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Bioinformatics. 2015 Dec 1;31(23):3799-806. doi: 10.1093/bioinformatics/btv427. Epub 2015 Jul 23.

Identifying kinase dependency in cancer cells by integrating high-throughput drug screening and kinase inhibition data.

Author information

1
Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine.
2
Department of Craniofacial Biology, School of Dental Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
3
Department of Computer Science and Engineering, Korea University, Seoul, Korea and.
4
Translational Bioinformatics and Cancer Systems Biology Laboratory, Division of Medical Oncology, Department of Medicine, Department of Computer Science and Engineering, Korea University, Seoul, Korea and Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Abstract

MOTIVATION:

Targeted kinase inhibitors have dramatically improved cancer treatment, but kinase dependency for an individual patient or cancer cell can be challenging to predict. Kinase dependency does not always correspond with gene expression and mutation status. High-throughput drug screens are powerful tools for determining kinase dependency, but drug polypharmacology can make results difficult to interpret.

RESULTS:

We developed Kinase Addiction Ranker (KAR), an algorithm that integrates high-throughput drug screening data, comprehensive kinase inhibition data and gene expression profiles to identify kinase dependency in cancer cells. We applied KAR to predict kinase dependency of 21 lung cancer cell lines and 151 leukemia patient samples using published datasets. We experimentally validated KAR predictions of FGFR and MTOR dependence in lung cancer cell line H1581, showing synergistic reduction in proliferation after combining ponatinib and AZD8055.

AVAILABILITY AND IMPLEMENTATION:

KAR can be downloaded as a Python function or a MATLAB script along with example inputs and outputs at: http://tanlab.ucdenver.edu/KAR/.

CONTACT:

aikchoon.tan@ucdenver.edu.

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
26206305
PMCID:
PMC4675831
DOI:
10.1093/bioinformatics/btv427
[Indexed for MEDLINE]
Free PMC Article

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