Strength of spatial correlation between structural brain network connectivity and brain-wide patterns of proto-oncogene and neural network construction gene expression is associated with diffuse glioma survival

Abstract

Like other forms of neuropathology, gliomas appear to spread along neural pathways. Accordingly, our group and others have previously shown that brain network connectivity is highly predictive of glioma survival. In this study, we aimed to examine the molecular mechanisms of this relationship via imaging transcriptomics. We retrospectively obtained presurgical, T1-weighted MRI datasets from 669 adult patients, newly diagnosed with diffuse glioma. We measured brain connectivity using gray matter networks and coregistered these data with a transcriptomic brain atlas to determine the spatial co-localization between brain connectivity and expression patterns for 14 proto-oncogenes and 3 neural network construction genes. We found that all 17 genes were significantly co-localized with brain connectivity (p < 0.03, corrected). The strength of co-localization was highly predictive of overall survival in a cross-validated Cox Proportional Hazards model (mean area under the curve, AUC = 0.68 +/- 0.01) and significantly (p < 0.001) more so for a random forest survival model (mean AUC = 0.97 +/- 0.06). Bayesian network analysis demonstrated direct and indirect causal relationships among gene-brain co-localizations and survival. Gene ontology analysis showed that metabolic processes were overexpressed when spatial co-localization between brain connectivity and gene transcription was highest (p < 0.001). Drug-gene interaction analysis identified 84 potential candidate therapies based on our findings. Our findings provide novel insights regarding how gene-brain connectivity interactions may affect glioma survival.

Keywords: MRI; connectome; glioma; imaging transcriptomics; transcriptome.

Figure 1.. Glioma Survival Data.

A: Median overall survival for our cohort of glioma patients was 81 months. B: The mean cross-validated, time dependent area under the curve for predicting survival from imaging transcriptomic relationships was 0.68 +/− 0.01 for Cox PH and 0.97 +/− 0.06 for random forest (RF). Random forest significantly outperformed Cox PH (p < 0.001, corrected).

Figure 2.. Bayesian Network Path Analysis.

Causal relationships are represented by directed arrows between imaging transcriptomes and glioma survival. The gene labeled nodes represent the strength of the relationship between the gene and brain connectivity, not gene expression itself. The direction of the coefficient is represented by a + for direct and − for inverse.

Figure 3.. Gene Ontology and Drug-Gene Interactions.

A: Gene ontology analysis of the genes whose relationship with brain connectivity was predictive of glioma survival. B: Drug-gene interaction results for these same genes. The magnitude of the Query Score is represented in the height of the gray box to the right of the drug name.