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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月9日

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  • Huancheng Zeng,
  • Rendong Zhang,
  • Qiongzhi Jiang,
  • Jundong Wu,
  • Zhemin Zhuang,
  • Yutong Fang

摘要

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.