Optimising the chick chorioallantoic membrane xenograft model of neuroblastoma for drug delivery

Background Neuroblastoma is a paediatric cancer that despite multimodal therapy still has a poor outcome for many patients with high risk tumours. Retinoic acid (RA) promotes differentiation of some neuroblastoma tumours and cell lines, and is successfully used clinically, supporting the view that differentiation therapy is a promising strategy for treatment of neuroblastoma. To improve treatment of a wider range of tumour types, development and testing of novel differentiation agents is essential. New pre-clinical models are therefore required to test therapies in a rapid cost effective way in order to identify the most useful agents. Methods As a proof of principle, differentiation upon ATRA treatment of two MYCN-amplified neuroblastoma cell lines, IMR32 and BE2C, was measured both in cell cultures and in tumours formed on the chick chorioallantoic membrane (CAM). Differentiation was assessed by 1) change in cell morphology, 2) reduction in cell proliferation using Ki67 staining and 3) changes in differentiation markers (STMN4 and ROBO2) and stem cell marker (KLF4). Results were compared to MLN8237, a classical Aurora Kinase A inhibitor. For the in vivo experiments, cells were implanted on the CAM at embryonic day 7 (E7), ATRA treatment was between E11 and E13 and tumours were analysed at E14. Results Treatment of IMR32 and BE2C cells in vitro with 10 μM ATRA resulted in a change in cell morphology, a 65% decrease in cell proliferation, upregulation of STMN4 and ROBO2 and downregulation of KLF4. ATRA proved more effective than MLN8237 in these assays. In vivo, 100 μM ATRA repetitive treatment at E11, E12 and E13 promoted a change in expression of differentiation markers and reduced proliferation by 43% (p < 0.05). 40 μM ATRA treatment at E11 and E13 reduced proliferation by 37% (p < 0.05) and also changed cell morphology within the tumour. Conclusion Differentiation of neuroblastoma tumours formed on the chick CAM can be analysed by changes in cell morphology, proliferation and gene expression. The well-described effects of ATRA on neuroblastoma differentiation were recapitulated within 3 days in the chick embryo model, which therefore offers a rapid, cost effective model compliant with the 3Rs to select promising drugs for further preclinical analysis

The deubiquitylase USP31 controls the Chromosomal Passenger Complex and spindle dynamics

We have identified USP31 as a microtubule and centrosome associated deubiquitylase, depletion of which leads to an increase in individual cell mass and defective proliferation. We have examined its dynamics and impact during mitosis. GFP-USP31 associates with the mitotic and central spindles, its levels are increased 2-3-fold in prometaphase compared to asynchronous cells and it is dynamically phosphorylated in a CDK1 dependent manner. We find that USP31 depleted cells display altered spindle morphology and chromosomal segregation errors, whilst stable expression of a catalytically inactive form of USP31 leads to polyploidy. At prometaphase, levels of multiple CPC components are destabilised, most prominently INCENP. Under anaphase conditions, depletion of USP31 impairs the translocation of both endogenous and ectopically expressed INCENP to the spindle mid-zone, whilst expression of catalytically inactive USP31 results in multiple ectopic cleavage furrows. In summary, our data indicate a multifaceted regulatory role for USP31 during mitosis, with a profound impact on chromosomal passenger complex abundance, dynamics and function

Characterisation of paediatric brain tumours by their MRS metabolite profiles

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

Noise suppression of proton magnetic resonance spectroscopy improves paediatric brain tumour classification

Proton magnetic resonance spectroscopy (1H‐MRS) is increasingly used for clinical brain tumour diagnosis, but suffers from limited spectral quality. This retrospective and comparative study aims at improving paediatric brain tumour classification by performing noise suppression on clinical 1H‐MRS. Eighty‐three/forty‐two children with either an ependymoma (ages 4.6 ± $$\pm$$ 5.3/9.3 ± $$\pm$$ 5.4), a medulloblastoma (ages 6.9 ± $$\pm$$ 3.5/6.5 ± $$\pm$$ 4.4), or a pilocytic astrocytoma (8.0 ± $$\pm$$ 3.6/6.3 ± $$\pm$$ 5.0), recruited from four centres across England, were scanned with 1.5T/3T short‐echo‐time point‐resolved spectroscopy. The acquired raw 1H‐MRS was quantified by using Totally Automatic Robust Quantitation in NMR (TARQUIN), assessed by experienced spectroscopists, and processed with adaptive wavelet noise suppression (AWNS). Metabolite concentrations were extracted as features, selected based on multiclass receiver operating characteristics, and finally used for identifying brain tumour types with supervised machine learning. The minority class was oversampled through the synthetic minority oversampling technique for comparison purposes. Post‐noise‐suppression 1H‐MRS showed significantly elevated signal‐to‐noise ratios (P .05, Wilcoxon signed‐rank test), and significantly higher classification accuracy (P < .05, Wilcoxon signed‐rank test). Specifically, the cross‐validated overall and balanced classification accuracies can be improved from 81% to 88% overall and 76% to 86% balanced for the 1.5T cohort, whilst for the 3T cohort they can be improved from 62% to 76% overall and 46% to 56%, by applying Naïve Bayes on the oversampled 1H‐MRS. The study shows that fitting‐based signal‐to‐noise ratios of clinical 1H‐MRS can be significantly improved by using AWNS with insignificantly altered line width, and the post‐noise‐suppression 1H‐MRS may have better diagnostic performance for paediatric brain tumours

Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors

Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging. Survival biomarkers are challenging to identify due to the relatively low numbers of individual tumor types. 69 children with biopsy-confirmed brain tumors were recruited into this study. All participants had perfusion and diffusion weighted imaging performed at diagnosis. Imaging data were processed using conventional methods, and a Bayesian survival analysis performed. Unsupervised and supervised machine learning were performed with the survival features, to determine novel sub-groups related to survival. Sub-group analysis was undertaken to understand differences in imaging features. Survival analysis showed that a combination of diffusion and perfusion imaging were able to determine two novel sub-groups of brain tumors with different survival characteristics (p < 0.01), which were subsequently classified with high accuracy (98%) by a neural network. Analysis of high-grade tumors showed a marked difference in survival (p = 0.029) between the two clusters with high risk and low risk imaging features. This study has developed a novel model of survival for pediatric brain tumors. Tumor perfusion plays a key role in determining survival and should be considered as a high priority for future imaging protocols

Development of a pre-operative scoring system for predicting risk of post-operative paediatric cerebellar mutism syndrome

BACKGROUND: Despite previous identification of pre-operative clinical and radiological predictors of post-operative paediatric cerebellar mutism syndrome (CMS), a unifying pre-operative risk stratification model for use during surgical consent is currently lacking. The aim of the project is to develop a simple imaging-based pre-operative risk scoring scheme to stratify patients in terms of post-operative CMS risk.METHODS: Pre-operative radiological features were recorded for a retrospectively assembled cohort of 89 posterior fossa tumour patients from two major UK treatment centers (age 2-23yrs; gender 28 M, 61 F; diagnosis: 38 pilocytic astrocytoma, 32 medulloblastoma, 12 ependymoma, 1 high grade glioma, 1 pilomyxoid astrocytoma, 1 atypical teratoid rhabdoid tumour, 1 hemangioma, 1 neurilemmoma, 2 oligodendroglioma). Twenty-six (29%) developed post-operative CMS. Based upon results from univariate analysis and C4.5 decision tree, stepwise logistic regression was used to develop the optimal model and generate risk scores.RESULTS: Univariate analysis identified five significant risk factors and C4.5 decision tree analysis identified six predictors. Variables included in the final model are MRI primary location, bilateral middle cerebellar peduncle involvement (invasion and/or compression), dentate nucleus invasion and age at imaging >12.4 years. This model has an accuracy of 88.8% (79/89). Using risk score cut-off of 203 and 238, respectively, allowed discrimination into low (38/89, predicted CMS probabilit

Metabolite selection for machine learning in childhood brain tumour classification

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

Exercise and Physical Therapy Interventions for Children with Ataxia: a systematic review

The effectiveness of exercise and physical therapy for children with ataxia is poorly understood. The aim of this systematic review was to critically evaluate the range, scope and methodological quality of studies investigating the effectiveness of exercise and physical therapy interventions for children with ataxia. The following databases were searched: AMED, CENTRAL, CDSR, CINAHL, ClinicalTrials.gov, EMBASE, Ovid MEDLINE, PEDro and Web of Science. No limits were placed on language, type of study or year of publication. Two reviewers independently determined whether the studies met the inclusion criteria, extracted all relevant outcomes, and conducted methodological quality assessments. A total of 1988 studies were identified, and 124 full texts were screened. Twenty studies were included in the review. A total of 40 children (aged 5-18 years) with ataxia as a primary impairment participated in the included studies. Data were able to be extracted from eleven studies with a total of 21 children (aged 5-18 years), with a range of cerebellar pathology. The studies reported promising results but were of low methodological quality (no RCTs), used small sample sizes and were heterogeneous in terms of interventions, participants and outcomes. No firm conclusions can be made about the effectiveness of exercise and physical therapy for children with ataxia. There is a need for further high-quality child-centred research