The potential crosstalk between tumor and plasma cells and its association with clinical outcome and immunotherapy response in bladder cancer

Abstract

Abstract Background Although immunotherapy is effective in improving the clinical outcomes of patients with bladder cancer (BC), it is only effective in a small percentage of patients. Intercellular crosstalk in the tumor microenvironment strongly influences patient response to immunotherapy, while the crosstalk patterns of plasma cells (PCs) as endogenous antibody-producing cells remain unknown. Here, we aimed to explore the heterogeneity of PCs and their potential crosstalk patterns with BC tumor cells. Methods Crosstalk patterns between PCs and tumor cells were revealed by performing integrated bulk and single-cell RNA sequencing (RNA-seq) and spatial transcriptome data analysis. A risk model was constructed based on ligand/receptor to quantify crosstalk patterns by stepwise regression Cox analysis. Results Based on cell infiltration scores inferred from bulk RNA-seq data (n = 728), we found that high infiltration of PCs was associated with better overall survival (OS) and response to immunotherapy in BC. Further single-cell transcriptome analysis (n = 8; 41,894 filtered cells) identified two dominant types of PCs, IgG1 and IgA1 PCs. Signal transduction from tumor cells of specific states (stress-like and hypoxia-like tumor cells) to PCs, for example, via the LAMB3/CD44 and ANGPTL4/SDC1 ligand/receptor pairs, was validated by spatial transcriptome analysis and associated with poorer OS as well as nonresponse to immunotherapy. More importantly, a ligand/receptor pair-based risk model was constructed and showed excellent performance in predicting patient survival and immunotherapy response. Conclusions PCs are an important component of the tumor microenvironment, and their crosstalk with tumor cells influences clinical outcomes and response to immunotherapies in BC patients. Graphical Abstract

Keywords

• Cell crosstalk
• Plasma cell
• Bladder cancer
• Immunotherapy
• Single-cell analysis