Cancer subtype identification from multi-omics profiles is important for understanding tumor heterogeneity and advancing computational precision oncology. Various subtyping methods have been proposed, among which graph-based approaches have become a widely adopted paradigm by capturing patient-to-patient relationships from heterogeneous molecular profiles. However, the reliability of learned patient similarity graphs remains a critical challenge, as noisy or spurious edges induced by high-dimensional and heterogeneous omics measurements can impair subtype discrimination. To address this issue, we propose topology-aware tensor graph learning (TaTGL), an unsupervised multi-omics clustering framework for cancer subtyping. TaTGL first constructs omics-specific patient graphs using a topology-aware distance metric that integrates molecular-profile distance with shared-neighborhood consistency. Furthermore, to exploit both complementary and consistent information across different omics views, the resulting graphs are integrated through frequency-weighted t-SVD-based tensor graph learning, which captures high-order cross-omics structural consistency. Experimental studies on several cancer subtype datasets show that TaTGL achieves competitive or superior clustering performance on labeled datasets and identifies clinically relevant patient subgroups on survival-based datasets. These results demonstrate that the proposed method can discover molecularly coherent and clinically meaningful cancer subtypes from heterogeneous multi-omics profiles.
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Shi et al. (2026) studied this question.