Abstract

Synthetic lethality—an interaction between two genetic events through which the co-occurrence of these two genetic events leads to cell death, but each event alone does not—can be exploited for cancer therapeutics1. DNA repair processes represent attractive synthetic lethal targets, because many cancers exhibit an impairment of a DNA repair pathway, which can lead to dependence on specific repair proteins2. The success of poly(ADP-ribose) polymerase 1 (PARP-1) inhibitors in cancers with deficiencies in homologous recombination highlights the potential of this approach3. Hypothesizing that other DNA repair defects would give rise to synthetic lethal relationships, we queried dependencies in cancers with microsatellite instability (MSI), which results from deficient DNA mismatch repair. Here we analysed data from large-scale silencing screens using CRISPR–Cas9-mediated knockout and RNA interference, and found that the RecQ DNA helicase WRN was selectively essential in MSI models in vitro and in vivo, yet dispensable in models of cancers that are microsatellite stable. Depletion of WRN induced double-stranded DNA breaks and promoted apoptosis and cell cycle arrest selectively in MSI models. MSI cancer models required the helicase activity of WRN, but not its exonuclease activity. These findings show that WRN is a synthetic lethal vulnerability and promising drug target for MSI cancers.

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Data availability

Source Data for Figs. 2a–g, 3c, 4a, c, e, f and Extended Data Figs. 3a, b, d, 4b–d, 5b, d, f, 6a, c, d, f, h, 7b, d, e, 8d, 10a–d, f are provided with online version of the paper. mRNA-seq data (shown in Fig. 3a) have been deposited in the Gene Expression Omnibus (GEO) repository under accession number GSE126464. DepMap omics and dependency data used for analyses are available as a FigShare repository: Cancer Data Science. DepMap Data sets for WRN manuscript. (2019). https://doi.org/10.6084/m9.figshare.7712756.v150.

Code availability

Code used for analysis can be found at https://github.com/cancerdatasci/WRN_manuscript. All materials can be accessed at https://depmap.org/WRN.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Acknowledgements

We thank C. R. Boland, A. Goel, M. Koi, R. J. Monnat and R. Weissleder for reagents; A. Tang for graphical assistance; and P. Montgomery for the website. This work was funded by The Carlos Slim Foundation/Slim Initiative for Genomic Medicine, Broad Institute, Team MG, NIH (2T32CA009172-42A1 to E.M.C.) and the Irving W. Janock Fellowship (to E.M.C.).

Author information

Author notes

  1. These authors contributed equally: Edmond M. Chan, Tsukasa Shibue

  2. These authors jointly supervised this work: Francisca Vazquez, Adam J. Bass

Affiliations

  1. Broad Institute of Harvard and MIT, Cambridge, MA, USA

    • Edmond M. Chan
    • , Tsukasa Shibue
    • , James M. McFarland
    • , Benjamin Gaeta
    • , Mahmoud Ghandi
    • , Nancy Dumont
    • , Alfredo Gonzalez
    • , Justine S. McPartlan
    • , Annie Apffel
    • , Syed O. Ali
    • , Rebecca Deasy
    • , Paula Keskula
    • , Raymond W. S. Ng
    • , Lisa Leung
    • , Maria Alimova
    • , Monica Schenone
    • , Mirazul Islam
    • , Yosef E. Maruvka
    • , Yang Liu
    • , Srivatsan Raghavan
    • , Marios Giannakis
    • , Yuen-Yi Tseng
    • , Zachary D. Nagel
    • , David E. Root
    • , Jesse S. Boehm
    • , Gad Getz
    • , Todd R. Golub
    • , Aviad Tsherniak
    • , Francisca Vazquez
    •  & Adam J. Bass
  2. Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

    • Edmond M. Chan
    • , Tianxia Li
    • , Yanxi Zhang
    • , Jie Bin Liu
    • , Syed O. Ali
    • , Raymond W. S. Ng
    • , Mirazul Islam
    • , Yang Liu
    • , Srivatsan Raghavan
    • , Marios Giannakis
    • , Francisca Vazquez
    •  & Adam J. Bass
  3. Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

    • Jean-Bernard Lazaro
    • , Emma A. Roberts
    • , Elizaveta Reznichenko
    •  & Alan D’Andrea
  4. Department of Laboratory Medicine, Yale University School of Medicine, New Haven, CT, USA

    • Peili Gu
    •  & Sandy Chang
  5. Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    • Cortt G. Piett
    •  & Zachary D. Nagel
  6. Massachusetts General Hospital Cancer Center, Boston, MA, USA

    • Yosef E. Maruvka
    •  & Gad Getz
  7. Department of Medicine, Division of Gastroenterology, Duke University, Durham, NC, USA

    • Jatin Roper
  8. Department of Pathology, Yale University School of Medicine, New Haven, CT, USA

    • Sandy Chang
  9. Department of Molecular Biophysics and Biochemistry, Yale University School of Medicine, New Haven, CT, USA

    • Sandy Chang
  10. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

    • Todd R. Golub
  11. Howard Hughes Medical Institute, Chevy Chase, MD, USA

    • Todd R. Golub

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Contributions

E.M.C., T.S., F.V. and A.J.B. initiated the project, and designed and supervised the research plan. J.M.M., M. Ghandi, Y.L. and Y.E.M. performed computational analysis of the CCLE and cancer dependency datasets under the supervision of D.E.R., J.S.B., G.G., T.R.G., A.T., F.V. and A.J.B. E.M.C., T.S., B.G. and J.S.M. performed the viability experiments to validate the cancer dependency dataset findings with help from M.S., A.A., S.O.A. and L.L. The rescue experiments with WRN overexpression were performed by E.M.C. and B.G. The HCT116 viability experiments were performed by T.S. and B.G. N.D., A.G., T.L. and Y.Z. performed in vivo experiments. The patient-derived organoids were established by J.S.B., Y.-Y.T., M. Giannakis, R.D. and P.K. Organoid experiments were conducted by E.M.C. and T.S. with help from S.R., R.W.S.N and J.R. RNA extraction for mRNA-seq was performed by T.S. and analysed by J.M.M. and M.I. J.S.M. and T.S. performed and analysed the cell cycle and apoptosis assays. Immunoblots were performed by T.S., E.M.C., B.G. and J.S.M. Immunofluorescence experiments were performed by T.S., E.M.C., J.B.L., J.-B.L., E.A.R, and E.R. and analysed by T.S., E.M.C., J.B.L., J.-B.L., M.A., and A.D.A. S.C. and P.G. performed the telomere PNA-FISH experiment. Z.D.N. and C.G.P. performed the fluorescence-based flow-cytometric host-cell reactivation assay. E.M.C., T.S., J.M.M., F.V. and A.J.B. wrote the manuscript. All the authors edited and approved the manuscript.

Competing interests

A.J.B. receives research funding from Merck and Novartis. D.E.R. receives research funding from the Functional Genomics Consortium (Abbvie, Jannsen, Merck and Vir) and is a director of Addgene. A.T. consults for Tango Therapeutics. T.R.G. has advised Foundation Medicine, GlaxoSmithKline, Sherlock Biosciences and Forma Therapeutics. A.D.A. consults for Lilly Oncology, EMD Serono, Intellia Therapeutics, Sierra Oncology, Formation Biologics and Cyteir Therapeutics, consults and holds stock in Ideaya, and co-founded and holds stock in Cedilla Therapeutics. G.G. receives research funding from IBM and Pharmacyclics and is an inventor on multiple patent applications related to bioinformatics tools, including applications related to MuTect, ABSOLUTE, MSMuTect, MSMutSig and MSIClass. Y.E.M. is an inventor on patent applications related to the bioinformatics tools MSMuTect, MSMutSig and MSIClass. The Broad Institute, on behalf of E.M.C., T.S., J.M.M., M. Ghandi, F.V. and A.J.B., filed a US patent application related to the target described in this manuscript.

Corresponding authors

Correspondence to Francisca Vazquez or Adam J. Bass.

Extended data figures and tables

  1. Extended Data Fig. 1 Functional genomic screening identifies that MSI cancers are selectively dependent on WRN.

    a, Screened cell lines plotted by number of deletions and fraction of deletions occurring in microsatellite (MS) regions. Genes involved in MMR that are lost are indicated by different colours. None, no predicted loss of MMR genes. Multiple, loss of more than one MMR gene. n/a, not available. b, Using PCR-based MSI phenotyping, P values (empirical Bayes moderated t-test) were plotted against the mean difference of dependency scores between MSI and MSS cell lines for Achilles (n = 19 MSI; n = 291 MSS) and DRIVE (n = 23 MSI; n = 252 MSS). c, Dependency scores for each RecQ helicase plotted for MSI and MSS cell lines from Achilles (n = 32 MSI; n = 413 MSS) and DRIVE (n = 38 MSI; n = 337 MSS). Q values (Wilcoxon rank-sum test) for Achilles/DRIVE are 5.0 × 10−8/1.7 × 10−8, 0.73/0.52, 0.73/0.85, 0.25/0.73 and 0.08/not available for WRN, RECQL, BLM, RECQL5 and RECQL4, respectively. Centre lines indicate medians. Boxes indicate 25th and 75th percentiles; whiskers extend to 1.5× IQR beyond the box and individual data points are represented by dots. d, Sensitivity and positive predictive value of indicated relationship between biomarker and genetic dependency. e, Dependency score distributions and associated biomarkers for example biomarker–genetic dependency relationships. Width of coloured regions represent density estimates. Horizontal dashed line: threshold used to separate dependent and non-dependent cell lines. n = 37, 14, 541 MSI cell lines from typical lineage, MSI cell lines from atypical lineage, MSS cell lines, respectively. n = 120/546 KRAS hotspot mutants/other; 65/601 BRAF hotspot mutants/other; 86/580 PIK3CA hotspot mutants/other.

  2. Extended Data Fig. 2 MSI cells from MSI-predominant lineages have a greater mutational burden and WRN dependency.

    a, WRN dependency scores plotted by lineage, sub-classified by MSI and MSS status. Boxes indicate 25th and 75th percentiles; whiskers extend to 1.5× IQR beyond the box and individual data points are represented by dots. b, Microsatellite deletions in cell lines classified as MSS (n = 541), MSI from an infrequent MSI lineage (n = 45), or MSI from an MSI-predominant lineage (n = 54). *P = 1.7 × 10−9, Wilcoxon signed-rank test. Width of coloured regions represent density estimates. c, MSI cell lines plotted by their average WRN dependency and number of microsatellite deletions. Lineages are colour-coded. d, MSI cell lines from MSI-predominant lineages are plotted by their average WRN dependency and number of microsatellite deletions. Lineages are colour-coded.

  3. Extended Data Fig. 3 WRN depletion preferentially impairs MSI cell viability.

    a, Immunoblot of WRN and GAPDH levels 4 days after sgRNA transduction. b, Relative viability following sgRNA transduction in a competitive growth assay. Data are mean ± s.e.m. (n = 6 biological replicates). Comparison between WRN sgRNAs and negative controls at day 10; two-way ANOVA; *P = 0.37, †P = 1.2 × 10−7, ‡P = 0.23, §P = 2.7 × 10−19. c, Clonogenic assay after shRNA transduction with a non-targeting negative control (RFP shRNA (shRFP)), a pan-essential control (PSMD2 shRNA (shPSMD2)) and two shRNAs against WRN (shWRN1 and shWRN2). d, Relative staining intensity of the clonogenic assay. Data are mean ± s.e.m. (n = 3 technical replicates). Representative data from one experiment are shown. All experiments were performed three times. Source data

  4. Extended Data Fig. 4 WRN depletion preferentially induces cell cycle arrest and apoptosis in MSI cells.

    a, Gating strategy. For cell cycle analyses (top), debris and dead cells were excluded based on forward scatter-area (FSC-A) and side scatter-area (SSC-A) profiles. Subsequently, singlets were identified based on FSC-A and forward scatter-height (FSC-H) profiles. These singlets were then analysed for DAPI (DNA content) and EdU–Alexa Fluor 647 (EdU–647) staining intensities. EdU+ cells (cells exhibiting higher staining intensity than unstained cells) were classified as ‘S phase’. EdU cells were classified either as ‘G1 phase’ or ‘G2/M phase’ based on their DNA content. For apoptosis analyses (bottom), debris was excluded based on FSC-A and SSC-A profiles. The remaining samples were analysed for annexin-V–FITC and propidium iodide (PI) staining intensities. Subsequently, annexin-V+ cells and PI+ cells (cells exhibiting higher staining intensity than unstained cells) were identified. On the basis of the positivity of these markers, cells were classified into one of the following three categories: viable (annexin-VPI), early apoptosis (annexin-V+PI) and late apoptosis/nonapoptotic death (annexin-VPI+ and annexin-V+PI+). b, Cell cycle evaluation 4 days after sgRNA transduction. Comparison between Chr.2-2 sgRNA and WRN sgRNAs for the percentage of S-phase cells; two-way ANOVA; *P = 0.16, †P = 0.67, ‡P = 6.1 × 10−7, §P = 3.5 × 10−4, P = 0.69, ¶P = 2.6 × 10−6. c, Annexin-V and propidium iodide staining evaluating early apoptosis and late apoptosis/non-apoptotic cell death 7 days after sgRNA transduction. Comparison between Chr.2-2 sgRNA and WRN sgRNAs for the percentage of dying/dead cells; two-way ANOVA; *P = 0.10, †P = 0.41, ‡P = 3.4 × 10−3, §P = 3.6 × 10−4, P = 0.57, ¶P = 3.6 × 10−5. d, Annexin V and propidium iodide staining 4 and 8 days after shRNA transduction. Comparison between RFP shRNA and WRN shRNAs; two-way ANOVA; 1.3 × 10−3 (SW837 day 4), 1.6 × 10−2 (SW837 day 8), 1.2 × 10−6 (KM12 day 4), 4.3 × 10−9 (KM12 day 8). Three biological replicates are presented in tandem for bd. Representative data from one experiment are shown. All experiments were performed twice. Source data

  5. Extended Data Fig. 5 WRN depletion activates a p53 response in MSI cells.

    a, Phosphorylated p53(S15) immunofluorescence images following sgRNA transduction in ovarian cell lines (ES2 and OVK18). Scale bar, 50 μm. b, Nuclear phosphorylated p53(S15) staining intensity per cell following WRN knockout compared to control sgRNA. Data were analysed as fold change (log(WRN sgRNA/control sgRNA)); mean = 0.059 (OVK18), mean = −0.037 (ES2). Difference in fold change between OVK18 and ES2; contrast test of least-squares means; P < 2 × 10−16. n indicates the number of cells treated with Chr.2-2 sgRNA, WRN sgRNA 2, WRN sgRNA 3, respectively, for OVK18 (3,982, 1,143, 2,740) and ES2 (4,916, 3,072, 3,690). c, p21 immunofluorescence images following sgRNA transduction in colon cell lines (SW620, KM12 and SW48). KM12 is a p53-impaired MSI cell line. Scale bar, 50 μm. d, Nuclear p21 staining per cell. Data were analysed as fold change (log(WRN sgRNA/control sgRNA)); WRN knockout compared to control in SW48 cells was compared to either SW620 (P < 2 × 10−16; contrast test of least-squares means) or KM12 cells (P < 2 × 10−16; contrast test of least-squares means). Mean = 0.13 (SW48), mean = −0.016 (SW620), mean = −0.032 (KM12). n indicates the number of cells analysed following treatment with Chr.2-2 sgRNA, WRN sgRNA 2, WRN sgRNA 3, respectively, for SW48 (16,203, 7,617, 13,257), SW620 (7,278, 13,768, 11,576) and KM12 (16,117, 14,200, 11,301). e, p21 immunofluorescence images following sgRNA transduction in ovarian cell lines. Scale bar, 50 μm. f, Nuclear p21 staining intensity per cell. Data were analysed as fold change (log(WRN sgRNA/control sgRNA)); WRN knockout compared to control in OVK18 cells was compared to ES2 cells; contrast test of least-squares means; P < 2 × 10−16. Mean = 0.157 (OVK18), mean = −0.010 (ES2). n indicates the number of cells analysed following treatment with Chr.2-2 sgRNA, WRN sgRNA 2, WRN sgRNA 3, respectively, for OVK18 (3,436, 5,876, 8,275) and ES2 (9,117, 6,834, 11,576). g, WRN dependency for cells lines classified as MSS (n = 514), MSI from an infrequent MSI lineage (n = 6 and 8 for p53-intact and -impaired), or MSI from an MSI-predominant lineage (n = 23 and 13 for p53-intact and -impaired) and further subclassified by p53 status. b, d, f, Centre line indicates the median, boxes indicate the 25 to 75th percentiles, whiskers indicate the 1st to 99th percentiles and dots indicate outliers. g, Boxes indicate 25th and 75th percentiles; whiskers extend to 1.5× IQR beyond the box and individual data points are represented by dots. Representative data from one experiment are shown. af, Experiments were performed twice. Source data

  6. Extended Data Fig. 6 WRN depletion preferentially induces DSBs in MSI cells.

    a, Nuclear γH2AX foci per cell following sgRNA transduction in colon cell lines. b, γH2AX immunofluorescence images following sgRNA transduction in ovarian cell lines. Scale bar, 50 μm. c, Nuclear γH2AX staining intensity per cell following sgRNA transduction. Difference in fold change between OVK18 and ES2; contrast test of least-squares mean; P < 2 × 10−16. Mean = 0.147 (OVK18) and mean = 0.055 (ES2). n indicates the number of cells analysed following treatment with Chr.2-2 sgRNA, WRN sgRNA 2, WRN sgRNA 3, respectively, for OVK18 (2,612, 4,823, 6,164) and ES2 (6,429, 6,469, 6,388). Centre line indicates the median, boxes indicate the 25 to 75th percentiles, whiskers indicate the 1st to 99th percentiles and dots indicate outliers. d, Nuclear γH2AX foci per cell following sgRNA transduction in ovarian cell lines. e, Fluorescence of Apple–53BP1 foci in colon cell lines exogenously expressing Apple–53BP1(truncated). Scale bar, 50 μm. f, Nuclear Apple–53BP1 foci per cell following sgRNA transduction in colon cell lines. g, Fluorescence of Apple–53BP1 foci following sgRNA transduction in ovarian cell lines exogenously expressing Apple–53BP1(truncated). Scale bar, 50 μm. h, Nuclear Apple–53BP1 foci per cell in ovarian cell lines. Representative data from one experiment are shown. All experiments were performed twice. Source data

  7. Extended Data Fig. 7 WRN depletion preferentially induces DSB responses in MSI cells.

    a, Phospho-ATM(S1981) immunofluorescence images following sgRNA transduction in colon cell lines. Scale bar, 50 μm. b, Nuclear phospho-ATM(S1981) foci per cell following sgRNA transduction in colon cell lines. c, Phospho-ATM(S1981) immunofluorescence images following sgRNA transduction in ovarian cell lines. Scale bar, 50 μm. d, Nuclear phospho-ATM(S1981) foci per cell following sgRNA transduction in ovarian cell lines. e, γH2AX, phospho-CHK2(T68), total CHK2, WRN and GAPDH levels following shRNA transduction. Representative data from one experiment are shown. All experiments were performed twice. Source data

  8. Extended Data Fig. 8 WRN is preferentially recruited to DNA in MSI cells.

    a, Telomere PNA-FISH of metaphase spreads with or without doxycycline induction of WRN shRNA 1. Hollow arrowhead, chromosomal breaks. Filled arrowhead, chromosomal fragments. b, WRN immunofluorescence images following treatment with WRN shRNA 1 or control shRNA (WRN-C911 shRNA 1). c, WRN immunofluorescence images. Scale bar, 20 μm. d, Analyses of WRN co-localization with the nucleolar marker, fibrillarin, by Pearson’s co-localization coefficients. Data are mean ± s.e.m. (n = 5 biological replicates); two-tailed Student’s t-test; *P = 1.0 × 10−3, †P = 4.3 × 10−5, ‡P = 0.014. Representative data from one experiment are shown. All experiments were conducted twice. Source data

  9. Extended Data Fig. 9 Paralogue dependencies and hypermutation alone cannot explain the WRN–MSI relationship.

    a, Estimated association between WRN dependency and MSI status after controlling for loss of indicated genes (effect size estimates for the linear model are plotted against significance). If loss of a gene can fully account for the MSI–WRN relationship, the difference in dependency and significance would be 0. Genes for which the loss are typically associated with insertion and deletion (indel) mutations (over half of loss events) are highlighted in red. n = 51 MSI, n = 541 MSS. b, Average WRN dependency score for MSS and MSI lines stratified by POLE status (n = 4, 5, 35, 497, 2, 12, 5, 10, 22 cell lines per category in order of left to right). Boxes indicate 25th and 75th percentiles; whiskers extend to 1.5× IQR beyond the box and individual data points are represented by dots.

  10. Extended Data Fig. 10 MMR deficiency contributes to WRN dependency.

    a, Flow-cytometric host-cell reactivation assay measuring the ability of the indicated cell lines to repair a G:G mismatch in a plasmid reporter, thus activating the fluorescence reporter and measuring MMR activity. Data are mean ± s.e.m. from three independent experiments; two-tailed Student’s t-test, *P = 5.5 × 10−2, †P = 2.3 × 10−3; two-way ANOVA, ‡P = 3.6 × 10−8. b, Immunoblot. γH2AX, WRN, MLH1, MSH3 and GAPDH levels following shRNA transduction in HCT116 cells with or without MMR restoration. c, Relative viability of HCT116 derivatives 7 days after shRNA transduction. Data are mean ± s.e.m. (n = 6 biological replicates); two-way ANOVA; P = 5.7 × 10−20 (* compared to †), P = 3.3 × 10−12 († compared to ‡), P = 1.6 × 10−16 († compared to §). c, Immunoblot. γH2AX, WRN, MLH1, MSH3 and GAPDH levels following shRNA transduction in HCT116 derivatives. d, Clonogenic assay after shRNA transduction for 15 days. e, Relative staining intensity of the clonogenic assay. Data are mean ± s.e.m. (n = 3 biological replicates); two-way ANOVA; P = 3.6 × 10−6 (* compared to †), P = 8.5 × 10−8 († compared to ‡), P = 2.8 × 10−8 († compared to §). bf, Representative data from one experiment are shown. All experiments were conducted twice except in a, for which experiments were conducted three times. Source data

Supplementary information

  1. Supplementary Table 1

    Cell line annotations. This table describes cell line ID, lineage, PCR (Genomics of Drug Sensitivity in Cancer)- and NGS (CCLE)-based MSI classifications, Project DRIVE (RNAi), Project Achilles (CRISPR) and average WRN dependency scores, WRN dependent classification, TP53 status, derivation from an MSI-common lineage, number of microsatellite deletions, fraction of deletions in microsatellite regions, predicted MMR loss, predicted MMR gene(s) loss, individual MMR gene damaging mutations, deletions, and loss, MSH2 and MSH6 expression by RPPA, any MMR gene mutation, POLE damaging mutations, POLE hotspot mutations, other POLE mutations, and comments.

  2. Reporting Summary

Source data

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DOI

https://doi.org/10.1038/s41586-019-1102-x

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