@article {49726,
	title = {Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality.},
	journal = {Cell},
	volume = {158},
	year = {2014},
	month = {2014 Aug 28},
	pages = {1199-209},
	abstract = {<p>Synthetic lethality occurs when the inhibition of two genes is lethal while the inhibition of each single gene is not. It can be harnessed to selectively treat cancer by identifying inactive genes in a given cancer and targeting their synthetic lethal (SL) partners. We present a data-driven computational pipeline for the genome-wide identification of SL interactions in cancer by analyzing large volumes of cancer genomic data. First, we show that the approach successfully captures known SL partners of tumor suppressors and oncogenes. We then validate SL predictions obtained for the tumor suppressor VHL. Next, we construct a genome-wide network of SL interactions in cancer and demonstrate its value in predicting gene essentiality and clinical prognosis. Finally, we identify synthetic lethality arising from gene overactivation and use it to predict drug efficacy. These results form a computational basis for exploiting synthetic lethality to uncover cancer-specific susceptibilities.</p>},
	keywords = {Breast Neoplasms, Cell Line, Tumor, Computational Biology, Data Mining, Genes, Tumor Suppressor, HUMANS, Neoplasms, Oncogenes, RNA, Small Interfering, workflow},
	issn = {1097-4172},
	doi = {10.1016/j.cell.2014.07.027},
	author = {Jerby-Arnon, Livnat and Pfetzer, Nadja and Waldman, Yedael Y and McGarry, Lynn and James, Daniel and Shanks, Emma and Seashore-Ludlow, Brinton and Weinstock, Adam and Geiger, Tamar and Clemons, Paul A and Gottlieb, Eyal and Ruppin, Eytan}
}
@article {49728,
	title = {Predicting selective drug targets in cancer through metabolic networks.},
	journal = {Mol Syst Biol},
	volume = {7},
	year = {2011},
	month = {2011},
	pages = {501},
	abstract = {<p>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 cytostatic drug targets, of which 40\% are targeted by known, approved or experimental anticancer drugs, and the rest are new. 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 selective treatments for specific cancers that depend on cancer type-specific downregulation of gene expression and somatic mutations are compiled.</p>},
	keywords = {Cell Line, Tumor, Cell Proliferation, Computational Biology, Cytostatic Agents, Down-Regulation, Drug Delivery Systems, Gene Expression Regulation, Neoplastic, HUMANS, Metabolic Networks and Pathways, Models, Biological, Neoplasms, RNA, Small Interfering},
	issn = {1744-4292},
	doi = {10.1038/msb.2011.35},
	author = {Folger, Ori and Jerby, Livnat and Frezza, Christian and Gottlieb, Eyal and Ruppin, Eytan and Shlomi, Tomer}
}
