Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patients

Autores
Carri, Ibel; Schwab, Erika; Podaza, Enrique Arturo; García Álvarez, Heli Magalí; Mordoh, José; Nielsen, Morten; Barrios, María Marcela
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In the last years, multiple efforts have been made to accurately predict neoantigens derived from somatic mutations in cancer patients, either to develop personalized therapeutic vaccines or to study immune responses after cancer immunotherapy. In this context, the increasing accessibility of paired whole-exome sequencing (WES) of tumor biopsies and matched normal tissue as well as RNA sequencing (RNA-Seq) has provided a basis for the development of bioinformatics tools that predict and prioritize neoantigen candidates. Most pipelines rely on the binding prediction of candidate peptides to the patient’s major histocompatibility complex (MHC), but these methods return a high number of false positives since they lack information related to other features that influence T cell responses to neoantigens. This review explores available computational methods that incorporate information on T cell preferences to predict their activation after encountering a peptide-MHC complex. Specifically, methods that predict i) biological features that may increase the availability of a neopeptide to be exposed to the immune system, ii) metrics of self-similarity representing the chances of a neoantigen to break immune tolerance, iii) pathogen immunogenicity, and iv) tumor immunogenicity. Also, this review describes the characteristics of these tools and addresses their performance in the context of a novel benchmark dataset of experimentally validated neoantigens from patients treated with a melanoma vaccine (VACCIMEL) in a phase II clinical study. The overall results of the evaluation indicate that current tools have a limited ability to predict the activation of a cytotoxic response against neoantigens. Based on this result, the limitations that make this problem an unsolved challenge in immunoinformatics are discussed.
Fil: Carri, Ibel. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Schwab, Erika. Fundación Cancer; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Podaza, Enrique Arturo. Englander Institute For Precision Medicine; Estados Unidos. Fundación Cancer; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: García Álvarez, Heli Magalí. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Mordoh, José. Fundación Cancer; Argentina
Fil: Nielsen, Morten. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Barrios, María Marcela. Fundación Cancer; Argentina
Materia
Neoantigen
cancer vaccine
melanoma
machine learning
neoepitope prediction
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/229616
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/229616
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patientsCarri, IbelSchwab, ErikaPodaza, Enrique ArturoGarcía Álvarez, Heli MagalíMordoh, JoséNielsen, MortenBarrios, María MarcelaNeoantigencancer vaccinemelanomamachine learningneoepitope predictionhttps://purl.org/becyt/ford/3.1https://purl.org/becyt/ford/3In the last years, multiple efforts have been made to accurately predict neoantigens derived from somatic mutations in cancer patients, either to develop personalized therapeutic vaccines or to study immune responses after cancer immunotherapy. In this context, the increasing accessibility of paired whole-exome sequencing (WES) of tumor biopsies and matched normal tissue as well as RNA sequencing (RNA-Seq) has provided a basis for the development of bioinformatics tools that predict and prioritize neoantigen candidates. Most pipelines rely on the binding prediction of candidate peptides to the patient’s major histocompatibility complex (MHC), but these methods return a high number of false positives since they lack information related to other features that influence T cell responses to neoantigens. This review explores available computational methods that incorporate information on T cell preferences to predict their activation after encountering a peptide-MHC complex. Specifically, methods that predict i) biological features that may increase the availability of a neopeptide to be exposed to the immune system, ii) metrics of self-similarity representing the chances of a neoantigen to break immune tolerance, iii) pathogen immunogenicity, and iv) tumor immunogenicity. Also, this review describes the characteristics of these tools and addresses their performance in the context of a novel benchmark dataset of experimentally validated neoantigens from patients treated with a melanoma vaccine (VACCIMEL) in a phase II clinical study. The overall results of the evaluation indicate that current tools have a limited ability to predict the activation of a cytotoxic response against neoantigens. Based on this result, the limitations that make this problem an unsolved challenge in immunoinformatics are discussed.Fil: Carri, Ibel. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Schwab, Erika. Fundación Cancer; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Podaza, Enrique Arturo. Englander Institute For Precision Medicine; Estados Unidos. Fundación Cancer; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: García Álvarez, Heli Magalí. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Mordoh, José. Fundación Cancer; ArgentinaFil: Nielsen, Morten. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Barrios, María Marcela. Fundación Cancer; ArgentinaOpen Exploration Publishing Inc2023-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/229616Carri, Ibel; Schwab, Erika; Podaza, Enrique Arturo; García Álvarez, Heli Magalí; Mordoh, José; et al.; Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patients; Open Exploration Publishing Inc; Exploration of Immunology; 3; 2; 4-2023; 82-1032768-6655CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.explorationpub.com/Journals/ei/Article/100391info:eu-repo/semantics/altIdentifier/doi/10.37349/ei.2023.00091info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:26:44Zoai:ri.conicet.gov.ar:11336/229616instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:26:44.5CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patients
title Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patients
spellingShingle Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patients
Carri, Ibel
Neoantigen
cancer vaccine
melanoma
machine learning
neoepitope prediction
title_short Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patients
title_full Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patients
title_fullStr Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patients
title_full_unstemmed Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patients
title_sort Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patients
dc.creator.none.fl_str_mv Carri, Ibel
Schwab, Erika
Podaza, Enrique Arturo
García Álvarez, Heli Magalí
Mordoh, José
Nielsen, Morten
Barrios, María Marcela
author Carri, Ibel
author_facet Carri, Ibel
Schwab, Erika
Podaza, Enrique Arturo
García Álvarez, Heli Magalí
Mordoh, José
Nielsen, Morten
Barrios, María Marcela
author_role author
author2 Schwab, Erika
Podaza, Enrique Arturo
García Álvarez, Heli Magalí
Mordoh, José
Nielsen, Morten
Barrios, María Marcela
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Neoantigen
cancer vaccine
melanoma
machine learning
neoepitope prediction
topic Neoantigen
cancer vaccine
melanoma
machine learning
neoepitope prediction
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.1
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv In the last years, multiple efforts have been made to accurately predict neoantigens derived from somatic mutations in cancer patients, either to develop personalized therapeutic vaccines or to study immune responses after cancer immunotherapy. In this context, the increasing accessibility of paired whole-exome sequencing (WES) of tumor biopsies and matched normal tissue as well as RNA sequencing (RNA-Seq) has provided a basis for the development of bioinformatics tools that predict and prioritize neoantigen candidates. Most pipelines rely on the binding prediction of candidate peptides to the patient’s major histocompatibility complex (MHC), but these methods return a high number of false positives since they lack information related to other features that influence T cell responses to neoantigens. This review explores available computational methods that incorporate information on T cell preferences to predict their activation after encountering a peptide-MHC complex. Specifically, methods that predict i) biological features that may increase the availability of a neopeptide to be exposed to the immune system, ii) metrics of self-similarity representing the chances of a neoantigen to break immune tolerance, iii) pathogen immunogenicity, and iv) tumor immunogenicity. Also, this review describes the characteristics of these tools and addresses their performance in the context of a novel benchmark dataset of experimentally validated neoantigens from patients treated with a melanoma vaccine (VACCIMEL) in a phase II clinical study. The overall results of the evaluation indicate that current tools have a limited ability to predict the activation of a cytotoxic response against neoantigens. Based on this result, the limitations that make this problem an unsolved challenge in immunoinformatics are discussed.
Fil: Carri, Ibel. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Schwab, Erika. Fundación Cancer; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Podaza, Enrique Arturo. Englander Institute For Precision Medicine; Estados Unidos. Fundación Cancer; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: García Álvarez, Heli Magalí. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Mordoh, José. Fundación Cancer; Argentina
Fil: Nielsen, Morten. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Barrios, María Marcela. Fundación Cancer; Argentina
description In the last years, multiple efforts have been made to accurately predict neoantigens derived from somatic mutations in cancer patients, either to develop personalized therapeutic vaccines or to study immune responses after cancer immunotherapy. In this context, the increasing accessibility of paired whole-exome sequencing (WES) of tumor biopsies and matched normal tissue as well as RNA sequencing (RNA-Seq) has provided a basis for the development of bioinformatics tools that predict and prioritize neoantigen candidates. Most pipelines rely on the binding prediction of candidate peptides to the patient’s major histocompatibility complex (MHC), but these methods return a high number of false positives since they lack information related to other features that influence T cell responses to neoantigens. This review explores available computational methods that incorporate information on T cell preferences to predict their activation after encountering a peptide-MHC complex. Specifically, methods that predict i) biological features that may increase the availability of a neopeptide to be exposed to the immune system, ii) metrics of self-similarity representing the chances of a neoantigen to break immune tolerance, iii) pathogen immunogenicity, and iv) tumor immunogenicity. Also, this review describes the characteristics of these tools and addresses their performance in the context of a novel benchmark dataset of experimentally validated neoantigens from patients treated with a melanoma vaccine (VACCIMEL) in a phase II clinical study. The overall results of the evaluation indicate that current tools have a limited ability to predict the activation of a cytotoxic response against neoantigens. Based on this result, the limitations that make this problem an unsolved challenge in immunoinformatics are discussed.
publishDate 2023
dc.date.none.fl_str_mv 2023-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/229616
Carri, Ibel; Schwab, Erika; Podaza, Enrique Arturo; García Álvarez, Heli Magalí; Mordoh, José; et al.; Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patients; Open Exploration Publishing Inc; Exploration of Immunology; 3; 2; 4-2023; 82-103
2768-6655
CONICET Digital
CONICET
url http://hdl.handle.net/11336/229616
identifier_str_mv Carri, Ibel; Schwab, Erika; Podaza, Enrique Arturo; García Álvarez, Heli Magalí; Mordoh, José; et al.; Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patients; Open Exploration Publishing Inc; Exploration of Immunology; 3; 2; 4-2023; 82-103
2768-6655
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.explorationpub.com/Journals/ei/Article/100391
info:eu-repo/semantics/altIdentifier/doi/10.37349/ei.2023.00091
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Open Exploration Publishing Inc
publisher.none.fl_str_mv Open Exploration Publishing Inc
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.22299

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