Skip to main content

An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients

Breast Cancer Research and Treatment volume 123, pages 725–731 (2010)Cite this article

Abstract

Validating prognostic or predictive candidate genes in appropriately powered breast cancer cohorts are of utmost interest. Our aim was to develop an online tool to draw survival plots, which can be used to assess the relevance of the expression levels of various genes on the clinical outcome both in untreated and treated breast cancer patients. A background database was established using gene expression data and survival information of 1,809 patients downloaded from GEO (Affymetrix HGU133A and HGU133+2 microarrays). The median relapse free survival is 6.43 years, 968/1,231 patients are estrogen-receptor (ER) positive, and 190/1,369 are lymph-node positive. After quality control and normalization only probes present on both Affymetrix platforms were retained (n = 22,277). In order to analyze the prognostic value of a particular gene, the cohorts are divided into two groups according to the median (or upper/lower quartile) expression of the gene. The two groups can be compared in terms of relapse free survival, overall survival, and distant metastasis free survival. A survival curve is displayed, and the hazard ratio with 95% confidence intervals and logrank P value are calculated and displayed. Additionally, three subgroups of patients can be assessed: systematically untreated patients, endocrine-treated ER positive patients, and patients with a distribution of clinical characteristics representative of those seen in general clinical practice in the US. Web address: www.kmplot.com. We used this integrative data analysis tool to confirm the prognostic power of the proliferation-related genes TOP2A and TOP2B, MKI67, CCND2, CCND3, CCNDE2, as well as CDKN1A, and TK2. We also validated the capability of microarrays to determine estrogen receptor status in 1,231 patients. The tool is highly valuable for the preliminary assessment of biomarkers, especially for research groups with limited bioinformatic resources.

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

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

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

Fig. 1
Fig. 2
Fig. 3

References

  1. Amat S, Penault-Llorca F, Cure H et al (2002) Scarff-Bloom-Richardson (SBR) grading: a pleiotropic marker of chemosensitivity in invasive ductal breast carcinomas treated by neoadjuvant chemotherapy. Int J Oncol 20:791–796

    CAS  PubMed  Google Scholar 

  2. Ravdin PM, Siminoff LA, Davis GJ et al (2001) Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J Clin Oncol 19:980–991

    CAS  PubMed  Google Scholar 

  3. Olivotto IA, Bajdik CD, Ravdin PM et al (2005) Population-based validation of the prognostic model ADJUVANT! for early breast cancer. J Clin Oncol 23:2716–2725

    Article  PubMed  Google Scholar 

  4. Harris L, Fritsche H, Mennel R et al (2007) American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J Clin Oncol 25:5287–5312

    Article  CAS  PubMed  Google Scholar 

  5. Paik S, Shak S, Tang G et al (2004) A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351:2817–2826

    Article  CAS  PubMed  Google Scholar 

  6. Draghici S, Khatri P, Eklund AC et al (2006) Reliability and reproducibility issues in DNA microarray measurements. Trends Genet 22:101–109

    Article  CAS  PubMed  Google Scholar 

  7. Shi L, Reid LH, Jones WD et al (2006) The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol 24:1151–1161

    Article  CAS  PubMed  Google Scholar 

  8. Gyorffy B, Molnar B, Lage H et al (2009) Evaluation of microarray preprocessing algorithms based on concordance with RT-PCR in clinical samples. PLoS One 4:e5645

    Article  PubMed  Google Scholar 

  9. Kaplan EL, Meier P (1958) Nonparametric estimation from incomplete observations. J Am Stat Assoc 53:457–481

    Article  Google Scholar 

  10. Colozza M, Azambuja E, Cardoso F et al (2005) Proliferative markers as prognostic and predictive tools in early breast cancer: where are we now? Ann Oncol 16:1723–1739

    Article  CAS  PubMed  Google Scholar 

  11. Tan PK, Downey TJ, Spitznagel EL Jr et al (2003) Evaluation of gene expression measurements from commercial microarray platforms. Nucleic Acids Res 31:5676–5684

    Article  CAS  PubMed  Google Scholar 

  12. Györffy B, Schäfer R (2009) Meta-analysis of gene expression profiles related to relapse-free survival in 1,079 breast cancer patients. Breast Cancer Res Treat 118(3):433–441

    Article  PubMed  Google Scholar 

  13. Gautier L, Cope L, Bolstad BM et al (2004) affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20:307–315

    Article  CAS  PubMed  Google Scholar 

  14. Sims AH, Smethurst GJ, Hey Y et al (2008) The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets—improving meta-analysis and prediction of prognosis. BMC Med Genomics 1:42

    Article  PubMed  Google Scholar 

  15. Gong Y, Yan K, Lin F et al (2007) Determination of oestrogen-receptor status and ERBB2 status of breast carcinoma: a gene-expression profiling study. Lancet Oncol 8:203–211

    Article  CAS  PubMed  Google Scholar 

  16. Dunnwald LK, Rossing MA, Li CI (2007) Hormone receptor status, tumor characteristics, and prognosis: a prospective cohort of breast cancer patients. Breast Cancer Res 9:R6

    Article  PubMed  Google Scholar 

  17. Lacroix M, Querton G, Hennebert P et al (2001) Estrogen receptor analysis in primary breast tumors by ligand-binding assay, immunocytochemical assay, and northern blot: a comparison. Breast Cancer Res Treat 67:263–271

    Article  CAS  PubMed  Google Scholar 

  18. Pusztai L, Ayers M, Stec J et al (2003) Gene expression profiles obtained from fine-needle aspirations of breast cancer reliably identify routine prognostic markers and reveal large-scale molecular differences between estrogen-negative and estrogen-positive tumors. Clin Cancer Res 9:2406–2415

    CAS  PubMed  Google Scholar 

  19. Paik S, Tang G, Shak S et al (2006) Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol 24:3726–3734

    Article  CAS  PubMed  Google Scholar 

  20. Darb-Esfahani S, Wirtz RM, Sinn BV, Budczies J, Noske A, Weichert W, Faggad A, Scharff S, Sehouli J, Oskay-Ozcelik G, Zamagni C, De Iaco P, Martoni A, Dietel M, Denkert C (2009) Estrogen receptor 1 mRNA is a prognostic factor in ovarian carcinoma: determination by kinetic PCR in formalin-fixed paraffin-embedded tissue. Endocr Relat Cancer 16(4):1229–1239

    Article  PubMed  Google Scholar 

  21. Bos PD, Zhang XH, Nadal C et al (2009) Genes that mediate breast cancer metastasis to the brain. Nature 459:1005–1009

    Article  CAS  PubMed  Google Scholar 

  22. Desmedt C, Giobbie-Hurder A, Neven P et al (2009) The Gene expression Grade Index: a potential predictor of relapse for endocrine-treated breast cancer patients in the BIG 1–98 trial. BMC Med Genomics 2:40

    Article  PubMed  Google Scholar 

  23. Zhang Y, Sieuwerts AM, McGreevy M et al (2009) The 76-gene signature defines high-risk patients that benefit from adjuvant tamoxifen therapy. Breast Cancer Res Treat 116:303–309

    Article  CAS  PubMed  Google Scholar 

  24. Schmidt M, Bohm D, von TC et al (2008) The humoral immune system has a key prognostic impact in node-negative breast cancer. Cancer Res 68:5405–5413

    Article  CAS  PubMed  Google Scholar 

  25. Loi S, Haibe-Kains B, Desmedt C et al (2008) Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen. BMC Genomics 9:239

    Article  PubMed  Google Scholar 

  26. Desmedt C, Piette F, Loi S et al (2007) Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multicenter independent validation series. Clin Cancer Res 13:3207–3214

    Article  CAS  PubMed  Google Scholar 

  27. Loi S, Haibe-Kains B, Desmedt C et al (2007) Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade. J Clin Oncol 25:1239–1246

    Article  CAS  PubMed  Google Scholar 

  28. Minn AJ, Gupta GP, Padua D et al (2007) Lung metastasis genes couple breast tumor size and metastatic spread. Proc Natl Acad Sci USA 104:6740–6745

    Article  CAS  PubMed  Google Scholar 

  29. Ivshina AV, George J, Senko O et al (2006) Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer. Cancer Res 66:10292–10301

    Article  CAS  PubMed  Google Scholar 

  30. Miller LD, Smeds J, George J et al (2005) An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. Proc Natl Acad Sci USA 102:13550–13555

    Article  CAS  PubMed  Google Scholar 

  31. Sotiriou C, Wirapati P, Loi S et al (2006) Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst 98:262–272

    Article  CAS  PubMed  Google Scholar 

  32. Wang YX, Klijn JGM, Zhang Y et al (2005) Gene-expression pro-files to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365:671–679

    CAS  PubMed  Google Scholar 

  33. Pawitan Y, Bjohle J, Amler L et al (2005) Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts. Breast Cancer Res 7:R953–R964

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

B.G. was supported by a Bolyai fellowship. Z.S. was supported by the Breast Cancer Research Foundation.

Author information

Authors and Affiliations

  1. Joint Research Laboratory of the Hungarian Academy of Sciences and the Semmelweis University, Semmelweis University 1st Department of Pediatrics, Bokay u. 53-54, 1083, Budapest, Hungary

    Balazs Györffy & Andras Lanczky

  2. Pazmany Peter University, Budapest, Hungary

    Andras Lanczky

  3. Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark

    Aron C. Eklund, Qiyuan Li & Zoltan Szallasi

  4. Charité Universitaetsmedizin, Berlin, Germany

    Carsten Denkert & Jan Budczies

  5. Children’s Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology (CHIP@HST), Harvard Medical School, Boston, MA, USA

    Zoltan Szallasi

Authors
  1. Balazs Györffy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andras Lanczky
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aron C. Eklund
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carsten Denkert
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jan Budczies
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Qiyuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Zoltan Szallasi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Balazs Györffy.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Györffy, B., Lanczky, A., Eklund, A.C. et al. An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients. Breast Cancer Res Treat 123, 725–731 (2010). https://doi.org/10.1007/s10549-009-0674-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10549-009-0674-9

Keywords

  • Survival analysis
  • Breast cancer
  • Prognosis