# Ligand-receptor pair-based signature score derived from on-treatment tumor specimens predicts immune checkpoint blockade response in metastatic melanoma

## TL;DR

This study shows that a new score based on ligand-receptor interactions in tumor samples can predict how well patients with metastatic melanoma will respond to immune therapy.

## Contribution

A novel ligand-receptor pair-based signature score (LRPS) derived from on-treatment tumor samples is introduced to predict immune checkpoint blockade response.

## Key findings

- Seven ligand-receptor pairs were identified as significantly associated with treatment outcomes.
- LRPS achieved an AUC exceeding 0.80 in predicting clinical outcomes across four independent cohorts.
- High-LRPS patients had better response rates, progression-free survival, and overall survival compared to low-LRPS patients.

## Abstract

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. In this study, we analyzed publicly available transcriptomic and clinical datasets comprising 144 patients and 168 on-treatment tumor samples from five independent cohorts (GEO: GSE120575, GSE115821, GSE168204; BioProject: PRJEB23709; dbGaP: phs001919.v1.p1; EGA: EGAD00001005738). 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 strong predictive power, achieving an area under the ROC curve (AUC) exceeding 0.80 in four independent cohorts. Patients in the high-LRPS group exhibited higher ICB response rates (up to 76.2%) and significantly better progression-free survival (PFS) and overall survival (OS) compared with the low-LRPS group. 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.

The online version contains supplementary material available at 10.1007/s12672-026-04396-4.