LGG-58. Understanding the transcriptional heterogeneity of pediatric low-grade gliomas and its implication for tumor pathophysiology. (3rd June 2022)

• Record Type: Journal Article
• Authors: Boisvert, Michelle; Perera, Ashwyn A; Condurat, Alexandra L; Jeang, John; Tsai, Jessica W; Novikov, Dana; Zhou, Kevin; Chacon, Madison; DiGiacomo, Jeromy; Kumbhani, Rushil; Wang, Dayle; Taylor, Michael D; Hansford, Jordan R; Ludlow, Louise; Jabado, Nada; Ligon, Keith L; Beroukhim, Rameen; Bandopadhayay, Pratiti; Jones, David T W
• Is Part Of: Neuro-oncology. Volume 24(2022)Supplement 1
• Page Start: i101
• Page End: i102
• Publication Date: 2022-06-03
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noac079.369
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: Pediatric low-grade gliomas (pLGGs) are the most frequent brain tumors in children and comprise a heterogeneous group of tumors with different locations, histologic subtypes, ages at presentation, and clinical behavior. Tumors frequently respond to treatment with chemotherapy or surgical removal, but they can regrow after a period of quiescence, requiring further therapy. Thus, a deeper understanding of the molecular processes involved in these tumors is required to develop therapeutic strategies that are effective against their disease mechanisms. To better understand the cellular behaviors of this heterogenous group of tumors, we have employed single-cell and single-nuclei RNA sequencing technologies to analyze a large-scale dataset (>250, 000 cells) of pLGGs. Analysis of this data identified a heterogenous population of cell types and cell states, detecting mature and progenitor-like astrocytes and oligodendrocytes, as well as cells exhibiting senescence or cycling programs. Moreover, we identify a significant immune infiltrate, comprised primarily of microglia. In addition to heterogeneity within pLGG tumors, heterogeneity between LGG subtypes represents another layer that stratifies pLGG biology. We performed a compositional analysis of the cell types present in these tumors and compared transcription signatures and gene expression programs across shared cellular populations of histologically and genetically distinct pLGGs. Finally, we optimized our integration and batch correction analyses by using external 293T cells as spike in controls during our single-cell and single-nuclei data generation steps to determine the most suitable method for batch-effect removal. Our analysis of human pLGGs at the single-cell and single-nuclei resolution provides critical insight into the heterogenous biological activities that constitute these tumors.