The Effect of Tumor Markers ER, Her2, and Ki67 on Long-Term and Short-Term Survival of Women with Breast Cancer Using Bayesian Cure Model

Document Type : Original Article (s)

Authors

1 PhD Student, Department of Biostatistics, School of Medical Sciences, Tarbiat Modares University, Tehran, Iran

2 Professor, Department of Biostatistics and Epidemiology, School of Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

3 Student of Medicine, School of Medicine, Kashan University of Medical Sciences, Kashan, Iran

4 Assistant Professor, Department of Pathology, School of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

Abstract

Background: Breast cancer is the second leading cause of death from cancer among women, and many factors are involved in its creation. The purpose of this study was to evaluate the effect of tumor markers on the survival of women with this cancer using Bayesian cure analysis.Methods: This was a population-based cohort study on 500 women with breast cancer registered in Shahid Ramazanzadeh hospital, Yazd City, Iran, from the April 2010 until March 2015, using Kaplan-Meier method and Bayesian cure model. The data were analyzed using R software. P < 0.050 was considered as the significance level.Findings: Based on Kaplan-Meier method, the 6-year cumulative survival for patients with breast cancer was 0.737. The mean age of breast cancer diagnosis was 48.03 ± 11.16 years, and the mean survival period was 97.64 ± 4.23 months. Bayesian cure model showed that Ki67 [hazard ratio (HR) = 1.34, 95% prediction interval (PI): 1.01-2.28] and ER (HR = 2.11, PI 95%: 1.99-2.36) were significantly related to hazard, and ER was significantly related to cure (OR = 0.38, PI 95%: 0.26-0.57).Conclusion: According to Bayesian cure analysis in this study, ER variable is also effective on short-term survival and long-term survival of patients. Cure models have the ability to analyze patients’ survival data, and can differentiate long-term survival from short- term survival. The interpretation of survival data with these statistical models could be more accurate.

Keywords

References

  1. Harirchi I, Karbakhsh M, Kashefi A, Momtahen AJ. Breast cancer in Iran: Results of a multi-center study. Asian Pac J Cancer Prev 2004; 5(1): 24-7.
  2. Asadi-Samani M, Rafieian-Kopaei M, Lorigooini Z, Shirzad H. A screening for discovering anticancer herbal drugs. J Pharm Pharmacogn Res 2019; 7(3): 213-22.
  3. Campone M, Fumoleau P, Bourbouloux E, Kerbrat P, Roche H. Taxanes in adjuvant breast cancer setting: Which standard in Europe? Crit Rev Oncol Hematol 2005; 55(3): 167-75.
  4. Kleinbaum DG, Klein M. Survival analysis: A self-learning text. vol 3. New York, NY: Springer; 2010.
  5. von Minckwitz G, Huang CS, Mano MS, Loibl S, Mamounas EP, Untch M, et al. Trastuzumab emtansine for residual invasive HER2-positive breast cancer. N Engl J Med 2019; 380(7): 617-28.
  6. Mohammadpour M, Yaseri M, Mahmoudi M, Entezar Mahdi R. Estimation of survival in women diagnosed with breast cancer with cure survival in West-Azerbaijan and East-Azerbaijan Provinces. J Sch Public Health Inst Public Health Res 2018; 16(1): 63-74. [In Persian].
  7. Wienke A. Frailty models in survival analysis. Boca Raton, FL: Chapman and Hall/CRC; 2010.
  8. Lunn D, Jackson C, Best N, Thomas A, Spiegelhalter D. The BUGS book: A practical introduction to Bayesian analysis. Boca Raton, FL: Chapman and Hall/CRC; 2012.
  9. van Ravenzwaaij D, Cassey P, Brown SD. A simple introduction to Markov Chain Monte-Carlo sampling. Psychon Bull Rev 2018; 25(1): 143-54.
  10. Sturtz S, Ligges U, Gelman A. R2WinBUGS: A package for running WinBUGS from R. J Stat Softw 2005; 1(3): 1-16.
  11. Plummer M, Best N, Cowles K, Vines K. CODA: Convergence diagnosis and output analysis for MCMC. R News 2005; 6(1): 7-11.
  12. Maller RA, Zhou X. Survival analysis with long-term survivors. Chichester, NY: Wiley; 1996.
  13. Rahimzadeh M, Baghestani AR, Gohari MR, Pourhoseingholi MA. Estimation of the cure rate in Iranian breast cancer patients. Asian Pac J Cancer Prev 2014; 15(12): 4839-42.
  14. Nishimura R, Osako T, Okumura Y, Hayashi M, Toyozumi Y, Arima N. Ki-67 as a prognostic marker according to breast cancer subtype and a predictor of recurrence time in primary breast cancer. Exp Ther Med 2010; 1(5): 747-54.
  15. Jurikova M, Danihel L, Polak S, Varga I. Ki67, PCNA, and MCM proteins: Markers of proliferation in the diagnosis of breast cancer. Acta Histochem 2016; 118(5): 544-52.
  16. Fallahzadeh H, Mohammadzadeh M, PahlavanV, Pahlavani N. A study on the prognostic factors of breast cancer survival time using Bayesian Cox model. J Isfahan Med Sch 2018; 36(466): 49-55. [In Persian].
  17. Savci-Heijink CD, Halfwerk H, Hooijer GK, Horlings HM, Wesseling J, van de Vijver MJ. Retrospective analysis of metastatic behaviour of breast cancer subtypes. Breast Cancer Res Treat 2015; 150(3): 547-57.
  18. Saadatmand S, Bretveld R, Siesling S, Tilanus-Linthorst MM. Influence of tumour stage at breast cancer detection on survival in modern times: Population based study in 173,797 patients. BMJ 2015; 351: h4901.
  19. Unni N, Sudhan DR, Arteaga CL. Neratinib: Inching Up on the Cure Rate of HER2(+) Breast Cancer? Clin Cancer Res 2018; 24(15): 3483-5.
  20. Vostakolaei FA, Broeders MJ, Rostami N, van Dijck JA, Feuth T, Kiemeney LA, et al. Age at diagnosis and breast cancer survival in Iran. Int J Breast Cancer 2012; 2012: 517976.
  21. Candido Dos Reis FJ, Wishart GC, Dicks EM, Greenberg D, Rashbass J, Schmidt MK, et al. An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation. Breast Cancer Res 2017; 19(1): 58.
  22. Jafari-Koshki T, Mansourian M, Mokarian F. Exploring factors related to metastasis free survival in breast cancer patients using Bayesian cure models. Asian Pac J Cancer Prev 2014; 15(22): 9673-8.
  23. Ibrahim JG, Chen MH, Sinha D. Bayesian survival analysis. In: Balakrishnan N, Colton T, Everitt B, Piegorsch W, Ruggeri F, Teugels JL, editors. Wiley StatsRef: Statistics Reference Online. Hoboken, NJ: Willey; 2014.
Journal of Isfahan Medical School
Volume 37, Issue 542 - Serial Number 542
July and August 2019
Pages 1074-1079

Files

History

  • Receive Date: 30 July 2019
  • Revise Date: 02 November 2024
  • Accept Date: 06 April 2022

Share

How to cite

Statistics