Supplementary Table S2 from Serum Metabolomic Profiles Identify ER-Positive Early Breast Cancer Patients at Increased Risk of Disease Recurrence in a Multicenter Population

Hart, Christopher D.; Vignoli, Alessia; Tenori, Leonardo; Uy, Gemma Leonora; Van To, Ta; Adebamowo, Clement; Hossain, Syed Mozammel; Biganzoli, Laura; Risi, Emanuela; Love, Richard R.; Luchinat, Claudio; Di Leo, Angelo

doi:10.1158/1078-0432.22465308.v1

Supplementary Table S2 from Serum Metabolomic Profiles Identify ER-Positive Early Breast Cancer Patients at Increased Risk of Disease Recurrence in a Multicenter Population

https://doi.org/10.1158/1078-0432.22465308

journal contribution

posted on 2023-03-31, 19:50 authored by Christopher D. Hart, Alessia Vignoli, Leonardo Tenori, Gemma Leonora Uy, Ta Van To, Clement Adebamowo, Syed Mozammel Hossain, Laura Biganzoli, Emanuela Risi, Richard R. Love, Claudio Luchinat, Angelo Di Leo

<p>Comparison of individual metabolite concentrations in arbitrary units in the serum NMR spectra of relapsing and non-relapsing EBC patients. Adjusted P-values for the comparison of relapsing and non-relapsing EBC Adjusted P-values are for the significance of any difference between EBC and MBC. MAD = median absolute deviation.</p>

Funding

Breast Cancer Research Foundation

Fondazione Veronesi

History

Related Materials

1.
DOI - Is supplement to Serum Metabolomic Profiles Identify ER-Positive Early Breast Cancer Patients at Increased Risk of Disease Recurrence in a Multicenter Population

ARTICLE ABSTRACT

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.