BACKGROUND: Muscle-invasive bladder cancer (MIBC) is a lethal disease with poor treatment response and a high death rate. Immune cells infiltrating the tumor tissues have been shown to play a vital role in tumorigenesis and tumor progression, but their prognostic significance in MIBC remains unclear. OBJECTIVES: To explore the landscape and prognostic significance of tumor-infiltrating immune cells (TIICs) in MIBC, and to develop a model to improve the prognostic predictions of MIBC. METHODS AND MATERIALS: The gene expression profile and clinical data of MIBC patients were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas portal. The fractions of 22 TIIC subtypes were calculated using the Cell Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm. A TIICs-based model was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression in a training cohort and validated in the validation cohort. RESULTS: Ten types of TIICs demonstrated different infiltration abundance between MIBC and normal tissue. We also found 11 types of TIICs that were significantly associated with overall survival (OS). A TIICs-based model was established, consisting of 15 types of immune cells, and an immunoscore was calculated. Significant differences in OS were found between the high and low immunoscore groups, in both training (n = 343) and validation (n = 146) cohorts. The model could identify patients who would have worse OS despite having similar clinical characteristics. Furthermore, multivariate analysis identified the immunoscore as an independent risk factor (hazard ratio, 3.23; 95% confidence interval; 2.22-4.70) for OS in MIBC patients. CONCLUSION: The landscape of immune infiltration is different between MIBC and normal tissue. The TIICs-based model could provide promising predictive value to complement the existing staging system for predicting the OS of MIBC patients. AJTR
BACKGROUND:Muscle-invasive bladder cancer (MIBC) is a lethal disease with poor treatment response and a high death rate. Immune cells infiltrating the tumor tissues have been shown to play a vital role in tumorigenesis and tumor progression, but their prognostic significance in MIBC remains unclear. OBJECTIVES: To explore the landscape and prognostic significance of tumor-infiltrating immune cells (TIICs) in MIBC, and to develop a model to improve the prognostic predictions of MIBC. METHODS AND MATERIALS: The gene expression profile and clinical data of MIBCpatients were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas portal. The fractions of 22 TIIC subtypes were calculated using the Cell Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm. A TIICs-based model was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression in a training cohort and validated in the validation cohort. RESULTS: Ten types of TIICs demonstrated different infiltration abundance between MIBC and normal tissue. We also found 11 types of TIICs that were significantly associated with overall survival (OS). A TIICs-based model was established, consisting of 15 types of immune cells, and an immunoscore was calculated. Significant differences in OS were found between the high and low immunoscore groups, in both training (n = 343) and validation (n = 146) cohorts. The model could identify patients who would have worse OS despite having similar clinical characteristics. Furthermore, multivariate analysis identified the immunoscore as an independent risk factor (hazard ratio, 3.23; 95% confidence interval; 2.22-4.70) for OS in MIBCpatients. CONCLUSION: The landscape of immune infiltration is different between MIBC and normal tissue. The TIICs-based model could provide promising predictive value to complement the existing staging system for predicting the OS of MIBCpatients. AJTR
Authors: Etienne Becht; Nicolas A Giraldo; Claire Germain; Aurélien de Reyniès; Pierre Laurent-Puig; Jessica Zucman-Rossi; Marie-Caroline Dieu-Nosjean; Catherine Sautès-Fridman; Wolf H Fridman Journal: Adv Immunol Date: 2016-01-29 Impact factor: 3.543
Authors: J Alfred Witjes; Eva Compérat; Nigel C Cowan; Maria De Santis; Georgios Gakis; Thierry Lebret; Maria J Ribal; Antoine G Van der Heijden; Amir Sherif Journal: Eur Urol Date: 2013-12-12 Impact factor: 20.096
Authors: Marko Babjuk; Maximilian Burger; Eva M Compérat; Paolo Gontero; A Hugh Mostafid; Joan Palou; Bas W G van Rhijn; Morgan Rouprêt; Shahrokh F Shariat; Richard Sylvester; Richard Zigeuner; Otakar Capoun; Daniel Cohen; José Luis Dominguez Escrig; Virginia Hernández; Benoit Peyronnet; Thomas Seisen; Viktor Soukup Journal: Eur Urol Date: 2019-08-20 Impact factor: 20.096