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bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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 genetic map of the response to DNA damage in cells

Michele Olivieri1,2, Tiffany Cho1,2, Alejandro Álvarez-Quilón1, Kejiao Li3, Matthew J. Schellenberg4,¥, Michal Zimmermann1,*, Nicole Hustedt1,Y, Silvia Emma Rossi1, Salomé Adam1, Henrique Melo1, Anne Margriet Heijink1, Guillermo Sastre-Moreno1, Nathalie Moatti1, Rachel Szilard1, Andrea McEwan1, Alexanda K. Ling5, Almudena Serrano-Benitez6, Tajinder Ubhi7,8, Irene 6 7,8 7,8 6,¤ Delgado-Sainz , Michael W. Ferguson , Grant W. Brown , Felipe Cortés-Ledesma , R. Scott Williams4, Alberto Martin5, Dongyi Xu3 and Daniel Durocher1,2#

1 Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, ON, M5G 1X5, Canada

2 Department of Molecular , University of Toronto, 1 King’s College Circle, Toronto, ON, M5S 1A8, Canada

3 State Key Laboratory of and Plant Research, School of Life Sciences, Peking University, 100871 Beijing, China

4 Integrity and Structural Biology Laboratory, National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC 27709, USA

5 Department of Immunology, University of Toronto, Medical Sciences Building, Toronto, ON M5S 1A8, Canada

6 Centro Andaluz de Biología Molecular y Medicina Regenerativa (CABIMER), CSIC-Universidad de Sevilla Universidad Pablo de Olavide, 41092 Sevilla, Spain.

7 Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, ON, M5S 3E1, Canada.

8 Department of Biochemistry, University of Toronto, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada

Y Present address: Ridgeline Therapeutics, Hochbergerstrasse 60C, CH-4057 Basel, Switzerland. ¥ Present address: Department of Biochemistry and Molecular Biology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA * Present address: Repare Therapeutics, 7210 Frederick-Banting, Suite 100, St-Laurent, QC, H4S 2A1, Canada ¤ Present address: Topology and DNA breaks group, Spanish National Research Centre (CNIO), Madrid 28029, Spain.

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1 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

SUMMARY

The response to DNA damage is critical for cellular homeostasis, tumor suppression, immunity and . In order to provide an unbiased and global view of the DNA damage response in human cells, we undertook 28 CRISPR/Cas9 screens against 25 genotoxic agents in the retinal pigment epithelium-1 (RPE1) cell line. These screens identified 840 whose loss causes either sensitivity or resistance to DNA damaging agents. Mining this dataset, we uncovered that ERCC6L2, which is mutated in a bone-marrow failure syndrome, codes for a canonical non-homologous end-joining pathway factor; that the RNA II component ELOF1 modulates the response to -blocking agents and that the cytotoxicity of the G-quadruplex ligand pyridostatin involves trapping II on DNA. This map of the DNA damage response provides a rich resource to study this fundamental cellular system and has implications for the development and use of genotoxic agents in cancer therapy.

INTRODUCTION

The preservation of genetic information is a fundamental cellular process that represents a formidable challenge given that, in each cell and on a daily basis, the DNA polymer is the subject of a wide variety of chemical and physical alterations. These alterations, known collectively as DNA damage, have the potential to cause loss of genetic information, block transcription, halt DNA replication, impair segregation, generate or produce chromosome rearrangements (Ciccia and Elledge, 2010; Jackson and Bartek, 2009; Lindahl and Barnes, 2000). These deleterious outcomes underlie many pathological conditions, including cancer and neurodegenerative diseases and are a likely cause of cellular and organismal aging (Hoeijmakers, 2009).

DNA damage can arise from many sources: from spontaneous chemical reactions such as base hydrolysis or deamination; from the interaction of DNA with reactive metabolites such as radicals, formaldehyde or S-adenosyl methionine (Lindahl and Barnes, 2000; Pontel et al., 2015; Tubbs and Nussenzweig, 2017); from the unscheduled or aberrant action of that act on DNA such as DNA , or cytidine deaminases; or through the action of 2 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

environmental or human-made agents that include (UV) light or cancer chemotherapeutics such as the topoisomerase I poison (Ciccia and Elledge, 2010; Tubbs and Nussenzweig, 2017). DNA replication is also liable to generate DNA damage through the incorporation of aberrant nucleotides such as ribonucleotides, when the encounters obstacles such as strand interruptions, non-B DNA structures such as G-quadruplexes; or when the replisome collides with a transcribing RNA polymerase, a set of conditions known as DNA replication stress (Gomez-Gonzalez and Aguilera, 2019; Zeman and Cimprich, 2014). The DNA at stalled can also be processed to generate DNA double-strand breaks (DSBs), single- stranded (ss) DNA gaps or other structures that can be recognized as DNA damage, in an attempt to stimulate resumption of DNA synthesis or to overcome obstacles (Neelsen and Lopes, 2015; Quinet et al., 2017).

To counteract the deleterious outcomes of DNA damage, a complex and interconnected network of processes detect, signal and repair DNA lesions, which is often referred to as the DNA damage response (Jackson and Bartek, 2009; Zhou and Elledge, 2000). At the centre of this genome quality- control system are the various DNA repair pathways. These include (BER), which deals with damaged bases and uracil; nucleotide excision repair (NER), which tackles helix- distorting lesions such as UV-induced dimers; mismatch repair (MMR), which removes misincorporated deoxyribonucleotides; single-strand break repair (SSBR), which re-seals single- strand breaks (SSBs) and the three DSB repair systems: non-homologous end-joining (NHEJ), (HR) and microhomology-mediated end-joining (MMEJ) (Caldecott, 2008; Gupta and Heinen, 2019; Hustedt and Durocher, 2016; Lindahl and Barnes, 2000; Marteijn et al., 2014; Sfeir and Symington, 2015). Some DNA lesions require the concerted action of multiple DNA repair pathways. One example is the repair of interstrand DNA crosslinks (ICLs), which involves the dual incision of the ICL directed by a subset of the Fanconi (FA) group of followed by HR, the bypass of the DNA lesion by translesion DNA and final removal of the remaining adduct by the NER machinery (Kottemann and Smogorzewska, 2013).

DNA damage detection also elicits the activation of cascades that are initiated by the ATM, ATR and DNA-PK protein kinases (Blackford and Jackson, 2017). DNA damage signaling coordinates DNA repair, modulates cell progression, controls DNA replication initiation and regulates a wide range of biological processes (Blackford and Jackson, 2017; Jackson and Bartek, 2009). Furthermore, since the response to DNA damage occurs on , DNA 3 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

repair takes advantage of, and is modulated by, and modification pathways (Papamichos-Chronakis and Peterson, 2013). The presence of DNA damage can also be communicated outside the cells harboring the lesion. As an example, DNA damage can lead to the activation of pattern recognition receptors, which can signal the presence of damaged cells to the immune systems for their elimination (Dhanwani et al., 2018). Therefore, the response to DNA damage is far-reaching and modulates multiple aspects of .

The cytotoxic, antiproliferative and immunostimulatory properties of DNA damage have been harnessed for the for decades, and DNA damaging agents remain at the core of the anti-cancer armamentarium (O'Connor, 2015; Pilie et al., 2019). DNA repair processes are also selectively targeted in cancer therapy, as exemplified by the ability of poly (ADP-ribose) polymerase (PARP) inhibitors to selectively kill HR-deficient cells (O'Connor, 2015; Pilie et al., 2019). There are likely many additional opportunities to exploit the DNA damage response for the benefit of cancer patients, given that is a hallmark of cancer and there is intense interest in developing new modulators of the DNA damage response (Hanahan and Weinberg, 2011).

Despite the fact that genome maintenance mechanisms have been extensively studied, important DNA repair factors are still being uncovered. Recent examples include shieldin, which promotes NHEJ and antagonizes end-resection (Setiaputra and Durocher, 2019), and HMCES, a universally conserved suicide that protects abasic sites in single-stranded (ss) DNA (Mohni et al., 2019). We also know much less about how DNA damage intersects with cellular processes that are not directly involved in the modulation of DNA repair. Therefore, there is scope in applying hypothesis-free and unbiased methods to uncover new biological features of genome maintenance control in human cells.

The emergence of CRISPR-based genetic screens has enabled genome-scale analyses of gene-gene and gene-drug interactions in human cells (Hart et al., 2015; Shalem et al., 2014; Shalem et al., 2015; Wang et al., 2015). It is therefore possible to chart the human response to genotoxic stress in an unbiased and comprehensive manner. Indeed, recent work has illustrated the power of such screens in identifying new vulnerabilities to PARP and ATR inhibitors as well as to agents like temozolomide, a DNA alkylator (Hustedt et al., 2019a; MacLeod et al., 2019; Wang et al., 2018; Zimmermann et al., 2018). However, as these screens were carried out in isolation, they do not 4 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

provide a global view of the human DNA damage response nor are they amenable to gene function inference by methods such as genetic interaction similarity profiling (Costanzo et al., 2019). We therefore aimed to map a genetic network of the response to DNA lesions and genotoxic agents, by undertaking 28 genome-scale CRISPR screens in an immortalized human diploid cell line (RPE-1 hTERT) that assessed the response to a wide variety of genotoxic agents that include multiple cancer therapeutics such as ionizing radiation, camptothecin, cisplatin and doxorubicin. The result is a rich dataset that uncovers new DNA repair factors, offers new insights into the mechanism-of-action of genotoxic drugs, and brings to light additional connections between genotoxic stress and extranuclear processes.

RESULTS

We undertook a set of 25 CRISPR/Cas9 dropout screens in an hTERT-immortalized RPE-1 cell line clone that expresses Flag-tagged Cas9 and in which the gene encoding is knocked out (Noordermeer et al., 2018). To this set, we added three other screens in the same cell line that have been previously presented (Hustedt et al., 2019a; Hustedt et al., 2019b; Noordermeer et al., 2018). In total, the 28 screens covered 25 genotoxic agents and the main features of the screens (library, genotoxin, concentration, etc.) are summarized in Table S1.

The screens were initially carried out using the TKOv2 sgRNA library but during the course of the study, we migrated to employing TKOv3, an all-in-one library with superior performance (Hart et al., 2017). The screens are schematized in Figure 1A and were carried out essentially as described previously (Hustedt et al., 2019b; Zimmermann et al., 2018). Briefly, cells were infected with the lentiviral sgRNA library at low multiplicity-of-infection (~0.35) and were then divided into control and treated groups. The control group was left untreated for the duration of the experiment and the treated group was grown in the presence of a sublethal dose of drug for approximately 10 population doublings (Table S1). In the case of ultraviolet (UV) or ionizing radiation (IR) screens, we irradiated cells every 3 d, at an LD20 dose (Table S1). Gene-level depletion scores were computed using DrugZ, which is optimized for chemogenomic CRISPR screens (Colic et al., 2019).

The 25 genotoxic agents were chosen to cover most types of DNA damage, which range from DNA strand breaks (bleomycin, etoposide, IR, camptothecin), base alkylation (methyl methanesulfonate 5 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

(MMS), and 1-methyl-3-nitro-1-nitrosoguanidine (MNNG)), inter- and intrastrand crosslinks

(cisplatin, formaldehyde), oxidative DNA damage (H2O2, KBrO3), DNA replication stress (hydroxurea, CD437, gemcitabine), G-quadruplex stabilizing drugs (pyridostatin, Phen-DC3), transcription-interfering compounds (illudin S, trabectedin) and helix-distorting lesions (cisplatin, UV radiation). We also included profiles of DNA damage repair and signaling inhibitors such as olaparib (a PARP inhibitor), AZD6738 (an ATR inhibitor) and ICRF187 (a catalytic TOP2 inhibitor), as well as highly cytotoxic agents used in -drug conjugates such as duocarmycin SA and calicheamicin (Table S1). The screens for cisplatin and camptothecin were carried out with both TKOv2 and TKOv3, which allowed us to ascertain the reproducibility of the screens across libraries and experimenters (Figure 1B and Table S1). The gene-level normalized Z-scores (NormZ) are presented in Table S2 and the read counts are presented in Datasets S1 and S2. Negative NormZ values represent genes whose leads to their depletion from the cell population after genotoxin exposure, whereas positive NormZ scores represent genes whose mutation leads to a selective growth advantage in the presence of the drug. Each of the screens passed a series of quality-control tests described in the Methods section.

Global view of the screens We identified hits in the screen as follows: for genes whose mutation caused sensitization to the genotoxin, we selected NormZ values less than -3 with false discovery rates (FDR) lower than 15%. For genes whose mutation caused resistance to the genotoxic insult, we selected a NormZ value greater than 6. We employed this asymmetric set of thresholds since the screens were optimized to identify mutations that sensitize cells to genotoxins. These filters identified a total of 813 genes whose loss led to sensitization and 57 genes whose mutation led to resistance, in at least one screen, for a total of 840 unique genes. The median number of sensitizing mutations per screen was 56.5, ranging from 214 (for MLN4924) to only 1 for calicheamicin (Figure 1B and Table S2). In the latter case, this low number of hits was likely due to difficulties in maintaining an LD20 concentration of the drug over the duration of the screen.

Functional term enrichment analyses using g:Profiler (Raudvere et al., 2019) showed that the 840 genes are highly enriched in DNA repair-associated terms in all categories assessed, which included (GO), KEGG, Reactome and WikiPathways (Table S3). For example, the top terms for the GO categories molecular function and biological process were “damaged DNA binding” (GO:0003684) and “DNA repair” (GO:0006281) respectively (Figures 1C, S1A and Table S3). The 6 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

DNA repair associated terms were varied and covered most DNA repair processes ranging from DNA double-strand break repair to mismatch repair. Highly enriched terms not directly associated with DNA repair included DNA replication (GO:0006260), regulation of chromosome organization (GO:0033044) and covalent chromatin modification (GO:0016569) pointing to other processes that are known to influence the response to DNA damage. While the hits from the screens were highly enriched for DNA repair-associated terms, DNA repair terms only covered 336 of the 840 genes identified, suggesting that the screens identified genes not previously associated with the response to DNA damage or that may be associated with pathways that indirectly influence the response to genotoxic agents.

To more specifically help us derive insights into the DNA repair mechanisms involved in the response to the agents screened, we curated a set of 197 bona fide DNA damage repair and signaling factors grouped according to their known function (Table S4). This list did not include genes that were not represented in our sgRNA library or that were only weakly linked to DNA repair. We next determined whether these groups were enriched in each of the screens using a Fischer exact test with Bonferroni correction. To facilitate visualization, we represented the data as heatmaps (Figure 1B) or radar plots (Figures 1D and S1B). For example, NER was important for survival to UV radiation, as expected, with additional contributions of FA/ICL and HR pathway genes (Figure 1B,D). Similarly, genes promoting survival to IR were highly enriched in NHEJ factor-coding genes, in line with previous results indicating that NHEJ is a major contributor to the cellular resistance to IR (Figure 1B,D). Some less characterized agents, like Illudin S, showed that multiple pathways, such as HR and DNA replication fork quality control (QC) in addition to NER, are involved in mediating cellular resistance to this agent (Jaspers et al., 2002) (Figure 1B,D). A few surprises arose from this

analysis. One example was the DNA repair sensitization profile of KBrO3, an oxidizing agent and

suspected used as food additive (IARC, 1999). KBrO3 is widely thought to oxidize DNA to produce 8-oxoguanine but whether this lesion is the root cause of bromate genotoxicity is

unknown (Ballmaier and Epe, 2006). We found that KBrO3 cytotoxicity was greatly enhanced by the loss of genes acting in NHEJ, MMEJ and HR, in addition to the expected sensitization by loss of BER/SSBR factors (Figure 1B,D). These observations strongly suggest that DSBs are a relevant

genotoxic lesion of KBrO3.

Pyridostatin and CX5461 cytotoxicity involves TOP2 trapping

7 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

Another unsuspected insight into genotoxic drug mechanism of action concerns pyridostatin, a compound that avidly binds and stabilizes G-quadruplex sequences in vitro and in cells (Muller et al., 2010; Rodriguez et al., 2012). We profiled pyridostatin along with another G-quadruplex binder, Phen-DC3 (De Cian et al., 2007). Both compounds had very different profiles, with pyridostatin displaying clear sensitization following loss of NHEJ (Figure 1B and Figure S1B). Furthermore, when we performed hierarchical clustering of the compounds based on Pearson correlation coefficients (PCC) of either DNA repair genes (Figure 1B) or gene hits (Figure S2A), pyridostatin not only clustered with other clastogenic agents but also clustered closely to etoposide and doxorubicin, two agents that cause DSBs via the poisoning of topoisomerase II (TOP2) (Delgado et al., 2018). In fact, TDP2, NBS1 (NBN) and ZATT (ZNF451) were among the top 3 hits in the pyridostatin screen and all three proteins are involved in the removal of trapped TOP2 (Aparicio et al., 2016; Schellenberg et al., 2017) (Figure 2A). These observations hinted that the mechanism of pyridostatin cytotoxicity involves the poisoning of TOP2.

To test this possibility, we confirmed that loss of TDP2 hypersensitizes cells to pyridostatin but not to Phen-DC3 (Figure 2B). The extent of sensitization by TDP2 loss was similar between pyridostatin and etoposide, a bona fide TOP2 poison (Figure 2B). Furthermore, overexpression of TOP2a fused to YFP in HEK293F cells, also led to sensitization to both etoposide and pyridostatin (Figures 2CD) consistent with the idea that cytotoxicity of pyridostatin involves TOP2. Assessment of TOP2 cleavage complexes (TOP2cc) using the RADAR (Kiianitsa and Maizels, 2014) showed that pyridostatin is a potent inducer of TOP2cc (Figure 2E). The kinetics of TOP2cc formation was strikingly different between etoposide and pyridostatin, with the etoposide-induced TOP2cc being barely above the detection threshold and displaying a biphasic accumulation characterized by a peak of TOP2cc 3 h post treatment (Figure 2EF). In contrast, pyridostatin caused TOP2cc to accumulate over the duration of the experiment (9 h), reaching levels of TOP2cc that were higher than those caused by etoposide (Figure 2EF). However, pyridostatin was much less potent at evoking g-H2AX than etoposide, with pyridostatin-induced g-H2AX formation being delayed and blunted compared to the response caused by etoposide, which was used at lower concentrations (Figure 2G and Figure S2B). These data suggest that pyridostatin-induced TOP2cc may be converted less efficiently into DSBs than those induced by etoposide, pointing to different mechanisms of TOP2 poisoning. Since g-H2AX formation in pyridostatin-treated cells tends to occur in regions with propensity to form G-quadruplex structures (Rodriguez et al., 2012), one

8 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

attractive possibility is that pyridostatin promotes TOP2 trapping following G-quadruplex stabilization.

As a means to see if these results could extend to other G-quadruplex ligands, we considered CX5461, which was originally identified as an RNA polymerase I inhibitor but was recently shown to be a G4 ligand (Xu et al., 2017). Like pyridostatin, CX5461 shows activity against HR-deficient cells and tumors (Xu et al., 2017; Zimmer et al., 2016), suggesting a common mechanism of action. We found that TDP2 loss hypersenstized cells to CX5461 (Figure 2H), consistent with the possibility that the cytotoxicity of CX5461 involves the trapping of TOP2.

Gene-level view of the screens To visualize the activity of genes across screens, we generated fingerprint plots that give a digital view of the gene activity across various screens (Figure 3A). As examples, we observed that the transcription-coupled (TC)-NER factor ERCC8 (also known as CSA), promotes cellular resistance to UV, illudin S and cisplatin, as expected (Figure 3A). In another example, the core NHEJ factors XRCC4 and LIG4 promoted survival against DSB-inducing agents such as IR, bleomycin and etoposide (Figure 3A). Some hits had exquisitely restricted profiles. For example, ATAD5, also known as ELG1, promotes cellular resistance exclusively to the alkylating agents MMS and MNNG (Figure 3A). ATAD5 forms an alternative RFC complex that may be involved in removing DNA- bound PCNA (Kanellis et al., 2003; Kang et al., 2019). Intriguingly, DSCC1 and CHTF8, which are components of another alternative RFC complex, RFC-CTF18, promote resistance to a wide variety of genotoxins and represent the group of genes that scored as hits in the highest number of screens (Figure 3A and Figure S3AB).

The similarity in profile of genes having related function (e.g. XRCC4 and LIG4 or ERCC6 and ERCC8) prompted us to undertake similarity profiling based on PCC. This allowed us to build a network where genes are nodes and their edges are constrained by their PCC values (Figure 3B and Table S5; see Figure S3C for a network with gene names included). In order to better visualize structure within this network, we employed a high PCC threshold of 0.725 in Figure 3B. The resulting 568-gene network is composed of a highly connected core subnetwork with clearly defined submodules, many of which are highly enriched in DNA repair pathways such as NHEJ, NER and ICL repair that are connected directly or indirectly to a central portion enriched in HR factors (Figure 3C-F). The DNA repair-associated genes composed only 42% of the total network 9 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

(240/568), suggesting that uncharacterized DNA repair factors are included in this network, and that other pathways are represented. As one example, we observed that Hippo pathway components such as NF2, FRYL, AMOTL2, LATS2 and TAOK1 clustered together in the network and their loss sensitized cells to multiple DNA damaging agents (Figures S4). This observation is consistent with the Hippo pathway acting as a major modulator of chemosensitivity to cancer therapeutics (Nguyen and Yi, 2019) and it suggests that additional Hippo signaling modulators could be found connected to these genes. Candidates for such proteins include KIRREL and FAM49B, which are connected to multiple known Hippo signaling molecules in the network (Figure S4). Therefore, we expect that exploration of this network will reveal insights about a wide range of pathways that impinge on survival to genotoxic stress including, but not limited to, DNA repair.

The failure syndrome gene ERCC6L2 encodes an NHEJ factor We next mined the DNA damage network to uncover uncharacterized or understudied DNA repair factors. One clear example resided in a subcluster enriched in NHEJ factor-coding genes (Figure 3C). This subcluster contained many core NHEJ factors (XRCC4, LIG4, NHEJ1), 53BP1-pathway modulators such as 53BP1, RIF1 and the shieldin components SHLD1-3 as well as the NHEJ regulator MRI/CYREN (Arnoult et al., 2017; Hung et al., 2018; Setiaputra and Durocher, 2019). This subcluster also contained ERCC6L2, a SWI/SNF-like ATPase remotely related to ERCC6 (CSB) and ERCC6L (PICH) (Figure 4A). ERCC6L2 was of particular interest since its mutation causes an inherited bone marrow failure (iBMF) syndrome, and (Douglas et al., 2019; Jarviaho et al., 2018; Shabanova et al., 2018; Tummala et al., 2014; Zhang et al., 2016). ERCC6L2 deficiency is also associated with additional clinical such as microcephaly, ataxia and other developmental delays that are often associated with NHEJ deficiency (Nijnik et al., 2007; Shabanova et al., 2018; Woodbine et al., 2014). While ERCC6L2 has previously been associated with genome instability, there is currently debate as to which DNA repair pathway it participates in, with most recent results pointing to ERCC6L2 participating in the resolution of R-loops (Tummala et al., 2018).

To assess the role of ERCC6L2 in DNA repair, we first generated independent knockout clones in the chicken DT40 cell line (Figure S5AB). The ERCC6L2 knockouts (ERCCL62-/-) were constructed in wild-type, XLF-/- and XRCC4-/- cell backgrounds to assess a possible genetic relationship between ERCCL2 and core NHEJ factors (Xing et al., 2015). As expected from the screens, loss of ERCC6L2 led to sensitivity to etoposide and bleomycin although the extent of sensitization was 10 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

milder than that caused by null mutations in XLF or XRCC4 (Figure 4B). The ERCC6L2-/- XRCC4-/- double-mutant lines consistently showed similar sensitivity to bleomycin or etoposide as XRCC4-/- cells, indicating that ERCC6L2 acts in the same genetic pathway as XRCC4 (Figure 4B and Figure S5C). The situation was similar with XLF deficiency, where the ERCC6L2-/- XLF-/- double-mutant cells showed a similar sensitivity profile as XLF-/- cells in response to bleomycin, but the double- mutant cells were slightly more sensitive to etoposide than XLF-/- cells (Figure 4B and S5C). These data indicate that ERCC6L2 acts in the canonical XRCC4-dependent NHEJ pathway but can provide support to NHEJ independently of XLF under certain conditions such as TOP2-driven DSBs.

In support for a role for ERCC6L2 in NHEJ, we employed the recently described EJ7-GFP reporter (Bhargava et al., 2018), which is unique among the many NHEJ assays by being highly dependent on canonical NHEJ factors, to assess the role of ERCC6L2 in this pathway in the human U2OS cell line. The assay monitors the end-joining of two distal Cas9-induced DSBs without indel formation (Figure 4C). We observed that the loss of ERCC6L2 following introduction of effective sgRNAs against its gene, led to a marked reduction in end-joining, albeit not as much as that caused by XRCC4 or LIG4 depletion (Figure 4D and Tables S6). These results are consistent with a role for ERCC6L2 in promoting canonical NHEJ.

To further assess the role of ERCC6L2 in physiological NHEJ, we examined the impact of its loss for immunoglobulin class switching (Methot and Di Noia, 2017) in mouse CH12F3-2 cells (Nakamura et al., 1996). We generated three independent Ercc6l2-/- clones and monitored switching from IgM to IgA by flow cytometry. All three Ercc6l2 knockout clones showed reduced numbers of IgA+ cells following induction of switching, with the magnitude of the defect approaching that of 53bp1-/- cells, which are highly impaired in class switching (Figure 4E). The defect was not due to reduced expression of transcripts (glt) at the Ig , or AID, which are both necessary for DSB induction, hinting at a direct role in the repair of DNA breaks (Figure S5DE). Collectively, these data suggest that ERCC6L2 is a bona fide NHEJ factor and indicates that the root cause of the bone marrow failure in individuals with mutated ERCC6L2 might be a failure to appropriately repair DSBs.

ELOF1 and STK19 modulate TC-NER

11 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

The NER cluster in the PCC network contains primarily TC-NER factors, such as ERCC8/CSB, UVSSA and various TFIIH components (ERCC2, GTF2H1, GTF2H4 and GTF2H5). Consistent with this cluster reflecting primarily TC-NER, the global-genome (GG) NER factor XPC was not connected to this group of genes even though it was a strong hit in the UV screen (Figures 3D and Figure 5A). We noted two genes within this cluster that were not previously associated with TC- NER. The first is ELOF1, an RNA polymerase II-associated protein that is conserved across and present in (Daniels et al., 2009). The ELOF1 homolog in budding yeast, Elf1, plays a role in transcriptional elongation and recent structural studies indicate that Elf1 collaborates with Spt5/6 (DSIF in human) to promote transcriptional elongation past the chromatin barrier (Ehara et al., 2019; Ehara et al., 2017; Prather et al., 2005). In the screens, loss of ELOF1 results in sensitivity to UV, illudin S, cisplatin (2 out of 3) as well as resistance to trabectedin (Figure 5A). Indeed, cells require the presence of TC-NER factors to survive the transcription- blocking agent illudin S; conversely, for the chemotherapeutic drug trabectedin, TC-NER factors are necessary for its cytotoxic effects (Takebayashi et al., 2001). This drug response profile of ELOF1 closely matched that of other TC-NER factors although the depletion scores for ELOF1 were generally lower than canonical TC-NER factor-coding genes (Table S2).

To validate these results, we undertook competitive growth assays using illudin S and trabectedin. As positive control, we used independent sgRNAs targeting UVSSA and ERCC8 (CSA) and as negative controls, we used guides targeting AAVS1 and XPC. As expected, loss of UVSSA and CSA led to sensitization to illudin S and resistance to trabectedin (Figures 5B, S6A and Table S6). Independent sgRNAs targeting ELOF1 similarly led to sensitization to illudin S and resistance to trabectedin in RPE1 cells, although the magnitude of the phenotypes caused by ELOF1 depletion was generally milder than those caused by depletion of UVSSA or CSA (Figures 5B, S6A and Table S7).

Given the presence of a yeast homolog of ELOF1 in the budding yeast (ELF1), we next assessed whether Elf1 also mediates the repair of UV-induced DNA lesions in fungi. To do so, we tested the sensitivity of an ELF1 deletion (elf1D) alone or in combination with various mutants. In particular, we combined elf1D with a rad16D mutation that disrupts GG-NER (Prakash and Prakash, 2000). In yeast, loss of TC-NER factors only causes mild UV sensitivity on their own but their loss greatly enhances sensitivity of GG-NER mutants such as rad16D. We also

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prepared the rad26D rad16D and rad26D elf1D double mutants as well as the elf1D rad26D rad16D triple mutant. Rad26 is the budding yeast ortholog of CSB (ERCC6) and is involved in TC- NER. Cultures were spotted on media and were subjected to increasing doses of UV light. While the elf1D and rad26D mutants had no discernable sensitivity to UV, combining these mutations with rad16D led to an increase in UV sensitivity (Figure 5C), a that could be reversed by re- introduction of ELF1 on a (Figure S6B). Interestingly, the elf1D rad26D rad16D triple mutant was slightly more sensitive to UV than the rad26D rad16D mutant indicating that ELF1 promotes UV resistance at least partly independently of RAD26 (Figure 5C). Given the location of Elf1 within the elongating RNA polymerase II complex, we speculate that ELOF1 may represent a previously unassigned and evolutionarily conserved modulator of transcriptional pausing in response to RNA polymerase-blocking lesions.

A second gene firmly embedded in the NER cluster was STK19. STK19 was first characterized as an atypical protein kinase (Gomez-Escobar et al., 1998) and STK19 mutations are recurrent in melanoma (Hodis et al., 2012). Recent work extended these original observations and identified a role for the kinase activity of STK19 in driving NRAS-dependent melanomagenesis (Yin et al., 2019). However, STK19 was also previously reported to participate in the response to UV, in particular the transcriptional recovery following UV irradiation (Boeing et al., 2016). The presence of STK19 in the NER cluster supports the observations of Boeing and colleagues. In particular, STK19 scored as the top illudin S-sensitizing hit and as the top trabectedin-resistance hit (Table S2). We confirmed these observations with two independent sgRNAs in competitive growth assays with both compounds (Figure 5B and Table S6). These results strongly suggest that STK19 can be assigned to the TC-NER pathway.

TXNDC17 links redox control to genotoxin resistance The FA/ICL cluster contains nearly all known FANC genes along with other genes coding for factors known to participate in ICL repair such as newly characterized C17orf53/HROB, which recruits the MCM8/MCM9 to DNA damage sites (Hustedt et al., 2019b) (Figure 3D). Two previously uncharacterized genes in this cluster were CCAR1 and TXNDC17. CCAR1 is the paralog of DBC1 (also known as CCAR2) and has been previously associated with DNA damage-induced although these studies have not excluded a more direct role of CCAR1 in the response to DNA damage. TXNDC17, on the other hand has no previous link to DNA damage and TXNDC17-

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targeting sgRNAs sensitized cells to a variety of DNA damaging agents, primarily but not limited to, alkylating agents MMS and MNNG as well as cisplatin (Figure 5A and Figure S6C). TXNDC17 is a -domain containing protein and is one of at least 27 such proteins encoded in the (Benham, 2012). Of the 25 thioredoxin-domain containing proteins screened, only a total of three showed any effect and TXNDC17 was remarkable in that it was the sole member whose loss led to strong sensitivity to more than one genotoxic agent (Figure S6C). This activity profile argues against the possibility that a non-specific redox imbalance caused by TXNDC17 depletion leads to genotoxin sensitivity, and is in line with the observation that genome maintenance mechanisms are regulated by redox sensing, such as the modulation of replication by the peroxiredoxin PRDX2 (Somyajit et al., 2017).

To validate these results, we generated independent clonal knockouts of TXNDC17 in RPE-hTERT p53-/- cells (Figure S6D) and undertook clonogenic survival assays with MMS, MNNG, cisplatin and

KBrO3. We observed that TXNDC17-KO cells are hypersensitive to MMS, MNNG and cisplatin but not to bromate, an oxidizing agent, suggesting some selectivity towards alkylating compounds (Figure S6E). The resistance to MNNG required its catalytic cysteines, suggesting that the activity of TXNDC17 is involved in resistance to these genotoxins (Figure 5DE), and pointing to the possibility that the redox state of specific proteins or metabolites controls the response to DNA alkylating agents.

CYB5R4 as a candidate modulator of protein phosphatases CYB5R4 (also known as NCB5OR), encodes another oxidoreductase that promotes the cellular resistance to multiple agents, including CPT for which it was the top hit in the CPT-2 screen (Figure 6A and Table S2). Mice lacking CYB5R4 are prone to developing diabetes (Xie et al., 2004), which made the link to CYB5R4 genotoxin resistance intriguing. We validated that independent CYB5R4- targeting sgRNAs lead to CPT sensitivity in competitive growth assays (Figure 6C and Figure S6F). Interestingly, in both the DNA damage network and in a co-dependency network derived from the DepMap project (Tsherniak et al., 2017) (Figure 6B), CYB5R4 is connected to Ser/Thr phosphatases and phosphatase regulators such as PPP2R4 and TIPRL, which encode proteins that regulate the assembly and disassembly of PP2A-family holoenzymes, respectively (Guo et al., 2014; Wu et al., 2017). Like those targeting CYB5R4, sgRNAs against PPP2R4 caused clear sensitization to CPT (Figures 6C, S6F and Table S6) and thus we speculate that CYB5R4 regulates the response to DNA damage indirectly via the modulation of PP2A-family phosphatases. 14 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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 selection of additional genes of interest Exploration of the individual screens and the correlation network reveals many additional notable genes. Below is a selection of those that caught our attention. One such gene is TMEM2 (also known as CEMIP2) that codes for a cell surface hyaluronidase (Yamamoto et al., 2017). Its loss in the context of the screen led to a strong sensitization to the radio-mimetic chemotherapeutic bleomycin (Figure 6D), which we have confirmed in competitive growth assays with independent sgRNAs (Figure 6E). Given the cell surface localization of CEMIP2 and its biochemical activity in degrading glycosaminoglycans (Yamamoto et al., 2017), a class of polysaccharides, we speculate that TMEM2 could be an enzyme that hydrolyzes the sugar moieties of the bleomycin molecule, even though the disaccharides of glycosaminoglycans and bleomycin are chemically distinct. These results also suggest that TMEM2 levels may be a major modulator of bleomycin responses in vivo.

Formaldehyde is an environmental genotoxin that is also produced endogenously from various demethylation reactions. Formaldehyde reacts with DNA to generate DNA-DNA and DNA-protein crosslinks as well as various DNA mono-adducts (Swenberg et al., 2013), and may represent the endogenous genotoxin causing the phenotype (Garaycoechea et al., 2018). Formaldehyde is detoxified primarily by the ADH5 enzyme in (Pontel et al., 2015) and accordingly, ADH5-targeting sgRNAs led to formaldehyde sensitization in our screens (Figure S7A). However, the same screen also identified ESD, encoding an S-formylglutathione , as mediating normal cellular resistance to formaldehyde treatment (Figure S7A). In a number of organisms, S-formylglutathione hydrolase plays a key role in a glutathione-dependent formaldehyde detoxification pathway (Chen et al., 2016) raising the possibility that a similar mechanism operates in human cells.

The USP37 and PHF12 genes are clustered in our network in proximity to POLE3 and POLE4, two non-essential subunits of DNA polymerase e (Bellelli et al., 2018a; Bellelli et al., 2018b) (Figure S3 and Table S5). POLE3/4 promote cellular resistance to ATR inhibition (Hustedt et al., 2019a) but also to a variety of other agents that perturb DNA replication, including CPT (Figure S7B and Table S2). Loss of USP37 also causes strong sensitization to ATR inhibition and CPT in our screens. While USP37 has been previously linked to genome stability and processes influenced by DNA replication (Hernandez-Perez et al., 2016; Typas et al., 2016; Yeh et al., 2015), we speculate that USP37 may modulate the ubiquitylation state of replisome components, a finding that is supported 15 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

by the observation that USP37 can be detected on nascent DNA by iPOND (Dungrawala et al., 2015) and that proximity proteomics placed USP37 in vicinity of core replisome components WDHD1 and MCM helicase subunits (Yeh et al., 2015).

Lastly, loss of the BTAF1-DRAP1 complex results in strong sensitivity to cisplatin and also to the DNA polymerase a inhibitor CD437 (Han et al., 2016) (Figure S7B and Table S2). BTAF1 is the human homolog of yeast Mot1 and is an inhibitor of TBP, the TATA-box binding protein (Auble and Hahn, 1993). DRAP1 is a component of NC2a, which stimulates the action of BTAF1 on TBP (Klejman et al., 2004). Exactly how BTAF1-DRAP1 promote resistance to cisplatin and other agents is not clear but one attractive possibility relates to the role of this complex in limiting pervasive transcription, thereby limiting the potential for transcription-replication conflicts (Xue et al., 2017).

Discussion

In this work, we present a comprehensive and unbiased characterization of the genetic architecture of the response to DNA damaging agents in a model human cell line using CRISPR screens. Using a stringent set of criteria, these screens uncovered 840 genes (nearly 5% of the known human protein- coding genes) that influence the viability of RPE1 human epithelial cells to one or more agents that cause genotoxic stress. This number highlights the far-reaching impact of DNA damage on cellular physiology and demonstrates that the optimal response to DNA lesions often necessitates the involvement of cellular processes outside the nucleus such as autophagy or Hippo signaling, which were both enriched in our dataset (Table S3 and Figure S4). Furthermore, based on PCC values, these 840 genes form a dense network that greatly help generate biological hypotheses.

To illustrate the power of this dataset in providing new insights into the organization of the DNA damage response, we uncovered previously uncharacterized DNA repair factors, revealed unsuspected drug mechanisms-of-action, and have evidence of new drug metabolism pathways. With respect to DNA repair factors, the assignment of ERCC6L2 as a new enzyme participating in the canonical NHEJ pathway is particularly noteworthy and surprising given that end-joining reactions can be efficiently reconstituted in vitro with a set of purified components that does not include ERCC6L2 (Zhao et al., 2019). While the phenotypes caused by ERCC6L2 mutations alone

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are relatively mild, our network analysis placed it firmly in the end-joining cluster, which we subsequently validated in a number of assays. It remains unclear what role an enzyme of the SWI/SNF family may have during the end-joining reaction. Given that this enzyme family functions as dsDNA , one simple model may be that it removes roadblocks that impair the completion of the end-joining reaction. These roadblocks could consist of , other DNA- bound proteins or even unproductive NHEJ assemblies that preclude completion of ligation. Understanding the role of ERCC6L2 in NHEJ will require that we understand both the biochemical activities of ERCC6L2 and the type of end-joining reaction substrates that necessitate the action of ERCC6L2. Our results also suggest that DSBs of unknown origin underlie the bone marrow failure in individuals with ERCC6L2 mutations. Therefore, finding the endogenous sources of DSBs in the hematopoietic compartment, similarly to what was done recently for Fanconi Anemia (Pontel et al., 2015) may provide important clues as to the pathogenesis of this syndrome and may reveal paths of intervention that could benefit those patients.

In addition to identifying new DNA repair factors and new biology in the response to genotoxic stress, this work also helps solidify a number of previous observations. A case in point is the homologous recombination gene cluster, which lies at the core of our human DNA damage network. A number of genes are embedded in this cluster for which evidence of a role in homologous recombination was either circumstantial, indirect or supported by very few studies. Examples are AUNIP, KDM8/JMJD5 and JMJD6 (Figure S3) (Amendola et al., 2017; Huo et al., 2019; Lou et al., 2017). In particular, recent work on KDM8 and JMJD6 suggest that instead of acting as demethylases they are rather acting as lysyl- and arginyl-hydroxylases, respectively (Webby et al., 2009; Wilkins et al., 2018). Whether or not this type of post-translational modification regulates HR remains to be determined but it represents a fascinating possibility.

Despite identifying nearly 5% of the human protein-coding genes as being involved in the response to DNA damaging agents, this list only represents a partial view of the architecture of the human DNA damage response. Indeed, there are inherent technical limitations of dropout chemogenomic CRISPR screens that preclude having a complete view of the genes surveyed. One such limitation is that dropout screens comparing two conditions (control vs perturbation) lack power to probe the function of many cell-essential genes. As an example of this phenomenon, the sgRNAs targeting the gene coding for the recombinase RAD51 are depleted too rapidly in our cell lines (with and without drugs) to produce statistically reliable results in most of our screens. Furthermore, our screens only 17 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

used a single cell line model, RPE1-hTERT, and previous work on ATR and PARP inhibitors, among others, have revealed substantial cell line-specific responses to these drugs that are likely reflecting their biology and tissues of origin (Hustedt et al., 2019a; Zimmermann et al., 2018). Finally, we have not explored the full range of genotoxic agents and this dataset will benefit from additional screens probing the response to compounds such as antimetabolites or environmental .

One class of genome maintenance regulators that was largely and conspicuously absent from our dataset were the modulators of DNA replication fork reversal and fork protection such as SMARCAL1, ZRANB3, HLTF and RADX (Quinet et al., 2017). An exception to this list was BOD1L, which was a prominent hit in multiple screens and clustered close to ATR and the 9-1-1 complex in our network, in line with its proposed function at replication forks (Higgs et al., 2015) (Figure S3). This relative lack of replication fork “quality control” factors is highly surprising given the current interest in fork reversal and nascent DNA degradation. These observations suggest either a high degree of genetic redundancy in this process or that these genes do not participate in the cellular resistance to the type of replication challenges that were induced by the agents screened in this study.

The work presented here has clear implications for the development of therapeutics and genetic toxicology. For example, the characterization of compounds using chemogenomic CRISPR screens or via the use of isogenic cell line panels derived from information produced in this work may enable the rapid identification of desired (or undesired) genotoxic effects such as formation of DNA double-strand breaks, DNA adducts or interference with transcription. Furthermore, the dataset presented can be mined for the development of patient-selection hypotheses as a means to identify subsets of tumors sensitive to a certain agent. For example, deep deletions and mutations in the MCPH1 gene occurs frequently in a number of cancer types such as bladder and (Figure S7C). Loss of MCPH1, a protein with dual function at centrosomes and as a poorly characterized reader of histone H2AX (Liu et al., 2016), leads to sensitization to multiple genotoxic drugs but scored particularly highly in the screen probing the response to etoposide, a cancer chemotherapeutic (Table S2). These results suggest that tumors with MCPH1 loss may be particularly vulnerable to etoposide treatment, a drug already used in the clinic. Finally, our data indicates that a subset of G quadruplex ligands have the unsuspected property of trapping TOP2 in a manner that produces signaling-competent DSBs at a slow rate. Since this mechanism of 18 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

action is clearly distinct from canonical TOP2 poisons like etoposide, it is conceivable that the TOP2-trapping property of these agents could be exploited therapeutically.

Methods

Cell lines Generation of RPE1-hTERT Cas9 TP53-KO (RPE1) has been previously described (Zimmermann et al., 2018). Cas9-expressing cell lines were maintained in the presence of 2 µg/mL blasticidin. RPE1 TDP2-/- cells were generated by transfecting with the sgRNA targeting TDP2 (see Table S6) using RNAiMAX (Invitrogen) according to manufacturer’s protocol. 24 h after transfection, cells were split and seeded in a 60 mm plate. After an additional 72 h, single cells were sorted into 96-well plates on a BD FACSAria Cell Sorter instrument and grown until colonies formed. TDP2-/- clones were selected on the basis of successful gene editing determined by PCR amplification and TIDE analysis (Brinkman et al., 2014).

sgRNAs used for transfection were ordered from Integrated DNA Technologies. crRNAs containing the target-specific sequence for guiding Cas9 protein to a genomic location were annealed with tracrRNA to form a functional sgRNA duplex. DT40 knockout constructs for ERCC6L2, XRCC4 and XLF were generated as previously described using a MultiSite Gateway three-fragment vector construction kit. 5’ and 3’ arms were cloned into the pDONR P4-P1R and pDONR P2R-P3 vector, respectively. The knockout constructs were generated by attL×attR recombination of the pDONR-5′ arm, pDONR-3′ arm, resistant gene cassette-containing pDONR-211 and the pDEST R4-R3 destination vector. TXNDC17 gene knockouts were generated by electroporation of LentiGuide vectors (see Table S6) using an Amaxa II Nucleofector (Lonza, Basel). 24 h after transfection, cells were selected for 48 h with 15 µg/mL puromycin, followed by single clone isolation. Gene mutations were further confirmed by PCR amplification and TIDE analysis. Stable integration of pHIV-TXNDC17 in RPE1-hTERT TP53-/- cells was achieved by lentiviral transduction. Transduced RPE1 cells were selected by culturing in the presence of 400 µg/ml nourseothricin (Jena Bioscience, cat # AB-102L). Anti-TRP14 antibody (1:1,000, Abcam #ab121725) was used to validate correct knock-out and complementation of TXNDC17. CH12F3-2 murine B cell lymphoma mutant lines were edited by transient transfection with pX330 plasmid constructs expressing sgRNAs against Trp53bp1 and Ercc6l2 and were selected on the basis of successful gene editing determined by PCR amplification and TIDE analysis (Brinkman et al., 2014) (see Table S6). HEK293F cells 19 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

(Invitrogen) were cultured in DMEM with 10% (v/v) fetal bovine serum (Gibco), 50 U/µL

penicillin, 50 µg/mL streptomycin, and 0.5 mmol/L sodium pyruvate at 37°C in 5% CO2 atmosphere.

Cell culture RPE1-hTERT and 293T cells were purchased from ATCC. U2OS EJ7 cells originate from the laboratory of Jeremy Stark and were a kind gift. RPE1-hTERT-derived cell lines, 293T and derived cell lines were grown in Dulbecco’s Modified Eagle Medium (DMEM; Gibco/Thermo Fisher #11965092) supplemented with 10% fetal bovine serum (FBS; Wisent), 1x GlutaMAX, 1× non- essential amino acids (both Gibco/Thermo Fisher), 100 U/mL penicillin and 100 μg/mL streptomycin (Pen/Strep; Wisent). U2OS-derived cell lines were grown in McCoy’s 5A (Gibco #1660-082) + 10% FBS (Gibco) + 100 U/mL penicillin and 100 μg/mL streptomycin (Pen/Strep; Wisent) + 1x L-Glutamine (final 2 mM, Wako Chemical #073-05391). DT40 cells were grown in RPMI-1640 medium + 10% FBS (Gibco) + 2% Chicken serum (Gibco) + 100 U/mL penicillin and 100 μg/mL streptomycin (Pen/Strep; Wisent) + 10 mM HEPES and grown at 39°C. All other cell

lines were grown at 37°C and atmospheric O2. All cell lines were routinely authenticated by STR and tested negative for mycoplasma. The two-color competitive growth assays were performed as previously described (Hustedt et al., 2019a). Imaging was done on an IN Cell Analyzer system (GE Healthcare Life Sciences) equipped with a 4X objective. Segmentation and counting of GFP- and mCherry-positive cells was performed using an Acapella script (PerkinElmer).

CRISPR screens RPE1-hTERT Cas9 TP53-KO cells were transduced with the lentiviral TKOv2 or TKOv3 library (Hart et al., 2015; Hart et al., 2017) at a low MOI (~0.35) and puromycin-containing medium was added the next day to select for transductants (see Table S1 for more details). The next day cells were trypsinized and replated in the same plates while maintaining the puromycin selection. 3 days after infection, which was considered the initial time point (t0), cells were pooled together and divided in 2 technical replicates (usually labelled as “A” and “B”). Negative-selection screens were performed by subculturing cells at days 3 and 6 (t3 and t6), at which point each replicate was divided into different treatments, including one that was left untreated as a control (NT), and then subcultured every 3 days (t9, t12 and t15) until the final timepoint at t18. For all screens, drugs and treatments were applied according to previous evaluation of LD20 concentrations in uninfected RPE1 cells treated for 12 days (except for gemcitabine, which was determined over 3d). Cells were 20 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

subcultured and medium with and without drugs was refreshed every 3 days. UV and IR treatments were applied one day after subculturing cells (t7, t10, t13 and t16). Cell pellets were frozen at day 18 for gDNA isolation. Screens were performed in technical duplicates and library coverage of ≥ 375 cells per sgRNA was maintained at every step with the exception of the gemcitabine screen which was carried out a 250-fold coverage. gDNA from cell pellets was isolated using the QIAamp Blood Maxi Kit (Qiagen) and genome-integrated sgRNA sequences were amplified by PCR using Q5 Mastermix (New England Biolabs Next UltraII, M5044S). i5 and i7 multiplexing barcodes were added in a second round of PCR and final gel-purified products were sequenced on Illumina NextSeq500 systems to determine sgRNA representation in each sample.

Functional Enrichment Analysis g:Profiler (https://biit.cs.ut.ee/gprofiler/gost) was used to identify functional enrichment analysis of the 840 hit genes (NormZ < -3 and FDR < 15% for sensitizing genes; NormZ > 6 for resistance genes), resulting in a list of 1230 statistically significant enriched terms (Benjamini-Hochberg FDR < 0.05). The hit genes list was treated as an unordered query, and statistical tests were conducted within a statistical domain scope of only annotated genes, selecting terms with sizes between 2 and 500 genes, considering the GO molecular function, GO cellular component, GO biological process, KEGG, Reactome and WikiPathways data sources, removing electronic GO annotations. The Ensembl ID with the most GO annotations were chosen for all 5 ambiguous genes (BABAM1, CHTF8, FOXP1, MATR3, SOD2).

DNA repair-associated terms gene list The list of 336 genes considered as ‘DNA repair genes’ was assembled by combining all genes present in at least one of 100 manually selected terms present in the g:Profiler data sources mentioned under ‘Functional Enrichment Analysis’. The terms were selected by their association with DNA damage and repair such as Homologous recombination (KEGG:03440), DNA Double- Strand Break Repair (REAC:R-HSA-5693532), response to UV (GO:0009411); DNA damage response (WP:WP707) and negative regulation of phase transition (GO:1901988).

Network Analysis The Network Analysis can be divided in two phases: the exploratory phase and the design phase. For the exploratory phase we developed an R shiny app (https://hs- durocherlab.shinyapps.io/ScreenClustering/) that would allow us to visualize several distance 21 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

measures, clustering techniques and networks graphs, with dynamically changing parameters to provide insights into our research. The design phase consisted of choosing how best to illustrate a single meaningful network of the 840 gene hits. For this step the interaction network was created using Cytoscape version 3.7.1, where the 568 nodes represent genes and the 1231 edges represent all pairwise connections that had a Pearson’s correlation coefficient (PCC) greater than 0.725, explaining why the network has fewer nodes than the initial 840 genes. These arbitrary threshold values were chosen as such to ensure that we captured interesting known genetic interactions but could filter out uncertain values and be able to observe some structure in the network. The PCC for each pair of genes was calculated using the default parameters of function ‘cor’, from the stats package, R version 3.5.2, from our data frame of 840 genes, each with 25 NormZ scores. Data from Cisplatin-1, Cisplatin-2 and CPT-1 screens were omitted when calculating the PCC to avoid skewness towards a specific drug.

Radar and Fingerprint Plots Radar charts were drawn in R version 3.5.2, using the fmsb package version 0.6.3, treating each screen independently. The one-sided alternative hypothesis Fisher Exact Test p-value was calculated by testing the enrichment [FDR < 15% for sensitizing genes and FDR < 1% for resistance genes] of each single pathway (Table S4) individually against the expected enrichment for that screen excluding that pathway, using the ‘fisher.test’ function from the stats package, version 3.5.2. The Bonferroni correction threshold, adjusted for the number of pathways tested [for 9 pathways, and a = 0.05, the threshold was therefore -log2(0.05/9) @ 7.49], was also added to the graph. Fingerprint plots were drawn using the ggplot2 package version 3.2.0.

Data Quality Control For each screen, at least two replicates of each condition were performed and to assess data quality, the two main data diagnostic tools used were the MAGeCK (Li et al., 2014)standard count summary R script and the BAGEL precision-recall curves using the core essential (CEGv2.txt) and nonessential (NEGv1.txt) gene lists from (https://github.com/hart-lab/bagel). From the MAGeCK count summary we observed that most samples had a mapped reads percentage above 78%, with only calicheamicin replicate B (67%) and trabectedin replicate B (68%) having mapped reads percentages below 70%. Gini indexes were acceptably low, with most screens scoring below 0.15; only camptothecin-1 replicate B had a high Gini index of 0.21 in comparison. From the precision-

22 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

recall curves it was possible to verify that known essential and nonessential genes behaved as expected, even though some samples had a steeper curve than most others.

EJ7 assay U2OS-EJ7 cells were infected with viruses produced in HEK293T using second generation vectors psPAX2 (Addgene #12259) as packaging plasmid, pMD2.G (Addgene #12259) as envelope and pLentiCRISPRv2-mCherry (Addgene #99154) as transfer vectors. All sgRNAs cloned into pLentiCRISPRv2-mCherry are reported in Table S6. After 6 d in culture, cells were plated at 70- 80% confluence in 6 well plates and transfected with 1μg of each plasmid containing 7a and 7b sgRNAs (Addgene #113620 and #113624) using Lipofectamine 2000 (ThermoFisher, #11668030). 48 h after transfection cells were resuspended, washed in PBS and analysed using a BD LSR Fortessa.

High content imaging Cells were seeded (~10,000 cells/well) in 96-well plates and cultured for 24 h. 20 μmol EdU (5- ethynyl-2-deoxyuridine, Life Technologies) was added 30 min prior washing with PBS and fixation with 4% paraformaldehyde (PFA) in PBS for 10 min. Cells were rinsed with PBS and permeabilized using 0.3% TritonX-100/ PBS for 30 min. Cells were washed with PBS and incubated in blocking buffer (0.2% fish skin gelatin, 0.5% BSA/PBS) for 30 min. Fresh blocking buffer containing mouse anti-γH2AX (Millipore #JBW301, 1:5,000) was added for 2 h. Cells were rinsed three times with PBS and blocking buffer with AlexaFluor 488-coupled goat anti-mouse antibody (Life Technologies, 1:1000) and 0.8 μg/mL DAPI (4,6-diamidino-2-phenylindole, Sigma) was added for 1h. After rinsing with PBS, immunocomplexes were fixed again using 4% PFA/PBS for 5 min. Cells were rinsed with PBS and incubated with EdU staining buffer (150 mM Tris/HCl pH8.8, 1mM CuSO4, 100 mM ascorbic acid and 10 μM AlexaFluor azide (Life Technologies)) for 30 min. After rinsing with PBS, images were acquired on an In Cell Analyzer 6000 automated microscope (GE Life Sciences) with a 60X objective. Image analysis was performed using Columbus (PerkinElmer). Cell cycle profiling and analysis was evaluated based on EdU and DAPI staining.

RADAR assay for detection of TOP2 cleavage complexes The assay was performed as previously described in (Anand et al., 2018). RPE1-hTERT Cas9 TP53- KO cells were seeded in 6 well plates (1x106 cells each). 24 h later cells were lysed directly in the plates using 1 mL of buffer containing 5 M guanidinium isothiocyanate, 10 mM Tris (pH 6.5), 20 23 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

mM EDTA, 4% TritonX-100/PBS, 1% Sarkosyl (Sigma-Aldrich #L9150), 1% DTT. Genomic DNA was precipitated incubating the lysate at -20°C for 5 min with 0.5 volumes of 100% ethanol, shaking vigorously, and centrifuging at 14,000 rpm for 15 min at 4 °C. The DNA pellet was washed 2 times in 75% EtOH by vortexing and centrifuging again at 14,000 rpm for 15 min at 4 °C. The final DNA pellet was resuspended in 100 µL of in freshly prepared 8 mM NaOH solution. The samples were incubated at 65°C for 5 min, passed through a 26G needle 10 times and then quantified using a Nanodrop (ThermoFisher). For each sample, 7 µg of DNA was resuspended in 200 µL of 25 mM

NaPO4 (pH 6.5) buffer and loaded in a slot blot apparatus (Bio-Dot® SF Microfiltration Apparatus, Bio-Rad). Membranes were blocked for 1 h in 5% milk/TBS Tween-20 solution and incubated for 1 h with anti-TOP2β (1:5,000, mouse anti-human, Clone 40/Topo IIβ, RUO – 611492, BD Biosciences-US) or anti-TOP2α (1:10,000, mouse anti-human, Topo IIα antibody (F-12), sc-365916, Santa Cruz) diluted in 5% milk/TBS Tween-20.

Class switch recombination assays To induce switching in CH12F3-2 murine B cell lymphoma cell or their derivatives, 2x105 cells were cultured in CH12 medium supplemented with a mixture of IL4 (10 ng/mL, R&D Systems #404- ML-050, Minneapolis, MN, USA), TGFβ (1 ng/mL, R&D Systems #7666-MB-005) and anti- CD40 antibody (1 μg/mL, #16-0401-86, eBioscience, Thermo Fisher) for 48 h. Cells were then stained with anti-IgA-PE and the fluorescence signal was acquired on an LSR II or Fortessa X-20 flow cytometer (BD Biosciences). AID protein was detected by immunoblotting with mouse mAb L7E7 ( #4975) and normalised to β- (Sigma #A2066) levels. Quantitative PCR was performed with qPCRBIO SyGreen Blue mix (PCR Biosystems) and CFX384 Real-Time PCR Detection System (Bio-Rad) according to manufacturers’ instructions. The Iμ sterile transcript was detected with primers 5’-gaacatgctggttggtggtt-3’ and 5’- tcacacagagcatgtggact-3’; the Iα transcript was detected with primers 5’-gggacaagagtctgcgagaa-3’ and 5’-tcaggcagccgattatcact-3’, and normalised to HPRT (primers 5’-cccagcgtcgtgattagc-3’ and 5’- ggaataaacactttttccaaat-3’). All samples were collected 48 h after stimulation with anti-CD40, IL4, and TGFβ as described above.

Clonogenic survival assays RPE1 and RPE TXNDC17-KO cells were seeded in 6-well plates (250 cells per well) and treated with indicated concentrations of MMS, cisplatin, MNNG, or KBrO3. After 14 days, colonies were stained with crystal violet solution (0.4% (w/v) crystal violet, 20% methanol) and manually counted. 24 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

Relative survival was calculated for the drug treatments by setting the number of colonies in non- treated controls at 100%.

Plasmids and viral vectors DNA corresponding to sgRNAs was cloned into LentiGuide-Puro (Addgene: 52963), LentiCRISPRv2 (Addgene: 52961), or a modified form of LentiGuide-Puro in which Cas9 was replaced by NLS-tagged GFP or mCherry using AgeI and BamHI (designated as LentiGuide-NLS– GFP or –mCherry respectively), as described (Noordermeer et al., 2018). Lentiviral particles were produced in 293T cells by co-transfection of the targeting vector with vectors expressing VSV-G, RRE and REV using calcium-phosphate, PEI (Sigma-Aldrich) or Lipofectamine 2000 (ThermoFisher). Viral transductions were performed in the presence of 8 µg/mL polybrene (Sigma- Aldrich) at an MOI <1, unless stated otherwise. Transduced RPE1 cells were selected by culturing in the presence of 20 µg/mL puromycin for 3 d. The TXNDC17 coding sequence was obtained from the ORFeome collection (http://horfdb. dfci.harvard.edu/), archived in the Lunenfeld-Tanenbaum Research Institute’s OpenFreezer (Olhovsky et al., 2011). The complete TXNDC17 coding sequence was PCR-amplified and cloned into the pHIV-NAT-T2A-hCD52 vector using NotI and EcoRI restriction enzymes. To generate a catalytic dead TXNDC17 mutant, the active-site Cys residues were replaced by Ser (C43/46S) using PCR-mediated site-directed mutagenesis.

Cell proliferation assays RPE1 cells were seeded in 96-well plates (2,000 cells per well) and treated with sequential serial dilutions of genotoxic agents as indicated. After 4 days of treatment, the cell confluency was measured using an IncuCyte Live-Cell Analysis (Sartorius). Confluence growth inhibition was calculated as the relative confluency compared to untreated cells.

YFP-TOP2α overexpression and pyridostatin sensitivity To generate cells that overexpress YFP–TOP2α, HEK293F were transfected with a plasmid encoding YFP-TOP2α (Schellenberg et al., 2018), followed by growth under selection with 20 μg/mL blasticidin for 3 weeks. Cells with high levels of YFP expression were selected by FACS. A single YFP-positive cell was propagated to generate the cell line that overexpresses YFP–TOP2α. 96-well plates were seeded with 2000 cells per well of the indicated cell line in 200 μL DMEM media. After growth for 24 h, the indicated concentration of DNA damaging agent was added. For proliferation (AUC) assays, cells were grown in an IncuCyte Zoom live cell imager (ESSEN 25 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

Biosciences) with images recorded every 2 h for 4 d after addition of drug. The confluence percentage for each image was calculated using the IncuCyte Zoom software and summed over the course of the experiment for each well. Viability measurements were normalized to that of untreated

cells and fit to a 4-parameter dose-response curve to calculate IC50 values.

Yeast strains and spot assay The S. cerevisiae strains in used this study are all derivatives of S288c BY4741 (Brachmann et al., 1998) and are described in Table S7. BY4741-derived elf1::KANMX and rad26::KANMX deletion strains as well as a Y7092-derived rad16::NATMX strain were kind gifts of Charlie Boone and were used as starting strains or sources of disrupted . We constructed all strains using standard genetic techniques. The original elf1::KANMX strain we received was discovered to have unusual growth properties on synthetic media, therefore we recreated the elf1 deletion strain by amplifying the elf1::KANMX genomic locus by PCR, transforming the reaction product into ELF1 wild-type yeast and screening G418-resistant colonies for normal growth rates and by diagnostic PCR for correct integration.

ELF1 rescue were created by ligating PCR products from wild-type genomic DNA template into the BamHI/XhoI sites of either pRS416 to insert the ELF1 locus (including the with 133 and 109 bp of up- and downstream flanking genomic DNA, respectively), or into a derivative of pRS416-ADH where the ELF1 open reading frame was placed under control of the ADH1 .

To measure UV sensitivity, cells were grown at 30°C overnight in 2% glucose-containing non- selective rich (XY) media or synthetic complete media lacking uracil to maintain plasmid selection. The next day serial ten-fold dilutions of cells were spotted on XY plates and exposed to the indicated dose of UV treatment. Plates were imaged after 3 days of incubation at 30°C in the dark.

Acknowledgments We thank Jing Yi Yuan for technical assistance on some of the screens. We are indebted to Jason Moffat for the TKOv2 and v3 libraries and to Stephane Angers and Traver Hart for developing tools and protocols for CRISPR screening in Toronto and to Charlie Boone for yeast strains. We also thank Feilong Meng, Sven Rottenberg and Martijn Luijsterburg for sharing unpublished results. AAQ, GSM and AMH are recipients of long-term EMBO fellowships (ALTF 910-2017, 795-2017 26 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

and 124-2019, respectively), NH was supported by a Human Frontier Science Program long-term Fellowship, SER is supported by a fellowship from AIRC and SA was a Banting post-doctoral fellow. ASB was supported by a PhD Studentship from AEFAT (Asociación Española Familia Ataxia Telangiectasia) and an EMBO short-term fellowship for a visit to the DD laboratory. The ICRF187 screen in FCL laboratory was funded by grants from the Spanish Government (SAF2017- 89619-R, European Regional Development Fund) and the European Research Council (ERC-CoG- 2014-647359). Work in the RSW laboratory was supported in part by the US National Institute of Health Intramural Program, US National Institute of Environmental Health Sciences (NIEHS, 1Z01ES102765). DD is a Canada Research Chair (Tier I) and the work was supported from grants from the CIHR (FDN143343 to DD; FRN 123518, PJT-156330 to AM) Canadian Cancer Society grant 705644 (to DD) with additional support to DD from the Krembil Foundation.

Conflict of interest statement DD is a shareholder and advisor of Repare Therapeutics. MZ is an employee of Repare Therapeutics.

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33 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

Figure legends

Figure 1. A chemogenomic view of the response to DNA damage. (A) Schematic of the dropout screens. (B) Heat map representation of the 28 CRISPR screens undertaken in RPE1 hTERT p53-/- Cas9 cells. The top panel indicates the library used. The histogram indicates the number of hits in each screen (determined as NormZ <-3 or >6, at FDR < 0.15) and the amount of DNA repair factor

identified (curated list in Table S4). The lower heat map panel indicates the log10-transformed p values for enrichment of different DNA repair pathways (rows) calculated using a one-sided Fisher's exact test. The color scale indicates fold-enrichment for resistance (orange) and sensitization (blue). Pathways are defined in the text except for ribonucleotide excision repair (RER). (C) Top 25 enriched GO terms, biological process, identified using g:Profiler (>10-fold enrichment; P < 0.05, with Benjamini-Hochberg FDR correction) among all 840 hits. Circle size indicates the number of genes from the common gene hit list included in each GO term, x-axis position indicates

-log10-transformed FDR score and color indicates the fold enrichment compared with the whole- genome reference set.

(D) Radar plots of genotoxic CRISPR screens performed with UV, IR, illudin S and KBrO3 depicting sensitization (red) or resistance (blue) for different DNA repair pathways (axes). Values

indicate the log2-transformed p-values of the Fisher's exact test score. Bonferroni thresholds are indicated in green. See also Figure S1.

Figure 2. Pyridostatin and CX5461 cytotoxicity involves TOP2 trapping. (A) CRISPR dropout screen results for RPE1 cells exposed to pyridostatin. The 5 genes with the lowest NormZ values are labeled. (B) Drug-response assays with the indicated compounds in RPE1 and isogenic TDP2-/- cells using confluency as a readout 6 d post-treatment. Data presented as mean ± SD.; N=3. (C) Immunoblotting of TOP2 in HEK293F cells expressing TOP2-YFP or a control vector. detection was used as loading control. (D) Drug-response assays with pyridostatin and etoposide in parental HEK293F cells and those expressing TOP2-YFP. Data presented as mean ± SEM; N=3. (E) RADAR assay for the detection of TOP2cc in RPE1 cells treated with etoposide (Etop.) and pyridostatin (Pyri.). The condition in upper left box represents samples that were untreated (NT).

34 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

The upper right box represents buffer alone. (F) Quantitation of the RADAR assay normalized to untreated cells. Data presented as mean ± SD; N=3. (G) Average gH2AX nuclear intensity in RPE1 cells treated with etoposide or pyridostatin determined by image segmentation. The dashed line represents the value for untreated (NT) cells. Data presented as mean ± SD; N=3. See also Figure S2.

Figure 3. The DNA damage response network. (A) Fingerprint plot of highlighted genes across the 28 screens (columns). The boxes are labeled according to whether mutations in these genes leads to sensitization (orange) or resistance (blue). (B) Network of genes displaying at least one connection with another gene at a PCC value >0.725 visualized in (cytoscape v3.7.1) using a perfused force directed layout. Nodes are colored according to a DNA repair pathway curation (Table S4). Highlighted are the clusters enriched in DNA repair genes. The same network with gene names can be found in Figure S3C. (C-F) Details of the clusters enriched in NHEJ (C), NER (D), FA/ICL (E) or HR (F) pathway coding genes. See also Figures S3 and S4.

Figure 4. ERCC6L2 promotes canonical NHEJ. (A) Schematic overview of ERCC6L2. ERCC6L2 also possess a short isoform of 712 residues produced by . (B) assays of DT40 cells of the indicated genotypes treated with either etoposide or bleomycin for a 3 d period. Data is presented as the mean of a technical triplicate. An independent experiment is shown in Figure S5C. (C) Schematic of the EJ7-GFP NHEJ assay. (D) End-joining frequency of U2OS-EJ7 cells transduced with sgRNA-expressing viruses targeting the indicated genes and subsequently transfected with a vector expressing an sgRNA targeting the EGFP stuffer region in the reporter transgene. Dashed line represents the mean of the end-joining frequency of the AAVS1-targeted condition. Error bars represent the mean ± SD; N=3. TIDE analysis is shown in Table S6. Note that sgERCC6L2-3 has low indel efficiency.

35 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

(E) Class switching recombination levels (% IgA+ cells) in CH12F3-2 cells of the indicated genotypes. Error bars represent the mean ± SD; N=3. See also Figure S5.

Figure 5. Characterization of ELOF1, STK19 and TXNDC17. (A) Fingerprint plots of ERCC6, GTF2H5, STK19, ELOF1 and TXNDC17. The boxes are labeled according to whether mutations in these genes leads to sensitization (orange) or resistance (blue or light blue). (B) Competitive growth assays ± Illudin S (30 nM) or trabectedin (0.5 nM) treatment in RPE1 cells

transduced with virus expressing the indicated sgRNAs. Data represent log10-transformed mean fraction of GFP-positive cells ± SD normalized to day 0 (N= 3, independent transductions). TIDE analysis is shown in Table S6. (C) Overnight cultures of S. cerevisiae strains with the indicated genotype were serially diluted and spotted onto YPD plates that were irradiated with the indicated UV dose, or left untreated. Plates were incubated at 30°C for 3 d before imaging. WT, wild type. (D) Immunoblotting for TXNDC17 in wild type (WT) or TXNDC17-KO cells transduced with the indicated TXNDC17-encoding viruses. Tubulin was used as a loading control. (E) Clonogenic survival of RPE1 cells of the indicated genotypes in response to increasing doses of MNNG. Representative images are shown (left) and quantified (right). Data represent mean ± SD (N = 3). TXNDC17 in square brackets indicates cells transfected with a wild type (WT) or mutant (C43S/C36S) expression construct. See also Figure S6.

Figure 6. CYB5R4 and CEMIP2 promote cellular resistance to genotoxins. (A) CRISPR dropout screen results for RPE1 cells exposed to camptothecin (screen CPT-2). The 9 genes with the lowest NormZ values are labeled. (B) Subnetworks derived from the PCC genotoxin network derived in this study or from the Broad DepMap data show that CYB5R4 loss correlates with the loss of the PPP2R4 and TIPRL phosphatase biogenesis factors. (C) Competitive growth assays ± CPT (7.5 nM) in RPE1 cells transduced with virus expressing the indicated sgRNAs. Data represent mean fraction of GFP-positive cells ± SD normalized

36 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

to day 0 (N= 3, independent transductions). TIDE analysis is shown in Table S6. (D) CRISPR dropout screen results for RPE1 cells exposed to bleomycin. The 6 genes with the lowest NormZ values are labeled. ERCC6L2 is also labeled. (E) Competitive growth assays ± bleomycin (67 nM) in RPE1 cells transduced with virus expressing the indicated sgRNAs. Data represent mean fraction of GFP-positive cells ± SEM normalized to day 0 (N= 3, independent transductions). TIDE analysis is shown in Table S6.

Figure S1. Gene term and DNA repair pathway enrichments. Relates to Figure 1. (A) Top 25 enriched GO terms, for molecular function (left) and cellular compartment (right), identified using g:Profiler (>10-fold enrichment; P < 0.05, with Benjamini-Hochberg FDR correction) among the 840 gene hits. Circle size indicates the number of genes from the common

gene hit list included in each GO term, x-axis position indicates -log10-transformed FDR score and the colors indicate the fold enrichment compared with the whole-genome reference set. (B) Radar plots of the genotoxic CRISPR screen performed with genotoxic agents not shown in Figure 1, depicting sensitization (red) or resistance (blue) for different DNA repair pathways (axes).

Values indicate the log2-transformed p-values of the Fisher's exact test score. Bonferroni thresholds are indicated in green.

Figure S2. Clustering of screens and control images. Relates to Figure 2. (A) Hierarchical cluster of the 28 genotoxic CRISPR screens based on PCC values calculated using the 840 genes hits. (B) Representative images depicting automated quantation of γH2AX foci using the IN Cell Analyzer 6000 coupled with Columbus image data analysis system. The initial nuclear masks were generated using images acquired from the DAPI channel. Upon selection of only completely in- frame nuclei, the number of foci and γH2AX intensity was evaluated in the FITC (g-H2AX) channel (additional details in Methods).

Figure S3. The DNA damage network with gene names. Relates to Figure 3. (A) Histogram that bins the number of genes and how many times they were identified as hits in the screens. (B) Histogram of the 10 genes that scored as hits in the highest number of screens. (C) The same PCC-based genetic network as in Figure 3 with gene names annontated. 37 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

Figure S4. Hippo signaling promotes resistance to DNA damaging agents. Relates to Figure 3. (A) Subnetwork highlighting NF2, FRYL, AMOTL2, LATS2, TAOK1 with genes that have PCC values > 0.725. The network was generated with Cytoscape (v3.7.1) using perfused force directed layout. (B) Fingerprint plots of NF2, FRYL, AMOTL2, LATS2, KIRREL, FAM49B and TAOK1. The boxes are colored according to whether mutations in these genes leads to sensitization (orange) or resistance (blue) in any of the 28 CRISPR screens.

Figure S5. Additional characterization of ERCC6L2. Relates to Figure 4. (A) Schematic of the strategy used to knockout ERCC6L2 in DT40 cells. Shown are the location of primers (arrows) used to confirm targeting. (B) Agarose gel images of the PCR-mediated amplification of the internal ERCC6L2 sequence to confirm gene-targeting of ERCC6L2, using the primers indicated in (A). (C) Cell proliferation assays of DT40 cells of the indicated genotypes treated with either etoposide or bleomycin for a 3 d period. Data is presented as the mean of a technical triplicate. An independent experiment is shown in Figure 4. (D) Quantitative PCR assessing the levels of Iμ sterile transcript and Iα transcript (normalised to HPRT) in CH12F3-2 cell lines of the indicated genotypes. CIT is the cocktail used to activate switching (anti-CD40, IL-4, and TGFβ). (E) Immunoblotting of AID in different CH12F3-2 cells of the indicated genotypes. Samples were collected 3 d after stimulation (N = 3). β-actin was used as loading control.

Figure S6. Further characterization of ELOF1, STK19, TXNDC17 and CYB5R4. Relates to Figures 5 and 6. (A) Competitive growth assays ± Illudin S (30 nM) or trabectedin (0.5 nM) in RPE1 cells

transduced with virus expressing the indicated sgRNAs. Data represent log10-transformed mean fraction of GFP-positive cells ± SD normalized to day 0 (N= 3, independent transductions). TIDE analysis is shown in Table S6. (B) Overnight cultures of S. cerevisiae strains with the indicated genotype and transformed with the indicated plasmids were serially diluted and spotted onto YPD plates that were irradiated with the indicated UV dose, or left untreated. Plates were incubated at 30°C for 3 d before being imaged. WT, wild-type. 38 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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.

(C) Fingerprint plots of the 25 thioredoxin-domain containing genes screened. The boxes are colored according to whether mutations in these genes leads to sensitization (orange) or resistance (blue) in any of the 28 CRISPR screens. (D) Immunoblotting to confirm successful knock-out of TXNDC17 in two independent clones of RPE1 hTERT p53-/- Cas9 cells. Tubulin was used as a loading control. (E) Quantitation of clonogenic survival of RPE1 cells of the indicated genotypes in response to

increasing doses of MNNG, MMS, cisplatin and KBrO3. (F) Competitive growth assays in untreated RPE1 cells transduced with virus expressing the indicated sgRNAs. Data represent mean fraction of GFP-positive cells ± SD normalized to day 0 (N= 3, independent transductions). TIDE analysis is shown in Table S6.

Figure S7. ESD, BTAF1, DRAP1 and MCPH1 as genes of interest. (A) CRISPR dropout screen results for RPE1 cells exposed to formaldehyde. Some of the top hits involved in the formaldehyde detoxification and the formaldehyde-induced DNA lesions repair have been highlighted. (B) Fingerprint plots of BTAF1, DRAP1, PHF12, POLE3, POLE4 and USP37. The boxes are colored according to whether mutations in these genes leads to sensitization (orange) or resistance (blue) in any of the 28 CRISPR screens. (C) Histogram plot generated from data at cBioPortal (http://www.cbioportal.org) showing the frequency of MCPH1 deletions and mutations across different types of cancer.

39 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. The copyright holder for this preprint (which was notA certified by peer review) is theLentiviral author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available sgRNA library under aCC-BY-NC-ND 4.0 International license. gDNA TKOv2 or TKOv3 extraction & nested PCR

subculture Passage with day 12 to 18 drug selection Illumina sequencing transduction (LD20) & DrugZ

RPE1-hTERT sgRNA expressing p53-/- Cas9 cell population cells (250-400x coverage) 3

B 2 xo rubicin

O Library 2 ormaldehyde Duocarmycin UV Illudin S MMS MNNG F Cisplatin-1 Cisplatin-2 Cisplatin-3 Phen-DC3 Trabectedin Calicheamicin Olaparib CPT-1 CPT-2 HU Gemcitabine CD437 AZD6738 MLN4924 H KBrO IR Bleomycin ICRF-187 Etoposide Do Pyridostatin TKOv2 TKO library: TKOv3 200 Number DNA repair of hits: 100 DNA repair 0 Non−DNA repair BER/SSBR NHEJ/TOP2 FORK QC HR Log10 FA/ICL repair (p-value) NER 20 MMR 0 RER −20 MMEJ −40

C GO Biological Process (top 25 terms n=840 genes) DNA repair (GO:0006281) double−strand break repair (GO:0006302) ● DNA replication (GO:0006260) ● interstrand cross−link repair (GO:0036297) ● DNA recombination (GO:0006310) ● double−strand break repair via homologous recombination (GO:0000724) ● recombinational repair (GO:0000725) ● regulation of DNA metabolic process (GO:0051052) ● regulation of chromosome organization (GO:0033044) ● (GO:0000075) ● nucleotide−excision repair (GO:0006289) ● response to radiation (GO:0009314) ● regulation of response to DNA damage stimulus (GO:2001020) ● positive regulation of DNA metabolic process (GO:0051054) ● DNA integrity checkpoint (GO:0031570) ● regulation of DNA repair (GO:0006282) ● DNA−dependent DNA replication (GO:0006261) ● maintenance (GO:0000723) ● DNA damage checkpoint (GO:0000077) ● telomere organization (GO:0032200) ● (GO:0007059) ● response to ionizing radiation (GO:0010212) ● mitotic cell cycle checkpoint (GO:0007093) ● regulation of cell cycle phase transition (GO:1901987) ● regulation of DNA recombination (GO:0000018) ● 20 40 60 −Log10(FDR)

Gene Set Hits 20 80 140 Fold Enrichment D 3 < 6.5 10+

UV radiation Ionizing radiation (IR) Illudin S KBrO3 BER/SSBR BER/SSBR BER/SSBR BER/SSBR NHEJ/ 80 NHEJ/ 140 NHEJ/ 80 NHEJ/ 100 TOP2 60 MMEJ TOP2 105 MMEJ TOP2 60 MMEJ TOP2 75 MMEJ

40 70 40 50

20 35 20 25 ATR/ RER ATR/ RER ATR/ RER ATR/ RER FORK QC 0 FORK QC 0 FORK QC 0 FORK QC 0

HR MMR HR MMR HR MMR HR MMR

FA/ICL NER FA/ICL NER FA/ICL NER FA/ICL NER repair repair repair repair

Log2 p−val (sensitizing hits) Log2 p-value (resistance hits) Bonferroni correction (α = 0.05) Figure 1 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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. RPE1-hTERT p53-/- (WT) A Pyridostatin screen B 5 TDP2-KO

100 100 100

0

50 50 50 Confluenc y -5 NormZ score PPP2R4 CDCA2 TDP2 0 0 0 NBN ZNF451 1 2 3 2 3 4 2 3 4 -10 Log10 [Etoposide] nM Log10 [Pyridostatin] nM Log10 [Phen-DC3] nM

HEK293F C D +TOP2-YFP E anti-TOP2α TOP2-YFP: - + 100 100 NT Buffer TOP2α-YFP 0.5 TOP2 1 10 3

10 (h) Time 6

% Proliferation (AUC ) 9 Tubulin 1 Pyri. Etop. HEK293F 0.01 0.1 1 10 100 1 10 100 1000 10000 (35 µM)(50 µM) [Pyridostatin] μM [Etoposide] nM

RPE1-hTERT p53-/- Cas9 (WT) Etoposide (50 μM) Etoposide (3 μM) F G H TDP2-KO Pyridostatin (35 μM) Pyridostatin (20 μM) 2.25 2000 100 2.00 1500 1.75

OP2cc level s 1.50 50 -H2AX intensit y 1000

1.25 Confluenc y 500 NT

Relative T 1.00 Nuclear γ 0 0.75 0 1 2 3 4 5 0 3 6 9 1 3 6 9 Time (h) Log10 [CX5461] nM Time (h)

Figure 2 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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 A under aCC-BY-NC-ND 4.0 International license. C 3

2 NHEJ KIF18A O 2 MRI/CYREN Duocarmycin UV Illudin S MMS MNNG Formaldehyde Cisplatin-1 Cisplatin-2 Cisplatin-3 Phen-DC3 Trabectedin Calicheamicin Olaparib KBrO IR Bleomycin CPT-1 CPT-2 HU Gemcitabine CD437 AZD6738 MLN4924 H ICRF-187 Etoposide Doxorubicin Pyridostatin BABAM1

POLL ATAD5 UIMC1 IREB2 PARP1 SHLD2

ERCC6 POLQ DCLRE1CRIF1 ERCC8 SHLD1 BRE LIG4 NHEJ1 LIG4 SHLD3 XRCC4 Sensitizing XRCC4 RNF168 TP53BP1 CHTF8 YRDC ERCC6L2 DSCC1 Resistance HMCES

Color legend FA/ICL repair REV3L NHEJ/TOP2 D ERCC2 B MMEJ NER NHEJ ATR/Fork QC ELOF1 UVSSA RFWD3 CAB39 MMR ERCC8

KIF18A FRYL NER AHR NSD1 ERCC1 NDST1

PDCD10 ERCC4 C7orf49 B3GAT3 XPA GTF2H1

BABAM1 RER

CAP1 SLC35B2 GTF2H5 ARNT HR

POLL

RALGAPB

UIMC1

IREB2 EXT2

BMPR1A

EXTL3 BER/SSBR GTF2H4 ERCC5 SHLD2 EXT1 Other DNA repair ERCC6

RIF1 TAOK1

DCLRE1C

POLQ

GCA

PIGF REV1

SHLD1

HIST1H1C

LIG4

BRE ERCC3

ABCC1

GNAS

BMX

NHEJ1

SHLD3

ARHGAP29

XRCC4 CIRBP STK19 AR POLK

PPM1F

SDCBP

RNF168 TP53BP1

PTPN14

YRDC

HTR3D

KCTD10

ERCC6L2

KIRREL

HMCES ZNF451

SPNS2

ACTB

NF2 FAM205A

DDRGK1

HOXB7

C1orf85

FXR1

ZNF823

EPB41L2 CUL3

FBXO32

MCPH1

PRTN3

AMOTL2

CPZ

NCOA4 NMUR2 MDC1 BBC3 TIGD3 E

RNF146

LRRK2

SPOP

TDP2

STOML2 PPP1R3B

CRYBB3 ESCO1

ACTG1

RAVER1

ATP1A1

ABL1 CCS FA/ICL C1orf86

B3GALT6

ZDHHC9 SLC25A37 SLX4 CRAMP1L

CAST UBOX5

PPP1R3A

H2AFX FANCD2

VPS37A

ATG9A

TRIM35

FAM49B

SCN11A

MAGEA1

LATS2 HIST1H1B HROB

STARD7 CRYBA2 USP1 C17orf70 GDPD2

AGK

TOP1

TMX2

TGIF2

RB1CC1

NUB1

PMVK DCLRE1A

RAMP1

MAP4K4 SOD2 FANCG MCM8

SCNM1

CWC27

SLC12A9 RMI1

RPA2 KIAA1524 FANCM FANCB FANCF

AP2M1

MTA2 FANCA AP2S1

RPA1

ATP6AP1

WDR7

DDI2

HNRNPLL GSG2 STIP1

HIRA

CDC6 C19orf40 PTEN

ATP6AP2 MBTPS2 FANCI

FNDC3B PSME3 BLM FANCL

CABIN1

LCMT1 UBE2T

TSSC1 FA/ICL

KIAA0907

CERS2

FAM180A

FAM135B RMI2

ESCO1 XPC FANCC

KIAA1432

C1orf86

GRB2 MBTPS1

CLUH

SHFM1

DCPS SLX4

FANCD2 UBE2M

POLH

SERF2

IFI30

CDCA5 TXNDC17 SRCAP CCAR1

HROB UBA5

ATRIP C17orf70 USP1 PSTK DPH1 HR BRIP1

TP53

HNRNPU NAE1 CLASRP

DCLRE1A BRD4 FANCE

FANCG

MCM8

NPLOC4

DHX36

RMI1

UFM1 LMNA

ELL

TMED10

RAD51

FANCM FANCF FANCB

UFSP2

FANCA ASNA1

SCAP MCM9

WRB

MMGT1

C19orf40 PHF12 TMED2

FANCI

POLE3

OLA1

RNASEH2C BLM

FANCL

RAD51C

UBE2T

UFL1

RMI2

RNASEH2A XRCC3 MCTS1

FANCC

WNK1 C1orf112

ZNHIT1 BAP1 BRCA2

DMAP1

TXNDC17 S1PR1

CAND1 CCAR1

PALB2

OXSM C7orf26 POLE4

BRIP1

FANCE

CXXC1

WDR48

RPL28

ECHS1 CTNNBL1

WDR83

TRAIP MCM9 MAD2L2

DUT

BRCA1 SRSF11 WDHD1

SKA1

UBA3

USP37

RAD51B BRAP

GET4

RTEL1 RAD18

TCEB2

NBN

SLC25A28

CENPO

GATAD1

PPP1R8 BARD1

KANSL3

C11orf30

GABPB1

DHX9

DHX35

BOD1L1

PUM1

MRTO4

HUWE1

UFC1

PDS5B

NAPG EIF4E2

FEN1

SNRNP40

GIGYF2

RAD51D

CDK2

PPIE

MAU2 UBE2L3

PTBP1

CDC25A

PPIL1

DNAJC8 GLMN

MRE11A

LMNB1 RAD9A F

CNOT2

RNF8 TRNAU1AP

SKA2

ATR

DYNC1LI2 ELAVL1

CCDC101

PPP4C RAD17

XRCC2

RNF111 ADSL HR ACTR1A

ARIH1

LIN37

FOXM1 BEND3 SLC39A10

HUS1

ANAPC15

DDX11

RAD1

CDC25B HDGFRP2 LMNA

DSCC1 CHTF8 DOLPP1

ASF1A

CLASP2 UBE2S RAD51

ARMC5

TAF11 ZNF827 ADSS

KDM8

PKMYT1

ESCO2

TAF3

WIPI2

C16orf80

RHOA CREB1 C12orf44 SCAP ARL1 NAA25 MMGT1 PPIL2

NAA50

ARFRP1 SUZ12

TAF13

MMS19

STAG1

SFR1

EXO1

CYFIP1

DAZAP1

CENPC PPP2CA CUL1

PPP6C BTAF1 PPP2R4

UBE2R2 TIPRL JMJD6 RNASEH2C

DRAP1

ISG20L2 RAD51C CYB5R4

ARPC4

FBXW11

TLK2

WEE1 RIC8A

DBF4

DNM2 ABCG2 MED12 PFN1 XRCC3 UBR4 RNASEH2A

RAD54L

EME1

XRCC1 WDR55

ADPGK

RCL1

MOCS3

YWHAE

SLC10A7

KRTAP10-3 BRCA2

GNA12

AUNIP

MPHOSPH8

TPI1 DOHH

DIS3

BAZ1B

FKBPL PALB2

CRYBA4 OXSM C7orf26

BCAP31

ANKRD49

TRIP12

CCNB1

MUS81

DKC1

RPL36

RTN4RL1 PARP1

ZNHIT3 ECHS1

REV3L WDR83 NER TRAIP

NUP188 ERCC2 WDHD1 BRCA1

C15orf56

CTDSPL2

ELOF1

UVSSA

RFWD3 N6AMT1

TDP1 ENO1

C16orf72 RAD51B RPRD1B

ERCC8

BRD7

FKBP8

ERCC1

ERCC4 XPA NAA30 GTF2H1 TCEB2 NBN SLC25A28 DPY19L2 CIRH1A

GTF2H5 HERC4 PPP1R8 BARD1

NEK2 ERCC5

GTF2H4

MIB1

RPRD2 MSL1 ERCC6

MEMO1

SEPHS1 REV1 DHX9

ASH2L ERCC3 UBE2H

DTYMK STK19 POLK PDS5B HUWE1

YPEL5

KIR3DL1

NAA35 SNAP29

DYRK1A FEN1

WDR26

FBXO42 MLH1 MAEA RAD51D

SCAI LIG1 CDK2

MSH2 SLC7A6OS MAU2 PTBP1 CALM3 DNAJC8 PMS2 MRE11A

KPNA2

UBE2G1

MSH6

CELF1

ZNF385D RNF8

ZC2HC1C

BRMS1 MMR

ATAD5 DYNC1LI2 PPP4C MAP7D1 XRCC2

SLBP

SDC4 PIAS1

CDH3

OR2T4 FOXM1 HRAS PAXIP1 CCM2

IRX6

CLDN9 SRR TMOD3 DDX11 PAGR1

KRIT1

ARID2 CDC25BHDGFRP2 DGAT2L6 DSCC1

STAG2

ITGB1BP1

XPR1 PBRM1 KDM8

ESD ESCO2 HSD17B12 WIPI2 ARID4B NAA25

TADA2B ROCK2 KANSL2 FLCN ATP13A1 RRAGC SYNE1 CNBP PPIL2 NAA50 ADH5

FOXP1

CDYL

EPC1 VPS39 MMS19

TADA1 PAWR FIGNL1 LAMTOR3 NPEPPS USP14 STAG1 SLC18B1 SFR1

VPS33A

CCDC15

SUPT20H DAPK3 EAPP LAMTOR2 NARG2 CBY1 CSNK2A2 TIPRL PPP2R4 DRAP1 CYB5R4 ISG20L2

TRAF2 TSC1 EREG F2R AMBRA1 AP2B1 ASIC3 GID8 HMCN1 CNOT10

CLK3 SMAD4 TRAF3 TSC2 RBM38 INTS5 SYCE1 VTA1 IKBKB SULT1E1 CKAP2 EP300 FUT1 DBF4 MED12 XRCC1 EME1 RAD54L

KRT74 MSL1 BRD9 TRAF2 ZNF207 CCDC15 CDCA2 CDYL

PRH2 NEK2 SMARCD1 SACS UBE2C TRAF3 ZWINT RWDD4 EPC1 HTR3D

SPTLC2

AXIN1 HMGCR KRT74 NPC1 KLHL4 BRD9 LCT

BCL2L1 HMGCS1 HSPA8 INTS10 PRH2 VPS16 SECISBP2L SMARCD1 PDS5A VWA9 RCL1 AUNIP TPI1 BAZ1B DOHH

MUS81

Figure 3 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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 ERCC6L2 R712V-STOP: short form 1561 N Tudor SNF2 Helicase domain C VIGSSK K165A

B 100 100

DT40 WT 10 10 ERCC6L2-/- (#36) ERCC6L2-/- (#34) 1 XRCC4-/- 1 ERCC6L2-/- XRCC4-/- (#10) 0.1 ERCC6L2-/- XRCC4/- (#42) Surviving fraction [% ] Surviving fraction [% ]

0.1 0.01 0.0 0.2 0.4 0.6 0.8 1.0 0 100 200 300 Bleomycin (μM) Etoposide (nM)

100 100

10 DT40 WT 10 ERCC6L2-/- (#36) ERCC6L2-/- (#34) 1 XLF-/- 1 ERCC6L2-/- XLF-/- (#6) 0.1 ERCC6L2-/- XLF-/- (#14) Surviving fraction [% ] Surviving fraction [% ]

0.1 0.01 0.0 0.2 0.4 0.6 0.8 1.0 0 100 200 300 Bleomycin (μM) Etoposide (nM)

C D U2OS EJ7-GFP cells E CH12F3-2 cells 25 50

20 cells) 40 G FP +

15 30 Distal NHEJ No indel 10 20 End-joining (% ) 5 10 GFP Class switching (%IgA 0 0 C9

sgRNA: WT F11 B12 AAVS 843/3 LIG4 #1 LIG4 #2 834/29

XRCC4 #1 XRCC4 #2 Ercc6l2-/- 53bp1-/- ERCC6L2 #1 ERCC6L2 #2 ERCC6L2 #3 ERCC6L2 #4

Figure 4 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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 A under aCC-BY-NC-ND 4.0 International license. Duocarmycin UV illudin S MMS MNNG Formaldehyde Cisplatin−1 Cisplatin−2 Cisplatin−3 Phen−DC3 Trabectedin Calicheamicin Olaparib CPT−1 CPT−2 HU Gemcitabine CD437 AZD6738 MLN4924 H2O2 KBrO3 IR Bleomycin ICRF−187 Etoposide Doxorubicin Pyridostatin

ERCC6 GTF2H5 Sensitizing STK19 ELOF1 Mild resistance (6>NormZ>4) TXNDC17 Resistance (NormZ>6)

Untreated B Illudin S (30 nM) Trabectedin (0.5 nM) sgAAVS1 #1 sgXPC #1 sgUVSSA #1 sgERCC8 #1 sgELOF1 #1 sgSTK19 #1 10 10 1000 1000 10 1000

100 100 100 1 1 1 10 10 10 0.1 1 1 1 (GFP/mChery ratio ) 0.1 0.1 10 0.01 0.1 0.1 0.1 Log

0.01 0.01 0.01 0.01 0.001 0.01 0 5 10 15 20 0 5 10 15 20 0 5 10 15 20 0 5 10 15 20 0 5 10 15 20 0 5 10 15 20 Time (d) Time (d) Time (d) Time (d) Time (d) Time (d) D C WT WT TXNDC17-KO (C2) rad26∆ C43S/ elf1∆ [TXNDC17] -- WT C46S rad26∆ elf1∆ TXNDC17 rad16∆ rad16∆ rad26∆ Tubulin rad16∆ elf1∆ rad16∆ rad26∆ elf1∆ unrradiated 20 J/m2 30 J/m2 40 J/m2

E RPE1 hTERT Cas9 TP53-KO WT TXNDC17-KO (C2) WT TXNDC17-KO (C2) TXNDC17-KO + [TXNDC17-WT] 0 TXNDC17-KO + [TXNDC17-C43S/C46S] 120

62 100

80

250 60 MNNG (nM) 40

500 20 Clonogenic survival (%) 0 10 100 1000 WT C43S/C46S [MNNG] nM + [TXNDC17]

Figure 5 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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 CPT screen (CPT-2) Genotoxin subnetwork DepMap (Broad) subnetwork 5

SOX10 SOX9 0 DBF4 JMJD6 PPP2R4 KDM8 DUSP4 PPME1 -5

XRCC2 PPP2R4 LCMT1 -10 CYB5R4 TIPRL

NormZ score PPP6C PARP1 TIPRL TLE3 RAD51D AUNIP PPP6R3 -15 EME1 ATM XRCC1 DSCC1 PPP2R4 RAD54L SFR1 PPM1G TIPRL CYB5R4 CYB5R4 -20 CCS PPM1A PPP4R2 C RPE1 hTERT p53-/- Cas9

sgAAVS1-1 1.0 sgAAVS1-1 (exp2) D Bleomycin screen sgPPP2R4-1 5 CPT (7.5 nM) sgPPP2R4-2 0.5 sgCYB5R4-3 0

GFP/mCherry rati o sgCYB5R4-5 (exp2)

0.0 -5 0 5 10 15 20 25 ERCC6L2 TP53BP1 time (d)

NormZ score ATM TMEM2 -10 LIG4 E RPE1 hTERT p53-/- Cas9 XRCC4 NHEJ1

1 sgAAVS1-1 -15 sgTMEM2-1 Untreated sgTMEM2-2 sgAAVS1-1 sgTMEM2-1 bleomycin (67 nM)

GFP/mCherry rati o sgTMEM2-2

0.1 0 5 10 15 20 time (d)

Figure 6 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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 GO Molecular Function (top 25 terms n=840 genes) GO Cellular Compartment (top 25 terms n=840 genes) damaged DNA binding (GO:0003684) ● chromosomal region (GO:0098687) ● catalytic activity, acting on DNA (GO:0140097) ● chromosome, telomeric region (GO:0000781) ● chromatin binding (GO:0003682) ● chromatin (GO:0000785) ● single−stranded DNA binding (GO:0003697) ● DNA repair complex (GO:1990391) ● DNA−dependent ATPase activity (GO:0008094) ● nuclear chromosome, telomeric region (GO:0000784) ● activity (GO:0004518) ● nuclear chromatin (GO:0000790) ● −like protein activity (GO:0019787) ● site of DNA damage (GO:0090734) ● endodeoxyribonuclease activity (GO:0004520) ● replication fork (GO:0005657) ● DNA secondary structure binding (GO:0000217) ● site of double−strand break (GO:0035861) ● deoxyribonuclease activity (GO:0004536) ● ubiquitin complex (GO:0000151) ● ubiquitin−protein transferase activity (GO:0004842) ● Fanconi anaemia nuclear complex (GO:0043240) ● protein N−terminus binding (GO:0047485) ● condensed chromosome (GO:0000793) ● activity (GO:0004519) ● nucleotide−excision repair complex (GO:0000109) ● ubiquitin−like protein ligase binding (GO:0044389) ● acetyltransferase complex (GO:1902493) ● activity (GO:0004527) ● protein acetyltransferase complex (GO:0031248) ● RNA polymerase II−specific DNA−binding transcription factor binding (GO:0061629) ● cytoplasmic ribonucleoprotein granule (GO:0036464) ● ubiquitin protein ligase binding (GO:0031625) ● −RING complex (GO:0031461) ● ATPase activity, coupled (GO:0042623) ● ribonucleoprotein granule (GO:0035770) ● four−way junction DNA binding (GO:0000400) ● DNA recombinase complex (GO:0033061) ● transcription activity (GO:0003713) ● transferase complex, transferring phosphorus−containing groups (GO:0061695) ● ATPase activity (GO:0016887) ● carboxy−terminal domain protein kinase complex (GO:0032806) ● DNA−binding transcription factor binding (GO:0140297) ● serine/threonine protein kinase complex (GO:1902554) ● exonuclease activity... producing 5'−phosphomonoesters (GO:0016796) ● RNA polymerase II transcription factor complex (GO:0090575) ● mismatch repair complex binding (GO:0032404) ● transcription factor TFIIH holo complex (GO:0005675) ● RNA polymerase II CTD heptapeptide repeat kinase activity (GO:0008353) ● RNA polymerase II, holoenzyme (GO:0016591) ● 5 10 15 20 5 10 15 20 −Log10(FDR) −Log10(FDR)

Gene Set Hits 20 80 140 Fold Enrichment 3 < 6.5 10+

B AZD6738 Bleomycin Calicheamicin CD437 Cisplatin-3

BER/SSBR BER/SSBR BER/SSBR BER/SSBR BER/SSBR 100 20 80 160 NHEJ/ 80 NHEJ/ NHEJ/ NHEJ/ NHEJ/ MMEJ MMEJ MMEJ MMEJ TOP2 60 MMEJ TOP2 75 TOP2 15 TOP2 60 TOP2 120

40 50 10 40 80

25 5 20 40 ATR/ 20 RER ATR/ RER ATR/ RER ATR/ RER ATR/ RER 0 0 0 FORK QC 0 FORK QC FORK QC 0 FORK QC FORK QC

HR HR HR HR HR MMR MMR MMR MMR MMR

FA/ICL NER FA/ICL NER FA/ICL NER FA/ICL NER FA/ICL NER repair repair repair repair repair

CPT-2 Doxorubicin Duocarmycin Etoposide Formaldehyde

BER/SSBR BER/SSBR BER/SSBR BER/SSBR BER/SSBR 100 NHEJ/ 80 NHEJ/ NHEJ/ 80 NHEJ/ 100 NHEJ/ 100 TOP2 60 MMEJ TOP2 75 MMEJ TOP2 60 MMEJ TOP2 75 MMEJ TOP2 75 MMEJ

40 50 40 50 50

20 25 20 25 25 ATR/ RER ATR/ RER ATR/ RER ATR/ RER ATR/ RER FORK QC 0 FORK QC 0 FORK QC 0 FORK QC 0 FORK QC 0

HR MMR HR MMR HR MMR HR MMR HR MMR

FA/ICL NER FA/ICL NER FA/ICL NER FA/ICL NER FA/ICL NER repair repair repair repair repair

HU ICRF-187 MLN4924 Gemcitabine H2O2 BER/SSBR BER/SSBR BER/SSBR BER/SSBR BER/SSBR 20 40 40 20 20 NHEJ/ NHEJ/ NHEJ/ NHEJ/ NHEJ/ MMEJ TOP2 15 MMEJ TOP2 30 MMEJ TOP2 30 TOP2 15 MMEJ TOP2 15 MMEJ 10 20 20 10 10 5 10 ATR/ 10 5 5 ATR/ RER ATR/ RER RER ATR/ RER ATR/ RER FORK QC 0 FORK QC FORK QC 0 FORK QC FORK QC

HR MMR HR MMR HR MMR HR MMR HR MMR

FA/ICL NER FA/ICL NER FA/ICL NER FA/ICL NER FA/ICL NER repair repair repair repair repair

MMS MNNG Olaparib PhenDC3 Pyridostatin

BER/SSBR BER/SSBR BER/SSBR BER/SSBR BER/SSBR 80 20 80 NHEJ/ 100 NHEJ/ 140 NHEJ/ NHEJ/ NHEJ/ TOP2 75 MMEJ TOP2 105 MMEJ TOP2 60 MMEJ TOP2 15 MMEJ TOP2 60 MMEJ

50 70 40 10 40

25 20 5 20 ATR/ RER ATR/ 35 RER ATR/ RER ATR/ RER ATR/ RER 0 FORK QC 0 FORK QC 0 FORK QC FORK QC FORK QC

HR MMR HR MMR HR MMR HR MMR HR MMR

FA/ICL NER FA/ICL NER FA/ICL NER FA/ICL NER FA/ICL NER repair repair repair repair repair

Trabectedin

BER/SSBR NHEJ/ 80 TOP2 60 MMEJ

40

ATR/ 20 RER

FORK QC 0

HR MMR

FA/ICL NER repair Figure S1 bioRxiv preprint B A not certifiedbypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmadeavailable γH2AX/DAPI foci masks foci masks 0.0 1.0 γH2AX (FITC) γH2AX DAPI doi:

ICRF-187 https://doi.org/10.1101/845446

KBrO3

nrae Pyridostatin(20 Untreated IR

Bleomycin

H2O2

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PhenDC3 under a ;

RPE1-hTERT p53 Trabectedin this versionpostedNovember18,2019. Figure S2 Calicheamicin CC-BY-NC-ND 4.0Internationallicense

HU

Pyridostatin

Etoposide

6 htreatment Doxorubicin μM)

-/- Olaparib Cas9 CPT-1

Etoposide (3μM) CPT-2

Formaldehyde The copyrightholderforthispreprint(whichwas

Cisplatin-1 .

Cisplatin-2

Cisplatin-3

MMS

MNNG

Original images (.tif) images Original In Cell masks (.bmp) masks Cell In Duocarmycin

UV

Illudin S

AZD6738

MLN4924

CD437 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. The copyright holder for this preprint (which was A not certified by peer review) is the author/funder, who has grantedB bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 400 15

300 12

Gene count 200 11

100 10 # screens where gene is hit 0 ATM 1 2 3 4 5 6 7 8 9 10 11 12 15 RAD1 CUL3 HUS1 NAE1 DSCC1 CAB39 UBE2M CAND1 CHTF8 DDX11 Screen frequency PPP2R4 MAD2L2 RAD51B RNASEH2B

NHEJ CAB39 KIF18A FRYL C AHR NSD1 NDST1 C7orf49 PDCD10 B3GAT3 BABAM1 SLC35B2 CAP1 ARNT POLL RALGAPB BMPR1A UIMC1 IREB2 EXT2 EXTL3 SHLD2 EXT1 POLQ DCLRE1CRIF1 TAOK1 GCA PIGF SHLD1 LIG4 HIST1H1C BRE ABCC1 BMX NHEJ1 GNAS SHLD3 ARHGAP29 AR XRCC4 CIRBP UBE3A PPM1F SDCBP RNF168 TP53BP1 YRDC PTPN14 HTR3D STK11IP ERCC6L2 KIRREL KCTD10 HMCES ACTB ZNF451 SPNS2 DDRGK1 FAM205A C1orf85 NF2 CDRT1 HOXB7 ZNF823 FXR1 NSUN5 EPB41L2 CUL3 MCPH1 FBXO32 PRTN3 AMOTL2 NCOA4 MDC1 NMUR2 CPZ BBC3 TIGD3 RNF146 LRRK2 SPOP STOML2 TDP2 CRYBB3 PPP1R3B ACTG1 RAVER1 ATP1A1 KCTD11 ABL1 CCS THOC1 B3GALT6 HSP90AB1 SLC25A37 ZDHHC9 CRAMP1L UBOX5 CAST PPP1R3A H2AFX VPS37A ATG9A FAM49B TRIM35 SCN11A LATS2 MAGEA1 LRRC28 STARD7 CRYBA2 HIST1H1B GDPD2 AGK TMX2 TOP1 TGIF2 RB1CC1 NUB1 RAMP1 PMVK SOD2 MAP4K4 SCNM1 SLFN11 SLC12A9 CWC27 RPA2 KIAA1524 AP2M1 MTA2 RPA1 AP2S1 ATP6AP1 WDR7 HNRNPLL DDI2 GSG2 STIP1 HIRA MBTPS2 ATP6AP2 PTEN FNDC3B PSME3 FA/ICL CABIN1 LCMT1 TSSC1 KIAA0907 CERS2 FAM180A XPC FAM135B ESCO1 KIAA1432 SHFM1 CLUH MBTPS1 GRB2 C1orf86 DCPS SLX4 POLH UBE2M FANCD2 SRCAP IFI30 CDCA5 SERF2 UBA5 HROB PSTK ATRIP DPH1 HR USP1 C17orf70 CLASRP TP53 NAE1 HNRNPU BRD4 DCLRE1A FANCG DHX36 NPLOC4 MCM8 ELL LMNA UFM1 RMI1 TMED10 RAD51 FANCM FANCF ASNA1 UFSP2 FANCBFANCA WRB SCAP MMGT1 PHF12 TMED2 C19orf40 FANCI POLE3 OLA1 RNASEH2C BLM RAD51C UFL1 FANCL UBE2T RMI2 WNK1 MCTS1 RNASEH2A XRCC3 C1orf112 FANCC BAP1 DMAP1 ZNHIT1 BRCA2 S1PR1 CAND1 PALB2 CCAR1 TXNDC17 POLE4 OXSM C7orf26 BRIP1 FANCE WDR48 RPL28 CXXC1 WDR83 ECHS1 CTNNBL1 DUT SKA1MAD2L2 SRSF11 WDHD1 BRCA1 TRAIP MCM9 UBA3 USP37 RAD51B RTEL1 GET4 BRAP RAD18 TCEB2 NBN SLC25A28 GATAD1 CENPO KANSL3 PPP1R8 BARD1 C11orf30 GABPB1 DHX9 DHX35 BOD1L1 PUM1 HUWE1 MRTO4 UFC1 NAPG FEN1 PDS5B EIF4E2 SNRNP40 GIGYF2 PPIE RAD51D CDK2 MAU2 PTBP1 UBE2L3 PPIL1 CDC25A GLMN DNAJC8 MRE11A RAD9A CNOT2 LMNB1 TRNAU1AP ATR SKA2 RNF8 ELAVL1 DYNC1LI2 PPP4C CCDC101 ADSL RAD17 XRCC2 RNF111 ARIH1 ACTR1A SLC39A10 FOXM1 LIN37 BEND3 HUS1 ANAPC15 DDX11 RAD1 CDC25BHDGFRP2 UBE2S CLASP2 CHTF8 DSCC1 DOLPP1 ASF1A ADSS ARMC5TAF11 ZNF827 PKMYT1 KDM8 ESCO2 TAF3 C16orf80 WIPI2 ARL1 CREB1 NAA25 C12orf44 RHOA ARFRP1 PPIL2 NAA50 SUZ12 TAF13 MMS19 SFR1 STAG1 CYFIP1 EXO1 CUL1 DAZAP1 PPP2CA CENPC UBE2R2 BTAF1 TIPRL PPP2R4 JMJD6 PPP6C DRAP1 ISG20L2 FBXW11 CYB5R4 ARPC4 TLK2 WEE1 DBF4 RIC8A ABCG2 DNM2 UBR4 MED12 PFN1 ADPGK XRCC1 EME1 RAD54L WDR55 RCL1 MOCS3 SLC10A7 YWHAE KRTAP10-3 GNA12 MPHOSPH8 AUNIP TPI1 FKBPL BAZ1B DOHH DIS3 BCAP31 CRYBA4 CCNB1 ANKRD49 TRIP12 MUS81 DKC1 RTN4RL1 PARP1 RPL36 ZNHIT3 REV3L NER NUP188 ERCC2 C15orf56 CTDSPL2 N6AMT1 ELOF1 UVSSA RFWD3 TDP1 ENO1 C16orf72 RPRD1B ERCC8 FKBP8 BRD7 NAA30 ERCC4 ERCC1 XPA GTF2H1 DPY19L2 CIRH1A HERC4 GTF2H5 NEK2 ERCC5 MSL1 MIB1 RPRD2 GTF2H4 ERCC6 MEMO1 SEPHS1 REV1 ASH2L UBE2H ERCC3 DTYMK STK19 POLK YPEL5 KIR3DL1 WDR26 DYRK1A NAA35 SNAP29

FBXO42 MAEA MLH1 LIG1 SCAI MSH2 SLC7A6OS CALM3 PMS2 KPNA2 UBE2G1 CELF1 MSH6 ZNF385D ZC2HC1C BRMS1 MMR ATAD5 Color legend SDC4 MAP7D1 PIAS1 SLBP CDH3 FA/ICL repair OR2T4 HRAS CCM2 SMARCA4 PAXIP1 CLDN9 SRR IRX6 TMOD3 NHEJ/TOP2 KRIT1 PAGR1 SMARCB1 DGAT2L6 ARID2 MMEJ STAG2 ITGB1BP1 ATR/Fork QC XPR1 PBRM1 ESD HSD17B12 MMR ARID4B NER TADA2B ROCK2 KANSL2 FLCN ATP13A1 RRAGC SYNE1 CNBP ADH5 RER FOXP1 VPS39 RAD54L2 CDCA2 EPC1 CDYL TADA1 PAWR FIGNL1 USP14 LAMTOR3 NPEPPS SLC18B1 HR VPS33A BER/SSBR SUPT20H DAPK3 EAPP LAMTOR2 NARG2 CSNK2A2 TMPRSS9 CBY1 RWDD4 CCDC15 Other DNA repair

TRAF2 TSC1 EREG F2R AMBRA1 AP2B1 ASIC3 GID8 HMCN1 FUT1 CKAP2 EP300 CLK3 SULT1E1 CNOT10 TRAF3 TSC2 RBM38 INTS5 SMAD4 SGOL1 SYCE1 VTA1 IKBKB

SPTLC2 BCL2L1 AXIN1 HMGCS1 HMGCR PDS5A HSPA8 VWA9 INTS10 SECISBP2L KLHL4 PRH2 KRT74 SMARCD1 BRD9 VPS16 NPC1 TMPRSS13 LCT TMEM30A

Figure S3 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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 SLC35B2 EXT2

RALGAPB

CAP1 GNAS

EXTL3 ARHGAP29 TAOK1

CUL3 FRYL

CAB39 NF2 RNF146 PDCD10 KCTD10

FAM49B

PTPN14 AMOTL2

LATS2 KIRREL

PPM1F CWC27

B Duocarmycin UV Illudin S MMS MNNG Formaldehyde Cisplatin−1 Cisplatin−2 Cisplatin−3 Phen−DC3 Trabectedin Calicheamicin Olaparib CPT−1 CPT−2 HU Gemcitabine CD437 AZD6738 MLN4924 H2O2 KBrO3 IR Bleomycin ICRF−187 Etoposide Doxorubicin Pyridostatin

NF2 FRYL AMOTL2 Sensitizing LATS2 TAOK1 KIRREL Resistance FAM49B

Figure S4 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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 ERCC6L2-/-

5’ arm 3’ arm WT #34 #36 ERCC6L2 locus ERCCL2 1 2 3 4 5 6

XRCC4 ERCCL2-/- neoR DT40

C 100 100

DT40 WT ERCC6L2-/- (#36) 10 ERCC6L2-/- (#34) 10 XRCC4-/- ERCC6L2-/- XRCC4-/- (#10) 1 ERCC6L2-/- XRCC4/- (#42) Surviving fraction [% ] Surviving fraction [% ]

0.1 1 0.0 0.5 1.0 1.5 2.0 0 10 20 30 40 50 Bleomycin (μm) Etoposide (nM)

100 100

DT40 WT 10 10 ERCC6L2-/- (#36) ERCC6L2-/- (#34) XLF-/- 1 1 ERCC6L2-/- XLF-/- (#6) -/- -/- Surviving fraction [% ] Surviving fraction [% ] ERCC6L2 XLF (#14)

0.1 0.1 0.0 0.5 1.0 1.5 2.0 0 100 200 300 Bleomycin (μm) Etoposide (nM)

D E CH12F3-2 1.5 Ercc6l2-/- 53bp1-/-

relative to W T 1.0 WT B12 C9 F11 T1 T2

0.5 AID

0.0 β-actin T/HP RT RN A L CIT: - + - + - + - + - + - + G CH12F3-2, 3 d post-stimulation C9 WT B12 F11 843/3 834/29 Ercc6l2-/- 53bp1-/-

Figure S5 bioRxiv preprint doi: https://doi.org/10.1101/845446; this version posted November 18, 2019. 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 A under aCC-BY-NC-ND 4.0 International license. sgXPC-2 sgUVSSA-2 sgERCC8-2 sgELOF1-4 sgSTK19-4 10 1000 1000 10 1000 100 Not Treated 100 100 10 Illudin S 30nM 1 1 10 10 1 Trabectedin 0.5nM 1 1 0.1 0.1 0.1 0.1 0.01 0.1 0.001 0.01

0.01 0.01 0.0001 0.01 0.001 0 5 10 15 20 0 5 10 15 20 0 5 10 15 20 0 5 10 15 20 0 5 10 15 20 Time (d) Time (d) Time (d) Time (d) Time (d)

C B Duocarmycin UV Illudin S MMS MNNG Formaldehyde Cisplatin−1 Cisplatin−2 Cisplatin−3 Phen−DC3 Trabectedin Calicheamicin Olaparib CPT−1 CPT−2 HU Gemcitabine CD437 AZD6738 MLN4924 H2O2 KBrO3 IR Bleomycin ICRF−187 Etoposide Doxorubicin Pyridostatin

AGR2 Sensitizing AGR3 WT [pRS416] DNAJC10 rad16∆ [pRS416] ERP27 Resistance elf1∆ rad16∆ [pRS416] ERP29 ERP44 elf1∆ rad16∆ [pRS416-ADHp] P4HB elf1∆ rad16∆ [pRS416-ELF1] PDIA2 elf1∆ rad16∆ [pRS416-ADHp-ELF1] PDIA3 PDIA4 Unirradiated 40 J/m2 PDIA5 PDIA6 PDILT TMX1 D TMX2 TMX3 TMX4 WT TXNDC17: KO (C2) KO (H4) TXNDC11 TXNDC12 TXNDC17 TXNDC15 TXNDC16 Tubulin TXNDC17 TXNDC2 -/- TXNDC5 RPE1 hTERT p53 TXNDC9

E WT TXNDC17-KO (C2) TXNDC17-KO (H4)

100 100 F RPE1 hTERT p53-/- Flag-Cas9 75 75 2.0 sgAAVS1-1 50 50 1.5 sgAAVS1-1 (exp2)

25 25 sgPPP2R4-1

Clonogenic survival (% ) Untreated 1.0 sgPPP2R4-2 0 0 1 10 100 1000 1 10 100 sgCYB5R4-3 MNNG (nM) MMS (μM) 0.5

GFP/mCherry rati o sgCYB5R4-5 (exp2)

0.0 100 100 0 5 10 15 20 25 Time (d) 75 75

50 50

25 25 Clonogenic survival (% )

0 0 0.01 0.1 1 10 1 10 100 1000 Cisplatin (μM) KBrO3 (μM)

Figure S6 A

-15 -10 NormZ score -5 0 5 BR A DH5 IP1 bioRxiv preprint not certifiedbypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmadeavailable Formaldehyde screen C Mutation data DSCC1 ESD

CNA data Alteration Frequency FANC genes GTF2H5 2% 4% 6% 8%

doi:

MCPH1 homozygousdeletionormutationfrequency(TCGA)retrievedSept272019 Uterine (TCGA PanCan 2018) PanCan (TCGA Uterine

RFWD3 https://doi.org/10.1101/845446

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Lung squ (TCGA PanCan) (TCGA squ Lung

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USP37 PHF12 B BT Ovarian (TCGA PanCan 2018) PanCan (TCGA Ovarian

AF1

Melanoma (TCGA PanCan 2018) PanCan (TCGA Melanoma

under a Lung adeno (TCGA PanCan) (TCGA adeno Lung ;

Duocarmycin this versionpostedNovember18,2019. Prostate (TCGA PanCan 2018) PanCan (TCGA Prostate UV CC-BY-NC-ND 4.0Internationallicense Figure S7

Stomach (TCGA PanCan 2018) PanCan (TCGA Stomach Illudin S

Breast Invasive Carcinoma Breast (TCGA PanCan 2018) PanCan (TCGA Breast Carcinoma Invasive Breast MMS

Esophagus (TCGA PanCan) (TCGA Esophagus MNNG Head & neck (TCGA PanCan) (TCGA neck & Head MultipleAlterations DeepDeletion Mutation Formaldehyde Cisplatin−1

Sarcoma (TCGA PanCan 2018) PanCan (TCGA Sarcoma Cisplatin−2

Cholangiocarcinoma (TCGA PanCan) (TCGA Cholangiocarcinoma Cisplatin−3

Pancreas (TCGA PanCan 2018) PanCan (TCGA Pancreas Phen−DC3

pRCC (TCGA PanCan) (TCGA pRCC Trabectedin DLBC (TCGA PanCan) (TCGA DLBC Calicheamicin

Olaparib Cervical (TCGA PanCan) (TCGA Cervical The copyrightholderforthispreprint(whichwas CPT−1 .

Uveal melanoma (TCGA PanCan 2018) PanCan (TCGA melanoma Uveal CPT−2

ccRCC (TCGA PanCan) (TCGA ccRCC HU

ACC (TCGA PanCan 2018) PanCan (TCGA ACC Gemcitabine Thyroid (TCGA PanCan 2018) PanCan (TCGA Thyroid CD437 AZD6738

LGG (TCGA PanCan) (TCGA LGG MLN4924

Testicular (TCGA PanCan 2018) PanCan (TCGA cell germ Testicular H2O2 GBM (TCGA PanCan) (TCGA GBM KBrO3 IR Bleomycin ICRF−187 Etoposide Doxorubicin Pyridostatin Sensitizing Resistance