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Deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy
T cell receptor (TCR) sequencing has been used to characterize the immune response to cancer. However, most analyses have been restricted to quantitative measures such as clonality that do not leverage the complementarity-determining region 3 (CDR3) sequence. We use DeepTCR, a framework of deep lear...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9481116/ https://www.ncbi.nlm.nih.gov/pubmed/36112691 http://dx.doi.org/10.1126/sciadv.abq5089 |
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author | Sidhom, John-William Oliveira, Giacomo Ross-MacDonald, Petra Wind-Rotolo, Megan Wu, Catherine J. Pardoll, Drew M. Baras, Alexander S. |
author_facet | Sidhom, John-William Oliveira, Giacomo Ross-MacDonald, Petra Wind-Rotolo, Megan Wu, Catherine J. Pardoll, Drew M. Baras, Alexander S. |
author_sort | Sidhom, John-William |
collection | PubMed |
description | T cell receptor (TCR) sequencing has been used to characterize the immune response to cancer. However, most analyses have been restricted to quantitative measures such as clonality that do not leverage the complementarity-determining region 3 (CDR3) sequence. We use DeepTCR, a framework of deep learning algorithms, to reveal sequence concepts that are predictive of response to immunotherapy. We demonstrate that DeepTCR can predict response and use the model to infer the antigenic specificities of the predictive signature and their unique dynamics during therapy. The predictive signature of nonresponse is associated with high frequencies of TCRs predicted to recognize tumor-specific antigens, and these tumor-specific TCRs undergo a higher degree of dynamic changes on therapy in nonresponders versus responders. These results are consistent with a biological model where the hallmark of nonresponders is an accumulation of tumor-specific T cells that undergo turnover on therapy, possibly because of the dysfunctional state of these T cells in nonresponders. |
format | Online Article Text |
id | pubmed-9481116 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-94811162022-09-29 Deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy Sidhom, John-William Oliveira, Giacomo Ross-MacDonald, Petra Wind-Rotolo, Megan Wu, Catherine J. Pardoll, Drew M. Baras, Alexander S. Sci Adv Biomedicine and Life Sciences T cell receptor (TCR) sequencing has been used to characterize the immune response to cancer. However, most analyses have been restricted to quantitative measures such as clonality that do not leverage the complementarity-determining region 3 (CDR3) sequence. We use DeepTCR, a framework of deep learning algorithms, to reveal sequence concepts that are predictive of response to immunotherapy. We demonstrate that DeepTCR can predict response and use the model to infer the antigenic specificities of the predictive signature and their unique dynamics during therapy. The predictive signature of nonresponse is associated with high frequencies of TCRs predicted to recognize tumor-specific antigens, and these tumor-specific TCRs undergo a higher degree of dynamic changes on therapy in nonresponders versus responders. These results are consistent with a biological model where the hallmark of nonresponders is an accumulation of tumor-specific T cells that undergo turnover on therapy, possibly because of the dysfunctional state of these T cells in nonresponders. American Association for the Advancement of Science 2022-09-16 /pmc/articles/PMC9481116/ /pubmed/36112691 http://dx.doi.org/10.1126/sciadv.abq5089 Text en Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited. |
spellingShingle | Biomedicine and Life Sciences Sidhom, John-William Oliveira, Giacomo Ross-MacDonald, Petra Wind-Rotolo, Megan Wu, Catherine J. Pardoll, Drew M. Baras, Alexander S. Deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy |
title | Deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy |
title_full | Deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy |
title_fullStr | Deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy |
title_full_unstemmed | Deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy |
title_short | Deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy |
title_sort | deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy |
topic | Biomedicine and Life Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9481116/ https://www.ncbi.nlm.nih.gov/pubmed/36112691 http://dx.doi.org/10.1126/sciadv.abq5089 |
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