Metabolomic Analysis of Plasma from Breast Tumour Patients. A Pilot Study

• Published in: Journal of public health research Vol. 10; no. 4
• Main Authors: Politi, Carola, Fattuoni, Claudia, Serra, Alessandra, Noto, Antonio, Loi, Silvia, Casanova, Andrea, Faa, Gavino, Ravarino, Alberto, Saba, Luca
• Format: Journal Article
• Language: English
• Published: Italy PAGEPress Publications, Pavia, Italy 25.05.2021; SAGE Publishing
• Subjects: Breast cancer; GC-MS; mammography; metabolomics; plasma
• ISSN: 2279-9036; 2279-9028; 2279-9036
• DOI: 10.4081/jphr.2021.2304

Patients at risk of breast cancer are submitted to mammography, resulting in a classification of the lesions following the Breast Imaging Reporting and Data System (BI-RADS®). Due to BI-RADS 3 classification problems and the great uncertainty of the possible evolution of this kind of tumours, the integration of mammographic imaging with other techniques and markers of pathology, as metabolic information, may be advisable. Our study aims to evaluate the possibility to quantify by gas chromatography-mass spectrometry (GC-MS) specific metabolites in the plasma of patients with mammograms classified from BI-RADS 3 to BI-RADS 5, to find similarities or differences in their metabolome. Samples from BI-RADS 3 to 5 patients were compared with samples from a healthy control group. This pilot project aimed at establishing the sensitivity of the metabolomic classification of blood samples of patients undergoing breast radiological analysis and to support a better classification of mammographic cases. Metabolomic analysis revealed a panel of metabolites more abundant in healthy controls, as 3-aminoisobutyric acid, cholesterol, cysteine, stearic, linoleic and palmitic fatty acids. The comparison between samples from BI-RADS 3 and BI-RADS 5 patients, revealed the importance of 4-hydroxyproline, found in higher amount in BI-RADS 3 subjects. Although the low sample number did not allow the attainment of high validated statistical models, some interesting data were obtained, revealing the potential of metabolomics for an improvement in the classification of different mammographic lesions.