Ligand-receptor pair-based signature score Derived from On-treatment Tumor Specimens Predicts Immune Checkpoint Blockade Response in Metastatic Melanoma

Immune checkpoint blockade (ICB) therapy has transformed the treatment landscape for metastatic melanoma, yet predicting therapeutic response remains a significant challenge. This study hypothesizes that coordinated ligand-receptor (LR) interactions within the tumor microenvironment (TME) critically influence ICB efficacy and proposes that a novel LR pair-based signature score (LRPS) derived from on-treatment samples can predict clinical outcomes. Using transcriptomic data from five independent cohorts, we identified seven LR pairs (FLT3-FLT3LG, LY9-LY9, CD5-CD5, CD40LG-ITGA2B/ITGB3, APP-CD74, TNFRSF17-TNFSF13, FCER2-ITGAV/ITGB3) significantly associated with treatment outcomes. LRPS demonstrated significant predictive power, achieving an area under the curve (AUC) exceeding 0.8 in four cohorts. Based on the LRPS signature, subjects were divided into high- and low-scores groups using the mean score. ICB response rates were higher in the high-scoring cohort subjects than the low-scoring subjects. Patient with high scores tended to have better survival outcomes than did those with low scores. In conclusion, we identified and verified an LRPS signature that provides a theoretical basis for applying such signatures derived from on-treatment tumor samples to predict therapeutic responses to ICB therapies.