Selective Vulnerability of Aneuploid Human Cancer Cells to Inhibition of the Spindle Assembly Checkpoint

bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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.

Selective vulnerability of aneuploid human cancer cells to inhibition of the spindle assembly checkpoint

Yael Cohen-Sharir1, James M. McFarland2, Mai Abdusamad2, Carolyn Marquis3, Helen Tang2, Marica R. Ippolito4, Sara V. Bernhard5, Kathrin Laue1, Heidi L.H. Malaby3, Andrew Jones2, Mariya Kazachkova2, Nicholas Lyons2, Ankur Nagaraja2,6, Adam J. Bass2,6, Rameen Beroukhim2,6, Stefano Santaguida4,7, Jason Stumpff 3, Todd R. Golub2,4, Zuzana Storchova5 and Uri Ben-David1,#

1 Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel 2 Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA 3 Department of Molecular Physiology and Biophysics, University of Vermont, Burlington, VT, USA 4 Department of Experimental Oncology at IEO, European Institute of Oncology IRCCS, Milan, Italy 5 Department of Molecular Genetics, TU Kaiserlautern, Kaiserlautern, Germany 6 Dana Farber Cancer Institute, Boston, MA, USA 7 Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy

# Corresponding author: [email protected]

Abstract

Selective targeting of aneuploid cells is an attractive strategy for cancer treatment. Here, we mapped the aneuploidy landscapes of ~1,000 human cancer cell lines and classified them by their degree of aneuploidy. Next, we performed a comprehensive analysis of large-scale genetic and chemical perturbation screens, in order to compare the cellular vulnerabilities between near- diploid and highly-aneuploid cancer cells. We identified and validated an increased sensitivity of aneuploid cancer cells to genetic perturbation of core components of the spindle assembly checkpoint (SAC), which ensures the proper segregation of chromosomes during mitosis. Surprisingly, we also found highly-aneuploid cancer cells to be less sensitive to short-term exposures to multiple inhibitors of the SAC regulator TTK. To resolve this paradox and to uncover its mechanistic basis, we established isogenic systems of near-diploid cells and their aneuploid derivatives. Using both genetic and chemical inhibition of BUB1B, MAD2 and TTK, we found that the cellular response to SAC inhibition depended on the duration of the assay, as aneuploid cancer cells became increasingly more sensitive to SAC inhibition over time. The increased ability of aneuploid cells to slip from mitotic arrest and to keep dividing in the presence of SAC inhibition was coupled to aberrant spindle geometry and dynamics. This resulted in a higher prevalence of mitotic defects, such as multipolar spindles, micronuclei formation and failed cytokinesis. Therefore, although aneuploid cancer cells can overcome SAC inhibition more readily than diploid cells, the proliferation of the resultant aberrant cells is jeopardized. At the molecular level, analysis of spindle proteins identified a specific mitotic kinesin, KIF18A, whose levels were drastically reduced in aneuploid cancer cells. Aneuploid cancer cells were particularly vulnerable to KIF18A depletion, and KIF18A overexpression restored the sensitivity of aneuploid cancer cells to SAC inhibition. In summary, we identified an increased vulnerability of aneuploid cancer cells to SAC inhibition and explored its cellular and molecular underpinnings. Our results reveal a novel synthetic lethal interaction between aneuploidy and the SAC, which may have direct therapeutic relevance for the clinical application of SAC inhibitors.

Aneuploidy, defined as copy number changes were independent of the specific that encompass an entire chromosome-arm or chromosome15,16. As cancer cells are almost a whole chromosome, is the most prevalent invariably aneuploid3, whereas normal cells genetic alteration in human cancer. ~90% of are (almost) always euploid17, the the solid tumors and ~75% of the identification of aneuploidy-targeting drugs hematologic malignancies are aneuploid1, has been a long sought-after goal of cancer and >25% of the tumor genome is affected, research. on average, by aneuploidy2,3. The process that gives rise to aneuploidy is chromosomal In yeast, several aneuploidy-specific cellular 18–20 instability (CIN), characterized by high vulnerabilities have been described . For prevalence of chromosome mis-segregation example, aneuploid budding yeast have and other mitotic aberrations4. Aneuploidy elevated levels of proteotoxic stress, and CIN are associated with clinical features, presumably due to the perturbation of the such as tumor stage, differentiation status and required stoichiometry of protein 19 disease aggressiveness5,6. However, our complexes . Another example is the knowledge of how CIN and aneuploidy drive increased sensitivity of aneuploid yeast to 20,21 tumorigenesis is rather limited4,7. perturbation of sphingolipid metabolism . Consequently, no existing therapeutics Although some of these findings were directly exploit this hallmark of cancer for the successfully recapitulated in mammalian 21,22 treatment of cancer patients. cells , the systematic identification of aneuploidy-induced vulnerabilities remains Aneuploidy comes with a substantial fitness elusive in human cancer. A major reason for cost, yet is well-tolerated by cancer cells1,8. that is that aneuploidy is notoriously difficult The genetic or epigenetic events that lead to to study: it affects multiple genes at once, it aneuploidy formation during tumorigenesis, plays distinct roles in different contexts, and or that allow cancer cells to tolerate it, may it is hard to engineer experimentally. also generate unique vulnerabilities in Moreover, a high degree of aneuploidy is aneuploid cells (compared to their euploid closely associated with other genomic counterparts). While some aneuploidy- alterations, such as whole-genome induced dependencies would be specific to duplication (WGD) and p53 the altered chromosome4,9–12, others may be inactivation3,23,24; and with cell division the consequence of more general cellular defects, such as CIN and micronuclei burdens associated with an abnormal number formation25–27. Large-scale studies are of chromosomes, independently of specific therefore required in order to control for karyotypes. Indeed, aneuploidy causes potentially-confounding factors, and isogenic various types of cellular stress responses: in vitro systems are then required to validate proteotoxic, metabolic, replicative, mitotic differential dependencies and dissect them and hypo-osmotic4,8,13,14. Consistent with a mechanistically. general cellular response to aneuploidy, introduction of aneuploidy into mammalian cells activated transcriptional programs that bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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.

Aneuploid cancer cells are selectively Data Fig. 1c). The full results of these sensitive to genetic perturbation of SAC comparisons are presented in core members Supplementary Table 2. DAVID functional enrichment analysis revealed that the list of To identify dependencies associated with a genes that are selectively essential in high degree of aneuploidy, we evaluated the aneuploid cancer cells is highly enriched for aneuploidy landscapes of 997 human cancer cell cycle-related pathways; in particular, the cell lines, using published copy number regulation of mitotic progression and the profiles from the Cancer Cell Line spindle assembly checkpoint came up as the 28 Encyclopedia . Each cell line was assigned top selectively-essential pathway (Fig. 1c, 3,11 an “aneuploidy score” (AS) based on the Extended Data Fig. 1d and Supplementary number of chromosome arms gained or lost Table 3). Two core members of the spindle in that cell line, relative to its basal ploidy assembly checkpoint (SAC; also known as (Fig. 1a, Extended Data Fig. 1a and the mitotic checkpoint), BUB1B (also known Supplementary Table 1). We then analyzed as BUBR1) and MAD2 (also known as the association of aneuploidy with gene MAD2L1), were at the top of the hit list (Fig. essentiality, using two distinct data sets of 1b,d, Extended Data Fig. 1b,e and loss-of-function shRNA screens across 689 Supplementary Table 2). Analysis of the 29–31 and 712 cell lines (Methods). Using the Achilles-CRISPR/Cas9 data set32 confirmed calculated aneuploidy scores as a novel that highly-aneuploid cell lines were also genomic feature, we performed a genome- more dependent on the SAC in the knockout wide comparison of the top (highly- screen (p=0.003; q=0.1; for the enrichment of aneuploid, a median of 5 chromosome-arm the GO term ‘mitotic cell cycle checkpoint’), alterations) and bottom (near-diploid, a but the association between aneuploidy and median of 3 chromosome-arm alterations) SAC essentiality was much weaker in this aneuploidy quartiles, in order to identify dataset, consistent with the inability of most differential vulnerabilities (Fig. 1a); mammalian cells to tolerate a complete loss specifically, we searched for genes whose of the SAC function33,34. Further analysis depletion is more lethal in highly-aneuploid showed that aneuploid cell lines had lower than in euploid (or near-euploid) cell lines. mRNA levels of both BUB1B and MAD2 (Fig. 1e), and expression levels were We identified 218 and 49 differential negatively correlated with essentiality (that dependencies of highly-aneuploid cells in the is, lower expression levels were associated DRIVE-shRNA and Achilles-shRNA data with greater sensitivity to genetic sets, respectively (effect size<-0.1, q<0.25; knockdown; Extended Data Fig. 1f). The Fig. 1b and Extended Data Fig. 1b). Of the other pathways found to be more essential in overlapping genes between the datasets, 14 of aneuploid cells, were the proteasome the 26 (54%) dependencies identified in the function and the DNA damage response (Fig. Achilles dataset were also identified in the 1c and Supplementary Table 3), two DRIVE dataset, indicating high concordance cellular processes previously linked to the between the datasets (p=8*10-16; Extended bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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.

cellular response to aneuploidy (reviewed in further set out to disentangle high-CIN from 13,14,35). high-AS experimentally, as described below.

We focused our downstream analyses on the Aneuploid cancer cells are selectively SAC dependency, as it was the top resistant to chemical perturbation of the SAC differential vulnerability identified in our regulator TTK analysis, and also considering that: a) SAC plays a key role in ensuring proper We next examined the association between chromosome segregation during mitosis36; b) aneuploidy and drug response, using three 12,45–48 A rich body of literature has demonstrated large-scale chemical screens . Similar that SAC perturbation leads to chromosomal to the genetic analysis, we used the cell line instability, resulting in aneuploid karyotypes AS for a comparison of drug sensitivity and frequently also in tumor formation37–42; between the top and bottom aneuploidy and c) Inhibitors of the SAC regulator TTK quartiles (Fig. 1a). We found that aneuploid (also known as MPS1) are currently used in cell lines are considerably more resistant to a clinical trials, either as single agents or in broad spectrum of drugs tested in multiple combination with chemotherapy43,44, but no large screens (Fig. 1f, Extended Data Fig. biomarkers of patients’ response to SAC 4a and Supplementary Table 4). inhibition have been reported. BUB1B and MAD2 work in concert with As noted above, the degree of tumor multiple other proteins to execute the crucial 49 aneuploidy is known to be associated with role of the SAC during mitosis . Of other genomic and cellular features, and in particular interest is the TTK serine- particular with tissue type, proliferation rate, threonine kinase, which is critical for SAC CIN, WGD, and p53 function3,5,6,23–27. recruitment to unattached kinetochores and 50 Indeed, we found all of these features to be for the complex formation . As a druggable strongly associated with AS in cancer cell kinase that is overexpressed in some cancer lines as well (Extended Data Fig. 2a-e). types, TTK has been at the focus of drug 51 Importantly, however, the increased development efforts to inhibit SAC activity . vulnerability of aneuploid cells to SAC Consequently, small molecule inhibitors perturbation was not explained by cell against this kinase have been developed and lineage, TP53 status, WGD, or proliferation entered clinical trials, either as a single agent 43,44 rate, remaining robust when accounting for or in combination with chemotherapy each of these factors (Extended Data Fig. (ClinicalTrials.gov identifier numbers: 3a-e). Of note, controlling for a gene NCT02138812, NCT02366949, expression signature of chromosomal NCT02792465, NCT03568422, instability, HET7015, also did not affect the NCT02366949, NCT03328494, strength or significance of the association NCT03411161). Importantly, each of the (Extended Data Fig. 3f). However, as three screens that we examined included a aneuploid cells are almost invariably TTK inhibitor: MPS1_IN1 in the GDSC 45,46 2 47,48 chromosomally unstable, and vice versa, we screen , AZ-3146 in the CTD screen , bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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 MPI-0479605 in the PRISM screen12. The effect of aneuploidy on the sensitivity to Aneuploid cancer cells were less sensitive to SAC inhibition evolves with time all three drugs (Fig. 1f-h, Extended Data Fig. 4a,b and Supplementary Table 4), in Why do aneuploid cells exhibit increased apparent contrast to the findings of the sensitivity to genetic perturbation of SAC genetic analysis. components, but reduced sensitivity to multiple TTK inhibitors? Three potential To confirm the drug screen results, we first explanations may underlie this discrepancy: exposed 5 near-diploid and 5 highly- 1) The degree of protein inhibition and the aneuploid cancer cell lines to the TTK target specificity may differ between genetic inhibitor reversine52. Confirming the screen and pharmacological perturbations. 2) results, aneuploid cells were more resistant to Perturbation of distinct SAC components exposure to 250nM of the compound for 72hr may have differential cellular consequences: (Extended Data Fig. 4c). In an attempt to BUB1B and MAD2 are core SAC identify genomic features associated with the components, whereas TTK is a master response to TTK inhibitors, we next regulator of the SAC. 3) The viability effect performed a pooled screen of barcoded cell may depend on the different assay time lines, using the PRISM platform53, and points: drug response was evaluated examined the response to reversine in 578 following 3d-5d of SAC inhibition, whereas adherent cancer cell lines. Viability was the response to genetic perturbations was evaluated following 5 days of exposure to 8 evaluated following ~21d of SAC inhibition, concentrations of reversine (ranging from as these are the typical time point for 0.9nM to 20mM), in triplicates. High-quality chemical and genetic perturbation screens, viability measurements could be generated respectively. for 530 cell lines, and Area Under the dose- response Curve (AUC) values were To resolve this conundrum, we turned to calculated for these cell lines isogenic models of TP53-WT near-diploid (Supplementary Table 5). A comparison of cells and their highly-aneuploid derivatives, reversine sensitivity between the top and based on HCT116, a chromosomally-stable, 54 bottom aneuploidy quartiles confirmed that near-diploid human colon cancer cell line , highly-aneuploid cells were significantly and RPE1, a chromosomally-stable, near- more resistant to reversine following 5d of diploid non-transformed human retinal 55 compound exposure (Fig. 1h and Extended epithelial cell line , that are commonly used Data Fig. 4d). Analysis of the association to study the cellular effects of 16,20,56–69 between cell line genomic features (gene- aneuploidy . We induced cytokinesis level expression, mutation and copy number failure in these cells, thus generating data) and response to reversine did not tetraploid cells, which then spontaneously 70 identify any other strong biomarker of drug became highly aneuploid . These otherwise- response (Extended Data Fig. 4e). isogenic aneuploid cell lines (termed HPT, HCT116-derived Post-Tetraploid; and RPT, RPE1-derived Post-Tetraploid) were, bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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.

together with their parental cell lines, with a complex karyotype, were significantly exposed to two TTK inhibitors, reversine and more resistant to the two TTK inhibitors (Fig. MPI-0479605. The highly-aneuploid 2c and Extended Data Fig. 5e), as well as to derivatives were more resistant to both drugs siRNA-mediated KD of BUB1B, MAD2 and in a 5-day assay (Fig. 2a and Extended Data TTK (Fig. 2d) in 5-day assays. These results Fig. 5a), consistent with the results described indicate that aneuploidy per se is a major above. Importantly, the differential effect determinant of SAC sensitivity, in line with could not be explained by different the cancer cell line data analysis presented proliferation rates (Extended Data Fig. 5b), above. nor was it a mere reflection of a general drug resistance of the aneuploid derivatives We next set out to determine whether the (Extended Data Fig. 5c). Next, we measured differences between the genetic and chemical viability of the isogenic near-diploid and screens were due to the different time points highly-aneuploid lines following siRNA- of viability assessment. We followed the mediated knockdown of BUB1B, MAD2 and proliferation of the HCT116 and HPT cell TTK. Strikingly, 72hr post-KD, the highly- lines in response to continuous genetic or aneuploid lines exhibited increased chemical SAC inhibition, using live-cell resistance to the genetic perturbation of all imaging. At d5, siRNA-mediated KD of three genes (Fig. 2b and Extended Data Fig. BUB1B, MAD2 or TTK all had a greater 5d). These results suggest that the observed effect on the near-diploid HCT116 cells, in differences between the genetic and chemical line with the previous viability measurements screens were neither due to the type of (Fig. 2e and Extended Data Fig. 6a); perturbation (i.e., genetic vs. chemical), nor however, by d14 of continuous knockdown due to the specific SAC gene being targeted. this trend reversed, and the highly-aneuploid HPT cells were much more sensitive to SAC The increased short-term resistance to SAC inhibition (Fig. 2e and Extended Data Fig. inhibition in the HPT and RPT cells may be a 6a). We observed the same reversal of consequence of their aneuploidy status, or relative sensitivity in long-term (d14) vs. may be associated with the CIN and/or the short-term (d5) viability assessment of the WGD in these highly-aneuploid cell lines. cells following continuous exposure to two Therefore, we established a third, distinct chemical TTK inhibitors (Fig. 2f-g and system of RPE1-based isogenic cell lines. We Extended Data Fig. 6b). The same effect induced transient chromosome mis- was also observed with the chromosomally- segregation in RPE1 cells68, and isolated stable, non-WGD diploid/aneuploid RPE1 stable clones of: 1) diploid cells; 2) aneuploid clones (Extended Data Fig. 6c). Thus, the cells with a single trisomy; and 3) aneuploid time point of viability assessment is critical cells with multiple trisomies. These cells for the results, which explains the apparent have not undergone WGD, and exhibited inconsistency between the genetic and stable karyotypes for >10 passages chemical screens. (unpublished data). We found that the aneuploid RPE1 lines, and especially those bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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.

Aneuploidy is associated with differential indicating that the observed expression transcriptional and cellular response to SAC changes are biologically relevant (Extended inhibition Data Fig. 7a). Next, we compared the expression changes induced by the drug in To gain insights into the response of nearly- near-diploid and highly-aneuploid cells, diploid and highly-aneuploid cancer cells to using gene set enrichment analysis SAC inhibition, we performed gene (GSEA)72. Negative regulation of cell cycle expression profiling of the HCT116/HPT cell and positive regulation of cell death were at lines, before and after SAC inhibition, using the top of the differentially-affected gene 71 the L1000 assay . Cells were exposed to sets. In particular, the GO gene sets ‘mitotic reversine (at 250nM or 500nM) or to MPI- cell cycle arrest’, ‘negative regulation of the 0479605 (250nM) and transcriptional cell cycle’ and ‘regulation of cell cycle arrest’ profiling was performed at 6hr, 24hr and 72hr were significantly more activated in the post drug exposure (Fig 3a). DMSO was highly-aneuploid cells lines (Fig. 3c). used as a negative control, and mitoxantrone Similarly, gene sets related to cell death were (at 1M) and reversine at high concentration activated in the highly-aneuploid cells more (10M) were used as positive cytotoxic strongly than in the near-diploid cells (Fig. controls. The resultant gene expression 3d). These findings suggest that at 72hr post- profiles are provided as Supplementary drug exposure, although the highly-aneuploid Table 6. We estimated the expression cells seem to be more resistant than their changes induced by each treatment relative to near-diploid counterparts, they are already its time-matched (DMSO) control and beginning to upregulate cell cycle inhibition performed unsupervised hierarchical and cell death pathways that will ultimately clustering on this differential expression lead to their elimination. matrix. Within each cell line, the transcriptional responses to the two We hypothesized that aneuploid cancer cells concentrations of reversine and to MPI- overcome SAC inhibition more readily than 0479605 were nearly identical (Fig. 3b). diploid cells, but consequently acquire severe Importantly, the two near-diploid cell lines aberrations that jeopardize their survival and clustered closer to each other than to the proliferation over time. Indeed, the HPT cells highly-aneuploid cell lines, with HPT2 overcame more quickly mitotic arrest clustering the furthest away (Fig. 3b). induced by the microtubule depolymerizing drug nocodazole (Fig. 3e). When treated with We next queried the expression profiles the SAC inhibitor reversine, the mitotic index against the Connectivity Map database, of HPT cells decreased less than that of their which compares the similarity between these parental HCT116 cells (Extended Data Fig. drugs’ transcriptional responses to those of 7b), and they were significantly more prone thousands of compounds and genetic to mitotic aberrations, such as multipolar cell reagents71. The transcriptional response to divisions, micronuclei formation and both SAC inhibitors connected strongly to cytokinesis failure (Fig. 3f,g and Extended perturbations related to cell cycle inhibition, Data Fig. 7c-e). These results confirm that bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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.

HPT cells can overcome SAC inhibition Extended Data Fig. 8b,c). Thus, highly more readily than their parental near-diploid aneuploid cells show differential expression cells, resulting in the accumulation of a of specific kinesin proteins and variety of mitotic aberrations. corresponding changes in spindle geometry.

Altered spindle geometry and dynamics Next, we asked whether the changes in underlie the selective vulnerability of KIF18A expression may be functionally aneuploid cancer cells to SAC inhibition associated with the sensitivity to SAC inhibition. To this end, we turned back to our We next set out to study the molecular large-scale genomic analysis of cancer cell underpinning of the differential response to lines (Supplementary Table 2). SAC inhibition between aneuploid and Remarkably, we found highly-aneuploid diploid cells. To this end, we analyzed the cancer cells to be significantly more changes in spindle proteins in the HPT and dependent on KIF18A KD and KO compared RPT cells compared to the parental cell lines. to near-diploid cancer cells (Fig. 4f and Strikingly, mRNA expression levels of one Extended Data Fig. 8d). KIF18A was the specific mitotic kinesin, KIF18A, were only differentially essential kinesin gene in drastically reduced in HPT cells (Fig. 4a). our analysis (out of 42 kinesin genes tested), Consequently, the protein levels of KIF18A and ranked #7 on the list of genes most were also significantly lower in the highly- selectively essential in aneuploid cancer cells aneuploid HPT cells, as measured by in the RNAi-DRIVE data set immunofluorescence and by Western blot (Supplementary Table 2). Moreover, (Fig. 4b and Extended Data Fig. 8a). siRNA-mediated KD of KIF18A in near- Interestingly, depletion of KIF18A was diploid HCT116 cells and in their highly- previously shown to alter the spindle aneuploid HPT derivatives confirmed that the geometry in a similar manner to what we aneuploid cells were more sensitive to observed in the HPT cells, making the KIF18A depletion (Fig. 4g and Extended 73,74 spindle longer and bulkier . Therefore, we Data Fig. 8e,f). Live-cell imaging identified assessed the spindle geometry and dynamics a modest mitotic delay in HPT cells in the near-diploid HCT116 cells and in their following siRNA-mediated KIF18A KD highly-aneuploid HPT derivatives. The HPT (Extended Data Fig. 8g), followed by a cells exhibited altered spindle geometry: significant increase in multipolar cell spindle length, width and angle were all divisions (Fig. 4h,i) and micronuclei significantly increased in the HPT cells, formation Extended Data Fig. 8h); in resulting in bulkier spindles (Fig. 4c,d). contrast, KIF18A depletion in the near- These structural changes were associated diploid HCT116 cells did not lead to similar with alterations in spindle activity: aberrations (Fig. 4h and Extended Data Fig. microtubule polymerization rate, EB1- 8g,h). tubulin co-localization and microtubule- kinetochore attachments were significantly Lastly, we examined whether the observed reduced in the HPT cells (Fig. 4e and association between the differential bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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.

sensitivity of aneuploid cells to SAC Our findings reveal that aneuploid cells can inhibition and KIF18A depletion was indeed initially overcome SAC inhibition more causal. We overexpressed KIF18A in the readily than diploid cells; however, the HPT cells (Extended Data Fig. 8i) and resultant aberrant cells exhibit severe examined their sensitivity to SAC inhibition. viability and proliferation defects (Fig. 5). Whereas KIF18A overexpression alone had These findings may have several important minimal effect on the viability and implications for the use of TTK inhibitors in proliferation of HPT cells, it sensitized them the clinic. First, the degree of aneuploidy to short-term SAC inhibition (Fig. 4j). This could potentially serve as a biomarker for ‘phenotypic rescue’ experiment demonstrates predicting the response of patients to this a causal link between KIF18A levels and the class of drugs. As aneuploidy assessment is cellular sensitivity to SAC inhibition. Further not routinely conducted in clinical trials, study is required in order to elucidate the future studies should test this hypothesis in a nature of this interaction at the molecular clinical setting. Second, we show that the level. estimate of the response to SAC inhibition strongly depends on the time of response Discussion assessment; therefore, standard in vitro drug response assays, which often last 3-5 days, The potential of targeting aneuploid cells to are insufficient for the evaluation of the long- selectively kill cancer cells remains term effect of SAC inhibition. Our findings unfulfilled due to the complexity of the explain observed discrepancies between problem and the paucity of relevant model genetic and pharmacological screens, and systems. Here, we assigned aneuploidy highlight the need to test drugs (especially, scores to ~1,000 cancer cell lines, performed those that target chromosome segregation a comprehensive analysis of large-scale and cell division) throughout longer periods genetic and chemical perturbation screens, of time. and identified increased dependency of aneuploid cancer cells on the SAC core We found that siRNAs against BUB1B, members, BUB1B and MAD2. Using three MAD2 and TTK similarly affect the viability model systems of isogenic near-diploid and and proliferation of cells, indicating that the highly-aneuploid cell lines, we confirmed the major reason for the discrepancy between the increased vulnerability of aneuploid cells to genetic screens and the chemical screens is SAC inhibition. Transcriptional profiling and the time of viability assessment. We note, imaging-based analyses of mitosis revealed however, that unlike BUB1B and MAD2, an altered response of aneuploid cells to SAC TTK did not come as a differential inhibition. Finally, we found the kinesin gene dependency of aneuploid cells in any of the KIF18A to be uniquely essential in aneuploid genetic screens, and was not transcriptionally cells, and functionally related to their downregulated in aneuploid cells. It is increased dependency on the SAC activity. therefore plausible that there is also a functional difference between these genes with regard to their essentiality in aneuploid bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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.

cells. It will be important to further elucidate findings may have direct therapeutic this issue in order to determine the relevance for the clinical application of TTK importance of developing selective inhibitors inhibitors. of BUB1B and MAD2 (in addition to TTK inhibitors). Lastly, our large-scale analyses revealed additional candidate vulnerabilities that are We identified reduced levels of the mitotic worthy of experimental validation (e.g., kinesin KIF18A in aneuploid cancer cells and increased sensitivity to proteasome increased sensitivity to its inhibition. This inhibition; Supplementary Table 3). sensitivity is interesting per se, given the Furthermore, our characterization of attempts to develop highly-selective and aneuploidy profiles and scores across the bioactive KIF18A inhibitors75,76. CCLE lines (Supplementary Table 1) will Importantly, we found that the increased be useful for the identification of additional dependency of aneuploid cells on KIF18A is genomic features and cellular vulnerabilities functionally related to their sensitivity to associated with high degree of aneuploidy or SAC inhibition. In line with our results, a with specific recurrent aneuploidies. To recent study reported that altering facilitate the further interrogation of this microtubule polymerization rates synergized resource, we have integrated the cell line with SAC inhibition in blocking cell aneuploidy profiles and scores into the proliferation65, and a parallel study indicates DepMap portal (www.depmap.org/portal/), that a loss of KIF18A activity, which alters where they can serve as novel genomic microtubule polymerization rates within the features for all of the portal’s applications. spindle, leads to extended mitotic delays, We hope that this study will thus pave the multipolar spindles, and reduced way for the routine integration of aneuploidy proliferation in aneuploid cells (Stumpff lab; status in the genomic analysis of cancer unpublished data). An important question dependencies. remains what selective advantage is provided to aneuploid cells by downregulation of KIF18A. Our current results suggest two non-exclusive hypotheses. First, low levels of KIF18A enable formation of a longer, bulky spindle that might better accommodate a large number of chromosomes. Second, altered microtubule dynamics due to reduced KIF18A levels may increase the tolerance of aneuploid cells to mitotic errors. Importantly, the selective vulnerability of highly aneuploid cells to KIF18A and SAC inhibition may open a novel window of opportunity for treatments of cancers with complex karyotypes. Most concretely, our bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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 and Table Legends large-scale CTD2 drug screen47,48. AZ-3146, the only SAC inhibitor included in the screen, Figure 1: Differential sensitivity of is highlighted in red. (g) The sensitivity of aneuploid cancer cells to inhibition of the near-euploid and highly-aneuploid cancer spindle assembly checkpoint. (a) cell lines to the SAC inhibitor AZ-3146 in the Schematics of our large-scale comparison of CTD2 drug screen. **, p=0.0017; two-tailed genetic and chemical dependencies between Student’s t-test. (h) The sensitivity of near- near-euploid and highly-aneuploid cancer euploid and highly-aneuploid cancer cell cell lines. Cell lines were assigned lines to the SAC inhibitor reversine, as aneuploidy scores (AS), and the genetic and evaluated by the PRISM assay12. ***, p=2e- chemical dependency landscapes were 04; two-tailed Student’s t-test. compared between the top and bottom AS quartiles. (b) A volcano plot showing the Figure 2: The effect of aneuploidy on differential genetic dependencies between cellular sensitivity to SAC inhibition in the near-euploid and highly-aneuploid cancer isogenic human cell lines. (a) Top: dose cell lines, based on the genome-wide Achilles response curves of near-diploid HCT116 RNAi screen29,30. BUB1B and MAD2, core cells and their highly-aneuploid derivatives members of the SAC, are highlighted in red. HPT cells, to the SAC inhibitor MPI- (c) The pathways enriched in the list of genes 0479605 following 120hr of drug exposure. that are more essential in highly-aneuploid Bottom: dose response curves of near-diploid than in near-euploid cancer cell lines (effect RPE1 cells and their highly-aneuploid size<-0.1,q<0.1) in the Achilles RNAi derivatives RPT cells, to the SAC inhibitor screen, based on DAVID functional MPI-0479605 following 120hr of drug annotation enrichment analysis77. Note that exposure. *, p<0.05; **, p<0.005; ***, the most enriched pathway is the SAC. *, p<0.0005; two-tailed Student’s t-test. Error adjusted-p value < 0.1. (d) The sensitivity of bars, s.d. (b) Top: the relative viability of near-euploid and highly-aneuploid cancer HCT116 and HPT cells following 72hr of cell lines to the knockdown of BUB1B (left) siRNA-mediated knockdown of the SAC and MAD2 (right) in the Achilles RNAi components BUB1B, MAD2 and TTK. screen. The more negative a value, the more Bottom: the relative viability of RPE1 and essential the gene is in that cell line. ****, RPT cells following 120hr of siRNA- p=5e-07 and p=1e-07 for BUB1B and MAD, mediated knockdown of three SAC respectively; two-tailed Student’s t-test. (e) components: BUB1B, MAD2 and TTK. The gene expression levels of BUB1B (left) Results are normalized to a non-targeting and MAD2 (right) in the Cancer Cell Line siRNA control. **, p<0.005; ***, p<0.0005; Encyclopedia28. **, p=0.002; ****, p=1e-05 two-tailed Student’s t-test. Error bars, s.d. (c) for BUB1B and MAD, respectively; two- Dose response curves of the near-diploid tailed Student’s t-test. (f) A volcano plot RPE1 clone SS48 and its isogenic aneuploid showing the differential drug sensitivities clones SS51 (+Ts7, +Ts22) and SS111 between the near-euploid and highly- (+Ts8, +Ts18), to the SAC inhibitor MPI- aneuploid cancer cell lines, based on the 0479605 following 120hr of drug exposure. bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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.

*, p<0.05; **, p<0.005; ***, p<0.0005; two- effect of SAC inhibition between the near- tailed Student’s t-test. Error bars, s.d. (d) The diploid and highly-aneuploid cell lines. (b) relative viability of 3 near-diploid and 4 Unsupervised hierarchical clustering of the 4 aneuploid RPE1 clones following 72hr of cell lines based on the transcriptional change siRNA-mediated knockdown of 3 SAC induced by the drugs. HCT116 cells cluster components: BUB1B, MAD2 and TTK. closer to each other than to the HPT cells. (c) Results are normalized to a non-targeting Functional enrichment of gene sets related to cell cycle regulation. Shown are the gene sets siRNA control. **, p<0.005; ***, p<0.0005; that were significantly more affected by two-tailed Student’s t-test. Error bars, s.d. (e) SACi in the highly-aneuploidy HPT1 and Time-lapse imaging-based proliferation HPT2 cells than in the nearly-diploid curves of HCT116 and HPT cells cultured in HCT116-WT and HCT116-GFP cells. *, the presence of siRNAs against BUB1B, p<0.05. (d) Functional enrichment of gene MAD2 and TTK, or a non-targeting control sets related to cell death. Shown are the gene siRNA. The top panels present days 1-5 of sets that were significantly more affected by the experiment, whereas the bottom panels SACi in the highly-aneuploidy HPT1 and present days 10-14 of the experiment. Error HPT2 cells than in the nearly-diploid bars, s.d. Linear growth slope-based doubling HCT116-WT and HCT116-GFP cells. *, times are presented in Extended Data Fig. 6a. p<0.05. (e) Time from mitotic arrest to (f) Time-lapse imaging-based proliferation slippage/division following exposure of curves of HCT116 and HPT cells cultured in HCT116-GFP, HPT1 and HPT2 cells to a the presence of MPI-0479605 (250nM) or high concentration (200ng/mL) of DMSO control (0nM). The top panels present nocodazole. **, p<0.005; two-tailed Student’s t-test. (f) The prevalence of days 1-5 of the experiment, whereas the micronuclei formation in HCT116 and HPT bottom panels present days 10-14 of the cells cultured under standard conditions or experiment. Error bars, s.d. Linear growth exposed to the SAC inhibitor reversine slope-based doubling times are presented in (500nM). *, p=0.007; ***, p<0.0001; two- Extended Data Fig. 6b. (g) Representative tailed Fisher’s exact test. (g) Representative images of the cells from the experiment images of micronuclei formation in HPT2 described in (f). Scale bar, 400µm. cells exposed to reversine (500nM). Scale bar, 10µm. Figure 3: Transcriptional and cellular characterization of SAC inhibition in Figure 4: Altered spindle geometry and aneuploid cells. (a) Schematics of the gene dynamics, and increased dependency on expression profiling experiment. HCT116 the mitotic kinesin KIF18A, in aneuploid and HPT cells were treated with two SAC cancer cells. (a) A volcano plot showing the inhibitors, reversine (250nM and 500nM) differential mRNA expression levels of all and MPI-0479605 (250nM), global gene mitotic kinesins between the near-diploid expression profiles were generated at 6hr, HCT116 and highly-aneuploid HPT cell 24hr and 72hr post-drug exposure, and gene lines. Data are based on genome-wide set enrichment analysis (GSEA) was microarray-based transcriptional profiling performed to compare the transcriptional (GSE47830). KIF18A is highlighted in red. bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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.

(b) Left: Western blot of KIF18A protein multipolar spindles in HPT2 cells following expression levels in HCT116 and HPT cell siRNA-mediated KIF18A knockdown. Scale lines. Right: Quantification of KIF18A bar, 10µm. (j) Time-lapse imaging-based expression levels (normalized to GAPDH). *, proliferation curves of highly-aneuploid p<0.05; two-tailed Student’s t-test. Error HPT1 and HPT2 cell lines before and after bars, s.d. (c) Imaging of metaphase spindle in transduction with a vector expressing the HCT116-GFP, HPT1 and HPT2 cells, Scale KIF18A open reading frame (KIF18A-ORF), bars, 10µm. (d) Imaging-based quantification in the absence or presence of the SAC of spindle length (top left), spindle width (top inhibitor MPI-0479605 (250nM). Note that right), and spindle angle (bottom left) in overexpression of KIF18A does not affect the HCT116 and HPT cell lines cultured under proliferation of the cells in the absence of standard conditions. The definition of length, SACi, but increases the inhibitory effect of width and angle is shown (bottom right). ***, SAC inhibition drastically. Error bars, s.d. p<0.0005. Bar, median; box, 25th and 75th percentile; whiskers, 1.5 X interquartile Figure 5: A model of the evolving response range of the lower and upper quartiles; of aneuploid cancer cells to SAC circles, individual cell lines. (e) Imaging- inhibition. Aneuploid cells have an altered based quantification of the percentage of spindle and altered activity of KIF18A. spindle microtubuli-bound kinetochores in Aneuploid cells overcome SAC inhibition HCT116 and HPT cell lines. ***, p<0.0005. more rapidly than diploid cells, leading to Bar, median; box, 25th and 75th percentile; their apparent reduced sensitivity to SAC whiskers, 1.5 X interquartile range of the inhibition, when viability is assessed early on lower and upper quartiles; circles, individual (3-5 days post treatment). However, this cell lines. (f) The sensitivity of near-euploid results in a significant increase of aberrant and highly-aneuploid cancer cell lines to the mitoses, giving rise to abnormal cell fates knockdown of KIF18A in the RNAi-DRIVE (e.g., detrimental karyotypes, micronuclei dataset. The more negative a value, the more formation, failed cytokinesis) that lead to essential the gene is in that cell line. ***, increased cell death. Consequently, p=3e-04; two-tailed Student’s t-test. (g) aneuploid cancer cells are more sensitive to Time-lapse imaging-based proliferation SAC inhibition, when viability is assessed curves of HCT116 and HPT cells cultured in after a few cell divisions (14-21 days post the presence of a KIF18A-targeting siRNA, treatment). The response of aneuploid cells to or a non-targeting control siRNA. Error bars, SAC inhibition is functionally associated s.d. (h) Imaging-based quantification of the with KIF18A activity, indicating an prevalence of cell divisions with multipolar important role for this mitotic kinesin in SAC spindles in HCT116 and HPT cell lines regulation. treated with non-targeting control or KIF18A-targeting siRNAs. n.s., p>0.05; *, p=0.03; two-tailed Student’s t-test. Bar, Extended Data Figure 1: Increased median; box, 25th and 75th percentile; sensitivity of aneuploid cancer cells to whiskers, 1.5 X interquartile range of the genetic inhibition of the spindle assembly lower and upper quartiles; circles, individual checkpoint. (a) Copy number profiles of 5 cell lines. (i) Representative images of representative breast cancer cell lines from bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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 highly-aneuploid cell line group (top lines. (a) The distribution of aneuploidy quartile of aneuploidy scores) and 5 scores across 23 cancer types. Bar, median; representative breast cancer cell lines from box, 25th and 75th percentile; whiskers, 1.5 X the near-euploid cell line group (bottom interquartile range of the lower and upper quartile of aneuploidy scores). (b) A volcano quartiles; circles, individual cell lines. (b) plot showing the differential genetic Comparison of aneuploidy scores between dependencies between the near-euploid and cancer cell lines with distinct TP53 mutation highly-aneuploid cancer cell lines, based on status (based on CCLE annotations)28. n.s., the genome-wide DRIVE RNAi screen31. p>0.05; ****, p=6e-22 and p=2e-14 for the BUB1B and MAD2, core members of the comparisons between TP53-WT and ‘non- SAC, are highlighted in red. (c) A Venn conserving’ and TP53-WT and ‘damaging’ diagram showing the overlap of the mutations, respectively; two-tailed Student’s differentially-dependent genes (q<0.25) t-test. (c) Comparison of aneuploidy scores between the Achilles and DRIVE RNAi between cancer cell with distinct genome screens. ****, p=8e-16, two-tailed Fisher’s doubling (WGD) status. ****, p=1e-192, Exact Test. (d) The pathways enriched in the p=2e-96 and p=6e-13 for the comparisons list of genes that are more essential in near- between WGD=0 and WGD=1, WGD=0 and euploid than in highly-aneuploid cancer cell WGD=2, and WGD=1 and WGD=2, lines (effect size<-0.1,q<0.1) in the DRIVE respectively; two-tailed Student’s t-test. (d) RNAi screen, based on DAVID functional Comparison of the HET70 score, a measure annotation enrichment analysis77. *, of karyotypic instability15, between the near- adjusted-p value < 0.1. (e) The sensitivity of diploid and highly-aneuploid cell line groups. near-euploid and highly-aneuploid cancer ****, p=4e-07; two-tailed Student’s t-test. (e) cell lines to the knockdown of BUB1B (top) Comparison of doubling time between the and MAD2 (bottom) in the DRIVE RNAi near-diploid and highly-aneuploid cell line screen. The more negative a value, the more groups. **, p=0.0029; two-tailed Student’s t- essential the gene is in that cell line. ****, test. p=1e-06 and p=2.3e-04 for BUB1B and MAD, respectively; two-tailed Student’s t- Extended Data Figure 3: Increased test. (f) The correlations between the mRNA sensitivity of aneuploid cancer cells to SAC expression levels of BUB1B (top) and inhibition remains significant when MAD2 (bottom) and the genetic dependency associated genomic and phenotypic on these genes in the Achilles (left) and features are controlled for. (a) The DRIVE (right) RNAi screens. Pearson’s r = sensitivity of near-euploid and highly- 0.27 (p=3.7e-05), 0.31 (p=2e-06), 0.26 aneuploid cancer cell lines to the knockdown (p=5e-03) and 0.32 (p=1.2e-05), of BUB1B (top) and MAD2 (bottom) in the respectively. Achilles (left) and DRIVE (right) RNAi screens across multiple cell lineages. *, Extended Data Figure 2: Genomic and p<0.05; **, p<0.005; two-tailed Student’s t- phenotypic features associated with the test. (b) The sensitivity of near-euploid and degree of aneuploidy in human cancer cell highly-aneuploid cancer cell lines to the bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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.

knockdown of BUB1B (top) and MAD2 RNAi-Achilles BUB1B, RNAi-Achilles (bottom) in the Achilles (left) and DRIVE MAD2 and RNAi-DRIVE BUB1B (right) RNAi screens, after accounting for dependencies, respectively; **, p=0.001; lineage-specific differences in gene two-tailed Student’s t-test. dependency scores using linear regression. ***, p=5e-04; ** p=0.0015; *, p=0.0139; Extended Data Figure 4: Reduced n.s., p=0.067; two-tailed Student’s t-test. (c) sensitivity of aneuploid cancer cells to The sensitivity of near-euploid and highly- chemical inhibition of the spindle assembly aneuploid cancer cell lines to the knockdown checkpoint. (a) Volcano plots showing the of BUB1B (top) and MAD2 (bottom) in the differential drug sensitivities between the Achilles (left) and DRIVE (right) RNAi near-euploid and highly-aneuploid cancer screens, across TP53 mutation classes. *, cell lines, based on the large-scale GDSC46 p<0.05; **, p<0.005, ***, p<0.0005; two- and PRISM screens12. MPS1-IN-1 and MPI- tailed Student’s t-test. (d) The correlations 0479605, the only SAC inhibitors included in between aneuploidy scores and the each screen, respectively, are highlighted in dependency on BUB1B (top) and MAD2 red. (b) The sensitivity of near-euploid and (bottom) in the Achilles (left) and DRIVE highly-aneuploid cancer cell lines to the SAC (right) RNAi screens, for cell lines that have inhibitors MPS1-IN-1 and MPI-0479605 in not undergone whole-genome duplication the GDSC (top) and PRISM (bottom) (i.e., cell lines with basal ploidy of n=2). screens. ****, p=3.2e-0.5; n.s., p=0.17; two- Pearson’s r = -0.27 (p=2e-04), -0.40 (p=3e- tailed Student’s t-test. (c) Experimental 08), -0.33 (p=2.8e-05) and -0.23 (p=0.003), validation of the response of 5 near-euploid respectively. (e) The sensitivity of near- (CAL51, EN, MHHNB11, SW48 and euploid and highly-aneuploid cancer cell VMCUB1) and 5 highly-aneuploid lines to the knockdown of BUB1B (top) and (MDAMB468, NCIH1693, PANC0813, MAD2 (bottom) in the Achilles (left) and SH10TC and A101D) cell lines to 72hr DRIVE (right) RNAi screens, after removing exposure to the SAC inhibitor reversine. **, the effect of doubling time on gene p=0.001, two-tailed Student’s t-test. (d) dependency scores using linear regression. Comparison of the sensitivity to reversine ****, p=2e-05 and p=5e-07, for RNAi- between near-euploid and highly-diploid Achilles BUB1B and MAD2 dependencies, cancer cell lines subjected to the PRISM cell respectively; ***, p=0002; **, p=0.003; two- viability assay, confirming the reduced tailed Student’s t-test. (f) The sensitivity of sensitivity of highly-aneuploid cells to a near-euploid and highly-aneuploid cancer 120hr exposure to SAC inhibitors. n.s., cell lines to the knockdown of BUB1B (top) p>0.05; *, p<0.05; **, p<0.005; two-tailed and MAD2 (bottom) in the Achilles (left) and Student’s t-test. (e) An association analysis DRIVE (right) RNAi screens, after removing failed to identify a genomic biomarker of the effect of HET70 scores on gene reversine sensitivity. Shown are the top 1000 dependency scores using linear regression. genomic features identified by our model ****, p=1e-06, p=4e-06 and p=9e-07 for (see Methods). No feature stands out in bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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.

terms of importance and/or correlation, and siRNAs against BUB1B, MAD2 or TTK. the overall predictive value is poor. The drug effect of SACi is stronger in the near-diploid HCT116 cells at d5, but is Extended Data Figure 5: The effect of stronger in the highly-aneuploid HPT cells at aneuploidy on cellular sensitivity to SAC d21. Error bars, s.d. (b) Comparison of the inhibition in isogenic human cell lines. (a) doubling times of HCT116 and HPT cells Top: dose response curves of the response of exposed to the SAC inhibitors MPI-0479605 near-diploid HCT116 cells and their highly- or reversine. The drug effect of SACi is aneuploid derivatives HPT cells, to the SAC stronger in the near-diploid HCT116 cells at inhibitor reversine following 120hr of drug d5, but at d21 it becomes stronger in the exposure. Bottom: dose response curves of highly-aneuploid HPT cells. *, p<0.05; **, the response of near-diploid RPE1 cells and p<0.005; one-tailed Student’s t-test. Error their highly-aneuploid derivatives RPT cells, bars, s.d. (c) The relative viability of the to the SAC inhibitor reversine following aneuploid RPE1 clones, SS111 and SS51, 120hr of drug exposure. *, p<0.05; **, following reversine exposure. The viability p<0.005; ***, p<0.0005; two-tailed effect was normalized to the effect of the drug Student’s t-test. Error bars, s.d. (b) Time- in the near-diploid RPE1 clone, SS48. The lapse imaging-based proliferation curves of drug effect of SACi is comparable during the HCT116 and HPT cells under standard first week of drug exposure, but the highly- culture conditions. Error bars, s.d. (c) Dose aneuploid cells become significantly more response curves of the response of HCT116 sensitive with time. *, p<0.05, **, p<0.005; and HPT cells to three drugs with unrelated two-tailed Student’s t-test. mechanisms of action. (d) Relative mRNA expression levels of BUB1B, MAD2 and Extended Data Figure 7: Transcriptional TTK, confirming successful siRNA- and cellular characterization of SAC mediated knockdown of each gene in all cell inhibition in aneuploid cells. (a) The top 10 lines. ****, p<5e-04; two-tailed Student’s t- results of a Connectivity Map (CMap) test. Error bars, s.d. (e) Dose response curves query71 of the transcriptional response of of the response of the near-diploid RPE1 HCT116 and HPT cells to the SAC clone SS48 and its isogenic aneuploid clones inhibitors, reversine (250nM and 500nM) SS51 (+Ts7, +Ts22) and SS111 (+Ts8, and MPI-0479605 (250nM). The top +Ts18), to the SAC inhibitor reversine connection is “Cell cycle inhibition”, following 120hr of drug exposure. *, p<0.05; correctly identifying the expected **, p<0.005; ***, p<0.0005; two-tailed mechanism of action of these compounds. Student’s t-test. Error bars, s.d. GOF, gain of function; OE, over-expression; KD, knockdown. (b) The mitotic index of Extended Data Figure 6: Time-dependent HCT116 and HPT cells cultured under increased sensitivity of aneuploid cancer standard conditions or exposed to the SAC cells to genetic and chemical SAC inhibitor reversine (500nM). *, p=0.035; n.s., inhibition. (a) Comparison of the doubling p=0.17; two-tailed Student’s t-test. (c) times of HCT116 and HPT cells exposed to Imaging-based quantification of the bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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.

prevalence of cell divisions with multipolar sensitivity of near-euploid and highly- spindles in HCT116 and HPT cell lines aneuploid cancer cell lines to the knockout of cultured under standard conditions or treated KIF18A in the CRISPR-Achilles dataset. The with reversine (500nM). (d) The prevalence more negative a value, the more essential the of premature mitotic exit (cytokinesis failure) gene is in that cell line. *, p=0.034; two-tailed in HCT116 and HPT cells exposed to the Student’s t-test. (e) Relative mRNA SAC inhibitor reversine (500nM). *, expression levels of KIF18A, confirming p=0.047; two-tailed Fisher’s exact test. (e) successful siRNA-mediated KD in all cell Representative images of premature mitotic lines. Error bars, s.d. ****, p<5e-04; two- exit in HPT2 cells exposed to reversine tailed Student’s t-test. (f) Comparison of the (500nM). T=0 defines nuclear envelope doubling times of HCT116 and HPT cells breakdown (NEB). Scale bar, 10µm. following siRNA-mediated KIF18A knockdown. Error bars, s.d. **, p=0.001; Extended Data Figure 8: Increased two-tailed Student’s t-test. (g) Time-lapse sensitivity of aneuploid cancer cells to imaging-based quantification of the time perturbation of the mitotic kinesin from nuclear envelope breakdown (NEBD) KIF18A. (a) Left: Imaging kinetochore- to anaphase onset in HCT116 and HPT cell bound KIF18A protein levels in HCT116- lines exposed to non-targeting or KIF18A- GFP, HPT1 and HPT2 cells, Scale bars, targeting siRNAs. n.s, p>0.05; **, p=0.003; 10µm. Right: Immunofluorescence-based two-tailed Student’s t-test. Bar, median; box, quantification of KIF18A protein levels. **, 25th and 75th percentile; whiskers, 1.5 X p<0.005, two-tailed Student’s t-test. (b) Left: interquartile range of the lower and upper Imaging-based quantification of microtubule quartiles; circles, individual cell lines. (h) polymerization rate in HCT116 and HPT The prevalence of micronuclei formation in cells cultured under standard conditions. HCT116 and HPT cells exposed to non- Right: Imaging-based quantification of targeting or KIF18A-targeting siRNAs. n.s., microtubule regrowth following complete p>0.05; ***, p<0.0005; two-tailed Fisher’s depolymerization in HCT116 and HPT cells. exact test. (i) Relative mRNA expression Bar, median; box, 25th and 75th percentile; levels of KIF18A, confirming successful whiskers, 1.5 X interquartile range of the KIF18 overexpression in the highly- lower and upper quartiles; circles, individual aneuploid HPT1 and HPT2 cell lines. ***, cell lines. *, p<0.05; **, p<0.005; ****, p<0.0005; ****, p<5e-04; two-tailed p<5e-4; two-tailed Student’s t-test. (c) Student’s t-test. Error bars, s.d. Imaging-based quantification of EB1- tubulin co-localization in HCT116 and HPT cells cultured under standard conditions. **, Supplementary Table 1: Chromosome- th th p<0.005. Bar, median; box, 25 and 75 arm copy number calls and aneuploidy percentile; whiskers, 1.5 X interquartile scores for 997 human cancer cell lines. For range of the lower and upper quartiles; each cell line, the copy number status of each circles, individual cell lines. (d) The chromosome arm was determined by bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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.

comparing the weighted median log2 copy HCT116-GFP, HPT1 and HPT2 cells treated number (wmed_CN) value of the arm to the with reversine (250nM or 500nM), MPI- basal ploidy of the cell line. Aneuploidy 0479605 (250nM), positive controls scores were determined as the number of (reversine at 10µM and Mitoxantrone at chromosome arms that were gained or lost in 10µM) or negative controls (DMSO). each cell line.

Supplementary Table 2: Genetic dependencies of highly-aneuploid cancer cells. The lists include all genes on which aneuploid cancer cell lines were found to be more dependent than euploid cancer cell lines (effect size<-0.1, q<0.25) in the RNAi- Achilles and RNAi-DRIVE genetic dependency screens. Supplementary Table 3: Functional annotation enrichment analysis of aneuploidy-associated genetic dependencies. DAVID functional enrichment analysis was performed on the genes that came up as differentially essential between the near-euploid and highly- aneuploid cell lines, focusing on the GO_BP gene sets. Gene sets with q<0.1 are listed. Supplementary Table 4: Chemical sensitivities of highly-aneuploid cancer cells. The lists present the differential dug sensitivities between near-euploid and highly-aneuploid cancer cell lines, for all of the compounds tested in the CTD2, GDSC and PRISM drug screens. Supplementary Table 5: Cancer cell line sensitivity to the SAC inhibitor reversine. Results of a PRISM screen of reversine (at 8 doses) across 530 human cancer cell lines. Shown are the Area Under the ROC Curve (AUC) values. Supplementary Table 6: Gene expression profiles of HCT116 and HPT cells exposed to SAC inhibitors. L1000-based expression values of 10,174 genes71 in HCT116-WT, bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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.

Methods https://doi.org/10.6084/m9.figshare.6025238 .v4 and CRISPR, mutation, and expression Aneuploidy score assignment data is available at Aneuploidy was quantified by estimating the https://doi.org/10.6084/m9.figshare.1138424 total number of arm-level gains and losses for 1.v2. The chemical perturbation datasets used each cell line, based on the published were the PRISM Repurposing Secondary ABSOLUTE copy number data (Ghandi et Screen12, CTD2 48,78, and GDSC79. Cell lines al., 2019). The median modal copy number were split into two groups: the upper and across segments was estimated for each lower quartiles of aneuploidy scores. Genes chromosome arm (weighted for segment that were preferentially dependent in highly- length), and compared to the cell lines’ aneuploid compared to near-diploid cell lines background ploidy in order to call the were identified using linear modelling chromosome-arm copy number status performed in parallel across genes using the (gain/loss/neutral). Aneuploidy score was R package Limma80. The difference in mean defined as the number of chromosome arms dependency between the groups was that were gained/lost. The cell lines with evaluated for each gene, and associated P- lower-quartile aneuploidy scores values were derived from empirical-Bayes- (corresponding to cell lines with a median of moderated t-statistics. Q values were 3 chromosome-arm copy number changes; computed using the Benjamini–Hochberg min = 0, max = 7) were defined as the “near- method81. This process was repeated with euploid” group, and the cell lines with the various features of the cell lines (cell lineage, upper-quartile aneuploidy scores HET70 score or doubling time) included as a (corresponding to cell lines with a median of covariate. To remove the effects of 25 chromosome-arm copy number changes; confounding variables (cell lineage, HET70 min = 22, max =39) were defined as the or doubling time), we fit linear regression “highly-aneuploid” group. models (Scikit-learn)82 and computed the residuals, maintaining the across-cell line Association of aneuploidy with genomic average dependency scores fixed. and phenotypic features Functional enrichment analysis Cell line doubling time measurements were obtained from Tsherniak et al29. The mutation The list of differentially-essential genes calls and mRNA expression levels were between the near-euploid and highly- obtained from the CCLE mutation and gene aneuploid groups (effect size<-0.1, q<0.1) expression data sets (19q4 DepMap release; was subjected to a DAVID functional CCLE_mutations.csv and annotation enrichment analysis77, focusing CCLE_expression_full.csv, respectively)28. on the GO Biological Process gene sets. The The genetic perturbation datasets used were full list of genes included in each screen was the gene_effect files from RNAi Achilles30, used as background. RNAi DRIVE30, and CRISPR Achilles (19q4 DepMap release). RNAi data is available at Reversine biomarker analysis bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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 scikit-learn’s RandomForestRegressor82 RPMI-1640 (Life Technologies) with 10% was used to predict Reversine AUC values fetal bovine serum (Sigma-aldrich) and 1% for 502 cell lines. The input features were penicillin-streptomycin-glutamine (Life (19Q4 release): RNA-Seq expression data for Technologies). PANC0813 medium was both protein-coding and non-coding regions supplemented with 10units/mL human (CCLE_expression_full.csv); mutation recombinant insulin (Sigma-Aldrich), and statuses, broken into three binary matrices: MHHNB11 medium was supplemented with damaging, hotspot and other MEM Non-Essential Amino Acids (Sigma- (CCLE_mutations.csv); and gene level copy Aldrich). Cells were incubated at 37°C, 5% number (CCLE_gene_cn.csv). Data are CO2 and passaged twice a week using available at Trypsin-EDTA (0.25%) (Life Technologies). https://doi.org/10.6084/m9.figshare.1138424 Cells were tested for mycoplasma 1.v2. Like in Dempster et al., we used tenfold contamination using the MycoAlert cross-validation, filtered features to the 1,000 Mycoplasma Detection Kit (Lonza), having the highest Pearson correlation with according to the manufacturer’s instructions. the Reversine AUC values in the training set, and reported accuracy via Pearson correlation PRISM screening between the measured AUC values and the The PRISM screen was performed as 83 complete set of out-of-sample predications . described in Corsello et al.12. Briefly, To estimate feature importance values we barcoded cell lines were pooled (25 cell lines retrained the model on all the samples and per pool) based on doubling time and frozen used RandomForestRegressor’s into assay-ready vials. Vials were thawed and feature_importances_ attribute. This attribute 1 pool was immediately plated in 384-well is a measure of the average contribution of a plate at 1,250 cells per well in triplicate. 24hr feature to decreasing the variance when later, cells were plated onto assay ready splitting values at nodes. plates containing 8 different concentrations Cell culture of reversine (3 fold-dilutions ranging from 0.9nM to 20µM) or control DMSO. 5d later, HCT116, RPE1 cells, and their aneuploid cells were lysed, and lysate plates were then derivatives, HPT and RPT, were cultured in pooled for amplification and barcode DMEM (Life Technologies) with 10% fetal measurement. Viability values were bovine serum (Sigma-aldrich) and 1% calculated by taking the median fluorescence penicillin-streptomycin-glutamine (Life intensity of beads corresponding to each cell Technologies). MDAMB468, A101D, EN, line barcode, and normalizing them by the VMCUB1, CAL51 and SW48 were also median of DMSO control treatments. Dose cultured in DMEM (Life Technologies) with response curves were calculated by fitting 10% fetal bovine serum (Sigma-aldrich) and four-parameter curves to viability data for 1% penicillin-streptomycin-glutamine (Life each compound and cell line using the R Technologies). SH10TC, NCIH1693, package “drc” and fixing the upper MHHNB11 and PANC0813 were cultured in asymptote of the logistic curves to 1; the area bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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.

under the dose response curve (AUC) was that were averaged and normalized to calculated using a normalized integral, as negative (DMSO-matched) control. discussed in Corsello et al.12 Cell transfections Cell growth rate analysis Cells were seeded in 100µL of medium in Kinetic cell proliferation assays were black, clear bottom 96-well plates (Corning 3 monitored using the IncuCyte S3 Live Cell 3904) excluding edge wells at 5 × 10 cells Analysis System (Essen Bioscience). 96-well per well a day prior to transfections. For plates were incubated at 37°C, 5% CO2. Four, siRNA experiments, cells were transfected non-overlapping planes of view phase with 25nM siRNA against BUB1B, contrast images were captured using a 10x MAD2L1, TTK, KIF18A, or a non-targeting objective, with data collected every 4hr for control (Dharmacon ON-TARGETplus the duration of each experiment. Incucyte SMARTpool) in triplicate using Base Software was used to calculate average DharmaFECT 1 Transfection Reagent confluence. Population doublings were (Dharmacon) as per the manufacturer’s calculated using the protocol. For KIF18A overexpression formula Tdoubling = (log2(ΔT))/(log(c2) − log(c experiments, cells were transfected with 25 1)), where c1 and c2 are the minimum and nM pMX229, a gift from Linda Wordeman maximum percentage confluency during the (Addgene plasmid #23002), using TransIT®- linear growth phase, respectively, and LT1 Transfection Reagent (Mirus). For ΔT was the time elapsed between c1 and c2. combination experiments with SAC inhibitors, cells were transfected and treated Drug response assays with drugs simultaneously. Following incubation with the siRNAs, the For drug experiments, cells were plated at 1 overexpressing vector and/or the drugs, 4 × 10 cells per well and treated with viability was assessed either by live-cell compounds 24hr later. MPI-0479605 was imaging using the IncuCyte S3 Live Cell purchased from MedChem Express Analysis System (Essen Bioscience) or using (Princeton, NJ, USA), reversine and CellTiter-Glo (Promega). Luminescence was mitoxantrone were purchased from Sigma- measured using an Envision Plate Reader Aldrich (Saint-Louis, MO, USA, and). (PerkinElmer). Experiments were performed Following incubation with the drug, viability in triplicates that were averaged and was assessed either by live-cell imaging normalized to negative (DMSO-matched) using the IncuCyte S3 Live Cell Analysis control. System (Essen Bioscience) or using CellTiter-Glo (Promega) or Crystal Violet RNA Extraction and Real-Time staining (Sigma). Luminescence and quantitative PCR analysis. absorbance were quantified using an RNA was extracted from cells using the Envision Plate Reader (PerkinElmer). RNeasy Plus Mini Kit (Qiagen) according to Experiments were performed in triplicates the manufacturer’s protocol. For gene bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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.

expression analysis, cDNA was generated MI-5 (Medical Index) or using Fujifilm from 1µg of RNA with the iScript cDNA Luminescent Image Analyzer (LAS-3000 synthesis kit (Bio-Rad) as per the Lite) system (Fujifilm). Protein band manufacturer’s protocol. Using the quantification was carried out using ImageJ QuantiTect SYBR Green PCR kit, 100ng of (National Institutes of Health, cDNA was amplified according to the http://rsb.info.nih.gov/ij/). The following manufacturer’s instructions with primers primary antibodies were used: anti-Kif18A targeting BUB1B (catalog no. QT00008701), rabbit (1:500), a gift from Dr. Thomas MAD2 (catalog no. QT00094955), TTK Mayer, University of Konstanz, Germany; (catalog no. QT00035168), KIF18A (catalog anti-GAPDH goat (1:1000), Abcam (catalog no. QT00042455), or GAPDH (catalog no. no. ab9483). QT00273322) as an endogenous control (Quantitect Primer Assay, Qiagen). Data Transcriptional profiling analysis was performed with the QuantStudio 6 and 7 Flex Real-Time PCR System The L1000 expression-profiling assay was 71,84 Software v1.0 (Applied Biosystems, Life performed as previously described . First, Technologies) using the ∆∆Ct method. mRNA was captured from cell lysate using an oligo dT-coated 384-well Magnefy Western blotting microspheres. First, mRNA was captured from cell lysate using an oligo dT-coated Processed total cell lysates were separated by Magnefy microspheres. The lysate was then SDS-PAGE. Protein size was estimated using removed, and a reverse-transcription mix ‘PrecisionPlus All Blue’ or ‘PrecisionPlus containing Superscript IV reverse Kaleidoscop’e protein markers (BioRad). transcriptase was added. The plate was Separated proteins were then transferred to a washed and a mixture containing both methanol-activated polyvinylidene difluoride upstream and downstream probes for each membrane (PVDF, Roche) using wet transfer gene was added. Each probe contained a Mini-PROTEAN II electrophoresis system gene-specific sequence, along with a (BioRad). Membranes were blocked in 5% universal primer site. The upstream probe skim milk (Fluka) in Tris-buffered saline also contained a microbead-specific barcode with 0.05% Tween20 (TBST), decorated sequence. The probes were annealed to the with respective primary antibodies diluted in cDNA over a 6-h period, and then ligated blocking solution overnight at 4°C with together to form a PCR template. After gentle agitation. Further, the membranes ligation, Platinum Taq and universal primers were rinsed for 30 min with TBST with a were added to the plate. The upstream primer triple buffer exchange, incubated with HRP- was biotinylated to allow later staining with conjugated secondary antibodies (R&D streptavidin–phycoerythrin. The PCR Systems), followed by triple TBST wash, amplicon was then hybridized to Luminex chemiluminescence using ECLplus kit and microbeads via the complimentary, probe- detection either on ECL hyperfilm (GE specific barcode on each bead. After Healthcare), on X-ray hyperfilm processor overnight hybridization the beads were bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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.

washed and stained with streptavidin– carried out using the ‘limma-trend’ phycoerythrin to prepare them for detection pipeline80, in which p-values were estimated in Luminex FlexMap 3D scanners. The based on empirical-Bayes moderated t- scanners measured each bead independently statistics. Unsupervised hierarchical and reported the bead colour and identity and clustering was performed on these the fluorescence intensity of the stain. A differential expression profiles using deconvolution algorithm converted these raw complete-linkage clustering, as implemented fluorescence intensity measurements into in the R function ‘hclust’. Pearson correlation median fluorescence intensities for each of was used as a similarity measure between the the 978 measured genes, producing the GEX expression profiles. For analysis of gene set level data. These GEX data were then enrichment of transcriptional response normalized based on an invariant gene set, signatures, enrichment was measured using and then quantile-normalized to produce the original GSEA method72 (based on the QNORM level data. An inference model was estimated log-fold-change), which estimates applied to the QNORM data to infer gene the concentration of each gene set in the list expression changes for a total of 10,174 of up- and down-regulated genes. We used features. Per-strain gene expression the GSEA implementation in the R package signatures were calculated using a weighted ‘fgsea’85. The collection of gene sets used average of the replicates, for which the was ‘Biological Processes’ gene set weights are proportional to the Spearman collection from MSigDB v6.286. correlation between the replicates. These signatures were then queried against the The analysis of the mRNA expression levels reference dataset Touchstone (GEO of mitotic kinesins was based on microarray- accession # GSE92742)71 to assess similarity. based transcriptional profiling of HCT116 The top 100 up- and down-regulated genes in and HPT cells (GEO accession # 16 each signature were compared to the GSE47830) . reference data, yielding a rank-ordered list of Microscopy most similar reference signatures. Cells were grown on plain glass or FBN- For downstream analyses (unsupervised coated or gelatin-coated coverslips. For clustering and GSEA), differential gene analysis, cells were either fixed in cold expression profiles were computed for the methanol followed by 4% paraformaldehyde, L1000 profiles. In order to maximize the blocked with 10% FBS in PBS-T, or cold expression signal, differential expression was methanol containing 1% paraformaldehyde, computed jointly using profiles measured at blocked with 20% goat serum in antibody 24 hours and 72 hours for each cell line and diluting buffer (Abdil; TBS, pH 7.4, 1% drug treatment. Specifically, log-fold-change BSA, 0.1% Triton X-100, and 0.1% sodium was estimated between drug-treated profiles azide) before incubating with the specified at 24 and 72 hours and DMSO-treated primary antibodies. Coverslips were mounted profiles at 24 and 72 hours for each onto slides using Prolong Gold anti-fade experimental condition. This estimation was bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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.

mounting medium with DAPI (Molecular analyzed after transfer to drug-free medium Probes). Images were acquired with a at 37°C. Cells were washed in PHEM buffer microscope (Axio Imager Z1; Carl Zeiss) and depolymerized tubulin was removed with equipped with CSU22 unit (Yokogawa 0.2% Triton in PHEM buffer for 1 min. The Corporation of America) and CoolSnap HQ2 cells were then washed in 1× PBS and fixed camera (Photometrics) controlled by in 3.7% formaldehyde for 15 min. An SlideBook software or a Ti-E inverted immunofluorescence assay for β-tubulin and microscope (Nikon Instruments) with a Clara pericentrin was performed after cooled charge-coupled device (CCD) permeabilization in 0.5% Triton and blocking camera, Spectra-X light engine (Lumencore) in PBTA. Quantification of mean β-tubulin (Andor) controlled by NIS Elements software fluorescence intensity in the region of the (Nikon Instruments). Imaging of z stacks centrosome was measured in ImageJ in a with 0.3 - 0.7 µm steps covering the entire circle of constant diameter across all samples volume of the mitotic apparatus were around the centrosome. At least 40 cells were collected with a Plan-Apochromatic 1.40 NA analyzed in each sample of three independent 60× or 100x immersion oil objective lens. biological experiments. Live-cell imaging of cells in CO2- independent media (Gibco) utilized Nikon Microtubule dynamics by EB3 tracking Plan Apo 20X or 40X DIC N2 0.75 NA The cells transfected with EB3-EGFP were objectives and an environmental chamber at seeded in 96-well glass bottom plate. 24 h 37oC. later, the VS83 was added for 18 h. Spinning Mitotic arrest assay disk confocal microscope with an incubator box was used for the microscopy. Live cell Cells were seeded in black 96-well plates two 60 s movies were taken using a spinning disk days prior to imaging and treated with confocal microscope with a 100x objective, Nocodazole at a concentration of 200 ng/ml. z-stacks 400 nm, time resolution 400 ms. The Imaging was performed with a 6-min time- mean velocity was calculated as the lapse for 50h with GFP (1000 ms exposure) instantaneous velocity between at least three and DIC (200 ms exposure) using 20x air consecutive time as v = mean distance objective. Image analysis was performed (micrometers)/time (seconds). using Slidebook 6 software (Intelligent Imaging Innovations). Quantitative analysis of spindle angle and length Microtubule regrowth assay Images were collected by taking z stacks with a step size of 0.3 µm covering the entire Microtubule regrowth assay was performed volume of the mitotic spindle. Fluorescence as previously described87. The cells were signal quantification in the spindle was incubated with 1 µg/ml nocodazole for 3h performed using the SlideBook software. and placed in ice for 1h to depolymerize Distances were measured after defining the microtubules. Microtubule regrowth was bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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.

position of the two poles and correcting for Information, or from the corresponding projection errors. authors upon request.

Quantification of multipolar spindles, micronuclei and unsuccessful cytokinesis Acknowledgments Multipolar spindles and micronuclei were counted in cells labeled with antibodies The authors would like to acknowledge against α-tubulin and γ-tubulin, as well as Jordan Bryan, Jennifer Roth and Sam Bender DAPI. The percentage of mitotic cells with for assistance with PRISM; Aviad Cherniack spindles containing more than two poles and and Mustafa Kocak for helpful discussions; the percentage of interphase cells with Dave Lam and Oana Enoche for assistance

micronuclei are reported. The percentage of with L1000 assay; Anastasya Y. Kuznetsova cells that exited mitosis as a single cell was for assistance with KIF18A experiments; and determined from live imaging of cells using Sharon Tsach for assistance with figure DIC and reported as those that fail preparation. This work was supported by the cytokinesis. Azrieli Foundation, the Richard Eimert Research Fund on Solid Tumors, the Tel-

Statistical analyses Aviv University Cancer Biology Research Center, and the Israel Cancer Association. The two-sided Student’s t-test was used to compare single gene dependency and Competing Interests expression between the near-euploid and highly-aneuploid cancer cell lines. The two- T.R.G. is a consultant to GlaxoSmithKline sided Fisher’s Exact test was used for and is a founder of Sherlock Biosciences. calculating the significance of the overlap of R.B. own shares in Ampressa and receives hits for the genetic perturbation datasets. The grant funding from Novartis. A.J.B. receives statistical analyses of all microscopy funding from Merck, Bayer and Novartis, experiments were performed using GraphPad and is an advisor to Earli and Helix Nano and Prism 7.0. Two-sided Student’s t-test was a co-founder of Signet Therapeutics. The used to determine the significance of other authors declare no competing interests. differences between the means of two groups. Fisher’s Exact test was used to determine the significance of differences in the prevalence of categorical events between groups.

Code availability. The code used to generate and/or analyze the data are publicly available, or available upon request.

Data availability. All datasets are available within the article, its Supplementary bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

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* 50 * 16 1.50 40 14 1.25 30 * 12 1.00 20 * 10 0.75 10 * * 0.50 * 8 AZ-3146 AUC AZ-3146 Fold-enrichment

0 AUC Reversine 0.25 , t Euploid Aneuploid Euploid Aneuploid ed ein ted volv ot ognion t pr ec oc spindle Cell division e r on in abolic processabolic process Mit ansport of virus at easome-media assembly checkpoint ranscripon-coupled ein cat T Prot ot Nucleode-excision repair nucleode-excision repair tracellular tr ein ubiquina ubiquin-dependen DNA damagIn Prot pr in ubiquin-dependen c d **** **** -0.25 0.0 -0.50

-0.5 -0.75 -1.00 -1.0 -1.25 -1.50 BUB1B dependency -1.5 MAD2 dependency -1.75

Euploid Aneuploid Euploid Aneuploid bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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 Figure 2 available under aCC-BY-NC-ND 4.0 International license. a e HCT116-WT HPT2 100 (near-diploid) (aneuploid) NT HCT116-WT ** BUB1B 1.2 HCT116-GFP 80 MAD2 HPT1 TTK 1 HPT2 60 *** ﬂ uence) * *** *** 0.8 * 40 Day 1-5 Day

e viability 20

0.6 (% con Cell prolifera 0.4 0

Rela 0.2 100 0 -4 -3 -2 -1 0 1 2 80

ﬂ uence) 60 40

1.2 RPE1-GFP 10-14 Day (% con 20 * RPT1 Cell prolifera 0 8 8 0 1 RPT3 0 16 24 28 36 44 52 60 68 76 84 92 16 24 28 36 44 52 60 68 76 84 92 100 108 116 ** RPT4 100 108 116 ** 0.8 * * Time (hours) Time (hours) 0.6 MPI-0479605: MPI-0479605: e viability f HCT116-WT 0nM HPT2 0nM 250nM 250nM 0.4 (near-diploid) (aneuploid) 100 80 Rela 0.2

ﬂ uence) 60 40 -4 -3 -2 -1 0 1 2 1-5 Day 20 (% con Concentra�on Cell prolifera 0 (log[MPI-0479605]) (μM) HCT116-WT HCT116-GFP HPT1 HPT2 100 b 80

*** *** *** ﬂ uence) 60 40

1 10-14 Day 20 (% con Cell prolifera 0 8 0 8 0.8 0 16 24 28 36 44 52 60 68 76 84 92 16 24 28 36 44 52 60 68 76 84 92 100 108 116 100 108 116 e viability 0.6 Time (hours) Time (hours) 0.4 0.2

Rela HCT16-WT (near-diploid) 0 g BUB1B - siRNA MAD2 - siRNA TTK - siRNA

RPE1-GFP RPT1 RPT3 ** *** ** 1 5 Day 0.8 0.6 e viability 0.4 0.2 Rela 0 BUB1B - siRNA MAD2 - siRNA TTK - siRNA c 14 Day RPE1: 1 SS48 (Diploid) SS51(+Ts7, +Ts22) 0.8 SS111(+Ts8, +Ts18) 0 nM 250 nM *** * * HPT2 (aneuploid) 0.6 ** e viability ** 0.4 *** 0.2 Rela 0 -4 -3 -2 -1 0 1 2 Concentra�on 5 Day (log[MPI-0479605]) (μM) Diploid: RPE1-SS48 RPE1-SS31 RPE1-SS77 d Aneuploid: RPE1-SS119 (+Ts8) RPE1-SS6(+Ts7) RPE1-SS51 (+Ts7, +Ts22) RPE1-SS111(+Ts8, +Ts18) *** ** ** 1 0.8 0.6 e viability Day 14 Day 0.4 0.2 Rela 0 0 nM 250 nM BUB1B - siRNA MAD2 - siRNA TTK - siRNA MPI-0479605 MPI-0479605 bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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 Figure 3 available under aCC-BY-NC-ND 4.0 International license. a 6hr 24hr 72hr e 50 ** ** 1 2 3 4 5 6 7 8 9 10 11 12 Color legend: A DMSO 40 Biological B (negave control) replicate #1 Reversine 10μM 30 C (posive control) oc arrest (hr) D Mitoxantrone 1μM 20 Biological (posive control) replicate #2 E Reversine 250nM F 10

Reversine 500nM Time in mit G MPI-0479605 250nM 0 H GFP HPT1 HPT2 HCT116-

HCT116 x2 near-diploid f *** Gene expression * *** proﬁling 100 vs. 98 96 HPT x2 Gene set 94 enrichment analysis aneuploid 92 with micrunuclei

% of cell divisions 90 Gene expression Control Reversine Control Reversine proﬁling HCT116-GFP HPT2 no micronuclei micronuclei b HCT116-WT HCT116-GFP g DAPI GFP Tag Merge HPT1 HPT2 HPT2 MPI-0479605 Reversine 250nM Reversine 500nM

c Mitoc cell cycle arrest * Posive regulaon of cell cycle G2M phase transion * Negave regulaon of cel l cycle arrest * Negave regulaon of cell cycle G2M phase transion * Regulaon of cell cycle G2M phase transion * Regulaon of cell cycle arrest * Cell cycle G1S phase transion * Negave regulaon of cell cycle process * Cell cycle arrest * Negave regulaon of cell cycle phase transion * Negave regulaon of cell cycle * Regulaon of cell cycle phase transion * Posive regulaon of cell cycle process * Regulaon of cell cycle * 0 5 10 15 20 25 30 35 40 Fold - enrichment d Posive regulaon of cell death * Regulaon of cell death * Cell death * 0 1 2 3 4 5 6 Fold-enrichment bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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 Figure 4 available under aCC-BY-NC-ND 4.0 International license. 1 *** a 2 f 1.8 1.6 0

ﬁ cance 1.4 1.2

signi -1 1 KIF18A

0.8 (RNAi) 0.6

(-log10(p-value)) 0.4 -2

0.2 KIF18A dependency Sta�s�cal 0 1- 0.5- 0 0.5 1 Euploid Aneuploid Lower in HPTs Higher in HPTs

Rela e gene expression HCT116-WT HPT1 (log2(fold-change)) g NT siRNA NT siRNA KIF18A siRNA KIF18A siRNA b 1.5 * 100 HCT116- GFP HPT1 HPT2 1.0 80

100Kd Kif18A ﬂ uence) 60 0.5 37Kd levels e protein 40 GAPDH (% con 0.0 Cell prolifera 20

GFP HPT1 HPT2 0 HCT116- Sytox Green HCT116-GFP HPT2 (DNA) a-Tubulin Merge NT siRNA NT siRNA c KIF18A siRNA KIF18A siRNA 100 HCT116- GFP 80

ﬂ uence) 60 40 (% con Cell prolifera 20 HPT1 0 8 0 8 0 16 24 28 36 44 52 60 68 76 84 92 16 24 28 36 44 52 60 68 76 84 92 100 108 116 Time (hours) 100 108 116 Time (hours)

h n.s. * i HPT2 HPT2 DAPI

d 20 *** 20 *** m] m] µ *** *** µ

15 15 divisiosns w/ % cell -tubulin α 10 10 0 2 4 6 8 10 5 5 Spindle length [ Spindle length Spindle width [ Control KIF18A-siRNA Control KIF18A-siRNA

0 0 Merge HCT116-GFP HPT2 GFP HPT1 HPT2 GFP HPT1 HPT2 HCT116- HCT116- 120 *** j HPT1 KIF18A-control Spindle length 100 *** 100 KIF18A-OE 80 80 KIF18A-control + MPI-0479605 (250nM) 60 60

ﬂ uence) KIF18A-OE 40 Spindle + MPI-0479605 (250nM) angle 40 20 eldnip S htdiw 20 (% con Cell prolifera Spindle angle [degrees] 0 0 0 20 40 60 80 100 120 GFP HPT1 HPT2 HCT116- Time (hours) e *** HPT2 *** 100 100 80 80 60 60 ﬂ uence) 40 40 (% con 20 Cell prolifera 20

%of bound kinetochores 0 0 0 20 40 60 80 100 120 GFP HPT1 HPT2 Time (hours) HCT116- Figure 5 Aneuploid cells Diploid cells Abormal spindle structureAbormal spindle Func�onal KIF18A structureNormal spindle Aberrant KIF18A Time (which wasnotcertifiedbypeerreview)istheauthor/funder,whohasgrantedbioRxivalicensetodisplaypreprintinperpetuity.Itmade bioRxiv preprint SAC doi: https://doi.org/10.1101/2020.06.18.159038 SAC mito�c aberra�ons High levels of mito�c aberra�ons Low levels of available undera cytokinesis Outcome ofcelldivision Failed Successful mitosis Micronuclei forma Detrimental karyotype CC-BY-NC-ND 4.0Internationallicense Outcome ofcelldivision Successful mitosis ; this versionpostedJune19,2020. karyotype Detrimental f Micronuclei orma�on cytokinesis Failed Cell survival Cell death . The copyrightholderforthispreprint bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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 Extended Data Figure 1 available under aCC-BY-NC-ND 4.0 International license. a e 1 3 5 7 9 11 13 15 17 19 21 2 4 6 8 10 12 14 16 18 2022 **** MDAMB468 HS578T 0.0 HCC70 -0.5

High AS EFM19 MDAMB415 -1.0 CAL51 HS274T -1.5 HS281T BUB1B depedency -2.0 Low AS HS742T HS343T -2.5

Euploid Aneuploid b RNAi DRIVE **** 12 1

10 0 ance 8 BUB1B 6 -1 al signiﬁc MAD2 4 -log10(p-value) -2 Sta

2 MAD2 dependency

0 -3 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 -4 More essen More essen Euploid Aneuploid in aneuploid cells in euploid cells Rela e dependency f c r=0.273 r=0.258 p = 8e-16 7 p=3.7e-05 7 p=0.0005 6 6 5 5 4 12 14 185 4 3 (log2(TPM+1)) BUB1B expression 2 3 RNA Achilles -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 BUB1B dependency (RNAi Achilles) BUB1B dependency (RNAi DRIVE)

r=0.311 7.5 r=0.322 7 p=2e-06 p=1.2e-05 RNAi DRIVE 7.0 6 6.5 6.0 d 5 5.5 18 5.0 16 * * 4

14 (log2(TPM+1)) 4.5

12 * MAD2 expression 10 * * 3 4.0 8 * * 3.5 6 * * -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 4 * MAD2 dependency (RNAi Achilles) MAD2 dependency (RNAi DRIVE)

Fold-enrichment 2 0 cle cle cle otein volved volved aphase t protein volved in volved cle arrest Cell division cle tr e regula e regula catabolic process catabolic plate congression plate Mit in mit otein lig otein Anaphase-pr P Nega lig DNA damage response, DNA damage Intrinsic apopt Intrinsic involved in mit involved otein lig otein pathway in response to DNA to in response pathway damage by p53 class mediator damage Regula signal tr Prot complex-dependent catabolic process catabolic complex-dependent G2/M tr mediator r mediator in regula�on bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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 Extended Data Figure 2 available under aCC-BY-NC-ND 4.0 International license. a 40 35 30 25 20 15 10

Aneuploidy score 5 0

y te eas ve oid act ary tral em tric em Skin ta ous erus Lung Liver v st Bone st Ov y Blood Breast Gas Kidne Ut ancr Thyr Bile duct P Pros Cen Esophagus Upper ner Colorectal So ssue Plasma cell Lymphocyte Urinary tr s aerodiges Peripheral b nervous sy 60 **** **** 50 n.s. d **** 40 7 30 6 20 5 10 Aneuploidy score 4 Het70 score Het70 0 3 -10 2 WT Silent Non-conserving Damaging Euploid Aneuploid TP53 mutaon status c e ** 50 **** **** 400 40 ****

300 30

200 20 Aneuploidy score 10 Doubling me (hr) 100

0 0

0 1 2 Euploid Aneuploid Number of genome doublings bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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 Extended Data Figure 3 available under aCC-BY-NC-ND 4.0 International license. a RNAi Achilles RNAi DRIVE Euploid d RNAi Achilles RNAi DRIVE 1 Aneuploid 0.00 r = -0.274 0.0 r = -0.325 -0.25 p = 0.0002 p = 2.8e-05 -0.50 -0.25 0 -0.5 -0.75 -0.50 -1 -1.0 -1.00 -0.75 -1.25 -2 -1.00 -1.5 -1.50 BUB1B dependency BUB1B dependency -1.25 -2.0 -1.75 -3 -1.50 al al 0 5 10 15 20 25 0 5 10 15 20 25 ary tral ous em tric ous em Skin Skin v t v t s Bone s Ov Ovary Uterus y Uterus y Gastric Cen Gas Central ner ner s s Colorect Colorect -0.4 r = -0.397 0.5 r = -0.233 -0.25 1 p = 3e-08 p = 0.003 -0.6 0.0 -0.50 0 -0.75 -0.8 -0.5 -1.00 -1 -1.0 -1.0 -1.25 -2 -1.2 -1.5 -2.0 -1.50 -3 -1.4 MAD2 dependency MAD2 dependency -2.5 -1.75 -1.6 -2.00 -4 -3.0

al al 0 5 10 15 20 25 0 5 10 15 20 25 ary tric ous em ary ous em Skin t erus Skin ect v v Aneuploidy score Aneuploidy score s Bone st Ov Ov Uterus y Ut y Gas Central Gastric Central ner ner s s Color Colorect b RNAi Achilles RNAi DRIVE e RNAi Achilles RNAi DRIVE -0.75 *** ** **** *** -0.5 -1.00 -0.5 -1.0 -1.0 -1.25 -1.0 -1.5 -1.50 -1.5 -1.5 -2.0 -1.75 -2.0 -2.5 -2.00 -2.5 -2.0 Lineage-controlled BUB1B dependency -3.0 -3.0

-2.25 BUB1B dependency

Doubling me-controlled -3.5 -2.50 -3.5 -2.5 Euploid Aneuploid Euploid Aneuploid Euploid Aneuploid Euploid Aneuploid n.s. -1.4 * **** ** -1.25 -1.6 -1 -1.50 -1.8 -1 -1.75 -2.0 -2 -2.00 -2 -2.2 -3 -2.25 -2.4 -3 -4 -2.50 Lineage-controlled

MAD2 dependency -2.6 MAD2 dependency -2.75 -4

-2.8 -5 Doubling me-controlled -3.00 Euploid Aneuploid Euploid Aneuploid Euploid Aneuploid Euploid Aneuploid

RNAi Achilles RNAi DRIVE c RNAi Achilles RNAi DRIVE f **** -0.5 *** Euploid -1.0 0.25 0.5 Aneuploid -1.0 0.00 0.0 -1.5 -0.25 -0.50 -0.5 -1.5 -2.0 -1.0 -0.75 -2.5 -1.00 -1.5 -2.0 -1.25 -2.0 -3.0 HET70-controlled

BUB1B dependency -1.50 -2.5 BUB1B dependency -3.5 -1.75 -3.0 -2.5 WT Silent Non- Damaging WT Silent Non- Damaging conserving conserving Euploid Aneuploid Euploid Aneuploid TP53 mutaon status TP53 mutaon status **** ** -1.50 -1 -0.25 2 -1.75 -0.50 1 -2 -2.00 -0.75 0 -1.00 -3 -1 -2.25 -1.25 -2 -2.50 -4

-1.50 HET70-controlled -3 MAD2 dependency -2.75

MAD2 dependency -1.75 -5 -2.00 -4 WT Silent Non- Damaging WT Silent Non- Damaging conserving conserving Euploid Aneuploid Euploid Aneuploid TP53 mutaon status TP53 mutaon status bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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 Extended Data Figure 4 available under aCC-BY-NC-ND 4.0 International license.

a GDSC PRISM 7 20 6 5 cance 15 cance ﬁ ﬁ 4 10 MPS1 inhibitor 3

al signi (MPS-1-IN-1) al signi 2 MPS1 inhibitor 5 (MPI-0479605)

(-log10(p-value)) (-log10(p-value)) 1

Sta 0 Sta 0 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 -0.1 0.1 0.1 0.2 Aneuploid cells Euploid cells Aneuploid cells Euploid cells more sensive more sensive more sensive more sensive Relave sensivity Relave sensivity

GDSC b **** d 700 Euploid 1.0 600 Aneuploid 500 0.8 400 300 0.6 200 % viability MPS-1-IN-1 AUC

0.4 DMSO control) to ve 100 0 0.2 (rela Euploid Aneuploid

20.0 PRISM 0.0009 0.0091 0.0274 0.0823 0.2469 0.7407 2.2222 6.6667 n.s. Reversine concentraon(μM) 1.75 1.50 e 1.25 0.035 1.00 0.030 0.75 0.50 0.025 MPI-0479605 AUC 0.25 0.020 Euploid Aneuploid 0.015

c 0.010

** importance Feature 1.0 0.005

0.9 0.000 -0.2 -0.1 0.0 0.1 0.2 0.3 0.8 Correlaon with Reversine AUC ve viability ve 0.7 Rela

in Reversine (250nM) in Reversine 0.6

Euploid Aneuploid bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. The copyright holder for this preprint Extended(which was not Data certified Figureby peer review) 5 is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made a available under aCC-BY-NC-ND 4.0 dInternational license. BUB1B HCT116-WT 1.4 **** **** **** **** 1.2 HCT116-GFP 1.2 HPT1 1 HPT2 0.8 1 0.6 ve mRNA ve 0.4 ** ** ** 0.8 Rela 0.2 expression levels expression 0 ** NT si-BUB1B NT si-BUB1B NT si-BUB1B NT si-BUB1B 0.6 HCT116-WT HCT116-GFP HPT1 HPT2

ve viability ve * 0.4 MAD2 Rela * ** 1.4 **** **** **** **** 1.2 0.2 1 0.8 0.6

0 mRNA ve -4 -3 -2 -1 0 1 2 0.4 0.2

Concentraon Rela (log[Reversine]) levels expression 0 NT si-MAD2 NT si-MAD2 NT si-MAD2 NT si-MAD2 HCT116-WT HCT116-GFP HPT1 HPT2 1.2 ** RPE1-GFP TTK ** 1 RPT1 1.4 **** **** **** **** RPT3 1.2 0.8 ** RPT4 1 0.8 * 0.6 0.6 mRNA ve 0.4

Rela 0.2 ve viability ve expression levels expression 0 0.4 NT si-TTK NT si-TTK NT si-TTK NT si-TTK Rela HCT116-WT HCT116-GFP HPT1 HPT2 0.2

0 -4 -3 -2 -1 0 1 2 Concentraon (log[Reversine]) b e 8 1.4 RPE1: 7 1.2 SS48 (Diploid) SS51(+Ts7, +Ts22) 6 * 1 SS111(+Ts8, +Ts18)

on 5 *** 0.8 ***

4 viability ve 0.6 * Rela

ve conﬂuence) ve 3 HCT116-WT ** Cell prolifera HCT116-GFP 0.4 (rela 2 HPT1 HPT2 1 0.2 0 0 0 20 40 60 80 100 120 -4 -3 -2 -1 0 1 2 Time (hours) Concentraon (log[Reversine])

HCT116-WT HCT116-GFP HPT1 c HPT2 1.2 1.4 1.2 1 1.2 1 0.8 1 0.8 0.8 0.6 0.6 0.6 ve viability ve 0.4 0.4 0.4 0.2 0.2 Rela 0.2 0 0 0 -5 -4 -3 -2 -1 0 1 2 -5 -4 -3 -2 -1 0 1 2 -5 -4 -3 -2 -1 0 1 2 Concentraon Concentraon Concentraon (log[Doxorubicin]) (log[Nutlin-3]) (log[Imanib]) bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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 Extended Data Figure 6 available under aCC-BY-NC-ND 4.0 International license. a Day 5 Day 21 c siRNA-BUB1B siRNA-BUB1B 1.5 2 SS111 ** 1.6 ** 1.5 **

ve to NT) to ve 1.4 1.2 Doubling me 1 (rela 1 WT GFP HPT1 HPT2 WT GFP HPT1 HPT2 1 siRNA-MAD2 siRNA-MAD2 0.8 2 0.6

2 viability ve 0.4 1.5 1.5 Rela 0.2

ve to NT) to ve 1 0 Day 3 Day 5 Day 7 Day 14

Doubling me 1 0.5 (rela WT GFP HPT1 HPT2 WT GFP HPT1 HPT2 Reversine: 250nM 500nM

siRNA-TTK siRNA-TTK 1.5 1.5 SS51 1 1.2 * ve to NT) to ve 1 Doubling me 1 0.5 (rela WT GFP HPT1 HPT2 WT GFP HPT1 HPT2 0.8 0.6 b MPI-0479605, Day 5 Reversine, Day 5

** ** viability ve 0.4 2.5 2

2 1.5 Rela 0.2 1.5 1 ve to NT) to ve 0 1 Day 3 Day 5 Day 7 Day 14 Day 21 0.5 0.5 Doubling me (rela Reversine: 250nM 500nM 0 0 WT GFP HPT1 HPT2 WT GFP HPT1 HPT2 MPI-0479605, Day 14 Reversine, Day 14 * * 10 8 4 6 3 ve to NT) to ve 4 2 1 Doubling me 2 (rela 0 0 WT GFP HPT1 HPT2 WT GFP HPT1 HPT2 bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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 Extended Data Figure 7 available under aCC-BY-NC-ND 4.0 International license. a

HCT116-WT posive 1 Cell Cycle Inhibion GOF connecvity HCT116-GFP 2 UBE2D1 KD HPT1 3 MAGEB6 OE HPT2 4 CDKN1A OE MPI-0479605 5 CDKN2C OE Reversine 250nM 6 HDAC5 KD Reversine 500nM negave 7 CDK4 KD connecvity 8 CDKN1B OE 9 HNF4A OE 10 KLF6 OE b 4 c 20 * n.s. 3 15

2 10 c Index c Mito 1 5

0 0

ol ol % of cell divisions w/ mulpolar spindles tr sine tr ol ol er ersine tr sine tr sine Con ev Con ev er R R Con ev Con ever R R HCT116-GFP HPT2 HCT116-GFP HPT2 d e *

100 -16 -8 0 +8 +16 +24 +32 +40 +56 +72 +88 +120 80 60 40 HPT2 20

% Successful cytokinesis % Successful 0 HCT116- HPT2 GFP NEB Time (min) Susccessful Unsuccessful bioRxiv preprint doi: https://doi.org/10.1101/2020.06.18.159038; this version posted June 19, 2020. 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. Extended Data Figure 8 ** a f 1.3

HCT116- 4000 1.2 GFP **

3000

escence 1.1

HPT1 2000 NT) to (Normalized ed ﬂuor

intensity [a.u.] intensity 1 1000 WT GFP HPT1 HPT2 Normaliz HPT2 0 g 120 n.s. ** HPT1 HPT2 HCT116- Sytox Green a-tubulin KIF18A Merge GFP

**** 100 b 20 **** m/s] µ ** 15 0.6 80 ate [ ate **

escence intensity) 10 0.4 MT regrowth 60 0.2 5 /nuclei ﬂuor

0.0 (MT 0 MT polymerisa

HPT1 HPT2 HPT1 HPT2 40 HCT116- HCT116-

GFP GFP anaphase onset NEBD to Time from c 2.0 ** ** 1.8 20

1.6

1.4 0

escence intensity) 1.2 -tubulin colocaliza 1.0 NT-siRNA NT-siRNA (ﬂuor

Eb1 α KIF18A-siRNA KIF18A-siRNA 0.8 HCT116-GFP HPT2

HPT1 HPT2 *** HCT116- n.s. GFP h *** 100 0.5 * d 98 0.0 96 94 -0.5 p=0.0341 92 % of cell divisions with micronuclei -1.0 90

(CRISPR) NT-siRNA KIF18A-siRNA NT-siRNA KIF18A-siRNA -1.5 HCT116-GFP HPT2

KIF18A dependency no micronuclei micronuclei -2.0

-2.5 Aneuploid i Euploid Aneuploid 1400 1200 *** **** e KIF18A-siRNA 1000 1.4 **** **** **** **** 800 1.2 e mRNA 1 600 0.8 e mRNA 0.6 Rela 400 expression levels expression 0.4 200 0.2 Rela

expression levels expression 0 0 NT si-KIF18A NT si-KIF18A NT si-KIF18A NT si-KIF18A KIF18A-WT KIF18A-OE KIF18A-WT KIF18A-OE HCT116-WT HCT116-GFP HPT1 HPT2 HPT1 HPT2