Causal network inference from gene transcriptional time-series response to glucocorticoids.

Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2768 genes and 31,945 directed edges (FDR ≤ 0.2). We validate inferred causal network edges using two external data sources: Overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS.

Breast Radiation Therapy Under COVID-19 Pandemic Resource Constraints-Approaches to Defer or Shorten Treatment From a Comprehensive Cancer Center in the United States.

PURPOSE: Breast radiation therapy accounts for a significant proportion of patient volume in contemporary radiation oncology practice. In the setting of anticipated resource constraints and widespread community infection with SARS-CoV-2 during the COVID-19 pandemic, measures for balancing both infectious and oncologic risk among patients and providers must be carefully considered. Here, we present evidence-based guidelines for omitting or abbreviating breast cancer radiation therapy, where appropriate, in an effort to mitigate risk to patients and optimize resource utilization. METHODS AND MATERIALS: Multidisciplinary breast cancer experts at a high-volume comprehensive cancer center convened contingency planning meetings over the early days of the COVID-19 pandemic to review the relevant literature and establish recommendations for the application of hypofractionated and abbreviated breast radiation regimens. RESULTS: Substantial evidence exists to support omitting radiation among certain favorable risk subgroups of patients with breast cancer and for abbreviating or accelerating regimens among others. For those who require either whole-breast or postmastectomy radiation, with or without coverage of the regional lymph nodes, a growing body of literature supports various hypofractionated approaches that appear safe and effective. CONCLUSIONS: In the setting of a public health emergency with the potential to strain critical healthcare resources and place patients at risk of infection, the parsimonious application of breast radiation therapy may alleviate a significant clinical burden without compromising long-term oncologic outcomes. The judicious and personalized use of immature study data may be warranted in the setting of a competing mortality risk from this widespread pandemic.