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Leveraging synthetic lethality to uncover potential therapeutic target in gastric cancer

Cancer Gene Therapy volume 31, pages 334–348 (2024)Cite this article

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Abstract

Since trastuzumab was approved in 2012 for the first-line treatment of gastric cancer (GC), no significant advancement in GC targeted therapies has occurred. Synthetic lethality refers to the concept that simultaneous dysfunction of a pair of genes results in a lethal effect on cells, while the loss of an individual gene does not cause this effect. Through exploiting synthetic lethality, novel targeted therapies can be developed for the individualized treatment of GC. In this study, we proposed a computational strategy named Gastric cancer Specific Synthetic Lethality inference (GSSL) to identify synthetic lethal interactions in GC. GSSL analysis was used to infer probable synthetic lethality in GC using four accessible clinical datasets. In addition, prediction results were confirmed by experiments. GSSL analysis identified a total of 34 candidate synthetic lethal pairs, which included 33 unique targets. Among the synthetic lethal gene pairs, TP53-CHEK1 was selected for further experimental validation. Both computational and experimental results indicated that inhibiting CHEK1 could be a potential therapeutic strategy for GC patients with TP53 mutation. Meanwhile, in vitro experimental validation of two novel synthetic lethal pairs TP53-AURKB and ARID1A-EP300 further proved the universality and reliability of GSSL. Collectively, GSSL has been shown to be a reliable and feasible method for comprehensive analysis of inferring synthetic lethal interactions of GC, which may offer novel insight into the precision medicine and individualized treatment of GC.

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Fig. 1: Study overview.
Fig. 2: Identifying driver genes in gastric cancer.
Fig. 3: Clinical and biological characteristics of SL interactions.
Fig. 4: Computational validation of the synthetic lethal interaction between TP53 and CHEK1.
Fig. 5: Estimation of drug response prediction analysis.
Fig. 6: TP53-mutant GC cells exhibited higher sensitivity to CHEK1 inhibitors than TP53-wild-type cell lines.
Fig. 7: CHEK1 inhibitors selectively killed TP53-mutant GC cells by inducing cell apoptosis and could synergize with DNA-damaging anticancer drugs to kill TP53-mutant GC cells.
Fig. 8: Further experiments validated the usability and reliability of GSSL.

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Data availability

All data and access to all databases used in this study are publicly available.

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Funding

This work was supported by The National Natural Science Foundation of China (81972206 and 82173215) and the Natural Science Foundation of Shanghai (22ZR1438800).

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Author notes

  1. These authors contributed equally: Haigang Geng, Ruolan Qian, Yiqing Zhong.

Authors and Affiliations

  1. Department of Gastrointestinal Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Haigang Geng, Yiqing Zhong, Zhongyi Dong, Zizhen Zhang & Chunchao Zhu

  2. State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Ruolan Qian, Xiangyu Tang, Xiaojun Zhang, Linmeng Zhang, Chen Yang & Cun Wang

  3. State Key Laboratory of Genetic Engineering, Human Phenome Institute, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China

    Tingting Li

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Contributions

Conceptualization, HG and RQ; methodology, HG, RQ, YZ, and XZ; validation, LZ, XT, and RQ; formal analysis, YX; investigation, CY, ZD, and TL; resources, SK and LZ; data curation, ZD and TL; writing—original draft preparation, HG, YZ, and LZ; writing—review and editing, WQ and CW; visualization, HG and RQ; supervision, ZZ and CZ; project administration, WQ and CW; funding acquisition, ZZ and CZ. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Zizhen Zhang or Chunchao Zhu.

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Geng, H., Qian, R., Zhong, Y. et al. Leveraging synthetic lethality to uncover potential therapeutic target in gastric cancer. Cancer Gene Ther 31, 334–348 (2024). https://doi.org/10.1038/s41417-023-00706-y

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