Article

Genome-wide CRISPR-Cas9 Screens Reveal Loss of Redundancy between PKMYT1 and 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 -wide CRISPR-Cas9 lethality screens in both GSCs and non- transformed NSCs, revealing PKMYT1 as a candidate GSC-lethal .

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 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 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- , 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 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 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 . 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 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 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 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 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 <1, FDR <0.05) after outgrowth. HEATR1 and MCM2 were top scoring essential gene hits, while TP53 showed strong enrichment in NSC screens. (D) GSEA for biological processes terms was conducted on all sgRNAs from screen results. Top 5 depleted gene sets in NSC-CB660 (FDR- corrected q < 0.0001) and GSC-0827 (FDR-corrected q < 0.011, 0.010, 0.012, 0.057, and 0.070 respectively) are displayed. Green line represents the point where the ratios (end point of screen/day 0) change from positive (left) to negative (right). Red line represents the point where the running sum statistic has its maximum deviation from 0 (enrichment score). (E) Overlapping human phenotype ontology gene sets enriched among candidate sensitive hits (logFC < 1.0, FDR < 0.05). Each set shown was among top ten gene sets enriched. (F) Significant screen hits (logFC <1, FDR <0.05) were overlapped with genes involved in cortical neural progenitor (NP) organization or orientation and the Fanconi anemia pathway. A gene was scored if one or more sgRNA(s) per gene met the criterion.

Surprisingly, only 10 GSC-specific hits out of 946 total (Fig- in the descriptive genomics data from these patient samples, ure 2A) overlapped core pathways altered in GBM (Figure S4A). except for TP53. GSC-0131s have a homozygous TP53V147D For GSC-0131s, only four genes were in the network: CCNE1, mutation (Table S1), which alters the requirement for MDM2 MLST8, SREBF2, and TP53. None of these genes are altered and MDM4 (as judged by loss of sgMDM2/4 in TP53wt isolates,

2428 Cell Reports 13, 2425–2439, December 22, 2015 ª2015 The Authors but not GSC-0131s) and how sgRNAs targeting TP53 score (R2 sgRNAs with –logFC, p < 0.05), while GSC-0827s had 17 (GSC-0131s possibly have a reliance on mutant TP53) screens hits meeting the same criteria (Figures S5B and S5C). (Figure S1F). Comparing in vitro GSC and NSC data revealed 18 hits with For GSC-0827s, seven screen hits were in its network (i.e., two or more sgRNAs at logFC % 1.0 for GSC-0131 and seven AKT1, ERBB3, GAB1, MLST8, NFKB1, PRKCA, and PRKCI)(Fig- hits meeting the same criteria for GSC-0827 (Figures S5D and ure S4B). Of these, four have missense mutations of unknown S5E). Both in vitro and in vivo retests yielded PKMYT1 as the function and two are overexpressed (relative to NSCs). However, top ‘‘convergent’’ GSC-lethal gene. Other hits consistent with because this isolate is a mutator (Figure S1D), many genes in the original screen results included: FBXO42 (0827 specific), network are altered. GSC-0827s have an activating mutation in HDAC2 (0827 specific), and TFAP2C (0131 specific), among PI3KCA and also an EGFR amplification and mutation. However, others (Figure 3C). these were not among the screen hits. Thus, counter to the notion of ‘‘oncogene addiction,’’ this analysis suggested that Comparisons with Short Hairpin RNA Screens core pathways altered in GBM are not good predictors of Performed in GSCs and NSCs CRISPR-Cas9-based lethality, as the majority of GBM lethal Since the CRISPR-Cas9 screens produced results consistent hits (>95%) occur outside of core GBM altered pathways. One with identification of GSC-lethal genes, we also compared the caveat though is that we do not know whether each library results to previously performed genome-wide short hairpin sgRNA is effective at targeting each gene in the GBM network; RNA (shRNA) screens conducted in NSC-CB660, GSC-0131, another is that these screens are not perfect with respect to pre- and GSC-0827 cells in identical outgrowth conditions, which cision and recall (Figure S2C) or retest rate (see below). also produced GSC-specific hits (Hubert et al., 2013). Consistent with CRISPR-Cas9 screens preferentially identifying essential Validation of Essential and GSC-Sensitive Genes In Vitro genes, there was greater number of total ‘‘essential hits’’ in and In Vivo sgRNA screens predicted to be lethal to all isolates (769 versus To initially validate lethal screen hits, we created a retest pool 95) (Figure S6). There was an agreement between GSC-sensitive consisting of 7 essential genes and 51 GSC-sensitive genes hits from both screens for several networks and pathways, picked with bias toward genes coding for with enzy- including pre-mRNA splicing, which includes genes previously matic function (e.g., PKMYT1 kinase), or part of complexes reported as GSC-sensitive involved in 30 splice-site recognition with enzymatic activity (e.g., FBXO42 E3 com- (Hubert et al., 2013); control of the G2/M transition, including plex) or transcriptional activity (e.g., AP-2 two key negative regulators of cyclin B/CDK1 activity, PKMYT1 gamma, TFAP2C) from edgeR analysis (three to four sgRNAs and WEE1; DNA damage checkpoint, including ATRIP, MDC1, per gene) comparing GSCs to NSCs sgRNAs with logFC < 1 and CLSPN; members of COP9 signalosome complex (Lee (Figure 3A). We first examined performance of individual sgRNAs et al., 2011), among others. Importantly, several nodes among from the pool in in vitro growth assays in NSC-CB660s, GSC- these complexes, including PKMYT1 and CAB39, were validated 0131s, and GSC-0827s, testing 47 individual sgRNAs (approxi- in the course of our sgRNA retests (Figure 3C). The results sug- mately two sgRNAs per gene) (Figures 3B and S5A; Table S6). gest that these pathways and complexes cross-validate be- Of 47 sgRNAs tested, 27 (57%) scored in a manner consistent tween the two technology platforms as GBM-sensitive. with the initial screen, which included PKMYT1, candidate GSC-sensitive gene, and HEATR1, candidate top scoring essen- PKMYT1 KO Causes Lethality in Multiple GSC Isolates tial gene that is involved in rDNA transcription (Prieto and To further evaluate retesting sgRNA screen hits, we next exam- McStay, 2007)(Figures 3B and S5A). ined targeting of PKMYT1, FBXO42, HDAC2, TFAP2C, and Next, we performed parallel screens with the full retest pool HEATR1 in ten different GSC isolates along with NSCs using both in vitro in GSCs and NSCs and in vivo in tumors derived in vitro viability assays and two control sgRNAs (Figure 3E). The re- from GSC cells. For the in vitro retest screens, two biological rep- sults revealed that PKMYT1 was required for viability in eight of licates of NSC-CB660, GSC-0131, and GSC-0827 cell pools these isolates, while HDAC2 and TFAP2C requirement appeared were outgrown for 21 days similar to the primary screen. For more specific to GSC-0827s and GSC-0131s, respectively. How- the in vivo tumor formation, five independently derived tumors ever, targeting of FBXO42, which can promote ubiquitination and were analyzed from GSC-0131s and GSC-0827s infected with degradation of p53 (Sun et al., 2009), showed profound sensitivity the retest pools (Supplemental Experimental Procedures). Like in both the GSC-0827 and GSC-G166 isolates (Figure 3E). This the main screen, the results were assayed by the changes in likely indicates that patient-specific genetic or epigenetic alter- sgRNA representation either at day 21 for in vitro studies or after ations drive differential requirement for these genes. In contrast, tumor formation for in vivo studies compared to day 0. Heatmaps HEATR1 sgRNAs were lethal to all isolates examined (Figure 3E), representing the change in representation for all sgRNAs in the demonstrating that the differences in GSC-specific requirement pool are shown in Figure 3C for in vitro and in vivo results, with for the other genes are not technical artifacts. callouts for genes that scored prominently as cancer-sensitive or essential. Analysis of in vitro versus in vivo results for this anal- Molecular and Phenotypic Analysis of PKMYT1 ysis suggested good concordance of changes in replication Depletion (R2 = 0.59 for GSC-0131s; R2 = 0.83 for GSC-0827s) (Figures Since PKMYT1 emerged as a robust GSC-sensitive hit both S5B and S5C). GSC-0131s had 22 screen hits displaying signif- in vivo and in vitro among our retests, we wished to further vali- icant loss of representation relative to control and EGFP sgRNAs date it as a candidate therapeutic target for GBM. PKMYT1 (aka

Cell Reports 13, 2425–2439, December 22, 2015 ª2015 The Authors 2429 A Candidate genes B required for in vitro expansion Individual retests in vitro (47 sgRNAs) PKMYT1 sgRNAs Proliferation Lethal hit retests: GSC-0827 index GSC-sensitive GSC-0131 NSCs GSC-0131 +1.5 -4.1 essential NSC-CB660 Log2FC misc. HEATR1 sgRNA Control sgRNA Consistent: 27 Miscategorized: 8 Failed to Score: 13

GSCs In vitro sgRNA-seq. LV-sgRNA NSCs expansion (day 21 vs. day 0) retest pool 58 genes GSC-0827 234 sgRNAs GSCs In vivo tumor sgRNA-seq. formation (tumor vs. day LogFC<-1, FDR<.05 of injection) KIAA1432 PKMYT1 SREBF2 HEATR1 GNB2L1 FBXO42 TFAP2C TXNL4A CIRH1A HDAC2 FARSB RAB6A MAT2A C CAB39 MCM2 PGD TBP

NSC-CB660-1 NSC-CB660-2 GSC-0131-1 GSC-0131-2 GSC-0827-1 GSC-0827-2

GSC-0131-T1-T5 GSC-0827-T1-T5

† * † * *******† † † † † *sgRNA-seq † Essential gene * GBM-sensitive +3.1 -7.1 Log2FC MVD‡ D SC5D‡ WEE1§ PKMYT1‡ RAB6A‡ STK11§ CAB39‡§ HELZ2‡ SREBF2‡ MBTPS1‡ KIAA1432‡ STRADA‡ ER/Golgi CDK1 CARM1‡ SCAP‡ transport CCNB1 SIK3‡ Regulation of cholesterol Control of G2/M LKB1-STRADA-MO25 biosynthesis by SREBP transition complex

Scored as GBM specific: § shRNA screens ‡ sgRNA screens

E Essential GBM-sensitive 0827-specifc 0131-specifc sgCtrl sgHEATR1 sgPKMYT1 sgFBXO42sgHDAC2 sgTFAP2C 134131224 0827p 0131m 1502m G166m 025Tc 0308c 578Tc G14m GSC isolates 022T* G179m NSC-CB660 Relative growth in vitro

Figure 3. Validation of CRISPR-Cas9 Screen Hits Required for GSC Expansion In Vitro and In Vivo (A) Venn diagram showing overlap among candidate genes. sgRNAs with logFC <1.0 (FDR <0.05) were considered candidate sensitive genes. For simplicity, NSC-U5 and NSC-CB660 were combined. (B) Heatmap of retested candidate sensitive individual sgRNAs (two sgRNAs/gene; 23 genes). Cells were infected with lentivirus containing individual sgRNAs, and cultured (15–22 days) in triplicate. Overall, growth of each sgRNA was calculated and normalized to sgControl. Each sgRNA was categorized as essential, GBM sensitive, or patient-specific according to screen results and then compared. Figure S5 contains sgRNAs scores, Table S5 contains individual sgRNA sequences used in retest, and Table S6 contains source data. (C) Heatmap of retested in vivo and in vitro pools (58 genes; three to four sgRNAs/gene). For in vivo studies, GSCs were injected into mice (n = 5) following selection (see Supplemental Experimental Procedures). Tumors were cut into two, sequenced, and scored using limma. -T, tumor. Table S6 contains source data. (D) STRING network (Szklarczyk et al., 2015) representations of GSC-specific hits scoring in pooled retest assays, along with other GSC-specific hits either scoring in CRISPR-Cas9 or shRNA genome-wide screens (see text and also Figure S5 for details of sgRNA and shRNA screen comparisons).

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2430 Cell Reports 13, 2425–2439, December 22, 2015 ª2015 The Authors Myt1) encodes a dual specificity protein kinase homologous to cells is 37 min compared to 47–51 min (Figures 4C and 4D). How- WEE1 that localizes to the ER-Golgi complex and, at least ever, loss of PKMYT1 and WEE1 activity together resulted in syn- in vitro, can inhibit cyclin B-CDK1 activity, by phosphorylating ergistic increases in MTTs to over 100 min on average with many CDK1’s ATP binding domain at T14 and to a lesser extent Y15 cells well over 150 min (Figures 4C and 4D). Importantly, (Booher et al., 1997; Liu et al., 1997). WEE1, by contrast, has concomitant synergistic increases in cell death during mitosis been shown to phosphorylate Y15 of both CDK1/2 but is inca- and cytokinesis failure were also observed (Figure 4E). Visual in- pable of phosphorylating T14 (Watanabe et al., 1995). Genetic spection of double inhibited cells with extended MTTs indicated experiments in suggest that PKMYT1 and WEE1 ho- that they spend most of their time arrested at metaphase, mologs act redundantly during fly development (Jin et al., 2008). consistent with a SAC-induced arrest (data not shown). However, loss-of-function experiments in mammals, which have We next repeated the same set of experiments in parallel in been performed mainly in HeLa cells, suggest that human NSC-CB660s and GSC-0827s, this time using a small interfering PKMYT1 and WEE1 are not functionally equivalent. For example, RNA (siRNA) pool to inhibit PKMYT1, which allowed for better knockdown of WEE1 in HeLa cells induces loss of Y15 phos- control of timing of PKMYT1 inhibition in GSCs. Importantly, phorylation, premature entry into mitosis before completion of the siRNA pool resulted in dramatic loss of PKMYT1 protein DNA replication (i.e., mitotic catastrophe), and apoptosis (Naka- expression (Figure 5D). In NSC-CB660s, the siPKMYT1 pool pre- jima et al., 2008),(Coulonval et al., 2011). By contrast, PKMYT1 cisely phenocopied the effects of PKMYT1 KO, showing the knockdown either fails to affect the timing of mitotic entry and same increases in MTTs for PKMYT1 alone and together with exit of HeLa cells or does so modestly, despite dramatically WEE1 inhibition (Figure 5A). By contrast and strikingly, in GSC- reducing CDK1-T14 phosphorylation, without affecting CDK1- 0827s, inhibition of PKMYT1 or WEE1 alone was sufficient to Y15 (Nakajima et al., 2008; Coulonval et al., 2011; Villeneuve cause dramatic increases in MTTs similar to those observed et al., 2013). Thus, in mammals it is unclear whether PKMYT1 for double inhibition in NSCs (Figure 5B). As before, extended is required for regulating cyclin B/CDK1 activity during the cell MTTs were associated with cell death during mitosis and also cycle, whereas there is ample evidence that WEE1 activity plays cytokinesis failure (Figure 5C). However, unlike NSCs, PKMYT1 key roles in preventing premature mitosis. depletion or WEE1 inhibition alone in GSCs resulted in cell death Given our results, we were interested to determine how during mitosis and also cytokinesis failure, and double treatment PKMYT1 and WEE1 might have roles in specifically sustaining resulted in the majority of the cells experiencing cell death during GSC viability. We began by examining the effects of PKMYT1 mitosis. Consistent with this notion, dose-response curves of and WEE1 inhibition on CDK1/2 T14 and Y15 phosphorylation WEE1 inhibitor alone also showed that GSCs are particularly in our NSC isolates, which permit KO of PKMYT1 without signif- sensitive, but not NSC-CB660s (Figure 5F). Furthermore, knock- icant loss of viability (Figure 4A). In NSC-CB660s, we observed down of PKMYT1 also compromised growth of GSC-0827s in that PKMYT1 KO results in dramatic reduction of PKMYT1 pro- limiting dilution sphere formation assays, a surrogate assay for tein and CDK1-T14 phosphorylation with little or no effect on self-renewal (Figure 5E). Importantly, these data demonstrate CDK1/2 Y15, consistent with previous studies. However, we that PKMYT1 and WEE1 are synthetic lethal in NSCs and act find that PKMYT1 does in fact act redundantly with WEE1 to redundantly to facilitate mitosis in human NSCs, and that this phosphorylate CDK1-Y15 in NSCs. Western blot analysis shows redundancy is lost in GBM cells, giving rise to differential require- that PKMYT1 activity sustains CDK1-Y15, but not CDK2-Y15, ment for PKMYT1. phosphorylation in the presence of a potent and specific WEE1 inhibitor (MK1775) (Figures 4A and 4B). Oncogenic Activation of EGFR and AKT1 Sensitize NSCs To investigate these effects phenotypically, we used time- to Loss of PKMYT1 Function lapse microscopy to measure mitotic transit times (MTTs) (from We next wondered what could cause loss of PKMYT1 and WEE1 nuclear envelop break down to cytokinesis). In Drosophila, loss redundancy in GSCs. Previous studies have established that the of myt1 and wee1 dramatically increases the mitotic index of AKT and MAP kinase pathways can negatively impact PKMYT1 imaginal wing disc cells (Jin et al., 2008), and a similar phenotype or WEE1 activity during meiosis/oocyte maturation (Okumura is observed in HeLa cells overexpressing CDK1-T14A-Y15F, et al., 2002; Palmer et al., 1998) and the somatic cell cycle (Ka- which cannot be phosphorylated by PKMYT1 or WEE1 activity tayama et al., 2005; Villeneuve et al., 2013). In human cells, AKT (Krek and Nigg, 1991). We reasoned that this is likely due to acti- has been shown to directly phosphorylate WEE1 at Ser-642, vation of the spindle assembly checkpoint (SAC), which blocks causing its retention in the cytoplasm and loss of WEE1 activity anaphase until end-on attachment of kinetochores and microtu- (Katayama et al., 2005). In addition, MEK1 activity has been impli- bules has occurred, and are properly aligned and cated in downregulation of PKMYT1 activity as HeLa cells enter stable (Santaguida and Musacchio, 2009); thus, we would mitosis (Villeneuve et al., 2013). Since activation of PI3K and expect MTT to be similarly delayed in our cells. First examining RTK signaling cascades are prominent features of GBM tumors, NSCs, we find that KO of PKMYT1 or WEE1 inhibition alone we next asked whether altering these pathways would be suffi- led to modest increases in MTT, where average MTT in control cient to trigger PKMYT1 KO sensitivity in our NSCs.

(E) In vitro viability assays retesting individual sgRNAs in multiple GSC isolates of different TCGA subtypes for genes indicated. Samples were outgrown for 12 days following selection or cultured for 18 days following selection and counted with each split every 5–7 days to determine total cell number and normalized to sgControl. See Table S6 for Student’s t tests. TCGA subtypes: p, proneural; m, mesenchymalm; c, classical; or *, unable to classify (see Table S7 and Sup- plemental Experimental Procedures for details).

Cell Reports 13, 2425–2439, December 22, 2015 ª2015 The Authors 2431 A

B

C

DE

Figure 4. Molecular and Phenotypic Characterization of PKMYT1 Function in GSCs and NSCs (A) PKMYT1 and WEE1 act redundantly to phosphorylate Cdk1-Y15 in NSC-CB660s. Western blot analysis on whole-cell lysates (WCLs) or following immu- noprecipitation (IP) of CDK1 or CDK2. NSC-CB660s were outgrown for 14 days following selection and then treated with 300 nM of MK1775 (WEE1 inhibitor) for 6 hr or mock treated. PKMYT1 antibody recognizes a non-specific protein that appears below PKMYT1 predicted molecular weight. (B) Semi-quantification of western blot in (A) using ImageJ. Each band was normalized to their respective sgControl (-MK1775) sample. (C) Representative images from time-lapse microscopy. NSC-CB660s were transduced with individual sgPKMTY1 and sgControl LV constructs, selected for 4 days, and outgrown for 15 days. Cells were then treated with 300 nM of the WEE1 inhibitor MK1775 or mock treated, followed by time-lapse microscopy for 72 hr. Images were acquired at 50-min intervals. Mitotic transit time was analyzed for individual cells following 6 hr of WEE1 inhibition.

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2432 Cell Reports 13, 2425–2439, December 22, 2015 ª2015 The Authors AB Figure 5. Loss of PKMYT1 and WEE1 Redundancy in GSC-0827 Cells (A and B) Quantitation of mitotic transit times (MTTs) of individual NSC-CB660s (A) and GSC- 0827s from (B) after PKMYT1 depletion, –/+ WEE1 inhibition (minimum of 6 hr). Cells were trans- fected, treated with MK1775 (300 nM) or mock treated (48 hr after initial transfection), and sub- jected to time-lapse microscopy for 48 hr. Mann- Whitney test; n R 60 cells/condition; ±SD. (C) Outcome of each mitosis from cells counted in (A) and (B). (D) Protein expression levels of PKMYT1 depletion C by RNAi –/+ WEE1 inhibition in NSCs and GSCs. PKMYT1 depletion with siRNAs in NSC-CB660 and GSC-0827 –/+ MK1775 (WEE1 inhibitor). Western blot analysis on whole-cell extracts that were transfected with siControl or siPKMYT1 (see Supplemental Experimental Procedures) for 24 hr. Following 48 hr from the initial transfection, cells were treated with 300 nM of MK1775 (WEE1 in- hibitor) for 6 hr or mock treated and harvested for protein extraction. (E) Limiting dilution sphere formation assays for GSC-0827s that were treated to knockdown DE PKMYT1. Cells were transfected with siControl or siPKMYT1 for 24 hr. Cells were then harvested and plated into 96-well plates at various seeding den- sities (0.125–256 cells per well, ten wells per con- dition). Linear regression analysis was performed to generate each line per sample, and then each line was compared (p value). (F) WEE1 inhibitor dose-response curves for NSC- CB660s and GSC-0827s. A differential response is observed between NSC-CB660s and GSC-0827s treated with the WEE1 inhibitor MK1775. Cells were plated into 96-well plates and treated with various doses of the WEE1 inhibitor dissolved in F 0.18% DMSO 24 hr later. Following 72 hr post- treatment, viability was assessed using CellTiter- glo (Promega). Samples were normalized to the DMSO only control sample, and the viability of GSC-0827s was compared to NSC-CB660s at each dose (Student’s t test [unpaired, unequal variance]; five to six replicates per dose for each line).

cence or apoptosis in our NSCs. Figures 6A and 6B shows the consequences of various combinations of these human To this end, we used constitutively active alleles of EGFR* oncogenes on CDK1-T14 and CDK1/2-Y15 phosphorylation (EGFRvIII) (Bachoo et al., 2002) and AKT1* (myristoylation levels. Interestingly, adding EGFR* and then AKT1* to these cells tagged) (Boehm et al., 2007) in combination with TERT, domi- dramatically suppressed T14 and Y15 phosphorylation to nant-negative TP53DD, and CCND1+CDK4R24C (p16 resistant) 2-fold below the baseline found in NSCs and >3-fold from (Kendall et al., 2005) in NSC-CB660s. Note that manipulating levels found in TP53/RB-axis altered cells (which were higher the p53 and Rb axis is required to bypass EGFR* induced senes- than baseline). AKT1* alone, however, had no effect. Instead,

(D) Quantitation of mitotic transit times (MTTs) of individual NSC-CB660s from (C) after PKMYT1 KO, –/+ WEE1 inhibition (minimum of 6 hr). NSC-CB660s were outgrown for 15 days following selection, treated with MK1775 (300 nM) or mock treated, and subjected to time-lapse microscopy for 72 hr. Mann-Whitney test; n R 60 cells/condition; ±SD. (E) Quantification of phenotypic outcome of mitosis from (C) and (D). See Supplemental Experimental Procedures for details. A cell was considered to enter mitosis when nuclear envelope breakdown was visible.

Cell Reports 13, 2425–2439, December 22, 2015 ª2015 The Authors 2433 AB Figure 6. Expression of Constitutively --+ + + TP53DD+TERT 2.0 Active Alleles of EGFR and AKT Sensitize --+ + + CCND1+CDK4R24C NSCs to PKMYT1 Depletion (A and B) EGFR* and AKT* cause depletion of --- ++EGFR* 1.5 - + - - + AKT1* steady-state levels of CDK1/2-Y15 and CDK1-T14 αP-Y15 phosphorylation in NSC-CB660s. (A) Western blot 1.0 analysis of total CDK1/2-Y15-P and CDK1-T14-P αCDK1 P-T14 using whole-cell lysates (WCL). Histone H4 was 0.5 used as a loading control. Lane number corre- αCDK1 sponds to its respective number in (B). Note that p53 and RB-axis pathway perturbations are αHistone H4 0.0 Relative to CDK1 expression 1 2 3 4 5 1 2 3 4 5 required to bypass EGFR*-induced senescence 1 2 3 4 5 CDK1 P-T14 P-Y15 and apoptosis in NSCs. Thus, EGFR* experiments NSC-CB660 could not be carried out alone. (B) Semi- NSC-CB660 NSC-CB660 quantitative analysis of western blot from (A). CDTP53DD+TERT+CCND1+CDK4R24C TP53DD+TERT+CCND1+CDK4R24C Samples were first normalized to their respective AKT1*+EGFR* loading control followed by their respective CDK1 Avg time= 40 49 51 102 Avg time= 69 129 84 272 expression. p=0.0001 p<0.0001 p<0.0001 p=0.0002 p<0.0001 300 1000 (C and D) Mitotic transit time of individual geneti- cally altered NSCs after PKMYT1 depletion, –/+ 250 800 WEE1 inhibition (minimum of 6 hr). Cells were 200 600 transfected, treated with MK1775 (300 nM) or 150 mock treated (48 hr after initial transfection), and 400 subjected to time-lapse microscopy for 48 hr. 100 200 Mann-Whitney test; n R 65 cells/condition; ±SD. 50 (E) Outcome of each mitosis from cells counted in 0 Mitotic Transit Time (mins) Time Mitotic Transit

Mitotic Transit Time (mins) Time Mitotic Transit 0 (C) and (D). siControl siPKMYT1 siControl siPKMYT1 siControl siPKMYT1 siControl siPKMYT1 +WEE1 inhibitor +WEE1 inhibitor

+WEE1 inhibitor E 3.1%1.5% 1.5% 12.1% 25.8% loss of PKMYT1, suggesting a general DD n=65 n=65 n=65 n=66 TP53 +TERT+ mechanism for PKMYT1 requirement in CCND1+CDK4R24C Successful mitosis GSCs. 100.0% 95.4% 98.5% 62.1% Cytokinesis failure 70.0% 96.9% 16.7% Cell death during mitosis Examining CRISPR-Cas9-Triggered TP53DD+TERT+ n=65 n=70 n=65 n=66 CCND1+CDK4R24C+ Insertion-Deletion Mutation 56.1% AKT1*+EGFR* Formation 100.0% 8.6% 21.4% 3.1% 27.3% Last, we confirmed sgRNA:Cas9 on- siControlsiPKMYT1 siControl siPKMYT1 target nuclease activity by deep sequencing target sites for multiple sgRNAs scoring in our screens. While AKT1* potentiated the effect of EGFR*. These results demon- we observed high frequencies of on-target indel formation (Fig- strate that activation of the EGFR and AKT pathways is sufficient ures 7 and S7), consistent with other recent studies (Bae et al., to suppress the steady-state levels of CDK1-T14 and CDK1/2- 2014; Shalem et al., 2014), we found unexpected biases in mu- Y15 phosphorylation in NSCs. tation spectra. In many cases, single nucleotide insertions To determine whether EGFR* and AKT1* affected were dramatically overrepresented (Figures 7B and 7C), biasing the requirement for PKMYT1, we again performed MTT assays. indels toward reading frameshifts. In fact, only one of the seven We found that NSC-CB660s with TERT+TP53DD+ sgRNA-target site combinations tested showed nearly unbiased CCND1+CDK4R24C behaved exactly like unmanipulated NSCs reading frameshifts after indel formation, which would be ex- (Figure 6C). Importantly, however, the addition of EGFR* and pected to occur one-third of the time (Figure 7D). Without this AKT1* to these cells in the presence of siPKMYT1 produces bias, small indels would have little affect on gene function nearly similar effects on MTTs that were observed for GSCs (Fig- a third of the time. Thereby, frameshift bias helps explain the ure 6D), where siPKMYT1 almost doubled MTTs. This pattern highly penetrant phenotypic effects produced by this technology also extends to increases in frequency of unsuccessful mitoses in human cells. (Figure 6E), with dramatic increases in cell death during mitosis and cytokinesis failure. It is also interesting to note that EGFR* DISCUSSION and AKT* expression in control experiments increased NSC MTTs from 40 to 69 min, as GSC-0827s show a similar trend. Here, we report the successful application of gene editing tech- Taken together, these results suggest that overactive EGFR nology to identify and characterize genes promoting symmetric and PI3K signaling is sufficient to cause loss of redundancy in vitro expansion in human GSCs and NSCs. Our results between PKMYT1 and WEE1 and differential sensitivity to contribute to a growing body of work demonstrating the power

2434 Cell Reports 13, 2425–2439, December 22, 2015 ª2015 The Authors of CRISPR-Cas9 based approaches in human cells to identify and PI3K pathways. This suggests that PKMYT1 inhibition is syn- context-specific and generally lethal genes (Blomen et al., thetic lethal with increased activity of these pathways, but not the 2015; Shi et al., 2015; Wang et al., 2015), in our case, using the particular lesions per se found in the patient isolates. Arguably, technology directly in patient-derived tumor isolates. Although this makes PKMYT1 a very intriguing GBM candidate therapeu- sgRNA-triggered KOs may not precisely replicate scenarios tic target. with small molecule inhibitors, they do provide clues as to which From a biological standpoint, our results help re-discover pathways and genes are likely to trigger a therapeutic response. PKMYT1 function in human cells. PKMYT1 has largely been Our results speak to the notion of what counts as a good overlooked as a key player in cell-cycle regulation in mammals, mono-therapeutic target for GBM. From our screen results, we despite convincing evidence in model metazoan systems that expected to confirm the concept of ‘‘oncogene addiction’’ for it plays key roles in regulating cyclin B/CDK1 activity during GBM, or that cancer cells become ‘‘addicted’’ to certain onco- meiosis/oocyte maturation (Okumura et al., 2002; Palmer et al., gene activities during their evolution, such that these activities 1998) and entry into mitosis (Jin et al., 2008; Mueller et al., represent ‘‘rationale’’ therapeutic targets (Weinstein and Joe, 1995). We find that, in human NSCs, PKMYT1 acts redundantly 2008). However, we found very little overlap between GSC-spe- with WEE1 to both maintain CDK1-Y-15 phosphorylation and cific screen hits and genes and pathways altered in GBM in gen- to promote timely completion of mitosis. Previous work in eral or in the patient GSC isolates in which the screens were HeLa cells has demonstrated sole reliance on WEE1 for CDK1- performed. Perhaps most importantly, we could not use descrip- Y-15-P and preventing premature entry into mitosis and mitotic tive data sets from our patient samples to predict screen catastrophe (Coulonval et al., 2011; Nakajima et al., 2008). Our outcome (and, thus, candidate therapeutic targets), which is data, however, demonstrate that PKMYT1 activity can compen- what current precision oncology paradigms attempt to do. While sate for both Y-15-P and preventing extended MTTs in WEE1 this does not disprove the oncogene addiction hypothesis for inhibited non-transformed NSCs (Figure 4). This is consistent GBM, it does suggest that targeting sensitivities caused by with the observation that Drosophila wee1 and myt1 have redun- oncogenic activity, rather than the oncogenic activities them- dant and overlapping roles during fly development (Jin et al., selves, may provide better therapeutic opportunities. This ap- 2008). We predict that the same redundancy will be observed pears to be a recurring theme in our GBM work, as each of our in other non-transformed vertebrate cell types. previous GSC-specific vulnerabilities found by shRNA screening We attribute the observed GSC-specific lethality of PKMYT1 (Hubert et al., 2013; Ding et al., 2013; Toledo et al., 2014) are KO to loss of redundancy of PKMYT1 and WEE1. In GSCs, likely caused by oncogene-induced changes in feedback regula- PKMYT1 loss alone leads to dramatic increases in MTTs, as tion of the underlying pathways. well as cell death during mitosis and cytokinesis failures Our screen results revealed at least two classes of GSC- (Figure 5). specific screen hits that arise from ‘‘individual-specific’’ We show that activation of EGFR and AKT1 pathways sup- dependencies found uniquely in patient samples and also press CDK1/2-Y15 and CDK1-T14 phosphorylation in NSCs ‘‘convergent’’ dependencies shared between the patient sam- (Figure 6). This strongly suggests that activation of these path- ples. Individual dependences likely arise from the specific epige- ways together results in net loss of inhibition of CDK1/2 during netic and genetic alterations arising during patient tumor the G2/M transition. It is conceivable that these effects are evolution. While we were able to successfully retest several mediated by direct negative regulation of PKMYT1 or WEE1 screen hits specific to GSC-0131 and GSC-0827 (e.g., activity. However, other mechanisms are possible. This in- FBXO42, HDAC2, TFAP2C), we were unable to find evidence cludes directly or indirect regulation of the activity of CDC25 that these hits correlated with a specific CNV, mutation, or tran- phosphatase, which is responsible for removing CDK-T14 scriptional signature from patient samples. More systematic and Y15 phosphorylation and which has been shown to be a testing of these hits (e.g., larger sample size of GSCs and target of EGFR signaling in a Drosophila model of glioma comprehensive retesting of all hits) will be required to determine (Read et al., 2009). Future experiments will be required to whether they are truly unique to these isolates. address whether EGFR and AKT signaling acts through direct Convergent screen hits, which score in multiple GSCs regard- or indirect regulation of PKMYT1 and/or WEE1 activity in less of particular oncogenic alterations, are more interesting GBM cells. from a therapeutic perspective. We predict these to arise from While PKMYT1 function has not previously been studied in general oncogenic pathway activity rather than specific alter- GBM or other cancers, there has been great interest in WEE1 ations in pathways. Thereby, it is conceivable that convergent as a potential therapeutic target as a cytotoxic chemotherapy screen hits may represent therapeutic targets that when in- and radiation sensitizer, since it is required for radiation-induced hibited are capable of producing durable responses in heteroge- arrest and repair (De Witt Hamer et al., 2011; Mir et al., 2010). For neous GBM tumors. Importantly, our follow-up experiments for GBM, GSCs appear more resistant to radiation through one such convergent hit, PKMYT1, suggests this would indeed increased repair proficiency (Bao et al., 2006; Liu et al., 2006). be the case. PKMYT1 arose from screens in patient isolates rep- Thus, WEE1 inhibition may enhance radiation treatment. Though resenting different developmental subtypes (e.g., proneural the WEE1 inhibitor, MK1775, and temozolomide preclinical versus mesenchymal) and also distinct genetic alterations combinational studies in mouse flank GBM models were highly (e.g., EGFR* versus NF1 loss and PTEN loss versus PI3KCA acti- effective, the combinational treatment in mouse brain orthotopic vation). Further, PKMYT1 dependency could be reproduced in xenograft models were ineffective due to the limited heteroge- NSCs through ectopic activation of receptor tyrosine kinase neous distribution of MK1775 across the blood-brain barrier

Cell Reports 13, 2425–2439, December 22, 2015 ª2015 The Authors 2435 A sgCREBBP_1 Total reads = 223,566 Fraction No Indel = .123

B 02040 1 4 7 10 14 48 70 76 90 94 99 105 109 % of all indel reads % C Insertion length () 012345

% of all indel reads % 1471014182226303438424650545863677175 8387919599 106 120 deletion length (base pair)

D Distribution of indel frame phase (base pair)

13% 14% 17% 10% 20% 22% 7%

73% 73% 60% 20% 71%

sgHDAC2_1 sgTFAP2C_1 sgNF2_3 sgCREBBP_1

13% 10%

53% 32% 32% 39% 3N 3N+1 3N+2 34% 36% 51%

sgHDAC2_2 sgTFAP2C_2 sgNF2_4

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2436 Cell Reports 13, 2425–2439, December 22, 2015 ª2015 The Authors (Pokorny et al., 2015). Because the current WEE1 inhibitor in clin- the experiments. Q.L., X.-N.L., D.-H.N., J.L., and S.M.P. provided cell isolates. ical trials is not effective in penetrating the blood-brain barrier, R.B., E.L., T.H., H.B., J.D., Q.Z., J.H., B.J.A., C.L.P., N.S.B., J.M., J.Z., and future studies are warranted in order to identify PKMYT1-spe- J.J.D. analyzed the data. C.M.T., Y.D., P.H., and P.J.P. wrote the paper. cific inhibitors, as our results suggest that inhibiting PKMYT1’s ACKNOWLEDGMENTS kinase activity alone may be a GBM-therapeutic target. Howev- er, it is currently unclear whether PKMYT1 inhibition would syn- We thank Prashant Mali, Eric Holland, Pierre Roger, Cassie Sather, and mem- ergize with cytotoxic therapies that engage WEE1 or whether this bers of J.M.O.’s and P.J.P.’s labs for helpful discussions and Pam Lindberg for could suppress requirement for PKMYT1. Future studies will administrative assistance. We thank Feng Zhang, Prashant Mali, David Saba- have to address this and also create pipelines for the identifica- tini, Valeri Vasioukhin, and Denise Galloway for providing reagents. We thank tion of PKMYT1-specific inhibitors, which, to our knowledge, Cory McCartan, Chris Yang, and Christina Toledo for assistance with experi- have not been successfully developed (Rohe et al., 2014a, ments. This work was supported by the following grants: National Science 2014b). Foundation Graduate Research Fellowship Program (DGE-0-718124 and DGE-1256082) (C.M.T.), Interdisciplinary Training in Cancer Research (T32CA080416) (P.H.), NCI/NIH (R01CA084069-11) (B.E.C.), NCI/NIH EXPERIMENTAL PROCEDURES (R01CA155360; R01CA114567) (J.M.O.), NCI/NIH (R01CA190957; R21CA170722; P30CA15704) (P.J.P.), DoD Translational New Investigator All in vivo experiments were conducted in accordance with the NIH Guide for Award CA100735 (P.J.P.), the Pew Biomedical Scholars Program (P.J.P.), the Care and Use of Experimental Animals, and with approval from the Fred and a Phi Beta Psi Sorority Cancer Research Grant (P.J.P.). Hutchinson Cancer Research Center Institutional Animal Care and Use Com- mittee (IR#1457). Received: September 1, 2015 Revised: October 12, 2015 Cell Culture Accepted: November 3, 2015 GSC and NSC lines were grown in N2B27 neural basal media (STEMCELL Published: December 3, 2015 Technologies) supplemented with EGF and FGF-2 (20 ng/ml) (PeproTech) on laminin (Sigma) -coated polystyrene plates and passaged as previously REFERENCES described (Pollard et al., 2009).

American Cancer Society. (2010). Cancer Facts and Figures. CRISPR-Cas9 Screening A human genome-wide CRISPR-Cas9 library (Shalem et al., 2014) was used in Bachoo, R.M., Maher, E.A., Ligon, K.L., Sharpless, N.E., Chan, S.S., You, M.J., lentiviral pooled format to transduce GSCs and NSCs. For each screen repli- Tang, Y., DeFrances, J., Stover, E., Weissleder, R., et al. (2002). Epidermal cate, cells were transduced at 500-fold representation of the library (at growth factor receptor and Ink4a/Arf: convergent mechanisms governing ter- 30% infection efficiency). 2 days after transduction, puromycin was added minal differentiation and transformation along the neural stem cell to astrocyte (1–4 mg/ml) for 3 days. A portion of cells were harvested as day 0 time point. axis. Cancer Cell 1, 269–277. The rest of the cells were then passaged to maintain 500-fold representation Bae, S., Kweon, J., Kim, H.S., and Kim, J.S. (2014). Microhomology-based and cultured for an additional 21–23 days (eight to ten cell doublings). Genomic choice of Cas9 nuclease target sites. Nat. Methods 11, 705–706. DNA was extracted, and a two-step PCR procedure was employed to amplify Bao, S., Wu, Q., McLendon, R.E., Hao, Y., Shi, Q., Hjelmeland, A.B., Dewhirst, sgRNA sequences and then to incorporate deep sequencing primer sites onto M.W., Bigner, D.D., and Rich, J.N. (2006). Glioma stem cells promote radiore- sgRNA amplicons. Purified PCR products were sequenced using HiSeq 2500 sistance by preferential activation of the DNA damage response. Nature 444, (Illumina). Raw and mapped data files are available at the Gene Expression 756–760. Omnibus databasae (GEO: GSE70038). Blomen, V.A., Ma´ jek, P., Jae, L.T., Bigenzahn, J.W., Nieuwenhuis, J., Staring, Additional experimental procedures are available in Supplemental J., Sacco, R., van Diemen, F.R., Olk, N., Stukalov, A., et al. (2015). Gene essen- Information. tiality and synthetic lethality in haploid human cells. Science, Published online October 15, 2015. SUPPLEMENTAL INFORMATION Boehm, J.S., Zhao, J.J., Yao, J., Kim, S.Y., Firestein, R., Dunn, I.F., Sjostrom, S.K., Garraway, L.A., Weremowicz, S., Richardson, A.L., et al. (2007). Integra- Supplemental Information includes Supplemental Experimental Procedures, tive genomic approaches identify IKBKE as a breast cancer oncogene. Cell seven figures, and seven tables and can be found with this article online at 129, 1065–1079. http://dx.doi.org/10.1016/j.celrep.2015.11.021. Booher, R.N., Holman, P.S., and Fattaey, A. (1997). Human Myt1 is a cell cycle- AUTHOR CONTRIBUTIONS regulated kinase that inhibits Cdc2 but not Cdk2 activity. J. Biol. Chem. 272, 22300–22306. C.M.T., Y.D., P.H., R.J.D., E.J.G., B.E.C., J.M.O., and P.J.P. conceived of and Brennan, C.W., Verhaak, R.G., McKenna, A., Campos, B., Noushmehr, H., Sal- designed the experiments. C.M.T., Y.D., P.H., R.J.D., and E.J.G. performed ama, S.R., Zheng, S., Chakravarty, D., Sanborn, J.Z., Berman, S.H., et al.;

Figure 7. Analysis of Mutation Induction by sgRNAs for Various Validating Screen Hits in NSC-CB660 Cells Cells were transduced with individual sgRNAs to CREBBP, NF2, TFAP2C, or HDAC2 at MOI <1, selected, and outgrown for 12 days (sgCREBBP) or 21 days (remaining sgRNAs). The 250 bp around the target was then PCR amplified and deep sequenced. Note that CREBBP and NF2 were among top enriched sgRNAs in NSC screens (Table S2). (A) Integrative Genomics Viewer (IGV) image of 220 representative deep sequencing reads for sgCREBBP_1. Red arrow denotes predicted Cas9 cutting site. Four-colored line above the red arrow shows the reference sequence. Black or purple bars in gray sequencing reads indicate deletions or insertions, respectively. Other colors within gray sequencing reads indicate SNPs, possibly due to PCR or sequencing error. (B) Frequency and distribution of insertion length for sgCREBBP_1. (C) Frequency and distribution of deletion length for sgCREBBP_1. (D) Frame phase of all indels for each of the seven sgRNA samples, calculated as the length of indels modulus 3. Note that 3N equals in-frame.

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Cell Reports 13, 2425–2439, December 22, 2015 ª2015 The Authors 2439 Cell Reports Supplemental Information Genome-wide CRISPR-Cas9 Screens Reveal Loss of Redundancy between PKMYT1 and WEE1 in Glioblastoma Stem-like Cells Chad M. Toledo, Yu Ding, Pia Hoellerbauer, Ryan J. Davis, Ryan Basom, Emily J. Girard, Eunjee Lee, Philip Corrin, Traver Hart, Hamid Bolouri, Jerry Davison, Qing Zhang, Justin Hardcastle, Bruce J. Aronow, Christopher L. Plaisier, Nitin S. Baliga, Jason Moffat, Qi Lin, Xiao-Nan Li, Do-Hyun Nam, Jeongwu Lee, Steven M. Pollard, Jun Zhu, Jeffery J. Delrow, Bruce E. Clurman, James M. Olson, and Patrick J. Paddison INVENTORY OF SUPPLEMENTAL MATERIALS

Figure S1. Molecular characterization of GSC-0131 and GSC-0827 isolates, Related to Figure 2. Figure S2. Analysis of CRISPR-Cas9 screen results, Related to Figures 2 and 3. Figure S3. Enrichment for Gene Ontology (GO) biological terms for CRISPR-Cas9 screen hits in NSCs and GSCs, Related to Figure 2. Figure S4. Mapping of GSC-specific screen hits onto a network containing core altered pathways and genes for GBM, Related to Figure 2. Figure S5. In vitro and in vivo CRISPR-Cas9 screen retest comparisons, Related to Figure 3. Figure S6. Comparison of genome-wide shRNA and sgRNA screen hits required for in vitro expansion of NSCs and GSC-0131 and GSC-0827 isolates, Related to Figures 2 & 3. Figure S7. Analysis of on- and off-target mutations induced by sgRNAs in NSC-CB660 cells.

Table S1. Source data for Figure S2, Related to Figures 2 and 3. Table S2. CRISPR-Cas9 screen results for NSC-CB660, NSC-U5, GSC-0131, & GSC-0827 cells, Related to Figures 2 and 3. Table S3. GSC and NSC CRISPR-Cas9 screen analysis using Bayesian classifier of gene essentiality, Related to Figures 2 and 3. Table S4. GSEA analysis results of CRISPR-Cas9 screen hits in NSCs and GSCs, Related to Figure 2. Table S5. sgRNA sequences used for retest studies, Related to Figure 3. Table S6. CRISPR-Cas9 in vitro and in vivo retests analyses, Related to Figure 3. Table S7. TCGA Subtype data for GSC isolates used in Figure 2E and references for each GSC isolate used in these studies, Related to Figure 2.

Supplemental Experimental Procedures

C A

Closest Gene Centroids Proneural TCGA GBMtumorsamplesbysubtype E F sgControl Rep#3 Neural sgControl Rep#1 sgControl Rep#2 sgControl Rep#1 sgControl Rep#2 sgControl Rep#3 NSC-CB660 NSC-U5

sgRNA logFC Classical -5 -2.5 0 2.5 5 CB660 NSC- Mesen- chymal 0131 NSC- GSC isolates TP53 wt Day 7(%) U5 12.1 1 5.4 5.7 6.0 12 1.8 0827 GSC- 0827 D B FDR>.05 Day 15(%)

TP53-V147D Mutations GSC- FDR<.05 0131 10.5 13.5 1 4.9 5.5 4.9 1.3 sgMDM4 sgMDM2 sgTP53 Day 23(%) 15.9 1 1 5.2 5.3 4.8 1.3 1.1 Figure S1 Supplemental Figure S1. Molecular characterization of GSC-0131 and GSC-0827 isolates, Related to Figure 2. (A) GBM subtype assignment for GSC-0131 and GSC-0827. Associations of GSCs with specific GBM subtypes were determined by minimum Manhattan distance to expression centroids (see Supplemental Methods). The y-axis represents the sum of subtype-associated gene centroids. TCGA tumor data were used to validate our classification approach and shows appropriate subtyping. 0131 and 0827 are most consistent with mesenchymal and proneural subtypes, respectively. (B) Relative expression values of examples of genes in mesenchymal and proneural classifications from Verhaak et al and Beier et al. Average FPKM normalized RNA-sequencing values (n=3) are shown with SDs in parentheses. The data reveal characteristic differences in expression of mesenchymal and proneural subtype-specific genes. (C) Genomic alterations observed in oncogenes and tumor suppressors, which are frequently altered in GBM, for GSCs used for CRISPR-Cas9 screens (Figure 2). (D) Analysis of point mutation frequency suggests that GSC-0827 cells are mutators, showing >7-folder more mutations than 0131 and other GSC isolates (not shown). The majority of 0827 point mutations are consistent with C to T transition mutations in forward or reverse strands of . Elevation in C->T mutations could result from CpG island methylator phenotype (CIMP) (Noushmehr et al. 2010), where cells have higher than normal 5-methyl-cytosine content in their DNA. 5-methyl-cytosine spontaneously deaminates in dsDNA resulting in conversion of C to T (, 2002). While glioma CIMP tumors characteristically contain IDH1 mutations and 0827 does not, a small number have been observed with wild type IDH1 (TCGA, 2012). Further study will be required to determine if GSC-0827 cells fit into this category. Full gene expression and exome and CNV profiles are available in Supplemental Table S1. (E) Expression of sgControl and Cas9 does not significantly impact outgrowth of NSC-CB660 or NSC-U5 cells. Puro selected LV-sgRNA:Cas9 cells were mixed with LV-GFP+ cells and allowed to outgrow for 23 days. Table shows percentage of LV-sgRNA:Cas9 cells at three times points. No significant differences were found. (F) Behavior of sgRNAs targeting MDM2, MDM4, and TP53 among screen results. Changes in representation of sgRNAs targeting MDM2, MDM4, and TP53 during the screening procedure. The results suggest that GSC-0131 cells are not affected by loss of MDM2 or MDM4 function, which is likely due to a mutation in TP53. However, sgRNAs targeting TP53 also scored significantly as possibly lethal to GSC-0131 cells, in contast to other isolates where these sgRNAs were growth-promoting, possibly suggesting that the TP53 mutation is required for viability (e.g., dominant negative or altered TP53 function). Figure S2 A Day 0 Day 21 or 23 E GSC-0131 GSC-0827 compared to: compared to: CB660 U5 CB660 U5 PKMYT1 NSC-CB660 TFAP2C

PKMYT1

RAB6A

NSC-U5

GSC-0131

HDAC2

GSC-0827

B D NSC-U5 edgeR logFC<-1 CCE training set

GSC-0827 edgeR logFC<-1 CCE training set

C

FBX042 = Scored in edgeR and BF analysis as GSC-sensitive Supplemental Figure S2. Analysis of CRISPR-Cas9 screen results, Related to Figures 2 and 3. (A) Comparison of Day 0 and Day 21 or 23 CRISPR-Cas9 screen replicates. Plots represent Log2 values for normalized sgRNA read counts from deep sequencing (counts per million reads mapped onto library sgRNAs). Pearson’s r values for each replicate are shown. (B) Precision versus recall graphs to assess screen performance. Screens were evaluated using predetermined “constitutive core essential (CCE)” gene and “non-essential” gene reference training sets, as described in Hart et al. 2014, to train a Bayesian classifier to identify essential genes in each screen. For each screen, genes are ranked by their “Baysian Factor (BF)” (i.e., the log likelihood that a gene’s sgRNAs were drawn from either essential or reference distribution) and compared to withheld reference sets to evaluate the culmulative precision [TP/(TP+FP)] and recall [TP/(TP+FN)]. TP= true positives, the number of genes in the essentials test set with BF scores greater than current gene. FP = false positives, the number of genes in the nonessentials test set with BF score greater than the current gene. The filled dot represents the point on the precision-recall curve where the BF crosses zero. (C) Summary statistics for CRISPR-Cas9 screens using Bayes classifier analyis from (A). F-measure represents the “harmonic mean of precision & recall” and can be used as a measure of quality. Hart et al. judged that screens with F-measures ≥ 0.75 to be high-performing. The results suggest that GSC-0827 screen under performed relative to other screens. (D) Comparison of overlaps of “constitutive core essential” genes used for deriving BFs and edgeR-scoring essential genes for NSC-U5 and GSC-0827 isolates. Data shows less overlap of CCEs with 0827 edgeR data, possibly suggesting why the 0827 screen under performed using BF analysis. (E) Identification of GSC sensitive genes using a Bayesian classifier of gene essentiality. Heatmaps of top 100 genes showing added sensitivity for GSC-0131 (left) and GSC-0827 (right) compared to NSC-CB660 and NSC-U5. The heatmaps represent comparisons of “Baysian Factors (BF)” (i.e., the log likelihood that a gene’s sgRNAs were drawn from either essential or non-essential gene distribution) for 0131 versus CB660 or U5 and

0827 versus CB660 or U5 by subtracting BFGSC from BFNSC for each scoring gene. Highly positive BFGSC -BFNSC values suggest GSC sensitivity. Heatmaps are rank ordered by BFGSC -BFNSC-U5 values. Boxed values and genes indicate hits that also scored as GSC sensitive by edgeR analysis. Importantly, BF analysis independently calls PKMYT1 as a top scoring GBM-sensitive hit and also reveals GBM isolate-specific genes that were validated in Figure 3 including: HDAC2, FBX042, RAB6A, and TFAP2C.

Figure S3 A NSC-U5 B GSC-0131 Enriched Depleted Enriched Depleted

Translation RNA processing

RNA splicing RNA splicing

Ribonucleoprotein complex RNA processing biogenesis and assembly

Ribonucleoprotein complex Translation biogenesis and assembly mRNA metabolic process DNA Replication

2000 6000 10000 14000 18000 2000 6000 10000 14000 18000 Gene rank Gene rank

C Shared GO biological processes in NSCs and GSCs # of genes Gene Set name % of core enrichment genes in gene set NSC-CB660 NSC-U5 GSC-0827 GSC-0131 Translation 171 25% 30% 19% 32% Ribonucleoprotein complex biogenesis and assembly 82 33% 43% 24% 37% RNA splicing 90 47% 46% 26% 41% RNA processing 169 41% 40% 22% 34% Protein RNA complex assembly 63 43% 38% 25% 33% Macromolecule biosynthetic process 311 17% 20% 14% 23% mRNA processing _GO 0006397 71 44% 46% 20% 37% mRNA metabolic process 81 41% 44% 19% 35% RNA splicing via transesterification reactions 34 38% 47% 26% 47% DNA replication 99 26% 29% 26% 36% Ribosome biogenesis and assembly 17 41% 53% 29% 47% Transcription from RNA polymerase III promoter 19 32% 47% 26% 47%

D The citric acid (TCA) cycle and Fanconi Anemia Pathway respiratory electron transport KEGG:03460 REAC:1428517

POLK ATP5E ATP5I CS ATP5H

REV1 UQCR10 COX6A1 ATP5J MT-ATP8 MT-CYB POLI ATP5F1 ATP5O CYC1 POLH BARD1 COX14 ATP5G1 MPC2 MT-CO1 ATP5C1 MT-ATP6 UQCRFS1 UQCRH

COX5A UQCRB UQCRQ BRCA1 C1orf86 SURF1 WDR48 COX5B UQCR11 UQCRC2 PALB2 C19orf40 ATP5B FANCF FANCE FANCI USP1 COX6B1 ATP5D TOP3B BRCA2 UQCRC1 XRCC3 RECQL5 FANCG COX8A TOP3A FANCD2 FANCM NDUFA5 FANCA COX7C NDUFA10 FANCC BLM FANCL ETFB ETFA RMI1 APITD1 ETFDH FANCB NDUFA9 C17orf70 NDUFS3 NDUFC1 NDUFV2

ATR G2E3 TERF2 NNT NDUFA8 NDUFB7 NDUFB6 UBE2T HSP90B1 ERCC4 PDHB NDUFB11

NDUFA4 MT-ND6 MUS81 PDPR SLX4

COX16 NDUFS6 EME1 = sgRNA scored in CB660s & U5s pathway co-localization predicted physical interaction Supplemental Figure S3. Enrichment for Gene Ontology (GO) biological terms for CRISPR-Cas9 screen hits in NSCs and GSCs, Related to Figure 2. Gene set enrichment analysis (GSEA) was conducted on all sgRNAs from the whole-genome CRISPR screen results (see Supplemental Methods). (A) and (B) GSEA reveal that most depleted sgRNAs targeted essential genes in biological processes such as translation. The top 5 most significantly depleted gene sets (false discovery rate (FDR)-corrected q<0.0001) in NSC-U5 (A) and GSC-0131 (B) by GSEA are displayed here. The green line represents the point where the ratios (end point of screen/day 0) change from positive (on the left) to negative (on the right). The red line represents the point where the running sum statistic has its maximum deviation from 0, which is the enrichment score for the gene set. (C) In common GO biological processes for all of NSCs and GSCs isolates used in the screen. The top 20 scoring gene sets in NSC-CB660, NSC-U5, GSC-0827, and GSC-0131 were analyzed for common gene sets shared among all of the lines and displayed here. The percentage of essential genes identified by the genome-wide CRISPR screens in each gene set for each isolate is also displayed. See Table S4 for complete GSEA results. (D) Shared NSC-U5 and NSC-CB660-specific hits (logFC<-1.0, FDR<0.05) where analysized using ToppGene tool suite (toppgene.cchmc.org) for pathway enrichment. The Fanconi anemia pathway (p=7.843E-8) and The Citric Acid (TCA) cycle and respiratory electron transport (p=3.467E-7) were the top scoring pahtways with 19 hits scoring among 53 total genes possible for the former and 32 screen hits among 136 total genes for the latter. The screen hits in these pathways were then input into GeneMANIA network viewer (www.genemania.org) to obtain the above networks. A Figure S4 GSC-0131

B GSC-0827 Supplemental Figure S4. Mapping of GSC-specific screen hits onto a network containing core altered pathways and genes for GBM, Related to Figure 2. (A) GSC-0131-specific screen hits. This figure represents a GBM network of core althered pathways and genes derived from TCGA data, experimentally validated interactions, and protein-protein interaction data bases (Supplemental Methods). We then added CNV, gene expression, and mutation data from GSC-0131 cells. Orange boarders indicate mutations; red nodes indicate >50% expression relative to NSC-CB660; green nodes, <25% gene expression relative to NSC-CB660; up-triangles represent amplified genes in GSC-0131 cells; down-triangles represent copy number loss in GSC-0131 cells; larger label nodes indicate GSC-0131-lethal screen hits (logFC<-1.0, FDR<.05). (B) GSC-0827-specific screen hits. Same as (A) expect GSC-0827 molecular data was used. Figure S5 A

B D # sgRNAs scoring 4 3 2 GMPPB CAB39 CIRH1A GNB2L1 MCM2 CNOT1 HEATR1 PKMYT1 FARSB METTL14 TFAP2C FBXO42 RAB6A TXNL4A KIAA1704 TYRO3 MAT2A NAA15 22 genes total PGD with ≥ 2 sgRNAs SREBF2 TBP TRNAU1AP Blue = essential gene Green = GSC-specific Red = GBM-sensitive C E # sgRNAs scoring 4 3 2 GNB2L1 CAB39 CIRH1A HEATR1 H2AFX FARSB RAB6A MCM2 FBXO42 PKMYT1 GMPPB HDAC2 KIAA1432 TAF5L 17 genes total TBP with ≥ 2 sgRNAs TRNAU1AP TXNL4A

Blue = essential gene Green = GSC-specific Red = GBM-sensitive Supplemental Figure S5. In vitro and in vivo CRISPR-Cas9 screen retest comparisons, Related to Figure 3. (A) Heat map depicting the results of the in vitro individual sgRNA retest of the lethal pool in NSCs and GSCs. NSCs and GSCs were infected with lentivirus containing individual sgRNAs to the respective gene or to control. Following selection, cells were harvested, counted, and plated in triplicate. Cells were routinely cultured for 15-22 days (split every 3-4 days), and counted at each split. The overall growth of each well containing an individual sgRNA was calculated and compared to the sgControl well. The growth defects were graphed using the ratio between the individual sgRNAs to sgControl. Following the screen results, each sgRNA was categorized as essential, GBM sensitive, or patient specific. Following the individual retest, each sgRNA was compared to its assigned category from the screen results to determine whether the sgRNA was scored correctly. Source data can be found in Table S6. (B) & (C) Comparison of in vitro and in vivo retest results for GSC-0131 and GSC-0827 isolates using sgRNA retest pool. Graphs show comparisons of in vitro and in vivo retests of an LV retest pool. This pool was separately used to infect GSC-0131 and GSC-0827 isolates. Afterwards, cells were allowed to outgrow in self-renewal conditions for 21 days or injected into mice for tumor formation (~3 weeks). Like the primary screen, sgRNA-seq was performed to determine in sgRNA representation at Day 21 versuses Day 0 (n=2). Heatmap representation of these results can be found in Figure 3E. Source data can be found in Table S6. See methods for additional details. (B) Comparison of GSC-0131 in vitro vs. in vivo sgRNA sequencing results. (B) Comparison of GSC-0827 in vitro vs. in vivo sgRNA sequencing results. Importantly, both comparisons show good replication of in vitro vs. in vivo screen pooled retest results. (D) & (E) compares this data with NSC-CB660 data. Notably, PKMYT1 scores prominantly both in vitro and in in vivo during tumor formation in both GSC-0131 and GSC-0827 isolates. (D) & (E) Results from pooled in vitro retests in GSC-0131 and GSC-0827 isolates. Graphs show in vitro retests of an LV retest pool containing 227 sgRNAs targeting 24 GSC-0131 or GSC-0827 sensitive genes, 17 GBM-sensitive genes, 7 essential genes, and sgEGFR and non-targeting controls. This pool was separately used to infect GSC-0131, GSC-0827, and NSC-CB660 isolates. Afterwards, cells were allowed to outgrow in self-renewal conditions for 21 days. Like the genome-wide screen, sgRNA-seq was perfomred to determine both in sgRNA representation at Day 21 versuses Day 0 (n=2). Heatmap representation of these results can be found in Figure 3E. Source data can be found in Table S6. (D) Validation of sgRNAs more sensitive to GSC-0131 cells than NSC-CB660. (E) Validation of sgRNAs more sensitive to GSC-0827 cells than NSC-CB660. The graphs show the differences between how sgRNAs scored between 0131 or 0827 and CB660 isolates. (B) & (C) compares this data with in vivo tumor formation using the same retest pool. Notably, PKMYT1 scores prominantly in both GSC-0131 and GSC-0827 retests as compared to results from NSC-CB660 cells. Figure S6 A Lethal screen hits from Lethal screen hits from shRNA screens (§) sgRNA screens (‡)

NSC-CB660 GSC-0131 NSCs GSC-0131

Total lethal hit overlap: 928 genes NSC-sensitive hit overlap: 422 genes GSC-0827 GSC-sensitive hit overlap: GSC-0827 65 genes B -logFC, pvalue<.05 logFC<-1, FDR<.05

C

WEE1§ PKMYT1‡ RPA3§ ATRIP‡ U2AF1§ RPA2§ HNRNPM‡ MDC1‡ HNRNPC§ PHF5A§ CDK1 CCNB1 CLSPN‡§ Control of G2/M DNA damage Processing of SF3B4§ Pre-mRNA transition checkpoint

CAND1‡§ IKBKAP‡§ STK11§ CAB39‡§ CDC73§ COPS5‡§ WDR61‡ CUL3§ STRADA‡ COPS4‡§ NUFIP1‡ COPS2‡§ SIK3‡ PAF1 COP9 signalosome LKB1-STRADA-MO25 complex complex complex

Scored as GBM specific in: § shRNA screens ‡ sgRNA screens Supplemental Figure S6. Comparison of genome-wide shRNA and sgRNA screen hits required for in vitro expansion of NSCs and GSC-0131 and GSC-0827 isolates, Related to Figures 2 & 3. (A) Venn diagrams showing overlap of lethal screen hits from previously published genome-wide shRNA screens (left) (Hubert et al., 2013) and CRISPR-Cas9 screens from the current studies (right) in NSC-CB660, GSC-0131, and GSC-0827 cells. (B) Pathway enrichmentment for combined candidate GBM-specific lethals for both shRNA and sgRNA screens, primarily showing enrichment for processing of pre-mRNA/mRNA splicing and cell cycle related genes among shared hits. Analysis was performed using ToppGene gene enrichment analysis (Chen et al., 2009). (B) Combined shRNA and sgRNA GBM-spe- cific hits were evaluated using STRING network analysis (Szklarcyk et al., 2015). Those shown are networks with 4 or more nodes. Note that CCNB1 and CDK1 were added to illustrate WEE1 and PKMYT1 interactions with cyclinB/CDK1 complex and did not score as GBM-specific. Thus, although there was little overlap among GBM-specific screen hits, the results suggest that screens were none-the-less converged on these networks/pathways, suggestive of cross validation.

Figure S7

Supplemental Figure S7. Analysis of on- and off-target mutations induced by sgRNAs in NSC-CB660 cells, Related to Figure 7. Cells were manipulated as in Figure 4C with individual sgRNAs to CREBBP, HDAC2, or non-targeting sgCTRL, except that 3-4 sequences with closest identify to primary target sequence were also sequenced. Percentage breakdown of reads with deletions, insertions, complex mutations, or no mutation (wild-type) are shown. Bases within off-target sites that differ from the on-target sgRNA sequence are shown in lower-case. sgCTRL does not have an on-target site, but the sequence is shown for comparison. Genomic PAM sequences are underlined. SUPPLEMENTAL EXPERIMENTAL PROCEDURES

GSC classifications

In order to classify GSC isolates by tumor subtypes according to gene expression signatures produced by The Cancer Genome Atlas (i.e., classical, mesenchymal, neural, and proneural) (Phillips et al., 2006; Verhaak et al., 2010), we first performed

RNA-seq (n=3) using an Illumina HiSeq 2000 according to the manufacturer’s instructions (FHCRC Genomics Shared Resource). RNA-Seq reads were aligned to the

GRCh37/hg19 assembly using Tophat (Trapnell et al., 2012) and counted for gene associations against the UCSC genes database with HTSeq, a python package for analysis of high-throughput sequencing data (Anders, 2010). All data was combined and normalized using a trimmed mean of M-values (TMM) method from the R package, edgeR (Robinson et al., 2010; Robinson and Smyth, 2007; Robinson and Smyth, 2008).

Normalized counts were then log transformed, and the means across all the cell lines were used to calculate relative gene expression levels. The GSC data was clustered using a Manhattan distance complete-linkage method to establish leaflets. Previously,

173 GBM tumors were subtyped using the expression of 840 signature genes (Verhaak et al., 2010). Our samples were clustered using 770 of these genes. Centroids were computed as the median expression of each gene across the core TCGA samples

(Verhaak et al., 2010). Each GSC sample replicate was compared against the centroids using Single Sample Predictor (SSP) method (Hu et al., 2006). In addition, samples are assigned to GBM subtypes by maximizing the Spearman rank based correlation between expression of new samples and GBM subtype centroids (presented in Table S10). Each replicate was assigned separately and then the consensus was used to assign a final classification.

Cell culture and drug treatment

GSC and NSC lines were grown in N2B27 neural basal media (StemCell Technologies) supplemented with EGF and FGF-2 (20ng/mL each) (Peprotech) on laminin (Sigma) coated polystyrene plates and passaged according to Ding(Ding et al., 2013; Toledo et al., 2014). Cells were detached from their plates using Accutase (Millipore). 293T

(ATCC) cells were grown in 10% FBS/DMEM (Invitrogen). Cells were treated with

0.75µg/mL Doxorubicin (Seattle’s Children Hospital) for 6 hours or treated with 300nM of theWEE1 inhibitor MK1775 ( Supplier: Fisher Scientific ; Part Number: 508890;

Manufacturer Name: Selleck Chemical Llc; Manufacturer Part Number: S1525-5MG) for

6 hours (IP/WBs) or 48-72 hours (time-lapse microscopy).

Library acquisition and Individual sgRNA assembly

The genome wide CRISPR library was provided by Dr. Zhang Feng (MIT). SgRNA sequences were obtained from (Shalem et al., 2014) and Addgene, and cloned into lentiCRISPR v2 plasmid. Briefly, DNA oligonucleotides were synthesized with sgRNA sequence flanked by the following:

5’: tatatcttGTGGAAAGGACGAAACACCg

3’: gttttagagctaGAAAtagcaagttaa

PCR was then performed with the following primers(Shalem et al., 2014):

ArrayF: TAACTTGAAAGTATTTCGATTTCTTGGCTTTATATATCTTGTGGAAAGGAC GAAACACCG

ArrayR: ACTTTTTCAAGTTGATAACGGACTAGCCTTATTTTAACTTGCTATTTCT

AGCTCTAAAAC

The PCR product was then ran on a 2% TAE gel, and purified using the ZymoClean Gel

DNA recovery kit (Zymo Research). Gibson Assembly Master Mix (NEB) was used to ligate the cut lentiCRISPR v2 plasmid with the purified PCR product (sgRNA). The ligated plasmid was then transformed into Stellar Competent cells (Clontech), and streaked onto LB agar plates.

siRNA

Reverse transfections were performed with RNAiMAX reagent (Life Technologies) according to manufacture instructions on siControl (AllStars Negative control siRNA;

Qiagen) and siPKMYT1 (ON-TARGETplus Human PKMYT1 (9088) siRNA –

SMARTpool; GE Dharmacon). Plates were coated with laminin for 3 hours, aspirated, and then coated with the respective siRNA/RNAiMAX/Opti-MEM (Life Technologies) mixture. Following 1-hour incubation, cells resuspended in media without antibiotics were then added to the siRNAs. After 24 hours, media was changed. Following 48 hours from the initial transfection, cells were treated with MK1775 (WEE1 inhibitor) or mock-treated, and harvested for protein or used for other experiments (i.e. time-lapse microscopy).

Lentiviral production LentiCRISPR v2 plasmids were transfected with Lipofectamine 2000 (Life

Technologies) into 293T cells along with psPAX and pMD2.G packing plasmids

(Addgene) to produce lentivirus. Approximately 24 hours after transfection, neural stem cell expansion medium was added to replace the original 293T growth medium. Virus was harvested and filtered approximately 24 hours after media change and stored at -

80˚C. GSCs and NSCs were infected at MOI <1 for all cell lines. Cells were infected for

48 hours followed by selection with 1-4µg/mL (depending on the target cell type) of puromycin for 2-4 days (Ding et al., 2013; Toledo et al., 2014). For the growth- promoting genes, cells were maintained under selection with 0.5µg/mL of puromycin in order to prevent outgrowth of residual uninfected cells. To produce lentivirus for the whole-genome wide CRISPR library, 25x150mm plates of 293T cells were seeded at

~15 million cells per plate.

Cell transduction and titering for CRISPR whole-genome library

Cells were transduced with the GeCKO library using the regular infection method. To determine optimal viral volumes for MOI <1 (~30% infection efficiency), each cell line was tested individually. In brief, 1x106 cells were plated onto 12 well plates. The next day, each well received viral supernatant in a dilution format. A non-transduction control was also included. Following 48 hours of infection, cells were split into duplicate wells, and selected with puromycin 24 hours later. After 3 days, cells were counted to calculate the percentage of transduction. The ratio is calculated as cell count from the replicate with puromycin versus the replicate without puromycin. The amount of virus that produced an MOI <1 and close to ~30% infection efficiency was chosen for the genome wide screen.

CRISPR-Cas9 screening

For large-scale transduction, ~ 220 million GSC or NSC cells were plated into T225 flasks at an appropriate density and such that each replicate had ~500 fold representation. 2 days after transduction, puromycin was added (1-4µg/ml) and maintained for 3 days. A portion of cells were harvested as Day 0 time point. The remaining cells were then passaged into T225 flasks maintaining 500 fold representation and cultured for an additional 23 days for NSC-CB660, GSC-0131, and

GSC-0827 or 21 days for NSC-U5 or between 8 to 10 cell doublings. Note that the choice to harvest at 23 or 21 days was based on when cells were passaged (i.e., plated out) relative to 3 week time point. We waited an additional two days for some to maximize cell number for each replicate to allow for multiple DNA preps per replicate

(incase one was compromised for any reason). Genomic DNA was extracted using

QiaAmp blood purification Midi kit (Qiagen). Two step PCR procedure was then performed to amplify sgRNA sequence: For the first PCR, the amount of genomic DNA for each sample was calculated in order to achieve 500-fold coverage over the library

(~6.6 µg of gDNA for 106 cells), which resulted in ~213 µg DNA per sample. For each sample, ~100 separate PCR reactions were performed with 2 µg genomic DNA in each reaction using Herculase II Fusion DNA Polymerase (Agilent). Afterwards, a second

PCR was performed to add on Illumina adaptors and to barcode samples, using 5ul of the product from the first PCR. We used a primer set to include both a variable 1-6 bp sequence to increase library complexity and 6 bp Illumina barcodes for multiplexing of different biological samples. Resulting amplicons from the second PCR were column purified using the combination of PureLink PCR purification kit (Life Technologies) and

MinElute PCR purification kit (Qiagen) to remove genomic DNA and first round PCR product. Purified products were quantified, mixed, and sequenced using HiSeq 2500

(Illumina). The whole amplification was carried out with 12 cycles for the first PCR and

21 cycles for the second PCR to maintain the linear amplification. The resulting reads were mapped onto a reference library containing library sgRNA sequences and filtered

(phred score= 37). Mapped reads were tallied and compared using R/Bioconductor package, edgeR, developed for RNA-seq analysis, which subtracts control from experimental replicates to calculate logFC and uses the Benjamini-Hochberg FDR calculation to adjust p-values for multiple comparisons. PCA was performed in R, using the log2 normalized CPM (counts per million) values generated by the Bioconductor package edgeR.

H2B Flow analysis

NSCs were infected with EGFP-H2B (Addgene) at MOI>2 and passaged for 1 week.

Cells were then infected with sgControl and sgEGFP at MOI<1 and selected by puromycin for 3 days. The cells were then plated onto 12-well plates and kept in culture for 14 days with regular passaging and media changing. Images were taken on day 14 using a fluorescent microscope (Nikon TI) to determine the knock out effect. Flow analysis (FACS Canto flow cytometer from Becton Dickinson) was then performed to analyze the percentage of eGFP in both sgControl and sgEGFP cells.

Growth assays

For short-term single clone validation assays, cells were infected with lentiviral gene pools containing 3-4 sgRNAs per gene (growth-limiting genes only) or with lentivirus containing a single sgRNA to the respective gene. Following selection, cells were harvested, counted (NucleoCounter, NBS) and plated in triplicate onto 96-well plates coated with laminin(Ding et al., 2013; Toledo et al., 2014) in dilution format starting at

1,000 cells to 3,750 cells per well (cell density depended on cell line and duration of assay). Cells were fed with fresh medium every 3-4 days. After 7-12 days under standard growth conditions, cell proliferative rates were measured using Alamar blue reagent (Invitrogen) or CellTiter-Glo (Promega) according to manufacturer’s instructions. For analysis, sgRNA-containing samples were normalized to their respective sgControl samples. For long-term growth assays (21 days or greater), cells infected with individual sgRNAs or sgControl were plated in triplicate. Cells were routinely cultured for 21 days (split every 3-4 days), and counted at each split. The overall growth of each well containing an individual sgRNA was calculated and compared to the sgControl well. The growth defects were graphed using the ratio between the individual sgRNAs to sgControl.

Western blotting

Cells were harvested following infection with their respective shRNA and selection, washed with PBS, and lysed with modified RIPA buffer or snap-frozen and stored at -

80˚C until lysis. Western blots were carried out using standard laboratory practices

(www.cshprotocols.org), except cells were lysed in a modified RIPA buffer (150mM NaCl, 50mM Tris, pH 7.5, 2mM MgCl2, 0.1% SDS, 2mM DDT, 0.4% deoxycholate, 0.4%

Triton X-100, 1X complete protease inhibitor cocktail (complete Mini EDTA-free,

Roche), and 1U/µL benzonase nuclease (Novagen)) at RT for 15 minutes(Ding et al.,

2013; Toledo et al., 2014). Cell lysates were quantified using Pierce 660nm protein assay reagent and identical amounts of proteins were loaded onto SDS-PAGE for western blot. Trans-Blot Turbo transfer system was used according to the manufacturer’s instructions. The following commercial antibodies were used: histone H4

(Abcam, # 17036-100, 1:2,000), Beta- (Cell Signaling, #3700, 1:1,000), and TP53

(Calbiochem, # OP03, 1:1,000). An Odyssey infrared imaging system was used to visualize blots (LI-COR) following the manufacturer’s instructions. The Odyssey software was used to semi-quantify the blots.

Immunoprecipitation-Western blotting

Frozen cell pellets were lysed in NP-40 lysis buffer (50 mM Tris pH 8.0, 150 mM NaCl, and 0.5% Nonidet P-40, 1mM DTT; supplemented with protease and phosphatase inhibitors), cleared by centrifugation, and quantified by Bio-Rad Protein Assay. For western blots, samples were normalized for total protein, separated on polyacrylamide gels, transferred to PVDF membranes, blocked in 5% milk/TBST, and incubated with primary antibodies overnight. For immunoprecipitations, lysates were normalized for total protein and rotated with specific antibodies at 4 degrees for two hours, followed by an additional one hour of rotation after addition of Protein G agarose. All IPs were washed three times with lysis buffer and eluted with Laemmli sample buffer and boiling.

The following commercial antibodies were used: Cdk1 (BD, #610037, 2 ul/IP, 1:500 WB), Cdk1 (Santa Cruz, clone p34 (17), Cat # SC-54, 1:750 dilution), Cdc2 (Cell

Signaling Technology, #POH1, Cat #9116), Cdk2 (Santa Cruz, #Clone D12, 2 ul/IP),

Cdk2 (Santa Cruz, #Clone M2, 1:1000), p-Y15 Cdk1/Cdk2 (Millipore, #219440, 1:1000), p-T14 Cdk1 (Abcam, # Ab58509, 1:750), Wee1 (Cell Signaling Technologies, # D10D2,

1:1000), Myt1 (Abcam, #114022, 1:1500), and γ-tubulin (Santa Cruz, #C-20, 1:1000).

RNA sequencing expression analysis

Cells were lysed with Trizol reagent (Life Technologies), and RNA was extracted according to manufacture instructions (Life Technologies). Total RNA integrity was checked using an Agilent 2200 TapeStation (Agilent Technologies, Inc., Santa Clara,

CA) and quantified using a Trinean DropSense96 spectrophotometer (Caliper Life

Sciences, Hopkinton, MA). RNA-seq libraries were prepared from total RNA using the

TruSeq RNA Sample Prep Kit (Illumina, Inc., San Diego, CA, USA) and libraries size distributions were validated using an Agilent 2200 TapeStation (Agilent Technologies,

Santa Clara, CA, USA). Additional library QC, blending of pooled indexed libraries, and cluster optimization was performed using Life Technologies’ Invitrogen Qubit® 2.0

Fluorometer (Life Technologies-Invitrogen, Carlsbad, CA, USA). RNA-seq libraries were pooled and clustered onto a flow cell lane using an Illumina cBot. Sequencing was performed using an Illumina HiSeq 2500 in Rapid Run mode and employed a paired- end, 50 base read length (PE50) sequencing strategy.

RNA sequencing data analysis Reads of low quality were discarded prior to alignment to the reference genome (UCSC hg19 assembly) using TopHat v2.0.12(Trapnell et al., 2009). Counts were generated from TopHat alignments for each gene using the Python package HTSeq v0.6.1(Anders et al., 2015). Genes with counts above threshold equal to at least the number of samples in the smallest group were retained, prior to identification of differentially expressed genes using the Bioconductor package edgeR v3.6.8(Robinson et al., 2010).

A false discovery rate (FDR) method was employed to correct for multiple testing(Reiner et al., 2003). Differential expression was defined as |log2 (ratio) | ≥ 0.585 (± 1.5-fold) with the FDR set to 5%.

Exome Sequencing and Preprocessing

Exome sequencing and preprocessing were performed at the Genome Core Facility of

Mount Sinai School of Medicine. Whole genome amplified was used for exome sequencing. Whole-exome capture libraries were constructed using ligation of Illumina adaptors. Each captured library was then loaded onto the HiSeq 2500 sequencing platform. Exome sequence preprocessing and analysis were performed using standard pipelines recommended by the Genome Analysis Toolkit (GATK)(McKenna et al.,

2010). Three GSC cell lines were aligned independently. For each sample, the reads were aligned to NCBI build 37 (hg19) human reference sequence using BWA(Li and

Durbin, 2009) (http://bio-bwa.sourceforge.net), and duplicated were marked using

Picard (http://picard.sourceforge.net). Local realignment around indels and the base recalibration process were performed, ending in an analysis-ready BAM file for each cell line.

Mutation detection and annotation

Mutation detection and annotation were performed at the Genome Core Facility of

Mount Sinai School of Medicine as follows. For each sample, GATK was used to detect all variants that differ from a reference genome. Variants identified were annotated using the snpEff software(Cingolani et al., 2012).

Variant Filtration

The variants were filtered in four steps following the previous study(Barretina et al.,

2012). First, the variants with low allelic fraction were excluded. The allelic fraction was calculated for each detected variant per cell line as a fraction of reads that supported an alternative allele (e.g., different from the reference) among reads overlapping the position. Only reads with allelic fractions above 0.25 were used in the downstream analysis. Additionally, the variants that were detected as common germline variants were excluded. Variants for which the global allele frequency (GAF) in dbSNP138 or allele frequency in the NHLBI Exome Sequencing Project

(http://evs.gs.washington.edu/EVS, data release ESP2500) was higher than 0.1% were excluded from further analysis. Furthermore, variants detected in a panel of 278 whole exomes sequenced at the Broad as part of the 1000 Project were excluded from further analysis. Finally, the variants with low quality (e.g. insufficient read depth and insufficient genotype quality) were filtered with the variant quality score tools.

Obtaining high-confidence mutations We selected high-confident mutations by their annotation obtained from snpEff17. We filtered silent mutations and extract high and moderate impact of mutations including non-synonymous, nonsense, frame shift, codon insertion/deletion mutations.

CNV Detection

The detection of copy number variations (CNVs) was carried out via Control-FREEC v7.2 (Boeva et al., 2012; Boeva et al., 2011). The software package was downloaded from http://bioinfo-out.curie.fr/projects/freec/. The paired-end alignment files of the tumor sample and its control, either CB660 or VM, were used as the input. R scripts provide by the software were used to add the significance to the predicted CNVs and the visualization of the results.

Transcript assembly and abundance estimation

The resulting aligned reads were analyzed further by Cufflinks(Trapnell et al., 2010) with the reference genome. Cufflinks assembled the aligned reads into transcripts with reference genome and reported the expression of those transcripts in Fragments Per

Kilobase of per Million fragments mapped (FPKM). FPKM is an expression of the relative abundance of transcripts. We set an FPKM value of 0.05 as the lower bound and FPKM value were log-transformed in our subsequent analyses. We determined the expression status of our cell lines for the genes within the frequently altered genomic regions in glioblastoma patients(Brennan et al., 2013). For the set of genes located in each altered genomic region, we clustered all available 67 GSC samples based on their expression levels using K-means clustering. For each sample and gene, we determined the expression status as overexpressed/ non-variable for the gene in amplified regions, and depleted/non-variable for the gene in deleted regions.

GBM network generation

For creation of GBM network shown in Figures S8 and S9, we started with the 2008

Cytoscape network built by TCGA

(http://cbio.mskcc.org/cancergenomics/gbm/pathways/) and then grew the network outward from this core by adding additional experimentally-validated interactions from the literature and known interactors of proteins in the curated network from the 'in-vivo' subset of 'http://hprd.org/'. An interactive version of the network with links to the supporting literature for every interaction is publicly available at http://oncoscape.sttrcancer.org/.

Time-lapse microscopy

NSCs were infected with lentiviral gene pools containing 3-4 sgRNAs per gene or with individual sgRNAs, puromycin selected, outgrown for 13-15 days, and plated onto 96- well plates or 24-well plates. Also NSCs and GSCs were transfected with siRNAs (see siRNA). Plates were then inserted into the IncuCyte ZOOM (Essen BioScience), which was in an incubator set to normal culturing conditions, and analyzed with its respective software for processing videos or determining the confluency of each well. For the growth-limiting assay, phase images were taken every hour for 72 hours. For the mitotic transit time, phase and fluorescence (GFP) images were taken every 5 minutes for 48-

72 hours.

Mitotic transit time

NSC-CB660s were transduced with individual sgRNA constructs to PKMYT1 and control for 48 hours, and puromycin selected for 96 hours. Cells were outgrown for 15 days, treated with 300nM of the WEE1 inhibitor MK1775, and followed by time-lapse microscopy for 72 hours using the IncuCyte ZOOM. Mitotic transit time was analyzed for individual cells following 6 hours of WEE1 inhibition. In addition, NSC-CB660s and

GSC-0827s were transfected with siRNAs for 24 hours, and treated with 300nM of the

WEE1 inhibitor MK1775 following 48 hours from the initial transfection. Plates were then placed into the IncuCyte ZOOM for time-lapse microscopy for 48-72. Videos were compiled and n>60 cells were analyzed to determine the mitotic transit time, and whether each cell successfully completed mitosis or experience cytokinesis failure or cell death in mitosis. A cell was considered to enter mitosis when nuclear envelope breakdown was visible or when a visible morphology change was observed (cell begins to go from flat to rounded-up). Following successful cytokinesis (proper cell division resulting in two daughter cells), a cell was categorized as successfully completing mitosis. A cell was classified as cytokinesis failure if the cell failed to divide following mitotic entry due to an abrupt mitotic exit while in metaphase or anaphase, or failure to complete cytokinesis. If a cell experienced cytokinesis failure, the cell was followed for additional amount of time to ensure that the cell indeed experienced cytokinesis failure and was categorized as such. A cell was categorized as cell death in mitosis if a cell erupted and died during mitosis (from nuclear envelop break down to cytokinesis).

In vivo validation with lentiviral sgRNA retest pool

0827 and 0131 GSCs were infected with the LV-retest-pool retest virus for 48 hours and selected for 4 days in puromycin (1 and 2µg/mL respectively). Cells were then harvested using Accutase (Sigma), counted, resuspended in an appropriate volume of culture media, washed with PBS, resuspended in a final concentration of 5 million cells/100µL of PBS, and kept on ice prior to immediate transplantation. Each mouse received 100µL of resuspended cells (5 million cells). All in vivo experiments were conducted in accordance with the NIH Guide for the Care and Use of Experimental

Animals, and with approval from the Fred Hutchinson Cancer Research Center

Institutional Animal Care and Use Committee (IR#1457). Cells were implanted subcutaneously into the right flank of female 6 week old athymic nude mice (Harlan).

Tumors were monitored three times weekly and allowed to grow to ~1500mm3 before harvesting the tumors. The DNeasy Blood and Tissue Kit (Qiagen) was used to dissociate the tumor and extract gDNA. Lethal-pool retest was also conducted in vitro as previously described (see CRISPR-Cas9 screening). Library pools for both the in vivo and in vitro samples were prepared as described earlier (see CRISPR-Cas9 screening). Raw counts from each sgRNA were transformed to log2 CPM (counts per million) using the Bioconductor package edgeR(Robinson et al., 2010), followed by normalization within each sample to a common control sgRNA. Differentially expressed sgRNAs were identified using the Bioconductor package limma(Ritchie et al., 2015), where a false discovery rate (FDR) method was employed to correct for multiple testing(Reiner et al., 2003). Differential expression was defined as |log2 (ratio)| ≥ 0.585

(± 1.5-fold) with the FDR set to 5%.

qRT-PCR

Cells containing shRNAs were harvested following infection/selection process and total

RNA from cells was extracted using TRIzol (Invitrogen) according to manufacturer’s instructions. QuantiTect quantitative real-time PCR (qRT-PCR) primer sets and SYBR

Green PCR Master Mix (Applied Biosystems) were used according to manufacturer’s instructions with the ABI PRISM 7900 Sequence detection System (Genomics

Resource, FHCRC). Ct values of the samples were normalized to beta-actin followed by the respective shControl. Relative transcript abundance was analyzed using the 2-ΔΔCt method (Toledo et al., 2014).

Limiting dilution assay

Cells were transfected with siControl or siPKMYT1 for 24 hours. Cells were then detached from their respective plate, dissociated into single-cell suspensions, counted with a nucleocounter, and then plated into non-tissue culture treated 96-well plates not coated with laminin with various seeding densities (0.125-256 cells per well, 10 wells per seeding density). Cells were incubated at 37˚C for ~2 weeks and fed with 10X EGF and FGF-2 neural stem cell expansion media every 3 to 4 days. At the time of quantification, each well was examined for the formation of tumorspheres (Toledo et al.,

2014).

Deep sequencing of CRISPR modified loci The genomic regions surrounding the CRISPR target loci of several different sgRNAs

were PCR amplified from corresponding CRISPR-transduced NSC-CB660 DNA

samples and prepared for deep sequencing using a two-step PCR procedure. In the

primary PCR, the genomic region of interest was amplified for 14 cycles using

Herculase II Fusion Polymerase (Agilent Technologies) and the genomic primers listed

below (preceded by an additional 24 or 28 bp sequence corresponding to the secondary

PCR primers).

Primer 1 (5’->3’) Primer 2 (5’->3’) sgCREBBP_1 …ATGCCGTACCCTACTCCAG …CACGTGGTCCCATTTTACGC sgNF2_4 …TGGTTTGTTATTGCAGATGAAGTG …GCTAGGCGCCTGCTCA sgTFAP2C_1 …AAAATGGAGGCCGGTCCTTG …CATCTCTACTTGCAACCAAGTTTTA sgHDAC2_2 …TATTTTTCGAACTGTTGCAGTATG …GTAATGAGAACACTTACCATTGGG

5 µl of the primary PCR reaction was then used as the template for the secondary PCR

reaction, which again used Herculase II Fusion Polymerase and was carried out for 23

cycles. Secondary PCR primers matched the additional sequence added on by the

primary PCR primers (“…” in the chart above) and also added Illumina adapters and

sample-specific barcodes. Final amplicons were then electrophoresed on a 1.5%

agarose gel, and bands with expected fragment sizes +/- 100 bp (to capture larger

indels) were excised and purified using the Zymoclean Gel DNA Recovery Kit (Zymo

Research). Amplicon concentration was measured using Life Technologies’ Invitrogen

Qubit® 2.0 Fluorometer (Life Technologies-Invitrogen, Carlsbad, CA, USA). The various

products were then pooled in equal proportions and sequenced on an Illumina MiSeq

machine (250 bp paired-end reads). Data were processed according to standard

Illumina sequencing analysis procedures, and reads were mapped to the PCR

amplicons as reference sequences. An R script was used to assess length and prevalence of insertions and deletions by analyzing cigar sequences found in sam/bam alignment files. Indel phase was calculated as the length of insertions or deletions modulus 3.

Genetically transformation of human neural stem cells

NSC-CB660 cells were simultaneously infected with retrovirus pBabe-TP53DD + human

Tert and pBabe-CyclinD1 + CDK4 R24C (Addgene) over three consecutive rounds of infection as previously described(Hubert et al., 2013). After recovery, cells containing the two constructs were infected with lentivirus pCDF1-MCS2-EF1-copGFP [CMV-ΔII-

VIII EGFR] (kindly provided by Robert Bachoo, UT Southwestern). After recovery and expansion, cells were sorted for GFP+ population and expanded. Following expansion, these cells and normal CB660 neural stem cells were infected with retrovirus pBabe-

Puro-Myr-Flag-AKT1 (Addgene) over three consecutive rounds of infection. After recovery, cells were selected with puromycin and expanded.

Statistics

All student’s t-tests were conducted using unpaired and unequal variance.

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