Learning Directed Acyclic Graphs for Ligands and Receptors Based on Spatially Resolved Transcriptomic Data of Ovarian Cancer

Authors

Shrabanti Chowdhury
Sammy Ferri-Borgogno
Peng Yang
Wenyi Wang
Jie Peng
Samuel C Mok
Pei Wang

Publication Date

3-4-2025

Journal

Briefings in Bioinformatics

DOI

10.1093/bib/bbaf085

PMID

40062614

PMCID

PMC11891659

PubMedCentral® Posted Date

3-10-2025

PubMedCentral® Full Text Version

Post-print

Abstract

To unravel the mechanism of immune activation and suppression within tumors, a critical step is to identify transcriptional signals governing cell-cell communication between tumor and immune/stromal cells in the tumor microenvironment. Central to this communication are interactions between secreted ligands and cell-surface receptors, creating a highly connected signaling network among cells. Recent advancements in in situ-omics profiling, particularly spatial transcriptomic (ST) technology, provide unique opportunities to directly characterize ligand-receptor signaling networks that power cell-cell communication. In this paper, we propose a novel statistical method, LRnetST, to characterize the ligand-receptor interaction networks between adjacent tumor and immune/stroma cells based on ST data. LRnetST utilizes a directed acyclic graph model with a novel approach to handle the zero-inflated distributions of ST data. It also leverages existing ligand-receptor regulation databases as prior information, and employs a bootstrap aggregation strategy to achieve robust network estimation. Application of LRnetST to ST data of high-grade serous ovarian tumor samples revealed both common and distinct ligand-receptor regulations across different tumors. Some of these interactions were validated through both a MERFISH dataset and a CosMx SMI dataset of independent ovarian tumor samples. These results cast light on biological processes relating to the communication between tumor and immune/stromal cells in ovarian tumors. An open-source R package of LRnetST is available on GitHub at https://github.com/jie108/LRnetST.

Keywords

Humans, Female, Ovarian Neoplasms, Ligands, Transcriptome, Tumor Microenvironment, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Receptors, Cell Surface, Signal Transduction, Computational Biology, spatial transcriptomics data, ligand–receptor network, hill climbing, bootstrap aggregation, prior domain knowledge

Published Open-Access

yes

Recommended Citation

Chowdhury, Shrabanti; Ferri-Borgogno, Sammy; Yang, Peng; et al., "Learning Directed Acyclic Graphs for Ligands and Receptors Based on Spatially Resolved Transcriptomic Data of Ovarian Cancer" (2025). Faculty, Staff and Student Publications. 5126.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/5126