Unraveling tumor specific neoantigen immunogenicity prediction: a comprehensive analysis
IntroductionIdentification of tumor specific neoantigen (TSN) immunogenicity is crucial to develop peptide/mRNA based anti-tumoral vaccines and/or adoptive T-cell immunotherapies; thus, accurate in-silico classification/prioritization proves critical for cost-effective clinical applications. Several...
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Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1094236/full |
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| author | Guadalupe Nibeyro Veronica Baronetto Juan I. Folco Pablo Pastore Maria Romina Girotti Laura Prato Gabriel Morón Gabriel Morón Hugo D. Luján Hugo D. Luján Elmer A. Fernández Elmer A. Fernández Elmer A. Fernández |
| author_facet | Guadalupe Nibeyro Veronica Baronetto Juan I. Folco Pablo Pastore Maria Romina Girotti Laura Prato Gabriel Morón Gabriel Morón Hugo D. Luján Hugo D. Luján Elmer A. Fernández Elmer A. Fernández Elmer A. Fernández |
| author_sort | Guadalupe Nibeyro |
| collection | DOAJ |
| description | IntroductionIdentification of tumor specific neoantigen (TSN) immunogenicity is crucial to develop peptide/mRNA based anti-tumoral vaccines and/or adoptive T-cell immunotherapies; thus, accurate in-silico classification/prioritization proves critical for cost-effective clinical applications. Several methods were proposed as TSNs immunogenicity predictors; however, comprehensive performance comparison is still lacking due to the absence of well documented and adequate TSN databases.MethodsHere, by developing a new curated database having 199 TSNs with experimentally-validated MHC-I presentation and positive/negative immune response (ITSNdb), sixteen metrics were evaluated as immunogenicity predictors. In addition, by using a dataset emulating patient derived TSNs and immunotherapy cohorts containing predicted TSNs for tumor neoantigen burden (TNB) with outcome association, the metrics were evaluated as TSNs prioritizers and as immunotherapy response biomarkers.ResultsOur results show high performance variability among methods, highlighting the need for substantial improvement. Deep learning predictors were top ranked on ITSNdb but show discrepancy on validation databases. In overall, current predicted TNB did not outperform existing biomarkers.ConclusionRecommendations for their clinical application and the ITSNdb are presented to promote development and comparison of computational TSNs immunogenicity predictors. |
| first_indexed | 2024-03-12T21:44:35Z |
| format | Article |
| id | doaj.art-ad5d77a1007d48128d7b7a79f122f733 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-03-12T21:44:35Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-ad5d77a1007d48128d7b7a79f122f7332023-07-26T13:13:49ZengFrontiers Media S.A.Frontiers in Immunology1664-32242023-07-011410.3389/fimmu.2023.10942361094236Unraveling tumor specific neoantigen immunogenicity prediction: a comprehensive analysisGuadalupe Nibeyro0Veronica Baronetto1Juan I. Folco2Pablo Pastore3Maria Romina Girotti4Laura Prato5Gabriel Morón6Gabriel Morón7Hugo D. Luján8Hugo D. Luján9Elmer A. Fernández10Elmer A. Fernández11Elmer A. Fernández12Centro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas (CIDIE), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)/Universidad Católica de Córdoba (UCC) & Fundación para el Progreso de la Medicina, Córdoba, ArgentinaCentro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas (CIDIE), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)/Universidad Católica de Córdoba (UCC) & Fundación para el Progreso de la Medicina, Córdoba, ArgentinaFacultad de Ingeniería, Universidad Católica de Córdoba (UCC), Córdoba, ArgentinaFacultad de Ingeniería, Universidad Católica de Córdoba (UCC), Córdoba, ArgentinaUniversidad Argentina de la Empresa (UADE), Instituto de Tecnología (INTEC), Buenos Aires, ArgentinaInstituto Académico Pedagógico de Ciencias Básicas y Aplicadas, Universidad Nacional de Villa María, Villa María, Córdoba, ArgentinaDepartamento de Bioquímica Clínica, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba (UNC), Córdoba, ArgentinaCentro de Investigaciones en Bioquímica Clínica e Inmunología (CIBICI), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Córdoba, ArgentinaCentro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas (CIDIE), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)/Universidad Católica de Córdoba (UCC) & Fundación para el Progreso de la Medicina, Córdoba, ArgentinaFacultad de Ciencias de la Salud, Universidad Católica de Córdoba (UCC), Córdoba, ArgentinaCentro de Investigación y Desarrollo en Inmunología y Enfermedades Infecciosas (CIDIE), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET)/Universidad Católica de Córdoba (UCC) & Fundación para el Progreso de la Medicina, Córdoba, ArgentinaFacultad de Ingeniería, Universidad Católica de Córdoba (UCC), Córdoba, ArgentinaFacultad de Ciencias Exactas, Físicas y Naturales (FCEFyN), Universidad Nacional de Córdoba (UNC), Córdoba, ArgentinaIntroductionIdentification of tumor specific neoantigen (TSN) immunogenicity is crucial to develop peptide/mRNA based anti-tumoral vaccines and/or adoptive T-cell immunotherapies; thus, accurate in-silico classification/prioritization proves critical for cost-effective clinical applications. Several methods were proposed as TSNs immunogenicity predictors; however, comprehensive performance comparison is still lacking due to the absence of well documented and adequate TSN databases.MethodsHere, by developing a new curated database having 199 TSNs with experimentally-validated MHC-I presentation and positive/negative immune response (ITSNdb), sixteen metrics were evaluated as immunogenicity predictors. In addition, by using a dataset emulating patient derived TSNs and immunotherapy cohorts containing predicted TSNs for tumor neoantigen burden (TNB) with outcome association, the metrics were evaluated as TSNs prioritizers and as immunotherapy response biomarkers.ResultsOur results show high performance variability among methods, highlighting the need for substantial improvement. Deep learning predictors were top ranked on ITSNdb but show discrepancy on validation databases. In overall, current predicted TNB did not outperform existing biomarkers.ConclusionRecommendations for their clinical application and the ITSNdb are presented to promote development and comparison of computational TSNs immunogenicity predictors.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1094236/fullimmunotherapycancer immunologyneopeptideimmunogenic neoantigen databaseimmunoinformatic |
| spellingShingle | Guadalupe Nibeyro Veronica Baronetto Juan I. Folco Pablo Pastore Maria Romina Girotti Laura Prato Gabriel Morón Gabriel Morón Hugo D. Luján Hugo D. Luján Elmer A. Fernández Elmer A. Fernández Elmer A. Fernández Unraveling tumor specific neoantigen immunogenicity prediction: a comprehensive analysis Frontiers in Immunology immunotherapy cancer immunology neopeptide immunogenic neoantigen database immunoinformatic |
| title | Unraveling tumor specific neoantigen immunogenicity prediction: a comprehensive analysis |
| title_full | Unraveling tumor specific neoantigen immunogenicity prediction: a comprehensive analysis |
| title_fullStr | Unraveling tumor specific neoantigen immunogenicity prediction: a comprehensive analysis |
| title_full_unstemmed | Unraveling tumor specific neoantigen immunogenicity prediction: a comprehensive analysis |
| title_short | Unraveling tumor specific neoantigen immunogenicity prediction: a comprehensive analysis |
| title_sort | unraveling tumor specific neoantigen immunogenicity prediction a comprehensive analysis |
| topic | immunotherapy cancer immunology neopeptide immunogenic neoantigen database immunoinformatic |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1094236/full |
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