Comprehensive molecular characterization of clear cell renal cell carcinoma

Abstract

Genetic changes underlying clear cell renal cell carcinoma (ccRCC) include alterations in genes controlling cellular oxygen sensing (for example, VHL) and the maintenance of chromatin states (for example, PBRM1). We surveyed more than 400 tumours using different genomic platforms and identified 19 significantly mutated genes. The PI(3)K/AKT pathway was recurrently mutated, suggesting this pathway as a potential therapeutic target. Widespread DNA hypomethylation was associated with mutation of the H3K36 methyltransferase SETD2, and integrative analysis suggested that mutations involving the SWI/SNF chromatin remodelling complex (PBRM1, ARID1A, SMARCA4) could have far-reaching effects on other pathways. Aggressive cancers demonstrated evidence of a metabolic shift, involving downregulation of genes involved in the TCA cycle, decreased AMPK and PTEN protein levels, upregulation of the pentose phosphate pathway and the glutamine transporter genes, increased acetyl-CoA carboxylase protein, and altered promoter methylation of miR-21 (also known as MIR21) and GRB10. Remodelling cellular metabolism thus constitutes a recurrent pattern in ccRCC that correlates with tumour stage and severity and offers new views on the opportunities for disease treatment.

Figures

Figure One. Somatic alterations in ccRCC

(a) Top histogram, mutation events per sample; left histogram, samples affected per alteration. Upper heat map, distribution of fusion transcripts and VHL methylation across samples (n=385 samples, with overlapping exome/CNA/RNA-seq/Methylation data); middle heatmap, mutation events; bottom heatmap, copy number gains (red) and losses (blue). Lower chart, mutation spectrum by indicated categories. (b) Left panel, frequency of arm level copy number alterations versus focal copy number alterations. Right panel, comparison of the average numbers of arm level and focal copy number changes in ccRCC, colon cancer (CRC), glioblastoma (GBM), breast cancer (BRCA) and ovarian cancer (OVCA). (c) Circos plot of fusion transcripts identified in 416 samples of ccRCC, with recurrent fusions highlighted.

Figure Two. DNA methylation and ccRCC

(a–b) Overall promoter DNA hypermethylation frequency in the tumor increases with rising stage (a) and grade (b). The promoter DNA hypermethylation frequency is calculated as the percentage of CpG loci hypermethylated among 15,101 loci which are unmethylated in the normal kidney tissue and normal white blood cells (boxplots, median with 95% confidence interval). (c) Volcano plots showing a comparison of DNA methylation for SETD2 mutant versus non-mutant tumors (n=224, Human Methylation 450 platform). Unshaded area: CpG loci with Benjamini-Hochberg FDR=0.001 and difference in mean beta value >0.1 (n=2,557). (d) Heatmap showing CpG loci with SETD2 mutation-associated DNA methylation (from part c); blue to red indicates low to high DNA methylation. The loci are split into those hypomethylated (top panel; n=1,251) or hypermethylated (bottom panel; n=1,306) in SETD2 mutants. Top color bars indicate SETD2 mRNA expression (red: high, green: low) and SETD2 mutation status. Gray-scale row-side color bar on left-hand side represents the relative number of overlapping reads, based on H3K36me3 ChIP-seq experiment in normal adult kidney (http://nihroadmap.nih.gov/epigenomics/); black, high read count. DNA methylation patterns include 14 normal kidney samples. Among the tumors without SETD2 mutations, six (arrowhead) have both the signature pattern of SETD2 mutation and low SETD2 mRNA expression.

Figure Three. mRNA and miRNA patterns reflect molecular subtypes of ccRCC

(a) By unsupervised analyses, tumors separated into four sample groups (i.e. “clusters”), based on either differentially expressed mRNA patterns (left panel, showing 500 representative genes: m1–4) or differentially expressed miRNA patterns (right panel, showing 26 representative miRNAs: mi1–4). (b) Significant differences in patient survival were identified among either the mRNA-based clusters (left panel) or the miRNA-based clusters (right panel). (c) Numbers of samples overlapping between the two sets of clusters, with significant concordance observed between m1 and mi3 and between m3 and mi2; Red, significant overlap (P<1E-5, chi-squared test). (d) mRNA-miRNA correlations, for predicted targeting interactions. Rows indicate miRNAs from part a (indicated by cluster specific color bar); columns, mRNAs (5000 differentially regulated genes selected for average RPKM>10 and at least one predicted miRNA interaction); mRNA-miRNA entries with no predicted targeting show as white. To the right of the correlation matrix, t-statistics (Spearman’s rank) indicate group target enrichment.

Figure Four. Genomically-altered pathways in ccRCC

(a) Alterations in chromatin remodeling genes were predicted to impact a large network of genes and pathways (larger implicated network in supplemental). 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); ‘Expression’, mRNA levels relative to normal (red, high); ‘Mutation’, somatic event; center, correlation of gene expression or activity to mutation events in chromatin-related genes (red, positive). Protein-protein relationships inferred using public resources. (b) For the PI3K/Akt/mTOR pathway (altered in ~28% of tumors), the MEMo algorithm identified a pattern of mutually exclusive gene alterations (somatic mutations, copy alterations, and aberrant mRNA expression) targeting multiple components, including 2 genes from the recurrent amplicon on 5q35.3. The alteration frequency and inferred alteration type (blue for inactivation, and red for activation) is shown for each gene in the pathway diagram.

Figure Five. Molecular correlates of patient survival involve metabolic pathways

(a) Sample profiles were separated into discovery and validation subsets, with the top survival correlates within the discovery subset being defined for each of the four platforms examined (mRNA, microRNA, protein, DNA methylation). Kaplan-Meier plots show results of applying the four prognostic signatures to the validation subset, comparing survival for patients with predicted higher risk (red, top third of signature scores), lower risk (blue, bottom third), or intermediate risk (gray, middle third); successful predictions were observed in each case. (b) When viewed in the context of metabolism, the molecular survival correlates highlight a widespread metabolic shift, with tumors altering their usage of key pathways and metabolites (red and blue shading representing the correlation of increased gene expression with worse or better survival respectively, univariate Cox based on extended cohort). Worse survival correlates with up-regulation of pentose phosphate pathway genes (G6PH, PGLS, TALDO and TKT), fatty acid synthesis genes (ACC and FASN), and PI3K pathway enhancing genes (miR-21). Better survival correlates with up-regulation of AMPK complex genes, multiple Krebs cycle genes, and PI3K pathway inhibitors (PTEN, TSC2). Additionally, specific promoter methylation events, including hypermethylation of PI3K pathway repressor GRB10, associate with outcome. (c) Heat map of selected key features from the metabolic shift schematic (b) demonstrating coordinate expression by stage at DNA methylation, RNA, and protein levels (data from validation subset).