Comprehensive molecular characterization of urothelial bladder carcinoma - Nature

Urothelial carcinoma of the bladder is a major cause of morbidity and mortality worldwide, causing an estimated 150,000 deaths per year¹. Previous studies have identified multiple regions of somatic copy number alteration, including amplification of PPARG, E2F3, EGFR, CCND1 and MDM2, as well as loss of CDKN2A and RB1 (refs 2, 3). Sequencing of candidate pathways has identified recurrent mutations in TP53, FGFR3, PIK3CA, TSC1, RB1 and HRAS (refs 2, 3). Whole-exome sequencing of nine bladder cancers, followed by a replication analysis of 88 cancers, identified mutations at >10% frequency in several chromatin remodelling genes: KDM6A, CREBBP, EP300 and ARID1A (ref. 4). Focused molecular analyses^5,6 have delineated tumour subtypes and identified kinase-activating FGFR3 gene fusions^7,8.

We report here a comprehensive, integrated study of 131 high-grade muscle-invasive urothelial bladder carcinomas as part of The Cancer Genome Atlas (TCGA) project. Included are data on DNA copy number, somatic mutation, messenger RNA and microRNA (miRNA) expression, protein and phosphorylated protein expression, DNA methylation, transcript splice variation, gene fusion, viral integration, pathway perturbation, clinical correlates and histopathology to characterize the molecular landscape of urothelial carcinoma. This study identifies a number of mutations and regions of copy number variation that involve genes not previously reported as altered in a significant fraction of bladder cancers. It also identifies potential therapeutic targets in most of the samples analysed.

The tumours displayed a large number of DNA alterations, slightly fewer than in lung cancer and melanoma, but more than in other adult malignancies studied by TCGA (Fig. 1)⁹. On average, there were 302 exonic mutations, 204 segmental alterations in genomic copy number and 22 genomic rearrangements per sample. We analysed somatic copy number alterations (CNAs) using both SNP 6.0 arrays and low-pass whole-genome sequencing; the two were strongly concordant (Supplementary Methods 6.1 and Supplementary Fig. 6.1). There were 22 significant arm-level copy number changes (Supplementary Data 6.1.1), and GISTIC (genomic identification of significant targets in cancer) (Supplementary Methods 6.2) identified 27 amplified and 30 deleted recurrent focal somatic CNAs (Supplementary Data 6.2.1 and 6.3.1). Focal amplifications involved genes previously reported to be altered in bladder cancer (Fig. 1c and Supplementary Fig. 6.2.1) and some not previously implicated. The latter included PVRL4, BCL2L1 and ZNF703. The most common recurrent focal deletion, seen in 47% of samples, contained CDKN2A (9p21.3) and correlated with reduced expression (Fig. 1 and Supplementary Fig. 2.7). Other focal deletions containing <10 genes appeared to target PDE4D, RB1, FHIT, CREBBP, IKZF2, FOXQ1, FAM190A (also called CCSER1), LRP1B and WWOX.

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.

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Whole-exome sequencing of 130 tumours and matched normal samples targeted 186,260 exons in 18,091 genes (mean coverage 100-fold, with 82% of target bases covered >30×). MuTect¹⁰ identified 39,312 somatic mutations (including 38,012 point mutations and 1,138 indels (insertions or deletions)), yielding mean and median somatic mutation rates of 7.7 and 5.5 per megabase (Mb), respectively (Fig. 1a and Supplementary Table 2.1.1). Thirty-two genes showed statistically significant levels of recurrent somatic mutation (Fig. 1b and Supplementary Table 2.1.2) by analysis using MutSig 1.5 (refs 9, 11) (Supplementary Methods 2.2). Three other genes identified by MutSig were not considered further because of low or undetectable expression (Supplementary Fig. 2.1.1). A similar analysis considering only mutations in the COSMIC database² identified three more significantly mutated genes: ERBB2, ATM and CTNNB1 (Supplementary Table 2.1.3). We validated the mutation findings in three ways: targeted re-sequencing of all significantly mutated gene mutations, comparison with RNA-seq data for 123 samples and comparison with whole-genome sequence data for 18 samples. Overall, the validation rate was >99% in selected mutations by a combination of the methods (Supplementary Methods 2.4).

Nearly half (49%) of the samples had TP53 mutations (Fig. 1b), which were mutually exclusive in their relationship with amplification (9%) and overexpression (29%) of MDM2; hence, TP53 function was inactivated in 76% of samples. Most RB1 mutations were inactivating, were associated with significantly reduced mRNA level (Supplementary Fig. 2.7) and were mutually exclusive with CDKN2A deletions (Supplementary Fig. 2.8 and Supplementary Table 2.8.1). FGFR3 mutations (12%) typically affected known kinase-activating sites. PIK3CA mutations were relatively common (20%), clustering in the helical domain near E545 (Supplementary Fig. 2.4). Most TSC1 mutations (8%) were truncating, and six were homozygous (allele fraction >0.5).

Many of the 32 genes identified in Fig. 1b have not previously been reported as statistically significantly mutated in bladder cancer: MLL2 (also called KMT2D; 27%), CDKN1A* (14%), ERCC2* (12%), STAG2 (11%), RXRA* (9%), ELF3* (8%), NFE2L2 (8%), KLF5* (8%), TXNIP (7%), FOXQ1* (5%), RHOB* (5%), FOXA1 (5%), PAIP1* (5%), BTG2* (5%), ZFP36L1 (5%), RHOA (4%) and CCND3 (4%). The nine genes marked with asterisks have not been reported as significantly mutated genes in any other TCGA cancer type or reported in another study as mutated at >3% frequency². CDKN1A (p21^CIP1), a cyclin-dependent kinase inhibitor¹², had predominantly null or truncating mutations, indicating loss of function. Fifteen of sixteen mutations in ERCC2, a nucleotide excision repair gene¹³, were deleterious missense mutations, suggesting dominant-negative effects. ERCC2-mutant tumours also had significantly fewer C>G mutations than did ERCC2-wild-type tumours (Supplementary Figs 2.3.1 and 2.3.2), and they trended towards higher overall mutation rate (Supplementary Fig. 2.12). Seven of twelve mutations in RXRA (retinoid X nuclear receptor alpha)¹⁴ occurred at the same amino acid (five S427F; two S427Y) in the ligand-binding domain. Those seven tumours showed increased expression of genes involved in adipogenesis and lipid metabolism (Supplementary Fig. 2.6 and Supplementary Data 2.6.1–2.6.3), suggesting that the mutations cause constitutive activation.

Eleven tumours (8%) had deleterious missense mutations in the Neh2 domain of NFE2L2, a transcription factor that regulates the anti-oxidant program in response to oxidative stress¹⁵. Those tumours showed markedly increased expression of genes involved in genotoxic metabolism and the reactive oxygen species (ROS) response (Supplementary Figs 2.5.1–2.5.3 and Supplementary Data 2.5.2). Furthermore, nine samples had mutations in redox regulator TXNIP (ref. 16) (five of them inactivating) and were mutually exclusive of samples with NFE2L2 mutations, providing another mechanism for dysregulation of redox metabolism. Predominant inactivating mutations were seen in STAG2, an X-linked cohesin complex component required for separation of sister chromatids during cell division¹⁷ (Supplementary Fig. 2.4).

Unsupervised clustering by non-negative matrix factorization of mutations and focal somatic CNAs in 125 samples identified three distinct groups (Fig. 1a and Supplementary Fig. 2.1.2). Group A (red), classified as ‘focally amplified’, is highly enriched in focal somatic CNAs in several genes, as well as mutations in MLL2 (Fig. 1 and Supplementary Tables 2.1.4 and 2.1.5). Group B (blue), classified as ‘papillary CDKN2A-deficient FGFR3 mutant’, is enriched in papillary histology. Nearly all group B samples show loss of CDKN2A, and most have one or more alterations in FGFR3. Group C (green), classified as ‘TP53/cell-cycle-mutant’, shows TP53 mutations in nearly all samples, as well as enrichment with RB1 mutations and amplifications of E2F3 and CCNE1 (Fig. 1 and Supplementary Table 2.1.4). These differences in pattern of mutation suggest the possibility of different oncogenic mechanisms.

Seventy-two per cent of the cancers in this study were from current or past smokers, consistent with extensive epidemiological studies indicating an association between smoking and urothelial cancer risk. In contrast with lung cancer, however, there was no statistically significant association between smoking status and the mutational spectrum, frequency of mutation in any significantly mutated gene, occurrence of focal somatic CNAs or expression subtype (Supplementary Tables 2.9.1 and 2.9.2). Never-smokers did have a slightly higher fraction of C>G mutations than did current/former smokers (28.5% versus 23.8%, P = 0.032; Supplementary Figs 2.3.2 and 2.3.3). Unsupervised clustering of promoter CpG island DNA methylation data revealed a major subgroup (34%) of tumours (CIMP) characterized by cancer-specific DNA hypermethylation (Supplementary Fig. 7.1). Multivariate regression analysis with age, sex and tumour stage as covariates identified smoking pack-years as the only significant predictor of CIMP phenotype, as has also been reported for colorectal cancer¹⁸.

Fifty-one per cent of mutations overall were Tp*C->(T/G) (Supplementary Table 2.1.1), a class of mutation recently reported to be mediated by one of the DNA cytosine deaminases, APOBEC (refs 19, 20). APOBEC3B was expressed at high levels in all of the tumours, suggesting a major role for APOBEC-mediated mutagenesis in bladder carcinogenesis (Supplementary Figs 12.1 and 12.2).

Four genes involved in epigenetic regulation were significantly mutated genes: MLL2, ARID1A, KDM6A and EP300 (Fig. 1). Truncating mutations were significantly enriched in each of those genes (Supplementary Fig. 2.2 and Supplementary Data 2.2.1–2). Three of the genes had previously been identified as mutated in urothelial cancers⁴, but mutation of MLL2, which encodes a histone H3 lysine 4 (H3K4) methyltransferase, is a novel finding. Several other chromatin-regulating genes had mutation rates ≥10% but were not statistically significant by MutSig analysis: MLL3, MLL, CREBBP, CHD7 and SRCAP. Many other epigenetic regulators were mutated at lower frequency but were also enriched with truncating mutations, indicating functional significance (Supplementary Fig. 2.2 and Supplementary Data 2.2.1 and 2.2.2). Non-silent mutations in chromatin regulatory genes overall were significantly enriched in bladder cancer in comparison with the entire exome, in contrast with all other epithelial cancers studied so far in the TCGA project (Supplementary Table 2.10). Mutations in MLL2 and KDM6A (the latter encoding a histone H3 lysine 27 (H3K27) demethylase) were mutually exclusive (Supplementary Fig. 2.8 and Supplementary Table 2.8.1), suggesting that mutations in the two genes have redundant downstream effects on carcinogenesis or that the combined loss is synthetically lethal.

To identify structural variations and pathogen sequences, we used low-pass, paired-end, whole-genome sequencing (WGS; 6–8× coverage) of 114 tumours and RNA sequencing of all tumours. We detected 2,529 structural aberrations, including 1,153 that involve gene–gene fusions. Among the translocations, 379 were inter-chromosomal, 237 were intra-chromosomal, 274 were the result of inversions and 263 resulted from deletions (Supplementary Table 3.1). We found several recurrent translocations of probable pathogenic significance, including an intra-chromosomal translocation on chromosome 4 involving FGFR3 and TACC3 (n = 3). The breakpoints were in intron 16 (two cases) or exon 17 (one case) of FGFR3 and intron 10 of TACC3 (confirmed by DNA sequencing and RNA-seq). All three lead to fusion mRNA products for which the predicted proteins include the amino-terminal 758 amino acids of FGFR3 fused with the carboxy-terminal 191 amino acids of TACC3 (Fig. 2a). On the basis of the structure of the FGFR3–TACC3 fusion protein, we predict that it can auto-dimerize, leading to constitutive activation of the kinase domain of FGFR3. FGFR3–TACC3 fusion, which was recently described in both glioblastoma²¹ and bladder cancer^7,8, represents a promising therapeutic target. The ERBB2 gene was also involved in translocations in four tumours, all with different fusion partners and all confirmed by DNA sequencing, RNA-seq or both. In one case, exons 4 to 29 of ERBB2 were fused to the promoter plus exon 1 of DIP2B, and the fusion product was amplified (Fig. 2b). Two other fusion products resulted in novel mRNA products, the biological significance of which is not known.

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.

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We identified viral DNAs in 7 of 122 tumours (6%), and viral transcripts in 5 of 122 (4%). Three tumours expressed cytomegalovirus (CMV) transcripts (encoding RL5A, RNA2.7, RL9A, RNA1.2, UL5 and UL22A), one expressed BK polyoma virus and one expressed human papilloma virus 16 (HPV16). HPV16 and human herpesvirus 6B DNA were each identified in one other sample but without expression. None of the tumours expressing CMV showed evidence of CMV integration into the host genome, suggesting the presence of a stable episome. In the BK-positive tumour, two BK genes were integrated into GRB14, a signalling adaptor protein for receptor tyrosine kinases. In the HPV-16-expressing case, the virus integrated into BCL2L1, an apoptosis-regulating gene (Fig. 2c). In that tumour, BCL2L1 was amplified (∼6×) and overexpressed (∼10× median; >2× any of the other samples). Overall, these findings indicate that viral infection may have a role in the development of a small percentage of urothelial carcinomas.

Analysis of RNA-seq data from 129 tumours identified four clusters (clusters I–IV) (Fig. 3 and Supplementary Fig. 4.1). Cluster I (‘papillary-like’) is enriched in tumours with papillary morphology (P = 0.0002), FGFR3 mutations (P = 0.0007, q = 0.02), FGFR3 copy number gain (P = 0.04, q = 0.1) and elevated FGFR3 expression (P < 0.0001) (Fig. 3a). It includes all three samples with FGFR3–TACC3 fusions. Cluster I samples also show significantly lower expression of miR-99a and miR-100, miRNAs that downregulate FGFR3 expression (P = 0.0002, Figs 3a and Supplementary Fig. 5.3)²². Cluster I samples also show lower expression of miR-145 and miR-125b, which have been reported as frequently downregulated in bladder cancer²³. Tumours with FGFR3 alterations, and perhaps other tumours that share the cluster I expression profile, may respond to inhibitors of FGFR or its downstream targets.

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.

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Reverse-phase protein array (RPPA) data indicate that clusters I and II express high HER2 (ERBB2) levels and an elevated oestrogen receptor beta (ESR2) signalling signature, indicating potential targets for hormone therapies such as tamoxifen or raloxifene (Fig. 3d). In fact, HER2 protein levels in a subset of the tumours are comparable to those found in TCGA HER2-positive breast cancers²³.

For comparison, we asked whether any of the four clusters show gene signatures similar to those identified in any other tumour type(s) among the first 11 analysed by TCGA. We found that the signature of bladder cancer cluster III (‘basal/squamous-like’) is similar to that of basal-like breast cancers, as well as squamous cell cancers of the head and neck and lung (Supplementary Fig. 4.2)^24,25. All four of those cancer types express characteristic epithelial lineage genes, including KRT14, KRT5, KRT6A and EGFR. Basal-like subtype²⁶ and squamous cell subtype²⁷ of urothelial carcinoma have been independently reported. Many of the samples in bladder cluster III express cytokeratins (that is, KRT14 and KRT5) that were recently reported to mark stem/progenitor cells²⁶. Some of those samples also show a level of variant squamous histology (Fig. 3b). Bladder clusters I and II show features similar to those of luminal A breast cancer, with high mRNA and protein expression of luminal breast differentiation markers, including GATA3 and FOXA1 (Fig. 3c). Markers of urothelial differentiation such as the uroplakins (for example, UPK3A) are also highly expressed in clusters I and II, as are the epithelial marker E-cadherin and members of the miR-200 family of miRNAs (which target multiple regulators of epithelial–mesenchymal transition)²⁸ (Fig. 3c). Taken together, these observations indicate that, despite their diverse tissue origins, some bladder, breast, head and neck and lung cancers share common pathways of tumour development.

To determine whether the expression-based clusters could be seen in other data sets, we used the muscle-invasive bladder cancer samples from ref. 27, hierarchically clustering them with the genes used in our analysis. From the sample dendrogram, we identified four groups (Supplementary Fig. 4.3a). The four groups identified in the data set of ref. 27 correlated well with the four clusters identified in our TCGA data (Supplementary Fig. 4.3b).

When we analysed the RNA-seq data for transcript splice variation using SpliceSeq²⁹ (Supplementary Information, section 11), one finding of interest was an average of 3% PKM1 and 97% PKM2 transcripts in the tumour samples. The PKM2 isoform of pyruvate kinase is the principal driver of a shift to aerobic glycolysis in tumours (the Warburg effect)³⁰. Therefore, urothelial bladder cancers (and other cancer types) may prove sensitive to inhibition of glycolysis or related metabolic pathways.

Integrated analysis of the mutation and copy-number data revealed three main pathways as frequently dysregulated in bladder cancer: cell cycle regulation (altered in 93% of cases); kinase and phosphatidylinositol-3-OH kinase (PI(3)K) signalling (72%); and chromatin remodelling, including mutations/somatic CNAs in histone-modifying genes (89%) and components of the SWI/SNF nucleosome remodelling complex (64%) (Fig. 4a). To complement these results for well-defined pathways, we applied network analysis methods to examine other possible interactions between genes and pathways (Fig. 4b). In particular, we used the TieDIE algorithm to search for causal regulatory interactions within the PARADIGM network, which connects mutated genes to active transcriptional hubs^31,32. The analysis identified a sub-network linking mutated histone-modifying genes to a large array of activated transcription factors, indicating potential far-reaching effects of histone modification on other pathways (Supplementary Fig. 8.2.1) converging on MYC/MAX regulation. Both MYC and MAX showed similar levels of pathway activity, independent of mutations in chromatin genes, suggesting that mutations in histone-modifying genes provide just one mechanism for disruption of the MYC/MAX hub. By contrast, tumours with chromatin-related mutations showed differential activity of transcription factors FOXA2 and SP1, implicating de-differentiation processes as a result of the mutations. Our network analysis also identified HSP90AA1 as a critical signalling hub, indicating that inhibitors of HSP90 may have therapeutic value in urothelial carcinoma. Although the linkages between mutations and transcriptional changes were statistically significant in terms of their proximity in the network (as determined by permutation tests; see Supplementary Fig. 8.2), further studies will be needed to assess the biological relevance of the findings.

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.

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Integrated analysis also identified mutations, copy number alterations or RNA expression changes affecting the PI(3)K/AKT/mTOR pathway in 42% of the tumours (Fig. 5a). Included were activating point mutations in PIK3CA (17%; potentially responsive to PI(3)K inhibitors), mutation or deletion of TSC1 or TSC2 (9%; potentially responsive to mTOR inhibitors) and overexpression of AKT3 (10%; potentially responsive to AKT inhibitors). We also observed mutations, genomic amplifications or gene fusions that affect the RTK/RAS pathway in 44% of the tumours (Fig. 5b, c). Included were events that can activate FGFR3 (17%; potentially responsive to FGFR inhibitors or antibodies), amplification of EGFR (9%; potentially responsive to EGFR antibodies or inhibitors), mutations of ERBB3 (6%; potentially sensitive to ERBB kinase inhibitors) and mutation or amplification of ERBB2 (9%; potentially sensitive to ERBB2 kinase inhibitors or antibodies). ERBB3 mutations in bladder cancer have been noted previously⁴, but statistically significant mutation of ERBB2 in bladder cancer has not been reported. Both genes are potential therapeutic targets in other diseases^33,34,35. Notably, ERBB2 alterations were approximately as frequent in this study as in TCGA breast cancers, but with fewer amplifications and more mutations (Fig. 5d)²⁴.

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.

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This integrated study of 131 invasive urothelial bladder carcinomas provides numerous novel insights into disease biology and delineates multiple potential opportunities for therapeutic intervention. Treatment for muscle-invasive bladder cancer has not advanced beyond cisplatin-based combination chemotherapy and surgery in the past 30 years³⁶, and no new drugs for the disease have been approved in that time. Median survival for patients with recurrent or metastatic bladder cancer remains 14–15 months with cisplatin-based chemotherapy, and there is no widely recognized second-line therapy³⁷. With the exception of a single case report, there is also no known benefit from treatment with newer, targeted agents³⁸. Several of the genomic alterations identified in this study, particularly those involving the PI(3)K/AKT/mTOR, CDKN2A/CDK4/CCND1 and RTK/RAS pathways, including ERBB2 (Her-2), ERBB3 and FGFR3, are amenable in principle to therapeutic targeting. Clinical trials based on patients with relevant druggable genomic alterations are warranted.

FGFR3 mutation is a common feature of low-grade non-invasive papillary urothelial bladder cancer, but it occurs at a much lower frequency in high-grade invasive bladder cancer. The cluster analysis in Fig. 3 highlights multiple mechanisms of FGFR3 activation, and its strong association with papillary morphology. The data presented here suggest a subset of muscle-invasive cancers that can potentially be targeted through FGFR3. Similarly, ERBB2 amplification may be targetable by strategies used in breast cancer, by small-molecule tyrosine kinase inhibitors or by novel immunotherapeutic approaches (NCT01353222)³⁴. The data here provide further support for several on-going ERBB2-targeted trials in bladder cancer and further define the subpopulation of cancers suited to that approach. Finally, cluster III of the integrated expression profiling analysis reveals the existence of a urothelial carcinoma subtype with cancer stem-cell expression features (including KRT14 and KRT5), perhaps providing another avenue for therapeutic targeting.

The alterations identified in epigenetic pathways also suggest new possibilities for bladder cancer treatment. Ninety-nine (76%) of the tumours analysed here had an inactivating mutation in one or more of the chromatin regulatory genes, and 53 (41%) had at least two such mutations. Overall, the bladder cancers showed a mutational spectrum highly enriched with mutations in chromatin regulatory genes (Supplementary Table 2.10). Furthermore, integrated network analyses revealed a profound impact of those mutations on the activity levels of various transcription factors and pathways implicated in cancer. Drugs that target chromatin modifications—for example, recently developed agents that bind acetyl-lysine binding motifs (bromodomains)—might prove useful for treatment of the subset of bladder tumours that exhibit abnormalities in chromatin-modifying enzymes³⁹. Our findings overall indicate bladder cancer as a prime candidate for exploration of that approach to therapy.

Tumour and normal samples were obtained with institutional-review-board-approved consent and processed using a modified AllPrep kit (Qiagen) to obtain purified DNA and RNA. Quality-control analyses revealed only modest batch effects (Supplementary Information, section ‘Batch effects’). The tumours were profiled using Affymetrix SNP 6.0 microarrays for somatic CNAs, low-pass WGS (HiSeq) for somatic CNAs and translocations, RNA-seq (HiSeq) for mRNA and miRNA expression, Illumina Infinium (HumanMethylation450) arrays for DNA methylation, HiSeq for exome sequencing and RPPA for protein expression and phosphorylation. Statistical analysis and biological interpretation of the data were spearheaded by the TCGA genome data analysis centres. Sequence files are in CGHub (https://cghub.ucsc.edu/). All other molecular, clinical and pathological data are available through the TCGA Data Portal (https://tcga-data.nci.nih.gov/tcga/). The data can be explored through a compendium of next-generation clustered heat maps (http://bioinformatics.mdanderson.org/TCGA/NGCHMPortal/), the cBio Cancer Genomics Portal (http://cbioportal.org), TieDIE (http://sysbiowiki.soe.ucsc.edu/tiedie), SpliceSeq (http://bioinformatics.mdanderson.org/main/SpliceSeq:Overview), MBatch batch effects assessor (http://bioinformatics.mdanderson.org/tcgambatch/) and Regulome Explorer (http://explorer.cancerregulome.org/). Also see Supplementary Information.

• 19 March 2014

  Sequences in figure 2a were incorrect and have been fixed.

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Authors and Affiliations

1. Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, 77030, Texas, USA

   John N. Weinstein, Rehan Akbani, Bradley M. Broom, Wenyi Wang, Roeland G. W. Verhaak, John N. Weinstein, Rehan Akbani, Bradley M. Broom, Wenbin Liu, Zhenlin Ju, Thomas Motter, Bo Peng, Michael Ryan, Wenyi Wang, Roeland G. W. Verhaak, Xiaoping Su, Ji-Yeon Yang, Philip L. Lorenzi, Hui Yao, Nianxiang Zhang, Jiexin Zhang, John N. Weinstein, Rehan Akbani, Bradley M. Broom & John N. Weinstein

2. Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, 77030, Texas, USA

   John N. Weinstein, John N. Weinstein, Wenbin Liu, Zhenlin Ju, Ji-Yeon Yang, Gordon B. Mills, John N. Weinstein & John N. Weinstein

3. The University of Texas MD Anderson Cancer Center, Houston, 77030, Texas, USA

   David McConkey, David McConkey & Kenna R. Mills Shaw

4. Scott Department of Urology, Baylor College of Medicine, Houston, 77030, Texas, USA

   Seth Lerner, Seth Lerner, Weiguo Jian, Sebrina Tello & Seth Lerner

5. Texas Cancer Research Biobank (TCRB), Baylor College of Medicine, Houston, 77030, Texas, USA

   Seth Lerner, Margaret Morgan, Seth Lerner, Weiguo Jian, Sebrina Tello, Michael Ittman, Patricia Castro, Whitney D. McClenden, Margaret Morgan, Richard Gibbs, Seth Lerner & Margaret Morgan

6. Human Genome Sequencing Center at Baylor College of Medicine, Houston, 77030, Texas, USA

   Margaret Morgan, Margaret Morgan, Richard Gibbs & Margaret Morgan

7. Dan L. Duncan Cancer Center, Human Genome Sequencing Center, Baylor College of Medicine, Houston, 77030, Texas, USA

   Chad J. Creighton, Chad J. Creighton & Lawrence Donehower

8. Baylor College of Medicine, Houston, 77030, Texas, USA

   Carolyn Smith

9. The Eli and Edythe L. Broad Institute of Massachusetts Institute of Technology and Harvard University Cambridge, 02142, Massachusetts, USA

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11. Harvard Medical School, Boston, 02115, Massachusetts, USA

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12. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, 02215, Massachusetts, USA

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14. Department of Urology, Oregon Health and Science University, 3303 SW Bond Avenue, CHH10U, Portland, Oregon 97239, USA.,

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20. UCSD Department of Pathology 9500 Gilman Drive, La Jolla, California 92093, USA.,

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21. Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, 27599, North Carolina, USA

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22. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.,

    William Y. Kim, Jeffrey S. Damrauer, Jeffrey S. Damrauer Alan P. Hoyle, William Y. Kim, Joel S. Parker, Charles M. Perou, Matthew D. Wilkerson & William Y. Kim

23. Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.,

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24. Cancer Biology Division, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, 21231, Maryland, USA

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26. Department of Molecular Virology and Microbiology, Baylor College of Medicine, 1 Baylor Plaza, Houston, Texas 77030, USA.,

    Lawrence Donehower

27. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, 02215, Massachusetts, USA

    Steven E. Schumacher

28. Department of Medicine, Harvard Medical School, Boston, 02215, Massachusetts, USA

    Rameen Beroukhim

29. Department of Pathology, Harvard Medical School, Boston, 02215, Massachusetts, USA

    Matthew Meyerson

30. Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z 4S6, Canada.,

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31. Department of Internal Medicine, Division of Medical Oncology, University of North Carolina at Chapel Hill, Chapel Hill, 27599, North Carolina, USA

    D. Neil Hayes

32. Research Computing Center, University of North Carolina at Chapel Hill, Chapel Hill, 27599, North Carolina, USA

    Jeffrey Roach

33. Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, 27599, North Carolina, USA

    Elizabeth Buda & Corbin D. Jones

34. Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.,

    Corbin D. Jones, Piotr A. Mieczkowski, Donghui Tan, Umadevi Veluvolu & Scot Waring

35. Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.,

    J. Todd Auman

36. Department of Genetics, Harvard Medical School, Boston, 02115, Massachusetts, USA

    Netty Santoso, Michael Parfenov, Xiaojia Ren, Angeliki Pantazi, Angela Hadjipanayis, Jonathan Seidman & Raju Kucherlapati

37. Division of Genetics, Brigham and Women’s Hospital, Boston, 02115, Massachusetts, USA

    Angela Hadjipanayis, Raju Kucherlapati, Peter J. Park & Andrew Wei Xu

38. The Center for Biomedical Informatics, Harvard Medical School, Boston, 02115, Massachusetts, USA

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39. Informatics Program, Children’s Hospital, Boston, 02115, Massachusetts, USA

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40. Department of Genomic Medicine, Institute for Applied Cancer Science, The University of Texas MD Anderson Cancer Center, Houston, 77030, Texas, USA

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41. Department of Pathology, The University of Texas MD Anderson Cancer Center, Unit 085; 1515 Holcombe Boulevard,

    Charles Guo, Charles Guo, Jolanta Bondaruk, Bogdan Czerniak, Charles Guo, Jolanta Bondaruk, Kenneth Aldape & Bogdan Czerniak

42. 1515 Holcombe Boulevard, Houston, 77030, Texas, USA

    Charles Guo, Charles Guo, Jolanta Bondaruk, Bogdan Czerniak, Charles Guo, Jolanta Bondaruk, Kenneth Aldape & Bogdan Czerniak

43. Institute for Systems Biology, 401 Terry Ave N, Seattle, Washington 98109, USA.,

    Brady Bernard, Dick Kreisberg, Sheila Reynolds, Hector Rovira & Ilya Shmulevich

44. Buck Institute for Research on Aging; 8001 Redwood Blvd, Novato, California 94945, USA.,

    Christopher Benz

45. 8001 Redwood Blvd, Novato, California 94945, USA.,

    Christopher Benz

46. University California Santa Cruz, 1156 High Street, Santa Cruz, California 95064, USA.,

    Daniel Carlin, David Haussler, Sam Ng, Evan O. Paull, Joshua Stuart & Jing Zhu

47. Department of Pathology, MD Anderson Cancer Center, Houston, 77030, Texas, USA

    Yuexin Liu, Wei Zhang, Yuexin Liu & Wei Zhang

48. Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, 94158, California, USA

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49. The Research Institute at Nationwide Children’s Hospital, Columbus, 43205, Ohio, USA

    Tara M. Lichtenberg, Erik Zmuda, Thomas Barr, Aaron D. Black, Myra George, Benjamin Hanf, Carmen Helsel, Cynthia McAllister, Nilsa C. Ramirez, Teresa R. Tabler, Stephanie Weaver, Lisa Wise, Jay Bowen & Julie M. Gastier-Foster

50. The Ohio State University, Columbus, 43210, Ohio, USA

    Nilsa C. Ramirez & Julie M. Gastier-Foster

51. Department of Pathology, Baylor College of Medicine, Houston, 77030, Texas, USA

    Michael Ittman & Patricia Castro

52. Analytical Biological Services, Inc., 701 Cornell Drive, Wilmington, Delaware 19801, USA.,

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53. Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, Ohio 44195, USA.,

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54. Georgia Regents University Cancer Center, Augusta, 30912, Georgia, USA

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55. Helen F. Graham Cancer Center at Christiana Care, 4701 Ogletown Stanton Road, Newark, Delaware 19713, USA.,

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56. International Genomics Consortium, 445 N. Fifth Street, Phoenix, Arizona 85004, USA.,

    Troy Shelton, Erin Curley, Johanna Gardner, David Mallery & Robert Penny

57. ILSbio, LLC 100 Radcliffe Drive, Chestertown, Maryland 21620, USA.,

    Nguyen Van Bang, Phan Thi Hanh, Bernard Kohl, Xuan Van Le, Bui Duc Phu, Richard Thorp, Nguyen Viet Tien & Le Quang Vinh

58. Hue Central Hospital, Hue City, Vietnam

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59. IU School of Medicine, Med Science Bldg, Room 128A, 635 Barnhill Drive, Indianapolis, Indiana 46202, USA.,

    George Sandusky

60. Lahey Hospital and Medical Center, Burlington, 01805, Massachusetts, USA

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61. University of North Carolina, Lineberger Cancer Center, 450 West Drive, Chapel Hill, North Carolina 27599, USA.,

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62. University of Pittsburgh, Pittsburgh, 15213, Pennsylvania, USA

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Consortia

Contributions

The Cancer Genome Atlas research network contributed collectively to this study. Biospecimens were provided by the tissue source sites and processed by the Biospecimen Core Resource. Data generation and analyses were performed by the genome-sequencing centres, cancer genome-characterization centres and genome data analysis centres. All data were released through the Data Coordinating Center. Project activities were coordinated by the NCI and NHGRI project teams. We also acknowledge the following TCGA investigators of the Bladder Analysis Working Group who contributed substantially to the project. Project leaders: J. N. Weinstein and S. P. Lerner. Data coordinator: C. J. Creighton. Analysis coordinators: R. Akbani and J. Kim. Manuscript coordinator: M. B. Morgan. Project coordinator: M. Sheth. Writing team: J. N. Weinstein, D. J. Kwiatkowski, S. P. Lerner, C. J. Creighton, P. W. Laird, R. Kucherlapati, R. Akbani, X. Su, K. A. Hoadley and M. C. Ryan. Clinical expertise: S. Lerner, D. J. Kwiatkowski, J. E. Rosenberg and D. Bajorin. Pathology review: H. Al-Ahmadie, B. A. Czerniak, D. Hansel, V. Reuter and B. Robinson. DNA sequence and copy number analysis: J. Kim, D. J. Kwiatkowski, A. D. Cherniack and J. E. Rosenberg. DNA methylation analysis: P. W. Laird and T. Hinoue. mRNA analysis: K. A. Hoadley, W. Y. Kim, J. S. Damrauer, W. Zhang, Y. Liu and R. Akbani. miRNA analysis: G. Robertson and A. J. Mungall. Transcript splicing analysis: M. Ryan and J. N. Weinstein. Protein analysis: R. Akbani and G. B. Mills. APOBEC: D. A. Gordenin. Pathway/integrated analysis: C. J. Creighton, N. Schultz, Evan O. Paull and J. Stuart. Chromosomal rearrangements and viral integration: X. Su, R. Kucherlapati, N. Santoso, S. Lee and M. Parfenov. Batch effects: R. Akbani and J. N. Weinstein. Manuscript review: R. Gibbs, C. Gunter and M. Meyerson. Contact PIs: J. N. Weinstein, S. P. Lerner and D. J. Kwiatkowski.

Corresponding authors

Correspondence to John N. Weinstein, Seth Lerner, David J. Kwiatkowski, David J. Kwiatkowski, John N. Weinstein, John N. Weinstein, Seth Lerner, John N. Weinstein, Seth Lerner or David J. Kwiatkowski.

Competing interests

The author declare no competing financial interests.

The primary and processed data used to generate the analyses presented here can be downloaded by registered users from The Cancer Genome Atlas at https://tcga-data.nci.nih.gov/tcga/tcgaDownload.jsp. All of the primary sequence files are deposited in CGHub and all other data are deposited at the Data Coordinating Center (DCC) for public access (http://cancergenome.nih.gov/, https://cghub.ucsc.edu/ and https://tcga-data.nci.nih.gov/docs/publications/blca_2013/).

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The Cancer Genome Atlas Research Network. Comprehensive molecular characterization of urothelial bladder carcinoma. Nature 507, 315–322 (2014). https://doi.org/10.1038/nature12965

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• Received: 17 June 2013

• Accepted: 19 December 2013

• Published: 29 January 2014

• Issue Date: 20 March 2014

• DOI: https://doi.org/10.1038/nature12965