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Systems Pharmacology Screening Reveals Targetable Kinases in Supratentorial Ependymoma Kinases in Supratentorial Ependymomas

GSE291213 Mus musculus Expression profiling by high throughput sequencing 8 samples Submitted 2025/12/30 Platform GPL17021
Summary
Ependymomas (EPN) are central nervous system tumors arising from ependymal cells, primarily affecting young children and accounting for 10% of intracranial tumors in children and 4% in adults. Current treatments are limited to surgery and radiotherapy, with chemotherapy providing minimal benefit. Over 70% of supratentorial ependymomas (ST-EPNs) are driven by ZFTARELA fusions (ZFTA-RELAfus), which are associated with poor prognosis. Recent molecular classifications have identified two distinct ST-EPN clusters, E1 and E2, with the E1 cluster predominantly composed of ZFTA-RELAfus tumors. However, effective targeted therapies for these tumors remain lacking. Here, we show that the ZFTA-RELAfus mouse model closely mirrors the human E1 cluster, validating its use for therapeutic discovery. Using this model and a systems polypharmacology approach called Kinome Regularization, we identified mer tyrosine kinase (Mertk) as a key regulator of ZFTA-RELAfus cell viability and proliferation. Genetic depletion or pharmacological inhibition of Mertk reduced cell growth ex vivo and treatment with the clinical-grade Mertk inhibitor significantly suppressed tumor proliferation in vivo. Mechanistically, both human E1 tumors and mouse ZFTA-RELAfus tumors showed elevated Mertk expression, with Mertk inhibition leading to decreased activity of pro-survival pathways. Further, substantial overlap (>80%) between genes upregulated in ZFTA-RELAfus tumors and those downregulated following Mertk inhibition treatment suggest a dependency on Mertkmediated signaling for tumor growth. These findings highlight Mertk as a promising therapeutic target and support the clinical development of Mertk inhibitors for treating human E1 subtype and ZFTA-RELAfus -driven ependymomas.
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Also filed as BioProject PRJNA1232209 and SRA study SRP568159. Searching any of these in the dataset finder brings you back here.

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