Deep graph embedding for prioritizing synergistic anticancer drug combinations

TL;DR

This study introduces a deep learning graph convolutional network model that integrates multiple biological networks to accurately predict synergistic drug combinations specific to cancer cell lines, aiding in efficient drug discovery.

Contribution

It presents a novel GCN-based approach that combines heterogeneous biological networks for predicting synergistic drug pairs in cancer treatment, demonstrating high accuracy and biological relevance.

Findings

Most models achieved AUC > 0.80, with a mean AUC of 0.84.

Many top predicted combinations have documented synergistic activity.

The method effectively predicts cell line-specific synergistic drug pairs.

Abstract

Drug combinations are frequently used for the treatment of cancer patients in order to increase efficacy, decrease adverse side effects, or overcome drug resistance. Given the enormous number of drug combinations, it is cost- and time-consuming to screen all possible drug pairs experimentally. Currently, it has not been fully explored to integrate multiple networks to predict synergistic drug combinations using recently developed deep learning technologies. In this study, we proposed a Graph Convolutional Network (GCN) model to predict synergistic drug combinations in particular cancer cell lines. Specifically, the GCN method used a convolutional neural network model to do heterogeneous graph embedding, and thus solved a link prediction task. The graph in this study was a multimodal graph, which was constructed by integrating the drug-drug combination, drug-protein interaction, and protein-protein interaction networks. We found that the GCN model was able to correctly predict cell line-specific synergistic drug combinations from a large heterogonous network. The majority (30) of the 39 cell line-specific models show an area under the receiver operational characteristic curve (AUC) larger than 0.80, resulting in a mean AUC of 0.84. Moreover, we conducted an in-depth literature survey to investigate the top predicted drug combinations in specific cancer cell lines and found that many of them have been found to show synergistic antitumor activity against the same or other cancers in vitro or in vivo. Taken together, the results indicate that our study provides a promising way to better predict and optimize synergistic drug pairs in silico.