Variation in chromatin accessibility in human kidney cancer links H3K36 methyltransferase loss with widespread RNA processing defects

Abstract

Comprehensive sequencing of human cancers has identified recurrent mutations in genes encoding chromatin regulatory proteins. For clear cell renal cell carcinoma (ccRCC), three of the five commonly mutated genes encode the chromatin regulators PBRM1, SETD2, and BAP1. How these mutations alter the chromatin landscape and transcriptional program in ccRCC or other cancers is not understood. Here, we identified alterations in chromatin organization and transcript profiles associated with mutations in chromatin regulators in a large cohort of primary human kidney tumors. By associating variation in chromatin organization with mutations in SETD2, which encodes the enzyme responsible for H3K36 trimethylation, we found that changes in chromatin accessibility occurred primarily within actively transcribed genes. This increase in chromatin accessibility was linked with widespread alterations in RNA processing, including intron retention and aberrant splicing, affecting ∼25% of all expressed genes. Furthermore, decreased nucleosome occupancy proximal to misspliced exons was observed in tumors lacking H3K36me3. These results directly link mutations in SETD2 to chromatin accessibility changes and RNA processing defects in cancer. Detecting the functional consequences of specific mutations in chromatin regulatory proteins in primary human samples could ultimately inform the therapeutic application of an emerging class of chromatin-targeted compounds.

Figure 1.

Regions of tumor-specific nucleosome eviction identify the underlying role of HIF in ccRCC. (A) Hierarchical clustering of median-centered FAIRE signal in windows with significant differences between tumors and normal kidney (two-sided t-test, P < 0.01). (B) FAIRE-seq tracks for ccRCC (black) and uninvolved kidney (red) at two loci. (Blue) ChIP-seq signals (Schodel et al. 2011) from HIF1A, HIF2A, and ARNT. (C) The top five Gene Ontology associations (q < 1 × 10⁻⁵) with sites in Cluster 1 are shown. (D) Transcription factor binding motifs enriched in Cluster 1 compared with local background 500-bp flanking windows (>2.5-fold over background and present in at least 10% of the Cluster 1 windows). P-values relative to local background are shown. (E) Fraction of HIF1A and HIF2A binding sites (Schodel et al. 2011) that overlap the loci in Clusters 1, 2, and 3 compared with permuted controls. Errors bars represent standard deviation (SD).

Figure 2.

SETD2 mutations link H3K36me3 loss with changes in chromatin accessibility. (A) Schematic representation of SETD2 mutations predicted to have high or moderate severity on protein structure. (B) Hierarchical clustering of median-centered FAIRE signal in windows with significant differences between SETD2 mutant tumors (red) and tumors without SETD2 mutation (gray) (2-sided t-test, P < 0.01). (White) Samples not genotyped. (C) Proportions of nucleosome-depleted loci overlapping H3K36me3-marked regions compared with loci with permuted genomic coordinates. Error bars represent SD. (D) Representative immunostaining of two ccRCC tumor-normal pairs on the tissue microarray. (E) Quantification of H3-normalized H3K36me3 intensity across 11 normal kidney and 69 renal tumors. Mutation severity (high, red; moderate, green; none, blue) is indicated. (White) Samples with unknown SETD2 mutation status. The threshold for H3K36me3 deficiency was set to the lowest observed intensity in normal tissue (dashed line).

Figure 3.

H3K36me3 deficiency is associated with intron retention. Intron retention scores for selected genes (Supplemental Fig. S1C) were compared between (A) H3K36me3-deficient tumors and H3K36me3-normal tumors, and (B) PBRM1-mutant and PBRM1-normal tumors. (C) Example genes exhibiting increased intron retention in H3K36me3-deficient tumors (top, PPP2CB; bottom, COX6C). Intron retention scores, genic coverage (calculated with both intron and exon reads), and exonic coverage (calculated only with exonic reads) are provided for two H3K36me3-deficient tumors (red) and two H3K36me3-normal tumors (black).

Figure 4.

Widespread RNA processing defects linked with SETD2 mutations persist in the mature RNA pool and are marked by altered chromatin accessibility. (A) Splicing differences (see Methods) between SETD2-mutant and SETD2-normal tumors (red) compared with a permuted control (blue) are plotted as a cumulative distribution function. (B) Significance of the difference in ratios between SETD2-mutant and SETD2-normal tumors (x-axis) plotted against the scrambled control (y-axis). Points are colored by the class of RNA processing aberrancy. (Gray box) Significance (P = 0.01) in the SETD2-mutant–normal comparison, but not significant in the control comparison. The percentages of significant differences in transcript processing are also presented. (C) Schematic of AP2A1 splicing. Exon skipping was represented as the ratio of included exon coverage to the sum of the exon and the spliced form. The skipped exon ratio is provided for SETD2-mutant tumors (red) and SETD2-normal tumors (black). (D) Quantitative PCR across two USH1C alternative exon utilization sites identified by RNA-seq for three SETD2-normal tumors (black) and two SETD2-mutant tumors (red). Error bars represent standard error. (E) FAIRE signal plotted around the exon start (±3 kb) of misspliced exons (left), random internal exon starts (middle), and random genic positions (right) for H3K36me3-deficient tumors (red) and H3K36me3-normal tumors (black). (Gray) H3K36me3 ChIP-seq signal from normal kidney tissue.