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Accounting for proximal variants improves neoantigen prediction

Recent efforts to design personalized cancer immunotherapies use predicted neoantigens, but most neoantigen prediction strategies do not consider proximal (nearby) variants that alter the peptide sequence and may influence neoantigen binding. We evaluated somatic variants from 430 tumors to understa...

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Autores principales: Hundal, Jasreet, Kiwala, Susanna, Feng, Yang-Yang, Liu, Connor J., Govindan, Ramaswamy, Chapman, William C., Uppaluri, Ravindra, Swamidass, S Joshua, Griffith, Obi L., Mardis, Elaine R., Griffith, Malachi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6309579/
https://www.ncbi.nlm.nih.gov/pubmed/30510237
http://dx.doi.org/10.1038/s41588-018-0283-9
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author Hundal, Jasreet
Kiwala, Susanna
Feng, Yang-Yang
Liu, Connor J.
Govindan, Ramaswamy
Chapman, William C.
Uppaluri, Ravindra
Swamidass, S Joshua
Griffith, Obi L.
Mardis, Elaine R.
Griffith, Malachi
author_facet Hundal, Jasreet
Kiwala, Susanna
Feng, Yang-Yang
Liu, Connor J.
Govindan, Ramaswamy
Chapman, William C.
Uppaluri, Ravindra
Swamidass, S Joshua
Griffith, Obi L.
Mardis, Elaine R.
Griffith, Malachi
author_sort Hundal, Jasreet
collection PubMed
description Recent efforts to design personalized cancer immunotherapies use predicted neoantigens, but most neoantigen prediction strategies do not consider proximal (nearby) variants that alter the peptide sequence and may influence neoantigen binding. We evaluated somatic variants from 430 tumors to understand how proximal somatic and germline alterations change the neoantigenic peptide sequence and also impact neoantigen binding predictions. On average, 241 missense somatic variants were analyzed per sample. Of these somatic variants, 5% had one or more in-phase missense proximal variants. Without incorporating proximal variant correction (PVC) for MHC Class I neoantigen peptides, the overall False Discovery Rate (FDR) (incorrect neoantigens predicted) and the False Negative Rate (FNR) (strong-binding neoantigens missed) across peptides of lengths 8–11 were estimated as 0.069 (6.9%) and 0.026 (2.6%), respectively.
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spelling pubmed-63095792019-06-03 Accounting for proximal variants improves neoantigen prediction Hundal, Jasreet Kiwala, Susanna Feng, Yang-Yang Liu, Connor J. Govindan, Ramaswamy Chapman, William C. Uppaluri, Ravindra Swamidass, S Joshua Griffith, Obi L. Mardis, Elaine R. Griffith, Malachi Nat Genet Article Recent efforts to design personalized cancer immunotherapies use predicted neoantigens, but most neoantigen prediction strategies do not consider proximal (nearby) variants that alter the peptide sequence and may influence neoantigen binding. We evaluated somatic variants from 430 tumors to understand how proximal somatic and germline alterations change the neoantigenic peptide sequence and also impact neoantigen binding predictions. On average, 241 missense somatic variants were analyzed per sample. Of these somatic variants, 5% had one or more in-phase missense proximal variants. Without incorporating proximal variant correction (PVC) for MHC Class I neoantigen peptides, the overall False Discovery Rate (FDR) (incorrect neoantigens predicted) and the False Negative Rate (FNR) (strong-binding neoantigens missed) across peptides of lengths 8–11 were estimated as 0.069 (6.9%) and 0.026 (2.6%), respectively. 2018-12-03 2019-01 /pmc/articles/PMC6309579/ /pubmed/30510237 http://dx.doi.org/10.1038/s41588-018-0283-9 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Hundal, Jasreet
Kiwala, Susanna
Feng, Yang-Yang
Liu, Connor J.
Govindan, Ramaswamy
Chapman, William C.
Uppaluri, Ravindra
Swamidass, S Joshua
Griffith, Obi L.
Mardis, Elaine R.
Griffith, Malachi
Accounting for proximal variants improves neoantigen prediction
title Accounting for proximal variants improves neoantigen prediction
title_full Accounting for proximal variants improves neoantigen prediction
title_fullStr Accounting for proximal variants improves neoantigen prediction
title_full_unstemmed Accounting for proximal variants improves neoantigen prediction
title_short Accounting for proximal variants improves neoantigen prediction
title_sort accounting for proximal variants improves neoantigen prediction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6309579/
https://www.ncbi.nlm.nih.gov/pubmed/30510237
http://dx.doi.org/10.1038/s41588-018-0283-9
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