Genomic Models of Short-Term Exposure Accurately Predict Long-Term Chemical Carcinogenicity and Identify Putative Mechanisms of Action

Abstract

BackgroundDespite an overall decrease in incidence of and mortality from cancer, about 40% of Americans will be diagnosed with the disease in their lifetime, and around 20% will die of it. Current approaches to test carcinogenic chemicals adopt the 2-year rodent bioassay, which is costly and time-consuming. As a result, fewer than 2% of the chemicals on the market have actually been tested. However, evidence accumulated to date suggests that gene expression profiles from model organisms exposed to chemical compounds reflect underlying mechanisms of action, and that these toxicogenomic models could be used in the prediction of chemical carcinogenicity.ResultsIn this study, we used a rat-based microarray dataset from the NTP DrugMatrix Database to test the ability of toxicogenomics to model carcinogenicity. We analyzed 1,221 gene-expression profiles obtained from rats treated with 127 well-characterized compounds, including genotoxic and non-genotoxic carcinogens. We built a classifier that predicts a chemical's carcinogenic potential with an AUC of 0.78, and validated it on an independent dataset from the Japanese Toxicogenomics Project consisting of 2,065 profiles from 72 compounds. Finally, we identified differentially expressed genes associated with chemical carcinogenesis, and developed novel data-driven approaches for the molecular characterization of the response to chemical stressors.ConclusionHere, we validate a toxicogenomic approach to predict carcinogenicity and provide strong evidence that, with a larger set of compounds, we should be able to improve the sensitivity and specificity of the predictions. We found that the prediction of carcinogenicity is tissue-dependent and that the results also confirm and expand upon previous studies implicating DNA damage, the peroxisome proliferator-activated receptor, the aryl hydrocarbon receptor, and regenerative pathology in the response to carcinogen exposure.

Prediction of Non-Genotoxic Carcinogenicity Based on Genetic Profiles of Short Term Exposure Assays

“…(2014) scanned most of the NTP database and built a classifier that englobed liver carcinogenesis (combining both genotoxic and non-genotoxic carcinogens) (29). They validated their model with TG-GATE liver data and estimated liver carcinogenesis with an AUC of 0.78 (56.8% sensitivity and 82.91% specificity).…”

High throughput toxicity screening and intracellular detection of nanomaterials

“…Connectivity mapping, i.e., grouping for similarities in gene expression profiles, can be viewed as a form of biological read‐across 178. Modeling efforts have indicated a need for investigating more than 200 and even thousands of agents, whether NMs or chemicals, to effectively characterize toxicity mechanisms through omics analysis 179. The well‐known Connectivity Map project,180 and its successor LINCS, have addressed this issue with over 1.5 million gene expression profiles covering over 10 5 variables to date 173.…”

The Carcinogenome Project: In-vitro Gene Expression Profiling of Chemical Perturbations to Predict Long-Term Carcinogenicity

“…The Drugmatrix-derived signatures were defined as the lists of genes in the Drugmatrix significantly associated with long-term carcinogenicity and genotoxicity. Data processing of the Drugmatrix data is consistent with methods described in Gusenleitner et al (2014). Gene features were mapped from rat Ensembl gene identifiers to human gene symbols using Biomart (Durinck et al 2005).…”

“…High-throughput transcriptional profiles from short-term chemical exposures have proven useful for predicting long-term carcinogenicity and for capturing multiple biological MoAs of long-term carcinogenicity. Many studies have explored the use of high-throughput transcriptional profiling in rodent models (Eichner et al 2013;Ellinger-Ziegelbauer et al 2008;Gusenleitner et al 2014;Kossler et al 2015;Uehara et al 2011). However, questions remain about the relevance of rodent models for characterizing human carcinogenicity, and most importantly, they are still excessively time-consuming and expensive for large-scale testing.…”