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

Summary High-grade gliomas (HGG) are a devastating group of cancers, representing the leading cause of brain tumor-related death in both children and adults. Therapies aimed at mechanisms intrinsic to the glioma cell have translated to only limited success; effective therapeutic strategies will need to also target elements of the tumor microenvironment that promote glioma progression. We recently demonstrated that neuronal activity robustly promotes the growth of a range of molecularly and clinically distinct HGG types, including adult glioblastoma (GBM), anaplastic oligodendroglioma, pediatric GBM, and diffuse intrinsic pontine glioma (DIPG)1. An important mechanism mediating this neural regulation of brain cancer is activity-dependent cleavage and secretion of the synaptic molecule neuroligin-3 (NLGN3), which promotes glioma proliferation through the PI3K-mTOR pathway1. However, neuroligin-3 necessity to glioma growth, proteolytic mechanism of secretion and further molecular consequences in glioma remain to be clarified. Here, we demonstrate a striking dependence of HGG growth on microenvironmental neuroligin-3, elucidate signaling cascades downstream of neuroligin-3 binding in glioma and determine a therapeutically targetable mechanism of secretion. Patient-derived orthotopic xenografts of pediatric GBM, DIPG and adult GBM fail to grow in Nlgn3 knockout mice. Neuroligin-3 stimulates numerous oncogenic pathways, including early focal adhesion kinase activation upstream of PI3K-mTOR, and induces transcriptional changes including upregulation of numerous synapse-related genes in glioma cells. Neuroligin-3 is cleaved from both neurons and oligodendrocyte precursor cells via the ADAM10 sheddase. ADAM10 inhibitors prevent release of neuroligin-3 into the tumor microenvironment and robustly block HGG xenograft growth. This work defines a promising strategy for targeting neuroligin-3 secretion, which could prove transformative for HGG therapy

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

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) andT2-weighted (T2) MRIs from 177 treatment-naive 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 graylevel 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

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

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