Effective Inhibition of MYC -Amplified Group 3 Medulloblastoma Through Targeting EIF4A1.

Purpose: In medulloblastoma (MB), group 3 (G3) patients with MYC amplification tend to exhibit worse prognosis, thus creating a need for novel effective therapies. As the driver and crucial dependency for MYC -amplified G3-MB, MYC has been proven to be a prospective therapeutic target. Here, we aimed to identify novel effective therapeutic strategies against MYC -amplified G3-MB via targeting MYC translation.
Materials and Methods: Major components of translation initiation complex eIF4F were subjected to MB tumor dataset analysis, and EIF4A1 was identified to be a potential therapeutic target of MYC -amplified G3-MB. Validation was performed through genetic or pharmacological approaches with multiple patient-derived tumor models of MYC -amplified G3-MB in vitro and in vivo. Underlying mechanisms were further explored by Western blot, quantitative real-time PCR and mass spectrometry (MS) analyses.
Results: MB tumor datasets analyses showed that EIF4A1 was significantly up-regulated in G3-MB patients relative to normal cerebella, positively correlated with MYC in G3-MB at transcriptional level and a crucial cancer dependency in MYC -amplified G3-MB cells. Targeting EIF4A1 with a CRISPR/Cas9 approach or small-molecule inhibitor silvestrol effectively attenuated growth in multiple preclinical models of MYC -amplified G3-MB via blocking proliferation and inducing apoptosis. Mechanistically, EIF4A1 inhibition effectively impeded MYC expression at translational level, and its potency was positively associated with MYC level. Whole-proteome MS analysis of silvestrol-treated cells further unveiled other biological functions and pathways influenced by EIF4A1 inhibition.
Conclusion: Our investigation shows that interrupting MYC translation by EIF4A1 inhibition could be a potential effective therapeutic approach when treating patients with MYC -amplified G3-MB.
Competing Interests: The authors declare that there are no conflicts of interest in this work.
(© 2020 Zhao et al.)