Journal
CELL
Volume 158, Issue 5, Pages 1199-1209Publisher
CELL PRESS
DOI: 10.1016/j.cell.2014.07.027
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Funding
- Edmond J. Safra bioinformatics center
- Israeli Center of Research Excellence program (I-CORE, Gene Regulation in Complex Human Disease Center) [41/11]
- Dan David foundation
- Adams Fellowship Program of the Israel Academy of Sciences and Humanities
- Eshkol fellowship (the Israeli Ministry of Science and Technology)
- Israeli Science Foundation (ISF)
- Israeli Cancer Research Fund (ICRF)
- I-CORE program
- Cancer Research UK [18278] Funding Source: researchfish
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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.
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