Spatial analysis of malignant-immune cell interactions in the tumor microenvironment using topological data analysis

Area of Science:

• Computational biology
• Cancer research
• Immunology

Background:

• Spatial interactions between tumor and immune cells are crucial for cancer progression and treatment response.
• Existing analytical tools struggle to extract interpretable spatial features linking these interactions to patient outcomes.

Purpose of the Study:

• To develop a novel framework for extracting interpretable spatial features of malignant-immune cell interactions.
• To link these spatial features to patient survival outcomes in diffuse large B-cell lymphoma.

Main Methods:

• Integration of topological data analysis (TDA) with statistical methods.
• Introduction of Topological Malignant Region (TopMR) for objective definition of malignant cell regions.
• Quantification of global infiltration and local interactions using signed distance-density (sDD) space.

Main Results:

• The framework successfully extracted interpretable spatial features from multiplex immunofluorescence images.
• Two-stage hierarchical clustering identified patient stratifications based on spatial interaction patterns.
• These patterns were significantly associated with survival outcomes in diffuse large B-cell lymphoma.

Conclusions:

• The proposed framework offers an end-to-end pipeline for region-aware spatial analysis in cancer research.
• This approach captures biologically meaningful spatial patterns linked to patient survival.
• It provides a valuable tool for understanding tumor immunobiology and improving clinical interpretation.