Nature Communications (Jun 2018)
Harnessing synthetic lethality to predict the response to cancer treatment
- Joo Sang Lee,
- Avinash Das,
- Livnat Jerby-Arnon,
- Rand Arafeh,
- Noam Auslander,
- Matthew Davidson,
- Lynn McGarry,
- Daniel James,
- Arnaud Amzallag,
- Seung Gu Park,
- Kuoyuan Cheng,
- Welles Robinson,
- Dikla Atias,
- Chani Stossel,
- Ella Buzhor,
- Gidi Stein,
- Joshua J. Waterfall,
- Paul S. Meltzer,
- Talia Golan,
- Sridhar Hannenhalli,
- Eyal Gottlieb,
- Cyril H. Benes,
- Yardena Samuels,
- Emma Shanks,
- Eytan Ruppin
Affiliations
- Joo Sang Lee
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland
- Avinash Das
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland
- Livnat Jerby-Arnon
- The Blavatnik School of Computer Science, Tel Aviv University
- Rand Arafeh
- Department of Molecular Cell Biology, Weizmann Institute
- Noam Auslander
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland
- Matthew Davidson
- Cancer Research UK, Beatson Institute
- Lynn McGarry
- Cancer Research UK, Beatson Institute
- Daniel James
- Cancer Research UK, Beatson Institute
- Arnaud Amzallag
- Massachusetts General Hospital Center for Cancer Research
- Seung Gu Park
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland
- Kuoyuan Cheng
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland
- Welles Robinson
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland
- Dikla Atias
- Division of Oncology, Sheba Medical Center Tel Hashomer
- Chani Stossel
- Division of Oncology, Sheba Medical Center Tel Hashomer
- Ella Buzhor
- Division of Oncology, Sheba Medical Center Tel Hashomer
- Gidi Stein
- The Sackler School of Medicine, Tel Aviv University
- Joshua J. Waterfall
- Genetics Branch, National Cancer Institute, National Institutes of Health
- Paul S. Meltzer
- Genetics Branch, National Cancer Institute, National Institutes of Health
- Talia Golan
- Division of Oncology, Sheba Medical Center Tel Hashomer
- Sridhar Hannenhalli
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland
- Eyal Gottlieb
- Cancer Research UK, Beatson Institute
- Cyril H. Benes
- Massachusetts General Hospital Center for Cancer Research
- Yardena Samuels
- Department of Molecular Cell Biology, Weizmann Institute
- Emma Shanks
- Cancer Research UK, Beatson Institute
- Eytan Ruppin
- Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland
- DOI
- https://doi.org/10.1038/s41467-018-04647-1
- Journal volume & issue
-
Vol. 9,
no. 1
pp. 1 – 12
Abstract
Synthetic lethality (SL) offers a new precision oncology approach, which is based on targeting cancer-specific vulnerabilities across the whole genome, going beyond cancer drivers. The authors develop an approach termed ISLE to identify clinically relevant SL interactions and use them for patient stratification and novel target identification.
