A Five-Gene Model Predicts Clinical Outcome in ER+/PR+, Early-Stage Breast Cancers Treated with Adjuvant Tamoxifen

被引:13
作者
Kerr II D.A. [1 ]
Wittliff J.L. [1 ]
机构
[1] Department of Biochemistry and Molecular Biology, Brown Cancer Center and Institute for Molecular Diversity and Drug Design, University of Louisville, Louisville
来源
Hormones and Cancer | 2011年 / 2卷 / 5期
关键词
Breast cancer; Disease-free survival; Estrogen receptor; Gene expression; Overall survival; Progesterone receptor; Tamoxifen;
D O I
10.1007/s12672-011-0080-8
中图分类号
学科分类号
摘要
Primary breast carcinomas expressing both estrogen and progesterone receptors are most likely to respond to tamoxifen therapy, especially in patients with early-stage lesions. However, certain patients exhibit clinicopathologic features suggesting good prognosis relapse within 10 years, justifying a search for biomarkers identifying patients at risk for recurrence. Nine candidate genes associated with estrogen signaling were selected from microarray studies and combined with those for conventional biomarkers (ESR1, PGR, ERBB2). Expression of this 12-gene subset was analyzed by RT-qPCR in frozen tissue specimens from 60 early-stage, estrogen receptor (ER)+/progestin receptor (PR)+ breast cancers from patients treated with adjuvant tamoxifen. A multivariate model was created by Cox regression using a training data set and applied to an independent validation set. A five-gene model was developed from the training set (n=36) that exhibited significant correlations with both relapse-free and overall survival. Applying this model to Kaplan-Meier regression, patients were separated into low-risk (100% relapse-free at 150 months) and high-risk (60% relapse-free at 150 months) groups (P = 0.03). When this model was applied to the validation set (n=24), similar risk stratification was achieved for both relapse-free and overall survival (P = 0.01 and 0.04, respectively). We developed a five-gene model composed of PgR, BCL2, ERBB4 JM-a, RERG, and CD34 that identified early-stage, ER+/PR+ breast cancers in patients treated with tamoxifen that relapsed, although they exhibited clinicopathologic features suggesting good prognosis. Within this multivariate model, increased expression of PgR, ERBB4 JM-a, RERG, and CD34 was associated with increased survival, while increased expression of BCL2 was associated with decreased survival. © 2011 Springer Science+Business Media, LLC.
引用
收藏
页码:261 / 271
页数:10
相关论文
共 45 条
[11]  
Ravdin P.M., Green S., Dorr T.M., Et al., Prognostic significance of progesterone receptor levels in estrogen receptor-positive patients with metastatic breast cancer treated with tamoxifen: results of a prospective Southwest Oncology Group study, J Clin Oncol, 10, pp. 1284-1291, (1992)
[12]  
Bardou V.J., Arpino G., Elledge R.M., Osborne C.K., Clark G.M., Progesterone receptor status significantly improves outcome prediction over estrogen receptor status alone for adjuvant endocrine therapy in two large breast cancer databases, J Clin Oncol, 21, pp. 1973-1979, (2003)
[13]  
Rakha E.A., El-Sayed M.E., Green A.R., Et al., Biologic and clinical characteristics of breast cancer with single hormone receptor positive phenotype, J Clin Oncol, 25, pp. 4772-4778, (2007)
[14]  
Bryant J., Fisher B., Dignam J., Duration of adjuvant tamoxifen therapy, J Natl Cancer Inst Monogr, 30, pp. 56-61, (2001)
[15]  
Clark G.M., McGuire W.L., Progesterone receptors and human breast cancer, Breast Cancer Res Treat, 3, pp. 157-163, (1983)
[16]  
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, 351, pp. 2817-2826, (2004)
[17]  
Sparano J.A., Paik S., Development of the 21-gene assay and its application in clinical practice and clinical trials, J Clin Oncol, 26, pp. 721-728, (2008)
[18]  
Vendrell J.A., Robertson K.E., Ravel P., Et al., A candidate molecular signature associated with tamoxifen failure in primary breast cancer, Breast Cancer Res, 10, 5, (2008)
[19]  
Ma X.J., Wang W., Salunga R., Et al., Gene expression associated with clinical outcome in breast cancer via laser capture microdissection, Breast Cancer Res Treat, 82, (2003)
[20]  
Wittliff J.L., Erlander M.G., Laser capture microdissection and its applications in genomics and proteomics, Methods Enzymol, 356, pp. 12-25, (2002)