As the field of precision medicine progresses, treatments for patients with cancer are starting to be tailored to their molecular as well as their clinical features. The emerging cancer subtypes defined by these molecular features require that dedicated resources be used to assist the discovery of drug candidates for preclinical evaluation. Voluminous gene expression profiles of patients with cancer have been accumulated in public databases, enabling the creation of cancer-specific expression signatures. Meanwhile, large-scale gene expression profiles of cellular responses to chemical compounds have also recently became available. By matching the cancer-specific expression signature to compound-induced gene expression profiles from large drug libraries, researchers can prioritize small molecules that present high potency to reverse expression of signature genes for further experimental testing of their efficacy. This approach has proven to be an efficient and cost-effective way to identify efficacious drug candidates. However, the success of this approach requires multiscale procedures, imposing considerable challenges to many labs. To address this, we developed Open Cancer TherApeutic Discovery (OCTAD; http://octad.org): an open workspace for virtually screening compounds targeting precise groups of patients with cancer using gene expression features. Its database includes 19,127 patient tissue samples covering more than 50 cancer types and expression profiles for 12,442 distinct compounds. The program is used to perform deep-learning-based reference tissue selection, disease gene expression signature creation, drug reversal potency scoring and in silico validation. OCTAD is available as a web portal and a standalone R package to allow experimental and computational scientists to easily navigate the tool.

随着精准医学领域的发展，癌症患者的治疗开始根据他们的分子和临床特征进行定制。由这些分子特征定义的新兴癌症亚型需要使用专门的资源来帮助发现用于临床前评估的候选药物。癌症患者的大量基因表达谱已在公共数据库中积累，从而能够创建癌症特异性表达特征。同时，最近也可以获得细胞对化合物反应的大规模基因表达谱。通过将癌症特异性表达特征与来自大型药物库的化合物诱导的基因表达谱相匹配，研究人员可以优先考虑表现出高效逆转特征基因表达的小分子，以进一步实验测试其功效。这种方法已被证明是一种识别有效候选药物的有效且具有成本效益的方法。然而，这种方法的成功需要多尺度程序，给许多实验室带来了相当大的挑战。为了解决这个问题，我们开发了 Open Cancer TherApeutic Discovery (OCTAD; http://octad.org)：一个开放的工作空间，用于使用基因表达特征虚拟筛选针对精确癌症患者群体的化合物。其数据库包括 19,127 个患者组织样本，涵盖 50 多种癌症类型和 12,442 种不同化合物的表达谱。该程序用于执行基于深度学习的参考组织选择，疾病基因表达特征创建、药物逆转效力评分和计算机验证。OCTAD 可作为一个门户网站和一个独立的 R 包提供，以使实验和计算科学家能够轻松地浏览该工具。