Molecular subgroups of medulloblastoma identification using noninvasive magnetic resonance spectroscopy

Characterisation of paediatric brain tumours by their MRS metabolite profiles

1H-magnetic resonance spectroscopy (MRS) has the potential to improve the noninvasive diagnostic accuracy for paediatric brain tumours. However, studies analysing large, comprehensive, multicentre datasets are lacking, hindering translation to widespread clinical practice. Single-voxel MRS (point-resolved single-voxel spectroscopy sequence, 1.5 T: echo time [TE] 23-37 ms/135-144 ms, repetition time [TR] 1500 ms; 3 T: TE 37-41 ms/135-144 ms, TR 2000 ms) was performed from 2003 to 2012 during routine magnetic resonance imaging for a suspected brain tumour on 340 children from five hospitals with 464 spectra being available for analysis and 281 meeting quality control. Mean spectra were generated for 13 tumour types. Mann-Whitney U-tests and Kruskal-Wallis tests were used to compare mean metabolite concentrations. Receiver operator characteristic curves were used to determine the potential for individual metabolites to discriminate between specific tumour types. Principal component analysis followed by linear discriminant analysis was used to construct a classifier to discriminate the three main central nervous system tumour types in paediatrics. Mean concentrations of metabolites were shown to differ significantly between tumour types. Large variability existed across each tumour type, but individual metabolites were able to aid discrimination between some tumour types of importance. Complete metabolite profiles were found to be strongly characteristic of tumour type and, when combined with the machine learning methods, demonstrated a diagnostic accuracy of 93% for distinguishing between the three main tumour groups (medulloblastoma, pilocytic astrocytoma and ependymoma). The accuracy of this approach was similar even when data of marginal quality were included, greatly reducing the proportion of MRS excluded for poor quality. Children's brain tumours are strongly characterised by MRS metabolite profiles readily acquired during routine clinical practice, and this information can be used to support noninvasive diagnosis. This study provides both key evidence and an important resource for the future use of MRS in the diagnosis of children's brain tumours.

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

BACKGROUND

The 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).

METHODS

Metabolite 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.

FINDINGS

Group-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).

INTERPRETATION

Tissue 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.

FUNDING

Children with Cancer UK, Cancer Research UK, Children's Cancer North and a Newcastle University PhD studentship.

Group-specific cellular metabolism in Medulloblastoma

BACKGROUND

Cancer metabolism influences multiple aspects of tumorigenesis and causes diversity across malignancies. Although comprehensive research has extended our knowledge of molecular subgroups in medulloblastoma (MB), discrete analysis of metabolic heterogeneity is currently lacking. This study seeks to improve our understanding of metabolic phenotypes in MB and their impact on patients' outcomes.

METHODS

Data from four independent MB cohorts encompassing 1,288 patients were analysed. We explored metabolic characteristics of 902 patients (ICGC and MAGIC cohorts) on bulk RNA level. Moreover, data from 491 patients (ICGC cohort) were searched for DNA alterations in genes regulating cell metabolism. To determine the role of intratumoral metabolic differences, we examined single-cell RNA-sequencing (scRNA-seq) data from 34 additional patients. Findings on metabolic heterogeneity were correlated to clinical data.

RESULTS

Established MB groups exhibit substantial differences in metabolic gene expression. By employing unsupervised analyses, we identified three clusters of group 3 and 4 samples with distinct metabolic features in ICGC and MAGIC cohorts. Analysis of scRNA-seq data confirmed our results of intertumoral heterogeneity underlying the according differences in metabolic gene expression. On DNA level, we discovered clear associations between altered regulatory genes involved in MB development and lipid metabolism. Additionally, we determined the prognostic value of metabolic gene expression in MB and showed that expression of genes involved in metabolism of inositol phosphates and nucleotides correlates with patient survival.

CONCLUSION

Our research underlines the biological and clinical relevance of metabolic alterations in MB. Thus, distinct metabolic signatures presented here might be the first step towards future metabolism-targeted therapeutic options.

Lipid Metabolic Reprogramming in Embryonal Neoplasms with MYCN Amplification

Tumor cells reprogram their metabolism, including glucose, glutamine, nucleotide, lipid, and amino acids to meet their enhanced energy demands, redox balance, and requirement of biosynthetic substrates for uncontrolled cell proliferation. Altered lipid metabolism in cancer provides lipids for rapid membrane biogenesis, generates the energy required for unrestricted cell proliferation, and some of the lipids act as signaling pathway mediators. In this review, we focus on the role of lipid metabolism in embryonal neoplasms with MYCN dysregulation. We specifically review lipid metabolic reactions in neuroblastoma, retinoblastoma, medulloblastoma, Wilms tumor, and rhabdomyosarcoma and the possibility of targeting lipid metabolism. Additionally, the regulation of lipid metabolism by the MYCN oncogene is discussed.

Identifying new biomarkers of aggressive Group 3 and SHH medulloblastoma using 3D hydrogel models, single cell RNA sequencing and 3D OrbiSIMS imaging

The most common malignant brain tumour in children, medulloblastoma (MB), is subdivided into four clinically relevant molecular subgroups, although targeted therapy options informed by understanding of different cellular features are lacking. Here, by comparing the most aggressive subgroup (Group 3) with the intermediate (SHH) subgroup, we identify crucial differences in tumour heterogeneity, including unique metabolism-driven subpopulations in Group 3 and matrix-producing subpopulations in SHH. To analyse tumour heterogeneity, we profiled individual tumour nodules at the cellular level in 3D MB hydrogel models, which recapitulate subgroup specific phenotypes, by single cell RNA sequencing (scRNAseq) and 3D OrbiTrap Secondary Ion Mass Spectrometry (3D OrbiSIMS) imaging. In addition to identifying known metabolites characteristic of MB, we observed intra- and internodular heterogeneity and identified subgroup-specific tumour subpopulations. We showed that extracellular matrix factors and adhesion pathways defined unique SHH subpopulations, and made up a distinct shell-like structure of sulphur-containing species, comprising a combination of small leucine-rich proteoglycans (SLRPs) including the collagen organiser lumican. In contrast, the Group 3 tumour model was characterized by multiple subpopulations with greatly enhanced oxidative phosphorylation and tricarboxylic acid (TCA) cycle activity. Extensive TCA cycle metabolite measurements revealed very high levels of succinate and fumarate with malate levels almost undetectable particularly in Group 3 tumour models. In patients, high fumarate levels (NMR spectroscopy) alongside activated stress response pathways and high Nuclear Factor Erythroid 2-Related Factor 2 (NRF2; gene expression analyses) were associated with poorer survival. Based on these findings we predicted and confirmed that NRF2 inhibition increased sensitivity to vincristine in a long-term 3D drug treatment assay of Group 3 MB. Thus, by combining scRNAseq and 3D OrbiSIMS in a relevant model system we were able to define MB subgroup heterogeneity at the single cell level and elucidate new druggable biomarkers for aggressive Group 3 and low-risk SHH MB.

Lipid Alterations in Glioma: A Systematic Review

Gliomas are highly lethal tumours characterised by heterogeneous molecular features, producing various metabolic phenotypes leading to therapeutic resistance. Lipid metabolism reprogramming is predominant and has contributed to the metabolic plasticity in glioma. This systematic review aims to discover lipids alteration and their biological roles in glioma and the identification of potential lipids biomarker. This systematic review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Extensive research articles search for the last 10 years, from 2011 to 2021, were conducted using four electronic databases, including PubMed, Web of Science, CINAHL and ScienceDirect. A total of 158 research articles were included in this study. All studies reported significant lipid alteration between glioma and control groups, impacting glioma cell growth, proliferation, drug resistance, patients' survival and metastasis. Different lipids demonstrated different biological roles, either beneficial or detrimental effects on glioma. Notably, prostaglandin (PGE2), triacylglycerol (TG), phosphatidylcholine (PC), and sphingosine-1-phosphate play significant roles in glioma development. Conversely, the most prominent anti-carcinogenic lipids include docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and vitamin D3 have been reported to have detrimental effects on glioma cells. Furthermore, high lipid signals were detected at 0.9 and 1.3 ppm in high-grade glioma relative to low-grade glioma. This evidence shows that lipid metabolisms were significantly dysregulated in glioma. Concurrent with this knowledge, the discovery of specific lipid classes altered in glioma will accelerate the development of potential lipid biomarkers and enhance future glioma therapeutics.

The role of apparent diffusion coefficient histogram metrics for differentiating pediatric medulloblastoma histological variants and molecular groups

BACKGROUND

Medulloblastoma, a high-grade embryonal tumor, is the most common primary brain malignancy in the pediatric population. Molecular medulloblastoma groups have documented clinically and biologically relevant characteristics. Several authors have attempted to differentiate medulloblastoma molecular groups and histology variants using diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps. However, literature on the use of ADC histogram analysis in medulloblastomas is still scarce.

OBJECTIVE

This study presents data from a sizable group of pediatric patients with medulloblastoma from a single institution to determine the performance of ADC histogram metrics for differentiating medulloblastoma variants and groups based on both histological and molecular features.

MATERIALS AND METHODS

In this retrospective study, we evaluated the distribution of absolute and normalized ADC values of medulloblastomas. Tumors were manually segmented and diffusivity metrics calculated on a pixel-by-pixel basis. We calculated a variety of first-order histogram metrics from the ADC maps, including entropy, minimum, 10th percentile, 90th percentile, maximum, mean, median, skewness and kurtosis, to differentiate molecular and histological variants. ADC values of the tumors were also normalized to the bilateral cerebellar cortex and thalami. We used the Kruskal-Wallis and Mann-Whitney U tests to evaluate differences between the groups. We carried out receiver operating characteristic (ROC) curve analysis to evaluate the areas under the curves and to determine the cut-off values for differentiating tumor groups.

RESULTS

We found 65 children with confirmed histopathological diagnosis of medulloblastoma. Mean age was 8.3 ± 5.8 years, and 60% (n = 39) were male. One child was excluded because histopathological variant could not be determined. In terms of medulloblastoma variants, tumors were classified as classic (n = 47), desmoplastic/nodular (n = 9), large/cell anaplastic (n = 6) or as having extensive nodularity (n = 2). Seven other children were excluded from the study because of incomplete imaging or equivocal molecular diagnosis. Regarding medulloblastoma molecular groups, there were: wingless (WNT) group (n = 7), sonic hedgehog (SHH) group (n = 14) and non-WNT/non-SHH (n = 36). Our results showed significant differences among the molecular groups in terms of the median (P = 0.002), mean (P = 0.003) and 90th percentile (P = 0.002) ADC histogram metrics. No significant differences among the various medulloblastoma histological variants were found.

CONCLUSION

ADC histogram analysis can be implemented as a complementary tool in the preoperative evaluation of medulloblastoma in children. This technique can provide valuable information for differentiating among medulloblastoma molecular groups. ADC histogram metrics can help predict medulloblastoma molecular classification preoperatively.

Application of Apparent Diffusion Coefficient Histogram Metrics for Differentiation of Pediatric Posterior Fossa Tumors : A Large Retrospective Study and Brief Review of Literature

PURPOSE

This study aimed to evaluate the application of apparent diffusion coefficient (ADC) histogram analysis to differentiate posterior fossa tumors (PFTs) in children.

METHODS

A total of 175 pediatric patients with PFT, including 75 pilocytic astrocytomas (PA), 59 medulloblastomas, 16 ependymomas, and 13 atypical teratoid rhabdoid tumors (ATRT), were analyzed. Tumors were visually assessed using DWI trace and conventional MRI images and manually segmented and post-processed using parametric software (pMRI). Furthermore, tumor ADC values were normalized to the thalamus and cerebellar cortex. The following histogram metrics were obtained: entropy, minimum, 10th, and 90th percentiles, maximum, mean, median, skewness, and kurtosis to distinguish the different types of tumors. Kruskal Wallis and Mann-Whitney U tests were used to evaluate the differences. Finally, receiver operating characteristic (ROC) curves were utilized to determine the optimal cut-off values for differentiating the various PFTs.

RESULTS

Most ADC histogram metrics showed significant differences between PFTs (p < 0.001) except for entropy, skewness, and kurtosis. There were significant pairwise differences in ADC metrics for PA versus medulloblastoma, PA versus ependymoma, PA versus ATRT, medulloblastoma versus ependymoma, and ependymoma versus ATRT (all p < 0.05). Our results showed no significant differences between medulloblastoma and ATRT. Normalized ADC data showed similar results to the absolute ADC value analysis. ROC curve analysis for normalized ADC_median values to thalamus showed 94.9% sensitivity (95% CI: 85-100%) and 93.3% specificity (95% CI: 87-100%) for differentiating medulloblastoma from ependymoma.

CONCLUSION

ADC histogram metrics can be applied to differentiate most types of posterior fossa tumors in children.

Pathological implications of metabolic reprogramming and its therapeutic potential in medulloblastoma

Tumor-specific alterations in metabolism have been recognized to sustain the production of ATP and macromolecules needed for cell growth, division and survival in many cancer types. However, metabolic heterogeneity poses a challenge for the establishment of effective anticancer therapies that exploit metabolic vulnerabilities. Medulloblastoma (MB) is one of the most heterogeneous malignant pediatric brain tumors, divided into four molecular subgroups (Wingless, Sonic Hedgehog, Group 3 and Group 4). Recent progresses in genomics, single-cell sequencing, and novel tumor models have updated the classification and stratification of MB, highlighting the complex intratumoral cellular diversity of this cancer. In this review, we emphasize the mechanisms through which MB cells rewire their metabolism and energy production networks to support and empower rapid growth, survival under stressful conditions, invasion, metastasis, and resistance to therapy. Additionally, we discuss the potential clinical benefits of currently available drugs that could target energy metabolism to suppress MB progression and increase the efficacy of the current MB therapies.

Metabolic determinants of stemness in medulloblastoma

Medulloblastomas (MBs) are the most prevalent brain tumours in children. They are classified as grade IV, the highest in malignancy, with about 30% metastatic tumours at the time of diagnosis. Cancer stem cells (CSCs) are a small subset of tumour cells that can initiate and support tumour growth. In MB, CSCs contribute to tumour initiation, metastasis, and therapy resistance. Metabolic differences among the different MB groups have started to emerge. Sonic hedgehog tumours show enriched lipid and nucleic acid metabolism pathways, whereas Group 3 MBs upregulate glycolysis, gluconeogenesis, glutamine anabolism, and glutathione-mediated anti-oxidant pathways. Such differences impact the clinical behaviour of MB tumours and can be exploited therapeutically. In this review, we summarise the existing knowledge about metabolic rewiring in MB, with a particular focus on MB-CSCs. Finally, we highlight some of the emerging metabolism-based therapeutic strategies for MB.

Advanced Neuroimaging Approaches to Pediatric Brain Tumors

Simple Summary

After leukemias, brain tumors are the most common cancers in children, and early, accurate diagnosis is critical to improve patient outcomes. Beyond the conventional imaging methods of computed tomography (CT) and magnetic resonance imaging (MRI), advanced neuroimaging techniques capable of both structural and functional imaging are moving to the forefront to improve the early detection and differential diagnosis of tumors of the central nervous system. Here, we review recent developments in neuroimaging techniques for pediatric brain tumors.

Abstract

Central nervous system tumors are the most common pediatric solid tumors; they are also the most lethal. Unlike adults, childhood brain tumors are mostly primary in origin and differ in type, location and molecular signature. Tumor characteristics (incidence, location, and type) vary with age. Children present with a variety of symptoms, making early accurate diagnosis challenging. Neuroimaging is key in the initial diagnosis and monitoring of pediatric brain tumors. Conventional anatomic imaging approaches (computed tomography (CT) and magnetic resonance imaging (MRI)) are useful for tumor detection but have limited utility differentiating tumor types and grades. Advanced MRI techniques (diffusion-weighed imaging, diffusion tensor imaging, functional MRI, arterial spin labeling perfusion imaging, MR spectroscopy, and MR elastography) provide additional and improved structural and functional information. Combined with positron emission tomography (PET) and single-photon emission CT (SPECT), advanced techniques provide functional information on tumor metabolism and physiology through the use of radiotracer probes. Radiomics and radiogenomics offer promising insight into the prediction of tumor subtype, post-treatment response to treatment, and prognostication. In this paper, a brief review of pediatric brain cancers, by type, is provided with a comprehensive description of advanced imaging techniques including clinical applications that are currently utilized for the assessment and evaluation of pediatric brain tumors.

Proton MR Spectroscopy of Pediatric Brain Disorders

In vivo MR spectroscopy is a non -invasive methodology that provides information about the biochemistry of tissues. It is available as a “push-button” application on state-of-the-art clinical MR scanners. MR spectroscopy has been used to study various brain diseases including tumors, stroke, trauma, degenerative disorders, epilepsy/seizures, inborn errors, neuropsychiatric disorders, and others. The purpose of this review is to provide an overview of MR spectroscopy findings in the pediatric population and its clinical use.

Metabolite selection for machine learning in childhood brain tumour classification

MRS can provide high accuracy in the diagnosis of childhood brain tumours when combined with machine learning. A feature selection method such as principal component analysis is commonly used to reduce the dimensionality of metabolite profiles prior to classification. However, an alternative approach of identifying the optimal set of metabolites has not been fully evaluated, possibly due to the challenges of defining this for a multi-class problem. This study aims to investigate metabolite selection from in vivo MRS for childhood brain tumour classification. Multi-site 1.5 T and 3 T cohorts of patients with a brain tumour and histological diagnosis of ependymoma, medulloblastoma and pilocytic astrocytoma were retrospectively evaluated. Dimensionality reduction was undertaken by selecting metabolite concentrations through multi-class receiver operating characteristics and compared with principal component analysis. Classification accuracy was determined through leave-one-out and k-fold cross-validation. Metabolites identified as crucial in tumour classification include myo-inositol (P < 0.05, AUC = 0 . 81 ± 0 . 01 ), total lipids and macromolecules at 0.9 ppm (P < 0.05, AUC = 0 . 78 ± 0 . 01 ) and total creatine (P < 0.05, AUC = 0 . 77 ± 0 . 01 ) for the 1.5 T cohort, and glycine (P < 0.05, AUC = 0 . 79 ± 0 . 01 ), total N-acetylaspartate (P < 0.05, AUC = 0 . 79 ± 0 . 01 ) and total choline (P < 0.05, AUC = 0 . 75 ± 0 . 01 ) for the 3 T cohort. Compared with the principal components, the selected metabolites were able to provide significantly improved discrimination between the tumours through most classifiers (P < 0.05). The highest balanced classification accuracy determined through leave-one-out cross-validation was 85% for 1.5 T 1 H-MRS through support vector machine and 75% for 3 T 1 H-MRS through linear discriminant analysis after oversampling the minority. The study suggests that a group of crucial metabolites helps to achieve better discrimination between childhood brain tumours.

Imaging diagnosis and treatment selection for brain tumors in the era of molecular therapeutics

Currently, most CNS tumors require tissue sampling to discern their molecular/genomic landscape. However, growing research has shown the powerful role imaging can play in non-invasively and accurately detecting the molecular signature of these tumors. The overarching theme of this review article is to provide neuroradiologists and neurooncologists with a framework of several important molecular markers, their associated imaging features and the accuracy of those features. A particular emphasis is placed on those tumors and mutations that have specific or promising imaging correlates as well as their respective therapeutic potentials.

MR Imaging of Pediatric Brain Tumors

Primary brain tumors are the most common solid neoplasms in children and a leading cause of mortality in this population. MRI plays a central role in the diagnosis, characterization, treatment planning, and disease surveillance of intracranial tumors. The purpose of this review is to provide an overview of imaging methodology, including conventional and advanced MRI techniques, and illustrate the MRI appearances of common pediatric brain tumors.

Added value of magnetic resonance spectroscopy for diagnosing childhood cerebellar tumours

¹ H-magnetic resonance spectroscopy (MRS) provides noninvasive metabolite profiles with the potential to aid the diagnosis of brain tumours. Prospective studies of diagnostic accuracy and comparisons with conventional MRI are lacking. The aim of the current study was to evaluate, prospectively, the diagnostic accuracy of a previously established classifier for diagnosing the three major childhood cerebellar tumours, and to determine added value compared with standard reporting of conventional imaging. Single-voxel MRS (1.5 T, PRESS, TE 30 ms, TR 1500 ms, spectral resolution 1 Hz/point) was acquired prospectively on 39 consecutive cerebellar tumours with histopathological diagnoses of pilocytic astrocytoma, ependymoma or medulloblastoma. Spectra were analysed with LCModel and predefined quality control criteria were applied, leaving 33 cases in the analysis. The MRS diagnostic classifier was applied to this dataset. A retrospective analysis was subsequently undertaken by three radiologists, blind to histopathological diagnosis, to determine the change in diagnostic certainty when sequentially viewing conventional imaging, MRS and a decision support tool, based on the classifier. The overall classifier accuracy, evaluated prospectively, was 91%. Incorrectly classified cases, two anaplastic ependymomas, and a rare histological variant of medulloblastoma, were not well represented in the original training set. On retrospective review of conventional MRI, MRS and the classifier result, all radiologists showed a significant increase (Wilcoxon signed rank test, p < 0.001) in their certainty of the correct diagnosis, between viewing the conventional imaging and MRS with the decision support system. It was concluded that MRS can aid the noninvasive diagnosis of posterior fossa tumours in children, and that a decision support classifier helps in MRS interpretation.

Magnetic Resonance Imaging in the Contemporary Management of Medulloblastoma: Current and Emerging Applications

Medulloblastoma, the most common malignant primary brain tumor in children, is now considered to comprise of four distinct molecular subgroups-wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4 medulloblastoma, each associated with distinct developmental origins, unique transcriptional profiles, diverse phenotypes, and variable clinical behavior. Due to its exquisite anatomic resolution, multiparametric nature, and ability to image the entire craniospinal axis, magnetic resonance imaging (MRI) is the preferred and recommended first-line imaging modality for suspected brain tumors including medulloblastoma. Preoperative MRI can reliably differentiate medulloblastoma from other common childhood posterior fossa masses such as ependymoma, pilocytic astrocytoma, and brainstem glioma. On T1-weighted images, medulloblastoma is generally iso- to hypointense, while on T2-weighted images, the densely packed cellular component of the tumor is significantly hypointense and displays restricted diffusion on diffusion-weighted imaging. Following intravenous gadolinium, medulloblastoma shows significant but variable and heterogeneous contrast enhancement. Given the propensity of neuraxial spread in medulloblastoma, sagittal fat-suppressed T1-postcontrast spinal MRI is recommended to rule out leptomeningeal metastases for accurate staging. Following neurosurgical excision, postoperative MRI done within 24-48 h confirms the extent of resection, accurately quantifying residual tumor burden imperative for risk assignment. Post-treatment MRI is needed to assess response and effectiveness of adjuvant radiotherapy and systemic chemotherapy. After completion of planned therapy, surveillance MRI is recommended periodically on follow-up for early detection of recurrence for timely institution of salvage therapy, as well as for monitoring treatment-related late complications. Recent studies suggest that preoperative MRI can reliably identify SHH and Group 4 medulloblastoma but has suboptimal predictive accuracy for WNT and Group 3 tumors. In this review, we focus on the role of MRI in the diagnosis, staging, and quantifying residual disease; post-treatment response assessment; and periodic surveillance, and provide a brief summary on radiogenomics in the contemporary management of medulloblastoma.

Proton and Multinuclear Spectroscopy of the Pediatric Brain

Magnetic resonance spectroscopy (MRS) is a valuable adjunct to structural brain imaging. State-of-the-art MRS has benefited greatly from recent technical advancements. Neurometabolic alterations in pediatric brain diseases have implications for diagnosis, prognosis, and therapy. Herein, the authors discuss MRS technical considerations and applications in the setting of various pediatric disease processes including tumors, metabolic diseases, hypoxic/ischemic encephalopathy/stroke, epilepsy, demyelinating disease, and infection.

Extra-axial tentorial medulloblastoma: a rare presentation of a common posterior fossa tumour

Medulloblastoma is a common paediatric posterior fossa tumour typically presenting as midline intra-axial mass involving the cerebellar vermis and/or roof of fourth ventricle with typical radiological features. These can be extra-axial in extremely rare instances with less than 50 cases reported so far in literature. We present a case of 18-year-old boy presenting with ataxia and headache. MRI showed dural mass (involving the left tentorium cerebellum) with typical imaging features of extra-axial lesion. The patient underwent near total excision of the tumour. Histopathology along with immunohistochemistry revealed the mass to be medulloblastoma. We present this case to highlight rarity of this location for medulloblastoma and the importance of considering this in the differential diagnosis of atypical posterior fossa extra-axial lesions. This can help in performing other relevant preoperative workup similar on the lines of medulloblastoma and planning of relevant management.

Mapping of Metabolic Heterogeneity of Glioma Using MR-Spectroscopy

Simple Summary

Radiomics is a research field that integrates radiological and genetic information, but the application of the techniques that have been developed to this purpose have not been widely established in daily clinical practice. The purpose of our study is the development of a straightforward tool that can easily be used to preoperatively predict and correlate the metabolic signature of different CNS-lesions. Particularly in gliomas, we hope to integrate the molecular profile of these tumors into our prediction model. Our goal is to deliver an open-software tool with the intention of advancing the diagnostic work-up of gliomas to the latest standards.

Abstract

Proton magnetic resonance spectroscopy (¹H-MRS) delivers information about the non-invasive metabolic landscape of brain pathologies. ¹H-MRS is used in clinical setting in addition to MRI for diagnostic, prognostic and treatment response assessments, but the use of this radiological tool is not entirely widespread. The importance of developing automated analysis tools for ¹H-MRS lies in the possibility of a straightforward application and simplified interpretation of metabolic and genetic data that allow for incorporation into the daily practice of a broad audience. Here, we report a prospective clinical imaging trial (DRKS00019855) which aimed to develop a novel MR-spectroscopy-based algorithm for in-depth characterization of brain lesions and prediction of molecular traits. Dimensional reduction of metabolic profiles demonstrated distinct patterns throughout pathologies. We combined a deep autoencoder and multi-layer linear discriminant models for voxel-wise prediction of the molecular profile based on MRS imaging. Molecular subtypes were predicted by an overall accuracy of 91.2% using a classifier score. Our study indicates a first step into combining the metabolic and molecular traits of lesions for advancing the pre-operative diagnostic workup of brain tumors and improve personalized tumor treatment.

Posterior fossa extra-axial variations of medulloblastoma: a pictorial review as a primer for radiologists

Manifestations of an atypical variant of medulloblastoma of the posterior fossa in extra-axial locations have been reported, and key questions concerning its interpretation have been raised previously. This review illustrated the clinico-radiological and histopathological features of the posterior fossa extra-axial medulloblastoma and described possible management strategies. We thoroughly reviewed all atypical anatomical locations of medulloblastoma reported within the posterior fossa and extra-axial spaces. The main characteristics of diagnostic imaging and histopathological results, primarily the distinctive radiopathological characteristics, were summarized to distinguish between intra- and extra-axial medulloblastoma, or pathologies mimicking this tumor. Most cases of posterior fossa extra-axial medulloblastoma have been reported in the cerebellopontine angle, followed by the tentorial and lateral cerebellar locations. The dural tail sign, which is commonly observed in meningioma, is rarely seen in intra- or extra-axial medulloblastoma and might be associated with other benign or malignant lesions. In addition to magnetic resonance imaging, the proposed new imaging techniques, including advances in modern neuroimaging modalities, were discussed, as potentially efficient modalities for characterizing extra-axial medulloblastoma. Radionuclide imaging and magnetic resonance perfusion imaging are practical alternatives to limit the number of differential diagnoses. We believe that medulloblastoma cases are likely under-reported because of publication bias and frequent tumors in unusual locations. Addressing these issues would help establish a more accurate understanding of this entity.

Imaging of brain tumors in children: the basics-a narrative review

Primary pediatric brain tumors comprise a broad group of neoplasm subtypes that can be categorized based on their histological and molecular features according to the 2016 World Health Organization (WHO) classification of central nervous system (CNS) tumors. The majority of the pediatric brain tumors demonstrate a singular preference for this age group and have a unique molecular profile. The separation of certain tumor entities, including different types of embryonal tumors, low-grade gliomas, and high-grade gliomas, may have a significant impact by guiding appropriate treatment for these children and potentially changing their outcomes. Currently, the focus of the imaging diagnostic studies is to follow the molecular updates, searching for potential imaging patterns that translate this information in molecular profile results, therefore helping the final diagnosis. Due to the high impact of accurate diagnosis in this context, the scientific community has presented extensive research on imaging pediatric tumors in recent years. This article summarizes the key characteristics of the imaging features of the most common primary childhood brain tumors, categorizing them according to the recent WHO classification update, which is based on each of their molecular profiles. The purpose of this review article is to familiarize radiologists with their key imaging features and thereby improve diagnostic accuracy.

Advancements in Neuroimaging to Unravel Biological and Molecular Features of Brain Tumors

Simple Summary

Advanced neuroimaging is gaining increasing relevance for the characterization and the molecular profiling of brain tumor tissue. On one hand, for some tumor types, the most widespread advanced techniques, investigating diffusion and perfusion features, have been proven clinically feasible and rather robust for diagnosis and prognosis stratification. In addition, 2-hydroxyglutarate spectroscopy, for the first time, offers the possibility to directly measure a crucial molecular marker. On the other hand, numerous innovative approaches have been explored for a refined evaluation of tumor microenvironments, particularly assessing microstructural and microvascular properties, and the potential applications of these techniques are vast and still to be fully explored.

Abstract

In recent years, the clinical assessment of primary brain tumors has been increasingly dependent on advanced magnetic resonance imaging (MRI) techniques in order to infer tumor pathophysiological characteristics, such as hemodynamics, metabolism, and microstructure. Quantitative radiomic data extracted from advanced MRI have risen as potential in vivo noninvasive biomarkers for predicting tumor grades and molecular subtypes, opening the era of “molecular imaging” and radiogenomics. This review presents the most relevant advancements in quantitative neuroimaging of advanced MRI techniques, by means of radiomics analysis, applied to primary brain tumors, including lower-grade glioma and glioblastoma, with a special focus on peculiar oncologic entities of current interest. Novel findings from diffusion MRI (dMRI), perfusion-weighted imaging (PWI), and MR spectroscopy (MRS) are hereby sifted in order to evaluate the role of quantitative imaging in neuro-oncology as a tool for predicting molecular profiles, stratifying prognosis, and characterizing tumor tissue microenvironments. Furthermore, innovative technological approaches are briefly addressed, including artificial intelligence contributions and ultra-high-field imaging new techniques. Lastly, after providing an overview of the advancements, we illustrate current clinical applications and future perspectives.

Medulloblastoma: "Onset of the molecular era"

Among brain tumors, Medulloblastoma (MB) is one of the most common, malignant, pediatric tumors of the cerebellum. It accounts for ~20% of all childhood central nervous system (CNS) tumors. Despite, tremendous advances in drug development processes, as well as novel drugs for MB the morbidity and mortality rates, remain high. Craniospinal radiation, high-dose chemotherapy, and surgical resection are the primary therapeutic strategies. Tremendous progress in the field of "genomics" with vast amounts of data has led to the identification of four distinct molecular subgroups in medulloblastoma: WNT group, SHH group, group-III, and group-IV. The identification of these subgroups has led to individualized treatment strategies for each subgroup. Here, we discuss the various molecular subgroups of medulloblastoma as well as the differences between them. We also highlight the latest treatment strategies available for medulloblastoma.

Radiomic Features From Multi-Parameter MRI Combined With Clinical Parameters Predict Molecular Subgroups in Patients With Medulloblastoma

The 2016 WHO classification of central nervous system tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3, and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multi-parameter magnetic resonance imaging (MRI) radiomics, tumor locations, and clinical factors. A total of 122 MB patients were enrolled retrospectively. After selecting robust, non-redundant, and relevant features from 5,529 extracted radiomics features, a random forest model was constructed based on a training cohort (n = 92) and evaluated on a testing cohort (n = 30). By combining radiographic features and clinical parameters, two combined prediction models were also built. The subgroup can be classified using an 11-feature radiomics model with a high area under the curve (AUC) of 0.8264 for WNT and modest AUCs of 0.6683, 0.6004, and 0.6979 for SHH, Group 3, and Group 4 in the testing cohort, respectively. Incorporating location and hydrocephalus into the radiomics model resulted in improved AUCs of 0.8403 and 0.8317 for WNT and SHH, respectively. After adding gender and age, the AUCs for WNT and SHH were further improved to 0.9097 and 0.8654, while the accuracies were 70 and 86.67% for Group 3 and Group 4, respectively. Prediction performance was excellent for WNT and SHH, while that for Group 3 and Group 4 needs further improvements. Machine learning algorithms offer potentials to non-invasively predict the molecular subgroups of MB.

Updates in Pediatric Malignant Gliomas

Malignant gliomas constitute a smaller portion of brain tumors in children compared with adults. Nevertheless, they can be devastating tumors with poor prognosis. Recent advances and improved understanding of the genetic and molecular characterization of pediatric brain tumors, including those of malignant gliomas, have led to the reclassification of many pediatric brain tumors and new entities have been defined. In this paper, we will present some of the more recent characterization and pertinent changes in pediatric high-grade gliomas, along with the conventional and advanced imaging features associated with these entities. Implications of the recent changes in pediatric malignant glioma classifications will also be discussed.

EANO-EURACAN clinical practice guideline for diagnosis, treatment, and follow-up of post-pubertal and adult patients with medulloblastoma

The European Association of Neuro-Oncology (EANO) and EUropean RAre CANcer (EURACAN) guideline provides recommendations for the diagnosis, treatment, and follow-up of post-pubertal and adult patients with medulloblastoma. The guideline is based on the 2016 WHO classification of tumours of the CNS and on scientific developments published since 1980. It aims to provide direction for diagnostic and management decisions, and for limiting unnecessary treatments and cost. In view of the scarcity of data in adults with medulloblastoma, we base our recommendations on adult data when possible, but also include recommendations derived from paediatric data if justified. Our recommendations are a resource for professionals involved in the management of post-pubertal and adult patients with medulloblastoma, for patients and caregivers, and for health-care providers in Europe. The implementation of this guideline requires multidisciplinary structures of care, and defined processes of diagnosis and treatment.

Nomograms based on preoperative multiparametric magnetic resonance imaging for prediction of molecular subgrouping in medulloblastoma: results from a radiogenomics study of 111 patients

Background

Novel biological insights have led to consensus classification of medulloblastoma into 4 distinct molecular subgroups-wingless (WNT), sonic hedgehog (SHH), Group 3, and Group 4. We aimed to predict molecular subgrouping in medulloblastoma based on preoperative multiparametric magnetic resonance imaging (MRI) characteristics.

Methods

A set of 19 MRI features were evaluated in 111 patients with histologic diagnosis of medulloblastoma for prediction of molecular subgrouping. MRI characteristics were correlated with molecular subgroups derived from tissue samples in 111 patients (WNT = 17, SHH = 44, Group 3 = 27, and Group 4 = 23). Multinomial logistic regression of imaging parameters was performed on a training cohort (TC) of 76 patients, representing two-thirds of randomly selected patients from each of 4 molecular subgroups, to generate binary nomograms. Validation of these nomograms was performed on the remaining 35 patients as the validation cohort (VC).

Results

Medulloblastoma subgroups could be accurately predicted by preoperative MRI features in 74% of cases. Predictive accuracy was excellent for SHH (95%), acceptably high for Group 4 (78%), modest for Group 3 (56%) and worst for WNT (41%) subgroup medulloblastoma. SHH-specific nomogram was associated with excellent correlation between TC and VC, with area under the curve (AUC) of 0.939 and 0.991, respectively. AUC for Group 4 was acceptable at 0.851 and 0.788 in TC and VC, respectively; however, these values were consistently suboptimal in WNT and Group 3 medulloblastoma.

Conclusion

The predictive accuracy of MRI-based nomograms was excellent for SHH and encouraging for Group 4 medulloblastoma. Further work is needed for Group 3 and WNT-pathway medulloblastoma.

Topics and trends in artificial intelligence assisted human brain research

Artificial intelligence (AI) assisted human brain research is a dynamic interdisciplinary field with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous growth in application scope of AI-assisted human brain research. A comprehensive understanding of this field is necessary to assess research efficacy, (re)allocate research resources, and conduct collaborations. This paper combines the structural topic modeling (STM) with the bibliometric analysis to automatically identify prominent research topics from the large-scale, unstructured text of AI-assisted human brain research publications in the past decade. Analyses on topical trends, correlations, and clusters reveal distinct developmental trends of these topics, promising research orientations, and diverse topical distributions in influential countries/regions and research institutes. These findings help better understand scientific and technological AI-assisted human brain research, provide insightful guidance for resource (re)allocation, and promote effective international collaborations.

Insights into the Chemical Biology of Childhood Embryonal Solid Tumors by NMR-Based Metabolomics

Most childhood cancers occur as isolated cases and show very different biological behavior when compared with cancers in adults. There are some solid tumors that occur almost exclusively in children among which stand out the embryonal solid tumors. These cancers main types are neuroblastoma, nephroblastoma (Wilms tumors), retinoblastoma and hepatoblastomas and tumors of the central nervous system (CNS). Embryonal solid tumors represent a heterogeneous group of cancers supposedly derived from undifferentiated cells, with histological features that resemble tissues of origin during embryogenesis. This key observation suggests that tumorigenesis might begin during early fetal or child life due to the errors in growth or pathways differentiation. There are not many literature data on genomic, transcriptomic, epigenetic, proteomic, or metabolomic differences in these types of cancers when compared to the omics- used in adult cancer research. Still, metabolomics by nuclear magnetic resonance (NMR) in childhood embryonal solid tumors research can contribute greatly to understand better metabolic pathways alterations and biology of the embryonal solid tumors and potential to be used in clinical applications. Different types of samples, such as tissues, cells, biofluids, mostly blood plasma and serum, can be analyzed by NMR to detect and identify cancer metabolic signatures and validated biomarkers using enlarged group of samples. The literature search for biomarkers points to around 20-30 compounds that could be associated with pediatric cancer as well as metastasis.

Cancer metabolism in a snapshot: MRS(I)

The contribution of MRS(I) to the in vivo evaluation of cancer-metabolism-derived metrics, mostly since 2016, is reviewed here. Increased carbon consumption by tumour cells, which are highly glycolytic, is now being sampled by ¹³ C magnetic resonance spectroscopic imaging (MRSI) following the injection of hyperpolarized [1-¹³ C] pyruvate (Pyr). Hot-spots of, mostly, increased lactate dehydrogenase activity or flow between Pyr and lactate (Lac) have been seen with cancer progression in prostate (preclinical and in humans), brain and pancreas (both preclinical) tumours. Therapy response is usually signalled by decreased Lac/Pyr ¹³ C-labelled ratio with respect to untreated or non-responding tumour. For therapeutic agents inducing tumour hypoxia, the ¹³ C-labelled Lac/bicarbonate ratio may be a better metric than the Lac/Pyr ratio. ³¹ P MRSI may sample intracellular pH changes from brain tumours (acidification upon antiangiogenic treatment, basification at fast proliferation and relapse). The steady state tumour metabolome pattern is still in use for cancer evaluation. Metrics used for this range from quantification of single oncometabolites (such as 2-hydroxyglutarate in mutant IDH1 glial brain tumours) to selected metabolite ratios (such as total choline to N-acetylaspartate (plain ratio or CNI index)) or the whole ¹ H MRSI(I) pattern through pattern recognition analysis. These approaches have been applied to address different questions such as tumour subtype definition, following/predicting the response to therapy or defining better resection or radiosurgery limits.

Pediatric Brain Tumor Genetics: What Radiologists Need to Know

Brain tumors are the most common solid tumors in the pediatric population. Pediatric neuro-oncology has changed tremendously during the past decade owing to ongoing genomic advances. The diagnosis, prognosis, and treatment of pediatric brain tumors are now highly reliant on the genetic profile and histopathologic features of the tumor rather than the histopathologic features alone, which previously were the reference standard. The clinical information expected to be gleaned from radiologic interpretations also has evolved. Imaging is now expected to not only lead to a relevant short differential diagnosis but in certain instances also aid in predicting the specific tumor and subtype and possibly the prognosis. These processes fall under the umbrella of radiogenomics. Therefore, to continue to actively participate in patient care and/or radiogenomic research, it is important that radiologists have a basic understanding of the molecular mechanisms of common pediatric central nervous system tumors. The genetic features of pediatric low-grade gliomas, high-grade gliomas, medulloblastomas, and ependymomas are reviewed; differences between pediatric and adult gliomas are highlighted; and the critical oncogenic pathways of each tumor group are described. The role of the mitogen-activated protein kinase pathway in pediatric low-grade gliomas and of histone mutations as epigenetic regulators in pediatric high-grade gliomas is emphasized. In addition, the oncogenic drivers responsible for medulloblastoma, the classification of ependymomas, and the associated imaging correlations and clinical implications are discussed. ^©RSNA, 2018.

Differential response of SHH-expressing adult medulloblastomas to the sonic hedgehog inhibitor vismodegib: whole-genome analysis

Medulloblastoma is an aggressive primitive neuroectodermal tumor of the cerebellum that is more common in children than in adults. In the past decade, advances in understanding the molecular drivers of medulloblastoma have identified four molecular subgroups defined by experimental gene expression profiles: the WNT pathway, sonic hedgehog (SHH) pathway, and subgroups 3 and 4 (non-SHH/WNT).  Medulloblastoma of adults belong primarily to the SHH category. Vismodegib, an SHH-pathway inhibitor, FDA-approved in 2012 for treatment of basal cell carcinoma, has been used successfully in the setting of chemorefractory medulloblastoma, but not as a first-line therapy. In 2016, we reported a case of an adult patient with a sustained response of an unresectable multifocal form of adult medulloblastoma to vismodegib. Molecular analysis in that case revealed mutations in TP53 and a cytogenetic abnormality, i17q, that is prevalent and most often associated with subgroup 4 rather than the SHH-activated form of medulloblastoma. Here, we report further whole-genome analysis of that patient (designated Patient A) as well as an additional adult patient (Patient B) whose tumor harbored the SHH molecular subgroup but which was unresponsive to visgmodegib therapy. Comparison of these disparate responses highlights the challenges to tailoring SHH-targeted treatment in individual patients with adult medulloblastoma.

Short-acquisition-time JPRESS and its application to paediatric brain tumours

OBJECTIVE

To develop and assess a short-duration JPRESS protocol for detection of overlapping metabolite biomarkers and its application to paediatric brain tumours at 3 Tesla.

MATERIALS AND METHODS

The short-duration protocol (6 min) was optimised and compared for spectral quality to a high-resolution (38 min) JPRESS protocol in a phantom and five healthy volunteers. The 6-min JPRESS was acquired from four paediatric brain tumours and compared with short-TE PRESS.

RESULTS

Metabolite identification between the 6- and 38-min protocols was comparable in phantom and volunteer data. For metabolites with Cramer-Rao lower bounds > 50%, interpretation of JPRESS increased confidence in assignment of lactate, myo-Inositol and scyllo-Inositol. JPRESS also showed promise for the detection of glycine and taurine in paediatric brain tumours when compared to short-TE MRS.

CONCLUSION

A 6-min JPRESS protocol is well tolerated in paediatric brain tumour patients. Visual inspection of a 6-min JPRESS spectrum enables identification of a range of metabolite biomarkers of clinical interest.

Magnetic resonance spectroscopy may serve as a presurgical predictor of somatostatin analog therapy response in patients with growth hormone-secreting pituitary macroadenomas

PURPOSE

Somatostatin analogs (SSAs) are considered one of the most effective medical treatments for patients with growth hormone-secreting pituitary adenomas (GH-PAs). The postoperative electron microscopy (EM) pathological subtype and SSTR2 expression in the tumor are the most established predictors of patient response to SSA therapy. The aim of this study was to evaluate how will magnetic resonance spectroscopy (MRS) measurements before surgery predict the EM pathological subtypes and SSTR2 expression of tumors, and thereby serve as an indicator for the therapeutic sensitivity to SSAs of patients with GH-PAs.

METHODS

Eighteen patients with GH pituitary macroadenomas who underwent transsphenoidal surgery were included in this retrospective study. The preoperative MRS data and T2 signal intensity were obtained from patients by 1.5 T MR spectroscopy of the sellar mass. The EM pathological subtypes of tumors were determined after surgery through examination of cell granulations. The expressions of somatostatin receptor 2 (SSTR2), SSTR5, P21, P27, and Ki-67 were evaluated by immunohistochemistry.

RESULTS

The MRS parameters that were found to significantly predict the EM pathological subtypes of tumors, as calculated by the receiver operating characteristic curve, were the choline (Ch) value at 3140.5 MR units (sensitivity 69.2%, specificity 100%) and the choline/creatine (Ch/Cr) ratio at 1.27 (sensitivity 92.3%, specificity 100%). Further, the Ch/Cr ratio, but not other MRS data, was shown to negatively correlate with the expression of SSTR2 (P = 0.02). The Ch/Cr ratio was also found to positively correlate with the Ki-67 value (P < 0.05) and T2 signal (P < 0.05), but not with other factors that were examined in this study. Moreover, the Ch/Cr ratio could predict the EM pathological subtypes of tumors with an accuracy of 83.3% (5/6) for patients with an isointense T2 signal.

CONCLUSION

The Ch/Cr ratio by MRS could effectively predict the tumor subtype and was significantly correlated with the expression of SSTR2, which was consistent with other predictors. It was also able to distinguish the patients with isointense T2 signals. Our results provide a potentially new and non-invasive method to predict the response to SSAs in patients with GH pituitary macroadenomas.

Childhood Medulloblastoma Revisited

Medulloblastoma is the most common malignant solid tumor in childhood and the most common embryonal neuroepithelial tumor of the central nervous system. Several morphological variants are recognized: classic medulloblastoma, large cell/anaplastic medulloblastoma, desmoplastic/nodular medulloblastoma, and medulloblastoma with extensive nodularity. Recent advances in transcriptome and methylome profiling of these tumors led to a molecular classification that includes 4 major genetically defined groups. Accordingly, the 2016 revision of the World Health Organization's Classification of Tumors of the Central Nervous System recognizes the following medulloblastoma entities: Wingless (WNT)-activated, Sonic hedgehog (SHH)-activated, Group 3, and Group 4. This transcriptionally driven classification constitutes the basis of new risk stratification schemes applied to current therapeutic clinical trials. Because additional layers of molecular tumor heterogeneities are being progressively unveiled, several clinically relevant subgroups within the 4 major groups have already been identified. The purpose of this article is to review the recent basic science and clinical advances in the understanding of "medulloblastomas," and their diagnostic imaging correlates and the implications of those on current neuroimaging practice.

The value of universally available raw NMR data for transparency, reproducibility, and integrity in natural product research

Covering: up to 2018With contributions from the global natural product (NP) research community, and continuing the Raw Data Initiative, this review collects a comprehensive demonstration of the immense scientific value of disseminating raw nuclear magnetic resonance (NMR) data, independently of, and in parallel with, classical publishing outlets. A comprehensive compilation of historic to present-day cases as well as contemporary and future applications show that addressing the urgent need for a repository of publicly accessible raw NMR data has the potential to transform natural products (NPs) and associated fields of chemical and biomedical research. The call for advancing open sharing mechanisms for raw data is intended to enhance the transparency of experimental protocols, augment the reproducibility of reported outcomes, including biological studies, become a regular component of responsible research, and thereby enrich the integrity of NP research and related fields.

Prospective multicentre evaluation and refinement of an analysis tool for magnetic resonance spectroscopy of childhood cerebellar tumours

BACKGROUND

A tool for diagnosing childhood cerebellar tumours using magnetic resonance (MR) spectroscopy peak height measurement has been developed based on retrospective analysis of single-centre data.

OBJECTIVE

To determine the diagnostic accuracy of the peak height measurement tool in a multicentre prospective study, and optimise it by adding new prospective data to the original dataset.

MATERIALS AND METHODS

Magnetic resonance imaging (MRI) and single-voxel MR spectroscopy were performed on children with cerebellar tumours at three centres. Spectra were processed using standard scanner software and peak heights for N-acetyl aspartate, creatine, total choline and myo-inositol were measured. The original diagnostic tool was used to classify 26 new tumours as pilocytic astrocytoma, medulloblastoma or ependymoma. These spectra were subsequently combined with the original dataset to develop an optimised scheme from 53 tumours in total.

RESULTS

Of the pilocytic astrocytomas, medulloblastomas and ependymomas, 65.4% were correctly assigned using the original tool. An optimized scheme was produced from the combined dataset correctly assigning 90.6%. Rare tumour types showed distinctive MR spectroscopy features.

CONCLUSION

The original diagnostic tool gave modest accuracy when tested prospectively on multicentre data. Increasing the dataset provided a diagnostic tool based on MR spectroscopy peak height measurement with high levels of accuracy for multicentre data.

Tissue metabolite profiles for the characterisation of paediatric cerebellar tumours

Paediatric brain tumors are becoming well characterized due to large genomic and epigenomic studies. Metabolomics is a powerful analytical approach aiding in the characterization of tumors. This study shows that common cerebellar tumors have metabolite profiles sufficiently different to build accurate, robust diagnostic classifiers, and that the metabolite profiles can be used to assess differences in metabolism between the tumors. Tissue metabolite profiles were obtained from cerebellar ependymoma (n = 18), medulloblastoma (n = 36), pilocytic astrocytoma (n = 24) and atypical teratoid/rhabdoid tumors (n = 5) samples using HR-MAS. Quantified metabolites accurately discriminated the tumors; classification accuracies were 94% for ependymoma and medulloblastoma and 92% for pilocytic astrocytoma. Using current intraoperative examination the diagnostic accuracy was 72% for ependymoma, 90% for medulloblastoma and 89% for pilocytic astrocytoma. Elevated myo-inositol was characteristic of ependymoma whilst high taurine, phosphocholine and glycine distinguished medulloblastoma. Glutamine, hypotaurine and N-acetylaspartate (NAA) were increased in pilocytic astrocytoma. High lipids, phosphocholine and glutathione were important for separating ATRTs from medulloblastomas. This study demonstrates the ability of metabolic profiling by HR-MAS on small biopsy tissue samples to characterize these tumors. Analysis of tissue metabolite profiles has advantages in terms of minimal tissue pre-processing, short data acquisition time giving the potential to be used as part of a rapid diagnostic work-up.

Biological material collection to advance translational research and treatment of children with CNS tumours: position paper from the SIOPE Brain Tumour Group

Paediatric CNS tumours are the most common cause of childhood cancer-related morbidity and mortality, and improvements in their diagnosis and treatment are needed. New genetic and epigenetic information about paediatric CNS tumours is transforming the field dramatically. For most paediatric CNS tumour entities, subgroups with distinct biological characteristics have been identified, and these characteristics are increasingly used to facilitate accurate diagnoses and therapeutic recommendations. Future treatments will be further tailored to specific molecular subtypes of disease, specific tumour predisposition syndromes, and other biological criteria. Successful biomaterial collection is a key requirement for the application of contemporary methodologies for the validation of candidate prognostic factors, the discovery of new biomarkers, the establishment of appropriate preclinical research models for targeted agents, a quicker clinical implementation of precision medicine, and for other therapeutic uses (eg, for immunotherapies). However, deficits in organisational structures and interdisciplinary cooperation are impeding the collection of high-quality biomaterial from CNS tumours in most centres. Practical, legal, and ethical guidelines for consent, storage, material transfer, biobanking, data sharing, and funding should be established by research consortia and local institutions to allow optimal collection of primary and subsequent tumour tissue, body fluids, and normal tissue. Procedures for the collection and storage of biomaterials and related data should be implemented according to the individual and organisational structures of the local institutions.

The Role of Advanced Brain Tumor Imaging in the Care of Patients with Central Nervous System Malignancies

OPINION STATEMENT

T1-weighted post-contrast and T2-weighted fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) constitute the gold standard for diagnosis and response assessment in neuro-oncologic patients but are limited in their ability to accurately reflect tumor biology and metabolism, particularly over the course of a patient's treatment. Advanced MR imaging methods are sensitized to different biophysical processes in tissue, including blood perfusion, tumor metabolism, and chemical composition of tissue, and provide more specific information on tissue physiology than standard MRI. This review provides an overview of the most common and emerging advanced imaging modalities in the field of brain tumor imaging and their applications in the care of neuro-oncologic patients.

MRI features as a helpful tool to predict the molecular subgroups of medulloblastoma: state of the art

Medulloblastoma is the most common malignant pediatric brain tumor. Medulloblastoma should not be viewed as a single disease, but as a heterogeneous mixture of various subgroups with distinct characteristics. Based on genomic profiles, four distinct molecular subgroups are identified: Wingless (WNT), Sonic Hedgehog (SHH), Group 3 and Group 4. Each of these subgroups are associated with specific genetic aberrations, typical age of onset as well as survival prognosis. Magnetic resonance imaging (MRI) is performed for all patients with brain tumors, and has a key role in the diagnosis, surgical guidance and follow up of patients with medulloblastoma. Several studies indicate MRI as a promising tool for early detection of medulloblastoma subgroups. The early identification of the subgroup can influence the extent of surgical resection, radiotherapy and chemotherapy targeted treatments. In this article, we review the state of the art in MRI-facilitated medulloblastoma subgrouping, with a summary of the main MRI features in medulloblastoma and a brief discussion on molecular characterization of medulloblastoma subgroups. The main focus of the article is MRI features that correlate with medulloblastoma subtypes, as well as features suggestive of molecular subgroups. Finally, we briefly discuss the latest trends in MRI studies and latest developments in molecular characterization.

Glycine: a non-invasive imaging biomarker to aid magnetic resonance spectroscopy in the prediction of survival in paediatric brain tumours

Paediatric brain tumours have a high mortality rate and are the most common solid tumour of childhood. Identification of high risk patients may allow for better treatment stratification. Magnetic Resonance Spectroscopy (MRS) provides a non-invasive measure of brain tumour metabolism and quantifies metabolite survival markers to aid in the clinical management of patients. Glycine can be identified using MRS and has been recently found to be important for cancer cell proliferation in tumours making it a valuable prognostic marker. The aims of this study were to investigate glycine and its added value to MRS as a prognostic marker for paediatric brain tumours in a clinical setting.

116 children with newly diagnosed brain tumours were examined with short echo-time MRS at the Birmingham Children’s Hospital and followed up for five years. Survival analysis was performed using Cox regression on the entire metabolite basis set with focus on glycine and three other established survival markers for comparison: n-acetylaspartate, scyllo-inositol and lipids at 1.3 ppm. Multivariate Cox regression was used in conjunction with risk values to establish if glycine added prognostic power when combined to the established survival markers.

Glycine was found to be a marker of poor prognosis in the cohort (p < 0.05) and correlated with tumour grade (p < 0.01). The addition of glycine improved the prognostic power of MRS compared to using the combination of established survival markers alone.

Tumour glycine was found to improve the MRS prediction of reduced survival in paediatric brain tumours aiding the non-invasive assessment of these children.

Oxamate, but Not Selective Targeting of LDH-A, Inhibits Medulloblastoma Cell Glycolysis, Growth and Motility

Medulloblastoma is the most common malignant paediatric brain tumour and current therapies often leave patients with severe neurological disabilities. Four major molecular groups of medulloblastoma have been identified (Wnt, Shh, Group 3 and Group 4), which include additional, recently defined subgroups with different prognosis and genetic characteristics. Lactate dehydrogenase A (LDHA) is a key enzyme in the aerobic glycolysis pathway, an abnormal metabolic pathway commonly observed in cancers, associated with tumour progression and metastasis. Studies indicate MBs have a glycolytic phenotype; however, LDHA has not yet been explored as a therapeutic target for medulloblastoma. LDHA expression was examined in medulloblastoma subgroups and cell lines. The effects of LDHA inhibition by oxamate or LDHA siRNA on medulloblastoma cell line metabolism, migration and proliferation were examined. LDHA was significantly overexpressed in Group 3 and Wnt MBs compared to non-neoplastic cerebellum. Furthermore, we found that oxamate significantly attenuated glycolysis, proliferation and motility in medulloblastoma cell lines, but LDHA siRNA did not. We established that aerobic glycolysis is a potential therapeutic target for medulloblastoma, but broader LDH inhibition (LDHA, B, and C) may be more appropriate than LDHA inhibition alone.

Anatomic mapping of molecular subtypes in diffuse glioma

Background

Tumor location served as an important prognostic factor in glioma patients was considered to postulate molecular features according to cell origin theory. However, anatomic distribution of unique molecular subtypes was not widely investigated. The relationship between molecular phenotype and histological subgroup were also vague based on tumor location. Our group focuses on the study of glioma anatomic location of distinctive molecular subgroups and histology subtypes, and explores the possibility of their consistency based on clinical background.

Methods

We retrospectively reviewed 143 cases with both molecular information (IDH1/TERT/1p19q) and MRI images diagnosed as cerebral diffuse gliomas. The anatomic distribution was analyzed between distinctive molecular subgroups and its relationship with histological subtypes. The influence of tumor location, molecular stratification and histology diagnosis on survival outcome was investigated as well.

Results

Anatomic locations of cerebral diffuse glioma indicate varied clinical outcome. Based on that, it can be stratified into five principal molecular subgroups according to IDH1/TERT/1p19q status. Triple-positive (IDH1 and TERT mutation with 1p19q codeletion) glioma tended to be oligodendroglioma present with much better clinical outcome compared to TERT mutation only group who is glioblastoma inclined (median overall survival 39 months VS 18 months). Five molecular subgroups were demonstrated with distinctive locational distribution. This kind of anatomic feature is consistent with its corresponding histological subtypes.

Discussion

Each molecular subgroup in glioma has unique anatomic location which indicates distinctive clinical outcome. Molecular diagnosis can be served as perfect complementary tool for the precise diagnosis. Integration of histomolecular diagnosis will be much more helpful in routine clinical practice in the future.

MELK and EZH2 Cooperate to Regulate Medulloblastoma Cancer Stem-like Cell Proliferation and Differentiation

Medulloblastoma is the most common malignant brain tumor in children. Although accumulated research has suggested that cancer stem-like cells play a key role in medulloblastoma tumorigenesis, the specific molecular mechanism regarding proliferation remains elusive. Here, we reported more abundant expression of maternal embryonic leucine-zipper kinase (MELK) and enhancer of zeste homolog 2 (EZH2) in medulloblastoma stem-like cells than in neural stem cells and the interaction between the two proteins could mediate the self-renewal of sonic hedgehog subtype medulloblastoma. In human medulloblastoma, extensive nodularity and large-cell/anaplastic subgroups differed according to the staining levels of MELK and EZH2 from the other two subgroups. The proportion of MELK- or EZH2-positive staining status could be considered as a potential indicator for survival. Mechanistically, MELK bound to and phosphorylated EZH2, and its methylation was induced by EZH2 in medulloblastoma, which could regulate the proliferation of cancer stem-like cells. In xenografts, loss of MELK or EZH2 attenuated medulloblastoma stem-like cell-derived tumor growth and promoted differentiation. These findings indicate that MELK-induced phosphorylation and EZH2-mediated methylation in MELK/EZH2 pathway are essential for medulloblastoma stem-like cell-derived tumor proliferation, thereby identifying a potential therapeutic strategy for these patients.Implications: This study demonstrates that the interaction occurring between MELK and EZH2 promotes self-proliferation and stemness, thus representing an attractive therapeutic target and potential candidate for diagnosis of medulloblastoma. Mol Cancer Res; 15(9); 1275-86. ©2017 AACR.