A risk score combining co-expression modules related to myeloid cells and alternative splicing associates with response to PD-1/PD-L1 blockade in non-small cell lung cancer - Han, Yichao; Liu, Si-Yang Maggie; Jin, Runsen; Meng, Wangyang; Wu, Yi-Long; Li, Hecheng (Frontiers in Immunology, 2023)
Background: Comprehensive analysis of transcriptomic profiles of non-small cell lung cancer (NSCLC) may provide novel evidence for biomarkers associated with response to PD-1/PD-L1 immune checkpoint blockade (ICB). Methods: We utilized weighted gene co-expression network analysis (WGCNA) to analyze transcriptomic data from two NSCLC datasets from Gene Expression Omnibus (GSE135222 and GSE126044) that involved patients received ICB treatment. We evaluated the correlation of co-expression modules with ICB responsiveness and functionally annotated ICB-related modules using pathway enrichment analysis, single-cell RNA sequencing, flow cytometry and alternative splicing analysis. We built a risk score using Lasso-COX regression based on hub genes from ICB-related modules. We investigated the alteration of tumor microenvironment between high- and low- risk groups and the association of the risk score with previously established predictive biomarkers. Results: Our results identified a black with positive correlation and a blue module with negative correlations to ICB responsiveness. The black module was enriched in pathway of T cell activation and antigen processing and presentation, and the genes assigned to it were consistently expressed on myeloid cells. We observed decreased alternative splicing events in samples with high signature scores of the blue module. The Lasso-COX analysis screened out three genes (EVI2B, DHX9, HNRNPM) and constructed a risk score from the hub genes of the two modules. We validated the predictive value of the risk score for poor response to ICB therapy in an in-house NSCLC cohort and a pan-cancer cohort from the KM-plotter database. The low-risk group had more immune-infiltrated microenvironment, with higher frequencies of precursor exhausted CD8+ T cells, tissue-resident CD8+ T cells, plasmacytoid dendritic cells and type 1 conventional dendritic cells, and a lower frequency of terminal exhausted CD8+ T cells, which may explain its superior response to ICB therapy. The significant correlation of the risk score to gene signature of tertiary lymphoid structure also implicated the possible mechanism of this predictive biomarker. Conclusions: Our study identified two co-expression modules related to ICB responsiveness in NSCLC and developed a risk score accordingly, which could potentially serve as a predictive biomarker for ICB response.