Multi-omics and AI-driven immune subtyping to optimize neoantigen-based vaccines for colorectal cancer
Abstract Colorectal cancer (CRC) presents significant challenges due to limited targeted therapeutic options. This study integrates multi-omics analysis and AI to identify tumor antigens and immune gene targets for personalized immunotherapy. Using TCGA, differential expression and mutation analysis...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-01680-1 |
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| author | Karthick Vasudevan Dhanushkumar T Sripad Rama Hebbar Prasanna Kumar Selvam Majji Rambabu Krishnan Anbarasu Karunakaran Rohini |
| author_facet | Karthick Vasudevan Dhanushkumar T Sripad Rama Hebbar Prasanna Kumar Selvam Majji Rambabu Krishnan Anbarasu Karunakaran Rohini |
| author_sort | Karthick Vasudevan |
| collection | DOAJ |
| description | Abstract Colorectal cancer (CRC) presents significant challenges due to limited targeted therapeutic options. This study integrates multi-omics analysis and AI to identify tumor antigens and immune gene targets for personalized immunotherapy. Using TCGA, differential expression and mutation analysis, we identified overexpressed and mutated genes in CRC. Among these, 62 neoantigens were shortlisted as potential tumor antigens. Survival analysis highlighted prognostic antigens, while their correlation with immune gene expression suggested these antigens could trigger immune activation. Three key neoantigens (TTK, EZH2, and KIF4A) emerged as promising candidates for immunotherapy. Based on immune gene activity, patients were categorized into three Immune Subtypes (IS). IS groups 1 and 2, characterized by high immune gene expression and immune activation markers, exhibited better survival outcomes, while IS 3, with low immune gene expression, showed poor survival and immune unresponsiveness. Neoantigen-based vaccines could potentially boost tumor recognition and improve survival for patients in immune-cold subtypes. Machine learning models like LightGBM, XGBoost, and XGBRF predicted optimal immune targets for vaccine design, validated through SHAP analysis. This study provides a machine learning- driven framework to identify tumor antigens and immune targets, offering a promising strategy for CRC immunotherapy tailored to immune subtype-specific responses. |
| format | Article |
| id | doaj-art-ff501ce845634466b7f2012b04115e13 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ff501ce845634466b7f2012b04115e132025-08-20T02:05:46ZengNature PortfolioScientific Reports2045-23222025-06-0115111410.1038/s41598-025-01680-1Multi-omics and AI-driven immune subtyping to optimize neoantigen-based vaccines for colorectal cancerKarthick Vasudevan0Dhanushkumar T1Sripad Rama Hebbar2Prasanna Kumar Selvam3Majji Rambabu4Krishnan Anbarasu5Karunakaran Rohini6Manipal Academy of Higher Education (MAHE)Department of Biotechnology, School of Applied Sciences, REVA UniversityDepartment of Biotechnology, School of Applied Sciences, REVA UniversityManipal Academy of Higher Education (MAHE)Department of Biotechnology, School of Applied Sciences, REVA UniversityDepartment of Computational Biology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha UniversityDepartment of Computational Biology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha UniversityAbstract Colorectal cancer (CRC) presents significant challenges due to limited targeted therapeutic options. This study integrates multi-omics analysis and AI to identify tumor antigens and immune gene targets for personalized immunotherapy. Using TCGA, differential expression and mutation analysis, we identified overexpressed and mutated genes in CRC. Among these, 62 neoantigens were shortlisted as potential tumor antigens. Survival analysis highlighted prognostic antigens, while their correlation with immune gene expression suggested these antigens could trigger immune activation. Three key neoantigens (TTK, EZH2, and KIF4A) emerged as promising candidates for immunotherapy. Based on immune gene activity, patients were categorized into three Immune Subtypes (IS). IS groups 1 and 2, characterized by high immune gene expression and immune activation markers, exhibited better survival outcomes, while IS 3, with low immune gene expression, showed poor survival and immune unresponsiveness. Neoantigen-based vaccines could potentially boost tumor recognition and improve survival for patients in immune-cold subtypes. Machine learning models like LightGBM, XGBoost, and XGBRF predicted optimal immune targets for vaccine design, validated through SHAP analysis. This study provides a machine learning- driven framework to identify tumor antigens and immune targets, offering a promising strategy for CRC immunotherapy tailored to immune subtype-specific responses.https://doi.org/10.1038/s41598-025-01680-1NeoantigensMachine learningImmune subtypesTumor antigensImmunotherapy |
| spellingShingle | Karthick Vasudevan Dhanushkumar T Sripad Rama Hebbar Prasanna Kumar Selvam Majji Rambabu Krishnan Anbarasu Karunakaran Rohini Multi-omics and AI-driven immune subtyping to optimize neoantigen-based vaccines for colorectal cancer Scientific Reports Neoantigens Machine learning Immune subtypes Tumor antigens Immunotherapy |
| title | Multi-omics and AI-driven immune subtyping to optimize neoantigen-based vaccines for colorectal cancer |
| title_full | Multi-omics and AI-driven immune subtyping to optimize neoantigen-based vaccines for colorectal cancer |
| title_fullStr | Multi-omics and AI-driven immune subtyping to optimize neoantigen-based vaccines for colorectal cancer |
| title_full_unstemmed | Multi-omics and AI-driven immune subtyping to optimize neoantigen-based vaccines for colorectal cancer |
| title_short | Multi-omics and AI-driven immune subtyping to optimize neoantigen-based vaccines for colorectal cancer |
| title_sort | multi omics and ai driven immune subtyping to optimize neoantigen based vaccines for colorectal cancer |
| topic | Neoantigens Machine learning Immune subtypes Tumor antigens Immunotherapy |
| url | https://doi.org/10.1038/s41598-025-01680-1 |
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