alexa Identification of a serum-detectable metabolomic fingerprint potentially correlated with the presence of micrometastatic disease in early breast cancer patients at varying risks of disease relapse by traditional prognostic methods.
Bioinformatics & Systems Biology

Bioinformatics & Systems Biology

Metabolomics:Open Access

Author(s): Oakman C, Tenori L, Claudino WM, Cappadona S, Nepi S,

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Abstract BACKGROUND: Prognostic tools in early breast cancer are inadequate. The evolving field of metabolomics may allow more accurate identification of patients with residual micrometastases. PATIENTS AND METHODS: Forty-four early breast cancer patients with pre- and postoperative serum samples had metabolomic assessment by nuclear magnetic resonance. Fifty-one metastatic patients served as control. Differential clustering was identified and used to calculate individual early patient 'metabolomic risk', calculated as inverse distance of each early patient from the metastatic cluster barycenter. Metabolomic risk was compared with Adjuvantionline 10-year mortality assessment. RESULTS: Innate serum metabolomic differences exist between early and metastatic patients. Preoperative patients were identified with 75\% sensitivity, 69\% specificity and 72\% predictive accuracy. Comparison with Adjuvantionline revealed discordance. Of 21 patients assessed as high risk by Adjuvantionline, 10 (48\%) and 6 (29\%) were at high risk by metabolomics in pre- and postoperative settings, respectively. Of 23 low-risk patients by Adjuvantionline, 11 (48\%) preoperative and 20 (87\%) postoperative patients were at low risk by metabolomics. CONCLUSIONS: This study identifies metabolomic discrimination between early and metastatic breast cancer. Micrometastatic disease may account for metabolomic misclassification of some early patients as metastatic. Metabolomics identifies more patients as low relapse risk compared with Adjuvantionline. Further exploration of this metabolomic fingerprint is warranted. This article was published in Ann Oncol and referenced in Metabolomics:Open Access

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