Trancriptional profiling of rat liver after short-term (up tp 14 days) administration of carcinogenic and non-carcinogenic chemicals

The carcinogenic potential of chemicals is currently evaluated with rodent life-time bioassays, which are time consuming, and expensive with respect to cost, number of animals and amount of compound required. Since the results of these 2-year bioassays are not known until quite late during development of new chemical entities, and since the short-term test battery to test for genotoxicity, a characteristic of genotoxic carcinogens, is hampered by low specificity, the identification of early biomarkers for carcinogenicity would be a big step forward. Using gene expression profiles from the livers of rats treated up to 14 days with genotoxic and non-genotoxic carcinogens we previously identified characteristic gene expression profiles for these two groups of carcinogens. We have now added expression profiles from further hepatocarcinogens and from non-carcinogens the latter serving as control profiles. We used these profiles to extract biomarkers discriminating genotoxic from non-genotoxic carcinogens and to calculate classifiers based on the support vector machine (SVM) algorithm. These classifiers then predicted a set of independent validation compound profiles with up to 88% accuracy, depending on the marker gene set. We would like to present this study as proof of the concept that a classification of carcinogens based on short-term studies may be feasible.当前化学品致癌潜力的评估多采用啮齿类动物终生生物试验（rodent life-time bioassays），该方法不仅耗时漫长，且在实验成本、动物使用量及受试化合物用量层面均耗资不菲。由于这类为期2年的生物试验结果需等到新化学实体（new chemical entities）开发的较晚阶段才能获取，且针对遗传毒性致癌物（genotoxic carcinogens）特征的遗传毒性（genotoxicity）短期试验组合（short-term test battery）特异性较差，因此鉴定致癌性早期生物标志物将是该领域的一项重大突破。此前，我们利用经遗传毒性致癌物与非遗传毒性致癌物（non-genotoxic carcinogens）处理14天的大鼠肝脏基因表达谱（gene expression profiles），成功识别出这两类致癌物的特征性基因表达特征。本次研究中，我们新增了更多肝致癌物（hepatocarcinogens）及非致癌物（non-carcinogens）的表达谱，其中非致癌物表达谱用作对照样本。我们依托这些表达谱提取了可区分遗传毒性与非遗传毒性致癌物的生物标志物，并基于支持向量机（support vector machine, SVM）算法构建分类模型。随后，这些分类模型对一组独立的验证化合物表达谱进行预测，根据选用的标记基因集不同，预测准确率最高可达88%。本研究旨在验证基于短期试验实现致癌物分类的可行性这一概念。