Modelling breast cancer requires identification and correction of a critical cell lineage-dependent transduction bias
Abstract
When trying to explore the biology and etiology of human cancers, clinically relevant human culture models are essential. Current breast tumour models, such as those from oncogenically transformed primary breast cells, produce predominantly basal-like properties, whereas the more common phenotype expressed by the vast majority of breast tumours are luminal. Reasons for this puzzling, yet important phenomenon, are not understood. We show here that luminal epithelial cells are significantly more resistant to viral transduction than their myoepithelial counterparts. Here, we suggest that this is a significant barrier to generating luminal cell lines and experimental tumours in vivo and to accurate interpretation of results. We show that the resistance is due to lower affinity of luminal cells for virus attachment, which can be overcome by pretreating cells—or virus—with neuraminidase. We present an analytical method for quantifying transductional differences between cell types and an optimized protocol for transducing unsorted primary human breast cells in context.
- Authors:
-
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1256019
- Grant/Contract Number:
- AC02-05CH1123; U01CA143233; U54CA112970; R01CA140663; R37CA064786
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nature Communications
- Additional Journal Information:
- Journal Volume: 6; Journal ID: ISSN 2041-1723
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; Cancer; breast cancer
Citation Formats
Hines, William C., Yaswen, Paul, and Bissell, Mina J. Modelling breast cancer requires identification and correction of a critical cell lineage-dependent transduction bias. United States: N. p., 2015.
Web. doi:10.1038/ncomms7927.
Hines, William C., Yaswen, Paul, & Bissell, Mina J. Modelling breast cancer requires identification and correction of a critical cell lineage-dependent transduction bias. United States. https://doi.org/10.1038/ncomms7927
Hines, William C., Yaswen, Paul, and Bissell, Mina J. Tue .
"Modelling breast cancer requires identification and correction of a critical cell lineage-dependent transduction bias". United States. https://doi.org/10.1038/ncomms7927. https://www.osti.gov/servlets/purl/1256019.
@article{osti_1256019,
title = {Modelling breast cancer requires identification and correction of a critical cell lineage-dependent transduction bias},
author = {Hines, William C. and Yaswen, Paul and Bissell, Mina J.},
abstractNote = {When trying to explore the biology and etiology of human cancers, clinically relevant human culture models are essential. Current breast tumour models, such as those from oncogenically transformed primary breast cells, produce predominantly basal-like properties, whereas the more common phenotype expressed by the vast majority of breast tumours are luminal. Reasons for this puzzling, yet important phenomenon, are not understood. We show here that luminal epithelial cells are significantly more resistant to viral transduction than their myoepithelial counterparts. Here, we suggest that this is a significant barrier to generating luminal cell lines and experimental tumours in vivo and to accurate interpretation of results. We show that the resistance is due to lower affinity of luminal cells for virus attachment, which can be overcome by pretreating cells—or virus—with neuraminidase. We present an analytical method for quantifying transductional differences between cell types and an optimized protocol for transducing unsorted primary human breast cells in context.},
doi = {10.1038/ncomms7927},
journal = {Nature Communications},
number = ,
volume = 6,
place = {United States},
year = {Tue Apr 21 00:00:00 EDT 2015},
month = {Tue Apr 21 00:00:00 EDT 2015}
}
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