Prediction of cancer drug response based on heterogeneous graph neural networks and multi-omics data.

Abstract

Precision prediction of cancer drug response remains a critical challenge in personalized medicine. With ongoing advancements in related research, substantial amounts of cell line omics data and drug feature information have been accumulated, offering valuable resources for investigating cancer drug responses. However, effectively integrating these multi-omics features and constructing accurate and interpretable network-based prediction models remain challenging. To address these issues, we propose GraphTCDR, a model based on heterogeneous graph neural networks and multi-omics data that can accurately predict cancer drug responses. The specific workflow of GraphTCDR is as follows: First, a cell line-drug heterogeneous network is constructed, using multi-omics data and drug features as node attributes. Next, node feature learning is conducted on the heterogeneous network. Finally, the learned features are fed into fully connected layers to predict IC50 values. Extensive experiments on the PRISM database demonstrate that GraphTCDR outperforms existing state-of-the-art methods across all evaluation metrics. Compared with the current best-performing model, GraphTCDR achieves improvements of 3.60 % in PCC, 4.30 % in SCC, 6.50 % in R², and a 1.60 % reduction in RMSE. The reliability of GraphTCDR's predictions on unlabeled samples is also validated. Moreover, GraphTCDR maintains stable performance even when the amount of training data is reduced, unlike other algorithms, indicating superior robustness. GraphTCDR offers a novel approach to drug response prediction and has significant implications for advancing personalized cancer therapy.