Artificial intelligence in tumor drug resistance: Mechanisms and treatment prospects
Jianyou Gu, Junfeng Zhang, Silüe Zeng, Wenying Zhang, Renpei Xia, Xianxing Wang, Qiang Zhou, Shixiang Guo, Huaizhi Wang, Zhe-Sheng Chen
Abstract
Artificial intelligence (AI) demonstrates unprecedented potential in the study of tumor drug resistance and precision therapy. With the rapid growth of multi-omics data and biomedical information, AI effectively deciphers the complex mechanisms underlying drug resistance, such as gene mutations, overexpression of drug efflux pumps, and changes in the tumor microenvironment. By leveraging the rapid growth of multi-omics data, AI identifies complex drug resistance mechanisms, including genetic mutations, overexpression of drug efflux pumps, and activation of alternative signaling pathways, as well as changes in the tumor microenvironment, such as hypoxia, immune evasion, and stromal interactions. AI promotes personalized treatment by accurately predicting patient responses and resistance risks, thereby supporting the design and dynamic adjustment of individualized therapeutic strategies. AI also plays a pivotal role in the optimization of drug combinations, the development of resistance-reversal strategies, and the design of nano-drug delivery systems. This review explores the cutting-edge advances made in AI and its key applications in tumor drug resistance research and analyzes its future prospects across interdisciplinary fields. Despite remaining challenges involving data privacy and model interpretability, the integration of AI with emerging technologies, such as single-cell sequencing, and cross-disciplinary collaboration will significantly advance the understanding of drug resistance mechanisms and promote progress in precision oncology.