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Deep learning enables the discovery of a novel cuproptosis-inducing molecule for the inhibition of hepatocellular carcinoma

Abstract

Hepatocellular carcinoma (HCC) is one of the most common and deadly cancers in the world. The therapeutic outlook for HCC patients has significantly improved with the advent and development of systematic and targeted therapies such as sorafenib and lenvatinib; however, the rise of drug resistance and the high mortality rate necessitate the continuous discovery of effective targeting agents. To discover novel anti-HCC compounds, we first constructed a deep learning-based chemical representation model to screen more than 6 million compounds in the ZINC15 drug-like library. We successfully identified LGOd1 as a novel anticancer agent with a characteristic levoglucosenone (LGO) scaffold. The mechanistic studies revealed that LGOd1 treatment leads to HCC cell death by interfering with cellular copper homeostasis, which is similar to a recently reported copper-dependent cell death named cuproptosis. While the prototypical cuproptosis is brought on by copper ionophore-induced copper overload, mechanistic studies indicated that LGOd1 does not act as a copper ionophore, but most likely by interacting with the copper chaperone protein CCS, thus LGOd1 represents a potentially new class of compounds with unique cuproptosis-inducing property. In summary, our findings highlight the critical role of bioavailable copper in the regulation of cell death and represent a novel route of cuproptosis induction.

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Fig. 1: The discovery of novel drug-like compounds with growth inhibitory properties in liver cancer by deep learning.
Fig. 2: The in vitro and in vivo anti-liver cancer activities of LGOd1.
Fig. 3: LGOd1 induces ROS accumulation.
Fig. 4: LGOd1 induces down-regulation of Fe-S cluster proteins.
Fig. 5: LGOd1 induces cuproptosis in HCC cells and xenograft tumors.
Fig. 6: LGOd1 interferes with copper homeostasis but not acting as an ionophore.
Fig. 7: LGOd1 impairs copper homeostasis and induces cuproptosis by targeting CCS.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (32070748, 81902787); Excellent Scientific and Technological Innovation Training Program of Shenzhen (RCYX20210706092040048); Natural Science Foundation of Top Talent of SZTU (Grant No. GDRC202125); Singapore’s National Research Foundation (NRF‐CRP22‐2019‐0003).

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Conceptualization: LYD; Methodology: LYD, JGW, PN, FY, NP; Investigation: FY, LJ, HCZ, JNH, MYH, ZJL, CZ, FTL; Visualization: FY, CBY; Supervision: LYD, JGW, PN; Writing—original draft: FY, LYD; Writing—review & editing: FY, LJ, JGW, LYD.

Corresponding authors

Correspondence to Ji-gang Wang or Ling-yun Dai.

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Competing interests

PN is the inventor of patents related to the CETSA method and is a cofounder and board member of Pelago Biosciences AB. LYD, JGW, FY, LJ, CBY, HCZ and MYH have a patent pending for compound LGOd1. The remaining authors declare they have no competing interests.

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Yang, F., Jia, L., Zhou, Hc. et al. Deep learning enables the discovery of a novel cuproptosis-inducing molecule for the inhibition of hepatocellular carcinoma. Acta Pharmacol Sin 45, 391–404 (2024). https://doi.org/10.1038/s41401-023-01167-7

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