Serum metabolomic profiles evaluated after surgery may identify patients with oestrogen receptor negative early breast cancer at increased risk of disease recurrence. Results from a retrospective study. Issue 1 (10th August 2014)

• Record Type: Journal Article
• Authors: Tenori, Leonardo; Oakman, Catherine; Morris, Patrick G.; Gralka, Ewa; Turner, Natalie; Cappadona, Silvia; Fornier, Monica; Hudis, Cliff; Norton, Larry; Luchinat, Claudio; Di Leo, Angelo
• Is Part Of: Molecular oncology. Volume 9:Issue 1(2015:Jan.)
• Page Start: 128
• Page End: 139
• Publication Date: 2014-08-10
• Subjects: Breast cancer -- Biomarker -- Metabolites -- Metabolomics -- Micrometastases -- Nuclear magnetic resonance spectroscopy; Cancer -- Molecular aspects -- Periodicals; 616.994005
• DOI: 10.1016/j.molonc.2014.07.012
• Languages: English
• ISSNs: 1574-7891

Abstract

Abstract : Purpose: Metabolomics is a global study of metabolites in biological samples. In this study we explored whether serum metabolomic spectra could distinguish between early and metastatic breast cancer patients and predict disease relapse. Methods: Serum samples were analysed from women with metastatic (n = 95) and predominantly oestrogen receptor (ER) negative early stage (n = 80) breast cancer using high resolution nuclear magnetic resonance spectroscopy. Multivariate statistics and a Random Forest classifier were used to create a prognostic model for disease relapse in early patients. Results: In the early breast cancer training set (n = 40), metabolomics correctly distinguished between early and metastatic disease in 83.7% of cases. A prognostic risk model predicted relapse with 90% sensitivity (95% CI 74.9–94.8%), 67% specificity (95% CI 63.0–73.4%) and 73% predictive accuracy (95% CI 70.6–74.8%). These results were reproduced in an independent early breast cancer set (n = 40), with 82% sensitivity, 72% specificity and 75% predictive accuracy. Disease relapse was associated with significantly lower levels of histidine (p = 0.0003) and higher levels of glucose (p = 0.01), and lipids (p = 0.0003), compared with patients with no relapse. Conclusions: The performance of a serum metabolomic prognostic model for disease relapse in individuals with ER‐negative early stage breast cancer is promising. A confirmation study is ongoing to better define the potential of metabolomics as a host and tumour‐derived prognostic tool. Highlights: The first clinical study exploring metabolomics in predicting breast cancer relapse. A serum‐derived signature predicted relapse (90% sensitivity, 67% specificity). In a multivariate the metabolomic signature maintained its prognostic value.