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Inferring Cancer Dependencies on Metabolic Genes from Large-Scale Genetic Screens
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Shoval Lagziel, M.Sc. Thesis Seminar
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Thursday, 25.1.2018, 12:30
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Taub 601
Alterations in metabolic activity in tumors provide novel means to selectively target cancer cells. A powerful tool for identifying genes essential for cancer cell proliferation and survival is genome-scale RNAi and CRISPR-based genetic silencing screens. Integration of the measured gene essentiality datasets with genomic characterization of genes was shown to provide mechanistic understanding of tumor-specific gene essentiality. Here, we analyze the essentiality of metabolic enzyme-coding genes in cancer by utilizing measurements from recent large-scale genetic screens, identifying a confounding effect of the tissue culture media on gene essentiality - which, quite surprisingly, was previously not accounted for. We find that gene expression may be helpful to predict essentiality in some cases while in most situations a more complex predictive model is required to infer the essentiality of a given gene. Computationally controlling for the effect of culture media, we characterize cancer dependence on metabolic enzyme-coding genes. We show that controlling for the effect of the culture media is fundamental for the identification of molecular signatures explaining cancer dependency on metabolic genes.
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