Predicting T cell receptor functionality against mutant epitopes
Felix Drost1, Emilio Dorigatti2, Adrian Straub3
1Institute of Computational Biology, Helmholtz Center Munich, 85764 Neuherberg, Germany; School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany.
Abstract:
Cancer cells and pathogens can evade T cell receptors (TCRs) via mutations in immunogenic epitopes. TCR cross-reactivity (i.e., recognition of multiple epitopes with sequence similarities) can counteract such escape but may cause severe side effects in cell-based immunotherapies through targeting self-antigens. To predict the effect of epitope point mutations on T cell functionality, we here present the random forest-based model Predicting T Cell Epitope-Specific Activation against Mutant Versions (P-TEAM). P-TEAM was trained and tested on three datasets with TCR responses to single-amino-acid mutations of the model epitope SIINFEKL, the tumor neo-epitope VPSVWRSSL, and the human cytomegalovirus antigen NLVPMVATV, totaling 9,690 unique TCR-epitope interactions. P-TEAM was able to accurately classify T cell reactivities and quantitatively predict T cell functionalities for unobserved single-point mutations and unseen TCRs. Overall, P-TEAM provides an effective computational tool to study T cell responses against mutated epitopes.
Insights
Predicting T Cell Epitope-Specific Activation against Mutant Versions (P-TEAM) is a new computational model that accurately forecasts T cell responses to mutated epitopes. This tool helps understand T cell functionality against cancer cells and pathogens evading immune detection.
Area of Science:
- Immunology
- Computational Biology
- Bioinformatics
Background:
- Cancer cells and pathogens mutate epitopes to evade T cell receptors (TCRs).
- TCR cross-reactivity can counter immune evasion but risks autoimmune side effects in immunotherapies.
- Predicting T cell responses to mutated epitopes is crucial for effective cancer and infectious disease treatments.
Purpose of the Study:
- To develop a predictive model for T cell functionality against mutated epitopes.
- To assess the impact of single-point mutations on T cell receptor interactions.
- To provide a computational tool for studying T cell responses in immunotherapy.
Main Methods:
- Developed a random forest-based model named Predicting T Cell Epitope-Specific Activation against Mutant Versions (P-TEAM).
- Trained and validated P-TEAM on three datasets covering single-amino-acid mutations of model epitopes (SIINFEKL, VPSVWRSSL, NLVPMVATV).
- Evaluated model performance on 9,690 unique TCR-epitope interactions, including unseen TCRs and mutations.
Main Results:
- P-TEAM accurately classified T cell reactivities against mutated epitopes.
- The model quantitatively predicted T cell functionalities for novel single-point mutations and TCRs.
- Demonstrated high accuracy in predicting T cell responses across diverse epitope datasets.
Conclusions:
- P-TEAM is an effective computational tool for analyzing T cell responses to mutated epitopes.
- The model can aid in designing safer and more effective cell-based immunotherapies.
- Facilitates understanding of TCR-epitope interactions in immune evasion and therapeutic strategies.
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