Development and prospective validation of a novel cfDNA-based diagnostic model for the early detection of pancreatic cancer.
Year: 2025
Type: conference-abstract
Abstract: 4191 Background: Pancreatic cancer (PC) is one of the most lethal malignancies, with a 5-year survival rate below 10%, primarily due to late-stage diagnosis. Existing diagnostic markers, like CA19-9, show inadequate sensitivity and specificity for early detection. To address this critical gap in early PC detection, this study developed and prospectively validated a cfDNA-based diagnostic model integrating fragmentomics features, including copy number variations (CNVs), fragment size ratios (FSR), and orientation-aware cfDNA fragmentation (OCF). Methods: A multicenter study was carried out with a case-control cohort (n = 467) for model development and a prospective cohort (n = 1,926) for clinical validation. Plasma cfDNA underwent low-pass whole-genome sequencing to extract fragmentomics features like CNVs, FSR, and OCF. A stacked ensemble machine-learning model was built based on case-control data and validated in the prospective cohort of PC elevated-risk individuals with diabetes or obesity. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were evaluated and compared with those of CA19-9. The follow-up was intended to last for 3 to 5 years, with the current follow-up period ranging from 12 to 24 months. Results: In the case-control cohort, the cfDNA-based model achieved AUCs of 0.9799 and 0.9622 in the training and validation sets, respectively, with both sensitivity and specificity exceeding 90%. In the prospective cohort (n = 1,926), for the 8 PC cases identified, the cfDNA model demonstrated a sensitivity of 75%, specificity of 98.1%, and PPV of 15.2% for detecting PC, significantly outperforming CA19-9 (sensitivity: 12.5%, specificity: 94.3%, PPV: 0.9%). Notably, the cfDNA model detected all 3 Stage 0 cases, 1 of 3 Stage I cases, and both Stage II cases, providing a median lead time of 227.5 days (range: 45–298 days) compared to imaging modalities. In contrast, CA19-9 detected only one Stage II case out of eight confirmed PC cases (12.5%). The model demonstrated significant potential in stratifying pancreatic cysts into high-risk and low-risk categories. While CA19-9 is ineffective in detecting either high-risk or benign cysts within the prospective cohort, the cfDNA model successfully differentiates between high-risk and low-risk pancreatic cysts (e.g., high-risk IPMN, 1/1 = 100% sensitivity; low-risk SCN, 0/1 = 0% false positive), which further underscores its clinical utility. Conclusions: This study is the first to validate a cfDNA-based diagnostic model for PC in a large elevated-risk population, showing superior performance and significant lead time benefits. The model detects PC earlier with much higher sensitivity and specificity than CA19-9, promising better outcomes with earlier treatment. The findings highlight cfDNA's potential for non-invasive PC screening in clinical settings.
Source: Journal of Clinical Oncology
Authors: Xiuchao Wang, Hongwei Wang, Song Gao, Tiansuo Zhao, Jian Wang, Weidong Ma, Hao Zhang, Dongqin Zhu, Shuang Chang, Jiangyan Zhang, Ruowei Yang, Haimeng Tang, Hua Bao, Xue Wu, Yang Shao, Jun Yu, Chuntao Gao, Jihui Hao
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W4410815437