# Targeting WEE1 kinase as a therapeutic strategy in ATIP3-deficient breast cancers

Maria Haykal, Sylvie Rodrigues-Ferreira, Rania El Botty, Laura Sourd, Elisabetta Marangoni, Marie Varin, Alexis Denis, Clara Nahmias

► To cite this version:

Maria Haykal, Sylvie Rodrigues-Ferreira, Rania El Botty, Laura Sourd, Elisabetta Marangoni, et al. Targeting WEE1 kinase as a therapeutic strategy in ATIP3-deficient breast cancers. Cancer Letters, 2025, 620, pp.217665. 10.1016/j.canlet.2025.217665. hal-05363718

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Submitted on 16 Jan 2026

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# Targeting WEE1 kinase as a therapeutic strategy in ATIP3-deficient breast cancers

Maria M. HAYKAL<sup>1,2</sup>, Sylvie RODRIGUES-FERREIRA<sup>1,2,3</sup>, Rania EL BOTTY<sup>4</sup>, Laura SOURD<sup>4</sup>, Elisabetta MARANGONI<sup>4</sup>, Marie VARIN<sup>5</sup>, Alexis DENIS<sup>5</sup> and Clara NAHMIAS<sup>1,2*</sup>

## Abstract

ATIP3-deficient breast cancers represent a subset of aggressive tumors with limited therapeutic options and poor prognosis. Here, we screened a panel of cell cycle kinase inhibitors to identify novel targets for these tumors. We show that loss of ATIP3 sensitizes breast cancer cells to WEE1 inhibition, resulting in aberrant mitoses characterized by detachment of centromere proteins from DNA and chromosome pulverization. This phenotype arises from excessive replication stress and DNA damage in S-phase, combined with premature mitotic entry driven by untimely CDK1 activation. Mechanistically, we identify DNA2 helicase/nuclease as a key mediator of chromosome pulverization. Importantly, the heightened sensitivity of ATIP3-deficient cells to WEE1 inhibition provides a strong rationale for clinical exploration of WEE1-targeted therapies. Furthermore, combining WEE1 and PKMYT1 inhibitors enhances therapeutic efficacy, offering a promising strategy for personalized treatment in ATIP3-deficient breast cancers.

**Keywords:** Aneuploidy, cell cycle kinase inhibitors, DNA2 helicase/nuclease, PKMYT1 kinase, precision medicine, targeted therapy.

<footnote n="1"><sup>1</sup>Institut Gustave Roussy, Inserm U981, Biomarqueurs prédictifs et nouvelles stratégies thérapeutiques en oncologie, 94800 Villejuif, France.</footnote>

<footnote n="2"><sup>2</sup>Université Paris-Saclay, 91400 Orsay, France.</footnote>

<footnote n="3"><sup>3</sup>Inovarion, 75005 Paris, France</footnote>

<footnote n="4"><sup>4</sup>Translational Research Department, Institut Curie, PSL University, 75005 Paris, France</footnote>

<footnote n="5"><sup>5</sup>IKTOS, 75017 Paris, France</footnote>

<footnote>*Correspondence: clara.nahmias@inserm.fr</footnote>

<footnote>*The authors declare no conflicts of interest related to the content of this manuscript.*</footnote>
**Introduction**

Breast cancer is a heterogeneous disease affecting one in eight women worldwide and a leading cause of cancer-related deaths¹. Despite significant advancements in detection and treatment, the prognosis of patients with aggressive breast cancer, such as triple negative breast cancer (TNBC), remains poor. One of the critical challenges is the development of personalized treatment approaches combined with reliable companion biomarkers.

ATIP3, a microtubule-stabilizing protein, has emerged as an important prognostic biomarker for breast cancer patient survival²,³. Its downregulation observed in 50–80% of breast cancers, particularly in TNBC, correlates with increased tumor aggressiveness and reduced patient survival. Interestingly, ATIP3 also functions as a predictive biomarker for taxane-based chemotherapy, as ATIP3-deficient breast tumors exhibit increased sensitivity to paclitaxel (PTX)⁴. This sensitivity results from the exacerbation of mitotic defects: ATIP3 deficiency promotes centrosome amplification and multipolar spindle formation, which, in conjunction with paclitaxel's mechanism of action⁵, drives chromosomal instability (CIN) to lethal levels, inducing cell death⁴,⁶.

ATIP3-deficient tumors are characterized by high levels of aneuploidy, a direct consequence of CIN⁷, detected in 90% of solid tumors⁸ and frequently observed in breast cancer genomes⁹⁻¹¹. High levels of aneuploidy have been correlated with poor prognosis in the majority of breast cancer cases, as they are associated with tumor aggressiveness and resistance to therapy¹²⁻¹⁴. While taxanes can exploit this vulnerability in ATIP3-deficient tumors, their clinical application is limited by toxicity and the fact that they benefit only approximately 30% of patients¹⁵. These limitations underscore the urgent need for more targeted therapies tailored to the molecular vulnerabilities of ATIP3-deficient cancers.

To address this challenge, we hypothesized that the aneuploid nature of ATIP3-deficient tumors could be exploited using a more targeted approach. Specifically, we focused on cell cycle checkpoint kinases, which are critical for maintaining genomic stability in aneuploid cells¹⁶,¹⁷. Through a screening of kinase inhibitors, we identified WEE1 kinase—a key regulator of the G2/M checkpoint¹⁸—as a promising therapeutic target. By inhibiting cyclin-dependent kinase 1 (CDK1) through phosphorylation of Tyr15, WEE1 helps to control the proper timing of cell division and prevents cells from entering mitosis until DNA replication and repair processes
are completed¹⁹. Beyond its role in mitosis, WEE1 also maintains genome stability during DNA replication, and its inhibition exacerbates replication stress and DNA damage²⁰⁻²⁴.

Here, we show that WEE1 inhibition is more cytotoxic in ATIP3-deficient cells and that this is linked to elevated levels of aneuploidy. In these cells, WEE1 inhibition induces higher levels of replication stress and DNA damage in S-phase, which is responsible for severe mitotic defects causing cell death. The helicase/nuclease DNA2 stands as a new molecular player involved in the severe phenotype induced by WEE1 inhibition in ATIP3-deficient breast cancer cells. Moreover, dual inhibition of WEE1 and PKMYT1, a complementary G2/M checkpoint regulator²⁵, shows enhanced cytotoxicity, providing a promising therapeutic strategy for ATIP3-deficient breast cancers.

# Results

## 1. Chemical synthetic screen identifies WEE1 kinase as a target for ATIP3-deficient breast cancer cells

The search for new therapeutic strategies against ATIP3-deficient breast cancers led us to perform a chemical synthetic lethality screen of 28 cell cycle kinase inhibitors. The SUM52PE breast cancer cell line expressing or not ATIP3 (Figure S1A) was grown in 3-dimensions as multicellular spheroids (MCSs) to mimic the features of solid tumors and was treated with increasing doses of each inhibitor (Figure 1A). After 72 h of treatment, cell viability was assessed and the IC50 of each inhibitor was calculated. We considered an inhibitor as a differential hit if the IC50-fold change between control and ATIP3-depleted cells was equal to or higher than 2 (Figure 1B, Table S1). ATIP3 depletion improved the cytotoxic response to six different inhibitors targeting ATR, ATM, WEE1, Aurora, and PLK4 kinases (S1B-C). Using the Sanger GDSC2 and the PRISM drug repurposing 24Q2 databases²⁶, we found a significant correlation between the levels of expression of *MTUS1* (the gene encoding for ATIP3) and the response to AZD1775 in 31 breast cancer cell lines (Figure 1C, Table S2), indicating that lower levels of ATIP3 correspond to a better response to AZD1775. The response to other hit compounds was not correlated with *MTUS1* expression levels (Figure S1D, Table S3), leading us to focus on AZD1775, the WEE1 kinase inhibitor. Indeed, AZD1775 was more potent in ATIP3-depleted breast cancer MCSs, as indicated by their size (Figure 1D) and lower IC50 values (mean IC50 in shCtrl is 1.31 µM vs. 0.43 µM in shATIP3) (Figure 1E). AZD1775 also exhibited lower IC50 values
in two other breast cancer MCSs models in which ATIP3 expression was depleted (Figure S1E).
In addition, AZD1775 abolished the phosphorylation of CDK1 at Tyr15 (Figure S1F), confirming
target engagement. Knockdown of *WEE1* using siRNA (Figure S1G) caused further cellular
toxicity in ATIP3-deficient cancer cells (Figure 1F) confirming WEE1 as a differential target.
Importantly, the doses of AZD1775 that caused maximal cell death in tumor cells had very little
effect on diploid, non-cancerous RPE-1 cells (Figure 1G).

We reasoned that increased vulnerability of ATIP3-depleted cells to WEE1 inhibition may be
driven by aneuploidy rather than other ATIP3-specific effects. To address this question, we used
near-diploid RPE-1 cells as a model. We induced aneuploidy using reversine, an inhibitor of
MPS1 kinase that induces chromosome missegregation<sup>27,28</sup>, and combined it with WEE1
inhibition (Figure S2A). MPS1 inhibition in RPE-1 cells resulted in aneuploidy, as shown by
increased variability in chromosome numbers (Figure S2B). Aneuploidy induction by reversine
in RPE-1 cells rendered them vulnerable to WEE1 inhibition, leading to cell death (Figure 1H),
indicating that sensitivity to WEE1 inhibition is driven by aneuploidy.

We tested the efficacy of AZD1775 *in vivo* using xenografts of the MDA-MB-468 breast cancer cell line in which ATIP3 was depleted. AZD1775 was administered daily by oral gavage at a dose of 90 mg/kg for 3 weeks. ATIP3 depletion increased tumor growth, in line with the high aggressiveness of tumors expressing low levels of ATIP3 (Figure 1I). Treatment with AZD1775 prevented tumor growth and had a more prominent effect on ATIP3-depleted tumors (Figure 1J, fold change in tumor volume of 1.6 and 2.3 in shCtl vs. shATIP3 upon treatment with AZD1775) in agreement with *in vitro* observations. Notably, since MDA-MB-468 cells are BRCA1-deficient, this heightened sensitivity to AZD1775 could result from the combined impact of both ATIP3 deficiency and defects in homologous recombination repair. We further tested the response to AZD1775 in organoids derived from patient-derived xenografts (PDXOs) that express either high or low levels of ATIP3 (Table S4). Results indicate that PDXOs expressing low levels of ATIP3 responded better to AZD1775 (Figure 1K, Figure S2C-D, Table S4). Considering 50% cell death as a criterion for sensitivity, 3 out of 4 low-ATIP3 PDXOs were sensitive to AZD1775, compared to 1 out of 4 high-ATIP3 PDXOs (Figure 1L).
## 2. WEE1 inhibition accelerates the mitotic entry of ATIP3-deficient cells and induces their cell death after failed mitosis

We then explored the molecular mechanism by which ATIP3-deficient cells are more sensitive to inhibition of the WEE1 kinase. We used HeLa cells as a model for live cell microscopy to investigate mitotic entry in control or ATIP3-depleted cells (Figure S3A) that were synchronized at the G1/S boundary and released in the presence or absence of AZD1775. Interestingly, only the cells treated with AZD1775 at the time of release entered mitosis within 3 hours post-release. ATIP3-depleted cells entered mitosis even earlier (3.1 h post-release for siATIP3 vs. 4.2 h for siCtrl), whereas most untreated cells did not enter mitosis within 6 hours (Figure 2A-B). In addition, WEE1 inhibition increased the phosphorylation of both histone H3 (Figure S3B) and CDK1 substrates (Figure S3C), indicators of mitotic entry, in ATIP3-depleted cells. In line with these observations, the proportion of mitotic cells increased in ATIP3-depleted cells in both 2D (Figure 2C) and MCSs (Figure S3D-E). To gain further insights into the effects induced by WEE1 inhibition in mitosis, we filmed HeLa cells stably expressing mCherry-histone H2B in which we depleted ATIP3 to closely track the fate of individual cells. As described previously⁴, untreated ATIP3-depleted cells spent more time in mitosis compared to control cells (Figure 2D-E, Movies S1 and S2). WEE1 inhibition further prolonged the time spent in mitosis. Notably, ATIP3-depleted cells treated with AZD1775 experienced significantly longer mitosis than control cells (Figure 2D-E, Movies S3 and S4). Strikingly, 75% of ATIP3-depleted cells treated with AZD1775 exhibited a back-and-forth movement of their DNA around the mitotic spindle, compared to only 1% of control cells that behaved in this manner (Figure S3F, Movie S4), which could be consistent with exceedingly long mitosis. AZD1775 induced two types of cell death. Cells either died during mitosis (mitotic catastrophe) (Figure 2D panel 3, Movie S3) or after mitotic exit (Figure 2D panel 4, Movie S4). Of note, cell death occurred in 95% of ATIP3-depleted cells treated with AZD1775 (63% after mitotic exit) and in 78% of control cells (12% after mitotic exit) (Figure 2F-G). Together, these results show that ATIP3 depletion accelerates mitotic entry, prolongs the time spent in mitosis, and exacerbates cell death upon exposure to the WEE1 inhibitor.

## 3. WEE1 inhibition causes severe mitotic defects and chromosome pulverization in ATIP3-deficient cells
WEE1 inhibition caused a very particular phenotype in mitosis, where a bulk of chromatin mass was on the outside of the mitotic spindle rather than in structured chromosomes aligned on the metaphase plate in both 2D (Figure 3A *enlarged panels*) and MCSs (Figure S3G). ATIP3 depletion exacerbated these mitotic abnormalities (Figure 3B). Similar results were obtained when WEE1 was depleted (Figure S3H). We then investigated the impact of CDK1 and 2, the major kinases controlled by WEE1. The abnormal mitosis phenotype was completely abolished upon combining WEE1 inhibition with RO-3306, a CDK1 inhibitor (Figure S3I-J) but not when CDK2 was depleted (Fig S3K) indicating that the abnormal mitotic phenotype was mainly due to the activation of CDK1 after WEE1 inhibition.

The observation of such a phenotype, characterized by exclusion of DNA from the spindle during mitosis, raised the question of the localization of centromere proteins (CENPs) in AZD1775-treated cells. Using anti-centromere antibodies (ACA) staining, we found that WEE1 inhibition induced a detachment of CENPs from the DNA, as evidenced by a chromatin mass located on the outside of the spindle and CENPs clustered on spindle fibers (Figure 3C). Similar results were obtained using CENP-A and CENP-B antibodies as centromeric markers (Figure S4A-B). We further distinguished two phenotypes, which we referred to as (i) "central chromatin mass" where the DNA is still mildly attached to the spindle (Figure 3D, central panel) and (ii) "side chromatin mass" where DNA is completely devoid of CENPs that are clustered on the spindle (Figure 3D, right panel). AZD1775-treated control and ATIP3-depleted cells exhibited equal proportions of the 'central chromatin mass' phenotype, whereas the 'side chromatin mass' phenotype was the major phenotype in ATIP3-depleted cells (46% compared to 7% in control cells) (Figure 3E). The detachment of DNA from the spindle prompted us to evaluate chromosome integrity by performing chromosome spreads. Following WEE1 inhibition, we observed two distinct types of chromosome spreads. The first type, termed as "Cen<sup>ON</sup>" showed CENPs attached to the chromosomes, although a few acentric chromosomes were present. In the second type, denoted as "Cen<sup>OFF</sup>", CENPs were clustered and chromosomes were disintegrated, a phenotype that we hereby refer to as "chromosome pulverization" (Figure 3F, S4C). The latter spreads were reminiscent of the "side chromatin mass" phenotype in mitosis. Importantly, WEE1 inhibition increased the percentage of "Cen<sup>OFF</sup>" spreads in ATIP3-depleted cells (65% compared to 23% in control cells) (Figure 3G). These results were confirmed using another independent siRNA targeting the non-coding region of ATIP3 (Figure S4D-F).
We next examined whether WEE1 inhibition may also trigger such chromosome pulverization in cells rendered aneuploid using reversine. WEE1 inhibition induced chromosome pulverization in aneuploid RPE-1, but not in their near-diploid counterparts (Figure 3H-I). Similar results were obtained in the chromosomally stable HCT116 cancer cell line, where WEE1 inhibition alone exhibited negligible effects but triggered chromosome pulverization upon increasing aneuploidy using reversine (Figure S5A-C). Accordingly, elevating aneuploidy using reversine in HeLa cells that are already aneuploid (Figure S5D), further increased the occurrence of chromosome pulverization (Figure S5E-F). These results underscore the relationship between aneuploidy and cell sensitivity to WEE1 inhibition.

To further examine centromeric aberrations, we investigated the binding of CENP-B to its specific DNA box, the CENP-B box. Immunofluorescence coupled to Fluorescence in situ hybridization (FISH) was performed to reveal both CENP-B and CENP-B box. Following WEE1 inhibition, the CENP-B box was no longer detected in "Cen<sup>OFF</sup>" spreads in which the protein CENP-B was clustered (Figure S5G-H), indicating loss or disruption of centromeric DNA. This suggests that inhibition of WEE1 results in DNA fragmentation in the centromeric region.

**4. Chromosome pulverization in mitosis is due to heightened levels of replication stress and DNA damage in S-phase**

We hypothesized that chromosome pulverization during mitosis may stem from events that occur earlier in S-phase. WEE1 inhibition was previously shown to cause replication stress by exhausting replication origins, leading to replication fork stalling<sup>21</sup>. Stalled forks can then be recognized by a nuclease for processing<sup>29</sup>. Importantly, uncontrolled action of nucleases can lead to excessive fork degradation and massive DNA double-strand breaks (DSBs). To investigate the early effects of WEE1 inhibition in S-phase before mitotic entry, cells were treated for 2 h with AZD1775. WEE1 inhibition did not change the proportion of cells in S-phase, as indicated by the proportion of EdU-positive cells (Figure S6A-B). In line with higher DNA replication levels, WEE1 inhibition induced higher levels of EdU incorporation, although no differences were observed between control and ATIP3-depleted cells (Figure S6C). One of the early events of WEE1 inhibition was the induction of replication stress, as shown by elevated levels of phosphorylated replication protein A (RPA32 pS4/S8) (Figure 4A-B). Interestingly, ATIP3-depleted cells showed significantly higher levels of replication stress in response to WEE1 inhibition compared to control cells (Figure 4A-B). Excessive replication stress is known
to cause DNA damage³⁰, that we assessed by analyzing pan-nuclear H2AX phosphorylation (γH2AX). DNA damage levels were elevated upon WEE1 inhibition and even further in ATIP3-depleted cells (Figure 4C-D). Similarly, our *in vivo* studies revealed that ATIP3-deficient tumors exhibit higher levels of DNA damage following treatment with AZD1775 (Figure S6D-E). Inversely, the percentage of EdU-positive cells with 53BP1 foci was reduced upon WEE1 inhibition, suggesting an impaired DNA damage response or reduced DNA repair activity (Figure S6F-G).

Importantly, after 6 h of treatment, when cells progressed to mitosis, the impact of WEE1 inhibition on DNA replication and damage remained pronounced in ATIP3-depleted cells. This is shown by the higher proportion of hyperphosphorylated RPA32 (Figure S7A-B) and γH2AX positive (Figure S7C-D) mitoses. Of note, EdU-positive mitotic cells were detected following WEE1 inhibition, and to a greater extent in ATIP3-depleted cells (Figure S7C,E), indicating either replication stress-associated mitotic DNA synthesis (MIDAS)³⁰,³¹ or abrupt mitotic entry of replicating cells. The observed higher levels of damage in ATIP3-depleted mitotic cells led us to investigate whether they could be attributed to the presence of pre-existing replication stress prior to WEE1 inhibition. Cells were pretreated with a low dose of the DNA polymerase inhibitor aphidicolin for 2 h and then exposed for 6 h to AZD1775. When control cells were exposed to aphidicolin and AZD1775, they exhibited the same proportions of hyperphosphorylated RPA32 (Figure S8A-B) and γH2AX (Figure S8C-D) mitoses as those observed in ATIP3-depleted cells treated with AZD1775 alone. These results point towards the existence of low levels of endogenous replication stress or a compromised replication stress response in ATIP3-depleted cells. Indeed, ATIP3-depleted cells showed higher levels of endogenous 53BP1 nuclear bodies (Figure S8E-G) a marker of unresolved replication stress transmitted throughout the cell cycle³².

We reasoned that WEE1 inhibition in S-phase may be responsible for the detachment of CENPs from DNA and the chromosome pulverization observed during mitosis. To address this question, G1/S synchronized cells were treated at different time points with AZD1775 following the scheme shown in Figure 4E. Control cells that completed at least half of their S-phase (mid-S and late-S) in the absence of AZD1775 presented much less abnormal phenotypes (side or central chromatin mass phenotypes) in mitosis (Figure 4F) and less chromosome pulverization (Figure 4G) compared to cells that were released into S-phase in the presence of AZD1775.
Remarkably, a similar trend was observed in ATIP3-depleted cells. When allowed to progress into S phase before exposure to AZD1775, ATIP3-depleted cells displayed diminished occurrence of abnormal mitosis and chromosome pulverization in subsequent mitosis (Figure 4F-G). These results imply that the effects of the WEE1 inhibitor on DNA replication are crucial for the development of abnormalities during subsequent mitosis. Notably, allowing cells to partially replicate their DNA helps to alleviate mitotic abnormalities.

We investigated whether the restoration of DNA replication by adding nucleosides, which would counteract the nucleotide depletion caused by WEE1 inhibition²¹, could prevent abnormal mitosis and chromosome pulverization. Simultaneous nucleoside supplementation in AZD1775-treated cells led to a comprehensive rescue of replication stress in S-phase (Figure S8H), as well as the subsequent abnormal mitoses (Figure S8I) and chromosome pulverization (Figure 4H-I) in both control and ATIP3-depleted cells, highlighting the dysregulation of DNA replication as a driving force behind mitotic aberrations induced by WEE1 inhibition.

**5. DNA2 nuclease is responsible for chromosome pulverization in mitosis**

Knowing that WEE1 inhibition during S phase results in chromosome pulverization in subsequent mitosis, we explored the potential involvement of key molecular players. Notably, the MUS81 endonuclease emerged as a candidate of interest due to its role in inducing DNA breakage at stalled replication forks after WEE1 inhibition²⁰. We co-depleted MUS81 and ATIP3 (Figure S9A) and studied the extent of replication stress and DNA damage after WEE1 inhibition. As expected, MUS81 silencing in ATIP3-depleted cells led to a significant reduction in the levels of DNA damage (Figure S9B-C) and replication stress (Figure S9D-E) induced by WEE1 inhibition. However, MUS81 depletion had no impact on the occurrence of the mitotic phenotypes (Figure S9F-G) or chromosome pulverization (Figure 5A-B), regardless of whether in control or ATIP3-depleted cells. This suggests that while MUS81 plays a role during S phase, it is not the sole factor responsible for subsequent abnormalities in mitosis. This led us to test the implication of three other nucleases (EXO1, MRE11 and DNA2) which are involved in DNA resection during DNA repair (EXO1), DNA double-strand break sensing and processing (MRE11) and replication fork restart (DNA2)³³⁻³⁵. Depletion of either EXO1 or MRE11 did not significantly influence abnormal mitoses (Figure S10A-C) or chromosome pulverization (Figure S10D-E), whereas DNA2 depletion rescued the abnormal mitotic phenotypes in ATIP3-depleted cells (Figure S11A-C) and prevented chromosome pulverization (15% 'Cen<sup>OFF</sup>' spreads when
DNA2 is co-depleted with ATIP3 vs. 55% 'Cen<sup>OFF</sup>' spreads in ATIP3-depleted cells) (Figure 5C-D). Furthermore, the depletion of DNA2 prevented the excessive levels of DNA damage induced in ATIP3-deficient cells in response to WEE1 inhibition (Figure 5E-F). Conversely, DNA2 overexpression led to an increase in the side chromatin mass phenotype during mitosis in control cells, mimicking ATIP3 depletion (Figure 5G-H). These findings point to a pivotal role of DNA2 in orchestrating the observed mitotic phenotypes subsequent to S-phase defects induced in ATIP3-deficient cells by WEE1 inhibition.

**6. Dual inhibition of WEE1 and PKMYT1 kinases enhances response in ATIP3-deficient cancer cells**

We next investigated the potential of targeting PKMYT1, a kinase closely related to WEE1, to determine whether its inhibition could exploit the vulnerabilities of ATIP3-deficient cancer cells. PKMYT1, known for its role in regulating CDK1 activity and maintaining genomic stability<sup>25,36,37</sup>, was targeted using RP-6306. We observed heightened sensitivity in ATIP3-depleted MCSs compared to controls in response to RP-6306 (mean IC50 in shCtrl is 3.7 µM vs. 1.4 µM in shATIP3, Figure 6A). Given that both WEE1 and PKMYT1 target CDK1, we sought to determine whether simultaneously inhibiting these kinases would provide greater therapeutic efficacy in ATIP3-deficient cells compared to targeting each kinase individually. The dual targeting of WEE1 and PKMYT1 with PD0166285, significantly reduced cell viability in ATIP3-deficient cells (mean IC50 in shCtrl is 47 nM vs. 14 nM in shATIP3, Figure 6B). The most striking results were obtained with the combination of AZD1775 and increasing doses of RP-6306, which led to enhanced reductions in viability in ATIP3-deficient cells compared to monotherapies (Figure 6C-D). Phenotypic analyses revealed severe mitotic abnormalities (Figure S11D) and extensive chromosome pulverization (Figure S11E), particularly in ATIP3-deficient cells treated with the dual-inhibition approach. The depletion of DNA2 in this scenario partially rescued the mitotic phenotypes and reduced chromosome pulverization in cells treated with both WEE1 and PKMYT1 inhibitors. These results demonstrate that the dual inhibition of WEE1 and PKMYT1 effectively exploits the vulnerabilities of ATIP3-deficient cells, highlighting a promising therapeutic approach for targeting this aggressive breast cancer population.
Discussion

The findings presented in this study address an urgent need for targeted therapies in the context of precision medicine, particularly for stratified patient populations. Breast cancers deficient in ATIP3 represent a clinically challenging subgroup with limited therapeutic options. By identifying WEE1 kinase as a vulnerability in ATIP3-deficient cancers, our work provides a step toward the development of personalized treatment strategies for these tumors. Furthermore, the dual inhibition of WEE1 and PKMYT1 offers a complementary approach that could improve therapeutic efficacy while minimizing side effects. Our study also provides detailed mechanistic insights into how ATIP3 deficiency sensitizes cells to WEE1 inhibition. In ATIP3-deficient cells, the uneven segregation of chromosomes and compromised DNA replication machinery routinely challenge cellular integrity, leading to aneuploidy (Figure 6E, left panel). In these cells, WEE1 inhibition intensifies replication stress and DNA damage to catastrophic levels in S-phase, culminating in replication failure. Untimely activation of CDK1 by WEE1 inhibition triggers a premature entry into mitosis, which, when combined with the defects in S-phase, causes aberrant mitotic phenotypes and chromosome pulverization, ultimately resulting in important cell death (Figure 6E, right panel).

One key player in this cascade is the DNA2 helicase/nuclease, a putative substrate of CDK1³⁸, which becomes deregulated upon WEE1 inhibition. The DNA2 helicase/nuclease has a high affinity for centromeric DNA³⁹ and plays a critical role in processing replication intermediates at fragile genomic sites⁴⁰. In our experiments, we showed that depletion of DNA2 rescues both DNA damage and abnormal mitotic phenotypes and prevents the severe chromosome pulverization associated with WEE1 inhibition. We also observed DNA2 localized at centromeres during mitosis (Figure S11F, Top panels). Upon WEE1 inhibition, DNA2 localized to the centromeres and more broadly across the chromatin in abnormal mitotic cells (Figure S11F, bottom panels), potentially reflecting a broader role in response to widespread mitotic DNA damage. These results support a model where untimely CDK1 activation drives unregulated DNA2 activity, contributing to chromosome pulverization and mitotic catastrophe.

Other studies have described a similar aberrant phenotype in response to WEE1 inhibition where cells underwent premature mitosis with under-replicated DNA⁴¹. This phenotype was referred to as 'centromere fragmentation' based on the observation of centromeres being
spatially separated from the main mass of chromosomes<sup>42-44</sup>. Our data provide further molecular insights into this phenotype and support a model in which centromere detachment from DNA is orchestrated by the action of nucleases, and highlight the DNA2 helicase/nuclease as a new molecular player of cell cycle regulation upon inhibition of WEE1 kinase.

While WEE1 inhibitors have demonstrated potential in preclinical models<sup>45,46</sup>, clinical trials have faced challenges, including toxicities and limited efficacy<sup>47</sup>. To address these issues, the identification of companion biomarkers such as ATIP3 could provide a strategy for patient stratification. ATIP3 deficiency, which is prevalent in approximately 50% of breast tumors and up to 80% of triple-negative breast cancers, makes it an ideal candidate for selecting patients who are more likely to benefit from WEE1 inhibition. Interestingly, ATIP3-deficient breast cancers have shown a better pathological complete response when treated with taxane-based chemotherapy<sup>4</sup>. In line with this, our results demonstrate that incorporating a low dose of paclitaxel into AZD1775 treatment further improves its efficacy in ATIP3-deficient cells (Figure S11G). This suggests that integrating low-dose paclitaxel with WEE1 inhibitors could represent a complementary approach to improve therapeutic outcomes.

To further enhance the clinical benefit while reducing toxicity, combining WEE1 inhibitors with PKMYT1 inhibitors emerges as a promising strategy. PKMYT1 shares functional similarities with WEE1 but has distinct regulatory mechanisms. By targeting both kinases simultaneously, we can more effectively disrupt the G2/M checkpoint in ATIP3-deficient cells, driving them towards mitotic catastrophe. This dual inhibition approach may allow for the use of lower doses of each inhibitor, thus minimizing the toxicity commonly associated with higher doses of WEE1 inhibitors alone. Therefore, combining WEE1 and PKMYT1 inhibitors could not only improve the therapeutic window but also offer a solution to the challenges of resistance and side effects, particularly in cancers with ATIP3 deficiency.

In summary, this study underscores the use of ATIP3 as a biomarker for guiding the use of WEE1 inhibitors in breast cancer treatment. The development of dual inhibition strategies targeting both WEE1 and PKMYT1 represents an exciting avenue for future research, with the potential to improve therapeutic outcomes in ATIP3-deficient cancers while minimizing toxicities. Future clinical investigations are warranted to translate them into personalized cancer therapy.
# Methods

## Cell culture, synchronization, and treatment

All cell lines were grown in a sterile cell culture environment and were routinely tested for mycoplasma contamination. MDA-MB-231-luc-D3H2LN (breast cancer cell line, RRID: CVCL_D257), MDA-MB-468 (breast cancer cell line, RRID: CVCL_0419), HeLa (cervical carcinoma cell line, RRID: CVCL_0030) and RPE-1 (hTERT immortalized retinal pigmented cell line, RRID: CVCL_4388) cells were cultured in Dulbecco's modified Eagle's medium (DMEM Gibco™, 61965026). SUM52PE (breast cancer cell line, RRID: CVCL_3425) and HCT116 (colon carcinoma cell line, RRID: CVCL_0291) cells were cultured in RPMI-1640 medium (Gibco™, 61870010). All cell culture media were supplemented with 10% fetal bovine serum (FBS, Sigma, F7514). MDA-MB-231 shCtrl and shATIP3, SUM52PE shCtrl and shATIP3, and HeLa H2B-mCherry cells were previously described⁴. Cell lines were grown under standard cell culture conditions in CO₂ incubators (37 °C; 5% CO₂). Cells were routinely tested for absence of mycoplasma contamination using Venor® GeM Advance Kit (MB Minerva biolabs®, 11-7024).

For synchronization at the G1/S boundary, HeLa cells were treated with 2 mM thymidine (Sigma, T1895) for 18 h. Thymidine was washed out and cells were released into fresh media for 8 h followed by a second exposition to 2 mM thymidine for 18 h.

All drugs were purchased from Selleckchem and resuspended in DMSO at 10 mM stock concentration. AZD1775 was used at 500 nM for all experiments except for cell viability assessment where it was used at increasing concentrations. RO-3306 was used at 10 µM, aphidicolin at 100 nM and reversine at 500 nM. Media was supplemented with nucleosides (EmbryoMax® Nucleosides, Sigma) 1/50 for DNA replication rescue experiments.

## siRNA transfection

Specific and control scrambled siRNA oligonucleotides were purchased from Dharmacon (Horizon Discovery).

The following sequences were used:

* ATIP3 (5'-UGGCAGAGGUUUAAGGUUA-3');
DNA2: GCUAAACCGUGAAGCAAGA, CUACGUCACUUUAAAGAUG, ACAGUUGCCUGCAUUCUAA, UGAUAUAGAUACCCCAUUA;

EXO1: GAAGUUUCGUUACAUGUGU, GUAAAUGGACCUACUAACA, ACUCGGAUCUCCUAGCUUU, GUUAGCAGCAUUUGGCAUA;

MRE11 GAUGAGAACUCUUGGUUUA, GAAAGGCUCUAUCGAAUGU, GCUAAUGACUCUGAUGAUA, GAGUAUAGAUUUAGCAGAA;

MUS81: GGGAGCACCUGAAUCCUAA, CAGGAGCCAUCAAGAAUAA, GGGUAUACCUGGUGGAAGA, CAGCCCUGGUGGAUCGAUA;

WEE1: AAUAGAACAUUCUGACUUA, AAUAUGAAGUCCCGGUAUA, GAUCAUAUGCUUAUACAGA, CGACAGACUCCUCAAGUGA

All siRNAs were transfected for 72 h (excepting the siWEE1 that was transfected for 48 h) using Lipofectamine RNAiMAX (Invitrogen) at a final concentration of 20 nM. Gene silencing efficiency was evaluated by qPCR or Western blot.

## Drug screening

SUM52PE breast cancer cells (2,000 cells) were seeded in round bottom 96-well ultra-low attachment plates (Nunclon Sphera, ThermoFisher, 174925) to form MCSs as previously described⁴. MCSs were treated with increasing doses of each kinase inhibitor for 3 days. Viability was determined by measurement of ATP using ATPlite assay (Perkin Elmer). IC50 values were calculated using dose-response curves generated by fitting the data to a nonlinear regression model (log[inhibitor] vs. response — variable slope using GraphPad Prism (RRID: SCR_002798).

## Xenografts and drug treatment

Animal experiments were performed in accordance with guidelines and approved by the ethical committee of the animal facility of Gustave Roussy Institute, Villejuif, France.

For tumor formation, 5 million MDA-MB-468 (shCt1 or shATIP3) cells were mixed with Geltrex (Gibco) and PBS (1:1) and injected in 100 µl subcutaneously into the left flank of 6–8-week-old female NOD SCID gamma (NSG) mice (Charles River, RRID: IMSR_JAX:005557). Tumor growth was measured every 4 days using a caliper and volume was assessed as (length × width²)/2.
When the tumor volumes reached approximately 60-70 mm³, mice were randomly segregated into 4 groups (n = 9 per group). Mice were treated daily with vehicle (0.5% methylcellulose) or 90 mg/kg AZD1775 (provided by AstraZeneca) (in 0.5% methylcellulose) via oral gavage for 26 days. Body weight was measured every 4 days as an indicator of toxicity. At the end of the experiment, tumors were harvested, fixed with Formalin and embedded in paraffin. Four µm thick paraffin sections were stained with hematoxilin-eosin-saffranin (HES). For immunostaining of γH2AX, the Bond automated immunostainer (Leica) instrument was used. Paraffin sections were processed for heat-induced antigen retrieval (ER2 corresponding EDTA buffer pH9 for 20 min at 100°C). Slides were incubated with the antibody (Cell Signaling 9718, RRID: AB_2118009, 1/750) for 60 min at room temperature and detected by Bond Polymer Refine Detection kit. The signal was revealed with DAB and counterstained with hematoxylin. The percentage of γH2AX cells was evaluated using QuPath (RRID: SCR_018257) open source software.

## Patient-derived organoid studies

Organoids were established from PDX of TNBC as described in⁴⁸. PDX cells were embedded in 100-µl Matrigel domes (Corning, 354230), which were plated in six-well tissue culture plates onto a 70-µl Matrigel base layer. After a 30 min incubation, organoid medium consisting of advanced DMEM/F12 with 5% FBS, 10 mmol/L HEPES, 1 Glutamax, 1 mg/mL hydrocortisone, 50 mg/mL gentamicin, 10 ng/mL hEGF, 100 ng/mL FGF2, 10 mmol/L Y-27632, and 1 mmol/L N-acetyl cysteine was added. Media was replenished every 3 to 4 days. Mature organoids were collected from culture using TrypleExpress (Life technologies, 12605010) for 20 min. Twenty thousand to fifty thousand cells (~200 organoids) were seeded per well in 96-well tissue culture plates in 10% matrigel. A range of 8 concentrations of AZD1775 was added to wells after 48h. Cell viability was assessed to evaluate drug response 3 days after treatment using CellTiter-Glo 3D (Promega, G9682) (v/v).

## Live cell imaging

For quantification of mitotic entry, HeLa cells were transfected with control or ATIP3 siRNAs for 24 h then synchronized using double thymidine block. Cells were released in the presence or absence of 500 nM AZD1775 and filmed using an incucyte at a magnification of 10x every 10 minutes for 6 h. For fluorescent live cell imaging, HeLa-mCherryH2B cells were transfected for
48 h with control or ATIP3 siRNAs, then incubated with siR-Tubulin dye (Spirochrome SC002, 10 nM) prior to treatment with 500 nM AZD1775. Cells were imaged using a confocal laser scanning microscope TCS SP8 MP (Leica) using a dry 40X objective, every 6 min for 48 h. Image analysis was performed using ImageJ software (RRID: SCR_002285).

## Immunofluorescence

Transfected cells were seeded on coverslips one day before treatment. For the analysis of mitotic phenotypes, cells were fixed with ice-cold methanol for 5 min then washed with PBS. For γH2AX and 53BP1 analysis, cells were fixed with 4% paraformaldehyde for 15 min then washed with PBS and permeabilized with 0.5% Triton X-100 in PBS for 30 min. For the analysis of phosphorylated RPA32, cells were pre-extracted using ice-cold 0.2% Triton X-100 for 1 min then fixed with 4% paraformaldehyde and permeabilized. Coverslips were subsequently blocked with 3% BSA in PBS for 1 h at room temperature and incubated with primary antibodies overnight at 4°C. Coverslips were washed with 3% BSA in PBS and incubated with Alexa Fluor-coupled secondary antibodies for 45 min at room temperature. Coverslips were counterstained with DAPI (1/1000) and mounted using FluorSave reagent (Millipore).

For detection of cells in S-phase, EdU incorporation was analyzed using the Click-iT EdU immunofluorescence kit (Invitrogen, C10338) according to the manufacturer's instructions. Cells were pulse labeled with 10 µM EdU for 30 min, fixed and subjected to the Click-iT reaction. The cells were then processed for immunofluorescence staining as described.

For immunofluorescence in 3D, MCSs were harvested and fixed in 4% paraformaldehyde, 0.5% Triton X-100 in PBS for 45 minutes, blocked in 3% BSA, 0.5% Triton X-100 in PBS overnight at 4°C and incubated 24 h in primary antibodies and overnight in secondary antibodies. MCSs were deposited onto slides and sealed with mounting media and coverslips.

The following primary antibodies were used: rabbit anti-pericentrin (ab4448, Abcam; 1/1000; RRID: AB_304461), rat anti-alpha-tubulin (ab6160, Abcam; 1/1000; RRID: AB_305328), rabbit anti-CENP-B (ab25734, Abcam; 1/1000; RRID: AB_726801), mouse anti-CENP-A (ADI-KAM-CC006-E, ENZO; 1/1000; RRID: AB_2038993), human ACA (Antibodies Inc.; 1/2000; RRID: AB_2939058), mouse γH2AX (05-636, Millipore; 1/1000; RRID: AB_309864), rabbit anti-53BP1 (ab172580, Abcam; 1/1000; RRID: AB_2924712), RPA32 phospho S4/S8 (A300-245A, Bethyl; 1/1000; RRID: AB_210547), rabbit anti-phospho-histone H3 (06-570, Millipore; 1/1000; RRID:
AB_310177). All secondary antibodies (Alexa Fluor dyes) were purchased from Jackson Immunoresearch.

## Chromosome spreads and FISH

Transfected cells were seeded on coverslips at low density (30% confluency) one day before treatment. Colcemid (KaryoMAX, Gibco) was added to culture media 6 h before spreading at a final concentration of 0.1 µg/ml. Media was replaced with hypotonic media (media 60%, H2O 40%) for 10 min at room temperature. Coverslips were centrifuged at 1800 rpm for 3 minutes and fixed with 4% paraformaldehyde, blocked with 3% BSA 0,1% Triton X-100 in PBS for 30 min at room temperature then immunofluorescence was performed as previously described. IF-FISH for CENP-B box detection was modified from Chardon *et al.*⁴⁹. Briefly, immunofluorescence was performed and cells were post-fixed in 2% formaldehyde for 10 mins. Cells were fixed in Carnoy's fixative for 15 min, rinsed in 80% ethanol and air-dried. Coverslips were incubated with CENP-B box probe-Cy3 (PNAbio) 1:1 in hybridization buffer (20mM Tris, pH 7.4, 60% formamide, 0.5% of blocking reagent (Roche, 11096176001)). Samples and probes were denatured by heating on 75°C for 2 mins and incubated at 37°C overnight. Coverslips were washed with 0.4X SSC, counterstained with DAPI (1/1000) and mounted using FluorSave reagent (Millipore).

## Image acquisition and analysis

Images were acquired with a confocal laser scanning microscope Dmi8-SP8 using a 63X objective. 5 to 10 fields were imaged/treatment. The pinhole diameter was set at 1 airy unit for all channels, and the exposure gain for each channel was kept constant in between image acquisition of all samples. Z-stack projection and image analysis was done using LAS-X analysis software. For intensity analysis (phosphorylated RPA32, γH2AX, EdU and DAPI), background was subtracted and areas of interest (nuclei) were delineated by ROIs. Mean intensities of each channel were calculated as the arithmetic mean of the determined gray-scale values in each ROI then normalized to DAPI mean intensities in the same ROI.

## Quantitative PCR

RNA extraction and reverse transcription were performed as previously described⁴. Briefly, RNA was isolated using TRIzol reagent (Thermo Fisher). RNA concentration was assessed using a
NanoDrop and Complementary DNA (cDNA) was generated using Superscript II reverse transcriptase (Thermo Fisher) following manufacturer's protocol.

Quantitative real-time PCR was performed on a Viia7 real-time PCR machine (Thermo Fisher).
Gene expression was normalized to RPL13. The following oligonucleotides were used for
assessment of gene silencing efficiency:

ATIP3 (F: GGCGGAACAGTGACAATA; R: GCAAATTCACCCATGACGA);

DNA2 (F: GATTTCTGGCACCAGCATAGCC; R: ACACCTCATGGAGAACCGTACC);

EXO1 (F: TCGGATCTCCTAGCTTTTGGCTG; R: AGCTGTCTGCACATTCCTAGCC);

MRE11 (F: CAGCAACCAACAAAGGAAGAGGC; R: GAGTTCCTGCTACGGGTAGAAG);

MUS81 (F: GATCCTACAGCACTTCGGAGAC; R: AAGAGTCCTGGACTTCCGCAAG);

WEE1 (F: GATGTGCGACAGACTCCTCAAG; R: CTGGCTTCCATGTCTTCACCAC).

Western blotting

Cells were lysed using RIPA buffer containing a cocktail of protease and phosphatase inhibitors. Protein lysates were denatured in Laemmli buffer, separated by SDS-PAGE and transferred onto a PVDF membrane. After blocking with 5% BSA in TBS-0.1% Tween-20, membranes were incubated with the following primary antibodies: rabbit anti-ATIP3 (ARP44419-P050, Aviva; 1/1000; RRID: AB_2047147), anti-phospho-histone H3 (06-570, Millipore; 1/1000; RRID: AB_310177), anti-phospho-tyrosine 15 Cdk1 (9111, Cell Signaling; 1/1,000; RRID: AB_331460), anti-CDK1 phosphorylated substrates (14371, Cell signaling; 1/1000; RRID: AB_2798466), rat anti-alpha-tubulin (ab6160, Abcam; 1/20000; RRID: AB_305328), goat anti-GAPDH (NB300-320, Novus biologicals; 1/50000; RRID: AB_10001796). Proteins were visualized using horseradish-peroxidase-conjugated secondary antibodies followed by chemiluminescence detection with ECL (Clarity Western ECL substrate, Bio-Rad).

Statistical analysis

All statistical analysis was performed using GraphPad Prism 9 software. Mean values are
presented with SEM, based on independent experiments, as indicated in figure legends (N=X
denotes the number of independent experiments. Statistical significance between two groups
was determined using two-tailed t-test. When more than two groups were compared,
statistical significance was determined using one or two-way ANOVA or Kruskal-Wallis when
data distribution did not pass normality tests. P-values < 0.05 were considered significant.
**Figure Legends**

<caption>**Figure 1. Chemical synthetic screen identifies WEE1 kinase as a target for ATIP3-deficient breast cancer cells**</caption>

(A) Pipeline scheme for drug screening using 28 cell cycle kinase inhibitors. The SUM52PE cell was cultured as MSCs. Screening was conducted in technical quadruplicates. (B) Plot representing the IC50 fold change of each inhibitor between shCtrl and shATIP3. (C) Plot showing the correlation between MTUS1 expression levels and the area under the curve (AUC) of AZD1775 in the Sanger GDSC2 database. (D) Representative images of SUM52PE MCSs treated or not with 500 nM AZD1775 for 72 h (Magnification 20x). (E) IC50 values of AZD1775 in MCSs (mean ± SEM. of N=6; two-tailed t-test; \*\*p<0.01). (F) Plot representing viability of shCtrl and shATIP3 SUM52PE MCSs where WEE1 was knocked down using siRNA (mean ± S.D.; one-way ANOVA; \*\*\*\*p<0.0001). (G) Plot representing viability of RPE-1 cells upon 72 h of treatment with increasing doses of AZD1775 (mean ± SEM. of N=3). (H) Plot representing viability of RPE-1 cells pre-treated with reversine for 48 h and 10 µM AZD1775 for 72 h (mean ± S.D.; one-way ANOVA; \*\*\*\*\*p<0.0001). (I-J) MDA-MB-468 shCtrl or shATIP3 subcutaneous xenografts were treated or not with 90 mg/kg of AZD1775 by oral gavage (mean ± SEM; N=1 with 9 mice per group) (I) Tumor growth curve (the beginning of treatment is indicated by an arrow). (J) Tumor volume (mm³) after 15 days of treatment (one-way ANOVA; \*p<0.05 \*\*\*\*\*p<0.0001). (K-L) PDXOs expressing high or low levels of ATIP3 were treated with increasing concentrations of AZD1775 for 72 h. (K) Plot representing cell death percentages of PDXOs treated with 300 nM AZD1775. (L) Plot representing expression levels of ATIP3 and the response to AZD1775.

<caption>**Figure 2: WEE1 inhibition accelerates mitotic entry in ATIP3-deficient cells and induces their cell death after failed mitosis**</caption>

(A-B) HeLa cells expressing or not ATIP3 were synchronized at the G1/S boundary, released in the presence or absence of 500 nM AZD1775, and filmed for 6 h. (A) Representative images (Magnification 10x). (B) Scattered dot plot showing the time to enter mitosis in response to AZD1775 (elapsed time between release and cell rounding). The number of analyzed cells is in
brackets (mean ± S.D.; Mann-Whitney test; ****p<0.0001). (C) HeLa cells expressing or not ATIP3 were treated or not with 500 nM AZD1775 for 6 h. Mitotic index was determined as the ratio of mitotic cells to the cellular population (mean ± SEM. of N=4; a minimum of 177 cells were analyzed per group; one-way ANOVA; \*p<0.05, \*\*p<0.01, \*\*\*p<0.0001). (D-G) HeLa mCherry-H2B expressing not ATIP3 were stained with siR-tubulin and treated or not with 500 nM AZD1775, then analyzed by timelapse fluorescence imaging for 48 h. (D) Representative images. Time is indicated on the top left as h:min. Microtubules (siR-tubulin) are shown in cyan and DNA (mCherry-H2B) in red. Scale bar = 10 µm. (E) Scattered dot plot showing the duration of mitosis. The number of analyzed cells is in brackets (mean ± S.D.; Kruskal-Wallis test followed by Dunn's multiple comparisons; \*\*p< 0.001, \*\*\*p<0.0001). (F) Cell fate profiles. (G) Proportion of cell fates measured in (F).

<caption>**Figure 3: WEE1 inhibition causes severe mitotic defects and chromosome pulverization in ATIP3-deficient cells**</caption>

HeLa cells expressing or not ATIP3 were treated or not with 500 nM AZD1775 for 6 h. (A) Immunofluorescence representative images showing centrosomes (pericentrin) in green, microtubules (tubulin) in red and DNA (DAPI) in blue. Scale bar = 20 µm (B) Quantification of abnormal mitosis shown in (A) (mean ± SEM. of N=4; a minimum of 128 cells was analyzed; one-way ANOVA; \*\*p<0.01, \*\*\*p<0.001, \*\*\*\*p<0.0001). (C) Immunofluorescence representative images showing CENPs (ACA) in magenta, microtubules (tubulin) in green and DNA (DAPI) in blue. Scale bar = 10 µm. (D) Immunofluorescence representative images of the mitotic phenotypes showing CENPs in magenta, microtubules in green and DNA in blue. Scale bar = 5 µm. (E) Quantification of the proportions of the mitotic phenotypes shown in (D) (mean ± SEM. of N=4; a minimum of 112 cells were analyzed per group; two-way ANOVA; side chromatin mass in siCtrl vs. siATIP3 \*\*\*p<0.0001). (F) Immunofluorescence representative images of chromosome spreads showing CENP-A in green and DNA in red. Scale bar = 5 µm. (G) Quantification of the proportions of chromosome spreads shown in (F) (mean ± SEM. of N=6; a minimum of 145 spreads were analyzed per group; two-way ANOVA; siCtrl Cen<sup>OFF</sup> vs. siATIP3 Cen<sup>OFF</sup> \*\*\*p<0.0001). (H-I) RPE-1 cells were treated with 500 nM reversine for 48 h then with 1 µM AZD1775 for 6 h. (H) Immunofluorescence representative images of chromosome spreads showing CENP-A in green and DNA in blue. Scale bar = 5 µm. (I)
Quantification of the proportion of chromosome spreads from (H) (mean ± S.E.M of N=2; a minimum of 54 spreads were analyzed per group; two-way ANOVA; AZD1775 Cen<sup>OFF</sup> vs AZD1775 + Reversine Cen<sup>OFF</sup> ****p<0.0001). Scale bar = 5 µm.

<caption>**Figure 4: Chromosome pulverization in mitosis is due to heightened levels of replication stress and DNA damage in S-phase**</caption>

(A-D) HeLa cells expressing or not ATIP3 were treated or not with 500 nM AZD1775 for 2 h. (A) Immunofluorescence representative images showing phosphorylated RPA32 (S4/S8) in red and DNA in blue. Scale bar = 10 µm. (B) Scattered dot plot of phosphorylated RPA32 mean intensity per nucleus (normalized to DAPI). (C) Immunofluorescence representative photographs showing γH2AX in green and DNA in blue. Scale bar = 10 µm. (D) Scattered dot plot of γH2AX mean intensity per nucleus (normalized to DAPI). (B-D) The number of analyzed cells is in brackets (mean ± S.D.; Kruskal-Wallis test followed by Dunn's multiple comparisons; ****p<0.0001). (E) Treatment scheme of HeLa cells synchronized at the G1/S boundary. Cells were either treated with 500 nM AZD1775 for 6 h directly after release from thymidine block (top axis, early-S); after 3 h into S phase progression (middle axis, mid-S), or after 6 h into S phase progression (bottom axis, late-S). (F) Quantification of the proportions of the mitotic phenotypes as described in (E) (mean ± SEM. of N=3; a minimum of 133 cells were analyzed per group; two-way ANOVA; side chromatin mass in siCtrl early-S vs. late-S p<0.0001; side chromatin mass in siATIP3 early-S vs. late-S p<0.0001) (G) Quantification of the proportions of chromosome spreads as described in (E) (mean ± SEM. of N=3; a minimum of 80 spreads were analyzed per group; two-way ANOVA; Cen<sup>OFF</sup> in siCtrl early-S vs. late-S p<0.0001; Cen<sup>OFF</sup> in siATIP3 early-S vs. late-S p<0.0001). (H) Immunofluorescence representative images of chromosome spreads from HeLa cells expressing or not ATIP3 and treated with 500 nM AZD1775 for 6 h in the presence or absence of nucleosides (1/50). CENP-A is shown in green and DNA in blue. Scale bar = 5 µm. (I) Quantification of the proportion of chromosome spreads shown in (H) (mean ± SEM. of N=2; a minimum of 61 spreads were analyzed per group; two-way ANOVA; Cen<sup>OFF</sup> in siCtrl AZD1775 vs. AZD1775 + Nucleosides p<0.0001; Cen<sup>OFF</sup> in siATIP3 AZD1775 vs. siATIP3 AZD1775 + Nucleosides Cen<sup>OFF</sup> p<0.0001).
<caption>**Figure 5:** DNA2 nuclease is responsible for chromosome pulverization in mitosis</caption>

(A-B) HeLa cells transfected with control, ATIP3, MUS81 or a combination of ATIP3 and MUS81 siRNAs were treated or not with 500 nM AZD1775 for 6 h. (A) Immunofluorescence representative images of chromosome spreads showing CENP-A in green and DNA in red. Scale bar = 5 µm. (B) Quantification of the proportion of chromosome spreads shown in (A) (mean ± S.E.M of N=2; a minimum of 67 spreads were analyzed per group; two-way ANOVA showing no significance (p>0.99) between Cen<sup>OFF</sup> in siATIP3 vs. siATIP3 + siMUS81). Scale bar = 5 µm. (C-F) HeLa cells transfected with control, ATIP3, DNA2 or a combination or ATIP3 and DNA2 siRNAs and treated or not with 500 nM AZD1775 for 6 h (C-D) or 2 h (E-F). (C) Immunofluorescence representative images of chromosome spreads showing CENP-A in green and DNA in blue. Scale bar = 5 µm. (D) Quantification of the proportion of chromosomes spreads shown in (C) (mean ± S.E.M of N=2; a minimum of 78 spreads were analyzed per group; two-way ANOVA; Cen<sup>OFF</sup> in siDNA2 vs. siDNA2 + siATIP3 **p<0.01). (E) Immunofluorescence representative images of chromosomes spreads showing γH2AX in green and DNA in blue. Scale bar = 10 µm. (F) Scattered dot plot of γH2AX mean intensity per nucleus (normalized to DAPI). The number of analyzed cells is in brackets (mean ± S.D.; Kruskal-Wallis test followed by Dunn's multiple comparisons; ****p<0.0001; *<0.05). (G-H) HeLa cells transfected with control or ATIP3 siRNAs were transfected with plasmids harboring GFP or GFP-DNA2 constructs and treated with AZD1775 for 6 h. (G) Immunofluorescence representative images of mitosis showing microtubules in red, ACA in blue, DNA2 in green and DNA in grey. (H) Quantification of the proportions of the mitotic phenotypes as described in (E) (mean ± SEM. of N=2; a minimum of 48 cells were analyzed per group; two-way ANOVA; side chromatin mass in treated siCtrl GFP-DNA2 vs. GFP p<0.05). Scale bar = 10 µm.

<caption>**Figure 6:** Dual inhibition of WEE1 and PKMYT1 kinases enhances response in ATIP3-deficient cancer cells.</caption>

(A) Left panel: Dose-response curves of RP-6306 in SUM52PE shCtrl or shATIP3 MCSs upon 72 h of treatment. Right panel: IC50 values of RP-6306 (mean ± SEM. of N=6; two-tailed t-test; *p<0.05) (B) Left panel: Dose-response curves of PD0166285 in SUM52PE shCtrl or shATIP3 MCSs upon 72 h of treatment. Right panel: IC50 values of PD0166285 (mean ± SEM. of N=3;
two-tailed t-test; \*p<0.05) (C) Dose-response curves of RP-6306 in combination with different doses of AZD1775 in SUM52PE shCtrl or shATIP3 MCSs upon 72 h of treatment. (D) IC50 values in µM of AZD1775 in SUM52PE shCtrl or shATIP3 MCSs upon 72 h of treatment combined with increasing doses of RP-6306. (E) **Graphical abstract: Left panel:** During S-phase, ATIP3-deficient aneuploid cells undergo replication stress due to the uneven allocation of chromosomes and flawed DNA replication machinery. This leads to the formation of DNA lesions and stalled replication forks. Consequently, DNA damage response pathways are activated with suboptimal DNA repair mechanisms. As these cells transition through mitosis, they experience chromosome missegregation causing dissemination of aneuploidy to subsequent daughter cells. **Right panel:** WEE1 inhibition in ATIP3-deficient aneuploid cells increases levels of replication stress and DNA damages in S-phase through the unrestricted activity of the MUS81 endonuclease and the DNA2 helicase/nuclease. The untimely activation of CDK1 after WEE1 inhibition forces cells to enter mitosis prematurely with under-replicated and damaged DNA. DNA2 helicase/nuclease causes chromosome pulverization which leads to cell death. Created with Biorender.
# Acknowledgements

This work has benefited from the facilities and expertise of PETRA (Nicolas Signolle), PFIC (Tudor Manoliu) and PFEP (Mélanie Polrot) platforms of the UMS AMMICa (CNRS UMS 3655 / Inserm US 23) - Gustave Roussy Cancer Campus Villejuif, France. Authors acknowledge help from Dr Daniele Fachinetti (Institut Curie, Paris, France) and Dr Michele Debatisse (Gustave Roussy, Villejuif, France) for providing reagents and Marianne Oulhen (Gustave Roussy, Villejuif, France) for technical assistance. We thank Dr Sophie Polo (Institut d'Epigénétique, Paris, France), Dr Daniele Fachinetti, Dr Renata Basto (Institut Curie, Paris, France), Dr Olivier Gavet and Dr Valeria Naim (Gustave Roussy, Villejuif, France) for constructive discussion throughout the project.

# Fundings

We thank the Inserm, the CNRS, the Gustave Roussy cancer center, the Ligue Nationale Contre le cancer (LNCC), the associations Odyssea and Prolific, the Fondation Janssen Horizon, the Fondation Rothschild, the Ruban Rose association, the GEFLUC Les entreprises contre le cancer, and the Taxe d'apprentissage of Gustave Roussy for financial support.

# Ethics approval

Human breast tumour fragments were obtained with patients' informed consent for the establishment of PDXs and PDXOs (Curie Institute).

# CRediT author statement

**Conceptualization**: MMH, SRF, CN; **Validation**: MMH, SRF, REB, LS; **Formal analysis**: MMH, SRF, REB, LS; **Investigation**: MMH, SRF, REB, LS; **Resources**: EM, MV, AD; **Writing original draft**: MH, CN; **Writing review and editing**: MH, SRF, CN.

# Conflict of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

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<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>
<figure><img src="image_14.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>E</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>F</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>G</figcaption></figure>

<figure><img src="image_20.png" /></figure>
<figure><img src="image_21.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>E</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>F</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>G</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>H</figcaption></figure>

<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>
<figure><img src="image_30.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_31.png" /><figcaption>E</figcaption></figure>

<figure><img src="image_32.png" /><figcaption>F</figcaption></figure>

<figure><img src="image_33.png" /><figcaption>G</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>H</figcaption></figure>

<figure><img src="image_35.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_36.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_37.png" /><figcaption>I</figcaption></figure>

<figure><img src="image_38.png" /></figure>
<figure><img src="image_39.png" /><figcaption>A</figcaption></figure>

CENPA DAPI

<figure><img src="image_40.png" /><figcaption>C</figcaption></figure>

CENPA DAPI

<figure><img src="image_41.png" /><figcaption>E</figcaption></figure>

γH2AX DAPI

<figure><img src="image_42.png" /><figcaption>G</figcaption></figure>

GFP ACA Tubulin DAPI

<figure><img src="image_43.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_44.png" /><figcaption>F</figcaption></figure>

<figure><img src="image_45.png" /><figcaption>H</figcaption></figure>

<figure><img src="image_46.png" /></figure>
<figure><img src="image_47.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_48.png" /></figure>

<figure><img src="image_49.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_50.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_51.png" /><figcaption>E</figcaption></figure>

Untreated
ATIP3-deficient aneuploid cancer cell

<figure><img src="image_52.png" /></figure>

AZD1775-treated
ATIP3-deficient aneuploid cancer cell

<figure><img src="image_53.png" /></figure>
# Targeting WEE1 kinase as a therapeutic strategy in ATIP3-deficient breast cancers

Maria M. HAYKAL<fnref n="1" />,<fnref n="2" />, Sylvie RODRIGUES-FERREIRA<fnref n="1" />,<fnref n="2" />,<fnref n="3" />, Rania EL BOTTY<fnref n="4" />, Laura SOURD<fnref n="4" />, Elisabetta MARANGONI<fnref n="4" />, Marie VARIN<fnref n="5" />, Alexis DENIS<fnref n="5" /> and Clara NAHMIAS<fnref n="1" />,<fnref n="2" />*

*The authors declare no conflicts of interest related to the content of this manuscript.*

## Supplementary data

<footnote n="1">¹Institut Gustave Roussy, Inserm U981, Biomarqueurs prédictifs et nouvelles stratégies thérapeutiques en oncologie, Villejuif, France.</footnote>

<footnote n="2">²Université Paris-Saclay, Orsay, France.</footnote>

<footnote n="3">³Inovarion, Paris, France</footnote>

<footnote n="4">⁴Translational Research Department, Institut Curie, PSL University, Paris, France</footnote>

<footnote n="5">⁵IKTOS, Paris, France</footnote>

<footnote>*Correspondence: clara.nahmias@inserm.fr</footnote>
A SUM52PE

shCt1 shATIP3

<figure><img src="image_54.png" /></figure>

ATIP3
(170 kDa)

<figure><img src="image_55.png" /></figure>

Tubulin
(55 kDa)

B

<figure><img src="image_56.png" /></figure>

log [AZ20], M
ATRi

<figure><img src="image_57.png" /></figure>

log [AZD0156], M
ATMi

<figure><img src="image_58.png" /></figure>

log [AZD1775], M
WEE1i

<figure><img src="image_59.png" /></figure>

log [Alisertib], M
AURKAi

log [CFI-400945], M
AURKB & PLK4i

log [Centrinone], M
PLK4i

<figure><img src="image_60.png" /></figure>

C

<figure><img src="image_61.png" /></figure>

D

<figure><img src="image_62.png" /></figure>

r = 0.1818

p = 0.4703

<figure><img src="image_63.png" /></figure>

r = 0.3654

p = 0.04327

<figure><img src="image_64.png" /></figure>

r = 0.1226

p = 0.4764

<figure><img src="image_65.png" /></figure>

r = 0.1638

p = 0.3785

MTUS1 log2(TPM+1)

MTUS1 log2(TPM+1)

MTUS1 log2(TPM+1)

MTUS1 log2(TPM+1)

E

<figure><img src="image_66.png" /></figure>

log [AZD1775], M
shCt1 shATIP3

ATIP3
(170 kDa)

GAPDH
(35 kDa)

<figure><img src="image_67.png" /></figure>

<figure><img src="image_68.png" /></figure>

MDA-MB-231

<figure><img src="image_69.png" /></figure>

log [AZD1775], M

ATIP3
(170 kDa)

GAPDH
(35 kDa)

<figure><img src="image_70.png" /></figure>

<figure><img src="image_71.png" /></figure>

F

<figure><img src="image_72.png" /></figure>

AZD1775

CDK1 pTyr15
(35 kDa)

<figure><img src="image_73.png" /></figure>

Tubulin
(55 kDa)

G

<figure><img src="image_74.png" /></figure>

WEE1/RPL13

MTUS1/RPL13
<figure><img src="image_75.png" /></figure>

RPE-1

<figure><img src="image_76.png" /></figure>

<figure><img src="image_77.png" /></figure>

HBCx-244-org

HBCx-66-org

HBCx-14-org

HBCx-226-org

<figure><img src="image_78.png" /></figure>

<figure><img src="image_79.png" /></figure>

<figure><img src="image_80.png" /></figure>

<figure><img src="image_81.png" /></figure>

<figure><img src="image_82.png" /></figure>
A

HeLa

siCtI siATIP3

<figure><img src="image_83.png" /></figure>

ATIP3
(170 kDa)

<figure><img src="image_84.png" /></figure>

GAPDH
(35 kDa)

B

siCtI

siATIP3

siCtI

siATIP3

+

AZD1775

<figure><img src="image_85.png" /></figure>

pHistone H3
(18 kDa)

<figure><img src="image_86.png" /></figure>

Tubulin
(55 kDa)

C

siCtI

siATIP3

-

+

+

-

-

+

+

-

AZD1775

PD0166285

MG-132

<figure><img src="image_87.png" /></figure>

CDK1
substrates

<figure><img src="image_88.png" /></figure>

Actin
(42 kDa)

D

shCtI

shATIP3

E

<figure><img src="image_89.png" /></figure>

pHisH3 DAPI

<figure><img src="image_90.png" /></figure>

F

<figure><img src="image_91.png" /></figure>

G

shCtI

shATIP3

<figure><img src="image_92.png" /></figure>

Tubulin pHisH3 DAPI

H

siCtI

siATIP3

I

siCtI

siATIP3

<figure><img src="image_93.png" /></figure>

Tubulin Pericentrin DAPI

<figure><img src="image_94.png" /></figure>

<figure><img src="image_95.png" /></figure>

Tubulin DAPI

J

<figure><img src="image_96.png" /></figure>

K

<figure><img src="image_97.png" /></figure>

Supplementary Figure S3
<figure><img src="image_98.png" /></figure>

D ATIP3 (mean gene expression relative to RPL13)

<figure><img src="image_99.png" /></figure>

<figure><img src="image_100.png" /></figure>

<figure><img src="image_101.png" /></figure>
<figure><img src="image_102.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_103.png" /></figure>

CENP-A DAPI

<figure><img src="image_104.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_105.png" /><figcaption>E</figcaption></figure>

<figure><img src="image_106.png" /></figure>

CENP-A DAPI

<figure><img src="image_107.png" /><figcaption>G</figcaption></figure>

<figure><img src="image_108.png" /></figure>

CENP-B box CENP-B DAPI

<figure><img src="image_109.png" /><figcaption>Supplementary Figure S5</figcaption></figure>
A

siCtI

siATIP3

AZD1775 Untreated

<figure><img src="image_110.png" /></figure>

EdU DAPI

EdU

EdU DAPI

EdU

B

<figure><img src="image_111.png" /></figure>

C

<figure><img src="image_112.png" /></figure>

D

shCtI

shATIP3

Untreated

<figure><img src="image_113.png" /></figure>

AZD1775

<figure><img src="image_114.png" /></figure>

E

<figure><img src="image_115.png" /></figure>

F

siCtI

siATIP3

AZD1775 Untreated

<figure><img src="image_116.png" /></figure>

53BP1 EdU DAPI

<figure><img src="image_117.png" /></figure>

53BP1

<figure><img src="image_118.png" /></figure>

53BP1 EdU DAPI

<figure><img src="image_119.png" /></figure>

53BP1

G

<figure><img src="image_120.png" /></figure>
A

Untreated

AZD1775

siCtI

<figure><img src="image_121.png" /></figure>

pRPA32 EdU DAPI

EdU

pRPA32

siATIP3

<figure><img src="image_122.png" /></figure>

B

<figure><img src="image_123.png" /></figure>

C

Untreated

AZD1775

siCtI

<figure><img src="image_124.png" /></figure>

γH2AX EdU DAPI

EdU

gH2AX

siATIP3

<figure><img src="image_125.png" /></figure>

γH2AX EdU DAPI

EdU

gH2AX

D

<figure><img src="image_126.png" /></figure>

E

<figure><img src="image_127.png" /></figure>
<caption>A</caption>

siCtI

siATIP3

Untreated

Aphidicolin

AZD1775

Aphidicolin +
AZD1775

<figure><img src="image_128.png" /></figure>

pRPA32 DAPI

<figure><img src="image_129.png" /><figcaption>C</figcaption></figure>

siCtI

siATIP3

Untreated

Aphidicolin

AZD1775

Aphidicolin +
AZD1775

<figure><img src="image_130.png" /></figure>

γH2AX DAPI

<figure><img src="image_131.png" /><figcaption>E</figcaption></figure>

siCtI

siCtI

<figure><img src="image_132.png" /></figure>

53BP1 EdU DAPI

siATIP3

<figure><img src="image_133.png" /></figure>

<figure><img src="image_134.png" /><figcaption>F</figcaption></figure>

siCtI

siATIP3

<figure><img src="image_135.png" /></figure>

<figure><img src="image_136.png" /></figure>

ATIP3
(170 kDa)

GAPDH
(35 kDa)

siCtI

<figure><img src="image_137.png" /></figure>

53BP1 EdU DAPI

siATIP3

<figure><img src="image_138.png" /><figcaption>G</figcaption></figure>

<figure><img src="image_139.png" /><figcaption>H</figcaption></figure>

<figure><img src="image_140.png" /></figure>

<figure><img src="image_141.png" /></figure>

Central chromatin mass

Side chromatin mass

Normal
<figure><img src="image_142.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_143.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_144.png" /><figcaption>F</figcaption></figure>

<figure><img src="image_145.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_146.png" /><figcaption>E</figcaption></figure>

<figure><img src="image_147.png" /><figcaption>G</figcaption></figure>

<figure><img src="image_148.png" /></figure>
<figure><img src="image_149.png" /></figure>

<figure><img src="image_150.png" /></figure>

ACA Tubulin DAPI

<figure><img src="image_151.png" /></figure>

<figure><img src="image_152.png" /></figure>

CENP-A DAPI

<figure><img src="image_153.png" /></figure>
<figure><img src="image_154.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_155.png" /></figure>

ACA Tubulin DAPI

<figure><img src="image_156.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_157.png" /><figcaption>F</figcaption></figure>

<figure><img src="image_158.png" /><figcaption>E</figcaption></figure>

<figure><img src="image_159.png" /><figcaption>G</figcaption></figure>

<figure><img src="image_160.png" /><figcaption>Supplementary Figure S11</figcaption></figure>
**Supplementary Figure S1:** (A) Western blot of ATIP3 protein levels in SUM52PE shCtrl or shATIP3 MCSs. Loading control: β-tubulin (B) Dose-response curves of the 6 differential inhibitors in SUM52PE shCtrl or shATIP3 MCSs upon 72 h of treatment. (C) IC50 values in µM of each differential inhibitor in SUM52PE shCtrl or shATIP3 MCSs upon 72 h of treatment. (D) Plots showing MTUS1 expression levels and the area under the response to AZ20, AZD0156, Alisertib and CFI-400942. (E) Top panels: Dose-response curves of AZD1775 in MDA-MB-468 or MDA-MB-231 shCtrl or shATIP3 MCSs upon 72 h of treatment. Bottom panels: Western blot of ATIP3 protein levels in shCtrl or shATIP3 MCSs. Loading control: GAPDH. (F) Western blot analysis of CDK1 Tyr15 phosphorylation in SUM52PE shCtrl or shATIP3 MCS treated or not with 500 nM AZD1775 for 6 h. Loading control: β-tubulin. (G) Relative gene expression levels of WEE1 and MTUS1 evaluated by qPCR after knock-down. Levels are normalized to RPL13.
**Supplementary Figure S2:** (A-B) RPE-1 cells were treated for 48 h with 500 nM reversine. Reversine was washed out and cells were treated with 10 µM AZD1775 for 72 h before cell viability was assessed. (A) Treatment scheme of RPE-1 cells. (B) Left panel: Violin plot representing chromosome counts. The number of analyzed spreads is indicated between brackets. Right panel: Frequency distribution of chromosome counts. (C) Dose-response curves of AZD1775 in each PDXO model upon 72 h of treatment. (D) Dose-response curves of AZD1775 in ATIP3-high PDXOs (left panel) and ATIP3-low PDXOs (right panel). Each dose-response curve represents a different PDXO.
**Supplementary Figure S3: (A-C) HeLa cells transfected with control or ATIP3 siRNAs and treated with AZD1775 or PD0166285 for 6 h. (A) Western blot of ATIP3 protein levels.**

Loading control: GAPDH. (B) Western blot analysis for histone H3 Ser10 phosphorylation.

Loading control: β-tubulin. (C) Western blot analysis of CDK1 phosphorylated substrates (pTPXK). Increased phosphorylated substrates are indicated in red. Loading control: Actin.

(D) Immunofluorescence representative images of SUM52PE shCtrl or shATIP3 MCSs treated with 500 nM AZD1775 for 14 h showing phosphorylated histone H3 in magenta and DNA in blue. Scale bar = 100 µm. (E) Quantification of the percentage of phosphohistone H3 positive cells in MCS shown in (D) (mean ± S.D.; Kruskal-Wallis test followed by Dunn's multiple comparisons; **p<0.01, ***p<0.001). (F) Percentage of cells exhibiting back and forth movement of their DNA around the spindle in mitosis. The number of analyzed cells in indicated between brackets. (G) Immunofluorescence representative images of SUM52PE shCtrl or shATIP3 MCSs treated with 500 nM AZD1775 for 14 h showing phosphorylated histone H3 in magenta, microtubules in green and DNA in blue. White arrows indicate the exclusion of DNA from the mitotic spindle. Scale bar = 10 µm.

(H) HeLa cells transfected with control, WEE1, ATIP3 or both siRNAs. Top panel: Immunofluorescence representative images showing centrosomes in green, microtubules in red and DNA in blue. Bottom panel: quantification of abnormal mitosis (mean ± SEM. of N=3; a minimum of 97 cells were analyzed per group; two-tailed t-test; **p<0.01). (I-J) HeLa cells transfected with control or ATIP3 siRNAs were treated with 500 nM AZD1775, 10 µM RO-3306 or a combination of both drugs for 6 h. (I) Immunofluorescence representative images showing microtubules in green and DNA in blue. (J) Quantification of abnormal mitosis shown in (I) (mean ± SEM. of N=3; the number of analyzed cells is in brackets; one-way ANOVA; ****p<0.0001).

Scale bar = 10 µm. (K) Quantification of abnormal mitosis of HeLa cells transfected with control, CDK2, ATIP3 or both siRNAs and treated with 500 nM AZD1775 for 6 h.
**Supplementary Figure S4: (A-C)** HeLa cells transfected with control or ATIP3 siRNAs were treated with 500 nM AZD1775 for 6 h. **(A)** Immunofluorescence representative images showing CENP-B in magenta, microtubules in green and DNA in blue. **(B)** Immunofluorescence representative images showing CENP-A in green, microtubules in red and DNA in blue. **(C)** Immunofluorescence representative images of chromosome spreads showing CENP-B in magenta and DNA in blue. Scale bar = 10 µm. **(D-F)** HCT116 cells were treated for 48 h with 500 nM reversine. Reversine was washed out and cells were treated with 500 nM AZD1775 for 6 h before chromosome spreading. **(D-F)** HeLa cells were transfected with control or ATIP3 siRNAs and were treated or not with 500 nM AZD1775 for 6 h. **(D)** Relative expression of ATIP3 evaluated by qPCR and normalized to RPL13. **(E)** Left panel: Immunofluorescence representative images showing CENPs (ACA) in magenta, microtubules (tubulin) in green and DNA (DAPI) in blue. Scale bar = 10 µm. Right panel: Quantification of the proportions of the mitotic phenotypes. **(F)** Left panel: Immunofluorescence representative images of chromosome spreads showing CENP-A in green and DNA in blue. Scale bar = 10 µm. Right panel: Quantification of the proportions of chromosome spreads.
**Supplementary Figure S5: (A-C)** HCT116 cells were treated for 48 h with 500 nM reversine. Reversine was washed out and cells were treated with 500 nM AZD1775 for 6 h before chromosome spreading. **(A)** Left panel: Violin plot representing chromosome counts. The number of analyzed spreads is indicated between brackets. Right panel: Frequency distribution of chromosome counts. **(B)** Representative images of chromosome spreads showing CENP-A in green and DNA in blue. Scale bar = 5 µm. **(C)** Quantification of the proportion of chromosome spreads shown in (B) (mean ± S.E.M of N=2; a minimum of 53 spreads were analyzed per group) two-way ANOVA; AZD1775 Cen<sup>OFF</sup> vs AZD1775 + Reversine Cen<sup>OFF</sup> ****p<0.0001). **(D-F)** HeLa cells were treated for 48 h with 500 nM reversine. Reversine was washed out and cells were treated with 500 nM AZD1775 for 6 h before chromosome spreading. **(D)** Left panel: Violin plot representing chromosome counts. The number of analyzed spreads is indicated between brackets. Right panel: Frequency distribution of chromosome counts. **(E)** Representative images of chromosome spreads showing CENP-A in green and DNA in blue. Scale bar = 5 µm. **(F)** Quantification of the proportion of chromosome spreads shown in (E) (mean ± S.E.M of N=2; a minimum of 57 spreads were analyzed per group) two-way ANOVA; AZD1775 Cen<sup>OFF</sup> vs AZD1775 + Reversine Cen<sup>OFF</sup> ***p<0.001). Scale bar = 5 µm. **(G-H)** HeLa cells transfected with control or ATIP3 siRNAs were treated with 500 nM AZD1775 for 6 h. **(G)** Immunofluorescence representative images of IF-FISH showing CENP-B box in magenta, CENP-B in green and DNA in blue. Scale bar = 5 µm. **(H)** Quantification of the proportion of chromosome spreads shown in (G) (mean ± SEM. of N=2; the number of analyzed spreads is in brackets; two-way ANOVA; Cen<sup>OFF</sup> in siCtrl AZD1775 vs. siATIP3 AZD1775 ****p<0.0001).
**Supplementary Figure S6:** HeLa cells transfected with control or ATIP3 siRNAs were treated with 500 nM AZD1775 for 2 h (A-C). (A) Immunofluorescence representative images showing EdU in yellow and DNA in blue. The gray channel representing EdU is shown on the right to each condition. Scale bar = 10 µm. (B) Quantification of the percentage of EdU-positive cells shown in (A) (mean ± SEM. of N=3; a minimum of 73 cells were analyzed per group; one-way ANOVA; ns). (C) Quantification of EdU mean intensity per nucleus (normalized to DAPI) (mean ± S.D.; the number of analyzed cells is in brackets; Kruskal-Wallis test followed by Dunn's multiple comparisons; \***p<0.001, \***\*p<0.0001). (D-E) Mice xenografted with MDA-MB-468 shCtrl or shATIP3 were treated or not with 90 mg/kg of AZD1775 by oral gavage daily for 4 days (1 mice per group) (D) Immunohistochemistry representative images showing γH2AX staining in xenografts. (E) Quantification of the percentage of γH2AX-positive nuclei shown in (D). (F-G) HeLa cells transfected with control or ATIP3 siRNAs were treated with 500 nM AZD1775 for 2 h. (F) Immunofluorescence representative images showing 53BP1 in magenta, EdU in yellow and DNA in blue. The gray channel representing 53BP1 is shown on the right to each condition. Scale bar = 10 µm. (G) Quantification of the percentage of EdU-positive cells with > 5 53BP1 foci shown in (F) (mean ± SEM. of N=4, a minimum of 173 cells were analyzed per group; one-way ANOVA; \***p<0.001, \***\*p<0.0001).
**Supplementary Figure S7:** HeLa cells transfected with control or ATIP3 siRNAs were treated with 500 nM AZD1775 for 6 h. (A) Immunofluorescence representative images showing phosphorylated RPA32 (S4/S8) in magenta, EdU in yellow and DNA in blue. The gray channels representing EdU or pRPA32 are shown on the right to each condition. Scale bar = 10 µm. (B) Quantification of the percentage of pRPA32-positive mitosis shown in (A) (mean ± SEM. of N=3; a minimum of 42 cells were analyzed per group; one-way ANOVA; ns, **p<0.01). (C) Immunofluorescence representative images showing γH2AX in red, EdU in green and DNA in blue. The gray channels representing EdU or γH2AX are shown on the right to each condition. Scale bar = 10 µm. (D-E) Quantification of the percentage of γH2AX-positive mitosis (D) and EdU-positive mitosis (E) shown in (C) (mean ± SEM. of N=4; a minimum of 50 cells were analyzed per group; one-way ANOVA; ns, *p<0.05, **p<0.01, ****p<0.0001).
**Supplementary Figure S8: (A-D)** HeLa cells transfected with control or ATIP3 siRNAs were pretreated with 100 nM Aphidicolin for 2 h then with 500 nM AZD1775 for 6 h. (A) Immunofluorescence representative images showing phosphorylated RPA32 (S4/S8) in magenta and DNA in blue. Scale bar = 10 µm. (B) Quantification of the percentage of pRPA32-positive mitosis shown in (A) (mean ± SEM. of N=2; a minimum of 51 cells were analyzed per group; one-way ANOVA; ns, ***p<0.001). (C) Immunofluorescence representative images showing γH2AX in green and DNA in blue. Scale bar = 10 µm. (D) Quantification of the percentage of γH2AX-positive mitosis shown in (C) (mean ± SEM. of N=2; a minimum of 58 cells were analyzed per group; one-way ANOVA; ns, ***p<0.001). (E) **Left panel:** Immunofluorescence representative images of HeLa cells transfected with control or ATIP3 siRNAs showing 53BP1 nuclear bodies in red, EdU in green and DNA in blue. **Right Panel:** Quantification of the percentage of G1 cells with 53BP1 nuclear bodies (mean ± SEM. of N=5; a minimum of 343 cells were analyzed per group; two-tailed t-test; ns, **p<0.01). Scale bar = 10 µm. (F-G) HCT116 cells were transfected with control or ATIP3 siRNAs. (F) **Right panel:** Western blot of ATIP3 protein levels. Loading control: GAPDH. **Left panel:** Immunofluorescence representative images showing 53BP1 nuclear bodies in magenta, EdU in green and DNA in blue. Scale bar = 10 µm. (G) Quantification of the percentage of G1 cells with 53BP1 nuclear bodies (mean ± SEM. of N=4; a minimum of 120 cells were analyzed per group; two-tailed t-test; ns, **p<0.01). (H-I) HeLa cells transfected with control or ATIP3 siRNAs were simultaneously with nucleosides (1/50) and 500 nM AZD1775 for 2 h (H) or 6 h (I). (H) Scattered dot-plot of phosphorylated RPA32 (S4/S8) per nucleus (normalized to DAPI). The number of analyzed cells is in brackets (mean ± S.D.; Kruskal-Wallis test followed by Dunn's multiple comparisons; ***p<0.001, ****p<0.0001). (I) Graph showing the % of mitotic phenotypes (mean ± SEM. of N=1; a minimum of 43 cells were analyzed per group; Side chromatin mass in siATIP3 AZD1775 vs. siATIP3 AZD1775 + Nuc p<0.0001).
**Supplementary Figure S9:** HeLa cells transfected with control, MUS81, ATIP3 or both siRNAs were treated with 500 nM AZD1775. (A) qPCR analysis of gene silencing efficiency. (B) Immunofluorescence representative images after 2 h of treatment showing phosphorylated γH2AX in green and DNA in blue. Scale bar = 10 µm. (C) Quantification of γH2AX mean intensity per nucleus (normalized to DAPI) (mean ± S.D.; the number of analyzed cells is in brackets; Kruskal-Wallis test followed by Dunn's multiple comparisons; \*p<0.05, \*\*\*p<0.0001). (D) Immunofluorescence representative images after 2 h of treatment showing phosphorylated RPA32 (S4/S8) in magenta and DNA in blue. Scale bar = 10 µm. (E) Quantification of phosphorylated RPA32 mean intensity per nucleus (normalized to DAPI) (mean ± S.D.; the number of analyzed cells is in brackets; Kruskal-Wallis test followed by Dunn's multiple comparisons; \*\*\*p<0.0001). (F) Immunofluorescence representative images after 6 h of treatment showing CENPs in magenta, microtubules in green and DNA in blue. Scale bar = 10 µm. (G) Quantification of the proportions of mitotic phenotypes shown in (F) (mean ± SEM. of N=2; a minimum of 68 cells were analyzed per group; two-way ANOVA; Side chromatin mass in siATIP3 AZD1775 vs. siATIP3 + siMUS81 AZD1775 ns).
**Supplementary Figure S10:** HeLa cells transfected with control, EXO1, MRE11, ATIP3 or a combination of siRNAs were treated with 500 nM AZD1775 for 6 h. (A) qPCR analysis of gene silencing efficiency. (B) Immunofluorescence representative images showing CENPs in green, microtubules in red and DNA in blue. Scale bar = 10 µm. (C) Quantification of the proportion of mitotic phenotypes shown in (B) (mean ± SEM. of N=1; a minimum of 67 cells were analyzed per group; two-way ANOVA; Side chromatin mass in siATIP3 AZD1775 vs. siATIP3 siMRE11 AZD1775 *ns*; Side chromatin mass in siATIP3 AZD1775 vs. siATIP3 siEXO1 AZD1775 *ns*). Scale bar = 10 µm. (D) Immunofluorescence representative images of chromosome spreads showing CENP-A in green and DNA in blue. Scale bar = 5 µm. (E) Quantification of the proportions of chromosome spreads shown in (D) (mean ± SEM. of N=1; a minimum of 73 spreads were analyzed per group; two-way ANOVA; Cen<sup>OFF</sup> in siATIP3 AZD1775 vs. siATIP3 siMRE11 AZD1775 *ns*; Cen<sup>OFF</sup> in siATIP3 AZD1775 vs. siATIP3 siEXO1 AZD1775 *ns*).
**Supplementary Figure S11: (A-C)** HeLa cells transfected with control, ATIP3, DNA2 or a combination or ATIP3 and DNA2 siRNAs and treated with 500 nM AZD1775 for 6 h. (A) qPCR analysis of gene silencing efficiency. (B) Immunofluorescence representative images of abnormal mitoses showing CENPs in magenta, microtubules in green and DNA in blue. Scale bar = 10 µm. (C) Quantification of the proportions of the mitotic phenotypes shown in (A) (mean ± S.E.M of N=2; a minimum of 88 cells were analyzed per group; two-way ANOVA; normal mitoses in siATIP3 vs. siATIP3 + siDNA2 \*\*p<0.01). Scale bar = 20 µm. (D-E) HeLa cells transfected with control, ATIP3, DNA2 or a combination or ATIP3 and DNA2 siRNAs and treated with 200 nM AZD1775 alone or in combination with 200 nM RP-6306 for 6 h. (D) Quantification of the proportions of the mitotic phenotypes. (E) Quantification of the proportion of chromosomes spreads. (F) Immunofluorescence representative images of HeLa cells transfected with GFP-DNA2 plasmid and treated or not with 500 nM AZD1775 for 6 h showing DNA2 in green, CENPs in cyan, microtubules in red and DNA in grey. Scale bar = 5 µm. White arrows and enlarged panels show DNA2 localization at centromeres in mitosis (upper and middle panels) or more broadly across the chromatin in mitotic cells (low panels). (G) Top panel: Dose-response curves of AZD1775 alone or in combination with 10 nM paclitaxel in SUM52PE shCtl or shATIP3 MCSs upon 72 h of treatment. Bottom panel: IC50 values.