Article
Genome-wide CRISPR-Cas9 Screens Reveal Loss of Redundancy between PKMYT1 and WEE1 in Glioblastoma Stem-like Cells
Graphical Abstract Authors Chad M. Toledo, Yu Ding, Pia Hoellerbauer, ..., Bruce E. Clurman, James M. Olson, Patrick J. Paddison
Correspondence [email protected] (J.M.O.), [email protected] (P.J.P.)
In Brief Patient-derived glioblastoma stem-like cells (GSCs) can be grown in conditions that preserve patient tumor signatures and their tumor initiating capacity. Toledo et al. use these conditions to perform genome-wide CRISPR-Cas9 lethality screens in both GSCs and non- transformed NSCs, revealing PKMYT1 as a candidate GSC-lethal gene.
Highlights d CRISPR-Cas9 lethality screens performed in patient brain- tumor stem-like cells d PKMYT1 is identified in GSCs, but not NSCs, as essential for facilitating mitosis d PKMYT1 and WEE1 act redundantly in NSCs, where their inhibition is synthetic lethal d PKMYT1 and WEE1 redundancy can be broken by over- activation of EGFR and AKT
Toledo et al., 2015, Cell Reports 13, 2425–2439 December 22, 2015 ª2015 The Authors http://dx.doi.org/10.1016/j.celrep.2015.11.021 Cell Reports Article
Genome-wide CRISPR-Cas9 Screens Reveal Loss of Redundancy between PKMYT1 and WEE1 in Glioblastoma Stem-like Cells
Chad M. Toledo,1,2,14 Yu Ding,1,14 Pia Hoellerbauer,1,2 Ryan J. Davis,1,2,3 Ryan Basom,4 Emily J. Girard,3 Eunjee Lee,5 Philip Corrin,1 Traver Hart,6,7 Hamid Bolouri,1 Jerry Davison,4 Qing Zhang,4 Justin Hardcastle,1 Bruce J. Aronow,8 Christopher L. Plaisier,9 Nitin S. Baliga,9 Jason Moffat,6,7 Qi Lin,10 Xiao-Nan Li,10 Do-Hyun Nam,11 Jeongwu Lee,12 Steven M. Pollard,13 Jun Zhu,5 Jeffery J. Delrow,4 Bruce E. Clurman,1,3 James M. Olson,3,* and Patrick J. Paddison1,2,* 1Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA 2Molecular and Cellular Biology Program, University of Washington, Seattle, WA 98195, USA 3Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA 4Genomics and Bioinformatics Shared Resources, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA 5Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA 6Department of Molecular Genetics, University of Toronto and Donnelly Centre, Toronto, ON M5S3E1, Canada 7Canadian Institute for Advanced Research, Toronto, ON M5G1Z8, Canada 8Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA 9Institute for Systems Biology, Seattle, WA 98109, USA 10Brain Tumor Program, Texas Children’s Cancer Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA 11Institute for Refractory Cancer Research, Samsung Medical Center, Seoul 135-710, Korea 12Department of Stem Cell Biology and Regenerative Medicine, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44192, USA 13Edinburgh CRUK Cancer Research Centre and MRC Centre for Regenerative Medicine, The University of Edinburgh, Edinburgh EH16 4UU, UK 14Co-first author *Correspondence: [email protected] (J.M.O.), [email protected] (P.J.P.) http://dx.doi.org/10.1016/j.celrep.2015.11.021 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
SUMMARY the discovery of patient-tailored therapeutic strategies. How- ever, it remains unclear whether analytic or computational ap- To identify therapeutic targets for glioblastoma proaches based solely on descriptive datasets are powerful (GBM), we performed genome-wide CRISPR-Cas9 enough to predict successful therapies. An alternative knockout (KO) screens in patient-derived GBM approach is to directly identify molecular vulnerabilities in pa- stem-like cells (GSCs) and human neural stem/pro- tient samples using functional genetic experimentation. This genitors (NSCs), non-neoplastic stem cell controls, has recently been achieved for glioblastoma (GBM) (Chudnov- for genes required for their in vitro growth. Surpris- sky et al., 2014; Ding et al., 2013; Gargiulo et al., 2013; Goidts et al., 2012; Hubert et al., 2013; Kitambi et al., 2014; Toledo ingly, the vast majority GSC-lethal hits were found et al., 2014; Wurdak et al., 2010), the most aggressive and outside of molecular networks commonly altered in common form of brain cancer in adults (American Cancer Soci- GBM and GSCs (e.g., oncogenic drivers). In vitro ety, 2010; Stupp et al., 2005). and in vivo validation of GSC-specific targets re- Loss of gene function RNAi screens have been performed vealed several strong hits, including the wee1-like directly in patient-derived GBM stem-like cells (GSCs) for candi- kinase, PKMYT1/Myt1. Mechanistic studies demon- date therapeutic targets and GBM regulatory networks (Chud- strated that PKMYT1 acts redundantly with WEE1 novsky et al., 2014; Ding et al., 2013; Gargiulo et al., 2013; Goidts to inhibit cyclin B-CDK1 activity via CDK1-Y15 phos- et al., 2012; Hubert et al., 2013; Toledo et al., 2014; Wurdak et al., phorylation and to promote timely completion of 2010). GSCs retain tumor-initiating potential and tumor-specific mitosis in NSCs. However, in GSCs, this redundancy genetic and epigenetic signatures in vitro (Lee et al., 2006; is lost, most likely as a result of oncogenic signaling, Pollard et al., 2009), under culture conditions that mimic the neu- ral progenitor perivascular niche (Kazanis et al., 2010; Lathia causing GBM-specific lethality. et al., 2012). By performing control screens in fetal neural stem cells (NSCs), which have similar expression profiles and devel- INTRODUCTION opmental potential but are not transformed (Lee et al., 2006; Pollard et al., 2009), candidate GSC-specific therapeutic targets One popular concept in cancer research is the notion that can be identified (Ding et al., 2013; Hubert et al., 2013; Toledo genomic and molecular profiling of patient samples will enable et al., 2014).
Cell Reports 13, 2425–2439, December 22, 2015 ª2015 The Authors 2425 With the emergence of CRISPR-Cas9 gene editing technol- We next performed genome-wide screens using two adult ogy, functional genetic single-guide RNA (sgRNA) libraries now GSC isolates, 0131 and 0827 (Son et al., 2009), and two control exist that are in theory capable of triggering biallelic insertion- NSC lines, CB660 and U5 (Figure 2A). These GSC isolates best deletion (indel) mutations in most genes in the human genome resemble mesenchymal and proneural GBM subtypes, respec- (Shalem et al., 2014; Wang et al., 2014). In contrast to gene tively (Figure S2), two subtypes accounting for over half of adult knockdown, these indels can cause knockout (KO)-like mu- GBM cases (Verhaak et al., 2010). These isolates harbor charac- tations that result in frameshifts in target genes leading to pre- teristic gene and pathway alterations commonly observed in mature stop codons, non-sense mediated mRNA decay, and GBM tumors (Brennan et al., 2013), including alterations in complete loss of protein function (Mali et al., 2013; Wiedenheft EGFR, NF1, MDM2/4, PI3KCA, PTEN, RB1, TERT, and/or et al., 2012). However, this technology may present unique chal- TP53 (Figures 2A and S1A–S1D; Table S1). Importantly, we did lenges for studying essential genes in mammals. For example, if not find growth defects in NSCs or GSCs when Cas9 was stably Cas9 cuts are repaired by the non-homologous end-joining expressed for over 3 weeks (Figure S1E). pathway in a non-biased manner, one-third of the time a small The screens were performed using a ‘‘shot gun’’ approach in-frame indel would be generated that might have little effect where GSCs and NSCs were transduced with a LV pool con- on protein activity. taining a human CRISPR-Cas9 library composed of 64,751 Here, we applied a genome-wide CRISPR-Cas9 library to unique sgRNAs targeting 18,080 genes (Shalem et al., 2014) GSCs and NSCs in an attempt to further identify GBM candi- and outgrown in self-renewal conditions for 3weeks(day date therapeutic targets, which when KO’d are essential to 21 for NSC-U5 or day 23 for all others) (Supplemental Exper- GSCs but non-essential in NSCs, suggestive of a large thera- imental Procedures) using two biological replicates per peutic window. The results from these screens provide evi- isolate. For the primary screen readout, we deep sequenced dence for both ‘‘individual’’ GSC-specific KO hits, which are library sgRNAs from transduced cell populations before and found only in individual patient samples, and ‘‘convergent’’ after outgrowth. Based on normalized read counts, we identi- KO hits, which are shared hits between GBM-isolates of fied 99.8% of all sgRNAs in the library pool. Each screen different developmental subtypes and genetic alterations. replicate tightly clustered at day 0 but displayed cell-type- Follow-up studies were focused on a strongly scoring ‘‘conver- specific differences after expansion (Figure 2B). Importantly, gent’’ screen hit, PKMYT1/Myt1 (Booher et al., 1997; Liu et al., sgRNA sequence reads were well correlated between bio- 1997). We find that PKMYT1 and WEE1 are redundant and logical replicates with Pearson’s r values of 0.98 for all day synthetic lethal in NSCs, where they redundantly phos- 0 replicates and R0.79 for 3-week-outgrown replicates phorylate CDK1-Y15 and block premature entry into mitosis. (Figure S2A). However, this redundancy is broken in GSCs or NSCs overex- To assess changes in individual sgRNA representation, edgeR pressing activated alleles of EGFR and AKT1, which results in (empirical analysis of digital gene expression in R) was used the essential requirement for PKMYT1 and timely completion (Robinson et al., 2010)(Supplemental Experimental Procedures). of mitosis. Further, we also demonstrate that repair of While edgeR has been mainly used for examining changes in CRISPR-Cas9-triggered indels exhibit frameshift bias, causing steady-state mRNA levels from SAGE and RNA sequencing more out-of-frame indels than expected by chance, which (RNA-seq) data, by design, edgeR was intended for use with explains the effectiveness of this technology. Our results sug- any type of count based sequence tag data in complex libraries, gest that PKMYT1 is a candidate therapeutic target for GBM. including nucleic acid bar codes (Dai et al., 2014). To do so, More generally, our results illustrate the utility of performing edgeR models the count variance across replicates as a CRISPR-Cas9 screens for essential genes in patient tumor nonlinear function of the mean counts using a negative binomial samples. distribution while accounting for over all data dispersion. The output of edgeR provides fold changes for each individual RESULTS sgRNA’s sequenced reads, in our case, between day 21 or 23 and day 0 and also provides a statistical test similar to a Fisher’s Genome-wide CRISPR-Cas9 Screens in Human GSCs exact test to determine significance. This approach revealed and NSCs thousands of significantly scoring sgRNAs for each screen at We first examined the efficacy of delivering a CRISPR-Cas9 tar- day 21 or 23, representing both candidate essential and growth geting system by lentiviral (LV) transduction in human GSC and limiting genes (Figure 2C; Table S2). NSC isolates. Consistent with previous reports, an all-in-one To assess screen and edgeR performance, we employed a LV-sgRNA:Cas9 platform system was highly effective at target- Bayesian classifier that uses predetermined essential and non- ing reporter and endogenous genes in both GSCs and NSCs essential gene training sets to help determine functional genetic (Figures 1A–1D), including randomly integrated copies of EGFP screen quality (Hart et al., 2014)(Figures S2B–S2D; Table S3). (>85%), a non-essential endogenous gene, TP53, assayed by This analysis allowed independent scoring of essential genes western blot, and an essential gene, MCM2 (O’Donnell et al., in each isolate, supporting observations reported below for 2013), assayed by viability of in vitro expanded cells. In each edgeR analysis (Figure S2E). case, we were able to observe profound reduction in target Further, given that CRISPR-Cas9 technology relies on gene activity in GSCs and NSCs. Importantly, peak suppression nuclease cleavage of target sites, there is the possibility that occurred 10–14 days post-selection and non-targeting sgRNA copy-number variation (CNV) in target sites could affect screen controls had no effect on cell viability (Figure 1). outcome. Using hypergeometric testing, we did not observe
2426 Cell Reports 13, 2425–2439, December 22, 2015 ª2015 The Authors A C
B
D
Figure 1. Validation of CRISPR-Cas9-Based Gene Targeting in Human GSCs and NSCs (A) Cartoon of lentiviral construct used for sgRNA:Cas9 expression. (B) sgEGFP:Cas9 was used to target stably expressed H2B-EGFP in GSCs and NSCs. Cells were first infected with LV-EGFP-H2B at MOI >2 and passaged for 1 week and then infected with sgControl or sgEGFP at MOI <1, selected, outgrown for 14 days, and flow analyzed. Similar results were obtained for each NSC- CB660s and GSC-0131s (data not shown). At day 5 post-selection, for EGFP+sgEGFP NSC-CB660s, we noted 19.5% of cells still positive for GFP, while by D12, this number was reduced to <1%, suggesting that peak suppression probably occurs around D10 for a single, mono-allelic genomic target. However, a small percentage of wild-type (non-edited) cells remained at D12 post-selection after targeting the endogenous gene CREBBP (Figure 7), and, thus, the peak sup- pression occurs between D10 and D14 depending on the target. (C) Western blot confirmation of TP53 protein expression after targeting TP53 gene with sgRNA:Cas9 in NSC-U5s. Cells were outgrown for >21 days following selection. Doxorubicin treatment (0.75 mg/ml for 6 hr) was used to stabilize TP53 in response to DNA damage. (D) CRISPR-Cas9-based targeting of an essential gene, MCM2. Cells were infected with sgRNAs and seeded 3 days post-selection for a 10-day culture in triplicate. Cell viability was then measured using alamarBlue reagent. *p < 0.01, Student’s t test (unpaired, unequal variance). enrichment of GSC screen hits at specific genome addresses, required to suppress apoptosis in mouse neural progenitors nor did we observe enrichment for genes contained within sites (Sun and Hevner, 2014), and also a network of citric acid cycle of GSC-specific CNV among screen hits (data not shown). This and respiratory electron transport genes (Figure S3D). The latter suggests that CNV differences in GSCs were not a major factor is consistent with the notion that GBM cells experience the affecting screen outcomes. Warburg effect where metabolism shifts from oxidative phos- Gene set enrichment analysis (GSEA) for each screen identi- phorylation to lactate production (Wu et al., 2014). fied core cellular processes, including translation, RNA splicing, and DNA replication, among others (Figures 2D and S3A–S3C; GSC-Specific CRISPR-Cas9 Screen Hits Fall Outside of Table S4). This indicated that the screens were effective at Core Genes and Pathways Altered in GBM revealing essential gene targets, which is consistent with previ- We next wondered whether our screen hits would be biased to- ous use of this library (Shalem et al., 2014). Interestingly, how- ward inclusion of gene hits found in networks and pathways ever, each screen was enriched for genes involved in cerebrum commonly found altered in GBM and in our patient isolates. and CNS development, suggesting that each of the isolates re- For example, the concept of ‘‘oncogene addiction’’ predicts tains the function of brain-specific gene networks (Figure 2E). that cancer cells should differentially require oncogene activities Closer examination of these hits revealed genes with critical to which they are ‘‘addicted’’ (Weinstein and Joe, 2008). To this roles in regulating asymmetric and symmetric divisions of neural end, patient-specific GBM networks were created by mapping progenitors during cortical development (Figure 2F)(Sun and the results from genomic data from GSC-0131 and GSC-0827 Hevner, 2014), consistent with GSCs and NSCs sharing underly- (i.e., RNA-seq, CNV, and exome-seq) onto genes and pathways ing neuroprogenitor biology. commonly altered in GBM from TCGA data (e.g., p53, PI3K, Interestingly, among hits specifically enriched in NSCs Rb-Axis, etc.) (Figure S4). We then incorporated GSC specific le- screens, but not GSCs, were sgRNAs belonging to the Fanconi thal screens hits, which also did not score in NSCs (to model anemia pathway gene network (Figures 2F and S3D), which is therapeutic window).
Cell Reports 13, 2425–2439, December 22, 2015 ª2015 The Authors 2427 A C 15 hGBM stem-like cells: 15 NSC-U5 GSC-0827 SOX2+, NES+
10 sgTP53 0131 0827 Adult Adult sgMCM2 Day 21 Day 23 sgMCM2 Mesenchymal Proneural sgHEATR1 sgHEATR1 mut/del hi/mut 05
NF1 EGFR Log2 (CPM) PTENmut PIK3CAmut
-5 0 5Day 10 0
-5 Day 0 RB1loss RB1mut -5 0 5 10 15 -5 0 5 10 15 TP53mut MDM2/4hi Log2 (CPM) TERT+ TERT+ D NSC-CB660 GSC-0827 hNeural stem cells: Enriched Depleted Enriched Depleted SOX2+, NES+ Translation Non-transformed controls RNA splicing Ribonucleoprotein complex CB660 U5 biogenesis and assembly Translation
TERT- TERT- RNA splicing RNA processing
LV-sgRNA:Cas9 RNA processing Ribosome biogenesis 18,080 genes and assembly Protein RNA Transcription from RNA complex assembly polymerase III promoter Expansion in self-renewal 2000 6000 10000 14000 18000 2000 6000 10000 14000 18000 media (21-23 days) Gene rank Gene rank
sgRNA-sequencing: E ID Human Phenotype NSC-CB660 NSC-U5 GSC-131 GSC-827 before vs. after expansion HP:0002060 Abnormality of the cerebrum 3.74E-13 8.56E-18 3.04E-12 1.15E-08 HP:0000252 Microcephaly 9.74E-11 2.07E-17 7.92E-12 1.90E-07 Expansion limiting and HP:0007364 Aplasia/Hypoplasia of the cerebrum 1.48E-11 2.06E-16 1.44E-11 8.46E-08 promoting genes HP:0002977 Aplasia/Hypoplasia involving the CNS 9.41E-11 2.14E-15 3.18E-11 7.04E-08 p-value B F NP spindle organization or orientation Fanconi anemia pathway Day 0 samples ERCC4 FANCE ASPM* FANCA* FANCF 50 NDE1* FANCB FANCG* FANCC FANCL 0 FANCD2 ZBTB32 131 Day 23 MCPH1* TCOF1* -50 827 Day 23 NP Apoptosis PC2 (15%) CB660 Day 23 PPP4C* -100 U5 Day 21 Sensitive to GSCs & NSCs Sensitive to NSCs only -50 0 50 100 PC1 (28%) * KO mice = microcephaly/smaller cortices
Figure 2. Genome-wide CRISPR-Cas9 KO Screens in GSCs and NSCs (A) Overview of GSC and NSC isolates used and screen procedure. (B) Principal component analysis of sgRNA-sequencing results of biological screen replicates. (C) Scatterplots showing log2 normalized library sgRNA read counts comparing day 21 or 23 to day 0 . Each dot represents a specific sgRNAs. Red dots indicate significantly overrepresented sgRNAs (LogFC >1, false discover rate [FDR] <0.05), while green dots indicate significantly underrepresented sgRNAs (LogFC <