IMAS resolves perturbation-sensitive regulatory architectures from matched tumour multiomics

Abstract

Tumour single-cell datasets contain weak, sparse and context-restricted regulatory signals that are difficult to distinguish from noise using expression measurements alone. Here we present IMAS, an integrative multiomic augmentation system that learns transferable regulatory structure from a pan-cancer foundation of matched single-cell RNA and chromatin-accessibility profiles and adapts it to data-limited target datasets. We refer to the resulting target-specific regulatory architectures as multi-layer target dependencies (MLTDs). MLTDs prioritize signals that remain supported across coordinated molecular, communication and perturbation-sensitive evidence, rather than by expression magnitude or any single prediction score. Rather than replacing the observed expression matrix, IMAS adds an interpretable regulatory-support layer for mechanism discovery. Across independent tumour datasets, IMAS preserved matched cross-layer supervision, concentrated predictive support into compact target-aligned structures and improved recovery of RNA and transcription-factor states. In colorectal cancer, MLTDs resolved a SOD2-associated perturbation-sensitive architecture that was distinct from native expression, conventional co-expression and the inherited pan-cancer hierarchy. These dependencies promoted propagation of perturbation-associated information through RNA-TF-regulatory-element bridges and into receiver-TF-aware communication across malignant cell states. A LAMB1-centred analysis further showed that successive regulatory, communication and temporal constraints restored an expected extracellular-matrix programme that was weakly represented in the original matrix. In head and neck squamous cell carcinoma, SOX2-centred MLTDs resolved malignant-state-specific perturbation programmes. In renal cancer, MLTD-guided analysis identified tumour-vascular coupling associated with spatially localized endothelial-to-mesenchymal-transition-like states in Xenium data. Together, IMAS reframes tumour multiomic augmentation as the recovery of compact, target-specific regulatory architectures rather than expression-matrix completion, providing an interpretable framework for prioritizing experimentally tractable mechanisms in heterogeneous tumour systems.