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Systematic identification of context-dependent gene essentiality in Glioblastoma: The GBM-CoDE platform

Foster, M. T.; Ewing, A.; Frame, M. C. T.; Brennan, P. M.; Khamseh, A.; Beentjes, S. V.; Carragher, N. O.; Semple, C. A.

2025-01-29 bioinformatics
10.1101/2025.01.27.634260 bioRxiv
Show abstract

Glioblastoma (GBM) is a heterogeneous and aggressive brain tumour that is invariably fatal despite maximal treatment. Genetic or transcriptomic biomarkers could be used to stratify patients for treatments, however, pairing biomarkers with appropriate therapeutic targets is challenging. Consequently, therapeutics have not yet been optimised for specific GBM patient subsets. Here we integrate genome-wide CRISPR/Cas9 knockout screening and genetic-annotation data for 60 distinct patient-derived, IDHwildtype, adult GBM cell lines, quantifying the essentiality of 15,145 genes. We describe a novel method using Targeted Learning, to estimate the effect size of GBM-relevant biomarkers on context-dependent gene essentiality (GBM-CoDE). We derive multiple target-biomarker pair hypotheses, which we release in an accessible platform to accelerate translation to biomarker-stratified clinical trials. Two of these (WWTR1 with EGFR mutation/amplification, and VRK1 with VRK2 expression suppression) have been validated in GBM, implying that our additional novel findings may be valid. Our method is readily translatable to other cancers of unmet need.

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"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.