Immunologic Gene Sets Reveal Features of the Tumor Immune Microenvironment and Predict Prognosis and Immunotherapy Response: A Pan-Cancer Analysis - Pan, Hongda; Pan, Jingxin; Li, Pei; Wu, Jianghong (Frontiers in Immunology, 2022)
In the treatment of cancer, anti-PD-1/PD-L1 immunotherapy has achieved unprecedented clinical success. However, a significant response to these therapies is limited to a small number of patients. This study aimed to predict immunotherapy response and prognosis using immunologic gene sets (IGS). The enrichment scores of 4,872 IGS in 348 patients with metastatic urothelial cancer treated with anti-PD-L1 therapy were computed using gene set variant analysis (GSVA). An IGS-based classification (IGSC) was constructed using a nonnegative matrix factorization (NMF) approach. An IGS-based risk prediction model (RPM) was developed using the least absolute shrinkage and selection operator (LASSO) method. The IMvigor210 cohort was divided into three distinct subtypes, among which subtype 2 had the best prognosis and the highest immunotherapy response rate. Subtype 2 also had significantly higher PD-L1 expression, a higher proportion of the immune-inflamed phenotype, and a higher tumor mutational burden (TMB). An RPM was constructed using four gene sets, and it could effectively predict prognosis and immunotherapy response in patients receiving anti-PD-L1 immunotherapy. Pan-cancer analyses also demonstrated that the RPM was capable of accurate risk stratification across multiple cancer types, and RPM score was significantly associated with TMB, microsatellite instability (MSI), CD8+ T cell infiltration, and the expression of cytokines (IFN-γ, TGF-β, and TNF-α), which are key predictors of immunotherapy response. The IGSC strengthens our understanding of the diverse biological processes in tumor immune microenvironment, and the RPM can be promising biomarkers for predicting the prognosis and response in cancer immunotherapy.