Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patients
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Open Exploration Publishing Inc.
2023-04-01
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| Series: | Exploration of Immunology |
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| Online Access: | https://www.explorationpub.com/Journals/ei/Article/100391 |
| _version_ | 1797831598465351680 |
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| author | Ibel Carri Erika Schwab Enrique Podaza Heli M. Garcia Alvarez José Mordoh Morten Nielsen María Marcela Barrio |
| author_facet | Ibel Carri Erika Schwab Enrique Podaza Heli M. Garcia Alvarez José Mordoh Morten Nielsen María Marcela Barrio |
| author_sort | Ibel Carri |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-09T13:54:16Z |
| format | Article |
| id | doaj.art-20a4ab5fe42740d892c2977300cc671e |
| institution | Directory Open Access Journal |
| issn | 2768-6655 |
| language | English |
| last_indexed | 2024-04-09T13:54:16Z |
| publishDate | 2023-04-01 |
| publisher | Open Exploration Publishing Inc. |
| record_format | Article |
| series | Exploration of Immunology |
| spelling | doaj.art-20a4ab5fe42740d892c2977300cc671e2023-05-08T08:57:16ZengOpen Exploration Publishing Inc.Exploration of Immunology2768-66552023-04-01328210310.37349/ei.2023.00091Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patientsIbel Carri0https://orcid.org/0000-0003-4561-1211Erika Schwab1https://orcid.org/0009-0007-0575-4601Enrique Podaza2https://orcid.org/0000-0002-3078-9458Heli M. Garcia Alvarez3https://orcid.org/0000-0002-4353-2942José Mordoh4https://orcid.org/0000-0002-8301-9617Morten Nielsen5https://orcid.org/0000-0001-7885-4311María Marcela Barrio6https://orcid.org/0000-0002-5617-8823Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM)—Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires B1650HMP, Argentina; Escuela de Bio y Nanotecnologías (EByN), Universidad Nacional de San Martín, Buenos Aires B1650HMP, ArgentinaCentro de Investigaciones Oncológicas, Fundación Cáncer, Ciudad Autónoma de Buenos Aires C1426ANZ, ArgentinaEnglander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USAInstituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM)—Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires B1650HMP, Argentina; Escuela de Bio y Nanotecnologías (EByN), Universidad Nacional de San Martín, Buenos Aires B1650HMP, ArgentinaCentro de Investigaciones Oncológicas, Fundación Cáncer, Ciudad Autónoma de Buenos Aires C1426ANZ, Argentina; Instituto Alexander Fleming, Ciudad Autónoma de Buenos Aires C1426ANZ, Argentina; Laboratory of Cancerology, Fundación Instituto Leloir, Ciudad Autónoma de Buenos Aires C1405BWE, ArgentinaInstituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM)—Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires B1650HMP, Argentina; Escuela de Bio y Nanotecnologías (EByN), Universidad Nacional de San Martín, Buenos Aires B1650HMP, Argentina; Section of Bioinformatics, Department of Health Technology, Technical University of Denmark, 2800 Lyngby, DenmarkCentro de Investigaciones Oncológicas, Fundación Cáncer, Ciudad Autónoma de Buenos Aires C1426ANZ, ArgentinaIn 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.https://www.explorationpub.com/Journals/ei/Article/100391neoantigencancer vaccinemelanomamachine learningneoepitope prediction |
| spellingShingle | Ibel Carri Erika Schwab Enrique Podaza Heli M. Garcia Alvarez José Mordoh Morten Nielsen María Marcela Barrio Beyond MHC binding: immunogenicity prediction tools to refine neoantigen selection in cancer patients Exploration of Immunology neoantigen cancer vaccine melanoma machine learning neoepitope prediction |
| title | 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_short | 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 |
| topic | neoantigen cancer vaccine melanoma machine learning neoepitope prediction |
| url | https://www.explorationpub.com/Journals/ei/Article/100391 |
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