Hong Proj-4

PROJECT SUMMARY/ABSTRACT Seizures are a common, severe neurologic symptom of brain tumors with up to 80% of patients experiencing at least one seizure. Recurrent seizures, termed brain-tumor-related epilepsy (BTRE), develop in half of brain tumor patients and are often refractory to medical management. Poorly controlled epilepsy is the leading risk-factor for long-term disability in brain tumor patients and complicates the course of treatment due to drug toxicities, loss of driving privileges, and decreased quality of life. Further, an emerging body of work investigating neuronal regulation of glioma growth suggests that epileptiform activity promotes tumor proliferation and invasion. Successful intervention to stop seizures in patients would profoundly reduce neurologic morbidity and may halt tumor progression. However, current first-line therapy for BTRE, the drug Leviteracetam, a synaptic release inhibitor, fails to suppress seizures in half of patients. A fundamental shift in our approach to treatment of BTRE is greatly needed. Recent studies on the pathogenesis of tumor-related epilepsy have uncovered novel roles for the brain tumor microenvironment (TME), including tumor-stromal and tumor-neuron interactions that result in bi- directional, synergistic efforts to re-sculpt the molecular structure of the TME, resulting in neuronal dysfunction and a feed-forward loop of loss of inhibition, repeated seizures, and tumor progression. In particular, the perineuronal net (PNN) has been shown to be degraded by gliomas, resulting in dysregulation of inhibitory interneurons in the adjacent cortex. The TME is a novel and potentially powerful therapeutic target in BTRE; however, the molecular and cellular mechanisms connecting tumor microenvironment to clinical seizures in patients remain undefined. To better understand seizure onset in patients with brain tumors, this project will perform digital spatial profiling (DSP), an automated system for multiplexed, spatially-linked RNA transcript and protein expression quantification, and Visium 10x Single-nuclei RNA sequencing on brain tumor specimens to evaluate PNN structure and composition in the TME. The project will then apply machine-learning approaches to analyze these high-dimensional data in order to identify PNN features that relate to a clinical history of seizures. This aim will test the hypothesis that tumor-related PNN degradation in patients is associated with severe neurologic symptoms. It will also result in major development of novel histologic and computational techniques for evaluation of PNN structure and function that can be broadly applied to other, similarly complex biomedical datasets. Completion of these aims will help the Project Leader and research team to better understand how brain pathologies affect PNNs in patients, generate new biological resources and technologies for the study of human PNNs, and provide evidence to support preserving or restoring the PNN as a novel, more effective strategy for treatment of BTRE.