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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.

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