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Background <p>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.</p> Methods <p>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, <i>n</i> = 183; patient-derived organoids, <i>n</i> = 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.</p> Results <p>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).</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月15日

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  • Meilong Shi,
  • Penghao Li,
  • Bo Li,
  • Huan Wang,
  • Xiaoyi Yin,
  • Xiaohan Shi,
  • Suizhi Gao,
  • Yikai Li,
  • Chuanqi Teng,
  • Shuai Yuan,
  • Xinyu Liu,
  • Zhendong Fu,
  • Xiaochao Kang,
  • Wei Jin,
  • Bin Song,
  • Kailian Zheng,
  • Yijie Zhang,
  • Xiongfei Xu,
  • Shiwei Guo,
  • Gang Jin

摘要

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.