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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.

BackgroundThunderstorm asthma is often characterised by a sudden surge in patients presenting with exacerbated symptoms of asthma linked to thunderstorm activity. On 17 June 2021, Public Health England (PHE) observed a large spike in health care seeking behaviour by patients presenting with asthma and difficulty breathing symptoms across parts of England.ObjectivesTo describe the epidemiology of the observed asthma spike and explore available meteorological and environmental data to understand potential causes of this episode.DesignA retrospective observational study was conducted of patient visits to health care services in England as monitored through surveillance systems routinely operated by PHE. The number of presentations during the asthma event was compared to expected levels for the overall population and across specific regions.SettingHealthcare services in England.Main outcome measuresNumber of patients presenting to healthcare services for asthma- and difficulty breathing-type symptoms.ResultsSpikes in asthma and difficulty breathing were detected across several PHE syndromic surveillance systems. Across affected areas ED attendances for asthma increased by 560% on 17 June compared to the average number of weekday daily attendances during the previous 4 weeks. GP out of hours contacts increased by 422%, NHS 111 calls 193%, NHS 111 online assessments 581% and ambulance call outs 54%. Increases were particularly noted in patient age groups 5-14 and 15-44 years. In non-affected regions, increases were small (ConclusionsAn unprecedented episode of asthma was recorded in England, characterised by significant surges in health care seeking behaviour. However, the links to meteorologically defined thunderstorm activity were not as clear as previous episodes, with less evidence of severe thunderstorm activity in those areas affected, prompting further discussion about the causes of these events and implications for public health management of the risk.

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.

The rare pediatric embryonal tumors retinoblastoma, medulloblastoma and neuroblastoma derive from neuroectodermal tissue and share similar histopathological features despite different anatomical locations and diverse clinical outcomes. As metabolism can reflect genetic and histological features, we investigated whether the metabolism of embryonal tumors reflects their similar histology, shared developmental and neural origins, or tumor location. We undertook metabolic profiling on 50 retinoblastoma, 39 medulloblastoma and 70 neuroblastoma using high resolution magic angle spinning magnetic resonance spectroscopy (1H-MRS). Mean metabolite concentrations identified several metabolites that were significantly different between the tumor groups including taurine, hypotaurine, glutamate, glutamine, GABA, phosphocholine, N-acetylaspartate, creatine, glycine and myoinositol, p < 0.0017. Unsupervised multivariate analysis found that each tumor group clustered separately, with a unique metabolic profile, influenced by their underlying clinical diversity. Taurine was notably high in all tumors consistent with prior evidence from embryonal tumors. Retinoblastoma and medulloblastoma were more metabolically similar, sharing features associated with the central nervous system (CNS). Neuroblastoma had features consistent with neural tissue, but also contained significantly higher myoinositol and altered glutamate-glutamine ratio, suggestive of differences in the underlying metabolism of embryonal tumors located outside of the CNS. Despite the histological similarities and shared neural metabolic features, we show that individual neuroectodermal derived embryonal tumors can be distinguished by tissue metabolic profile. Pathway analysis suggests the alanine-aspartate-glutamate and taurine-hypotaurine metabolic pathways may be the most pertinent pathways to investigate for novel therapeutic strategies. This work strengthens our understanding of the biology and metabolic pathways underlying neuroectodermal derived embryonal tumors of childhood.

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.