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Genomic copy number dictates a gene-independent cell response to CRISPR-Cas9 targeting Andrew J. Aguirre1,2,3,4*, Robin M. Meyers2*, Barbara A. Weir1,2, Francisca Vazquez1,2, Cheng-Zhong Zhang1,2, Uri Ben-David2, April Cook1,2, Gavin Ha1,2, William F. Harrington2, Mihir B. Doshi1,2, Maria Kost-Alimova2, Stanley Gill1,2, Han Xu2, Levi D. Ali2, Guozhi Jiang2, Sasha Pantel2, Yenarae Lee2, Amy Goodale2, Andrew D. Cherniack2, Coyin Oh2, Gregory Kryukov1,2, Glenn S. Cowley2, Levi A. Garraway1,2,3,4,5, Kimberly Stegmaier1,2,4,6, Charles W. Roberts2,7, Todd R. Golub1,2,4,5, Matthew Meyerson1,2,4,8,9, David E. Root2, Aviad Tsherniak2#, William C. Hahn1,2,3,4,9# 1 Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215 USA 2 Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142 USA 3 Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, 75 Francis Street, Boston, MA 02115 4 Harvard Medical School, 25 Shattuck Street, Boston, Massachusetts 02115, USA. 5 Howard Hughes Medical Institute, 4000 Jones Bridge Road, Chevy Chase, Maryland 20815, USA. 6 Boston Childrens Hospital, Boston, MA, USA 7 St. Jude Childrens Research Hospital, 262 Danny Thomas Place, Memphis, TN 8 Department of Pathology, Harvard Medical School, Boston, Massachusetts, USA. 9 Center for Cancer Genome Discovery, Dana-Farber Cancer Institute, Boston, MA, USA * These authors contributed equally # Co-corresponding authors William C. Hahn, M.D., Ph.D., 450 Brookline Avenue, Dana 1538, Boston, MA 02215 USA, 617-632-2641 (phone), 617-632-4005 (fax), william_ Aviad Tsherniak, Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142 USA, 617-714-7506 (phone), Running Title: Genomic copy number impacts CRISPR-Cas9 screens Research. on June 6, 2016. 2016 American Association for C Downloaded from Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 3, 2016; DOI: 10.1158/2159-8290.CD-16-0154 2 Conflict of Interest Disclosure: Levi A. Garraway and William C. Hahn are consultants for Novartis. Matthew Meyerson receives research support from Bayer. Research. on June 6, 2016. 2016 American Association for C Downloaded from Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 3, 2016; DOI: 10.1158/2159-8290.CD-16-0154 3 Abstract: The CRISPR-Cas9 system enables genome editing and somatic cell genetic screens in mammalian cells. We performed genome scale loss-of-function screens in 33 cancer cell lines to identify genes essential for proliferation/survival and found a strong correlation between increased gene copy number and decreased cell viability after genome editing. Within regions of copy number gain, CRISPR-Cas9 targeting of both expressed and unexpressed genes, as well as intergenic loci, led to significantly decreased cell proliferation through induction of a G2 cell cycle arrest. By examining single guide RNAs that map to multiple genomic sites, we found that this cell response to CRISPR-Cas9 editing correlated strongly with the number of target loci. These observations indicate that genome targeting by CRISPR-Cas9 elicits a gene- independent anti-proliferative cell response. This effect has important practical implications for interpretation of CRISPR-Cas9 screening data and confounds the use of this technology for identification of essential genes in amplified regions. Significance: We found that the number of CRISPR-Cas9-induced DNA breaks dictates a gene- independent anti-proliferative response in cells. These observations have practical implications for using CRISPR-Cas9 to interrogate cancer gene function and illustrate that cancer cells are highly sensitive to site-specific DNA damage, which may provide a path to novel therapeutic strategies. Research. on June 6, 2016. 2016 American Association for C Downloaded from Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 3, 2016; DOI: 10.1158/2159-8290.CD-16-0154 4 Introduction Genome engineering using site-specific DNA endonucleases has operationalized functional somatic cell genetics, enabling precise perturbation of both coding and non- coding regions of the genome in cells from a range of different organisms. Zinc-finger nucleases (ZFNs) and transcription activator-like effector nucleases (TALENs) are custom-designed endonucleases that enable site-specific genome editing, but their widespread application has been limited by reagent complexity and cost (1, 2). The bacterial CRISPR-Cas9 (clustered regularly interspaced short palindromic repeats CRISPR-associated 9) system, which serves as an adaptive immune mechanism, has been shown to serve as a versatile and highly effective technology for genome editing (3-8). CRISPR-Cas9 applications require introduction of two fundamental components into cells: (i) the RNA-guided CRISPR-associated Cas9 nuclease derived from Streptococcus pyogenes and (ii) a single guide RNA (sgRNA) that directs the Cas9 nuclease through complementarity with specific regions of the genome (3, 7-11). Genome editing occurs through induction of double stranded breaks in DNA by the Cas9 endonuclease in an sgRNA-directed sequence-specific manner. These DNA breaks can be repaired by one of two mechanisms: non-homologous end joining (NHEJ) or homology-directed repair (HDR)(3, 12). CRISPR-Cas9-mediated gene knock- out results from a DNA break being repaired in an error-prone manner through NHEJ and introduction of an insertion/deletion (indel) mutation with subsequent disruption of the translational reading frame (11). Alternatively, HDR-mediated repair in the presence of an exogenously supplied nucleotide template can be utilized to generate specific point mutations or other precise sequence alterations. Furthermore, nuclease-dead versions of Cas9 (dCas9) can also be fused to transcriptional activator or repressor domains to modulate gene expression at specific sites in the genome (13-17). CRISPR- Cas9 technology has been effectively utilized in cultured cells from a myriad of organisms (12), and has also been successfully employed for in vivo modeling in the mouse germline (18, 19) as well as for somatic gene editing to generate novel mouse models of cancer (20-24). Recent studies have shown that CRISPR-Cas9 can be effectively used for loss- of-function genome scale screening in human and mouse cells (9-11, 25-28). These Research. on June 6, 2016. 2016 American Association for C Downloaded from Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 3, 2016; DOI: 10.1158/2159-8290.CD-16-0154 5 approaches rely upon lentiviral delivery of the gene encoding the Cas9 nuclease and sgRNAs targeting annotated human or mouse genes. Multiple different CRISPR-Cas9 knock-out screening libraries have been developed, including both single-vector (Cas9 and the sgRNA on the same vector) and dual-vector systems (9, 25, 29). Pooled CRISPR-Cas9 screening is typically performed through massively parallel introduction of sgRNAs targeting all genes into Cas9-expressing cells, with a single sgRNA per cell. Positive- or negative-selection proliferation screens are performed and sgRNA enrichment or depletion is measured by next generation sequencing (9, 10). To date, only a limited number of genome-scale CRISPR-Cas9 knock-out screens have been reported, and these screens have demonstrated a high rate of target gene validation (9-11, 25-28). Wang et al. recently reported an analysis of cell essential genes using CRISPR-Cas9-mediated loss-of-function screens in four leukemia and lymphoma cell lines (28). Hart et al. also reported identification of core and cell line- specific essential genes in five cancer cell lines of differing lineages (25). This approach has enabled the identification of known oncogene dependencies as well as many novel essential genes and pathways in individual cancer cell lines (25, 28). In addition to knock-out screens, proof-of-concept CRISPR-activator or inhibitor screens using dCas9 and genome-scale sgRNA libraries have also been successfully conducted (30, 31). Moreover, in vivo genome-scale screens with CRISPR-Cas9 have also been performed for cancer-relevant phenotypes (32). To identify cancer cell vulnerabilities in a genotype- and phenotype-specific manner, we performed genome-scale loss-of-function genetic screens in 33 cancer cell lines representing a diversity of cancer types and genetic contexts of both adult and pediatric lineages (Table S1)(29). When we analyzed essential genes across the entire dataset, we unexpectedly found a robust correlation between apparent gene essentiality and genomic copy number, where the number of CRISPR-Cas9-induced DNA cuts predict the cellular response to genome editing. Research. on June 6, 2016. 2016 American Association for C Downloaded from Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 3, 2016; DOI: 10.1158/2159-8290.CD-16-0154 6 Results High-resolution CRISPR-Cas9 screening in cancer cell lines for gene dependencies Using the dual-vector GeCKOv2 CRISPR-Cas9 system, we performed genome- scale pooled screening in 33 cancer cell lines representing a wide diversity of adult and pediatric cancer types (Table S1; Fig. 1A). Cancer cell lines were transduced with a lentiviral vector expressing the Cas9 nuclease under blasticidin selection. These stable cell lines were then infected in replicate (n = 3 or 4) at low multiplicity of infection (MOI 2. Gene dependency scores are shown as global Z-scores for both CRISPR-Cas9 and RNAi screening datasets, with Z-scores representing standard deviations from the mean of all genes evaluated in all cell lines screened (CRISPR-Cas9, n = 33 cell lines; RNAi, n = 503 cell lines). Figure 4. CRISPR-Cas9 sensitivity correlates with number of predicted cuts for both guides targeting single loci and multiple loci. Data from two representative cell lines are shown (PA-TU-8902, A-B; Panc 08.13, C-D) (A, C) CRISPR-Cas9 sensitivity for sgRNAs targeting only a single locus is plotted against copy number of that locus. The black hash marks represent the median CRISPR guide score for all guides targeting a locus at that copy number. The linear trendline is shown. (B, D) CRISPR-Cas9 guide scores for sgRNAs targeting multiple loci, are plotted against the predicted number of cuts for each sgRNA. Only sgRNAs targeting non-amplified regions are included, thus allowing segregation of the impact of multiple CRISPR-Cas9-induced DNA cuts due to Research. on June 6, 2016. 2016 American Association for C Downloaded from Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 3, 2016; DOI: 10.1158/2159-8290.CD-16-0154 31 either copy number or number of target loci. The influence of the number of predicted DNA cuts on CRISPR-Cas9 guide scores was modeled for each cell line as the slope of the trend line in A-D and termed the CRISPR-Cut Index (CCI). The CCI was determined for both copy number-driven (CCI-CN) (A, C) and multiple alignment-driven effects (CCI-MA) (B, D). (E) Scatter plot of CCI-MA versus CCI-CN showing strong correlation of the effect on CRISPR-Cas9 guide scores for either multiple alignment driven or copy number-driven DNA cuts across the cell lines. Figure 5. sgRNAs targeting multiple chromosomes show greater sensitivity to CRISPR- Cas9-induced cutting. Data from two representative cell lines are shown (PA-TU-8902, A, C; Panc 08.13, B, D) (A, B) Boxplots of CRISPR-Cas9 sensitivity to the predicted number of CRISPR-Cas9-induced DNA cuts. CRISPR-Cas9 guide scores are shown on the Y-axis and the predicted number of DNA cuts is shown on the X-axis. sgRNAs are divided into three groups. In red are sgRNAs that target a single locus, and therefore total number of predicted cuts is based on copy number. In yellow are sgRNAs that target multiple loci within a single chromosome (intra-chromosomal). In blue are sgRNAs that target multiple loci across multiple chromosomes (inter-chromosomal). The analysis demonstrates a more potent detrimental influence on cell viability for multiple CRISPR-Cas9-induced DNA cuts across multiple chromosomes (inter-chromosomal) as compared to those restricted to a single chromosome (intra-chromosomal). Multiple linear regression accounting for difference in total number of cuts for inter-chromosomal vs. intra-chromosomal: panel A, = -0.27, p = 2.64e-22; panel C, = -0.16, p = 4.97e- 22. (C, D) Waterfall plots showing CRISPR guide scores for all sgRNAs in the pooled screens performed on the indicated cell lines. sgRNAs from the multiple alignment analysis targeting multiple chromosomes with 10 predicted target sites are shown in red and are significantly enriched with negative CRISPR-Cas9 guide scores relative to all other sgRNAs in the library (one-sided KolmogorovSmirnov test: p = 2.13E-159, C; p = 9.17E-88, D) These data highlight the potent detrimental effect that these sgRNAs have on cell proliferation and viability within the screen. Research. on June 6, 2016. 2016 American Association for C Downloaded from Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 3, 2016; DOI: 10.1158/2159-8290.CD-16-0154 32 Figure 6. CRISPR-Cas9 targeting of amplified regions or multiple genomic loci induces DNA damage and a G2 cell cycle arrest. (A) Schematic of the PANC-1 19q13 amplicon demonstrating ABSOLUTE DNA copy number (top panel) and CRISPR guide scores (middle panel) mapped by genomic position. Schematic and color scheme are similar to that detailed in Fig. 2. (B) In vitro validation experiment measuring arrayed proliferation and viability response of PANC-1 cells at 6 d post-infection with sgRNAs targeting regions inside (red) and outside (blue) of the demonstrated amplicon. sgRNAs targeting intergenic regions are labeled by chromosomal locus and columns are given a checkered pattern. Multi-targeted sgRNAs (MT-1 and MT-2) are indicated by black bars. sgRNAs targeting an alternative unamplified locus (12q, orange) and known essential genes (green) are also shown. Non-targeting negative control sgRNAs are shown in yellow. Dots placed below the copy number panel correspond to the validation sgRNAs targeting the indicated genes or intergenic regions on the locus, and are matched by color and left-to-right genomic position. Cell-Titer-Glo was performed at 6- days post-infection. Error bars indicate SD of biologic replicates (n=3). p 0.0001 for two-tailed T-test comparing sgRNAs inside (red) vs outside (blue and orange) the amplicon. (C) Plot of the percentage of PANC-1 cells in each phase of the cell cycle at 48 hours post-infection with the indicated sgRNAs targeting inside (red) or outside (blue) the amplicon. Data for a multi-targeted sgRNA (MT-2) and a control sgRNA targeting an alternative locus (12q-5), as well as for control genes are also shown. Fraction of cells in each phase of the cell cycle is indicated by a unique pattern within the column corresponding to each cell cycle phase. Colors scheme is as indicated above, with coloration of the G2 and S phases for emphasis. Error bars represent the standard deviation for the mean of three replicates. (D) Plot of the number of -H2AX foci present in PANC-1 cells at 48 hours post-infection with the indicated sgRNAs. Color scheme is as indicated above, with checkered pattern corresponding to sgRNAs targeting intergenic regions. Figure 7. Cell essential genes and copy number. Cumulative distribution function (CDF) of the correlation coefficient between ABSOLUTE CN and CRISPR-Cas9 sensitivity for the indicated gene sets across all 33 cell lines screened with pooled CRISPR-Cas9. Research. on June 6, 2016. 2016 American Association for C Downloaded from Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 3, 2016; DOI: 10.1158/2159-8290.CD-16-0154 33 Known cell essential KEGG gene sets are displayed separately (proteasome, red; ribosome, blue; spliceosome, green, Table S2) from all other genes in the screen (black). Cel

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