Predicting T cell receptor functionality against mutant epitopes

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.

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Publication Details

Journal
Cell Genomics
Published
2024-08-15
DOI
https://doi.org/10.1016/j.xgen.2024.100634
Citations
13
Primary Topic
vaccines and immunoinformatics approaches
Type
article
Field-Weighted Citation Impact
2.81

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article

Predicting T cell receptor functionality against mutant epitopes

Kilian Schober, Benjamin Schubert, K Wagner, Marta Montes et al.
13 citations
Cell Genomics
vaccines and immunoinformatics approaches
2.81
article

Predicting T cell receptor functionality against mutant epitopes

Kilian Schober, Benjamin Schubert, K Wagner, Marta Montes, Kersten Heyer, Felix Drost, Dirk Hans Busch, Emilio Dorigatti, Bernd Bischl, Adrian Straub, Philipp Hilgendorf
article en
13 citations

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.

Cell GenomicsVol. 4(9)
Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), Helmholtz Zentrum München (DE), Universitätsklinikum Erlangen (DE), German Center for Infection Research (DE), Institute of Medical Microbiology and Hygiene (DE), Technical University of Munich (DE), Ludwig-Maximilians-Universität München (DE)
Joachim Herz Stiftung, Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung
Zero hunger
Openalex Percentile: Top 9%
vaccines and immunoinformatics approaches
2.81
DOIOpen Access PDFOpenAlex
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