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.

About Geneseeq

Geneseeq Technology Inc. (Geneseeq) is a research-driven leader in precision oncology, utilizing cutting-edge next-generation sequencing (NGS) technologies to advance cancer care. The company provides comprehensive genomic profiling solutions for all tumor types, including pan-cancer and cancer-specific panels, alongside cutting-edge tools for minimal residual disease (MRD) monitoring and multi-cancer early detection (MCED). Geneseeq has reached key regulatory milestones to date, including CE-IVD certification for its solid tumor and hematologic cancer panels, and FDA Breakthrough Device Designation for its MCED test, CanScan®. The company has also received approval from the National Medical Products Administration (NMPA) for GeneseeqPrime, designed for tumor mutational burden (TMB) detection in lung cancer.

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This study recruited 103 LARC patients who received nCRT at the Fudan University Shanghai Cancer Center from February 2016, to October 2017. The treatment response of the patients was evaluated using pCR status and pathological or MRI tumor regression grade (mrTRG). Plasma samples were collected at baseline (T1), before the 15th (T2), before the 25th (T3) fractions of nCRT and after nCRT (T4). The extracted cell-free DNAs (cfDNAs) were subjected to targeted deep sequencing of 422 cancer-related genes at an average depth of 4000X, and cfDNA 5’-end motif profiles were extracted to construct an elastic-net logistic regression model to predict non-pCR status before surgery.

Using cross-validation, the constructed model based on the 5’-end motif profile alone showed a high AUC of 0.90 (95%CI: 0.89-0.91). The combination of the 5’-end motif and mrTRG further improves the model’s prediction power, achieving an AUC of 0.92 (95%CI: 0.90-1.00). cfDNA detection and mrTRG detection are two investigated methods that have been used for distinguishing pCR from non-pCR patients. cfDNA detection alone had a high specificity and a low sensitivity while mrTRG1 detection had a high sensitivity with low specificity. Here the combination model based on the combination of the 5’-end motif and mrTRG showed greatly improved sensitivity and specificity compared to cfDNA detection or mrTRG detection alone. Furthermore, the models based on the 5′-end motif profile alone or in combination with mrTRG consistently demonstrated good predictive ability for patients without detectable cfDNA mutations (AUC 0.94, 95% CI, 0.93–0.95; AUC 0.95, 95% CI, 0.94–0.96).

“The combination model using 5’-end motif and mrTRG showed promising performance for predicting pCR in different cohorts, which can be of great value to improve disease outcomes and quality of life for LARC patients.”, says Dr. Hua Bao, author, and director of Geneseeq Research Institute.

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