Center for Computation and Technology, Louisiana State University, Baton Rouge, LA 70803, USA.
These authors contributed equally to this work.
Oncotarget. 2022 May 19;13:695-706. doi: 10.18632/oncotarget.28234. eCollection 2022.
Development of novel anti-cancer treatments requires not only a comprehensive knowledge of cancer processes and drug mechanisms of action, but also the ability to accurately predict the response of various cancer cell lines to therapeutics. Numerous computational methods have been developed to address this issue, including algorithms employing supervised machine learning. Nonetheless, high prediction accuracies reported for many of these techniques may result from a significant overlap among training, validation, and testing sets, making existing predictors inapplicable to new data. To address these issues, we developed CancerOmicsNet, a graph neural network with sophisticated attention propagation mechanisms to predict the therapeutic effects of kinase inhibitors across various tumors. Emphasizing on the system-level complexity of cancer, CancerOmicsNet integrates multiple heterogeneous data, such as biological networks, genomics, inhibitor profiling, and gene-disease associations, into a unified graph structure. The performance of CancerOmicsNet, properly cross-validated at the tissue level, is 0.83 in terms of the area under the receiver operating characteristics, which is notably higher than those measured for other approaches. CancerOmicsNet generalizes well to unseen data, i.e., it can predict therapeutic effects across a variety of cancer cell lines and inhibitors. CancerOmicsNet is freely available to the academic community at https://github.com/pulimeng/CancerOmicsNet.
开发新的抗癌疗法不仅需要全面了解癌症过程和药物作用机制，还需要能够准确预测各种癌细胞系对治疗的反应。已经开发出许多计算方法来解决这个问题，包括使用监督机器学习的算法。尽管如此，许多这些技术报告的高预测精度可能是由于训练、验证和测试集之间存在显著重叠，使得现有预测器不适用于新数据。为了解决这些问题，我们开发了 CancerOmicsNet，这是一种具有复杂注意力传播机制的图神经网络，可预测各种肿瘤中激酶抑制剂的治疗效果。CancerOmicsNet 强调癌症的系统级复杂性，将多种异构数据（如生物网络、基因组学、抑制剂分析和基因疾病关联）集成到一个统一的图结构中。CancerOmicsNet 在组织水平上进行了适当的交叉验证，其性能在接收器操作特征曲线下的面积方面达到 0.83，明显高于其他方法的测量值。CancerOmicsNet 可以很好地推广到未见数据，即可以预测各种癌细胞系和抑制剂的治疗效果。CancerOmicsNet 可在 https://github.com/pulimeng/CancerOmicsNet 上免费供学术界使用。
