Shashvath Venkatesh

Ivy Neurological Sciences Internship Program

Glioblastoma (GBM) is an aggressive and lethal form of brain cancer, characterized by rapid tumor growth and invasion, necessitating improved treatment strategies. Non-germline genetically engineered models (nGEM) have been developed to faithfully replicate the molecular and cellular features of GBM in mice, enabling researchers to study disease mechanisms and evaluate therapeutic interventions. The Collaborative Cross is a collection of mice generated by cross breeding several founding mouse strains to create a genetically diverse population of mice. To unravel the complex relationship between host genomics and GBM growth rate, we implanted an nGEM glioma model into several histocompatible strains of genetically diverse mice and observed a wide range of tumor growth rates, highlighting the intricate interplay between the host genome and tumor aggressiveness. The purpose of this study is to validate spatial transcriptomics and single nuclei gene expression analyses of fast and slow growing nGEM tumors using immunofluorescence, a widely used technique for visualizing specific proteins in tissues. Six carefully selected tumor markers (GFAP, Olig2, PDGFRa, HIF1alpha, CD3, IBA1), based on their relevance to GBM biology and previous research, were examined to determine if protein-level expression detected by immunofluorescence correlates with transcript-level expression, observed in single nuclei expression and spatial transcriptomic analyses. In addition, we tested for the functional state of cellular senescence using an SA-𝜷-Gal assay. This study will help to confirm the identification and characterization of individual tumor cells and functional states within the tumor and the surrounding complex microenvironment. Through the integration of these techniques, this project aims to uncover the intricate relationships between host genomics, tumor growth rate, and protein expression patterns in GBM. Understanding the impact of host genomics on GBM growth rate has profound implications for precision medicine, facilitating the development of personalized treatment strategies tailored to the unique genetic characteristics of individual patients.