GCLM-CDR: a graph contrastive learning method with multi-omics for cancer drug response prediction

Zeyu Yao, Qingchen Zhang

Abstract: Cancer has emerged as a significant threat to human health, leading to numerous fatalities globally. It is important for improving the level of cancer treatment to predict the response of cancer patients to drugs,which is based on their personalized differences. Existing methods for cancer drug response prediction are difficult to effectively capture the differences and correlations between multi-omics features and extract the complex structural patterns of drug molecules, resulting in insufficient accuracy of drug response prediction. To solve these problems, we propose a graph contrastive learning method with multi-omics for cancer drug response prediction (GCLM-CDR). Firstly, we construct a multi-omics drug feature representation module to extract multi-omics features and complex structural patterns of drug molecular graphs. Specifically, for multi-omics data, we use deep neural networks to extract multi-omics features, then we construct a multi-omics neighbor interaction module to capture differences and correlations between different omics data. For drugs, the graph attention network is used to effectively extract complex structural patterns of drug molecular graphs. Secondly, we construct a graph contrastive learning module to further enhance the feature representation after the fusion of multi-omics and drug molecular graphs. In this task, the graph construction strategies of effective positive and negative sample are designed for two types of data. Finally, we construct a cancer drug response prediction module to obtain the prediction results. The experimental results on the GDSC dataset and CCLE dataset showed that the AUC were 0.8534 and AUPR were 0.5327, which were superior to existing methods.