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Context-dependent tumor-suppressive BMP signaling in diffuse intrinsic pontine glioma regulates stemness through epigenetic regulation of CXXC5

Nature Cancer volume 3, pages 1105–1122 (2022)Cite this article

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Abstract

The most lethal subtype of diffuse intrinsic pontine glioma (DIPG) is H3K27M. Although ACVR1 mutations have been implicated in the pathogenesis of this currently incurable disease, the impacts of bone morphogenetic protein (BMP) signaling on more than 60% of H3K27M DIPG carrying ACVR1 wild-type remain unknown. Here we show that BMP ligands exert potent tumor-suppressive effects against H3.3K27M and ACVR1 WT DIPG in a SMAD-dependent manner. Specifically, clinical data revealed that many DIPG tumors have exploited the capacity of CHRDL1 to hijack BMP ligands. We discovered that activation of BMP signaling promotes the exit of DIPG tumor cells from ‘prolonged stem-cell-like’ state to differentiation by epigenetically regulating CXXC5, which acts as a tumor suppressor and positive regulator of BMP signaling. Beyond showing how BMP signaling impacts DIPG, our study also identified the potent antitumor efficacy of Dacinostat for DIPG. Thus, our study delineates context-dependent features of the BMP signaling pathway in a DIPG subtype.

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Fig. 1: BMP signaling is downregulated in H3.3K27M and ACVR1 WT DIPG subtype due to high expression of CHRDL1.
Fig. 2: BMP4 inhibits H3.3K27M and ACVR1 WT DIPG growth.
Fig. 3: BMP4 inhibits DIPG subtype growth through SMAD1/5 and SMAD4.
Fig. 4: BMP4 forces DIPG subtype cells to exit from a prolonged stem-cell-like state and induces differentiation.
Fig. 5: Epigenetic landscape is changed by BMP4 treatment in DIPG subtype.
Fig. 6: CXXC5 functions as a tumor suppressor in DIPG subtype.
Fig. 7: CXXC5 positively regulates BMP signaling.
Fig. 8: HDAC inhibitors are potential therapeutic strategy for this subtype of DIPG, boosting BMP signaling.

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Data availability

The raw RNA-seq, ChIP–seq and ATAC-seq data that support the findings of this study have been deposited with the Genome Sequence Archive in BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, https://bigd.big.ac.cn/gsa-human, under the accession number: HRA000612. Previously published data that were reanalyzed here are available under accession numbers GSE50021, GSE126319, GSE94259, GSE128745 and GSE1105722. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Change history

  • 05 September 2022

    In the version of this article initially published, the three columns at the right of the SU-DIPG17 panel of Fig. 2b were obscured by a white box, which has been removed from the HTML and PDF versions of the article.

References

  1. Hargrave, D., Bartels, U. & Bouffet, E. Diffuse brainstem glioma in children: critical review of clinical trials. Lancet Oncol. 7, 241–248 (2006).

    Article  PubMed  Google Scholar 

  2. Buczkowicz, P. et al. Genomic analysis of diffuse intrinsic pontine gliomas identifies three molecular subgroups and recurrent activating ACVR1 mutations. Nat. Genet. 46, 451–456 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Fontebasso, A. M. et al. Recurrent somatic mutations in ACVR1 in pediatric midline high-grade astrocytoma. Nat. Genet. 46, 462–466 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Taylor, K. R. et al. Recurrent activating ACVR1 mutations in diffuse intrinsic pontine glioma. Nat. Genet. 46, 457–461 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Wu, G. et al. The genomic landscape of diffuse intrinsic pontine glioma and pediatric non-brainstem high-grade glioma. Nat. Genet. 46, 444–450 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Mackay, A. et al. Integrated molecular meta-analysis of 1,000 pediatric high-grade and diffuse intrinsic pontine glioma. Cancer Cell 32, 520–537.e5 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Funato, K., Major, T., Lewis, P. W., Allis, C. D. & Tabar, V. Use of human embryonic stem cells to model pediatric gliomas with H3.3K27M histone mutation. Science 346, 1529–1533 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Pathania, M. et al. H3.3K27M cooperates with Trp53 loss and PDGFRA gain in mouse embryonic neural progenitor cells to induce invasive high-grade gliomas. Cancer Cell 32, 684–700.e9 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Filbin, M. G. et al. Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq. Science 360, 331–335 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Nagaraja, S. et al. Histone variant and cell context determine H3K27M reprogramming of the enhancer landscape and oncogenic state. Mol. Cell 76, 965–980.e12 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Castel, D. et al. Transcriptomic and epigenetic profiling of ‘diffuse midline gliomas, H3 K27M-mutant’ discriminate two subgroups based on the type of histone H3 mutated and not supratentorial or infratentorial location. Acta Neuropathol. Commun. 6, 117 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Khuong-Quang, D. A. et al. K27M mutation in histone H3.3 defines clinically and biologically distinct subgroups of pediatric diffuse intrinsic pontine gliomas. Acta Neuropathol. 124, 439–447 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Castel, D. et al. Histone H3F3A and HIST1H3B K27M mutations define two subgroups of diffuse intrinsic pontine gliomas with different prognosis and phenotypes. Acta Neuropathol. 130, 815–827 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Carvalho, D. et al. ALK2 inhibitors display beneficial effects in preclinical models of ACVR1 mutant diffuse intrinsic pontine glioma. Commun. Biol. 2, 156 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Mucha, B. E., Hashiguchi, M., Zinski, J., Shore, E. M. & Mullins, M. C. Variant BMP receptor mutations causing fibrodysplasia ossificans progressiva (FOP) in humans show BMP ligand-independent receptor activation in zebrafish. Bone 109, 225–231 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Fortin, J. et al. Mutant ACVR1 arrests glial cell differentiation to drive tumorigenesis in pediatric gliomas. Cancer Cell 37, 308–323.e12 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Chaikuad, A. & Bullock, A. N. Structural basis of intracellular TGF-β signaling: receptors and Smads. Cold Spring Harb. Perspect. Biol. 8, a022111 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Urist, M. R. Bone: formation by autoinduction. Science 150, 893–899 (1965).

    Article  CAS  PubMed  Google Scholar 

  19. Sanchez-Duffhues, G., Williams, E., Goumans, M. J., Heldin, C. H. & Ten Dijke, P. Bone morphogenetic protein receptors: structure, function and targeting by selective small molecule kinase inhibitors. Bone 138, 115472 (2020).

    Article  CAS  PubMed  Google Scholar 

  20. Sun, Y. et al. Potent anti-tumor efficacy of palbociclib in treatment-naive H3.3K27M-mutant diffuse intrinsic pontine glioma. Ebiomedicine 43, 171–179 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Piunti, A. et al. Therapeutic targeting of polycomb and BET bromodomain proteins in diffuse intrinsic pontine gliomas. Nat. Med. 23, 493–500 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Grasso, C. S. et al. Functionally defined therapeutic targets in diffuse intrinsic pontine glioma. Nat. Med. 21, 555–559 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Nagaraja, S. et al. Transcriptional dependencies in diffuse intrinsic pontine glioma. Cancer Cell 31, 635–652.e6 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Anastas, J. N. Re-programing chromatin with a bifunctional LSD1/HDAC inhibitor induces therapeutic differentiation in DIPG. Cancer Cell 36, 528–544.e10 (2019).

    Article  CAS  PubMed  Google Scholar 

  25. Xu, C. et al. Patient-derived DIPG cells preserve stem-like characteristics and generate orthotopic tumors. Oncotarget 8, 76644–76655 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Ouahoud, S., Hardwick, J. C. H. & Hawinkels, L. Extracellular BMP antagonists, multifaceted orchestrators in the tumor and Its microenvironment. Int. J. Mol. Sci. 21, 3888 (2020).

    Article  CAS  PubMed Central  Google Scholar 

  27. Puget, S. et al. Mesenchymal transition and PDGFRA amplification/mutation are key distinct oncogenic events in pediatric diffuse intrinsic pontine gliomas. PLoS ONE 7, e30313 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Walsh, D. W., Godson, C., Brazil, D. P. & Martin, F. Extracellular BMP-antagonist regulation in development and disease: tied up in knots. Trends Cell Biol. 20, 244–256 (2010).

    Article  CAS  PubMed  Google Scholar 

  29. Cyr-Depauw, C. et al. Chordin-Like 1 suppresses bone morphogenetic protein 4-induced breast cancer cell migration and invasion. Mol. Cell. Biol. 36, 1509–1525 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Massagué, J. TGFβ signalling in context. Nat. Rev. Mol. Cell Biol. 13, 616–630 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  31. VanBrocklin, M. W., Verhaegen, M., Soengas, M. S. & Holmen, S. L. Mitogen-activated protein kinase inhibition induces translocation of Bmf to promote apoptosis in melanoma. Cancer Res. 69, 1985–1994 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Yoo, J. et al. Transforming growth factor-β-induced apoptosis is mediated by Smad-dependent expression of GADD45b through p38 activation. J. Biol. Chem. 278, 43001–43007 (2003).

    Article  CAS  PubMed  Google Scholar 

  33. Mohammad, F. et al. EZH2 is a potential therapeutic target for H3K27M-mutant pediatric gliomas. Nat. Med. 23, 483–492 (2017).

    Article  CAS  PubMed  Google Scholar 

  34. Harutyunyan, A. S. et al. H3K27M induces defective chromatin spread of PRC2-mediated repressive H3K27me2/me3 and is essential for glioma tumorigenesis. Nat. Commun. 10, 1262 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Whyte, WarrenA. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153, 307–319 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Loven, J. et al. Selective inhibition of tumor oncogenes by disruption of super-enhancers. Cell 153, 320–334 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Xiong, X., Tu, S., Wang, J., Luo, S. & Yan, X. CXXC5: a novel regulator and coordinator of TGF-beta, BMP and Wnt signaling. J. Cell. Mol. Med. 23, 740–749 (2019).

    Article  CAS  PubMed  Google Scholar 

  38. Andersson, T. et al. CXXC5 is a novel BMP4-regulated modulator of Wnt signaling in neural stem cells. J. Biol. Chem. 284, 3672–3681 (2009).

    Article  CAS  PubMed  Google Scholar 

  39. Yan, X. et al. CXXC5 suppresses hepatocellular carcinoma by promoting TGF-beta-induced cell cycle arrest and apoptosis. J. Mol. Cell. Biol. 10, 48–59 (2018).

    Article  CAS  PubMed  Google Scholar 

  40. Voo, K. S., Carlone, D. L., Jacobsen, B. M., Flodin, A. & Skalnik, D. G. Cloning of a mammalian transcriptional activator that binds unmethylated CpG motifs and shares a CXXC domain with DNA methyltransferase, human trithorax, and methyl-CpG binding domain protein 1. Mol. Cell Biol. 20, 2108–2121 (2000).

    Article  CAS  PubMed  Google Scholar 

  41. Ravichandran, M. et al. Rinf regulates pluripotency network genes and Tet enzymes in embryonic stem cells. Cell Rep 28, 1993–2003.e5 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Vitanza, N. A. et al. Optimal therapeutic targeting by HDAC inhibition in biopsy-derived treatment-naive diffuse midline glioma models. Neuro Oncol. 23, 376–386 (2021).

    Article  CAS  PubMed  Google Scholar 

  43. Subramanian, A. et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell 171, 1437–1452.e17 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Sun, X. et al. A chemical approach for global protein knockdown from mice to non-human primates. Cell Discov. 5, 10 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Sachdeva, R. et al. ID1 is critical for tumorigenesis and regulates chemoresistance in glioblastoma. Cancer Res. 79, 4057–4071 (2019).

    Article  CAS  PubMed  Google Scholar 

  46. Xi, Q. et al. A poised chromatin platform for TGF-beta access to master regulators. Cell 147, 1511–1524 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Wang, Q. et al. The p53 family coordinates Wnt and Nodal inputs in mesendodermal differentiation of embryonic stem cells. Cell Stem Cell 20, 70–86 (2017).

    Article  CAS  PubMed  Google Scholar 

  48. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Ramirez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Thorvaldsdóttir, H., Robinson, J. T. & Mesirov, J. P. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform. 14, 178–192 (2013).

    Article  PubMed  Google Scholar 

  52. Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal 6, pl1 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank all the patients and families for donating tissues for this research. We are grateful to H. Zheng for the luciferase-GFP plasmid; to Y. Lu from the Laboratory Animal Research Center (Tsinghua University) for technical assistance with histological analyses and immunohistochemistry experiments; to G. Wang (Tsinghua University) for the cleaved-CASPASE3 antibody and helpful discussion and to J. Massagué (Memorial Sloan Kettering Cancer Center), Y.-G. Chen, C. David, H. Zheng, W. Wu, Y. Rao (Tsinghua University) and Y. Sun (Tiantan Hospital) for valuable discussions; and to staff of the Laboratory Animal Research Center for animal experiments during the COVID-19 pandemic. Finally, we thank Z. Zhou (Tsinghua University) for the graphic design. Q.X. is supported by Ministry of Science and Technology of China (2018YFA0107702), National Natural Science Foundation of China (31471229, 31771622 and 91540108) and Tsinghua-Peking Center for Life Sciences. Y.T. is supported by The Recruitment Program of Global Experts of China, National Natural Science Foundation of China (81772655), and Innovative Research Team of High-Level Local Universities in Shanghai (SSMU-ZDCX20180800). L.Z. is supported by Beijing municipal administration of Hospitals Clinical Medicine Development of Special Funding Support (ZYLX201608), Beijing Municipal Natural Science Foundation (7161004) and National Natural Science Foundation of China (81872048).

Author information

Author notes

  1. These authors contributed equally: Ye Sun, Kun Yan, Yi Wang, Cheng Xu.

Authors and Affiliations

  1. MOE Key Laboratory of Protein Sciences, School of Life Sciences, Tsinghua University, Beijing, China

    Ye Sun, Dan Wang, Wei Zhou & Qiaoran Xi

  2. Tsinghua-Peking Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing, China

    Kun Yan, Shuning Guo, Yanqiu Shao & Qiangfeng C. Zhang

  3. Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China

    Yi Wang, Cheng Xu, Shaobo Shan & Liwei Zhang

  4. Department of Pathophysiology, State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of National Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Yujie Han & Yujie Tang

  5. Joint Graduate Program of Peking-Tsinghua-NIBS, Tsinghua University, Beijing, China

    Lei Tang, Qiangfeng C. Zhang & Qiaoran Xi

  6. MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China

    Qiangfeng C. Zhang

  7. China National Clinical Research Center for Neurological Diseases, Beijing, China

    Liwei Zhang

  8. Beijing Key Laboratory of Brain Tumor, Beijing, China

    Liwei Zhang

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Contributions

Y. Sun and K.Y. performed most of the experiments. Y.W., C.X., S.S. and L. Z. provided the biopsy samples and clinical data of DIPG patients from Tiantan hospital (Beijing). Y.H. and Y.T. provided SU-DIPG4 and SU-DIPG17 cell samples. D.W. performed 3D culture of DIPG cells. S.G. helped with western blot assay. Y. Sun and K.Y. performed RNA-seq and ChIP–seq experiments. L.T. performed ATAC-seq experiments. Q.C.Z. supervised the ATAC-seq experiments. K.Y. performed RNA-seq, ChIP–seq, and ATAC-seq analyses. K.Y. and W.Z. performed scRNA-seq analysis; Y. Shao provided bioinformatic analysis tools. Q.X., Y. Sun and K.Y. designed the experiments. All authors contributed ideas to the project. Q.X. supervised the project. Q.X., Y. Sun and K.Y. wrote the manuscript. All authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Liwei Zhang or Qiaoran Xi.

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The authors declare no competing interests.

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Nature Cancer thanks Rosemary Akhurst, Joan Seoane 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 BMP signaling is downregulated and CHRDL1 is highly expressed in H3.3K27M and ACVR1 WT DIPG subtype.

(a) Heatmap to show single sample GSEA (ssGSEA) scores of hallmark gene sets in control group and the drug (JQ1, Panobinostat, THZ1, or Corin) treatments group in SU-DIPG6 or SU-DIPG13 cells (GEO: GSE94259, GSE1105722). Each column represents a sample with or without drug. Each row represents the specific gene signature of the pathway and the normalized z-score of the ssGSEA score corresponding to the sample is displayed in the heatmap where the colors correspond to the normalized z-score. (b) GSEA analysis using the “BMP signaling signature” gene set to compare PPCs and the H3.3K27M and ACVR1 WT DIPG cells (SU-DIPG6 and SU-DIPG13). (c, d) GSEA analysis using the “BMP signaling signature” gene set to compare PPCs and the H3.1K27M and ACVR1 mutant DIPG cell (SU-DIPG4)23 (c); or to compare 10 normal tissues and 18 H3.3K27M and ACVR1 WT DIPG tissues (d). (e) FPKM (Fragments Per Kilobase of exon model per Million mapped fragments) values of CHRDL1 in RNA-seq data of TT150630, TT150714, and PPCs (n = 2 independent experiments). (f) FPKM values of CHRDL1 in RNA-seq data of pons and a group of H3K27M DIPG tissues. Genotype of H3 and ACVR1 in each sample is marked10. (g) Unsupervised clustering of single-cell H3K27M DIPG RNA-seq data using the most variable genes9. (h-k) Gene expression of OLIG2, ASCL1, SOX2 and CHRDL1 in (g). Expression of each gene was scaled to [0, 2] for visualization. (l) Among the CHRDL1-expressing cells in (g), boxplots representing CHRDL1 gene expression differences between ACVR1 mutant (MUT, blue, n = 286 tumor single cells) and wild-type (WT, red, n = 136 tumor single cells) (left). Boxplots representing CHRDL1 gene expression differences between H3.3K27M (red, n = 406 tumor single cells) and H3.1K27M (blue, n = 16 tumor single cells) (right). Boxplots define the interquartile range (IQR) split by the median, with whiskers extending to the most extreme values within 1.5 × IQR beyond the box, statistical significance was calculated by two-tailed unpaired Student’s t-test.

Source data

Extended Data Fig. 2 High expression of CHRDL1 contributes to the low activity of BMP signaling and tumor progression in this DIPG subtype.

(a) Kaplan–Meier survival curves for DIPG patients in the UCSC xena patient cohort27, separated into CHRDL1 high(n = 8 patients) and CHRDL1 low(n = 9 patients) survival groups. Log-rank test was performed. (b) qPCR analysis of CHRDL1 expression in indicated Ctr or CHRDL1 KD cells. n = 3 independent experiments. (c) Immunoblotting analysis of p-SMAD1/5 and SMAD1 in TT150714 and SU-DIPG17 (Ctr or CHRDL1 KD DIPG cells) with or without BMP4 (25 ng/mL) for 2 hr. (d) qPCR analysis of BMP signaling response genes mRNA expression in Ctr and CHRDL1 KD TT150714 and SU-DIPG17 cells with or without BMP4 (25 ng/mL) treatment. Data represents the mean ± S.D., statistical significance was calculated by two-tailed unpaired Student’s t-test, n = 3 independent experiments. (e) Neural sphere formation of indicated cell lines (Ctr or CHRDL1 KD DIPG cells) for 10 days (n = 3 independent experiments). (f) Neural sphere counts from (e). (g) The normalized bioluminescence activity was plotted and the statistical difference between TT150630 Ctr and CHRDL1 KD groups was significant (n = 5 mice in Ctr and n = 5 mice in CHRDL1 KD). (h) Representative bioluminescence images from animals implanted with 5 ×105 Ctr (n = 5 mice) or CHRDL1 KD(n = 5 mice) of luciferase-GFP engineered-SU-DIPG17 cells in the pons at day 238. The heatmap superimposed over the mouse heads represents the degree of photon emission by DIPG cells expressing firefly luciferase. (i) The normalized bioluminescence activity was plotted and the statistical difference between SU-DIPG17 Ctr and CHRDL1 KD groups was significant (n = 5 mice in Ctr and n = 5 mice in CHRDL1 KD). (j) Kaplan–Meier analysis from animals implanted with SU-DIPG17 cells with (n = 5 mice) or without (n = 5 mice) CHRDL1 KD in the pons. Log-rank test was performed. (k) Immunofluorescence of pons section from animals implanted with TT150630 cells with or without CHRDL1 KD for anti-human nuclear antigen (HNA), OLIG2 and GFAP. Scale bars, 50 µm. This figure represents 9 independent tissues. (l) Quantification of OLIG2-positive cells in all tumor cells (HNA positive) from animals implanted with TT150630 cells with or without CHRDL1 KD. n = 9 independent tissue samples. For g and i, boxplots define the interquartile range (IQR) split by the median, with whiskers extending to the most extreme values within 1.5 × IQR beyond the box, statistical significance was calculated by two-tailed unpaired Student’s t-test. The experiments in c have been repeated three times with similar results. For b, f and i, data represents the mean ± S.D., statistical significance was calculated by two-tailed unpaired Student’s t-test.

Source data

Extended Data Fig. 3 BMPs inhibit ACVR1 WT DIPG subtype cells sphere formation and growth.

(a) Immunoblotting analysis of p-SMAD1/5 and SMAD1 levels in TT150630 and TT150714 treated with LDN-193189 (LDN) (200 nM), BMP4 (50 ng/mL), or BMP4 (50 ng/mL) plus LDN-193189 (200 nM). (b) top: Neural sphere formation of the indicated cell lines treated with vehicle, 50 ng/mL BMP2 or 25 ng/mL BMP2 for 10 days, n = 3 independent experiments; bottom: Neural sphere counts from top panel. All p value were generated by comparing to control group. (c) top: Neural sphere formation of the indicated cell lines treated with vehicle or indicated concentration of BMP4 for 10 days, n = 3 independent experiments; bottom: Neural sphere counts from top panel. All p value were generated by comparing to control group.(d) Viability (metabolic capacity) of indicated cells (n = 3 independent experiments) treated with vehicle or indicated concentration of BMP4.(e) Viability (metabolic capacity) of SU-DIPG4 cells (n = 3 independent experiments) treated with vehicle or indicated concentration of BMP4 or LDN. For b-e, data represents the mean ± S.D., statistical significance was calculated by two-tailed unpaired Student’s t-test. The experiments in a have been repeated three times with similar results.

Source data

Extended Data Fig. 4 BMP4 prolongs the survival of DIPG xenograft model mice.

(a) Schematic for the construction of a xenograft mouse model upon orthotopic injection of 1 ×105 BMP4-pretreated (50 ng/mL) 24 hr or untreated DIPG cells. Created with BioRender.com. (b) The normalized bioluminescence activity in TT150630 mouse model was plotted and the statistical difference between BMP4 pre-treated and vehicle treated groups was significant (n = 6 mice in each group). (c) The normalized bioluminescence activity in SU-DIPG17 mouse model was plotted and the statistical difference between BMP4 pre-treated and vehicle treated groups was significant (n = 6 mice in each group).(d) Kaplan–Meier analysis from animals implanted with TT150630 cells with indicated treatment (n = 6 mice in Ctr group vs n = 5 mice in BMP4 pretreatment group) in the pons. Log-rank test was performed. (e) Representative images of pons from animals implanted with SU-DIPG17 cells with or without BMP4 treatment analyzed by H&E staining. Regions marked by the box are magnified below. Scale bars, 1,000 µm (top), 50 µm (middle) and 20 µm (bottom). (f) Immunofluorescence of pons section from animals implanted with SU-DIPG17 cells with or without BMP4 treatment for anti-human nuclear antigen (HNA). Scale bars, 1,000 µm (top), 50 µm (middle) and 20 µm (bottom). For b and c, boxplots define the interquartile range (IQR) split by the median, with whiskers extending to the most extreme values within 1.5 × IQR beyond the box, statistical significance was calculated by two-tailed unpaired Student’s t-test. The experiments in e and f have been repeated three times with similar results.

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Extended Data Fig. 5 BMP4 forces DIPG subtype cells to exit from a prolonged stem-cell-like state.

(a) The heatmap of OPC, AC and cell-cycle-related genes in RNA-seq transcriptome analysis of TT150714 cells treated with BMP4 (50 ng/mL) at indicated timepoints (n = 2 independent experiments). (b) Volcano plots showing differentially expressed genes from transcriptome datasets of TT150714 with or without BMP4 (50 ng/mL) treatment for 24 hr. Upregulated genes (n = 323; |LFC | ≥ 1.5-fold; p < 0.05) are red dots; downregulated genes (n = 193; |LFC | ≥ 1.5-fold; p < 0.05) are in blue. Individual genes of interest are depicted. (c) The heatmap of OPC, AC and cell-cycle-related genes in RNA-seq transcriptome analysis of SU-DIPG4 cells treated with BMP4 (50 ng/mL) at indicated timepoints (n = 2 independent experiments). (d) Volcano plots showing differentially expressed genes from transcriptome datasets of SU-DIPG4 with or without BMP4 treatment for 24 hr. Upregulated genes (n = 31; |LFC | ≥ 1.5-fold; p < 0.05) are red dots; downregulated genes (n = 55; |LFC | ≥ 1.5-fold; p < 0.05) are in blue. Individual genes of interest are depicted. (e) Immunofluorescence staining for GFAP, OLIG2 and phosphorylated SMAD1/5 proteins in TT150714 cells with or without BMP4 (50 ng/mL) treatment for 48 hr. Scale bars, 100 μm. (f) Representative immunohistochemistry of OLIG2 and GFAP in pons from animals implanted with TT150630 cells with or without BMP4 treatment. Scale bars, 50 μm.(g) Immunoblotting for p-SMAD1/5, SMAD1, p21CIP1 and GAPDH proteins in TT150630 and TT150714 DIPG cells with BMP4 treatment (50 ng/mL) at indicated timepoints. (h) Cell cycle analysis of indicated cells with or without BMP4 treatment (50 ng/mL) for 24 hr by propidium Iodide staining. n = 3 independent experiments. (i) Quantification of cells (%) in each cell cycle phase of cells analyzed by flow cytometry in (h), n = 3 independent experiments, data represents the mean ± S.D, statistical significance was calculated by two-tailed unpaired Student’s t-test. (j) Plots of annexin V (AV) and Propidium Iodide (PI) FACS analyses in TT150630 DIPG cells treated with indicated concentration at indicated conditions. n = 3 independent experiments (k) Bar plots show early apoptotic (AV + DAPI − ) or late apoptotic (AV + DAPI + ) cell population percentage from each condition. n = 3 independent experiments, two-tailed unpaired Student’s t-tests was performed. For b and d, p value was calculated by Cuffdiff. The experiments in e, f and g have been repeated three times with similar results.

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Extended Data Fig. 6 Global alteration in H3K27ac, H3K27me3, and H3K27M-modified chromatin in DIPG subtype cells treated with BMP4.

(a) Pie charts show the percentage of genomic distribution of SMAD1 ChIP–seq peaks in the vehicle or BMP4 (50 ng/mL) treated TT150630 cells. The hg38 reference genome distribution was set up for control. (b) Metagene plots showing the average ChIP–seq signal for SMAD1, H3K27ac, H3K27me3 and H3K27M for all of the BMP4 up- (top) and downregulated (down) genes in TT150630 cells treated with vehicle or BMP4 for 2 hr. (c) Identification of subgroup-specific genes with concordant changes in both expression and SEs in TT150630 DIPG cells. x axis: LFC of gene expression between vehicle and BMP4 treatment for 2 hr in TT150630 DIPG cells. y axis: LFC of SEs associated with the gene. Red: significantly upregulated genes. Blue: significantly downregulated genes. Gray: no significant changes in expression. (d) The HOXA family genes heatmap from RNA-seq transcriptome analysis of TT150630 cells treated with BMP4 at the indicated timepoints (n = 2 independent experiments). (e) IGV tracks for H3K27me3 ChIP–seq (with vehicle or BMP4 treated for 24 hr or CHRDL1 KD) in TT150630 cells at indicated genes loci. (f) Identification of subgroup-specific genes with concordant changes in both expression and H3K27ac signals in TT150630 cells. X axis: LFC of gene expression between control (n = 2 independent experiments) and BMP4 treatment (n = 2 independent experiments). Y axis: LFC of H3K27ac signals at a promoter or enhancer associated with the gene. Genes with significantly differentially acetylated regulatory regions between control and BMP4 treatment are shown. Red: significantly upregulated genes. Blue: significantly downregulated genes. Gray: no significant changes in expression. (g) Super enhancers (SEs) detected in TT150630 DIPG CHRDL1 KD cells.

Extended Data Fig. 7 CXXC5 is the primary target gene of BMP signaling in DIPG subtype cells.

(a) qPCR analysis of CXXC5 mRNA expression in control or 2 hr-BMP4 (50 ng/mL) -treated indicated DIPG cells with or without pretreatment with 10 µg/mL cycloheximide (CHX) for 1 hr. n = 3 independent experiments. (b) FPKM values of CXXC5 in RNA-seq data of TT150630 DIPG cells treated with BMP4 at indicated timepoints (n = 2 independent experiments). (c) Immunoblotting analysis of CXXC5 and TUBULIN in indicated DIPG cell lines treated with BMP4 (50 ng/mL) at the indicated timepoints. The relative intensity of the CXXC5 protein level compared to TUBULIN is indicated. (d, e) qPCR analysis of CXXC5 relative expression in control and SMAD4 KD (d) or control, SMAD1 KD, and SMAD1/5 KD (e) DIPG cells treated with BMP4 (50 ng/mL) at indicated timepoints. n = 3 independent experiments. (f) Immunoblotting analysis for phosphorylated SMAD1/5, total SMAD1, CXXC5 and GAPDH proteins in the indicated cell lines. (g) qPCR analysis of ID1, SMAD7 and CXXC5 expression in SU-DIPG4 cells treated with BMP4 (50 ng/mL) at the indicated timepoints. n = 3 independent experiments. (h) Immunoblotting analysis for phosphorylated SMAD1/5, total SMAD1, CXXC5 and GAPDH proteins in SU-DIPG4 cell lines treated with BMP4 (50 ng/mL) at the indicated timepoints. (i) Immunoblotting analysis of CXXC5 and TUBULIN in indicated DIPG cell lines and PPC. SU-DIPG17 (1), TT150714 (2), TT150630 (3), TT160728 (4), TT150603 (5), TT160310 (6), TT151201 (7) and TT160518 (8). The relative intensity of the CXXC5 protein level is indicated. The experiments in c, f, h and i have been repeated three times with similar results. For a, d, e and g, data represents the mean ± S.D., statistical significance was calculated by two-tailed unpaired Student’s t-test.

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Extended Data Fig. 8 CXXC5 functions as a tumor suppressor in DIPG subtype.

(a) CXXC5 KD verification in TT150630 and TT150714 cells. Cells were validated by immunoblotting analysis using antibodies against CXXC5 and TUBULIN. (b) Neural sphere formation of control and CXXC5 KD TT150714 cells (n = 3 independent experiments) and neural sphere counts. (c) Cell cycle analysis of indicated cells by propidium Iodide staining. (d) Quantification of cells (%) in each cell cycle phase of cells analyzed by flow cytometry in (c), n = 3 independent experiments samples, data represents the mean ± S.D, statistical significance was calculated by two-tailed unpaired Student’s t-test. (e) Immunoblotting for PARP1, cleaved PARP1, CXXC5 and GAPDH in the indicated TT150630 DIPG cell line.(f) Overlapping plots of Annexin V (AV) and Propidium Iodide (PI) FACS analyses in CXXC5 instantaneous overexpression TT150630 cells. (g) Overlapping plots of Annexin V (AV) and Propidium Iodide (PI) FACS analyses in Dox-inducible CXXC5 overexpression TT150630 DIPG cells at indicated timepoints. (h) Neural sphere formation of indicated cells treated with Dox (750 ng/mL) or none for 10 days. (n = 3 independent experiments). (i) Neural sphere counts in (h). For b and i, data represents the mean ± S.D., statistical significance was calculated by two-tailed unpaired Student’s t-test. The experiments in a and e have been repeated three times with similar results.

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Extended Data Fig. 9 Depletion of CXXC5 alters transcription of cell-cycle-related genes.

(a) Pie charts show the percentage of genomic distribution of CXXC5 ChIP–seq peaks in the vehicle or BMP4 treated TT150630 cells. The hg38 reference genome distribution was set up for control. (b) IGV tracks for RNA-seq and CXXC5 ChIP–seq (with vehicle or BMP4 treated for 24 hr) in TT150630 cells at indicated genes loci. (c) qPCR analysis of indicated genes expression in control and CXXC5 KD DIPG cells treated with BMP4 (50 ng/mL) at indicated timepoints. n = 3 independent experiments.(d) Immunoblotting for CXXC5, p21CIP1 and GAPDH proteins in TT150630 and TT150714 control and CXXC5 KD cells with or without BMP4 treatment (50 ng/mL) for 24 hr. (e) qPCR analysis of SMAD1, SMAD5 and p21CIP1 relative expression in TT150630 control, SMAD1 KD and SMAD1/5 double KD cells treated with BMP4 (50 ng/mL) at indicated timepoints. n = 3 independent experiments. (f) qPCR analysis of SMAD4 and p21CIP1 relative expression in TT150630 control and SMAD4 KD cells treated with BMP4 (50 ng/mL) at indicated timepoints. n = 3 independent experiments. (g) GSEA analysis using the G2M checkpoint gene set to compare control and CXXC5-inducible overexpression TT150630 DIPG cells. (h) qPCR analysis of p21CIP1 relative expression in SU-DIPG4 cells treated with BMP4 (50 ng/mL) at indicated timepoints. Data represents the mean ± S.D., statistical significance was calculated by two-tailed unpaired Student’s t-test, n = 3 independent experiments. The experiments in d have been repeated three times with similar results. For c, e and f, data represents the mean ± S.D., statistical significance was calculated by two-tailed unpaired Student’s t-test.

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Extended Data Fig. 10 CXXC5 positively regulates BMP signaling.

(a) IGV tracks for RNA-seq and CXXC5 ChIP–seq (treated with vehicle or BMP4 for 2 hr) in TT150630 DIPG cells at BMP2, BMPR2 and ACVR1 gene loci. (b) Immunoblotting for phosphorylated SMAD1/5 (Ser 463/465), total SMAD1, CXXC5 and GAPDH in control and CXXC5 KD TT150714 DIPG cells. The experiments have been repeated three times with similar results. (c) Heatmaps of ChIP–seq signals of CXXC5 at top 4000 SMAD1 significant peaks or 5868 H3K27ac upregulated peaks respectively in TT150630 DIPG cells with or without BMP4 treatment for 2 hr (top). Average CXXC5 ChIP–seq signals corresponding with their ChIP–seq profiles in the indicated conditions (bottom). (d) Kaplan–Meier survival curves for DIPG patients in the UCSC xena patient cohort27, separated into ACVR1 high (n = 26 patients) and ACVR1 low (n = 25 patients) survival groups. Log-rank test was performed.

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Sun, Y., Yan, K., Wang, Y. et al. Context-dependent tumor-suppressive BMP signaling in diffuse intrinsic pontine glioma regulates stemness through epigenetic regulation of CXXC5. Nat Cancer 3, 1105–1122 (2022). https://doi.org/10.1038/s43018-022-00408-8

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