Summary
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.
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.
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| Web resources: | https://cordis.europa.eu/project/id/101116823 |
| Start date: | 01-09-2023 |
| End date: | 31-08-2028 |
| Total budget - Public funding: | 1 497 756,00 Euro - 1 497 756,00 Euro |
Cordis data
Original description
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.
Status
SIGNEDCall topic
ERC-2023-STGUpdate Date
12-03-2024
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