Large-Scale Profiling of Kinase Dependencies in Cancer Cell Lines.
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Date
2016-03-15Author
Campbell, J
Ryan, CJ
Brough, R
Bajrami, I
Pemberton, HN
Chong, IY
Costa-Cabral, S
Frankum, J
Gulati, A
Holme, H
Miller, R
Postel-Vinay, S
Rafiq, R
Wei, W
Williamson, CT
Quigley, DA
Tym, J
Al-Lazikani, B
Fenton, T
Natrajan, R
Strauss, SJ
Ashworth, A
Lord, CJ
Type
Journal Article
Metadata
Show full item recordAbstract
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.
Subject
Cell Line, Tumor
Humans
Neoplasms
Protein Kinases
Receptor, erbB-2
RNA, Small Interfering
Gene Expression Profiling
RNA Interference
Mutation
Receptor, Fibroblast Growth Factor, Type 1
Smad4 Protein
Research team
Computational Biology and Chemogenomics
Ashworth Collaborators
Functional Genomics
Gene Function
Language
eng
Date accepted
2016-02-01
License start date
2016-03-03
Citation
Cell reports, 2016, 14 (10), pp. 2490 - 2501
Publisher
CELL PRESS