Learning directed acyclic graphs for ligands and receptors based on spatially resolved transcriptomic data of ovarian cancer

• Published in: Briefings in bioinformatics Vol. 26; no. 2
• Main Authors: Chowdhury, Shrabanti, Ferri-Borgogno, Sammy, Yang, Peng, Wang, Wenyi, Peng, Jie, C Mok, Samuel, Wang, Pei
• Format: Journal Article
• Language: English
• Published: England Oxford Publishing Limited (England) 04.03.2025; Oxford University Press
• Subjects: Biological activity; Case Study; Cell interactions; Communication; Computational Biology - methods; Datasets; Female; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Graph theory; Humans; Immune response; Ligands; Ovarian cancer; Ovarian Neoplasms - genetics; Ovarian Neoplasms - metabolism; Ovarian Neoplasms - pathology; Receptors; Receptors, Cell Surface - genetics; Receptors, Cell Surface - metabolism; Signal Transduction; Statistical methods; Stromal cells; Transcriptome; Transcriptomics; Tumor Microenvironment; Tumors; hill climbing; bootstrap aggregation; spatial transcriptomics data; prior domain knowledge; ligand–receptor network
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbaf085

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.