On May 7, 2025 Geneseeq Technology Inc., in collaboration with leading clinical institutions, reported it has developed a cutting-edge blood-based screening test that could transform early detection of pancreatic cancer-potentially saving by identifying the disease at more treatable stages (Press release, Geneseeq, MAY 7, 2025, View Source [SID1234652666]). Published in the Journal of Clinical Oncology (Impact Factor: 50.7), this study represents the most comprehensive assessment to date of using cell-free DNA (cfDNA) fragmentomics and artificial intelligence (AI) for early pancreatic cancer detection.
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Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies, largely because it is rarely caught early and diagnosed too late for curative treatment. The five-year survival rate remains around 12%, and currently tools-such as imaging and CA19-9 blood test-often miss early-stage cases. There is currently no recommended population-wide screening method for PDAC.
The new test model from Geneseeq analyzes cfDNA fragmentomics-specific patterns of DNA fragments shed into the bloodstream by cancer cells. By applying advanced machine learning algorithm to shallow whole-genome sequencing data, the test can detect subtle genomic and epigenetic changes associated with early-stage PDAC.
Key clinical results:
Achieved 93.4% sensitivity and 95.2% specificity in the training cohort
Reached 90.91-97.3% sensitivity and 92.8-94.5% specificity in multiple validation cohorts
Demonstrated strong performance even in early-state cancers
Outperformed CA19-9, especially in individuals with normal bilirubin levels
"Our cfDNA fragmentomics model offers a practical, highly accurate, and non-invasive option for detecting pancreatic cancer early," said Dr. Hua Bao, VP of R&D at Geneseeq. "It could support earlier identification of at-risk individuals, allowing timely clinical follow-up and potentially improving outcomes."
What makes this approach especially promising is its clinical feasibility. The test uses low-coverage sequencing (as little as 0.5×), making it cost-effective and suitable for broader population screening. The test also showed high stability, even with lower DNA sequencing data, and could be used to monitor high-risk patients or suspicious pancreatic lesions. The researchers also estimated that applying this test at the population level could reduce pancreatic cancer mortality by up to 27%, by catching more cancers at a treatable stage.
Further research is underway to refine the model’s application in screening programs and to validate its effectiveness in more diverse populations. Clinicians may soon have a powerful new tool to help combat one of the hardest-to-detect cancers.