Metabolically optimised NK cell therapies for glioblastoma
Background: Glioblastoma Multiforme (GBM) is an incurable form of brain cancer. However, there is now an opportunity to apply the advances in cellular immunotherapy to treat GBM. Natural Killer (NK) cells are cytotoxic lymphocytes that kill tumour cells. However, GBM tumours create an environment rich in metabolites (eg. fatty acids) and proteins (eg. TGF?) that potently suppress NK cell metabolism and cytotoxicity.
Hypothesis: The metabolic microenvironment of GBM is a key driver of NK cell dysfunction and a limiting factor for NK cell immunotherapies.
Aims: Our primary aim is to establish the nature the suppressive metabolic tumour microenvironment (TME) and to understand how this interferes with infiltrating NK cells. This will guide our secondary aim of developing novel approaches to bolster NK cell metabolism for enhanced cytotoxic activities against GBM tumours.
Methods: Spatial distribution of the metabo-lipidome and TGF? actions within GBM tumours will be performed by DESI-/MALDI-mass spec imaging (Germany) and multiplex immunofluorescence imaging (Belgium). Modelling will estimate the relationship between metabolites, lipids, TGF? pathway components and the immunological landscape with respect to NK cells abundance and functionality (Ireland/Germany). Flow cytometry, confocal and electron microscopy (Ireland/Norway), will define the metabolic phenotype of GBM infiltrating NK cells.
Identified strategies such as genetic engineering of NK cells and/or antibody blockade of TGF? axis for metabolic resilience will be tested in a murine GBM model and applied to human NK cell therapeutic platforms (Norway) towards generating cellular products for clinical trials.
Expected results and potential impact: This research will determine the metabolic restraints experienced by GBM infiltrating NK cells that impair cytotoxicity and develop new strategies to bolster therapeutic NK cells to open a new horizon for effective NK cell-based immunotherapies for GBM.
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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.