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Imaging regional gene expression variability in brain tumor associated seizures

Jong Woo Lee

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National Institutes of Health (NIH)
Tumor associated seizures (TAS) are present in approximately half of the patients with brain tumors and cause a marked decrease in the quality of life in these patients. Understanding the underlying mechanisms of TAS, crucial in developing new therapies, is hindered by its multifactorial nature, as both imaging and genomic characteristics play key roles. Examining the contribution of neuroimaging and gene expression in TAS in concert will more readily lead to discovery of biologically relevant mechanisms of TAS than by assessment of either modality in isolation. The central hypothesis of this study is that tumor gene expression is associated with TAS in a brain region-specific manner. This study will utilize the genomic and MRI data from carefully curated public repositories: The Cancer Genome Atlas (TCGA), Repository for Molecular BRAin Neoplasia DaTa (REMBRANDT, for validation), and The Cancer Imaging Archive (TCIA). The specific aims are: 1) Using TCGA dataset, identify genes and gene sets that are differentially expressed in patients with versus without TAS. 2) Using TCGA and TCIA datasets, characterize brain regions where gene expression has significant correlation with TAS. Particular focus will be on the expression of SLC7A11 which encodes the system xc- glutamate transporter, which causes a well-established mechanism of TAS through dysregulation of glutamate. The datasets will be assessed for differential expression of other novel genes and gene sets. Newly developed gene expression statistical image mapping tools will be utilized to analyze in what brain region the expression of SLC7A11 and other key genes are significantly associated with TAS, with the hypothesis that SLC7A11 expression have a higher correlation with TAS in the temporal, as compared to the frontal, lobes. Genes that are correlated to TAS because of association with tumor location, but without specific epileptogenic mechanisms, will demonstrate no regions of significance. Incorporating both gene expression and imaging information simultaneously is a novel and powerful method of characterizing TAS. In accordance with the NINDS Benchmarks for Epilepsy Research, completion of this will result in 1) improved identification of tumors that are most likey to respond to anti-glutaminergic treatments, 2) identification of genes and pathways associated with epilepsy; 3) advancement in the understanding of the epileptogenic process of TAS.

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