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Comparison of Prognostic Gene Profiles Using qRT-PCR in Paraffin Samples: A Retrospective Study in Patients with Early Breast Cancer

Author

Listed:
  • Enrique Espinosa
  • Iker Sánchez-Navarro
  • Angelo Gámez-Pozo
  • Álvaro Pinto Marin
  • David Hardisson
  • Rosario Madero
  • Andrés Redondo
  • Pilar Zamora
  • Belén San José Valiente
  • Marta Mendiola
  • Manuel González Barón
  • Juan Ángel Fresno Vara

Abstract

Introduction: Gene profiling may improve prognostic accuracy in patients with early breast cancer, but this technology is not widely available. We used commercial assays for qRT-PCR to assess the performance of the gene profiles included in the 70-Gene Signature, the Recurrence Score and the Two-Gene Ratio. Methods: 153 patients with early breast cancer and a minimum follow-up of 5 years were included. All tumours were positive for hormonal receptors and 38% had positive lymph nodes; 64% of patients received adjuvant chemotherapy. RNA was extracted from formalin-fixed paraffin-embedded (FFPE) specimens using a specific kit. qRT-PCR amplifications were performed with TaqMan Gene Expression Assays products. We applied the three gene-expression-based models to our patient cohort to compare the predictions derived from these gene sets. Results: After a median follow-up of 91 months, 22% of patients relapsed. The distant metastasis-free survival (DMFS) at 5 years was calculated for each profile. For the 70-Gene Signature, DMFS was 95% -good prognosis- versus 66% -poor prognosis. In the case of the Recurrence Score, DMFS was 98%, 81% and 69% for low, intermediate and high-risk groups, respectively. Finally, for the Two-Gene Ratio, DMFS was 86% versus 70%. The 70-Gene Signature and the Recurrence Score were highly informative in identifying patients with distant metastasis, even in multivariate analysis. Conclusion: Commercially available assays for qRT-PCR can be used to assess the prognostic utility of previously published gene expression profiles in FFPE material from patients with early breast cancer. Our results, with the use of a different platform and with different material, confirm the robustness of the 70-Gene Signature and represent an independent test for the Recurrence Score, using different primer/probe sets.

Suggested Citation

  • Enrique Espinosa & Iker Sánchez-Navarro & Angelo Gámez-Pozo & Álvaro Pinto Marin & David Hardisson & Rosario Madero & Andrés Redondo & Pilar Zamora & Belén San José Valiente & Marta Mendiola & Manuel , 2009. "Comparison of Prognostic Gene Profiles Using qRT-PCR in Paraffin Samples: A Retrospective Study in Patients with Early Breast Cancer," PLOS ONE, Public Library of Science, vol. 4(6), pages 1-9, June.
  • Handle: RePEc:plo:pone00:0005911
    DOI: 10.1371/journal.pone.0005911
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    References listed on IDEAS

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    1. Michael Schemper & Robin Henderson, 2000. "Predictive Accuracy and Explained Variation in Cox Regression," Biometrics, The International Biometric Society, vol. 56(1), pages 249-255, March.
    2. Laura J. van 't Veer & Hongyue Dai & Marc J. van de Vijver & Yudong D. He & Augustinus A. M. Hart & Mao Mao & Hans L. Peterse & Karin van der Kooy & Matthew J. Marton & Anke T. Witteveen & George J. S, 2002. "Gene expression profiling predicts clinical outcome of breast cancer," Nature, Nature, vol. 415(6871), pages 530-536, January.
    3. Andy J. Minn & Gaorav P. Gupta & Peter M. Siegel & Paula D. Bos & Weiping Shu & Dilip D. Giri & Agnes Viale & Adam B. Olshen & William L. Gerald & Joan Massagué, 2005. "Genes that mediate breast cancer metastasis to lung," Nature, Nature, vol. 436(7050), pages 518-524, July.
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