Developmental interneuron subtype deficits after targeted loss of Arx

Abstract Background Aristaless-related homeobox (ARX) is a paired-like homeodomain transcription factor that functions primarily as a transcriptional repressor and has been implicated in neocortical interneuron specification and migration. Given the role interneurons appear to play in numerous human conditions including those associated with ARX mutations, it is essential to understand the consequences of mutations in this gene on neocortical interneurons. Previous studies have examined the effect of germline loss of Arx, or targeted mutations in Arx, on interneuron development. We now present the effect of conditional loss of Arx on interneuron development. Results To further elucidate the role of Arx in forebrain development we performed a series of anatomical and developmental studies to determine the effect of conditional loss of Arx specifically from developing interneurons in the neocortex and hippocampus. Analysis and cell counts were performed from mouse brains using immunohistochemical and in situ hybridization assays at 4 times points across development. Our data indicate that early in development, instead of a loss of ventral precursors, there is a shift of these precursors to more ventral locations, a deficit that persists in the adult nervous system. The result of this developmental shift is a reduced number of interneurons (all subtypes) at early postnatal and later time periods. In addition, we find that X inactivation is stochastic, and occurs at the level of the neural progenitors. Conclusion These data provide further support that the role of Arx in interneuron development is to direct appropriate migration of ventral neuronal precursors into the dorsal cortex and that the loss of Arx results in a failure of interneurons to reach the cortex and thus a deficiency in interneurons.http://deepblue.lib.umich.edu/bitstream/2027.42/134595/1/12868_2016_Article_265.pd

Non-invasive detection of 2-hydroxyglutarate in IDH-mutated gliomas using two-dimensional localized correlation spectroscopy (2D L-COSY) at 7 Tesla

BACKGROUND: Mutations in the isocitrate dehydrogenase enzyme are present in a majority of lower-grade gliomas and secondary glioblastomas. This mis-sense mutation results in the neomorphic reduction of isocitrate dehydrogenase resulting in an accumulation of the “oncometabolite” 2-hydroxyglutarate (2HG). Detection of 2HG can thus serve as a surrogate biomarker for these mutations, with significant translational implications including improved prognostication. Two dimensional localized correlated spectroscopy (2D L-COSY) at 7T is a highly-sensitive non-invasive technique for assessing brain metabolism. This study aims to assess tumor metabolism using 2D L-COSY at 7T for the detection of 2HG in IDH-mutant gliomas. METHODS: Nine treatment-naïve patients with suspected intracranial neoplasms were scanned at 7T MRI/MRS scanner using the 2D L-COSY technique. 2D-spectral processing and analyses were performed using a MATLAB-based reconstruction algorithm. Cross and diagonal peak volumes were quantified in the 2D L-COSY spectra and normalized with respect to the creatine peak at 3.0 ppm and quantified data were compared with previously-published data from six normal subjects. Detection of 2HG was validated using findings from immunohistochemical (IHC) staining in patients who subsequently underwent surgical resection. RESULTS: 2HG was detected in both of the IDH-mutated gliomas (grade III Anaplastic Astrocytoma and grade II Diffuse Astrocytoma) and was absent in IDH wild-type gliomas and in a patient with breast cancer metastases. 2D L-COSY was also able to resolve complex and overlapping resonances including phosphocholine (PC) from glycerophosphocholine (GPC), lactate (Lac) from lipids and glutamate (Glu) from glutamine (Gln). CONCLUSIONS: This study demonstrates the ability of 2D L-COSY to unambiguously detect 2HG in addition to other neuro metabolites. These findings may aid in establishing 2HG as a biomarker of malignant progression as well as for disease monitoring in IDH-mutated gliomas

SETD2 mutations in primary central nervous system tumors

Abstract Mutations in SETD2 are found in many tumors, including central nervous system (CNS) tumors. Previous work has shown these mutations occur specifically in high grade gliomas of the cerebral hemispheres in pediatric and young adult patients. We investigated SETD2 mutations in a cohort of approximately 640 CNS tumors via next generation sequencing; 23 mutations were detected across 19 primary CNS tumors. Mutations were found in a wide variety of tumors and locations at a broad range of allele frequencies. SETD2 mutations were seen in both low and high grade gliomas as well as non-glial tumors, and occurred in patients greater than 55 years of age, in addition to pediatric and young adult patients. High grade gliomas at first occurrence demonstrated either frameshift/truncating mutations or point mutations at high allele frequencies, whereas recurrent high grade gliomas frequently harbored subclones with point mutations in SETD2 at lower allele frequencies in the setting of higher mutational burdens. Comparison with the TCGA dataset demonstrated consistent findings. Finally, immunohistochemistry showed decreased staining for H3K36me3 in our cohort of SETD2 mutant tumors compared to wildtype controls. Our data further describe the spectrum of tumors in which SETD2 mutations are found and provide a context for interpretation of these mutations in the clinical setting

Detecting Histologic & Clinical Glioblastoma Patterns of Prognostic Relevance

Glioblastoma is the most common and aggressive malignant adult tumor of the central nervous system, with a grim prognosis and heterogeneous morphologic and molecular profiles. Since adopting the current standard-of-care treatment 18 years ago, no substantial prognostic improvement has been noticed. Accurate prediction of patient overall survival (OS) from histopathology whole slide images (WSI) integrated with clinical data using advanced computational methods could optimize clinical decision-making and patient management. Here, we focus on identifying prognostically relevant glioblastoma characteristics from H&E stained WSI & clinical data relating to OS. The exact approach for WSI capitalizes on the comprehensive curation of apparent artifactual content and an interpretability mechanism via a weakly supervised attention-based multiple-instance learning algorithm that further utilizes clustering to constrain the search space. The automatically placed patterns of high diagnostic value classify each WSI as representative of short or long-survivors. Further assessment of the prognostic relevance of the associated clinical patient data is performed both in isolation and in an integrated manner, using XGBoost and SHapley Additive exPlanations (SHAP). Identifying tumor morphological & clinical patterns associated with short and long OS will enable the clinical neuropathologist to provide additional relevant prognostic information to the treating team and suggest avenues of biological investigation for understanding and potentially treating glioblastoma

Integrated deep learning model for predicting DNA methylation profiles and tumor types from histopathology in central nervous system tumors

DEPLOY codes in the manuscript "Integrated deep learning model for predicting DNA methylation and tumor types from histopathology in central nervous system tumors" are uploaded here. Please see the README for details

Abstract 3428: Validation and utilization of next generation sequencing in the clinical assessment of gliomas

Abstract Background. Identifying prognostic and potentially therapeutic genetic alterations in gliomas is crucial to improve clinical outcome and guide future targeted therapies in this devastating disease. Here, we establish the feasibility of next generation sequencing (NGS) profiling of glioma samples in a clinical setting. Methods. We optimized and validated a 47 gene panel for use on both fresh and Formalin Fixed Paraffin embedded (FFPE) tissue specimens in a CLIA-certified laboratory. Validation was performed in 27 samples by comparing mutation detection on two different NGS platforms, and we have since incorporated routine molecular tumor profiling into the standard workup of glioma patients seen at our institution. Results. From analysis of 120 clinical samples, we found disease-associated changes in 90 patients (75%). Overall 184 changes including disease-associated point mutations and variants of unclear significance were identified across 98 samples, resulting in an average of 1.85 mutations per sample and a range of 1- 6 mutations. This included 49 amplifications across several genes (EGFR, PDGFRA and KIT). Conclusions. The final validated assay allows for a cost effective and efficient analysis of a spectrum of clinically relevant and actionable biomarkers including point mutations, insertions, deletions and gene amplifications. Given the high fraction of tumors presenting with known disease associated changes, routine genomic profiling has the promise of improving patient outcomes and allow for access to targeted therapies. Citation Format: MacLean P. Nasrallah, Maria Martinez-Lage, Alan Fox, Shrey Sukhadia, Arati Desai, Donald M. O'Rourke, Steven Brem, David Roth, Jennifer J.D. Morrissette, Robert D. Daber. Validation and utilization of next generation sequencing in the clinical assessment of gliomas. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3428. doi:10.1158/1538-7445.AM2014-3428</jats:p

MRI-Based Radiomics Combined with Deep Learning for Distinguishing IDH-Mutant WHO Grade 4 Astrocytomas from IDH-Wild-Type Glioblastomas

This study aimed to investigate the potential of quantitative radiomic data extracted from conventional MR images in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type glioblastomas (GBMs). A cohort of 57 treatment-na&iuml;ve patients with IDH-mutant grade 4 astrocytomas (n = 23) and IDH-wild-type GBMs (n = 34) underwent anatomical imaging on a 3T MR system with standard parameters. Post-contrast T1-weighted and T2-FLAIR images were co-registered. A semi-automatic segmentation approach was used to generate regions of interest (ROIs) from different tissue components of neoplasms. A total of 1050 radiomic features were extracted from each image. The data were split randomly into training and testing sets. A deep learning-based data augmentation method (CTGAN) was implemented to synthesize 200 datasets from the training sets. A total of 18 classifiers were used to distinguish two genotypes of grade 4 astrocytomas. From generated data using 80% training set, the best discriminatory power was obtained from core tumor regions overlaid on post-contrast T1 using the K-best feature selection algorithm and a Gaussian na&iuml;ve Bayes classifier (AUC = 0.93, accuracy = 0.92, sensitivity = 1, specificity = 0.86, PR_AUC = 0.92). Similarly, high diagnostic performances were obtained from original and generated data using 50% and 30% training sets. Our findings suggest that conventional MR imaging-based radiomic features combined with machine/deep learning methods may be valuable in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type GBMs