Discriminating Different Classes of Toxicants by Transcript Profiling

Abstract

Male rats were treated with various model compounds or the appropriate vehicle controls. Most substances were either well-known hepatotoxicants or showed hepatotoxicity during preclinical testing. The aim of the present study was to determine if biological samples from rats treated with various compounds can be classified based on gene expression profiles. In addition to gene expression analysis using microarrays, a complete serum chemistry profile and liver and kidney histopathology were performed. We analyzed hepatic gene expression profiles using a supervised learning method (support vector machines; SVMs) to generate classification rules and combined this with recursive feature elimination to improve classification performance and to identify a compact subset of probe sets with potential use as biomarkers. Two different SVM algorithms were tested, and the models obtained were validated with a compound-based external cross-validation approach. Our predictive models were able to discriminate between hepatotoxic and nonhepatotoxic compounds. Furthermore, they predicted the correct class of hepatotoxicant in most cases. We provide an example showing that a predictive model built on transcript profiles from one rat strain can successfully classify profiles from another rat strain. In addition, we demonstrate that the predictive models identify nonresponders and are able to discriminate between gene changes related to pharmacology and toxicity. This work confirms the hypothesis that compound classification based on gene expression data is feasible.

Alterations in Gene Expression in Human Mesothelial Cells Correlate with Mineral Pathogenicity

“…Models of transcript profiling for discrimination of toxic and nontoxic compounds in liver and other organs have also been developed in rodents (18), confirming the hypothesis that predictive modeling for classification of toxic agents and carcinogens is feasible. Here we used toxicogenomic approaches in human mesothelial cells, a cell type exquisitely sensitive to asbestos (19) and human contact-inhibited ovarian epithelial cells, a cell type not linked to carcinogenesis by asbestos, to determine whether the magnitude of altered gene expression by insoluble particulates correlated with their toxicity to cells and documented pathogenicity in humans.…”

NSAID-Induced Acute Phase Response is Due to Increased Intestinal Permeability and Characterized by Early and Consistent Alterations in Hepatic Gene Expression

“…The value created from mining vast amounts of gene expression data has allowed the linking of gene expression changes with traditional measurements of pharmacological and toxicological activity. This connection of traditional and novel data provides context and meaning to the alterations in gene expression caused by a candidate drug or chemical (Amin et al, 2002;Bushel et al, 2002;Hamadeh et al, 2002aHamadeh et al, , 2002bSteiner et al, 2004;Fielden et al, 2005;Ruepp et al, 2005). Further, this database has been mined using rigorous, statistical approaches based on sophisticated classification algorithms and logistic regression producing a library of linear, robust binary classifiers (Ganter et al, 2005;Natsoulis et al, 2005).…”

Increased levels of urinary phenylacetylglycine associated with mitochondrial toxicity in a model of drug-induced phospholipidosis

“…To classify individual gene expression profiles, previous liver gene expression profiles from male Wistar rats were used to generate a predictive support vector machine (SVM)-based predictive model for the following hepatotoxic categories: control/nontoxic, direct acting, peroxisome proliferation, cholestasis and steatosis [Lee, 1995]. Subsequently, gene expression profiles from the livers of control and treated rats were compared with the SVM Hepatotox_SVM_OVA_Versionl [Steiner et al 2004], and classified according to confidence.…”