Triple-negative breast cancer: Present challenges and new perspectives

Contains fulltext : 83751.pdf (publisher's version ) (Open Access)21 p

Feasibility of MR Metabolomics for Immediate Analysis of Resection Margins during Breast Cancer Surgery

Contains fulltext : 111501.pdf (publisher's version ) (Open Access

Quality control of prostate 1 H MRSI data.

Contains fulltext : 140579.pdf (publisher's version ) (Closed access)MRSI of prostate cancer provides a potential clinical tool to aid in the detection and characterisation of this disease, but its clinical use is limited by the need for the specialist training of radiologists to read these datasets. An essential part of this reading is the assessment of the usability and reliability of MRSI spectra because they can be affected by artefacts such as poor signal to noise, lipid signal contamination and broad resonances that could cause errors of interpretation. We have developed an automated quality control algorithm that classifies every voxel of an MRSI dataset as either acceptable or unacceptable for further analysis, based on the spectral profile alone. The method was trained and tested based on a gold standard of agreement of four experts. It was highly accurate: testing with a novel set of data from MRSI patients produced agreement with the experts' consensus decisions with a specificity of 0.95 and sensitivity of 0.95. This method provides fast quality control of three-dimensional MRSI datasets of the prostate, removing the need for radiologists to perform this time consuming, but necessary, task prior to further analysis. Copyright � 2012 John Wiley & Sons, Ltd

Short echo time 1H MRSI of the human brain at 3T with adiabatic slice-selective refocusing pulses; reproducibility and variance in a dual center setting.

Contains fulltext : 89280.pdf (publisher's version ) (Closed access)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 + glycine, and glutamate + 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.01 januari 201

IDH1 R132H Mutation Generates a Distinct Phospholipid Metabolite Profile in Glioma

Contains fulltext : 135917.pdf (publisher's version ) (Closed access)Many patients with glioma harbor specific mutations in the isocitrate dehydrogenase gene IDH1 that associate with a relatively better prognosis. IDH1-mutated tumors produce the oncometabolite 2-hydroxyglutarate. Because IDH1 also regulates several pathways leading to lipid synthesis, we hypothesized that IDH1-mutant tumors have an altered phospholipid metabolite profile that would impinge on tumor pathobiology. To investigate this hypothesis, we performed (31)P-MRS imaging in mouse xenograft models of four human gliomas, one of which harbored the IDH1-R132H mutation. (31)P-MR spectra from the IDH1-mutant tumor displayed a pattern distinct from that of the three IDH1 wild-type tumors, characterized by decreased levels of phosphoethanolamine and increased levels of glycerophosphocholine. This spectral profile was confirmed by ex vivo analysis of tumor extracts, and it was also observed in human surgical biopsies of IDH1-mutated tumors by (31)P high-resolution magic angle spinning spectroscopy. The specificity of this profile for the IDH1-R132H mutation was established by in vitro (31)P-NMR of extracts of cells overexpressing IDH1 or IDH1-R132H. Overall, our results provide evidence that the IDH1-R132H mutation alters phospholipid metabolism in gliomas involving phosphoethanolamine and glycerophosphocholine. These new noninvasive biomarkers can assist in the identification of the mutation and in research toward novel treatments that target aberrant metabolism in IDH1-mutant glioma. Cancer Res; 74(17); 4898-907. (c)2014 AACR

Alignment of high resolution magic angle spinning magnetic resonance spectra using warping methods

Contains fulltext : 83424.pdf (publisher's version ) (Closed access)11 p

In Vivo (31) P magnetic resonance spectroscopic imaging (MRSI) for metabolic profiling of human breast cancer xenografts

Contains fulltext : 153127.pdf (publisher's version ) (Closed access)To study cancer associated with abnormal metabolism of phospholipids, of which several have been proposed as biomarkers for malignancy or to monitor response to anticancer therapy. We explored 3D (31) P magnetic resonance spectroscopic imaging (MRSI) at high magnetic field for in vivo assessment of individual phospholipids in two patient-derived breast cancer xenografts representing good and poor prognosis (luminal- and basal-like tumors).Metabolic profiles from luminal-like and basal-like xenograft tumors were obtained in vivo using 3D (31) P MRSI at 11.7T and from tissue extracts in vitro at 14.1T. Gene expression analysis was performed in order to support metabolic differences between the two xenografts.In vivo (31) P MR spectra were obtained in which the prominent resonances from phospholipid metabolites were detected at a high signal-to-noise ratio (SNR >7.5). Metabolic profiles obtained in vivo were in agreement with those obtained in vitro and could be used to discriminate between the two xenograft models, based on the levels of phosphocholine, phosphoethanolamine, glycerophosphocholine, and glycerophosphoethanolamine. The differences in phospholipid metabolite concentration could partly be explained by gene expression profiles.Noninvasive metabolic profiling by 3D (31) P MRSI can discriminate between subtypes of breast cancer based on different concentrations of choline- and ethanolamine-containing phospholipids.J. Magn. Reson. Imaging 2014. © 2014 Wiley Periodicals, Inc

Peripheral Zone Prostate Cancer Localization by Multiparametric Magnetic Resonance at 3 T: Unbiased Cancer Identification by Matching to Histopathology

Contains fulltext : 109020.pdf (publisher's version ) (Closed access)OBJECTIVES: The aim of this study was to assess the diagnostic accuracy of peripheral zone prostate cancer localization by multiparametric magnetic resonance (MR) at 3 T using segmental matching of histopathology and MR images to avoid bias by image features in selection of cancer and noncancer regions. MATERIALS AND METHODS: Forty-eight patients underwent multiparametric MR imaging (MRI) on a 3 T system using a phased array body coil and spine coil elements for signal detection before prostatectomy. The examination included T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), dynamic contrast-enhanced imaging (DCE-MRI), and MR spectroscopic imaging (MRSI). Histopathology slides were correlated to T2W images and a stringent matching procedure was performed to define cancer and noncancer areas of the peripheral zone without influence of the MR image appearance. Mean T2W signal intensity, apparent diffusion coefficient, area under the enhancement curve, and choline + creatine-to-citrate signal ratio were calculated for cancer and noncancer areas. Receiver operating characteristic (ROC) analysis was performed on MR-derived parameters from the selected areas. Logistic regression was used to create models based on best combination of parameters. A simplified approach assigning a parametric score to each segment based on cutoff values from ROC analysis was also explored. RESULTS: By using the stringent matching procedure, 138 noncancer and 41 cancer segments were selected in the T2W images and transferred to the images of the other MR methods. A significant difference between mean values in cancer and noncancer segments was observed for all MR parameters analyzed (P < 0.001). Apparent diffusion coefficient was the best performing single parameter, with an area under the ROC curve Az,DWI of 0.90 for prostate cancer detection. Any combination of T2WI, DWI, and DCE-MRI was significantly better than T2WI alone in separating cancer from noncancer segments (Az,T2WI + DWI + DCE-MRI = 0.94, Az,T2WI + DWI = 0.92, Az,T2WI + DCE-MRI = 0.91, Az,T2WI = 0.85). The combination of T2WI and MRSI was also better than T2WI alone (Az, T2WI + MRSI = 0.90); however, the logistic regression models including MRSI did not have significant parameters. The simplified approach combining all parameters gave similar results to logistic regression combining all parameters (Az = 0.95 and 0.97, respectively). CONCLUSION: By selecting histopathology defined cancer and noncancer areas without influence of image contrast, this study objectively reveals that all investigated MR parameters have the ability to separate cancer from noncancer areas in the peripheral zone individually and that any combination is better than T2WI alone

The Cinderella story of metabolic profiling: does metabolomics get to go to the functional genomics ball?

To date most global approaches to functional genomics have centred on genomics, transcriptomics and proteomics. However, since a number of high-profile publications, interest in metabolomics, the global profiling of metabolites in a cell, tissue or organism, has been rapidly increasing. A range of analytical techniques, including (1)H NMR spectroscopy, gas chromatography–mass spectrometry (GC–MS), liquid chromatography–mass spectrometry (LC–MS), Fourier Transform mass spectrometry (FT–MS), high performance liquid chromatography (HPLC) and electrochemical array (EC-array), are required in order to maximize the number of metabolites that can be identified in a matrix. Applications have included phenotyping of yeast, mice and plants, understanding drug toxicity in pharmaceutical drug safety assessment, monitoring tumour treatment regimes and disease diagnosis in human populations. These successes are likely to be built on as other analytical and bioinformatic approaches are developed to fully exploit the information obtained in metabolic profiles. To assist in this process, databases of metabolomic data will be necessary to allow the passage of information between laboratories. In this prospective review, the capabilities of metabolomics in the field of medicine will be assessed in an attempt to predict the impact this ‘Cinderella approach’ will have at the ‘functional genomic ball’