Early detection of recurrent breast cancer using metabolite profiling.

Abstract

1037 Background: The growing field of metabolite profiling (or metabolomics) focuses on the quantitative detection of multiple small molecules that provide tremendous information on biological status. In particular, detectable perturbations in the metabolite profile often result from pathophysiological stimuli, such as from cancer. We report on the development of metabolite profile for use as an early test for recurrent breast cancer. As is the case for early detection of primary breast cancer, it is anticipated that earlier recurrence diagnoses will not only improve survival but also help clinicians determine the best therapeutic strategies for patients by avoiding under or over treatment. Methods: We apply a combination of nuclear magnetic resonance (NMR) and gas chromatography-mass spectrometry (GC-MS) to analyze the metabolite profiles of 257 serial serum samples from breast cancer patients consisting 116 samples from breast cancer recurrence and 141 samples from breast cancer patients with no evidence of disease (NED). NMR and GC-MS data were analyzed by combining advanced univariate and multivariate statistical methods and comparison of individual spectral features between patients with and without recurrent breast cancer. Results: From multivariate analysis of 42 targeted metabolites, ten metabolite markers (7 from NMR and 3 from GC-MS) were used to build a regression model with high accuracy (AUROC >0.89 using 10 fold cross validation) with a sensitivity of 82% and specificity of 84% using a training set of samples. When the model was tested on an independent set of patient samples, it yielded a sensitivity of 76% and a specificity of 83% (AUROC >0.85). Strikingly, over 60% of the patients could be correctly predicted to have recurrence on average 10 months before clinical diagnosis, which represents a large improvement over the current diagnostic assays CA 27.29 and CA 15-3. To the best of our knowledge, this is the first study to develop and validate a prediction model for early detection of recurrent breast cancer based on metabolic profiles. Conclusions: The combination of NMR and MS provide a powerful approach for the development of metabolic profile-based diagnostic tests for detecting breast cancer recurrence. Author Disclosure Employment or Leadership Position Consultant or Advisory Role Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration Matrix-Bio Matrix-Bio Matrix-Bio Matrix-Bio