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.
References
[1]
Goldhirsch A, Wood WC, Gelber RD, Coates AS, Thurlimann B, et al. (2007) Progress and promise: highlights of the international expert consensus on the primary therapy of early breast cancer 2007. Ann Oncol 18: 1133–1144.
[2]
Olivotto IA, Bajdik CD, Ravdin PM, Speers CH, Coldman AJ, et al. (2005) Population-based validation of the prognostic model ADJUVANT! for early breast cancer. J Clin Oncol 23: 2716–2725.
[3]
Bergh J, Holmquist M (2001) Who should not receive adjuvant chemotherapy? International databases. J Natl Cancer Inst Monogr 103–108.
[4]
Paik S, Shak S, Tang G, Kim C, Baker J, et al. (2004) A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351: 2817–2826.
[5]
van't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, et al. (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415: 530–536.
[6]
Ma XJ, Wang Z, Ryan PD, Isakoff SJ, Barmettler A, et al. (2004) A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell 5: 607–616.
[7]
Antonov J, Goldstein DR, Oberli A, Baltzer A, Pirotta M, et al. (2005) Reliable gene expression measurements from degraded RNA by quantitative real-time PCR depend on short amplicons and a proper normalization. Lab Invest 85: 1040–1050.
[8]
Cronin M, Pho M, Dutta D, Stephans JC, Shak S, et al. (2004) Measurement of gene expression in archival paraffin-embedded tissues: development and performance of a 92-gene reverse transcriptase-polymerase chain reaction assay. Am J Pathol 164: 35–42.
[9]
Buyse M, Loi S, van't Veer L, Viale G, Delorenzi M, et al. (2006) Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J Natl Cancer Inst 98: 1183–1192.
[10]
van de Vijver MJ, He YD, van't Veer LJ, Dai H, Hart AA, et al. (2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347: 1999–2009.
[11]
Mook S, Schmidt MK, Viale G, Pruneri G, Eekhout I, et al. (2008) The 70-gene prognosis-signature predicts disease outcome in breast cancer patients with 1–3 positive lymph nodes in an independent validation study. Breast Cancer Res Treat.
[12]
Minn AJ, Gupta GP, Siegel PM, Bos PD, Shu W, et al. (2005) Genes that mediate breast cancer metastasis to lung. Nature 436: 518–524.
[13]
Albain K, Barlow W, Shak S, Hortobagyi G, R L, et al. (2007) Prognostic and predictive value of the 21-gene recurrence score assay in postmenopausal, node-positive, ER-positive breast cancer
[14]
Paik S, Tang G, Shak S, Kim C, Baker J, et al. (2006) Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol 24: 3726–3734.
[15]
Ma XJ, Hilsenbeck SG, Wang W, Ding L, Sgroi DC, et al. (2006) The HOXB13:IL17BR expression index is a prognostic factor in early-stage breast cancer. J Clin Oncol 24: 4611–4619.
[16]
Ma XJ, Salunga R, Dahiya S, Wang W, Carney E, et al. (2008) A Five-Gene Molecular Grade Index and HOXB13:IL17BR Are Complementary Prognostic Factors in Early Stage Breast Cancer. Clin Cancer Res 14: 2601–2608.
[17]
Sotiriou C, Wirapati P, Loi S, Harris A, Fox S, et al. (2006) Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst 98: 262–272.
[18]
Fan C, Oh DS, Wessels L, Weigelt B, Nuyten DS, et al. (2006) Concordance among gene-expression-based predictors for breast cancer. N Engl J Med 355: 560–569.
[19]
Habel LA, Shak S, Jacobs MK, Capra A, Alexander C, et al. (2006) A population-based study of tumor gene expression and risk of breast cancer death among lymph node-negative patients. Breast Cancer Res 8: R25.
[20]
Espinosa E, Vara JA, Redondo A, Sanchez JJ, Hardisson D, et al. (2005) Breast cancer prognosis determined by gene expression profiling: a quantitative reverse transcriptase polymerase chain reaction study. J Clin Oncol 23: 7278–7285.
[21]
Reyal F, van Vliet MH, Armstrong NJ, Horlings HM, de Visser KE, et al. (2008) A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the proliferation, immune response and RNA splicing modules in breast cancer. Breast Cancer Res 10: R93.
[22]
Wirapati P, Sotiriou C, Kunkel S, Farmer P, Pradervand S, et al. (2008) Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures. Breast Cancer Res 10: R65.
[23]
Goldstein LJ, Gray R, Badve S, Childs BH, Yoshizawa C, 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: 4063–4071.
[24]
Mook S, Schmidt MK, Viale G, Pruneri G, Eekhout I, et al. (2007) Breast cancer patients with 1–3 positive lymph nodes and a low risk 70-gene profile have an excellent survival. Proc of 2007 SABCS abstract 50.
[25]
Dunkler D, Michiels S, Schemper M (2007) Gene expression profiling: does it add predictive accuracy to clinical characteristics in cancer prognosis? Eur J Cancer 43: 745–751.
[26]
Pfaffl MW (2001) A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res 29: e45.
[27]
Koscielny S (2008) Critical review of microarray-based prognostic tests and trials in breast cancer. Curr Opin Obstet Gynecol 20: 47–50.
[28]
Harrell FE Jr, Lee KL, Mark DB (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15: 361–387.
[29]
Pencina MJ, D'Agostino RB (2004) Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med 23: 2109–2123.
[30]
Lusa L, Miceli R, Mariani L (2007) Estimation of predictive accuracy in survival analysis using R and S-PLUS. Comput Methods Programs Biomed 87: 132–137.
[31]
Schemper M, Henderson R (2000) Predictive accuracy and explained variation in Cox regression. Biometrics 56: 249–255.