Survival-Informed Multi-Omics Kernel Fusion for Cancer Subtyping

Cancer molecular heterogeneity impedes precise subtyping and personalized therapy. Current multi-omics integration methods often overlook clinical relevance and kernel redundancy, yielding subtypes with limited prognostic utility. Here, we introduce Survival-Informed Multi-omics Kernel Fusion (SIMKF), a framework that synergizes survival-guided kernel selection with distribution-aware fusion to uncover clinically distinct subtypes. SIMKF addresses the limitations of current multi-omics integration methods by combining survival-guided kernel selection, adaptive weighting based on maximum mean discrepancy, and spectral clustering to integrate survival information with multi-omics data. This approach significantly outperforms existing techniques across five TCGA cancer datasets. Notably, in breast cancer, it successfully identifies five distinct subtypes with pronounced survival differences, revealing a nonlinear relationship between methylation levels (hypermethylation correlating with better prognosis, hypomethylation with poorer outcomes) and survival outcomes, while aligning closely with established clinical subtypes. As an automated, tuning-free tool for precision oncology, SIMKF not only uncovers prognostic biological mechanisms but also translates directly into clinically applicable subtyping models.