Ma Aolong, Xiang Lingyan, Yuan Jingping, Wang Qianwen, Zhao Lina, Yan Honglin
Department of Pathology, Renmin Hospital of Wuhan University, Wuhan 430060, China.
Biomolecules. 2025 Jul 24;15(8):1067. doi: 10.3390/biom15081067.
Breast cancer, the most prevalent malignancy among women worldwide, exhibits significant heterogeneity, particularly in the tumor microenvironment (TME), which poses challenges for treatment. Spatial transcriptomics (ST) has emerged as a transformative technology, enabling gene expression analysis while preserving tissue spatial architecture. This provides unprecedented insights into tumor heterogeneity, cellular interactions, and disease mechanisms, offering a powerful tool for advancing breast cancer research and therapy. This review aims to synthesize the applications of ST in breast cancer research, focusing on its role in decoding tumor heterogeneity, characterizing the TME, elucidating progression and metastasis dynamics, and predicting therapeutic responses. We also explore how ST can bridge molecular profiling with clinical translation to enhance precision therapy. The key scientific concepts of review included the following: We summarize the technological advancements in ST, including imaging-based and sequencing-based methods, and their applications in breast cancer. Key findings highlight how ST resolves spatial heterogeneity across molecular subtypes and histological variants. ST reveals the dynamic interplay between tumor cells, immune cells, and stromal components, uncovering mechanisms of immune evasion, metabolic reprogramming, and therapeutic resistance. Additionally, ST identifies spatial prognostic markers and predicts responses to chemotherapy, targeted therapy, and immunotherapy. We propose that ST serves as a hub for integrating multi-omics data, offering a roadmap for precision oncology and personalized treatment strategies in breast cancer.
乳腺癌是全球女性中最常见的恶性肿瘤，具有显著的异质性，尤其是在肿瘤微环境（TME）中，这给治疗带来了挑战。空间转录组学（ST）已成为一项变革性技术，能够在保留组织空间结构的同时进行基因表达分析。这为肿瘤异质性、细胞间相互作用和疾病机制提供了前所未有的见解，为推进乳腺癌研究和治疗提供了强大工具。本综述旨在综合ST在乳腺癌研究中的应用，重点关注其在解码肿瘤异质性、表征TME、阐明进展和转移动态以及预测治疗反应方面的作用。我们还探讨了ST如何将分子谱分析与临床转化联系起来，以增强精准治疗。综述的关键科学概念包括以下内容：我们总结了ST的技术进展，包括基于成像和基于测序的方法，以及它们在乳腺癌中的应用。主要发现突出了ST如何解决不同分子亚型和组织学变体之间的空间异质性。ST揭示了肿瘤细胞、免疫细胞和基质成分之间的动态相互作用，揭示了免疫逃逸、代谢重编程和治疗耐药的机制。此外，ST还能识别空间预后标志物，并预测对化疗、靶向治疗和免疫治疗的反应。我们认为，ST作为整合多组学数据的枢纽，为乳腺癌的精准肿瘤学和个性化治疗策略提供了路线图。
