CGP-Net: A Cross-Modal Attention Network with Graph Priors for Integrated Multi-Omics Disease Subtyping

Abstract

Integrating upstream genomic variations and downstream functional proteomic signalling is necessary for developing precision oncology. There are challenges in the integration of multi-omics data due to large discrepancies in dataset dimensions, structural heterogeneity, and non-linear interdependencies between modalities. All of these challenges can cause high-dimensional data of genomics to overshadow important data of proteomics. To remedy these issues, we developed a new multi-omics integration framework and called it CrossGeneProtein-Net (CGP-Net). CrossGeneProtein-Net utilizes a Sparse Autoencoder (SAE) that compresses the high-dimensional genome data and a Protein-Protein Interaction (PPI) focused Graph Attention Network (GAT) that forms a structure of interrelated biological activities that are incorporated within the proteomic communication framework. Capturing cross talking of the different modalities was accomplished through a bidirectional cross-attention architecture. An InfoNCE objective with a contrastive approach served to keep relative positions of the data according to the degree of intermodal noise and were more easily interpretable and consistent in relation to the data. Validation of CGP-Net was conducted on multiple TCGA and CPTAC datasets and it outperformed baseline models MOGONET and OASIS with statistical significance of p < 0.01. On the TCGA-BRCA benchmark, CGP-Net achieved a macro-balanced accuracy of 91.82% for PAM50 molecular subtyping and an overall survival concordance index (C-index) of 0.784, significantly outperforming state-of-the-art baselines including MOGONET and OASIS (p < 0.01). The Integrated Gradients method of feature attribution validated CGP-Net’s superior performance through its emphasis on important oncogenes as well as pathways.