Yang Qian, Li Mengmeng, Xiao Zian, Feng Yekai, Lei Lanjie, Li Shisheng
Department of Otorhinolaryngology Head and Neck Surgery, The Second Xiangya Hospital, Central South University, Changsha 410011, Hunan, China.
Key Laboratory of Artificial Organs and Computational Medicine in Zhejiang Province, Institute of Translational Medicine, Zhejiang Shuren University, Hangzhou 310015, Zhejiang, China.
Biomater Res. 2025 Mar 24;29:0171. doi: 10.34133/bmr.0171. eCollection 2025.
Precision medicine is a personalized medical model based on the individual's genome, phenotype, and lifestyle that provides tailored treatment plans for patients. In this context, tumor organoids, a 3-dimensional preclinical model based on patient-derived tumor cell self-organization, combined with digital analysis methods, such as high-throughput sequencing and image processing technology, can be used to analyze the genome, transcriptome, and cellular heterogeneity of tumors, so as to accurately track and assess the growth process, genetic characteristics, and drug responsiveness of tumor organoids, thereby facilitating the implementation of precision medicine. This interdisciplinary approach is expected to promote the innovation of cancer diagnosis and enhance personalized treatment. In this review, the characteristics and culture methods of tumor organoids are summarized, and the application of multi-omics, such as bioinformatics and artificial intelligence, and the digital methods of organoids in precision medicine research are discussed. Finally, this review explores the main causes and potential solutions for the bottleneck in the clinical translation of digital tumor organoids, proposes the prospects of multidisciplinary cooperation and clinical transformation to narrow the gap between laboratory and clinical settings, and provides references for research and development in this field.
精准医学是一种基于个体基因组、表型和生活方式的个性化医疗模式，它为患者提供量身定制的治疗方案。在此背景下，肿瘤类器官作为一种基于患者来源的肿瘤细胞自组织形成的三维临床前模型，与高通量测序和图像处理技术等数字分析方法相结合，可用于分析肿瘤的基因组、转录组和细胞异质性，从而准确追踪和评估肿瘤类器官的生长过程、遗传特征和药物反应性，进而推动精准医学的实施。这种跨学科方法有望促进癌症诊断的创新并加强个性化治疗。在这篇综述中，总结了肿瘤类器官的特征和培养方法，并讨论了生物信息学和人工智能等多组学以及类器官数字方法在精准医学研究中的应用。最后，本综述探讨了数字肿瘤类器官临床转化瓶颈的主要原因和潜在解决方案，提出了多学科合作和临床转化以缩小实验室与临床差距的前景，并为该领域的研发提供参考。
