Skip to main content

Advertisement

SpringerLink
Log in

Gene expression profile testing for breast cancer and the use of chemotherapy, serious adverse effects, and costs of care

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

Abstract

As gene expression profile (GEP) testing for breast cancer may provide additional prognostic information to guide the use of adjuvant chemotherapy, we examined the association between GEP testing and use of chemotherapy, serious chemotherapy-related adverse effects, and total charges during the 12 months following diagnosis. Medical record review was conducted for women age 30–64 years, with incident, non-metastatic, invasive breast cancer diagnosed 2006–2008 in a large, national health plan. Of 534 patients, 25.8% received GEP testing, 68.2% received chemotherapy, and 10.5% experienced a serious chemotherapy-related adverse effect. GEP testing was most commonly used in women at moderate clinical risk of recurrence (52.0 vs. 25.0% of low-risk women and 5.5% of high-risk). Controlling for the propensity to receive GEP testing, women who had GEP were less likely to receive chemotherapy (propensity adjusted odds ratio, 95% confidence interval 0.62, 0.39–0.99). Use of GEP was associated with more chemotherapy use among women at low risk based on clinical characteristics (OR = 42.19; CI 2.50–711.82), but less use among women with a high risk based on clinical characteristics (OR = 0.12; CI 0.03–0.47). Use of GEP was not associated with chemotherapy for the moderate risk group. There was no significant relationship between GEP use and either serious chemotherapy-associated adverse effects or total charges. While GEP testing was associated with an overall decrease in adjuvant chemotherapy, we did not find differences in serious chemotherapy-associated adverse events or charges during the 12 months following diagnosis.

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

Similar content being viewed by others

References

  1. Jemal A, Siegel R, Xu J, Ward E (2010) Cancer statistics, 2010. CA Cancer J Clin 60(5):277–300

    Article  PubMed  Google Scholar 

  2. National Comprehensive Cancer Network (NCCN) Clinical practice guidelines in oncology: breast cancer v.1.20092009

  3. Eifel P, Axelson JA, Costa J, Crowley J, Curran WJ Jr, Deshler A et al. (2001) National Institutes of Health Consensus Development Conference Statement: adjuvant therapy for breast cancer, November 1–3, 2000. J Natl Cancer Inst 93(13):979–989

    Article  PubMed  CAS  Google Scholar 

  4. Early Breast Cancer Trialists’ Collaborative Group (EBCTCG) (2005) Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Lancet 365(9472):1687–1717

    Article  Google Scholar 

  5. Warren JL, Yabroff KR, Meekins A, Topor M, Lamont EB, Brown ML (2008) Evaluation of trends in the cost of initial cancer treatment. J Natl Cancer Inst 100(12):888–897

    Article  PubMed  Google Scholar 

  6. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M et al. (2004) A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351(27):2817–2826

    Article  PubMed  CAS  Google Scholar 

  7. Goldstein LJ, Gray R, Badve S, Childs BH, Yoshizawa C, Rowley S et al. (2008) Prognostic utility of the 21-gene assay in hormone receptor-positive operable breast cancer compared with classical clinicopathologic features. J Clin Oncol 26(25):4063–4071

    Article  PubMed  Google Scholar 

  8. Albain K, Barlow W, Shak S, Hortobagyi G, Livingston R, Yeh I et al. (2007) Prognostic and predictive value of the 21-gene recurrence score assay in postmenopausal, node-positive, ER-positive breast cancer. San Antonio Breast Cancer Symposium, San Antonio, 2007

  9. Paik S, Tang G, Shak S, Kim C, Baker J, Kim W et al. (2006) Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol 24(23):3726–3734

    Article  PubMed  CAS  Google Scholar 

  10. Sotiriou C, Pusztai L (2009) Gene-expression signatures in breast cancer. N Engl J Med 360(8):790–800

    Article  PubMed  CAS  Google Scholar 

  11. Genomic Health 2005 Annual Report. Available from http://files.shareholder.com/downloads/GHDX/1288212664x0x237978/DBCCB2E0-6584-496A-ACB1-D67DDA497401/2005AR.pdf. Accessed 11 June 2011

  12. Breastcancer.org. OcotypeDX Test (2011) Available at http://www.breastcancer.org/symptoms/testing/types/oncotype_dx.jsp. Accessed 21 May 2011

  13. Lyman GH, Cosler LE, Kuderer NM, Hornberger J (2007) Impact of a 21-gene RT-PCR assay on treatment decisions in early-stage breast cancer: an economic analysis based on prognostic and predictive validation studies. Cancer 109(6):1011–1018

    Article  PubMed  Google Scholar 

  14. Oestreicher N, Ramsey SD, Linden HM, McCune JS, van’t Veer LJ, Burke W et al. (2005) Gene expression profiling and breast cancer care: what are the potential benefits and policy implications? Genet Med 7(6):380–389

    Article  PubMed  Google Scholar 

  15. Hornberger J, Lyman GH, Chien R (2010) Economic implications of 21-gene recurrence score assay: US multicenter experience. J Clin Oncol 28(22):e382

    Article  PubMed  Google Scholar 

  16. Hornberger J, Cosler LE, Lyman GH (2005) Economic analysis of targeting chemotherapy using a 21-gene RT-PCR assay in lymph-node-negative, estrogen-receptor-positive, early-stage breast cancer. Am J Manag Care 11(5):313–324

    PubMed  Google Scholar 

  17. Tsoi DT, Inoue M, Kelly CM, Verma S, Pritchard KI (2010) Cost-effectiveness analysis of recurrence score-guided treatment using a 21-gene assay in early breast cancer. Oncologist 15(5):457–465

    Article  PubMed  Google Scholar 

  18. Klang SH, Hammerman A, Liebermann N, Efrat N, Doberne J, Hornberger J (2010) Economic implications of 21-gene breast cancer risk assay from the perspective of an Israeli-managed health-care organization. Value Health 13(4):381–387

    Article  PubMed  Google Scholar 

  19. Gold JM, Najita JS, Lester S, Richardson AL, Morganstern DE, Chen WY et al. (2009) Personalizing treatment in early-stage breast cancer: the role of standard clinical factors and genomic information in adjuvant chemotherapy decision making. ASCO Annual Meeting, Orlando, 2009

  20. Ademuyiwa FO, Miller A, O’Connor T, Edge SB, Thorat MA, Sledge GW et al (2011) The effects of oncotype DX recurrence scores on chemotherapy utilization in a multi-institutional breast cancer cohort. Breast Cancer Res Treat 126(3):797–802

    Article  PubMed  CAS  Google Scholar 

  21. Marchionni L, Wilson RF, Wolff AC, Marinopoulos S, Parmigiani G, Bass EB et al. (2008) Systematic review: gene expression profiling assays in early-stage breast cancer. Ann Intern Med 148(5):358–369

    PubMed  Google Scholar 

  22. Webber EM, Lin JS, Whitlock EP (2010) Oncotype DX tumor gene expression profiling in stage II colon cancer. Application: prognostic, risk prediction. PLoS Curr 2:RRN1177

    Google Scholar 

  23. Hassett MJ, O’Malley AJ, Pakes JR, Newhouse JP, Earle CC (2006) Frequency and cost of chemotherapy-related serious adverse effects in a population sample of women with breast cancer. J Natl Cancer Inst 98(16):1108–1117

    Article  PubMed  Google Scholar 

  24. Liang SY, Phillips KA, Wang G, Keohane C, Armstrong J, Morris WM et al. (2011) Tradeoffs of using administrative claims and medical records to identify the use of personalized medicine for patients with breast cancer. Med Care 49(6):e1–e8

    Article  PubMed  Google Scholar 

  25. Albain KS, Barlow WE, Shak S, Hortobagyi GN, Livingston RB, Yeh IT et al. (2010) Prognostic and predictive value of the 21-gene recurrence score assay in postmenopausal women with node-positive, oestrogen-receptor-positive breast cancer on chemotherapy: a retrospective analysis of a randomised trial. Lancet Oncol 11(1):55–65

    Article  PubMed  CAS  Google Scholar 

  26. Carlson B (2010) Payers try new approaches to manage molecular diagnostics. Biotechnol Healthcare 7(3):26–30

    Google Scholar 

  27. Deverka PA (2009) Pharmacogenomics, evidence, and the role of payers. Public Health Genomics 12(3):149–157

    Article  PubMed  CAS  Google Scholar 

  28. Klabunde CN, Potosky AL, Legler JM, Warren JL (2000) Development of a comorbidity index using physician claims data. J Clin Epidemiol 53(12):1258–1267

    Article  PubMed  CAS  Google Scholar 

  29. Adams JL, Mehrotra A, Thomas JW, McGlynn EA (2010) Physician cost profiling–reliability and risk of misclassification. N Engl J Med 362(11):1014–1021

    Article  PubMed  CAS  Google Scholar 

  30. Cepeda MS, Boston R, Farrar JT, Strom BL (2003) Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders. Am J Epidemiol 158(3):280–287

    Article  PubMed  Google Scholar 

  31. Lo SS, Mumby PB, Norton J, Rychlik K, Smerage J, Kash J et al. (2010) Prospective multicenter study of the impact of the 21-gene recurrence score assay on medical oncologist and patient adjuvant breast cancer treatment selection. J Clin Oncol 28(10):1671–1676

    Article  PubMed  Google Scholar 

  32. Cardoso F, Van’t Veer L, Rutgers E, Loi S, Mook S, Piccart-Gebhart MJ (2008) Clinical application of the 70-gene profile: the MINDACT trial. J Clin Oncol 26(5):729–735

    Article  PubMed  Google Scholar 

  33. Stukel TA, Fisher ES, Wennberg DE, Alter DA, Gottlieb DJ, Vermeulen MJ (2007) Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods. JAMA 297(3):278–285

    Article  PubMed  CAS  Google Scholar 

  34. Kerlikowske K, Cook AJ, Buist DS, Cummings SR, Vachon C, Vacek P et al. (2010) Breast cancer risk by breast density, menopause, and postmenopausal hormone therapy use. J Clin Oncol 28(24):3830–3837

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

The study was supported by a grant from the National Cancer Institute (P01CA130818). Drs. Haas, Phillips, and Liang received funding from a research grant from the Aetna Foundation for earlier related research.

Author information

Authors and Affiliations

  1. Division of General Medicine and Primary Care, Brigham and Women’s Hospital, 1620 Tremont Street, Boston, MA, 02120, USA

    Jennifer S. Haas

  2. University of California, San Francisco, CA, USA

    Su-Ying Liang, Stephen Shiboski & Kathryn A. Phillips

  3. Dana-Farber Cancer Institute, Boston, MA, USA

    Michael J. Hassett

  4. Memorial Sloan-Kettering Cancer Center, New York, NY, USA

    Elena B. Elkin

Authors
  1. Jennifer S. Haas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Su-Ying Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael J. Hassett
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Stephen Shiboski
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Elena B. Elkin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kathryn A. Phillips
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jennifer S. Haas.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Haas, J.S., Liang, SY., Hassett, M.J. et al. Gene expression profile testing for breast cancer and the use of chemotherapy, serious adverse effects, and costs of care. Breast Cancer Res Treat 130, 619–626 (2011). https://doi.org/10.1007/s10549-011-1628-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10549-011-1628-6

Keywords

Search

Navigation