The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.
A single‐cell RNA expression atlas of normal, preneoplastic and tumorigenic states in the human breast
Open accessAbstract
To examine global changes in breast heterogeneity across different states, we determined the single‐cell transcriptomes of > 340,000 cells encompassing normal breast, preneoplastic BRCA1+/– tissue, the major breast cancer subtypes, and pairs of tumors and involved lymph nodes. Elucidation of the normal breast microenvironment revealed striking changes in the stroma of post‐menopausal women. Single‐cell profiling of 34 treatment‐naive primary tumors, including estrogen receptor (ER)+, HER2+, and triple‐negative breast cancers, revealed comparable diversity among cancer cells and a discrete subset of cycling cells. The transcriptomes of preneoplastic BRCA1+/– tissue versus tumors highlighted global changes in the immune microenvironment. Within the tumor immune landscape, proliferative CD8+ T cells characterized triple‐negative and HER2+ cancers but not ER+ tumors, while all subtypes comprised cycling tumor‐associated macrophages, thus invoking potentially different immunotherapy targets. Copy number analysis of paired ER+ tumors and lymph nodes indicated seeding by genetically distinct clones or mass migration of primary tumor cells into axillary lymph nodes. This large‐scale integration of patient samples provides a high‐resolution map of cell diversity in normal and cancerous human breast. To examine global changes in breast heterogeneity across different states, this gene expression resource integrates large‐scale patient samples from diverse tissue states and breast cancer subtypes, offering a refined high‐resolution map of cell diversity in the normal and cancerous human mammary gland. A large‐scale gene expression resource integrates diverse tissue samples and reveals unexpected heterogeneity of breast cancer subtypes.
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Cites
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Cited by
Janesick, A., Shelansky, R., Gottscho, A. D., Wagner, F., Williams, S. R., Rouault, M., Beliakoff, G., Morrison, C. A., Oliveira, M. F., Sicherman, J. T., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43458-x · Cited by 909
Deciphering breast cancer: from biology to the clinic
Nolan, E., Lindeman, G. J., & Visvader, J. E. (2023) · Cell · doi:10.1016/j.cell.2023.01.040 · Cited by 730
Self-citationApplications of single-cell sequencing in cancer research: progress and perspectives
Lei, Y., Tang, R., Xu, J., Wang, W., Zhang, B., Liu, J., Yu, X., & Shi, S. (2021) · Journal of Hematology & Oncology · doi:10.1186/s13045-021-01105-2 · Cited by 592
Unsupervised spatially embedded deep representation of spatial transcriptomics
Xu, H., Fu, H., Long, Y., Ang, K. S., Sethi, R., Chong, K., Li, M., Uddamvathanak, R., Lee, H. K., Ling, J., et al. (2024) · Genome Medicine · doi:10.1186/s13073-024-01283-x · Cited by 391
Towards targeting the breast cancer immune microenvironment
Harris, M. A., Savas, P., Virassamy, B., O’Malley, M. M. R., Kay, J., Mueller, S. N., Mackay, L. K., Salgado, R., & Loi, S. (2024) · Nature Reviews Cancer · doi:10.1038/s41568-024-00714-6 · Cited by 363
Metadata
- Authors
- Pal, B., Chen, Y., Vaillant, F., Capaldo, B. D., Joyce, R., Song, X., Bryant, V. L., Penington, J. S., Di Stefano, L., Tubau Ribera, N., Wilcox, S., Mann, G. B., kConFab, Papenfuss, A. T., Lindeman, G. J., Smyth, G. K., & Visvader, J. E.
- Venue
- The EMBO Journal
- Published
- 2021-05-05
- Volume
- 40
- Issue
- 11
- Pages
- e107333-e107333
- ISSN
- 0261-4189 (ISSN-L)
- 1460-2075
- OpenAlex
- W3158998710
- Cited by
- 469
- References
- 71
- FWCI
- 20.06
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Pal, B., Chen, Y., Vaillant, F., Capaldo, B. D., Joyce, R., Song, X., Bryant, V. L., Penington, J. S., Di Stefano, L., Tubau Ribera, N., Wilcox, S., Mann, G. B., kConFab, Papenfuss, A. T., Lindeman, G. J., Smyth, G. K., & Visvader, J. E. (2021). A single‐cell RNA expression atlas of normal, preneoplastic and tumorigenic states in the human breast. The EMBO Journal. https://doi.org/10.15252/embj.2020107333
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
