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...

Full description

Saved in:

Bibliographic Details
Published in:	Bioinformatics (Oxford, England) Vol. 41; no. 3
Main Authors:	Jeon, Jaemin, Yu, Suwan, Lee, Sangam, Kim, Sang Cheol, Jo, Hye-Yeong, Jung, Inuk, Kim, Kwangsoo
Format:	Journal Article
Language:	English
Published:	England Oxford University Press 04.03.2025
Subjects:	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 Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1367-4811 1367-4803 1367-4811
DOI:	10.1093/bioinformatics/btaf080