Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Original Article
  • Published:

Pathway-based analysis of genetic susceptibility to cervical cancer in situ: HLA-DPB1 affects risk in Swedish women

Abstract

We have conducted a pathway-based analysis of genome-wide single-nucleotide polymorphism (SNP) data in order to identify genetic susceptibility factors for cervical cancer in situ. Genotypes derived from Affymetrix 500k or 5.0 arrays for 1076 cases and 1426 controls were analyzed for association, and pathways with enriched signals were identified using the SNP ratio test. The most strongly associated KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways were Asthma (empirical P=0.03), Folate biosynthesis (empirical P=0.04) and Graft-versus-host disease (empirical P=0.05). Among the 11 top-ranking pathways were 6 related to the immune response with the common denominator being genes in the major histocompatibility complex (MHC) region on chromosome 6. Further investigation of the MHC revealed a clear effect of HLA-DPB1 polymorphism on disease susceptibility. At a functional level, DPB1 alleles associated with risk and protection differ in key amino-acid residues affecting peptide-binding motifs in the extracellular domains. The results illustrate the value of pathway-based analysis to mine genome-wide data, and point to the importance of the MHC region and specifically the HLA-DPB1 locus for susceptibility to cervical cancer.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2
Figure 3

Similar content being viewed by others

References

  1. Parkin DM, Bray F, Ferlay J, Pisani P . Global cancer statistics, 2002. CA Cancer J Clin 2005; 55: 74–108.

    Article  Google Scholar 

  2. Walboomers JM, Jacobs MV, Manos MM, Bosch FX, Kummer JA, Shah KV et al. Human papillomavirus is a necessary cause of invasive cervical cancer worldwide. J Pathol 1999; 189: 12–19.

    Article  CAS  Google Scholar 

  3. Baseman JG, Koutsky LA . The epidemiology of human papillomavirus infections. J Clin Virol 2005; 32 (Suppl 1): S16–S24.

    Article  Google Scholar 

  4. Magnusson PK, Sparen P, Gyllensten UB . Genetic link to cervical tumours. Nature 1999; 400: 29–30.

    Article  CAS  Google Scholar 

  5. Magnusson PK, Lichtenstein P, Gyllensten UB . Heritability of cervical tumours. Int J Cancer 2000; 88: 698–701.

    Article  CAS  Google Scholar 

  6. Apple RJ, Erlich HA, Klitz W, Manos MM, Becker TM, Wheeler CM . HLA DR-DQ associations with cervical carcinoma show papillomavirus-type specificity. Nat Genet 1994; 6: 157–162.

    Article  CAS  Google Scholar 

  7. Beskow AH, Josefsson AM, Gyllensten UB . HLA class II alleles associated with infection by HPV16 in cervical cancer in situ. Int J Cancer 2001; 93: 817–822.

    Article  CAS  Google Scholar 

  8. Wang SS, Hildesheim A . Chapter 5: viral and host factors in human papillomavirus persistence and progression. J Natl Cancer Inst Monogr 2003; 31: 35–40.

    Article  CAS  Google Scholar 

  9. Zoodsma M, Nolte IM, Te Meerman GJ, De Vries EG, Van der Zee AG . HLA genes and other candidate genes involved in susceptibility for (pre)neoplastic cervical disease. Int J Oncol 2005; 26: 769–784.

    CAS  PubMed  Google Scholar 

  10. Sanjeevi CB, Hjelmstrom P, Hallmans G, Wiklund F, Lenner P, Angstrom T et al. Different HLA-DR-DQ haplotypes are associated with cervical intraepithelial neoplasia among human papillomavirus type-16 seropositive and seronegative Swedish women. Int J Cancer 1996; 68: 409–414.

    Article  CAS  Google Scholar 

  11. Ghaderi M, Wallin KL, Wiklund F, Zake LN, Hallmans G, Lenner P et al. Risk of invasive cervical cancer associated with polymorphic HLA DR/DQ haplotypes. Int J Cancer 2002; 100: 698–701.

    Article  CAS  Google Scholar 

  12. Ivansson EL, Magnusson JJ, Magnusson PK, Erlich HA, Gyllensten UB . MHC loci affecting cervical cancer risk: distinguishing the effects of HLA-DQB1 and non-HLA genes TNF, LTA, TAP1 and TAP2. Genes Immun 2008; 9: 613–623.

    Article  CAS  Google Scholar 

  13. Ho GY, Bierman R, Beardsley L, Chang CJ, Burk RD . Natural history of cervicovaginal papillomavirus infection in young women. N Engl J Med 1998; 338: 423–428.

    Article  CAS  Google Scholar 

  14. Ivansson EL, Gustavsson IM, Magnusson JJ, Steiner LL, Magnusson PK, Erlich HA et al. Variants of chemokine receptor 2 and interleukin 4 receptor, but not interleukin 10 or Fas ligand, increase risk of cervical cancer. Int J Cancer 2007; 121: 2451–2457.

    Article  CAS  Google Scholar 

  15. Wang SS, Gonzalez P, Yu K, Porras C, Li Q, Safaeian M et al. Common genetic variants and risk for HPV persistence and progression to cervical cancer. PLoS One 2010; 5: e8667.

    Article  Google Scholar 

  16. Castro FA, Haimila K, Sareneva I, Schmitt M, Lorenzo J, Kunkel N et al. Association of HLA-DRB1, interleukin-6 and cyclin D1 polymorphisms with cervical cancer in the Swedish population--a candidate gene approach. Int J Cancer 2009; 125: 1851–1858.

    Article  CAS  Google Scholar 

  17. Johnson LG, Schwartz SM, Malkki M, Du Q, Petersdorf EW, Galloway DA et al. Risk of cervical cancer associated with allergies and polymorphisms in genes in the chromosome 5 cytokine cluster. Cancer Epidemiol Biomarkers Prev 2011; 20: 199–207.

    Article  CAS  Google Scholar 

  18. Ivansson EL, Juko-Pecirep I, Gyllensten UB . Interaction of immunological genes on chromosome 2q33 and IFNG in susceptibility to cervical cancer. Gynecol Oncol 2009; 116: 544–548.

    Article  Google Scholar 

  19. Manolio TA, Brooks LD, Collins FS . A HapMap harvest of insights into the genetics of common disease. J Clin Invest 2008; 118: 1590–1605.

    Article  CAS  Google Scholar 

  20. Wang K, Li M, Bucan M . Pathway-based approaches for analysis of genomewide association studies. Am J Hum Genet 2007; 81: 1278–1283.

    Article  CAS  Google Scholar 

  21. Holmans P, Green EK, Pahwa JS, Ferreira MA, Purcell SM, Sklar P et al. Gene ontology analysis of GWA study data sets provides insights into the biology of bipolar disorder. Am J Hum Genet 2009; 85: 13–24.

    Article  CAS  Google Scholar 

  22. Eleftherohorinou H, Wright V, Hoggart C, Hartikainen AL, Jarvelin MR, Balding D et al. Pathway analysis of GWAS provides new insights into genetic susceptibility to 3 inflammatory diseases. PLoS One 2009; 4: e8068.

    Article  Google Scholar 

  23. O’Dushlaine C, Kenny E, Heron EA, Segurado R, Gill M, Morris DW et al. The SNP ratio test: pathway analysis of genome-wide association datasets. Bioinformatics 2009; 25: 2762–2763.

    Article  Google Scholar 

  24. Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M . KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res 2010; 38 (Database issue): D355–D360.

    Article  CAS  Google Scholar 

  25. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000; 25: 25–29.

    Article  CAS  Google Scholar 

  26. Ivansson EL, Rasmussen F, Gyllensten UB, Magnusson PK . Reduced incidence of cervical cancer in mothers of sons with allergic rhinoconjunctivitis, asthma or eczema. Int J Cancer 2006; 119: 1994–1998.

    Article  CAS  Google Scholar 

  27. Weiss MJ, Cole DE, Ray K, Whyte MP, Lafferty MA, Mulivor RA et al. A missense mutation in the human liver/bone/kidney alkaline phosphatase gene causing a lethal form of hypophosphatasia. Proc Natl Acad Sci USA 1988; 85: 7666–7669.

    Article  CAS  Google Scholar 

  28. Mornet E . Hypophosphatasia: the mutations in the tissue-nonspecific alkaline phosphatase gene. Hum Mutat 2000; 15: 309–315.

    Article  CAS  Google Scholar 

  29. Tanaka T, Scheet P, Giusti B, Bandinelli S, Piras MG, Usala G et al. Genome-wide association study of vitamin B6, vitamin B12, folate, and homocysteine blood concentrations. Am J Hum Genet 2009; 84: 477–482.

    Article  CAS  Google Scholar 

  30. Engelmark M, Beskow A, Magnusson J, Erlich H, Gyllensten U . Affected sib-pair analysis of the contribution of HLA class I and class II loci to development of cervical cancer. Hum Mol Genet 2004; 13: 1951–1958.

    Article  CAS  Google Scholar 

  31. Ivansson EL, Magnusson JJ, Magnusson PK, Erlich HA, Gyllensten UB . MHC loci affecting cervical cancer risk: distinguishing the effects of HLA-DQB1 and non-HLA genes TNF, LTA, TAP1 and TAP2. Genes Immun 2008; 9: 613–623.

    Article  CAS  Google Scholar 

  32. Leslie S, Donnelly P, McVean G . A statistical method for predicting classical HLA alleles from SNP data. Am J Hum Genet 2008; 82: 48–56.

    Article  CAS  Google Scholar 

  33. Rioux JD, Goyette P, Vyse TJ, Hammarstrom L, Fernando MM, Green T et al. Mapping of multiple susceptibility variants within the MHC region for 7 immune-mediated diseases. Proc Natl Acad Sci USA 2009; 106: 18680–18685.

    Article  CAS  Google Scholar 

  34. Gyllensten UB, Lashkari D, Erlich HA . Allelic diversification at the class II DQB locus of the mammalian major histocompatibility complex. Proc Natl Acad Sci USA 1990; 87: 1835–1839.

    Article  CAS  Google Scholar 

  35. Gyllensten UB, Sundvall M, Erlich HA . Allelic diversity is generated by intraexon sequence exchange at the DRB1 locus of primates. Proc Natl Acad Sci USA 1991; 88: 3686–3690.

    Article  CAS  Google Scholar 

  36. Stern LJ, Brown JH, Jardetzky TS, Gorga JC, Urban RG, Strominger JL et al. Crystal structure of the human class II MHC protein HLA-DR1 complexed with an influenza virus peptide. Nature 1994; 368: 215–221.

    Article  CAS  Google Scholar 

  37. Sidney J, Steen A, Moore C, Ngo S, Chung J, Peters B et al. Five HLA-DP molecules frequently expressed in the worldwide human population share a common HLA supertypic binding specificity. J Immunol 2010; 184: 2492–2503.

    Article  CAS  Google Scholar 

  38. Diaz G, Amicosante M, Jaraquemada D, Butler RH, Guillen MV, Sanchez M et al. Functional analysis of HLA-DP polymorphism: a crucial role for DPbeta residues 9, 11, 35, 55, 56, 69 and 84-87 in T cell allorecognition and peptide binding. Int Immunol 2003; 15: 565–576.

    Article  CAS  Google Scholar 

  39. Rabbee N, Speed TP . A genotype calling algorithm for Affymetrix SNP arrays. Bioinformatics 2006; 22: 7–12.

    Article  CAS  Google Scholar 

  40. Di X, Matsuzaki H, Webster TA, Hubbell E, Liu G, Dong S et al. Dynamic model based algorithms for screening and genotyping over 100 K SNPs on oligonucleotide microarrays. Bioinformatics 2005; 21: 1958–1963.

    Article  CAS  Google Scholar 

  41. Zheng SL, Sun J, Wiklund F, Smith S, Stattin P, Li G et al. Cumulative association of five genetic variants with prostate cancer. N Engl J Med 2008; 358: 910–919.

    Article  CAS  Google Scholar 

  42. Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PI, Chen H et al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 2007; 316: 1331–1336.

    Article  CAS  Google Scholar 

  43. Duggan D, Zheng SL, Knowlton M, Benitez D, Dimitrov L, Wiklund F et al. Two genome-wide association studies of aggressive prostate cancer implicate putative prostate tumor suppressor gene DAB2IP. J Natl Cancer Inst 2007; 99: 1836–1844.

    Article  CAS  Google Scholar 

  44. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 2010; 42: 937–948.

    Article  CAS  Google Scholar 

  45. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81: 559–575.

    Article  CAS  Google Scholar 

  46. Tian C, Gregersen PK, Seldin MF . Accounting for ancestry: population substructure and genome-wide association studies. Hum Mol Genet 2008; 17 (R2): R143–R150.

    Article  CAS  Google Scholar 

  47. Barrett JC, Fry B, Maller J, Daly MJ . Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 2005; 21: 263–265.

    Article  CAS  Google Scholar 

  48. Erlich H, Bugawan T, Begovich AB, Scharf S, Griffith R, Saiki R et al. HLA-DR, DQ and DP typing using PCR amplification and immobilized probes. Eur J Immunogenet 1991; 18: 33–55.

    Article  CAS  Google Scholar 

  49. Lindblom B . Proceedings of the 11th International Histocompatibility Workshop 1995.

  50. Aldener-Cannava A, Olerup O . HLA-DPB1 typing by polymerase chain reaction amplification with sequence-specific primers. Tissue Antigens 2001; 57: 287–299.

    Article  CAS  Google Scholar 

  51. Allen M, Sandberg-Wollheim M, Sjogren K, Erlich HA, Petterson U, Gyllensten U . Association of susceptibility to multiple sclerosis in Sweden with HLA class II DRB1 and DQB1 alleles. Hum Immunol 1994; 39: 41–48.

    Article  CAS  Google Scholar 

  52. Gonzalez-Galarza FF, Christmas S, Middleton D, Jones AR . Allele frequency net: a database and online repository for immune gene frequencies in worldwide populations. Nucleic Acids Res 2011; 39 (Database issue): D913–D919.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This study was supported by grants from the Swedish Cancer Foundation and the Knut and Alice Wallenberg Foundation to Ulf Gyllensten. Henry Erlich is employed by Roche Molecular Systems who kindly provided reagents and protocols for HLA-DPB1 typing. The population allele and genotype frequencies were based on samples regionally selected from Sweden obtained from the data source funded by the Nordic Center of Excellence in Disease Genetics. The study from which control set 1 originated was supported by Novartis Pharmaceuticals, Sigrid Juselius Foundation, Folkhälsan Research Foundation and the Swedish Research Council Linné grant. The study from which control sets 2 and 3 originated was supported by grants from the National Cancer Institute (CA106523, CA105055, CA95052, CA112517, CA58236, CA86323); Department of Defense (PC051264); Swedish Cancer Society; and Swedish Research Council.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to U B Gyllensten.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information

Supplementary Information accompanies the paper on Genes and Immunity website

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ivansson, E., Juko-Pecirep, I., Erlich, H. et al. Pathway-based analysis of genetic susceptibility to cervical cancer in situ: HLA-DPB1 affects risk in Swedish women. Genes Immun 12, 605–614 (2011). https://doi.org/10.1038/gene.2011.40

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/gene.2011.40

Keywords

This article is cited by

Search

Quick links