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Catalytic-independent functions of the Integrator–PP2A complex (INTAC) confer sensitivity to BET inhibition

Nature Chemical Biology volume 21, pages 959–970 (2025) Cite this article

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Abstract

Chromatin and transcription regulators are critical to defining cell identity through shaping epigenetic and transcriptional landscapes, with their misregulation being closely linked to oncogenesis. Pharmacologically targeting these regulators, particularly the transcription-activating BET proteins, has emerged as a promising approach in cancer therapy, yet intrinsic or acquired resistance frequently occurs, with poorly understood mechanisms. Here, using genome-wide CRISPR screens, we find that BET inhibitor efficacy in mediating transcriptional silencing and growth inhibition depends on the auxiliary/arm/tail module of the Integrator–PP2A complex (INTAC), a global regulator of RNA polymerase II pause–release dynamics. This process bypasses a requirement for the catalytic activities of INTAC and instead leverages direct engagement of the auxiliary module with the RACK7/ZMYND8–KDM5C complex to remove histone H3K4 methylation. Targeted degradation of the COMPASS subunit WDR5 to attenuate H3K4 methylation restores sensitivity to BET inhibitors, highlighting how simultaneously targeting coordinated chromatin and transcription regulators can circumvent drug-resistant tumors.

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Fig. 1: The INTAC auxiliary/arm/tail module regulates BET inhibition sensitivity independently of the catalytic activities of INTAC.
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Fig. 2: The INTAC auxiliary/arm/tail module facilitates BET inhibitor-induced transcriptional repression and cell cycle arrest.
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Fig. 3: The INTAC auxiliary/arm/tail module has broad roles in regulating gene expression and the epigenetic landscape.
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Fig. 4: The INTAC auxiliary/arm/tail module directly recruits the RACK7–KDM5C H3K4 demethylase complex.
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Fig. 5: WDR5 degradation reduces H3K4 methylation levels and resensitizes cells to BET inhibition.
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Data availability

Raw data and BigWig files of the RNA-seq and CUT&Tag data have been deposited at the NCBI GEO repository with the accession number GSE274476. Expression of the INTAC auxiliary module and sensitivity to BET inhibitor across different cancer cell lines was analyzed and visualized by DepMap (https://depmap.org/portal/). Source data are provided with this paper.

Code availability

The scripts used to analyze the data from this study are freely available via Zenodo at https://doi.org/10.5281/zenodo.14032289 (ref. 71).

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Acknowledgements

This work was supported by grants from the National Key R&D Program of China (2021YFA1300100 to F.X.C., 2021YFA1301700 to F.X.C., 2020YFA0509001 to H.J.), the National Natural Science Foundation of China (32400444 to X.-Y.S., 32070636 to F.X.C., 32300437 to Z.W.), the Research Funds of Hangzhou Institute for Advanced Study, UCAS (2023HIAS-V005 to H.J.), the Guangdong Basic and Applied Basic Research Foundation (2022A1515110272 to J.L.).

Author information

Author notes

  1. These authors contributed equally: Pengyu Fan, Xue-Ying Shang, Aixia Song, Shuo Chen.

Authors and Affiliations

  1. Fudan University Shanghai Cancer Center, Institutes of Biomedical Sciences, State Key Laboratory of Genetic Engineering, Shanghai Key Laboratory of Medical Epigenetics, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China

    Pengyu Fan, Xue-Ying Shang, Aixia Song, Run-Yuan Mao, Jiwei Chen, Zhenning Wang, Hai Zheng, Bolin Tao, Lei Hong, Jiaxian Liu, Hongjie Shen, Fei Lan, Jingdong Cheng & Fei Xavier Chen

  2. Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China

    Pengyu Fan & Peng Zhang

  3. Key Laboratory of RNA Innovation, Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China

    Shuo Chen, Jingchuan Ma & Hai Jiang

  4. Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, the First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, China

    Jiaxian Liu

  5. Department of Orthopedic Oncology, Changzheng Hospital, Second Military Medical University, Shanghai, China

    Wei Xu

  6. Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China

    Wei Jiang & Huijuan Yang

  7. South Australian immunoGENomics Cancer Institute, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, South Australia, Australia

    Qi Zhang

  8. Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Anhui Institute of Innovative Drugs, School of Pharmacy, Anhui Medical University, Hefei, China

    Xiao-Ming Meng

  9. Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China

    Congling Xu

  10. Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China

    Hai Jiang

Authors
  1. Pengyu Fan
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  2. Xue-Ying Shang
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  3. Aixia Song
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  4. Shuo Chen
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  5. Run-Yuan Mao
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  22. Peng Zhang
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  23. Hai Jiang
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  24. Fei Xavier Chen
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Contributions

F.X.C., H.J. and P.Z. conceptualized the study with help from F.L., J. Cheng, C.X, Q.Z. and X.-M.M.; S.C. performed the genome-wide CRISPR screens and J.M. analyzed the CRISPR screens data. P.F. and X.-Y.S. performed most of the cell-based and biochemistry experiments, with help from Z.W., H.Z., B.T., L.H. and J.L.; A.S. analyzed the sequencing and proteomics data with the help from R.-Y. M. and J. Chen; J. Cheng performed the protein–protein interaction prediction using AlphaFold-Mutimer. W.X., W.J., H.S. and H.Y. contributed intellectual input. F.X.C., P.F., X.-Y.S. and A.S. wrote the manuscript with input from all authors. F.X.C. supervised the work.

Corresponding authors

Correspondence to Peng Zhang, Hai Jiang or Fei Xavier Chen.

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Nature Chemical Biology thanks Christopher A. French and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 The INTAC auxiliary module sensitizes cells to BET inhibition.

(a) Table listing the top genes identified through CRISPR screens. (b) Volcano plot showing the log2 fold change (JQ1 versus DMSO) and P-value of sgRNA reads in surviving cells post JQ1 treatment. (c) Cell proliferation analysis for corresponding Eμ-Myc cells in response to JQ1 (300 nM) treatment, measured by cell number counts. Data are mean ± s.d. n = 3 biological replicates, P values were generated using two-way analysis of variance (ANOVA) with Turkey’s multiple comparisons. (d and e) Cell survival analysis using a CCK8 assay in corresponding Eμ-Myc cells, treated with different concentrations of BET inhibitors ABBV-075 (d) and OTX015 (e) for 48 hours. Data are mean ± s.d. n = 3 biological replicates, P values were generated using two-way analysis of variance (ANOVA) with Turkey’s multiple comparisons. (f–i) Correlations between cell sensitivity to JQ1 and gene expression of INTAC auxiliary subunits, including INTS15 (f) (n = 185 cancer cell lines), INTS10 (g) (n = 276 cancer cell lines), INTS13 (h) (n = 276 cancer cell lines), and INTS14 (i) (n = 276 cancer cell lines). Statistical analysis was performed using two-sided t-tests based on the Pearson’s product moment correlation coefficient (left). For the violin plots (right), the center line indicates the median, the top and bottom hinges indicate the first and third quartiles, respectively. (j) Comparison between cell sensitivity to JQ1 and the average expression of the four INTAC auxiliary subunits. n = 185 cancer cell lines. The center line indicates the median, the top and bottom hinges indicate the first and third quartiles, respectively, and the whiskers extend to the quartiles ± 1.5 × interquartile range. (k and l) Cell survival analysis using a CCK8 assay in THP-1 (k) and HGC-27 cells (l) transduced with sgRNAs targeting INTAC auxiliary module subunits, treated with different concentrations of JQ1 for 48 hours. Data are mean ± s.d. n = 3 biological replicates, P values were generated using two-way analysis of variance (ANOVA) with Turkey’s multiple comparisons.

Extended Data Fig. 2 Gene expression and cell cycle changes mediated by the INTAC auxiliary module.

(a) Western blotting analysis of BRD2, BRD3, and BRD4 in their respective knockout Eμ-Myc cells. Data represent three independent experiments. (b) PCA analysis of transcriptomes from sgCtr, sgINTS10 and sgINTS15 cells under DMSO or JQ1 treatment. (c and d) MA plots illustrating the differentially expressed genes (|log2 FC|> 1, adjusted p-values < 0.05) in sgINTS15 (c) and sgINTS10 (d) Eμ-Myc cells following JQ1 (300 nM) treatment, compared to sgCtr cells treated with DMSO for 48 hours. (e and f) MA plots illustrating the differentially expressed genes in sgINTS15 (e) and sgINTS10 cells (f) compared to sgCtr Eμ-Myc cells. Genes with adjusted p-values less than 0.05 and |log2 FC| greater than 1 were considered differentially expressed. (g) Table listing cell cycle-related genes that are downregulated by JQ1 treatment in sgCtr Eμ-Myc cells and rescued by knockouts of INTS15 and INTS10. (h) RT-qPCR analysis of representative cell cycle genes that are downregulated by JQ1 treatment in sgCtr Eμ-Myc cells and rescued by knockouts of INTS15 and INTS10. Data are mean ± s.d. n = 3 biological replicates, P values were generated using one-way analysis of variance (ANOVA) with Dunnett’s multiple comparison. (i) Cell survival analysis using a CCK8 assay in sgINTS15 and sgINTS10 Eμ-Myc cells treated with JQ1 (300 nM) alone or in combination with different concentrations of Abemaciclib for 48 hours. Data are mean ± s.d. n = 3 biological replicates.

Source data

Extended Data Fig. 3 The INTAC auxiliary module regulates gene expression and H3K4 methylation.

(a–c) Curve plots showing gene expression changes (JQ1 versus DMSO) in sgCtr (a), sgINTS15 (b), and sgINTS10 (c) Eμ-Myc cells, with all plots ranked by increasing fold change in sgCtr cells. (d) Western blotting analysis of BRD2, BRD3, BRD4, and c-MYC in DMSO- and JQ1-treated corresponding Eμ-Myc cells. Data represent three independent experiments. (e) Genome browser examples of RNA-seq at the Myc gene in DMSO- and JQ1-treated corresponding Eμ-Myc cells. (f) Venn diagram showing the overlap of CUT&Tag peaks for INTS10 and INTS15. (g and h) Western blotting analysis of H3K4me3, H3K4me2 and c-MYC in THP-1 (g) and HGC-27 cells (h) transduced with sgRNAs targeting INTS10 or INTS15. Data represent three independent experiments. (i) Pie chart showing the proportion of H3K4me3 occupancy changes at promoters of downregulated genes by JQ1 treatment in sgCtr cells. (j) Heatmaps showing INTS15 occupancy at all INTAC auxiliary module-bound peaks in sgCtr and sgINTS15 cells, ranked by decreasing INTS15 occupancy in sgCtr Eμ-Myc cells. (k) Heatmaps showing the H3K4me3 occupancy at all INTAC auxiliary module-bound peaks in DMSO or JQ1 treated corresponding Eμ-Myc cells, ranked by decreasing INTS15 occupancy in sgCtr cells. (l) Metaplot showing the average levels of H3K4me3 centered at all INTAC auxiliary module-bound peaks in corresponding Eμ-Myc cells following DMSO or JQ1 treatment.

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Extended Data Fig. 4 The INTAC auxiliary module recruits RACK7-KDM5C to chromatin.

(a-d) Volcano plots displaying proteins identified by mass spectrometry from Flag-tagged INTS15 (a), INTS10 (b), INTS6 (c) and INTS11 (d) compared to Flag-vector control immunoprecipitations in 293 T cells. INTAC components are highlighted in red. (e and f) Overlaps of CUT&Tag peaks for INTAC auxiliary module (INTS15 and INTS10) with RACK7–KDM5C (e) or NuRD subunits (f). (g) Venn diagram showing the overlap of the genomic peaks between RACK7–KDM5C, NuRD, and INTAC auxiliary module components. (h) Heatmaps showing the occupancy of INTS15, INTS10, RACK7, KDM5C and BRD4 at INTAC auxiliary module-bound promoters, ranked by decreasing INTS15 occupancy. (i) Venn diagram showing the overlap of the genomic peaks between BRD4, RACK7–KDM5C and INTAC auxiliary module components. (j) Genome browser examples showing the CUT&Tag signals of RACK7 and KDM5C at representative cell cycle genes in sgCtr or sgINTS15 Eμ-Myc cells.

Extended Data Fig. 5 The INTAC auxiliary module specifically interacts with complexes of RACK7-KDM5C.

(a and b) Heatmaps (a) and metaplots (b) showing MBD3 occupancy in sgCtr and sgINTS15 Eμ-Myc cells at INTAC auxiliary module-bound promoters. The heatmaps are ranked by decreasing INTS15 occupancy. (c and d) Heatmaps (c) and metaplots (d) showing CHD4 occupancy in sgCtr and sgINTS15 Eμ-Myc cells at INTAC auxiliary module-bound promoters. The heatmaps are ranked by decreasing INTS15 occupancy. (e-h) Metaplots showing the average levels of RACK7 (e), KDM5C (f), MBD3 (g), and CHD4 (h) in sgCtr and sgINTS15 Eμ-Myc cells, centered at all INTAC auxiliary module-bound peaks. (i) In vitro pull-down assays using the immobilized auxiliary, shoulder, and endonuclease modules of INTAC as baits, incubated with the RACK7-KDM5C complex. The bound proteins were subjected to SDS-PAGE followed by Coomassie blue staining. Data represent three independent experiments. (j) Gradient centrifugation of the purified auxiliary module of INTAC incubated with RACK7-KDM5C (top), INTAC auxiliary module alone (median), and RACK7-KDM5C alone (bottom). Fractions were analyzed by SDS-PAGE followed by western blotting. Data represent three independent experiments.

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Extended Data Fig. 6 WDR5 degradation resensitizes cells to BET inhibition.

(a and b) Western blots demonstrating knockout efficiency for RACK7 (a) and KDM5C (b) in Eμ-Myc cells. Data represent three independent experiments. (c) Western blot analysis of MBD2 and MBD3 knockout efficiency in Eμ-Myc cells. Data represent three independent experiments. (d) Western blot analysis of H3K4me2/3 following JQ1 treatment in sgCtr Eμ-Myc cells and cells with either individual or combined knockouts of MBD2 and MBD3. Data represent three independent experiments. (e) Overexpression of HA-tagged wild-type KDM5C or the catalytic-deficient mutant (H514K) in sgKDM5C Eμ-Myc cells. Data represent three independent experiments. (f) Reciprocal immunoprecipitation of endogenous RACK7 and overexpressed HA-KDM5C or HA-KDM5D followed by western blot analysis in 293 T cells. Data represent three independent experiments. (g) Genome browser examples of RNA-seq at Kdm5c and Kdm5d loci in Eμ-Myc cells. (h) Western blot analysis of H3K4me2/3 in JQ1-treated sgKDM5C Eμ-Myc cells with overexpression of KDM5D. Data represent three independent experiments. (i) Western blot analysis of H3K27ac by JQ1 treatment alone or combined with KDM5-C70 in Eμ-Myc cells. Data represent three independent experiments. (j) Cell survival analysis using a CCK8 assay in sgCtr, sgRACK7, and sgKDM5C Eμ-Myc cells treated with different concentrations of JQ1 for 48 hours. Data are mean ± s.d. n = 3 biological replicates. (k) Western blot analysis of H3K4me2/3 in sgCtr and sgINTS10 Eμ-Myc cells post-treatment with DMSO, JQ1, or a combination of MS67 and JQ1. Data represent three independent experiments. (l-n) Curve plot showing gene expression changes by JQ1 treatment alone in sgCtr (l) and sgINTS15 cells (m), and simultaneous treatment with MS67 and JQ1 in sgINTS15 Eμ-Myc cells (n). (o) RT-qPCR analysis of representative cell cycle genes following individual or combined treatment with JQ1 and MS67 in sgINTS10/15 Eμ-Myc cells. Data are mean ± s.d. n = 3 biological replicates, P values were generated using one-way analysis of variance (ANOVA) with Dunnett’s multiple comparison.

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Extended Data Fig. 7 WDR5 degradation restores sensitivity to BET inhibition.

(a) Flow cytometric evaluation of propidium iodide (PI) staining for cell cycle analysis in sgCtr, sgINTS10, sgINTS15 Eμ-Myc cells treated with DMSO, JQ1, or a combination of MS67 and JQ1. (b and c) Cell survival analysis using a CCK8 assay (b) and cell proliferation analysis (c) in sgCtr, sgINTS15 and sgINTS10 Eμ-Myc cells treatment with BET inhibitor ABBV-075 (20 nM) alone or in combination with MS67 (10 μM). Data are mean ± s.d. n = 3 biological replicates, P values were generated using two-way analysis of variance (ANOVA) with Turkey’s multiple comparisons. (d and e) Cell survival analysis using a CCK8 assay (d) and cell proliferation analysis (e) in sgCtr, sgINTS15 and sgINTS10 Eμ-Myc cells treatment with BET inhibitor OTX015 (300 nM) alone or in combination with MS67 (10 μM). Data are mean ± s.d. n = 3 biological replicates, P values were generated using two-way analysis of variance (ANOVA) with Turkey’s multiple comparisons. (f–g) Cell survival analysis using a CCK8 assay in sgINTS15/10 THP-1 (f) (n = 4 biological replicates) and HGC-27 cells (g) (n = 3 biological replicates) treated with JQ1 alone or in combination with different concentrations of MS67 for 48 hours. Data are mean ± s.d.

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Fan, P., Shang, XY., Song, A. et al. Catalytic-independent functions of the Integrator–PP2A complex (INTAC) confer sensitivity to BET inhibition. Nat Chem Biol 21, 959–970 (2025). https://doi.org/10.1038/s41589-024-01807-x

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