Gain-of-function p53 mutants co-opt chromatin pathways to drive cancer growth (original) (raw)

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ChIP-seq and RNA-seq data can be accessed through NCBI Gene Expression Omnibus (GEO) database under accession number GSE59176.

References

  1. Lawrence, M. S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495–501 (2014)
    Article ADS CAS PubMed PubMed Central Google Scholar
  2. Lang, G. A. et al. Gain of function of a p53 hot spot mutation in a mouse model of Li-Fraumeni syndrome. Cell 119, 861–872 (2004)
    Article CAS PubMed Google Scholar
  3. Olive, K. P. et al. Mutant p53 gain of function in two mouse models of Li-Fraumeni syndrome. Cell 119, 847–860 (2004)
    Article CAS PubMed Google Scholar
  4. Freed-Pastor, W. A. et al. Mutant p53 disrupts mammary tissue architecture via the mevalonate pathway. Cell 148, 244–258 (2012)
    Article CAS PubMed PubMed Central Google Scholar
  5. Zhang, C. et al. Tumour-associated mutant p53 drives the Warburg effect. Nat. Commun. 4, 2935 (2013)
    Article ADS PubMed CAS Google Scholar
  6. Subramanian, M. et al. A mutant p53/let-7i-axis-regulated gene network drives cell migration, invasion and metastasis. Oncogene 34, 1094–1104 (2015)
    Article CAS PubMed Google Scholar
  7. Weissmueller, S. et al. Mutant p53 drives pancreatic cancer metastasis through cell-autonomous PDGF receptor β signaling. Cell 157, 382–394 (2014)
    Article CAS PubMed PubMed Central Google Scholar
  8. Do, P. M. et al. Mutant p53 cooperates with ETS2 to promote etoposide resistance. Genes Dev. 26, 830–845 (2012)
    Article CAS PubMed PubMed Central Google Scholar
  9. Scian, M. J. et al. Modulation of gene expression by tumor-derived p53 mutants. Cancer Res. 64, 7447–7454 (2004)
    Article CAS PubMed Google Scholar
  10. Garritano, S., Inga, A., Gemignani, F. & Landi, S. More targets, more pathways and more clues for mutant p53. Oncogenesis 2, e54 (2013)
    Article CAS PubMed PubMed Central Google Scholar
  11. Dawson, M. A. & Kouzarides, T. Cancer epigenetics: from mechanism to therapy. Cell 150, 12–27 (2012)
    Article CAS PubMed Google Scholar
  12. Tam, W. L. & Weinberg, R. A. The epigenetics of epithelial-mesenchymal plasticity in cancer. Nature Med. 19, 1438–1449 (2013)
    Article CAS PubMed Google Scholar
  13. Kouzarides, T. Chromatin modifications and their function. Cell 128, 693–705 (2007)
    Article CAS PubMed Google Scholar
  14. Li, B., Carey, M. & Workman, J. L. The role of chromatin during transcription. Cell 128, 707–719 (2007)
    Article CAS PubMed Google Scholar
  15. The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012)
  16. Gertz, J. et al. Distinct properties of cell-type-specific and shared transcription factor binding sites. Mol. Cell 52, 25–36 (2013)
    Article CAS PubMed Google Scholar
  17. Hollenhorst, P. C., McIntosh, L. P. & Graves, B. J. Genomic and biochemical insights into the specificity of ETS transcription factors. Annu. Rev. Biochem. 80, 437–471 (2011)
    Article CAS PubMed PubMed Central Google Scholar
  18. Xiong, S. et al. Pla2g16 phospholipase mediates gain-of-function activities of mutant p53. Proc. Natl Acad. Sci. USA 111, 11145–11150 (2014)
    Article ADS CAS PubMed PubMed Central Google Scholar
  19. Voss, A. K., Collin, C., Dixon, M. P. & Thomas, T. Moz and retinoic acid coordinately regulate H3K9 acetylation, Hox gene expression, and segment identity. Dev. Cell 17, 674–686 (2009)
    Article CAS PubMed Google Scholar
  20. Shilatifard, A. The COMPASS family of histone H3K4 methylases: mechanisms of regulation in development and disease pathogenesis. Annu. Rev. Biochem. 81, 65–95 (2012)
    Article CAS PubMed PubMed Central Google Scholar
  21. Cao, F. et al. Targeting MLL1 H3K4 methyltransferase activity in mixed-lineage leukemia. Mol. Cell 53, 247–261 (2014)
    Article CAS PubMed PubMed Central Google Scholar
  22. Wang, P. et al. Global analysis of H3K4 methylation defines MLL family member targets and points to a role for MLL1-mediated H3K4 methylation in the regulation of transcriptional initiation by RNA polymerase II. Mol. Cell. Biol. 29, 6074–6085 (2009)
    Article CAS PubMed PubMed Central Google Scholar
  23. Milne, T. A. et al. MLL targets SET domain methyltransferase activity to Hox gene promoters. Mol. Cell 10, 1107–1117 (2002)
    Article CAS PubMed Google Scholar
  24. Nakamura, T. et al. ALL-1 is a histone methyltransferase that assembles a supercomplex of proteins involved in transcriptional regulation. Mol. Cell 10, 1119–1128 (2002)
    Article CAS PubMed Google Scholar
  25. Lim, L. Y., Vidnovic, N., Ellisen, L. W. & Leong, C. O. Mutant p53 mediates survival of breast cancer cells. Br. J. Cancer 101, 1606–1612 (2009)
    Article CAS PubMed PubMed Central Google Scholar
  26. Alexandrova, E. M. et al. Improving survival by exploiting tumour dependence on stabilized mutant p53 for treatment. Nature 532, 352–356 (2015)
    Article ADS CAS Google Scholar
  27. Zhu, Q., Wani, G., Wani, M. A. & Wani, A. A. Human homologue of yeast Rad23 protein A interacts with p300/cyclic AMP-responsive element binding (CREB)-binding protein to down-regulate transcriptional activity of p53. Cancer Res. 61, 64–70 (2001)
    CAS PubMed Google Scholar
  28. Dawson, M. A., Kouzarides, T. & Huntly, B. J. Targeting epigenetic readers in cancer. N. Engl. J. Med. 367, 647–657 (2012)
    Article CAS PubMed Google Scholar
  29. Huang, J. et al. The same pocket in menin binds both MLL and JUND but has opposite effects on transcription. Nature 482, 542–546 (2012)
    Article ADS CAS PubMed PubMed Central Google Scholar
  30. Yokoyama, A. et al. Leukemia proto-oncoprotein MLL forms a SET1-like histone methyltransferase complex with menin to regulate Hox gene expression. Mol. Cell. Biol. 24, 5639–5649 (2004)
    Article CAS PubMed PubMed Central Google Scholar
  31. Caslini, C. et al. Interaction of MLL amino terminal sequences with menin is required for transformation. Cancer Res. 67, 7275–7283 (2007)
    Article CAS PubMed PubMed Central Google Scholar
  32. Thiel, A. T., Huang, J., Lei, M. & Hua, X. Menin as a hub controlling mixed lineage leukemia. Bioessays 34, 771–780 (2012)
    Article CAS PubMed PubMed Central Google Scholar
  33. Yokoyama, A. et al. The menin tumor suppressor protein is an essential oncogenic cofactor for MLL-associated leukemogenesis. Cell 123, 207–218 (2005)
    Article CAS PubMed Google Scholar
  34. Grembecka, J. et al. Menin-MLL inhibitors reverse oncogenic activity of MLL fusion proteins in leukemia. Nature Chem. Biol. 8, 277–284 (2012)
    Article CAS Google Scholar
  35. Shi, A. et al. Structural insights into inhibition of the bivalent menin–MLL interaction by small molecules in leukemia. Blood 120, 4461–4469 (2012)
    Article CAS PubMed PubMed Central Google Scholar
  36. Karatas, H. et al. High-affinity, small-molecule peptidomimetic inhibitors of MLL1/WDR5 protein–protein interaction. J. Am. Chem. Soc. 135, 669–682 (2013)
    Article CAS PubMed Google Scholar
  37. Karatas, H., Townsend, E. C., Bernard, D., Dou, Y. & Wang, S. Analysis of the binding of mixed lineage leukemia 1 (MLL1) and histone 3 peptides to WD repeat domain 5 (WDR5) for the design of inhibitors of the MLL1–WDR5 interaction. J. Med. Chem. 53, 5179–5185 (2010)
    Article CAS PubMed PubMed Central Google Scholar
  38. Grebien, F. et al. Pharmacological targeting of the Wdr5-MLL interaction in C/EBPα N-terminal leukemia. Nature Chem. Biol. 11, 571–579 (2015)
    Article CAS Google Scholar
  39. Lee, K. H. et al. A genomewide study identifies the Wnt signaling pathway as a major target of p53 in murine embryonic stem cells. Proc. Natl Acad. Sci. USA 107, 69–74 (2010)
    Article ADS CAS PubMed Google Scholar
  40. Shah, P. P. et al. Lamin B1 depletion in senescent cells triggers large-scale changes in gene expression and the chromatin landscape. Genes Dev. 27, 1787–1799 (2013)
    Article CAS PubMed PubMed Central Google Scholar
  41. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nature Methods 9, 357–359 (2012)
    Article CAS PubMed PubMed Central Google Scholar
  42. Senisterra, G. et al. Small-molecule inhibition of MLL activity by disruption of its interaction with WDR5. Biochem. J. 449, 151–159 (2013)
    Article CAS PubMed Google Scholar

Download references

Acknowledgements

We thank M. Tainsky for the LFS cell lines; A. Weller, J. Glover and the Stem Cell and Xenograft Core at the University of Pennsylvania for help with the tumour xenograft experiments. S.L.B. is supported by NIH grant R01 CA078831. M.A.S. is supported by a Postdoctoral Fellowship from the American Cancer Society. X.H. is supported in part by a pilot grant from ITMAT of the University of Pennsylvania. A.S. is supported by NIH grant R01 GM069905. The Structural Genomics Consortium is a registered charity (number 1097737) that receives funds from AbbVie, Bayer, Boehringer Ingelheim, Genome Canada through the Ontario Genomics Institute (OGI-055), GlaxoSmithKline, Janssen, Lilly Canada, Merck, the Novartis Research Foundation, the Ontario Ministry of Economic Development and Innovation, Pfizer, Takeda, and the Wellcome Trust (092809/Z/10/Z). Funding was also provided to C.H.A. from the Canadian Cancer Society Research Institute.

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Authors and Affiliations

  1. Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, USA
    Jiajun Zhu, Morgan A. Sammons, Greg Donahue, Zhixun Dou & Shelley L. Berger
  2. Epigenetics Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, USA
    Jiajun Zhu, Morgan A. Sammons, Greg Donahue, Zhixun Dou & Shelley L. Berger
  3. Biomedical Graduate Studies, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, USA
    Jiajun Zhu
  4. Structural Genomics Consortium, University of Toronto, Toronto, M5G 1L7, Ontario, Canada
    Masoud Vedadi, Dalia Barsyte-Lovejoy & Cheryl H. Arrowsmith
  5. Department of Pharmacology and Toxicology, University of Toronto, Toronto, M5S 1A8, Ontario, Canada
    Masoud Vedadi & Rima Al-awar
  6. Drug Discovery Program, Ontario Institute for Cancer Research, Toronto, M5G 0A3, Ontario, Canada
    Matthäus Getlik & Rima Al-awar
  7. Department of Cancer Biology, Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, USA
    Bryson W. Katona & Xianxin Hua
  8. Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Northwestern University, 320 E. Superior Street, Chicago, 60611, Illinois, USA
    Ali Shilatifard
  9. Cancer and Stem Cell Epigenetics, Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, Bethesda, 20892, Maryland, USA
    Jing Huang
  10. and Department of Medical Biophysics, Princess Margaret Cancer Centre, University of Toronto, Toronto, M5G 2C4, Ontario, Canada
    Cheryl H. Arrowsmith

Authors

  1. Jiajun Zhu
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  2. Morgan A. Sammons
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  3. Greg Donahue
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  4. Zhixun Dou
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  5. Masoud Vedadi
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  6. Matthäus Getlik
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  7. Dalia Barsyte-Lovejoy
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  8. Rima Al-awar
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  9. Bryson W. Katona
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  10. Ali Shilatifard
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  11. Jing Huang
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  12. Xianxin Hua
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  13. Cheryl H. Arrowsmith
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  14. Shelley L. Berger
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Contributions

J.Z. and S.L.B. initiated and led the project. J.Z., M.A.S., Z.D. and S.L.B. designed the experiments and interpreted the data. J.Z. and M.A.S. performed the experiments. J.Z., M.A.S. and G.D. analysed all next-generation sequencing data. M.V., M.G., D.B.-L., R.A. and C.H.A. developed OICR9429. B.W.K. and X.H. contributed to the use of menin inhibitor. A.S. and J.H. contributed to reagents used in this study. J.Z., M.A.S., C.H.A. and S.L.B. composed the manuscript. All authors reviewed and commented on the manuscript.

Corresponding author

Correspondence toShelley L. Berger.

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Competing interests

J.Z., M.A.S., X.H. and S.L.B. have a related patent.

Extended data figures and tables

Extended Data Figure 1 Distinct GOF p53 mutants have similar genome-wide binding patterns, but are different from that of wild-type p53.

a, Heat maps showing the enrichment of p53 peaks (±2,500 bp around peak centre) identified from each cell line (rows) in all five cell lines (columns) examined by ChIP-seq. b–e, Area under the curve, meta-peak analysis showing GOF p53(R248W) or IgG ChIP-seq signal enrichment from MDAH087 cells over TSS-proximal peaks identified in MDA-MB-468 (b), HCC70 (c), MCF7 (d) and MDA-MB-175VII (e) cells.

Extended Data Figure 2 GOF p53 genome-wide binding is in association with ETS family proteins.

a, Canonical ETS binding motif (top), and discovered motif from all TSS-proximal peaks in MDA-MB-468 predicted by MEME/TomTom (middle), or SeqPos (bottom). b, MEME/TomTom identified wild-type p53 motif from MDA-MB-175VII TSS-proximal peaks. c, GST pulldown of bacterially expressed GST or GST–ETS2 with in vitro translated wild-type p53 or p53(R175H). d, e, Co-immunoprecipitation at endogenous protein levels of ETS2 and GOF p53(R273H) (d) or wild-type p53 (e) in MDA-MB-468 (d) or MCF7 (e) cells. f, g, Box plots showing overlap of GOF p53 (f) TSS-proximal peaks from MDA-MB-468 cells or wild-type p53 (g) TSS-proximal peaks from MCF7 cells, with ETS family proteins (blue), all other transcription factors (grey) or Pol II (white) peaks from ENCODE ChIP-seq data sets. Whiskers on the box plots represent the inter-quartile range. Mann–Whitney _U_-tests were performed to compute significance. h, GO analysis of wild-type p53 TSS-proximal peaks (statistics are shown in Supplementary Table 1).

Extended Data Figure 3 UCSC Genome Browser views showing distinct wild-type p53 and GOF p53 binding patterns over representative canonical wild-type p53 targets and novel GOF p53 targets.

a–d, UCSC Genome Browser views of p53 occupancy over promoter regions of MLL1 (a), MLL2 (b), CDKN1A (c) and MOZ (d) in MCF7, HCC70, and BT-549 cells. e–g, Re-aligned GOF p53(R248W) and IgG ChIP-seq data from LFS MDAH087 cells, showing enrichment of GOF p53 at promoter regions of MLL1 (e), MLL2 (f), and MOZ (g). h–j, UCSC Genome Browser views of p53 occupancy over promoter regions of RBBP5 (h), MDM2 (i) and PUMA (j), in MCF7, MDA-MB-175VII, HCC70, BT-549, and MDA-MB-468 cells.

Extended Data Figure 4 ChIP–qPCR validation of GOF p53 binding at newly identified chromatin regulator genes.

a, Schematic of amplicon locations for ChIP-qPCR validations performed in this study. b, c, ChIP–qPCR showing p53 (DO-1 antibody) or IgG (mouse) enrichment (ChIP/input) over MLL1, MLL2 and MOZ peak regions, in BT-549 (b) and HCC70 (c) cells. d–f, ChIP–qPCR showing p53 (DO-1 antibody) or IgG (mouse) enrichment over OGT, PPP1CC, RBBP5, SMARCD2, and DCAF10 peak regions in BT-549 (d), HCC70 (e) and MDA-MB-468 (f) cells. g, h, ChIP–qPCR showing p53 (FL393 antibody) or IgG (rabbit) enrichment over MDM2, CDKN1A, MLL1, MLL2 and MOZ regions, in MDA-MB-468 (g) and MDA-MB-175VII (h) cells. i. ChIP–qPCR showing p53 (DO-1 antibody) or IgG (mouse) enrichment over MLL1, MLL2 and MOZ peak regions in PANC-1 cells. Error bars represent mean ± s.e.m.; n = 3; two-tailed Student’s _t_-test: *P < 0.05; **P < 0.01; ***P < 0.001.

Extended Data Figure 5 GOF p53 regulates expression of MLL1, MLL2, and MOZ, and corresponding histone post-translational modifications in cancer cells.

a, b, RT–qPCR analysis measuring mRNA level changes upon siRNA-mediated GOF p53 knockdown in MDA-MB-468 cells (a), and shRNA-mediated wild-type p53 knockdown in MDA-MB-175VII cells (b). c, d, RT–qPCR analysis of mRNA levels (c), and western blot analysis of protein levels upon DMSO or nutlin treatment in MCF7 cells (d). e, Western blot analysis of MLL1 protein level upon shRNA-mediated wild-type p53 knockdown in MDA-MB-175VII cells. f, Western blot analysis of MOZ protein level change upon shRNA-mediated GOF p53 knockdown in MDA-MB-468 cells. g, RT–qPCR measuring mRNA levels changes upon shRNA-mediated ETS2 knockdown in MDA-MB-468 cells. h, i, RT–qPCR measuring mRNA levels (h) and western blot measuring protein levels (i) upon shRNA-mediated ETS2 knockdown in BT-549 cells. j, k, RT–qPCR measuring mRNA levels changes upon shRNA-mediated ETS1 knockdown in BT-549 (j) and MDA-MB-468 (k) cells. Numbers 89 and 91 denote two short hairpins targeting ETS1, sequences of which are shown in Supplementary Table 3. l, m, ChIP–qPCR showing p53 occupancy (l) and Pol II occupancy (m) upon shRNA-mediated ETS1 knockdown in MDA-MB-468 cells. n, o, Western blot analysis of histone methylation and acetylation level changes upon siRNA-mediated (n) or shRNA-mediated (o) knockdown of GOF p53 in MDA-MB-468 cells. p, Western blot analysis of histone methylation and acetylation level changes upon GOF p53 knockdown in PANC-1 cells. q, Western blot of H3K9ac change upon MOZ knockdown in MDA-MB-468 cells. Uncropped blots are shown in Supplementary Fig. 1. Error bars represent mean ± s.e.m.; n = 3; two-tailed Student’s _t_-test; *P < 0.05; **P < 0.01; ***P < 0.001.

Extended Data Figure 6 GOF p53 regulates expression of Mll1, Mll2, and Moz, and corresponding histone post-translational modifications in primary MEFs.

a, RT–qPCR analysis comparing Mll1 expression levels between MEFs bearing wild-type p53, GOF p53(R172H), and p53 null. b, Western blot comparing Mll1 protein level between MEFs with wild-type p53 and GOF p53. c, RT–qPCR analysis comparing Mll2 and Moz expression levels between MEFs bearing wild-type p53, GOF p53(R172H), and p53 null. d, RT–qPCR measuring mRNA changes upon shRNA-mediated p53 knockdown in GOF p53(R172H) knock-in MEFs. e, f, RT–qPCR analysis of mRNA levels (e) and western blot of protein levels (f) upon retroviral expression of GOF p53(R172H) in MEFs with p53 knockout. g, Western blot comparing H3K4me3 and H3K9ac levels between MEFs with wild-type p53 and GOF p53(R172H). h, Western blot showing H3K4me3 and H3K9ac levels upon p53 knockdown in wild-type p53 MEFs. i, Growth curve analysis of wild-type p53 MEF proliferation upon shRNA-mediated p53 knockdown. j, k, Box plot analysis of RNA levels (left) and H3 normalized H3K4me3 levels (right) at previously discovered Mll1 target genes (j) or Hoxa cluster genes (k) compared with all genes, from RNA-seq and H3K4me3 ChIP-seq in MEFs with wild-type p53 or GOF p53 R172H. Plots are presented as ratios of GOF p53(R172H) values over wild-type p53 values. l, UCSC Genome Browser views of H3K4me3 enrichment (top) and RNA levels (bottom) of Cdkn1a, from H3K4me3 ChIP-seq and RNA-seq of MEFs with wild-type p53 or GOF p53(R172H). Tracks are presented as overlay of wild-type p53 and GOF p53 signals. Blue denotes more enriched in wild-type p53, red denotes more enriched in GOF p53(R172H), black denotes overlap. m, Box plot of H3 normalized H3K4me3 levels over all gene TSSs, from H3K4me3 ChIP-seq in MEFs with wild-type p53 or GOF p53(R172H). n, RT–qPCR analysis comparing Hox gene expression levels between MEFs bearing wild-type p53, GOF p53(R172H), and p53 null. Uncropped blots are shown in Supplementary Fig. 1. For all bar graphs, two-tailed Student’s _t_-test; *P <0.05; **P < 0.01; ***P < 0.001. Error bars represent mean ± s.e.m.; n = 3. For all box plots, Mann–Whitney _U_-test; *P < 0.05; **P < 0.01; ***P < 0.001.

Extended Data Figure 7 MLL knockdown reduces proliferation and cancer phenotype specifically in GOF p53 cancer cells.

a, b, Growth curve analysis of MDA-MB-468 (a) and MDA-MB-175VII (b) cells with either non-targeting control shRNA or p53 shRNA knockdown. c, d, Growth curve analysis of MDA-MB-468 (c) and MDA-MB-175VII (d) cells with non-targeting control shRNA, MLL1 shRNA, or MLL2 shRNA knockdown. e, Growth curve analysis of MCF7 cells with non-targeting control shRNA or MLL1 shRNA knockdown. f, g, Colony-formation assay of MDA-MB-468 (f) and MCF7 (g) cells with either non-targeting control shRNA or MLL1 shRNA knockdown. Corresponding to Fig. 4a, b. h, i, Colony-formation assay of BT-549 (h) and PANC-1 (i) cells with either non-targeting control shRNA or two different MLL1 shRNA knockdown, and quantification by crystal violet staining over three biological replicates. Reduction of MLL1 protein is also shown by western blot. j, k, Anchorage-independent soft agar assay of MDA-MB-468 (j) and MCF7 (k) cells with either non-targeting control shRNA or MLL1 shRNA knockdown. Dashed boxes denote enlarged images of the selected areas. White arrows indicate visible colonies in j. Quantifications are shown as number of visible colonies. Error bars represent mean ± s.e.m.; n = 3; two-tailed Student’s _t_-test; **P < 0.01; ***P < 0.001.

Extended Data Figure 8 MLL knockdown reduces proliferation specifically of GOF p53 MEFs and LFS cells.

a, Growth curve analysis of GOF p53(R172H) MEFs with either non-targeting control shRNA or two different Mll1 shRNA knockdowns. b, Western blot analysis of MLL1 levels upon shRNA-mediated knockdown in LFS MDAH087 and MDAH041 cells. c, Western blot analysis of p53 protein levels in LFS MDAH087 and MDAH041 cells. d, e, Growth curve analysis of LFS MDAH087 cells upon MLL1 (d) knockdown or p53 (e) knockdown. f, Growth curve analysis of LFS MDAH041 cells upon MLL1 knockdown. g, h, Western blot analysis of MLL1 level (g) and growth curve analysis (h) of proliferation upon shRNA-mediated MLL1 knockdown in IMR90 cells. i, Growth curve analysis of LFS MDAH087 cells with non-targeting control shRNA plus empty vector, p53 shRNA plus vector, and p53 shRNA plus MLL1 expressing vector. j, k, Growth curve analysis of LFS MDAH087 (j) and LFS MDAH041 (k) cells with either non-targeting control shRNA or MLL2 shRNA knockdown.

Extended Data Figure 9 TCGA RNA expression profile analysis.

a–f, TCGA RNA expression profile of GOF p53 target genes (top), housekeeping genes (middle), and wild-type p53 target genes (bottom) in brain lower grade glioma (a), head and neck squamous cell carcinoma (b), bladder urothelial carcinoma (c), colon adenocarcinoma (d), oesophageal carcinoma (e) or pancreatic adenocarcinoma tumours (f) with wild-type p53 (blue), GOF p53 (orange), or p53 null (white). Expression values are normalized read counts (a–d, f), or RPKM values (e) from TCGA RNA-seq data sets. Mann–WhitneyU-tests were performed to compute significance.

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Zhu, J., Sammons, M., Donahue, G. et al. Gain-of-function p53 mutants co-opt chromatin pathways to drive cancer growth.Nature 525, 206–211 (2015). https://doi.org/10.1038/nature15251

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