Spots Global Cancer Trial Database for Precise Gene Signature for Predicting Outcomes in PDAC
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Trial Identification
Brief Title: Precise Gene Signature for Predicting Outcomes in PDAC
Official Title: Gene Signature Developed Using Machine Learning for Precise Prediction of Relapse and Survival in Resected Stage I-II Pancreatic Ductal Adenocarcinoma
Study ID: NCT05441189
Interventions
Study Description
Brief Summary: The current TNM staging system is not sufficient for prediction of prognosis and cannot precisely identify the patients who are in greater need of adjuvant therapy in pancreatic ductal adenocarcinoma (PDAC). Tumor mutation and copy number variation (CNV) markers may have a higher predictive value. In this study, whole exosome sequencing was performed for patients with stage I-II PDAC undergoing R0 resection. The investigators aimed to identify genes with discrepant statuses of mutations or CNVs between patients with and without relapse within 1 year after R0 resection, and then to construct a support vector machine (SVM)-based prognostic classifier (the SVM signature) for PDAC using machine learning; the investigators then aimed to further validate the SVM signature in an independent cohort.
Detailed Description:
Keywords
Eligibility
Minimum Age: 18 Years
Eligible Ages: ADULT, OLDER_ADULT
Sex: ALL
Healthy Volunteers: No
Locations
Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, Shanghai, China
Contact Details
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