STARComm Scalably Detects Emergent Modules of Spatial Cell-Cell Communication in Inflammation and Cancer.

Area of Science:

• Spatial biology
• Computational pathology
• Systems immunology

Background:

• Cell-cell communication is crucial for tissue function but challenging to map spatially.
• Existing methods struggle to link intercellular communication networks to clinical outcomes.

Purpose of the Study:

• Introduce STARComm, a scalable computational method for identifying Multicellular Communication Interaction Modules (MCIMs).
• Apply STARComm to analyze spatial transcriptomics data across diverse human cancers and diseases.
• Establish a framework linking spatial communication to clinical outcomes and therapeutic targets.

Main Methods:

• Developed STARComm to detect spatially co-located receptor-ligand activity from high-plex spatial transcriptomics.
• Applied STARComm to a large atlas of over 14 million cells from 8 cancer types.
• Utilized STARComm on minor salivary gland biopsies from patients with chronic graft-versus-host disease (GVHD).

Main Results:

• Identified 24 conserved and tumor-specific MCIMs in cancers, including a fibro-immune module associated with immunotherapy resistance.
• Discovered three salivary gland MCIMs in chronic GVHD predictive of patient survival.
• Highlighted druggable axes (CXCL12-CXCR4, CCL5-SDC4) in GVHD with existing FDA-approved therapeutics.

Conclusions:

• STARComm provides the first outcome-linked spatial communication framework in human disease.
• Spatial profiling of peripheral tissues, like minor salivary glands, can predict patient outcomes years in advance.
• STARComm enables scalable biomarker discovery, drug targeting, and advances spatial medicine for clinical translation.