A computational workflow for predicting cancer neo-antigens

Neo-antigens presented on cell surface play a pivotal role in the success of immunotherapies. Peptides derived from mutant proteins are thought to be the primary source of neo-antigens presented on the surface of cancer cells. Mutation data from cancer genome sequencing is often used to predict canc...

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Autores principales: Kasaragod, Sandeep, Kotimoole, Chinmaya Narayana, Gurtoo, Sumrati, Keshava Prasad, Thottethodi Subrahmanya, Gowda, Harsha, Modi, Prashant Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Biomedical Informatics 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9722413/
https://www.ncbi.nlm.nih.gov/pubmed/36518130
http://dx.doi.org/10.6026/97320630018214
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author Kasaragod, Sandeep
Kotimoole, Chinmaya Narayana
Gurtoo, Sumrati
Keshava Prasad, Thottethodi Subrahmanya
Gowda, Harsha
Modi, Prashant Kumar
author_facet Kasaragod, Sandeep
Kotimoole, Chinmaya Narayana
Gurtoo, Sumrati
Keshava Prasad, Thottethodi Subrahmanya
Gowda, Harsha
Modi, Prashant Kumar
author_sort Kasaragod, Sandeep
collection PubMed
description Neo-antigens presented on cell surface play a pivotal role in the success of immunotherapies. Peptides derived from mutant proteins are thought to be the primary source of neo-antigens presented on the surface of cancer cells. Mutation data from cancer genome sequencing is often used to predict cancer neo-antigens. However, this strategy is associated with significant false positives as many coding mutations may not be expressed at the protein level. Hence, we describe a computational workflow to integrate genomic and proteomic data to predictpotential neo-antigens.
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spelling pubmed-97224132022-12-13 A computational workflow for predicting cancer neo-antigens Kasaragod, Sandeep Kotimoole, Chinmaya Narayana Gurtoo, Sumrati Keshava Prasad, Thottethodi Subrahmanya Gowda, Harsha Modi, Prashant Kumar Bioinformation Research Article Neo-antigens presented on cell surface play a pivotal role in the success of immunotherapies. Peptides derived from mutant proteins are thought to be the primary source of neo-antigens presented on the surface of cancer cells. Mutation data from cancer genome sequencing is often used to predict cancer neo-antigens. However, this strategy is associated with significant false positives as many coding mutations may not be expressed at the protein level. Hence, we describe a computational workflow to integrate genomic and proteomic data to predictpotential neo-antigens. Biomedical Informatics 2022-03-31 /pmc/articles/PMC9722413/ /pubmed/36518130 http://dx.doi.org/10.6026/97320630018214 Text en © 2022 Biomedical Informatics https://creativecommons.org/licenses/by/3.0/This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
spellingShingle Research Article
Kasaragod, Sandeep
Kotimoole, Chinmaya Narayana
Gurtoo, Sumrati
Keshava Prasad, Thottethodi Subrahmanya
Gowda, Harsha
Modi, Prashant Kumar
A computational workflow for predicting cancer neo-antigens
title A computational workflow for predicting cancer neo-antigens
title_full A computational workflow for predicting cancer neo-antigens
title_fullStr A computational workflow for predicting cancer neo-antigens
title_full_unstemmed A computational workflow for predicting cancer neo-antigens
title_short A computational workflow for predicting cancer neo-antigens
title_sort computational workflow for predicting cancer neo-antigens
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9722413/
https://www.ncbi.nlm.nih.gov/pubmed/36518130
http://dx.doi.org/10.6026/97320630018214
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