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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.

Aims Magnetic resonance imaging (MRI) is a valuable tool for non-invasive diagnosis of paediatric brain tumours. The rarity of the disease dictates multi-centre studies and imaging biomarkers that are robust to protocol variability. We investigated diffusion tensor MRI (DT-MRI), combined with machine learning, as an aid to diagnosis and evaluated the robustness of the imaging metrics. Method A multi-centre cohort of 52 clinical DT-MRI scans (20 medulloblastomas (MB), 21 pilocytic astrocytomas (PA), 11 ependymomas (EP)) were analysed retrospectively. Histograms for regions of solid tumour for fractional anisotropy (FA), mean diffusivity (MD), pure anisotropic diffusion (q) and pure isotropic diffusion (p) were compared to assess diagnostic capability. Linear discriminate analysis (LDA) was used for classification and validated using leave-one-out-cross-validation (LOOCV). Results Histogram medians for FA, MD, q and p were all different between tumor groups (P Conclusion DT-MRI metrics from multi-centre acquisitions can classify paediatric brain tumours. FA is the least robust metric to protocol variability and q provides the most robust quantification of anisotropic behaviour.

Medulloblastoma (MB) is the most common paediatric malignant brain tumour and is classified into four distinct molecular subgroups (WNT, SHH, G3 and G4), each of them further subdivided into subtypes with different prognosis and responses to therapy. Deregulation of chromatin modifier genes plays an essential role in MB, particularly in the G4 subgroup, the least understood of all subgroups, despite being the most common and associated with poor prognosis. A BMI1High; CHD7Low molecular signature identifies patients with poor survival within this subgroup. We show that BMI1High; CHD7Low mediates a novel epigenetic regulation of inositol metabolism in both G4 MB cells and patients. These tumours display hyperactivation of the AKT/mTOR pathway which leads to energetic rewiring characterized by enhanced glycolytic capacity and reduced mitochondrial function. We demonstrate that inositol administration counteracts this metabolic alteration, impairs MB proliferation in vitro and significantly extends survival in an in vivo pre-clinical model. Moreover, inositol synergises with cisplatin, a chemotherapy agent currently used in MB treatment, enhancing its therapeutic effect in vivo. Importantly, cerebellar neural stem cells bearing the BMI1High; CHD7Low signature do not show metabolic adaptation and are thus resistant to inositol treatment, highlighting a fundamental difference between normal and neoplastic metabolism in the developing cerebellum. In summary, we have identified an actionable vulnerability in a pre-clinical setting modelling a molecularly defined group of MB patients, the translational value of which can now be explored in signature-matched clinical trials in MB.

SHH medulloblastoma patients have a variable prognosis. Infants (

INTRODUCTION Magnetic Resonance Imaging (MRI) is routinely used in the assessment of children’s brain tumours. Reduced diffusion and increased perfusion on MRI are commonly associated with higher grade but there is a lack of quantitative data linking these parameters to survival. Machine learning is increasingly being used to develop diagnostic tools but its use in survival analysis is rare. In this study we combine quantitative parameters from diffusion and perfusion MRI with machine learning to develop a model of survival for paediatric brain tumours. METHOD: 69 children from 4 centres (Birmingham, Liverpool, Nottingham, Newcastle) underwent MRI with diffusion and perfusion (dynamic susceptibility contrast) at diagnosis. Images were processed to form ADC, cerebral blood volume (CBV) and vessel leakage correction (K2) parameter maps. Parameter mean, standard deviation and heterogeneity measures (skewness and kurtosis) were calculated from tumour and whole brain and used in iterative Bayesian survival analysis. The features selected were used for k-means clustering and differences in survival between clusters assessed by Kaplan-Meier and Cox-regression. RESULTS Bayesian analysis revealed the 5 top features determining survival to be tumour volume, ADC kurtosis, CBV mean, K2 mean and whole brain CBV mean. K-means clustering using these features showed two distinct clusters (high- and low-risk) which bore significantly different survival characteristics (Hazard Ratio = 5.6). DISCUSSION AND CONCLUSION Diffusion and perfusion MRI can be used to aid the prediction of survival in children’s brain tumours. Tumour perfusion played a particularly important role in predicting survival despite being less routinely measured than diffusion.

OBJECTIVE: Paediatric low-grade glioma can progress or recur following primary surgical/non-surgical treatment and require further therapy. Studies have identified clinical characteristics predictive of disease progression. This study aims to determine whether size of residual disease following primary treatment is a prognostic indicator for predicting progression. METHODS: A retrospective analysis of 61 children with histopathologically confirmed low-grade glioma treated at Birmingham Children’s Hospital between 2004 and 2015 was performed. Residual tumour size following primary treatment was measured from MR imaging and analysed in context of clinical characteristics. RESULTS: 47.5% (29/61) children progressed during the follow-up period. A residual tumour size of ≤1cm(3) was predictive of better progression free survival for the entire cohort (p2cm(3)) progressed earlier (minimum 0.36 years) than those with smaller residuals (

Metabolic profiling by Magnetic Resonance Spectroscopy has shown the three most common paediatric cerebellar tumours are metabolically distinct; however, these techniques are limited by their sensitivity. Mass spectrometry is more sensitive and able to detect many more metabolites. In this study, metabolic phenotyping was performed to investigate the water-soluble metabolome and the lipidome of the three most common paediatric cerebellar tumours with the aim of informing a detailed metabolic pathway analysis. A discovery based experiment was performed on 60 surgically resected snap frozen pilocytic astrocytomas, ependymomas and medulloblastomas using UPLC-MS assays to detect water soluble and lipid metabolites. Principal component analysis demonstrates separation of the tumours based on the relative concentration of thousands of detected metabolites. The relative concentrations of glucose and glycolytic intermediates varied between the tumour types, as did metabolites involved in purine and pyrimidine metabolism. Key metabolites within the taurine metabolism pathway were found to be higher in medulloblastoma. Alterations were observed in lipid metabolism, with a wide range of relative concentration differences in di- and triacylglycerides, sphingolipids, ceramides, cardiolipins and glycerophospholipids. This study has shown that these tumours are metabolically distinct and offer the opportunities for stratified diagnosis and treatment. Some of the observed changes in relative concentration between the tumours reflect findings from spectroscopic studies, particularly with regards to taurine which is known to be higher in medulloblastoma. The ability of mass spectrometry to identify and measure lipid species and low concentration metabolites from tissue could allow targetable metabolic differences to be characterised and exploited.

MB-84. IDENTIFICATION OF MEDULLOBLASTOMA MOLECULAR SUBGROUPS USING METABOLITE PROFILES Sarah Kohe1,2, Simrandip K. Gill1,2, Debbie Hicks3, Ed C. Schwalbe3, Stephen Crosier3, Lisa Storer4, Anbarasu Lourdusamy4, Christopher D. Bennett1,2, Martin Wilson1,2, Simon Bailey3, Daniel Williamson3, Richard G. Grundy4, Steven C. Clifford3, and Andrew C. Peet1,2; Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK; Birmingham Childrens Hospital, Birmingham, UK; Northern Institute for Cancer Research, Newcastle University, Newcastle, UK; Childrens Brain Tumour Research Centre, Queens Medical Centre, University of Nottingham, Nottingham, UK Identification of the four consensus molecular subgroups of medulloblastoma is becoming increasingly important for determining risk-based treatment strategies. Metabolite profiles can distinguish between brain tumour types, therefore we investigated whether profiling can discriminate the molecular subgroups within medulloblastoma. Metabolite concentrations were determined using high resolution magnetic resonance spectroscopy (MRS) on biopsied tissue from 29 medulloblastomas. Molecular subgroup was determined by Illumina 450K DNA methylation array and consensus clustering. Mean metabolite concentrations showed taurine, characteristic of PNETs, was prominent in all subgroups. Lipid was significantly elevated in high-risk Group 3 tumours (n 1⁄4 5, p , 0.0002) consistent with it being a marker of poor prognosis. The ratio of glutamate to glutamine was significantly higher in SHH (n 1⁄4 6), and lower in Group 4 (n 1⁄4 15), p , 0.03. Decreased creatine was detected in SHH tumours, p , 0.002. Only low risk WNT tumours (n 1⁄4 3) contained GABA suggesting it may be a subgroup specific marker, supported by links between GABA and WNT signalling in the developing cerebellum. Glycine, typically associated with poor prognosis, was also significantly lower in WNT tumours (p , 0.003). Multivariate PLS discriminant analysis found metabolite profiles could discriminate subgroup with a classification accuracy of 85% in this pilot set. Lipid, glutamate, glutamine, taurine, hypotaurine, GABA, myoinositol, glycine and creatine were most discriminatory. Matched 1.5T in-vivo MRS concentrations (n 1⁄4 19) found significant correlations with ex-vivo values for taurine, glutamate, glutamine, glycine, creatine, and myoinositol (Pearson’s r range:0.61-0.67, p , 0.05). Identified profiles will inform non-invasive MRS methods for pre-operative subgroup identification, with potential to guide extent of surgical resection and enhanced disease monitoring. Neuro-Oncology 18:iii97–iii122, 2016. doi:10.1093/neuonc/now076.80 #The Author(s) 2016. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Molecular subgroup is now influencing risk stratification and disease management in medulloblastoma. Tissue metabolite profiles have shown promise in identifying the four consensus subgroups. A smaller number of metabolites can be measured non-invasively in patients using magnetic resonance spectroscopy (MRS). We investigated if a classifier constructed from tissue metabolites could be applied to in-vivo metabolite profiles to accurately predict subgroup non-invasively. Machine learning was used to construct a classifier with tissue concentrations from 10 metabolites reliably detected in-vivo. Retrospectively acquired diagnostic in-vivo MRS was available for 37 cases from four treatment centres. Although we identified WNT tumours by the presence of GABA with 100% accuracy in tissue, GABA was not reliability quantified in this in-vivo dataset. Therefore the classifier was developed using tissue profiles of known molecular subgroup (determined using DNA methylation array) from group 3(n=20), group 4(n=34) and SHH(n=23). The cross-validated accuracy of the tissue classifier was 86%. When applied to in-vivo metabolite profiles, subgroup was predicted non-invasively with an overall accuracy of 76%. Group 3 had the highest proportion of incorrectly classified cases (4/11), followed by SHH (2/10), and group 4 (3/16), largely due to differences in measuring lipids, glutamate, glutamine and hypotaurine in-vivo. We have established the feasibility of non-invasive metabolite profiling to identify medulloblastoma subgroups. With the ongoing optimization of MRS to target specific metabolites including GABA, we can further improve accuracy. Rapid, non-invasive preoperative diagnosis of subgroup will offer opportunities to stratify early therapeutic intervention especially surgery, avoiding long-term sequalae and improving quality of survival.

Pilocytic astrocytomas are the most common brain tumours arising in children, accounting for ~18% of primary brain tumours. 42% are cerebellar in origin and, whilst they have an excellent 10-year overall survival rate of >90%, progression-free survival for these tumours is variable. Identifying tumours more likely to progress will help determine those requiring more aggressive treatment. Metabolite profiles were obtained from 23 cerebellar pilocytic astrocytomas using High Resolution Magic Angle Spinning NMR (HR-MAS). Cox regression tested the ability of individual metabolites to predict progression-free survival. Progression was defined as the time point at which the local multi-disciplinary team decided to further treat the tumour. Low normalised tissue glutamine concentration was found to be associated with a worse progression-free survival (HR=4.58; P

BACKGROUND: Molecularly targeted approaches informing next-generation glioblastoma (GBM) chemotherapy must account for the intra-heterogeneous genome landscape and aim to eradicate invasive residual sub-populations. We outline an analytical strategy that forms a functional genomic corollary to existing genome-wide mutation and gene expression studies. METHODS: Biopsies from spatially distinct intra-GBM regions were surgically resected via Stealth and 5-ALA fluorescence guidance, including core, enhancing and invasive margin regions. Cellular intra-tumour heterogeneity (ITH) was confirmed by immunohistochemistry and qPCR arrays, whilst molecular analytics were conducted using liquid chromatography-mass spectrometry (metabolomic/lipidomic) magnetic resonance spectroscopy (metabolic) and SDS PAGE phosphoprotein enrichment (proteomic). RESULTS: Cellular ITH was confirmed via distinct neural stem cell expression in medial and posterior rims relative to the central enhancement region. Metabolites present in the invasive margin (5-ALA-determined), central enhancement and peri-necrotic core were distinct within each GBM. Controlling for the high proportion of normal brain cells surrounding the invasive margin, high tumour-specific glutamine levels were consistently present. Relative abundance of cytosolic phosphoproteins distinguished active protein pathways within intra-GBM regions, including invasive margin. Metabolomic network analyses confirmed that GBM residual disease is distinct from core ITH regions with respect to metabolic dependence. CONCLUSIONS: This innovative concept is the first to address ITH with multi-region biopsies using advanced analytical bioscience techniques. Identification of metabolites, lipids and phospho-proteins that are functional determinants of GBM infiltration reveal putative molecular targets on residual disease cells and represent a route to expedite new chemotherapy agents to clinic within a precision medicine framework.