Trans-Driver: a deep learning approach for cancer driver gene discovery with multi-omics data

Driver genes play a crucial role in the growth of cancer cells. Accurate identification of cancer driver genes is essential for deepening our understanding of cancer pathogenesis and facilitating the development of cancer therapies and drug-targeted driver genes. However, the diversity and complexity of multi-omics data still make cancer driver identification highly challenging. In this study, we propose Transformer-Driver (Trans-Driver), a deep supervised learning method based on a novel transformer architecture, which integrates multi-omics data to learn the differences and associations between different omics modalities for cancer driver discovery. Trans-Driver introduces a kernel-based multi-head self-attention mechanism with gated residual connections, as well as a Dynamic Tanh (DyT) normalization function, to enhance the integration and modeling of heterogeneous multi-omics features. Compared with other state-of-the-art driver gene identification methods, Trans-Driver achieved excellent performance on TCGA, CGC, and PCAWG datasets. Among approximately 20,000 protein-coding genes, Trans-Driver reported 269 candidate driver genes, of which 132 genes (about 49.1%) were included in the gold standard CGC dataset. Feature contribution analysis further demonstrated that integrating multi-omics data improved performance compared to using somatic mutation data alone. Finally, detailed analysis revealed that the candidate drivers are clinically meaningful, demonstrating the practical value of Trans-Driver.