Personalized medicine is increasingly important in cancer treatment for its role in staging and its potential to improve stratification of patients. Different types of molecules, genes, proteins, and metabolites are being extensively explored as potential biomarkers. This review discusses the major findings and potential of tissue metabolites determined by high-resolution magic angle spinning magnetic resonance spectroscopy for cancer detection, characterization, and treatment monitoring. Cancer Res; 70(17); 6692-6. ©2010 AACR.
The Concept of MetabolomicsThe metabolome is the complete set of small-molecule metabolites in an organism and the final downstream product of the preceding gene expression and protein activity. Disease and influences from environmental factors, such as diet and drugs, are important factors in shaping the dynamic composition of the metabolome. The purpose of using metabolomics is to monitor the metabolic state at a certain time point and thereby understand more complex biological interactions, or to define biomarkers related to specific conditions. Compared with the more well-established "omics" technologies (genomics, transcriptomics, and proteomics), metabolomics is a relatively new and emerging field. Metabolomics contributes to the diversity of technologies within systems biology, thus enabling a more holistic picture of the chosen biological system. Magnetic resonance spectroscopy (MRS) offers safe, nondestructive, and quantitative metabolite identification in an automated and high-throughput fashion. Its sensitivity is substantially lower than for mass spectrometry (microgram compared with picogram level), but MRS enables investigation of tissue samples by high-resolution magic angle spinning (HR-MAS) MRS with minimal sample preparation, keeping the sample intact after analysis (1). This review focuses on the use of HR-MAS in cancer detection, characterization, and treatment evaluation, in which several studies show that MR metabolomics has promise. Patients with identical clinical and morphologic cancer diagnosis may have very different outcomes. Moreover, the metabolic state in cancer compared with control tissue, or metabolic changes following response and/or resistance to therapy, usually involves multiple metabolites. Multivariate analysis, which handles interactions between multiple variables (metabolites), has, thus, become a commonly used strategy for analysis of large spectral data sets. Statistical methods, such as principal component analysis (PCA) and partial least squares regression (PLS), are then applied to matrices of spectral data for exploration and model building to establish classifiers that enable predictions and classifications related to the biological problem in question. Proper validation of the classifiers, preferably using separate data for training and testing, is of great importance. By this means, MR metabolomics can establish a more detailed tumor portrait by defining specific fingerprints reflecting diagnostic status or therapeutic response. O...