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 journalArticlepeer-review

    16 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

    Bibliographical note

    Publisher Copyright:
    © The Author 2015. Published by Oxford University Press. All rights reserved.

    ASJC Scopus subject areas

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

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