Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-25 @ 3:38 AM
Ignite Modification Date:
2025-12-25 @ 3:38 AM
NCT ID:
NCT07224802
Brief Summary:
Early liver metastasis (Early-LiM) is the most significant prognostic factor in pancreatic ductal adenocarcinoma (PDAC) and is thought to originate from occult micrometastases present at the time of surgery. Reliable preoperative detection of such lesions remains an unmet clinical need.
The EXELiM study aims to develop and validate a circulating exosomal microRNA (exo-miRNA)-based liquid biopsy assay to accurately identify PDAC patients at high risk of occult liver metastasis before surgery. By integrating machine learning with multi-institutional plasma exosome profiling, this study seeks to enable biology-driven patient stratification and guide treatment sequencing toward precision oncology.
Detailed Description:
Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest human malignancies, with a 5-year overall survival rate below 10%. Even after curative-intent pancreatectomy, over 70% of patients experience disease recurrence, with early liver metastasis (within six months postoperatively) representing the most aggressive and fatal pattern.
Emerging evidence indicates that pancreatic tumors release exosomes that modulate the liver microenvironment, establishing a premetastatic niche that facilitates early colonization. We hypothesize that circulating exosomal microRNAs reflect these biological processes and can serve as non-invasive biomarkers for detecting occult liver metastasis.
In this multi-center observational study, preoperative plasma-derived exosomes from PDAC patients are analyzed using next-generation small RNA sequencing and validated by RT-qPCR. A machine learning model is employed to integrate exo-miRNA expression profiles and clinical variables, yielding a predictive score for early liver metastasis risk.
The study evaluates the diagnostic accuracy, prognostic utility, and clinical benefit of the exo-miRNA panel compared to conventional biomarkers such as CA19-9.
Study:
NCT07224802
Study Brief:
Protocol Section:
NCT07224802