GAE-Δ: A Graph-Learning Framework for Gene Network Rewiring and Clinical Outcome Prediction from Multi-Omics Data

Cancer progression and outcomes are driven in part by changes to molecular networks thatresult from genetic and/or environmental perturbations. These network changes manifestacross multiple interconnected network layers and include accumulation of somatic mutations, altered protein-protein interactions and dysregulated gene-expression. Here wedescribe a graph autoencoder based framework (Graph Autoencoder-Delta (GAE-{Delta})), for characterizing phenotype-specific gene role shifts across multiomics data. Given samples stratified into two contrasting phenotypic groups and a prior gene interaction network,GAE-{Delta} constructs group-specific gene graphs for each omics modality and trains, for each modality, a single graph autoencoder jointly on both group graphs, so that the two group conditional embeddings share a common latent space. Contrasting these embeddings defines a multiomics embedding-shift representation for each gene that reflects how its network role reorganizes across phenotypic contexts. These gene-level shifts are subsequently used for unsupervised gene prioritization, multiomics late fusion andsample-level classification. Applied to five TCGA cancer types with a survival endpoint, GAE-{Delta} achieves competitive or superior predictive performance compared with classical network based methods and multiomics matrix factorisation methods (MOFA+, iNMF), with statistically significant AUC gains over MOFA+ in three of five cohorts and statistical ties on the remaining two. Beyond predictive performance, the consensus shift genes are significantly enriched for known cancer drivers in three of five cohorts (hypergeometric p < 0.01; 11 - 17x fold enrichment), whereas matrix factorisation baselines reach p < 0.05 in zero of five cohorts (best per cancer p = 0.06), indicating that GAE-{Delta} captures biological signal that linear factor methods miss. In summary, the GAE- {Delta} approach provides for both improved outcome classification as well as for biological and mechanistic discovery through deep network-based integration of disease-associated multi-omics data.

Tang, Z., Chen, Z., Chen, M., Wang, Y., Ennis, S., Niranjan, M., Ewing, R.

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