Transcriptomic profiles of tissues from rats treated with drug combinations [Study 1]

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.抗癌药物联合使用或可产生协同抗肿瘤效应，但伴随的毒性增强往往会限制其临床应用。通过对每种化合物活性的全面分析，可推测出两种及以上药物联合使用时，引发的不良反应较单药治疗更为严重的风险。我们采集了单药单次给药与药物联合给药两种处理方式下的微阵列基因表达数据（microarray gene expression data）。本研究计划的核心目标是生成并公开共享多组抗癌药物联合疗法对应的关键高内涵基因表达数据集（high-content gene expression data sets），为相关机制性假说的构建提供支撑。我们期望，源自靶组织的组织源性基因组信息将助力机制性假说的构建与验证。我们认为，向生物信息学家及其他科研人员开放此类数据集，将有助于开展此类数据的分析工作，并对相关研究方法进行评估。