Accurate and actionable prognostic models can meaningfully influence follow-up scheduling, therapeu tic prioritization, and resource allocation in oncology. We propose GLLM, a multimodal graph learning framework that integrates RNA-seq profiles, routine clinical variables, and structural protein embeddings derived from protein language models to stratify patients by 5-year risk across multiple cancer types. Each gene is represented as a node within a protein-protein interaction graph, and we intro duce SCANE, a fusion mechanism that modulates each gene's structural embedding using patient-specific expression values. This design enables the graph neural network to propagate expression-conditioned molecular sig nals while preserving the underlying biophysical context. Across breast cancer, lung adenocarcinoma, and colorectal cancer cohorts, GLLM improves the area under the precision-recall curve relative to strong clinical and molecular baselines, while maintaining competitive concordance indices. The contributions of this work include: (1) an effective fusion strategy that enhances node representations by combining protein structural embeddings with gene expression for improved risk prediction; (2) a sys tematic evaluation demonstrating that sequence-derived structural embeddings outperform text-based biomedical embeddings; and (3) patient-level interpretability analyses showing that the model highlights established biomarkers and aligns with perturbation-based sensitivity profiles. Clinical significance: GLLM supports personalized surveillance planning by identifying high-risk patients who may benefit from earlier imaging, shorter follow-up inter vals, or prioritization for treatment discussions and clinical trial screening. Its lightweight architecture (<7 MFLOPs) enables seamless integration into existing oncology work f lows without additional computational burden. The result ing risk score is designed to complement, rather than replace, mutation profiling and clinicopathological staging, reflecting the biological and operational heterogeneity across cancer types.
Arriagada et al. (Thu,) studied this question.