Abstract
Transcriptional co-regulators have been widely pursued as targets for disrupting oncogenic gene regulatory programs. However, many proteins in this target class are universally essential for cell survival, which limits their therapeutic window. Here we unveil a genetic interaction between histone deacetylase 1 (HDAC1) and HDAC2, wherein each paralog is synthetically lethal with hemizygous deletion of the other. This collateral synthetic lethality is caused by recurrent chromosomal deletions that occur in diverse solid and hematological malignancies, including neuroblastoma and multiple myeloma. Using genetic disruption or dTAG-mediated degradation, we show that targeting HDAC2 suppresses the growth of HDAC1-deficient neuroblastoma in vitro and in vivo. Mechanistically, we find that targeted degradation of HDAC2 in these cells prompts the degradation of several members of the nucleosome remodeling and deacetylase (NuRD) complex, leading to diminished chromatin accessibility at HDAC2–NuRD-bound sites of the genome and impaired control of enhancer-associated transcription. Furthermore, we reveal that several of the degraded NuRD complex subunits are dependencies in neuroblastoma and multiple myeloma, providing motivation to develop paralog-selective HDAC1 or HDAC2 degraders that could leverage HDAC1/2 synthetic lethality to target NuRD vulnerabilities. Altogether, we identify HDAC1/2 collateral synthetic lethality as a potential therapeutic target and reveal an unexplored mechanism for targeting NuRD-associated cancer dependencies.
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Data availability
For availability of the data from DepMap and cBioportal see ‘Analyses of datasets from DepMap’ and ‘Analyses of datasets from cBioportal.’ All raw next-generation sequencing data (SLAM-seq, 3′ end mRNA-seq, CUT&RUN, and ATAC-seq) and the related processed data were deposited in the NCBI Gene Expression Omnibus (GEO) database under accession number GSE202706. Quantitative proteomics data were deposited in the PRIDE database by EMBL’s European Bioinformatics Institute (EMBL-EBI) under accession number PXD034444. Previously published datasets (H3K27ac ChIP and H3K4me3 ChIP in BE(2)-C cells) used in this study can be found on GEO under accession number GSE80154. No restrictions on data availability apply. Source data are provided with this paper.
Code availability
The sources of the codes used in this study can be found in corresponding method sections and are available online.
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Acknowledgements
This work was supported by the National Institutes of Health (NIH) through a NIH Director’s Early Independence Award (DP5-OD26380, M.A.E.) and by the Ono Pharma Foundation. We thank M. G. Jaeger for his critical feedback on the paper prior to submission. We also thank the flow cytometry core, the NGS core at The Scripps Research Institute, and the NGS at La Jolla Institute for Immunology for supporting this work.
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Y.Z. designed and executed most experiments, analyzed the data, and produced figures. D.R. designed and executed the proteomics experiments supervised by B.F.C. U.O. designed and executed the in vivo experiments, supervised by M.J. B.K. designed and executed the ATAC-seq experiments supervised by C.J.O. C.J.O., S.N., and A.D.D. provided critical cell lines for the experiments. J.N.A., A.R., K.B., T.R.B., and P.A.B. contributed to construct cloning, cell proliferation assays, lentivirus production, and data analysis. Y.Z. and M.A.E. wrote the manuscript. M.A.E. conceived, planed, and supervised the research project.
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Nature Structural & Molecular Biology thanks Shaun Cowley and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Carolina Perdigoto and Dimitris Typas were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. Peer reviewer reports are available.
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Extended data
Extended Data Fig. 1 HDAC1 and HDAC2 are selective dependencies in neuroblastoma and lymphoid malignancies.
a, Volcano plots of HDAC1 (left) and HDAC2 (right) differential dependencies in cancer lineages. P value were determined by two-tailed Student’s t-test. b, Boxplots of dependency scores of core regulatory circuitry (CRC) transcription factor genes in neuroblastoma (blue, n = 34) and other cancer cell lines (black, n = 1,020). Boxes represent 25–75 percentiles with whiskers extending to 10–90 percentiles and the center line represents the median of the data. P values were determined by two-tailed Student’s t-test. c, Schematic illustration of CRISPR/Cas9-based competitive growth assay. d, Validation of on-target effects of HDAC1 and HDAC2 guides by immunoblot. e, TIDE (tracking of indels by decomposition) analysis shows a cutting efficiency of 88.7% for sgAAVS1. P values were determined by two-tailed t-test of the variance.
Extended Data Fig. 2 Selective dependencies of HDAC1 and HDAC2 are validated in human cell lines and in vivo.
a, Representative plots of flow cytometry gating for CRISPR/Cas9-based competitive growth assay in BE(2)-C and RPMI-8226 cells. b, Protein levels of HDAC1 and HDAC2 in pre-xenografting BE(2)-C cells with sgLuc or HDAC2-targeting guide and end-point tumors with HDAC2-targeting guide. c. HDAC1 expression versus HDAC1 copy number in all neuroblastoma cell lines. d. HDAC2 expression versus HDAC2 copy number in all lymphoid cell lines. Data from DepMap, CCLE expression and gene copy number 22Q4. e. Protein levels of HDAC1, HDAC2, HDAC3 in the neuroblastoma and lymphoid cell line, including multiple myeloma (MM) and chronic lymphocytic lymphoma (CLL) with/without HDAC1/2 hemizygous deletions highlighted in (c) and (d). f. Proliferation of BE(2)-C cells overexpressing GFP or HDAC1. Mean ± s.e.m., n = 3. g, Boxplots of HDAC1 dependency scores in DLBCL lines (purple, n = 8), non-DLBCL lymphocyte lines (blue, n = 19), and other lineages (grey, n = 962). h, Boxplot of HDAC2 transcript levels in DLBCL lines (purple, n = 20), non-DLBCL lymphocyte lines (blue, n = 63), and other lineages (grey, n = 1,292). Data from DepMap, CRISPR_genetic_effect 22Q1 and CCLE_expression 22Q1. Boxes represent 25–75 percentiles with whiskers extending to 10–90 percentiles and the center line represents the median of the data. P values were determined by two-tailed Student’s t-test.
Extended Data Fig. 3 Hemizygous deletion of HDAC1 leads to high dependency on HDAC2 neuroblastoma.
a, Competitive growth assays with HDAC2-targeting guides in and RPMI-8226. Mean ± s.e.m., n = 3. Experiments were performed at the same time with Fig. 1e, hence shared the same control groups. b, Competitive growth assays with HDAC1-targeting guides in and BE(2)-C cells. Mean ± s.e.m., n = 3. Experiments were performed at the same time with Fig. 1d, hence shared the same control groups. c,d HDAC1 RNA expression (c) or HDAC1 copy number (d) versus HDAC2 dependency in plasma cell lines (left) and soft tissue lines (right). P values (two-tailed) were determined by Pearson correlation coefficient (r). Data from DepMap, CCLE expression and gene copy number 22Q4. e, Representative plots of flow cytometry gating for CRISPR/Cas9-based competitive growth assay in SK-N-AS, MM.1S, KELLY, and CHP-212 cells. f, Competitive growth assays with HDAC1-targeting guides in and SK-N-AS cells. Mean ± s.e.m., n = 3. Experiments were performed at the same time with Fig. 3b, hence shared the same control groups.
Extended Data Fig. 4 HDAC1 and HDAC2 form synthetic lethality in multiple myeloma and neuroblastoma.
a. Representative plots of flow cytometry gating for CRISPR/Cas9-based two-color competitive growth assay in SK-N-AS, OCI-AML2, BE(2)-C, RPMI-8226, and MM.1S cells. b, Control groups related to the two-color competitive growth assay in Fig. 3e. c, Two-color competitive growth assay with HDAC1-sg1 and HDAC2-sg1 in BE(2)-C, RPMI-8226, MM.1 S, and OCI-AML2 cells. Mean ± s.d., n = 3. Proportion of each sub-population normalized to day 4. d, Copy number of genes located at 1p36-1p34.3 in neuroblastoma cell lines highlighted in Extended Data Fig. 3a. Data from CCLE copy number, 22Q4. e. Representative plots of flow cytometry gating for CRISPR/Cas9-based competitive growth assay in GI-ME-N cells. f. Competitive growth assay with HDAC1 or HDAC2 targeting guides in GI-ME-N cells which harbor 1p36 deletion but not HDAC1 deletion. Mean ± s.e.m., n = 3. g, Competitive growth assay with HDAC2 targeting guides MM.1S cells. Mean ± s.e.m., n = 3. Experiments were performed at the same time with Fig. 3f, hence shared the same control groups.
Extended Data Fig. 5 dTAG system allows efficient degradation of HDAC2 without affecting its normal functions.
a, Schematic illustration of the dTAG system. dTAG PROTACs mediate dimerization of the FKBP12F36A-tagged protein of interest and an E3 ubiquitin ligase, which results in ubiquitination and proteasomal degradation of the target protein. b, Representative Sanger sequencing chromatograms of HDAC2 locus of a clone with successful dTAG knock-in. c, Immunoblot validation of HDAC2-dTAG cell lines with and without HDAC1 overexpression. d, Dose response of dTAG-13 treatment in BE(2)-C-HDAC2-dTAG cells (2 h). e, Rank-ordered heatmaps of CUT&RUN signal for HDAC2 in wild-type BE(2)-C cells and HA in BE(2)-C-HDAC2-dTAG cells (ranked based on HA signal at HDAC2-HA binding sites in BE(2)-C-HDAC2-dTAG cells). f,g Correlations of HDAC2 CUT&RUN in BE(2)-C cells versus HA CUT&RUN in BE(2)-C-HDAC2-dTAG cells at HDAC2 binding sites in wild-type BE(2)-C cells (f) (n = 10,832) and at HDAC2-HA binding sites in BE(2)-C-HDAC2-dTAG cells (g) (n = 8,661). P values (two-tailed) were determined by Pearson correlation coefficient (r). h, Co-immunoprecipitation of HDAC2-dTAG (IP: HA) and NuRD subunits. i, Competitive growth assay with HDAC1- or HDAC2-targeting guides in BE(2)-C-HDAC2-dTAG cells. Mean ± s.e.m., n = 3. j, Proliferation of BE(2)-C wild-type cells are not affected by dTAG-13 (500 nM) (blue) compared to the DMSO group (black). Mean ± s.e.m., n = 3.
Extended Data Fig. 6 HDAC2 degradation disrupts transcriptional regulation.
a, Boxplot of SLAM-seq changes at genes with (n = 5,082) or without (n = 8,117) HDAC2 bound at the promoter (TSS ± 1 kb) with ACY-957 (5 µM) or dTAG-13 (500 nM) for 2 h. b, CUT&RUN signals with DMSO control and 2-h dTAG-13 (500 nM) treatment. c, Genomic feature distribution of typical and asymmetric HDAC2 sites. d, Volcano plots of changes in total mRNA abundance (3′-end mRNA-seq) following dTAG-13 treatment (500 nM) for 8 h. e, Boxplots of 3′-end mRNA-seq changes following 8-h dTAG-13 (500 nM) treatment. Genes are grouped by 2-h SLAM-seq data (significantly downregulated, n = 59; significantly upregulated, n = 224; steady, n = 12,916). f,g Volcano plots of 3′-end mRNA-seq changes following dTAG-13 treatment (500 nM) for 24 h (f), and 72 h (g). n = 3. h, Boxplots of 3′-end mRNA-seq change of genes with (n = 5,082) or without (n = 8,117) HDAC2-bound promoter. i, Boxplot of 3′-end mRNA-seq changes following 72-h dTAG-13 treatment (500 nM) in BE(2)C-HDAC2-dTAG cells (No enhancer, n = 7,588; TE, n = 5,026; SE, n = 585). j, Boxplot of neuroblastoma-specific differential dependencies for genes in SK-N-BE(2) cells (unexpressed, CPM < 3 by 3′-end mRNA-seq data, n = 7,973; expressed-and-no-enhancer, n = 7,588; TE-controlled, n = 5,026; SE-controlled, n = 585). k, Volcano plot of total transcript changes following 24-h dTAG-13 (500 nM) treatment in BE(2)-C-HDAC2-dTAG cells overexpressing HDAC1. l, Volcano plots of changes of genes with neuroblastoma-selective dependencies (differential dependency < −0.2, n = 91) by 3′-end mRNA-seq following 8-h, 24-h, and 72-h dTAG-13 treatments (500 nM) in BE(2)C-HDAC2-dTAG cells and 24-h treatment in HDAC1-overexpressing BE(2)C-HDAC2-dTAG cells. For volcano plots, P values were determined by two-tailed Student’s t-test. For boxplots in e and i boxes represent 25–75 percentiles with whiskers extending to 10–90 percentiles and the center line represents the median. P values were determined by two-tailed Student’s t-test. For other boxplots, boxes represent 25–75 percentiles with whiskers extending 1.5 IQR and the center line represents the median. P values were determined by two-tailed Welch’s t-test.
Extended Data Fig. 7 HDAC2 degradation transiently upregulated transcriptome and suppressed mRNA synthesis in long term.
a,b Volcano plots depicting in SLAM-seq changes following 500 nM dTAG-13 treatments for 8 h (a) and 24 h (b). n = 3. P values were determined by two-tailed Student’s t-test. c. Boxplot of SLAM-seq changes at genes not associated with an enhancer (n = 7,588), associated with typical enhancers (n = 5,026), or associated with super enhancers (n = 585) with dTAG-13 (500 nM) for 2 h, 8 h, or 24 h. Boxes represent 25–75 percentiles with whiskers extending 1.5 IQR and the center line represents the median of the data. P values were determined by two-tailed Welch’s t-test.
Extended Data Fig. 8 HDAC2 loss destabilizes the NuRD complex.
a, GSEA of proteomic changes following 2-h dTAG-13 (500 nM) treatment. b, Changes of mRNA abundance given by 3′ end mRNA-seq from Extended Data Fig. 6e versus changes of protein abundance given by quantitative proteomics from Fig. 5b. P value (two-tailed) was determined by Pearson correlation coefficient (r). n = 8,109. c, DMSO-normalized changes in gene expression (qRT-PCR) following dTAG-13 treatments (500 nM). Gene expression levels are normalized to B2M transcript level. See Source Data File for raw data. Mean ± propagated error (∆∆Ct), n = 3. IRS2 was used as a positive control as it was significantly downregulated by 24-h dTAG-13 treatment shown by 3′-end mRNA-seq (Extended Data Fig. 6l). d,e Immunoblots of NuRD subunits (d) and MiDAC subunits (e) with 2-h and 24-h dTAG-13 treatments in BE(2)-C-HDAC2-dTAG cells. f. Dependency scores of MiDAC subunits in neuroblastoma (n = 34) and other cell lines (n = 1,020). Boxes represent 25–75 percentiles with whiskers extending to 10–90 percentiles and the center line represents the median of the data. P values were determined by two-tailed Student’s t-test.
Extended Data Fig. 9 Destabilized NuRD complex leads to dysregulated chromatin accessibility.
a, Genomic feature distribution of HDAC2, MBD3, and CHD4 sites. b. Heatmaps (top) and metaplots (bottom) of ATAC-seq signals with DMSO and dTAG-13 (500 nM) treatments for 2 h (left) and 24 h (right) in triplicates. c, Volcano plots of chromatin accessibility changes measured by ATAC-seq following dTAG-13 (500 nM) treatment for 2 h (left) or 24 h (right). n = 3. d, Boxplots of chromatin accessibility changes grouped by genomic localizations. For 2-h treatment, TSS (n = 14,142), TE (n = 19,687), SE (n = 2,557), and other sites (n = 71,044). For 24-h treatment, TSS (n = 14,167), TE (n = 19,369), SE (n = 2,546), and other sites (n = 68,458). e. Boxplots of chromatin accessibility changes grouped by genomic localizations and HDAC1, MBD3, and CHD4 co-occupancy. For 2-h treatment, at TSS HDAC2-occupied sites n = 4,370, MBD3-occupied sites n = 2,041, and CHD4-occupied sites n = 324; at TE HDAC2-occupied sites n = 3,565, MBD3-occupied sites n = 5,871, and CHD4-occupied sites n = 2,697; at SE HDAC2-occupied sites n = 880, MBD3-occupied sites n = 1,102, and CHD4-occupied sites n = 612; at other sites HDAC2-occupied sites n = 766, MBD3-occupied sites n = 2,045, and CHD4-occupied sites n = 748. For 24-h treatment, at TSS HDAC2-occupied sites n = 4,361, MBD3-occupied sites n = 2,028, and CHD4-occupied sites n = 320; at TE HDAC2-occupied sites n = 3,572, MBD3-occupied sites n = 5,837, and CHD4-occupied sites n = 2,655; at SE HDAC2-occupied sites n = 874, MBD3-occupied sites n = 1,098, and CHD4-occupied sites n = 604; at other sites HDAC2-occupied sites n = 766, MBD3-occupied sites n = 1,969, and CHD4-occupied sites n = 738. f, Immunoblots with subcellular fractionation in BE2C-HDAC2-dTAG cells with dTAG-13 treatments (500 nM). Experiments were performed in two biological replicates. For volcano plots, P values were determined by two-tailed Student’s t-test. Boxplots represent 25–75 percentiles with whiskers extending 1.5 IQR and the center line represents the median. P values were determined by two-tailed Welch’s t-test.
Extended Data Fig. 10 HDAC2 degradation exploits lineage-specific NuRD dependencies.
a, Dependency scores of HDAC1/2-containing complexes in neuroblastoma (n = 34), multiple myeloma (n = 21), and other cell lines (n = 999). b, Boxplots of Pearson correlation coefficients between MBD2, MBD3, MTA2, and MTA3 dependency scores compared to all other genes (n = 17,386). c, Unsupervised clustering of dependency scores of NuRD subunits in all cell lines. For boxplots, boxes represent 25–75 percentiles with whiskers extending to 10–90 percentiles and the center line represents the median of the data. P values were determined by two-tailed Student’s t-test.
Supplementary information
Supplementary Table 1
SLAM-seq_conversion_rate_all, Expressed_gene_annotations, mRNA-seq_ERCC-normalized_CPM, Quantitative_proteomics_2hdtag, Quantitative_proteomics_24hdtag, Expressed_gene_annotations
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Zhang, Y., Remillard, D., Onubogu, U. et al. Collateral lethality between HDAC1 and HDAC2 exploits cancer-specific NuRD complex vulnerabilities. Nat Struct Mol Biol 30, 1160–1171 (2023). https://doi.org/10.1038/s41594-023-01041-4
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DOI: https://doi.org/10.1038/s41594-023-01041-4
