Background: Translational medicine aims at transferring advances in basic science research into new approaches for diagnosis and treatment of diseases. Low-grade gliomas (LGG) have a heterogeneous clinical behavior that can be only partially predicted employing current state-of-the-art markers, hindering the decision-making process. To deepen our comprehension on tumor heterogeneity, we dissected the mechanism of interaction between tumor cells and relevant components of the neoplastic environment, isolating, from LGG and high-grade gliomas (HGG), proliferating stem cell lines from both the glioma stroma and, where possible, the neoplasm. Methods and Findings: We isolated glioma-associated stem cells (GASC) from LGG (n=40) and HGG (n=73). GASC showed stem cell features, anchorage-independent growth, and supported the malignant properties of both A172 cells and human glioma-stem cells, mainly through the release of exosomes. Finally, starting from GASC obtained from HGG (n=13) and LGG (n=12) we defined a score, based on the expression of 9 GASC surface markers, whose prognostic value was assayed on 40 subsequent LGG-patients. At the multivariate Cox analysis, the GASC-based score was the only independent predictor of overall survival and malignant progression free-survival. Conclusions: The microenvironment of both LGG and HGG hosts non-tumorigenic multipotent stem cells that can increase in vitro the biological aggressiveness of glioma-initiating cells through the release of exosomes. The clinical importance of this finding is supported by the strong prognostic value associated with the characteristics of GASC. This patient-based approach can provide a groundbreaking method to predict prognosis and to exploit novel strategies that target the tumor stroma. Stem Cells 2014;32:1239–1253

Low-grade gliomas (LGG) are infiltrative primary brain tumors that in 70% of the cases undergo anaplastic transformation, deeply affecting prognosis. However, the timing of progression is heterogeneous. Recently, the tumor microenvironment (TME) has gained much attention either as prognostic factor or therapeutic target. Through the release of extracellular vesicles, the TME contributes to tumor progression by transferring bioactive molecules such as microRNA. The aim of the study was to take advantage of glioma-associated stem cells (GASC), an in vitro model of the glioma microenvironment endowed with a prognostic significance, and their released exosomes, to investigate the possible role of exosome miRNAs in favoring the anaplastic transformation of LGG. Therefore, by deep sequencing, we analyzed and compared the miRNA profile of GASC and exosomes obtained from LGG patients characterized by different prognosis. Results showed that exosomes presented a different signature, when compared to their cellular counterpart and that, although sharing several miRNAs, exosomes of patients with a bad prognosis, selectively expressed some miRNAs possibly responsible for the more aggressive phenotype. These findings get insights into the value of TME and exosomes as potential biomarkers for precision medicine approaches aimed at improving LGG prognostic stratification and therapeutic strategies.

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 (ALA-PALE) 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.

Exosomes are one of the most important mediators of the cross talk occurring between glioma stem cells (GSCs) and the surrounding microenvironment. We have previously shown that exosomes released by patient-derived glioma-associated stem cells (GASC) are able to increase, in vitro, the aggressiveness of both GSC and glioblastoma cell lines. To understand which molecules are responsible for this tumour-supporting function, we performed a descriptive proteomic analysis of GASC-exosomes and identified, among the others, Semaphorin7A (SEMA7A). SEMA7A was described as a promigratory cue in physiological and pathological conditions, and we hypothesised that it could modulate GSC migratory properties. Here, we described that SEMA7A is exposed on GASC-exosomes’ surface and signals to GSC through Integrin β1. This interaction activates focal adhesion kinase into GSC and increases their motility, in our patient-based in vitro model. Our findings suggest SEMA7A-β1-integrin as a new target to disrupt the communication between GSCs and the supporting microenvironment.

The persistence of long-term coronavirus-induced disease 2019 (COVID-19) sequelae demands better insights into its natural history. Therefore, it is crucial to discover the biomarkers of disease outcome to improve clinical practice. In this study, 160 COVID-19 patients were enrolled, of whom 80 had a “non-severe” and 80 had a “severe” outcome. Sera were analyzed by proximity extension assay (PEA) to assess 274 unique proteins associated with inflammation, cardiometabolic, and neurologic diseases. The main clinical and hematochemical data associated with disease outcome were grouped with serological data to form a dataset for the supervised machine learning techniques. We identified nine proteins (i.e., CD200R1, MCP1, MCP3, IL6, LTBP2, MATN3, TRANCE, α2-MRAP, and KIT) that contributed to the correct classification of COVID-19 disease severity when combined with relative neutrophil and lymphocyte counts. By analyzing PEA, clinical and hematochemical data with statistical methods that were able to handle many variables in the presence of a relatively small sample size, we identified nine potential serum biomarkers of a “severe” outcome. Most of these were confirmed by literature data. Importantly, we found three biomarkers associated with central nervous system pathologies and protective factors, which were downregulated in the most severe cases.

The main aim of this study was to identify the most relevant cytokines which, when assessed in the earliest stages from hospital admission, may help to select COVID-19 patients with worse prognosis. A retrospective observational study was conducted in 415 COVID-19 patients (272 males; mean age 68 ± 14 years) hospitalized between May 2020 and March 2021. Within the first 72 h from hospital admission, patients were tested for a large panel of biomarkers, including C-reactive protein (CRP), Mid-regional proadrenomedullin (MR-proADM), Interferon-γ, interleukin 6 (IL-6), IL-1β, IL-8, IL-10, soluble IL2-receptor-α (sIL2Rα), IP10 and TNFα. Extensive statistical analyses were performed (correlations, t-tests, ranking tests and tree modeling). The mortality rate was 65/415 (15.7%) and a negative outcome (death and/or orotracheal intubation) affected 98/415 (23.6%) of cases. Univariate tests showed the majority of biomarkers increased in severe patients, but ranking tests helped to select the best variables to put on decisional tree modeling which identified IL-6 as the first dichotomic marker with a cut-off of 114 pg/mL. Then, a good synergy was found between IL-10, MR-proADM, sIL2Rα, IP10 and CRP in increasing the predictive value in classifying patients at risk or not for a negative outcome. In conclusion, beside IL-6, a panel of other cytokines representing the degree of immunoparalysis and the anti-inflammatory response (IP10, sIL2Rα and IL-10) showed synergic role when combined to biomarkers of systemic inflammation and endothelial dysfunction (CRP, MR-proADM) and may also better explain disease pathogenesis and suggests targeted intervention.