Exploring the Effects of Experimental Parameters and Data Modeling Approaches on In Vitro Transcriptomic Point-of-Departure Estimates

Multiple new approach methods (NAMs) are being developed to rapidly screen large numbers of chemicals to aid in hazard evaluation and risk assessments. High-throughput transcriptomics (HTTr) in human cell lines has been proposed as a first-tier screening approach for determining the types of bioactivity a chemical can cause (activation of specific targets vs. generalized cell stress) and for calculating transcriptional points of departure (tPODs) based on changes in gene expression. In the present study, we examine a range of computational methods to calculate tPODs from HTTr data, using six data sets in which MCF7 cells cultured in two different media formulations were treated with a panel of 44 chemicals for 3 different exposure durations (6, 12, 24 hr). Multiple computational approaches for determining tPODs are compared using six HTTr datasets, all generated from a single cell type (MCF7, a breast cancer cell line), but using three different exposure durations and with two different media formulations. Each dataset included 44 chemicals in an eight-point concentration-response. We previously published a subset of these data (GSE162855) corresponding to one exposure time (6 hrs) and media formulation (DMEM + 10% HI-FBS). In the current study we incorporate additional data for all 5 additional combinations of exposure times (6, 12, and 24 hrs) and media formulations (DMEM + either 10% HI-FBS or 10% charcoal-stripped FBS), and compare results across a broader set of computational approaches for determining an overall transcriptomic point of departure (tPOD) for each chemical.