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

Karen A. Ryall, Jimin Shin, Minjae Yoo, Trista K. Hinz, Jihye Kim, Jaewoo Kang, Lynn E. Heasley, Aik Choon Tan

Research output: Contribution to journalArticle

11 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)3799-3806
Number of pages8
JournalBioinformatics
Volume31
Issue number23
DOIs
Publication statusPublished - 2015 Jun 18

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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