Prospective, multicenter study on the economic and clinical impact of gene-expression assays in early-stage breast cancer from a single region: the PREGECAM registry experience

Abstract

Introduction

The aim of this study is to evaluate the cost-effectiveness and impact of gene-expression assays (GEAs) on treatment decisions in a real-world setting of early-stage breast cancer (ESBC) patients.

Methods

This is a regional, prospective study promoted by the Council Health Authorities in Madrid. Enrolment was offered to women with estrogen receptor-positive, human epidermal growth factor receptor 2-negative, node-negative or micrometastatic, stage I or II breast cancer from 21 hospitals in Madrid. Treatment recommendations were recorded before and after knowledge of tests results. An economic model compared the cost-effectiveness of treatment, guided by GEAs or by common prognostic factors.

Results

907 tests (440 Oncotype DX® and 467 MammaPrint®) were performed between February 2012 and November 2014. Treatment recommendation changed in 42.6% of patients. The shift was predominantly from chemohormonal (CHT) to hormonal therapy (HT) alone, in 30.5% of patients. GEAs increased patients’ confidence in treatment decision making. Tumor grade, progesterone receptor positivity and Ki67 expression were associated with the likelihood of change from CHT to HT (P < 0.001) and from HT to CHT (P < 0.001). Compared with current clinical practice genomic testing increased quality-adjusted life years by 0.00787 per patient and was cost-saving from a national health care system (by 13.867€ per patient) and from a societal perspective (by 32.678€ per patient).

Conclusion

Using GEAs to guide adjuvant therapy in ESBC is cost-effective in Spain and has a significant impact on treatment decisions.

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References

  1. 1.

    Bray F, Ferlay J, Soerjomataram I, et al: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018;68:394–424. https://gco.iarc.fr/today/data/factsheets/cancers/39-All-cancers-fact-sheet.pdf and https://gco.iarc.fr/today/data/factsheets/populations/724-spain-fact-sheets.pdf. Accessed 13 May 2019.

  2. 2.

    Goldhirsch A, Winer E, Coates A, et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen international expert consensus on the primary therapy of early breast cancer. Ann Oncol. 2013;24:2206–23.

    CAS  Article  Google Scholar 

  3. 3.

    Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351:2817–26.

    CAS  Article  Google Scholar 

  4. 4.

    Van de Vijver MJ, He YD, van ’t Veer LJ, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002;347:1999–2009.

  5. 5.

    Lo SS, Mumby PB, Norton J, et al. 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. 2010;28:1671–6.

    Article  Google Scholar 

  6. 6.

    Albanell J, González A, Ruiz-borrego M, et al. Prospective transGEICAM study of the impact of the 21-gene recurrence score assay and traditional clinicopathological factors on adjuvant clinical decision making in women with estrogen receptor-positive (ER+) node-negative breast cancer. Ann Oncol. 2012;23:625–31.

    CAS  Article  Google Scholar 

  7. 7.

    Eiermann W, Rezai M, Kümmel S, et al. The 21-gene recurrence score assay impacts adjuvant therapy recommendations for ER-positive, node-negative and node-positive early breast cancer resulting in a risk-adapted change in chemotherapy use. Ann Oncol. 2013;24:618–24.

    CAS  Article  Google Scholar 

  8. 8.

    Gligorov J, Pivot XB, Naman HL, et al. Prospective clinical utility study of the use of 21-gene assay in adjuvant decision making in women with estrogen receptor positive early invasive breast cancer: results from the SWITCH study. Oncologist. 2015;20:873–9.

    CAS  Article  Google Scholar 

  9. 9.

    Exner R, Bago-Horvath Z, Bartsch R, et al. The multigene signature MammaPrint® impacts on multidisciplinary team decisions in ER+, HER2- early breast cancer. Br J Cancer. 2014;111:837–42.

    CAS  Article  Google Scholar 

  10. 10.

    Cusumano PG, Generali D, Ciruelos E, et al. European inter-institutional impact study of MammaPrint®. Breast. 2014;23:423–8.

    CAS  Article  Google Scholar 

  11. 11.

    Rouzier R, Pronzato P, Chéreau E, et al. Multigene assays and molecular markers in breast cancer: systematic review of health economic analyses. Breast Cancer Res Treat. 2013;139:621–37.

    CAS  Article  Google Scholar 

  12. 12.

    Hillner BE, Smith TJ. Efficacy and cost effectiveness of adjuvant chemotherapy in women with node-negative breast cancer. A decision-analysis model. N Engl J Med. 1991;324:160–8.

    CAS  Article  Google Scholar 

  13. 13.

    Hornberger J, Cosler LE, Lyman GH. 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. 2005;11:313–24.

    PubMed  Google Scholar 

  14. 14.

    INE. Tablas de mortalidadde la población de España por año, sexo, edad y funciones. 2013. https://www.ine.es. Accessed 10 Mar 2015.

  15. 15.

    Tsoi DT, Inoue M, Kelly CM, et al. Cost-effectiveness analysis of recurrence score-guided treatment using a 21-gene assay in early breast cancer. Oncologist. 2010;15:457–65.

    Article  Google Scholar 

  16. 16.

    Yang M, Rajan S, Issa AM, et al. Cost effectiveness of gene expression profiling for early stage breast cancer: a decision-analytic model. Cancer. 2012;118:5163–70.

    Article  Google Scholar 

  17. 17.

    Lidgren M, Wilking N, Jönsson B, et al. Health related quality of life in different states of breast cancer. Qual Life Res. 2007;16:1073–81.

    Article  Google Scholar 

  18. 18.

    Chen E, Tong KB, Malin JL, et al. Cost-effectiveness of 70-gene MammaPrint® signature in node-negative breast cancer. Am J Manag Care. 2010;16:333–42.

    Google Scholar 

  19. 19.

    Retèl VP, Joore MA, Knauer M, et al. Cost-effectiveness of the 70-gene signature versus St. Gallen guidelines and Adjuvant Online for early breast cancer. Eur J Cancer. 2010;46:1382–91.

    Article  Google Scholar 

  20. 20.

    Ward S, Scope A, Rafia R, et al. Gene expression profiling and expanded immunohistochemistry tests to guide the use of adjuvant chemotherapy in breast cancer management: a systematic review and cost-effectiveness analysis. Health Technol Assess. 2013;17:1–30.

    CAS  Article  Google Scholar 

  21. 21.

    Campbell HE, Epstein D, Bloomfield D, et al. The cost-effectiveness of adjuvant chemotherapy for early breast cancer: a comparison of no chemotherapy and first, second, and third generation regimens for patients with differing prognoses. Eur J Cancer. 2011;47:2517–30.

    CAS  Article  Google Scholar 

  22. 22.

    Arrospide A, Soto-Gordoa M, Acaiturri T, et al. Cost of breast cancer treatment by clinical stage in the Basque Country. Spain. Rev Española Salud Pública. 2015;89:93–7.

    Article  Google Scholar 

  23. 23.

    Calculador Universal del Gasto por km de un Automóvil. https://dim.usal.es/eps/mmt/?page_id=990. Accessed 10 Mar 2015.

  24. 24.

    Consorcio trasportes Madrid. Tarifas anuales 2015. https://www.crtm.es/media/236501/tarifas_anual.pdf. Accessed 10 Mar 2015.

  25. 25.

    Gamma Psicólogos. Tarifas. https://gammapsicologosmadrid.es/tarifas/. Accessed 10 Mar 2015.

  26. 26.

    Hassett MJ, O’Malley AJ, Pakes JR, et al. Frequency and cost of chemotherapy-related serious adverse effects in a population sample of women with breast cancer. J Natl Cancer Inst. 2006;98:1108–17.

    Article  Google Scholar 

  27. 27.

    ORDEN 731/2013, de 6 de septiembre del Consejero de Sanidad, por la que se fijan los precios públicos por la prestación de los servicios y actividades de naturaleza sanitaria de la Red de Centros de la Comunidad de Madrid. BOCM No 215, 10 de septiembre de 2013.

  28. 28.

    Sparano JA. TAILORx: trial assigning individualized options for treatment (Rx). Clin Breast Cancer. 2006;7:347–50.

    Article  Google Scholar 

  29. 29.

    Hack TF, Degner LF, Watson P, et al. Do patients benefit from participating in medical decision making? Longitudinal follow-up of women with breast cancer. Psychooncology. 2006;15:9–19.

    Article  Google Scholar 

  30. 30.

    Bombard Y, Rozmovits L, Trudeau ME, et al. Patients’ perceptions of gene expression profiling in breast cancer treatment decisions. Curr Oncol. 2014;21:203–11.

    Article  Google Scholar 

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Acknowledgements

We would like to acknowledge all PREGECAM study investigators, the Pharmacoeconomic Company Health value and R. Pla, head of the quality department at Hospital General Universitario Gregorio Marañón, for their contribution, support and advice.

Funding

This work was supported by the local health council in Madrid and CIBERONC.

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Corresponding author

Correspondence to M. Martín.

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Conflict of interest

SPR received consultant/advisory honorarium from Janssen, Novartis, Roche, Pharma-Mar and Bayer; SLTC received consultant/advisory honorarium from Astrazeneca, Novartis, Roche, Pfizer, Gelgene, Pierre-Fabre, Eissai and Lilly; NMJ received consultant/advisory honorarium from Roche, Amgen, Pfizer, Gelgene and Eissai; IMR received consultant/advisory honorarium from BMS, MSD, Novartis, Roche, Pierre-Fabre, Bioncotech and Sanofi; CRT received funding from Hospital General Universitario Gregorio Marañón; DRR received funding from Hospital General Universitario Gregorio Marañón; JAGS received consultant/advisory honorarium from Novartis, Lilly, Celgene and Roche and Funding from AstraZeneca; FMA received consultant/advisory honorarium from Roche, Pfizer, Novartis and AstraZeneca; PZA received consultant/advisory honorarium from Roche and Novartis; MLA received consultant/advisory honorarium from Novartis, Celgene, Roche and Pfizer; EMCG received remuneration from Novartis, Lilly, Pfizer, Roche and consultant/advisory honorarium from Sama; LMS received consultant/advisory honorarium from Tesaro, Astra-Zeneca, Roche, Novartis and Celgene, and funding from Tesaro; SGA reports personal fees from Celgene, Roche, Pierre Fabre, Novartis and Astra Zeneca and non-financial support from Roche, outside the submitted work; MM received remuneration from Pfizer, Lilly; consultant/advisory honorarium from Roche, Novartis, Pfizer, Astrazeneca, Lilly, Glaxo, PharmaMar, Taiho; and funding from Roche and Novartis. MDMM, FLS, YIP, MAY, MJEG, JAGM, CJS, CBM, RCG, VVM declare that they have no conflict of interest.

Research involving human participants and/or animals

This study has been approved by the Ethical Committee (Area 1 CEIm Hospital General Universitario Gregorio Marañón) and it has also been authorised by the local health council in Madrid and has therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

Informed consent

All eligible patients provided written informed consent prior to the inclusion in the study.

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Pérez Ramírez, S., del Monte-Millán, M., López-Tarruella, S. et al. Prospective, multicenter study on the economic and clinical impact of gene-expression assays in early-stage breast cancer from a single region: the PREGECAM registry experience. Clin Transl Oncol 22, 717–724 (2020). https://doi.org/10.1007/s12094-019-02176-x

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Keywords

  • Breast cancer
  • Gene-expression profiling
  • Cost analysis
  • Quality-adjusted life years