Rapid assessment of T-cell receptor specificity of the immune repertoire
Accurate assessment of T-cell-receptor (TCR)–antigen specificity across the whole immune repertoire lies at the heart of improved cancer immunotherapy, but predictive models capable of high-throughput assessment of TCR–peptide pairs are lacking. Recent advances in deep sequencing and crystallography...
| Main Authors: | , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Science and Business Media LLC
2021
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| Online Access: | https://hdl.handle.net/1721.1/131255 |
| _version_ | 1811095074168111104 |
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| author | Lin, Xingcheng George, Jason T. Schafer, Nicholas P. Ng Chau, Kevin Birnbaum, Michael E Clementi, Cecilia Onuchic, José N. Levine, Herbert |
| author2 | Massachusetts Institute of Technology. Department of Chemistry |
| author_facet | Massachusetts Institute of Technology. Department of Chemistry Lin, Xingcheng George, Jason T. Schafer, Nicholas P. Ng Chau, Kevin Birnbaum, Michael E Clementi, Cecilia Onuchic, José N. Levine, Herbert |
| author_sort | Lin, Xingcheng |
| collection | MIT |
| description | Accurate assessment of T-cell-receptor (TCR)–antigen specificity across the whole immune repertoire lies at the heart of improved cancer immunotherapy, but predictive models capable of high-throughput assessment of TCR–peptide pairs are lacking. Recent advances in deep sequencing and crystallography have enriched the data available for studying TCR–peptide systems. Here, we introduce RACER, a pairwise energy model capable of rapid assessment of TCR–peptide affinity for entire immune repertoires. RACER applies supervised machine learning to efficiently and accurately resolve strong TCR–peptide binding pairs from weak ones. The trained parameters further enable a physical interpretation of interacting patterns encoded in each TCR–peptide system. When applied to simulate thymic selection of a major-histocompatibility-complex (MHC)-restricted T-cell repertoire, RACER accurately estimates recognition rates for tumor-associated neoantigens and foreign peptides, thus demonstrating its utility in helping address the computational challenge of reliably identifying properties of tumor antigen-specific T-cells at the level of an individual patient’s immune repertoire. |
| first_indexed | 2024-09-23T16:10:13Z |
| format | Article |
| id | mit-1721.1/131255 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:10:13Z |
| publishDate | 2021 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1312552022-10-02T06:48:08Z Rapid assessment of T-cell receptor specificity of the immune repertoire Lin, Xingcheng George, Jason T. Schafer, Nicholas P. Ng Chau, Kevin Birnbaum, Michael E Clementi, Cecilia Onuchic, José N. Levine, Herbert Massachusetts Institute of Technology. Department of Chemistry Koch Institute for Integrative Cancer Research at MIT Massachusetts Institute of Technology. Department of Biological Engineering Accurate assessment of T-cell-receptor (TCR)–antigen specificity across the whole immune repertoire lies at the heart of improved cancer immunotherapy, but predictive models capable of high-throughput assessment of TCR–peptide pairs are lacking. Recent advances in deep sequencing and crystallography have enriched the data available for studying TCR–peptide systems. Here, we introduce RACER, a pairwise energy model capable of rapid assessment of TCR–peptide affinity for entire immune repertoires. RACER applies supervised machine learning to efficiently and accurately resolve strong TCR–peptide binding pairs from weak ones. The trained parameters further enable a physical interpretation of interacting patterns encoded in each TCR–peptide system. When applied to simulate thymic selection of a major-histocompatibility-complex (MHC)-restricted T-cell repertoire, RACER accurately estimates recognition rates for tumor-associated neoantigens and foreign peptides, thus demonstrating its utility in helping address the computational challenge of reliably identifying properties of tumor antigen-specific T-cells at the level of an individual patient’s immune repertoire. 2021-09-15T19:20:14Z 2021-09-15T19:20:14Z 2021-05 2020-09 2021-09-13T17:01:53Z Article http://purl.org/eprint/type/JournalArticle 2662-8457 https://hdl.handle.net/1721.1/131255 Lin, Xingcheng et al. "Rapid assessment of T-cell receptor specificity of the immune repertoire." Nature Computational Science 1, 5 (May 2021): 362–373. © 2021 The Author(s) en http://dx.doi.org/10.1038/s43588-021-00076-1 Nature Computational Science Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf application/pdf Springer Science and Business Media LLC Prof. Michael Birnbaum |
| spellingShingle | Lin, Xingcheng George, Jason T. Schafer, Nicholas P. Ng Chau, Kevin Birnbaum, Michael E Clementi, Cecilia Onuchic, José N. Levine, Herbert Rapid assessment of T-cell receptor specificity of the immune repertoire |
| title | Rapid assessment of T-cell receptor specificity of the immune repertoire |
| title_full | Rapid assessment of T-cell receptor specificity of the immune repertoire |
| title_fullStr | Rapid assessment of T-cell receptor specificity of the immune repertoire |
| title_full_unstemmed | Rapid assessment of T-cell receptor specificity of the immune repertoire |
| title_short | Rapid assessment of T-cell receptor specificity of the immune repertoire |
| title_sort | rapid assessment of t cell receptor specificity of the immune repertoire |
| url | https://hdl.handle.net/1721.1/131255 |
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