Targeting de novo pyrimidine synthesis confers vulnerability to copper-mediated ATR inactivation in PARP inhibitor-resistant ovarian cancer

Domain: chemistry
Published: 2026-02-25
Editorial outcome: major_revision

Abstract
Although poly(ADP-ribose) polymerase (PARP) inhibitors (PARPi) as monotherapy or in combination with other DNA-damaging agents exhibit promising clinical efficacy, the therapeutic responses are usually transient, with subsequent development of acquired resistance posing a significant challenge. Here, through a small-molecule compound screening, we identify elesclomol, a potent copper ionophore, which sensitizes BRCA-proficient ovarian cancer cells to PARPi by inhibiting activation of the ATR-CHK1 pathway. Mechanistically, we demonstrate that copper directly binds to ATRIP, a critical cofactor of ATR activation, disrupting the ATR-ATRIP interaction, further impairing ATR-mediated DNA damage repair signaling and potentiating PARPi sensitivity. Importantly, we reveal a secondary metabolic vulnerability in PARPi-resistant ovarian cancer associated with de novo pyrimidine synthesis, suggesting that targeting this pathway as an effective strategy to eradicate drug-adaptive residual tumors and resistant patient-derived xenograft models following ATR and PARP co-inhibition. These findings propose de novo pyrimidine synthesis as an adaptive metabolic vulnerability that can be therapeutically targeted to overcome PARPi resistance in BRCA-proficient ovarian cancer.

Introduction
Poly(ADP-ribose) polymerase (PARP) inhibition is a highly effective therapeutic strategy in BRCA1/2-mutated and homologous recombination (HR) repair-deficient cancers1,2. PARP inhibitors (PARPi) therapy combined with chemotherapy or immune checkpoint blockade has also yielded encouraging preliminary clinical efficacy3–6. However, PARPi exhibit notable limitations in HR-proficient cancers, and even in HR-deficient cancers where they are initially effective, acquired resistance inevitably arises with the restoration of HR7–9. Moreover, several BRCA-deficient cancers also display intrinsic resistance to PARPi10. These constraints have promoted considerable interest in combining PARPi with other inhibitors capable of disabling HR repair, aiming to enhance the sensitivity of BRCA-wild-type cancers to PARPi and surmount both primary and acquired resistance in BRCA-mutated cancers.

DNA damage response acts as a complicated signaling cascade to exert a pivotal role in maintaining genomic stability and triggering PARPi resistance11,12. The ataxia-telangiectasia mutated (ATM)/check kinase 2 (CHK2) and ATM-related and RAD3-related (ATR)/check kinase 1 (CHK1) signaling cascades represent two major pathways that respond to DNA damage13,14. Single-strand DNA break typically triggers the ATR/CHK1 pathway, whereas the ATM/CHK2 signaling pathway is predominantly activated in response to double-strand DNA damage15,16. Although the advantages of PARPi appear most pronounced in patients with BRCA1/2 mutations, emerging preclinical data suggest that deficiencies in other HR genes can also overcome PARPi resistance17,18. Several studies involving patients with ovarian and prostate cancers have shown that those with BRCA1/2 wild-type tumors containing detrimental variations in other DNA repair genes, including ATR, ATM, CHK1, and CHK2, may derive benefits from PARPi19,20. Moreover, mutations in other pivotal HR-related genes, such as DNA repair protein RAD51 homolog 1, are likely to increase sensitivity to PARPi21. Consequently, the use of combination therapies targeting alternative DNA repair pathways represents a reasonable strategy to combat PARPi resistance, even in BRCA-proficient cancers.

Copper, a mineral nutrient, is increasingly recognized for its involvement in both cell proliferation and cell death22. Copper acts as a double-edged sword in cancer cells, and its status can be strategically harnessed as a vulnerability for cancer therapy. Currently, two major therapeutic strategies targeting this nutrient include copper chelators to deplete copper pools, which inhibit tumor proliferation and metastasis in several copper-dependent cancers23–27, and copper ionophores to supplement copper, which trigger cuproptosis or induce reactive oxygen species-dependent cell death28–32. However, the impact of copper on the sensitivity of PARPi remains largely unexplored.

In this study, we demonstrate that elesclomol-copper synergizes with PARPi to induce lethality in ovarian cancer cells by directly binding to ATR-interacting protein (ATRIP), a critical cofactor of ATR activation, thereby disrupting the ATR-ATRIP interaction and eventually suppressing the ATR-CHK1 repair pathway. Moreover, we show that although combined ATR and PARP inhibition exerts substantial tumor-killing effects, this strategy is ineffective against a subset of tumors dependent on de novo pyrimidine synthesis. Therefore, targeting de novo pyrimidine synthesis markedly enhances the therapeutic efficacy of ATR and PARP co-inhibition in these resistant tumors. These findings highlight the therapeutic potential of ATR inactivation, achieved through elesclomol-copper or selective ATR inhibitors, in enhancing PARPi efficacy for BRCA-proficient ovarian cancer, and propose a selective strategy based on pyrimidine vulnerability in patient-derived preclinical models, thereby providing a strong rationale for broader preclinical investigations and future translational applications.

Results
Elesclomol-copper synergizes with PARPi in BRCA-proficient ovarian cancer
To identify critical lethal targets involved in PARPi resistance, we treated the BRCA-proficient clear cell ovarian cancer cell line ES-2 with the FDA-approved PARPi olaparib in combination with 144 cell death-related compounds (Supplementary Data 1). The screening was conducted in two rounds at 50 μM and 10 μM concentrations (Fig. 1a). We identified five compounds consistently ranked in the top 10 for synergistic lethality across both rounds, including two ferroptosis inducers, ML162 and ML210 (Fig. 1a). Recent studies have reported that combining PARPi with ferroptosis inducers results in synergistic lethality in both BRCA-proficient and BRCA-deficient cancers33–36, we therefore focused on the remaining three compounds: elesclomol, perhexiline, and LAS17 (Fig. 1a). Further validation of these three compounds at lower concentrations (1 μM and 500 nM) in combination with PARPi revealed that elesclomol exhibited the most significant synergy with olaparib in a dose-dependent manner (Fig. 1b). Elesclomol is a potent copper ionophore that transports copper into cells, inducing cuproptosis37. Next, to investigate the role of copper in ovarian cancer cell death, we treated ES-2 and another BRCA-proficient mucinous ovarian cancer cell line, 3AO, with elesclomol under various conditions. Cell viability assays indicated that copper supplementation significantly increased cell sensitivity to elesclomol, whereas copper chelation with tetrathiomolybdate (TTM) restored cell survival (Supplementary Fig. 1a). Additionally, cells cultured without serum exhibited resistance to elesclomol (Supplementary Fig. 1a). These findings collectively indicate that elesclomol-copper induces cell death dose-dependently in ovarian cancer cells.

We subsequently explored whether elesclomol-copper could enhance the sensitivity of ovarian cancer cells to PARPi. By analyzing the drug sensitivity correlation between elesclomol and two clinically approved PARPi, olaparib and niraparib, in pan-cancer cell lines from the DepMap portal, we observed significant positive correlations between elesclomol and both PARPi (Supplementary Fig. 1b). Furthermore, we determined the half-maximal inhibitory concentration (IC50) of elesclomol-copper and PARPi across a panel of BRCA-proficient ovarian cancer cells. Notably, ovarian cancer cells exhibited nearly 1000-fold greater sensitivity to elesclomol-copper compared to either PARPi (Supplementary Fig. 1c). Correlation analysis indicated a significant positive correlation between the IC50 values of elesclomol-copper and PARPi (Fig. 1c), as well as between the IC50 values of olaparib and fluzoparib (Supplementary Fig. 1d). We then investigated the potential for synergy when combining PARPi with elesclomol-copper. We observed a significant synergistic effect on cell inhibition across a range of olaparib concentrations (50–100 μM in ES-2 cells and 100–150 μM in 3AO cells) when combined with elesclomol-copper (160–240 nM in ES-2 cells and 160–320 nM in 3AO cells) (Fig. 1d, e).

Finally, we investigated the therapeutic potential of elesclomol-copper in vivo. Xenograft assays revealed that single-agent treatments of olaparib or elesclomol-copper only marginally slowed growth in ES-2-derived tumors (Fig. 1f, g) and had negligible effects on 3AO-derived tumors (Fig. 1i, j). Surprisingly, the combination of elesclomol-copper and olaparib completely retarded tumor growth in both xenograft models, although tumors were not fully eradicated (Fig. 1f, g, i, j). These results highlight the synergistic effect of elesclomol-copper with PARPi in BRCA-proficient ovarian cancer. Furthermore, immunohistochemistry (IHC) staining of xenograft-derived tumors for γ-H2AX, a marker of DNA damage, showed that treatment with elesclomol-copper or its combination with olaparib significantly increased γ-H2AX expression (Fig. 1h, k), suggesting enhanced DNA damage due to combined treatment. Notably, olaparib monotherapy induced only a slight increase in DNA damage (Fig. 1h, k), confirming the substantial resistance of BRCA-proficient ovarian cancer cells to PARPi. Furthermore, H&E staining of major organs from both control and elesclomol-copper-treated mice showed no apparent histopathological abnormalities or treatment-related toxicity (Supplementary Fig. 1e).

Given recent insights into the mechanisms of copper-induced regulated cell death, cuproptosis, we next sought to determine whether elesclomol-copper-mediated sensitivity to PARPi occurs through the cuproptosis pathway. Analysis of the DepMap database revealed a negative correlation between the transcriptional expression level of ferredoxin 1 (FDX1), a key mediator of cuproptosis, and sensitivity to elesclomol across pan-cancer cell lines (Supplementary Fig. 2a), confirming the pro-cuproptotic role of FDX1. However, no significant correlation was observed between FDX1 expression and sensitivity to either PARPi (Supplementary Fig. 2a). Furthermore, to investigate whether elesclomol-copper-mediated PARPi sensitivity involves interaction with classic FDX1-mediated cuproptosis signaling, we silenced FDX1 in ES-2 and 3AO cells (Supplementary Fig. 2b) and then determined the response to olaparib and elesclomol-copper in both parental and FDX1 knockout cells. Although FDX1 knockout inhibited elesclomol-copper-induced cell death, which is consistent with previous studies28,37, there was no effect of FDX1 knockout on PARPi sensitivity (Supplementary Fig. 2c, d). Moreover, we performed drug combination experiments using olaparib and elesclomol-copper in both FDX1 wild-type and knockout ES-2 and 3AO cells. The results further showed a significant synergy between olaparib and elesclomol-copper in both wild-type and FDX1 knockout cells (Supplementary Fig. 2e, f). In summary, elesclomol-copper synergizes with PARPi to induce cell death in BRCA-proficient ovarian cancer preclinical models derived from ES-2 and 3AO cells, which is independent of the canonical cuproptosis signaling pathway.

Drug-adaptive survival cells preferentially activate ATR to resist PARP inhibition
To explore the mechanism underlying the synergistic effect of elesclomol-copper and PARPi, we treated ES-2 cells continuously with high doses of elesclomol-copper or olaparib for seven days. While most cells underwent cell death, a small subpopulation survived the treatment. Cell viability assays showed that the surviving cells derived from elesclomol-copper treatment (ES-2_EC) and olaparib treatment (ES-2_Olap) exhibited increased resistance to their respective treatments compared to untreated parental cells (Fig. 2a). Notably, cells that survived combined elesclomol-copper and olaparib treatment (ES-2_Olap&EC) showed the strongest resistance to both agents (Fig. 2a), underscoring potential mechanistic crosstalk between these resistances. Further analyses of cell proliferation and cell-cycle distribution revealed that drug-adapted subpopulations exhibited reduced cell proliferation rates and G1-phase arrest compared to parental cells (Fig. 2b, c). To determine whether the duration of drug exposure affected the characteristics of surviving cells, we compared subpopulations generated after 7 or 14 days of treatment. Both groups exhibited comparable drug responses, proliferation rates, and cell-cycle profiles (Supplementary Fig. 3a–c). Additionally, we performed rechallenge experiments using BRCA1-mutant high-grade serous ovarian cancer (HGSOC) cell line COV362 and BRCA-proficient HGSOC cell line OVCAR4, which represent the primary clinical target population for PARPi therapy (Supplementary Fig. 3d, h). In both lines, cells that survived 7 days of olaparib exposure exhibited significantly increased resistance compared to parental controls (Supplementary Fig. 3e, i). After drug withdrawal, residual and parental cells were cultured in drug-free medium and monitored for growth. The residual cells proliferated markedly more slowly than parental cells (Supplementary Fig. 3f, j). Upon retreatment with olaparib after seven days of regrowth, the rechallenged cells maintained partial resistance relative to parental cells but regained partial sensitivity compared with the initially drug-adapted population (Supplementary Fig. 3g, k). Collectively, these results show that short-term (7-day) drug exposure selects for a minor population of surviving ovarian cancer cells with transient resistance and reduced proliferative capacity. Finally, to explore potential resistance mechanisms, we analyzed the expression of FDX1, protein lipoylation, and PARP1 to explore potential resistance mechanisms in these drug-adapted subpopulations. FDX1 and lipoylation levels were reduced in cells adapted to elesclomol-copper or the combination treatment, but not in olaparib-induced survivors (Supplementary Fig. 3l), consistent with previous studies37. Moreover, the levels of total and cleaved PARP1 remained unchanged between parental and drug-adaptive cells (Supplementary Fig. 3l).

Next, we performed the RNA sequencing (RNA-seq) in both parental and drug-adaptive survival cells. Gene Set Enrichment Analysis (GSEA) revealed significant enrichment of pathways related to DNA replication and cell cycle in all three drug-adaptive survival populations compared to parental cells following olaparib treatment (Fig. 2d). Differential gene expression analysis indicated that most genes involved in the cell cycle and HR repair were substantially upregulated in drug-adaptive cells compared to parental cells (Supplementary Fig. 4a). Consistently, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed activation of the cell cycle, DNA replication, and mismatch repair pathways in drug-adapted subpopulations (Supplementary Fig. 4b, c). Notably, we found that the ATM-CHK2 pathway exhibited transcriptional downregulation, while CHK1 was upregulated in drug-adapted subpopulations compared to parental cells (Supplementary Fig. 4a). Given the critical roles of the ATM-CHK2 and ATR-CHK1 pathways in DNA damage repair and PARPi resistance, we considered whether one or both pathways were dysregulated in drug-adaptive cells. Surprisingly, western blot analysis revealed that the ATR-CHK1 pathway, but not the ATM-CHK2 pathway, was dramatically activated in drug-adaptive cells to respond PARPi-induced replication stress, particularly in ES-2_Olap&EC cells, which displayed the strongest phosphorylation of CHK1 following olaparib treatment (Fig. 2e). Immunofluorescence (IF) analysis further confirmed that drug-adaptive cells preferentially activate ATR-CHK1 pathway to resist PARP inhibition (Fig. 2f, Supplementary Fig. 4d). Similarly, in another BRCA-proficient ovarian cancer cell line, 3AO, PARPi treatment selectively activated the ATR-CHK1 pathway in drug-adaptive cells, without significantly affecting ATM-CHK2 signaling (Supplementary Fig. 4e, f).

Finally, we assessed the synergistic effects of elesclomol-copper or ATR inhibitor AZD6738 with olaparib in a panel of both parental and drug-adaptive ovarian cancer cell lines. Surprisingly, while combined inhibition of PARP and ATR significantly elicited cell death in parental cells, neither elesclomol-copper nor AZD6738 sensitized drug-adapted cells to PARPi (Fig. 2g, h, Supplementary Fig. 5a, b), suggesting that these drug-adapted resistant cell subpopulations harbor a unique dependency that enables them to resist PARPi and ATR inhibitor treatment.

Copper disrupts ATR-ATRIP interaction to inactivate ATR-mediated DNA repair signaling
Given that drug-adaptive survival cells preferentially activate the ATR-CHK1 pathway to cope with DNA damage stress and the observed synergistic effect of either elesclomol-copper or ATR inhibitor in combination with olaparib in parental cells, we next investigated whether copper could modulate ATR activity and thereby sensitize ovarian cancer cells to PARPi. Western blot analysis indicated that treatment with either elesclomol-copper or AZD6738 markedly inhibited olaparib-induced ATR-CHK1 pathway activation in ES-2 and 3AO parental cells (Fig. 3a, Supplementary Fig. 6a). Furthermore, IF analysis showed that combined treatment with olaparib and either elesclomol-copper or AZD6738 significantly increased γ-H2AX foci compared to control or olaparib monotherapy (Fig. 3b). These results indicate that elesclomol-copper inhibits ATR-CHK1 pathway-mediated DNA repair and synergizes with PARPi to elicit DNA damage.

Next, we sought to investigate how copper inactivates ATR signaling. The activation of ATR and its downstream CHK1 phosphorylation requires interaction with its essential cofactor, ATRIP38,39. Therefore, we proposed three possibilities by which copper might interfere with ATR signaling: (1) copper directly binds to ATR, inhibiting its kinase activity or disrupting its interaction with ATRIP; (2) copper binds to ATRIP, preventing its association with ATR; or (3) copper facilitates formation of an inhibitory ATR-ATRIP complex, as in a previous study40. To investigate these possibilities, we first utilized AlphaFold3 to predict the binding potential of copper to ATR, ATRIP, and the ATR-ATRIP complex. The prediction results revealed that copper has the highest binding affinity for ATRIP (ipTM score = 0.98), followed by ATR (ipTM score = 0.87), while its potential to promote an inhibitory ATR-ATRIP complex was relatively low (ipTM score = 0.64) (Fig. 3c, Supplementary Fig. 6b, c). To experimentally validate these predictions, we performed pull-down assays to assess the binding of copper, as well as other common metal ions, to ATR and ATRIP. Western blot results demonstrated that copper selectively binds to ATRIP, but not ATR, whereas other metal ions showed no significant binding with ATRIP (Fig. 3d), which is consistent with the AlphaFold3 prediction. Given recent findings that copper can directly bind to hypoxia-inducible factor 1-alpha (HIF-1α) and disrupt its interaction with the E3 ligase von Hippel–Lindau (VHL), thus stabilizing HIF-1α32, our results potentially suggest that copper may directly bind to ATRIP and disrupt its interaction with ATR, thereby blocking ATR kinase activation and inhibiting downstream CHK1 phosphorylation.

Previous studies have identified the C-terminal region of ATRIP as essential for its interaction with ATR41,42. Consistent with this, AlphaFold3 structural predictions indicated that copper binds to key cysteine residues within the ATRIP C-terminal region (C624, C630, C680, and C682) (Fig. 3c). To determine whether copper binding alters ATRIP conformation and disrupts ATR-ATRIP interaction, we analyzed ATRIP structural changes before and after copper binding. Structural alignment in PyMOL predicted substantial conformational changes in ATRIP upon copper binding (RMSD = 35.141), with the most pronounced structural alterations occurring in the C-terminal 200 amino acids (RMSD = 38.956) (Fig. 3e). Subsequently, we performed co-immunoprecipitation (co-IP) experiments in ES-2 and 3AO cells. Western blot results showed that olaparib treatment significantly induced ATR-ATRIP interaction, consistent with the activation of ATR-dependent DNA repair in PARPi-resistant cells (Fig. 3f, Supplementary Fig. 6d). Strikingly, elesclomol-copper treatment markedly disrupted ATR-ATRIP interaction, with higher elesclomol-copper concentrations (100 nM) completely abolishing ATR-ATRIP binding (Fig. 3f, Supplementary Fig. 6d). These results were further confirmed by proximity ligation assays (PLA), indicating that copper effectively prevents the interaction between ATR and ATRIP (Fig. 3g, h, Supplementary Fig. 6e, f). Collectively, our results identify copper as a kinase-inhibitory cofactor that directly binds to ATRIP, disrupts its interaction with ATR, and thereby suppresses ATR-mediated DNA damage repair and sensitizes BRCA-proficient ovarian cancer cells to PARPi.

PARPi-resistant cells exhibit activation of de novo pyrimidine synthesis
Augmented synthesis and utilization of nucleotide triphosphates are well-established as critical metabolic dependencies across diverse cancer types, playing a fundamental role in both DNA damage repair and cancer progression43,44. Given our previous results demonstrated that drug-adaptive cells were resistant to the combined inhibition of PARP and ATR, we performed untargeted metabolomic analysis on both parental and drug-adaptive cells to identify unique metabolic adaptations. Strikingly, KEGG analysis revealed a pronounced activation of nucleotide metabolism, especially pyrimidine metabolism, in all three drug-adapted surviving subpopulations compared to parental cells (Fig. 4a). We next analyzed metabolite levels involved in de novo and salvage pathways of pyrimidine and purine synthesis. Several metabolites involved in the de novo pyrimidine synthesis pathway, including dihydroorotate, orotate, and UMP, as well as pyrimidine nucleotides, were significantly upregulated in drug-adaptive cells compared to parental cells (Fig. 4b–d). In contrast, metabolites involved in pyrimidine salvage synthesis exhibited no significant alterations (Supplementary Fig. 7a), and no consistent alterations were detected in either de novo or salvage purine synthesis (Supplementary Fig. 7b, c). To confirm whether drug-adaptive cells depend on de novo pyrimidine synthesis, we performed metabolic flux analysis using the established 15N-glutamine tracing method45,46. Isotopic analysis revealed significantly increased incorporation of 15N from glutamine into pyrimidine intermediates in drug-adaptive cells compared to parental cells (Fig. 4e), indicating enhanced flux through the de novo pyrimidine synthesis. Notably, this metabolic shift was specific to pyrimidines, as purine synthesis showed no comparable increase (Supplementary Fig. 7d). These findings are consistent with the untargeted metabolomics data and suggest that PARPi-resistant cells potentially develop a metabolic adaptation associated with de novo pyrimidine synthesis.

To investigate the functional relevance of this metabolic shift, we assessed whether elevated pyrimidine metabolites contribute to PARPi resistance. Stimulation of the pyrimidine nucleotide salvage with supraphysiological uridine increased PARPi resistance and completely abolished the synergistic effects of elesclomol-copper or ATR inhibitor with PARPi (Fig. 4f, Supplementary Fig. 7e). Likewise, treatment with exogenous thymidine suppressed the efficacy of PARP and ATR inhibition, mirroring the effect of uridine, while cytosine only conferred a modest protective effect (Fig. 4f, Supplementary Fig. 7e). This difference arises because thymidine directly enters the downstream DNA synthesis pathway, whereas cytosine primarily contributes to RNA synthesis. These findings suggest that enhanced pyrimidine nucleotide availability plays a critical role in mediating resistance to PARPi. We then investigated whether targeting de novo pyrimidine synthesis could increase the vulnerability of these drug-adapted surviving subpopulations and enhance the therapeutic efficacy of combined PARP and ATR inhibition. As our hypothesis, treatment with BAY-2402234, a selective dihydroorotate dehydrogenase (DHODH) inhibitor currently in clinical trials for AML (ClinicalTrials.gov Identifier: NCT03404726) and glioma (ClinicalTrials.gov Identifier: NCT05061251)43,44,47, in combination with either elesclomol-copper or AZD6738, significantly resensitized drug-adaptive cells to PARPi (Supplementary Fig. 7f). Importantly, we extended these observations to two HGSOC cell lines, COV362 and OVCAR4, which similarly demonstrated that inhibition of de novo pyrimidine synthesis, together with either elesclomol-copper or ATR inhibition, enhanced PARPi sensitivity and eradicated drug-adaptive HGSOC cells (Supplementary Fig. 7g). Finally, to further validate the clinical potential of combining ATR and PARP inhibitors and confirm the role of de novo pyrimidine synthesis in PARPi resistance, we generated a patient-derived organoid (PDO) model from BRCA1/2 wild-type ovarian cancer. In this model, treatment with either elesclomol-copper or AZD6738 enhanced sensitivity to PARPi, which was completely abolished by uridine supplementation (Fig. 4g, h), highlighting the critical role of pyrimidine metabolism in therapeutic resistance. Collectively, these results underscore the specific reprogramming of PARPi-resistant cells on de novo pyrimidine synthesis and suggest that targeting this metabolic pathway may offer a promising strategy for overcoming PARPi resistance.

Residual drug-adaptive tumors exhibit elevated pyrimidine metabolism
Given the observed PARPi-induced activation of ATR signaling in drug-adaptive cells and the synergistic effect of the ATR inhibitor and PARPi, we investigated whether co-targeting ATR and PARP could prevent tumor relapse and extend remission. To explore this, we established xenograft models using control and ATR-silenced ES-2 cells. Mice were treated with olaparib from days 14 to 21 (Supplementary Fig. 8a). In mice harboring control ES-2 cells, olaparib treatment moderately suppressed tumor growth, whereas in ATR-silenced ES-2 cells, olaparib treatment led to a significant reduction in tumor size (Supplementary Fig. 8b). After day 21, olaparib treatment was discontinued in half of the ATR-silenced group, while the remaining half continued treatment (Supplementary Fig. 8a). As expected, residual tumors that regressed under olaparib treatment relapsed more slowly in the continued treatment group compared to the discontinuation group (Supplementary Fig. 8b). Furthermore, the combination of ATR silencing and olaparib treatment significantly improved survival outcomes compared to single-agent interference (Supplementary Fig. 8c).

To further assess the effects of pharmacological inhibition of ATR and PARP, ES-2 xenograft-bearing mice were treated with olaparib, AZD6738, or both (Fig. 5a). Compared to monotherapy, continued combined treatment significantly inhibited tumor growth and extended survival (Fig. 5b, c). However, when olaparib and AZD6738 were discontinued on day 25, tumors rapidly relapsed, leading to a poorer prognosis (Fig. 5b, c). Collectively, these results highlight that dual inhibition of ATR and PARP substantially delays tumor relapse and improves survival in BRCA-proficient ovarian cancer models.

To further confirm the role of de novo pyrimidine synthesis in resistance to combined PARP and ATR inhibition, we collected ES-2-derived tumors at day 14 (untreated), day 25 (co-treated with olaparib and AZD6738), and day 40 (treated with vehicle, AZD6738, olaparib, or both) for targeted metabolomic analysis (Fig. 5d). Pathway enrichment analysis showed significant activation of pyrimidine metabolism in residual tumors from the olaparib plus AZD6738 group at both days 25 and 40, compared to the untreated group (Fig. 5e). Further investigation of differentially abundant metabolites showed a pronounced upregulation of pyrimidine nucleotides and other metabolites involved in pyrimidine synthesis in residual tumors post-treatment (Fig. 5f, g). Notably, as treatment progressed, pyrimidine metabolism became increasingly activated, with residual tumors exhibiting an escalating dependency on pyrimidine nucleotides (Fig. 5h, Supplementary Fig. 8d–f). Additionally, residual tumors in the combination treatment group showed a markedly stronger dependency on pyrimidine nucleotides than those in the control or monotherapy groups (Fig. 5h, Supplementary Fig. 8g–i). In summary, these findings underscore the pivotal role of pyrimidine metabolism in driving resistance to combined PARP and ATR inhibition, suggesting that targeting de novo pyrimidine synthesis may represent a promising therapeutic strategy for overcoming resistance in PARPi-resistant residual tumors.

Selective therapeutic strategies for PARPi-resistant ovarian cancer
To further define effective targeted therapeutic strategies in ovarian cancer preclinical models, particularly the therapeutic potential targeting de novo pyrimidine synthesis, for BRCA-proficient PARPi-resistant ovarian cancer, we established eight patient-derived xenograft (PDX) models to evaluate the efficacy of different combination treatments (Fig. 6a, Supplementary Fig. 9a). Initially, olaparib monotherapy was administered to all eight PDX models, revealing that only three responded to treatment, while the remaining five were resistant (Fig. 6a, b, Supplementary Fig. 9b). Next, in the five resistant models, we tested two combination strategies: elesclomol plus olaparib and ATR inhibitor plus olaparib. Both strategies exhibited a similar response profile, significantly inhibiting tumor growth in three models but proving ineffective in the other two (Fig. 6a, c, Supplementary Fig. 9c). These findings further support that elesclomol-copper sensitizes tumors to PARPi through targeting the ATR pathway and suggest that some patients may not respond to combined PARP and ATR inhibition in clinical practice. Finally, for the two models resistant to the combined inhibition of PARP and ATR, we employed a strategy targeting DHODH, the key enzyme in de novo pyrimidine synthesis. Remarkably, this approach led to significant tumor suppression in both models (Fig. 6a, d, Supplementary Fig. 9d). To further assess the correlation between PARPi sensitivity and pyrimidine dependency, we measured pyrimidine metabolite levels in baseline samples from all eight PDX models. We found that the three most PARPi-sensitive models (PDX#1, PDX#4, PDX#5) exhibited low levels of pyrimidine-related metabolites, while the two most resistant models (PDX#3, PDX#6) had the highest levels (Fig. 6e). This result further confirms the involvement of de novo pyrimidine synthesis in PARPi resistance and highlights a potential selective therapeutic strategy for PARPi-resistant tumors. Additional toxicity assessments, including serum biochemical analyses and H&E staining of major organs from vehicle- and elesclomol-treated mice, consistently revealed no treatment-related toxicity (Supplementary Fig. 10a, b). Finally, we reviewed the clinicopathological information of the patients from whom the PDX models were derived: six were classified as HGSOC, one as clear cell carcinoma, and one as moderately differentiated endometrioid carcinoma (Supplementary Fig. 10c). These findings suggest that the proposed strategy, targeting de novo pyrimidine synthesis in combination with PARP and ATR inhibition, is effective not only in two BRCA-proficient non-HGSOC cases, but also in six HGSOC cases. This highlights its potential for validation in larger preclinical patient cohorts and underscores its translational value, with the possibility of applicability across different subtypes of PARPi-resistant ovarian cancer.

Methods
Cell culture
The following cell lines were utilized in this study: 293T (CRL-3216), ES-2 (CRL-1978), SK-OV-3 (HTB-77) and TOV-21G (CRL-3577) cell lines were purchased from the American Type Culture Collection (ATCC, VA, USA); A2780 (1101HUM-PUMC001020) cell line was obtained from the National Experimental Cell Resource Sharing Platform (Beijing, China); 3AO and OVTOKO cell lines were graciously provided by Dr. Zhigang Zhang (Shanghai Jiao Tong University); OVCA432 and OVCA429 cell lines were kindly provided by Dr. Zhe Liu (Tianjin Medical University); COV362 (07071910) and OVCAR4 (SCC258) cell lines were purchased from Sigma-Aldrich (MO, USA). 293T cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS). COV362 cells were cultured in DMEM supplemented with 2 mM glutamine and 10% FBS. SK-OV-3 cells were cultured in McCoy’s 5A medium supplemented with 10% FBS. OVCA429 cells were cultured in DMEM/F12 supplemented with 10% FBS. All other cells were cultured in RPMI 1640 medium supplemented with 10% FBS. Before any experimental use, all cell lines were routinely authenticated using short tandem repeat DNA fingerprinting and were tested with a MycoBlue Mycoplasma Detector (D201, Vazyme Biotech, Nanjing, China) to confirm the absence of mycoplasma contamination.

Animal experiments
All mice were housed under specific pathogen-free conditions in an accredited animal facility, with controlled temperature (22±1°C), relative humidity (50–60%), and a 12-h light/dark cycle. Standard chow and water were provided ad libitum. Our study focused exclusively on female mice, as the disease modeled in this study is relevant only to females. All animal protocols related to mouse experiments were approved by the Animal Care and Use Committee of the Chinese Academy of Medical Sciences Cancer Hospital (approval number: NCC2022A371). Mice were euthanized if tumor volume exceeded 2000 mm3 or the maximum tumor diameter reached 2 cm. In this study, all animal experiments remained within these limits. Six-week-old female BALB/c nude mice (HFK Bioscience, Beijing, China) were used for subcutaneous xenograft experiments, while six-week-old female NOD-Prkdcscid-Il2rgem1IDMO (NPI) mice (IDMO, Beijing, China) were used for the establishment of the PDX model.

To verify the synergistic effect of copper and olaparib in cell-derived xenograft models, six-week-old female immunodeficient BALB/c nude mice were subcutaneously injected with either 5×106 3AO cells or 2×106 ES-2 cells. Mice were treated with either the vehicle, olaparib (50 mg/kg), elesclomol-copper (5 mg/kg), or combinations of these compounds. The specific duration of drug treatments is provided in the corresponding figures and legends.

For in vivo experiments aimed at assessing the effects of targeting ATR and PARP individually or in combination on tumor growth and relapse, control ES-2 cells or shRNA-mediated ATR-silenced ES-2 cells (2×106 cells per mouse) were subcutaneously injected into six-week-old female immunodeficient BALB/c nude mice. Following tumor establishment, tumor-bearing mice with ES-2 control and ATR knockdown were randomly assigned to receive either the vehicle or olaparib (50 mg/kg) treatment. Additionally, parental ES-2 cells (2×106 cells per mouse) were subcutaneously injected into 6-week-old female BALB/c nude mice. Once xenografts were established, mice were randomly allocated to groups to receive either olaparib (50 mg/kg) or AZD6738 (50 mg/kg) alone or in combination treatment. The specific duration of drug treatments is provided in the corresponding figures and legends.

For PDX models, patient samples were obtained from the Chinese Academy of Medical Sciences Cancer Hospital. This study was approved by the Ethics Committee of the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College (approval number: 22/328-3530), and informed consent was obtained from all participating patients. To establish PDX models, freshly collected tumor specimens were immediately transferred into a DMEM medium containing penicillin and streptomycin. Within 12 h of excision, the tumor samples were cut into sections approximately 50 mm3 in size and then implanted subcutaneously into the flanks of six-week-old female NPI mice, with a severe deficiency of functional T cells, B cells, and NK cells. Once the xenografts reached approximately 1 cm3 in volume, they were removed and transplanted into a new generation of mice for subsequent experiments. Considering the profound immunodeficiency of NPI mice and previous studies reporting that 10 mg/kg elesclomol monotherapy is sufficient to achieve effective antitumor activity65,66, we employed a treatment strategy of 10 mg/kg elesclomol alone in the PDX experiments. The detailed treatment regimen is provided in the corresponding figure.

Tumor volume was calculated using the formula: 0.52 × length × width2. The combination index (CI) was calculated using the formula following a previously published study67: CI = (Comb/S2)/(S1/Ctrl), where Comb represents the mean tumor volume or weight of the combination group. S1 and S2 represent the mean tumor volume or weight of the two corresponding single-agent groups. Ctrl represents the mean tumor volume or weight of the vehicle control group. By this metric, CI < 1 indicates synergy, CI = 1 indicates an additive effect, and CI > 1 indicates antagonism.

PDO model
The patient samples used to establish the PDO model were obtained from the Chinese Academy of Medical Sciences Cancer Hospital. This study was approved by the Ethics Committee of the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College (approval number: 22/328-3530). Written informed consent was obtained from the patient. Briefly, a tissue sample was obtained during the surgical procedure from a BRCA-proficient ovarian cancer patient and then was collected in a tissue preservation solution and treated with tissue enzyme solution, followed by incubation on a temperature-controlled shaking incubator at 37°C for 30–60 min. Following enzyme treatment, the cell suspension was transferred to an organoid washing solution and filtered through a 100 μm cell sieve. The collected cells were then centrifuged, and the resultant pellet was resuspended in matrix gel and seeded in 24-well cell culture plates. After solidification in the incubator, 500 μl of ovarian cancer organoid culture medium was added. The organoid culture medium was replenished with anti-apoptotic factors every 2 days before the complete medium was changed every 3–4 days. For drug sensitivity testing, the digested organoids were mixed uniformly with 60% concentration matrix gel and seeded at a density of 1×103–2×103 cells/5 μl/well in the IBACS1 chip microwells, and cultured with organoid culture medium for 3 days. Subsequently, the organoids were incubated with the drug-containing organoid culture medium for 5 days. After drug treatment, the ATP activity was measured using the organoid viability assay reagent.

Plasmids and lentivirus
Lentiviruses were produced in 293T cells using a second-generation packaging system involving psPAX2 (#12260, Addgene, MA, USA) and pMD2.G (#12259, Addgene) plasmids. The pSIH1-puro vector (#26597, Addgene) was used to incorporate the shRNA sequences. The sgRNA sequences targeting human FDX1 were inserted into the LentiCRISPR v.2 (#52961, Addgene) backbone. Transfection was carried out using the Hieff Trans Liposomal Transfection Reagent (40802, Yeasen, Shanghai, China) following the manufacturer’s instructions. Lentiviral infection was performed twice within a 48-h interval, with the virus-containing medium replaced with a complete culture medium 8 h after each infection. Subsequently, cells were selected using 2 μg/ml puromycin (A610593, Sangon Biotech, Shanghai, China) for 4–7 days before commencing subsequent experimental procedures. The ATR shRNA sequence used was as follows (5′–3′): AATGGAGTAAACCAGTGAAA, and the FDX1 sgRNA sequence used was as follows (5′–3′): GCAGGCCGCTGGATCCAGCG.

Drug screening and cell viability assay
Cell viability was measured using the Cell Counting Kit-8 (CCK-8) (CK04, Dojindo Laboratories, Kumamoto, Japan) according to the manufacturer’s instructions. For compound screening, ES-2 cells were seeded at 1.2×104 cells per well into 96-well plates and treated with olaparib alone or in combination with a panel of 144 cell death-related compounds at concentrations of 10 μM and 50 μM. After 24 h, cell viability was assessed using the CCK-8 assay. For drug response assays, approximately 1×104–1.5×104 ovarian cancer cells (with slight variations depending on cell type) were seeded in 96-well plates. After allowing the cells to adhere for approximately 12 h, drug treatments were initiated. Cell viability was evaluated 48 h post-treatment by measuring absorbance at 450 nm and normalizing it to the negative control group. Compound screening results and the screening summary are provided in Supplementary Data 1 (normalized activity of original data) and Supplementary Information (screening summary). Drug synergy was assessed by calculating the Bliss synergy score using SynergyFinder 3.068.

Flow cytometry for cell-cycle analysis
To evaluate cell-cycle distribution, ES-2 parental and drug-adaptive cells were analyzed using the Click-iT™ Plus EdU Alexa Fluor™ 488 Flow Cytometry Assay Kit (C10632, Thermo Scientific, MA, USA) in combination with FxCycle™ Violet Stain (F10347, Thermo Scientific), following the manufacturer’s instructions. Briefly, cells were incubated with 10 μM EdU at 37°C for 2 h, then harvested and washed once with 3 ml of 1% BSA in PBS. After centrifugation, cells were fixed with 100 μl of Click-iT™ fixative (Component D) for 15 min at room temperature in the dark. Subsequently, cells were washed again with 3 ml of 1% BSA in PBS, permeabilized with 100 μl of 1× Click-iT™ permeabilization and wash reagent for 15 min, and then stained with 0.5 ml of Click-iT™ EdU reaction cocktail. Samples were incubated for 30 min at room temperature, protected from light. Following incubation, cells were washed once with 3 ml of 1× Click-iT™ permeabilization and wash reagent, centrifuged, and resuspended in 500 μl of the same reagent. Then, 1 μl of FxCycle™ Violet stain was added to each tube. Samples were incubated for an additional 30 min at room temperature in the dark before analysis. Flow cytometric data were acquired using an LSR Fortessa HTS analyzer (BD Biosciences), with 1×104 events collected per sample. Data were analyzed using FlowJo software (BD Biosciences). The gating strategy used for flow cytometry is presented in Supplementary Fig. 11.

RNA-seq and data analysis
RNA-seq and subsequent data analysis were conducted by Shanghai Applied Protein Technology Co., Ltd (Shanghai, China). Six independent replicates were prepared for each group from distinct cell cultures. For the transcriptome of drug-adaptive survival cells and parental ES-2 cells, the cells were pretreated with 10 μM olaparib for 48 h before RNA isolation. Paired-end libraries were generated using an NEBNext Ultra RNA Library Prep Kit for the Illumina system (New England Biolabs, MA, USA) following the provided protocols, and sequencing was executed on the Illumina NovaSeq 6000 platform. Differentially expressed genes were identified based on a fold change of greater than 2 and a P-value of less than 0.05. The RNA-seq data have been deposited in the Gene Expression Omnibus under the accession number GSE246085.

Metabolomics
Metabolomics analysis was conducted by Shanghai Applied Protein Technology Co., Ltd. with six replicates per group derived from independent cell cultures. Briefly, 800 μl of cold methanol/acetonitrile (1:1, v/v) was added to cultured cells for metabolite extraction. The analysis was performed using a UHPLC system (1290 Infinity LC, Agilent Technologies) connected to a quadrupole time-of-flight mass spectrometer (AB Sciex TripleTOF 6600). Raw MS data were converted to MzXML files via ProteoWizard MSConvert and imported into XCMS software for analysis. Metabolites were identified by comparing accurate m/z values (<10 ppm) and MS/MS spectra with an in-house database of authentic standards. After normalization, data were analyzed using multivariate methods such as Pareto-scaled PCA and OPLS-DA, through the R package. The VIP value for each variable in the OPLS-DA model indicated its contribution to group classification. Significant metabolite changes were identified based on VIP > 1 and P-value < 0.05 using two-sided Student’s t-test. Pearson correlation analysis was used to assess relationships between variables.

Metabolic flux analysis using 15N-glutamine tracing
To assess de novo pyrimidine synthesis flux, both ES-2 parental and drug-adaptive cells were cultured in medium supplemented with 3 mM 15N-glutamine (NLM-557, Cambridge Isotope Laboratories, MA, USA) at 37°C for 4 h. Following incubation, cells were harvested by trypsinization, pelleted, and subjected to metabolite extraction. For each sample, 500 μl of cold extraction buffer (methanol/acetonitrile/water = 2:2:1, v/v/v) was added, followed by sonication for 2 min. Samples were then centrifuged at 1.4×104 × g for 5 min at 4°C. The resulting supernatants were completely lyophilized (FreeZone 6 Liter, Labconco, USA) and reconstituted in 50 μl of methanol/water (1:1, v/v) immediately before analysis. LC-MS analysis was performed on a Thermo Q Exactive Plus hybrid quadrupole-orbitrap mass spectrometer coupled to a Thermo Vanquish UPLC system. Data acquisition and analysis were conducted using TraceFinder 5.0 and Xcalibur 4.0 (Thermo Fisher). Metabolite mass distribution vectors (MDV) were calculated as the ratio of each isotopomer’s integrated peak area to the sum of all isotopomers of the given metabolite. All data were corrected for natural isotope abundance before biological interpretation.

Antibodies and reagents
The following antibodies were used for western blot analysis: anti-p-ATR (S428) (1:1000; 2853, Cell Signaling Technology, MA, USA), anti-ATR (1:1000; 13934, Cell Signaling Technology), anti-p-CHK1 (S345) (1:1000; 2348, Cell Signaling Technology), anti-CHK1 (1:1000; ab40866, Abcam, Cambridge, UK), anti-ATRIP (1:1000; 2737, Cell Signaling Technology), anti-p-ATM (S1981) (1:1000; 13050, Cell Signaling Technology), anti-ATM (1:1000; 2873, Cell Signaling Technology), anti-p-CHK2 (T68) (1:1000; 2197, Cell Signaling Technology), anti-CHK2 (1:1000; 6334, Cell Signaling Technology), anti-γ-H2AX (1:5000; AP0687, ABclonal, Wuhan, China), anti-β-actin (1:4000; A5316, Sigma-Aldrich). The following antibodies were used for IF analysis: anti-p-ATR (S428) (1:100; AP0676, ABclonal), anti-ATR (1:200; ab313435, Abcam), anti-p-CHK1 (S345) (1:50; 2348, Cell Signaling Technology), anti-CHK1 (1:200; ab40866, Abcam), anti-γ-H2AX (1:50; AP0687, ABclonal). The anti-γ-H2AX (1:100; AP0687, ABclonal) was used for IHC analysis. The following reagents were purchased from MedChemExpress (Monmouth Junction, NJ, USA): elesclomol (HY-12040), SBE-β-CD (HY-17031), tetrathiomolybdate (TTM, HY-128530), olaparib (HY-10162), fluzoparib (HY-114778), BAY-2402234 (HY-112645), AZD6738 (HY-19323), uridine (HY-B1449), cytosine (HY-I0626), thymidine (HY-N1150) and Iron(III) chloride (HY-Y0266). Copper(II) chloride dihydrate (102733), Nickel(II) chloride (451193), Cobalt(II) chloride (232696), and Magnesium chloride (M8266) were purchased from Sigma-Aldrich.

Co-immunoprecipitation (Co-IP) and western blot
Co-IP and western blot analyses were performed according to a previously established protocol69. Initially, proteins from ovarian cancer cells were extracted using RIPA buffer (CW2333, CWBIO, Taizhou, China) along with protease inhibitor cocktail tablets (04693132001, Roche, Prague, Czech Republic). The protein concentrations were determined using the BCA assay kit (23225, Thermo Scientific). Subsequently, the samples were separated on 10–15% gels, based on the molecular weights of the proteins, and then transferred onto polyvinylidene difluoride membranes (Millipore, MA, USA). To assess metal-binding affinity, a previously published protocol was followed32. Briefly, the Profinity IMAC resin (1560121, Bio-Rad, CA, USA) was pre-loaded with 0.4 M solutions of various metal ions at 4°C for 2 h. Approximately 10 million cells were lysed in NP-40 buffer, and the lysates were incubated with the metal-charged resin at 4°C for 4 h. Metal-binding proteins were then eluted and analyzed by Western blot.

Immunofluorescence (IF) and immunohistochemical (IHC) staining
The cells were seeded in a μ-Slide V10.4 (80606, ibidi, Martinsried, Germany) and cultured overnight to facilitate cell adhesion. Subsequently, the cells were fixed with 4% paraformaldehyde for 15 min at room temperature and permeabilized with 0.2% Triton X-100 for an additional 10 min at room temperature. After blocking with 5% bovine serum albumin for 30 min, the cells were incubated with a primary antibody overnight. The proteins were visualized by incubating with Alexa Fluor 488-conjugated anti-rabbit IgG (4412, Cell Signaling Technology) or Alexa Fluor 594-conjugated anti-rabbit IgG (8889, Cell Signaling Technology) for 2 h at room temperature. Nuclei were stained with Hoechst 33342 (62249, Thermo Scientific) for 15 min at room temperature. For the statistical analysis of p-ATR and p-CHK1, the percentage of positive cells was counted in each field of view, with a total of five fields analyzed per sample. For the statistical analysis of γ-H2AX, the number of positive foci within each cell was counted, with at least 100 cells analyzed per sample. IHC experiments and calculation of H-scores were performed as previously described70.

Proximity ligation assay (PLA)
PLA was performed using the Duolink In Situ Red Starter Kit (DUO92101, Sigma-Aldrich) according to the manufacturer’s instructions. The following primary antibodies were used: anti-ATR (1:100; sc-515173, Santa Cruz Biotechnology, TX, USA) and anti-ATRIP (1:100; A5041, ABclonal). Cells were pretreated with 20 μM olaparib in combination with either 20 nM or 50 nM elesclomol-copper for 12 h prior to PLA analysis.

AlphaFold3 structure prediction
Protein sequences for ATR (accession number: Q13535) and ATRIP (accession number: Q8WXE1) were retrieved from UniProtKB (https://www.uniprot.org/). The structures of ATR and ATRIP, along with their potential copper-binding sites and abilities, were predicted using AlphaFold3 (https://alphafoldserver.com/), and the corresponding structure CIF files were downloaded. To analyze the detailed local interaction interfaces and specific amino acid residues, the CIF files were examined using the Protein Viewer extension in VSCode. Structural alignment and RMSD calculations were performed using PyMOL.

Statistics
Statistical analysis was conducted using GraphPad Prism 8 or GraphPad Prism 9 (GraphPad Software, CA, USA). Two-sided unpaired or paired Student’s t-test, two-way ANOVA, and the Kaplan–Meier method were employed for comparisons between two groups, comparisons among multiple groups, and survival analysis, respectively. Statistical significance was considered for P-values ≤ 0.05, and results from at least three biologically independent experiments with consistent outcomes are presented. The data are expressed as the mean±s.e.m. or the mean±s.d. as indicated in the related figure legends.

Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability
The data supporting the findings of this study are available within the paper and its supplementary information files. The RNA sequencing data generated in this study have been deposited in the Gene Expression Omnibus and are accessible through accession number GSE246085. The metabolomics data generated in this study have been publicly released and deposited in the OMIX at the National Genomics Data Center, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences, under accession numbers OMIX013458. The remaining data are available within the Article, Supplementary Information, or Source Data file. Source data are provided with this paper.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "I read the article with great pleasure. The presentation of the results is rather clear. The authors approached the subject holistically. Very interesting and important research. I appreciate the enormous amount of work put into the research. My comments are aimed at objectively organizing the content and in no way diminishing the value of the work.\nMajor remarks\n1. For the work on the PDO model, the authors obtained permission for the research from the Research Ethics Committee of the Chinese Academy of Medical Sciences Cancer Hospital. Was a similar consent also obtained for the work on PDX, where the tissue was also collected from patients? Please also provide the numbers of the obtained permits.\n2. In vivo studies were conducted on several types of mice. Why was one model not chosen, e.g. immunodeficient mice? I did not find a justification for the choice of mice in the text\n3. It is not clear to me how the model based on AZD+olaparib+anti PD1 is related to the dependence on de novo pirimidine synthesis (abstract, line 41): “Additionally, both copper and ATR inhibitor in combination with PARPi activate the cancer cell-intrinsic cGAS-STING pathway, thereby enhancing the antitumor immune response in pyrimidine-independent ovarian cancer. These findings propose selective therapeutic strategies for PARPi-resistant ovarian cancer, implicated by the dependency on de novo pyrimidine synthesis.”\n4. Scheme 6a is clear and this is basically the idea that the work is based on. In conducting the second model: AZD + olaparib + anti PD1 introduces unnecessary confusion. The relationship between olaparib and the cGAS-STING pathway has long been known.\n5. It would be useful to have a summary focusing not only on the role of pyrimidines in PARP resistance, but also a summary of the drug regimens used and what final conclusions were drawn based on their use.\nMinor remarks\n1. Line 133, page 7:” Notably, ovarian cancer cells exhibited nearly 1000-fold greater sensitivity to elesclomol-copper compared to either PARPi (Supplementary Fig. 1c).” This is correct except for the 3AO line where after elesclomol-copper IC 50 is 244 and after olaparib 240.\n2. There are a few missing words about the BAY inhibitor, which is crucial for this work.\n3. Figure 3h. The description of the figure is not clear. Is the top part of the panel cells treated with ES-CU, AZD, EC+uridine, AZD+uridine, and the 2nd row the same compounds with additionally added olaparib? I propose to improve the description to make it more readable", "reviewer_2": "PARP inhibitor (PARPi) resistance is a significant challenge in ovarian cancer and other malignancies. Nan et al. investigated the role of copper-induced programmed cell death and the use of a copper ionophore, which they found can synergize with PARPi by inhibiting activation of the ATR-CHK1 pathway. Their results demonstrated enhanced tumor growth inhibition when combining these therapies. Additionally, the authors identified that resistance might involve de novo pyrimidine synthesis, which are important findings supported by extensive experiments and robust data from traditional cancer cell biology perspectives.\nHowever, there are notable caveats in this study. At the histological level, targeting the described cell death in cell lines is unlikely to yield significant clinical benefits. Advanced-stage tumors often exhibit extensive necrosis, indicating that cell death may be an associated phenomenon rather than a causative factor. In fact, cell death could potentially promote tumor growth.\nThe authors may have overlooked a critical conceptual advancement in understanding therapeutic resistance: the role of polyploid giant cancer cells (PGCCs).\nDid the authors examine drug-tolerant persister cells following treatment? While individual molecular pathways, such as those highlighted in this study, are relevant, the mechanisms underlying resistance likely involve PGCCs emerging after Olaparib treatment. Recent studies (Liu et al., Seminars in Cancer Biology, 2022; Zhang et al., Science Advances, 2023) have demonstrated that PGCCs reorganize their genomes through whole-genome duplications to generate therapy-resistant cells under therapeutic stress.\nThese holistic mechanisms are more impactful than individual pathways alone. The observed de novo pyrimidine synthesis may support the increased DNA synthesis required for whole-genome duplication in PGCCs, potentially underlying the mechanism of resistance.\nWhile additional experiments may not be necessary for this manuscript, if the editors decide to accept it, the authors should re-examine their data and images to determine if PGCC-related phenomena were inadvertently missed (e.g., Figure 3h). If evidence of PGCCs is identified, it should be incorporated into the experimental results, and the discussion should be revised to reflect this new perspective.", "reviewer_3": "PARPi is a powerful drug for cancer therapy; however, drug resistance remains a significant challenge to its clinical application. To address this problem, combining PARPi with other treatments, such as chemotherapy or immunotherapy, is considered a way to overcome the drug resistance. In this manuscript, Nan et al. screened a small molecule library for a potential lethal compound to overcome PARPi resistance. They identified a previously recognized anticancer drug, elesclomol (a potent copper ionophore that can induce cuproptosis), to have such an activity. Specifically, they found that elesclomol (ES) can enhance DNA damage and sensitize BRCA-proficient ovarian cancer cells to PARPi by suppressing the activation of the ATR-CHK1 DNA damage response pathway in a manner independent of elesclomol-induced cuproptosis. The combined treatment of copper-ES and PARPi significantly inhibits tumor progression, although it does not completely eradicate the residual tumors. In addition, the authors show that de novo pyrimidine synthesis plays a pivotal role in the PARPi resistance, as abrogating de novo pyrimidine synthesis significantly inhibits the growth of PARPi-resistant ovarian tumors. Mechanistically, this study reveals that both copper and ATR inhibitors, in combination with PARPi, activate the cGAS-STING pathway to enhance the antitumor immune response. Finally, the authors evaluate therapeutic strategies for PARPi-resistant ovarian cancer using patient-derived xenograft (PDX) models.\nMost of the data shown in the study is solid, and provides a potentially feasible treatment option for patients with BRCA-proficient ovarian cancer. However, there are several concerns that require to be clarified.\n1. A major finding of this manuscript is the identification of elesclomol as an inhibitor of the ATR-CHK1 signaling pathway. However, how elesclomol does this is not clear. The authors showed that copper or AXZD 6738 treatment blocks CHK1 phosphorylation, suggesting inhibition of the ATR kinase activity. Intriguingly, as the authors showed, elesclomol induces garma-H2AX production, an indicator of DNA breaks. This usually activates the ATR-mediated DNA damage response, which is contradictory to the authors’ observation that elesclomol inhibits ATR signaling. In addition to inducing ROS-caused DNA breaks, does elesclomol directly interact with ATR to inhibit ATR kinase activity? These issues should be addressed.\n2. In Fig. 5, the author showed that PARPi fails to activate the cGAS-STING pathway. This result is inconsistent with previous studies (e.g., 10.1172/JCI123319 and 10.1016/j.canlet.2023.216507), where PARP inhibition enhances the cGAS-STING pathway and the downstream type I IFN signaling. This discripency should be addressed.\n3. The authors mentioned (lines 338-340) that combinational treatment of PARPi and ATRi suppresses tumor growth, and activates the STING signaling, thereby enhancing the efficacy of anti-PD-1 immunotherapy. The observation is interesting. Again, what is the mechanism? Does the combinational treatment stimulates immune cell infiltration and/or T cell-mediated tumor cell killing?\n4. In Fig. 2d and Fig. S2c, why are the mRNA and protein levels of ATM-CHEK2 and ATR-CHEK1 pathway genes not consistent？", "reviewer_4": "In their manuscript, Nan and colleagues investigate strategies to overcome PARP inhibitor (PARPi) resistance in ovarian cancer models, identifying elesclomol-copper (ES-Cu) as a possible synergistic agent. The authors present data suggesting that ES-Cu enhances PARPi efficacy by inhibiting ATR signaling, with additional insights into the dependency of resistant cells on de novo pyrimidine synthesis. While the study explores a potentially interesting therapeutic combination for PARPi resistance, its impact is limited by a lack of essential experimental controls, insufficient mechanistic detail, and inconsistent presentation. Specific comments below:\n1. Some critical experiments are not well controlled. In Figure 5, the absence of single-agent controls for ES-Cu and AZD6738 treatments makes it impossible to evaluate the combinatorial effects properly. In Figure 6, single-agent activities for combination therapies are not presented. Without these, claims about synergy are unsubstantiated, as additive effects are indistinguishable from true synergy. Dose information for all treatments is missing, further obscuring interpretation. In Figure 3, uridine alone inhibits olaparib’s activity, but this control is inconsistently applied in other experiments. For combinations where uridine rescues PARPi effects, it is unclear whether the inhibition is specific to olaparib or extends to ATR inhibitors or ES-Cu. There are too many experiments that lack essential controls and information required for proper interpretation.\n2. There is insufficient mechanistic exploration to substantiate some of the authors’ conclusions. The authors claim that copper sensitization is independent of canonical cuproptosis (FDX1-dependent pathways), but the data provided (e.g., Figure S1f-h) are unconvincing. The role of FDX1 in regulating ES-Cu has been established in numerous cell lines and publications. The authors need to show that FDX1 KO indeed affects ES-Cu sensitivity and, in those conditions, show whether it impacts synergy with PARPi. There is no discussion of Fe-S cluster proteins or mitochondrial lipoylation, both known targets of ES-Cu, that could explain its effects on pyrimidine synthesis and DNA damage. Additionally, the authors do not evaluate levels of FDX1 or lipoylation in their resistant model analysis, which are key to substantiating their conclusions. Potential involvement of nucleotide metabolism could also be explained through the targeting of mitochondrial Fe-S proteins by ES-Cu or through DNA damage caused by targeting Fe-S cluster DNA damage repair complexes. Both mechanisms remain unexplored, despite their clear relevance to the proposed mechanisms of ES-Cu and PARPi. Without this data, the conclusions are speculative.\n3. In many cases, the doses used are not described, preventing proper evaluation. For ES-Cu, it has been established that specificity is retained at doses below 100 nM, while higher doses target non-specific pathways (PMID: 31133756). If higher doses are used, the authors should address these non-specific effects explicitly. Additionally, throughout the manuscript, critical details on dose and treatment conditions are missing, including for the synergy experiments in Figure 2f, leaving the methods unclear and the data difficult to interpret.\nOther Comments:\n1. It is not clear why the authors try to connect the PARPi response with ES-Cu. If PARPi-resistant cells are not resistant to ES-Cu, shouldn’t this be interpreted as evidence that they work through different mechanisms, hence the potential synergy?\n2. In Figure 2, the levels of FDX1 and cellular protein lipoylation should be shown, as these are the most trivial explanations for resistance. Similarly, PARP levels should be assessed, as the loss of PARP would promote resistance to PARPi.\n3. I am not a PARPi researcher, but a quick search showed there is existing work on the synergy of PARPi and nucleotide biosynthesis (PMID: 33833118). This should be addressed in the text as this is not a novel concept.\n4. Uridine treatment completely abolished the synergistic effects of copper or ATR inhibitors with PARPi. Wouldn’t this suggest that ES-Cu contributes to PARPi efficacy via its effects on mitochondria and inhibition of nucleotide synthesis? Could it be through targeting different Fe-S cluster proteins? A deeper mechanistic analysis is warranted to clarify this connection.\n5. The authors use the term “treatment with copper.” It is NOT copper; it is elesclomol-copper (ES-Cu). This distinction is important and should be made consistently throughout the manuscript.\n6. In Figure 2h-i, it is unclear how the synergy score was calculated or what doses were used. This information is not included in the text, legends, or methods.\n7. Why do the authors switch to inhibition rates in Figure 3, when standard viability curves are shown in Figure 2? This inconsistency needs to be addressed.\nSmall Comments:\n1. Line 155: Replace “programmed” with “regulated” cell death (and throughout the text).\n2. In Figure 1h, k, add the stained protein name directly in the figure.\n3. The legend for Figure 1 is disorganized and lacks a description for Figure 1k.", "reviewer_5": "Review of NCOMMS-24-68581\nSummary: The manuscript put forth by Nan et al. explores mechanisms of PARP inhibitor resistance in ovarian cancer. To identify pharmacological agents that potentially cooperate with the PARP inhibitor, Olaparib, the authors treat the BRCA-proficient cell line ES-2 with 144 cytotoxic agents. From this analysis the authors identify 5 compounds that cooperate with Olaparib, including two ferroptotic inducing agents previously shown to synergize with Olaparib. Of the three remaining compounds, elesclomol (ELE) showed the strongest synergy. ELE is a potent copper ionophore that 116 transports copper into cells that can induce cell death. The authors then provide several pieces of data suggesting that PARPi cooperate with ELE in killing ovarian cancer cell lines in vitro. The authors also show evidence that ELE and Olaparib cooperate in reducing tumor volume and weight in two cell line xenograft models. The authors also provided evidence that PARPi cooperate with ELE independent of FDX1, which antagonizes cuproptosis (copper-mediated cell death).\nTo investigate the molecular consequences of PARPi-ELE cooperativity, the authors ES-2 cell lines that are resistant to PARPi alone, ELE alone or the combination of the two and then performed gene expression analyses on parental and each of the three resistance cell lines (called DTP cells). A GSEA revealed an enrichment of pathways related to DNA replication in all three DTP cell lines and in particular that DTPs displayed elevated CHEK1 compared to parental cells. The authors then utilized western blotting and immunofluorescence to confirm that ATR- CHEK1 signaling was elevated in all three DTPs compared to parental cell lines. The authors then show that Olaparib treatment induced CHEK activation (i.e. phosphorylation) in parental cells, which can be reversed with ELE treatment or an ATR inhibitor. However, neither ELE nor ATR inhibitor was unable to cooperate with PARPi in DTP cells. As a result, the authors explored additional mechanisms that could be driving PARPi resistance in DTPs. To do so, the authors perform untargeted total polar metabolite analysis of parental and all three DTP cell lines. From this analysis, the authors observe that multiple pyrimidines (and pyrimidine synthesis intermediates) are significantly elevated in all three DTP lines, however, levels of pyrimidines are the highest in the DTPs resistant to both PARPi and ELE. The authors also show that excess uridine (to engage salvage pathways) promotes resistance to Olaparib alone or the combination of Olaparib and ELE. The authors also show that the DHODH inhibitor BAY-2402234 can resensitize DTPs to Olaparib and ELE or the ATR inhibitor.\nTo therapeutically translate these findings the authors then assess how the combination of Olaparib and the ATR inhibitor, AZD6738 (using two treatment schedules) impacts tumor growth in cell line xenograft models of ovarian cancer. These studies indicate the sustained combined inhibition of PARP and ATR is most effective in minimizing tumor burden. The authors then carry out additional RNA-seq analyses comparing vehicle treated cells to combined Olaparib with AZD6738 or ELE and they find an enrichment of cytosolic DNA sensing and interferon pathways, i.e. the STING signaling pathway. The authors then show that the combination of PARPi with either ELE or AZD6738 cooperate in reducing tumor volume in a syngeneic mouse model of ovarian cancer (ID8) injected sub-cutaneous. The authors also show that the combination of a PD1 antibody further increases the anti-tumor effect of either PARPi plus ELE or PARPi plus AZD6738. Finally, the authors examine how effective various pharmacological strategies are in 8 patient-derived xenograft models of subcutaneously injected ovarian cancer. They find that three PDX models are sensitive to Olaparib alone and that another three are sensitive to the combination of Olaparib with either AZD6738 or ELE. The remaining two PDX models they show are sensitive to the combination of Olap+AZD+DHODH inhibitor or Olap+ELE+DHODH.\nOverall, there are several interesting aspects to the manuscript, including the novel therapeutic combination of Olaparib and elesclomol in BRCA1-proficient Ovarian cancer cell lines as well as a possible mechanistic connection between copper-mitochondrial biology and ATR as well as the pyrimidine metabolism. However, there are several technical and scientific issues that detract enthusiasm for this manuscript, which are detailed in the comments below:\nComments:\n1. One the key observations is that elesclomol and ATR inhibition are equally blocking CHEK activation and inducing DNA damage, however, the authors do not explore the possible mechanistic connection between these two observations. Is elesclomol an unknown ATR inhibitor? Is there some unknown connection between mitochondrial copper biology and ATR?\n2. Additionally, while resistance to Olaparib + elesclomol/AZD6738 is associated with increased steady-state levels of pyrimdines, the authors do not provide any molecular connection between these observations. Since de novo pyrimidine synthesis utilizes glutamine, carbon tracing experiments should be employed to prove de novo pyrimidine synthesis is upregulated in DTPs compared to parental cells.\n3. While the study reports that exogenous uridine supplementation increases PARPi resistance, data on the effects of exogenous cytosine and thymidine are missing. Including this data would help determine whether PARPi resistance is driven by DNA synthesis via TTP, RNA synthesis via CTP, or both.\n4. Much of the data in Figure 5 is confusing, unexplained and I would recommend removing. Specifically, the authors provide no molecular explanation as to how these inhibitors lead to cGAS-STING activation. Furthermore, the data is missing key controls such as elesclomol or AZD6738 alone. The authors have provided zero explanation for why they combined the anti-PD1 with their other drug combinations. It is also unlcear if an anti-IgG control was included in the experimental treatment arms.\n5. The only data from Figure 5 that may have any value to this manuscript is that elesclomol or AZD6738 cooperate with Olaparib in the ID8 model (move to supplemental). However, the ID8 model is usually injected intraperitoneal, which could influence drug delivery.\n6. Copper sensitizes ovarian cancer to PARPi by inhibiting ATR signaling i.e. more information about DTPs should be added to the manuscript. What were the concentrations of Olaparib and Elesclomol used, given that previous figure show that that both of these drugs reduce cell viability. Is 7 days too short a time to generate DTPs? Additionally, characterizations as cell proliferation and cell cycles allow readers to understand how different DTPS are from parental cells. These studies/observations should be provided.\n7. Regarding Figure S8:\na. A more descriptive description should be provided of the mechanistic insights.\nb. It is depicted that Copper reduces ATR expression or protein levels. There are no experiments in this study which validates that point. This manuscript only shows increase in pCHEK1 when cells are treated with ATRi or ES-Cu along with Olaparib.", "reviewer_6": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Point-by-Point Responses to Reviewers’ Comments\nRE: NCOMMS-24-68581\nTitle: Targeting de novo pyrimidine synthesis confers vulnerability to copper-mediated ATR\ninactivation in PARP inhibitor-resistant ovarian cancer\nWe sincerely thank the reviewers and editor for their thoughtful and insightful evaluation of our\nwork. All reviewers were positive about the novelty and potential significance of our study. They\nasked for additional data to support all the conclusions"}]

Decision letter