Integrative and Personalized QSAR Analysis in Cancer by Kernelized Bayesian Matrix Factorization
Integrative and Personalized QSAR Analysis in Cancer by Kernelized Bayesian Matrix Factorization
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DOI：
10.1021/ci500152b
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发表时间：
2014-08-01
影响因子：
5.6
通讯作者：
Kaski, Samuel
中科院分区：
文献类型：
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作者：
Amnnad-ud-din, Muhammad;Georgii, Elisabeth;Kaski, Samuel
利用最近大规模药物敏感性测量活动的数据，现在可以建立和测试模型，预测一百多种抗癌药物对数百种人类癌细胞系的反应。传统的定量构效关系（quantitative structure-activity relationship，QSAR）方法主要研究小分子在单个细胞系或单个组织类型中的生物活性。我们在两个方向上扩展了这一研究路线：（1）同时预测多个已知癌细胞系对新药反应的综合QSAR方法和（2）预测新癌细胞系对新药反应的个性化QSAR方法。为了解决建模任务，我们采用了一种新的核化贝叶斯矩阵分解方法。为了最大的适用性和预测性能，除了化学药物描述符之外，该方法任选地利用细胞系的基因组特征和药物的靶信息。在116种抗癌药物和650种细胞系的案例研究中，我们证明了该方法在几种相关预测场景中的实用性，不同的可用信息量，并分析了各种类型的药物特征对响应预测的重要性。此外，在预测数据集的缺失值后，探索药物反应的完整全局图，以评估治疗上感兴趣的抗癌药物的治疗潜力和治疗范围。
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.