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Cell Rep. 2016 Mar 15;14(10):2490-501. doi: 10.1016/j.celrep.2016.02.023. Epub 2016 Mar 3.

Large-Scale Profiling of Kinase Dependencies in Cancer Cell Lines.

Author information

1
The Breast Cancer Now Research Centre and CRUK Gene Function Laboratory, The Institute of Cancer Research, London SW3 6JB, UK.
2
Systems Biology Ireland, University College Dublin, Dublin 4, Ireland.
3
The Breast Cancer Now Research Centre and CRUK Gene Function Laboratory, The Institute of Cancer Research, London SW3 6JB, UK; Royal Marsden Hospital, London SW3 6JJ, UK.
4
The Breast Cancer Now Research Centre and CRUK Gene Function Laboratory, The Institute of Cancer Research, London SW3 6JB, UK; UCL Cancer Institute, University College London, London WC1E 6DD, UK.
5
The Breast Cancer Now Research Centre and CRUK Gene Function Laboratory, The Institute of Cancer Research, London SW3 6JB, UK; Gustave Roussy Cancer Campus, 94805 Villejuif, France.
6
UCSF Helen Diller Family Comprehensive Cancer Centre, San Francisco, CA 94158, USA.
7
Cancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research, Sutton SM2 5NG, UK.
8
UCL Cancer Institute, University College London, London WC1E 6DD, UK.
9
Functional Genomics Laboratory, The Breast Cancer Now Research Centre, The Institute of Cancer Research, London SW3 6JB, UK.
10
The Breast Cancer Now Research Centre and CRUK Gene Function Laboratory, The Institute of Cancer Research, London SW3 6JB, UK. Electronic address: alan.ashworth@ucsf.edu.
11
The Breast Cancer Now Research Centre and CRUK Gene Function Laboratory, The Institute of Cancer Research, London SW3 6JB, UK. Electronic address: chris.lord@icr.ac.uk.

Abstract

One approach to identifying cancer-specific vulnerabilities and therapeutic targets is to profile genetic dependencies in cancer cell lines. Here, we describe data from a series of siRNA screens that identify the kinase genetic dependencies in 117 cancer cell lines from ten cancer types. By integrating the siRNA screen data with molecular profiling data, including exome sequencing data, we show how vulnerabilities/genetic dependencies that are associated with mutations in specific cancer driver genes can be identified. By integrating additional data sets into this analysis, including protein-protein interaction data, we also demonstrate that the genetic dependencies associated with many cancer driver genes form dense connections on functional interaction networks. We demonstrate the utility of this resource by using it to predict the drug sensitivity of genetically or histologically defined subsets of tumor cell lines, including an increased sensitivity of osteosarcoma cell lines to FGFR inhibitors and SMAD4 mutant tumor cells to mitotic inhibitors.

PMID:
26947069
PMCID:
PMC4802229
DOI:
10.1016/j.celrep.2016.02.023
[Indexed for MEDLINE]
Free PMC Article

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