Metabolomes of breast cancer cell lines highlight the differences in the metabolic reprogramming of diverse tumour subtypes

Background and goals. Metabolic adaptation is one of the hallmarks of cancer cells. The metabolic reprogramming process is complex, influenced by multiple factors, and depends on the cancer type. This study has characterized the metabolic profiles of six breast cancer (BrCa) cells often used for in vitro studies. The selected cell lines derive from tumor subtypes with different aggressiveness: MCF7 and T47D (Luminal A, LA), SkBr3 (HER2+), MDA-MB-468 and MDA-MB436 (triple negative A, TNA), and MDA-MB-231 (TNB). The aim was to produce a database of the polar metabolites of these cells and identify some common or discriminating features to better understand the metabolic adaptation of BrCa. Methods. BrCa cells were cultured in conditions where cell growth is optimal for each cell line. Cells were harvested and lysed. The lysates were centrifuged, filtered, lyophilized, and analyzed by Proton Nuclear Magnetic Resonance Spectroscopy (1H-NMR). Chenomx NMR Suite 9 was used for metabolite identification and quantification. Multivariate statistics was performed using the Metaboanalyst 5.0 platform. Results. A supervised approach succeeded in classifying the metabolomes of the BrCa cell lines according to the current subtype classification: LA, HER2+, TNA, and TNB. Using the most discriminant metabolites able to separate the subtypes of cells, we identified the tricarboxylic acids cycle, Warburg effect, alanine and glycine and serine metabolisms, urea cycle, and glutathione metabolism as the most differently impacted pathways. In addition, known metabolic adaptative features of cancer were investigated and compared within the six BrCa cell lines. Conclusions. This study provides a database of the intracellular metabolite profiles of selected BrCa cell lines useful for future studies. The metabolomes of the evaluated cell lines reflect the heterogeneity of BrCa tumors. In addition, the observed metabolic fingerprints correlate with the tumor’s aggressiveness.