Glioblastoma Proteomics: Uncovering Patient-Specific Drug Responses
See how a Gillies McIndoe Research Institute researcher used Omics Playground for glioblastoma proteomics, uncovering patient-specific drug responses.
Goal
Glioblastoma is a highly aggressive and treatment-resistant brain tumor with limited therapeutic options. As part of her PhD research, Freya Weth investigated whether repurposed
medicines could be combined to create more effective treatments in patient-derived 3D glioblastoma models.
She used proteomics to understand whether the effect of a drug combination could be predicted from its individual components, and whether different patient samples responded through the same molecular mechanisms.
Challenges
Analysing a complex proteomics dataset across untreated samples, four single-drug treatments, a predicted additive effect, and a four-drug combination.
Managing multiple treatment comparisons and research questions through a manual R-based workflow.
Addressing missing values and strong patient-to-patient variation to focus analysis on treatment-driven effects.
Moving from cohort-level findings to pathway- and protein-level insights for individual patient-derived models.
Results with Omics Playground
Identified a four-drug repurposed combination that achieved efficacy across all eight patient-derived glioblastoma models in this study.
Showed that the observed molecular effects of the combination could not be predicted by summing the effects of the individual drugs.
Revealed stronger downregulation of the TCA cycle, oxidative phosphorylation, and electron transport chain in the combination condition than predicted by the additive model.
Identified distinct molecular response patterns across models, including a subset with elevated resistance-associated proteins.