Probing T-cell response by sequence-based probabilistic modeling
With the increasing ability to use high-throughput next-generation sequencing to quantify the diversity of the human T cell receptor (TCR) repertoire, the ability to use TCR sequences to infer antigen-specificity could greatly aid potential diagnostics and therapeutics. Here, we use a machine-learni...
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| Vydáno v: | PLoS computational biology Ročník 17; číslo 9; s. e1009297 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Public Library of Science
02.09.2021
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| Abstract | With the increasing ability to use high-throughput next-generation sequencing to quantify the diversity of the human T cell receptor (TCR) repertoire, the ability to use TCR sequences to infer antigen-specificity could greatly aid potential diagnostics and therapeutics. Here, we use a machine-learning approach known as Restricted Boltzmann Machine to develop a sequence-based inference approach to identify antigen-specific TCRs. Our approach combines probabilistic models of TCR sequences with clone abundance information to extract TCR sequence motifs central to an antigen-specific response. We use this model to identify patient personalized TCR motifs that respond to individual tumor and infectious disease antigens, and to accurately discriminate specific from non-specific responses. Furthermore, the hidden structure of the model results in an interpretable representation space where TCRs responding to the same antigen cluster, correctly discriminating the response of TCR to different viral epitopes. The model can be used to identify condition specific responding TCRs. We focus on the examples of TCRs reactive to candidate neoantigens and selected epitopes in experiments of stimulated TCR clone expansion. |
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| AbstractList | With the increasing ability to use high-throughput next-generation sequencing to quantify the diversity of the human T cell receptor (TCR) repertoire, the ability to use TCR sequences to infer antigen-specificity could greatly aid potential diagnostics and therapeutics. Here, we use a machine-learning approach known as Restricted Boltzmann Machine to develop a sequence-based inference approach to identify antigen-specific TCRs. Our approach combines probabilistic models of TCR sequences with clone abundance information to extract TCR sequence motifs central to an antigen-specific response. We use this model to identify patient personalized TCR motifs that respond to individual tumor and infectious disease antigens, and to accurately discriminate specific from non-specific responses. Furthermore, the hidden structure of the model results in an interpretable representation space where TCRs responding to the same antigen cluster, correctly discriminating the response of TCR to different viral epitopes. The model can be used to identify condition specific responding TCRs. We focus on the examples of TCRs reactive to candidate neoantigens and selected epitopes in experiments of stimulated TCR clone expansion. Large repertoires of immune cells, such as T cells, are increasingly made available by high-throughput sequencing. Exploiting such datasets to infer how T cells respond to antigens could help design vaccines and adoptive T-cell therapies. We here propose an approach based on probabilistic machine learning to identify and characterize responding T cells. After learning, this approach is able to distinguish clones that specifically respond to different antigen stimulations. The model parameters and the low-dimensional representations of the T-cell sequences identify sequence motifs underlying T-cell recognition at the molecular level. The approach is illustrated on repertoire data describing in vitro stimulation of T cells by cancer-related neoantigens, as well as on data for common infectious diseases. With the increasing ability to use high-throughput next-generation sequencing to quantify the diversity of the human T cell receptor (TCR) repertoire, the ability to use TCR sequences to infer antigen-specificity could greatly aid potential diagnostics and therapeutics. Here, we use a machine-learning approach known as Restricted Boltzmann Machine to develop a sequence-based inference approach to identify antigen-specific TCRs. Our approach combines probabilistic models of TCR sequences with clone abundance information to extract TCR sequence motifs central to an antigen-specific response. We use this model to identify patient personalized TCR motifs that respond to individual tumor and infectious disease antigens, and to accurately discriminate specific from non-specific responses. Furthermore, the hidden structure of the model results in an interpretable representation space where TCRs responding to the same antigen cluster, correctly discriminating the response of TCR to different viral epitopes. The model can be used to identify condition specific responding TCRs. We focus on the examples of TCRs reactive to candidate neoantigens and selected epitopes in experiments of stimulated TCR clone expansion. With the increasing ability to use high-throughput next-generation sequencing to quantify the diversity of the human T cell receptor (TCR) repertoire, the ability to use TCR sequences to infer antigen-specificity could greatly aid potential diagnostics and therapeutics. Here, we use a machine-learning approach known as Restricted Boltzmann Machine to develop a sequence-based inference approach to identify antigen-specific TCRs. Our approach combines probabilistic models of TCR sequences with clone abundance information to extract TCR sequence motifs central to an antigen-specific response. We use this model to identify patient personalized TCR motifs that respond to individual tumor and infectious disease antigens, and to accurately discriminate specific from non-specific responses. Furthermore, the hidden structure of the model results in an interpretable representation space where TCRs responding to the same antigen cluster, correctly discriminating the response of TCR to different viral epitopes. The model can be used to identify condition specific responding TCRs. We focus on the examples of TCRs reactive to candidate neoantigens and selected epitopes in experiments of stimulated TCR clone expansion.With the increasing ability to use high-throughput next-generation sequencing to quantify the diversity of the human T cell receptor (TCR) repertoire, the ability to use TCR sequences to infer antigen-specificity could greatly aid potential diagnostics and therapeutics. Here, we use a machine-learning approach known as Restricted Boltzmann Machine to develop a sequence-based inference approach to identify antigen-specific TCRs. Our approach combines probabilistic models of TCR sequences with clone abundance information to extract TCR sequence motifs central to an antigen-specific response. We use this model to identify patient personalized TCR motifs that respond to individual tumor and infectious disease antigens, and to accurately discriminate specific from non-specific responses. Furthermore, the hidden structure of the model results in an interpretable representation space where TCRs responding to the same antigen cluster, correctly discriminating the response of TCR to different viral epitopes. The model can be used to identify condition specific responding TCRs. We focus on the examples of TCRs reactive to candidate neoantigens and selected epitopes in experiments of stimulated TCR clone expansion. |
| Audience | Academic |
| Author | Greenbaum, Benjamin D. Walczak, Aleksandra M. Monasson, Rémi Bravi, Barbara Mora, Thierry Balachandran, Vinod P. Cocco, Simona |
| AuthorAffiliation | 3 Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York State, United States of America Emory University, UNITED STATES 2 Immuno-Oncology Service, Human Oncology and Pathogenesis Program, Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York State, United States of America 1 Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, Paris, France |
| AuthorAffiliation_xml | – name: 2 Immuno-Oncology Service, Human Oncology and Pathogenesis Program, Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York State, United States of America – name: 3 Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York State, United States of America – name: Emory University, UNITED STATES – name: 1 Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, Paris, France |
| Author_xml | – sequence: 1 givenname: Barbara surname: Bravi fullname: Bravi, Barbara – sequence: 2 givenname: Vinod P. surname: Balachandran fullname: Balachandran, Vinod P. – sequence: 3 givenname: Benjamin D. surname: Greenbaum fullname: Greenbaum, Benjamin D. – sequence: 4 givenname: Aleksandra M. surname: Walczak fullname: Walczak, Aleksandra M. – sequence: 5 givenname: Thierry orcidid: 0000-0002-5456-9361 surname: Mora fullname: Mora, Thierry – sequence: 6 givenname: Rémi orcidid: 0000-0002-4459-0204 surname: Monasson fullname: Monasson, Rémi – sequence: 7 givenname: Simona orcidid: 0000-0002-1852-7789 surname: Cocco fullname: Cocco, Simona |
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| CitedBy_id | crossref_primary_10_1209_0295_5075_ada636 crossref_primary_10_3389_fimmu_2022_939394 crossref_primary_10_1038_s41586_022_04735_9 crossref_primary_10_7554_eLife_85126 crossref_primary_10_1038_s41541_023_00795_8 crossref_primary_10_1038_s42003_023_04447_4 crossref_primary_10_1073_pnas_2213264120 |
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| Copyright | COPYRIGHT 2021 Public Library of Science 2021 Bravi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. licence_http://creativecommons.org/publicdomain/zero 2021 Bravi et al 2021 Bravi et al |
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| Notes | new_version ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 PMCID: PMC8476001 I have read the journal’s policy and the authors of this manuscript have the following competing interests: B.G. has received honoraria for speaking engagements from Merck, Bristol-Meyers Squibb, and Chugai Pharmaceuticals, has received research funding from Bristol-Meyers Squibb, and has been a compensated consultant for Darwin health, PMV Pharma and Rome Therapeutics of which he is a cofounder. |
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