Ligand-receptor interaction profiling as a predictive biomarker for anti-PD-1 therapy response in melanoma.
Cell-to-cell communication through ligand-receptor (LR) interactions can fundamentally shape the tumor microenvironment and immune responses, but the full spectrum of these interactions in anti-PD-1 therapy remains unexplored. We developed a predictive model for anti-PD-1 therapy responses incorporating 2,705 LR pairs across 121 melanoma samples. Using a random forest classifier, our model achieved robust accuracy in the training and test datasets as well as in two independent external validation cohorts. Feature importance analysis revealed nine key LR pairs with substantial predictive power, including both known immune checkpoint molecules and novel interaction pairs. The genes comprising these top-ranking pairs were significantly enriched in tumor-related pathways, particularly MAPK and PI3K/AKT signaling pathways. Importantly, our systematic LR profiling approach identified previously uncharacterized ligand-receptor interactions that may represent alternative therapeutic targets beyond the established PD-1/PD-L1 axis. These findings demonstrate the clinical utility of integrated LR expression analysis for predicting anti-PD-1 therapy responses and reveal potential novel biomarkers and combination therapy targets that could expand treatment options for immunotherapy-resistant patients.