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Alternative signaling network activation through different insulin receptor family members caused by pro-mitogenic antidiabetic insulin analogues in human mammary epithelial cells

Overview of attention for article published in Breast Cancer Research, July 2015
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Title
Alternative signaling network activation through different insulin receptor family members caused by pro-mitogenic antidiabetic insulin analogues in human mammary epithelial cells
Published in
Breast Cancer Research, July 2015
DOI 10.1186/s13058-015-0600-5
Pubmed ID
Authors

Bas ter Braak, Steven Wink, Esmee Koedoot, Chantal Pont, Christine Siezen, Jan Willem van der Laan, Bob van de Water

Abstract

Insulin analogues are designed to have improved pharmacokinetic parameters compared to regular human insulin. This provides a sustained control of blood glucose levels in diabetic patients. All novel insulin analogues are tested for their mitogenic side effects, however these assays do not take into account the molecular mode of action of different insulin analogues. Insulin analogues can bind the insulin receptor and the insulin-like growth factor 1 receptor with different affinities and consequently will activate different downstream signaling pathways. Here we used a panel of MCF7 human breast cancer cell lines that selectively express either one of the isoforms of the INSR or the IGF1R. We applied a transcriptomics approach to assess the differential transcriptional programs activated in these cells by either insulin, IGF1 or X10 treatment. Based on the differentially expressed genes between insulin versus IGF1 and X10 treatment, we retrieved a mitogenic classifier gene set. Validation by RT-qPCR confirmed the robustness of this gene set. The translational potential of these mitogenic classifier genes was examined in primary human mammary cells and in mammary gland tissue of mice in an in vivo model. The predictive power of the classifier genes was evaluated by testing all commercial insulin analogues in the in vitro model and defined X10 and glargine as the most potent mitogenic insulin analogues. We propose that these mitogenic classifier genes can be used to test the mitogenic potential of novel insulin analogues as well as other alternative molecules with an anticipated affinity for the IGF1R.

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The data shown below were collected from the profiles of 4 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 8 27%
Student > Ph. D. Student 6 20%
Researcher 6 20%
Student > Master 4 13%
Professor 2 7%
Other 3 10%
Unknown 1 3%
Readers by discipline Count As %
Medicine and Dentistry 10 33%
Biochemistry, Genetics and Molecular Biology 5 17%
Agricultural and Biological Sciences 4 13%
Pharmacology, Toxicology and Pharmaceutical Science 3 10%
Nursing and Health Professions 2 7%
Other 2 7%
Unknown 4 13%
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 21 November 2018.
All research outputs
#16,722,913
of 25,374,917 outputs
Outputs from Breast Cancer Research
#1,480
of 2,053 outputs
Outputs of similar age
#154,924
of 275,154 outputs
Outputs of similar age from Breast Cancer Research
#33
of 44 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,053 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 25th percentile – i.e., 25% 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 275,154 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 44 others from the same source and published within six weeks on either side of this one. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.