Detecting signals of micrometastatic disease in early breast cancer (EBC) patients could improve risk stratification and allow better tailoring of adjuvant therapies. We have previously shown that postoperative serum metabolomic profiles are predictive of relapse in a single-centre cohort of ER-negative EBC patients. Here, we investigated this further using pre-operative serum samples from ER-positive, premenopausal women with EBC who were enrolled in an international phase III trial.
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 based on the likelihood of the sample being misclassified as metastatic.
In the training set, the RF model separated EBC from MBC with discrimination accuracy of 84.9%. In the test set, the RF recurrence risk score correlated with relapse, with an area under the curve of 0.747 in receiver operator characteristics analysis. Accuracy was maximised 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 AdjuvantOnline risk of relapse score.
In a multicentre group of EBC patients, we developed a model based on preoperative serum metabolomic profiles that was prognostic for disease recurrence, independent of traditional clinicopathological risk factors.
Clinical trial identification
Legal entity responsible for the study
Hospital of Prato
BCRF Veronesi Foundation Sandro Pitigliani Foundation
All authors have declared no conflicts of interest.