Top

BMC Cancer

Published in:

Open Access 01-12-2021 | Research article

Differential chromatin accessibility landscape of gain-of-function mutant p53 tumours

Authors: Bhavya Dhaka, Radhakrishnan Sabarinathan

Published in: BMC Cancer | Issue 1/2021

Abstract

Background

Mutations in TP53 not only affect its tumour suppressor activity but also exerts oncogenic gain-of-function activity. While the genome-wide mutant p53 binding sites have been identified in cancer cell lines, the chromatin accessibility landscape driven by mutant p53 in primary tumours is unknown. Here, we leveraged the chromatin accessibility data of primary tumours from The Cancer Genome Atlas (TCGA) to identify differentially accessible regions in mutant p53 tumours compared to wild-type p53 tumours, especially in breast and colon cancers.

Results

We identified 1587 lost and 984 gained accessible chromatin regions in breast, and 1143 lost and 640 gained regions in colon cancers. However, only less than half of those regions in both cancer types contain sequence motifs for wild-type or mutant p53 binding. Whereas, the remaining showed enrichment for master transcriptional regulators, such as FOX-Family TFs and NF-kB in lost and SMAD and KLF TFs in gained regions of breast. In colon, ATF3 and FOS/JUN TFs were enriched in lost, and CDX family TFs and HNF4A in gained regions. By integrating the gene expression data, we identified known and novel target genes regulated by the mutant p53.

Conclusion

This study reveals the direct and indirect mechanisms by which gain-of-function mutant p53 targets the chromatin and subsequent gene expression patterns in a tumour-type specific manner. This furthers our understanding of the impact of mutant p53 in cancer development.

Available only for authorised users

Harms KL, Chen X. The functional domains in p53 family proteins exhibit both common and distinct properties. Cell Death Differ. 2006;13(6):890–7. https://doi.org/10.1038/sj.cdd.4401904.CrossRefPubMed

Sullivan KD, Galbraith MD, Andrysik Z, Espinosa JM. Mechanisms of transcriptional regulation by p53. Cell Death Differ. 2018;25(1):133–43. https://doi.org/10.1038/cdd.2017.174.CrossRefPubMed

Levine AJ. p53: 800 million years of evolution and 40 years of discovery. Nat Rev Cancer. 2020;20(8):471–80. https://doi.org/10.1038/s41568-020-0262-1.CrossRefPubMed

Hafner A, Bulyk ML, Jambhekar A, Lahav G. The multiple mechanisms that regulate p53 activity and cell fate. Nat Rev Mol Cell Biol. 2019;20(4):199–210. https://doi.org/10.1038/s41580-019-0110-x.CrossRefPubMed

Integrated analysis of TP53 gene and pathway alterations in the cancer genome atlas. Cell Rep. 2019;28:1370–84.e5. https://doi.org/10.1016/j.celrep.2019.07.001.

Sabapathy K, Lane DP. Therapeutic targeting of p53: all mutants are equal, but some mutants are more equal than others. Nat Rev Clin Oncol. 2018;15(1):13–30. https://doi.org/10.1038/nrclinonc.2017.151.CrossRefPubMed

Olivier M, Bouaoun L, Sonkin D, Ardin M, Hollstein M, Byrnes G, et al. TP53 variations in human cancers: new lessons from the IARC TP53 database and genomic studies. Eur J Cancer. 2016;61:S15. https://doi.org/10.1016/s0959-8049(16)61042-0.CrossRef

Eldar A, Rozenberg H, Diskin-Posner Y, Rohs R, Shakked Z. Structural studies of p53 inactivation by DNA-contact mutations and its rescue by suppressor mutations via alternative protein–DNA interactions. Nucleic Acids Res. 2013;41(18):8748–59. https://doi.org/10.1093/nar/gkt630.CrossRefPubMedPubMedCentral

Willis A, Jung EJ, Wakefield T, Chen X. Mutant p53 exerts a dominant negative effect by preventing wild-type p53 from binding to the promoter of its target genes. Oncogene. 2004;23(13):2330–8. https://doi.org/10.1038/sj.onc.1207396.CrossRefPubMed

10.

Xu H, El-Gewely MR. Differentially expressed downstream genes in cells with normal or mutated p53. Oncol Res. 2003;13(6):429–36. https://doi.org/10.3727/096504003108748456.CrossRefPubMed

11.

William A, Freed-Pastor CP. Mutant p53: one name, many proteins. Genes Dev. 2012;26(12):1268–86. https://doi.org/10.1101/gad.190678.112.CrossRef

12.

Kim MP, Lozano G. Mutant p53 partners in crime. Cell Death Differ. 2018;25(1):161–8. https://doi.org/10.1038/cdd.2017.185.CrossRefPubMed

13.

Bouaoun L, Sonkin D, Ardin M, Hollstein M, Byrnes G, Zavadil J, et al. TP53 variations in human cancers: new lessons from the IARC TP53 database and genomics data. Hum Mutat. 2016;37(9):865–76. https://doi.org/10.1002/humu.23035.CrossRefPubMed

14.

Rahnamoun H, Hong J, Sun Z, Lee J, Lu H, Lauberth SM. Mutant p53 regulates enhancer-associated H3K4 monomethylation through interactions with the methyltransferase MLL4. J Biol Chem. 2018;293(34):13234–46. https://doi.org/10.1074/jbc.RA118.003387.CrossRefPubMedPubMedCentral

15.

Zhu J, Sammons MA, Donahue G, Dou Z, Vedadi M, Getlik M, et al. Prevalent p53 mutants co-opt chromatin pathways to drive cancer growth. Nature. 2015;525(7568):206–11. https://doi.org/10.1038/nature15251.CrossRefPubMedPubMedCentral

16.

Corces MR, Granja JM, Shams S, Louie BH, Seoane JA, Zhou W, et al. The chromatin accessibility landscape of primary human cancers. Science. 2018;362(6413):eaav1898. https://doi.org/10.1126/science.aav1898.CrossRefPubMedPubMedCentral

17.

Knudson AG. Mutation and cancer: statistical study of retinoblastoma. Proc Natl Acad Sci U S A. 1971;68(4):820–3. https://doi.org/10.1073/pnas.68.4.820.CrossRefPubMedPubMedCentral

18.

Tamborero D, Rubio-Perez C, Deu-Pons J, Schroeder MP, Vivancos A, Rovira A, et al. Cancer genome interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med. 2018;10(1):25. https://doi.org/10.1186/s13073-018-0531-8.CrossRefPubMedPubMedCentral

19.

Tebaldi T, Zaccara S, Alessandrini F, Bisio A, Ciribilli Y, Inga A. Whole-genome cartography of p53 response elements ranked on transactivation potential. BMC Genomics. 2015;16(1):1–13. https://doi.org/10.1186/s12864-015-1643-9.CrossRef

20.

Fornes O, Castro-Mondragon JA, Khan A, van der Lee R, Zhang X, Richmond PA, et al. JASPAR 2020: update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 2020;48:D87–92. https://doi.org/10.1093/nar/gkz1001.CrossRefPubMed

21.

Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell. 2010;38(4):576–89. https://doi.org/10.1016/j.molcel.2010.05.004.CrossRefPubMedPubMedCentral

22.

Maria L, Golson KHK. Fox transcription factors: from development to disease. Development. 2016;143(24):4558–70. https://doi.org/10.1242/dev.112672.CrossRef

23.

Verzi MP, Shin H, Ak SR, Liu XS, Shivdasani RA. Intestinal master transcription factor CDX2 controls chromatin access for partner transcription factor binding. Mol Cell Biol. 2013;33(2):281–92. https://doi.org/10.1128/MCB.01185-12.CrossRefPubMedPubMedCentral

24.

Hess J, Angel P, Schorpp-Kistner M. AP-1 subunits: quarrel and harmony among siblings. J Cell Sci. 2004;117 Pt 25. https://doi.org/10.1242/jcs.01589.

25.

Fishilevich S, Nudel R, Rappaport N, Hadar R, Plaschkes I, Stein TI, et al. GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. Database. 2017;2017. https://doi.org/10.1093/database/bax028.

26.

Petr M, Helma R, Polášková A, Krejčí A, Dvořáková Z, Kejnovská I, et al. Wild-type p53 binds to MYC promoter G-quadruplex. Biosci Rep. 2016;36(5). https://doi.org/10.1042/BSR20160232.

27.

Perez-Llamas C, Lopez-Bigas N. Gitools: analysis and visualisation of genomic data using interactive heat-maps. PLoS One. 2011;6(5):e19541. https://doi.org/10.1371/journal.pone.0019541.CrossRefPubMedPubMedCentral

28.

Kurtenbach S, William HJ. SparK: a publication-quality ngs visualization tool. Cold Spring Harbor Lab. 2019;845529. https://doi.org/10.1101/845529.

29.

Garritano S, Inga A, Gemignani F, Landi S. More targets, more pathways and more clues for mutant p53. Oncogenesis. 2013;2(7):e54. https://doi.org/10.1038/oncsis.2013.15.CrossRefPubMedPubMedCentral

30.

Dai K, Qin F, Zhang H, Liu X, Guo C, Zhang M, et al. Low expression of BMPRIB indicates poor prognosis of breast cancer and is insensitive to taxane-anthracycline chemotherapy. Oncotarget. 2016;7. https://doi.org/10.18632/oncotarget.6613.

31.

Bokobza SM, Ye L, Kynaston HE, Mansel RE, Jiang WG. Reduced expression of BMPR-IB correlates with poor prognosis and increased proliferation of breast cancer cells. Cancer Genomics Proteomics. 2009;6 https://pubmed.ncbi.nlm.nih.gov/19451094/. Accessed 30 Oct 2020.

32.

Zhao J, Zhao D, Poage GM, Mazumdar A, Zhang Y, Hill JL, et al. Death-associated protein kinase 1 promotes growth of p53-mutant cancers. J Clin Invest. 2015;125(7):2707–20. https://doi.org/10.1172/JCI70805.CrossRefPubMedPubMedCentral

33.

Li Y, Liu J, Xiao Q, Tian R, Zhou Z, Gan Y, et al. EN2 as an oncogene promotes tumor progression via regulating CCL20 in colorectal cancer. Cell Death Dis. 2020;11(7):1–11. https://doi.org/10.1038/s41419-020-02804-3.CrossRefPubMedPubMedCentral

34.

Pernodet N, Hermetet F, Adami P, Vejux A, Descotes F, Borg C, et al. High expression of QSOX1 reduces tumorogenesis, and is associated with a better outcome for breast cancer patients. Breast Cancer Res. 2012;14(5):1–15. https://doi.org/10.1186/bcr3341.CrossRef

35.

Douglas F, Lake DOF. The emerging role of QSOX1 in Cancer. Antioxid Redox Signal. 2014;21(3):485–96. https://doi.org/10.1089/ars.2013.5572.CrossRef

36.

Involvement of sulfhydryl oxidase QSOX1 in the protection of cells against oxidative stress-induced apoptosis. Exp Cell Res. 2007;313(19):3971–82. https://doi.org/10.1016/j.yexcr.2007.09.003.

37.

Kim MP, Zhang Y, Lozano G. Mutant p53: multiple mechanisms define biologic activity in Cancer. Front Oncol. 2015;5. https://doi.org/10.3389/fonc.2015.00249.

38.

Miller LD, Smeds J, George J, Vega VB, Vergara L, Ploner A, et al. An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. Proc Natl Acad Sci U S A. 2005;102(38):13550–5. https://doi.org/10.1073/pnas.0506230102.CrossRefPubMedPubMedCentral

39.

Ellrott K, Bailey MH, Saksena G, Covington KR, Kandoth C, Stewart C, et al. Scalable Open Science approach for mutation calling of tumor exomes using multiple genomic pipelines. Cell Syst. 2018;6(3):271–281.e7. https://doi.org/10.1016/j.cels.2018.03.002.CrossRefPubMedPubMedCentral

40.

Martincorena I, Raine KM, Gerstung M, Dawson KJ, Haase K, Van Loo P, et al. Universal patterns of selection in Cancer and somatic tissues. Cell. 2017;171(5):1029–1041.e21. https://doi.org/10.1016/j.cell.2017.09.042.CrossRefPubMedPubMedCentral

41.

Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47. https://doi.org/10.1093/nar/gkv007.CrossRefPubMedPubMedCentral

42.

Reske JJ, Wilson MR, Chandler RL. ATAC-seq normalization method can significantly affect differential accessibility analysis and interpretation. Epigenetics Chromatin. 2020;13(1):1–17. https://doi.org/10.1186/s13072-020-00342-y.CrossRef

43.

Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26(6):841–2. https://doi.org/10.1093/bioinformatics/btq033.CrossRefPubMedPubMedCentral

44.

Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, et al. TCGAbiolinks : an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 2015;44(8):e71. https://doi.org/10.1093/nar/gkv1507.CrossRefPubMedPubMedCentral

45.

Taylor AM, Shih J, Ha G, Gao GF, Zhang X, Berger AC, et al. Genomic and functional approaches to understanding cancer aneuploidy. Cancer Cell. 2018;33:676–89.e3. https://doi.org/10.1016/j.ccell.2018.03.007.CrossRefPubMedPubMedCentral

46.

Oki S, Ohta T, Shioi G, Hatanaka H, Ogasawara O, Okuda Y, et al. ChIP-Atlas: a data-mining suite powered by full integration of public ChIP-seq data. EMBO Rep. 2018;19. https://doi.org/10.15252/embr.201846255.

47.

Ramírez F, Ryan DP, Grüning B, Bhardwaj V, Kilpert F, Richter AS, et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 2016;44. https://doi.org/10.1093/nar/gkw257.

48.

Cer RZ, Donohue DE, Mudunuri US, Temiz NA, Loss MA, Starner NJ, et al. Non-B DB v2.0: a database of predicted non-B DNA-forming motifs and its associated tools. Nucleic Acids Res. 2013;41(Database issue):D94–100. https://doi.org/10.1093/nar/gks955.CrossRefPubMed

49.

Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci. 2005;102(43):15545–50. https://doi.org/10.1073/pnas.0506580102.CrossRefPubMedPubMedCentral

50.

Mootha VK, Lindgren CM, Eriksson K-F, Subramanian A, Sihag S, Lehar J, et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003;34(3):267–73. https://doi.org/10.1038/ng1180.CrossRefPubMed

Title: Differential chromatin accessibility landscape of gain-of-function mutant p53 tumours
Authors: Bhavya Dhaka
Radhakrishnan Sabarinathan
Publication date: 01-12-2021
Publisher: BioMed Central
Published in: BMC Cancer / Issue 1/2021
Electronic ISSN: 1471-2407
DOI: https://doi.org/10.1186/s12885-021-08362-x

Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras

Prof. Fabrice Barlesi

Developed by: Springer Medicine

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 1/2021

Catastrophic health expenditure and its determinants in households with lung cancer patients in China: a retrospective cohort study

Genomic features of rapid versus late relapse in triple negative breast cancer

Patients harboring uncommon EGFR exon 19 deletion-insertion mutations respond well to first-generation EGFR inhibitors and osimeritinib upon acquisition of T790M

Evaluating the prognostic performance of a polygenic risk score for breast cancer risk stratification

Incidence, clinical risk and prognostic factors for liver metastasis in patients with cervical cancer: a population-based retrospective study

Pooled CRISPR screening in pancreatic cancer cells implicates co-repressor complexes as a cause of multiple drug resistance via regulation of epithelial-to-mesenchymal transition

Keynote webinar | Spotlight on antibody–drug conjugates in cancer