Cancer cells adapt their metabolism during tumorigenesis. We studied two isogenic breast cancer cells lines (highly metastatic 4T1; nonmetastatic 67NR) to identify differences in their glucose and glutamine metabolism in response to metabolic and environmental stress. Dynamic magnetic resonance spectroscopy of (13)C-isotopomers showed that 4T1 cells have higher glycolytic and tricarboxylic acid (TCA) cycle flux than 67NR cells and readily switch between glycolysis and oxidative phosphorylation (OXPHOS) in response to different extracellular environments. OXPHOS activity increased with metastatic potential in isogenic cell lines derived from the same primary breast cancer: 4T1 > 4T07 and 168FARN (local micrometastasis only) > 67NR. We observed a restricted TCA cycle flux at the succinate dehydrogenase step in 67NR cells (but not in 4T1 cells), leading to succinate accumulation and hindering OXPHOS. In the four isogenic cell lines, environmental stresses modulated succinate dehydrogenase subunit A expression according to metastatic potential. Moreover, glucose-derived lactate production was more glutamine dependent in cell lines with higher metastatic potential. These studies show clear differences in TCA cycle metabolism between 4T1 and 67NR breast cancer cells. They indicate that metastases-forming 4T1 cells are more adept at adjusting their metabolism in response to environmental stress than isogenic, nonmetastatic 67NR cells. We suggest that the metabolic plasticity and adaptability are more important to the metastatic breast cancer phenotype than rapid cell proliferation alone, which could 1) provide a new biomarker for early detection of this phenotype, possibly at the time of diagnosis, and 2) lead to new treatment strategies of metastatic breast cancer by targeting mitochondrial metabolism.

Fetal growth may be impaired by poor placental function or maternal conditions, each of which can influence the transfer of nutrients and oxygen from the mother to the developing fetus. Large-scale studies of metabolites (metabolomics) are key to understand cellular metabolism and pathophysiology of human conditions. Herein, maternal and cord blood plasma samples were used for NMR-based metabolic fingerprinting and profiling, including analysis of the enrichment of circulating lipid classes and subclasses, as well as the number of sub-fraction particles and their size. Changes in phosphatidylcholines and glycoproteins were prominent in growth-restricted fetuses indicating significant alterations in their abundance and biophysical properties. Lipoprotein profiles showed significantly lower plasma concentrations of cholesterol-intermediate density lipoprotein (IDL), triglycerides-IDL and high-density lipoprotein (HDL) in mothers of growth-restricted fetuses compared to controls (p < 0.05). In contrast, growth-restricted fetuses had significantly higher plasma concentrations of cholesterol and triglycerides transporting lipoproteins [LDL, IDL, and VLDL, (p < 0.005; all)], as well as increased VLDL particle types (large, medium and small). Significant changes in plasma concentrations of formate, histidine, isoleucine and citrate in growth-restricted fetuses were also observed. Comprehensive metabolic profiling reveals that both, mother and fetuses of pregnancies complicated with fetal growth restriction have a substantial disruption in lipid metabolism.

DCE@urLAB is a software application for analysis of dynamic contrast-enhanced magnetic resonance imaging data (DCE-MRI). The tool incorporates a friendly graphical user interface (GUI) to interactively select and analyze a region of interest (ROI) within the image set, taking into account the tissue concentration of the contrast agent (CA) and its effect on pixel intensity.

Pattern Recognition techniques can provide invaluable insights in the field of neuro-oncology. This is because the clinical analysis of brain tumors requires the use of non-invasive methods that generate complex data in electronic format. Magnetic Resonance (MR), in the modalities of spectroscopy (MRS) and spectroscopic imaging (MRSI), has been widely applied to this purpose. The heterogeneity of the tissue in the brain volumes analyzed by MR remains a challenge in terms of pathological area delimitation. A pre-clinical study was carried out using seven brain tumor-bearing mice. Imaging and spectroscopy information was acquired from the brain tissue. A methodology is proposed to extract tissue type-specific sources from these signals by applying Convex Non-negative Matrix Factorization (Convex-NMF). Its suitability for the delimitation of pathological brain area from MRSI is experimentally confirmed by comparing the images obtained with its application to selected target regions, and to the gold standard of registered histopathology data. The former showed good accuracy for the solid tumor region (proliferation index (PI)>30%). The latter yielded (i) high sensitivity and specificity in most cases, (ii) acquisition conditions for safe thresholds in tumor and non-tumor regions (PI>30% for solid tumoral region; ≤5% for non-tumor), and (iii) fairly good results when borderline pixels were considered. The unsupervised nature of Convex-NMF, which does not use prior information regarding the tumor area for its delimitation, places this approach one step ahead of classical label-requiring supervised methods for discrimination between tissue types, minimizing the negative effect of using mislabeled voxels. Convex-NMF also relaxes the non-negativity constraints on the observed data, which allows for a natural representation of the MRSI signal. This should help radiologists to accurately tackle one of the main sources of uncertainty in the clinical management of brain tumors, which is the difficulty of appropriately delimiting the pathological area.