Somatic Versus Germline: A Case Series of Three Children With ATM-Mutated Medulloblastoma.

Somatic versus Germline-A Case Series of Three Children with ATM- mutated Medulloblastoma.

Maladaptive myelination promotes generalized epilepsy progression.

Activity-dependent myelination can fine-tune neural network dynamics. Conversely, aberrant neuronal activity, as occurs in disorders of recurrent seizures (epilepsy), could promote maladaptive myelination, contributing to pathogenesis. In this study, we tested the hypothesis that activity-dependent myelination resulting from absence seizures, which manifest as frequent behavioral arrests with generalized electroencephalography (EEG) spike-wave discharges, promote thalamocortical network hypersynchrony and contribute to epilepsy progression. We found increased oligodendrogenesis and myelination specifically within the seizure network in two models of generalized epilepsy with absence seizures (Wag/Rij rats and Scn8a+/mut mice), evident only after epilepsy onset. Aberrant myelination was prevented by pharmacological seizure inhibition in Wag/Rij rats. Blocking activity-dependent myelination decreased seizure burden over time and reduced ictal synchrony as assessed by EEG coherence. These findings indicate that activity-dependent myelination driven by absence seizures contributes to epilepsy progression; maladaptive myelination may be pathogenic in some forms of epilepsy and other neurological diseases.

MRI Radiogenomics of Pediatric Medulloblastoma: A Multicenter Study.

Background Radiogenomics of pediatric medulloblastoma (MB) offers an opportunity for MB risk stratification, which may aid therapeutic decision making, family counseling, and selection of patient groups suitable for targeted genetic analysis. Purpose To develop machine learning strategies that identify the four clinically significant MB molecular subgroups. Materials and Methods In this retrospective study, consecutive pediatric patients with newly diagnosed MB at MRI at 12 international pediatric sites between July 1997 and May 2020 were identified. There were 1800 features extracted from T2- and contrast-enhanced T1-weighted preoperative MRI scans. A two-stage sequential classifier was designed-one that first identifies non-wingless (WNT) and non-sonic hedgehog (SHH) MB and then differentiates therapeutically relevant WNT from SHH. Further, a classifier that distinguishes high-risk group 3 from group 4 MB was developed. An independent, binary subgroup analysis was conducted to uncover radiomics features unique to infantile versus childhood SHH subgroups. The best-performing models from six candidate classifiers were selected, and performance was measured on holdout test sets. CIs were obtained by bootstrapping the test sets for 2000 random samples. Model accuracy score was compared with the no-information rate using the Wald test. Results The study cohort comprised 263 patients (mean age ± SD at diagnosis, 87 months ± 60; 166 boys). A two-stage classifier outperformed a single-stage multiclass classifier. The combined, sequential classifier achieved a microaveraged F1 score of 88% and a binary F1 score of 95% specifically for WNT. A group 3 versus group 4 classifier achieved an area under the receiver operating characteristic curve of 98%. Of the Image Biomarker Standardization Initiative features, texture and first-order intensity features were most contributory across the molecular subgroups. Conclusion An MRI-based machine learning decision path allowed identification of the four clinically relevant molecular pediatric medulloblastoma subgroups. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Chaudhary and Bapuraj in this issue.

Radiomic signatures of posterior fossa ependymoma: Molecular subgroups and risk profiles.

BACKGROUND: The risk profile for posterior fossa ependymoma (EP) depends on surgical and molecular status [Group A (PFA) versus Group B (PFB)]. While subtotal tumor resection is known to confer worse prognosis, MRI-based EP risk-profiling is unexplored. We aimed to apply machine learning strategies to link MRI-based biomarkers of high-risk EP and also to distinguish PFA from PFB. METHODS: We extracted 1800 quantitative features from presurgical T2-weighted (T2-MRI) and gadolinium-enhanced T1-weighted (T1-MRI) imaging of 157 EP patients. We implemented nested cross-validation to identify features for risk score calculations and apply a Cox model for survival analysis. We conducted additional feature selection for PFA versus PFB and examined performance across three candidate classifiers. RESULTS: For all EP patients with GTR, we identified four T2-MRI-based features and stratified patients into high- and low-risk groups, with 5-year overall survival rates of 62% and 100%, respectively (P < .0001). Among presumed PFA patients with GTR, four T1-MRI and five T2-MRI features predicted divergence of high- and low-risk groups, with 5-year overall survival rates of 62.7% and 96.7%, respectively (P = .002). T1-MRI-based features showed the best performance distinguishing PFA from PFB with an AUC of 0.86. CONCLUSIONS: We present machine learning strategies to identify MRI phenotypes that distinguish PFA from PFB, as well as high- and low-risk PFA. We also describe quantitative image predictors of aggressive EP tumors that might assist risk-profiling after surgery. Future studies could examine translating radiomics as an adjunct to EP risk assessment when considering therapy strategies or trial candidacy.

Machine-Learning Approach to Differentiation of Benign and Malignant Peripheral Nerve Sheath Tumors: A Multicenter Study.

BACKGROUND: Clinicoradiologic differentiation between benign and malignant peripheral nerve sheath tumors (PNSTs) has important management implications. OBJECTIVE: To develop and evaluate machine-learning approaches to differentiate benign from malignant PNSTs. METHODS: We identified PNSTs treated at 3 institutions and extracted high-dimensional radiomics features from gadolinium-enhanced, T1-weighted magnetic resonance imaging (MRI) sequences. Training and test sets were selected randomly in a 70:30 ratio. A total of 900 image features were automatically extracted using the PyRadiomics package from Quantitative Imaging Feature Pipeline. Clinical data including age, sex, neurogenetic syndrome presence, spontaneous pain, and motor deficit were also incorporated. Features were selected using sparse regression analysis and retained features were further refined by gradient boost modeling to optimize the area under the curve (AUC) for diagnosis. We evaluated the performance of radiomics-based classifiers with and without clinical features and compared performance against human readers. RESULTS: A total of 95 malignant and 171 benign PNSTs were included. The final classifier model included 21 imaging and clinical features. Sensitivity, specificity, and AUC of 0.676, 0.882, and 0.845, respectively, were achieved on the test set. Using imaging and clinical features, human experts collectively achieved sensitivity, specificity, and AUC of 0.786, 0.431, and 0.624, respectively. The AUC of the classifier was statistically better than expert humans (P = .002). Expert humans were not statistically better than the no-information rate, whereas the classifier was (P = .001). CONCLUSION: Radiomics-based machine learning using routine MRI sequences and clinical features can aid in evaluation of PNSTs. Further improvement may be achieved by incorporating additional imaging sequences and clinical variables into future models.

Effects of Age on White Matter Microstructure in Children With Neurofibromatosis Type 1.

Children with neurofibromatosis type 1 (NF1) often report cognitive challenges, though the etiology of such remains an area of active investigation. With the advent of treatments that may affect white matter microstructure, understanding the effects of age on white matter aberrancies in NF1 becomes crucial in determining the timing of such therapeutic interventions. A cross-sectional study was performed with diffusion tensor imaging from 18 NF1 children and 26 age-matched controls. Fractional anisotropy was determined by region of interest analyses for both groups over the corpus callosum, cingulate, and bilateral frontal and temporal white matter regions. Two-way analyses of variance were done with both ages combined and age-stratified into early childhood, middle childhood, and adolescence. Significant differences in fractional anisotropy between NF1 and controls were seen in the corpus callosum and frontal white matter regions when ages were combined. When stratified by age, we found that this difference was largely driven by the early childhood (1-5.9 years) and middle childhood (6-11.9 years) age groups, whereas no significant differences were appreciable in the adolescence age group (12-18 years). This study demonstrates age-related effects on white matter microstructure disorganization in NF1, suggesting that the appropriate timing of therapeutic intervention may be in early childhood.

MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: an international study.

BACKGROUND: Diffuse intrinsic pontine gliomas (DIPGs) are lethal pediatric brain tumors. Presently, MRI is the mainstay of disease diagnosis and surveillance. We identify clinically significant computational features from MRI and create a prognostic machine learning model. METHODS: We isolated tumor volumes of T1-post-contrast (T1) and T2-weighted (T2) MRIs from 177 treatment-naïve DIPG patients from an international cohort for model training and testing. The Quantitative Image Feature Pipeline and PyRadiomics was used for feature extraction. Ten-fold cross-validation of least absolute shrinkage and selection operator Cox regression selected optimal features to predict overall survival in the training dataset and tested in the independent testing dataset. We analyzed model performance using clinical variables (age at diagnosis and sex) only, radiomics only, and radiomics plus clinical variables. RESULTS: All selected features were intensity and texture-based on the wavelet-filtered images (3 T1 gray-level co-occurrence matrix (GLCM) texture features, T2 GLCM texture feature, and T2 first-order mean). This multivariable Cox model demonstrated a concordance of 0.68 (95% CI: 0.61-0.74) in the training dataset, significantly outperforming the clinical-only model (C = 0.57 [95% CI: 0.49-0.64]). Adding clinical features to radiomics slightly improved performance (C = 0.70 [95% CI: 0.64-0.77]). The combined radiomics and clinical model was validated in the independent testing dataset (C = 0.59 [95% CI: 0.51-0.67], Noether's test P = .02). CONCLUSIONS: In this international study, we demonstrate the use of radiomic signatures to create a machine learning model for DIPG prognostication. Standardized, quantitative approaches that objectively measure DIPG changes, including computational MRI evaluation, could offer new approaches to assessing tumor phenotype and serve a future role for optimizing clinical trial eligibility and tumor surveillance.

Cerebral volume and diffusion MRI changes in children with sensorineural hearing loss.

PURPOSE: Sensorineural hearing loss (SNHL) is the most prevalent congenital sensory deficit in children. Information regarding underlying brain microstructure could offer insight into neural development in deaf children and potentially guide therapies that optimize language development. We sought to quantitatively evaluate MRI-based cerebral volume and gray matter microstructure children with SNHL. METHODS & MATERIALS: We conducted a retrospective study of children with SNHL who obtained brain MRI at 3 T. The study cohort comprised 63 children with congenital SNHL without known focal brain lesion or structural abnormality (33 males; mean age 5.3 years; age range 1 to 11.8 years) and 64 age-matched controls without neurological, developmental, or MRI-based brain macrostructure abnormality. An atlas-based analysis was used to extract quantitative volume and median diffusivity (ADC) in the following brain regions: cerebral cortex, thalamus, caudate, putamen, globus pallidus, hippocampus, amygdala, nucleus accumbens, brain stem, and cerebral white matter. SNHL patients were further stratified by severity scores and hearing loss etiology. RESULTS: Children with SNHL showed higher median ADC of the cortex (p = .019), thalamus (p < .001), caudate (p = .005), and brainstem (p = .003) and smaller brainstem volumes (p = .007) compared to controls. Patients with profound bilateral SNHL did not show any significant differences compared to patients with milder bilateral SNHL, but both cohorts independently had smaller brainstem volumes compared to controls. Children with unilateral SNHL showed greater amygdala volumes compared to controls (p = .021), but no differences were found comparing unilateral SNHL to bilateral SNHL. Based on etiology for SNHL, patients with Pendrin mutations showed higher ADC values in the brainstem (p = .029, respectively); patients with Connexin 26 showed higher ADC values in both the thalamus (p < .001) and brainstem (p < .001) compared to controls. CONCLUSION: SNHL patients showed significant differences in diffusion and volume in brain subregions, with region-specific findings for patients with Connexin 26 and Pendrin mutations. Future longitudinal studies could examine macro- and microstructure changes in children with SNHL over development and potential predictive role for MRI after interventions including cochlear implant outcome.

Association of Pediatric Acute-Onset Neuropsychiatric Syndrome With Microstructural Differences in Brain Regions Detected via Diffusion-Weighted Magnetic Resonance Imaging.

Importance: Epidemiological studies indicate a link between obsessive-compulsive disorder and infections, particularly streptococcal pharyngitis. Pediatric acute-onset neuropsychiatric syndrome (PANS) manifests suddenly with obsessions, compulsions, and other behavioral disturbances, often after an infectious trigger. The current working model suggests a unifying inflammatory process involving the central nervous system, particularly the basal ganglia. Objective: To investigate whether diffusion-weighted magnetic resonance imaging (DWI) detects microstructural abnormalities across the brain regions of children with PANS. Design, Setting, and Participants: Case-control study performed at a single-center, multidisciplinary clinic in the United States focusing on the evaluation and treatment of children with PANS. Sixty consecutive patients who underwent 3 Tesla (T) magnetic resonance imaging (MRI) before immunomodulation from September 3, 2012, to March 30, 2018, were retrospectively reviewed for study inclusion. Six patients were excluded by blinded investigators because of imaging or motion artifacts, 3 patients for major pathologies, and 17 patients for suboptimal atlas image registration. In total, 34 patients with PANS before initiation of treatment were compared with 64 pediatric control participants. Main Outcomes and Measures: Using atlas-based MRI analysis, regional brain volume, diffusion, and cerebral blood flow were measured in the cerebral white matter, cerebral cortex, thalamus, caudate, putamen, pallidum, hippocampus, amygdala, nucleus accumbens, and brainstem. An age and sex-controlled multivariable analysis of covariance was used to compare patients with control participants. Results: This study compared 34 patients with PANS (median age, 154 months; age range, 55-251 months; 17 girls and 17 boys) and 64 pediatric control participants (median age, 139 months; age range, 48-213 months); 41 girls and 23 boys). Multivariable analysis demonstrated a statistically significant difference in MRI parameters between patients with PANS and control participants (F21,74 = 6.91; P < .001; partial η2 = 0.662). All assessed brain regions had statistically significantly increased median diffusivity compared with 64 control participants. Specifically, the deep gray matter (eg, the thalamus, basal ganglia, and amygdala) demonstrated the most profound increases in diffusivity consistent with the cardinal clinical symptoms of obsessions, compulsions, emotional dysregulation, and sleep disturbances. No statistically significant differences were found regarding volume and cerebral blood flow. Conclusions and Relevance: This study identifies cerebral microstructural differences in children with PANS in multiple brain structures, including the deep gray matter structures (eg, the thalamus, basal ganglia, and amygdala). Further study of MRI is warranted in prospective, clinical trials as a potential quantitative method for assessing patients under evaluation for PANS.

Electrical and synaptic integration of glioma into neural circuits.

High-grade gliomas are lethal brain cancers whose progression is robustly regulated by neuronal activity. Activity-regulated release of growth factors promotes glioma growth, but this alone is insufficient to explain the effect that neuronal activity exerts on glioma progression. Here we show that neuron and glioma interactions include electrochemical communication through bona fide AMPA receptor-dependent neuron-glioma synapses. Neuronal activity also evokes non-synaptic activity-dependent potassium currents that are amplified by gap junction-mediated tumour interconnections, forming an electrically coupled network. Depolarization of glioma membranes assessed by in vivo optogenetics promotes proliferation, whereas pharmacologically or genetically blocking electrochemical signalling inhibits the growth of glioma xenografts and extends mouse survival. Emphasizing the positive feedback mechanisms by which gliomas increase neuronal excitability and thus activity-regulated glioma growth, human intraoperative electrocorticography demonstrates increased cortical excitability in the glioma-infiltrated brain. Together, these findings indicate that synaptic and electrical integration into neural circuits promotes glioma progression.

Methotrexate Chemotherapy Induces Persistent Tri-glial Dysregulation that Underlies Chemotherapy-Related Cognitive Impairment.

Chemotherapy results in a frequent yet poorly understood syndrome of long-term neurological deficits. Neural precursor cell dysfunction and white matter dysfunction are thought to contribute to this debilitating syndrome. Here, we demonstrate persistent depletion of oligodendrocyte lineage cells in humans who received chemotherapy. Developing a mouse model of methotrexate chemotherapy-induced neurological dysfunction, we find a similar depletion of white matter OPCs, increased but incomplete OPC differentiation, and a persistent deficit in myelination. OPCs from chemotherapy-naive mice similarly exhibit increased differentiation when transplanted into the microenvironment of previously methotrexate-exposed brains, indicating an underlying microenvironmental perturbation. Methotrexate results in persistent activation of microglia and subsequent astrocyte activation that is dependent on inflammatory microglia. Microglial depletion normalizes oligodendroglial lineage dynamics, myelin microstructure, and cognitive behavior after methotrexate chemotherapy. These findings indicate that methotrexate chemotherapy exposure is associated with persistent tri-glial dysregulation and identify inflammatory microglia as a therapeutic target to abrogate chemotherapy-related cognitive impairment. VIDEO ABSTRACT.

Targeting neuronal activity-regulated neuroligin-3 dependency in high-grade glioma.

High-grade gliomas (HGG) are a devastating group of cancers, and represent the leading cause of brain tumour-related death in both children and adults. Therapies aimed at mechanisms intrinsic to glioma cells have translated to only limited success; effective therapeutic strategies will need also to target elements of the tumour microenvironment that promote glioma progression. Neuronal activity promotes the growth of a range of molecularly and clinically distinct HGG types, including adult and paediatric glioblastoma (GBM), anaplastic oligodendroglioma, and diffuse intrinsic pontine glioma (DIPG). An important mechanism that mediates this neural regulation of brain cancer is activity-dependent cleavage and secretion of the synaptic adhesion molecule neuroligin-3 (NLGN3), which promotes glioma proliferation through the PI3K-mTOR pathway. However, the necessity of NLGN3 for glioma growth, the proteolytic mechanism of NLGN3 secretion, and the further molecular consequences of NLGN3 secretion in glioma cells remain unknown. Here we show that HGG growth depends on microenvironmental NLGN3, identify signalling cascades downstream of NLGN3 binding in glioma, and determine a therapeutically targetable mechanism of secretion. Patient-derived orthotopic xenografts of paediatric GBM, DIPG and adult GBM fail to grow in Nlgn3 knockout mice. NLGN3 stimulates several oncogenic pathways, such as early focal adhesion kinase activation upstream of PI3K-mTOR, and induces transcriptional changes that include upregulation of several synapse-related genes in glioma cells. NLGN3 is cleaved from both neurons and oligodendrocyte precursor cells via the ADAM10 sheddase. ADAM10 inhibitors prevent the release of NLGN3 into the tumour microenvironment and robustly block HGG xenograft growth. This work defines a promising strategy for targeting NLGN3 secretion, which could prove transformative for HGG therapy.