TY - JOUR
T1 - Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality
AU - Jerby-Arnon, Livnat
AU - Pfetzer, Nadja
AU - Waldman, Yedael Y.
AU - McGarry, Lynn
AU - James, Daniel
AU - Shanks, Emma
AU - Seashore-Ludlow, Brinton
AU - Weinstock, Adam
AU - Geiger, Tamar
AU - Clemons, Paul A.
AU - Gottlieb, Eyal
AU - Ruppin, Eytan
N1 - Funding Information:
We thank A. Wagner, D. Horn, D. Steinberg, E. Halperin, I. Meilijson, L. Wolf, M. Kupiec, M. Oberhardt, and R. Sharan for their help and comments. We thank E. MacKenzie for technical support. L.J.A. and A.W. are partially funded by the Edmond J. Safra bioinformatics center and the Israeli Center of Research Excellence program (I-CORE, Gene Regulation in Complex Human Disease Center No 41/11). L.J.A. was also funded by the Dan David foundation and by the Adams Fellowship Program of the Israel Academy of Sciences and Humanities. Y.Y.W. was supported in part by Eshkol fellowship (the Israeli Ministry of Science and Technology). E.R.’s research in cancer is supported by grants from the Israeli Science Foundation (ISF) and Israeli Cancer Research Fund (ICRF). E.R. and T.G. are supported by the I-CORE program.
PY - 2014/8/28
Y1 - 2014/8/28
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84907333139&partnerID=8YFLogxK
U2 - 10.1016/j.cell.2014.07.027
DO - 10.1016/j.cell.2014.07.027
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AN - SCOPUS:84907333139
SN - 0092-8674
VL - 158
SP - 1199
EP - 1209
JO - Cell
JF - Cell
IS - 5
ER -
