Purpose: Detecting signals of micrometastatic disease in patients with early breast cancer (EBC) could improve risk stratification and allow better tailoring of adjuvant therapies. We previously showed that postoperative serum metabolomic profiles were predictive of relapse in a single-center cohort of estrogen receptor (ER)-negative EBC patients. Here, we investigated this further using preoperative serum samples from ER-positive, premenopausal women with EBC who were enrolled in an international phase III trial.Experimental Design: Proton nuclear magnetic resonance (NMR) spectroscopy of 590 EBC samples (319 with relapse or ≥6 years clinical follow-up) and 109 metastatic breast cancer (MBC) samples was performed. A Random Forest (RF) classification model was built using a training set of 85 EBC and all MBC samples. The model was then applied to a test set of 234 EBC samples, and a risk of recurrence score was generated on the basis of the likelihood of the sample being misclassified as metastatic.Results: In the training set, the RF model separated EBC from MBC with a discrimination accuracy of 84.9%. In the test set, the RF recurrence risk score correlated with relapse, with an AUC of 0.747 in ROC analysis. Accuracy was maximized at 71.3% (sensitivity, 70.8%; specificity, 71.4%). The model performed independently of age, tumor size, grade, HER2 status and nodal status, and also of Adjuvant! Online risk of relapse score.Conclusions: In a multicenter group of EBC patients, we developed a model based on preoperative serum metabolomic profiles that was prognostic for disease recurrence, independent of traditional clinicopathologic risk factors. Clin Cancer Res; 23(6); 1422-31. ©2017 AACR.
©2017 American Association for Cancer Research.