Single-cell morphological and topological atlas reveals the ecosystem diversity of human breast cancer

Shen Zhao, Chen De-Pin, Tong Fu, Jing-Cheng Yang, Ding Ma, Xiu-Zhi Zhu, Xiang-Xue Wang, Yi-Ping Jiao, Xi Jin, Yi Xiao, Wen-Xuan Xiao, Hu-Yunlong Zhang, Hong Lv, Anant Madabhushi, Wen-Tao Yang (), Yi-Zhou Jiang (), Jun Xu () and Zhi-Ming Shao ()
Additional contact information
Shen Zhao: Fudan University Shanghai Cancer Center
Chen De-Pin: Nanjing University of Information Science and Technology
Tong Fu: Fudan University Shanghai Cancer Center
Jing-Cheng Yang: Fudan University Shanghai Cancer Center
Ding Ma: Fudan University Shanghai Cancer Center
Xiu-Zhi Zhu: Fudan University Shanghai Cancer Center
Xiang-Xue Wang: Nanjing University of Information Science and Technology
Yi-Ping Jiao: Nanjing University of Information Science and Technology
Xi Jin: Fudan University Shanghai Cancer Center
Yi Xiao: Fudan University Shanghai Cancer Center
Wen-Xuan Xiao: Fudan University Shanghai Cancer Center
Hu-Yunlong Zhang: Fudan University Shanghai Cancer Center
Hong Lv: Fudan University Shanghai Cancer Center
Anant Madabhushi: Georgia Institute of Technology and Emory University
Wen-Tao Yang: Fudan University Shanghai Cancer Center
Yi-Zhou Jiang: Fudan University Shanghai Cancer Center
Jun Xu: Nanjing University of Information Science and Technology
Zhi-Ming Shao: Fudan University Shanghai Cancer Center

Nature Communications, 2023, vol. 14, issue 1, 1-22

Abstract: Abstract Digital pathology allows computerized analysis of tumor ecosystem using whole slide images (WSIs). Here, we present single-cell morphological and topological profiling (sc-MTOP) to characterize tumor ecosystem by extracting the features of nuclear morphology and intercellular spatial relationship for individual cells. We construct a single-cell atlas comprising 410 million cells from 637 breast cancer WSIs and dissect the phenotypic diversity within tumor, inflammatory and stroma cells respectively. Spatially-resolved analysis identifies recurrent micro-ecological modules representing locoregional multicellular structures and reveals four breast cancer ecotypes correlating with distinct molecular features and patient prognosis. Further analysis with multiomics data uncovers clinically relevant ecosystem features. High abundance of locally-aggregated inflammatory cells indicates immune-activated tumor microenvironment and favorable immunotherapy response in triple-negative breast cancers. Morphological intratumor heterogeneity of tumor nuclei correlates with cell cycle pathway activation and CDK inhibitors responsiveness in hormone receptor-positive cases. sc-MTOP enables using WSIs to characterize tumor ecosystems at the single-cell level.

Date: 2023
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DOI: 10.1038/s41467-023-42504-y

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