Motion correction of Single Voxel Spectroscopy by Independent Component Analysis applied to spectra from non-anesthetized pediatric subjects

For Single Voxel Spectroscopy (SVS), the acquisition of the spectrum is typically repeated n times and then combined with a factor in order to improve the Signal-to-Noise Ratio (SNR). In practice the acquisitions are not only affected by random noise, but also by physiological motion and subject movements. Since the influence of physiological motion such as cardiac and respiratory motion on the data is limited, it can be compensated for without data-loss. Individual acquisitions hampered by subject movements on the other hand need to be rejected, if no correction or compensation is possible. If the individual acquisitions are stored, it is possible to identify and reject the motion-disturbed acquisitions before averaging. Several automatic algorithms were investigated using a dataset of spectra from non-anesthetized infants with a gestational age of 40 weeks. Median filtering removed most subject movement artifacts, but at the cost of increased sensitivity to random noise. Neither Independent Component Analysis (ICA) nor outlier identification with multiple comparisons has this problem. These two algorithms are novel in this context. The peak height values of the metabolites were increased compared to the mean of all acquisitions for both methods, although primarily for the ICA method

Increasing the sensitivity of hyperpolarized [15 N2 ]urea detection by serial transfer of polarization to spin-coupled protons.

PURPOSE: Hyperpolarized 15 N-labeled molecules have been proposed as imaging agents for investigating tissue perfusion and pH. However, the sensitivity of direct 15 N detection is limited by the isotope's low gyromagnetic ratio. Sensitivity can be increased by transferring 15 N hyperpolarization to spin-coupled protons provided that there is not significant polarization loss during transfer. However, complete polarization transfer would limit the temporal window for imaging to the order of the proton T1 (2-3 s). To exploit the long T1 offered by storing polarization in 15 N and the higher sensitivity of 1 H detection, we have developed a pulse sequence for partial polarization transfer. METHODS: A polarization transfer pulse sequence was modified to allow partial polarization transfer, as is required for dynamic measurements, and that can be implemented with inhomogeneous B1 fields, as is often the case in vivo. The sequence was demonstrated with dynamic spectroscopy and imaging measurements with [15 N2 ]urea. RESULTS: When compared to direct 15 N detection, the sequence increased the signal-to-noise ratio (SNR) by a factor of 1.72 ± 0.25, where both experiments depleted ~20% of the hyperpolarization (>10-fold when 100% of the hyperpolarization is used). Simulations with measured cross relaxation rates showed that this sequence gave up to a 50-fold increase in urea proton polarization when compared to spontaneous polarization transfer via cross relaxation. CONCLUSION: The sequence gave an SNR increase that was close to the theoretical limit and can give a significant SNR benefit when compared to direct 13 C detection of hyperpolarized [13 C]urea

In vivo magnetic resonance imaging of glucose - initial experience

A new noninvasive, nonradioactive approach for glucose imaging using spin hyperpolarization technology and stable isotope labeling is presented. A glucose analog labeled with 13C at all six positions increased the overall hyperpolarized imaging signal; deuteration at all seven directly bonded proton positions prolonged the spin-lattice relaxation time. High-bandwidth 13C imaging overcame the large glucose carbon chemical shift dispersion. Hyperpolarized glucose images in the live rat showed time-dependent organ distribution patterns. At 8s after the start of bolus injection, the inferior vena cava was demonstrated at angiographic quality. Distribution of hyperpolarized glucose in the kidneys, vasculature, and heart was demonstrated at 12 and 20s. The heart-to-vasculature intensity ratio at 20s suggests myocardial uptake. Cancer imaging, currently performed with 18F-deoxyglucose positron emission tomography (FDG-PET), warrants further investigation, and glucose imaging could be useful in a vast range of clinical conditions and research fields where the radiation associated with the FDG-PET examination limits its use. © 2012 John Wiley & Sons, Ltd

Distinct choline metabolic profiles are associated with differences in gene expression for basal-like and luminal-like breast cancer xenograft models

<p>Abstract</p> <p>Background</p> <p>Increased concentrations of choline-containing compounds are frequently observed in breast carcinomas, and may serve as biomarkers for both diagnostic and treatment monitoring purposes. However, underlying mechanisms for the abnormal choline metabolism are poorly understood.</p> <p>Methods</p> <p>The concentrations of choline-derived metabolites were determined in xenografted primary human breast carcinomas, representing basal-like and luminal-like subtypes. Quantification of metabolites in fresh frozen tissue was performed using high-resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS).</p> <p>The expression of genes involved in phosphatidylcholine (PtdCho) metabolism was retrieved from whole genome expression microarray analyses.</p> <p>The metabolite profiles from xenografts were compared with profiles from human breast cancer, sampled from patients with estrogen/progesterone receptor positive (ER+/PgR+) or triple negative (ER-/PgR-/HER2-) breast cancer.</p> <p>Results</p> <p>In basal-like xenografts, glycerophosphocholine (GPC) concentrations were higher than phosphocholine (PCho) concentrations, whereas this pattern was reversed in luminal-like xenografts. These differences may be explained by lower choline kinase (<it>CHKA</it>, <it>CHKB</it>) expression as well as higher PtdCho degradation mediated by higher expression of phospholipase A2 group 4A (<it>PLA2G4A</it>) and phospholipase B1 (<it>PLB1</it>) in the basal-like model. The glycine concentration was higher in the basal-like model. Although glycine could be derived from energy metabolism pathways, the gene expression data suggested a metabolic shift from PtdCho synthesis to glycine formation in basal-like xenografts. In agreement with results from the xenograft models, tissue samples from triple negative breast carcinomas had higher GPC/PCho ratio than samples from ER+/PgR+ carcinomas, suggesting that the choline metabolism in the experimental models is representative for luminal-like and basal-like human breast cancer.</p> <p>Conclusions</p> <p>The differences in choline metabolite concentrations corresponded well with differences in gene expression, demonstrating distinct metabolic profiles in the xenograft models representing basal-like and luminal-like breast cancer. The same characteristics of choline metabolite profiles were also observed in patient material from ER+/PgR+ and triple-negative breast cancer, suggesting that the xenografts are relevant model systems for studies of choline metabolism in luminal-like and basal-like breast cancer.</p

Integrative analyses identify modulators of response to neoadjuvant aromatase inhibitors in patients with early breast cancer

Introduction Aromatase inhibitors (AIs) are a vital component of estrogen receptor positive (ER+) breast cancer treatment. De novo and acquired resistance, however, is common. The aims of this study were to relate patterns of copy number aberrations to molecular and proliferative response to AIs, to study differences in the patterns of copy number aberrations between breast cancer samples pre- and post-AI neoadjuvant therapy, and to identify putative biomarkers for resistance to neoadjuvant AI therapy using an integrative analysis approach. Methods Samples from 84 patients derived from two neoadjuvant AI therapy trials were subjected to copy number profiling by microarray-based comparative genomic hybridisation (aCGH, n = 84), gene expression profiling (n = 47), matched pre- and post-AI aCGH (n = 19 pairs) and Ki67-based AI-response analysis (n = 39). Results Integrative analysis of these datasets identified a set of nine genes that, when amplified, were associated with a poor response to AIs, and were significantly overexpressed when amplified, including CHKA, LRP5 and SAPS3. Functional validation in vitro, using cell lines with and without amplification of these genes (SUM44, MDA-MB134-VI, T47D and MCF7) and a model of acquired AI-resistance (MCF7-LTED) identified CHKA as a gene that when amplified modulates estrogen receptor (ER)-driven proliferation, ER/estrogen response element (ERE) transactivation, expression of ER-regulated genes and phosphorylation of V-AKT murine thymoma viral oncogene homolog 1 (AKT1). Conclusions These data provide a rationale for investigation of the role of CHKA in further models of de novo and acquired resistance to AIs, and provide proof of concept that integrative genomic analyses can identify biologically relevant modulators of AI response

Breast imaging technology: Imaging biochemistry - applications to breast cancer

The use of magnetic resonance spectroscopy (MRS) to investigate breast tumour biochemistry in vivo is reviewed. To this end, results obtained both from patients in vivo and from tumour extracts and model systems are discussed. An association has been observed between transformation and an increase in phosphomonoesters (PMEs) detected in the (31)P MRS spectrum, as well as an increase in choline-containing metabolites detected in the (1)H spectrum. A decrease in PME content after treatment is associated with response to treatment as assessed by tumour volume. Experiments in model systems aimed at understanding the underlying biochemical processes are presented, as well as data indicating the usefulness of MRS in monitoring the uptake and metabolism of some chemotherapeutic agents

Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles

Saliva is a readily accessible and informative biofluid, making it ideal for the early detection of a wide range of diseases including cardiovascular, renal, and autoimmune diseases, viral and bacterial infections and, importantly, cancers. Saliva-based diagnostics, particularly those based on metabolomics technology, are emerging and offer a promising clinical strategy, characterizing the association between salivary analytes and a particular disease. Here, we conducted a comprehensive metabolite analysis of saliva samples obtained from 215 individuals (69 oral, 18 pancreatic and 30 breast cancer patients, 11 periodontal disease patients and 87 healthy controls) using capillary electrophoresis time-of-flight mass spectrometry (CE-TOF-MS). We identified 57 principal metabolites that can be used to accurately predict the probability of being affected by each individual disease. Although small but significant correlations were found between the known patient characteristics and the quantified metabolites, the profiles manifested relatively higher concentrations of most of the metabolites detected in all three cancers in comparison with those in people with periodontal disease and control subjects. This suggests that cancer-specific signatures are embedded in saliva metabolites. Multiple logistic regression models yielded high area under the receiver-operating characteristic curves (AUCs) to discriminate healthy controls from each disease. The AUCs were 0.865 for oral cancer, 0.973 for breast cancer, 0.993 for pancreatic cancer, and 0.969 for periodontal diseases. The accuracy of the models was also high, with cross-validation AUCs of 0.810, 0.881, 0.994, and 0.954, respectively. Quantitative information for these 57 metabolites and their combinations enable us to predict disease susceptibility. These metabolites are promising biomarkers for medical screening