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Molecular characterization of breast cancer cell lines through multiple omic approaches

Overview of attention for article published in Breast Cancer Research, June 2017
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Title
Molecular characterization of breast cancer cell lines through multiple omic approaches
Published in
Breast Cancer Research, June 2017
DOI 10.1186/s13058-017-0855-0
Pubmed ID
Authors

Shari E. Smith, Paul Mellor, Alison K. Ward, Stephanie Kendall, Megan McDonald, Frederick S. Vizeacoumar, Franco J. Vizeacoumar, Scott Napper, Deborah H. Anderson

Abstract

Breast cancer cell lines are frequently used as model systems to study the cellular properties and biology of breast cancer. Our objective was to characterize a large, commonly employed panel of breast cancer cell lines obtained from the American Type Culture Collection (ATCC 30-4500 K) to enable researchers to make more informed decisions in selecting cell lines for specific studies. Information about these cell lines was obtained from a wide variety of sources. In addition, new information about cellular pathways that are activated within each cell line was generated. We determined key protein expression data using immunoblot analyses. In addition, two analyses on serum-starved cells were carried out to identify cellular proteins and pathways that are activated in these cells. These analyses were performed using a commercial PathScan array and a novel and more extensive phosphopeptide-based kinome analysis that queries 1290 phosphorylation events in major signaling pathways. Data about this panel of breast cancer cell lines was also accessed from several online sources, compiled and summarized for the following areas: molecular classification, mRNA expression, mutational status of key proteins and other possible cancer-associated mutations, and the tumorigenic and metastatic capacity in mouse xenograft models of breast cancer. The cell lines that were characterized included 10 estrogen receptor (ER)-positive, 12 human epidermal growth factor receptor 2 (HER2)-amplified and 18 triple negative breast cancer cell lines, in addition to 4 non-tumorigenic breast cell lines. Within each subtype, there was significant genetic heterogeneity that could impact both the selection of model cell lines and the interpretation of the results obtained. To capture the net activation of key signaling pathways as a result of these mutational combinations, profiled pathway activation status was examined. This provided further clarity for which cell lines were particularly deregulated in common or unique ways. These two new kinase or "Kin-OMIC" analyses add another dimension of important data about these frequently used breast cancer cell lines. This will assist researchers in selecting the most appropriate cell lines to use for breast cancer studies and provide context for the interpretation of the emerging results.

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 266 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 266 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 43 16%
Student > Bachelor 39 15%
Researcher 35 13%
Student > Master 33 12%
Professor > Associate Professor 12 5%
Other 43 16%
Unknown 61 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 98 37%
Agricultural and Biological Sciences 32 12%
Medicine and Dentistry 20 8%
Chemistry 11 4%
Engineering 9 3%
Other 26 10%
Unknown 70 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 January 2018.
All research outputs
#16,051,091
of 25,382,440 outputs
Outputs from Breast Cancer Research
#1,431
of 2,054 outputs
Outputs of similar age
#188,796
of 331,621 outputs
Outputs of similar age from Breast Cancer Research
#21
of 28 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,054 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.2. This one is in the 28th percentile – i.e., 28% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 331,621 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.