基于空间域识别的乳腺浸润性导管癌细胞通讯分析
Cell Communication Analysis of Invasive Ductal Carcinoma of the Breast Based on Spatial Domain Recognition
摘要: 乳腺浸润性导管癌(Invasive Ductal Carcinoma, IDC)具有显著的空间异质性，肿瘤细胞、免疫细胞、成纤维细胞等多种细胞共同构成其复杂的微环境结构。为系统解析IDC的空间组织特征及局部细胞通讯规律，本文基于5组IDC Visium空间转录组样本，构建了融合多模态质量控制、自适应图神经网络空间域识别及基于空间约束推断细胞通讯的分析框架。结果表明，各样本空间域识别结果均可以归纳出肿瘤上皮相关、反应性间质相关、免疫浸润相关和界面/应激相关等空间生态位。进一步的域内通讯分析发现，不同空间生态位的通讯功能存在差异。域间通讯分析结果显示，跨域信号具有明显的方向性和模块化特征，其中界面/间质相关区域在不同空间域之间发挥重要的连接和整合作用。本研究纳入样本主要为ER+/PR−/HER2+分子特征的IDC病例，因此本文的结果主要反映该类IDC样本中的空间组织与局部通讯特征。本文为理解特定分子背景下IDC肿瘤微环境的空间异质性提供了分析思路。
Abstract: Invasive Ductal Carcinoma (IDC) of the breast exhibits significant spatial heterogeneity, with its complex microenvironmental structure being collectively constituted by various cell types, including tumor cells, immune cells, and fibroblasts. To systematically elucidate the spatial organizational features and local cellular communication patterns of IDC, this study—based on five IDC Visium spatial transcriptomics samples—established an analytical framework that integrates multimodal quality control, adaptive graph neural network-based spatial domain identification, and spatially constrained inference of cellular communication. The results demonstrate that the spatial domain identification outcomes for each sample can be broadly categorized into distinct spatial niches, such as those associated with tumor epithelium, reactive stroma, immune infiltration, and the interface/stress response. Further intra-domain communication analysis revealed functional differences in communication patterns across these distinct spatial niches. Inter-domain communication analysis indicated that cross-domain signaling exhibits distinct directionality and modular characteristics, with interface/stroma-associated regions playing a pivotal role in connecting and integrating the various spatial domains. The sample set included in this study consists primarily of IDC cases characterized by an ER+/PR−/HER2+ molecular profile; consequently, the results presented herein primarily reflect the spatial organization and local communication features within this specific subset of IDC samples. This study offers an analytical framework for understanding the spatial heterogeneity of the IDC tumor microenvironment within a specific molecular context.
文章引用：王宇, 谭建军. 基于空间域识别的乳腺浸润性导管癌细胞通讯分析[J]. 生物医学, 2026, 16(3): 471-484. https://doi.org/10.12677/hjbm.2026.163050

参考文献

[1] Harris, M.A., Savas, P., Virassamy, B., O’Malley, M.M.R., Kay, J., Mueller, S.N., et al. (2024) Towards Targeting the Breast Cancer Immune Microenvironment. Nature Reviews Cancer, 24, 554-577. [Google Scholar] [CrossRef] [PubMed]
[2] Rodríguez-Bejarano, O.H., Parra-López, C. and Patarroyo, M.A. (2024) A Review Concerning the Breast Cancer-Related Tumour Microenvironment. Critical Reviews in Oncology/Hematology, 199, Article ID: 104389. [Google Scholar] [CrossRef] [PubMed]
[3] Wu, S.Z., Al-Eryani, G., Roden, D.L., Junankar, S., Harvey, K., Andersson, A., et al. (2021) A Single-Cell and Spatially Resolved Atlas of Human Breast Cancers. Nature Genetics, 53, 1334-1347. [Google Scholar] [CrossRef] [PubMed]
[4] Danenberg, E., Bardwell, H., Zanotelli, V.R.T., Provenzano, E., Chin, S., Rueda, O.M., et al. (2022) Breast Tumor Microenvironment Structures Are Associated with Genomic Features and Clinical Outcome. Nature Genetics, 54, 660-669. [Google Scholar] [CrossRef] [PubMed]
[5] Hunter, M.V., Moncada, R., Weiss, J.M., Yanai, I. and White, R.M. (2021) Spatially Resolved Transcriptomics Reveals the Architecture of the Tumor-Microenvironment Interface. Nature Communications, 12, Article No. 6278. [Google Scholar] [CrossRef] [PubMed]
[6] Wang, X., Wang, Z., Liao, Q., Yuan, P., Mei, J., Zhang, Y., et al. (2025) Spatially Resolved Atlas of Breast Cancer Uncovers Intercellular Machinery of Venular Niche Governing Lymphocyte Extravasation. Nature Communications, 16, Article No. 3348. [Google Scholar] [CrossRef] [PubMed]
[7] Williams, C.G., Lee, H.J., Asatsuma, T., Vento-Tormo, R. and Haque, A. (2022) An Introduction to Spatial Transcriptomics for Biomedical Research. Genome Medicine, 14, Article No. 68. [Google Scholar] [CrossRef] [PubMed]
[8] Liu, Y., Dai, Y. and Wang, L. (2026) Spatial Omics at the Forefront: Emerging Technologies, Analytical Innovations, and Clinical Applications. Cancer Cell, 44, 24-49. [Google Scholar] [CrossRef] 
[9] Longo, S.K., Guo, M.G., Ji, A.L. and Khavari, P.A. (2021) Integrating Single-Cell and Spatial Transcriptomics to Elucidate Intercellular Tissue Dynamics. Nature Reviews Genetics, 22, 627-644. [Google Scholar] [CrossRef] [PubMed]
[10] Genomics, X. (2022) Visium Spatial Gene Expression User Guide (CG000239 Rev H). 10x Genomics.
https://www.10xgenomics.com/support/spatial-gene-expression-fresh-frozen/documentation/steps/library-construction/visium-spatial-gene-expression-reagent-kits-user-guide
[11] Eagles, N.J., Bach, S.V., Tippani, M., Ravichandran, P., Du, Y., Miller, R.A., et al. (2024) Integrating Gene Expression and Imaging Data across Visium Capture Areas with VisiumStitched. bioRxiv. [Google Scholar] [CrossRef] [PubMed]
[12] Juwayria, Shrivastava, P., Yadav, K., Das, S., Mittal, S., Kumar, S., et al. (2024) Microarray Integrated Spatial Transcriptomics (MIST) for Affordable and Robust Digital Pathology. npj Systems Biology and Applications, 10, Article No. 142. [Google Scholar] [CrossRef] [PubMed]
[13] Wang, Z., Geng, A., Duan, H., Cui, F., Zou, Q. and Zhang, Z. (2024) A Comprehensive Review of Approaches for Spatial Domain Recognition of Spatial Transcriptomes. Briefings in Functional Genomics, 23, 702-712. [Google Scholar] [CrossRef] [PubMed]
[14] Sun, S., Liu, J., Li, G. and Liu, B. (2025) DeepGFT: Identifying Spatial Domains in Spatial Transcriptomics of Complex and 3D Tissue Using Deep Learning and Graph Fourier Transform. Genome Biology, 26, Article No. 153. [Google Scholar] [CrossRef] [PubMed]
[15] Palla, G., Spitzer, H., Klein, M., Fischer, D., Schaar, A.C., Kuemmerle, L.B., et al. (2022) Squidpy: A Scalable Framework for Spatial Omics Analysis. Nature Methods, 19, 171-178. [Google Scholar] [CrossRef] [PubMed]
[16] Song, W., Wang, D., Li, J. and Zhang, R. (2026) Spatial Transcriptomics: Integrating Platforms and Computational Approaches for Clinical Insights. Biotechnology Advances, 87, Article ID: 108791. [Google Scholar] [CrossRef] 
[17] Jin, S. and Ramos, R. (2022) Computational Exploration of Cellular Communication in Skin from Emerging Single-Cell and Spatial Transcriptomic Data. Biochemical Society Transactions, 50, 297-308. [Google Scholar] [CrossRef] [PubMed]
[18] Cang, Z., Zhao, Y., Almet, A.A., Stabell, A., Ramos, R., Plikus, M.V., et al. (2023) Screening Cell-Cell Communication in Spatial Transcriptomics via Collective Optimal Transport. Nature Methods, 20, 218-228. [Google Scholar] [CrossRef] [PubMed]
[19] Zhu, J., Wang, Y., Chang, W.Y., Malewska, A., Napolitano, F., Gahan, J.C., et al. (2024) Mapping Cellular Interactions from Spatially Resolved Transcriptomics Data. Nature Methods, 21, 1830-1842. [Google Scholar] [CrossRef] [PubMed]
[20] Yang, W., Wang, P., Xu, S., Wang, T., Luo, M., Cai, Y., et al. (2024) Deciphering Cell-Cell Communication at Single-Cell Resolution for Spatial Transcriptomics with Subgraph-Based Graph Attention Network. Nature Communications, 15, Article No. 7101. [Google Scholar] [CrossRef] [PubMed]
[21] Keesling, A.R., Rondini, E.A. and Granneman, J.G. (2025) Spatial Transcriptomics of Adipose Tissue: Technologies, Applications, and Challenges. Journal of Obesity & Metabolic Syndrome, 34, 362-377. [Google Scholar] [CrossRef] 
[22] An, J., Lu, Y., Chen, Y., Chen, Y., Zhou, Z., Chen, J., et al. (2024) Spatial Transcriptomics in Breast Cancer: Providing Insight into Tumor Heterogeneity and Promoting Individualized Therapy. Frontiers in Immunology, 15, Article 1499301. [Google Scholar] [CrossRef] [PubMed]
[23] Tang, Z., Zhang, T., Yang, B., Su, J. and Song, Q. (2023) spaCI: Deciphering Spatial Cellular Communications through Adaptive Graph Model. Briefings in Bioinformatics, 24, bbac563. [Google Scholar] [CrossRef] [PubMed]
[24] Müller-Bötticher, N., Sahay, S., Eils, R. and Ishaque, N. (2025) Spatialleiden: Spatially Aware Leiden Clustering. Genome Biology, 26, Article No. 24. [Google Scholar] [CrossRef] [PubMed]
[25] Sciaraffa, N., Gagliano, A., Augugliaro, L. and Coronnello, C. (2025) Optimization of Clustering Parameters for Single-Cell RNA Analysis Using Intrinsic Goodness Metrics. Frontiers in Bioinformatics, 5, Article 1562410. [Google Scholar] [CrossRef] [PubMed]
[26] Efremova, M., Vento-Tormo, M., Teichmann, S.A. and Vento-Tormo, R. (2020) Cellphonedb: Inferring Cell-Cell Communication from Combined Expression of Multi-Subunit Ligand-Receptor Complexes. Nature Protocols, 15, 1484-1506. [Google Scholar] [CrossRef] [PubMed]
[27] Zohora, F.T., Paliwal, D., Flores-Figueroa, E., Li, J., Gao, T., Notta, F., et al. (2025) Cellnest Reveals Cell-Cell Relay Networks Using Attention Mechanisms on Spatial Transcriptomics. Nature Methods, 22, 1505-1519. [Google Scholar] [CrossRef] [PubMed]
[28] Pong, A., Mah, C.K., Yeo, G.W. and Lewis, N.E. (2024) Computational Cell-Cell Interaction Technologies Drive Mechanistic and Biomarker Discovery in the Tumor Microenvironment. Current Opinion in Biotechnology, 85, Article ID: 103048. [Google Scholar] [CrossRef] [PubMed]
[29] Wu, G., Wang, D., Xiong, F., Wang, Q., Liu, W., Chen, J., et al. (2024) The Emerging Roles of CEACAM6 in Human Cancer (Review). International Journal of Oncology, 64, Article No. 27. [Google Scholar] [CrossRef] [PubMed]
[30] Montenegro, R.C., de Souza, P.F.N. and Mesquita, F.P. (2025) APOE and Cancer Immunology. In: Oria, R., Ashford, J.W. and Smith, C.J., Eds., Apolipoprotein E, Springer, 1-18. [Google Scholar] [CrossRef] 
[31] Zheng, H., An, M., Luo, Y., Diao, X., Zhong, W., Pang, M., et al. (2024) PDGFRα+ITGA11+ Fibroblasts Foster Early-Stage Cancer Lymphovascular Invasion and Lymphatic Metastasis via ITGA11-SELE Interplay. Cancer Cell, 42, 682-700.e12. [Google Scholar] [CrossRef] [PubMed]
[32] Sankar, R., John, S., Sharma, K., Devi, P., Chundawat, P. and Gupta, S. (2025) Integrin Α11: Key Signaling Pathways and Tumor Dynamics. Journal of Oral and Maxillofacial Surgery, Medicine, and Pathology, 37, 546-551. [Google Scholar] [CrossRef] 
[33] Guerrero-Barberà, G., Burday, N. and Costell, M. (2024) Shaping Oncogenic Microenvironments: Contribution of Fibronectin. Frontiers in Cell and Developmental Biology, 12, Article 1363004. [Google Scholar] [CrossRef] [PubMed]
[34] Lin, Y., Choukrani, G., Dubbel, L., Rockstein, L., Freile, J.A., Qi, Y., et al. (2024) VISTA Drives Macrophages towards a Pro-Tumoral Phenotype That Promotes Cancer Cell Phagocytosis Yet Down-Regulates T Cell Responses. Experimental Hematology & Oncology, 13, Article No. 35. [Google Scholar] [CrossRef] [PubMed]
[35] Lou, J., Zhang, J., Song, Z., Zhang, S., Zhao, S., Wei, Y., et al. (2025) M2 Macrophage Polarization Mediated by Complement C3 from Hedgehog-Activated Fibroblasts Establishes an Immunosuppressive Niche in Gastric Cancer. Cancers, 17, Article 3164. [Google Scholar] [CrossRef] 
[36] Ghasemi, K. (2025) C-C Motif Glycoprotein Ligand 5 (CCL5) and Its GPCR CCR5: Macromolecular Game-Changers in Cancer Biology. International Journal of Biological Macromolecules, 329, Article ID: 147737. [Google Scholar] [CrossRef] 
[37] Garg, P., Jallepalli, V.R. and Verma, S. (2024) Unravelling the CXCL12/CXCR4 Axis in Breast Cancer: Insights into Metastasis, Microenvironment Interactions, and Therapeutic Opportunities. Human Gene, 40, Article ID: 201272. [Google Scholar] [CrossRef] 
[38] Luo, Y., Peng, H., Yao, Q., Xie, Y., Liu, D., Wang, Y., et al. (2026) Immunosuppressive Immune Microenvironment Landscapes in Vista-High Gastric Cancer. British Journal of Cancer, 134, 1066-1079. [Google Scholar] [CrossRef] 
[39] Kouzeli, A., Collins, P.J., Metzemaekers, M., Meyrath, M., Szpakowska, M., Artinger, M., et al. (2020) CXCL14 Preferentially Synergizes with Homeostatic Chemokine Receptor Systems. Frontiers in Immunology, 11, Article 561404. [Google Scholar] [CrossRef] [PubMed]
[40] Belhabib, I., Zaghdoudi, S., Lac, C., Bousquet, C. and Jean, C. (2021) Extracellular Matrices and Cancer-Associated Fibroblasts: Targets for Cancer Diagnosis and Therapy? Cancers, 13, Article 3466. [Google Scholar] [CrossRef] [PubMed]
[41] Hui, B., Lu, C., Li, H., Hao, X., Liu, H., Zhuo, D., et al. (2022) Inhibition of APOE Potentiates Immune Checkpoint Therapy for Cancer. International Journal of Biological Sciences, 18, 5230-5240. [Google Scholar] [CrossRef] [PubMed]
[42] Yang, Y., Li, J., Lei, W., Wang, H., Ni, Y., Liu, Y., et al. (2023) CXCL12-CXCR4/CXCR7 Axis in Cancer: From Mechanisms to Clinical Applications. International Journal of Biological Sciences, 19, 3341-3359. [Google Scholar] [CrossRef] [PubMed]
[43] Holderried, T.A.W., de Vos, L., Bawden, E.G., Vogt, T.J., Dietrich, J., Zarbl, R., et al. (2019) Molecular and Immune Correlates of TIM-3 (HAVCR2) and Galectin 9 (LGALS9) mRNA Expression and DNA Methylation in Melanoma. Clinical Epigenetics, 11, Article No. 161. [Google Scholar] [CrossRef] [PubMed]

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