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

Abstract

Non-genotoxic carcinogens are substances that induce tumorigenesis by non-mutagenic mechanisms and long term rodent bioassays are required to identify them. Recent studies have shown that transcription profiling can be applied to develop early identifiers for long term phenotypes. In this study, we used rat liver expression profiles from the NTP (National Toxicology Program, Research Triangle Park, USA) DrugMatrix Database to construct a gene classifier that can distinguish between non-genotoxic carcinogens and other chemicals. The model was based on short term exposure assays (3 days) and the training was limited to oxidative stressors, peroxisome proliferators and hormone modulators. Validation of the predictor was performed on independent toxicogenomic data (TG-GATEs, Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System, Osaka, Japan). To build our model we performed Random Forests together with a recursive elimination algorithm (VarSelRF). Gene set enrichment analysis was employed for functional interpretation. A total of 770 microarrays comprising 96 different compounds were analyzed and a predictor of 54 genes was built. Prediction accuracy was 0.85 in the training set, 0.87 in the test set and increased with increasing concentration in the validation set: 0.6 at low dose, 0.7 at medium doses and 0.81 at high doses. Pathway analysis revealed gene prominence of cellular respiration, energy production and lipoprotein metabolism. The biggest target of toxicogenomics is accurately predict the toxicity of unknown drugs. In this analysis, we presented a classifier that can predict non-genotoxic carcinogenicity by using short term exposure assays. In this approach, dose level is critical when evaluating chemicals at early time points.

Mutagenicity and genotoxicity of ClearTaste

“…The authors submit that to fully understand the nature of ClearTaste’s safety, studies regarding non-genotoxic mechanisms of carcinogenesis should be conducted to finalize comprehension of ClearTaste’s full carcinogenic potential [ [23] , [24] , [25] ]. With that consideration it is understood that most carcinogenic compounds are mutagenic/genotoxic.…”

Identification of Time-Invariant Biomarkers for Non-Genotoxic Hepatocarcinogen Assessment

“…Especially, dose level is critical when evaluating chemicals, and a high-dose level may increase specificity compared with a lower-dose level. A previous study also concluded that the optimal exposure style for assessing NGHCs is a 3-day daily high dose [57]. However, given that we are looking for time-invariant biomarkers and the prediction model performed well in the external database, the effect of the differences in the experimental study design should not be an issue.…”

A study of differential microRNA expression profile in migraine: the microMIG exploratory study

“…We used Random Forests (RF) models for the classification task, a machine learning method widely used in the microarray analysis [50][51][52]. RF algorithm uses an ensemble of classification trees, internally and randomly constructed using a bootstrap sample of the data.…”