Biologically Informed Multi-Omics Integration Reveals Clinically Meaningful Patient Representations in Acute Myeloid Leukemia

Integrating heterogeneous molecular and clinical data into unified, clinically meaningful patient representations remains a major challenge in precision medicine. Here, we present SurvMOCA-GNN, a biologically informed multi-omics integration framework that explicitly incorporates biological network structure, cross-omics regulatory relationships, and patient-specific clinical information to generate unified patient representations. Using gene expression, microRNA expression, and clinical data from the TCGA-LAML cohort, SurvMOCA-GNN revealed patient representations that identified prognostically distinct patient groups and further stratified patients within established European LeukemiaNet 2022 risk categories. Systematic ablation analyses demonstrated that incorporation of graph-based modeling, cross-omics attention, adaptive clinical integration, and biological prior knowledge progressively improved integration quality, resulting in more structured patient representations and stronger prognostic stratification. Compared with existing multi-omics integration approaches, SurvMOCA-GNN consistently produced more biologically coherent patient representations with improved survival discrimination. Evaluation in an independent AML cohort, together with transfer learning analyses, further demonstrated the robustness and transferability of the integrated representations across heterogeneous patient populations. Together, these findings demonstrate that biologically informed multi-omics integration reveals clinically meaningful patient representations and provides a general framework for integrating heterogeneous molecular and clinical data to improve patient stratification in AML and potentially other diseases. The SurvMOCA-GNN framework is freely available at https://github.com/tjgu/SurvMOCA-GNN.git.