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journal article·2024-12Open access

CDC7 Inhibition Potentiates Antitumor Efficacy of PARP Inhibitor in Advanced Ovarian Cancer

Shini Liu, Peng Deng, Zhaoliang Yu, Jing Han Hong, Jiuping Gao, Yulin Huang, Rong Xiao, Jiaxin Yin, Xian Zeng, Yichen Sun, Peili Wang, Ruizi Geng, Jason Yongsheng Chan, Peiyong Guan, Qiang Yu, Bin‐Tean Teh, Qingping Jiang, Xiaojun Xia, Ying Xiong, Jianfeng Chen, Yongliang Huo, Jing Tan

Advanced Science· Volume 11· Issue 45
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Source abstract

Abstract

Abstract Poly (ADP‐ribose) Polymerase inhibitors (PARPi) have demonstrated remarkable clinical efficacy in treating ovarian cancer (OV) with BRCA1/2 mutations. However, drug resistance inevitably limits their clinical applications and there is an urgent need for improved therapeutic strategies to enhance the clinical utility of PARPi, such as Olaparib. Here, compelling evidence indicates that sensitivity of PARPi is associated with cell cycle dysfunction. Through high‐throughput drug screening with a cell cycle kinase inhibitor library, XL413, a potent cell division cycle 7 (CDC7) inhibitor, is identified which can synergistically enhance the anti‐tumor efficacy of Olaparib. Mechanistically, the combined administration of XL413 and Olaparib demonstrates considerable DNA damage and DNA replication stress, leading to increased sensitivity to Olaparib. Additionally, a robust type‐I interferon response is triggered through the induction of the cGAS/STING signaling pathway. Using murine syngeneic tumor models, the combination treatment further demonstrates enhanced antitumor immunity, resulting in tumor regression. Collectively, this study presents an effective treatment strategy for patients with advanced OV by combining CDC7 inhibitors (CDC7i) and PARPi, offering a promising therapeutic approach for patients with limited response to PARPi.
Source: crossref · Quality: baseline

Research context

Polymers

OpenAlex topics: PARP inhibition in cancer therapy · Plant Virus Research Studies · Ovarian cancer diagnosis and treatment

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

State Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for Cancer Sun Yat‐sen University Cancer Center Guangzhou Guangdong 510060 P. R. China

Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences School of Medicine Southern Medical University Guangzhou Guangdong 510080 P. R. China

Biotherapy Center Sun Yat‐Sen Memorial Hospital Sun Yat‐Sen University Guangzhou 510120 P. R. China

Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases The Sixth Affiliated Hospital of Sun Yat‐sen University Guangzhou Guangdong 510655 P. R. China

Cancer and Stem Cell Biology Program Duke‐NUS Medical School Singapore 169857 Singapore

Department of Laboratory Medicine Guangzhou First People's Hospital School of Medicine South China University of Technology Guangzhou 510180 P. R. China

Experimental Animal Center Guangzhou Municipal and Guangdong Provincial Key Laboratory of Protein Modification and Degradation School of Basic Medical Sciences Guangzhou Medical University Guangzhou 511436 P. R. China

Division of Medical Sciences Laboratory of Cancer Epigenome National Cancer Centre Singapore Singapore 169610 Singapore

Genome Institute of Singapore A*STAR Singapore 138672 Singapore

Department of Patholgy Guangdong Provincial Key Laboratory of Major Obstetric Disease The Third Affiliated Hospital of Guangzhou Medical University Guangzhou Guangdong 510150 China

Hainan Academy of Medical Science Hainan Medical University Haikou 571199 P. R. China

Funding

National Natural Science Foundation of China · National Key Research and Development Program of China · China Postdoctoral Science Foundation · Guangdong Innovative and Entrepreneurial Research Team Program

References (73)

Show source-provided references
1. 10.3322/caac.2170810.3322/caac.21708 ↗
2. 10.1056/NEJMoa090021210.1056/NEJMoa0900212 ↗
3. 10.1016/j.annonc.2020.06.00410.1016/j.annonc.2020.06.004 ↗
4. 10.1038/s41573-020-0076-610.1038/s41573-020-0076-6 ↗
5. 10.1038/s41571-021-00532-x10.1038/s41571-021-00532-x ↗
6. 10.1038/s41467-019-10993-510.1038/s41467-019-10993-5 ↗
7. 10.1016/j.ccell.2018.01.01910.1016/j.ccell.2018.01.019 ↗
8. 10.1016/j.molcel.2018.10.04510.1016/j.molcel.2018.10.045 ↗
9. 10.1158/0008-5472.CAN-21-062810.1158/0008-5472.CAN-21-0628 ↗
10. 10.1158/0008-5472.CAN-14-083910.1158/0008-5472.CAN-14-0839 ↗
11. 10.1158/2159-8290.CD-18-121810.1158/2159-8290.CD-18-1218 ↗
12. 10.1016/j.celrep.2018.11.05410.1016/j.celrep.2018.11.054 ↗
13. 10.1016/S1470-2045(22)00369-210.1016/S1470-2045(22)00369-2 ↗
14. 10.1158/1078-0432.CCR-20-005610.1158/1078-0432.CCR-20-0056 ↗
15. 10.1001/jamaoncol.2019.104810.1001/jamaoncol.2019.1048 ↗
16. 10.1016/j.ejca.2021.02.03510.1016/j.ejca.2021.02.035 ↗
17. 10.1158/1078-0432.CCR-18-096810.1158/1078-0432.CCR-18-0968 ↗
18. 10.1056/NEJMoa191096210.1056/NEJMoa1910962 ↗
19. 10.1038/ncomms436110.1038/ncomms4361 ↗
20. 10.1016/j.cell.2020.05.04010.1016/j.cell.2020.05.040 ↗
21. 10.1002/path.464910.1002/path.4649 ↗
22. 10.1158/1078-0432.CCR-16-227310.1158/1078-0432.CCR-16-2273 ↗
23. 10.15252/embr.20194892010.15252/embr.201948920 ↗
24. 10.1186/1471-2105-14-710.1186/1471-2105-14-7 ↗
25. 10.1093/bioinformatics/btr26010.1093/bioinformatics/btr260 ↗
26. 10.1186/s13073-021-00981-010.1186/s13073-021-00981-0 ↗
27. 10.1016/j.xpro.2022.10137110.1016/j.xpro.2022.101371 ↗
28. 10.1158/2159-8290.CD-19-076110.1158/2159-8290.CD-19-0761 ↗
29. 10.7554/eLife.6063710.7554/eLife.60637 ↗
30. 10.1038/nature1230610.1038/nature12306 ↗
31. 10.1158/2159-8290.CD-18-102010.1158/2159-8290.CD-18-1020 ↗
32. 10.1158/0008-5472.CAN-18-100310.1158/0008-5472.CAN-18-1003 ↗
33. 10.1172/JCI15962810.1172/JCI159628 ↗
34. 10.1172/JCI14503510.1172/JCI145035 ↗
35. 10.1126/science.125223010.1126/science.1252230 ↗
36. 10.1056/NEJMoa161131010.1056/NEJMoa1611310 ↗
37. 10.1016/S1470-2045(14)70228-110.1016/S1470-2045(14)70228-1 ↗
38. 10.1016/j.ccell.2021.03.01010.1016/j.ccell.2021.03.010 ↗
39. 10.1038/nrc.2016.13810.1038/nrc.2016.138 ↗
40. 10.1200/JCO.2012.46.547610.1200/JCO.2012.46.5476 ↗
41. 10.1200/JCO.2014.57.502710.1200/JCO.2014.57.5027 ↗
42. 10.1016/j.ebiom.2019.03.02710.1016/j.ebiom.2019.03.027 ↗
43. 10.1016/j.celrep.2016.10.07710.1016/j.celrep.2016.10.077 ↗
44. 10.1016/j.phrs.2022.10616210.1016/j.phrs.2022.106162 ↗
45. 10.1038/s41416-023-02326-710.1038/s41416-023-02326-7 ↗
46. 10.1101/gad.290957.11610.1101/gad.290957.116 ↗
47. 10.1002/advs.20210075910.1002/advs.202100759 ↗
48. 10.1038/s41586-019-1607-310.1038/s41586-019-1607-3 ↗
49. 10.1158/1078-0432.CCR-08-127610.1158/1078-0432.CCR-08-1276 ↗
50. 10.15252/embr.20164334710.15252/embr.201643347 ↗
51. 10.1038/s41467-023-43274-310.1038/s41467-023-43274-3 ↗
52. 10.1038/s41420-023-01315-210.1038/s41420-023-01315-2 ↗
53. 10.1016/j.ebiom.2018.09.03010.1016/j.ebiom.2018.09.030 ↗
54. 10.1080/13543776.2023.226213810.1080/13543776.2023.2262138 ↗
55. 10.1158/2159-8290.CD-18-036710.1158/2159-8290.CD-18-0367 ↗
56. 10.1001/jamaoncol.2018.625810.1001/jamaoncol.2018.6258 ↗
57. 10.1038/s43018-023-00599-810.1038/s43018-023-00599-8 ↗
58. 10.1016/j.cell.2024.06.01310.1016/j.cell.2024.06.013 ↗
59. 10.1101/gad.348314.12110.1101/gad.348314.121 ↗
60. 10.1038/s41467-020-16142-710.1038/s41467-020-16142-7 ↗
61. 10.1172/JCI12331910.1172/JCI123319 ↗
62. 10.1016/j.celrep.2019.12.03410.1016/j.celrep.2019.12.034 ↗
63. 10.1158/2159-8290.CD-12-059510.1158/2159-8290.CD-12-0595 ↗
64. 10.1038/s41556-021-00812-910.1038/s41556-021-00812-9 ↗
65. 10.3389/fonc.2022.88881010.3389/fonc.2022.888810 ↗
66. 10.1016/j.redox.2013.12.02010.1016/j.redox.2013.12.020 ↗
67. 10.1038/ncomms1020110.1038/ncomms10201 ↗
68. 10.1093/nar/gkac01110.1093/nar/gkac011 ↗
69. 10.1038/nature1415610.1038/nature14156 ↗
70. 10.1093/bioinformatics/bty56010.1093/bioinformatics/bty560 ↗
71. 10.1093/bioinformatics/bts63510.1093/bioinformatics/bts635 ↗
72. 10.1186/1471-2105-12-32310.1186/1471-2105-12-323 ↗
73. 10.1073/pnas.050658010210.1073/pnas.0506580102 ↗