Cargando…
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...
Autores principales: | , , , , , , , , , , |
---|---|
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 |
_version_ | 1783383375953788928 |
---|---|
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. |
format | Online Article Text |
id | pubmed-6309579 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT hundaljasreet accountingforproximalvariantsimprovesneoantigenprediction AT kiwalasusanna accountingforproximalvariantsimprovesneoantigenprediction AT fengyangyang accountingforproximalvariantsimprovesneoantigenprediction AT liuconnorj accountingforproximalvariantsimprovesneoantigenprediction AT govindanramaswamy accountingforproximalvariantsimprovesneoantigenprediction AT chapmanwilliamc accountingforproximalvariantsimprovesneoantigenprediction AT uppaluriravindra accountingforproximalvariantsimprovesneoantigenprediction AT swamidasssjoshua accountingforproximalvariantsimprovesneoantigenprediction AT griffithobil accountingforproximalvariantsimprovesneoantigenprediction AT mardiselainer accountingforproximalvariantsimprovesneoantigenprediction AT griffithmalachi accountingforproximalvariantsimprovesneoantigenprediction |