A Computational Framework for Characterizing the Melanoma Immune Interactome in Response to Immune Checkpoint Inhibitors 108 views

Immune checkpoint inhibitors (ICIs) are the standard of care in late-stage melanoma, though their efficacy remains limited by refractory response. While prior studies have implicated immune cell interactions as key drivers of ICI response, systematic approaches to directly quantify and compare immune interactions in relation to treatment response remain limited. Here, we present a computational pipeline that combines previously developed methods to infer and analyze immune cell interaction networks using single-cell RNA sequencing data. The pipeline models ligand-receptor communication between cell types as a weighted and directed network and enables quantitative comparison of these networks across biological conditions. From these networks, the pipeline extracts interpretable features, including cell pair communication frequencies and enriched ligand-receptor interactions, which are analyzed alongside relative immune cell abundances using both univariate and multivariate statistical approaches. Applied to melanoma data, the pipeline successfully distinguishes biological groups based on interaction patterns and highlights plausible, literature-supported communication pathways relevant to ICI response. This work demonstrates the utility of the pipeline in capturing meaningful variation in immune communication. This comprehensive framework has the potential to be broadly applicable for investigating immune cell interactions across diverse biological contexts.

MS (Master of Science)

Melanoma; Immunotherapy; Computational Biology; Systems Immunology

English

All rights reserved (no additional license for public reuse)

2025-07-26