Imaging and Tissue Biomarkers of Choline Metabolism in Diffuse Adult Glioma: 18F-Fluoromethylcholine PET/CT, Magnetic Resonance Spectroscopy, and Choline Kinase α

The cellular and molecular basis of choline uptake on PET imaging and MRS-visible choline containing compounds is not well understood. Choline kinase alpha (ChoKa) is an enzyme that phosphorylates choline, an essential step in membrane synthesis. We investigate choline metabolism through 18F-fluoromethylcholine (18F-FMC) PET, MRS and tissue ChoKa in human glioma. 14 patients with suspected diffuse glioma underwent multimodal 3T MRI and dynamic 18F FMC PET/CT prior to surgery. Co-registered PET and MRI data were used to target biopsies to regions of high and low choline signal, and immunohistochemistry for ChoKa expression was performed. 18F-FMC/PET differentiated WHO grade IV from grade II and III tumours, whereas MRS differentiated grade III/IV from grade II tumours. Tumoural 18F-FMC/PET uptake was higher than in normal-appearing white matter across all grades and markedly elevated within regions of contrast enhancement. 18F-FMC/PET correlated weakly with MRS Cho ratios. ChoKa expression on IHC was negative or weak in all but one GBM sample, and did not correlate with tumour grade or imaging choline markers. MRS and 18F-FMC/PET provide complimentary information on glioma choline metabolism. Tracer uptake is, however, potentially confounded by blood-brain barrier permeability. ChoKa overexpression does not appear to be a common feature in diffuse glioma

Reliability of dynamic contrast-enhanced magnetic resonance imaging data in primary brain tumours: a comparison of Tofts and shutter speed models

Purpose To investigate the robustness of pharmacokinetic modelling of DCE-MRI brain tumour data and to ascertain reliable perfusion parameters through a model selection process and a stability test. Methods DCE-MRI data of 14 patients with primary brain tumours were analysed using the Tofts model (TM), the extended Tofts model (ETM), the shutter speed model (SSM) and the extended shutter speed model (ESSM). A no-effect model (NEM) was implemented to assess overfitting of data by the other models. For each lesion, the Akaike Information Criteria (AIC) was used to build a 3D model selection map. The variability of each pharmacokinetic parameter extracted from this map was assessed with a noise propagation procedure, resulting in voxel-wise distributions of the coefficient of variation (CV). Results The model selection map over all patients showed NEM had the best fit in 35.5% of voxels, followed by ETM (32%), TM (28.2%), SSM (4.3%) and ESSM (<0.1%). In analysing the reliability of Ktrans, when considering regions with a CV<20%, ≈25% of voxels were found to be stable across all patients. The remaining 75% of voxels were considered unreliable. Conclusions The majority of studies quantifying DCE-MRI data in brain tumours only consider a single model and whole-tumour statistics for the output parameters. Appropriate model selection, considering tissue biology and its effects on blood brain barrier permeability and exchange conditions, together with an analysis on the reliability and stability of the calculated parameters, is critical in processing robust brain tumour DCE-MRI data

Identification of genetic variants associated with Huntington's disease progression: a genome-wide association study

Background Huntington's disease is caused by a CAG repeat expansion in the huntingtin gene, HTT. Age at onset has been used as a quantitative phenotype in genetic analysis looking for Huntington's disease modifiers, but is hard to define and not always available. Therefore, we aimed to generate a novel measure of disease progression and to identify genetic markers associated with this progression measure. Methods We generated a progression score on the basis of principal component analysis of prospectively acquired longitudinal changes in motor, cognitive, and imaging measures in the 218 indivduals in the TRACK-HD cohort of Huntington's disease gene mutation carriers (data collected 2008–11). We generated a parallel progression score using data from 1773 previously genotyped participants from the European Huntington's Disease Network REGISTRY study of Huntington's disease mutation carriers (data collected 2003–13). We did a genome-wide association analyses in terms of progression for 216 TRACK-HD participants and 1773 REGISTRY participants, then a meta-analysis of these results was undertaken. Findings Longitudinal motor, cognitive, and imaging scores were correlated with each other in TRACK-HD participants, justifying use of a single, cross-domain measure of disease progression in both studies. The TRACK-HD and REGISTRY progression measures were correlated with each other (r=0·674), and with age at onset (TRACK-HD, r=0·315; REGISTRY, r=0·234). The meta-analysis of progression in TRACK-HD and REGISTRY gave a genome-wide significant signal (p=1·12 × 10−10) on chromosome 5 spanning three genes: MSH3, DHFR, and MTRNR2L2. The genes in this locus were associated with progression in TRACK-HD (MSH3 p=2·94 × 10−8 DHFR p=8·37 × 10−7 MTRNR2L2 p=2·15 × 10−9) and to a lesser extent in REGISTRY (MSH3 p=9·36 × 10−4 DHFR p=8·45 × 10−4 MTRNR2L2 p=1·20 × 10−3). The lead single nucleotide polymorphism (SNP) in TRACK-HD (rs557874766) was genome-wide significant in the meta-analysis (p=1·58 × 10−8), and encodes an aminoacid change (Pro67Ala) in MSH3. In TRACK-HD, each copy of the minor allele at this SNP was associated with a 0·4 units per year (95% CI 0·16–0·66) reduction in the rate of change of the Unified Huntington's Disease Rating Scale (UHDRS) Total Motor Score, and a reduction of 0·12 units per year (95% CI 0·06–0·18) in the rate of change of UHDRS Total Functional Capacity score. These associations remained significant after adjusting for age of onset. Interpretation The multidomain progression measure in TRACK-HD was associated with a functional variant that was genome-wide significant in our meta-analysis. The association in only 216 participants implies that the progression measure is a sensitive reflection of disease burden, that the effect size at this locus is large, or both. Knockout of Msh3 reduces somatic expansion in Huntington's disease mouse models, suggesting this mechanism as an area for future therapeutic investigation

Primed infusion with delayed equilibrium of Gd.DTPA for enhanced imaging of small pulmonary metastases

To use primed infusions of the magnetic resonance imaging (MRI) contrast agent Gd.DTPA (Magnevist), to achieve an equilibrium between blood and tissue (eqMRI). This may increase tumor Gd concentrations as a novel cancer imaging methodology for the enhancement of small tumor nodules within the low signal-to-noise background of the lung.A primed infusion with a delay before equilibrium (eqMRI) of the Gd(III) chelator Gd.DTPA, via the intraperitoneal route, was used to evaluate gadolinium tumor enhancement as a function of a bolus injection, which is applied routinely in the clinic, compared to gadolinium maintained at equilibrium. A double gated (respiration and cardiac) spin-echo sequence at 9.4T was used to evaluate whole lungs pre contrast and then at 15 (representative of bolus enhancement), 25 and 35 minutes (representative of eqMRI). This was carried out in two lung metastasis models representative of high and low tumor cell seeding. Lungs containing discrete tumor nodes where inflation fixed and taken for haematoxylin and eosin staining as well as CD34 staining for correlation to MRI.We demonstrate that sustained Gd enhancement, afforded by Gd equilibrium, increases the detection of pulmonary metastases compared to bolus enhancement and those tumors which enhance at equilibrium are sub-millimetre in size (<0.7 mm(2)) with a similar morphology to early bronchoalveolar cell carcinomas.As Gd-chelates are routinely used in the clinic for detecting tumors by MRI, this methodology is readily transferable to the clinic and advances MRI as a methodology for the detection of small pulmonary tumors

Low tumor cell seeding model mean values for signal-to noise (SNR) and the percentage increase in observed tumours for eqMRI.

<p>A) SNR of tumors already visible on pre contrast images, B) SNR of tumors that were visible due to Gd enhancement from 15 minutes onwards, C) the percentage increase in observed tumors by Gd enhancement at each time point compared to pre contrast. Values are mean ± s.e.m. * p = <0.05 from pre (A and B) or from 15 min (C).</p

An MR image of a low tumor cell seeding model mouse lung the corresponding whole lung composite H & E stained section, a blown up area and sizing of tumor (white box), and CD34 immunohistochemistry image.

<p>A) a single enhancing tumour (at 25 minutes) with corresponding CD34 image (C), B) two visible tumors (pre contrast image) with corresponding CD34 images left (D) and right (E).</p

MR images of a high tumor cell seeding model mouse at day 14 over the 35 minutes for high dose Gd.DTPA (white arrowsheads denote visible tumors) (A), and the corresponding mean values for the percentage increase in observable tumors compared to that of high tumor cell seeding mice at day 14 for eqMRI (B).

<p>Values are mean ± s.e.m. * p = <0.05 from high dose.</p

Experimental plan showing timings of bolus and infusion as well as scan times.

<p>The bars above are an assumed grey scale representation of gadolinium concentration within the blood where black is high and white low concentration.</p