Machine learning-enhanced T cell neoepitope discovery for immunotherapy design
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/66174 |
Resumo: | Immune responses mediated by T cells are aimed at specific peptides, designated T cell epitopes, that are recognized when bound to human leukocyte antigen (HLA) molecules. The HLA genes are remarkably polymorphic in the human population allowing a broad and fine-tuned capacity to bind a wide array of peptide sequences. Polymorphisms might generate neoepitopes by impacting the HLA-peptide interaction and potentially alter the level and type of generated T cell responses. Multiple algorithms and tools based on machine learning (ML) have been implemented and are able to predict HLA-peptide binding affinity with considerable accuracy. Challenges in this field include the availability of adequate epitope datasets for training and benchmarking and the development of fully integrated pipelines going from next-generation sequencing to neoepitope prediction and quality analysis metrics. Effectively predicting neoepitopes from in silico data is a demanding task that has been facilitated by ML and will be of great value for the future of personalized immunotherapies against cancer and other diseases. |
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Machine learning-enhanced T cell neoepitope discovery for immunotherapy designneoepitopesT cellsimmunotherapymachine learningepitope predictionScience & TechnologyImmune responses mediated by T cells are aimed at specific peptides, designated T cell epitopes, that are recognized when bound to human leukocyte antigen (HLA) molecules. The HLA genes are remarkably polymorphic in the human population allowing a broad and fine-tuned capacity to bind a wide array of peptide sequences. Polymorphisms might generate neoepitopes by impacting the HLA-peptide interaction and potentially alter the level and type of generated T cell responses. Multiple algorithms and tools based on machine learning (ML) have been implemented and are able to predict HLA-peptide binding affinity with considerable accuracy. Challenges in this field include the availability of adequate epitope datasets for training and benchmarking and the development of fully integrated pipelines going from next-generation sequencing to neoepitope prediction and quality analysis metrics. Effectively predicting neoepitopes from in silico data is a demanding task that has been facilitated by ML and will be of great value for the future of personalized immunotherapies against cancer and other diseases.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research received funding from Fundação para a Ciência e a Tecnologia (FCT) contract IF/00474/2014; PhD scholarship SFRH/BD/132797/2017.info:eu-repo/semantics/publishedVersionSAGE PublicationsUniversidade do MinhoMartins, JoanaMagalhães, CarlosRocha, MiguelOsório, Nuno S.20192019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/66174engMartins, Joana; Magalhães, Carlos; Rocha, Miguel; Osório, Nuno S., Machine learning-enhanced T cell neoepitope discovery for immunotherapy design. Cancer Informatics, 18, 1-2, 20191176-935110.1177/1176935119852081https://journals.sagepub.com/home/cixinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:48:52Zoai:repositorium.sdum.uminho.pt:1822/66174Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:47:13.097008Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Machine learning-enhanced T cell neoepitope discovery for immunotherapy design |
title |
Machine learning-enhanced T cell neoepitope discovery for immunotherapy design |
spellingShingle |
Machine learning-enhanced T cell neoepitope discovery for immunotherapy design Martins, Joana neoepitopes T cells immunotherapy machine learning epitope prediction Science & Technology |
title_short |
Machine learning-enhanced T cell neoepitope discovery for immunotherapy design |
title_full |
Machine learning-enhanced T cell neoepitope discovery for immunotherapy design |
title_fullStr |
Machine learning-enhanced T cell neoepitope discovery for immunotherapy design |
title_full_unstemmed |
Machine learning-enhanced T cell neoepitope discovery for immunotherapy design |
title_sort |
Machine learning-enhanced T cell neoepitope discovery for immunotherapy design |
author |
Martins, Joana |
author_facet |
Martins, Joana Magalhães, Carlos Rocha, Miguel Osório, Nuno S. |
author_role |
author |
author2 |
Magalhães, Carlos Rocha, Miguel Osório, Nuno S. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Martins, Joana Magalhães, Carlos Rocha, Miguel Osório, Nuno S. |
dc.subject.por.fl_str_mv |
neoepitopes T cells immunotherapy machine learning epitope prediction Science & Technology |
topic |
neoepitopes T cells immunotherapy machine learning epitope prediction Science & Technology |
description |
Immune responses mediated by T cells are aimed at specific peptides, designated T cell epitopes, that are recognized when bound to human leukocyte antigen (HLA) molecules. The HLA genes are remarkably polymorphic in the human population allowing a broad and fine-tuned capacity to bind a wide array of peptide sequences. Polymorphisms might generate neoepitopes by impacting the HLA-peptide interaction and potentially alter the level and type of generated T cell responses. Multiple algorithms and tools based on machine learning (ML) have been implemented and are able to predict HLA-peptide binding affinity with considerable accuracy. Challenges in this field include the availability of adequate epitope datasets for training and benchmarking and the development of fully integrated pipelines going from next-generation sequencing to neoepitope prediction and quality analysis metrics. Effectively predicting neoepitopes from in silico data is a demanding task that has been facilitated by ML and will be of great value for the future of personalized immunotherapies against cancer and other diseases. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019 2019-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/66174 |
url |
http://hdl.handle.net/1822/66174 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Martins, Joana; Magalhães, Carlos; Rocha, Miguel; Osório, Nuno S., Machine learning-enhanced T cell neoepitope discovery for immunotherapy design. Cancer Informatics, 18, 1-2, 2019 1176-9351 10.1177/1176935119852081 https://journals.sagepub.com/home/cix |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
SAGE Publications |
publisher.none.fl_str_mv |
SAGE Publications |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799133044616986624 |