Qin Yong, Conley Anthony P, Grimm Elizabeth A, Roszik Jason
Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
Department of Sarcoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
PLoS One. 2017 Apr 28;12(4):e0176763. doi: 10.1371/journal.pone.0176763. eCollection 2017.
The sensitivity of cancer cells to anticancer drugs is a crucial factor for developing effective treatments. However, it is still challenging to precisely predict the effectiveness of therapeutics in humans within a complex genomic and molecular context. We developed an interface which allows the user to rapidly explore drug sensitivity and gene expression associations. Predictions for how expression of various genes affect anticancer drug activity are available for all genes for a set of therapeutics based on data from various cell lines of different origin in the Cancer Cell Line Encyclopedia and the Genomics of Drug Sensitivity in Cancer projects. Our application makes discovery or validation of drug sensitivity and gene expression associations efficient. Effectiveness of this tool is demonstrated by multiple known and novel examples.
癌细胞对抗癌药物的敏感性是开发有效治疗方法的关键因素。然而，在复杂的基因组和分子背景下精确预测治疗方法在人体中的有效性仍然具有挑战性。我们开发了一个界面，允许用户快速探索药物敏感性和基因表达之间的关联。基于癌症细胞系百科全书和癌症药物敏感性基因组学项目中不同来源的各种细胞系的数据，对于一组治疗药物的所有基因，都可以预测各种基因的表达如何影响抗癌药物活性。我们的应用程序使药物敏感性和基因表达关联的发现或验证变得高效。多个已知和新颖的例子证明了该工具的有效性。
