Department of Computer Engineering, Bioinformatics and Computational Biology Lab, Sharif University of Technology, Tehran, Iran.
Faculty of Mathematics and Computer Science, Kharazmi University, Tehran, Iran.
PLoS One. 2024 Jul 26;19(7):e0307649. doi: 10.1371/journal.pone.0307649. eCollection 2024.
Cancer treatment has become one of the biggest challenges in the world today. Different treatments are used against cancer; drug-based treatments have shown better results. On the other hand, designing new drugs for cancer is costly and time-consuming. Some computational methods, such as machine learning and deep learning, have been suggested to solve these challenges using drug repurposing. Despite the promise of classical machine-learning methods in repurposing cancer drugs and predicting responses, deep-learning methods performed better. This study aims to develop a deep-learning model that predicts cancer drug response based on multi-omics data, drug descriptors, and drug fingerprints and facilitates the repurposing of drugs based on those responses. To reduce multi-omics data's dimensionality, we use autoencoders. As a multi-task learning model, autoencoders are connected to MLPs. We extensively tested our model using three primary datasets: GDSC, CTRP, and CCLE to determine its efficacy. In multiple experiments, our model consistently outperforms existing state-of-the-art methods. Compared to state-of-the-art models, our model achieves an impressive AUPRC of 0.99. Furthermore, in a cross-dataset evaluation, where the model is trained on GDSC and tested on CCLE, it surpasses the performance of three previous works, achieving an AUPRC of 0.72. In conclusion, we presented a deep learning model that outperforms the current state-of-the-art regarding generalization. Using this model, we could assess drug responses and explore drug repurposing, leading to the discovery of novel cancer drugs. Our study highlights the potential for advanced deep learning to advance cancer therapeutic precision.
癌症治疗已成为当今世界面临的最大挑战之一。针对癌症，有多种治疗方法，基于药物的治疗方法显示出了更好的效果。另一方面，设计新的癌症药物既昂贵又耗时。一些计算方法，如机器学习和深度学习，已经被提出用于药物重用来解决这些挑战。尽管经典机器学习方法在重新利用癌症药物和预测反应方面有一定的前景，但深度学习方法表现得更好。本研究旨在开发一种基于多组学数据、药物描述符和药物指纹的深度学习模型，用于预测癌症药物反应，并基于这些反应促进药物的再利用。为了降低多组学数据的维度，我们使用自动编码器。作为一种多任务学习模型，自动编码器与 MLP 相连。我们使用三个主要数据集（GDSC、CTRP 和 CCLE）来广泛测试我们的模型，以确定其疗效。在多项实验中，我们的模型始终优于现有的最先进方法。与最先进的模型相比，我们的模型实现了令人印象深刻的 AUPRC 为 0.99。此外，在跨数据集评估中，我们的模型在 GDSC 上进行训练，在 CCLE 上进行测试，其性能优于之前的三项工作，AUPRC 达到 0.72。总之，我们提出了一种在泛化方面优于当前最先进技术的深度学习模型。我们可以使用这个模型来评估药物反应并探索药物再利用，从而发现新的癌症药物。我们的研究强调了先进的深度学习在提高癌症治疗精度方面的潜力。
