Deciphering immune response against glioblastoma to find new targets
Glioblastoma (GBM) is the most frequent brain tumor. Currently survival is poor and few treatments are available. Recent data show that there is no immune privilege of the CNS and that GBM are invaded by effector CD8 T cells, letting us hypothesize that GBM growth depend on immunosurveillance. Our aim is to better understand the antitumor immune response against GBM to unravel new effectors and immunosuppressive pathways important for the regulation of anticancer immunity and to discover new immune activating strategies with the objective to isolate subgroups of GBMs that could benefit from an immunotherapy approach. In this project, GBM tumor samples will be collected during the initial tumor resection. Each tumor biopsy will be subdivided into samples. One of them will be frozen for molecular classification and immune infiltrate eveluation using multiplex PCR. The second sample will be used for histological labeling. Finally, in situ immune response will be analyzed by flow cytometry and functional assay in the third sample. One team will decipher the ability of gamma delta T cells to eliminate GBM cells to make the proof of concept that such cells could be used for cellular therapy. The second team will study CD8 T cell response and investigate the role of nectin and checkpoint inhibitor to define the best strategy to boost local immune response. The third team will study antigen specific immune response to find new vaccine strategy. The last team will study the role and biology of MDSC to define optimal ways to blunt immunosuppression. We will achieve a clear description of the immune response in each molecular class of GBM and its impact on prognosis. In addition, we will identify which strategy modulating the immune response, developed at the moment for other types of cancer, could be efficient in GBM. This will allow the beginning of dedicated clinical trials for GBM with the hope to increase the currently median survival of 15 months.
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This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 964264.