nature.com

Analysis of Ugandan cervical carcinomas identifies human papillomavirus clade–specific epigenome and transcriptome landscapes - Nature Genetics

  • ️Marra, Marco A.
  • ️Mon Aug 03 2020

Data availability

All molecular and clinical data used in this publication can be found on the National Cancer Institute’s Genome Data Commons Publication Page at https://gdc.cancer.gov/about-data/publications/CGCI-HTMCP-CC-2020. Data from this publication are publicly available for download through dbGaP (phs000528), as part of the NCI Cancer Genome Characterization Initiative (CGCI; phs000235). Sample metadata are reported in Supplementary Table 2. TCGA cervical cancer data (file name: CESC.snp__genome_wide_snp_6__broad_mit_edu__Level_3__segmented_scna_minus_germline_cnv_hg19__seg.seg.txt) were obtained from http://gdac.broadinstitute.org/runs/stddata__2016_01_28/data/CESC/20160128/. Source data are provided with this paper.

Code availability

Bioinformatics analyses in this study were conducted with open-source software, with the exception of tumor purity and ploidy estimation, which was performed with Ploidetect (https://github.com/lculibrk/ploidetect).

References

  1. Bodily, J. & Laimins, L. A. Persistence of human papillomavirus infection: keys to malignant progression. Trends Microbiol. 19, 33–39 (2011).

    Article  CAS  PubMed  Google Scholar 

  2. de Sanjose, S. et al. Human papillomavirus genotype attribution in invasive cervical cancer: a retrospective cross-sectional worldwide study. Lancet Oncol. 11, 1048–1056 (2010).

    Article  PubMed  CAS  Google Scholar 

  3. Wright, J. D. et al. Human papillomavirus type and tobacco use as predictors of survival in early stage cervical carcinoma. Gynecol. Oncol. 98, 84–91 (2005).

    Article  PubMed  Google Scholar 

  4. Yang, S.-H., Kong, S.-K., Lee, S.-H., Lim, S.-Y. & Park, C.-Y. Human papillomavirus 18 as a poor prognostic factor in stage I–IIA cervical cancer following primary surgical treatment. Obstet. Gynecol. Sci. 57, 492–500 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Lai, C.-H. et al. Role of human papillomavirus genotype in prognosis of early-stage cervical cancer undergoing primary surgery. J. Clin. Oncol. 25, 3628–3634 (2007).

    Article  PubMed  Google Scholar 

  6. Garland, S. M. et al. Impact and effectiveness of the quadrivalent human papillomavirus vaccine: a systematic review of 10 years of real-world experience. Clin. Infect. Dis. 63, 519–527 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Bruni, L. et al. Global estimates of human papillomavirus vaccination coverage by region and income level: a pooled analysis. Lancet Glob. Health 4, e453–e463 (2016).

    Article  PubMed  Google Scholar 

  8. Nakisige, C., Schwartz, M. & Ndira, A. O. Cervical cancer screening and treatment in Uganda. Gynecol. Oncol. Rep. 20, 37–40 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Zubizarreta, E. H., Fidarova, E., Healy, B. & Rosenblatt, E. Need for radiotherapy in low and middle income countries—the silent crisis continues. Clin. Oncol. (R. Coll. Radiol.) 27, 107–114 (2015).

    Article  CAS  Google Scholar 

  10. Ferlay, J. et al. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int. J. Cancer 144, 1941–1953 (2019).

    Article  CAS  PubMed  Google Scholar 

  11. Cancer Genome Atlas Research Network. Integrated genomic and molecular characterization of cervical cancer. Nature 543, 378–384 (2017).

    Article  CAS  Google Scholar 

  12. Ojesina, A. I. et al. Landscape of genomic alterations in cervical carcinomas. Nature 506, 371–375 (2014).

    Article  CAS  PubMed  Google Scholar 

  13. Li, X. Emerging role of mutations in epigenetic regulators including MLL2 derived from The Cancer Genome Atlas for cervical cancer. BMC Cancer 17, 252 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Kelley, D. Z. et al. Integrated analysis of whole-genome ChIP–seq and RNA-seq data of primary head and neck tumor samples associates HPV integration sites with open chromatin marks. Cancer Res. 77, 6538–6550 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lleras, R. A. et al. Unique DNA methylation loci distinguish anatomic site and HPV status in head and neck squamous cell carcinoma. Clin. Cancer Res. 19, 5444–5455 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Alexandrov, L. B., Nik-Zainal, S., Wedge, D. C., Campbell, P. J. & Stratton, M. R. Deciphering signatures of mutational processes operative in human cancer. Cell Rep. 3, 246–259 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Rosenthal, R., McGranahan, N., Herrero, J., Taylor, B. S. & Swanton, C. DeconstructSigs: delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution. Genome Biol. 17, 31 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Henderson, S., Chakravarthy, A., Su, X., Boshoff, C. & Fenton, T. R. APOBEC-mediated cytosine deamination links PIK3CA helical domain mutations to human papillomavirus-driven tumor development. Cell Rep. 7, 1833–1841 (2014).

    Article  CAS  PubMed  Google Scholar 

  19. Wallace, N. A. & Münger, K. The curious case of APOBEC3 activation by cancer-associated human papillomaviruses. PLoS Pathog. 14, e1006717 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Zhang, H.-M. et al. AnimalTFDB 2.0: a resource for expression, prediction and functional study of animal transcription factors. Nucleic Acids Res. 43, D76–D81 (2015).

    Article  CAS  PubMed  Google Scholar 

  21. Huang, F. W. et al. Highly recurrent TERT promoter mutations in human melanoma. Science 339, 957–959 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Horn, S. et al. TERT promoter mutations in familial and sporadic melanoma. Science 339, 959–961 (2013).

    Article  CAS  PubMed  Google Scholar 

  23. Nik-Zainal, S. et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature 534, 47–54 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Garinet, S. et al. High prevalence of a hotspot of noncoding somatic mutations in intron 6 of GPR126 in bladder cancer. Mol. Cancer Res. 17, 469–475 (2019).

    Article  CAS  PubMed  Google Scholar 

  25. Wu, S. et al. Whole-genome sequencing identifies ADGRG6 enhancer mutations and FRS2 duplications as angiogenesis-related drivers in bladder cancer. Nat. Commun. 10, 720 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Coetzee, S. G., Coetzee, G. A. & Hazelett, D. J. motifbreakR: an R/Bioconductor package for predicting variant effects at transcription factor binding sites. Bioinformatics 31, 3847–3849 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Chu, J. et al. BioBloom tools: fast, accurate and memory-efficient host species sequence screening using bloom filters. Bioinformatics 30, 3402–3404 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Schiffman, M., Clifford, G. & Buonaguro, F. M. Classification of weakly carcinogenic human papillomavirus types: addressing the limits of epidemiology at the borderline. Infect. Agent Cancer 4, 8 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Maranga, I. O. et al. HIV infection alters the spectrum of HPV subtypes found in cervical smears and carcinomas from Kenyan women. Open Virol. J. 7, 19–27 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Clifford, G. M. et al. Effect of HIV infection on human papillomavirus types causing invasive cervical cancer in Africa. J. Acquir. Immune Defic. Syndr. 73, 332–339 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Morris, T. J. et al. ChAMP: 450k Chip Analysis Methylation Pipeline. Bioinformatics 30, 428–430 (2014).

    Article  CAS  PubMed  Google Scholar 

  32. Tian, Y. et al. ChAMP: updated methylation analysis pipeline for Illumina BeadChips. Bioinformatics 33, 3982–3984 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Sandoval, J. et al. Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics 6, 692–702 (2011).

    Article  CAS  PubMed  Google Scholar 

  35. Shen, J. et al. Exploring genome-wide DNA methylation profiles altered in hepatocellular carcinoma using Infinium HumanMethylation 450 BeadChips. Epigenetics 8, 34–43 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Szklarczyk, D. et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).

    Article  CAS  PubMed  Google Scholar 

  37. Doolittle-Hall, J. M., Cunningham Glasspoole, D. L., Seaman, W. T. & Webster-Cyriaque, J. Meta-analysis of DNA tumor–viral integration site selection indicates a role for repeats, gene expression and epigenetics. Cancers 7, 2217–2235 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Moody, C. A. & Laimins, L. A. Human papillomavirus oncoproteins: pathways to transformation. Nat. Rev. Cancer 10, 550–560 (2010).

    Article  CAS  PubMed  Google Scholar 

  39. Monk, B. J., Tian, C., Rose, P. G. & Lanciano, R. Which clinical/pathologic factors matter in the era of chemoradiation as treatment for locally advanced cervical carcinoma? Analysis of two Gynecologic Oncology Group (GOG) trials. Gynecol. Oncol. 105, 427–433 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Rader, J. S. et al. Genetic variations in human papillomavirus and cervical cancer outcomes. Int. J. Cancer 144, 2206–2214 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Hoadley, K. A. et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158, 929–944 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Ross-Innes, C. S. et al. Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature 481, 389–393 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Lin-Shiao, E. et al. KMT2D regulates p63 target enhancers to coordinate epithelial homeostasis. Genes Dev. 32, 181–193 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Herz, H.-M. et al. Enhancer-associated H3K4 monomethylation by Trithorax-related, the Drosophila homolog of mammalian Mll3/Mll4. Genes Dev. 26, 2604–2620 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Hu, D. et al. The MLL3/MLL4 branches of the COMPASS family function as major histone H3K4 monomethylases at enhancers. Mol. Cell. Biol. 33, 4745–4754 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Lee, J.-E. et al. H3K4 mono- and di-methyltransferase MLL4 is required for enhancer activation during cell differentiation. eLife 2, e01503 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Hu, Z. et al. Genome-wide profiling of HPV integration in cervical cancer identifies clustered genomic hot spots and a potential microhomology-mediated integration mechanism. Nat. Genet. 47, 158–163 (2015).

    Article  CAS  PubMed  Google Scholar 

  48. Pokholok, D. K. et al. Genome-wide map of nucleosome acetylation and methylation in yeast. Cell 122, 517–527 (2005).

    Article  CAS  PubMed  Google Scholar 

  49. Gates, L. A., Foulds, C. E. & O’Malley, B. W. Histone marks in the ‘driver’s seat’: functional roles in steering the transcription cycle. Trends Biochem. Sci. 42, 977–989 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Hurst, T. P. & Magiorkinis, G. Activation of the innate immune response by endogenous retroviruses. J. Gen. Virol. 96, 1207–1218 (2015).

    Article  CAS  PubMed  Google Scholar 

  51. Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Okoye, A. A. & Picker, L. J. CD4+ T-cell depletion in HIV infection: mechanisms of immunological failure. Immunol. Rev. 254, 54–64 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Hensley-McBain, T. & Klatt, N. R. The dual role of neutrophils in HIV infection. Curr. HIV/AIDS Rep. 15, 1–10 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Sitole, B. N. & Mavri-Damelin, D. Peroxidasin is regulated by the epithelial–mesenchymal transition master transcription factor Snai1. Gene 646, 195–202 (2018).

    Article  CAS  PubMed  Google Scholar 

  55. Zheng, Y.-Z. & Liang, L. High expression of PXDN is associated with poor prognosis and promotes proliferation, invasion as well as migration in ovarian cancer. Ann. Diagn. Pathol. 34, 161–165 (2018).

    Article  PubMed  Google Scholar 

  56. Gifford, C. A. et al. Transcriptional and epigenetic dynamics during specification of human embryonic stem cells. Cell 153, 1149–1163 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. McBride, A. A. & Warburton, A. The role of integration in oncogenic progression of HPV-associated cancers. PLoS Pathog. 13, e1006211 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Kajitani, N., Satsuka, A., Kawate, A. & Sakai, H. Productive lifecycle of human papillomaviruses that depends upon squamous epithelial differentiation. Front. Microbiol. 3, 152 (2012).

  59. Ou, H. D., May, A. P. & O’Shea, C. C. The critical protein interactions and structures that elicit growth deregulation in cancer and viral replication. Wiley Interdiscip. Rev. Syst. Biol. Med. 3, 48–73 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Jeon, S., Allen-Hoffmann, B. L. & Lambert, P. F. Integration of human papillomavirus type 16 into the human genome correlates with a selective growth advantage of cells. J. Virol. 69, 2989–2997 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Groves, I. J., Knight, E. L. A., Ang, Q. Y., Scarpini, C. G. & Coleman, N. HPV16 oncogene expression levels during early cervical carcinogenesis are determined by the balance of epigenetic chromatin modifications at the integrated virus genome. Oncogene 35, 4773–4786 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Pleasance, E. et al. Pan-cancer analysis of advanced patient tumors reveals interactions between therapy and genomic landscapes. Nat. Cancer 1, 452–468 (2020).

    Article  CAS  PubMed  Google Scholar 

  63. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Saunders, C. T. et al. Strelka: accurate somatic small-variant calling from sequenced tumor–normal sample pairs. Bioinformatics 28, 1811–1817 (2012).

    Article  CAS  PubMed  Google Scholar 

  66. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly 6, 80–92 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Chun, H.-J. E. et al. Genome-wide profiles of extra-cranial malignant rhabdoid tumors reveal heterogeneity and dysregulated developmental pathways. Cancer Cell 29, 394–406 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Kent, W. J., Zweig, A. S., Barber, G., Hinrichs, A. S. & Karolchik, D. BigWig and BigBed: enabling browsing of large distributed datasets. Bioinformatics 26, 2204–2207 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Landt, S. G. et al. ChIP–seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 22, 1813–1831 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Zhao, E. Y. et al. Homologous recombination deficiency and platinum-based therapy outcomes in advanced breast cancer. Clin. Cancer Res. 23, 7521–7530 (2017).

    Article  CAS  PubMed  Google Scholar 

  73. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Mermel, C. H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  75. Arthur, S. E. et al. Genome-wide discovery of somatic regulatory variants in diffuse large B-cell lymphoma. Nat. Commun. 9, 4001 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. Ding, J. et al. Feature-based classifiers for somatic mutation detection in tumour–normal paired sequencing data. Bioinformatics 28, 167–175 (2012).

    Article  CAS  PubMed  Google Scholar 

  77. Wilkerson, M. D. & Hayes, D. N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26, 1572–1573 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Quinlan, A. R. BEDTools: the Swiss-army tool for genome feature analysis. Curr. Protoc. Bioinformatics 47, 11.12.1–11.12.34 (2014).

    Article  Google Scholar 

  79. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Ramírez, F., Dündar, F., Diehl, S., Grüning, B. A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Pellacani, D. et al. Analysis of normal human mammary epigenomes reveals cell-specific active enhancer states and associated transcription factor networks. Cell Rep. 17, 2060–2074 (2016).

    Article  CAS  PubMed  Google Scholar 

  82. Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. https://doi.org/10.1200/PO.17.00011 (2017).

Download references

Acknowledgements

This project has been funded in whole or in part with US federal funds from the National Cancer Institute, National Institutes of Health, under contract no. HHSN261200800001E and HHSN261201500003I. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products or organizations imply endorsement by the US government. We gratefully acknowledge the Fred Hutchinson Cancer Research Center and the Uganda Cancer Institute for overseeing sample and data collection in Uganda. We are grateful for contributions from the other members of the HTMCP Cervical Cancer Working Group at the Department of Epidemiology, University of Alabama at Birmingham, the Pancreas Centre BC and various groups at Canada’s Michael Smith Genome Sciences Centre, including those from the Biospecimen, Library Construction, Sequencing, Bioinformatics, Technology Development, Quality Assurance, LIMS, Purchasing and Project Management teams. We thank the AIDS and Cancer Specimen Resource for logistical coordination and support of this project through NIH grants U01CA066535, U01CA096230 and UM1CA181255. L.C. and V.L.P. are the recipients of CIHR Frederick Banting and Charles Best Canada Graduate Scholarships GSD-164207 and GSD-152374, respectively. S.J.M.J. is the recipient of the Canada Research Chair in Computational Genomics. This research was supported by the Intramural Research Program of the NIH, National Cancer Institute (R.Y.). C.C. is supported by NIH grant P30AI027757. G.B.M. is supported by NCI grants U01CA217842 and P50CA098258. M.A.M. is the recipient of the Canada Research Chair in Genome Science. This work was supported in part by funding provided by the Canadian Institutes for Health Research (CIHR award FDN-143288) to M.A.M. A.I.O. was supported in part by the Endlichhofer Trust (OCCC 3120957) and a V Foundation grant (DVP2018-007).

Author information

Author notes

  1. These authors contributed equally: Alessia Gagliardi, Vanessa L. Porter, Zusheng Zong, Reanne Bowlby, Emma Titmuss.

  2. These authors jointly supervised this work: Janet S. Rader, Akinyemi I. Ojesina, Daniela S. Gerhard, Andrew J. Mungall, Marco A. Marra.

Authors and Affiliations

  1. Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada

    Alessia Gagliardi, Vanessa L. Porter, Zusheng Zong, Reanne Bowlby, Emma Titmuss, Simon K. Chan, Luka Culibrk, Yussanne Ma, Karen L. Mungall, Steven J. M. Jones, Karen Novik, Andrew J. Mungall & Marco A. Marra

  2. Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada

    Vanessa L. Porter, Steven J. M. Jones & Marco A. Marra

  3. Uganda Cancer Institute, Kampala, Uganda

    Constance Namirembe, Carolyn Nakisige, Jackson Orem & Martin Origa

  4. Office of Cancer Genomics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    Nicholas B. Griner, Patee Gesuwan & Daniela S. Gerhard

  5. Nationwide Children’s Hospital, Columbus, OH, USA

    Hilary Petrello, Jay Bowen & Julie M. Gastier-Foster

  6. Department of Pathology, University of California at San Francisco, San Francisco, CA, USA

    Teresa M. Darragh

  7. Department of Pathology, University of Virginia, Charlottesville, VA, USA

    Mark H. Stoler

  8. Department of Pathology and Cell Biology, Columbia University, New York, NY, USA

    Thomas C. Wright

  9. Frederick National Laboratory for Cancer Research, Frederick, MD, USA

    Maureen A. Dyer

  10. Departments of Pathology and Pediatrics, The Ohio State University, Columbus, OH, USA

    Julie M. Gastier-Foster

  11. Office of HIV and AIDS Malignancy, National Cancer Institute, National Institues of Health, Bethesda, MD, USA

    Robert Yarchoan

  12. HIV and AIDS Malignancy Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    Robert Yarchoan

  13. Infectious Disease Research Institute, Seattle, WA, USA

    Corey Casper

  14. Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA

    Gordon B. Mills

  15. Department of Obstetrics and Gynecology, Medical College of Wisconsin, Milwaukee, WI, USA

    Janet S. Rader

  16. Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA

    Akinyemi I. Ojesina

  17. O’Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA

    Akinyemi I. Ojesina

  18. HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA

    Akinyemi I. Ojesina

Authors

  1. Alessia Gagliardi

    You can also search for this author inPubMed Google Scholar

  2. Vanessa L. Porter

    You can also search for this author inPubMed Google Scholar

  3. Zusheng Zong

    You can also search for this author inPubMed Google Scholar

  4. Reanne Bowlby

    You can also search for this author inPubMed Google Scholar

  5. Emma Titmuss

    You can also search for this author inPubMed Google Scholar

  6. Constance Namirembe

    You can also search for this author inPubMed Google Scholar

  7. Nicholas B. Griner

    You can also search for this author inPubMed Google Scholar

  8. Hilary Petrello

    You can also search for this author inPubMed Google Scholar

  9. Jay Bowen

    You can also search for this author inPubMed Google Scholar

  10. Simon K. Chan

    You can also search for this author inPubMed Google Scholar

  11. Luka Culibrk

    You can also search for this author inPubMed Google Scholar

  12. Teresa M. Darragh

    You can also search for this author inPubMed Google Scholar

  13. Mark H. Stoler

    You can also search for this author inPubMed Google Scholar

  14. Thomas C. Wright

    You can also search for this author inPubMed Google Scholar

  15. Patee Gesuwan

    You can also search for this author inPubMed Google Scholar

  16. Maureen A. Dyer

    You can also search for this author inPubMed Google Scholar

  17. Yussanne Ma

    You can also search for this author inPubMed Google Scholar

  18. Karen L. Mungall

    You can also search for this author inPubMed Google Scholar

  19. Steven J. M. Jones

    You can also search for this author inPubMed Google Scholar

  20. Carolyn Nakisige

    You can also search for this author inPubMed Google Scholar

  21. Karen Novik

    You can also search for this author inPubMed Google Scholar

  22. Jackson Orem

    You can also search for this author inPubMed Google Scholar

  23. Martin Origa

    You can also search for this author inPubMed Google Scholar

  24. Julie M. Gastier-Foster

    You can also search for this author inPubMed Google Scholar

  25. Robert Yarchoan

    You can also search for this author inPubMed Google Scholar

  26. Corey Casper

    You can also search for this author inPubMed Google Scholar

  27. Gordon B. Mills

    You can also search for this author inPubMed Google Scholar

  28. Janet S. Rader

    You can also search for this author inPubMed Google Scholar

  29. Akinyemi I. Ojesina

    You can also search for this author inPubMed Google Scholar

  30. Daniela S. Gerhard

    You can also search for this author inPubMed Google Scholar

  31. Andrew J. Mungall

    You can also search for this author inPubMed Google Scholar

  32. Marco A. Marra

    You can also search for this author inPubMed Google Scholar

Contributions

A.G., V.L.P., Z.Z., R.B. and E.T. contributed equally to this work. J.S.R., A.I.O., D.S.G., A.J.M. and M.A.M. equally supervised this work. The HTMCP Cervical Cancer Working Group contributed collectively to this work. Project management and data coordination: K.N., M.A.D. and P.G. Cohort and clinical data collection: C.C., C. Nakisige, C. Namirembe, J.O., M.O., N.B.G., H.P., J.B. and J.M.G.-F. Pathology and molecular review: T.M.D., M.H.S., T.C.W. and R.B. Data were generated by Canada’s Michael Smith Genome Sciences Centre at BC Cancer and analyses were performed by V.L.P., Z.Z., R.B. and E.T. Contribution to analyses: G.B.M., R.Y., S.J.M.J., Y.M., K.L.M., A.G., S.K.C. and L.C. A.G., A.J.M., V.L.P., E.T. and M.A.M. wrote the manuscript. All authors reviewed and edited the manuscript.

Corresponding author

Correspondence to Marco A. Marra.

Ethics declarations

Competing interests

G.B.M. reports the following potentially competing interests: SAB/consultant: AstraZeneca, Chrysallis Biotechnology, ImmunoMET, Ionis, Lilly, PDX Pharmaceuticals, Signalchem Lifesciences, Symphogen, Tarveda, Zentalis; stock/options/financial: Catena Pharmaceuticals, ImmunoMet, SignalChem, Tarveda; licensed technology: HRD assay to Myriad Genetics, DSP patents with Nanostring; sponsored research: Nanostring Center of Excellence, Ionis (provision of tool compounds). R.Y. reports the following potentially competing interests: research support from a CRADA with Celgene/BMS. T.C.W. reports the following potentially competing interests: consultant to Roche, BD and Inovio with respect to HPV diagnostic tests and therapeutic vaccines.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Additional characteristics of the HTMCP discovery and extension cohorts.

a. Coding mutations per Mb in samples exhibiting low (≤ 0.4) and high (> 0.4) APOBEC signatures. b. Difference in PIK3CA expression by HIV status. c. Mutations in the top 20 most mutated epigenetic modifiers, ordered by frequency of alterations for the cohort (n = 118). APOBEC signature proportion and homologous recombination deficiency (HRD) scores are reported above. HIV status, age at diagnosis, tumor histology (”other” includes neuroendocrine and undifferentiated) and stage are also annotated. d. Comparison of mutation frequencies of the 12 SMGs in the discovery vs. extension cohorts. Boxplots in a and b represent the median, upper and lower quartiles of the distribution and whiskers represent the limits of the distribution (1.5-times interquartile range), and statistics were determined using two-sided Wilcoxon rank sum tests.

Source data

Extended Data Fig. 2 Association of HPV clades with HIV status, gene expression, DNA methylation and survival.

a. HPV types in our cohort separated by HIV status (n = 72 positive samples, n = 45 negative), and clade. The x axis indicates the percentage of samples in that cohort infected by the indicated HPV type, and in brackets is the number of samples. b. Unsupervised clustering of the top 1,000 most variable genes across our cohort (n = 118 samples). q-values were determined using Benjamini-Hochberg (BH) corrected Fisher exact tests. c. Percentage of differentially methylated probes between clades (A7 = 51 samples, A9 = 56 samples) at different genomic features, by HPV clade. d. Log2 fold change and adjusted (BH) p-value of differentially expressed genes between clade A7- (n = 52) and A9-infected (n = 57) samples. e. Volcano plots showing the log2 fold change and adjusted p-value (BH) of differentially expressed genes between clade A7- (n = 52) and A9-infected (n = 57) samples associated with A9 hypermethylated (top), and A7 hypermethylated (bottom) differentially methylated regions (DMRs). f, g. top: Kernel density of E6 (f) and E7 (g) expression in the HTMCP cohort separates samples into high- and low expressing cases. bottom: gene ontologies enriched in differentially expressed genes in samples with low / high E6 (n = 68 / n = 48) (f) and E7 (n = 58 / n = 59) (g). h. Multivariate cox proportional hazards model for HPV clade, HIV status and disease stage for 66 patients. Hazard ratios and p-values reported for each variable were determined using log-rank tests. Where relevant, all statistical tests were two-sided.

Source data

Extended Data Fig. 3 Correlations between histone modifications and gene expression.

a. Cluster of clusters analysis for 54 consensus clustering solutions for all histone marks on 52 samples (solutions with k = 2 to 10 for each mark). The heatmap color indicates the sample probabilities in the consensus matrix. q-values for each variable were determined using Benjamini-Hochberg corrected Fisher exact tests. b. Schematic showing the cluster of clusters solution (k = 5 for H3K27ac and H3K4me3 and k = 4 for the other marks) for all histone marks and for the 3 active marks. Each dot represents a sample and dot color represents the cluster membership of the sample. Hollow circles indicate no available ChIP data for that sample. c. Fold change of H3K4me3 abundance and gene expression between clades associated with TSS of genes (−5/+20 kb) found at intersecting H3K4me3 and H3K27ac peaks. Sample Ns used for differential analyses (and derivation of adjusted p-values) were: expression A7 = 52, A9 = 57; H3K4me3 and H3K27ac A7 = 25, A9 = 22. Genes with BH-adjusted p-values <0.05 (DESeq, Methods) are highlighted. d. Expression of the genes reported in Fig. 4f separated by HPV clade. Boxplots represent the median, upper and lower quartiles of the distribution and whiskers represent the limits of the distribution (1.5-times interquartile range), and p-values were calculated by Wilcoxon rank sum tests. Where relevant, all statistical tests were two-sided.

Source data

Extended Data Fig. 4 HPV integration events and tumor microenvironments.

a. Number of HPV integration sites per event separated by HPV clade. b, c, e, f. Distribution of the number (b, e) and fold change in integrated samples (c, f) of genes (b, c) and ERVs (e, f) near integration events. d. Expression (RPKM) of selected genes near HPV integration events in each sample (n = 118). g. Fold change of ERVs nearby integration events separated based on the clusters identified in Fig. 5f. h. Histone mark coverage of a 115 kb genomic region containing ERVs. The line represents an integration event, and arrows indicate individual integration sites. Top tracks refer to a case with integration, and the bottom to a control case without integration. i. Total T-cell scores and estimated tumor content of samples with HPV integration events that are associated with significant changes in expression of ERVs or genes, and those that are not. j, k. CIBERSORT scores for all CD4 + T-cells (sum) and CD8 + T-cells (j), Follicular helper T-cells and neutrophils (k) separated by HIV status (HIV + n = 72, HIV- n = 45). Boxplots in a, d, g, i–k represent the median, upper and lower quartiles of the distribution and whiskers represent the limits of the distribution (1.5-times interquartile range). All p-values were determined by Wilcoxon tests unless otherwise stated, and q-values were corrected using the Benjamini-Hochberg method. Where relevant, all statistical tests were two-sided.

Source data

Supplementary information

Source data

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gagliardi, A., Porter, V.L., Zong, Z. et al. Analysis of Ugandan cervical carcinomas identifies human papillomavirus clade–specific epigenome and transcriptome landscapes. Nat Genet 52, 800–810 (2020). https://doi.org/10.1038/s41588-020-0673-7

Download citation

  • Received: 23 October 2019

  • Accepted: 26 June 2020

  • Published: 03 August 2020

  • Issue Date: August 2020

  • DOI: https://doi.org/10.1038/s41588-020-0673-7