OCTAD: an open workspace for virtually screening therapeutics targeting precise cancer patient groups using gene expression features.
OCTAD: an open workspace for virtually screening therapeutics targeting precise cancer patient groups using gene expression features.
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OCTAD：一个开放的工作空间，用于使用基因表达特征对精确的癌症患者群体进行虚拟筛选治疗。
DOI：
10.1038/s41596-020-00430-z
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发表时间：
2021-03
期刊：
影响因子：
14.8
通讯作者：
Chen B
中科院分区：
文献类型：
--
作者：
Zeng B;Glicksberg BS;Newbury P;Chekalin E;Xing J;Liu K;Wen A;Chow C;Chen B
随着精准医学领域的发展，癌症患者的治疗开始根据其分子和临床特征进行定制。由这些分子特征定义的新出现的癌症亚型需要使用专用资源来帮助发现用于临床前评价的候选药物。癌症患者的大量基因表达谱已经在公共数据库中积累，使得能够创建癌症特异性表达特征。与此同时，细胞对化合物反应的大规模基因表达谱最近也已可用。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具有高效力的小分子来逆转特征基因的表达，以进一步实验测试其功效。这种方法已被证明是一种有效的和具有成本效益的方式来确定有效的候选药物。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。为了解决这一问题，我们开发了开放式癌症治疗发现（OCECHO; 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靶向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。OCCENTRAL作为一个门户网站和一个独立的R软件包提供，允许实验和计算科学家轻松地浏览该工具。
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.
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影响因子：
7.3
作者：
Gao J;Aksoy BA;Dogrusoz U;Dresdner G;Gross B;Sumer SO;Sun Y;Jacobsen A;Sinha R;Larsson E;Cerami E;Sander C;Schultz N
通讯作者：
Schultz N
影响因子：
4.4
作者：
Kucukural, Alper;Yukselen, Onur;Garber, Manuel
通讯作者：
Garber, Manuel
影响因子：
4.4
作者：
Lee BK;Tiong KH;Chang JK;Liew CS;Abdul Rahman ZA;Tan AC;Khang TF;Cheong SC
通讯作者：
Cheong SC
影响因子：
14.9
作者：
Kim S;Chen J;Cheng T;Gindulyte A;He J;He S;Li Q;Shoemaker BA;Thiessen PA;Yu B;Zaslavsky L;Zhang J;Bolton EE
通讯作者：
Bolton EE
DOI：
10.1073/pnas.1501597112
发表时间：
2015-10-13
影响因子：
11.1
作者：
Brum, Andrea M.;van de Peppel, Jeroen;van Leeuwen, Johannes P. T. M.
通讯作者：
van Leeuwen, Johannes P. T. M.