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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| Published in: | Bioinformatics (Oxford, England) Vol. 41; no. 3 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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England
Oxford University Press
04.03.2025
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| ISSN: | 1367-4811, 1367-4803, 1367-4811 |
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| Abstract | 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. |
|---|---|
| AbstractList | 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.MOTIVATIONCorrectly 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.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.RESULTSFrom 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.The EpicPred Software is available at https://github.com/jaeminjj/EpicPred.AVAILABILITY AND IMPLEMENTATIONThe EpicPred Software is available at https://github.com/jaeminjj/EpicPred. 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. 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. The EpicPred Software is available at https://github.com/jaeminjj/EpicPred. 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. |
| Author | Kim, Sang Cheol Lee, Sangam Kim, Kwangsoo Yu, Suwan Jo, Hye-Yeong Jung, Inuk Jeon, Jaemin |
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Correctly identifying epitope-binding T-cell receptors (TCRs) is important to both understand their underlying biological mechanism in association... Correctly identifying epitope-binding T-cell receptors (TCRs) is important to both understand their underlying biological mechanism in association to some... |
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| SubjectTerms | 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 |
| Title | EpicPred: predicting phenotypes driven by epitope-binding TCRs using attention-based multiple instance learning |
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