Taoma Kittisak, Ruengjitchatchawalya Marasri, Kusonmano Kanthida, Termsaithong Teerasit, Sutthibutpong Thana, Liangruksa Monrudee, Laomettachit Teeraphan
Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi, Bangkok, 10150, Thailand.
School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, 10140, Thailand.
Sci Rep. 2025 May 22;15(1):17735. doi: 10.1038/s41598-025-02444-7.
Breast cancer is a complex and challenging disease to treat, and despite progress in combating it, drug resistance remains a significant hindrance. Drug combinations have shown promising results in improving therapeutic outcomes, and many machine learning models have been proposed to identify potential drug combinations. Recently, there has been a growing emphasis on enhancing the interpretability of machine learning models to improve our biological understanding of the drug mechanisms underlying the predictions. In this study, we developed a random forest model using simulated protein activities derived from Boolean modeling of breast cancer signaling pathways as input features. The model demonstrates a moderate Pearson's correlation coefficient of 0.40 between the predicted and experimentally observed synergistic scores, with the area under the curve (AUC) of 0.67. Despite its moderate performance, the model offers insights into the interpretable mechanisms behind its predictions. The model's input features consist solely of the individual protein activities simulated in response to drug treatments. Therefore, the framework allows for the analysis of each protein's contribution to the synergy level of each drug pair, enabling a direct interpretation of the drugs' actions on the signaling networks of breast cancer. We demonstrated the interpretability of our approach by identifying proteins responsible for drug resistance and sensitivity in specific cell lines. For example, the analysis revealed that the combination of MEK and STAT3 inhibitors exhibits only a moderate synergistic effect on MDA-MB-468 due to the negative contributions of mTORC1 and NF-κB that diminish the efficacy of the drug pair. The model further predicted that hyperactive PTEN would sensitize the cells to the drug pair. Our framework enhances the understanding of drug mechanisms at the level of the signaling pathways, potentially leading to more effective treatment designs.
乳腺癌是一种治疗起来复杂且具有挑战性的疾病，尽管在抗击乳腺癌方面取得了进展，但耐药性仍然是一个重大障碍。药物联合使用在改善治疗效果方面已显示出有前景的结果，并且已经提出了许多机器学习模型来识别潜在的药物联合。最近，人们越来越强调提高机器学习模型的可解释性，以增进我们对预测背后药物机制的生物学理解。在本研究中，我们使用从乳腺癌信号通路的布尔模型派生的模拟蛋白质活性作为输入特征，开发了一个随机森林模型。该模型在预测的和实验观察到的协同得分之间显示出适度的皮尔逊相关系数0.40，曲线下面积（AUC）为0.67。尽管其性能中等，但该模型为其预测背后的可解释机制提供了见解。该模型的输入特征仅由响应药物治疗模拟的单个蛋白质活性组成。因此，该框架允许分析每种蛋白质对每个药物对协同水平的贡献，从而能够直接解释药物对乳腺癌信号网络的作用。我们通过识别特定细胞系中负责耐药性和敏感性的蛋白质，证明了我们方法的可解释性。例如，分析表明，MEK和STAT3抑制剂的联合对MDA-MB-468仅表现出适度的协同作用，因为mTORC1和NF-κB的负面作用降低了该药物对的疗效。该模型进一步预测，过度活跃的PTEN会使细胞对该药物对敏感。我们的框架增强了在信号通路水平上对药物机制的理解，可能会带来更有效的治疗设计。
