OncoDrug+ data 2.0

Combinations of cancer drugs have the potential to overcome resistance, improve the response rate of existing drugs and reduce dose-limiting toxicity associated with single agents. Existing drug combination databases only provide response data, such as synergy scores between two drugs, without important contextual information to assist oncologists in matching their patients with these combinations in an evidence-based way. To address this gap, we constructed an unique dataset by manually collecting and integrating drug combinations and corresponding evidences from FDA databases, clinical guidelines, clinical trials, clinical case reports, patient-derived tumor xenograft models, cell line models and bioinformatics predictions.Records classified into Level A were derived from professional clinical guidelines or included in the FDA database.Records classified into Level B were collected from clinical trials and individual case reports in electronic medical records.Records classified into Level C were obtained from in vivo or in vitro experiments, including PDX mouse models, cell line models and high-throughput drug screening experiments.Records classified into Level D dataset were derived from drug combination predictions based on bioinformatic algorithms.Our manuscript about this dataset has been submitted.癌症药物联合疗法具备克服肿瘤耐药性、提升现有药物临床应答率，且降低单药治疗伴随的剂量限制性毒性的潜力。现有药物联合疗法数据库仅提供应答数据（如两种药物间的协同评分），却缺乏关键背景信息，无法帮助肿瘤医师以循证方式为患者匹配合适的联合疗法方案。为填补这一研究空白，我们通过手动收集并整合来自FDA数据库、临床指南、临床试验、临床病例报告、患者来源肿瘤异种移植（patient-derived tumor xenograft, PDX）模型、细胞系模型以及生物信息学预测结果的药物联合疗法与对应证据，构建了一套独特的数据集。被归类为A级的记录源自专业临床指南，或已收录于FDA数据库；B级记录采集自临床试验与电子病历中的单个病例报告；C级记录来源于体内或体外实验，包括PDX小鼠模型、细胞系模型以及高通量药物筛选实验；被归类为D级的记录则来源于基于生物信息学算法的药物联合疗法预测结果。本团队关于该数据集的研究论文已提交。