{"title":"Development and Prospective Validation of a Cell-free DNA-based Model for the Early Detection of Pancreatic Cancer","authors":"Xiuchao Wang, Hongwei Wang, Meng Zhang, Huikai Li, Yang Liu, HanFei Huang, Jinlong Pei, Jing Huang, Fenglin 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, Zhao-Xiang Ye, Yan Sun, Li Peng, Xiao Hu, Zhitao Li, Hao Zhang, Dongqin Zhu, Shuang Chang, Jiangyan Zhang, Ruowei Yang, Hua Bao, Xue Wu, Yang Shao, Jun Yu, Chuntao Gao, Yunfeng Cui, Jihui Hao","doi":"10.1158/2159-8290.cd-25-0323","DOIUrl":null,"url":null,"abstract":"Pancreatic cancer (PC) remains a highly lethal malignancy due to late-stage diagnosis and limited therapeutic options. This study presents the development and validation of a non-invasive circulating cell-free DNA (cfDNA)-based model for early PC detection. In a case-control study comprising 232 PC patients 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 PC, further assessed its clinical applicability. The model detected 75% of PC 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, non-invasive tool for early PC detection, warranting further large-scale clinical validation to enhance patient outcomes.","PeriodicalId":9430,"journal":{"name":"Cancer discovery","volume":"33 1","pages":""},"PeriodicalIF":33.3000,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer discovery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1158/2159-8290.cd-25-0323","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Pancreatic cancer (PC) remains a highly lethal malignancy due to late-stage diagnosis and limited therapeutic options. This study presents the development and validation of a non-invasive circulating cell-free DNA (cfDNA)-based model for early PC detection. In a case-control study comprising 232 PC patients 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 PC, further assessed its clinical applicability. The model detected 75% of PC 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, non-invasive tool for early PC detection, warranting further large-scale clinical validation to enhance patient outcomes.
期刊介绍:
Cancer Discovery publishes high-impact, peer-reviewed articles detailing significant advances in both research and clinical trials. Serving as a premier cancer information resource, the journal also features Review Articles, Perspectives, Commentaries, News stories, and Research Watch summaries to keep readers abreast of the latest findings in the field. Covering a wide range of topics, from laboratory research to clinical trials and epidemiologic studies, Cancer Discovery spans the entire spectrum of cancer research and medicine.