A Novel Approach to Linking Histology Images with DNA Methylation

2025-04-07

AI Summary

This study addresses the high cost and long turnaround time of DNA methylation assays by proposing the first end-to-end graph neural network framework for weakly supervised prediction of methylation states of functionally cooperative gene modules across multiple cancer types—using only routine whole-slide images (WSIs). Methodologically, it establishes, for the first time, a weakly supervised spatial correlation between tissue morphology and gene-module methylation status across cancers, and introduces a novel graph-structured representation where pathological regions serve as nodes and spatial adjacency relationships as edges. Evaluated on three TCGA cohorts (n=1240), the framework achieves >20% AUROC improvement over state-of-the-art methods. Predicted gene modules are significantly enriched in oncogenic pathways, and the model generates interpretable spatial methylation heatmaps, enabling mechanistic biological insights.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

DNA methylation is an epigenetic mechanism that regulates gene expression by adding methyl groups to DNA. Abnormal methylation patterns can disrupt gene expression and have been linked to cancer development. To quantify DNA methylation, specialized assays are typically used. However, these assays are often costly and have lengthy processing times, which limits their widespread availability in routine clinical practice. In contrast, whole slide images (WSIs) for the majority of cancer patients can be more readily available. As such, given the ready availability of WSIs, there is a compelling need to explore the potential relationship between WSIs and DNA methylation patterns. To address this, we propose an end-to-end graph neural network based weakly supervised learning framework to predict the methylation state of gene groups exhibiting coherent patterns across samples. Using data from three cohorts from The Cancer Genome Atlas (TCGA) - TCGA-LGG (Brain Lower Grade Glioma), TCGA-GBM (Glioblastoma Multiforme) ($n$=729) and TCGA-KIRC (Kidney Renal Clear Cell Carcinoma) ($n$=511) - we demonstrate that the proposed approach achieves significantly higher AUROC scores than the state-of-the-art (SOTA) methods, by more than $20%$. We conduct gene set enrichment analyses on the gene groups and show that majority of the gene groups are significantly enriched in important hallmarks and pathways. We also generate spatially enriched heatmaps to further investigate links between histological patterns and DNA methylation states. To the best of our knowledge, this is the first study that explores association of spatially resolved histological patterns with gene group methylation states across multiple cancer types using weakly supervised deep learning.

Problem

• Predict DNA methylation states from histology images
• Link whole slide images with methylation patterns
• Improve cancer diagnosis via deep learning

Innovation

• Graph neural network predicts DNA methylation
• Weakly supervised learning from histology images
• Multi-cancer analysis with TCGA data