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Nature. 2019 Apr 10. doi: 10.1038/s41586-019-1103-9. [Epub ahead of print]

Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens.

Author information

1
Wellcome Sanger Institute, Cambridge, UK.
2
Open Targets, Cambridge, UK.
3
European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK.
4
Candiolo Cancer Institute-FPO, IRCCS, Turin, Italy.
5
Department of Oncology, University of Torino, Turin, Italy.
6
GlaxoSmithKline Research and Development, Stevenage, UK.
7
GlaxoSmithKline Research and Development, Collegeville, PA, USA.
8
Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University, Aachen, Germany.
9
Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, Bioquant, Heidelberg, Germany.
10
Heidelberg University Hospital, Heidelberg, Germany.
11
Wellcome Sanger Institute, Cambridge, UK. k.yusa@infront.kyoto-u.ac.jp.
12
Open Targets, Cambridge, UK. k.yusa@infront.kyoto-u.ac.jp.
13
Stem Cell Genetics, Institute for Frontier Life and Medical Sciences, Kyoto University, Kyoto, Japan. k.yusa@infront.kyoto-u.ac.jp.
14
Wellcome Sanger Institute, Cambridge, UK. mathew.garnett@sanger.ac.uk.
15
Open Targets, Cambridge, UK. mathew.garnett@sanger.ac.uk.

Abstract

Functional genomics approaches can overcome limitations-such as the lack of identification of robust targets and poor clinical efficacy-that hamper cancer drug development. Here we performed genome-scale CRISPR-Cas9 screens in 324 human cancer cell lines from 30 cancer types and developed a data-driven framework to prioritize candidates for cancer therapeutics. We integrated cell fitness effects with genomic biomarkers and target tractability for drug development to systematically prioritize new targets in defined tissues and genotypes. We verified one of our most promising dependencies, the Werner syndrome ATP-dependent helicase, as a synthetic lethal target in tumours from multiple cancer types with microsatellite instability. Our analysis provides a resource of cancer dependencies, generates a framework to prioritize cancer drug targets and suggests specific new targets. The principles described in this study can inform the initial stages of drug development by contributing to a new, diverse and more effective portfolio of cancer drug targets.

PMID:
30971826
DOI:
10.1038/s41586-019-1103-9

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