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SETD2 loss perturbs the kidney cancer epigenetic landscape to promote metastasis and engenders actionable dependencies on histone chaperone complexes

Nature Cancer volume 3, pages 188–202 (2022)Cite this article

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Abstract

SETD2 is a histone H3 lysine 36 (H3K36) trimethyltransferase that is mutated with high prevalence (13%) in clear cell renal cell carcinoma (ccRCC). Genomic profiling of primary ccRCC tumors reveals a positive correlation between SETD2 mutations and metastasis. However, whether and how SETD2 loss promotes metastasis remains unclear. In this study, we used a SETD2-mutant (SETD2MT) metastatic ccRCC human-derived cell line and xenograft models and showed that H3K36me3 restoration greatly reduced distant metastases of ccRCC in mice in a matrix metalloproteinase 1 (MMP1)-dependent manner. An integrated multiomics analysis using assay for transposase-accessible chromatin using sequencing (ATAC-seq), chromatin immunoprecipitation–sequencing (ChIP–seq) and RNA sequencing (RNA-seq) established a tumor suppressor model in which loss of SETD2-mediated H3K36me3 activates enhancers to drive oncogenic transcriptional output through regulation of chromatin accessibility. Furthermore, we uncovered mechanism-based therapeutic strategies for SETD2-deficient cancer through the targeting of specific histone chaperone complexes, including ASF1A/ASF1B and SPT16. Overall, SETD2 loss creates a permissive epigenetic landscape for cooperating oncogenic drivers to amplify transcriptional output, providing unique therapeutic opportunities.

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Fig. 1: SETD2 is highly mutated in metastatic ccRCC tumors, and restoration of H3K36me3 in SETD2MT ccRCC suppresses tumor metastasis.
Fig. 2: Loss of SETD2-mediated H3K36me3 induces a genome-wide increase in chromatin accessibility that correlates with increased oncogenic transcriptional output.
Fig. 3: Loss of SETD2-mediated H3K36me3 induces genome-wide epigenetic reprogramming and activation of enhancers.
Fig. 4: SETD2 loss of function induces MMP1 expression to promote ccRCC metastasis.
Fig. 5: Loss of SETD2-mediated H3K36me3 increases histone chaperone recruitment and sensitizes cancer cells to genetic inactivation of histone chaperones.
Fig. 6: Loss of SETD2-mediated H3K36me3 sensitizes cancer cells to inhibition of the FACT complex.

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Data availability

Raw ATAC-seq, RNA-seq and ChIP–seq sequencing data and the normalized tracks that support the findings of this study have been deposited in the Gene Expression Omnibus (GEO) database under GSE146583. Further information and requests for resources and reagents should be directed to the corresponding author. All unique/stable reagents generated in this study are available from the corresponding author with a completed materials transfer agreement. Source data are provided with this paper.

Code availability

Sequencing data processing and analysis was performed using open-source software packages. The RNA-seq analysis was conducted using Trimmomatic (v0.38), STAR (v2.7.1a), HTSeq (v0.11.2) and DESeq2 (v1.22.2), as reported in ref. 59. The ATAC-seq analysis was performed using Trimmomatic (v0.38), Bowtie2 (v2.3.4.3), MACS2 (v2.1.2) and IDR (v2.0.3) by using scripts in https://github.com/ENCODE-DCC/atac-seq-pipeline/tree/master/src with setting ‘–extsize 200–shift -100–nomodel’ for the peak calling step. ChIP–seq analysis was performed using BWA (v0.7.17-r1188), SAMtools (v1.9), Picard (v2.18.16), MACS2 (v2.1.2) and IDR (v2.0.3) by using scripts in https://github.com/ENCODE-DCC/chip-seq-pipeline2/tree/master/src. Each ATAC-seq and ChIP–seq peak was assigned to the closest gene as in https://github.com/hchintalapudi/ATAC-seq-ATAC-array/blob/777fbb6d9e281e7715ba15b7522c846f3b6234b2/As_Atlas_creation.R, and counts in these peaks were obtained using featureCounts (v1.6.4). The differential analyses were performed using gene counts for RNA-seq and peak counts for ATAC-seq and ChIP–seq following the DESeq2 (v1.22.2) pipeline (https://github.com/mikelove/DESeq2). Bedgraph files for each genomic dataset were generated using bedtools (v2.27.1) genomeCoverageBed scaled with (-scale) DESeq2 size factors (https://github.com/arq5x/bedtools/blob/master/docs/content/tools/genomecov.rst). Those scaled bedgraph files were converted to normalized bigwig tracks using UCSC bedgraph2bigwig. Heat maps and metaplots of ChIP–seq and ATAC-seq data were generated using deepTools (v3.1.1) plotHeatmap (https://github.com/deeptools/deepTools/blob/master/docs/content/tools/plotHeatmap.rst) and plotProfile (https://github.com/deeptools/deepTools/blob/master/docs/content/tools/plotProfile.rst) functions by providing peaks as bed files and normalized bigwig files.

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Acknowledgements

We apologize to all the investigators whose research could not be appropriately cited due to space limitations. This work was supported by an NIH grant to E.H.C. (R01 CA223231) as well as an NCI Cancer Center Support Grant (P30 CA008748). Y.X. was supported by an NCI Predoctoral to Postdoctoral Fellow Transition Award (F99 CA234949).

Author information

Author notes

  1. These authors contributed equally: Yuchen Xie, Merve Sahin.

Authors and Affiliations

  1. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Yuchen Xie, Sonali Sinha, Yufeng Wang, Amrita M. Nargund, Song Han, Yiyu Dong & Emily H. Cheng

  2. Gerstner Sloan Kettering Graduate School of Biomedical Sciences, New York, NY, USA

    Yuchen Xie

  3. Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Merve Sahin & Christina S. Leslie

  4. Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, USA

    Merve Sahin

  5. Molecular Oncology, Department of Medicine, Washington University, St. Louis, MO, USA

    Yang Lyu & James J. Hsieh

  6. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Emily H. Cheng

  7. Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, Cornell University, New York, NY, USA

    Emily H. Cheng

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Contributions

Y.X. designed and conducted experiments and analyzed the data. E.H.C. designed the research, analyzed the data and supervised the project. S.S., Y.W., S.H. and A.M.N. conducted some experiments. Y.L. analyzed some data. J.J.H. supervised the project. M.S. performed computational analyses. C.S.L. supervised the computational analyses.

Corresponding authors

Correspondence to James J. Hsieh, Christina S. Leslie or Emily H. Cheng.

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Competing interests

J.J.H. has consulted for Eisai and BostonGene and has received clinical trial funding from Bristol Myers Squibb, Merck, AstraZeneca, Exelixis, Calithera and SillaJen. J.J.H. has received research funding from Merck, BostonGene and TScan. All other authors have no competing interests to declare.

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Extended data

Extended Data Fig. 1 Restoration of H3K36me3 in SETD2 mutant ccRCC cells suppresses tumor metastasis.

a-b, Representative bioluminescence images of NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (NSG) mice injected with the indicated JHRCC12 cells into subrenal capsules of unilateral kidneys are shown in a and the quantification of bioluminescence is shown in b (mean ± s.d., n = 5 for H3K36me3-deficient and n = 4 for H3K36me3-proficient). n.s., not significant (two-tailed unpaired Student’s t-test). c, Representative gross images of the indicated organs in mice received subrenal capsule injection of the indicated JHRCC12 cells. Yellow arrowheads indicate metastatic tumors.

Source data

Extended Data Fig. 2 Summary of differentially expressed genes in H3K36me3− compared to H3K36me3+ JHRCC12 cells as well as in SETD2MT compared to SETD2WT ccRCC.

a, Venn diagram showing overlap of differentially upregulated genes (FDR < 0.05, log2(FC) > 0) in H3K36me3− compared to H3K36me3+ (SETD2∆N-transduced) JHRCC12 cells and differentially upregulated genes (FDR < 0.05, log2(FC) > 0) in SETD2MT compared to SETD2WT ccRCC from the TCGA-KIRC dataset. b, Venn diagram showing overlap of differentially downregulated genes (FDR < 0.05, log2(FC) < 0) in H3K36me3− compared to H3K36me3+ (SETD2∆N-transduced) JHRCC12 cells and differentially downregulated genes (FDR < 0.05, log2(FC) < 0) in SETD2MT compared to SETD2WT ccRCC from the TCGA-KIRC dataset. c, The open chromatin peaks comparing H3K36me3− with H3K36me3+ JHRCC12 cells were enriched for the binding motifs of STAT family transcription factors.

Extended Data Fig. 3 Genes that are upregulated in SETD2∆N-transduced compared to SETD2-deficient JHRCC12 cells have higher H3K36me3 levels than downregulated genes in SETD2∆N-transduced cells.

a, Normalized H3K36me3 ChIP-seq counts in SETD2-deficient JHRCC12 cells over gene bodies of stringently up- and down-regulated genes (FDR < 0.05 and log2(FC) > 1) in SETD2-proficient (SETD2∆N-transduced) compared to SETD2-deficient cells. b, Normalized H3K36me3 ChIP-seq counts in SETD2∆N-transduced JHRCC12 cells over gene bodies of stringently up- and down-regulated genes (FDR < 0.05 and log2(FC) > 1) in SETD2-proficient (SETD2∆N-transduced) compared to SETD2-deficient cells. Centers of the boxes indicate median values, the lower and upper hinges correspond to the first and third quartiles and the upper (lower) whiskers extend from the hinge to the largest (smallest) value no further than 1.5 times the distance between the first and third quartiles. P values were calculated using one-sided Wilcoxon rank sum tests. c, Cumulative distribution of H3K36me3 levels in SETD2∆N-transduced cells over gene bodies of significantly upregulated (red) or downregulated (blue) genes (FDR < 0.05) in SETD2-proficient (SETD2∆N-transduced) compared to SETD2-deficient cells. P values were calculated using one-sided KS test comparing H3K36me3 levels in differentially expressed genes to all genes. d, Metaplots showing the normalized average levels of histone marks across gene bodies of stringently upregulated genes (FDR < 0.05 and log2(FC) > 1, n = 174) comparing H3K36me3+ (SETD2∆N-transduced) with H3K36me3− (control) JHRCC12 cells by ChIP-seq. e, Metaplots showing the normalized average levels of histone marks across gene bodies of stringently downregulated genes (FDR < 0.05 and log2(FC) < -1, n = 194) comparing H3K36me3+ (SETD2∆N-transduced) with H3K36me3− (control) JHRCC12 cells by ChIP-seq. TSS, transcription start site; TES, transcription end site.

Extended Data Fig. 4 Loss of SETD2-mediated H3K36me3 induces genome-wide epigenetic changes.

a. Pie chart showing the percentage of differentially enriched ChIP-seq peaks (FDR < 0.05) for each histone mark in promoter, intronic, intergenic, and exonic regions comparing H3K36me3− with H3K36me3+ (SETD2∆N-transduced) JHRCC12 cells. b. Heatmap of differentially accessible ATAC-seq peaks (FDR < 0.05 and log2(FC) > 1) assigned to an upregulated gene, in a 5 kb window grouped by localization at promoter, intron, and intergenic regions (n = 1012). c. Heatmap of differentially accessible ATAC-seq peaks (FDR < 0.05 and log2(FC) > 1) assigned to a downregulated gene, in a 5 kb window grouped by localization at promoter, intron, and intergenic regions (n = 456).

Extended Data Fig. 5 Open chromatin regions that are not affected by the status of H3K36me3 show no differences in both enhancer and promoter marks.

a. Heatmaps for non-differential ATAC-seq peaks (FDR > 0.05; n = 24016) in 5 kb window grouped by localization at promoter, intron, and intergenic regions and heatmaps showing histone modifications in 5 kb window in the same regions of ATAC-seq peaks. b. Metapeak plots of non-differential ATAC-seq peaks (FDR > 0.05) in 5 kb window grouped by localization at promoter, intron, and intergenic regions and metapeak plots of histone modifications in 5 kb window in the same regions of ATAC-seq peaks.

Extended Data Fig. 6 Open chromatin regions that are not affected by the status of H3K36me3 show no differences in histone modifications.

a. Heatmap of non-differential ATAC-seq peaks assigned to an upregulated gene in a 5 kb window grouped by localization at promoter, intron, and intergenic regions (n = 6313) and heatmaps showing histone modifications in 5 kb window in the same regions of ATAC-seq peaks. b. Heatmap of non-differential ATAC-seq peaks assigned to a downregulated gene in a 5 kb window grouped by localization at promoter, intron, and intergenic regions (n = 7835) and heatmaps showing histone modifications in 5 kb window in the same regions of ATAC-seq peaks.

Extended Data Fig. 7 Loss of SETD2-mediated H3K36me3 induces a genome-wide increase of active/permissive histone marks that correlate with increased chromatin accessibility.

a, The extent of co-occurrence of two up/downregulated histone modification ChIP-seq or ATAC-seq peaks were assessed using Fisher’s exact test for each pairwise comparison of those peaks. The plot shows the odds ratio and Bonferroni adjusted P value from Fisher’s exact test. Enrichment ratios greater than 1 implies the peaks of interest are more likely to co-occur, whereas enrichment ratios less than 1 means that the peaks of interest are less likely to co-occur (*, P < 0.05; **, P < 0.01; ***, P < 0.001). b, The extent of co-occurrence of up- or downregulated genes with up- or downregulated histone modification ChIP-seq or ATAC-seq peaks were assessed using Fisher’s exact test for each pairwise comparison. The plot shows the odds ratio and Bonferroni adjusted P value from Fisher’s exact test. Enrichment ratios greater than 1 implies that up- (or down-) regulated genes are enriched in up- (or down-) regulated peaks, while enrichment ratios less than 1 implies that up- (or down-) regulated genes are depleted in up- (or down-) regulated peaks (*, P < 0.05; **, P < 0.01; ***, P < 0.001).

Extended Data Fig. 8 Comparison of ATAC-seq and ChIP-seq at the MMP1 locus.

ATAC-seq tracks and ChIP-seq tracks for the indicated histone marks at the MMP1 locus in H3K36me3− or H3K36me3+ (SETD2∆N-transduced) JHRCC12 cells.

Extended Data Fig. 9 Loss of SETD2-mediated H3K36me3 increases H3K56ac levels.

a. Metaplots showing the normalized distribution profiles of H3K56ac across gene bodies. b. Pie chart showing the percentage of differentially enriched ChIP-seq peaks for H3K56ac (FDR < 0.05; n = 11251) in promoter, intronic, intergenic, and exonic regions comparing H3K36me3− (control) with H3K36me3+ (SETD2∆N-transduced) JHRCC12 cells.

Extended Data Fig. 10 Loss of SETD2-mediated H3K36me3 sensitizes cancer cells to KO of both ASF1A and ASF1B but not the inhibitor of p300/CBP, CCS1477.

a-b, JHRCC12 cells infected with control retrovirus or retrovirus expressing SETD2ΔN were subjected to lentiviral CRISPR/Cas9-mediated KO of ASF1A (exon 3), ASF1B, or both ASF1A (exon 3) and ASF1B and analyzed by the indicated immunoblots in a. Cell death was quantified by annexin-V staining (mean ± s.d., n = 3) in b. **, P < 0.01 (two-tailed unpaired Student’s t-test). c, JHRCC12 cells infected with control retrovirus or retrovirus expressing SETD2ΔN were treated with the p300/CBP inhibitor CCS1477 at the indicated concentrations. Cell death was quantified by annexin-V staining (mean ± s.d., n = 3).

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Xie, Y., Sahin, M., Sinha, S. et al. SETD2 loss perturbs the kidney cancer epigenetic landscape to promote metastasis and engenders actionable dependencies on histone chaperone complexes. Nat Cancer 3, 188–202 (2022). https://doi.org/10.1038/s43018-021-00316-3

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