Bevacizumab, Irinotecan, or Topotecan Added to Temozolomide for Children With Relapsed and Refractory Neuroblastoma: Results of the ITCC-SIOPEN BEACON-Neuroblastoma Trial

PURPOSE

Outcomes for children with relapsed and refractory high-risk neuroblastoma (RR-HRNB) remain dismal. The BEACON Neuroblastoma trial (EudraCT 2012-000072-42) evaluated three backbone chemotherapy regimens and the addition of the antiangiogenic agent bevacizumab (B).

MATERIALS AND METHODS

Patients age 1-21 years with RR-HRNB with adequate organ function and performance status were randomly assigned in a 3 × 2 factorial design to temozolomide (T), irinotecan-temozolomide (IT), or topotecan-temozolomide (TTo) with or without B. The primary end point was best overall response (complete or partial) rate (ORR) during the first six courses, by RECIST or International Neuroblastoma Response Criteria for patients with measurable or evaluable disease, respectively. Safety, progression-free survival (PFS), and overall survival (OS) time were secondary end points.

RESULTS

One hundred sixty patients with RR-HRNB were included. For B random assignment (n = 160), the ORR was 26% (95% CI, 17 to 37) with B and 18% (95% CI, 10 to 28) without B (risk ratio [RR], 1.52 [95% CI, 0.83 to 2.77]; P = .17). Adjusted hazard ratio for PFS and OS were 0.89 (95% CI, 0.63 to 1.27) and 1.01 (95% CI, 0.70 to 1.45), respectively. For irinotecan ([I]; n = 121) and topotecan (n = 60) random assignments, RRs for ORR were 0.94 and 1.22, respectively. A potential interaction between I and B was identified. For patients in the bevacizumab-irinotecan-temozolomide (BIT) arm, the ORR was 23% (95% CI, 10 to 42), and the 1-year PFS estimate was 0.67 (95% CI, 0.47 to 0.80).

CONCLUSION

The addition of B met protocol-defined success criteria for ORR and appeared to improve PFS. Within this phase II trial, BIT showed signals of antitumor activity with acceptable tolerability. Future trials will confirm these results in the chemoimmunotherapy era.

Applying machine learning classifiers to automate quality assessment of paediatric dynamic susceptibility contrast (DSC-) MRI data

OBJECTIVE

Investigate the performance of qualitative review (QR) for assessing dynamic susceptibility contrast (DSC-) MRI data quality in paediatric normal brain and develop an automated alternative to QR.

METHODS

1027 signal-time courses were assessed by Reviewer 1 using QR. 243 were additionally assessed by Reviewer 2 and % disagreements and Cohen's κ (κ) were calculated. The signal drop-to-noise ratio (SDNR), root mean square error (RMSE), full width half maximum (FWHM) and percentage signal recovery (PSR) were calculated for the 1027 signal-time courses. Data quality thresholds for each measure were determined using QR results. The measures and QR results trained machine learning classifiers. Sensitivity, specificity, precision, classification error and area under the curve from a receiver operating characteristic curve were calculated for each threshold and classifier.

RESULTS

Comparing reviewers gave 7% disagreements and κ = 0.83. Data quality thresholds of: 7.6 for SDNR; 0.019 for RMSE; 3 s and 19 s for FWHM; and 42.9 and 130.4% for PSR were produced. SDNR gave the best sensitivity, specificity, precision, classification error and area under the curve values of 0.86, 0.86, 0.93, 14.2% and 0.83. Random forest was the best machine learning classifier, giving sensitivity, specificity, precision, classification error and area under the curve of 0.94, 0.83, 0.93, 9.3% and 0.89.

CONCLUSION

The reviewers showed good agreement. Machine learning classifiers trained on signal-time course measures and QR can assess quality. Combining multiple measures reduces misclassification.

ADVANCES IN KNOWLEDGE

A new automated quality control method was developed, which trained machine learning classifiers using QR results.

RARE-11. 60 years single centre experience of craniopharyngioma management

Adamantinomatous craniopharyngiomas are challenging intracranial tumours associated with significant morbidity. Management includes surgery and radiotherapy, with a shift towards more conservative surgery in recent years, aimed at preserving hypothalamic function. The West Midlands Regional Children’s Tumour Registry collects detailed clinical, pathological and follow up information on patients treated within the region from 1957. 52 cases (26 male, 26 female) of craniopharyngioma treated at Birmingham Children’s Hospital 1957-2018, were identified, with further clinical details obtained from patient records, where available. Visual symptoms were the commonest presenting feature (63%), followed by headache (48%), vomiting (31%), neurological symptoms (31%) and features of endocrine disorders (21%) with a median symptom duration of 6 months (range <1-24). Initial management was with gross total resection (GTR) in 14 patients, subtotal resection in 22 patients and subtotal resection with adjuvant radiotherapy in seven patients. Two patients received radiotherapy without resection, and five patients underwent cystic drainage procedures alone. Two patients initially underwent shunt insertion alone, but received radiotherapy at progression. 30 (58%) patients underwent relapse/progression, with a median time to progression of 1.2 years (range 0.2-6.3). 15 had further surgery. Radiotherapy was used in 14/15 patients who had not previously received radiotherapy, with the other undergoing a GTR. To date 10 patients have died, nine from tumour related reasons and one from pulmonary embolism. Where data was available at follow up, all patients had at least one endocrinopathy, with 38/45 patients having diabetes insipidus. Hypothalamic obesity was identified in 14/36 (39%) patients with sufficient records, with this more common in those undergoing GTR (7/9 (78%)) compared to other surgical procedures (7/27)(26%)(p<0.05). Three patients have developed neurovascular complications and three fatty liver disease. This experience is consistent with the literature and supports the increasing usage of hypothalamic sparing surgical management.

GCT-03. MonoGerm: A proposed phase II trial of carboplatin or vinblastine monotherapy induction prior to radiotherapy for intracranial germinoma

BACKGROUND: Intracranial germinoma is chemosensitive but radiotherapy (RT) is needed for cure. In localised disease, three-drug standard-of-care (SOC) inpatient chemotherapy is used to reduce RT fields/dose. Concomitant diabetes insipidus is common, making chemotherapy delivery challenging. Small studies have demonstrated benefits from single-agent carboplatin or vinblastine in germinoma as an alternative to SOC. However, this needs prospective evaluation in a clinical trial. METHODS: We developed a trial, with a patient-public-involvement workstream, primarily evaluating whether single-agent chemotherapy (carboplatin or vinblastine) is non-inferior to SOC for inducing radiological complete response (CR) in localised disease, and is associated with reduced toxicity and improved quality-of-life (QoL), evaluated through patient-reported-outcome-measures (PROMs). RESULTS: The resultant proposed multi-centre, phase II proof-of-principle trial will investigate, in parallel, two single agents as monotherapy induction, in children/teenagers/adults with intracranial germinoma. Trial features include: a) Bayesian statistical design determining whether CR rate for either agent is sufficiently non-inferior to SOC; b) ‘Flip-flop’ design with alternating, continuous enrolment to the two single-agents, interim assessments after each recruited cohort, and early stopping rules for inferiority; c) Safety MRI, after 6-weeks of chemotherapy with real-time central-radiological-review; d) Proof-of-principle vinblastine monotherapy arm for metastatic patients awaiting definitive craniospinal-irradiation; e) State-of-the-art integrated imaging acquisition, QoL/PROM, pharmacokinetics and circulating microRNA studies to maximise information/learning; f) European and North American neuroradiological response criteria comparison and prospective evaluation of new consensus criteria. CONCLUSIONS: Trial results will: a) establish whether monotherapy is a treatment option in this setting, which may be practice-altering; b) use QoL/PROM data to inform on optimal treatment if results similar; c) use embedded radiological assessments to develop intracranial germinoma trials and facilitate European/US study comparisons; d) describe vinblastine pharmacokinetic data to inform future dosing schedules in this and other malignancies; and e) quantify circulating microRNAs, facilitating future non-invasive diagnosis/risk-stratification.

IMG-11. A COMPUTERISED CLINICAL DECISION SUPPORT SYSTEM FOR DIAGNOSING CHILDREN’S BRAIN TUMOURS USING FUNCTIONAL IMAGING AND MACHINE LEARNING

INTRODUCTION: Magnetic resonance imaging is a key investigation in the diagnosis of childhood solid tumours. Advanced techniques such as diffusion weighted imaging (DWI), magnetic resonance spectroscopy (MRS) and perfusion imaging probe the underlying cellular, chemical and vascular nature of the disease. Coupled with machine learning these scanning methods show improvement in diagnostic accuracy compared with conventional imaging. Advanced image analysis is not routinely available in hospitals. We present a clinical decision support system (CDSS) developed for advanced MR analysis and interpretation. METHOD: The CDSS was developed in house. The Children’s Cancer and Leukaemia Group Functional Imaging Group (CCLGFIG) Database, a national resource, was used to provide a repository of cases together with their advanced imaging and machine learning diagnostic classifiers. A new case is displayed alongside cases in the repository with known diagnoses, including summary statistics for relevant diagnostic categories. The CDSS was made available to radiologists, in their clinical environment for technical and clinical evaluation. Structured interviews were undertaken. The CDSS was developed as a computer app for multi-centre distribution. RESULTS: 436 MRS, 240 DWI and 85 perfusion cases were available for building repositories. Machine learning classifiers showed diagnostic accuracies for the major childhood brain tumour types of 85-95%. Comparison of MRS with a data repository was found to improve non-invasive diagnosis. Results from the CDSS can be uploaded to the CCLGFIG to support multicentre research. Positive feedback on the CDSS from clinicians included: ready access to advanced analysis; simple and efficient integration into clinical workflow; and assisted interpretation of advanced analysis. DISCUSSION: Advanced MR analysis techniques provide improved non-invasive diagnostic accuracy but are difficult to implement on clinical systems due to technical, infrastructure and training limitations. CONCLUSION: We have successfully released a CDSS for paediatric cancer within the hospital environment and assessed its suitability for clinical use.

MEDB-79. MYC-driven upregulation of thede novo serine and glycine pathway is a novel therapeutic target for Group 3 MYC-amplified Medulloblastoma

Despite advances in the molecular sub-classification and risk-stratification of medulloblastoma (MB), a subset of tumours remain refractory to current multimodal therapies. Group 3 (MB_Group3) patients represent around 25% of MBs, and amplification and elevated expression of MYC in this group correlates with dismal clinical outcomes. Since direct targeting of MYC remains elusive, understanding and exploiting metabolic dependencies in MYC-amplified MB_Group3 may reveal novel therapeutic opportunities. We engineered three independent regulable MYC-amplified MB_Group3 cell-based models, each harbouring doxycycline-inducible anti-MYC shRNAs (two independent species) or a non-silencing shRNA control. In all three models, MYC knockdown (KD) revealed persistent MYC-dependent cancer phenotypes, reduction in proliferation and cell cycle progression. We utilised ¹H high-resolution magic angle spectroscopy (HRMAS) and stable isotope-resolved metabolomics to assess changes in intracellular metabolites and pathway dynamics when MYC expression was modulated. Profiling revealed consistent MYC-dependent changes in metabolite concentrations across models. Notably, glycine was consistently accumulated following MYC KD suggesting altered pathway dynamics. ¹³C-glucose tracing further revealed a reduction in glucose-derived serine and glycine (de novo synthesis) following MYC KD which was attributable to lower expression of PHGDH, the rate-limiting enzyme of this pathway. Furthermore, in human primary tumours, elevated expression of PHGDH was associated with MYC amplification and poorer survival outcomes. MYC expressing cells showed greater sensitivity to pharmacological inhibition of PHGDH compared to MYC KD (MB_Group3) and MB_SHH subgroup cell lines in vitro. Critically, targeting PHGDH in vivo, using MYC-dependent xenografts and genetically engineered mouse models, consistently slowed tumour progression and increased survival. In summary, metabolic profiling has uncovered MYC-dependent metabolic alterations and revealed the de novo serine/glycine synthesis pathway as a novel and clinically relevant therapeutic target in MYC-amplified MB_Group3. Together, these findings reveal metabolic vulnerabilities of MYC-amplified MB_Group3 which represent novel therapeutic opportunities for this poor-prognosis disease group.

MRI-based Whole-Tumor Radiomics to Classify the Types of Pediatric Posterior Fossa Brain Tumor

BACKGROUND

Differential diagnosis between medulloblastoma (MB), ependymoma (EP) and astrocytoma (PA) is important due to differing medical treatment strategies and predicted survival. The aim of this study was to investigate non-invasive MRI-based radiomic analysis of whole tumors to classify the histologic tumor types of pediatric posterior fossa brain tumor and improve the accuracy of discrimination, using a random forest classifier.

METHODS

MRI images of 99 patients, with 59 MBs, 13 EPs and 27 PAs histologically confirmed by surgery and pathology before treatment, were included in this retrospective study. Registration was performed between the three sequences, and high- throughput features were extracted from manually segmented tumors on MR images of each case. The forest-based feature selection method was adopted to select the top ten significant features. Finally, the results were compared and analyzed according to the classification.

RESULTS

The top ten contributions according to the classifier of wavelet features all came from the ADC sequence. The random forest classifier achieved 100% accuracy on the training data and validated the best accuracy (0.938): sensitivity = 1.000, 0.948 and 0.808, specificity = 0.952, 0.926 and 1.000 for EP, MB and PA, respectively.

CONCLUSION

A random forest classifier based on the ADC sequence of the whole tumor provides more quantitative information than TIWI and T2WI in differentiating pediatric posterior fossa brain tumors. In particular, the histogram percentile value showed great superiority, which added diagnostic value in pediatric neuro-oncology.

OTHR-20. Precision neuro-oncology in the real world. Opportunity and challenges from a UK Oncology Centre

The last five years has shown advances in the molecular classification of brain tumour, molecular profiling techniques and an increased use of targeted therapies. We reviewed the molecular analysis pathways and use of targeted agents at Birmingham Children’s Hospital (BCH), a large (~55 new cases/year) neuro-oncology centre, between 2016-2021. Having previously been analysed locally by limited directed immuno-histochemical stains and referral for specific genetic tests, tissue is now referred for a range of second histopathological opinions and in depth molecular classification, via methylation array, panel sequencing, RNA fusion analysis, and whole genome sequencing. These are accessed through different evolving pathways and consent processes, including referral to other centres, national reference laboratories, clinical studies, and local genetics laboratories with links to national sequencing infrastructures. Different routes result in different reporting structures, timescales and with varying levels of interpretation, often without adequate access to clinical information and context. 21 patients were treated on five targeted agent clinical trials (Afatanib (n=6), Biomede (n=3), eSmart(n=1), PARC (n=7), Vinilo (n=5)), with one patient on both Afatanib and PARC trials. A further two patients visited other centres for trials. Eight patients received MAPK pathway inhibitors through compassionate access pathways, with benefit, including radiological response, in four. Cardiac toxicity was observed in three and retinal oedema in one. Two patients received immune checkpoint inhibition, with rapid fatal enlargement, either progression or pseudo-progression, in one case. These rapid changes in diagnostic and management options offer new opportunities for patients, but bring challenges to the delivery of neuro-oncology services, including the logistics of sample, report, clinical trial, compassionate access management and the increased multi-specialist support required for monitoring and management of toxicities. Integration of targeted agents into the appropriate part of a patient’s treatment strategy requires skilled interpretation of the benefits compared to conventional therapies.

Artificial intelligence for early prediction of treatment response in glioblastoma

Aims

Treatment response assessment in glioblastoma is challenging. Patients routinely undergo conventional magnetic resonance imaging (MRI), but it has a low diagnostic accuracy for distinguishing between true progression (tPD) and pseudoprogression (psPD) in the early post-chemoradiotherapy time period due to similar imaging appearances. The aim of this study was to use artificial intelligence (AI) on imaging data, clinical characteristics and molecular information within machine learning models, to distinguish between and predict early tPD from psPD in patients with glioblastoma.

Method

The study involved retrospective analysis of patients with newly-diagnosed glioblastoma over a 3.5 year period (n=340), undergoing surgery and standard chemoradiotherapy treatment, with an increase in contrast-enhancing disease on the baseline MRI study 4-6 weeks post-chemoradiotherapy. Studies had contrast-enhanced T1-weighted imaging (CE-T1WI), T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences, acquired at 1.5 Tesla with 6-months follow-up to determine the reference standard outcome. 76 patients (mean age 55 years, range 18-76 years, 39% female, 46 tPD, 30 psPD) were included. Machine learning models utilised information from clinical characteristics (age, gender, resection extent, performance status), O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and 307 quantitative imaging features; extracted from baseline study CE-T1WI/ADC and T2WI sequences using semi-automatically segmented enhancing disease and perilesional oedema masks respectively. Feature selection was performed within bootstrapped cross-validated recursive feature elimination with a random forest algorithm and Naïve Bayes five-fold cross-validation to validate the final model.

Results

Treatment response assessment based on the standard-of-care reports by clinical neuroradiologists showed an accuracy of 33% (sensitivity/specificity 52%/3%) to distinguish between tPD and psPD from the early post-treatment MRI study at 4-6 weeks. Machine learning-based models based on clinical and molecular features alone demonstrated an AUC of 0.66 and models using radiomic features alone from the early post-treatment MRI demonstrated an AUC of 0.46-0.69 depending on the feature and mask subset. A combined clinico-radiomic model utilising top common features demonstrated an AUC of 0.80 and an accuracy of 74% (sensitivity/specificity 78%/67%). The features in the final model were age, MGMT promoter methylation status, two shape-based features from the enhancing disease mask (elongation and sphericity), three radiomic features from the enhancing disease mask on ADC (kurtosis, correlation, contrast) and one radiomic feature from the perilesional oedema mask on T2WI (dependence entropy).

Conclusion

Current standard-of-care glioblastoma treatment response assessment imaging has limitations. In this study, the use of AI through a machine learning-based approach incorporating clinical characteristics and MGMT promoter methylation status with quantitative radiomic features from standard MRI sequences at early 4-6 weeks post-treatment imaging showed the best model performance and a higher accuracy to distinguish between tPD and psPD for early prediction of glioblastoma treatment response.

The role of diffusion tensor imaging metrics in machine learning-based characterisation of paediatric brain tumors and their practicality for multicentre clinical assessment

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<.0001, Kruskal Wallis test). Median MD, p and q values were highest in PA, then EP and lowest in MB (P<.0001, Pairwise Wilcox test). FA median was higher for EP than PA (P=.004) with no significant difference between EP and MB (P=.591). ROC analysis showed that median MD, q and p perform best as a diagnostic marker (AUC= 0.92 to 0.99). LOOCV showed an overall accuracy of the LDA classification, ranging between 67% - 87%. FA values were highly dependent on protocol parameters, whereas pure anisotropic diffusion, q, was not.

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 role of Magnetic Resonance Images (MRIs) in coping for patients with brain tumours and their parents: a qualitative study

BACKGROUND

When children and young people (CYP) are diagnosed with a brain tumour, Magnetic Resonance Imaging (MRI) is key to the clinical management of this condition. This can produce hundreds, and often thousands, of Magnetic Resonance Images (MRIs).

METHODS

Semi-structured interviews were undertaken with 14 families (15 parents and 8 patients), and analysed using Grounded Theory. Analysis was supported by the Framework Method.

RESULTS

Although the focus of the research was whether paediatric patients and their families find viewing MRIs beneficial, all patients and parents discussed difficult times during the illness and using various strategies to cope. This article explores the identified coping strategies that involved MRIs, and the role that MRIs can play in coping. Coping strategies were classified under the aim of the strategy when used: 'Normalising'; 'Maintaining hope and a sense of the future'; 'Dealing with an uncertain future'; and 'Seeking Support'.

CONCLUSIONS

Coping and finding ways to cope are clearly used by patients and their families and are something that they wish to discuss, as they were raised in conversations that were not necessarily about coping. This suggests clinicians should always allow time and space (in appointments, consultations, or impromptu conversations on the ward) for patient families to discuss ways of coping. MRIs were found to be used in various ways: to maintain or adapt normal; maintain hope and a sense of the future; deal with an uncertain future; and seek support from others. Clinicians should recognise the potential for MRIs to aid coping and if appropriate, suggest that families take copies of scans (MRIs) home. Professional coaches or counsellors may also find MRIs beneficial as a way to remind families that the child is in a more stable or 'better' place than they have been previously.

European Society for Paediatric Oncology (SIOPE) MRI guidelines for imaging patients with central nervous system tumours

INTRODUCTION

Standardisation of imaging acquisition is essential in facilitating multicentre studies related to childhood CNS tumours. It is important to ensure that the imaging protocol can be adopted by centres with varying imaging capabilities without compromising image quality.

MATERIALS AND METHOD

An imaging protocol has been developed by the Brain Tumour Imaging Working Group of the European Society for Paediatric Oncology (SIOPE) based on consensus among its members, which consists of neuroradiologists, imaging scientists and paediatric neuro-oncologists. This protocol has been developed to facilitate SIOPE led studies and regularly reviewed by the imaging working group.

RESULTS

The protocol consists of essential MRI sequences with imaging parameters for 1.5 and 3 Tesla MRI scanners and a set of optional sequences that can be used in appropriate clinical settings. The protocol also provides guidelines for early post-operative imaging and surveillance imaging. The complementary use of multimodal advanced MRI including diffusion tensor imaging (DTI), MR spectroscopy and perfusion imaging is encouraged, and optional guidance is provided in this publication.

CONCLUSION

The SIOPE brain tumour imaging protocol will enable consistent imaging across multiple centres involved in paediatric CNS tumour studies.

Molecular characterization of DICER1-mutated pituitary blastoma

Pituitary blastoma (PitB) has recently been identified as a rare and potentially lethal pediatric intracranial tumor. All cases that have been studied molecularly possess at least one DICER1 pathogenic variant. Here, we characterized nine pituitary samples, including three fresh frozen PitBs, three normal fetal pituitary glands and three normal postnatal pituitary glands using small-RNA-Seq, RNA-Seq, methylation profiling, whole genome sequencing and Nanostring® miRNA analyses; an extended series of 21 pituitary samples was used for validation purposes. These analyses demonstrated that DICER1 RNase IIIb hotspot mutations in PitBs induced improper processing of miRNA precursors, resulting in aberrant 5p-derived miRNA products and a skewed distribution of miRNAs favoring mature 3p over 5p miRNAs. This led to dysregulation of hundreds of 5p and 3p miRNAs and concomitant dysregulation of numerous mRNA targets. Gene expression analysis revealed PRAME as the most significantly upregulated gene (500-fold increase). PRAME is a member of the Retinoic Acid Receptor (RAR) signaling pathway and in PitBs, the RAR, WNT and NOTCH pathways are dysregulated. Cancer Hallmarks analysis showed that PI3K pathway is activated in the tumors. Whole genome sequencing demonstrated a quiet genome with very few somatic alterations. The comparison of methylation profiles to publicly available data from ~ 3000 other central nervous system tumors revealed that PitBs have a distinct methylation profile compared to all other tumors, including pituitary adenomas. In conclusion, this comprehensive characterization of DICER1-related PitB revealed key molecular underpinnings of PitB and identified pathways that could potentially be exploited in the treatment of this tumor.

EMBR-10. INOSITOL TREATMENT INHIBITS MEDULLOBLASTOMA THROUGH SUPPRESSION OF EPIGENETIC-DRIVEN METABOLIC ADAPTATION

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.

Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma

AIM

To investigate machine learning based models combining clinical, radiomic, and molecular information to distinguish between early true progression (tPD) and pseudoprogression (psPD) in patients with glioblastoma.

MATERIALS AND METHODS

A retrospective analysis was undertaken of 76 patients (46 tPD, 30 psPD) with early enhancing disease following chemoradiotherapy for glioblastoma. Outcome was determined on follow-up until 6 months post-chemoradiotherapy. Models comprised clinical characteristics, O⁶-methylguanine-DNA methyltransferase (MGMT) promoter methylation status, and 307 quantitative imaging features extracted from enhancing disease and perilesional oedema masks on early post-chemoradiotherapy contrast-enhanced T1-weighted imaging, T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) maps. Feature selection was performed within bootstrapped cross-validated recursive feature elimination with a random forest algorithm. Naive Bayes five-fold cross-validation was used to validate the final model.

RESULTS

Top selected features included age, MGMT promoter methylation status, two shape-based features from the enhancing disease mask, three radiomic features from the enhancing disease mask on ADC, and one radiomic feature from the perilesional oedema mask on T2WI. The final model had an area under the receiver operating characteristics curve (AUC) of 0.80, sensitivity 78.2%, specificity 66.7%, and accuracy of 73.7%.

CONCLUSION

Incorporating a machine learning-based approach using quantitative radiomic features from standard-of-care magnetic resonance imaging (MRI), in combination with clinical characteristics and MGMT promoter methylation status has a complementary effect and improves model performance for early prediction of glioblastoma treatment response.

Inositol treatment inhibits medulloblastoma through suppression of epigenetic-driven metabolic adaptation

Deregulation of chromatin modifiers plays an essential role in the pathogenesis of medulloblastoma, the most common paediatric malignant brain tumour. Here, we identify a BMI1-dependent sensitivity to deregulation of inositol metabolism in a proportion of medulloblastoma. We demonstrate mTOR pathway activation and metabolic adaptation specifically in medulloblastoma of the molecular subgroup G4 characterised by a BMI1High;CHD7Low signature and show this can be counteracted by IP6 treatment. Finally, we demonstrate that IP6 synergises with cisplatin to enhance its cytotoxicity in vitro and extends survival in a pre-clinical BMI1High;CHD7Low xenograft model.

Classification of paediatric brain tumours by diffusion weighted imaging and machine learning

To determine if apparent diffusion coefficients (ADC) can discriminate between posterior fossa brain tumours on a multicentre basis. A total of 124 paediatric patients with posterior fossa tumours (including 55 Medulloblastomas, 36 Pilocytic Astrocytomas and 26 Ependymomas) were scanned using diffusion weighted imaging across 12 different hospitals using a total of 18 different scanners. Apparent diffusion coefficient maps were produced and histogram data was extracted from tumour regions of interest. Total histograms and histogram metrics (mean, variance, skew, kurtosis and 10th, 20th and 50th quantiles) were used as data input for classifiers with accuracy determined by tenfold cross validation. Mean ADC values from the tumour regions of interest differed between tumour types, (ANOVA P < 0.001). A cut off value for mean ADC between Ependymomas and Medulloblastomas was found to be of 0.984 × 10⁻³ mm² s⁻¹ with sensitivity 80.8% and specificity 80.0%. Overall classification for the ADC histogram metrics were 85% using Naïve Bayes and 84% for Random Forest classifiers. The most commonly occurring posterior fossa paediatric brain tumours can be classified using Apparent Diffusion Coefficient histogram values to a high accuracy on a multicentre basis.

Clinical Outcomes and Complications of Pituitary Blastoma

CONTEXT

Pituitary blastoma is a rare, dysontogenetic hypophyseal tumor of infancy first described in 2008, strongly suggestive of DICER1 syndrome.

OBJECTIVE

This work aims to describe genetic alterations, clinical courses, outcomes, and complications in all known pituitary blastoma cases.

DESIGN AND SETTING

A multi-institutional case series is presented from tertiary pediatric oncology centers.

PATIENTS

Patients included children with pituitary blastoma.

INTERVENTIONS

Genetic testing, surgery, oncologic therapy, endocrine support are reported.

OUTCOME MEASURES

Outcome measures included survival, long-term morbidities, and germline and tumor DICER1 genotypes.

RESULTS

Seventeen pituitary blastoma cases were studied (10 girls and 7 boys); median age at diagnosis was 11 months (range, 2-24 months). Cushing syndrome was the most frequent presentation (n = 10). Cushingoid stigmata were absent in 7 children (2 with increased adrenocorticotropin [ACTH]; 5 with normal/unmeasured ACTH). Ophthalmoplegia and increased intracranial pressure were also observed. Surgical procedures included gross/near-total resection (n = 7), subtotal resection (n = 9), and biopsy (n = 1). Six children received adjuvant therapy. At a median follow-up of 6.7 years, 9 patients were alive; 8 patients died of the following causes: early medical/surgical complications (n = 3), sepsis (n = 1), catheter-related complication (n = 1), aneurysmal bleeding (n = 1), second brain tumor (n = 1), and progression (n = 1). Surgery was the only intervention for 5 of 9 survivors. Extent of resection, but neither Ki67 labeling index nor adjuvant therapy, was significantly associated with survival. Chronic complications included neuroendocrine (n = 8), visual (n = 4), and neurodevelopmental (n = 3) deficits. Sixteen pituitary blastomas were attributed to DICER1 abnormalities.

CONCLUSIONS

Pituitary blastoma is a locally destructive tumor associated with high mortality. Surgical resection alone provides long-term disease control for some patients. Quality survival is possible with long-term neuroendocrine management.

MBCL-52. ENDOCRINE PROFILE AFTER MEDULLOBLASTOMA TREATMENT

BACKGROUND

Treatment of medulloblastoma has evolved substantially with more chemotherapy, risk-adapted dosing of radiotherapy (RT) and new RT techniques. We present the endocrine profile for our patients treated over a 20-year period.

METHODS

The charts of patients treated for medulloblastoma between 1/1/00 and 31/12/19 were reviewed. 105 were available. Group 1 received chemotherapy alone, Group 2 received 23.4 Gy whole CNS RT with a posterior fossa (PF) boost to 54 Gy, Group 3 received > 35 Gy whole CNS RT with PF boost to 54–59 Gy, Group 4 received PF RT to 54 Gy. All received chemotherapy according to national guidelines or clinical trials relevant at the time.

RESULTS

Group 1 (M:F 11:6, 7 survivors mean age 2 years range 1–7) had no endocrinopathies. At 5 years from diagnosis Group 2 (M:F 15:13) and Group 3 (M:F 35:14) had the following %

RESULTS

Survival 77:61; Growth Hormone deficiency 92:100; Thyroid deficiency 75:81; ACTH deficiency 42:33. Girls were more likely to need sex hormone replacement than boys. Group 4 (M:F 7:5 mean age 2) were all treated in the first decade. 3 survivors, one GH deficiency, one thyroxine deficiency, one both.

CONCLUSIONS

There is a trend to earlier endocrinopathies in the group 3 vs group 2 patients, but it does not reach statistical significance. Girls are more likely to need sex hormone replacement than boys. This investigation provides a contemporary profile of endocrinopathy after treatment for medulloblastoma that can be used for future comparisons.

MBRS-69. METABOLITE PROFILING OF SHH MEDULLOBLASTOMA IDENTIFIES A SUBSET OF CHILDHOOD TUMOURS ENRICHED FOR HIGH-RISK MOLECULAR BIOMARKERS AND CLINICAL FEATURES

SHH medulloblastoma patients have a variable prognosis. Infants (&lt;3–5 years at diagnosis) are associated with a good prognosis, while disease-course in childhood is associated with specific prognostic biomarkers (MYCN amplification, TP53 mutation, LCA histology; all high-risk). There is an unmet need to identify prognostic subgroups of SHH tumours rapidly in the clinical setting, to aid in real-time risk stratification and disease management. Metabolite profiling is a powerful technique for characterising tumours. High resolution magic angle spinning NMR spectroscopy (HR-MAS) can be performed on frozen tissue samples and provides high quality metabolite information. We therefore assessed whether metabolite profiles could identify subsets of SHH tumours with prognostic potential. Metabolite concentrations of 22 SHH tumours were acquired by HR-MAS and analysed using unsupervised hierarchical clustering. Methylation profiling assigned the infant and childhood SHH subtypes, and clinical and molecular features were compared between clusters. Two clusters were observed. A significantly higher concentration of lipids was observed in Cluster 1 (t-test, p=0.012). Cluster 1 consisted entirely of childhood-SHH whilst Cluster 2 included both childhood-SHH and infant-SHH subtypes. Cluster 1 was enriched for high-risk markers - LCA histology (3/7 v. 0/5), MYCN amplification (2/7 v. 0/5), TP53 mutations (3/7 v. 1/5) and metastatic disease - whilst having a lower proportion of TERT mutations (0/7 v. 2/5) than Cluster 2. These pilot results suggest that (i) it is possible to identify childhood-SHH patients linked to high-risk clinical and molecular biomarkers using metabolite profiles and (ii) these may be detected non-invasively in vivo using magnetic-resonance spectroscopy.

Brain Age Prediction of Children Using Routine Brain MR Images via Deep Learning

Predicting brain age of children accurately and quantitatively can give help in brain development analysis and brain disease diagnosis. Traditional methods to estimate brain age based on 3D magnetic resonance (MR), T1 weighted imaging (T1WI), and diffusion tensor imaging (DTI) need complex preprocessing and extra scanning time, decreasing clinical practice, especially in children. This research aims at proposing an end-to-end AI system based on deep learning to predict the brain age based on routine brain MR imaging. We spent over 5 years enrolling 220 stacked 2D routine clinical brain MR T1-weighted images of healthy children aged 0 to 5 years old and randomly divided those images into training data including 176 subjects and test data including 44 subjects. Data augmentation technology, which includes scaling, image rotation, translation, and gamma correction, was employed to extend the training data. A 10-layer 3D convolutional neural network (CNN) was designed for predicting the brain age of children and it achieved reliable and accurate results on test data with a mean absolute deviation (MAE) of 67.6 days, a root mean squared error (RMSE) of 96.1 days, a mean relative error (MRE) of 8.2%, a correlation coefficient (R) of 0.985, and a coefficient of determination (R 2) of 0.971. Specially, the performance on predicting the age of children under 2 years old with a MAE of 28.9 days, a RMSE of 37.0 days, a MRE of 7.8%, a R of 0.983, and a R 2 of 0.967 is much better than that over 2 with a MAE of 110.0 days, a RMSE of 133.5 days, a MRE of 8.2%, a R of 0.883, and a R 2 of 0.780.

Qualitative study: patients' and parents' views on brain tumour MRIs

BACKGROUND

MRI is essential to the clinical management of children and young people with brain tumours. Advances in technology have made images more complicated to interpret, yet more easily available digitally. It is common practice to show these to patients and families, but how they emotionally respond to, understand and value, seeing brain tumour MRIs has not been formally studied.

METHODS

Qualitative semi-structured interviews were undertaken with 14 families (8 patients, 15 parents) purposively sampled from paediatric patients (0 to 18 years) attending a large UK children's hospital for treatment or monitoring of a brain tumour. Transcripts were analysed thematically using the Framework Method.

RESULTS

Four themes were identified: Receiving results (waiting for results, getting results back, preferences to see images), Emotional responses to MRIs, Understanding of images (what they can show, what they cannot show, confusion) and Value of MRIs (aesthetics, aiding understanding, contextualised knowledge/emotional benefits, enhanced control, enhanced working relationships, no value). All families found value in seeing MRIs, including reassurance, hope, improved understanding and enhanced feeling of control over the condition. However emotional responses varied enormously.

CONCLUSIONS

Clinical teams should always explain MRIs after 'framing' the information. This should minimise participant confusion around meaning, periodically evident even after many years. Patient and parent preferences for being shown MRIs varied, and often changed over time, therefore clinicians should identify, record and update these preferences. Time between scanning and receiving the result was stressful causing 'scanxiety', but most prioritised accuracy over speed of receiving results.

Utilising functional imaging to predict survival in paediatric brain tumours

Introduction

Brain tumours are a common cause of death in the paediatric population. We have previously shown that MR imaging and spectroscopy can be used to non-invasively differentiate between tumour types. Here, we demonstrate that functional imaging can be highly predictive of survival and grade in a paediatric cohort.

Methods

Perfusion (PWI) and diffusion weighted imaging (DWI) were performed in a multi-site (Birmingham Children’s Hospital, Royal Victoria Infirmary, Alder Hey, Nottingham) cohort ([grade, 5-year survival alive:dead number] = [I,15:1],[II, 5:1],[III,2:3],[IV,8:11]). ROIs were drawn on T2 imaging and functional imaging features (mean, standard deviation, skewness, and kurtosis) were derived. Supervised machine learning was used to predict 5-year survival and tumour grade from features. ANOVA and post-hoc tests were used to assess differences in features between grade and 5-year survival status.

Results

5-year survival was predicted with 89%, 85%, and 87% accuracy with all imaging, perfusion, or diffusion features, respectively.

A significant difference in perfusion was found between surviving and diseased participants (1.71 ± 0.82 vs 2.62 ± 1 mL/100g/min, respectively, p < 0.05). A significant difference in ADC (mm2 s-1) between tumour grades was found (1 vs 4 (1533 ± 458 vs 857 ± 239), 4 vs 3 (857 ± 239 vs 1197 ± 137), 4 vs 2 (857 ± 239 vs 1440 ± 557), corrected p < 0.05).

Conclusion

We have shown that perfusion and diffusion imaging features can be used to non-invasively assess tumour grade and estimate 5-year survival status in a cohort of paediatric brain tumours.

Multicentre study of perfusion magnetic resonance imaging in paediatric brain tumours

Studies in adults have shown that brain tumour perfusion correlates with grade. These studies are dominated by gliomas grade II to IV which are rare in children. The standard method, Dynamic Susceptibility Contrast MRI, provides estimates of relative cerebral blood volume (rCBV) but contrast agent leakage affects rCBV accuracy. The majority of perfusion studies have been conducted at single centres and variation in acquisition protocols makes the generalizability of results questionable. The aim of this study was to compare leakage-corrected rCBV with grade in paediatric brain tumours at multiple centres. Scans were analysed from 85 patients at 4 centres on 6 scanners prior to treatment. MRI protocols varied between centres. Histological diagnoses including grade were obtained. Whole-tumour median rCBV was significantly higher in the 45 high grade than the 40 low grade tumours (2.54 ± 1.63 ml/100ml vs 1.68 ± 1.36 ml/100ml, p=0.010). Low grade tumours, particularly pilocytic astrocytomas (grade I), displayed more contrast agent leakage consistent with their appearance on contrast enhanced images and required more leakage correction than high grade tumours. This finding differs from that in adults where contrast agent uptake is usually associated with higher grade. A cut-off of 1.70 ml/100ml for rCBV gave sensitivity and specificity of 76% and 65% respectively for discriminating grade. In summary, perfusion MRI can be used to help distinguish between low and high grade paediatric brain tumours. This finding is robust across multiple centres and acquisition protocols but correction should be made for leakage of contrast agent from the vessels.

A study of 51 subtypes of peripheral blood immune cells in newly diagnosed young type 1 diabetes patients

Type 1 diabetes (T1D) results from autoimmune destruction of insulin-producing beta cells in pancreatic islets. Various immune cell populations are involved in disease development and natural course. However, to our knowledge, so far there are no comprehensive comparative investigations of all main immune cell populations and their most important subsets at the onset of disease. Therefore, in the current study, we analyzed 51 peripheral blood immune cell populations in 22 young T1D patients and in 25 age-matched controls using a comprehensive polychromatic flow cytometry panel developed for whole blood by the COST Action no. BM0907 ENTIRE (European Network for Translational Immunology Research and Education: From Immunomonitoring to Personalized Immunotherapy) consortium. We found that in T1D patients, frequencies and absolute counts of natural killer (NK) cells, dendritic cells (DC) and T cells, as well as their respective subsets, were significantly altered compared to controls. Further, we observed that changes in several cell populations (e.g. CD14⁺ CD16⁺ non-classical monocytes, plasmablasts) were dependent on the age of the patient. In addition to age-related changes, we also found that alterations in immune cell patterns were associated with parameters such as the presence of ketoacidosis and C-peptide serum levels. Our study provides a foundation for future studies investigating different cell lineages and their role in T1D and illustrates the value of polychromatic flow cytometry for evaluating all main peripheral immune cells and their subsets in whole blood samples.

The utility of routine surveillance screening with magnetic resonance imaging to detect tumor recurrence/progression in children with high-grade central nervous system tumors: a systematic review

BACKGROUND

Surveillance magnetic resonance imaging (MRI) is routinely used to detect recurrence in children with high-grade central nervous system (CNS) tumors, although no consensus has been reached regarding its effectiveness and whether earlier detection is associated with improved patient outcomes. This review aimed to evaluate this practice and any associated benefits and harms.

METHODS

Systematic searches for relevant studies were undertaken in a number of databases, including MEDLINE and EMBASE, from 1985 to August 2018. Study selection and data extraction was undertaken independently by two reviewers. Due to heterogeneity between studies, no pooling of data was undertaken. Reporting followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

RESULTS

No comparative studies were identified. Three retrospective observational studies involving 306 patients were reviewed. All had high risk of bias by virtue of study design. Two studies reported outcomes by symptomatic status-both recurrence rates and overall survival for asymptomatic patients were comparable with those for clinically symptomatic patients. No quality-of-life outcomes were reported.

CONCLUSION

There is a paucity of evidence to guide clinical practice as to the effectiveness of MRI surveillance in pediatric patients with high-grade CNS tumors. These studies do not clearly demonstrate benefit or harm for the practice. With more research needed, there is a role for researchers to build into future trials data collection on surveillance imaging to give more information for the assessment of imaging frequency and duration in asymptomatic patients. This is an important question not only to clinicians and patients and their families but also from a health service resource perspective.

Evaluation of Response to Stereotactic Radiosurgery in Brain Metastases Using Multiparametric Magnetic Resonance Imaging and a Review of the Literature

AIMS

Following stereotactic radiosurgery (SRS), brain metastases initially increase in size in up to a third of cases, suggesting treatment failure. Current imaging using structural magnetic resonance imaging (MRI) cannot differentiate between tumour recurrence and SRS-induced changes, creating difficulties with patient management. Combining multiparametric MRI techniques, which assess tissue physiological and metabolic information, has shown promise in answering this clinical question.

MATERIALS AND METHODS

Multiparametric MRI techniques, including spectroscopy, diffusion and perfusion imaging, were used for the differentiation of radiation-related changes and tumour recurrence after SRS for intracranial metastases in six cases. All patients presented with enlargement of the treated lesion, an increase in perilesional brain oedema and aggravation or appearance of neurological signs and symptoms from 7 to 29 weeks after primary treatment.

RESULTS

Multiparametric imaging helped to differentiate features of tumour progression (n = 4) from radiation-related changes (n = 2). A low apparent diffusion coefficient (ADC) <1000 × 10^-6 mm²/s, high relative cerebral blood volume (rCBV) ratio > 2.1, high choline:creatine (Cho:Cr) ratio > 1.8 suggested tumour recurrence. A high ADC > 1000 × 10^-6 mm²/s, low rCBV ratio < 2.1, Cho:Cr ratio < 1.8 suggested SRS-induced radiation changes. Multiparametric MRI diagnosis was confirmed by histology or radiological and clinical follow-up.

CONCLUSION

Multiparametric MRI was helpful in the early identification of radiation-related changes and tumour recurrence and may be useful for monitoring treatment changes in intracranial neoplasms after SRS treatment.

The utility of routine surveillance screening with magnetic resonance imaging (MRI) to detect tumour recurrence in children with low-grade central nervous system (CNS) tumours: a systematic review

BACKGROUND

Magnetic resonance imaging (MRI) is routinely used as a surveillance tool to detect early asymptomatic tumour recurrence with a view to improving patient outcomes. This systematic review aimed to assess its utility in children with low-grade CNS tumours.

METHODS

Using standard systematic review methods, twelve databases were searched up to January 2017.

RESULTS

Seven retrospective case series studies (n = 370 patients) were included, with average follow-up ranging from 5.6 to 7 years. No randomised controlled trials (RCTs) were identified. Due to study heterogeneity only a descriptive synthesis could be undertaken. Imaging was most frequent in the first year post-surgery (with 2-4 scans) reducing to around half this frequency in year two and annually thereafter for the duration of follow-up. Diagnostic yield ranged from 0.25 to 2%. Recurrence rates ranged from 5 to 41%, with most recurrences asymptomatic (range 65-100%). Collectively, 56% of recurrences had occurred within the first year post-treatment (46% in the first 6-months), 68% by year two and 90% by year five. Following recurrence, 90% of patients underwent treatment changes, mainly repeat surgery (72%). Five-year OS ranged from 96 to 100%, while five-year recurrence-free survival ranged from 67 to 100%. None of the studies reported quality of life measures.

CONCLUSION

This systematic review highlights the paucity of evidence currently available to assess the utility of MRI surveillance despite it being routine clinical practice and costly to patients, their families and healthcare systems. This needs to be evaluated within the context of an RCT.

What is MR spectroscopy?

¹H-Magnetic Resonance Spectroscopy (MRS) is a novel advanced imaging technique used as an adjunct to MRI to reveal complementary non-invasive information about the biochemical composition of imaged tissue. Clinical uses in paediatrics include aiding diagnosis of brain tumours, neonatal disorders such as hypoxic-ischaemic encephalopathy, inherited metabolic diseases, traumatic brain injury, demyelinating conditions and infectious brain lesions. MRS has potential to improve diagnosis and treatment monitoring of childhood brain tumours and other CNS diseases, facilitate biopsy and surgical planning, and provide prognostic biomarkers. MRS is employed as a research tool outside the brain in liver disease and disorders of muscle metabolism. The range of clinical uses is likely to increase with growing evidence for added value. Multicentre trials are needed to definitively establish the benefits of MRS in specific clinical scenarios and integrate this promising new technique into routine practice to improve patient care. This article gives a brief overview of MRS and its potential clinical applications, and addresses challenges surrounding translation into practice.

Automated Modular Magnetic Resonance Imaging Clinical Decision Support System (MIROR): An Application in Pediatric Cancer Diagnosis

BACKGROUND

Advances in magnetic resonance imaging and the introduction of clinical decision support systems has underlined the need for an analysis tool to extract and analyze relevant information from magnetic resonance imaging data to aid decision making, prevent errors, and enhance health care.

OBJECTIVE

The aim of this study was to design and develop a modular medical image region of interest analysis tool and repository (MIROR) for automatic processing, classification, evaluation, and representation of advanced magnetic resonance imaging data.

METHODS

The clinical decision support system was developed and evaluated for diffusion-weighted imaging of body tumors in children (cohort of 48 children, with 37 malignant and 11 benign tumors). Mevislab software and Python have been used for the development of MIROR. Regions of interests were drawn around benign and malignant body tumors on different diffusion parametric maps, and extracted information was used to discriminate the malignant tumors from benign tumors.

RESULTS

Using MIROR, the various histogram parameters derived for each tumor case when compared with the information in the repository provided additional information for tumor characterization and facilitated the discrimination between benign and malignant tumors. Clinical decision support system cross-validation showed high sensitivity and specificity in discriminating between these tumor groups using histogram parameters.

CONCLUSIONS

MIROR, as a diagnostic tool and repository, allowed the interpretation and analysis of magnetic resonance imaging images to be more accessible and comprehensive for clinicians. It aims to increase clinicians' skillset by introducing newer techniques and up-to-date findings to their repertoire and make information from previous cases available to aid decision making. The modular-based format of the tool allows integration of analyses that are not readily available clinically and streamlines the future developments.

Early childhood infections precede development of beta-cell autoimmunity and type 1 diabetes in children with HLA-conferred disease risk

BACKGROUND

The etiology of type 1 diabetes (T1D) is largely unknown. Infections and microbial exposures are believed to play a role in the pathogenesis and in the development of islet autoimmunity in genetically susceptible individuals.

OBJECTIVE

To assess the relationships between early childhood infections, islet autoimmunity, and progression to T1D in genetically predisposed children.

METHODS

Children with human leukocyte antigen (HLA)-conferred disease susceptibility (N=790; 51.5% males) from Finland (n = 386), Estonia (n = 322), and Russian Karelia (n = 82) were observed from birth up to the age of 3 years. Children attended clinical visits at the age of 3, 6, 12, 18, 24, and 36 months. Serum samples for analyzing T1D-associated autoimmune markers were collected and health data recorded during the visits.

RESULTS

Children developing islet autoimmunity (n = 46, 5.8%) had more infections during the first year of life (3.0 vs 3.0, mean rank 439.1 vs 336.2; P = .001) and their first infection occurred earlier (3.6 vs 5.0 months; P = .005) than children with no islet autoimmunity. By May 2016, 7 children (0.9%) had developed T1D (progressors). Compared with non-diabetic children, T1D progressors were younger at first infection (2.2 vs 4.9 months; P = .004) and had more infections during the first 2 years of life (during each year 6.0 vs 3.0; P = .001 and P = .027, respectively). By 3 years of age, the T1D progressors had twice as many infections as the other children (17.5 vs 9.0; P = .006).

CONCLUSIONS

Early childhood infections may play an important role in the pathogenesis of T1D. Current findings may reflect either differences in microbial exposures or early immunological aberrations making diabetes-prone children more susceptible to infections.

High-Speed Tracer Analysis of Metabolism (HS-TrAM)

Tracing the fate of stable isotopically-enriched nutrients is a sophisticated method of describing and quantifying the activity of metabolic pathways. Nuclear Magnetic Resonance (NMR) offers high resolution data, yet is under-utilised due to length of time required to collect the data, quantification requiring multiple samples and complicated analysis. Here we present two techniques, quantitative spectral filters and enhancement of the splitting due to J-coupling in 1H,13C-HSQC NMR spectra, which allow the rapid collection of NMR data in a quantitative manner on a single sample. The reduced duration of HSQC spectra data acquisition opens up the possibility of real-time tracing of metabolism including the study of metabolic pathways in vivo. We show how these novel techniques can be used to trace the fate of labelled nutrients in a whole organ model of kidney preservation prior to transplantation using a porcine kidney as a model organ, and also show how the use of multiple nutrients, differentially labelled with 13C and 15N, can be used to provide additional information with which to profile metabolic pathways.

High-Speed Tracer Analysis of Metabolism (HS-TrAM)

Tracing the fate of stable isotopically-enriched nutrients is a sophisticated method of describing and quantifying the activity of metabolic pathways. Nuclear Magnetic Resonance (NMR) spectroscopy offers high resolution data in terms of resolving metabolic pathway utilisation. Despite this, NMR spectroscopy is under-utilised due to length of time required to collect the data, quantification requiring multiple samples and complicated analysis. Here we present two techniques, quantitative spectral filters and enhancement of the splitting of  ¹³C signals due to homonuclear  ¹³C, ¹³C or heteronuclear  ¹³C, ¹⁵N J-coupling in  ¹H, ¹³C-HSQC NMR spectra. Together, these allow the rapid collection of NMR spectroscopy data in a quantitative manner on a single sample. The reduced duration of HSQC spectra data acquisition opens up the possibility of real-time tracing of metabolism including the study of metabolic pathways  in vivo. We show how these techniques can be used to trace the fate of labelled nutrients in a whole organ model of kidney preservation prior to transplantation using a porcine kidney as a model organ. In addition, we show how the use of multiple nutrients, differentially labelled with  ¹³C and  ¹⁵N, can be used to provide additional information with which to profile metabolic pathways.

Application of pattern recognition techniques for classification of pediatric brain tumors by in vivo 3T 1 H-MR spectroscopy-A multi-center study

Purpose

3T magnetic resonance scanners have boosted clinical application of ¹H‐MR spectroscopy (MRS) by offering an improved signal‐to‐noise ratio and increased spectral resolution, thereby identifying more metabolites and extending the range of metabolic information. Spectroscopic data from clinical 1.5T MR scanners has been shown to discriminate between pediatric brain tumors by applying machine learning techniques to further aid diagnosis. The purpose of this multi‐center study was to investigate the discriminative potential of metabolite profiles obtained from 3T scanners in classifying pediatric brain tumors.

Methods

A total of 41 pediatric patients with brain tumors (17 medulloblastomas, 20 pilocytic astrocytomas, and 4 ependymomas) were scanned across four different hospitals. Raw spectroscopy data were processed using TARQUIN. Borderline synthetic minority oversampling technique was used to correct for the data skewness. Different classifiers were trained using linear discriminative analysis, support vector machine, and random forest techniques.

Results

Support vector machine had the highest balanced accuracy for discriminating the three tumor types. The balanced accuracy achieved was higher than the balanced accuracy previously reported for similar multi‐center dataset from 1.5T magnets with echo time 20 to 32 ms alone.

Conclusion

This study showed that 3T MRS can detect key differences in metabolite profiles for the main types of childhood tumors. Magn Reson Med 79:2359–2366, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Differences in B7 and CD28 family gene expression in the peripheral blood between newly diagnosed young-onset and adult-onset type 1 diabetes patients

Type-1 diabetes (T1D) is a heterogeneous autoimmune disease, and there are pathogenetic differences between young- and adult-onset T1D patients. We hypothesized that the expressions of genes involved in costimulatory immune system pathways in peripheral blood are differently regulated in young- and adult-onset T1D. Study group I consisted of 80 children, adolescents, and young adults (age range 1.4-21.4 y; 31 controls and 49 T1D patients). Study group II consisted of 48 adults (age range 22.0-78.4 y; 30 controls and 18 T1D patients). The mRNA expression levels of CD86, CD28, CD25, CD226, CD40, BTLA, GITR, PDCD1, FoxP3, TGF-β, ICOS, sCTLA4, flCTLA4, and CD80 were measured in peripheral blood. Genetic polymorphisms (HLA haplotypes; rs231806, rs231775, and rs3087243 in CTLA4; rs763361 in CD226; and rs706778 in CD25) and T1D-associated autoantibodies were analyzed. In group I, there was significantly lower expression of CD226 in T1D patients than in the controls. In group II, there were significantly higher expression levels of CD86 and TGF-β in T1D patients than in the controls. In the T1D patients in group I, the upregulated CD80 expression correlated with the expression of both CTLA4 splice variants (sCTLA4 and flCTLA4). In contrast, in group II, upregulated CD86 correlated with TGF-β and CD25. In group I, the inhibitory CD80-CTLA4 pathway was activated, whereas, in group II, the activation CD86-CD28 pathway and TGF-β production were activated. These results emphasize the differences between young-onset and adult-onset T1D in the regulation of costimulatory pathways. These differences should be considered when developing novel treatments for T1D.