Skip to main content

Advertisement

SpringerLink
Log in

A multigenic approach to predict breast cancer risk

  • Epidemiology
  • Published:
Breast Cancer Research and Treatment Aims and scope Submit manuscript

Abstract

In the biology of complex disorders, such as breast cancer, interactions among genetic factors may play an important role and theoretical considerations suggest that gene–gene interactions are quite common in such diseases. In this case-control study with 500 breast cancer patients and 500 population-based healthy sex- and age-matched control subjects, we applied a multigenic approach to examine the associations with breast cancer risk of a comprehensive panel of 16 selected polymorphisms in a variety of pathways using classification tree analysis (CART). Overall, 79.6% of all breast cancer patients and 80.6% of all control subjects were correctly classified on the basis of their individual genetic profile by the classification procedure. CART analysis of the data identified the heterozygous vascular endothelial growth factor (VEGF) and matrix metalloproteinase 3 (MMP3) genotype and homozygous cyclooxygenase-2 (PTGS2) mutant as the initial splits, indicating that these genotypes exert the greatest impact on the classification process. Breast cancer patients were primarily indicated by 30 distinct genetic profiles. The odds ratio of these genetic risk profiles for breast cancer was 16.12 (95% confidence interval 11.09–23.49). Five genetic profiles formed homogenous breast cancer subgroups and represented highest risk genetic profiles. This is the first comprehensive study to use a multigenic analysis for breast cancer and the data suggest that individuals with distinct genetic profiles are at an increased risk for breast cancer, confirming the importance of taking a multigenic approach for risk assessment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Greenlee RT, Hill-Harmon MB, Murray T et al (2001) Cancer statistics. CA Cancer J Clin 51(1):15–36

    Article  PubMed  CAS  Google Scholar 

  2. Collaborative Group on Hormonal Factors in Breast Cancer (1997) Breast cancer and hormone replacement therapy: collaborative reanalysis of data from 51 epidemiological studies of 52,705 women with breast cancer and 108,411 women without breast cancer. Collaborative Group on Hormonal Factors in Breast Cancer. Lancet 350(9084):1047–1059

    Article  Google Scholar 

  3. Collaborative Group on Hormonal Factors in Breast Cancer (1996) Breast cancer and hormonal contraceptives: collaborative reanalysis of individual data on 53 297 women with breast cancer and 100 239 women without breast cancer from 54 epidemiological studies. Collaborative Group on Hormonal Factors in Breast Cancer. Lancet 347(9017):1713–1727

    Article  Google Scholar 

  4. Hulka BS, Stark AT (1995) Breast cancer: cause and prevention. Lancet 346(8979):883–887

    Article  PubMed  CAS  Google Scholar 

  5. Kelsey JL (1993) Breast cancer epidemiology: summary and future directions. Epidemiol Rev 15(1):256–263

    PubMed  CAS  Google Scholar 

  6. Potter J (1997) World cancer research fund—American institute of cancer research. Food, nutrition and the prevention of cancer: a global perspective. Banta Book Group, Mebasha, USA

    Google Scholar 

  7. Antoniou AC, Pharoah PD, McMullan G et al (2001) Evidence for further breast cancer susceptibility genes in addition to BRCA1 and BRCA2 in a population-based study. Genet Epidemiol 21(1):1–18

    Article  PubMed  CAS  Google Scholar 

  8. Rannala B (2001) Finding genes influencing susceptibility to comples diseases in the post-genome era. Am J Pharmacogenomics 1(3):203–221

    Article  PubMed  CAS  Google Scholar 

  9. Zhang H, Bonney G (2000) Use of classification trees for association studies. Genet Epidemiol 19(4):323–332

    Article  PubMed  CAS  Google Scholar 

  10. Popanda O, Schattenberg T, Phong CT et al (2004) Specific combinations of DNA repair gene variants and increased risk for non-small cell lung cancer. Carcinogenesis 25(12):2433–2441

    Article  PubMed  CAS  Google Scholar 

  11. Han J, Colditz GA, Samson LD et al (2004) Polymorphisms in DNA double-strand break repair genes and skin cancer risk. Cancer Res 64(9):3009–3013

    Article  PubMed  CAS  Google Scholar 

  12. Gu J, Zhao H, Dinney CP et al (2005) Nucleotide excision repair gene polymorphisms and recurrence after treatment for superficial bladder cancer. Clin Cancer Res 11(4):1408–1415

    Article  PubMed  CAS  Google Scholar 

  13. Cheng TC, Chen ST, Huang CS et al (2005) Breast cancer risk associated with genotype polymorphism of the catechol estrogen-metabolizing genes: a multigenic study on cancer susceptibility. Int J Cancer 113(3):345–353

    Article  PubMed  CAS  Google Scholar 

  14. Esterbauer H, Schneitler C, Oberkofler H et al (2001) A common polymorphism in the promoter of UCP2 is associated with decreased risk of obesity in middle-aged humans. Nat Genet 28(2):178–183

    Article  PubMed  CAS  Google Scholar 

  15. Langsenlehner U, Yazdani-Biuki B, Eder T et al (2006) The cyclooxygenase-2 (PTGS2) 8473T > C polymorphism is associated with breast cancer risk. Clin Cancer Res 12(4):1392–1394

    Article  PubMed  Google Scholar 

  16. Langsenlehner U, Renner W, Yazdani-Biuki B et al (2006) Integrin alpha-2 and beta-3 gene polymorphisms and breast cancer risk. Breast Cancer Res Treat 97(1):67–72

    Article  PubMed  CAS  Google Scholar 

  17. Langsenlehner U, Krippl P, Renner W et al (2005) Interleukin-10 promoter polymorphism is associated with decreased breast cancer risk. Breast Cancer Res Treat 90(2):113–115

    Article  PubMed  CAS  Google Scholar 

  18. Krippl P, Langsenlehner U, Samonigg H et al (2004) Vascular endothelial growth factor in predicting outcome in breast cancer. Clin Cancer Res 10(24):8752–8753

    Article  PubMed  Google Scholar 

  19. Krippl P, Langsenlehner U, Renner W et al (2004) Re: Polymorphisms of death pathway genes FAS and FASL in esophageal squamous-cell carcinoma. J Natl Cancer Inst 96(19):1478–1479

    Article  PubMed  Google Scholar 

  20. Langsenlehner U, Krippl P, Renner W et al (2004) Genetic variants of the sulfotransferase 1A1 and breast cancer risk. Breast Cancer Res Treat 87(1):19–22

    Article  PubMed  CAS  Google Scholar 

  21. Krippl P, Langsenlehner U, Renner W et al (2004) The 5A/6A polymorphism of the matrix metalloproteinase 3 gene promoter and breast cancer. Clin Cancer Res 10(10):3518–3520

    Article  PubMed  CAS  Google Scholar 

  22. Krippl P, Langsenlehner U, Renner W et al (2004) The 825C > T polymorphism of the G-protein beta-3 subunit gene (GNB3) and breast cancer. Cancer Lett 206(1):59–62

    Article  PubMed  CAS  Google Scholar 

  23. Krippl P, Langsenlehner U, Renner W et al (2003) The 870G > A polymorphism of the cyclin D1 gene is not associated with breast cancer. Breast Cancer Res Treat 82(3):165–168

    Article  PubMed  CAS  Google Scholar 

  24. Krippl P, Langsenlehner U, Renner W et al (2003) The L10P polymorphism of the transforming growth factor-beta 1 gene is not associated with breast cancer risk. Cancer Lett 201(2):181–184

    Article  PubMed  CAS  Google Scholar 

  25. Langsenlehner U, Krippl P, Renner W et al (2003) The common 677C > T gene polymorphism of methylenetetrahydrofolate reductase gene is not associated with breast cancer risk. Breast Cancer Res Treat 81(2):169–172

    Article  PubMed  CAS  Google Scholar 

  26. Krippl P, Langsenlehner U, Renner W et al (2003) A common 936 C/T gene polymorphism of vascular endothelial growth factor is associated with decreased breast cancer risk. Int J Cancer 106(4):468–471

    Article  PubMed  CAS  Google Scholar 

  27. Breiman L, Friedman J (1984) Classification and regression trees. Pacific Grove, Wadsworth

  28. Steinberg D, Colla P (1995) CART: tree-structured non-parametric data analysis. Salford Systems, San Diego

    Google Scholar 

Download references

Acknowledgment

Armin Gerger and Uwe Langsenlehner contributed equally to this work.

Author information

Authors and Affiliations

  1. Department of Internal Medicine, Division of Oncology, Medical University Graz, Auenbruggerplatz 15, 8036, Graz, Austria

    Armin Gerger, Uwe Langsenlehner, Werner Weitzer, Günter Hofmann, Hellmut Samonigg & Peter Krippl

  2. Clinical Institute of Medical and Laboratory Diagnostics, Medical University Graz, Auenbruggerplatz 15, 8036, Graz, Austria

    Wilfried Renner

  3. Clinic of Therapeutic Radiology and Oncology, Medical University Graz, Auenbruggerplatz 15, 8036, Graz, Austria

    Tanja Eder

  4. Department of Internal Medicine, Division of Rheumatology, Medical University Graz, Auenbruggerplatz 15, 8036, Graz, Austria

    Babak Yazdani-Biuki

Authors
  1. Armin Gerger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Uwe Langsenlehner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wilfried Renner
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Werner Weitzer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tanja Eder
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Babak Yazdani-Biuki
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Günter Hofmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Hellmut Samonigg
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Peter Krippl
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Armin Gerger.

Additional information

Armin Gerger and Uwe Langsenlehner contributed equally to this work

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gerger, A., Langsenlehner, U., Renner, W. et al. A multigenic approach to predict breast cancer risk. Breast Cancer Res Treat 104, 159–164 (2007). https://doi.org/10.1007/s10549-006-9408-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10549-006-9408-4

Keywords

Search

Navigation