EpicPred: predicting phenotypes driven by epitope-binding TCRs using attention-based multiple instance learning

Motivation Correctly identifying epitope-binding T-cell receptors (TCRs) is important to both understand their underlying biological mechanism in association to some phenotype and accordingly develop T-cell mediated immunotherapy treatments. Although the importance of the CDR3 region in TCRs for epi...

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Veröffentlicht in:Bioinformatics (Oxford, England) Jg. 41; H. 3
Hauptverfasser: Jeon, Jaemin, Yu, Suwan, Lee, Sangam, Kim, Sang Cheol, Jo, Hye-Yeong, Jung, Inuk, Kim, Kwangsoo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Oxford University Press 04.03.2025
Schlagworte:
Computational Biology - methods
COVID-19 - immunology
COVID-19 - virology
Epitopes - immunology
Epitopes, T-Lymphocyte - immunology
Epitopes, T-Lymphocyte - metabolism
Humans
Machine Learning
Multiple-Instance Learning Algorithms
Neoplasms - immunology
Original Paper
Phenotype
Receptors, Antigen, T-Cell - chemistry
Receptors, Antigen, T-Cell - genetics
Receptors, Antigen, T-Cell - immunology
Receptors, Antigen, T-Cell - metabolism
SARS-CoV-2 - immunology
Software
ISSN:1367-4811, 1367-4803, 1367-4811
Online-Zugang:Volltext
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Zusammenfassung:Motivation Correctly identifying epitope-binding T-cell receptors (TCRs) is important to both understand their underlying biological mechanism in association to some phenotype and accordingly develop T-cell mediated immunotherapy treatments. Although the importance of the CDR3 region in TCRs for epitope recognition is well recognized, methods for profiling their interactions in association to a certain disease or phenotype remains less studied. We developed EpicPred to identify phenotype-specific TCR–epitope interactions. EpicPred first predicts and removes unlikely TCR–epitope interactions to reduce false positives using the Open-set Recognition (OSR). Subsequently, multiple instance learning was used to identify TCR–epitope interactions specific to a cancer type or severity levels of COVID-19 infected patients. Results From six public TCR databases, 244 552 TCR sequences and 105 unique epitopes were used to predict epitope-binding TCRs and to filter out non-epitope-binding TCRs using the OSR method. The predicted interactions were used to further predict the phenotype groups in two cancer and four COVID-19 TCR-seq datasets of both bulk and single-cell resolution. EpicPred outperformed the competing methods in predicting the phenotypes, achieving an average AUROC of 0.80 ± 0.07. Availability and implementation The EpicPred Software is available at https://github.com/jaeminjj/EpicPred.
Bibliographie:ObjectType-Article-1
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ISSN:1367-4811
1367-4803
1367-4811
DOI:10.1093/bioinformatics/btaf080