Tumour Microenvironment (TME) Characterization Identified Prognosis and Immunotherapy Response in Muscle-Invasive Bladder Cancer (MIBC)

Abstract

Background: Tumour microenvironment (TME), consisting of a great diversity of immune and stromal cells together with the factors that they secrete, are collaborating to build a chronic inflammatory, immunosuppressive, and pro-angiogenic intratumoural atmosphere, which have been reported to correlated with patients’ outcomes and treatment efficacy. In this study, we aimed at establishing a signature for prediction of prognosis and immunotherapeutic response in muscle-invasive bladder cancer (MIBC) through tumour microenvironment (TME) characterization. Methods: We depicted the TME pattern in MIBC through “Estimation of STromal and Immune cells in MAlignant Tumours using Expression data” (ESTIMATE) algorithm and differential expression genes (DEGs) were submitted to build a TME-related signature (TMERS). Then, the influence of the TMERS on MIBC was comprehensively analysed. Results: TMERS was strongly capable to predict the overall survival (OS) and disease free survival (DFS), serving as an independent prognostic factor through univariate and multivariate cox regression analyses in MIBC. Compared with other clinicopathological features, the TMERS showed much higher predictive accuracy and contributed more in the nomogram. Moreover, we found that TMERS scores are highly positive correlated with immune infiltration as well as expression of immune checkpoints. The value of TMERS in assessing immunotherapy response was conducted using TIDE algorithm/subclass mapping and confirmed in several cohorts receiving immune-checkpoint inhibitors (ICIs). Furthermore, TMERS has a negative correlation with tumour mutation burden (TMB), which is a potential predictive biomarker for immunotherapy. Remarkably, combination of TMERS and TMB was a more effective tool for survival and ICIs therapy prediction. Conclusion: We established a novel TMERS, which have depicted the TME pattern, acting as a robust negative independent prognostic factor and predictive biomarker for response to ICIs when compiling with TMB. Funding Statement: This work was supported by grants from National Natural Science Foundation of China (Grant No. 81802550), Beijing Postdoctoral Research Foundation (Grant No. ZZ2019‐ 04), Hubei Provincial Natural Science Foundation of China (Grant No. 2018CFB221), Fundamental Research Funds for the Central Universities (Grant No. 2042018kf0055). Declaration of Interests: The authors declare that they have no competing interests. Ethics Approval Statement: The authors stated: Not applicable.