MDSGCN: Predicting Multiple Types of Mutation-Drug Association Through Signed Graph Convolution Network

Cancer constitutes a significant global public health challenge, resulting in millions of fatalities annually. Gene mutations are pivotal in initiating and advancing cancer, disrupting regular cellular growth and differentiation mechanisms, thereby fostering tumor development. Consequently, comprehending the intricacies of gene mutations and their interplay with pharmaceuticals is imperative for cancer prevention, diagnosis, and treatment. Despite drug therapy being a cornerstone in cancer treatment, prognosticating and assessing multiple types of mutation-drug association remains a laborious and costly work. To address this problem, we develop a deep learning model grounded in signed graph convolution network (MDSGCN) to predict multiple types of mutation-drug association. We establish mutation-drug association as a signed bipartite network, comprising mutation nodes, drug nodes and two edge types including sensitive or resistant of mutations in drugs. MDSGCN extracts the subgraphs from the mutation-drug pairs in signed bipartite network, utilizing a label algorithm to learn subgraph structural features. Furthermore, MDSGCN integrates biological features (i.e., mutation-mutation similarity and drug-drug similarity) as the auxiliary information with the subgraph structural features to construct the prediction model. Experimental results demonstrate that our model consistently outperforms the state-of-the-art methods. The case study shows that MDSGCN can discover novel mutation-drug association and the association type.