Impact of Genomics Platform and Statistical Filtering on Transcriptional Benchmark Doses (BMD) and Multiple Approaches for Selection of Chemical Point of Departure (PoD).

Many regulatory agencies are exploring ways to integrate toxicogenomic data into their chemical risk assessments. The major challenge lies in determining how to distill the complex data produced by high-content, multi-dose gene expression studies into quantitative information. It has been proposed that benchmark dose (BMD) values derived from toxicogenomics data be used as point of departure (PoD) values in chemical risk assessments. However, there is limited information regarding which genomics platforms are most suitable and how to select appropriate PoD values. In this study, we compared BMD values modeled from RNA sequencing-, microarray-, and qPCR-derived gene expression data from a single study, and explored multiple approaches for selecting a single PoD from these data. The strategies evaluated include several that do not require prior mechanistic knowledge of the compound for selection of the PoD, thus providing approaches for assessing data-poor chemicals. We used RNA extracted from the livers of female mice exposed to non-carcinogenic (0, 2 mg/kg/day, mkd) and carcinogenic (4, 8 mkd) doses of furan for 21 days. We show that transcriptional BMD values were consistent across technologies and highly predictive of the two-year cancer bioassay-based PoD. We also demonstrate that filtering data based on statistically significant changes in gene expression prior to BMD modeling creates more conservative BMD values. Taken together, this case study on mice exposed to furan demonstrates that high-content toxicogenomics studies produce robust data for BMD modelling that are minimally affected by inter-technology variability and highly predictive of cancer-based PoD doses

Gene expression levels are correlated between genomics platforms.

<p>Correlation analysis of the fold changes of differentially expressed genes (FDR p < 0.05, fold change > ±1.5 in at least one dataset) for 2 mkd (left), 4 mkd (middle), and 8 mkd (right) furan relative to controls. All regressions were significant (p < 0.05).</p

BMD-means of furan MoA pathways are consistent across platforms.

<p>Symbols indicate BMD values and whiskers indicate the lower confidence interval of the BMD. The confidence intervals (or the range between the BMDL and BMD) for HCA and HCC are shaded in grey.</p

Filtering gene expression data changes the distribution of pathway BMD-mean values.

<p>Distributions of pathway BMD-mean values for RNA-seq (top), microarray (center) and qPCR (bottom). Mode BMD (BMDL) values are labeled. Modes, means and medians decrease as filtering stringency increases. Pathways were only considered in this analysis if they had 4 or more molecules with p fit>0.1. Overlain bar charts indicate the number of transcripts used to model each group.</p

BMD values are not well correlated between genomics platforms.

<p>Inter-platform comparisons of BMDs for genes (top), pathway means (center), and pathway medians (bottom) for ANOVA filtered data. The BMD range for furan-induced HCA-HCC of 2.6–5.1 mkd is shaded in grey. Statistically significant correlations are indicated in red (regression p < 0.05).</p

Four approaches to deriving transcriptomic PoD values.

<p>(A) the lowest BMD(L)-mean for a molecular pathway; (B) the lowest BMD(L)-mean for a molecular pathway that has been validated using a second gene expression platform; (C) the mode, mean, or median of the BMD(L)-mean values; and (D) the BMD of a key MoA-based signaling pathway. PoDs are represented as the pathway BMD-mean values (mkd) with lower confidence intervals indicated. All pathways had a minimum of four molecules that were modeled. The BMD confidence interval for each furan-dependent liver cancer is shaded in grey. NKC = Natural Killer Cells; CV = Cardiovascular; TWEAK = TNF-related weak inducer of apoptosis.</p