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

2024-10-23

arXiv.org

AI Summary

This study addresses the challenges of high intertumoral heterogeneity and poorly understood progression mechanisms in glioblastoma (GBM) by proposing a novel multimodal deep learning framework that jointly models whole-slide histopathology images and RNA-seq data for the first time. We innovatively design a graph neural network encoder for RNA-seq sequences and construct an image–molecular joint embedding space to systematically link histomorphological patterns with genetic profiles. The model identifies multiple previously unreported GBM driver genes and defines genetically informed subtypes that are both prognostically discriminative and clinically interpretable. It substantially enhances the pathological interpretability of molecular subtyping. Our work establishes an explainable, multimodal biomarker framework for precision GBM classification and uncovers promising novel therapeutic targets.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Glioblastoma
• Genetic Causes
• Potential Therapeutic Targets

Innovation

• Deep Learning
• Glioblastoma Genomics
• Target Discovery