Hypergraph-driven spatial multimodal fusion for precise domain delineation and tumor microenvironment decoding
Chengyang Zhang1, Xulong Li2, Bo Li3
1School of Big Data and Software Engineering, Chongqing University, Chongqing, China.
Communications Biology
|December 1, 2025
Summary
HAST, a new tool, precisely maps spatial domains in tumors using multimodal data. This advances understanding of the tumor microenvironment and aids cancer research.
Area of Science:
- Oncology
- Computational Biology
- Bioinformatics
Background:
- Spatial transcriptomics offers insights into tumor microenvironment complexity.
- Accurate spatial domain delineation is crucial but challenging for existing methods.
- Current techniques struggle with single-view data and complex spot relationships.
Purpose of the Study:
- To develop a novel tool, HAST, for precise spatial domain delineation and tumor microenvironment decoding.
- To overcome limitations of existing methods by integrating multimodal spatial data.
- To enhance understanding of spatial heterogeneity and cellular interactions within tumors.
Main Methods:
- HAST utilizes a hypergraph-driven approach to fuse gene expression, spatial coordinates, and histological features.
- It constructs local hypergraphs modeling many-to-many spatial relationships, aggregated into a global hypergraph.
- A hypergraph convolutional network with self-supervised contrastive learning learns fused representations.
Main Results:
- HAST accurately delineates spatial domains and identifies domain-associated genes across multiple datasets.
- Performance benchmarks show HAST outperforms state-of-the-art methods.
- Functional enrichment analyses reveal biologically relevant pathways and novel tumor microenvironment insights.
Conclusions:
- HAST provides a robust framework for decoding tumor spatial complexity using multimodal data.
- The tool enables precise spatial omics analyses in cancer research.
- HAST advances the study of tumor microenvironments and cellular interactions.
