Computational methods for characterizing electrophysiological and genomic profiles of gliomas

About This Grant

1 Malignant gliomas are the most common and deadly form of primary brain tumors in adults. Despite decades of 2 research, their formation and progression mechanisms remain poorly understood, and survival rates have 3 remained unchanged over the past 30 years. These glial tumors include isocitrate dehydrogenase (IDH1) mutant 4 (IDH1mut) and IDH wild-type (IDH1WT) subtypes, each of which presents with unique clinical and histopathological 5 correlates. Prognostic outcomes for IDH1WT tumors are poor, conferring a median survival of less than 14 6 months. In contrast, IDH1mut tumors confer significantly better prognoses, with a median survival of 31–65 7 months after diagnosis. Molecular and genomic studies have revealed that the disparity in survival outcomes 8 between glioma subtypes is primarily attributed to differences in tumor cell proliferation and invasiveness. 9 Emerging evidence, including our own studies, suggests that glioma progression is closely linked to neuronal 10 activity, where interactions between glioma cells and neurons create a vicious cycle that drives tumorigenesis. 11 Despite recent findings on tumor-neuron interactions, there remains a key knowledge gap in glioma biology 12 regarding the electrophysiological profiles of tumor cells and their role in tumor progression. Therefore, the 13 overarching goal of this proposal is to develop computational methods to integrate multi-modal data and 14 systematically link the molecular and electrophysiological states of glioma cells, enabling a deeper understanding 15 of the mechanisms underlying electrophysiological function that contribute to tumor progression. 16 Patch-sequencing (Patch-seq), which couples electrophysiological recordings, morphological analysis, 17 and single-cell RNA-sequencing (scRNA-seq), has unveiled various neuronal subtypes that feature distinct 18 properties. In our preliminary studies, in situ Patch-seq on surgically resected human gliomas to 19 ascertain whether glioma cells exhibit neuronal features. We have identified a unique subset of glioma cells, 20 termed neuronal-like tumor (NLT) cells, which exhibit partial neuronal characteristics. These cells are capable of 21 firing action potentials, mimicking the electrophysiological behavior of neurons, while retaining the morphological 22 features of glial cells. While Patch-seq provides paired electrophysiological and transcriptomic data, technical 23 limitations restrict profiling to hundreds of cells. In contrast, scRNA-seq alone allows the sequencing of millions 24 of cells but lacks corresponding electrophysiological profiles. To bridge this gap, we aim to develop computational 25 models to predict electrophysiological profiles within large scRNA-seq datasets. These models will identify cell 26 types and gene programs underlying electrophysiological function, uncover postsynaptic partners, and reveal 27 spatially colocalized cells of electrophysiologically active NLT cells. Collectively, our preliminary findings have 28 led us to our central hypothesis that understanding how glioma cells disrupt normal brain physiology by mimicking 29 neuronal functions will provide critical insights into their contribution to tumor progression and guide the 30 repurposing of approved neurological and psychiatric drugs that target neural-cancer signaling. we performed