Search

OBJECTIVE: Approximately half of glioblastoma (GBM) cases develop in geriatric patients, and this trend is destined to increase with the aging of the population. The optimal strategy for management of GBM in elderly patients remains controversial. The aim of this study was to assess the role of surgery in the elderly (≥ 65 years old) based on clinical, molecular, and imaging data routinely available in neurosurgical departments and to assess a prognostic survival score that could be helpful in stratifying the prognosis for elderly GBM patients. METHODS: Clinical, radiological, surgical, and molecular data were retrospectively analyzed in 322 patients with GBM from 9 neurosurgical centers. Univariate and multivariate analyses were performed to identify predictors of survival. A random forest approach (classification and regression tree [CART] analysis) was utilized to create the prognostic survival score. RESULTS: Survival analysis showed that overall survival (OS) was influenced by age as a continuous variable (p = 0.018), MGMT (p = 0.012), extent of resection (EOR; p = 0.002), and preoperative tumor growth pattern (evaluated with the preoperative T1/T2 MRI index; p = 0.002). CART analysis was used to create the prognostic survival score, forming six different survival groups on the basis of tumor volumetric, surgical, and molecular features. Terminal nodes with similar hazard ratios were grouped together to form a final diagram composed of five classes with different OSs (p < 0.0001). EOR was the most robust influencing factor in the algorithm hierarchy, while age appeared at the third node of the CART algorithm. The ability of the prognostic survival score to predict death was determined by a Harrell's c-index of 0.75 (95% CI 0.76–0.81). CONCLUSIONS: The CART algorithm provided a promising, thorough, and new clinical prognostic survival score for elderly surgical patients with GBM. The prognostic survival score can be useful to stratify survival risk in elderly

The glioblastoma microenvironment plays a substantial role in glioma biology. However, few studies have investigated its spatial heterogeneity. Exploiting 5-ALA Fluorescence Guided Surgery (FGS), we were able to distinguish between the tumor core (ALA+), infiltrating area (ALAPALE) and healthy tissue (ALA-) of the glioblastoma, based on the level of accumulated fluorescence. The aim of this study was to investigate the properties of the microenvironments associated with these regions. For this purpose, we isolated glioma-associated stem cells (GASC), resident in the glioma microenvironment, from ALA+, ALA-PALE and ALA-samples and compared them in terms of growth kinetic, phenotype and for the expression of 84 genes associated with cancer inflammation and immunity. Differentially expressed genes were correlated with transcriptomic datasets from TCGA/GTEX. Our results show that GASC derived from the three distinct regions, despite a similar phenotype, were characterized by different transcriptomic profiles. Moreover, we identified a GASC-based genetic signature predictive of overall survival and disease-free survival. This signature, highly expressed in ALA+ GASC, was also well represented in ALA PALE GASC. 5-ALA FGS allowed to underline the heterogeneity of the glioma microenvironments. Deepening knowledge of these differences can contribute to develop new adjuvant therapies targeting the crosstalk between tumor and its supporting microenvironment.