CNSC-05. ELECTROPHYSIOLOGICAL PATTERNS OF GLIOMA-INDUCED NEURONAL NETWORK REMODELING ARE CONSERVED ACROSS TUMOR SUBTYPE. (14th November 2022)

• Record Type: Journal Article
• Authors: Aabedi, Alexander; Lipkin, Benjamin; Young, Jacob S; Hinkley, Leighton; Findlay, Anne; Daniel, Andy; Krishna, Saritha; Umbach, Gray; Kaur, Jasleen; Berger, Mitchel S; Molinaro, Annette; Brang, David; Nagarajan, Srikantan; Hervey-Jumper, Shawn L
• Is Part Of: Neuro-oncology. Volume 24(2022)Supplement 7
• Page Start: vii22
• Page End: vii22
• Publication Date: 2022-11-14
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noac209.086
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: Recent evidence indicates that diffuse gliomas engage with neurons at the single-unit and circuit level through differing mechanisms. Certain malignant gliomas form glioma-neuron excitatory glutamatergic synapses and modulate neuron-neuron synapses through activity-dependent paracrine signaling, while others establish glioma-glioma connections via tumor microtubes. It is therefore possible that diffuse gliomas remodel neuronal circuits in a defined and predictable manner and demonstrate distinct electrophysiological profiles with prognostic and therapeutic significance. Here we apply machine learning principles in 140 patients across glioma subtypes to uncover unique electrophysiological features non-invasively via magnetoencephalography (discovery dataset) followed by feature validation using subdural electrocorticography (validation dataset). Following spatial-temporal registration, we fit an elastic net logistic regression classifier to distinguish between power spectra arising from glioma-remodeled cortex and within-subject control conditions. Model significance was determined non-parametrically by re-training each model 1, 000 times with randomly permuted class labels and testing the true phi coefficient against the null distribution. In the discovery dataset, we were able to classify glioma infiltration based on tumor intrinsic neuronal activity (p < 0.05) in 127 patients (90.7%). We identified 30 electrophysiological features which revealed increased power in the delta range (1-4 Hz) and decreased power in the beta range (12-20 Hz) as a unique signature of glioma remodeling (p < 0.05) which was preserved in the validation dataset as well as across WHO 2021 diffuse glioma subtypes. In order to identify gene expression programs and signaling mechanisms that may contribute to glioma-induced remodeling but are potentially not identified in the current clinical classification scheme, we assessed targeted, next generation sequencing and DNA mutations as covariates, which again demonstrated the significance of the delta-beta spectral features. These data support converging mechanisms of glioma-induced neuronal network remodeling across tumor subtypes, setting the stage for novel therapies such as neuromodulation.