CRISPR Enriches for Cells with Mutations in a P53-Related Interactome, And

bioRxiv preprint doi: https://doi.org/10.1101/2021.03.10.434760; this version posted March 10, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Jiang L et al., 2021

1 CRISPR enriches for cells with mutations in a p53-related interactome, and

2 this can be inhibited.

3 Long Jiang1, Katrine Ingelshed2, Yunbing Shen1, Sanjaykumar V. Boddul1, Vaishnavi 4 Srinivasan Iyer1,3, Zsolt Kasza1, Saikiran Sedimbi2, David P. Lane2, 4, and Fredrik Wermeling1* 5 6 1. Department of Medicine Solna, Center for Molecular Medicine, Karolinska University 7 Hospital and Karolinska Institutet, Stockholm, Sweden. 8 2. Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 9 Sweden. 10 3. School of Physical and Mathematical Sciences, Nanyang Technological University, 11 Singapore. 12 4. p53 Laboratory (p53Lab), Agency for Science, Technology, and Research (A*STAR), 13 Singapore. 14 15 * Corresponding author. Center for Molecular Medicine, L8:03, Karolinska University 16 Hospital, 171 76 Stockholm, Sweden. Email: [email protected]. 17 18 CRISPR/Cas9 can be used to inactivate or modify genes by inducing double-stranded 19 DNA breaks1-3. As a protective cellular response, DNA breaks result in p53-mediated cell 20 cycle arrest and activation of cell death programs4,5. Inactivating p53 mutations are the 21 most commonly found genetic alterations in cancer, highlighting the important role of the 22 gene6-8. Here, we show that cells deficient in p53, as well as in genes of a core CRISPR- 23 p53 tumor suppressor interactome, are enriched in a cell population when CRISPR is 24 applied. Such enrichment could pose a challenge for clinical CRISPR use. Importantly, 25 we identify that transient p53 inhibition suppresses the enrichment of cells with these 26 mutations. Furthermore, in a data set of >800 human cancer cell lines, we identify 27 parameters influencing the enrichment of p53 mutated cells, including strong baseline 28 CDKN1A expression as a predictor for an active CRISPR-p53 axis. Taken together, our 29 data identify strategies enabling safe CRISPR use. 30 The CRISPR molecular biology tool has immense potential for clinical gene therapy use9. 31 Correcting disease-causing mutations in congenital monogenic disorders affecting for example 32 the hematopoietic system are apparent candidates, and clinical trials for sickle cell anemia and 33 beta-thalassemia are ongoing10-12. CRISPR-mediated modifications of cells used for chimeric

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34 antigen receptor (CAR) based immunotherapy is another clinical setting where CRISPR is 35 likely to have a large impact13,14. Concerns about the safety of CRISPR-based gene therapy are 36 successfully being addressed at multiple levels. This includes risks related to CRISPR off-target 37 activity, where, for example, sgRNA design with low off-target activity15, high fidelity Cas9 38 versions16, and methods for the evaluation of off-target mutations17 has been an intense research 39 focus. Another risk that has been suggested is that the CRISPR-mediated DNA damage could 40 give cells with mutations in p53 (also referred to as TP53 in humans, and Trp53 in mice) a 41 selective advantage and thereby be enriched in a cell population exposed to CRISPR18-20. 42 Notably, TP53 mutations are seen in human embryonic stem cell lines21 and also contribute to 43 clonal hematopoiesis22,23 showing that premalignant TP53 mutations can be found in different 44 cell populations of relevance for clinical CRISPR use. 45 To address the risk of enriching for cells with p53 mutations using CRISPR, we initially 46 studied the cellular response following CRISPR (electroporation of a GFP targeting sgRNA 47 with very low off-target activity, as calculated by Cas-OFFinder24), and compared it to the 48 response following a pulse of Etoposide (a topoisomerase II inhibitor causing DNA damage 49 and p53 activaton25), or AMG232 (an inhibitor activating p53 by interfering with the MDM2- 50 p53 interaction26). Using a cell line derived from mouse hematopoietic stem cells of Cas9+ 51 GFP+ mice (Supplementary Fig. 1a-d), we observed that the CRISPR event resulted in partial 52 delayed cell growth (Fig. 1b), apoptosis induction (Fig. 1c), and transcription of Cdkn1a (also 53 referred to as p21, a p53 target gene central to DNA damage-induced cell cycle arrest 54 response27, Fig. 1d), although at a lower magnitude compared to AMG232 (Fig. 1b), and 55 Etoposide (Fig. 1b-d). To explore if this relatively mild phenotype was sufficient to give a 56 selective advantage to cells with mutations in Trp53, we established a competitive assay, where 57 Trp53 KO and WT cells were mixed at a 1:4 ratio, and subsequently exposed to CRISPR 58 (electroporation or lentiviral delivery of sgRNA), or to pharmacological p53 activation with 59 AMG232 or Etoposide (Fig. 1e). Notably, the proportion of Trp53 KO cells in the population 60 did not expand significantly by only being cultured, or by being transduced by non-targeting 61 control (NTC) lentiviral particles, as identified by sequencing the Trp53 locus after seven days 62 in culture. However, the proportion of Trp53 KO cells did significantly expand after being 63 exposed to CRISPR, AMG232, and Etoposide (Fig. 1f and Supplementary Fig. 1e), as well 64 as hypoxia (Supplementary Fig. 1f-h). The level of enrichment mirrored the severity of the 65 cellular phenotype shown in figure 1b-d, with Etoposide and AMG232 causing a significantly 66 higher enrichment of Trp53 KO cells compared to CRISPR. Based on this, we hypothesized 67 that the level of DNA damage response induced by sgRNAs could be a parameter affecting the

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68 enrichment of Trp53 KO cells. To this end, we designed a set of sgRNAs with different off- 69 target activity and used them alone or in combination at equimolar concentrations, and 70 compared the induction of Cdkn1a transcription at early time points (0-24h), as a proxy for p53 71 activation, to the enrichment of cells with Trp53 mutations at a later time points (seven days). 72 We found that the level of early CRISPR-induced Cdkn1a transcription correlated with the 73 enrichment of Trp53 KO cells, and that the level of enrichment could be estimated based on the 74 level of off-target activity one, or a combination, of sgRNAs would give (Fig. 1g-h and 75 Supplementary Fig. 2a-c). This suggests that the level of induced Cdkn1a expression could be 76 a relevant parameter for sgRNA selection. 77 The enrichment of cells with inactive p53 could pose a challenge to the clinical use of 78 CRISPR. Therefore, we set out to identify strategies that could be used to suppress the 79 enrichment. We hypothesized that inhibiting p53, or proteins playing a non-redundant role in 80 the p53 pathway could be a viable strategy. We gathered a selection of potentially relevant 81 inhibitors (Fig. 1i), and pretreated cells with the inhibitors followed by exposing them to 82 CRISPR. Importantly, we found that treating the cells with a Trp53 siRNA completely inhibited 83 the enrichment of cells with Trp53 mutations, while the other used inhibitors did not show 84 sufficient activity (Fig. 1j and Supplementary Fig. 3). We also noted that the pan-Caspase 85 inhibitor Z-VAD did not significantly suppress the enrichment of cells with Trp53 mutations 86 (Fig. 1k), suggesting that apoptosis is not a significant driver in the CRISPR-mediated 87 enrichment of Trp53 KO cells. We concluded (i) that cells with inactivating Trp53 mutations 88 are enriched following exposure to CRISPR, (ii) that this correlates to the level of triggered 89 DNA damage response, and (iii) that transient inhibition of p53 can completely abrogate this 90 enrichment. 91 Next, we set out to identify p53 linked genes playing a non-redundant role in the CRISPR- 92 induced DNA damage response. Such genes could represent additional drug targets to modify 93 the CRISPR-p53 response and, importantly, cells with mutations in such genes could be 94 enriched by CRISPR-mediated DNA damage. To this end, we applied a custom CRISPR screen 95 library targeting 395 DNA damage-related genes and controls, with 1640 sgRNAs 96 (Supplementary Table 1), to WT and Trp53 KO cells. Relying on the DNA damage response 97 induced by the introduction of the sgRNA library into Cas9+ cells, as has been used in the 98 past19,28, we found that Trp53 sgRNAs were enriched and that Mdm2 sgRNAs were depleted 99 in a p53 dependent manner, in both Hox and B16 cells (Supplementary Fig. 4a-d). In this 100 regard, Mdm2 behaved as an essential gene, where mutated cells are rapidly lost over time 101 (Supplementary Fig. 4e-f). We also noted that the Hox cells, both Trp53 WT and KO, but not

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102 B16 cells, over time enriched for sgRNAs relating to type I IFN signaling (Stat1, Jak1, and 103 Ptprc), and were sensitive to induced type I IFN signaling (Supplementary Fig. 4g-j, and 104 Supplementary Table 2). 105 Next, we performed a screen using the same sgRNA library, but this time culturing the cells 106 for 14 days after the introduction of the library into the Hox cell population, and then applied a 107 controlled CRISPR DNA damage event by electroporating a GFP targeting sgRNA, comparing 108 it to an eight-hour pulse of AMG232 or Etoposide (Fig. 2a). In this way, we could separate the 109 studied CRISPR event from the introduction of the CRISPR library. Seven days later we 110 collected the different cell populations, sequenced the sgRNA barcodes, and deconvoluted the 111 sgRNA enrichment/depletion using MAGeCK29. Comparing the different interventions to 112 control-treated cells, we identified that CRISPR enriched for cells with mutations in Chek2, 113 Trp53, and Cdkn1a (Fig. 2b); AMG232 enriched for cells with mutations in Trp53 and Cdkn1a 114 (Fig. 2c); and Etoposide enriched for cells with mutations in Atm, Chek2, Trp53 and Cdkn1a 115 (Fig. 2d). Notably, AMG232 also enriched for mutations in Stat1, and Eif2ak2, two genes 116 related to type I IFN signaling (Fig. 2c), again highlighting this pathway in the Hox cells. 117 Focusing on the Atm-Chek2-Trp53-Cdkn1a pathway, linking double-stranded DNA damage to 118 cell cycle arrest4,5,27, we found that CRISPR caused a significant enrichment of sgRNAs 119 targeting all these genes when comparing WT and Trp53 KO cells (Fig. 2e-h) and that a Trp53 120 siRNA could significantly suppress the enrichment of cells with mutations in Chek2, Trp53, 121 and Cdkn1a, but not for Atm for which the enrichment phenotype also was the weakest (Fig. 2i 122 and Supplementary Fig. 5a). Noteworthy, we did not observe any enrichment of sgRNAs 123 targeting genes related to apoptosis (Fig. 2j and Supplementary Fig. 5b), in line with data 124 presented by Haapaniemi et. al19, and the absence of activity of the pan-Caspase inhibitor Z- 125 VAD (Fig. 1k). We concluded (i) that the Atm-Chek2-Trp53-Cdkn1a pathway plays a non- 126 redundant role in the CRISPR-mediated DNA damage response (Fig. 2k), and as a consequence 127 (ii) that cells with mutations in these tumor suppressor genes could be enriched in a cell 128 population as CRISPR is applied, also if they are found at a low frequency (1:1640 in figure 2 129 compared to 1:4 in figure 1e-j), and (iii) that a Trp53 siRNA can suppress the enrichment of 130 mutated cells to a large degree. 131 To expand our understanding of which mutations could be enriched in a cell population as 132 CRISPR is applied, we next turned to the Depmap portal, containing full genome CRISPR 133 screen data of 808 human cell lines (Public 20Q4 release), as well as, for example, baseline 134 gene expression, mutation status, and drug sensitivity data for a large proportion of the same 135 cells30-32. Exploring the database, we found that 103 of the 808 included cell lines (12.7%)

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136 enriched for TP53 sgRNAs as defined by a score >1. Stratifying these cells based on TP53 137 mutation status (WT or any type of mutation, making up 32.3% and 67.7% of the cell lines, 138 respectively), we found that 94 of the 103 cell lines (91.3%) that enriched for TP53 sgRNAs 139 were confined to the TP53 WT group (Fig. 3a), supporting the validity of the analysis. As an 140 anecdote, we also noted that the cell line with the strongest enrichment for TP53 sgRNAs is a 141 version of the RPE-1 cell line (Supplementary Table 3), that has been used in a significant 142 portion of previous publications related to p53 and CRISPR19,28,33,34. Next, we compared the 143 TP53 sgRNA enrichment to the sensitivity of the cells to p53 modulating drugs. We found a 144 clear correlation between TP53 sgRNA enrichment and the sensitivity to AMG232 (Fig. 3b-c), 145 as well as to Nutlin-3 and CMG097 (both with a similar mode of action as AMG232), but not, 146 for comparison, to the p53 inhibitor Pifithrin- (Supplementary Fig. 6). Taken together, we 147 concluded that TP53 sgRNA enrichment in the Depmap dataset could be used to identify cells 148 where the CRISPR-p53 pathway is active, and, as a consequence, that correlation to TP53 149 sgRNA enrichment also could identify other factors influencing the pathway. In line with this 150 concept, we found that TP53 sgRNA enrichment correlated strongly with MDM2 sgRNA 151 depletion (Fig. 3d). Performing the same correlation analysis, but on a full genome basis, we 152 identified a list of genes where sgRNA enrichment (+) or depletion (-) correlated with TP53 153 sgRNA enrichment (Fig. 3e and Supplementary Table 4). The list contained the genes we had 154 identified in our experimental data and expanded the list of genes playing a non-redundant, 155 TP53-related role in the CRISPR-mediated DNA damage response. Furthermore, analyzing the 156 top 10 enriched and depleted genes using geneMANIA, we noted a high level of physical 157 interaction between the linked proteins (Fig. 3f). An alternative analysis approach of the data, 158 identifying genes where sgRNA enrichment/depletion correlated with the TP53 mutation status, 159 instead of TP53 sgRNA enrichment (as in Fig. 3e), resulted in a list of genes with a large level 160 of overlap with the list in figure 3e (Supplementary Fig. 7 and Supplementary Table 5). 161 Based on the overlap of these lists, and the experimental data we highlight TP53, TP53BP1, 162 CHEK2, ATM, CDKN1A, USP28, UBE2K, XPO7 as a core CRISPR-p53 tumor suppressor 163 interactome, where cells with inactivating mutations or silencing of these genes (something that 164 is commonly found in cancers6-8,35-39, Supplementary Fig. 8) can be expected to be enriched 165 in cell population as CRISPR is applied. Furthermore, cells with copy number amplifications, 166 overexpression, or activating mutations of MDM2, PPM1D, MDM4, DDX31, USP7, PPM1G, 167 WDR89, and TERF1 also observed in cancer 40-45, could similarly be enriched by CRISPR. 168 Finally, we explored if specific gene expression patterns could predict if the CRISPR-p53 169 axis is active in a cell, and thus if the cell would enrich for TP53 sgRNAs. Based on our data

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170 identifying a central, non-redundant role for CDKN1A in the CRISPR-induced response, we 171 tested if a strong baseline expression of CDKN1A could predict if cells would enrich for TP53 172 sgRNAs. We found that this assumption was correct and that cells that enrich for TP53 sgRNAs 173 with a few exceptions had a strong baseline CDKN1A expression (Fig. 3g-h), while, for 174 example, TP53 expression itself did not predict cells that would enrich for TP53 sgRNAs 175 (Supplementary Fig. 9a-b). Correlating strong baseline gene expression with TP53 sgRNA 176 enrichment, resulted in a list of genes that to a large extent were identified as transcription target 177 genes for p53 (Fig. 3i), and these genes were also identified by geneMANIA to display a strong 178 co-expression pattern (Supplementary Fig. 9c-f). Notably, the gene set we identify by 179 analyzing the 800 cell lines overlaps to a large extent with published gene expression patterns 180 induced by CRISPR-mediated DNA damage in specific cell lines18,46, as well as genes 181 upregulated in cancers WT for TP5347. Even more, performing a tSNE dimensional reduction 182 analysis based on the expression of the top 10 genes, identified that cells broadly clustered 183 based on TP53 mutation status and TP53 sgRNA enrichment (Fig. 3j and Supplementary Fig. 184 9g). Interestingly, most of the cell lines that enriched for TP53 sgRNAs despite having TP53 185 mutations also clustered with the TP53 WT cells based on expression of the top 10 genes, 186 suggesting that the specific mutations in those cell lines are not abrogating the p53 function 187 sufficiently to cause a phenotype. Apart from Cdkn1a, the expression gene set list (Fig. 3i) does 188 not overlap with genes identified to play a central role in the CRISPR-p53 pathway (Fig. 3e). 189 This suggests that the upregulated genes are mainly an indication of active p53-mediated 190 transcription in the cell, and not directly involved in the CRISPR response. Eda2r and Ptchd4 191 knockout cells supported this notion by not behaving differently than WT cells in response to 192 CRISPR in a mixed cell population (Supplementary Fig. 9h-i). This observation challenges 193 the notion that p53 is only active during stress, by showing that a baseline p53-regulated gene 194 expression pattern before exposure to the specific stressor, CRISPR in this case, predicts the 195 subsequent p53-related response to the stressor. 196 In conclusion, here we set out to explore if and how cells with inactive p53 are enriched as 197 CRISPR is applied to a cell population, something that could represent a challenge for the 198 clinical use of CRISPR. We identify that cells with mutations in p53 indeed are enriched as 199 CRISPR is applied, and that it correlates to the level of induced DNA damage response, 200 highlighting the induction of CDKN1A expression as a sgRNA selection criterion. Furthermore, 201 we identify a core CRISPR-p53 interactome, with genes that display a large level of physical 202 interaction. Mutations or duplications of these genes could be enriched as CRISPR is applied, 203 and these genes should, therefore, be monitored in the clinical CRISPR setting. Transient p53

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204 inhibition has been proposed as a strategy to increase CRISPR efficiency18-20 and to retain the 205 functionality of targeted cells46,48. Significantly, our data also show that transient p53 inhibition 206 can limit the enrichment of mutations in the CRISPR-p53 tumor suppressor interactome, further 207 supporting using transient p53 inhibition in the clinical CRISPR setting. Finally, we identify a 208 gene expression profile, including strong baseline CDKN1A expression, that defines cells where 209 the CRISPR-p53 axis is active. 210 211 Methods 212 Methods, including statements of data availability and any associated accession codes and 213 references, are available as supplemental methods. 214 215 Acknowledgments 216 We are grateful to Drs. Robert M. Anthony, Kate Jeffrey, Eduardo Villablanca, and Bernhard 217 Schmierer for their critical review of the manuscript, as well as to Drs. Michael Sundström, 218 Lars Klareskog, Rasmus O. Bak, Alexander Espinosa, Klas G. Wiman, Lisa Westerberg, and 219 Richard Rosenquist Brandell for important suggestions. psPAX2 (Addgene plasmid 12260) and 220 pMD2.G (Addgene plasmid 12259) were kind gifts from Dr. Didier Trono. The ER-Hoxb8, and 221 EcoPac plasmids were kind gifts from Dr. Mark P. Kamps. The R-script used to represent 222 individual sgRNAs in Fig. S4a-b was a kind gift from Dr. Eric Shifrut. This work benefitted 223 from data assembled by the DepMap Consortium for which we are grateful. The authors 224 acknowledge support from the National Genomics Infrastructure in Stockholm funded by 225 Science for Life Laboratory, the Knut and Alice Wallenberg Foundation and the Swedish 226 Research Council, and SNIC/Uppsala Multidisciplinary Center for Advanced Computational 227 Science for assistance with massively parallel sequencing and access to the UPPMAX 228 computational infrastructure. This research was partly funded by grants from the Swedish 229 Research Council (FW and DPL), the Swedish Cancer Society, Karolinska Institutet, Åke 230 Olssons stiftelse (to FW), the China Scholarship Council (LJ and YS), and the Nanyang 231 Technological University–Karolinska Institutet Joint PhD Programme (VSI). 232 233 Author contributions 234 LJ and FW designed experiments with input from KI, SS and DPL. LJ and KI performed 235 experiments. LJ, KI, YS, SVB, VSI, ZK, SS, DPL, and FW analyzed the data. LJ, DPL, and 236 FW wrote the manuscript. The final manuscript was read and approved by all the authors. 237

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238 Competing interest statement 239 None declared.

240

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1 Online Methods 2 3 Cells 4 The Hox bone marrow cell line was generated by transducing bone marrow cells of C57BL/6 5 Cas9+ GFP+ mice (The Jackson Laboratory, #026179) with an estrogen inducible retroviral 6 construct expressing HoxB8 (ER-Hoxb8, a kind gift from Mark P. Kamps, University of 7 California, San Diego) as described in1,2. Cells were cultured in 1 µM β-estradiol (BE, Sigma- 8 Aldrich #E2758) and 25 nM mouse Stem Cell Factor (SCF, Peprotech #250-03) for several 9 weeks with HoxB8 expression turned on to establish a cell line like population. Hox cells were 10 cultured in RPMI-1640 (Sigma-Aldrich #R0883) with 10% heat-inactivated fetal bovine serum, 11 1% penicillin-streptomycin-glutamine (Gibco #10378016), 1 μM BE, and 25 nM SCF. 12 B16-F10 cell is a mouse melanoma cell line, purchased from ATCC (#CRL6475) and used 13 at a low passage number. Cas9 expressing cells were generated by transducing B16-F10 cells 14 with lentiCas9-Blast (Addgene #52962) lentiviral particles. B16 cells were cultured in RPMI- 15 1640 (Sigma-Aldrich #R0883) with 10% heat-inactivated fetal bovine serum, and 1% 16 penicillin-streptomycin-glutamine (Gibco #10378016) 17 18 Viral preparation and transduction 19 Lentiviral particles were generated by seeding 2106 HEK293T cells in 10 cm plate in 10 ml 20 of DMEM (Sigma-Aldrich #D6546) with 10% heat-inactivated fetal bovine serum and 1% L- 21 glutamine (Gibco #A2916801). After ~24 h of culture, the cells reached ~60-70% confluency, 22 and the medium was replaced by 5 ml of prewarmed fresh media. Transfer plasmids (lentiCas9- 23 Blast, Addgene #52962; or LentiGuide-Puro-P2A-EGFP_mRFPstuf, Addgene #137730), 24 pMD2.G (Addgene #12259), and psPAX2 (Addgene #12260) were mixed at 4:5:1 ratio (10 µg : 25 12.5 µg : 2.5 µg for 10 cm plate), and transfected into HEK293T cells using Lyovec (Invivogen 26 #lyec) according to the manufacturer’s protocol. After 12 h, the medium was replaced by 8 ml 27 of DMEM with 30% heat-inactivated fetal bovine serum and 1% L-glutamine. After another 36 28 h, the supernatant containing the virus was collected and centrifuged to remove the cell debris 29 and used to spin infect cells. 30 For ER-Hoxb8 retrovirus preparation, plasmids including ER-Hoxb8 and the EcoPac gag- 31 pol-env plasmid (both are kind gifts from Mark P. Kamps, University of California, San Diego) 32 were mixed at 1:1 ratio (12 µg:12 µg for 10 cm plate) for transfection into HEK293T following 33 the same approach as for generating lentiviral particles.

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34 To transduce Hox or B16 cells with LentiGuide-Puro-P2A-EGFP_mRFPstuf, the 35 multiplicity of infection (MOI) of the viral particles was tested by infection with serial dilutions 36 of virus particles, to find a dilution resulting in a suitable MOI described below. Virus 37 supernatant was added to each well of 6-well plate containing cells (4105 for Hox cells with

38 SCF and BE, and 1105 for B16 cells) with 8 µg/ml polybrene (Sigma-Aldrich #H9268). The 39 plate was centrifuged at 37°C, 1200g (120 min for Hox cells, and 45 min for B16 cells). After 40 24h, the virus-containing medium was replaced with fresh medium, and the infection rate was 41 measured by the percentage of GFP+ cells if the vector contains GFP. Puromycin (Invivogen 42 #ant-pr) selection (10 g/ml for HoxB8 cells, and 5ug/ml for B16 cells) or Blasticidin

43 (Invivogen #ant-bl) selection (10 g/ml for B16 cells) was performed for 24 h to remove the 44 non-infected cells. 45 46 Electroporation and transfection 47 sgRNAs were designed using the Green Listed software3,4 using sgRNA design from the 48 Doench mouse library5. 2’-O-methyl and phosphorothioate stabilized sgRNAs 49 (Supplementary Table 8) were ordered from Sigma-Aldrich. Off-target activity was calculated 50 using Cas-OFFinder (http://www.rgenome.net/cas-offinder/)6, and On-target activity was 51 extracted from CHOPCHOP (https://chopchop.cbu.uib.no/)7 by searching for Ccr1, or entering 52 the EGFP FASTA sequence from the LentiGuide-Puro-P2A-EGFP_mRFPstuf (Addgene 53 #137730). 54 For Hox cells, Neon Transfection System (Invitrogen #MPK5000) was used to perform the 55 electroporation following the manufacturer’s instructions (Pulse voltage: 1700, Pulse width: 20 56 ms, Pulse number: 1, for Hox cells). 100 pmol of sgRNA were electroporated into 2x105 Hox 57 cells for each electroporation experiment using Neon Transfection System 10 µL Kit 58 (Invitrogen #MPK1096). For B16 cells, Lipofectamine 2000 Transfection Reagent (Invitrogen, 59 #11668019) was used following the recommended protocol. 100pmol of sgRNA were 60 transfected into 1x105 B16 cells for each transfection experiment. 61 Trp53 ON-TARGETplus siRNA SMARTPool was ordered from Horizon. For Hox cells, 62 100 pmol of siRNA was electroporated into 2x105 Hox cells for each electroporation 63 experiment. For B16 cells, 100 pmol of siRNA was transfected into 1x105 B16 cells for each 64 transfection experiment. The Trp53 siRNA was typically delivered in the same reaction as the 65 sgRNAs. 66

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67 CRISPR KO genotyping 68 1x105 cells were collected for genomic DNA extraction using DNeasy Blood & Tissue Kit 69 (Qiagen #69504) following the recommended protocol. Primers (Sigma-Aldrich) were 70 designed using Primer-BLAST (Supplementary Table 8), aiming for a 400-1000 bp amplicon 71 with the sgRNA target in the middle. Amplicons were gel purified and recovered using 72 Zymoclean Gel DNA Recovery Kit (Zymo Research #D4007/D4008). The PCR products were 73 quantified using Nanodrop and sequenced by Eurofins Genomics. The Sanger sequencing data 74 was subsequently analyzed by ICE (Synthego, https://ice.synthego.com). 75 76 Growth curve characterization 77 Hox cells were cultured with the following interventions: electroporation with a GFP targeting 78 sgRNA; 0.5 µg/ml Etoposide (Sigma-Aldrich #E1383); 3 µM AMG232 (Axon Medchem 79 #2639). The Etoposide and AMG232 were removed after 8 h by removing the medium and 80 adding fresh medium without Etoposide or AMG232. The cells were cultured in 6 well plates 81 with 4ml medium and passaged every day at the ratio of 1:3. 80 ul of cells were taken every 82 day for flow cytometry (BD Accuri) with the existence of CountBright Absolute Counting 83 Beads (Invitrogen #C36950). The absolute viable cell number was calculated by comparing the 84 events number of viable cells and counting beads in the flow cytometry data. 85 86 Real-Time PCR 87 TRIzol Reagent (Invitrogen #15596026) and Direct-zol RNA MiniPrep Kit (Zymo Research 88 #R2051) was used to extract RNA. RNA was then converted into cDNA using High Capacity 89 RNA-to cDNA kit (Applied Biosystem #4388950). The expression of Cdkn1a was quantified 90 with a CFX 384 Real-Time PCR machine (Bio-Rad) using TaqMan gene expression FAM 91 assays for Cdkn1a (Mm00432448_m1) with the TaqMan Gene Expression Master Mix 92 (Applied Biosystem #4369542) as suggested by the manufacturer. Expression was normalized 93 by TaqMan gene expression VIC assays for β-actin (Mm00607939) and Gene expression was 94 quantified using the ddCT method. 95 96 Apoptosis TUNEL assay 97 Cells were collected and fixed by PFA at different time point, and FlowTAC Apoptosis 98 Detection Kit (R&D Systems #4817-60-K) was used to stain apoptotic cells following the 99 manufacturer’s instructions. Stained cells were analyzed by flow cytometry (BD Accuri). 100

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101 Cloning of sgRNAs into lentiviral transfer plasmid, and CRISPR screens 102 sgRNAs with overhangs for the plasmids (Supplementary Table 1) were designed using the 103 Green Listed software3,4 using sgRNA design from the Doench mouse library5 and, for 104 intergenic controls, the Wang mouse library8. Individual sgRNAs were ordered from Sigma- 105 Aldrich, and the sgRNA library was ordered from CustomArray as a DNA oligo pool. Cloning 106 was performed using 150 ng of BsmBI (New England Biolabs #R0739) cleaved lentiGuide- 107 Puro-P2A-EGFP_mRFPstuf plasmid (Addgene #137730), and 10 ng of the library oligo pool, 108 using NEBuilder HiFi DNA assembly master mix (New England Biolabs #E2621). Endura 109 ElectroCompetent Cells (Lucigen #60242), were subsequently transformed with the cloned 110 plasmid pool using electroporation (1.0 mm cuvette, 10 µF, 600 Ohms, 1800 Volts) following 111 the suggested protocol. The electroporated cells were combined and seeded on ten 20 cm LB 112 agar plates with 100 g/ml carbenicillin and grown at 37°C overnight. The lentiGuide-Puro- 113 P2A-EGFP_mRFPstuf plasmid (Addgene #137730) makes bacteria red if the stuffer has not 114 been exchanged by a sgRNA, and a few red clones were removed before collecting all other 115 white clones. Then the plasmids were purified using EndoFree Plasmid Maxi Kit (Qiagen 116 #12362). 117 The sgRNA cloned lentiGuide-Puro-P2A-EGFP_mRFPstuf was used as transfer plasmid for 118 lentiviral preparation and transduction. The total amount of transduced cells was calculated 119 based on MOI (0.25 for B16 cells, and 0.05-0.1 for Hox cells, which were difficult to transduce), 120 based on the % GFP+ cells before puromycin selection, aiming for 1000 transduced cells for 121 each sgRNA. 122 The CRISPR library transduced cells were exposed to GFP targeting sgRNA electroporation 123 with or without Trp53 siRNA, 0.5 µg/ml Etoposide (Sigma-Aldrich #E1383) 8 h pulse 124 stimulation, or 3 µM AMG232 (Axon Medchem #2639) 8 h pulse stimulation. 125 Cells were collected for genomic DNA extraction using DNeasy Blood & Tissue Kit (Qiagen 126 #69504). Genomic DNA was then amplified using Q5 High-Fidelity DNA Polymerase (New 127 England Biolabs #M0491) while introduced sample-specific barcodes and adapters for Illumina 128 Sequencing similar as described in Joung J et al.9 using primers specified in Supplementary 129 Table 9. The final PCR products were gel purified and recovered using Zymoclean Gel DNA 130 Recovery Kit (Zymo Research #D4007/D4008), and quantified with Qubit 4 Fluorometer 131 (Invitrogen #Q33238) using Qubit dsDNA HS Assay Kit (Invitrogen #Q32851) and pooled for 132 next-generation sequencing (Illumina MiSeq v3 run, 2x75bp reads). The raw FASTQ data were 133 analyzed by MAGeCK10. Read counts from CRISPR screens found in Supplementary Table 134 10-11. - 4/7 -

135 JAK1/STAT1 signaling assay 136 Hox cells were cultured with or without mouse Interferon Beta (IFNβ, Nordic BioSite #12405- 137 1) and the Jak1 inhibitor Solcitinib (Selleckchem #S5917) for 7 days. Cells were passaged at 138 the ratio of 1:8 every other day. 100 ul of cells were taken on day 7 for flow cytometry (BD 139 Accuri) with the existence of CountBright Absolute Counting Beads (Invitrogen #C36950). The 140 absolute viable cell number was calculated by comparing the events number of viable cells and 141 counting beads in the flow cytometry data. 142 143 Competitive co-culture assay 144 Trp53 KO and WT cells were mixed at 1:4 ratio, and were electroporated with sgRNA or 145 transduced with lentivirus (lentiGuide-Puro-P2A-EGFP_mRFPstuf) and cultured for 7 days; or 146 cultured with Etoposide (Sigma-Aldrich, #E1383, 0.05 g/ml) or AMG232 (Axon Medchem 147 #2639, 0.5 µM for Hox cells, 4 µM for B16 cells) for 7 days; or cultured with Cobalt(II) chloride

148 (CoCl2, Sigma-Aldrich #232696, 10-20 g/ml) for 7 days. Different p53 related inhibitors were 149 added during culture: Trp53 ON-TARGETplus siRNA SMARTPool (Horizon), KU55933 150 (Sigma-Aldrich #SML1109, 100 ng/ml for B16, 10 ng/ml for Hox), VE821 (Sigma-Aldrich 151 #SML1415, 50 ng/ml), Pifithrin-µ (Sigma-Aldrich #P0122, 2 µg/ml for B16, 200 ng/ml for 152 Hox), Cyclic Pifithrin-α (Sigma-Aldrich #P4236, 700 ng/ml for B16, 70 ng/ml for Hox), C646 153 (Sigma-Aldrich, #SML0002, 1 µg/ml), AZD2461 (Selleckchem #S7029, 250 ng/ml), LJI308 154 (Sigma-Aldrich #SML1788, 2 ng/ml), Z-VAD-FMK (Selleckchem #S7023, 50 µM). siRNA 155 was delivered to cells 1 day before CRISPR/Etoposide/AMG232 exposure (100 pmol of siRNA 156 electroporated into 2x105 Hox cells, or transfected into 1x105 B16 cells), or together with 157 sgRNA for the transfection/electroporation groups. Other inhibitors were added to cell culture 158 media 1 day before CRISPR/Etoposide/AMG232 exposure and cultured for 7 days. Cells were 159 then collected for Trp53 KO genotyping. Supplementary Table 7 further describes how stock 160 solutions of inhibitors were generated and stored. 161 162 Flow Cytometry Analysis 163 Fresh bone marrow cells from C57BL/6 Cas9+ GFP+ mice and Hox cells were stained with the 164 following antibodies: FITC Rat anti-Mouse CD34 (BD Biosciences #553733, 1:500), PE anti- 165 mouse CD150 (BioLegend #115904, 1:200), PerCP/Cyanine5.5 anti-mouse Ly-6A/E 166 (BioLegend #122524, 1:200), APC anti-mouse CD117 (BioLegend #105812, 1:500), 167 APC/Cyanine7 anti-mouse CD16/32 (BioLegend #156612, 1:500), Biotin anti-mouse Lineage 168 Panel (BioLegend #133307, 1:100), BV421 Streptavidin (BD Biosciences #563259, 1:1000), - 5/7 -

169 LIVE/DEAD Fixable Aqua Dead Cell Stain Kit (Invitrogen #L34957, 1:2000). After 30 min of 170 staining, the cells were washed and analyzed by flow cytometry (BD FACSVerse). FACS FCS 171 files were analyzed by FlowJo version 10 (FlowJo, LLC). 172 173 Analysis of data from the Depmap portal 174 sgRNA enrichment (CRISPR (Avana) Public 20Q4 release), mutation profile (Mutation Public 175 20Q4 release), drug sensitivity (PRISM Repurposing Primary Screen 19Q4 release), and 176 mRNA expression levels (Expression Public 20Q4 release) was extracted December 13th 2020 177 from the Depmap portal (https://depmap.org/portal/)11-13. Connectivity maps were generated 178 using the geneMANIA plugin for Cytoscape14,15. tSNE plots were made with the Rtsne package 179 (https://github.com/jkrijthe/Rtsne) to analyze the cluster and ggplot2 180 (https://github.com/tidyverse/ggplot2) to visualize the data, or tSNE-online 181 (https://github.com/jefworks/tsne-online). The “ENCODE and ChEA Consensus TFs from 182 ChIP-X” functionality of Enrichr (https://maayanlab.cloud/Enrichr/index.html)16,17 was used to 183 identify transcription factor binding to gene sets. 184 185 References 186 1. Wang, G.G., et al. Quantitative production of macrophages or neutrophils ex vivo using 187 conditional Hoxb8. Nat Methods 3, 287-293 (2006). 188 2. Panda, S.K., et al. IL-4 controls activated neutrophil FcgammaR2b expression and 189 migration into inflamed joints. Proc Natl Acad Sci U S A 117, 3103-3113 (2020). 190 3. Panda, S.K., et al. Green listed-a CRISPR screen tool. Bioinformatics 33, 1099-1100 191 (2017). 192 4. Iyer, V.S., et al. Designing custom CRISPR libraries for hypothesis-driven drug target 193 discovery. Comput Struct Biotechnol J 18, 2237-2246 (2020). 194 5. Doench, J.G., et al. Optimized sgRNA design to maximize activity and minimize off- 195 target effects of CRISPR-Cas9. Nat Biotechnol 34, 184-191 (2016). 196 6. Bae, S., Park, J. & Kim, J.S. Cas-OFFinder: a fast and versatile algorithm that searches 197 for potential off-target sites of Cas9 RNA-guided endonucleases. Bioinformatics 30, 198 1473-1475 (2014). 199 7. Labun, K., et al. CHOPCHOP v3: expanding the CRISPR web toolbox beyond genome 200 editing. Nucleic Acids Res 47, W171-W174 (2019). 201 8. Wang, T., et al. Gene Essentiality Profiling Reveals Gene Networks and Synthetic 202 Lethal Interactions with Oncogenic Ras. Cell 168, 890-903 e815 (2017). 203 9. Joung, J., et al. Genome-scale CRISPR-Cas9 knockout and transcriptional activation 204 screening. Nat Protoc 12, 828-863 (2017). 205 10. Li, W., et al. MAGeCK enables robust identification of essential genes from genome- 206 scale CRISPR/Cas9 knockout screens. Genome Biol 15, 554 (2014). 207 11. Dempster, J.M., et al. Extracting Biological Insights from the Project Achilles Genome- 208 Scale CRISPR Screens in Cancer Cell Lines. bioRxiv, 720243 (2019).

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209 12. Meyers, R.M., et al. Computational correction of copy number effect improves 210 specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nat Genet 49, 1779- 211 1784 (2017). 212 13. Ghandi, M., et al. Next-generation characterization of the Cancer Cell Line 213 Encyclopedia. Nature 569, 503-508 (2019). 214 14. Franz, M., et al. GeneMANIA update 2018. Nucleic Acids Res 46, W60-W64 (2018). 215 15. Montojo, J., et al. GeneMANIA Cytoscape plugin: fast gene function predictions on the 216 desktop. Bioinformatics 26, 2927-2928 (2010). 217 16. Chen, E.Y., et al. Enrichr: interactive and collaborative HTML5 gene list enrichment 218 analysis tool. BMC Bioinformatics 14, 128 (2013). 219 17. Kuleshov, M.V., et al. Enrichr: a comprehensive gene set enrichment analysis web 220 server 2016 update. Nucleic Acids Res 44, W90-97 (2016). 221

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a b c d Control 10 CRISPR ETOP, 8h 6 ETOP, 8h CRISPR DNA damage 8 80 CRISPR CRISPR AMG232, 8h (%)

Etoposide ells ** 6 ETOP, 8h 60 *** ***

(ddCT) 4

Mdm2 Trp53 change)

** cells

4 relative 40

iabl ec 2 AMG232 Apop- Cell fold fv 0 20 2 tosis cycle #o -2 0 Cdkn1a (log2 Apoptotic arrest expression *** -4 * 0 ** 024487296 0824 48 024824 48 Time (h) Time (h) Time (h) e f g h

culture 7 days Cdkn1a relative expression (ddCT) n 2 - CRISPR *** 2.8 R =0.84 2.5 P < 0.001 "Off- - Etoposide 100 *** 0h 2.6 - AMG232 target" 75 *** 2h 2.4

2.0 relative WT WT ? fraction (%) GFP+ ? 50 4h lectroporatio 2.2 Time Ccr1

? KO n.s. 1.5 te KO ? 8h GFP Ccr1 WT ? 25 2.0 ? Cdkn1a

WT ? expression (ddCT) Trp53 0 24h 1.0 1.8 2h pos GFP "Off- 30 35 40 45 50 55 Ctrl

Input Day 7 irus irus irus GFPCcr1 sgRNA

Input +Ccr1 target" gRNA ETOP Trp53 KO fraction (%), day 7 yv Cv 100 100 50+50 100 [pmol] AMG232 NT GF Pv Librar GF Ps i j k Inhibitor # Name Target 80 80 1 Trp53 siRNA p53 *** n.s. 2 KU55933 ATM 60 60 3 VE821 ATR n.s. fraction (%) 4 Pifithrin-µ p53 fraction (%) 40 40 5 Cyclic Pifithrin-α p53 KO 6 C646 p300/CBP KO 20 20 7 AZD2461 PARP1/2 Trp53 8 LJI308 pan-RSK Trp53 0 0 - Z-VAD pan-Caspase Day 0 7 7 7 7 7 7 7 7 7 7 Day 0 7 7 7 7 7 GFP virus - - + + + + + + + + + GFP virus - - - - + + Inhibitor # - - - 1 2 3 4 5 6 7 8 GFP sgRNA - - + + - - Z-VAD ---+-+

Figure 1. CRISPR-mediated DNA damage enriches for cells with mutations in Trp53, and this can be inhibited. (a) Model describing how CRISPR and the used drugs are expected to affect targeted cells. (b) Growth characteristics of Hox expanded bone marrow cells (from Cas9+ GFP+ mice), exposed to CRISPR (electroporated with a GFP sgRNA, or control), or pulsed for 8h with AMG232, or Etoposide (ETOP). (c) Kinetic analysis of apoptosis by flow cytometry-based TUNEL assay of Hox cells exposed to CRISPR (electroporated with GFP sgRNA) or ETOP. (d) Kinetic qPCR analysis of Cdkn1a expression of Hox cells exposed to CRISPR (electroporated with GFP sgRNA) or ETOP. (e) Model describing experimental setup. (f) WT and Trp53 KO Hox cells (Cas9+ and GFP+) were mixed and subjected to CRISPR (electroporated with a GFP sgRNA, non-targeting ctrl (NTC) virus, CRISPR library virus, or GFP targeting sgRNA virus), or an 8h pulse with ETOP or AMG232. After seven days in culture, cells were sequenced, and the fraction of Trp53 KO sequences determined. (g) Hox cells (Cas9+ and GFP+) were electroporated with indicated sgRNAs, and Cdkn1a expression was analyzed by qPCR at different time points as indicated in figure. (h) Comparison of the Cdkn1a expression by qPCR 2h post electroporation with indicated sgRNAs, and enrichment of Trp53 KO sequences day seven. (i) Inhibitors used in (j-k). (j) WT and Trp53 KO Hox cells (Cas9+ and GFP+) were mixed and transduced with a GFP targeting sgRNA virus in the presence of inhibitors. Cells were then cultured for seven days, followed by sequencing of Trp53, and the frequency of Trp53 mutations quantified. (k) WT and Trp53 KO Hox cells (Cas9+ and GFP+), were mixed and transduced with a GFP targeting sgRNA virus or electroporated with a GFP sgRNA in the presence of the caspase inhibitor Z-VAD. Cells were then cultured for seven days, followed by sequencing of Trp53, and the frequency of Trp53 mutations quantified. Data is shown as mean +/- SEM, n=3 (b-d, h), mean and individual values, n=3-4 (f, j-k), or heatmap based on the average signal, n=3 (g). Data is representative of two or more experiments. n.s. = non-significant, * = p < 0.05, ** = p < 0.01, *** = p < 0.001 by two-way ANOVA and Turkey's post-test (b-d), one-way ANOVA and Tukey´s post-test (f, j-k), or PearsonCdkn1a r correlation and simple linearrelative regression line (h). expression (ddCT) bioRxiv preprint doi: https://doi.org/10.1101/2021.03.10.434760; this version posted March 10, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

a b c d AMG232 4 CRISPR 4 4 Etoposide sgRNA Cdkn1a Cdkn1a library - CRISPR +/- siRNA Stat1 Trp53 3 Trp53 3 3 Trp53 - AMG232 Cdkn1a Atm - Etoposide 2 2 2 Chek2 Analysis Chek2 Eif2ak2 of sgRNA 1 1 1 p-value (-log10) p-value (-log10) Hox BM cells >14 days 7 days enrichment/ p-value (-log10) Cas9+ GFP+ depletion 0 0 0 -5 05-5 05 -5 05 Z-score Z-score Z-score e f g h 3 Atm sgRNAs 3 Chek2 sgRNAs 4 Trp53 sgRNAs 4 Cdkn1a sgRNAs * * * * 2 2 * * * * n.s. * n.s. 2 * 1 1 2 0 0 Z-score Z-score Z-score Z-score 0 0 -1 -1 -2 -2 -2 -2 Trp53 Trp53 Trp53 KO KO KO KO KO KO KO KO KO Trp53 KO KO KO WT WT WT WT WT WT WT WT WT WT WT WT mock mock mock mock CRISPR AMG ETOP CRISPR AMG ETOP CRISPR AMG ETOP CRISPR AMG ETOP i j k 3 * 3 Apoptosis related sgRNAs * n.s. n.s. CRISPR 2 n.s. DNA damage 2 * n.s. n.s. Etoposide n.s. n.s. n.s. 1 Atm + Chek2 1 0 Z-score Z-score Mdm2 Trp53 0 -1 AMG232 Cdkn1a -1 -2 Trp53 siRNA -+ -+ -+ -+ Trp53 Cell cycle arrest KO KO KO KO KO KO KO WT WT WT WT WT WT WT sgRNAs AtmCChek2 Trp53 dkn1a sgRNAs Apaf1 Bak1 BaxCasp3 Casp7Casp8 Casp9

Figure 2. CRISPR enriches for low-frequency mutations in tumor suppressor genes. (a) Model describing experimental setup. (b-d) Hox cells (Cas9+ and GFP+) were transduced with a custom CRISPR library and cultured for >14 days. Cells were then either exposed to CRISPR (GFP targeting sgRNA) (b), AMG232 8h pulse (c), or Etoposide 8h pulse (d). Cells were cultured for seven days and then the sgRNA representation was analyzed by next-generation sequencing, and enrichment/depletion deconvoluted by MAGeCK. (e-h) Z-scores of individual sgRNAs (n=4/gene) for Atm (e), Chek2 (f), Trp53 (g), and Cdkn1a (h) in WT and Trp53 KO Hox cells treated with mock (electroporation without sgRNA), CRISPR (electroporation with GFP targeting sgRNA), AMG232 or ETOP. (i) Z-scores of individual sgRNAs (n=4/gene) in Hox cells treated with Trp53 siRNA at the same time as being electroporated with a GFP targeting sgRNA as described in (a). (j) Z-scores of individual sgRNAs (n=4/gene) for genes linked to apoptosis. (k) Model indicating genes playing a non-redundant role in the DNA damage response. Data presented as volcano plots with Z-score (log2 fold change) and adjusted p-values (b-d), or mean and individual values for 4 gRNAs from the exploratory screen (e-j). * = P < 0.05, and n.s. = non-significant by Mann-Whitney test. bioRxiv preprint doi: https://doi.org/10.1101/2021.03.10.434760; this version posted March 10, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

a b c

p < 0.0001 WT TP53 Mut TP53 ) p < 0.0001 6 5 2 4 cell# 4 0 94 cell 9 cell 3 MG232

lines lines enrichment 2 2 >1 >1 oA -2 yt

1 change in

gRNA enrichment 0

0 sgRNA -4 fold

3s -1

-2 -2 Sensitivit -6 TP5 TP53 No TP53 Any TP53 -6 -4 -2 02 (Log2 mutation mutation Sensitivity to AMG232 TP53 sgRNA Yes No (n=261) (n=547) (Log2 fold change in cell#) enriched? (>1) (<1) d e f Positive + Negative - + 6 Gene Correlation Gene Correlation - TP53BP1 0.74 MDM2* -0.71 TP53BP1 MDM4 4 CDKN1A* 0.73 PPM1D* -0.59 + + +

enrichment USP28 0.67 MDM4* -0.49 USP28 CHEK2 CDKN1A 2 r2=0.51 CHEK2 0.67 PPM1G -0.46 - TP53 + p<0.0001 ATM 0.60 WDR89 -0.45 USP7 ZNF326 XPO7* 0.42 USP7 -0.44 + sgRNA - 0 ATM UBE2K 0.42 DDX31 -0.44 - - MDM2 PPM1G PPM1D -2 RPL22 0.41 FERMT2 -0.40 TP53 FAM193A 0.40 TERF1 -0.38 + - + -3 -2 -1 01 ZNF326 0.37 USP38 -0.36 XPO7 TERF1 UBE2K MDM2 sgRNA enrichment g h i j WT TP53 Mut TP53 Gene Correlation p < 0.0001 6 10 EDA2R* 0.63

A PTCHD4 0.55 4 8 ZMAT3* 0.53 RPS27L* 0.51 enrichment 6 SPATA18* 0.49 2 CDKN1 e 4 CDKN1A* 0.47 RRM2B* 0.42 expression

sgRNA 0 2 PHLDA3* 0.41

Baselin BAX* 0.40 -2 0 TP53 mutation No Yes No Yes

TP53 WDR63 0.40 0246810 TP53 sgRNA Yes No TP53 sgRNA No No Yes Yes CDKN1A baseline enriched? (>1) (<1) enrichment (<1) (<1) (>1) (>1) expression

Figure 3. Enrichment of TP53 sgRNAs in full genome CRISPR screens of human cancer cell lines. (a) Enrichment score of TP53 gRNAs in 808 cell lines, stratified based on the presence or absence of any mutation in TP53. (b) Correlation between the enrichment of TP53 sgRNAs and AMG232 sensitivity. TP53 WT cells are indicated in red. (c) Sensitivity to AMG232 in cell lines stratified based on TP53 sgRNA enrichment. (d) Correlation between the enrichment of TP53 sgRNAs and MDM2 sgRNAs. (e) Top 10 genes with the strongest positive (+) and negative (-) correlation with TP53 sgRNA enrichment from full genome CRISPR screens of 808 cell lines. * indicates genes identified as transcription factor target genes for p53 identified by Enricher. Bold indicates genes identified experimentally in figure 2. (f) Physical interactions of genes in (e) defined by geneMANIA. + indicates genes that positively correlate, and - indicates genes that negatively correlate with TP53 sgRNA enrichment. (g) Correlation of TP53 sgRNA enrichment to baseline CDKN1A expression. TP53 WT cells are indicated in red. (h) Baseline CDKN1A expression in cells stratified based on the enrichment of TP53 sgRNAs. (i) Top 10 genes with expression correlating with enrichment of TP53 sgRNAs. * indicates genes identified as transcription factor target genes for p53. (j) tSNE dimensionality reduction analysis of cell, n=808, based on expression of the ten genes in (i). Data includes all available data in the Depmap CRISPR (Avana) 20Q4, Expression Public 20Q4, as well as Drug sensitivity (PRISM Repurposing Primary Screen) 19Q4 releases. Each dot represents one cell line (a-d, g, h, j), and the data is based on n=808 (a, d-f) n=408 (b-c), n=800 (g-j). Statistic based on unpaired T-test (a, c, h), Pearson r correlation and simple linear regression line (d), and calculated in Depmap (e, i). bioRxiv preprint doi: https://doi.org/10.1101/2021.03.10.434760; this version posted March 10, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

a Gate: Viable, singlets b Gate: Viable, Lin-, singlets

Fresh BM

Hox- modified BM cells Kit R Kit - g Lin - c c Fc FSC Sca-1 CD34 CD150 c d e gRNA PAM 120 GFP+ (%) with GFP virus WT GGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTT 120 T

r 100 p 60 5

Indel Contribution 100 3 )

) 80 K

% +1 66% GGTCGAGCTGGACGGCGACGTNAAACGGCCACAAGTT (

80 O

( +

60 -12 14% GGTCGAGCTGGACGGC------CACAAGTT GFP virus 40

r + P 60

F P

40 -12 13% GGTCGAGCTGGACGGCGACG------AGTT NTC virus c F

t G 40 i -12 1% GGTCGAGCTGGACGGCGAC------AAGTT o G 20 20 n

-1 1% GGTCGAGCTGGACGGCGACG-AAACGGCCACAAGTT 20 ( 0 %

expected cut site 0 0 )

4 4 5 0 2 4 6 8 10 Time (h) Time (days)

f g h )

) 100 100

% (

( ***

CoCl (g), hypoxia (h) *** 2 n 80

n 80 *

t c

WT WT ? c 60 * 60 a

? a

r f

? f

KO ? 40 40 O

? O K

WT ? K 3

WT ? 3 20 20

p r r 0 0 T Input Day 7 T Day 0 7 7 7 Day 0 7 7 Hypoxia CoCl2 0 0 10 20 - - + (mg/ml) (1% O2)

Supplementary Figure 1. Hox cells, CRISPR-mediated GFP inactivation, and hypoxia. (a-b) Flow cytometry analysis of fresh bone marrow (BM) and Hox expanded BM cells from C57BL/6 Cas9+ GFP+ mice. Hox cells display a Granulocyte-Monocyte Progenitor (GMP) phenotype (Lin-, c- kit+, Sca-1-, FcgR+, CD34+, CD150-). (c) Kinetic flow cytometer analysis of GFP signal of Hox cells electroporated with a GFP targeting sgRNA. (d) Sanger sequencing and ICE mutation analysis of the GFP targeted region in Hox cells 48h after electroporation of a GFP targeting sgRNA. (e) Kinetic flow cytometer analysis of GFP signal of Hox cells transduced with lentiviral particles carrying a GFP targeting sgRNA. Triangles indicate the kinetics of GFP KO (left y-axis), showing a substantially slower dynamics compared to sgRNA electroporation in (c). Circles indicate the kinetics of the enrichment of Trp53 KO cells (right y-axis). (f-h) WT and Trp53 KO Hox cells were mixed and cultured for seven days in the presence of different concentrations of Cobalt Cloride (CoCL2) that stabilizes hypoxia-inducible factor-1a (g) or in a hypoxia chamber (h). Data presented as mean +/- SEM, n=3 (c, e), or mean and individual values (g-h). Data is representative of two or more experiments. bioRxiv preprint doi: https://doi.org/10.1101/2021.03.10.434760; this version posted March 10, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

a c GFP sgRNA *** GAGCUGGACGGCGACGUAAA ** n.s. ) 80

On-target (GFP) score: 37.6 %

( n.s.

* n.s.

Off-target: 0, 0, 0, 2, 19 n ***

o 60

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Supplementary Figure 2. The level of DNA damage induced by CRISPR affects the magnitude of enrichment of cells with Trp53 mutations. (a) Information about sgRNAs used in the figure. On- target score calculated by CHOPCHOP (PMID: 31106371). Off-target indicates number of targets with 0, 1, 2, 3, and 4 miss matches to the mouse genome as identified by Cas-OFFinder. (b) Hox cells (Cas9+ and GFP+) were electroporated by different sgRNAs at the indicated doses (pmol x 100), and cells collected for Cdkn1a qPCR (same data as shown in heatmap, Fig. 1g, but with quantification). (c) Trp53 KO and WT Hox cells (Cas9+ and GFP+) were mixed 1:5 and electroporated by different sgRNAs at the indicated doses (pmol x 100). Seven days later cells were collected and sequenced to determine the % Trp53 KO sequences. Data presented as mean +/- SEM, n=3 (b), or mean and individual data, n=3 (c). Data is representative of two or more experiments. * = p < 0.05, ** = p < 0.01, *** = p < 0.001, and n.s. = non-significant by two-way ANOVA and Tukey´s post-test (b), or one-way ANOVA and Tukey´s post-test (b). bioRxiv preprint doi: https://doi.org/10.1101/2021.03.10.434760; this version posted March 10, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

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r T T 0 0 1 2 3 4 5 Day 0 7 7 7 7 7 Day 0 7 7 7 7 7 Time (days) ETOP - - + + - - ETOP - - + + - - AMG232 - - - - + + AMG232 - - - - + + Trp53 siRNA - - - + - + Trp53 siRNA - - - + - + Supplementary Figure 3. Transient inhibition of p53 limits enrichment of cells with mutations in Trp53 while retaining, or increasing, the KO efficiency. (a) Hox cells (Cas9+ and GFP+) were electroporated +/- Trp53 siRNA and subsequently transduced with a virus carrying a GFP targeting sgRNA. The KO efficiency of GFP was followed over time by flow cytometry. (b) WT and Trp53 KO Hox cells were mixed and electroporated with a Ccr1 sgRNA +/- Trp53 siRNA. Cells were cultured for seven days, followed by sequencing of Trp53 to quantify the frequency of mutations. (c) The Ccr1 knockout efficiency was measured by sequencing Ccr1 in Hox cells treated as in (b). (d) WT and p53 KO B16 cells (Cas9+ and GFP+), were mixed and transduced with a GFP targeting sgRNA virus in the presence of a selection of inhibitors (see figure 1i-j for details). Inhibitor #1 = Trp53 siRNA, #2 = KU55933 (an Atm inhibitor). Cells were then cultured for seven days, followed by sequencing of Trp53, and the frequency of Trp53 mutations quantified. (e-f) Same experiment as (b-c), but with B16 cells. (g) Growth characteristics of Cas9+ GFP+ Hox cells electroporated with a GFP sgRNA +/- Trp53 siRNA. (h-i) WT and Trp53 KO Hox cells (h) or B16 cells (i) were mixed and incubated with an 8h pulse of ETOP or AMG232 in the presence or absence of Trp53 siRNA. Data is shown as mean +/- SEM, n=3 (a, g), or mean and individual values, n=3-4 (b-c, d-f, h-i). Data is representative of two or more experiments. ** = P < 0.01, *** = P < 0.001 by one-way ANOVA and Tukey´s post-test (b-c, d- f, h-i), or two-way ANOVA and Tukey´s post-test (g, indicated significance relates to CRISPR +/- siRNA). bioRxiv preprint doi: https://doi.org/10.1101/2021.03.10.434760; this version posted March 10, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

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p p Mdm2 e 0 0 R 0.0 sgRNAs sgRNAs sgRNAs sgRNAs -5depleted0 enriched5 -5depleted0 enriched5 IFNb (U/ml) 0 0 0 10 10 10 in cells vs in cells vs in cells vs in cells vs 0 10 50 0 10 50 plasmid plasmid plasmid plasmid Jak1 inhibitor (nM) Z-score Z-score (log2 fold change) (log2 fold change) Supplementary Figure 4. p53 and MDM2 play a central role in the CRISPR-mediated DNA damage response, and JAK1/STAT1 signaling negatively affects Hox cell survival independently of p53 status. (a-b) Exploratory CRISPR screen targeting 400 cell death-related genes and controls in Hox cells (a), and B16 cells (b). The sgRNA representation was analyzed by next-generation sequencing, and enrichment/depletion deconvoluted by MAGeCK. Genes (left) and individual sgRNAs of non-targeting controls (NTC), intergenic controls, Trp53, and Mdm2 (right) enriched and depleted after seven days by the CRISPR-induced DNA damage. (c-d) Enrichment and depletion of individual sgRNAs for Trp53 (c), and Mdm2 (d) in the CRISPR screen, comparing WT and Trp53 KO Hox and B16 cells. (e-f) Hox cells WT or with mutations (any Indels, including insertions and deletion of 3 nucleotides) in Mdm2 were mixed and exposed to CRISPR (sgRNA electroporation or sgRNA virus). Cells were cultured for seven days, followed by sequencing of Mdm2 to quantify the frequency of indels (e) and KO (f). (g) Top WP term identified by g:Profiler querying STAT1, JAK1, and PTPRC. (h-i) Enrichment of Jak1, Stat1, and Ptprc sgRNAs in Hox WT (h), and Hox Trp53 KO cells (i). (h) is the same data as (a), but indicating Jak1, Stat1, Ptprc. (j) Hox cells cultured for five days with type I interferon (IFNb) and Jak1 inhibitor. Data presented as relative cell number compared to the control (no IFNb or Jak1 inhibitor). Data is presented as volcano plots with adjusted p-values (-log10; >1.3 = p < 0.05) and Z-score (log2 fold enrichment/depletion of sgRNAs (a-b, g-h, adjusted p-value <0.05, and Z- score >1 or <-1 are indicated in color), or mean and individual values (c-f, j). * = p < 0.05, and ** = p < 0.01 by Mann-Whitney test (c, d), or one-way ANOVA and Turkey's post-test (e-f, j). bioRxiv preprint doi: https://doi.org/10.1101/2021.03.10.434760; this version posted March 10, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

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Supplementary Figure 5. Enrichment of mutations in Cdkn1a but not Bbc3 by CRISPR. (a) WT and Cdkn1a KO Hox cells were mixed and electroporated with a GFP sgRNA +/- Trp53 siRNA. Cells were cultured for seven days, followed by sequencing of Cdkn1a to quantify the frequency of mutations. (b) WT and Bbc3 KO Hox cells were mixed and electroporated with a GFP sgRNA, or exposed to a 8h pulse with Etoposide or AMG232. Cells were subsequently cultured for seven days, followed by sequencing of Bbc3 to quantify the frequency of mutations. Data is presented as mean and individual values (pooled from three independent experiments) n.s. = non-significant, and ** = p < 0.01 by one- way ANOVA and Turkey's post-test. bioRxiv preprint doi: https://doi.org/10.1101/2021.03.10.434760; this version posted March 10, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

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Supplementary Figure 6. The sensitivity of cell lines to Nutlin-3, CMG097, and Pifithrin-m. (a-c) TP53 sgRNA enrichment score from the Depmap, and sensitivity to Nutlin-3 (a), CGM097 (b), and Pifithrin-m (c). Sensitivity is defined as the number of cells after a five-day culture, compared to control-treated cells (log2 fold change). Upper graphs show both parameters and TP53 mutation status (WT or mutated, Mut) indicated by color, lower graphs show sensitivity in cells stratified based on TP53 sgRNA enrichment. Data includes all available data in the Depmap CRISPR (Avana) 20Q4, Mutation Public 20Q4, and Drug sensitivity (PRISM Repurposing Primary Screen) 19Q4 releases. Each dot represents one cell line. Statistics based on unpaired T-test (a-c). bioRxiv preprint doi: https://doi.org/10.1101/2021.03.10.434760; this version posted March 10, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

a b c

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TP53BP1 3 XPO7 16 MRPS6 45 MDM2 2 WDR89 13 C16orf72 31 PSME3 44 C2orf49 58 RPS27L 96 CDKN1A 7 RPL22 19 BRD7 46 PPM1D 4 TERF1 19 BRAP 32 DCLRE1B 45 ACD 63 NOLC1 97 CHEK2 7 FAM193A 23 METTL14 46 MDM4 6 RPS3 20 KIF18B 34 CENPF 50 HEATR1 72 ERCC6L2 108 ATM 9 ZNF318 25 ATXN3 47 PPM1G 10 DNTTIP2 28 ZWILCH 36 FAF1 52 CEP120 78 USP28 9 RPRD2 30 CHD8 63 DDX31 11 FERMT2 29 NCL 37 CCNI 53 PPP2R2A 88 UBE2K 16 ZNF326 43 GPATCH8 67 USP7 11 ADRM1 31 QRICH1 41 USP38 55 RPL30 90

Supplementary Figure 7. sgRNA enrichment/depletion that correlates with TP53 mutation status (a) MDM2 gRNA enrichment score stratified based on TP53 mutation status. (b) Top ten negative and positive correlating genes comparing sgRNA enrichment in cells that are WT for TP53 or that has any mutation in TP53. # indicates genes that overlap with the sgRNA correlation list in figure 3e. (c) WT and Trp53bp1 KO Hox cells were mixed and electroporated with a GFP sgRNA or transduced with a virus carrying the same GFP sgRNA. Cells were cultured for seven days, followed by sequencing of Trp53bp1 to quantify the frequency of mutations. (d-e) Venn diagram showing overlap of genes identified to correlate with the two different approaches, divided into genes that functionally behave similarly as TP53 (d), and MDM2 (e), in the CRISPR context. Overlapping genes are sorted based on the combined ranking of the two different approaches. Data includes all available data in the Depmap CRISPR Avana 20Q4, and Mutation Public 20Q4 releases. Each dot represents one cell line. Statistics based on unpaired T test (a), correlation calculated by Depmap (b), or one-way ANOVA and Tukey´s post-test, * = p < 0.05, ** = p < 0.01 (c). suppressor analysis interactome Supplementary available the found Includes mutation) geneMANIA indicated bioRxiv preprint in (which wasnotcertifiedbypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.It one of all data in Depmap : genes a b genes . all of Red genes (

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t E 0 P 0 Days 0 7 7 7 Days 0 7 7 7 GFP sgRNA - - + - GFP sgRNA - - + - GFP virus - - - + GFP virus - - - + Supplementary Figure 9. p53 target gene expression, but not TP53 expression, predicts TP53 sgRNA enrichment. (a-b) Correlation between TP53 sgRNA enrichment and TP53 baseline expression. (c) Baseline expression of the ten selected genes in 800 cell lines sorted by TP53 sgRNA enrichment rank. For visualization purposes, the expression is normalized to 0-100%, where the highest expression of each gene is defined as 100% and the lowest as 0%. (d) Interactions of the ten genes identified by geneMANIA: purple = co-expression. (e-f) Transcription factor binding to the top ten genes analyzed by the Enrichr software: ENCODE and ChEA Consensus TFs from ChIP-X. (e) Target genes (y-axis) for different transcription factors (TF, x-axis) indicated with black color. (f) Statistical analysis of as calculated by the Enrichr software of TFs linked to the gene set. (g) tSNE dimensionality reduction analysis of cells based on the expression of the 10 selected genes (same data as Fig. 3j but divided into two graphs for clarity). The colors indicate TP53 mutation state (left) and TP53 sgRNA enrichment (right). (h-i) WT and Eda2r (h), or Ptchd4 (i) KO Hox cells (Cas9+ and GFP+) were mixed and electroporated with a GFP sgRNA or transduced with a GFP sgRNA virus. Cells were cultured for seven days, followed by sequencing of Eda2r (h), or Ptchd4 (i) to quantify the frequency of mutations. Data presented as individual cell lines (each dot/line represents one human cell line), n=800 (a-c, g), or as mean and individual data (h-i). Data includes all available data in the Depmap CRISPR (Avana), and Expression Public 20Q4 releases. Statistics based on unpaired T-test (b, h-i), n.s. = non-significant. bioRxiv preprint doi: https://doi.org/10.1101/2021.03.10.434760; this version posted March 10, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Supplementary tables:

1. sgRNA library. 2. Genes enriched/depleted in primary screen (related to Sup. Fig. 4a-d, g-i). 3. Cell lines included in Depmap with TP53 sgRNA enrichment (related to Fig. 3a). 4. Extended list of sgRNAs correlating to TP53 sgRNA enrichment (related to Fig. 3e). 5. Extended list of sgRNAs correlating to TP53 mutation status (related to Sup. Fig. 7b). 6. Extended list of gene expression correlating to TP53 sgRNA enrichment (related to Fig. 3i). 7. Reagents (stock solution and storage) 8. sgRNAs and primers used for electroporation. 9. Primers used for NGS analysis of CRISPR screens. 10. Read count first screen 11. Read count second screen