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Purpose: Immune checkpoint blockades (ICB) have been successful in gastric cancer (GC). However, the majority of unselected patients with GC fail to respond to ICB. It is crucial to identify precise biomarkers to predict response to ICB. Methods: Gene expression profiling of formalin-fixed and paraffin-embedded GC tissues from 25 patients treated with ICB (pembrolizumab) targeting programmed cell death protein 1 (PD-1) was performed using NanoString (NanoString Technologies). For development of a gene signature to predict response to ICB, differential gene expression analysis with linear regression modeling was performed with area under the curve packages in R. Results: From the analysis, 10 genes were differentially expressed between patients with response and no response to ICB (P<0.01). To identify a biomarker predicting response to ICB, four genes were selected based on |log2 (foldchange)|≥1. After calculating the IMmunotherapy Against GastrIc Cancer (IMAGiC) score, patients were divided into two groups: to be responder and to be non-responder, according to Response Evaluation Criteria in Solid Tumors (RECIST) guidelines. The IMAGiC score was significantly associated with RECIST groups (P=0.0057), Epstein-Barr virus (P=0.048), and tumor mutational load (P=0.023); however, was not significantly correlated with microsatellite instability status (P=0.14) and programmed death ligand 1 (PD-L1) expression (P=0.095). To reproduce IMAGiC with different technology, we retested the results with a quantitative real-time polymerase chain reaction (qRT-PCR) method, and the precision of reproduction of 87.5%. In validation cohort with 17 samples from the ongoing trial with nivolumab, the precision of IMAGiC qRT-PCR was 100%. Conclusion: Our identified gene signatures and proposed IMAGiC model for predicting response to pembrolizumab in patients with GC showed validity.

Combined biomarker for prediction of response to an immune checkpoint inhibitor in metastatic gastric cancer