Single-cell landscape in mammary epithelium reveals bipotent-like cells associated with breast cancer risk and outcome.
Weiyan Chen, Samuel J Morabito, Kai Kessenbrock, Tariq Enver, Kerstin B Meyer, Andrew E Teschendorff
Author Information
- Weiyan Chen: 1CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031 China.
- Samuel J Morabito: 2Chao Family Comprehensive Cancer Center, University of California, Irvine 839 Health Science Road, Sprague Hall 114 Irvine, Irvine, CA 92697-3905 USA. ORCID
- Kai Kessenbrock: 2Chao Family Comprehensive Cancer Center, University of California, Irvine 839 Health Science Road, Sprague Hall 114 Irvine, Irvine, CA 92697-3905 USA. ORCID
- Tariq Enver: 3UCL Cancer Institute, Paul O'Gorman Building, University College London, 72 Huntley Street, London, WC1E 6BT United Kingdom.
- Kerstin B Meyer: 4Wellcome Sanger Institute, Cambridge, CB10 1SA UK. ORCID
- Andrew E Teschendorff: 1CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031 China. ORCID
Adult stem-cells may serve as the cell-of-origin for cancer, yet their unbiased identification in single cell RNA sequencing data is challenging due to the high dropout rate. In the case of breast, the existence of a bipotent stem-like state is also controversial. Here we apply a marker-free algorithm to scRNA-Seq data from the human mammary epithelium, revealing a high-potency cell-state enriched for an independent mammary stem-cell expression module. We validate this stem-like state in independent scRNA-Seq data. Our algorithm further predicts that the stem-like state is bipotent, a prediction we are able to validate using FACS sorted bulk expression data. The bipotent stem-like state correlates with clinical outcome in basal breast cancer and is characterized by overexpression of and , two modulators of basal breast cancer risk. This study illustrates the power of a marker-free computational framework to identify a novel bipotent stem-like state in the mammary epithelium.
Algorithms
Breast
Breast Neoplasms
Diffusion
Epithelium
Female
Gene Expression Regulation, Neoplastic
Humans
Neoplastic Stem Cells
Reproducibility of Results
Risk Factors
Single-Cell Analysis
Treatment Outcome
