Wu Yang, Chen Ming, Qin Yufang
College of Information Technology, Shanghai Ocean University, Shanghai, China.
Key Laboratory of Fisheries Information Ministry of Agriculture, Shanghai, China.
PLoS Comput Biol. 2025 Mar 31;21(3):e1012905. doi: 10.1371/journal.pcbi.1012905. eCollection 2025 Mar.
Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. While numerous predictive methodologies for cancer drug response have been proposed, the precise prediction of an individual patient's response to drug and a thorough understanding of differences in drug responses among individuals continue to pose significant challenges. This study introduced a deep learning model PASO, which integrated transformer encoder, multi-scale convolutional networks and attention mechanisms to predict the sensitivity of cell lines to anticancer drugs, based on the omics data of cell lines and the SMILES representations of drug molecules. First, we use statistical methods to compute the differences in gene expression, gene mutation, and gene copy number variations between within and outside biological pathways, and utilized these pathway difference values as cell line features, combined with the drugs' SMILES chemical structure information as inputs to the model. Then the model integrates various deep learning technologies multi-scale convolutional networks and transformer encoder to extract the properties of drug molecules from different perspectives, while an attention network is devoted to learning complex interactions between the omics features of cell lines and the aforementioned properties of drug molecules. Finally, a multilayer perceptron (MLP) outputs the final predictions of drug response. Our model exhibits higher accuracy in predicting the sensitivity to anticancer drugs comparing with other methods proposed recently. It is found that PARP inhibitors, and Topoisomerase I inhibitors were particularly sensitive to SCLC when analyzing the drug response predictions for lung cancer cell lines. Additionally, the model is capable of highlighting biological pathways related to cancer and accurately capturing critical parts of the drug's chemical structure. We also validated the model's clinical utility using clinical data from The Cancer Genome Atlas. In summary, the PASO model suggests potential as a robust support in individualized cancer treatment. Our methods are implemented in Python and are freely available from GitHub (https://github.com/queryang/PASO).
癌症药物敏感性的个体化预测在精准医学中至关重要。虽然已经提出了许多用于预测癌症药物反应的方法，但精确预测个体患者对药物的反应以及深入了解个体间药物反应的差异仍然面临重大挑战。本研究引入了一种深度学习模型PASO，该模型集成了Transformer编码器、多尺度卷积网络和注意力机制，基于细胞系的组学数据和药物分子的SMILES表示来预测细胞系对抗癌药物的敏感性。首先，我们使用统计方法计算生物途径内外基因表达、基因突变和基因拷贝数变异的差异，并将这些途径差异值作为细胞系特征，结合药物的SMILES化学结构信息作为模型的输入。然后，该模型集成了各种深度学习技术——多尺度卷积网络和Transformer编码器，从不同角度提取药物分子的特性，同时注意力网络致力于学习细胞系组学特征与上述药物分子特性之间的复杂相互作用。最后，多层感知器（MLP）输出药物反应的最终预测结果。与最近提出的其他方法相比，我们的模型在预测抗癌药物敏感性方面表现出更高的准确性。在分析肺癌细胞系的药物反应预测时发现，PARP抑制剂和拓扑异构酶I抑制剂对小细胞肺癌特别敏感。此外，该模型能够突出与癌症相关的生物途径，并准确捕捉药物化学结构的关键部分。我们还使用来自癌症基因组图谱的临床数据验证了该模型的临床实用性。总之, PASO模型显示出作为个体化癌症治疗有力支持的潜力。我们的方法用Python实现，可从GitHub（https://github.com/queryang/PASO）免费获取。
