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 expressio...
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| Format: | Article |
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eLife Sciences Publications Ltd
2025-03-01
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| Online Access: | https://elifesciences.org/articles/95010 |
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| author | Almudena Mendez-Perez Andres M Acosta-Moreno Carlos Wert-Carvajal Pilar Ballesteros-Cuartero Ruben Sánchez-García Jose R Macias Rebeca Sanz-Pamplona Ramon Alemany Carlos Oscar Sorzano Arrate Munoz-Barrutia Esteban Veiga |
| author_facet | Almudena Mendez-Perez Andres M Acosta-Moreno Carlos Wert-Carvajal Pilar Ballesteros-Cuartero Ruben Sánchez-García Jose R Macias Rebeca Sanz-Pamplona Ramon Alemany Carlos Oscar Sorzano Arrate Munoz-Barrutia Esteban Veiga |
| author_sort | Almudena Mendez-Perez |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4c3eabc2eba64950b0c0bdbfebb6452d |
| institution | OA Journals |
| issn | 2050-084X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj-art-4c3eabc2eba64950b0c0bdbfebb6452d2025-08-20T01:57:24ZengeLife Sciences Publications LtdeLife2050-084X2025-03-011310.7554/eLife.95010Unraveling the power of NAP-CNB’s machine learning-enhanced tumor neoantigen predictionAlmudena Mendez-Perez0Andres M Acosta-Moreno1Carlos Wert-Carvajal2Pilar Ballesteros-Cuartero3Ruben Sánchez-García4Jose R Macias5Rebeca Sanz-Pamplona6Ramon Alemany7Carlos Oscar Sorzano8Arrate Munoz-Barrutia9https://orcid.org/0000-0002-1573-1661Esteban Veiga10https://orcid.org/0000-0002-7333-2466Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas, Madrid, SpainCentro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas, Madrid, SpainCentro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas, Madrid, Spain; Departamento de Bioingenieria, Universidad Carlos III de Madrid, Leganés, Madrid, SpainCentro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas, Madrid, SpainCentro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas, Madrid, Spain; University of Oxford, Department of Statistics & XChem, Oxford, United KingdomCentro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas, Madrid, SpainCatalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; University Hospital Lozano Blesa, Aragon Health Research Institute (IISA), ARAID Foundation, Aragon Government, Zaragoza, SpainProcure Program, Institut Català d'Oncologia-Oncobell Program, Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, SpainCentro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas, Madrid, SpainDepartamento de Bioingenieria, Universidad Carlos III de Madrid, Leganés, Madrid, SpainCentro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas, Madrid, SpainIn 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.https://elifesciences.org/articles/95010vaccinationantigen identificationcancermachine learning-based platform |
| spellingShingle | Almudena Mendez-Perez Andres M Acosta-Moreno Carlos Wert-Carvajal Pilar Ballesteros-Cuartero Ruben Sánchez-García Jose R Macias Rebeca Sanz-Pamplona Ramon Alemany Carlos Oscar Sorzano Arrate Munoz-Barrutia Esteban Veiga Unraveling the power of NAP-CNB’s machine learning-enhanced tumor neoantigen prediction eLife vaccination antigen identification cancer machine learning-based platform |
| title | Unraveling the power of NAP-CNB’s machine learning-enhanced tumor neoantigen prediction |
| title_full | Unraveling the power of NAP-CNB’s machine learning-enhanced tumor neoantigen prediction |
| title_fullStr | Unraveling the power of NAP-CNB’s machine learning-enhanced tumor neoantigen prediction |
| title_full_unstemmed | Unraveling the power of NAP-CNB’s machine learning-enhanced tumor neoantigen prediction |
| title_short | Unraveling the power of NAP-CNB’s machine learning-enhanced tumor neoantigen prediction |
| title_sort | unraveling the power of nap cnb s machine learning enhanced tumor neoantigen prediction |
| topic | vaccination antigen identification cancer machine learning-based platform |
| url | https://elifesciences.org/articles/95010 |
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