PHISA: Polarized Hypergraphs Induce Spectral Adaptation of Biomedical LLMs for Survival Prediction

Tissue organization and molecular information provide complementary perspectives that jointly inform cancer survival prediction. However, existing multimodal survival methods struggle to characterize the diverse relational patterns of tissue interactions, weakening the connection between tissue organization and molecular representation learning. To address this limitation, we propose PHISA, which encodes diverse interaction tendencies into a structural representation of patient tissue to guide molecular representation learning with pretrained biomedical knowledge for survival prediction. Specifically, PHISA first forms a polarized hypergraph by learning phase orientations for regions within each hyperedge, such that relative phases encode cooperative and antagonistic tendencies. The resulting polarized hypergraph governs the propagation of representations across tissue regions, with global structural coordinates and local responses capturing tissue structure. PHISA then aggregates the global and local structural components into a patient tissue descriptor. This structural descriptor modulates spectral coefficients along fixed principal singular directions of pretrained biomedical LLM weights. Finally, PHISA combines tissue representations with molecular representations refined by spectral adaptation conditioned on the patient tissue descriptor for survival prediction. Experiments on four TCGA cohorts show that PHISA achieves a mean C-index of 0.750, representing a 6.1% relative improvement over the strongest evaluated baseline. Ablations show predictive gains from polarized tissue modeling, with additional benefit from conditioning molecular refinement on patient tissue structure. Further analyses reveal spatial variation in tissue region importance and significant survival differences between groups defined by predicted risk scores.