Summary
T cells are a key element of the human immune system. Upon binding to antigenic peptides (called T cell epitopes), T cells can induce the death of infected cells or prime and regulate other immune cells. In cancer immunotherapy treatments, T cells are genetically re-engineered to recognize cancer epitopes and destroy malignant cells. Unfortunately, it still remains challenging to determine which T cells can target a specific epitope, both from a computational and experimental point of view. This limits mechanistic understanding of T-cell-mediated immunity and translational applications for disease treatments.
Thanks to advances in high-throughput sequencing technologies, sequence data of T cells coupled with their cognate epitopes are accumulating at an unprecedented pace, offering unique opportunities to develop data-driven T cell-epitope interaction predictors.
The goal of the MT-PoINT project (Motif in T cells for the Prediction of INTeractions) is to identify patterns in T cells sequences that underlie the binding specificity, interpret them at the structural level, and to develop sequence-based predictors of T cell-epitope interactions (Aim1) with a special focus on T cells targeting cancer epitopes (Aim2). My project will capitalize on a unique dataset of publicly available and in-house generated data that was not available in previous studies, and T cell sequence data from cancer patients of Lausanne University Hospital will allow me to benchmark the in-silico predictors in a clinically relevant setting.
Accurate predictions of TCR-epitope interactions can narrow down the list of T cell candidates for personalized cancer immunotherapies, and significantly accelerate cancer immunotherapy clinical developments.
Thanks to advances in high-throughput sequencing technologies, sequence data of T cells coupled with their cognate epitopes are accumulating at an unprecedented pace, offering unique opportunities to develop data-driven T cell-epitope interaction predictors.
The goal of the MT-PoINT project (Motif in T cells for the Prediction of INTeractions) is to identify patterns in T cells sequences that underlie the binding specificity, interpret them at the structural level, and to develop sequence-based predictors of T cell-epitope interactions (Aim1) with a special focus on T cells targeting cancer epitopes (Aim2). My project will capitalize on a unique dataset of publicly available and in-house generated data that was not available in previous studies, and T cell sequence data from cancer patients of Lausanne University Hospital will allow me to benchmark the in-silico predictors in a clinically relevant setting.
Accurate predictions of TCR-epitope interactions can narrow down the list of T cell candidates for personalized cancer immunotherapies, and significantly accelerate cancer immunotherapy clinical developments.
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| Web resources: | https://cordis.europa.eu/project/id/101027973 |
| Start date: | 01-07-2021 |
| End date: | 30-06-2023 |
| Total budget - Public funding: | 191 149,44 Euro - 191 149,00 Euro |
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Original description
T cells are a key element of the human immune system. Upon binding to antigenic peptides (called T cell epitopes), T cells can induce the death of infected cells or prime and regulate other immune cells. In cancer immunotherapy treatments, T cells are genetically re-engineered to recognize cancer epitopes and destroy malignant cells. Unfortunately, it still remains challenging to determine which T cells can target a specific epitope, both from a computational and experimental point of view. This limits mechanistic understanding of T-cell-mediated immunity and translational applications for disease treatments.Thanks to advances in high-throughput sequencing technologies, sequence data of T cells coupled with their cognate epitopes are accumulating at an unprecedented pace, offering unique opportunities to develop data-driven T cell-epitope interaction predictors.
The goal of the MT-PoINT project (Motif in T cells for the Prediction of INTeractions) is to identify patterns in T cells sequences that underlie the binding specificity, interpret them at the structural level, and to develop sequence-based predictors of T cell-epitope interactions (Aim1) with a special focus on T cells targeting cancer epitopes (Aim2). My project will capitalize on a unique dataset of publicly available and in-house generated data that was not available in previous studies, and T cell sequence data from cancer patients of Lausanne University Hospital will allow me to benchmark the in-silico predictors in a clinically relevant setting.
Accurate predictions of TCR-epitope interactions can narrow down the list of T cell candidates for personalized cancer immunotherapies, and significantly accelerate cancer immunotherapy clinical developments.
Status
CLOSEDCall topic
MSCA-IF-2020Update Date
28-04-2024
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