ProgModule: A novel computational framework to identify mutation driver modules for predicting cancer prognosis and immunotherapy response
Abstract Background Cancer originates from dysregulated cell proliferation driven by driver gene mutations. Despite numerous algorithms developed to identify genomic mutational signatures, they often suffer from high computational complexity and limited clinical applicability. Methods Here, we prese...
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| Format: | Article |
| Language: | English |
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BMC
2025-05-01
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| Series: | Journal of Translational Medicine |
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| Online Access: | https://doi.org/10.1186/s12967-025-06497-0 |
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| author | Xiangmei Li Bingyue Pan Xilong Zhao Yinchun Su Jiyin Lai Siyuan Li Yalan He Jiashuo Wu Junwei Han |
| author_facet | Xiangmei Li Bingyue Pan Xilong Zhao Yinchun Su Jiyin Lai Siyuan Li Yalan He Jiashuo Wu Junwei Han |
| author_sort | Xiangmei Li |
| collection | DOAJ |
| description | Abstract Background Cancer originates from dysregulated cell proliferation driven by driver gene mutations. Despite numerous algorithms developed to identify genomic mutational signatures, they often suffer from high computational complexity and limited clinical applicability. Methods Here, we presented ProgModule, an advanced computational framework designed to identify mutation driver modules for cancer prognosis and immunotherapy response prediction. In ProgModule, we introduced the Prognosis-Related Mutually Exclusive Mutation (PRMEM) score, which optimizes the balance between exclusive mutation coverage and the incorporation of mutation combination mechanisms critical for cancer prognosis. Results Applying to BLCA and HNSC cohorts, ProgModule successfully identified driver modules that stratify patients into distinct prognostic subgroups, and the combination of these modules could serve as an effective prognostic biomarker. Extending our method to diverse cancers, ProgModule presented robust prognostic performance and stability across model parameters, including stopping criteria and network topology. Moreover, our analysis suggested that driver modules can predict immunotherapeutic benefit more effectively than existing signatures. Further analyses based on published CRISPR data indicated that genes within these modules may serve as potential therapeutic targets. Conclusions Altogether, ProgModule emerges as a powerful tool for identifying mutation driver modules as prognostic and immunotherapy response biomarkers, and genes within these modules may be used as potential therapeutic targets for cancer, offering new insights into precision oncology. |
| format | Article |
| id | doaj-art-570c2ea877d34d79821e9573286c2dc3 |
| institution | OA Journals |
| issn | 1479-5876 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj-art-570c2ea877d34d79821e9573286c2dc32025-08-20T01:49:36ZengBMCJournal of Translational Medicine1479-58762025-05-0123111510.1186/s12967-025-06497-0ProgModule: A novel computational framework to identify mutation driver modules for predicting cancer prognosis and immunotherapy responseXiangmei Li0Bingyue Pan1Xilong Zhao2Yinchun Su3Jiyin Lai4Siyuan Li5Yalan He6Jiashuo Wu7Junwei Han8College of Bioinformatics Science and Technology, Harbin Medical UniversityCollege of Bioinformatics Science and Technology, Harbin Medical UniversityCollege of Bioinformatics Science and Technology, Harbin Medical UniversityDepartment of Neurobiology, Harbin Medical UniversityCollege of Bioinformatics Science and Technology, Harbin Medical UniversityCollege of Bioinformatics Science and Technology, Harbin Medical UniversityCollege of Bioinformatics Science and Technology, Harbin Medical UniversityCollege of Bioinformatics Science and Technology, Harbin Medical UniversityCollege of Bioinformatics Science and Technology, Harbin Medical UniversityAbstract Background Cancer originates from dysregulated cell proliferation driven by driver gene mutations. Despite numerous algorithms developed to identify genomic mutational signatures, they often suffer from high computational complexity and limited clinical applicability. Methods Here, we presented ProgModule, an advanced computational framework designed to identify mutation driver modules for cancer prognosis and immunotherapy response prediction. In ProgModule, we introduced the Prognosis-Related Mutually Exclusive Mutation (PRMEM) score, which optimizes the balance between exclusive mutation coverage and the incorporation of mutation combination mechanisms critical for cancer prognosis. Results Applying to BLCA and HNSC cohorts, ProgModule successfully identified driver modules that stratify patients into distinct prognostic subgroups, and the combination of these modules could serve as an effective prognostic biomarker. Extending our method to diverse cancers, ProgModule presented robust prognostic performance and stability across model parameters, including stopping criteria and network topology. Moreover, our analysis suggested that driver modules can predict immunotherapeutic benefit more effectively than existing signatures. Further analyses based on published CRISPR data indicated that genes within these modules may serve as potential therapeutic targets. Conclusions Altogether, ProgModule emerges as a powerful tool for identifying mutation driver modules as prognostic and immunotherapy response biomarkers, and genes within these modules may be used as potential therapeutic targets for cancer, offering new insights into precision oncology.https://doi.org/10.1186/s12967-025-06497-0Mutually exclusive mutationDriver modulePrognostic biomarkersImmune checkpoint inhibitorsTherapeutic targets |
| spellingShingle | Xiangmei Li Bingyue Pan Xilong Zhao Yinchun Su Jiyin Lai Siyuan Li Yalan He Jiashuo Wu Junwei Han ProgModule: A novel computational framework to identify mutation driver modules for predicting cancer prognosis and immunotherapy response Journal of Translational Medicine Mutually exclusive mutation Driver module Prognostic biomarkers Immune checkpoint inhibitors Therapeutic targets |
| title | ProgModule: A novel computational framework to identify mutation driver modules for predicting cancer prognosis and immunotherapy response |
| title_full | ProgModule: A novel computational framework to identify mutation driver modules for predicting cancer prognosis and immunotherapy response |
| title_fullStr | ProgModule: A novel computational framework to identify mutation driver modules for predicting cancer prognosis and immunotherapy response |
| title_full_unstemmed | ProgModule: A novel computational framework to identify mutation driver modules for predicting cancer prognosis and immunotherapy response |
| title_short | ProgModule: A novel computational framework to identify mutation driver modules for predicting cancer prognosis and immunotherapy response |
| title_sort | progmodule a novel computational framework to identify mutation driver modules for predicting cancer prognosis and immunotherapy response |
| topic | Mutually exclusive mutation Driver module Prognostic biomarkers Immune checkpoint inhibitors Therapeutic targets |
| url | https://doi.org/10.1186/s12967-025-06497-0 |
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