Dataset Information

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

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.

SUBMITTER: Campbell J

PROVIDER: S-EPMC4802229 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Publications

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

Campbell James J Ryan Colm J CJ Brough Rachel R Bajrami Ilirjana I Pemberton Helen N HN Chong Irene Y IY Costa-Cabral Sara S Frankum Jessica J Gulati Aditi A Holme Harriet H Miller Rowan R Postel-Vinay Sophie S Rafiq Rumana R Wei Wenbin W Williamson Chris T CT Quigley David A DA Tym Joe J Al-Lazikani Bissan B Fenton Timothy T Natrajan Rachael R Strauss Sandra J SJ Ashworth Alan A Lord Christopher J CJ

Cell reports 20160303 10

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 ...[more]

PMID: 26947069

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Project description:BACKGROUND:Cancer cells reprogram their metabolism to survive and propagate. Thus, targeting metabolic rewiring in tumors is a promising therapeutic strategy. Genome-wide RNAi and CRISPR screens are powerful tools for identifying genes essential for cancer cell proliferation and survival. Integrating loss-of-function genetic screens with genomics and transcriptomics datasets reveals molecular mechanisms that underlie cancer cell dependence on specific genes; though explaining cell line-specific essentiality of metabolic genes was recently shown to be especially challenging. RESULTS:We find that variability in tissue culture medium between cell lines in a genetic screen is a major confounding factor affecting cell line-specific essentiality of metabolic genes-while, quite surprisingly, not being previously accounted for. Additionally, we find that altered expression level of a metabolic gene in a certain cell line is less indicative of its essentiality than for other genes. However, cell line-specific essentiality of metabolic genes is significantly correlated with changes in the expression of neighboring enzymes in the metabolic network. Utilizing a machine learning method that accounts for tissue culture media and functional association between neighboring enzymes, we generated predictive models for cancer cell line-specific dependence on 162 metabolic genes (representing a ~?2.2-fold increase compared to previous studies). The generated predictive models reveal numerous novel associations between molecular features and cell line-specific dependency on metabolic genes. Specifically, we demonstrate how cancer cell dependence on one-carbon metabolic enzymes is explained based on cancer lineage, oncogenic mutations, and RNA expression of neighboring enzymes. CONCLUSIONS:Considering culture media as well as accounting for molecular features of functionally related metabolic enzymes in a metabolic network significantly improves our understanding of cancer cell line-specific dependence on metabolic genes. We expect our approach and predictive models of metabolic gene essentiality to be a useful tool for investigating metabolic abnormalities in cancer.

Dataset Information

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

Publications

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

Similar Datasets

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