School of Computer Science and Engineering, Central South University, 410075 Changsha, China.
Aliyun School of Big Data, Changzhou University, 213164 Changzhou, China.
J Chem Inf Model. 2020 Oct 26;60(10):4497-4505. doi: 10.1021/acs.jcim.0c00331. Epub 2020 Aug 31.
To efficiently save cost and reduce risk in drug research and development, there is a pressing demand to develop methods to predict drug sensitivity to cancer cells. With the exponentially increasing number of multi-omics data derived from high-throughput techniques, machine learning-based methods have been applied to the prediction of drug sensitivities. However, these methods have drawbacks either in the interpretability of the mechanism of drug action or limited performance in modeling drug sensitivity. In this paper, we presented a pathway-guided deep neural network (DNN) model to predict the drug sensitivity in cancer cells. Biological pathways describe a group of molecules in a cell that collaborates to control various biological functions like cell proliferation and death, thereby abnormal function of pathways can result in disease. To take advantage of the excellent predictive ability of DNN and the biological knowledge of pathways, we reshaped the canonical DNN structure by incorporating a layer of pathway nodes and their connections to input gene nodes, which makes the DNN model more interpretable and predictive compared to canonical DNN. We have conducted extensive performance evaluations on multiple independent drug sensitivity data sets and demonstrated that our model significantly outperformed the canonical DNN model and eight other classical regression models. Most importantly, we observed a remarkable activity decrease in disease-related pathway nodes during forward propagation upon inputs of drug targets, which implicitly corresponds to the inhibition effect of disease-related pathways induced by drug treatment on cancer cells. Our empirical experiments showed that our method achieves pharmacological interpretability and predictive ability in modeling drug sensitivity in cancer cells. The web server, the processed data sets, and source codes for reproducing our work are available at http://pathdnn.denglab.org.
为了在药物研发中有效地节省成本和降低风险，迫切需要开发预测癌细胞对药物敏感性的方法。随着高通量技术产生的越来越多的多组学数据，基于机器学习的方法已被应用于药物敏感性的预测。然而，这些方法要么在药物作用机制的可解释性方面存在缺陷，要么在建模药物敏感性方面的性能有限。在本文中，我们提出了一种基于通路的深度神经网络（DNN）模型来预测癌细胞中的药物敏感性。生物通路描述了细胞中一组协同控制各种生物功能（如细胞增殖和死亡）的分子，因此通路的异常功能会导致疾病。为了利用 DNN 的优异预测能力和通路的生物学知识，我们通过将一层通路节点及其与输入基因节点的连接重新塑造规范 DNN 结构，使 DNN 模型比规范 DNN 更具可解释性和预测性。我们在多个独立的药物敏感性数据集上进行了广泛的性能评估，并证明我们的模型明显优于规范 DNN 模型和其他八个经典回归模型。最重要的是，我们观察到在输入药物靶标时，疾病相关通路节点在正向传播过程中活性显著下降，这与药物治疗对癌细胞中疾病相关通路的抑制作用隐含对应。我们的实验表明，我们的方法在癌症细胞药物敏感性建模中实现了药理学可解释性和预测能力。用于重现我们工作的网络服务器、处理后的数据集和源代码可在 http://pathdnn.denglab.org 上获得。
