Bioinformatics tools for cancer metabolomics

It is well known that significant metabolic change take place as cells are transformed from normal to malignant. This review focuses on the use of different bioinformatics tools in cancer metabolomics studies. The article begins by describing different metabolomics technologies and data generation techniques. Overview of the data pre-processing techniques is provided and multivariate data analysis techniques are discussed and illustrated with case studies, including principal component analysis, clustering techniques, self-organizing maps, partial least squares, and discriminant function analysis. Also included is a discussion of available software packages

Potential Markers of Cisplatin Treatment Response Unveiled by NMR Metabolomics of Human Lung Cells

In this work, H-1 high resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) spectroscopy was used to characterize the variations in the metabolome (small metabolites and mobile lipids) of A549 human lung cells in response to exposure to the alkylating drug cisplatin. Multivariate analysis and signal integration of spectral data were carried out to unveil exposure-induced effects and follow their time course. Parallel and strongly correlated increases in lipids (particularly unsaturated triglycerides) and nucleotide sugars (particularly uridine diphosphate N-acetylglucosamine) were found in cisplatin-treated cells, highlighting these compounds as potential biomarkers of treatment response. Other significant changes upon drug exposure comprised an increase in sorbitol and decreases in niacinamide and several amino acids (glutamine, alanine, lysine, methionine, citrulline, phenylalanine and tyrosine). These results show that in vitro NMR metabolomics is a powerful tool for detecting variations in a range of intracellular compounds upon drug exposure, thus offering the possibility of identifying candidate metabolite markers for in vivo monitoring of tumor responsiveness to treatment