Development and Prospective Validation of a Cell-free DNA–Based Model for the Early Detection of Pancreatic Cancer

作者：Xiuchao Wang, Hongwei Wang, Meng Zhang, Huikai Li, Yang Liu, Hanfei Huang, Jinlong Pei, Jing Huang, Fenggang Zang, Yanhui Zhang, Xingyun Chen, Song Gao, Tiansuo Zhao, Jian Wang, Weidong Ma, Yuexiang Liang, Shangheng Shi, Shuo Li, Wei Li, Tianxing Zhou, Ying Zhang, Xiaonan Cui, Zhaoxiang Ye, Yan Sun, Li Peng, Xiao Hu, Zhitao Li, Hao Zhang, Dongqin Zhu, Shuang Chang, Jun-Ye Zhang, Ruowei Yang, Hua Bao, Xue Wu, Yang Shao, Jun Yu, Chuntao Gao, Yunfeng Cui, Jihui Hao · 发表于：Cancer Discovery · 年份：2025 · DOI：10.1158/2159-8290.cd-25-0323 · 被引用次数：1 · 研究领域：Cancer Genomics and Diagnostics、Pancreatic and Hepatic Oncology Research、Single-cell and spatial transcriptomics

Pancreatic cancer remains a highly lethal malignancy due to late-stage diagnosis and limited therapeutic options. This study presents the development and validation of a noninvasive circulating cell-free DNA (cfDNA)-based model for early pancreatic cancer detection. In a case-control study comprising 232 patients with pancreatic cancer and 235 healthy controls, the model demonstrated high diagnostic accuracy (AUC = 0.9799 in training; 0.9622 in validation). A prospective cohort study involving 1,926 individuals with diabetes and obesity established risk factors for pancreatic cancer and further assessed its clinical applicability. The model detected 75% of pancreatic cancer cases, including all stage 0 patients, with a lead time of up to 298 days, significantly outperforming CA19-9. Additionally, it demonstrates potential for distinguishing high-risk from low-risk pancreatic cysts, thereby facilitating more precise risk stratification. This study highlights the potential of cfDNA-based screening as a scalable, noninvasive tool for early pancreatic cancer detection, warranting further large-scale clinical validation to enhance patient outcomes. SIGNIFICANCE: This study develops a cfDNA-based model for early pancreatic cancer detection, demonstrating high accuracy and prospective clinical validation. By enabling presymptomatic identification and risk stratification, this noninvasive approach enhances early intervention and improves outcomes, supporting potential clinical applic...