Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens
Abstract T cell recognition of a cognate peptide–major histocompatibility complex (pMHC) presented on the surface of infected or malignant cells is of the utmost importance for mediating robust and long-term immune responses. Accurate predictions of cognate pMHC targets for T cell receptors would gr...
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Published in | Briefings in bioinformatics Vol. 23; no. 3 |
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Main Authors | , , , , , , , , , , , |
Format | Journal Article |
Language | English |
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England
Oxford University Press
13.05.2022
Oxford Publishing Limited (England) |
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Abstract | Abstract
T cell recognition of a cognate peptide–major histocompatibility complex (pMHC) presented on the surface of infected or malignant cells is of the utmost importance for mediating robust and long-term immune responses. Accurate predictions of cognate pMHC targets for T cell receptors would greatly facilitate identification of vaccine targets for both pathogenic diseases and personalized cancer immunotherapies. Predicting immunogenic peptides therefore has been at the center of intensive research for the past decades but has proven challenging. Although numerous models have been proposed, performance of these models has not been systematically evaluated and their success rate in predicting epitopes in the context of human pathology has not been measured and compared. In this study, we evaluated the performance of several publicly available models, in identifying immunogenic CD8+ T cell targets in the context of pathogens and cancers. We found that for predicting immunogenic peptides from an emerging virus such as severe acute respiratory syndrome coronavirus 2, none of the models perform substantially better than random or offer considerable improvement beyond HLA ligand prediction. We also observed suboptimal performance for predicting cancer neoantigens. Through investigation of potential factors associated with ill performance of models, we highlight several data- and model-associated issues. In particular, we observed that cross-HLA variation in the distribution of immunogenic and non-immunogenic peptides in the training data of the models seems to substantially confound the predictions. We additionally compared key parameters associated with immunogenicity between pathogenic peptides and cancer neoantigens and observed evidence for differences in the thresholds of binding affinity and stability, which suggested the need to modulate different features in identifying immunogenic pathogen versus cancer peptides. Overall, we demonstrate that accurate and reliable predictions of immunogenic CD8+ T cell targets remain unsolved; thus, we hope our work will guide users and model developers regarding potential pitfalls and unsettled questions in existing immunogenicity predictors. |
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AbstractList | T cell recognition of a cognate peptide-major histocompatibility complex (pMHC) presented on the surface of infected or malignant cells is of the utmost importance for mediating robust and long-term immune responses. Accurate predictions of cognate pMHC targets for T cell receptors would greatly facilitate identification of vaccine targets for both pathogenic diseases and personalized cancer immunotherapies. Predicting immunogenic peptides therefore has been at the center of intensive research for the past decades but has proven challenging. Although numerous models have been proposed, performance of these models has not been systematically evaluated and their success rate in predicting epitopes in the context of human pathology has not been measured and compared. In this study, we evaluated the performance of several publicly available models, in identifying immunogenic CD8+ T cell targets in the context of pathogens and cancers. We found that for predicting immunogenic peptides from an emerging virus such as severe acute respiratory syndrome coronavirus 2, none of the models perform substantially better than random or offer considerable improvement beyond HLA ligand prediction. We also observed suboptimal performance for predicting cancer neoantigens. Through investigation of potential factors associated with ill performance of models, we highlight several data- and model-associated issues. In particular, we observed that cross-HLA variation in the distribution of immunogenic and non-immunogenic peptides in the training data of the models seems to substantially confound the predictions. We additionally compared key parameters associated with immunogenicity between pathogenic peptides and cancer neoantigens and observed evidence for differences in the thresholds of binding affinity and stability, which suggested the need to modulate different features in identifying immunogenic pathogen versus cancer peptides. Overall, we demonstrate that accurate and reliable predictions of immunogenic CD8+ T cell targets remain unsolved; thus, we hope our work will guide users and model developers regarding potential pitfalls and unsettled questions in existing immunogenicity predictors. Abstract T cell recognition of a cognate peptide–major histocompatibility complex (pMHC) presented on the surface of infected or malignant cells is of the utmost importance for mediating robust and long-term immune responses. Accurate predictions of cognate pMHC targets for T cell receptors would greatly facilitate identification of vaccine targets for both pathogenic diseases and personalized cancer immunotherapies. Predicting immunogenic peptides therefore has been at the center of intensive research for the past decades but has proven challenging. Although numerous models have been proposed, performance of these models has not been systematically evaluated and their success rate in predicting epitopes in the context of human pathology has not been measured and compared. In this study, we evaluated the performance of several publicly available models, in identifying immunogenic CD8+ T cell targets in the context of pathogens and cancers. We found that for predicting immunogenic peptides from an emerging virus such as severe acute respiratory syndrome coronavirus 2, none of the models perform substantially better than random or offer considerable improvement beyond HLA ligand prediction. We also observed suboptimal performance for predicting cancer neoantigens. Through investigation of potential factors associated with ill performance of models, we highlight several data- and model-associated issues. In particular, we observed that cross-HLA variation in the distribution of immunogenic and non-immunogenic peptides in the training data of the models seems to substantially confound the predictions. We additionally compared key parameters associated with immunogenicity between pathogenic peptides and cancer neoantigens and observed evidence for differences in the thresholds of binding affinity and stability, which suggested the need to modulate different features in identifying immunogenic pathogen versus cancer peptides. Overall, we demonstrate that accurate and reliable predictions of immunogenic CD8+ T cell targets remain unsolved; thus, we hope our work will guide users and model developers regarding potential pitfalls and unsettled questions in existing immunogenicity predictors. |
Author | Koohy, Hashem Rei, Margarida Simmons, Alison Woodhouse, Isaac Buckley, Paul R Shughay, Mikhail Woo, Jeongmin Tsvetkov, Vasily O Ma, Ruichong Lee, Chloe H Shcherbinin, Dmitrii S Antanaviciute, Agne |
AuthorAffiliation | 10 Alan Turning Fellow, University of Oxford , Oxford, United Kingdom 8 Institute of Translational Medicine, Pirogov Russian National Research Medical University , Moscow, 117997, Russia 5 Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford , Oxford, United Kingdom 9 The Ludwig Institute for Cancer Research, Old Road Campus Research Building, University of Oxford , Oxford, United Kingdom 2 MRC WIMM Centre for Computational Biology, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford , Oxford, United Kingdom 7 Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry , Moscow, 117997, Russia 3 Department of Neurosurgery, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, University of Oxford , Oxford, United Kingdom 6 Chief Scientific Officer, ImmunoMind Inc , Berkeley, CA, USA 1 MRC Human Immunology Unit, Medical Research Council |
AuthorAffiliation_xml | – name: 6 Chief Scientific Officer, ImmunoMind Inc , Berkeley, CA, USA – name: 5 Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford , Oxford, United Kingdom – name: 1 MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford , Oxford, United Kingdom – name: 8 Institute of Translational Medicine, Pirogov Russian National Research Medical University , Moscow, 117997, Russia – name: 3 Department of Neurosurgery, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, University of Oxford , Oxford, United Kingdom – name: 9 The Ludwig Institute for Cancer Research, Old Road Campus Research Building, University of Oxford , Oxford, United Kingdom – name: 10 Alan Turning Fellow, University of Oxford , Oxford, United Kingdom – name: 2 MRC WIMM Centre for Computational Biology, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford , Oxford, United Kingdom – name: 4 Nuffield Department of Surgical Sciences, University of Oxford , Oxford, United Kingdom – name: 7 Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry , Moscow, 117997, Russia |
Author_xml | – sequence: 1 givenname: Paul R surname: Buckley fullname: Buckley, Paul R email: paul.buckley@rdm.ox.ac.uk organization: MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom – sequence: 2 givenname: Chloe H surname: Lee fullname: Lee, Chloe H email: chloehyun-jung.lee@rdm.ox.ac.uk organization: MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom – sequence: 3 givenname: Ruichong surname: Ma fullname: Ma, Ruichong email: rui.ma@nds.ox.ac.uk organization: MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom – sequence: 4 givenname: Isaac surname: Woodhouse fullname: Woodhouse, Isaac email: isaac.woodhouse@ndm.ox.ac.uk organization: Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom – sequence: 5 givenname: Jeongmin surname: Woo fullname: Woo, Jeongmin email: jeongmin.woo@rdm.ox.ac.uk organization: MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom – sequence: 6 givenname: Vasily O surname: Tsvetkov fullname: Tsvetkov, Vasily O email: seaneuron@gmail.com organization: Chief Scientific Officer, ImmunoMind Inc, Berkeley, CA, USA – sequence: 7 givenname: Dmitrii S surname: Shcherbinin fullname: Shcherbinin, Dmitrii S email: amid.dima@gmail.com organization: Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia – sequence: 8 givenname: Agne surname: Antanaviciute fullname: Antanaviciute, Agne email: agne.antanaviciute@ndm.ox.ac.uk organization: MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom – sequence: 9 givenname: Mikhail surname: Shughay fullname: Shughay, Mikhail email: Mikail.shugay@gmail.com organization: Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia – sequence: 10 givenname: Margarida surname: Rei fullname: Rei, Margarida email: margarida.rei@ludwig.ox.ac.uk organization: The Ludwig Institute for Cancer Research, Old Road Campus Research Building, University of Oxford, Oxford, United Kingdom – sequence: 11 givenname: Alison surname: Simmons fullname: Simmons, Alison email: Alison.simmons@ndm.ox.ac.uk organization: MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom – sequence: 12 givenname: Hashem orcidid: 0000-0002-3640-7043 surname: Koohy fullname: Koohy, Hashem email: hashem.koohy@rdm.ox.ac.uk organization: MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom |
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Keywords | cancer neoantigen peptide immunogenicity CD8 T-cell target peptide presentation immunotherapy T-cell response |
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License | This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. The Author(s) 2022. Published by Oxford University Press. |
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Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Paul R. Buckley, Chloe H. Lee and Ruichong Ma authors contributed equally to this work. |
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T cell recognition of a cognate peptide–major histocompatibility complex (pMHC) presented on the surface of infected or malignant cells is of the... T cell recognition of a cognate peptide-major histocompatibility complex (pMHC) presented on the surface of infected or malignant cells is of the utmost... T cell recognition of a cognate peptide–major histocompatibility complex (pMHC) presented on the surface of infected or malignant cells is of the utmost... |
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SubjectTerms | Cancer Cancer immunotherapy CD8 antigen CD8-Positive T-Lymphocytes - metabolism Cell recognition Computer applications Computer Simulation Context Coronaviruses COVID-19 Epitopes Epitopes, T-Lymphocyte Histocompatibility antigen HLA Human pathology Humans Immunogenicity Lymphocytes Lymphocytes T Major histocompatibility complex Mathematical models Neoantigens Neoplasms Pathogens Peptides Performance evaluation Performance prediction Predictions Problem Solving Protocol Severe acute respiratory syndrome Severe acute respiratory syndrome coronavirus 2 T cell receptors Vaccines Viral diseases Viruses |
Title | Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens |
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