The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Single-Cell Transcriptomic Analysis of Tumor-Derived Fibroblasts and Normal Tissue-Resident Fibroblasts Reveals Fibroblast Heterogeneity in Breast Cancer
Open accessAbstract
Cancer-associated fibroblasts (CAFs) are a prominent stromal cell type in solid tumors and molecules secreted by CAFs play an important role in tumor progression and metastasis. CAFs coexist as heterogeneous populations with potentially different biological functions. Although CAFs are a major component of the breast cancer stroma, molecular and phenotypic heterogeneity of CAFs in breast cancer is poorly understood. In this study, we investigated CAF heterogeneity in triple-negative breast cancer (TNBC) using a syngeneic mouse model, BALB/c-derived 4T1 mammary tumors. Using single-cell RNA sequencing (scRNA-seq), we identified six CAF subpopulations in 4T1 tumors including: 1) myofibroblastic CAFs, enriched for α-smooth muscle actin and several other contractile proteins; 2) ‘inflammatory’ CAFs with elevated expression of inflammatory cytokines; and 3) a CAF subpopulation expressing major histocompatibility complex (MHC) class II proteins that are generally expressed in antigen-presenting cells. Comparison of 4T1-derived CAFs to CAFs from pancreatic cancer revealed that these three CAF subpopulations exist in both tumor types. Interestingly, cells with inflammatory and MHC class II-expressing CAF profiles were also detected in normal breast/pancreas tissue, suggesting that these phenotypes are not tumor microenvironment-induced. This work enhances our understanding of CAF heterogeneity, and specifically targeting these CAF subpopulations could be an effective therapeutic approach for treating highly aggressive TNBCs.
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Cites
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314
Özdemir, B. C., Pentcheva-Hoang, T., Carstens, J. L., Zheng, X., Wu, C.-C., Simpson, T. R., Laklai, H., Sugimoto, H., Kahlert, C., Novitskiy, S. V., et al. (2014) · Cancer Cell · doi:10.1016/j.ccr.2014.04.005 · Cited by 2,681
Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer
Öhlund, D., Handly-Santana, A., Biffi, G., Elyada, E., Almeida, A. S., Ponz-Sarvise, M., Corbo, V., Oni, T. E., Hearn, S. A., Lee, E. J., et al. (2017) · Journal of Experimental Medicine · doi:10.1084/jem.20162024 · Cited by 2,590
Organoid models of human and mouse ductal pancreatic cancer
Boj, S. F., Hwang, C.-I., Baker, L. A., Chio, I. I. C., Engle, D. D., Corbo, V., Jager, M., Ponz-Sarvise, M., Tiriac, H., Spector, M. S., et al. (2015) · Cell · doi:10.1016/j.cell.2014.12.021 · Cited by 2,150
Fibroblast Heterogeneity and Immunosuppressive Environment in Human Breast Cancer
Costa, A., Kieffer, Y., Scholer-Dahirel, A., Pelon, F., Bourachot, B., Cardon, M., Sirven, P., Magagna, I., Fuhrmann, L., Bernard, C., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.01.011 · Cited by 1,705
Cancer-associated fibroblasts: an emerging target of anti-cancer immunotherapy
Liu, T., Han, C., Wang, S., Fang, P., Ma, Z., Xu, L., & Yin, R. (2019) · Journal of Hematology & Oncology · doi:10.1186/s13045-019-0770-1 · Cited by 860 · cited 2×
“CAF markers Pdpn, Thy1 and Pdgfra [25] and CAF-secreted collagens Col1a1 and Col3a1 were expressed in all these clusters (Figure 2B).”
BackgroundCancer-Associated Fibroblasts: Their Characteristics and Their Roles in Tumor Growth
Shiga, K., Hara, M., Nagasaki, T., Sato, T., Takahashi, H., & Takeyama, H. (2015) · Cancers · doi:10.3390/cancers7040902 · Cited by 774
Guerrero-Juarez, C. F., Dedhia, P. H., Jin, S., Ruiz-Vega, R., Ma, D., Liu, Y., Yamaga, K., Shestova, O., Gay, D. L., Yang, Z., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-08247-x · Cited by 521
Cancer-Associated Fibroblasts Build and Secure the Tumor Microenvironment
Liu, T., Zhou, L., Li, D., Andl, T., & Zhang, Y. (2019) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2019.00060 · Cited by 446 · cited 1×
“CAFs have been shown to play a major role in extracellular matrix (ECM) synthesis and organization [28].”
Background
Cited by
Mao, X., Xu, J., Wang, W., Liang, C., Hua, J., Liu, J., Zhang, B., Meng, Q., Yu, X., & Shi, S. (2021) · Molecular Cancer · doi:10.1186/s12943-021-01428-1 · Cited by 2,290
Signaling pathways in cancer-associated fibroblasts and targeted therapy for cancer
Wu, F., Yang, J., Liu, J., Wang, Y., Mu, J., Zeng, Q., Deng, S., & Zhou, H. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00641-0 · Cited by 773
Cancer-associated fibroblasts in the single-cell era
Lavie, D., Ben-Shmuel, A., Erez, N., & Scherz-Shouval, R. (2022) · Nature Cancer · doi:10.1038/s43018-022-00411-z · Cited by 616
Cancer-associated fibroblasts: from basic science to anticancer therapy
Yang, D., Liu, J., Qian, H., & Zhuang, Q. (2023) · Experimental & Molecular Medicine · doi:10.1038/s12276-023-01013-0 · Cited by 572
Fibroblasts in cancer: Unity in heterogeneity
Chhabra, Y., & Weeraratna, A. T. (2023) · Cell · doi:10.1016/j.cell.2023.03.016 · Cited by 474
Metadata
- Authors
- Sebastian, A., Hum, N. R., Martin, K. A., Gilmore, S. F., Peran, I., Byers, S. W., Wheeler, E. K., Coleman, M. A., & Loots, G. G.
- Venue
- Cancers
- Publisher
- MDPI AG
- Published
- 2020-05-21
- Volume
- 12
- Issue
- 5
- Pages
- 1307
- ISSN
- 2072-6694 (ISSN-L)
- OpenAlex
- W3026654828
- Cited by
- 260
- References
- 12
- FWCI
- 9.34
- Type
- journal article
- Language
- en
- Open access
- gold
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- Sebastian, A., Hum, N. R., Martin, K. A., Gilmore, S. F., Peran, I., Byers, S. W., Wheeler, E. K., Coleman, M. A., & Loots, G. G. (2020). Single-Cell Transcriptomic Analysis of Tumor-Derived Fibroblasts and Normal Tissue-Resident Fibroblasts Reveals Fibroblast Heterogeneity in Breast Cancer. Cancers. https://doi.org/10.3390/cancers12051307
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