Identifying tumor type and cell type-specific gene expression alterations in pediatric central nervous system tumors.
Min Kyung LeeNasim AzizgolshaniJoshua A ShapiroLananh N NguyenFred Kolling IvGeorge J ZanazziHildreth Robert FrostBrock C ChristensenPublished in: Nature communications (2024)
Central nervous system (CNS) tumors are the leading cause of pediatric cancer death, and these patients have an increased risk for developing secondary neoplasms. Due to the low prevalence of pediatric CNS tumors, major advances in targeted therapies have been lagging compared to other adult tumors. We collect single nuclei RNA-seq data from 84,700 nuclei of 35 pediatric CNS tumors and three non-tumoral pediatric brain tissues and characterize tumor heterogeneity and transcriptomic alterations. We distinguish cell subpopulations associated with specific tumor types including radial glial cells in ependymomas and oligodendrocyte precursor cells in astrocytomas. In tumors, we observe pathways important in neural stem cell-like populations, a cell type previously associated with therapy resistance. Lastly, we identify transcriptomic alterations among pediatric CNS tumor types compared to non-tumor tissues, while accounting for cell type effects on gene expression. Our results suggest potential tumor type and cell type-specific targets for pediatric CNS tumor treatment. Here we address current gaps in understanding single nuclei gene expression profiles of previously under-investigated tumor types and enhance current knowledge of gene expression profiles of single cells of various pediatric CNS tumors.
Keyphrases
- gene expression
- single cell
- rna seq
- stem cells
- blood brain barrier
- induced apoptosis
- healthcare
- risk factors
- end stage renal disease
- cell cycle arrest
- risk assessment
- childhood cancer
- machine learning
- chronic kidney disease
- young adults
- climate change
- deep learning
- cerebrospinal fluid
- high resolution
- subarachnoid hemorrhage
- neuropathic pain
- artificial intelligence
- bone marrow
- genetic diversity
- data analysis
- pi k akt