Characterizing subtype-specific alternative splicing events in pediatric high-grade gliomas
Mentor Name: Yi Xing
Pediatric high-grade glioma (pHGG) is a highly aggressive pediatric brain tumor with poor survival rates and limited treatment options. pHGG is classified into subtypes based on histone H3 mutations (H3 K27M, H3 G34R/V, and wild-type), which influence alternative splicing (AS) and may generate tumor-specific protein isoforms, presenting immunotherapy opportunities. This project aims to compare AS events and antigen profiles across three pHGG subtypes to assess their therapeutic potential, addressing: 1) What significant AS events distinguish pHGG subtypes? and 2) How do gene expression differences drive splicing variation in tumors? Ms. Grace Zhu (Student) is actively involved in this study. Under the direction of Dr. Yi Xing (Mentor), Grace has been analyzing short-read RNA sequencing (RNA-seq) data to identify differences in gene expression and AS across pHGG subtypes, using innovative bioinformatics tools to identify significant molecular patterns. Her preliminary analysis revealed interesting patterns in differential gene expression and splicing between subtypes, potentially providing insights into their molecular distinctions and prompting deeper functional and therapeutic exploration. For this ALSF POST project, Grace will perform further analysis of AS and gene expression differences across pHGG subtypes. This work will leverage computational biology approaches, utilizing both established and custom computational pipelines to process, analyze, and integrate these datasets. Grace conducted preliminary analyses with a limited and publicly available dataset. To expand on these results, our lab has sequenced cell lines derived from patients at the Children’s Hospital of Philadelphia using Oxford Nanopore Technologies long-read RNA-seq. Long-read data will enable identification of full-length transcript isoforms and discovery of novel, complex splicing events with immunotherapy potential that are not resolvable with short-read data. This information will crucially provide context for the effects on gene function, facilitating interpretation of differential subtype splicing events. Grace will integrate the short- and long-read RNA-seq data to obtain a comprehensive understanding of how AS contributes to the molecular characteristics of pHGG subtypes. By the end of the summer, the goal is for Grace to 1) globally characterize gene expression and splicing trends/differences between the three pHGG subtypes, and 2) identify specific, interesting splicing events that are likely to affect cancer, by integrating short- and long-read RNA-seq data. Finally, Grace will nominate specific splicing events for future experimental validation.
