Comparative performance analysis of neoepitope prediction algorithms in head and neck cancer
BackgroundMutations in cancer cells can result in the production of neoepitopes that can be recognized by T cells and trigger an immune response. A reliable pipeline to identify such immunogenic neoepitopes for a given tumor would be beneficial for the design of cancer immunotherapies. Current metho...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1494453/full |
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| author | Leila Y. Chihab Leila Y. Chihab Julie G. Burel Aaron M. Miller Aaron M. Miller Luise Westernberg Brandee Brown Jason Greenbaum Michael J. Korrer Stephen P. Schoenberger Sebastian Joyce Sebastian Joyce Young J. Kim Zeynep Koşaloğlu-Yalçin Bjoern Peters Bjoern Peters |
| author_facet | Leila Y. Chihab Leila Y. Chihab Julie G. Burel Aaron M. Miller Aaron M. Miller Luise Westernberg Brandee Brown Jason Greenbaum Michael J. Korrer Stephen P. Schoenberger Sebastian Joyce Sebastian Joyce Young J. Kim Zeynep Koşaloğlu-Yalçin Bjoern Peters Bjoern Peters |
| author_sort | Leila Y. Chihab |
| collection | DOAJ |
| description | BackgroundMutations in cancer cells can result in the production of neoepitopes that can be recognized by T cells and trigger an immune response. A reliable pipeline to identify such immunogenic neoepitopes for a given tumor would be beneficial for the design of cancer immunotherapies. Current methods, such as the pipeline proposed by the Tumor Neoantigen Selection Alliance (TESLA), aim to select short peptides with the highest likelihood to be MHC-I restricted minimal epitopes. Typically, only a small percentage of these predicted epitopes are recognized by T cells when tested experimentally. This is particularly problematic as the limited amount of sample available from patients that are acutely sick restricts the number of peptides that can be tested in practice. This led our group to develop an in-house pipeline termed Identify-Prioritize-Validate (IPV) that identifies long peptides that cover both CD4 and CD8 epitopes.MethodsHere, we systematically compared how IPV performs compared to the TESLA pipeline. Patient peripheral blood mononuclear cells were cultured in vitro with their corresponding candidate peptides, and immune recognition was measured using cytokine-secretion assays.ResultsThe IPV pipeline consistently outperformed the TESLA pipeline in predicting neoepitopes that elicited an immune response in our assay. This was primarily due to the inclusion of longer peptides in IPV compared to TESLA.ConclusionsOur work underscores the improved predictive ability of IPV in comparison to TESLA in this assay system and highlights the need to clearly define which experimental metrics are used to evaluate bioinformatic epitope predictions. |
| format | Article |
| id | doaj-art-e129c37a8bc94a7b8fc2da1b990470c0 |
| institution | DOAJ |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-e129c37a8bc94a7b8fc2da1b990470c02025-08-20T02:47:18ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-03-011610.3389/fimmu.2025.14944531494453Comparative performance analysis of neoepitope prediction algorithms in head and neck cancerLeila Y. Chihab0Leila Y. Chihab1Julie G. Burel2Aaron M. Miller3Aaron M. Miller4Luise Westernberg5Brandee Brown6Jason Greenbaum7Michael J. Korrer8Stephen P. Schoenberger9Sebastian Joyce10Sebastian Joyce11Young J. Kim12Zeynep Koşaloğlu-Yalçin13Bjoern Peters14Bjoern Peters15Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United StatesDepartment of Chemistry and Biochemistry, University of California, San Diego, San Diego, CA, United StatesCenter for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United StatesCenter for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United StatesDivision of Hematology and Oncology, UCSD Moores Cancer Center, San Diego, CA, United StatesCenter for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United StatesDepartment of Otolaryngology-Head and Neck Surgery, Vanderbilt University, Nashville, TN, United StatesCenter for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United StatesDepartment of Otolaryngology-Head and Neck Surgery, Vanderbilt University, Nashville, TN, United StatesCenter for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United StatesDepartment of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, United StatesDepartment of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, TN, United StatesGlobal Clinical Development, Regeneron Pharmaceuticals, Tarrytown, NY, United StatesCenter for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United StatesCenter for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United StatesDepartment of Medicine, University of California, San Diego, San Diego, CA, United StatesBackgroundMutations in cancer cells can result in the production of neoepitopes that can be recognized by T cells and trigger an immune response. A reliable pipeline to identify such immunogenic neoepitopes for a given tumor would be beneficial for the design of cancer immunotherapies. Current methods, such as the pipeline proposed by the Tumor Neoantigen Selection Alliance (TESLA), aim to select short peptides with the highest likelihood to be MHC-I restricted minimal epitopes. Typically, only a small percentage of these predicted epitopes are recognized by T cells when tested experimentally. This is particularly problematic as the limited amount of sample available from patients that are acutely sick restricts the number of peptides that can be tested in practice. This led our group to develop an in-house pipeline termed Identify-Prioritize-Validate (IPV) that identifies long peptides that cover both CD4 and CD8 epitopes.MethodsHere, we systematically compared how IPV performs compared to the TESLA pipeline. Patient peripheral blood mononuclear cells were cultured in vitro with their corresponding candidate peptides, and immune recognition was measured using cytokine-secretion assays.ResultsThe IPV pipeline consistently outperformed the TESLA pipeline in predicting neoepitopes that elicited an immune response in our assay. This was primarily due to the inclusion of longer peptides in IPV compared to TESLA.ConclusionsOur work underscores the improved predictive ability of IPV in comparison to TESLA in this assay system and highlights the need to clearly define which experimental metrics are used to evaluate bioinformatic epitope predictions.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1494453/fullcancerneoepitope predictionneoepitope screeningbioinformaticsimmunogenicity |
| spellingShingle | Leila Y. Chihab Leila Y. Chihab Julie G. Burel Aaron M. Miller Aaron M. Miller Luise Westernberg Brandee Brown Jason Greenbaum Michael J. Korrer Stephen P. Schoenberger Sebastian Joyce Sebastian Joyce Young J. Kim Zeynep Koşaloğlu-Yalçin Bjoern Peters Bjoern Peters Comparative performance analysis of neoepitope prediction algorithms in head and neck cancer Frontiers in Immunology cancer neoepitope prediction neoepitope screening bioinformatics immunogenicity |
| title | Comparative performance analysis of neoepitope prediction algorithms in head and neck cancer |
| title_full | Comparative performance analysis of neoepitope prediction algorithms in head and neck cancer |
| title_fullStr | Comparative performance analysis of neoepitope prediction algorithms in head and neck cancer |
| title_full_unstemmed | Comparative performance analysis of neoepitope prediction algorithms in head and neck cancer |
| title_short | Comparative performance analysis of neoepitope prediction algorithms in head and neck cancer |
| title_sort | comparative performance analysis of neoepitope prediction algorithms in head and neck cancer |
| topic | cancer neoepitope prediction neoepitope screening bioinformatics immunogenicity |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1494453/full |
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