An effective way to facilitate the development of modern oncology precision medicine is the systematical analysis of the known drug sensitivities that have emerged in recent years. Meanwhile, the screening of drug response in cancer cell lines provides an estimable genomic and pharmacological data towards high accuracy prediction. Existing works primarily utilize genomic or functional genomic features to classify or regress the drug response. Here in this work, by the migration and extension of the conventional merchandise recommendation methods, we introduce an innovation model on accurate drug sensitivity prediction by using dual-layer strengthened collaborative topic regression (DS-CTR), which incorporates not only the graphic model to jointly learn drugs and cell lines feature from pharmacogenomics data but also drug and cell line similarity network model to strengthen the correlation of the prediction results. Using Genomics of Drug Sensitivity in Cancer project (GDSC) as benchmark datasets, the 5-fold cross-validation experiment demonstrates that DS-CTR model significantly improves drug response prediction performance compared with four categories of state-of-the-art algorithms as for both Receiver Operator Curve (ROC) and the Area Under Receiver Operator Curve (AUC). By uncovering the unknown cell-drug associations with advanced literature evidences, our novel model DS-CTR is validated and supported. The model also provides the possibility to make the discovery of new anti-cancer therapeutics in the preclinical trials cheaper and faster.

中文翻译：

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双层增强协作主题回归模型，用于预测药物敏感性。

促进现代肿瘤精密医学发展的有效途径是对近年来出现的已知药物敏感性进行系统分析。同时，对癌细胞系中药物反应的筛选为高准确度预测提供了可估计的基因组和药理学数据。现有作品主要利用基因组或功能基因组特征对药物反应进行分类或回归。在此工作中，通过对传统商品推荐方法的移植和扩展，我们引入了一种通过使用双层加强协作主题回归（DS-CTR）进行准确药物敏感性预测的创新模型，它不仅包含图形模型以共同从药物基因组学数据中学习药物和细胞系特征，而且还包含药物和细胞系相似性网络模型以加强预测结果的相关性。使用癌症药物敏感性基因组学项目（GDSC）作为基准数据集，5倍交叉验证实验表明，与四类最新算法相比，DS-CTR模型显着提高了药物反应的预测性能接收器操作员曲线（ROC）和接收器操作员曲线下方的面积（AUC）。通过发现具有先进文献证据的未知细胞药物关联，我们的新型模型DS-CTR得到了验证和支持。