MassIVE MSV000090357

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High-throughput identification of repurposable neuroactive drugs with potent anti-glioblastoma activity

Description

Glioblastoma, the most aggressive primary brain cancer, has dismal prognosis, yet systemic treatment is limited to DNA-alkylating chemotherapies. New therapeutic strategies may emerge from exploring neurodevelopmental and neurophysiological vulnerabilities of glioblastoma. To this end, we here systematically screen repurposable neuroactive drugs in glioblastoma patient surgery material using a clinically concordant and single-cell resolved platform. Profiling >2,500 ex vivo drug responses across 27 patients and 132 drugs identified class-diverse neuroactive drugs with potent anti-glioblastoma efficacy that were validated across model systems. Interpretable molecular machine learning of drug-target networks revealed neuroactive convergence on AP-1/BTG-driven glioblastoma suppression, enabling expanded in silico screening of >1 million compounds with high patient validation accuracy. Deep multi-modal profiling confirmed Ca2+-driven AP-1/BTG-pathway induction as a neuro-oncological glioblastoma vulnerability, epitomized by the antidepressant Vortioxetine synergizing with current standard-of-care chemotherapies in vivo. These findings establish an actionable framework for glioblastoma treatment rooted in its neural etiology. [doi:10.25345/C58C9R82V] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: Glioblastoma, DIA, PRM, Multi-omic profiling

Contact

Principal Investigators:
(in alphabetical order)
Bernd Wollscheid, ETHZ, Switzerland
Submitting User: Sandra

Publications

Lee S, Weiss T, Bühler M, Mena J, Lottenbach Z, Wegmann R, Sun M, Bihl M, Augustynek B, Baumann SP, Goetze S, van Drogen A, Pedrioli PGA, Penton D, Festl Y, Buck A, Kirschenbaum D, Zeitlberger AM, Neidert MC, Vasella F, Rushing EJ, Wollscheid B, Hediger MA, Weller M, Snijder B.
High-throughput identification of repurposable neuroactive drugs with potent anti-glioblastoma activity.
Nat Med. Epub 2024 Sep 20.

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Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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