Clinical-Genomic Models of Node-Positive Breast Cancer: Training, Testing, and Validation.

Abstract

There is no useful model for predicting the risk of recurrence in node-positive patients regardless of breast cancer subtype. We developed and validated 2 clinical-genomic models (recurrence index [RI]-local recurrence [LR]) and RI-distant recurrence (RI-DR) for stratifying these patients into low- and high-risk groups. The 4 data sets were (1) training group (n = 112); (2) testing group (n = 46); (3) validation group (n = 388); and (4) E-MTAB-365 data set (n = 426). Patients who had undergone mastectomy or breast-conserving surgery and mRNA microarray analysis of their primary tumor tissue and had a pathologic stage of I to III were enrolled in the training, testing, and validation groups. Using preset cut-offs obtained from the training group, the models were tested and validated in the 3 other independent groups. In the validation data set, the RI-LR distinguished between low- and high-risk groups according to 10-year LR-free interval (100% vs 93.0%, P = .015) and relapse-free survival (RFS; 85.0% vs 76.9%, P = .032). The RI-DR distinguished the low-risk group from the high-risk group according to RFS (85.7% vs 77.4%, P = .025). RI-DR and RI-LR scores were independent prognostic factors in N1-N2 patients (hazard ratio [HR], 3.3; 95% confidence interval, 1.1-10.2; and HR, 2.7; 95% confidence interval, 1.1-6.7, respectively) by multivariate analysis. The RI-DR and RI-LR genetic models were tested similarly using the E-MTAB data set with HRs of 2.5 (P = .0048) and 2.7 (P = .0285), respectively, in node-positive patients. Both RI-DR and RI-LR can partition N1-N2 patients into low- and high-risk groups for RFS; however, the latter is superior for predicting locoregional recurrence.