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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.

Brain tumours are the most common cause of cancer death in children. Molecular studies have greatly improved our understanding of these tumours but tumour metabolism is underexplored. Metabolites measured in vivo have been reported as prognostic biomarkers of these tumours but analysis of surgically resected tumour tissue allows a more extensive set of metabolites to be measured aiding biomarker discovery and providing validation of in vivo findings. In this study, metabolites were quantified across a range of paediatric brain tumours using 1H-High-Resolution Magic Angle Spinning nuclear magnetic resonance spectroscopy (HR-MAS) and their prognostic potential investigated. HR-MAS was performed on pre-treatment frozen tumour tissue from a single centre. Univariate and multivariate Cox regression was used to examine the ability of metabolites to predict survival. The models were cross validated using C-indices and further validated by splitting the cohort into two. Higher concentrations of glutamine were predictive of a longer overall survival, whilst higher concentrations of lipids were predictive of a shorter overall survival. These metabolites were predictive independent of diagnosis, as demonstrated in multivariate Cox regression models. Whilst accurate quantification of metabolites such as glutamine in vivo is challenging, metabolites show promise as prognostic markers due to development of optimised detection methods and increasing use of 3 T clinical scanners.

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.

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 (

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.

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.