Integrating genome-wide association study data with single-cell `omics' to reveal the cellular effectors of glioblastoma predisposition

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults and remains uniformly lethal, with median survival <15 months despite intensive therapy. High-dose ionizing radiation remains the only confirmed environmental risk factor, but the first stages of tumor initiation appear to occur decades prior to clinical diagnosis. Although the etiology of GBM remains poorly understood, genome-wide association studies (GWAS) have identified seventeen independent risk alleles associated with heritable predisposition. These alleles are non-coding regulatory variants, and their impact on disease risk reflects the varied gene expression patterns and epigenetic programs of the underlying cell types and states involved in tumor initiation and maintenance. We recently performed stratified Linkage Disequilibrium Score Regression (SLDSR) analysis to partition the heritability of GBM across tissue types, observing significant enrichment in deep gray matter structures of the brain and in lymphocytes at the interface of innate and adaptive immunity. However, SLDSR is limited by the underlying reference datasets employed. We hypothesize that a subset of cell states/types comprising the tumor, the tumor microenvironment, and the developing brain will be enriched for GBM heritability, reflecting their critical roles in cellular transformation and tumor establishment. Our OVERALL GOAL is to incorporate single-cell transcriptomic and epigenomic datasets into an improved platform for partitioning GBM heritability at unprecedented resolution, revealing both early neurodevelopmental and late pathologic cell types that serve as effectors of GBM predisposition. This collaborative project will leverage a wealth of existing data resources to test our hypothesis through three complementary aims. In Aim 1, we will partition the genetic heritability of GBM across meta-modules of malignant cell states and microenvironmental cell types generated from harmonized snRNA-seq and snATAC-seq datasets in order to identify the cell populations through which genetic predisposition precipitates GBM formation. In Aim 2, we will overcome the lack of ancestral diversity in GBM profiling data by performing multiome snRNA-seq and snATAC-seq of GBM cells from 24 African American patients, evaluate the impact of ancestry on meta-module construction, and partition GBM heritability across cell states/types. In Aim 3, we will use existing snRNA-seq and snATAC-seq datasets from fetal brain tissues to partition GBM heritability across early cell lineages in order to examine the neurodevelopmental origins of GBM. Novel cell states and tumor cell subpopulations enriched for GBM heritability in these aims will be further characterized and validated using spatially-resolved, in situ RNA sequencing of patient tumors. Despite extensive molecular and epidemiologic research, the origin of GBM remains poorly defined. This proposal represents an innovative approach for integrating existing GWAS and single-cell datasets to reveal potentially unanticipated cell populations involved in GBM formation. Such cell populations may serve as targets for GBM interception or for the development of more effectively targeted therapies. Project Number: 1R01CA295845-01A1 | Fiscal Year: 2025 | NIH Institute/Center: National Cancer Institute (NCI) | Principal Investigator: Quinn Ostrom | Institution: DUKE UNIVERSITY, DURHAM, NC | Award Amount: $1,882,680 | Activity Code: R01 | Study Section: Basic Mechanisms of Cancer Health Disparities Study Section[BMCD] View on NIH RePORTER: https://reporter.nih.gov/project-details/11225209