Deep Integrative Multi-Omics Fusion Using Adversarial Augmentation and Graph Attention Networks for Precision Oncology

The rapid expansion of biological data in oncology has created a significant “data deluge,” making clinical decision-making increasingly complex. However, traditional multi-omics data integration methods face severe problems with extreme high-dimensionality, extensive data noise, missing features, and cross-platform distribution shifts, leading to a critical lack of model interpretability and a high risk of downstream data leakage. Motivated by the urgent need for transparent, reproducible, and robust computational screening tools that preserve cross-layer biological relationships without artificial performance inflation, this study introduces a Deep Integrative Multi-Omics Fusion framework utilizing conditional Generative Adversarial Networks (cGAN), Graph-Attention Integrated Fusion (GAIF), and a Deep Survival-Subtyping Engine (DSSE). The primary objective is to map intricate multi-modal oncogenic interactions into a low-dimensional representation space while enforcing strict train-test fold isolation. The main contribution is a mathematically defined, heterogeneous feature graph that integrates database priors with statistical filters. Retrospective evaluations across major malignancy cohorts demonstrate high-fidelity results: the model achieves a cancer subtype classification accuracy of 96.5% in BRCA cohorts, a continuous survival forecasting concordance index (C-index) of 0.89 (σ = 0.012), and an AUC-ROC of up to 0.97 for binary risk stratification. Furthermore, out-of-sample pharmacogenomic screenings yield a Pearson correlation coefficient of r = 0.89 for targeted molecular therapies across unseen cell lines. Local Shapley attributions provide highly reproducible feature importance rankings without asserting unverified biological causality. Future scope involves extending the framework to model multi-drug combination synergy metrics and initiating prospective multi-institutional clinical trial tracking to advance this retrospective research model toward bedside application.