Rui V. Simões

Metabolic plasticity of metastatic breast cancer cells: adaptation to changes in the microenvironment.

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 13C-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.

Convex Non-Negative Matrix Factorization for Brain Tumor Delimitation from MRSI Data

Background 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. Methodology/Principal Findings 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. Conclusions/Significance 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.

1H-MRSI pattern perturbation in a mouse glioma: the effects of acute hyperglycemia and moderate hypothermia.

MR spectroscopic Imaging (MRSI), with PRESS localization, is used here to monitor the effects of acute hyperglycemia in the spectral pattern of 11 mice bearing GL261 gliomas at normothermia (36.5–37.5°C) and at hypothermia (28.5–29.5°C). These in vivo studies were complemented by ex vivo high resolution magic angle spinning (HR‐MAS) analysis of GL261 tumor samples from 6 animals sacrificed by focused microwave irradiation, and blood glucose measurements in 12 control mice. Apparent glucose levels, monitored by in vivo MRSI in brain tumors during acute hyperglycemia, rose to an average of 1.6‐fold during hypothermia (p < 0.05), while no significant changes were detected at normothermia, or in control experiments performed at euglycemia, or in normal/peritumoral brain regions. Ex vivo analysis of glioma‐bearing mouse brains at hypothermia revealed higher glucose increases in distinct regions during the acute hyperglycemic challenge (up to 6.6‐fold at the tumor center), in agreement with maximal in vivo blood glucose changes (5‐fold). Phantom studies on taurine plus glucose containing solutions explained the differences between in vivo and ex vivo measurements. Our results also indicate brain tumor heterogeneity in the four animal tumors investigated in response to a defined metabolic challenge. Copyright

Proton Magnetic Resonance Spectroscopy Assessment of Fetal Brain Metabolism in Late-Onset ‘Small for Gestational Age' versus ‘Intrauterine Growth Restriction' Fetuses

Objectives: We used magnetic resonance spectroscopy (MRS) to evaluate brain metabolic differences in small fetuses near term as compared to appropriate for gestational age (AGA) fetuses. Study Design: 71 term small fetuses (estimated fetal weight <10th centile for gestational age with normal umbilical artery Doppler sonography) were subclassified as late intrauterine growth restriction (IUGR) (n = 50) or small for gestational age (SGA) (n = 21), and compared with 65 AGA fetuses. IUGR was defined by either abnormal middle cerebral artery, abnormal uterine artery Doppler sonography or estimated fetal weight <3rd centile. All participants underwent brain magnetic resonance imaging at 37 weeks of gestation, and single-voxel magnetic resonance spectra were obtained from the frontal lobe on a 3-tesla scanner. N-acetylaspartate (NAA)/choline (Cho), NAA/creatine (Cr) and Cho/Cr ratios were calculated and compared between cases and controls. The association of the metabolic ratios with the study groups was tested. Results: After MRS processing and applying quality control criteria, 31 spectra from late-onset IUGR, 11 from SGA and 30 from AGA fetuses were selected for further analysis. Both SGA and late-onset IUGR fetuses showed significantly reduced NAA/Cho levels when compared to AGA fetuses. This decrease followed a linear trend across the three clinical groups that were considered. Conclusions: Both SGA and late-onset IUGR fetuses showed differences in MRS brain metabolic ratios. The findings suggest that despite near-normal perinatal outcomes, SGA fetuses are not constitutionally small and may represent a form of growth disorder that needs to be clarified.

DCE@urLAB: a dynamic contrast-enhanced MRI pharmacokinetic analysis tool for preclinical data

BackgroundDCE@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.ResultsPixel-wise model-based quantitative parameters are estimated by fitting DCE-MRI data to several pharmacokinetic models using the Levenberg-Marquardt algorithm (LMA). DCE@urLAB also includes the semi-quantitative parametric and heuristic analysis approaches commonly used in practice. This software application has been programmed in the Interactive Data Language (IDL) and tested both with publicly available simulated data and preclinical studies from tumor-bearing mouse brains.ConclusionsA user-friendly solution for applying pharmacokinetic and non-quantitative analysis DCE-MRI in preclinical studies has been implemented and tested. The proposed tool has been specially designed for easy selection of multi-pixel ROIs. A public release of DCE@urLAB, together with the open source code and sample datasets, is available at http://www.die.upm.es/im/archives/DCEurLAB/.

Preliminary characterization of an experimental breast cancer cells brain metastasis mouse model by MRI/MRS

PurposeChemotherapy increases survival in breast cancer patients. Consequently, cerebral metastases have recently become a significant clinical problem, with an incidence of 30–40% among breast carcinoma patients. As this phenomenon cannot be studied longitudinally in humans, models which mimic brain metastasis are needed to investigate its pathogenesis. Such models may later be used in experimental therapeutic approaches.Material and methods/resultsWe report a model in which 69% of the animals (9/13 BALB/cnude mice) developed MR-detectable abnormal masses in the brain parenchyma within a 20 to 62-day time window post intra-carotid injection of 435-Br1 human cells. The masses detected in vivo were either single (7 animals) or multiple (2 animals). Longitudinal MR (MRI/MRS) studies and post-mortem histological data were correlated, revealing a total incidence of experimental brain metastases of 85% in the cases studied (11/13 animals). ADC maps perfectly differentiated edema and/or CSF areas from metastasis. Preliminary MRS data also revealed additional features: decrease in N-acetyl aspartate (NAA) was the first MRS-based marker of metastasis growth in the brain (micrometastasis); choline-containing compounds (Cho) rose and creatine (Cr) levels decreased as these lesions evolved, with mobile lipids and lactate also becoming visible. Furthermore, MRS pattern recognition-based analysis suggested that this approach may help to discriminate different growth stages.ConclusionsThis study paves the way for further in vivo studies oriented towards detection of different tumor progression states and for improving treatment efficiency.

Association of brain metabolism with sulcation and corpus callosum development assessed by MRI in late-onset small fetuses

OBJECTIVE We sought to determine the relationship between fetal brain metabolism and microstructure expressed by brain sulcation, and corpus callosum (CC) development assessed by fetal brain magnetic resonance (MR) imaging and proton MR spectroscopy ((1)H-MRS). STUDY DESIGN A total of 119 fetuses, 64 that were small for gestational age (estimated fetal weight <10th centile and normal umbilical artery Doppler) and 55 controls underwent a 3T MR imaging/(1)H-MRS exam at 37 weeks. Anatomical T2-weighted images were obtained in the 3 orthogonal planes and long echo time (TE) (1)H-MRS acquired from the frontal lobe. Head biometrics, cortical fissure depths (insula, Sylvian, parietooccipital, cingulate, and calcarine), and CC area and biometries were blindly performed by manual and semiautomated delineation using Analyze software and corrected creating ratios for biparietal diameter and frontooccipital diameter, respectively, for group comparison. Spectroscopic data were processed using LCModel software and analyzed as metabolic ratios of N-acetylaspartate (NAA) to choline (Cho), Cho to creatine (Cr), and myo-inositol (Ino) to Cho. Differences between cases and controls were assessed. To test for the association between metabolic ratios and microstructural parameters, bivariate correlation analyses were performed. RESULTS Spectroscopic findings showed decreased NAA/Cho and increased Cho/Cr ratios in small fetuses. They also presented smaller head biometrics, shorter and smaller CC, and greater insular and cingulate depths. Frontal lobe NAA/Cho significantly correlated with biparietal diameter (r = 0.268; P = .021), head circumference (r = 0.259; P = .026), CC length (r = 0.265; P = .026), CC area (r = 0.317; P = .007), and the area of 6 from the 7 CC subdivisions. It did not correlate with any of the cortical sulcation parameters evaluated. None of the other metabolic ratios presented significant correlations with cortical development or CC parameters. CONCLUSION Frontal lobe NAA/Cho levels-which are considered a surrogate marker of neuronal activity-show a strong association with CC development. These results suggest that both metabolic and callosal alterations may be part of the same process of impaired brain development associated with intrauterine growth restriction.

Improving the classification of brain tumors in mice with perturbation enhanced (PE)-MRSI

Classifiers based on statistical pattern recognition analysis of MRSI data are becoming important tools for the non-invasive diagnosis of human brain tumors. Here we investigate the potential interest of perturbation-enhanced MRSI (PE-MRSI), in this case acute hyperglycemia, for improving the discrimination between mouse brain MRS patterns of glioblastoma multiforme (GBM), oligodendroglioma (ODG), and non-tumor brain parenchyma (NT). Six GBM-bearing mice and three ODG-bearing mice were scanned at 7 Tesla by PRESS-MRSI with 12 and 136 ms echo-time, during euglycemia (Eug) and also during induced acute hyperglycemia (Hyp), generating altogether four datasets per animal (echo time + glycemic condition): 12Eug, 136Eug, 12Hyp, and 136Hyp. For classifier development all spectral vectors (spv) selected from the MRSI matrix were unit length normalized (UL2) and used either as a training set (76 GBM spv, four mice; 70 ODG spv, two mice; 54 NT spv) or as an independent testing set (61 GBM spv, two mice; 31 ODG, one mouse; 23 NT spv). All Fishers LDA classifiers obtained were evaluated as far as their descriptive performance-correctly classified cases of the training set (bootstrapping)-and predictive accuracy-balanced error rate of independent testing set classification. MRSI-based classifiers at 12Hyp were consistently more efficient in separating GBM, ODG, and NT regions, with overall accuracies always >80% and up to 95-96%; remaining classifiers were within the 48-85% range. This was also confirmed by user-independent selection of training and testing sets, using leave-one-out (LOO). This highlights the potential interest of perturbation-enhanced MRSI protocols for improving the non-invasive characterization of preclinical brain tumors.

A new ex vivo method to evaluate the performance of candidate MRI contrast agents: a proof-of-concept study

BackgroundMagnetic resonance imaging (MRI) plays an important role in tumor detection/diagnosis. The use of exogenous contrast agents (CAs) helps to improve the discrimination between lesion and neighbouring tissue, but most of the currently available CAs are non-specific. Assessing the performance of new, selective CAs requires exhaustive assays and large amounts of material. Accordingly, in a preliminary screening of new CAs, it is important to choose candidate compounds with good potential for in vivo efficiency. This screening method should reproduce as close as possible the in vivo environment. In this sense, a fast and reliable method to select the best candidate CAs for in vivo studies would minimize time and investment cost, and would benefit the development of better CAs.ResultsThe post-mortem ex vivo relative contrast enhancement (RCE) was evaluated as a method to screen different types of CAs, including paramagnetic and superparamagnetic agents. In detail, sugar/gadolinium-loaded gold nanoparticles (Gd-GNPs) and iron nanoparticles (SPIONs) were tested. Our results indicate that the post-mortem ex vivo RCE of evaluated CAs, did not correlate well with their respective in vitro relaxivities. The results obtained with different Gd-GNPs suggest that the linker length of the sugar conjugate could modulate the interactions with cellular receptors and therefore the relaxivity value. A paramagnetic CA (GNP (E_2)), which performed best among a series of Gd-GNPs, was evaluated both ex vivo and in vivo. The ex vivo RCE was slightly worst than gadoterate meglumine (201.9 ± 9.3% versus 237 ± 14%, respectively), while the in vivo RCE, measured at the time-to-maximum enhancement for both compounds, pointed to GNP E_2 being a better CA in vivo than gadoterate meglumine. This is suggested to be related to the nanoparticule characteristics of the evaluated GNP.ConclusionWe have developed a simple, cost-effective relatively high-throughput method for selecting CAs for in vivo experiments. This method requires approximately 800 times less quantity of material than the amount used for in vivo administrations.

In Vivo Detection of Perinatal Brain Metabolite Changes in a Rabbit Model of Intrauterine Growth Restriction (IUGR)

Background Intrauterine growth restriction (IUGR) is a risk factor for abnormal neurodevelopment. We studied a rabbit model of IUGR by magnetic resonance imaging (MRI) and spectroscopy (MRS), to assess in vivo brain structural and metabolic consequences, and identify potential metabolic biomarkers for clinical translation. Methods IUGR was induced in 3 pregnant rabbits at gestational day 25, by 40–50% uteroplacental vessel ligation in one horn; the contralateral horn was used as control. Fetuses were delivered at day 30 and weighted. A total of 6 controls and 5 IUGR pups underwent T2-w MRI and localized proton MRS within the first 8 hours of life, at 7T. Changes in brain tissue volumes and respective contributions to each MRS voxel were estimated by semi-automated registration of MRI images with a digital atlas of the rabbit brain. MRS data were used for: (i) absolute metabolite quantifications, using linear fitting; (ii) local temperature estimations, based on the water chemical shift; and (iii) classification, using spectral pattern analysis. Results Lower birth weight was associated with (i) smaller brain sizes, (ii) slightly lower brain temperatures, and (iii) differential metabolite profile changes in specific regions of the brain parenchyma. Specifically, we found estimated lower levels of aspartate and N-acetylaspartate (NAA) in the cerebral cortex and hippocampus (suggesting neuronal impairment), and higher glycine levels in the striatum (possible marker of brain injury). Our results also suggest that the metabolic changes in cortical regions are more prevalent than those detected in hippocampus and striatum. Conclusions IUGR was associated with brain metabolic changes in vivo, which correlate well with the neurostructural changes and neurodevelopment problems described in IUGR. Metabolic parameters could constitute non invasive biomarkers for the diagnosis and abnormal neurodevelopment of perinatal origin.