Highly parallel identification of essential genes in cancer cells

Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, MA 02142, USA.
Proceedings of the National Academy of Sciences (Impact Factor: 9.81). 01/2009; 105(51):20380-5. DOI: 10.1073/pnas.0810485105
Source: PubMed

ABSTRACT More complete knowledge of the molecular mechanisms underlying cancer will improve prevention, diagnosis and treatment. Efforts such as The Cancer Genome Atlas are systematically characterizing the structural basis of cancer, by identifying the genomic mutations associated with each cancer type. A powerful complementary approach is to systematically characterize the functional basis of cancer, by identifying the genes essential for growth and related phenotypes in different cancer cells. Such information would be particularly valuable for identifying potential drug targets. Here, we report the development of an efficient, robust approach to perform genome-scale pooled shRNA screens for both positive and negative selection and its application to systematically identify cell essential genes in 12 cancer cell lines. By integrating these functional data with comprehensive genetic analyses of primary human tumors, we identified known and putative oncogenes such as EGFR, KRAS, MYC, BCR-ABL, MYB, CRKL, and CDK4 that are essential for cancer cell proliferation and also altered in human cancers. We further used this approach to identify genes involved in the response of cancer cells to tumoricidal agents and found 4 genes required for the response of CML cells to imatinib treatment: PTPN1, NF1, SMARCB1, and SMARCE1, and 5 regulators of the response to FAS activation, FAS, FADD, CASP8, ARID1A and CBX1. Broad application of this highly parallel genetic screening strategy will not only facilitate the rapid identification of genes that drive the malignant state and its response to therapeutics but will also enable the discovery of genes that participate in any biological process.

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    • "Enrichment of pY-modified peptides was performed by using PhosphoScan reagents (CST) essentially following the manufacturer's recommendations [44]. Inline reversed-phase chromatography was performed using an integrated nano- LC system (Easy-nLC, Proxeon Biosystems A/S, Odense, Denmark). "
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    Proteomics 01/2015; 15(2-3). DOI:10.1002/pmic.201400259 · 3.97 Impact Factor
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    • "To mitigate these effects, analytical approaches have been developed that look for phenotypic consistency across multiple hairpins targeting a gene (Luo et al, 2008; Cheung et al, 2011; Marcotte et al, 2012) and among the same hairpins in different screens (Shao et al, 2013). Not surprisingly, different approaches can yield different results, and the degree to which false positives contaminate results is largely unknown (Kaelin, 2012). "
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    Molecular Systems Biology 07/2014; 10(7). DOI:10.15252/msb.20145216 · 14.10 Impact Factor
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    • "The " second best " method assigns scores to genes based on each gene's second most differentially scoring reagent between classes (Cheung et al. 2011), requiring – similar to the " frequency approach " – that favorable genes have at least two high-scoring reagents. Alternatively, RNAi Gene Enrichment Ranking (RIGER) ranks all the reagents by their differential effects and generates a gene-level score for each gene based on the rank distribution of its reagents (Luo et al. 2008; Barbie et al. 2009), analogous to RSA. However, the requirement of two pre-defined classes can limit full interrogation of the data. "
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