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...

Purpose: To assess the reproducibility of 1 H-MR spectroscopic imaging (MRSI) of the human brain at 3T with volume selection by a double spin echo sequence for localization with adiabatic refocusing pulses (semi-LASER). Materials and Methods:Twenty volunteers in two different institutions were measured twice with the same pulse sequence at an echo time of 30 msec. Magnetic resonance (MR) spectra were analyzed with LCModel with a simulated basis set including an experimentally acquired macromolecular signal profile. For specific regions in the brain mean metabolite levels, within and between subject variance, and the coefficient of variation (CoV) were calculated (for taurine, glutamate, total N-acetylaspartate, total creatine, total choline, myo-inositol þ glycine, and glutamate þ glutamine).Results: Repeated measurements showed no significant differences with a paired t-test and a high reproducibility (CoV ranging from 3%-30% throughout the selected volume). Mean metabolite levels and CoV obtained in similar regions in the brain did not differ significantly between two contributing institutions. The major source of differences between different measurements was identified to be the between-subject variations in the volunteers. Conclusion:We conclude that semi-LASER 1 H-MRSI at 3T is an adequate method to obtain quantitative and reproducible measures of metabolite levels over large parts of the brain, applicable across multiple centers.

The objectives of this study were to (a) explore the spectral characteristics of brain metastases, focusing on the origin of the primary cancer, and (b) evaluate the correlation with clinical outcome using multivariate analysis. High-resolution magic angle spinning (HR-MAS) MR spectra (n = 26) were obtained from 16 patients with brain metastases using a Bruker Avance DRX600 instrument. Standard pulse-acquired and spin-echo (TE 32 and 285 ms) (1)H spectra were obtained. These were examined using principal component analysis (PCA) and partial least squares regression analysis (PLS) relating spectral data to clinical outcome. The PCA score plot of pulse-acquired HR-MAS spectra showed a trend of clustering due to the origin of the metastases, mainly based on differences in the lipid signals at 1.3 and 0.9 ppm. With PLS, spectra of patients who died less than 5 months after surgery appeared to cluster in the lower left quadrant of the score plot. These preliminary results on brain metastasis classification and prediction of survival must be validated in a larger patient cohort. However, the possibility of differentiating metastases according to origin and predicting survival on the basis of HR-MAS spectra suggests that this method may be useful for diagnosing and planning treatment for brain metastases and also for guiding decisions about terminating further treatment.

3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1205-1216.

The concentrations of manganese, copper, and zinc in cerebrospinal fluid (CSF) from patients with multiple sclerosis (MS) and patients with no known neurological disease (control group) were measured. Manganese and copper levels were determined by two different analytical methods: atomic absorption spectrometry (AAS) and high-resolution inductively coupled plasma-mass spectrometry (HR-ICP-MS), whereas zinc levels were determined by HR-ICP-MS only. Manganese levels (mean+/-SEM) were significantly decreased in the CSF of MS patients (1.07+/-0.13 microg/L, ICP-MS; 1.08+/-0.11 microg/L, AAS) compared to the levels in the control group (1.78+/-0.26 microg/L, ICP-MS; 1.51+/-0.17 microg/L, AAS). Copper levels were significantly elevated in the CSF of MS patients (10.90+/-1.11 microg/L; ICP-MS, 11.53+/-0.83 microg/L, AAS) compared to the levels in the control group (8.67+/-0.49 microg/L, ICP-MS; 9.10+/-0.62 microg/L, AAS). There were no significant differences between the CSF zinc levels of MS and control patients. The physiological basis for the differences in manganese and copper concentrations between MS patients and controls is unknown, but could be related to alterations in the manganese- containing enzyme glutamine synthetase and the copper-containing enzyme cytochrome oxidase.