Gene signatures for predicting the response to immune checkpoint inhibitor by integrating bulk and single-cell sequence data in patients with bladder cancer.

Abstract

535 Background: Although immune checkpoint inhibitor (ICI) is regarded as a breakthrough in bladder cancer (BC) therapy, only a limited fraction of patients benefit from it. Thus, we aim to establish gene signature (iGS) and develop a model that predicts the response of ICI for the patients with BC. Methods: We collected bulk RNA expression data from BC cohort (GSE176307) receiving ICI and single-cell sequencing data from patients with BC (GSE135337). Single-sample gene set enrichment (ssGSEA) analysis was used to develop predictive iGS. Clinical benefit of ICI was defined as a complete or partial response or stable disease at week 24. The gene ontology (GO) and pathway enrichment analysis were established through DAVID database. Logistic regression model was constructed with iGS and clinical factors. The final model was validated by the K-fold cross validation technique. Results: We integrated bulk RNA expression data and single-cell sequencing data, and identified subset of cells (N= 157) that were associated with the overall survival (OS) of patients receiving ICI. These cells were compared with all other cells, and 85 up-regulated differentially expressed genes (DEGs) were identified. DEGs were mainly enriched in 'antigen processing and presentation of exogenous peptide antigen via MHC class II', 'peptide antigen assembly with MHC class II protein complex' and 'immune response' in the biological process enrichment analysis. High iGS score, that was derived from ssGSEA, was significantly associated with longer OS in multivariate Cox regression analysis (Hazard ratio: 0.40, P = 0.02) Final prediction model for clinical benefit included iGS score [odds ratio (OR) = 3.89], tumor mutation burden (OR = 9.8) and age (OR = 0.93) with accuracy of 0.80 and an area under the receiver operator characteristic curve (AUROC) of 0.77. Conclusions: We showed the potential of iGS from bulk and single cell RNA sequencing for predicting clinical benefit of ICI in BC.