EPCO-48. STEMNESS SIGNATURE STRATIFIES GLIOMAS WITH CLINICAL IMPORTANCE AND REVEALS A PROLIFERATIVE PHENOTYPE IN SINGLE CELL DATA

Abstract

Abstract Gliomas are the most common central nervous system neoplasm and despite the past significant progress, its diagnostics faces suboptimal classification which impacts patient management. The stem cell-like phenotype of various cancers is correlated with the worst overall prognosis. We propose a Stemness prediction model based on gene expression signatures of neural progenitors that can be used to measure the dedifferentiation state (or Stemness) of glioma samples. To built the model, publicly available single-cell RNA sequencing data was used to identify gene expression from the fetal astrocyte (AST) population. Subpopulations of interest were identified through the expression of marker genes. We applied a one-class logistic regression to built the prediction model using the AST population. The model was applied to glioma bulk transcriptomic data to generate an fetal astrocyte stemness index (ASTsi). The ASTsi was able to stratify gliomas based on grade, histology, and molecular subtypes. Grade 4, glioblastoma, IDHwt, and the mitochondrial and proliferative functional subtypes had the highest stemness. When applied to longitudinal samples we observed an increase of ASTsi in IDHmut recurrent and a decrease in IDHwt recurrent tumors, compared to primary samples. Additionally, we applied the model to single-cell RNAseq of adult IDHwt glioblastomas and found clusters of high-stemness cells (ASTsi > 0.8). A differential gene expression combined with pathway analysis between high- and low-stemness cells revealed cell cycle, DNA repair mechanisms and histone modifications upregulated in the high-stemness population. The balance between histone methylation and demethylation may be directly related to the phenotype of these cells. More in-depth analysis of these genes and pathways are being carried out and may provide important information about the oncogenesis and phenotypic characterization of glioma stemness. Our stemness prediction model stratified glioma samples by pathological and molecular features and revealed tumor subpopulations with distinct stemness degree in gliomas IDHwt.