An integrated bioinformatics analysis to dissect kinase dependency in triple negative breast cancer

BMC Genomics. 2015;16 Suppl 12(Suppl 12):S2. doi: 10.1186/1471-2164-16-S12-S2. Epub 2015 Dec 9.


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. We validated 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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Cell Line, Tumor
  • Cell Proliferation / drug effects
  • Computational Biology / methods*
  • Databases, Genetic
  • Female
  • Gene Expression Regulation, Neoplastic / drug effects
  • Humans
  • Protein Kinase Inhibitors / pharmacology*
  • Protein Kinases / genetics*
  • Protein Kinases / metabolism
  • Triple Negative Breast Neoplasms / enzymology*
  • Triple Negative Breast Neoplasms / genetics


  • Protein Kinase Inhibitors
  • Protein Kinases