Abstract This abstact is being presented as a short talk in Concurrent Session 9. A full abstract is printed in the Proffered Abstracts section (PR-06) of the Conference Proceedings. Citation Information: Cancer Prev Res 2010;3(12 Suppl):B20.
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
5 Background: The detection of recurrent breast cancer is limited by poorly performing CA markers that are both insensitive and late markers. Because of their sensitivity to biological status, metabolite markers may provide better diagnostic performance and earlier detection. Detectable perturbations in the metabolic profiles of patients often result from altered metabolism in cancer. We report on the discovery and initial validation of a metabolite profile for the early detection of recurrent breast cancer. Methods: We applied a combination of nuclear magnetic resonance (NMR) and gas chromatography-mass spectrometry (GC-MS) to analyze the metabolite profiles of 116 serial serum samples from 20 recurring patients and 141 serial samples from 36 breast cancer survivors with no evidence of disease (NED). Multivariate analysis was used to identify 11 metabolite markers that were used to build a model with high accuracy (AUROC >0.88 using 10 fold cross validation) with a sensitivity of 68% and specificity of 94%. Strikingly, over 55% of the patients could be correctly predicted to have recurrence on average 13 months before clinical diagnosis, representing a large improvement over the current diagnostic assays CA 27.29 and CA 15-3 (Cancer Res. 2010; 70, 8309-18). The metabolites were then ported to an LC-MS/MS platform and a method was developed to quantify these metabolites using stable isotope labeled compounds. Due to difficulty in obtaining some labeled compounds the profile was reduced to 9 metabolites. The multivariate algorithm was recalculating using five-fold cross validation and using 211 patient samples. During this work we found that the performance of the assay could be improved by adding CA27-29 values to the assay, which mainly ensured excellent specificity. Results: The profile was tested using a separate validation set of 96 patient samples run identically. The performance was similar to the training set with a sensitivity of 65% and specificity of 93%. Recurrence detection was approximately 11 months ahead of clinical diagnosis (based on imaging for symptomatic patients) and about 2 years ahead of CA 27-29 alone. Conclusions: This metabolite profile has promise for early recurrence detection.
