Personalized tumor-informed circulating tumor DNA monitoring for early detection of recurrence in postoperative pancreatic cancer
Year: 2026
Type: article
Study design: Observational Study
Abstract: Background: Up to 80% of patients with resected pancreatic cancer experience recurrence within 2 years. We evaluated the feasibility and accuracy of a personalized, tumor-informed circulating tumor DNA (ctDNA) test for the early detection of recurrence risk during long-term postoperative surveillance. Methods: We recruited 43 patients with pancreatic cancer who underwent curative surgical resections. A personalized panel was developed to detect ctDNA in plasma based on whole-exome mutation information derived from tumor tissues. A total of 139 plasma samples were analyzed to assess recurrence risk and the efficacy of adjuvant therapy. Results: A personalized ctDNA monitoring panel was successfully customized in 35 of 43 cases. Sixteen patients relapsed within a median of 15.7 months (range: 5.4-30.0 months) postsurgery. For the 11 patients with positive ctDNA, the median lead time from initial ctDNA positivity to radiological relapse was 4.59 months (range: 0.88-15.61). After completion of adjuvant chemotherapy (ACT), 94.3% (33/35) of patients contributed 52.5% (73/139) of the ctDNA testing samples. These samples exhibited an elevated rate of ctDNA detection (48.5%, 16/33) compared to samples obtained prior to and during the commencement of ACT, with a negative predictive value of 82.4% (14/17) and a positive predictive value of 75.0% (12/16). The presence of ctDNA was significantly correlated with shorter disease-free survival and overall survival. Conclusions: Long-term dynamic ctDNA monitoring after pancreatic cancer resection, particularly following the completion of ACT, is predictive of recurrence risk. The proactive implementation of ctDNA monitoring after ACT in patients with resectable pancreatic cancer has important implications for clinical practice.
Source: Frontiers in Oncology
Authors: Jingjing Chen, Lu Zou, Xinyuan Bai, Fan Tong, Jiayao Ni, Haochen Tang, Yaru Liu, Xiang Kong, Jiani Yin, Fufeng Wang, Huizi Sha, Fanyan Meng, Juan Du
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,801
Award: G427218624
OpenAlex ID: W7125397000
