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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

  • S. Pérez Ramírez
  • M. del Monte-Millán
  • S. López-Tarruella
  • N. Martínez Jáñez
  • I. Márquez-Rodas
  • F. Lobo Samper
  • Y. Izarzugaza Perón
  • C. Rubio Terres
  • D. Rubio Rodríguez
  • J. Á. García-Sáenz
  • F. Moreno Antón
  • P. Zamora Auñón
  • M. Arroyo Yustos
  • M. Á. Lara Álvarez
  • E. M. Ciruelos Gil
  • L. Manso Sánchez
  • M. J. Echarri González
  • J. A. Guerra Martínez
  • C. Jara Sánchez
  • C. Bueno Muiño
  • S. García Adrián
  • J. R. Carrión Galindo
  • V. Valentín Maganto
  • M. MartínEmail author
Research Article
  • 17 Downloads

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.

Keywords

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

Notes

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.

Compliance with ethical standards

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.

Supplementary material

12094_2019_2176_MOESM1_ESM.docx (14 kb)
Supplementary file1 (DOCX 14 kb)
12094_2019_2176_MOESM2_ESM.doc (118 kb)
Supplementary file2 (DOC 118 kb)
12094_2019_2176_MOESM3_ESM.pdf (149 kb)
Supplementary file3 (PDF 149 kb)
12094_2019_2176_MOESM4_ESM.pdf (135 kb)
Supplementary file4 (PDF 135 kb)

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.CrossRefGoogle 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.CrossRefGoogle 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.Google Scholar
  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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
  10. 10.
    Cusumano PG, Generali D, Ciruelos E, et al. European inter-institutional impact study of MammaPrint®. Breast. 2014;23:423–8.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.Google Scholar
  28. 28.
    Sparano JA. TAILORx: trial assigning individualized options for treatment (Rx). Clin Breast Cancer. 2006;7:347–50.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar

Copyright information

© Federación de Sociedades Españolas de Oncología (FESEO) 2019

Authors and Affiliations

  • S. Pérez Ramírez
    • 1
  • M. del Monte-Millán
    • 2
  • S. López-Tarruella
    • 2
  • N. Martínez Jáñez
    • 3
  • I. Márquez-Rodas
    • 2
  • F. Lobo Samper
    • 4
  • Y. Izarzugaza Perón
    • 4
  • C. Rubio Terres
    • 5
  • D. Rubio Rodríguez
    • 5
  • J. Á. García-Sáenz
    • 6
  • F. Moreno Antón
    • 6
  • P. Zamora Auñón
    • 7
  • M. Arroyo Yustos
    • 8
  • M. Á. Lara Álvarez
    • 9
  • E. M. Ciruelos Gil
    • 10
  • L. Manso Sánchez
    • 10
  • M. J. Echarri González
    • 11
  • J. A. Guerra Martínez
    • 12
  • C. Jara Sánchez
    • 13
  • C. Bueno Muiño
    • 14
  • S. García Adrián
    • 15
  • J. R. Carrión Galindo
    • 16
  • V. Valentín Maganto
    • 17
  • M. Martín
    • 18
    Email author
  1. 1.Medical Oncology ServiceHospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM)MadridSpain
  2. 2.Medical Oncology ServiceHospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), CiberOncMadridSpain
  3. 3.Medical Oncology ServiceHospital Universitario Ramón y CajalMadridSpain
  4. 4.Medical Oncology ServiceHospital Universitario Fundación Jiménez DíazMadridSpain
  5. 5.HEALTH VALUE, Health Economics & Research of Outcomes ConsultingMadridSpain
  6. 6.Medical Oncology ServiceHospital Universitario Clínico San Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC)MadridSpain
  7. 7.Medical Oncology ServiceHospital Universitario La PazMadridSpain
  8. 8.Medical Oncology ServiceHospital Universitario Príncipe de AsturiasMadridSpain
  9. 9.Medical Oncology ServiceHospital Universitario Infanta LeonorMadridSpain
  10. 10.Medical Oncology ServiceHospital Universitario 12 de OctubreMadridSpain
  11. 11.Medical Oncology ServiceHospital Universitario Severo OchoaMadridSpain
  12. 12.Medical Oncology ServiceHospital Universitario de FuenlabradaMadridSpain
  13. 13.Medical Oncology ServiceHospital Universitario Fundación Alcorcón, Universidad Rey Juan CarlosMóstolesSpain
  14. 14.Medical Oncology ServiceHospital Universitario Infanta CristinaParlaSpain
  15. 15.Medical Oncology ServiceHospital Universitario de MóstolesMadridSpain
  16. 16.Medical Oncology ServiceHospital del Sureste Arganda del ReyMadridSpain
  17. 17.Regional Oncology CoordinatorMadridSpain
  18. 18.Medical Oncology ServiceHospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), CIBERONC, GEICAM (Spanish Breast Cancer Group), Universidad ComplutenseMadridSpain

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