A Distinguished Roadmap of Fibroblast Senescence in Predicting Immunotherapy Response and Prognosis Across Human Cancers

• DOI: 10.1002/advs.202406624
• Publication Date: 2024-12-30T10:46:31Z
• Authors: Dongjie Chen; Pengyi Liu; Jiayu Lin; Longjun Zang; Yihao Liu; Shuyu Zhai; Xiongxiong Lu; Yuanchi Weng; Hongzhe Li
• Keywords: Senescence; Cellular senescence

The resistance of tumors to immune checkpoint inhibitors (ICI) may be intricately linked cellular senescence, although definitive clinical validation remains elusive. In this study, comprehensive pan-cancer scRNA-seq analyses identify fibroblasts as exhibiting the most pronounced levels senescence among tumor-associated cell populations. To elucidate phenomenon, a fibroblast senescence-associated transcriptomic signature (FSS), which correlated strongly with protumorigenic signaling pathways and dysregulation that fosters tumor progression, is developed. Leveraging FSS, machine learning (ML) framework demonstrates exceptional accuracy in predicting ICI response survival outcomes, achieving superior area under curve (AUC) values across validation, testing, in-house cohorts. Strikingly, FSS consistently outperforms established signatures predictive robustness diverse cancer subtypes. From an integrative analysis 17 CRISPR/Cas9 libraries, CDC6 emerges pivotal biomarker for prognostic stratification. Mechanistically, experimental evidence reveals cells orchestrates via TGF-β1 secretion oxidative stress, subsequently reprogramming microenvironment modulating response. These findings underscore translational potential targeting novel therapeutic strategy mitigate enhance antitumor efficacy.