Metabolite Levels in Paediatric Brain Tumours Correlate with Histological Features

&lt;b&gt;&lt;i&gt;Aims:&lt;/i&gt;&lt;/b&gt; Metabolite levels can be measured non-invasively using in vivo &lt;sup&gt;1&lt;/sup&gt;H magnetic resonance spectroscopy (MRS). These tumour metabolite profiles are highly characteristic for tumour type in childhood brain tumours; however, the relationship between metabolite values and conventional histopathological characteristics has not yet been fully established. This study systematically tests the relationship between metabolite levels detected by MRS and specific histological features in a range of paediatric brain tumours. &lt;b&gt;&lt;i&gt;Methods:&lt;/i&gt;&lt;/b&gt; Single-voxel MRS was performed routinely in children with brain tumours along with the clinical imaging prior to treatment. Metabolites were quantified using LCModel. Histological features were assessed semi-quantitatively for 27 children on H&amp;amp;E and immunostained slides, blind to the metabolite values. Statistical analysis included 2-tailed independent-samples &lt;i&gt;t&lt;/i&gt; tests and 2-tailed Spearman rank correlation tests. &lt;b&gt;&lt;i&gt;Results:&lt;/i&gt;&lt;/b&gt; Ki67, cellular atypia, and mitosis correlated positively with choline metabolites, and phosphocholine in particular. Apoptosis and necrosis were both associated with lipid levels, with the relationship dependent on the use of long or short echo time MRS acquisitions. Neuronal components correlated negatively and glial components positively with N-acetyl-aspartate. Glial components correlated positively with myoinositol. &lt;b&gt;&lt;i&gt;Conclusion:&lt;/i&gt;&lt;/b&gt; Metabolite levels in children's brain tumours measured by MRS are closely associated with key histological features routinely assessed by histopathologists in the diagnostic process. This further elucidates our understanding of this important non-invasive diagnostic tool and strengthens our understanding of the relationship between metabolites and histological features.</jats:p

Metabolite profiles of medulloblastoma for rapid and non-invasive detection of molecular disease groups

BackgroundThe malignant childhood brain tumour, medulloblastoma, is classified clinically into molecular groups which guide therapy. DNA-methylation profiling is the current classification ‘gold-standard’, typically delivered 3–4 weeks post-surgery. Pre-surgery non-invasive diagnostics thus offer significant potential to improve early diagnosis and clinical management. Here, we determine tumour metabolite profiles of the four medulloblastoma groups, assess their diagnostic utility using tumour tissue and potential for non-invasive diagnosis using in vivo magnetic resonance spectroscopy (MRS).MethodsMetabolite profiles were acquired by high-resolution magic-angle spinning NMR spectroscopy (MAS) from 86 medulloblastomas (from 59 male and 27 female patients), previously classified by DNA-methylation array (WNT (n = 9), SHH (n = 22), Group3 (n = 21), Group4 (n = 34)); RNA-seq data was available for sixty. Unsupervised class-discovery was performed and a support vector machine (SVM) constructed to assess diagnostic performance. The SVM classifier was adapted to use only metabolites (n = 10) routinely quantified from in vivo MRS data, and re-tested. Glutamate was assessed as a predictor of overall survival.FindingsGroup-specific metabolite profiles were identified; tumours clustered with good concordance to their reference molecular group (93%). GABA was only detected in WNT, taurine was low in SHH and lipids were high in Group3. The tissue-based metabolite SVM classifier had a cross-validated accuracy of 89% (100% for WNT) and, adapted to use metabolites routinely quantified in vivo, gave a combined classification accuracy of 90% for SHH, Group3 and Group4. Glutamate predicted survival after incorporating known risk-factors (HR = 3.39, 95% CI 1.4–8.1, p = 0.025).InterpretationTissue metabolite profiles characterise medulloblastoma molecular groups. Their combination with machine learning can aid rapid diagnosis from tissue and potentially in vivo. Specific metabolites provide important information; GABA identifying WNT and glutamate conferring poor prognosis

Metabolite profiling in retinoblastoma identifies novel clinicopathological subgroups

BACKGROUND: Tumour classification, based on histopathology or molecular pathology, is of value to predict tumour behaviour and to select appropriate treatment. In retinoblastoma, pathology information is not available at diagnosis and only exists for enucleated tumours. Alternative methods of tumour classification, using noninvasive techniques such as magnetic resonance spectroscopy, are urgently required to guide treatment decisions at the time of diagnosis. METHODS: High-resolution magic-angle spinning magnetic resonance spectroscopy (HR-MAS MRS) was undertaken on enucleated retinoblastomas. Principal component analysis and cluster analysis of the HR-MAS MRS data was used to identify tumour subgroups. Individual metabolite concentrations were determined and were correlated with histopathological risk factors for each group. RESULTS: Multivariate analysis identified three metabolic subgroups of retinoblastoma, with the most discriminatory metabolites being taurine, hypotaurine, total-choline and creatine. Metabolite concentrations correlated with specific histopathological features: taurine was correlated with differentiation, total-choline and phosphocholine with retrolaminar optic nerve invasion, and total lipids with necrosis. CONCLUSIONS: We have demonstrated that a metabolite-based classification of retinoblastoma can be obtained using ex vivo magnetic resonance spectroscopy, and that the subgroups identified correlate with histopathological features. This result justifies future studies to validate the clinical relevance of these subgroups and highlights the potential of in vivo MRS as a noninvasive diagnostic tool for retinoblastoma patient stratification

A Novel Rat Model of ADHD-like Hyperactivity/Impulsivity after Delayed Reward Has Selective Loss of Dopaminergic Neurons in the Right Ventral Tegmental Area

In attention deficit hyperactivity disorder (ADHD), hyperactivity and impulsivity occur in response to delayed reward. Herein we report a novel animal model in which male Sprague-Dawley rats exposed to repeated hypoxic brain injury during the equivalent of extreme prematurity were ADHD-like hyperactive/impulsive in response to delayed reward and attentive at 3 months of age. Thus, a unique animal model of one of the presentations/subtypes of ADHD was discovered. An additional finding is that the repeated hypoxia rats were not hyperactive in the widely used open field test, which is not ADHD specific. Hence, it is recommended that ADHD-like hyperactivity and ADHD-like impulsivity, specifically in response to delayed reward, be a primary component in the design of future experiments that characterize potential animal models of ADHD, replacing open field testing of hyperactivity. Unknown is whether death and/or activity of midbrain dopaminergic neurons contributed to the ADHD-like hyperactivity/impulsivity detected after delayed reward. Hence, we stereologically measured the absolute number of dopaminergic neurons in four midbrain subregions and the average somal/nuclear volume of those neurons. Repeated hypoxia rats had a significant specific loss of dopaminergic neurons in the right ventral tegmental area (VTA) at 2 weeks of age and 18 months of age, providing new evidence of a site of pathology. No dopaminergic neuronal loss occurred in three other midbrain regions. Fewer VTA dopaminergic neurons correlated with increased ADHD-like hyperactivity and impulsivity. Novel early intervention therapies to rescue VTA dopaminergic neurons and potentially prevent ADHD-like hyperactivity/impulsivity can now be investigated