CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Science, Shanghai 200031, China.
Bioinformatics. 2024 Oct 1;40(10). doi: 10.1093/bioinformatics/btae604.
Accurate and robust estimation of the synergistic drug combination is important for medicine precision. Although some computational methods have been developed, some predictions are still unreliable especially for the cross-dataset predictions, due to the complex mechanism of drug combinations and heterogeneity of cancer samples.
We have proposed JointSyn that utilizes dual-view jointly learning to predict sample-specific effects of drug combination from drug and cell features. JointSyn outperforms existing state-of-the-art methods in predictive accuracy and robustness across various benchmarks. Each view of JointSyn captures drug synergy-related characteristics and makes complementary contributes to the final prediction of the drug combination. Moreover, JointSyn with fine-tuning improves its generalization ability to predict a novel drug combination or cancer sample using a small number of experimental measurements. We also used JointSyn to generate an estimated atlas of drug synergy for pan-cancer and explored the differential pattern among cancers. These results demonstrate the potential of JointSyn to predict drug synergy, supporting the development of personalized combinatorial therapies.
Source code and data are available at https://github.com/LiHongCSBLab/JointSyn.
准确而稳健的协同药物组合估计对于医学精准性非常重要。尽管已经开发了一些计算方法，但由于药物组合的复杂机制和癌症样本的异质性，一些预测仍然不可靠，特别是对于跨数据集的预测。
我们提出了 JointSyn，它利用双视图联合学习，从药物和细胞特征中预测药物组合对特定样本的作用。在各种基准测试中，JointSyn 在预测准确性和稳健性方面均优于现有的最先进方法。JointSyn 的每个视图都捕获了与药物协同作用相关的特征，并对药物组合的最终预测做出了互补的贡献。此外，通过微调 JointSyn 可以提高其在使用少量实验测量值预测新的药物组合或癌症样本时的泛化能力。我们还使用 JointSyn 生成了泛癌的药物协同作用估计图谱，并探索了癌症之间的差异模式。这些结果表明了 JointSyn 预测药物协同作用的潜力，支持了个性化组合疗法的发展。
源代码和数据可在 https://github.com/LiHongCSBLab/JointSyn 上获得。
