Ammad-ud-din Muhammad, Georgii Elisabeth, Gönen Mehmet, Laitinen Tuomo, Kallioniemi Olli, Wennerberg Krister, Poso Antti, Kaski Samuel
Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University , P.O. Box 15400, Espoo 00076, Finland.
J Chem Inf Model. 2014 Aug 25;54(8):2347-59. doi: 10.1021/ci500152b. Epub 2014 Aug 6.
With data from recent large-scale drug sensitivity measurement campaigns, it is now possible to build and test models predicting responses for more than one hundred anticancer drugs against several hundreds of human cancer cell lines. Traditional quantitative structure-activity relationship (QSAR) approaches focus on small molecules in searching for their structural properties predictive of the biological activity in a single cell line or a single tissue type. We extend this line of research in two directions: (1) an integrative QSAR approach predicting the responses to new drugs for a panel of multiple known cancer cell lines simultaneously and (2) a personalized QSAR approach predicting the responses to new drugs for new cancer cell lines. To solve the modeling task, we apply a novel kernelized Bayesian matrix factorization method. For maximum applicability and predictive performance, the method optionally utilizes genomic features of cell lines and target information on drugs in addition to chemical drug descriptors. In a case study with 116 anticancer drugs and 650 cell lines, we demonstrate the usefulness of the method in several relevant prediction scenarios, differing in the amount of available information, and analyze the importance of various types of drug features for the response prediction. Furthermore, after predicting the missing values of the data set, a complete global map of drug response is explored to assess treatment potential and treatment range of therapeutically interesting anticancer drugs.
借助近期大规模药物敏感性测量活动的数据，现在能够构建并测试预测一百多种抗癌药物对数百种人类癌细胞系反应的模型。传统的定量构效关系（QSAR）方法专注于小分子，以寻找其在单一细胞系或单一组织类型中预测生物活性的结构特性。我们从两个方向扩展了这一研究方向：（1）一种综合QSAR方法，可同时预测一组多个已知癌细胞系对新药的反应；（2）一种个性化QSAR方法，可预测新癌细胞系对新药的反应。为了解决建模任务，我们应用了一种新颖的核化贝叶斯矩阵分解方法。为了实现最大的适用性和预测性能，该方法除了化学药物描述符外，还可选择利用细胞系的基因组特征和药物的靶点信息。在一项针对116种抗癌药物和650个细胞系的案例研究中，我们展示了该方法在几种相关预测场景中的有用性，这些场景在可用信息量上有所不同，并分析了各种类型的药物特征对反应预测的重要性。此外，在预测数据集的缺失值后，探索了完整的药物反应全局图，以评估具有治疗意义的抗癌药物的治疗潜力和治疗范围。
