Project description
Metabolic hijack of immune cells by tumours
Despite its great promise, immunotherapy has proved beneficial only for a fraction of cancer patients in clinical practice. Novel approaches are needed to enhance clinical outcomes. Funded by the European Research Council, the SpatialTMEMetabolism project will focus on the metabolism of human immune cells. Researchers will work under the hypothesis that tumours create defined metabolic niches to suppress immune responses. Using single-cell metabolic profiling, researchers will uncover how different cancers influence immune cell metabolism and identify predictive immune cell states for immunotherapy response. The work will lead to the identification of novel molecular targets that can be exploited therapeutically.
Objective
The success of cancer immunotherapy, especially immune checkpoint inhibition (ICI), demonstrates the ability of the immune system to fight tumors. However, only a fraction of patients benefit from currently available therapies, and we need to find novel approaches to improve clinical responses. Cellular metabolism has emerged as a key determinant of multiple aspects of immune cell function, especially T cell exhaustion and anti-inflammatory macrophage polarization. However, we currently do not have a good understanding of the metabolic states of human immune cells since no technology has been available to quantify them directly in clinical tumor tissues.
I hypothesize that tumors create spatially defined metabolic environments, also called metabolic niches, to suppress immune cells and that this mechanism can be targeted to improve cancer immunotherapy. To test this, we will (1) quantify the metabolic states of immune cells in solid human cancers, (2) identify metabolic immune cell states that predict response to ICI, and (3) reveal the mechanism of metabolic niche formation in tumor organoids. We will quantify cellular metabolism and phenotype directly in human tumor tissues, using the innovative single-cell metabolic profiling (scMEP) approach I have recently developed. We will combine this with multiplexed ion beam imaging (MIBI), a technology that enables 40-dimensional proteomic imaging. MIBI imaging will be complemented by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and machine learning frameworks for the analysis of these multi-omic datasets.
Taken together, this project will uncover generalizable concepts of how different tumor entities influence the cellular metabolism of immune cells to modulate their function. The potential therapeutic targets that will emerge from this analysis could thus contribute to improved treatment options for various types of human cancer.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- medical and health sciencesclinical medicineoncology
- medical and health sciencesbasic medicineimmunologyimmunotherapy
- natural sciencesphysical sciencesopticslaser physics
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Keywords
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Topic(s)
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
69120 Heidelberg
Germany