Unraveling the power of NAP-CNB’s machine learning-enhanced tumor neoantigen prediction

In this study, we present a proof-of-concept classical vaccination experiment that validates the in silico identification of tumor neoantigens (TNAs) using a machine learning-based platform called NAP-CNB. Unlike other TNA predictors, NAP-CNB leverages RNA-seq data to consider the relative expression of neoantigens in tumors. Our experiments show the efficacy of NAP-CNB. Predicted TNAs elicited potent antitumor responses in mice following classical vaccination protocols. Notably, optimal antitumor activity was observed when targeting the antigen with higher expression in the tumor, which was not the most immunogenic. Additionally, the vaccination combining different neoantigens resulted in vastly improved responses compared to each one individually, showing the worth of multiantigen-based approaches. These findings validate NAP-CNB as an innovative TNA identification platform and make a substantial contribution to advancing the next generation of personalized immunotherapies.

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Publication Details

Journal
eLife
Published
2025-03-05
DOI
https://doi.org/10.7554/elife.95010.3
Primary Topic
Cancer Immunotherapy and Biomarkers
Type
article
Field-Weighted Citation Impact
0.00

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article

Unraveling the power of NAP-CNB’s machine learning-enhanced tumor neoantigen prediction

Carlos Óscar S. Sorzano, Arrate Muñoz‐Barrutia, José Ramón Macías, Rubén Sánchez-García et al.
eLife
Cancer Immunotherapy and Biomarkers
article

Unraveling the power of NAP-CNB’s machine learning-enhanced tumor neoantigen prediction

Carlos Óscar S. Sorzano, Arrate Muñoz‐Barrutia, José Ramón Macías, Rubén Sánchez-García, Almudena Méndez‐Pérez, Carlos Wert-Carvajal, Rebeca Sanz‐Pamplona, Esteban Veiga, Ramón Alemany, Andrés M. Acosta-Moreno, Pilar Ballesteros-Cuartero
article en

Abstract

In this study, we present a proof-of-concept classical vaccination experiment that validates the in silico identification of tumor neoantigens (TNAs) using a machine learning-based platform called NAP-CNB. Unlike other TNA predictors, NAP-CNB leverages RNA-seq data to consider the relative expression of neoantigens in tumors. Our experiments show the efficacy of NAP-CNB. Predicted TNAs elicited potent antitumor responses in mice following classical vaccination protocols. Notably, optimal antitumor activity was observed when targeting the antigen with higher expression in the tumor, which was not the most immunogenic. Additionally, the vaccination combining different neoantigens resulted in vastly improved responses compared to each one individually, showing the worth of multiantigen-based approaches. These findings validate NAP-CNB as an innovative TNA identification platform and make a substantial contribution to advancing the next generation of personalized immunotherapies.

eLifeVol. 13
Consejo Superior de Investigaciones Científicas (ES), University of Oxford (GB), Fundacion Agencia Aragonesa para la Investigacion y el Desarrollo (ES), Institut d'Investigació Biomédica de Bellvitge (ES), Institut Català d'Oncologia (ES), Universidad Carlos III de Madrid (ES)
Ministerio de Ciencia e Innovación, Fundación Fero, Agencia Estatal de Investigación
Openalex Percentile: Top 94%
Cancer Immunotherapy and Biomarkers
DOIOpen Access PDFOpenAlex
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