Abstract AIMS Glioblastoma (GBM) is a heterogeneous and aggressive brain tumour that is invariably fatal despite maximal treatment. GBM-relevant ‘biomarkers’ (such as known driver-mutations or cell-states) could be used to stratify patients for treatments. However, pairing biomarkers with appropriate therapeutic ‘targets’ is challenging. If a gene is more essential in GBM cells which contain such a biomarker, inhibiting the gene product may be therapeutically useful in GBM tumours where that biomarker is present. METHOD We describe a novel computational tool which distils new translational insights from existing disparate large- scale biological data (genomics, transcriptomics and functional genomics data from 60 distinct patient-derived, IDH-wildtype, adult GBM cell lines) using Targeted Learning (a subfield of mathematics and causal machine learning) to systematically identify context-dependent gene essentiality in Glioblastoma (GBM-CoDE). RESULTS We derived multiple target-biomarker pair hypotheses, two of which (WWTR1 is more essential in EGFR- mutated/amplified GBM, and VRK1 is more essential in VRK2-methylated GBM) have recently been published by external groups, implying that our additional novel findings may be valid. We developed an interactive plat- form to make our findings accessible to other researchers to prioritise pairs for experimental validation and subsequent translation. CONCLUSIONS GBM-CoDE represents a potent new approach to solving the problem of developing patient-tailored therapeutics in GBM, made possible with recent innovations in computational biology. While the majority of these target- biomarker hypotheses have yet to be experimentally validated, our results may in time accelerate effective translation to biomarker stratified clinical trials in GBM. Our method is translatable to other cancers of unmet need.

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