LTBK-08. Inferring cell type and cell state composition in glioblastoma from bulk DNA methylation profiles using multi-omic single-cell analyses

alexandrossfakianakis shared this article with you from Inoreader LTBK-08. Inferring cell type and cell state composition in glioblastoma from bulk DNA methylation profiles using multi-omic single-cell analyses via # Neuro-Oncology Current Issue Abstract OBJECTIVE Cellular heterogeneity determines tumor phenotype and response to therapy. This is particularly pronounced in glioblastoma (GBM), which is characterized by multiple malignant cell states with distinct proliferation potentials, and different cell types of the microenvironment. Ideally, cellular heterogeneity is characterized using single cell genomic profiling techniques. However, these techniques remain challenging to apply in a diagnostic setting and to large retrospective patient cohorts (TCGA, GLASS, DKFZ and clinical trials). Instead, clinicians routinely support their diagnosis with bulk DNA methylation profiling, which generates robust results from low quality material but does not inform on cellular heterogeneity. We have developed a powerful new computational method to deconvolute bulk DNA methylation data and infer cellular heterogeneity within individual tumors, to support prognostic accuracy and personalized treatme nt decisions. METHODS Using both bulk and single-cell multi-omic datasets, we created a DNA methylation-based reference of cell types (malignant, glial, neuronal, and immune) within GBM tumors, and the state of malignant cells therein (stem-like vs. differentiated-like). Using this reference, our computational approach accurately deconvolutes bulk DNA methylation profiles of individual query samples. RESULTS High deconvolution accuracy of GBM heterogeneity was achieved from frozen and FFPE tissue samples, including those of low quality or purity (Jensen Shannon divergence for composition similarity < 0.05). Our approach eliminates bias derived from the microenvironment, and results in patient stratification that harmonizes the DNA methylation- and RNA-based classifications of GBM. It also reveals the inter- and intra-tumoral links between the genetic, DNA methylation, and transcriptomic components of GBM pathology, and suggests their specific impacts on treatmen t efficacy. To facilitate clinical translation, we created a public website that allows clinicians to infer the relative abundance of different cell states within a tumor at the click of a button. View on Web

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