EPCO-09. CHARACTERIZING THE GBM CELLULAR LANDSCAPE BY LARGE-SCALE SINGLE-NUCLEUS RNA-SEQUENCING

Abstract Tumor heterogeneity is a well-known hallmark of glioblastoma (GBM). Single-cell RNA-sequencing (scRNA-seq) technologies significantly extended our understanding of GBM intra-tumor heterogeneity. While the landmark scRNA-seq studies were limited by scale, recent technological advances have enabled analyzing increasing numbers of cells. Here, leveraging a large-scale single-nucleus RNA-sequencing (snRNA-seq) dataset containing 457,442 cells (271,444 malignant and 185,995 non-malignant) from 121 IDH-wildtype GBM tumor samples and matching whole-exome/whole-genome sequencing data, we dissected the GBM malignant and tumor microenvironment (TME) compartments with unprecedented resolution and scale. We identified three novel malignant cellular states - Glial progenitor cell-like (GPC-like), Neuron-like and Cilia-like - in addition to the cellular states previously defined in GBM (NPC-like, OPC-like, AC-like and MES-like). Cross-referencing our dataset with published scRNA-seq datasets suggests that GBM states mirror the developmental hierarchy observed in the developing human brain. Functional enrichment analysis demonstrated the heterogeneous nature of pathway and metabolic activities across the spectrum of cellular states. Analysis of the matched DNA-sequencing data revealed novel associations between the occurrence of certain genetic events and the abundance of specific cellular states. Similarly, we dissected the TME and robustly defined cellular states for different TME cell types, underscoring the heterogeneity found also within the TME compartment. Leveraging the large number of samples in our dataset and controlling for intra-tumor cell state frequency exposed three baseline gene expression programs - neuronal activity, glial development and extra-cellular matrix remodeling - termed State-Controlled Profiles (SCPs), that reflect the inter-tumor heterogeneity and are not influenced by the intra-tumor cell state distribution. Integrated analysis of the intra- and inter-tumor heterogeneity components revealed striking associations between certain TME cell types and states, malignant cell states and SCPs, suggesting that an interplay between the TME and malignant cells shapes the frequency of malignant states and baseline expression profile in each tumor.