Tuan Zea Tan1, Mathieu Rouanne2, Kien Thiam Tan3, Ruby Yun-Ju Huang4, Jean-Paul Thiery5. 1. Cancer Science Institute of Singapore, National University of Singapore, Center for Translational Medicine, Singapore. Electronic address: csittz@nus.edu.sg. 2. Department of Urology, Hôpital Foch, Université Versailles-Saint-Quentin-en-Yvelines, Université Paris-Saclay, Suresnes, France; INSERM Unit 1015, Laboratoire de Recherche Translationnelle en Immunologie (LRTI), Gustave Roussy, Université Paris-Saclay, Villejuif, France. 3. ACT Genomics Co., Ltd., Taipei city, Taiwan. 4. Cancer Science Institute of Singapore, National University of Singapore, Center for Translational Medicine, Singapore; Department of Obstetrics and Gynecology, National University Health System, Singapore; Department of Anatomy, Yong Loo Lin School of Medicine, National University of Singapore, Singapore. 5. Biochemistry Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Guangzhou Institute of Biomedicine and Health, Chinese Academy of Science, Guangzhou, People's Republic of China; CNRS Emeritus CNRS UMR 7057 Matter and Complex Systems, University Paris Denis Diderot, Paris, France; INSERM UMR 1186, Integrative Tumor Immunology and Genetic Oncology, Gustave Roussy, EPHE, PSL, Fac. de Médecine - University Paris-Sud, Université Paris-Saclay, Villejuif, France. Electronic address: bchtjp@nus.edu.sg.
Abstract
BACKGROUND: Previous molecular subtyping for bladder carcinoma (BLCA) involved <450 samples, with diverse classifications. OBJECTIVE: To identify molecular subtypes by curating a large BLCA dataset. DESIGN, SETTING, AND PARTICIPANTS: Gene expression publicly available were combined and reanalyzed. The dataset contained 2411 unique tumors encompassing non-muscle-invasive (NMIBC) and muscle-invasive BLCA (MIBC). Subtypes were reproduced on The Cancer Genome Atlas, UROMOL, and IMvigor210. INTERVENTION: Subtypes were assigned by gene expression. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Kaplan-Meier analyses were performed for subtype-clinical outcome correlations; Chi-square/Fisher exact tests were used for subtype-clinicopathological parameters associations. RESULTS AND LIMITATIONS: We identified six molecular subtypes with different overall survival (OS) and molecular features. Subtype Neural-like (median OS, 87 mo) is prevalent in MIBC and characterized by high WNT/β-catenin signaling. HER2-like (107.7 mo) is distributed evenly across NMIBC and MIBC, with higher ERBB2 amplification and signaling. Papillary-like (>135 mo), an NMIBC subtype enriched in urothelial differentiation genes, shows a high frequency of actionable FGFR3 mutations, amplifications, and FGFR3-TACC3 fusion. Luminal-like (91.7 mo), predominantly NMIBC, has higher MAPK signaling and more KRAS and KMT2C/D mutations than other subtypes. Mesenchymal-like (MES; 86.6 mo) and Squamous-cell carcinoma-like (SCC; 20.6 mo) are predominant in MIBC. MES is high in AXL signaling, whereas SCC has elevated PD1, CTLA4 signaling, and macrophage M2 infiltration. About 20% of NMIBCs show MIBC subtype traits and a lower 5-yr OS rate than Papillary-like NMIBC (81% vs 96%). The main limitations of our study are the incomplete clinical annotation, and the analyses were based on transcriptome subset due to comparisons across gene expression quantification technologies. CONCLUSIONS: BLCA can be stratified into six molecular subtypes. NMIBC, with a high risk of progression, displays the molecular features of MIBC. PATIENT SUMMARY: Biomarkers are urgently needed to guide patient treatment selection and avoid unnecessary toxicities in those who fail to respond. We believe molecular subtyping is a promising way to tailor disease management for those who will benefit most.
BACKGROUND: Previous molecular subtyping for bladder carcinoma (BLCA) involved <450 samples, with diverse classifications. OBJECTIVE: To identify molecular subtypes by curating a large BLCA dataset. DESIGN, SETTING, AND PARTICIPANTS: Gene expression publicly available were combined and reanalyzed. The dataset contained 2411 unique tumors encompassing non-muscle-invasive (NMIBC) and muscle-invasive BLCA (MIBC). Subtypes were reproduced on The Cancer Genome Atlas, UROMOL, and IMvigor210. INTERVENTION: Subtypes were assigned by gene expression. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Kaplan-Meier analyses were performed for subtype-clinical outcome correlations; Chi-square/Fisher exact tests were used for subtype-clinicopathological parameters associations. RESULTS AND LIMITATIONS: We identified six molecular subtypes with different overall survival (OS) and molecular features. Subtype Neural-like (median OS, 87 mo) is prevalent in MIBC and characterized by high WNT/β-catenin signaling. HER2-like (107.7 mo) is distributed evenly across NMIBC and MIBC, with higher ERBB2 amplification and signaling. Papillary-like (>135 mo), an NMIBC subtype enriched in urothelial differentiation genes, shows a high frequency of actionable FGFR3 mutations, amplifications, and FGFR3-TACC3 fusion. Luminal-like (91.7 mo), predominantly NMIBC, has higher MAPK signaling and more KRAS and KMT2C/D mutations than other subtypes. Mesenchymal-like (MES; 86.6 mo) and Squamous-cell carcinoma-like (SCC; 20.6 mo) are predominant in MIBC. MES is high in AXL signaling, whereas SCC has elevated PD1, CTLA4 signaling, and macrophage M2 infiltration. About 20% of NMIBCs show MIBC subtype traits and a lower 5-yr OS rate than Papillary-like NMIBC (81% vs 96%). The main limitations of our study are the incomplete clinical annotation, and the analyses were based on transcriptome subset due to comparisons across gene expression quantification technologies. CONCLUSIONS: BLCA can be stratified into six molecular subtypes. NMIBC, with a high risk of progression, displays the molecular features of MIBC. PATIENT SUMMARY: Biomarkers are urgently needed to guide patient treatment selection and avoid unnecessary toxicities in those who fail to respond. We believe molecular subtyping is a promising way to tailor disease management for those who will benefit most.
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