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VENUS, a Novel Selection Approach to Improve the Accuracy of Neoantigens’ Prediction

Neoantigens are tumor-specific antigens able to induce T-cell responses, generated by mutations in protein-coding regions of expressed genes. Previous studies demonstrated that only a limited subset of mutations generates neoantigens in microsatellite stable tumors. We developed a method, called VEN...

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Autores principales: Leoni, Guido, D’Alise, Anna Morena, Tucci, Fabio Giovanni, Micarelli, Elisa, Garzia, Irene, De Lucia, Maria, Langone, Francesca, Nocchi, Linda, Cotugno, Gabriella, Bartolomeo, Rosa, Romano, Giuseppina, Allocca, Simona, Troise, Fulvia, Nicosia, Alfredo, Lahm, Armin, Scarselli, Elisa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8402534/
https://www.ncbi.nlm.nih.gov/pubmed/34452005
http://dx.doi.org/10.3390/vaccines9080880
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author Leoni, Guido
D’Alise, Anna Morena
Tucci, Fabio Giovanni
Micarelli, Elisa
Garzia, Irene
De Lucia, Maria
Langone, Francesca
Nocchi, Linda
Cotugno, Gabriella
Bartolomeo, Rosa
Romano, Giuseppina
Allocca, Simona
Troise, Fulvia
Nicosia, Alfredo
Lahm, Armin
Scarselli, Elisa
author_facet Leoni, Guido
D’Alise, Anna Morena
Tucci, Fabio Giovanni
Micarelli, Elisa
Garzia, Irene
De Lucia, Maria
Langone, Francesca
Nocchi, Linda
Cotugno, Gabriella
Bartolomeo, Rosa
Romano, Giuseppina
Allocca, Simona
Troise, Fulvia
Nicosia, Alfredo
Lahm, Armin
Scarselli, Elisa
author_sort Leoni, Guido
collection PubMed
description Neoantigens are tumor-specific antigens able to induce T-cell responses, generated by mutations in protein-coding regions of expressed genes. Previous studies demonstrated that only a limited subset of mutations generates neoantigens in microsatellite stable tumors. We developed a method, called VENUS (Vaccine-Encoded Neoantigens Unrestricted Selection), to prioritize mutated peptides with high potential to be neoantigens. Our method assigns to each mutation a weighted score that combines the mutation allelic frequency, the abundance of the transcript coding for the mutation, and the likelihood to bind the patient’s class-I major histocompatibility complex alleles. By ranking mutated peptides encoded by mutations detected in nine cancer patients, VENUS was able to select in the top 60 ranked peptides, the 95% of neoantigens experimentally validated including both CD8 and CD4 T cell specificities. VENUS was evaluated in a murine model in the context of vaccination with an adeno vector encoding the top ranked mutations prioritized in the MC38 cell line. Efficacy studies demonstrated anti tumoral activity of the vaccine when used in combination with checkpoint inhibitors. The results obtained highlight the importance of a combined scoring system taking into account multiple features of each tumor mutation to improve the accuracy of neoantigen prediction.
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spelling pubmed-84025342021-08-29 VENUS, a Novel Selection Approach to Improve the Accuracy of Neoantigens’ Prediction Leoni, Guido D’Alise, Anna Morena Tucci, Fabio Giovanni Micarelli, Elisa Garzia, Irene De Lucia, Maria Langone, Francesca Nocchi, Linda Cotugno, Gabriella Bartolomeo, Rosa Romano, Giuseppina Allocca, Simona Troise, Fulvia Nicosia, Alfredo Lahm, Armin Scarselli, Elisa Vaccines (Basel) Article Neoantigens are tumor-specific antigens able to induce T-cell responses, generated by mutations in protein-coding regions of expressed genes. Previous studies demonstrated that only a limited subset of mutations generates neoantigens in microsatellite stable tumors. We developed a method, called VENUS (Vaccine-Encoded Neoantigens Unrestricted Selection), to prioritize mutated peptides with high potential to be neoantigens. Our method assigns to each mutation a weighted score that combines the mutation allelic frequency, the abundance of the transcript coding for the mutation, and the likelihood to bind the patient’s class-I major histocompatibility complex alleles. By ranking mutated peptides encoded by mutations detected in nine cancer patients, VENUS was able to select in the top 60 ranked peptides, the 95% of neoantigens experimentally validated including both CD8 and CD4 T cell specificities. VENUS was evaluated in a murine model in the context of vaccination with an adeno vector encoding the top ranked mutations prioritized in the MC38 cell line. Efficacy studies demonstrated anti tumoral activity of the vaccine when used in combination with checkpoint inhibitors. The results obtained highlight the importance of a combined scoring system taking into account multiple features of each tumor mutation to improve the accuracy of neoantigen prediction. MDPI 2021-08-09 /pmc/articles/PMC8402534/ /pubmed/34452005 http://dx.doi.org/10.3390/vaccines9080880 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Leoni, Guido
D’Alise, Anna Morena
Tucci, Fabio Giovanni
Micarelli, Elisa
Garzia, Irene
De Lucia, Maria
Langone, Francesca
Nocchi, Linda
Cotugno, Gabriella
Bartolomeo, Rosa
Romano, Giuseppina
Allocca, Simona
Troise, Fulvia
Nicosia, Alfredo
Lahm, Armin
Scarselli, Elisa
VENUS, a Novel Selection Approach to Improve the Accuracy of Neoantigens’ Prediction
title VENUS, a Novel Selection Approach to Improve the Accuracy of Neoantigens’ Prediction
title_full VENUS, a Novel Selection Approach to Improve the Accuracy of Neoantigens’ Prediction
title_fullStr VENUS, a Novel Selection Approach to Improve the Accuracy of Neoantigens’ Prediction
title_full_unstemmed VENUS, a Novel Selection Approach to Improve the Accuracy of Neoantigens’ Prediction
title_short VENUS, a Novel Selection Approach to Improve the Accuracy of Neoantigens’ Prediction
title_sort venus, a novel selection approach to improve the accuracy of neoantigens’ prediction
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8402534/
https://www.ncbi.nlm.nih.gov/pubmed/34452005
http://dx.doi.org/10.3390/vaccines9080880
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