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

• Type of Material: Article
• Authors: Chen Zhao, Fudan University; De-Pin Chen, Nanjing University; Tong Fu, Fudan University; Jing-Cheng Yang, Fudan University; Ding Ma, Fudan University; Xiu-Zhi Zhu, Fudan University; Xiang-Xue Wang, Nanjing University; Yi-Ping Jiao, Nanjing University; Xi Jin, Fudan University; Yi Xiao, Fudan University; Wen-Xuan Xiao, Fudan University; Hu-Yunlong Zhang, Fudan University; Hong Lv, Fudan University; Anant Madabhushi, Emory University; Wen-Tao Yang, Fudan University; Yi-Zhou Jian, Fudan University; Jun Xu, Nanjing University; Zhi-Ming Shao, Fudan University
• Language: English
• Date: 2023-10-25
• Publisher: Springer Nature
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2023
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): https://doi.org/10.1038%2Fs41467-023-42504-y
• Title of Journal or Parent Work: Nature Communications
• Volume: 14
• Start Page: 6796
• Grant/Funding Information: This work was supported by grants from the National Key Research and Development Program of China (Grant number 2021YFF1201300 to Y.Z.J.), the National Natural Science Foundation of China (Grant numbers 82103039 to S.Z., 91959207 and 92159301 to Z.M.S.), the Shanghai Key Laboratory of Breast Cancer (12DZ2260100), the SHDC Municipal Project for Developing Emerging and Frontier Technology in Shanghai Hospitals (SHDC12021103) and China Postdoctoral Science Foundation (Grant number 2022M720790 to S.Z.).
• Keywords: Breast cancer; Tumour heterogeneity; Cancer imaging
• Research Categories: Health Sciences, Oncology

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.