Background: Triple-Negative Breast Cancer (TNBC) is an aggressive disease with a poor prognosis. Clinically, TNBC patients have limited treatment options besides chemotherapy. The goal of this study was to determine the kinase dependency in TNBC cell lines and to predict compounds that could inhibit these kinases using integrative bioinformatics analysis. Results: We integrated publicly available gene expression data, high-throughput pharmacological profiling data, and quantitative in vitro kinase binding data to determine the kinase dependency in 12 TNBC cell lines. We employed Kinase Addiction Ranker (KAR), a novel bioinformatics approach, which integrated these data sources to dissect kinase dependency in TNBC cell lines. We then used the kinase dependency predicted by KAR for each TNBC cell line to query K-Map for compounds targeting these kinases. Wevalidated our predictions using published and new experimental data. Conclusions: In summary, we implemented an integrative bioinformatics analysis that determines kinase dependency in TNBC. Our analysis revealed candidate kinases as potential targets in TNBC for further pharmacological and biological studies.
Bibliographical noteFunding Information:
This work is partly supported by the National Institutes of Health under Ruth L. Kirschstein National Research Service Award T32CA17468 (K.A.R.), the National Institutes of Health P30CA046934, Cancer League of Colorado, the David F. and Margaret T. Grohne Family Foundation. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the funders.
© 2015 Ryall et al.
- High-throughput screening
- Kinase dependency
- Triple-Negative Breast Cancer
ASJC Scopus subject areas