Senescence-driven molecular subtyping in pancreatic cancer: a multi-omics framework for precision medicine.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains a lethal malignancy with a five-year survival rate below 15%, largely due to tumor heterogeneity and limited therapeutic options. While senescence-related genes (SRGs) are implicated in cancer progression, their pancreas-specific roles in PDAC subtyping and treatment remain unexplored. METHODS: We integrated multi-omics data (RNA-seq, ATAC-seq, and whole-genome sequencing) from 402 pancreas-specific SRGs to classify PDAC subtypes through unsupervised clustering. Independent validation cohorts (TCGA-PAAD, n = 183; patient-derived organoids, n = 40) and drug sensitivity screens were used to define subtype-specific therapeutic vulnerabilities. A machine learning-based random forest model identified key SRG biomarkers for clinical stratification. RESULTS: Three distinct PDAC subtypes were identified: Cluster 1, characterized by extensive immune infiltration; Cluster 2, mixed features with moderate prognosis; and Cluster 3, defined by significant metabolic reprogramming. Drug screens revealed Cluster 3 as uniquely sensitive to Metformin and Trametinib, suggesting combinatory therapy potential. A 20-gene random forest classifier achieved high accuracy in subtype prediction (AUC = 0.96). CONCLUSION: This study establishes the first pancreas-specific SRG-driven classification of PDAC, resolving prior inconsistencies in Metformin trial outcomes. Our framework enables risk stratification and subtype-guided therapy, with immediate clinical implications: Metabolic-targeting agents (Metformin) may benefit the high-risk Cluster 3, while immunotherapy could be prioritized for Cluster 1.