The genomic and transcriptomic landscape of metastastic urothelial cancer

Abstract

Metastatic urothelial carcinoma (mUC) is a lethal cancer, with limited therapeutic options. Large-scale studies in early settings provided critical insights into the genomic and transcriptomic characteristics of non-metastatic UC. The genomic landscape of mUC remains however unclear. Using Whole Exome (WES) and mRNA sequencing (RNA-seq) performed on metastatic biopsies from 111 patients, we show that driver genomic alterations from mUC were comparable to primary UC (TCGA data). APOBEC, platin, and HRD mutational signatures are the most prevalent in mUC, identified in 56%, 14%, and 9% of mUC samples, respectively. Molecular subtyping using consensus transcriptomic classification in mUC shows enrichment in neuroendocrine subtype. Paired samples analysis reveals subtype heterogeneity and temporal evolution. We identify potential therapeutic targets in 73% of mUC patients, of which FGFR3 (26%), ERBB2 (7%), TSC1 (7%), and PIK3CA (13%) are the most common. NECTIN4 and TACSTD2 are highly expressed regardless of molecular subtypes, FGFR3 alterations and sites of metastases.

Fig. 1. Study design and biopsy sites.

A Study design. B Biopsies’ sites. WES Whole Exome Sequencing. Adapted from Servier Medical Art under the licence Creative Commons Attribution 3.0 France.

Fig. 2. Genomic alterations in mUC.

A Driver gene mutations in mUC. Clinical features, immunohistochemical subgroups, and somatic genetic alterations identified in 97 mUC subjected to whole-exome sequencing. The effects of the somatic alterations are color-coded according to the legend. B Main altered pathways in mUC. Frequently altered genes were regrouped into specific signaling pathways detailed in Supplementary Table 4. Clinical features, immunohistochemical subgroups, and somatic genetic alterations identified in 97 mUC subjected to whole-exome sequencing. ICI Immune Checkpoint Inhibitor. Differential analysis between groups was performed using Fisher’s exact test, and Benjamini–Hochberg correction, two-sided. Source data are provided as a Source Data file.

Fig. 3. Genomic alterations in early and metastatic MIBC.

A Driver gene mutations in eMIBC (right panel; n = 219) and mMIBC (left panel; n = 68). Clinical features, immunohistochemical subgroups, and somatic genetic alterations identified in 219 eMIBC and 68 mMIBC subjected to whole-exome sequencing. The effects of the somatic alterations are color-coded according to the legend. Differential analysis between groups was performed using Fisher’s exact test, and Benjamini–Hochberg correction, two-sided. B Main altered pathways in eMIBC (right panel; n = 219) and mMIBC (left panel; n = 68). Frequently altered genes were regrouped into specific signaling pathways detailed in Sup Table 4. Clinical features, immune histochemical subgroups, and somatic genetic alterations identified 219 eMIBC and 68 mMIBC subjected to whole-exome sequencing. The effects of the somatic alterations are color-coded according to the legend. Differential analysis between groups was performed using Fisher’s exact test, and Benjamini–Hochberg correction, two-sided. C Comparison with eMIBC from the TCGA cohort revealed significantly mutated genes in mMIBC. Scatter plots depicting the mutational frequencies (percentage of patients) between the overall cohorts of mMIBC (n = 68) and eMIBC (n = 219) (FGFR3 - p = 0.043/ARID2 – p = 0.060/BCL2L1 – p = 0.057, Fisher’s exact test and Benjamini–Hochberg correction, two-sided). D Evolution of the clonal composition from the eMIBC to the matched metastases in four patients. The intersections represent clonal and subclonal variants common to both eMIBC and mMIBC. The blue circles represent variants (clonal or subclonal) detected only in eMIBC or variants that are contradictory between the two categories (i.e. subclonal in eMIBC and clonal in mMIBC). The orange circles represent variants (clonal or subclonal) detected only in mMIBC or variants that are contradictory between the two categories. ICI Immune Checkpoint Inhibitor. Source data are provided as a Source Data file.

Fig. 4. Molecular subtypes analysis.

N = 98 samples. A Proportion of molecular subtypes in eMIBC vs mUC. Side by side proportions of each subtype in the mMIBC (large bars) cohort and in the eMIBC from TCGA (narrow bars). P-value shown is Fisher’s exact test for NE-like vs others. B Subtype proximity in mMIBC depending on metastatic site. Intra-tumor proportion by subtype deconvolution from whole-transcriptomic profiles are shown grouped by metastatic site. Kruskal–Wallis (K-W) test results are shown. C Evolution of subtype composition in paired primary-metastasis. Pie chart of intra-tumor proportions are shown for the 6-subtype consensus classification and for the same classification after removing the ‘stroma-rich’ subtype. Adapted from Servier Medical Art under the licence Creative Commons Attribution 3.0 France. D Overall survival Kaplan–Meier in mUC stratified by molecular subtype. Box boundaries show the first and third quartiles, with the median line shown. Whiskers spread at 1.5 times the interquartile range. Basal/Squamous (Ba.Sq): n = 25, Luminal Non Specified (LumNS): n = 1, Luminal Papillary (LumP): n = 30, Luminal Unstable (LumU): n = 21, Neuroendocrine-like (NE.like): n = 10 and Stroma rich (Stroma.rich): n = 11. Source data are provided as a Source Data file.

Fig. 5. Immune infiltration in mUC.

Different bioinformatics methods were applied to deconvolute immune cell populations from bulk transcriptome data using immune cell–specific signatures (CIBERSORT, XCELL, MCPcounter, Epic) and microenvironment composition signatures (ESTIMATE). N = 98 samples. Source data are provided as a Source Data file. Global immune infiltration (estimated from transcriptomic profiles using the ESTIMATE algorithm) was associated to TMB (a), prior FGFR inhibitor therapy (N = 88 naive and N = 10 treated patients) (b), prior immunotherapy (N = 80 naive and N = 18 treated patients) (c), and biopsy sampling site (N = 20 lung; N = 34 lymph nodes; N = 25 liver and N = 19 other) (d). Whiskers box plot represent interquartiles with 1.5x IQR, and outliers dots. e Subtype-specific immune enrichment in eMIBC vs mUC. The level of immune and stromal infiltration (estimated by MCPcounter in upper panel and ESTIMATE in lower panel) was compared in one subtype versus the others, or in FGFR3-mutated tumors vs wild-type, in either mUC after removing lymph-node biopsies (left panel) or in eMIBC from the TCGA (right panel). f Forest plot of univariate survival models in mUC of stromal and immune estimates, stratified by presence of visceral metastasis and lymph node biopsy site. The squares represent HR with the confidence intervals in gray lines. Two-tailed Mann–Whitney test was applied g Forest plot of univariate survival models in eMIBC of stromal and immune estimates. The squares represent HR with the confidence intervals in gray lines. Two-tailed Mann–Whitney test was applied. TMB Tumor Mutational Burden, LN lymph node, MCP Microenvironment Cell Population, HR Hazard Ratio. N = 98 mUC versus N = 408 eMIBC from TCGA.

Fig. 6. Oncoprint representation of druggable alterations in mUC.

Frequently altered genes were regrouped according to druggable alterations detailed in Supplementary Data 16. Clinical features, immunohistochemical subgroups, and somatic druggable genetic alterations identified in n = 97 mUC were analyzed by whole-exome sequencing. The effects of the somatic alterations are color-coded according to the legend. Source data are provided as a Source Data file.