Clinical, pathologic, and genomic characteristics of two pediatric glioneuronal tumors with a CLIP2::MET fusion.

Integration of molecular data with histologic, radiologic, and clinical features is imperative for accurate diagnosis of pediatric central nervous system (CNS) tumors. Whole transcriptome RNA sequencing (RNAseq), a genome-wide and non-targeted approach, allows for the detection of novel or rare oncogenic fusion events that contribute to the tumorigenesis of a substantial portion of pediatric low- and high-grade glial and glioneuronal tumors. We present two cases of pediatric glioneuronal tumors occurring in the occipital region with a CLIP2::MET fusion detected by RNAseq. Chromosomal microarray studies revealed copy number alterations involving chromosomes 1, 7, and 22 in both tumors, with Case 2 having an interstitial deletion breakpoint in the CLIP2 gene. By methylation profiling, neither tumor had a match result, but both clustered with the low-grade glial/glioneuronal tumors in the UMAP. Histologically, in both instances, our cases displayed characteristics of a low-grade tumor, notably the absence of mitotic activity, low Ki-67 labeling index and the lack of necrosis and microvascular proliferation. Glial and neuronal markers were positive for both tumors. Clinically, both patients achieved clinical stability post-tumor resection and remain under regular surveillance imaging without adjuvant therapy at the last follow-up, 6 months and 3 years, respectively. This is the first case report demonstrating the presence of a CLIP2::MET fusion in two pediatric low-grade glioneuronal tumors (GNT). Conservative clinical management may be considered for patients with GNT and CLIP2:MET fusion in the context of histologically low-grade features.

Machine learning-based integration of network features and chemical structure of compounds for SARS-CoV-2 drug effect analysis.

High drug development costs and the limited number of new annual drug approvals increase the need for innovative approaches for drug effect prediction. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), led to a global pandemic with high morbidity and mortality. Although effective preventive measures exist, there are few effective treatments for hospitalized patients with SARS-CoV-2 infection. Drug repurposing and drug effect prediction are promising strategies that could shorten development time and reduce costs compared with de novo drug discovery. In this work, we present a machine learning framework to integrate a variety of target network features and physicochemical properties of compounds, and analyze their influence on the therapeutic effects for SARS-CoV-2 infection and on host cell cytotoxic effects. Random forest models trained on compounds with known experimental effects on SARS-CoV-2 infection and subsequent feature importance analysis based on Shapley values provided insights into the determinants of drug efficacy and cytotoxicity, which can be incorporated into novel drug discovery approaches. Given the complexity of molecular mechanisms of drug action and limited sample sizes, our models achieve a reasonable mean area under the receiver operating characteristic curve (ROC-AUC) of 0.73 on an unseen validation set. To our knowledge, this is the first work to incorporate a combination of network and physicochemical features of compounds into a machine learning model to predict drug effects on SARS-CoV-2 infection. Our systems pharmacology-based machine learning framework can be used to classify other existing drugs for SARS-CoV-2 infection and can easily be adapted to drug effect prediction for future viral outbreaks.