Transcriptomic profiles of tissues from rats treated with drug combinations [Study 3]

Combinations of anticancer agents may have synergistic anti-tumor effects, but enhanced toxicity often limit their clinical use. The risk that combinations of two or more drugs will cause adverse effects that are more severe than drugs used as monotherpies can be hypothesized from comprehensive analysis of each compound’s activity. We generated microarray gene expression data following a single dose of agents administered individually with that of the agents administered in a combination. The key objective of this initiative is to generate and make publicly available key high-content gene expression data sets for mechanistic hypothesis generation for several anticancer drug combinations. The expectation is that availability of tissue-based genomic information that are derived from target tissues will facilitate the generation and testing of mechanistic hypotheses. The view is that availability of these data sets for bioinformaticians and other scientists will contribute to analysis of these data and evaluation of the approach.抗癌药物联合使用可产生协同抗肿瘤效应，但伴随的毒性增强往往限制了其临床应用。通过对每种化合物活性的全面分析，可推测出两种或多种药物联合使用时，其不良反应较单药治疗更为严重的风险。我们获取了单药单剂量单独给药与联合给药后的微阵列（microarray）基因表达数据。本项目的核心目标是生成并公开发布针对多种抗癌药物联合方案的关键高内涵基因表达数据集，以助力机制性假说的构建。我们期望，源自靶组织的组织源性基因组信息的公开共享，将推动机制性假说的提出与验证。我们认为，该数据集的公开可及将为生物信息学家及其他科研人员开展数据分析与研究方法评估提供重要支撑。