Signature based on B cells-related gene pairs could predict prognosis and immunotherapeutic response in NSCLC.

Abstract

e21051 Background: Tumor-infiltrating B cells accompanying several widely used B cell-related biomarkers, such as CD19 and CD20, have inconsistent clinical prognostic values in non-small-cell lung cancer (NSCLC) patients. Considering only one B cell-related biomarker could not distinguish the anti-tumor and pro-tumor B cell subsets in tumor, we aimed to construct a more accurate B-cells related gene pairs (BRGPs) prognostic model and evaluate its potential predictive ability to immunotherapy in NSCLC patients. Methods: Using public single-cell RNA sequencing data, the B cells-related genes (BRGs) in NSCLC samples were identified. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were utilized to construct the BRGPs model which was not affected by the technical bias of different platforms. With no dependence upon specific gene expression levels, a novel signature based on BRGPs was established in this study. In addition, the prognostic value and immunotherapeutic response for this signature with regard to the TME components and potential molecular mechanism were explored. Results: Based on the TCGA database, we built a novel prognostic signature of 23 BRGPs comprising 28 unique BRGs. This risk model showed significant power in distinguishing good or poor prognosis and could serve as an independent prognostic factor for NSCLC patients in the TCGA cohort. The prognostic accuracy of the model was further verified in the GSE31210 dataset from the GEO database. Likewise, the prognostic value of the risk score for the BRGPs model was demonstrated in the GEO cohorts by the univariate and multivariate cox regression analysis. In addition, the risk model was significantly associated with sex, TNM stage, immune score, tumor purity and various tumor-infiltrating immune cells. GSEA analysis indicated that low-risk group enriched with several immune-related pathways, such as activation of immune response, antigen receptor mediated signaling pathway, B cell activation and B cell mediated immunity, whereas several proliferation-related pathways, such as nuclear chromosome segregation, sister chromatid segregation and mitotic sister chromatid segregation were most enriched in high-risk group. Besides, the tumor mutational burden (TMB) score rather than CD274(PD-L1 mRNA) expression was positively correlated with the risk score (P＜0.001; P = 0.94, respectively). NSCLC patients with high-risk exhibited significantly higher TMB score compared with low-risk patients (P < 0.001). Correspondingly, we demonstrated that immune checkpoint blockade therapy may be more efficacious in high-risk group NSCLC patients according to TIDE method (P＜0.01). Conclusions: This novel BRGPs model can assess the prognosis of patients with NSCLC, and may be helpful to guide immune checkpoint inhibitors treatment in our clinical practice.