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Gliomas harboring oncogenic ROS1 alterations are uncommon and primarily described in infants. Our goal was to characterize the clinicopathological features and molecular signatures of the full spectrum of ROS1 fusion-positive gliomas across all age groups. Through a retrospective multi-institutional collaboration, we report a collection of unpublished ROS1 fusion gliomas along with the characterization and meta-analysis of new and published cases. A cohort of 32 new and 58 published cases was divided into the following 3 age groups: 19 infants, 40 pediatric patients, and 31 adults with gliomas. Tumors in infants and adults showed uniformly high-grade morphology; however, tumors in pediatric patients exhibited diverse histologic features. The GOPC::ROS1 fusion was prevalent (61/79, 77%) across all age groups, and 10 other partner genes were identified. Adult tumors showed recurrent genomic alterations characteristic of IDH wild-type glioblastoma, including the +7/-10/CDKN2A deletion; amplification of CDK4, MDM2, and PDGFRA genes; and mutations involving TERTp, TP53, PIK3R1, PIK3CA, PTEN, and NF1 genes. Infant tumors showed few genomic alterations, whereas pediatric tumors showed moderate genomic complexity. The outcomes were significantly poorer in adult patients. Although not statistically significant, tumors in infant and pediatric patients with high-grade histology and in hemispheric locations appeared more aggressive than tumors with lower grade histology or those in nonhemispheric locations. In conclusion, this study is the largest to date to characterize the clinicopathological and molecular signatures of ROS1 fusion-positive gliomas from infant, pediatric, and adult patients. We conclude that ROS1 likely acts as a driver in infant and pediatric gliomas and as a driver or codriver in adult gliomas. Integrated comprehensive clinical testing might be helpful in identifying such patients for possible targeted therapy., (Copyright © 2023. Published by Elsevier Inc.)

Adrenocortical insufficiency (AI) is a clinical condition defined by deficient production of glucocorticoids that can result in life-threatening complications. We examined the prevalence of AI in children with brain tumors and those undergoing hematopoietic cell transplantation. Adrenocorticotropic hormone stimulation (stim) testing was used for the assessment of adrenocortical function. On the basis of 155 stim tests in 117 patients, AI was diagnosed in 27.4% of patients with brain tumors and in 21% of hematopoietic cell transplantation recipients. A number of risk factors associated with AI were identified. Adrenocorticotropic hormone stim testing led to a definitive diagnosis of AI or recovery of adrenal function and unambiguous medical management., Competing Interests: R.K.G. is an employee and a stockholder at RAPT Therapeutics. The remaining authors declare no conflict of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

The OncoScan CNV Plus Assay (OS+) is a single-nucleotide polymorphism microarray platform that can detect 74 hotspot somatic mutations (SMs) in nine genes via molecular inversion probes. We report validation of the SM component of OS+ using a cohort of pediatric high-grade brain tumor specimens. SM calls were generated from 46 brain tumor cases, most tested orthogonally via bidirectional Sanger sequencing. The initial calling algorithm result showed that 31 tumors were positive and 15 were negative for SM, with a total of 71 OS+ SM calls [28 high-confidence (HC) and 43 low-confidence (LC)]. Sanger sequencing was performed for 54 of the 71 calls (27 HC and 27 LC), as well as for 21 randomly selected hotspots across the 15 OS+ negative cases. HC calls (except EGFR) Sanger sequencing confirmed positive, negative calls confirmed negative, but none of the LC calls were Sanger-confirmed positive. An update of the OS+ algorithm resolved the LC calls, but of the 11 HC SM EGFR calls, Sanger sequencing confirmed only one. Two PTEN SM calls by OS+ in two separate cases were also negative per Sanger sequencing. We conclude that a majority of HC OS+ SM calls were accurate, except calls identified in EGFR and PTEN. Clinically, we report SMs identified by OS+ only after Sanger sequencing verification., (Copyright © 2021 Association for Molecular Pathology and American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.)

Deep learning (DL) is a widely applied mathematical modeling technique. Classically, DL models utilize large volumes of training data, which are not available in many healthcare contexts. For patients with brain tumors, non-invasive diagnosis would represent a substantial clinical advance, potentially sparing patients from the risks associated with surgical intervention on the brain. Such an approach will depend upon highly accurate models built using the limited datasets that are available. Herein, we present a novel genetic algorithm (GA) that identifies optimal architecture parameters using feature embeddings from state-of-the-art image classification networks to identify the pediatric brain tumor, adamantinomatous craniopharyngioma (ACP). We optimized classification models for preoperative Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and combined CT and MRI datasets with demonstrated test accuracies of 85.3%, 83.3%, and 87.8%, respectively. Notably, our GA improved baseline model performance by up to 38%. This work advances DL and its applications within healthcare by identifying optimized networks in small-scale data contexts. The proposed system is easily implementable and scalable for non-invasive computer-aided diagnosis, even for uncommon diseases.