Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges
Abstract Drug resistance is one of the main reasons for cancer treatment failure, leading to a rapid recurrence/disease progression of the cancer. Recently, artificial intelligence (AI) has empowered physicians to use its powerful data processing and pattern recognition capabilities to extract and m...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2025-04-01
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| Series: | Molecular Cancer |
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| Online Access: | https://doi.org/10.1186/s12943-025-02321-x |
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| _version_ | 1849712778167713792 |
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| author | Yuan Mao Dangang Shangguan Qi Huang Ling Xiao Dongsheng Cao Hui Zhou Yi-Kun Wang |
| author_facet | Yuan Mao Dangang Shangguan Qi Huang Ling Xiao Dongsheng Cao Hui Zhou Yi-Kun Wang |
| author_sort | Yuan Mao |
| collection | DOAJ |
| description | Abstract Drug resistance is one of the main reasons for cancer treatment failure, leading to a rapid recurrence/disease progression of the cancer. Recently, artificial intelligence (AI) has empowered physicians to use its powerful data processing and pattern recognition capabilities to extract and mine valuable drug resistance information from large amounts of clinical or omics data, to study drug resistance mechanisms, to evaluate and predict drug resistance, and to develop innovative therapeutic strategies to reduce drug resistance. In this review, we proposed a feasible workflow for incorporating AI into tumor drug resistance research, highlighted current AI-driven tumor drug resistance applications, and discussed the opportunities and challenges encountered in the process. Based on a comprehensive literature analysis, we systematically summarized the role of AI in tumor drug resistance research, including drug development, resistance mechanism elucidation, drug sensitivity prediction, combination therapy optimization, resistance phenotype identification, and clinical biomarker discovery. With the continuous advancement of AI technology and rigorous validation of clinical data, AI models are expected to fuel the development of precision oncology by improving efficacy, guiding therapeutic decisions, and optimizing patient prognosis. In summary, by leveraging clinical and omics data, AI models are expected to pioneer new therapy strategies to mitigate tumor drug resistance, improve efficacy and patient survival, and provide novel perspectives and tools for oncology treatment. Graphical Abstract |
| format | Article |
| id | doaj-art-bc3dee7170e84e8cbff63f153b31df00 |
| institution | DOAJ |
| issn | 1476-4598 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Molecular Cancer |
| spelling | doaj-art-bc3dee7170e84e8cbff63f153b31df002025-08-20T03:14:09ZengBMCMolecular Cancer1476-45982025-04-0124112210.1186/s12943-025-02321-xEmerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challengesYuan Mao0Dangang Shangguan1Qi Huang2Ling Xiao3Dongsheng Cao4Hui Zhou5Yi-Kun Wang6Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South UniversityXiangya School of Pharmaceutical Sciences, Central South UniversityDepartment of Pharmacy, Xiangya Hospital, Central South UniversityDepartment of Histology and Embryology of Xiangya School of Medicine, Central South UniversityHunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South UniversityDepartment of Lymphoma and Hematology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South UniversityHunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South UniversityAbstract Drug resistance is one of the main reasons for cancer treatment failure, leading to a rapid recurrence/disease progression of the cancer. Recently, artificial intelligence (AI) has empowered physicians to use its powerful data processing and pattern recognition capabilities to extract and mine valuable drug resistance information from large amounts of clinical or omics data, to study drug resistance mechanisms, to evaluate and predict drug resistance, and to develop innovative therapeutic strategies to reduce drug resistance. In this review, we proposed a feasible workflow for incorporating AI into tumor drug resistance research, highlighted current AI-driven tumor drug resistance applications, and discussed the opportunities and challenges encountered in the process. Based on a comprehensive literature analysis, we systematically summarized the role of AI in tumor drug resistance research, including drug development, resistance mechanism elucidation, drug sensitivity prediction, combination therapy optimization, resistance phenotype identification, and clinical biomarker discovery. With the continuous advancement of AI technology and rigorous validation of clinical data, AI models are expected to fuel the development of precision oncology by improving efficacy, guiding therapeutic decisions, and optimizing patient prognosis. In summary, by leveraging clinical and omics data, AI models are expected to pioneer new therapy strategies to mitigate tumor drug resistance, improve efficacy and patient survival, and provide novel perspectives and tools for oncology treatment. Graphical Abstracthttps://doi.org/10.1186/s12943-025-02321-xTumor drug resistanceArtificial intelligence-driven precision therapiesMachine learningDeep learning |
| spellingShingle | Yuan Mao Dangang Shangguan Qi Huang Ling Xiao Dongsheng Cao Hui Zhou Yi-Kun Wang Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges Molecular Cancer Tumor drug resistance Artificial intelligence-driven precision therapies Machine learning Deep learning |
| title | Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges |
| title_full | Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges |
| title_fullStr | Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges |
| title_full_unstemmed | Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges |
| title_short | Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges |
| title_sort | emerging artificial intelligence driven precision therapies in tumor drug resistance recent advances opportunities and challenges |
| topic | Tumor drug resistance Artificial intelligence-driven precision therapies Machine learning Deep learning |
| url | https://doi.org/10.1186/s12943-025-02321-x |
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