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Single-cell morphological and topological atlas reveals the ecosystem diversity of human breast cancer.
Zhao, Shen; Chen, De-Pin; Fu, Tong; Yang, Jing-Cheng; Ma, Ding; Zhu, Xiu-Zhi; Wang, Xiang-Xue; Jiao, Yi-Ping; Jin, Xi; Xiao, Yi; Xiao, Wen-Xuan; Zhang, Hu-Yunlong; Lv, Hong; Madabhushi, Anant; Yang, Wen-Tao; Jiang, Yi-Zhou; Xu, Jun; Shao, Zhi-Ming.
Afiliação
  • Zhao S; Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
  • Chen DP; Institute for Artificial Intelligence in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China.
  • Fu T; Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
  • Yang JC; Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
  • Ma D; Greater Bay Area Institute of Precision Medicine, Guangzhou, China.
  • Zhu XZ; Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
  • Wang XX; Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
  • Jiao YP; Institute for Artificial Intelligence in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China.
  • Jin X; Institute for Artificial Intelligence in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China.
  • Xiao Y; Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
  • Xiao WX; Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
  • Zhang HY; Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
  • Lv H; Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
  • Madabhushi A; Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China.
  • Yang WT; Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
  • Jiang YZ; Atlanta Veterans Affairs Medical Center, Atlanta, GA, USA.
  • Xu J; Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China. yangwt2000@163.com.
  • Shao ZM; Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China. yizhoujiang@fudan.edu.cn.
Nat Commun ; 14(1): 6796, 2023 10 25.
Article em En | MEDLINE | ID: mdl-37880211
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Neoplasias de Mama Triplo Negativas Limite: Female / Humans Idioma: En Revista: Nat Commun Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Mama / Neoplasias de Mama Triplo Negativas Limite: Female / Humans Idioma: En Revista: Nat Commun Ano de publicação: 2023 Tipo de documento: Article