Latent Variables Capture Pathway-Level Points of Departure in High-Throughput Toxicogenomic Data

Estimation
of points of departure (PoDs) from high-throughput transcriptomic
data (HTTr) represents a key step in the development of next-generation
risk assessment (NGRA). Current approaches mainly rely on single key
gene targets, which are constrained by the information currently available
in the knowledge base and make interpretation challenging as scientists
need to interpret PoDs for thousands of genes or hundreds of pathways.
In this work, we aimed to address these issues by developing a computational
workflow to investigate the pathway concentration–response
relationships in a way that is not fully constrained by known biology
and also facilitates interpretation. We employed the Pathway-Level
Information ExtractoR (PLIER) to identify latent variables (LVs) describing
biological activity and then investigated in vitro LVs’ concentration–response
relationships using the ToxCast pipeline. We applied this methodology
to a published transcriptomic concentration–response data set
for 44 chemicals in MCF-7 cells and showed that our workflow can capture
known biological activity and discriminate between estrogenic and
antiestrogenic compounds as well as activity not aligning with the
existing knowledge base, which may be relevant in a risk assessment
scenario. Moreover, we were able to identify the known estrogen activity
in compounds that are not well-established ER agonists/antagonists
supporting the use of the workflow in read-across. Next, we transferred
its application to chemical compounds tested in HepG2, HepaRG, and
MCF-7 cells and showed that PoD estimates are in strong agreement
with those estimated using a recently developed Bayesian approach
(cor = 0.89) and in weak agreement with those estimated using a well-established
approach such as BMDExpress2 (cor = 0.57). These results demonstrate
the effectiveness of using PLIER in a concentration–response
scenario to investigate pathway activity in a way that is not fully
constrained by the knowledge base and to ease the biological interpretation
and support the development of an NGRA framework with the ability
to improve current risk assessment strategies for chemicals using
new approach methodologies.