihood of being a better prognostic indicator and is useful for identifying alterations in pathways and networks that drive tumor progression and growth. The information obtained can potentially be translated into more effective and less toxic targeted therapies. We tested a method for genome wide expression profiling of formalin-fixed, paraffin-embedded tissues. We applied the method to the analysis of the clinical outcome of meningioma tumor. Materials and Methods: The training set consisted of tissue samples from 63 patients who were consecutively treated with surgery for meningioma between 1990 and 2005. For each patient data on clinical outcomes and formalin-fixed, paraffin-embedded blocks of tumor were available. The validation set included tissue samples from 189 patients with meningioma who consecutively underwent surgery between 1992 and 2006. We used a custom 60-mer amino modified oligo- array, containing 912 probes, a lot of which specific for genes commonly altered in cancer. Functional annotation was performed by means of gene set enrichment analysis (GSEA, www. broad.mit.edu/gsea/). Survival analyses were performed with the use of the log-rank test and Cox regression modeling. All analyses were performed with the use of GenePattern. Results: We investigated whether gene-expression profiles of meningioma tumors were associated with the clinical outcome. Using a standard leaveone- out cross-validation procedure, we found the meningioma signature to be significantly correlated with survival (P = 0.0001). The survival correlated signature contained 219 genes and was tested in the validation set. Conclusion: These results support the validity of the survival signature and highlight the potential role of tumoral meningioma tissue in predicting the outcome for patients with meningioma tumors.

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Patient-Derived Meningioma Organoids: A Reliable Model for Studying Human Tumor Pathophysiology

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