therefore a feasible tool is urgently needed to predict the patients who might benefit from ICIs in clinical practice. Methods:We enrolled 76 patients with advanced NSCLC and treated them with ICIs in three independent cohorts. Using transcriptome data analysis of a training set (n¼35), we constructed a predictive signature consisting of immune-related gene pairs (IRGPs). The predictive signature was first validated in the testing set (n¼20) and then validated in an independent cohort containing 19 patients recruited from the Cancer Hospital/Institute, Chinese Academy of Medical Sciences (CICAMS).Results: Based on a gene expression profile from the GSE93157 database, we proposed the IRGP index (IRGPI): four IRGPs were significantly associated with progression-free survival (PFS) of patients with NSCLC treated with ICIs. We then validated the IRGPI using a test set and its prognostic performance was further verified at various protein levels in an additional independent set. Stratification and multivariate Cox regression analyses revealed that IRGPI was an independent prognostic predictor. Notably, IRGPI exhibited more powerful predictive performance than PD-L1. Further analysis revealed that the IRGPI-low and PD-L1-high subgroups showed the best response to anti-PD-1 immunotherapy.Conclusions: Our study highlights the potential predictive value of IRGPI for responses of ICIs in advanced NSCLC. This signature may be a powerful prognostic tool and help further optimise the use of ICIs.

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