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

Cell
Authors
Keywords
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.

Year of Publication
2014
Journal
Cell
Volume
158
Issue
5
Pages
1199-209
Date Published
2014 Aug 28
ISSN
1097-4172
DOI
10.1016/j.cell.2014.07.027
PubMed ID
25171417
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