Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality

Livnat Jerby-Arnon, Nadja Pfetzer, Yedael Y. Waldman, Lynn McGarry, Daniel James, Emma Shanks, Brinton Seashore-Ludlow, Adam Weinstock, Tamar Geiger, Paul A. Clemons, Eyal Gottlieb, Eytan Ruppin

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)1199-1209
Number of pages11
JournalCell
Volume158
Issue number5
DOIs
StatePublished - 28 Aug 2014

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