Format

Send to

Choose Destination
Cell. 2014 Aug 28;158(5):1199-1209. doi: 10.1016/j.cell.2014.07.027.

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

Author information

1
The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 6997801, Israel. Electronic address: livnatje@post.tau.ac.il.
2
Cancer Research UK, The Beatson Institute for Cancer Research, Switchback Road, Glasgow G61 1BD, Scotland, UK.
3
The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 6997801, Israel.
4
Center for the Science of Therapeutics, Broad Institute of Harvard and MIT, 7 Cambridge Center, Cambridge, MA 02142, USA.
5
The Sackler School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel.
6
The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 6997801, Israel; The Sackler School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel. Electronic address: ruppin@post.tau.ac.il.

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.

PMID:
25171417
DOI:
10.1016/j.cell.2014.07.027
[Indexed for MEDLINE]
Free full text

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center