@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}
}
