NSD1 Inactivation and SETD2 Mutation Drive a Convergence toward Loss of Function of H3K36 Writers in Clear Cell Renal Cell Carcinomas

Abstract

Extensive dysregulation of chromatin-modifying genes in clear cell renal cell carcinoma (ccRCC) has been uncovered through next-generation sequencing. However, a scientific understanding of the cross-talk between epigenetic and genomic aberrations remains limited. Here we identify three ccRCC epigenetic clusters, including a clear cell CpG island methylator phenotype (C-CIMP) subgroup associated with promoter methylation of VEGF genes (FLT4, FLT1, and KDR). C-CIMP was furthermore characterized by silencing of genes related to vasculature development. Through an integrative analysis, we discovered frequent silencing of the histone H3 K36 methyltransferase NSD1 as the sole chromatin-modifying gene silenced by DNA methylation in ccRCC. Notably, tumors harboring NSD1 methylation were of higher grade and stage in different ccRCC datasets. NSD1 promoter methylation correlated with SETD2 somatic mutations across and within spatially distinct regions of primary ccRCC tumors. ccRCC harboring epigenetic silencing of NSD1 displayed a specific genome-wide methylome signature consistent with the NSD1 mutation methylome signature observed in Sotos syndrome. Thus, we concluded that epigenetic silencing of genes involved in angiogenesis is a hallmark of the methylator phenotype in ccRCC, implying a convergence toward loss of function of epigenetic writers of the H3K36 histone mark as a root feature of aggressive ccRCC. Cancer Res; 77(18); 4835-45. ©2017 AACR.

Figure 1.

Clustering of TCGA samples in ccRCC reveals a CpG Island methylator phenotype with poor outcome. A, Unsupervised hierarchical clustering for the most variable methylated probes in promoter CpG islands among 271 ccRCCs. The (β value) level of DNA methylation is represented by the color scale. Each column represents a sample; each row a probe set. The transcriptomic subtype in TCGA, the copy number variation at 9p23.1 locus (CDKN2A), somatic mutation status of four genes (PBRM1, BAP1, SETD2, and VHL) are indicated by red, green, and gray squares, with annotations in the legend. B, Kaplan-Meier curves showing distinct outcomes of patients according to the three subgroups of DNA methylation classification, with patients belonging to C-CIMP subgroup having the worst outcome.

Figure 2.

Charting methylation of VEGF receptors in ccRCC. A, Heatmap for methylation of FLT4, KDR, and FLTÌ in TCGA ccRCC data. B, Correlation of methylation in VEGF receptor genes and expression assessed by RNA-seq. C, Kaplan-Meier curves for overall survival of patients according to FLT4 methylation status.

Figure 3.

Correlation between DNA methylation and polycomb mark. A, Distribution of CCIMP genes marked by H3K27me3 in fetal kidney samples. B, Distribution of genes that gain DNA methylation in ccRCC according to H3K27me3 mark status in fetal kidney samples. C, Twenty-five genes identified as frequently methylated and repressed in ccRCC. Rate of Λ/SD7 methylation (red) is high compared with that of VHL (black).

Figure 4.

Prognostic impact of NSDΊ methylation and correlation with SETD2 mutation. A, Association between NSDΊ methylation and SETD2 somatic mutations in TCGA dataset shows high rate of NSDΊ methylation in tumors with SETD2 mutations. B, Kaplan-Meier curves for overall survival according to NSDΊ methylation in TCGA cohort (450 K). C, Kaplan-Meier curves for recurrence-free survival according to NSDΊ methylation in Pitié-Salpêtrière cohort. D, Progression-free survival according to NSDΊ methylation in patients with ccRCC treated with sunitinib (Beuselinck cohort).

Figure 5.

Representative images of IHC staining for NSDΊ protein on whole slides of ccRCCs. A and B, Microphotographs are from a ccRCC sample positive for NSDΊ expression. A, Original magnification, ×ΊO. B, Original magnification, ×20.C,Weakstaining in one ccRCC case with NSDΊ promoter methylation. Original magnification, ×20. D, Negative staining in one ccRCC case with NSDΊ promoter methylation. Original magnification, ×ΊO.

Figure 6.

Heterogeneity of NSD1 methylation and SETD2 somatic mutation. A, Heterogeneity of NSD1 methylation in 20 primary ccRCCs. Each column represents a section; each row a primary ccRCC sample. Red, NSD1 methylation; blue, no DNA methylation. B, Association between NSD1 methylation and SETD2 mutations in a cohort of 13 primary ccRCCs. Red, methylation of NSD1 and mutations of SETD2\ blue, unmethylated NSD1 and wild-type SETD2. Annotation according to NM_000551 for VHL and NM_014159 for SETD2.