Predicting selective drug targets in cancer through metabolic networks


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Tomer Shlomi1
1Technion, Haifa, Israel

Abstract

The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome-scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell-lines. The model predicts 52 anticancer drug targets whose inhibition selectively affects cancer cells, of which 40% are targeted by known, approved or experimental anticancer drugs, and the rest are new – testifying to its promise in identifying novel viable anticancer drug targets. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI-60 cancer cell-line collection. Finally, potential treatments for specific cancers that depend on cancer type-specific down regulation of gene expression and somatic mutations are compiled. A specific synthetic lethal drug target prediction was experimentally validated in a cancer kidney cell line suggesting a new treatment strategy for kidney cancer.

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