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Multimodal molecular analysis of astroblastoma enables reclassification of most cases into more specific molecular entities.

ABSTRACT: Astroblastoma is a rare and controversial glioma with variable clinical behavior. The diagnosis currently rests on histologic findings of a circumscribed glioma with astroblastomatous pseudorosettes and vascular hyalinization. Immunohistochemical studies have suggested different oncogenic drivers, such as BRAF p.V600E, but very few cases have been studied using genome-wide methodologies. Recent genomic profiling identified a subset of CNS embryonal tumors with astroblastoma-like morphology that harbored MN1 gene fusions, termed "CNS high-grade neuroepithelial tumors with MN1 alteration" (CNS-HGNET-MN1). To further characterize the genetic alterations that drive astroblastomas, we performed targeted next-generation sequencing (NGS) of 500 cancer-associated genes in a series of eight cases. We correlated these findings with break-apart fluorescence in situ hybridization (FISH) analysis of the MN1 locus and genome-wide DNA methylation profiling. Four cases showed MN1 alteration by FISH, including two pediatric cases that lacked other pathogenic alterations, and two adult cases that harbored other cancer-associated gene mutations or copy number alterations (eg, CDKN2A/B homozygous deletion, TP53, ATM and TERT promoter mutations). Three of these cases grouped with the CNS-HGNET-MN1 entity by methylation profiling. Two of four MN1 intact cases by FISH showed genetic features of either anaplastic pleomorphic xanthoastrocytoma (BRAF p.V600E mutation, CDKN2A/B homozygous deletion and TERT promoter mutation) or IDH-wildtype glioblastoma (trisomy 7, monosomy 10, CDK4 amplification and TP53, NRAS and TERT promoter mutations) and these cases had an aggressive clinical course. Two clinically indolent cases remained unclassifiable despite multimodal molecular analysis. We conclude that astroblastoma histology is not specific for any entity including CNS-HGNET-MN1, and that additional genetic characterization should be considered for astroblastomas, as a number of these tumors likely contain a methylation profile or genetic alterations that suggest classification as other tumor entities. Our heterogeneous molecular findings help to explain the clinical unpredictability of astroblastoma.

SUBMITTER: Wood MD

PROVIDER: S-EPMC5843525 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The Biopharmaceutics Drug Disposition Classification System (BDDCS) was successfully employed for predicting drug-drug interactions (DDIs) with respect to drug metabolizing enzymes (DMEs), drug transporters and their interplay. The major assumption of BDDCS is that the extent of metabolism (EoM) predicts high versus low intestinal permeability rate, and vice versa, at least when uptake transporters or paracellular transport is not involved. We recently published a collection of over 900 marketed drugs classified for BDDCS. We suggest that a reliable model for predicting BDDCS class, integrated with in vitro assays, could anticipate disposition and potential DDIs of new molecular entities (NMEs). Here we describe a computational procedure for predicting BDDCS class from molecular structures. The model was trained on a set of 300 oral drugs, and validated on an external set of 379 oral drugs, using 17 descriptors calculated or derived from the VolSurf+ software. For each molecule, a probability of BDDCS class membership was given, based on predicted EoM, FDA solubility (FDAS) and their confidence scores. The accuracy in predicting FDAS was 78% in training and 77% in validation, while for EoM prediction the accuracy was 82% in training and 79% in external validation. The actual BDDCS class corresponded to the highest ranked calculated class for 55% of the validation molecules, and it was within the top two ranked more than 92% of the time. The unbalanced stratification of the data set did not affect the prediction, which showed highest accuracy in predicting classes 2 and 3 with respect to the most populated class 1. For class 4 drugs a general lack of predictability was observed. A linear discriminant analysis (LDA) confirming the degree of accuracy for the prediction of the different BDDCS classes is tied to the structure of the data set. This model could routinely be used in early drug discovery to prioritize in vitro tests for NMEs (e.g., affinity to transporters, intestinal metabolism, intestinal absorption and plasma protein binding). We further applied the BDDCS prediction model on a large set of medicinal chemistry compounds (over 30,000 chemicals). Based on this application, we suggest that solubility, and not permeability, is the major difference between NMEs and drugs. We anticipate that the forecast of BDDCS categories in early drug discovery may lead to a significant R&D cost reduction.

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Multimodal molecular analysis of astroblastoma enables reclassification of most cases into more specific molecular entities.

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