Neoadjuvant Chemotherapy Improves the Immunosuppressive Microenvironment of Bladder Cancer and Increases the Sensitivity to Immune Checkpoint Blockade

Abstract

Although tumor immune microenvironment plays an important role in antitumor therapy, few studies explored the gene signatures associated with the tumor immune microenvironment of bladder cancer after neoadjuvant chemotherapy. We examined and analyzed differentially expressed genes from 9 patients with stage I-III bladder cancer by RNA immune-oncology profiling platform. After neoadjuvant chemotherapy, the expressions of 43 genes in 19 pathways and 10 genes in 5 pathways were upregulated and downregulated, respectively. Neoadjuvant chemotherapy also promoted the expression of genes related to the activation of antitumor immune responses and decreased the expression of genes related to tumor proliferation pathways. In addition, neoadjuvant chemotherapy improved tumor response to immune checkpoint blockade. Furthermore, this study also identified several genes that can be used to predict the efficacy of neoadjuvant chemotherapy and their possible molecular mechanisms. In conclusion, neoadjuvant chemotherapy may promote the activation of antitumor effects, improve the suppressive tumor immune microenvironment, and increase the sensitivity of bladder cancer to immune checkpoint blockade.

Figure 1

Transcriptomic signatures of bladder cancer in the immune microenvironment before and after neoadjuvant chemotherapy. (a) Heatmap showing the differentially expressed genes in bladder cancer tissue samples. (b) Bar plot showing the differentially expressed genes with significant fold changes. (c) Gene ontology (GO) analysis. (d) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. (e) Hierarchical clustering heatmap showing the gene set variation analysis (GSVA) of gene signatures. (f) Bar plot showing immune infiltration genes analyzed by single-sample gene set enrichment analysis (ssGSEA). (g) Hierarchical clustering heatmap for innate anti-PD-1 resistance signature (IPRES). (h) Bar plot showing the score and fold change of T cell-inflamed gene expression profile (GEP). P value was calculated by the Wilcoxon rank-sum test or described in the panel. Differences were found to be statistically significant at ^∗P < 0.05 and ^∗∗P < 0.01.

Figure 2

Identification of differentially expressed immune-related genes related to efficacy before neoadjuvant chemotherapy. Seven cases of bladder cancer tissues before neoadjuvant chemotherapy were divided into a favorable prognosis group (n = 4) and a poor prognosis group (n = 3) according to the final curative effect. (a) Heatmap showing the differentially expressed genes. (b) Bar plot showing the differentially expressed genes with significant fold change. (c) Hierarchical clustering heatmap showing the gene set variation analysis (GSVA) of gene signatures. (d) Chord diagram showing the top 5 enriched KEGG terms for 6 differentially expressed genes. (e) The Spearman correlation analysis between therapeutic effect score and monocyte. (f) The Spearman correlation analysis between therapeutic effect score and neutrophil. (g) The Spearman correlation analysis between pathological score and Th1 cell. (h) The pathway interaction network showing the relationship with pathological score and therapeutic effect score by IPRSE analysis. P values in the (a) and (b) subfigures were calculated by the Wilcoxon rank-sum test, while P values in the (e–h) subfigures were calculated by Spearman correlation analysis. Differences were found to be statistically significant at ^∗P < 0.05.

Figure 3

IHC of differentially expressed immune-related genes before and after neoadjuvant chemotherapy. The tumor tissues from patients before neoadjuvant chemotherapy were stained for (a) LAG3 and VEGFA and, after neoadjuvant chemotherapy, were stained for (b) VEGFA and TNFSF14. Magnifications: ×400.

Figure 4

Identification of differentially expressed immune-related genes related to efficacy after neoadjuvant therapy. Seven cases of bladder cancer tissues after neoadjuvant chemotherapy were divided into the favorable prognosis group (n = 3) and the poor prognosis group (n = 4) according to the final curative effect. (a) Heatmap showing the differentially expressed genes with significant fold change. (b) Bar plot showing the differentially expressed genes with significant fold change. (c) Gene ontology (GO) analysis. (d) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. (e) Bar plot showing immune infiltration genes analyzed by single-sample gene set enrichment analysis (ssGSEA). (f) The Spearman correlation analysis between pathological score and activated/immature B cell. (g) The Pearson correlation analysis between therapeutic effect score and neutrophil. P values in the (a), (b), and (e) subfigures were calculated by the Wilcoxon rank-sum test (two-tailed). The P values in the (f) and (g) subfigure were calculated by Spearman correlation analysis and Pearson correlation analysis, respectively. Differences were found to be statistically significant at ^∗P < 0.05.