Takeaways from meta-analysis: indications of combinational treatments for glioblastoma

Patients with brain cancers are diagnosed based on MRI in the clinical setting while molecular signatures offer potential therapeutic targets. The necessity to re-form molecular and imaging information motivated our meta-analysis to decipher the correlation between the MRI-classified tumor locations, gene expression, and protein signatures in GBM.

We analyzed spatial and omics data alongside the assessment of post-translational modifications. We first utilized MRI data to classify GBM into 4 groups. We then integrated imaging groups with RNA-Seq and proteomic data to determine the association between tumor locations, gene signatures, and protein abundance. Furthermore, we scrutinized independent measurements of post-translational modifications in each group of MRI-classified GBM.

The coherent layer of imaging and molecular data collectively showed the dysregulation of cell cycle, ECM organization, immune infiltration or surveillance in all GBM cases regardless of tumor locations. Several neuronal and synaptic-specific genes were differentially altered, indicative of aberrant neuroactivity in GBM. These dysregulated genes and networks provided druggable targets that led to small compounds identification, possessing cytotoxicity against primary GBM and spanning histological boundaries. Our analysis also revealed lesion-specific molecular signatures in each group of GBM, suggesting pathological features uniquely in subgroups of GBM with prognostic or therapeutic potential. Moreover, alterations in post-translational modifications would be noteworthy to explore clinical applications.

Deliverables from our meta-analysis hold the potential to inform therapeutic intervention. Despite heterogeneity in GBM, our findings implicate new directions of emerging treatments that may be used as concomitants to chemo-, radio- or immunological therapies.