Uncovering the Genetic Basis of Glioblastoma Heterogeneity through Multimodal Analysis of Whole Slide Images and RNA Sequencing Data

TL;DR

This study uses multimodal deep learning to analyze whole-slide images and RNA sequencing data, uncovering genetic factors that contribute to glioblastoma heterogeneity and progression, offering potential therapeutic targets.

Contribution

It introduces novel methods to encode RNA-seq data and combines image and genetic analysis to identify genes linked to glioblastoma heterogeneity.

Findings

Identification of novel genes associated with glioblastoma

Insights into genetic profiles influencing tumor progression

Potential targets for therapeutic intervention

Abstract

Glioblastoma is a highly aggressive form of brain cancer characterized by rapid progression and poor prognosis. Despite advances in treatment, the underlying genetic mechanisms driving this aggressiveness remain poorly understood. In this study, we employed multimodal deep learning approaches to investigate glioblastoma heterogeneity using joint image/RNA-seq analysis. Our results reveal novel genes associated with glioblastoma. By leveraging a combination of whole-slide images and RNA-seq, as well as introducing novel methods to encode RNA-seq data, we identified specific genetic profiles that may explain different patterns of glioblastoma progression. These findings provide new insights into the genetic mechanisms underlying glioblastoma heterogeneity and highlight potential targets for therapeutic intervention. Code and data downloading instructions are available at: https://github.com/ma3oun/gbheterogeneity.