1. Independent component analysis uncovers the landscape of the bladder tumor transcriptome and reveals insights into luminal and basal subtypes.
- Author
-
Biton A, Bernard-Pierrot I, Lou Y, Krucker C, Chapeaublanc E, Rubio-Pérez C, López-Bigas N, Kamoun A, Neuzillet Y, Gestraud P, Grieco L, Rebouissou S, de Reyniès A, Benhamou S, Lebret T, Southgate J, Barillot E, Allory Y, Zinovyev A, and Radvanyi F
- Subjects
- Carcinogenesis genetics, Cell Differentiation genetics, Cell Survival genetics, Databases, Genetic, Disease Progression, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Genes, Neoplasm, Humans, Muscles pathology, Neoplasm Invasiveness, PPAR gamma metabolism, Reproducibility of Results, Urinary Bladder Neoplasms pathology, Urothelium pathology, Algorithms, Transcriptome genetics, Urinary Bladder Neoplasms classification, Urinary Bladder Neoplasms genetics
- Abstract
Extracting relevant information from large-scale data offers unprecedented opportunities in cancerology. We applied independent component analysis (ICA) to bladder cancer transcriptome data sets and interpreted the components using gene enrichment analysis and tumor-associated molecular, clinicopathological, and processing information. We identified components associated with biological processes of tumor cells or the tumor microenvironment, and other components revealed technical biases. Applying ICA to nine cancer types identified cancer-shared and bladder-cancer-specific components. We characterized the luminal and basal-like subtypes of muscle-invasive bladder cancers according to the components identified. The study of the urothelial differentiation component, specific to the luminal subtypes, showed that a molecular urothelial differentiation program was maintained even in those luminal tumors that had lost morphological differentiation. Study of the genomic alterations associated with this component coupled with functional studies revealed a protumorigenic role for PPARG in luminal tumors. Our results support the inclusion of ICA in the exploitation of multiscale data sets., (Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.)
- Published
- 2014
- Full Text
- View/download PDF