《药物化学》课程文献资料（Medicinal Chemistry）Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens

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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

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RESEARCH Article Of the pan-cancer core fitness genes identified using ADaM, 399 were previously defined as essential genes8,9 and 125 are genes involved in essential cellular processes10,11 (Extended Data Fig. 4a). The remaining 132 (24%) genes were newly identified and are also significantly enriched in cellular housekeeping genes and pathways (Extended Data Fig. 4b, c and Supplementary Table 4). In comparison to previously identified reference core fitness gene sets8,9 , our pan￾cancer core fitness gene set showed greater recall of genes involved in essential processes (median = 67%, versus 28% and 51% in the previously published gene sets of refs. 8 and 9 , respectively, Extended Data Fig. 4d), with similar false discovery rates (FDRs) for putative context-specific fitness genes (taken from a previous study12; Extended Data Fig. 4e). Blood cancer cell lines had the most distinctive pro￾file of core fitness genes (31 exclusive core fitness genes; Extended Data Fig. 4f). Cancer-type-specific core fitness genes are generally highly expressed in matched healthy tissues (Extended Data Fig. 4g), consistent with their predicted role in fundamental cellular processes and suggesting that they show potential toxicity if used as targets. Notably, five genes were core fitness in a single cancer type and were lowly or not expressed at the basal level in the matched normal tissues (Extended Data Fig. 4g), suggesting that they could induce cancer-cell-specific dependencies in these tissues. Overall, using a statistical approach, we refined and expanded our current knowledge of core fitness genes in humans and identified genes that have a high likelihood of toxicity, which thus represent less favour￾able therapeutic targets. Furthermore, owning to the large scale of our dataset, we could now define context-specific fitness genes (median n = 2,813 genes per cancer type), many of which had a loss-of-fitness effect that was similar to or stronger than core fitness genes (Fig. 1c). A quantitative framework for target prioritization To nominate promising therapeutic targets from our list of context￾specific fitness genes, we developed a computational framework that integrated multiple lines of evidence to assign each gene a target pri￾ority score—which ranged from 0 to 100—and generated ranked lists of candidates for an individual cancer type or a pan-cancer candidate (Fig. 1a and Extended Data Fig. 5a). To exclude genes that are likely to be poor targets because of potential toxicity, core fitness genes were scored as ‘0’, as were potential false-positive genes, such as genes that were not expressed or homozygously deleted. For each gene, 70% of the priority score was derived from CRISPR–Cas9 experimental evidence and averaged across dependent cell lines on the basis of the fitness effect size, the significance of fitness deficiency, target gene expression, target mutational status and evidence for other fitness genes in the same path￾way. The remaining 30% of the priority score was based on evidence of a genetic biomarker that was associated with a target dependency and the frequency at which the target was somatically altered in tumours in patients7 . For the biomarker analysis, we performed an analysis of variance (ANOVA; Fig. 2a, Extended Data Fig. 5b and supplementary data 1) to test associations between fitness genes and the presence of 484 cancer driver events (151 single-nucleotide variants and 333 copy number variants)7 or MSI, in each cancer type with a sufficiently large sample size (n ≥ 10 cell lines) and pan-cancer. We derived a priority score threshold (55 and 41 for pan-cancer and cancer-type-specific analyses, respectively) based on scores calculated for targets with approved or preclinical cancer compounds (Extended Data Fig. 5c and Supplementary Table 5). In total, we identified 628 unique priority targets, including 92 pan-cancer and 617 cancer-type-specific targets (Fig. 2b and Ovarian carcinoma priority targets Novel set of human core tness genes Oesophagus Large intestine Breast Bone ADaM Patient genomic data Priority score KRAS WRN Colorectal carcinoma priority targets Approved or in development Supporting evidence Weak or no supporting evidence Linked genomic marker High expression ... Tractability Fitness Scores Context￾specic tness genes Pathway annotation Genome-wide CRISPR–Cas9 drop-out screen (324 cell lines) Lung ... ... 42 32 34 25 32 26 17 34 14 12 23 12 8 28 22 24 19 23 20 14 26 12 11 18 10 7 No. cell lines ADaM threshold Number of genes (×1,000) 1 2 3 Signicant tness effect –2 0 –4 –6 Core tness Context-specic tness Median b a c 7,470 tness genes Number of dependent cell lines 0 50 100 150 200 250 300 1 10 102 103 Number of genes 40 60 80 Genes (%) 20 Lung Ovary CNS Breast Large intestine Oesophagus PNS Head and neck Stomach Bone Haemat. and lymphoid Pancreas Endometrium 50% cell lines WWW Fig. 1 | Target prioritization framework. a, Strategy to prioritize targets in multiple cancer types, incorporating CRISPR–Cas9 gene fitness effects, genomic biomarkers and target tractability for drug development. ADaM (adaptive daisy model) distinguishes context-specific and core fitness genes. Datasets are available on the project Score website (https://score. depmap.sanger.ac.uk/). b, Number of genes exerting a fitness defect in a given number of cell lines. The bars indicate the percentage of genes that induce a dependency in less than (bottom bar) or at least (top bar) 50% of cell lines. c, Bottom, number of core and context-specific fitness genes identified by ADaM for 13 cancer types (median = 866 and 2,813, respectively). The ADaM threshold is the number of cell lines for a gene to be classified as core fitness. Top, comparison of the effect size for ADaM core and context-specific fitness genes (only significant genes are shown, BAGEL FDR = 5%). CNS, central nervous system; Haemat., haematological; PNS, peripheral nervous system. N A t U r e | www.nature.com/nature

ARTICLE RESEARCH 6 -2 0 50 ation a molecular ups ng.weak nce of e a (P 1 ng thei AKTL.ESRI.TYMS ast carcinoma and PIK3CA TOR idony in the pasu canc the target that nce cer-type- in hat they ciated with at leas ificant fitne OVA CSNK2A1in tal c 29 that c).Fo FLT3 and WASF ig.3b)and ther priority targets in ence of ERBB2amplif on.CDK2 dep cy in ASXL-amplified =11719% ne ets with PT ereidcentinedinmolieleh y Table ) vithout d t with evid ce that of th (h in n KRAS- -type t cance ts vary in ntibod ned each and pan-cance (Fig.3b) s-ref ets with their t GPX4is ncer types(Fig.4,E ded NAT UR EIwww.nature.com/nature

Article RESEARCH Supplementary Tables 6, 7). The number of priority targets varied approximately threefold across cancer types with a median of 88 targets. The majority of cancer-type priority targets (n = 457, 74%) were iden￾tified in only one (56%) or two (18%) cancer types, underscoring their context specificity. Most priority pan-cancer targets (88%) were also identified in the cancer-type-specific analyses (Extended Data Fig. 5d). The 11 priority targets that were identified only in the pan-cancer anal￾ysis typically included dependencies that occurred in a small subset of cell lines from multiple cancer types (for example, CREBBP and JUP) or in a cancer type for which the limited numbers of available cell lines prevented a cancer-type-specific analysis being performed (for exam￾ple, SOX10 in melanoma; Extended Data Fig. 5e). Of the 628 priority targets, 120 (19%) were associated with at least one biomarker identified using ANOVA with high significance and large effect size (defined as class A targets) and these proteins would therefore be of particular interest for drug development (Fig. 2c). For example, PIK3CA is a class A target in breast, oesophageal, colorectal and ovarian carcinoma; PI3K inhibitors are in clinical development for cancers with mutations in PIK3CA13. Using progressively less stringent significance thresholds expanded the targets with at least one biomarker association as identified by ANOVA, which were defined as class B (n = 61, 10%) followed by class C (n = 117, 19%) targets, some of which were identified in multiple cancer types (Supplementary Table 8). Taken together, these results highlight the potential of a data-driven quan￾titative framework to prioritize targets by combining CRISPR–Cas9 screening data from multiple cell lines and associated genomic features. Tractability assessment of priority targets On the basis of current drug-development strategies, targets vary in their suitability for pharmaceutical intervention and this informs target selection. Using a target tractability assessment for the development of small molecules and antibodies, we previously assigned each gene to 1 of 10 tractability buckets (with 1 indicating the highest tractabil￾ity)14. We cross-referenced the 628 priority targets with their tracta￾bility and categorized them into three tractability groups (Fig. 2b and Supplementary Table 9). Tractability group 1 (buckets 1–3) comprised targets of approved anticancer drugs or compounds in clinical or preclinical development, and included 40 unique priority targets, such as ERBB2, ERBB3, CDK4, AKT1, ESR1, TYMS and PIK3CB in breast carcinoma and PIK3CA, IGF1R, MTOR and ATR in colorectal carcinoma (Figs. 3a, 4 and Extended Data Fig. 6). Of these 40 priority targets, 20 have at least one drug that has been developed for the cancer type in which the target was identified as priority, whereas the remaining 20 targets have drugs that have been used or developed for treatment of other cancer types, which present opportunities for the repurposing of these drugs. A third of the priority targets in group 1 have a class A biomarker, indicating that they are highly desirable targets (Supplementary Tables 8, 9). An example is CSNK2A1, which is encoded by the highly significant fitness gene CSNK2A1 in colorectal cancer cell lines with amplification of a chromo￾somal segment that contains FLT3 and WASF3 (P = 6.65 × 10−6 , Glass’s Δ > 2.9, Fig. 3b) and targeted by silmasertib. Other priority targets in group 1 with markers show ERBB2 or ERBB3 dependency in the pres￾ence of ERBB2 amplification, CDK2 dependency in ASXL-amplified oesophageal cancer cell lines, PIK3CA dependency in the presence of PIK3CA mutations, and PIK3CB dependency in breast cancer cell lines with PTEN mutations (Fig. 3b and supplementary data 1). Tractability group 2 (buckets 4–7) contained 277 priority targets without drugs in clinical development but with evidence that support target tractability (Figs. 3a, 4, Extended Data Fig. 6 and Supplementary Table 9). Of these, 18% have a class A biomarker, including KRAS dependency in KRAS-mutant cell lines, USP7 dependency in APC wild-type colorectal cell lines, KMT2D dependency in breast cancer cell lines with amplification of a chromosomal segment that contains PPM1D and CLTC, and TRIAP1 dependency in MYC-amplified bone and gastric cancer cell lines (Fig. 3b and supplementary data 1). Of note, we observed a class A biomarker-type dependency on WRN in colorectal and ovarian cell lines with MSI and pan-cancer (Fig. 3b). Of the priority targets in group 2 that were not associated with a bio￾marker, GPX4 is a target in multiple cancer types (Fig. 4, Extended Data Fig. 6 and Supplementary Table 9). Sensitivity to GPX4 inhibition has been associated with epithelial–mesenchymal transition15 and we Increased dependency Decreased dependency Signed effect size –log10(P) 30 50 –6 –4 –2 0 246 0 5 10 15 WRN MSI NRAS NRASmut ERBB2 ERBB2 gain KRAS KRASmut PIK3CA PIK3CAmut WRN MSI FLI1 EWSR1-FLI1 fusion WRN MSI BRAF BRAFmut SHOC2 NRASmut MYB G6PD loss PIK3CA PIK3CAmut FOXA1 17q22 gain RANBP2 MSI CCND1 LARP4B loss PFDN3 MYCN gain TP53mut MDM2 PELP1 3q27.1 gain 25% FDR 5% MSI FDR Dependency Molecular feature a Oral cavity Head and neck Squamous cell CNS Pancreatic Neuroblastoma Lung adenocarcinoma Breast Haemat. and lymphoid Oesophagus Bone Colorectal Gastri Ovarian c Pan-cancer Total unique Cancer-type specic 0 600 500 400 300 200 100 0 250 200 150 100 50 0 100 80 60 40 20 0 150 100 50 No. of priority targets No. of priority targets b Genomic marker evidence Class A Class B Class C Tractability Group 1 Group 2 Group 3 c Fig. 2 | Target prioritization and biomarker discovery. a, Differential dependency biomarkers were analysed by ANOVA. Each point is an association between the fitness effect of a gene (top name) and a molecular feature or MSI (bottom name). Colours indicate results from 13 cancer￾type-specific (number of cell lines indicated in Supplementary Table 1) or pan-cancer (n = 319) analyses. FDRs were calculated using the Storey–Tibshirani method. b, Cancer-type-specific and pan-cancer priority targets classified based on tractability for drug development as groups 1, 2 and 3 (strong, weak and absence of evidence, respectively). c, Priority targets with a genomic biomarker defined as class A, B or C (from strongest to weakest, based on statistical significance and effect size). N A t U r e | www.nature.com/nature

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RESEARCH Article observed differential expression of markers associated with epithelial– mesenchymal transition in GPX4-dependent cell lines (Extended Data Fig. 7a and supplementary data 2). This is indicative of how future refine￾ment of our target prioritization scheme can capture priority targets that are associated with an expanded set of molecular features, including gene expression, chromatin modifications and differentiation states. Lastly, group 3 (buckets 8–10) included 311 priority targets that had no support or a lack of information that could inform tractabil￾ity (Figs. 3a, 4 and Extended Data Fig. 6); this group is significantly enriched in transcription factors (Extended Data Fig. 7b and supple￾mentary data 3). Examples of priority targets in group 3 with class A biomarkers include FOXA1 and GATA3 in breast cancer, MYB in hae￾matological and lymphoid cancer, STX5 in ovarian cancer and PFDN5 in neuroblastoma cell lines (Fig. 3b). Priority targets in tractability group 1 were enriched in protein kinases, highlighting a major focus of drug development against this class of targets, compared to groups 2 and 3, which included a more functionally diverse set of targets (Extended Data Fig. 7b and supple￾mentary data 3). Targets in group 2 are most likely to be novel and tractable through conventional modalities and, therefore, represent good candidates for drug development. Newer therapeutic modali￾ties, such as proteolysis-targeting chimaeras, may increase the range of proteins that are amenable to pharmaceutical intervention to include targets in group 3. Overall, our framework informed a data-driven list of prioritized therapeutic targets that would be strong candidates for the development of cancer drugs. WRN is a target in cancers with MSI To substantiate our target prioritization strategy, we investigated WRN helicase as a promising target in MSI cancers (Figs. 3, 4). WRN is one of five RecQ family DNA helicases, of which it is the only one that has both a helicase and an exonuclease domain, and has diverse roles in DNA repair, replication, transcription and telomere maintenance16. The MSI phenotype is caused by impaired DNA mismatch repair (MMR) due to silencing or inactivation of MMR pathway genes. MSI is associ￾ated with a high mutational load and occurs in more than 20 tumours types and is frequent in colon, ovarian, endometrial and gastric cancers (3–28%)17. Dependency on WRN was highly associated with MSI in the pan-cancer ANOVA, and analyses of colon and ovarian cancer cell lines (Figs. 2a, 3b and supplementary data 1). Most endometrial and gastric cancer cell lines with MSI were dependent on WRN; how￾ever, the association with MSI was not significant (for gastric) or not tested because of small sample sizes (Extended Data Fig. 7c). MSI is A A A 40 50 60 70 80 –5 –4 –2 –1 0 ERBB2: ERBB2 gain –5 –4 –3 –2 –1 0 –4 –3 –2 –1 0 Tractability buckets Approved drug In clinical/preclinical development Supporting evidence Weak or no supporting evidence Priority score Pan-cancer threshold Cancer￾specic threshold a Dependency Molecular feature ERBB3: ERBB2 gain PIK3CA: PIK3CAmut PIK3CB: PTENmut CSNK2A1: FLT3, WSF3 gain CDK2: ASXL gain KRAS: KRASmut WRN: MSI KMT2D: CLTC, PPM1D gain NRAS: NRASmut USP7: APCmut TRIAP1: MYC gain FOXA1: CLTC, PPM1D gain MYB: G6PD loss PFDN5: MYCN gain STX5: EIF2B5, EPHB3 gain GATA3: CLTC, PPM1D gain A A A A A A A A A A A A A A C C C A C C A A A – + – + – + – + – + – + – + – + – + – + – + Fitness effect Fitness effect Fitness effect FDR < 25%, P < 0.001, Glass > 1 FDR < 30% (and Glass > 1 for pan-cancer) P < 0.001 (and Glass > 1 for pan-cancer) Class A Class B Class C Genomic marker evidence Bone Cancer types Breast carcinoma CNS Colorectal Oesophagus Gastric Neuroblastoma Ovarian Haemat. and lymphoid Head and neck Lung adenocarcinoma Oral cavity Pancreatic Squamous cell lung b ERBB2 ERBB2ERBB3 PIK3CA PIK3CB KRAS KRAS WRN Pa ERBB2 ERBB2 ERBB3 PIK3CA PIK3CB KRAS KRAS WRN WRN NRAS USP7 USP7 FOXA1 MYB PFDN5 STX5 GATA3 MET EGFR TYMS EGFRTUBB4B CDK4 CDK6 TYMS MET IGF1R BCL2L1 MTOR MCL1 MCL1 ATR ATR PLK4 CREBBP RHOA SKP1 AP2M1 VPS4A CFLAR ATP6V0E1 CELSR2 TERF1 CCND1 CCND1 CCND1 PPM1D HMGCS1 LRR1 LRR1 FOSL1 SEC61A1 MYBL2 TIMM17A NUP85 MTX2 FAM96B ANAPC10 t 12345678 9 10 Group 1 Group 2 Group 3 Pan-cancer A A A FLT3, WSF3 gain XL gain PIK3CAmut P7: APCmut AP1: MYC gain D: CLTC, PPM1D gain A A A A A 2B5, EPHB3 gain LTC, PPM1D gain A MYB: G6PD loss A –3 – + – + – + – + – + – + – + – + – + – + – + – + – + – + – + – + – + + Altered – Wild type Δ Δ Δ Fig. 3 | Priority targets and biomarker-linked dependencies. a, All priority targets from cancer-type and pan-cancer analyses and their tractability. Priority score thresholds are indicated and selected examples labelled. b, Differential fitness analysis (quantile-normalized gene depletion fold change between the average of targeting sgRNAs versus plasmid library) for selected priority targets comparing cells with (+) or without (−) a genomic marker (classes A–C as previously defined from ANOVAs). Each data point is a cell line and colours represent cancer type. Box-and-whisker plots show 1× interquartile ranges and 5–95th percentiles, centres indicate medians. 123456789 10 Group 1 Group 2 Group 3 Approved drug Supporting evidence Weak or no supporting evidence 40 60 80 40 60 80 40 60 80 40 60 80 CDK4 TUBB4B MET TYMS CDK6 FGFR1 CDK2 TFRC GRB2 ATP5A1 MAT2A SKP2 CPD EIF4A1 NDC1 TMED10 POLRMT YRDC CCND1 LRR1 STX5 SOCS3SKA3 SEH1L TYMS HDAC1 CDK4 EGFR PIK3CA CDK6 IGF1R CDK2 BCL2L1 PDPK1 CDK12 MCL1 KRAS WRN CLTC MDM4 DNAJC9 CHMP4B CELSR2 PCYT1A CCND1 FPGS LRR1HSD17B12 STX5 MFN2 HYPK GRWD1 CPSF1 TIMM17A ISY1 ATP5D TUBB4B FASN PIK3CA BCL2L1 IGF1R CSNK2A1 MTOR MCL1 ATR KRAS WRN GRB2 DLD XRN1 DNAJC11 USP7 TBCD POLRMT CYB5R4 ZNF407 RPTOR UFL1 PIK3CA ERBB2 ERBB3 GPX4 PTPN23 GPX NDUFB4 4 CKD4 ESR1 AKT1 BCL2L1 PIK3CB PPP2CA KMT2D RANBP2 CFLAR TBX3 TBCD CCND1 LRR1 NRBP1 FOXA1 GATA3 SPAG5 SPDEF MED12 GTF2H2C Priority score Priority score Priority score Priority score Anticancer specic Anticancer Other disease Targeting agent indication PTP N11 FZR1 Class A Class C Genomic marker evidence Class B Colorectal carcinoma Ovarian carcinoma Central nervous system Breast carcinoma In clinical/preclinical development Tractability Buckets Fig. 4 | Cancer-type priority targets. Results for 4 of the 13 cancer￾type-specific analyses. Points are target priority scores and the shapes indicate approved or preclinical compounds to the corresponding target (other disease (squares), anticancer targets (triangles) or those specific to the cancer type considered (rhombus)), or the absence of a compound (circles). Symbols indicate the strength of a genomic biomarker. Selected priority targets are labelled. N A t U r e | www.nature.com/nature

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Article RESEARCH rare (<1%) in many other tumour types, such as kidney, melanoma and prostate cancers17 and most (4 out of 5 tested) MSI cell lines from these tissues were not dependent on WRN (Extended Data Fig. 7c). Other tested RecQ family members (BLM, RECQL and RECQL5) were not associated as fitness genes in MSI cell lines. A focused analysis of non-synonymous mutations, promoter methylation and homozygous deletions of MMR pathway genes confirmed a significant association between WRN dependency and hypermethylation of the MLH1 pro￾moter (Student’s t-test, FDR = 7.72 × 10−3 ) or mutations in MSH6 (FDR = 3.85 × 10−2 ); as well as mutations in the epigenetic regulator MLL2 (also known as KMT2D) (FDR = 1.43 × 10−4 ) (Fig. 5a). To further validate WRN, we performed CRISPR-based co￾competition assays in which the relative fitness of WRN-knockout versus wild-type cells was compared. WRN knockout using four individual sgRNAs decreased fitness of WRN-knockout compared to wild-type cells in six MSI cell lines from colon, ovarian, endometrial and gastric cancers (Fig. 5b, Extended Data Fig. 8a and Supplementary Table 10). By contrast, there was no difference in all microsatellite stable cell lines from these four tissues. Consistently, WRN was selectively essen￾tial for MSI cells in clonogenic assays (Extended Data Fig. 8b, c). Of note, WRN knockout had a potent effect on cell fitness with an effect size similar to core fitness genes (Fig. 5a, b). Furthermore, we mined data from systematic RNA interference screens and confirmed WRN dependency in MSI cancer cell lines12 (Extended Data Fig. 8d), and confirmed that WRN downregulation by RNA interference robustly impaired growth in MSI HCT116 cells (Extended Data Fig. 8e, f), thus providing validation in an orthogonal experimental system. Despite the strong association between MMR deficiency and WRN dependency, knockout of MLH1 in microsatellite-stable SW620 cell line did not induce WRN dependency; conversely complementation of HCT116 cells with chromosomes that contain MLH1 and/or MSH3—to restore their expression and correct MMR deficiency18—did not revert the effect of WRN knockout (Extended Data Fig. 9). To determine whether the loss-of-fitness effect was selective to WRN and identify a potential strategy for drug targeting, we performed func￾tional rescue experiments using wild-type, or hypomorphic versions of mouse Wrn (resistant to the WRN sgRNAs that we used) with a mutation in the exonuclease (E78A) or helicase (R799C or T1052G) domain to impair protein function19–21. Expression of wild-type or exonuclease-deficient Wrn rescued knockout of WRN in MSI cells, whereas expression of helicase-deficient Wrn led to no (R799C) or weak (T1052G) rescue (Fig. 5c and Extended Data Fig. 10a, b). Thus, the helicase activity of WRN is required and is an important domain that can be used for therapeutic targeting. To evaluate in vivo sensitivity of MSI cells to WRN depletion, we developed a doxycycline-inducible WRN sgRNA system in HCT116 cells (Extended Data Fig. 10c, d). Following subcutaneous engraftment of WRN sgRNA-expressing HCT116 cells in mice, treatment with dox￾ycycline led to significant growth suppression of established tumours and a reduction in the number of proliferating cells (Fig. 5d–f and Extended Data Fig. 10e, f). These findings confirm that WRN is nec￾essary to sustain in vivo growth of colorectal cancer cells with MSI. Discussion New approaches are needed to effectively prioritize candidate ther￾apeutic targets for cancer treatments. We performed CRISPR–Cas9 screens in a diverse collection of cancer cells lines and combined this with genomic and tractability data to systematically nominate new can￾cer targets in an unbiased way. Confirmatory studies are necessary to further evaluate the priority targets that we identified. Even a modest improvement in drug-development success rates, and an expanded repertoire of targets, through approaches such as ours could provide benefits to patients with cancer. Our CRISPR–Cas9 screening results are also a resource with diverse applications in fundamental and evo￾lutionary biology, genome engineering and disease genetics. Results are available through the project Score database (https://score.depmap. sanger.ac.uk/). We identified WRN as a promising new synthetic lethal target in MSI tumours. This finding is corroborated by the accompanying study by Chan et al.22. WRN physically interacts with MMR proteins23, can resolve DNA recombination intermediates24, and the yeast homologue Sgs1 has a redundant function with MMR proteins to suppress home￾ologous recombination in regions of nucleotide mismatch25. Together with our finding that modulation of MMR proteins alone is insufficient to confer WRN dependency, this suggests a model in which WRN is required to resolve the genomic structures present in MMR-deficient cells, which are possibly homeologous recombination structures, and failure to efficiently resolve these underpins the synthetic lethal dependency. Mutation of WRN leads to Werner syndrome, an auto￾somal recessive disorder characterized by premature ageing and an increased risk of cancer16. Thus, loss of WRN is compatible with human development; however, targeting WRN could result in damage to nor￾mal cells. Consideration should be given to maximizing therapeutic benefits through patient selection and dose scheduling. A possible route 0.5 1.0 Normalised cell viability Control sgRNA Control vector WRN sgRNA WT Exonuclease def. Helicase def. Helicase def. NS Wrn E78A R799C T1052G Vehicle Dox Vehicle Dox 0 20 40 60 80 KI-67+ nuclei (% area) P = 4.8 × 10–16 MSI MSS Colorectal Ovarian Other MLH1 meth. MLL2 mut. MSH6 mut. –4 –2 0 10 100 WRN Core fitness genes Mutation rate (per Mb) a b 1,750 1,500 1,250 1,000 750 500 250 Doxycycline Days from treatment start –10 –5 0 5 10 15 20 25 Tumour volume (mm3 ) n = 9 n = 10 d f e c MSI MSS 0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 Co-competition score sgEss P = 0.69 MSI MSS sgNon P = 0.62 MSI MSS WRN sgRNA Gastric Endometrial Colorectal Ovarian P = 2.8 × 10–19 P = 4.7 × 10–3 P = 2.3 × 10–2 Fig. 5 | WRN is a target in MSI cancer cells. a, Circle plot of cell lines. From the outer ring to inner ring the following are shown: the fitness effect of WRN knockout and mean effect of core fitness genes (red dashed line); cancer-type; MLH1 methylation (meth.) status; mutation (mut.) status of MLL2 and MSH6; and the DNA mutation rate. b, WRN dependency in a co-competition assay. sgRNAs that target essential (sgEss) and non-essential (sgNon) genes were used as controls. Each point represents the mean co-competition score for a cell line (seven MSI and seven microsatellite stable (MSS) lines in duplicate); four WRN sgRNA guides were used. A score less than 1 denotes selective depletion of sgRNA-expressing knockout cells. Box-and-whisker plots show 1.5× the interquartile range and the median. P values were determined using a two￾sided Welch’s t-test. c, WRN rescue using wild-type (WT), exonuclease￾deficient (def.) or helicase-deficient mouse Wrn in SW48 cells with MSI. Mean ± s.d. from 3 independent experiments. P values were calculated using a standard two-sided t-test assuming equal variance; comparison to wild-type Wrn. NS, not significant. d, Tumour volume of WRN sgRNA-expressing HCT116 (clone a) xenografts treated with doxycycline (yellow line) or vehicle (grey line). P = 0.006, two-way ANOVA. Data are mean ± s.e.m. Numbers of mice in each cohort are indicated. e, Representative KI-67 immunohistochemistry assessment of WRN sgRNA￾expressing HCT116 (clone a) tumours explanted after one week. Scale bar, 50 μm; 40× magnification. f, Quantification of KI-67 staining. Data are mean ± s.d. of 10 fields from three different samples. n = 30; P value were calculated using a two-sided Welch’s t-test. N A t U r e | www.nature.com/nature