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Understanding drugs in breast cancer through drug sensitivity screening

Overview of attention for article published in SpringerPlus, October 2015
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Title
Understanding drugs in breast cancer through drug sensitivity screening
Published in
SpringerPlus, October 2015
DOI 10.1186/s40064-015-1406-8
Pubmed ID
Authors

Katharina Uhr, Wendy J. C. Prager-van der Smissen, Anouk A. J. Heine, Bahar Ozturk, Marcel Smid, Hinrich W. H. Göhlmann, Agnes Jager, John A. Foekens, John W. M. Martens

Abstract

With substantial numbers of breast tumors showing or acquiring treatment resistance, it is of utmost importance to develop new agents for the treatment of the disease, to know their effectiveness against breast cancer and to understand their relationships with other drugs to best assign the right drug to the right patient. To achieve this goal drug screenings on breast cancer cell lines are a promising approach. In this study a large-scale drug screening of 37 compounds was performed on a panel of 42 breast cancer cell lines representing the main breast cancer subtypes. Clustering, correlation and pathway analyses were used for data analysis. We found that compounds with a related mechanism of action had correlated IC50 values and thus grouped together when the cell lines were hierarchically clustered based on IC50 values. In total we found six clusters of drugs of which five consisted of drugs with related mode of action and one cluster with two drugs not previously connected. In total, 25 correlated and four anti-correlated drug sensitivities were revealed of which only one drug, Sirolimus, showed significantly lower IC50 values in the luminal/ERBB2 breast cancer subtype. We found expected interactions but also discovered new relationships between drugs which might have implications for cancer treatment regimens.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 27%
Student > Ph. D. Student 6 23%
Student > Doctoral Student 3 12%
Student > Master 2 8%
Student > Bachelor 2 8%
Other 0 0%
Unknown 6 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 23%
Agricultural and Biological Sciences 6 23%
Psychology 2 8%
Mathematics 1 4%
Computer Science 1 4%
Other 4 15%
Unknown 6 23%