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Ultrasound-Guided Orthotopic Implantation of Murine Pancreatic Ductal Adenocarcinoma
Published on: November 19, 2019
Mature tertiary lymphoid structures tumor microenvironment-based risk model to assess patients with pancreatic ductal
Zheng Gu1, Chaoran He1, Sheng Cheng1
1Department of Pharmacy, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Background:
Tertiary lymphoid structures (TLS) are associated with favorable prognosis and immunotherapy response in various cancers. However, a definitive, TLS-derived gene signature for predicting outcomes in pancreatic ductal adenocarcinoma (PDAC) is lacking. This study aimed to develop and validate a robust TLS-based prognostic model for PDAC.
Methods:
We evaluated TLS presence in PDAC samples from a The Cancer Genome Atlas (TCGA) cohort using an 11-chemokine gene signature, stratifying patients into TLS-high and TLS-low groups. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) networking were employed to identify TLS-related hub genes. A prognostic risk model was constructed via Cox regression analysis. The model's association with the tumor immune microenvironment (TIME) and immunotherapy response was further explored using computational algorithms (CIBERSORT, ESTIMATE) and validated in the IMvigor210 cohort.
Results:
TLS-high PDAC tumors exhibited an inflamed immune phenotype with enhanced immune cell infiltration. We identified a gene module and key hub genes included CD8A, CD247, SYK, GRB2, LYN, HLA-A, LCK, FYN, PIK3CD, and VAV1. From this, a seven-gene prognostic signature containing CXCL11, CASC8, REEP2, TNNT1, SLC16A11, DUSP26, and CHGA was developed. This signature effectively stratified patients into high- and low-risk groups with distinct survival outcomes. Crucially, validation in the IMvigor210 cohort confirmed that patients in the TLS-high- and low-risk groups demonstrated significantly better prognosis and improved response to immunotherapy.
Conclusions:
We successfully developed a novel TLS-derived gene signature that robustly predicts patient survival and immunotherapy efficacy in PDAC. This model serves as a valuable prognostic biomarker and provides insights into the immune mechanisms of PDAC, supporting the strategy of inducing TLS formation to augment cancer immunotherapy.
