MTGCL: Multi-Task Graph Contrastive Learning for Identifying Cancer Driver Genes from Multi-omics Data

Abstract Cancer is a complex disease that typically arises from the accumulation of mutations in driver genes. Identification of cancer driver genes is crucial for understanding the molecular mechanisms of cancer, and developing the targeted therapeutic approaches. With the development of high-throughput biological technology, a large amount of genomic data and protein interaction network data have been generated, which provides abundant data resources for identifying cancer driver genes through computational methods. Given the ability of graph neural networks to effectively integrate graph structure topology information and node features information, some graph neural network-based methods have been developed for identifying cancer driver genes. However, these methods suffer from the sparse supervised signals, and also neglect a large amount of unlabeled node information, thereby affecting their ability to identify cancer driver genes. To tackle these issues, in this work we propose a novel Multi-Task Graph Contrastive Learning framework (called MTGCL) to identify cancer driver genes. By using self-supervised graph contrastive learning to fully utilize the unlabeled node information, MTGCL designs an auxiliary task module to enhance the performance of the main task of driver gene identification. MTGCL simultaneously trains the auxiliary task and main task, and shares the graph convolutional encoder weights, so that the main task enhances the discriminative ability of the auxiliary task via supervised learning, whereas the auxiliary task exploits the unlabeled node information to refine the node representation learning of the main task. The experimental results on pan-cancer and some specific cancers demonstrate the effectiveness of MTGCL in identifying the cancer driver genes. In addition, integrating multi-omics features extracted from multiple cancer-related databases can greatly enhance the performance of identifying cancer driver genes, especially, somatic mutation features can effectively improve the performance of identifying specific cancer driver genes. The source code and data are available at https://github.com/NWPU-903PR/MTGCL . Author Summary Identifying cancer driver genes that causally contribute to cancer initiation and progression is essential for comprehending the molecular mechanisms of cancer and developing the targeted therapeutic strategies. However, wet-lab experiments are time-consuming and labor-intensive. The advent of high-throughput multi-omics technology provides an opportunity for identifying the cancer driver genes through data-driven computing approaches. Nevertheless, effectively integrating these omics data to identify cancer driver genes poses significant challenges. Existing computational methods exhibit certain limitations. For instance, conventional approaches (e.g., gene mutation frequency-based methods, network-based methods) often focus on a single omics data, while existing deep learning-based methods have not fully utilized the abundant unlabeled node information, so that their identification accuracy is not high enough. Thus, by fully utilizing multidimensional genomics data and molecular interaction networks, we propose a multi-task learning framework (called MTGCL) to identify cancer driver genes. MTGCL synergistically combines graph convolutional neural networks with graph contrastive learning. The experimental results validate the power of MTGCL for identifying cancer driver genes.