Development and characterization of a mass cytometry panel for detecting the effect of acute doxorubicin exposure on murine cardiac nonmyocytes.

Childhood cancer survivors (CCSs) face lifelong side effects related to their treatment with chemotherapy. Anthracycline agents, such as doxorubicin (DOX), are important in the treatment of childhood cancers but are associated with cardiotoxicity. Cardiac toxicities represent a significant source of chronic disability that cancer survivors face; despite this, the chronic cardiotoxicity phenotype and how it relates to acute toxicity remains poorly defined. To address this critical knowledge gap, we studied the acute effect of DOX on murine cardiac nonmyocytes in vivo. Determination of the acute cellular effects of DOX on nonmyocytes, a cell pool with finite replicative capacity, provides a basis for understanding the pathogenesis of the chronic heart disease that CCSs face. To investigate the acute cellular effects of DOX, we present single-cell RNA sequencing (scRNAseq) data from homeostatic cardiac nonmyocytes and compare it with preexisting datasets, as well as a novel CyTOF datasets. SCANPY, a python-based single-cell analysis, was used to assess the heterogeneity of cells detected in scRNAseq and CyTOF. To further assist in CyTOF data annotation, joint analyses of scRNAseq and CyTOF data using an artificial neural network known as sparse autoencoder for clustering, imputation, and embedding (SAUCIE) are performed. Lastly, the panel is tested on a mouse model of acute DOX exposure at two time points (24 and 72 h) after the last dose of doxorubicin and examined with joint clustering. In sum, we report the first ever CyTOF study of cardiac nonmyocytes and characterize the effect of acute DOX exposure with scRNAseq and CyTOF.NEW & NOTEWORTHY We describe the first mass cytometry studies of murine cardiac nonmyocytes. The mass cytometry panel is compared with single-cell RNA sequencing data. Homeostatic cardiac nonmyocytes are characterized by mass cytometry to identify and quantify four major cell populations: endothelial cells, fibroblasts, leukocytes, and pericytes. The single-cell acute nonmyocyte response to doxorubicin is studied at 24 and 72 h after doxorubicin exposure given daily for 5 days at a dose of 4 mg/kg/day.

Factors Affecting Outcomes in Geriatric Traumatic Subdural Hematoma in a Neurosurgical Intensive Care Unit.

BACKGROUND AND OBJECTIVE: Geriatric patients (age ≥65 years) who sustain a traumatic brain injury have an increased risk of poor outcomes and higher mortality compared with younger cohorts. We aimed to evaluate the risk factors for discharge outcomes in a geriatric traumatic subdural hematoma population, stratified by age and pretraumatic medical comorbidities. This was a single-center retrospective cohort study of geriatric patients (N = 207). METHODS: Patient charts were evaluated for factors including patient characteristics, comorbidities, injury-related and seizure-related factors, neurosurgical intervention, and patient disposition on discharge. RESULTS: Bivariate and multivariate analyses showed that age was nonpredictive of patient outcomes. Underlying vasculopathic comorbidities were the primary determinant of posttraumatic seizure, surgical, and discharge outcomes. Multifactor analysis showed that patients who went on to develop status epilepticus (n = 11) had a higher frequency of vasculopathic comorbidities with strong predictive power in poor patient outcomes. CONCLUSIONS: Our findings suggest a need to establish unique prognostic risk factors based on patient outcomes that guide medical and surgical treatment in geriatric patients.

Predicting and characterizing a cancer dependency map of tumors with deep learning.

Genome-wide loss-of-function screens have revealed genes essential for cancer cell proliferation, called cancer dependencies. It remains challenging to link cancer dependencies to the molecular compositions of cancer cells or to unscreened cell lines and further to tumors. Here, we present DeepDEP, a deep learning model that predicts cancer dependencies using integrative genomic profiles. It uses a unique unsupervised pretraining that captures unlabeled tumor genomic representations to improve the learning of cancer dependencies. We demonstrated DeepDEP's improvement over conventional machine learning methods and validated the performance with three independent datasets. By systematic model interpretations, we extended the current dependency maps with functional characterizations of dependencies and a proof-of-concept in silico assay of synthetic essentiality. We applied DeepDEP to pan-cancer tumor genomics and built the first pan-cancer synthetic dependency map of 8000 tumors with clinical relevance. In summary, DeepDEP is a novel tool for investigating cancer dependency with rapidly growing genomic resources.

Reconstruction of Ewing Sarcoma Developmental Context from Mass-Scale Transcriptomics Reveals Characteristics of EWSR1-FLI1 Permissibility.

Ewing sarcoma is an aggressive pediatric cancer of enigmatic cellular origins typically resulting from a single translocation event t (11; 22) (q24; q12). The resulting fusion gene, EWSR1-FLI1, is toxic or unstable in most primary tissues. Consequently, attempts to model Ewing sarcomagenesis have proven unsuccessful thus far, highlighting the need to identify the cellular features which permit stable EWSR1-FLI1 expression. By re-analyzing publicly available RNA-Sequencing data with manifold learning techniques, we uncovered a group of Ewing-like tissues belonging to a developmental trajectory between pluripotent, neuroectodermal, and mesodermal cell states. Furthermore, we demonstrated that EWSR1-FLI1 expression levels control the activation of these developmental trajectories within Ewing sarcoma cells. Subsequent analysis and experimental validation demonstrated that the capability to resolve R-loops and mitigate replication stress are probable prerequisites for stable EWSR1-FLI1 expression in primary tissues. Taken together, our results demonstrate how EWSR1-FLI1 hijacks developmental gene programs and advances our understanding of Ewing sarcomagenesis.