OMNIS: a spatially informed multi-omics deep-learning framework for tumor recurrence prediction and primary–metastatic tumor differentiation title page
TL;DR
By embedding three-dimensional genome organization into deep-learning models, OMNIS nominates biologically coherent, context-specific drivers of progression and may guide future biomarker development and personalized therapy in precision oncology.
Abstract
Background Cancer recurrence and distant metastasis are major causes of cancer-related death, yet existing biomarkers and single-omics models have limited accuracy and interpretability across tumor types. Methods We developed OMNIS (OMics Network Integration and Spatial representation), a convolutional deep-learning framework that embeds multi-omics profiles into a five-channel genomic image ordered by Hi-C–derived chromosomal proximity. Somatic mutation, copy-number alteration, DNA methylation and gene-expression data from 1,578 TCGA tumors across 33 cancer types were used to train classifiers for recurrence risk and for primary-versus-metastatic status. Performance was assessed by 10-fold cross-validation using AUROC, AUPR and threshold-based metrics. Integrated gradients yielded per-gene attribution scores; top-ranked genes were evaluated for prognostic value in two independent non-small cell lung cancer cohorts (GSE31210, n = 226; GSE135222, n = 27) using survival analyses. Results OMNIS achieved high discrimination for recurrence (AUROC/AUPR 0.970/0.937) and metastasis (0.980/0.883), with accuracies of 0.873–0.911 and negative predictive values ≥0.970 across tasks. Spatial genomic embedding accelerated convergence and outperformed non-spatial baselines. Attribution highlighted seven recurrence-associated genes (including IBA57, DNTTIP1, SLC20A2 and TMEM201) and ten metastasis-associated genes (including PLXNA1, POLR3D, TTLL4, SREBF2, TYMP and ZBTB7C). In external cohorts, expression of these genes showed independent, stage-dependent associations with progression-free and overall survival. Conclusion OMNIS is a spatially informed multi-omics framework that couples accurate prediction with gene-level interpretability. By embedding three-dimensional genome organization into deep-learning models, OMNIS nominates biologically coherent, context-specific drivers of progression and may guide future biomarker development and personalized therapy in precision oncology.