Comprehensive molecular characterization of urothelial bladder carcinoma

Abstract

Urothelial carcinoma of the bladder is a common malignancy that causes approximately 150,000 deaths per year worldwide. So far, no molecularly targeted agents have been approved for treatment of the disease. As part of The Cancer Genome Atlas project, we report here an integrated analysis of 131 urothelial carcinomas to provide a comprehensive landscape of molecular alterations. There were statistically significant recurrent mutations in 32 genes, including multiple genes involved in cell-cycle regulation, chromatin regulation, and kinase signalling pathways, as well as 9 genes not previously reported as significantly mutated in any cancer. RNA sequencing revealed four expression subtypes, two of which (papillary-like and basal/squamous-like) were also evident in microRNA sequencing and protein data. Whole-genome and RNA sequencing identified recurrent in-frame activating FGFR3-TACC3 fusions and expression or integration of several viruses (including HPV16) that are associated with gene inactivation. Our analyses identified potential therapeutic targets in 69% of the tumours, including 42% with targets in the phosphatidylinositol-3-OH kinase/AKT/mTOR pathway and 45% with targets (including ERBB2) in the RTK/MAPK pathway. Chromatin regulatory genes were more frequently mutated in urothelial carcinoma than in any other common cancer studied so far, indicating the future possibility of targeted therapy for chromatin abnormalities.

Figure 1. The genomic landscape of bladder cancer.

a, Mutation rate and type, histological subtype, smoking status, gender, tumour stage and cluster type. b, Genes with statistically significant levels of mutation (MutSig, false discovery rate <0.1) and mutation types. c, Deletions and amplifications for genomic regions with statistically significant focal copy number changes (GISTIC2.0). ‘Copy number’ refers to absolute copy number. Note that two amplification peaks (*) contain several genes, any of which could be the target, as opposed to the single gene listed here. d, RNA expression level for selected genes, expressed as fold change from the median value for all samples. Tumour samples were grouped into three clusters (red, blue and green) using consensus NMF clustering (see the main text and Supplementary Fig. 2.1.2). Three samples with no copy number data and two samples with no mutations in the genes were not used in the clustering and are shown in grey. PowerPoint slide

Figure 2. Structural rearrangements and viral integration.

a, FGFR3–TACC3 fusion in sample TCGA-CF-A3MH showing the breakpoints in the two genes, the breakpoint junction sequences and the predicted fusion protein. b, Rearrangement involving DIP2B and ERBB2 in TCGA-DK-A2I6. The ERBB2 gene has swapped its promoter with that of DIP2B, resulting in overexpression of ERBB2. c, Insertion of human papilloma virus 16 (HPV16) into the BCL2L1 gene on chromosome 20 in TCGA-GC-A3I6. The region of BCL2L1 into which the virus has integrated and the integration junction sequence are shown. PowerPoint slide

Figure 3. Expression characteristics of bladder cancer.

Integrated analysis of mRNA, miRNA and protein data led to identification of distinct subsets of urothelial carcinoma. Data for mRNA, miRNA and protein were z-normalized, and samples were organized in the horizontal direction by mRNA clustering. a, Papillary histology, FGFR3 alterations, FGFR3 expression and reduced FGFR3-related miRNA expression are enriched in cluster I. b, Expression of epithelial lineage genes and stem/progenitor cytokeratins are generally high in cluster III, some of which show variant squamous histology. c, Luminal breast and urothelial differentiation factors are enriched in clusters I and II. d, ERBB2 mutation and oestrogen receptor beta (ESR2) expression are enriched in clusters I and II. PowerPoint slide

Figure 4. Altered pathways and networks in bladder cancer.

a, Somatic mutations and copy number alterations (CNA) in components of the p53/Rb pathway, RTK/RAS/PI(3)K pathway, histone modification system and SWI/SNF complex. Red, activating genetic alterations; blue, inactivating genetic alterations. Percentages shown denote activation or inactivation of at least one allele. b, The network connecting mutated histone-modifying genes to transcription factors with differential activity (methodology and larger implicated network in Supplementary Fig. 8.2.1). Each gene is depicted as a multi-ring circle with various levels of data, plotted such that each ‘spoke’ in the ring represents a single patient sample (same sample ordering for all genes). ‘PARADIGM’ ring, bioinformatically inferred levels of gene activity (red, higher activity); ‘Transcriptional activity’, mean mRNA levels of all of the targets of each transcription factor; ‘expression’, mRNA levels relative to normal (red, high); ‘Mutation in gene’, somatic mutation; ‘Mutation in histone modifier genes’, somatic mutation in at least one such gene; ‘IPL anti-correlation’, genes with PARADIGM integrated pathway levels (IPLs) inversely correlated with histone-gene mutation status. Gene–gene relationships are inferred using public resources. PowerPoint slide

Figure 5. Potential targets in bladder cancer.

a, Alterations in the PI(3)K/AKT/mTOR pathway are mutually exclusive. Tumour samples are shown in columns; genes in rows. Only samples with at least one alteration are shown. AKT3 shows elevated expression in 10% of samples, independent of copy number (right panel). Hetloss, heterozygous loss. b, Receptor tyrosine kinases are altered, by any of several different mechanisms (amplification, mutation or fusion), in 45% of samples. Only mutations that are recurrent in this data set or previously reported in COSMIC are shown. c, Recurrent mutations in ERBB2 and ERBB3. The mutations shown in black are either recurrent in the TCGA data set or reported in COSMIC. Green, receptor L domain; red, furin-like cysteine-rich region; blue, growth factor receptor domain IV; yellow, tyrosine kinase domain. d, ERBB2 amplifications and recurrent mutations in other cancers profiled by TCGA. Missense mutations were counted in the following positions: G309, S310, L313, R678, T733, L755, V777, D769, V842, T862, R896 and M916I. In-frame insertions were counted between amino acids 774 and 776. Only tumour types with an alteration frequency ≥2% are shown. PowerPoint slide