ARTICLE htps/4 oi.or/10.1038/s41586-019-1103-9 Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens o lorio 27 ov e etanyp t in heodru dmnerend oreecve portoi ofer generate molecular features t the molecular featur 3 es can hthat are common to the majority of of Wer ne drome ATP. instability (MSD). Genome-scale CRISPR-Cas9 screens in cancer cell lines at cancer cell fit g 941 CRISPR- 5 ns in 33 l3atacontolG nt.Stev erg University.F b.He NAT UR EIwww.nature.com/nature Article https://doi.org/10.1038/s41586-019-1103-9 Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens Fiona M. Behan1,2,12, Francesco Iorio1,2,3,12, Gabriele Picco1,12, Emanuel Gonçalves1 , Charlotte M. Beaver1 , Giorgia Migliardi4,5, Rita Santos6, Yanhua Rao7 , Francesco Sassi4, Marika Pinnelli4,5, Rizwan Ansari1 , Sarah Harper1 , David Adam Jackson1 , Rebecca McRae1 , Rachel Pooley1 , Piers Wilkinson1 , Dieudonne van der Meer1 , David Dow2,6, Carolyn Buser-Doepner2,7, Andrea Bertotti4,5, Livio Trusolino4,5, Euan A. Stronach2,6, Julio Saez-Rodriguez2,3,8,9,10, Kosuke Yusa1,2,11,13* & Mathew J. Garnett1,2,13* 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. The molecular features of a patient’s tumour influence clinical responses and can be used to guide therapy, leading to more effective treatments and reduced toxicity1 . However, most patients do not ben￾efit from such targeted therapies in part owing to a limited knowledge of candidate targets2 . Lack of efficacy is a leading cause of the 90% attrition rate in the development of cancer drugs, and fewer molecular entities to new targets are being developed3 . Unbiased strategies that effectively identify and prioritize targets in tumours could expand the range of targets, improve success rates and accelerate the development of new cancer therapies. CRISPR–Cas9 screens that use libraries of single-guide RNAs (sgRNAs) have been used to study gene function and their role in cellular fitness4,5 . CRISPR–Cas9-based genome editing provides high specificity and produces penetrant phenotypes as null alleles can be generated. Here we present genome-scale CRISPR–Cas9 fitness screens in 324 cancer cell lines and an integrative analysis that enables the pri￾oritization of candidate cancer therapeutic targets (Fig. 1a), which we illustrate through the identification of Werner syndrome ATP￾dependent helicase (WRN) as a target for tumours with microsatellite instability (MSI). Genome-scale CRISPR–Cas9 screens in cancer cell lines To comprehensively catalogue genes that are required for cancer cell fit￾ness (defined as genes required for cell growth or viability), we performed 941 CRISPR–Cas9 fitness screens in 339 cancer cell lines, targeting 18,009 genes (Extended Data Fig. 1a, b and Supplementary Table 1). Following stringent quality control (Extended Data Fig. 1c–h), the final analysis set included 324 cell lines from 30 different cancer types, across 19 different tissues (Extended Data Fig. 1i). These cell lines are part of the collec￾tion of Cell Model Passports of highly genomically annotated cell lines6 , broadly represent the molecular features of tumours in patients7 , and include common cancers (such as lung, colon and breast cancers) and cancers of particular unmet clinical need (such as lung and pancreatic cancers). Analysis of screen data from these 324 cell lines demonstrated high sensitivity, specificity and precision in classifying essential and non-essential genes8 (Extended Data Fig. 1g, h, j), and results were not biased by experimental factors (Extended Data Fig. 2a–e). Defining core and context-specific fitness genes Genes required for cell fitness in specific molecular or histological con￾texts are likely to encode favourable drug targets, because of a reduced likelihood of inducing toxic effects in healthy tissues. Conversely, fitness genes that are common to the majority of tested cell lines or common within a cancer type (referred to as pan-cancer or cancer-type-specific core fitness genes, respectively) may be involved in essential processes in cells and have greater toxicity. It is therefore important to distinguish context-specific fitness genes from core fitness genes. We identified a median of 1,459 fitness genes in each cell line (Extended Data Fig. 2f–n and Supplementary Table 2). In total, 41% (n = 7,470) of all targeted genes induced a fitness effect in one or more cell lines and the majority (83%) of these genes induced a dependency in less than 50% of the tested cell lines (Fig. 1b). To identify core fit￾ness genes, we developed a statistical method, the adaptive daisy model (ADaM; Extended Data Fig. 3a–d), to adaptively determine the min￾imum number of dependent cell lines that are required for a gene to be classified as a core fitness gene (Fig. 1c). Genes that were defined as core fitness in at least 12 out of 13 cancer types (also adaptively determined) were classified as pan-cancer core fitness genes (Extended Data Fig. 3e–g). This yielded a median of 866 cancer-type-specific and 553 pan-cancer core fitness genes (Fig. 1c and Supplementary Table 3). 1Wellcome Sanger Institute, Cambridge, UK. 2Open Targets, Cambridge, UK. 3European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK. 4Candiolo Cancer Institute-FPO, IRCCS, Turin, Italy. 5Department of Oncology, University of Torino, Turin, Italy. 6GlaxoSmithKline Research and Development, Stevenage, UK. 7GlaxoSmithKline Research and Development, Collegeville, PA, USA. 8Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University, Aachen, Germany. 9Institute for Computational Biomedicine, Heidelberg University, Faculty of Medicine, Bioquant, Heidelberg, Germany. 10Heidelberg University Hospital, Heidelberg, Germany. 11Present address: Stem Cell Genetics, Institute for Frontier Life and Medical Sciences, Kyoto University, Kyoto, Japan. 12These authors contributed equally: Fiona M. Behan, Francesco Iorio, Gabriele Picco. 13These authors jointly supervised this work: Kosuke Yusa, Mathew J. Garnett. *e-mail: k.yusa@infront.kyoto-u.ac.jp; mathew.garnett@sanger.ac.uk N A t U r e | www.nature.com/nature