Toxicogenomic module associations with pathogenesis: a network-based approach to understanding drug toxicity

OPEN The Pharmacogenomics Journal (2018) 18, 377–390 www.nature.com/tpj ORIGINAL ARTICLE Toxicogenomic module associations with pathogenesis: a network-based approach to understanding drug toxicity JJ Sutherland, YW Webster, JA Willy, GH Searfoss, KM Goldstein, AR Irizarry, DG Hall and JL Stevens Despite investment in toxicogenomics, nonclinical safety studies are still used to predict clinical liabilities for new drug candidates. Network-based approaches for genomic analysis help overcome challenges with whole-genome transcriptional profiling using limited numbers of treatments for phenotypes of interest. Herein, we apply co-expression network analysis to safety assessment using rat liver gene expression data to define 415 modules, exhibiting unique transcriptional control, organized in a visual representation of the transcriptome (the ‘TXG-MAP’). Accounting for the overall transcriptional activity resulting from treatment, we explain mechanisms of toxicity and predict distinct toxicity phenotypes using module associations. We demonstrate that early network responses complement traditional histology-based assessment in predicting outcomes for longer studies and identify a novel mechanism of hepatotoxicity involving endoplasmic reticulum stress and Nrf2 activation. Module-based molecular subtypes of cholestatic injury derived using rat translate to human. Moreover, compared to gene-level analysis alone, combining module and gene-level analysis performed in sequence identifies significantly more phenotype-gene associations, including established and novel biomarkers of liver injury. The Pharmacogenomics Journal (2018) 18, 377–390; doi:10.1038/tpj.2017.17; published online 25 April 2017 INTRODUCTION Safety remains a major cause of attrition during clinical trials.1–5 Prior to clinical testing, all clinical candidates are evaluated in animals to define the spectrum of toxicities that might occur in human subjects and safe doses for clinical testing.6 However, continued occurrences of clinical safety terminations calls into question the value of nonclinical testing in predicting human risk.7,8 Nonetheless, when confidence in nonclinical safety data is high compounds are more likely to be safe in humans.9 Uncertainty regarding safety predictions occurs at three major transition points in biopharmaceutical testing: (1) the transition inherent in using simple in vitro models to predict in vivo nonclinical (animal) results early in discovery; (2) the transition from nonclinical testing to human clinical trials; and (3) the transition from testing in well-controlled clinical trials to the larger diverse patient population post approval. In other work, we addressed the first transition by associating chemical properties with toxicity early10 and by developing a systems level framework using co-expression networks to evaluate how well mechanisms extrapolate from primary cell cultures to the same organ in vivo.11 Here we address the second transition by investigating the utility of network-based toxicogenomic approaches for predicting mechanisms of drug-induced liver injury and the translation from rodent to human. Considerable effort has been invested applying transcript profiling to risk assessment using methodologies such as gene signatures,12 pathway-based enrichment analysis,13 co-expression networks,14,15 and adverse outcome pathways.16 However, toxicogenomic approaches to safety testing remain challenging and have achieved only modest utility in addressing uncertainty in safety predictions, largely as an investigative tool. Nonclinical safety testing remains largely dependent on traditional clinical chemistry and histologic evaluation. Gene signatures are effective as classifiers but their development requires large and costly compendia of transcript profiles and may not translate to other models and mechanisms. Limitations in measurement technologies and the inherent stochastic nature of biological systems pose additional analytical challenges to establishing the relationship between thousands of variables (genes) and toxicity properties using small sets of training compounds. Pathway or Gene Ontology (GO) enrichment analysis can reduce noise but are biased toward known biology captured in existing repositories.13,17 Unsupervised methods that organize high-dimensional data into networks based on biologically relevant coalescent properties reduce noise and boost signal detection.18,19 This seems intuitive since organisms demonstrate modularity and conservation of biology across evolution.20,21 One such approach, weighted gene co-expression network analysis (WGCNA), uses the property of coexpression to organize genes into gene networks or modules.22 Here we develop a co-expression framework called the ‘toxicogenomic module associations with pathogenesis’ (the TXG-MAP) and integrate it with standard pathology evaluation to characterize mechanisms of drug-induced liver injury. We demonstrate the utility of the TXG-MAP for common applications. First, we illustrate how co-expression modules reveal mechanisms of pathogenesis concurrent with or preceding toxicity phenotypes. Second, we illustrate the utility of modules for identifying marker genes in small data sets, while controlling for false discovery. Third, we use case studies to illustrate utility in elucidating specific mechanisms of liver injury. Fourth, we identify transcription factors that couple upstream signals to co-expression changes. Finally, we demonstrate that module-based molecular phenotypes for rodent liver injury translate to human liver disease. Lilly Research Laboratories, Eli Lilly and Company, Lilly Corporate Center, Indianapolis IN, USA. Correspondence: Dr JL Stevens, Lilly Research Laboratories, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA. E-mail: Received 2 November 2016; revised 19 February 2017; accepted 28 February 2017; published online 25 April 2017 Toxicogenomic module associations with pathogenesis JJ Sutherland et al 378 MATERIALS AND METHODS Microarray data processing from Drug Matrix, TG-GATEs and GEO The Drug Matrix (DM)23 and open TG-GATEs (TG)24 databases constitute two large publicly available resources describing the effects of drugs and other compounds in rat liver. They contain gene expression data from Affymetrix microarrays, linked to traditional histology and clinical chemistry results for 3528 treatment groups from TG and 654 from DM. A treatment group denotes three or more animals receiving a given dose of drug or vehicle, usually administered daily by oral gavage, and killed following drug exposures lasting from 3 h to 29 days. The treatment groups analyzed in this work are given in Supplementary Table S1. Methods for obtaining, processing and analyzing rat liver microarray data from DM and TG are described in detail elsewhere;11 details for Gene Expression Omnibus (GEO) sets are provided in Supplementary Methods. phenotype label, means ‘any other (...truncated)