Back

Identification of a new inhibitor of Ran GTPase with therapeutic value in epithelial ovarian cancer

Boudhraa, Z.; Tian, X.; Ritch, S.; Gam, R.; Kendall-Dupont, J.; Carmona, E.; Provencher, D.; Wu, J. H.; Mes-Masson, A.-M.

2025-03-07 cancer biology
10.1101/2025.03.02.641080 bioRxiv
Show abstract

Compiled research studies suggest that the small GTPase Ran is critical in cancer initiation and progression. Compounds that block Ran activity would be valuable for cancer treatment. Yet, to date, there is no inhibitor proven to be efficient and specific to Ran. Here, by learning lessons from the discovery of KRAS G12C inhibitors, we generated a structural model of the switch II pocket of GDP-bound Ran to identify potential inhibitors of Ran by virtual screening. After in vitro verification, compound M26 was detected. Subsequent hit optimization lead us to identify compound M36 as an inhibitor of Ran with a promising therapeutic value. Binding of M36 to Ran was confirmed by cellular thermal shift assay. The specificity of M36 towards Ran was demonstrated by the evaluation of the active GTP-bound forms of a range of GTPases including Ran, RhoA, Cdc42 and Rac1; and by the expression of a dominant active mutant of Ran. Remarkably, similar to depletion of Ran by siRNA, M36 exhibits a specific toxicity in aneuploid ovarian cancer cells and represses DNA repair systems. In accordance with this, we demonstrated a synergistic relationship between M36 and the FDA approved PARP inhibitor Olaparib. In vivo, M36 presents acceptable pharmacokinetic properties and, more importantly, inhibits the tumor growth of an aggressive epithelial ovarian cancer xenograft model. Clinically relevant, M36 was able to induce cell death in ex vivo EOC patient derived micro-dissected tumor. Overall, our study is the first to provide a small-molecule compound inhibitor of Ran with a promising therapeutic potential.

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.