Application of Genomic Biomarkers to Predict Increased Lung Tumor Incidence in 2-Year Rodent Cancer Bioassays

Abstract

Rodent cancer bioassays are part of a legacy of safety testing that has not changed significantly over the past 30 years. The bioassays are expensive, time consuming, and use hundreds of animals. Fewer than 1500 chemicals have been tested in a rodent cancer bioassay compared to the thousands of environmental and industrial chemicals that remain untested for carcinogenic activity. In this study, we used existing data generated by the National Toxicology Program (NTP) to identify gene expression biomarkers that can predict results from a rodent cancer bioassay. A set of 13 diverse chemicals was selected from those tested by the NTP. Seven chemicals were positive for increased lung tumor incidence in female B6C3F1 mice and six were negative. Female mice were exposed subchronically to each of the 13 chemicals, and microarray analysis was performed on the lung. Statistical classification analysis using the gene expression profiles identified a set of eight probe sets corresponding to six genes whose expression correctly predicted the increase in lung tumor incidence with 93.9% accuracy. The sensitivity and specificity were 95.2 and 91.8%, respectively. Among the six genes in the predictive signature, most were enzymes involved in endogenous and xenobiotic metabolism, and one gene was a growth factor receptor involved in lung development. The results demonstrate that increases in chemically induced lung tumor incidence in female mice can be predicted using gene biomarkers from a subchronic exposure and may form the basis of a more efficient and economical approach for evaluating the carcinogenic activity of chemicals.