Transcriptomic Profiling Identifies an Exosomal microRNA Signature for Predicting Recurrence Following Surgery in Patients With Pancreatic Ductal Adenocarcinoma.

Abstract

We performed genome-wide expression profiling to develop an exosomal miRNA panel for predicting recurrence following surgery in patients with PDAC. Pretreatment risk stratification is essential for offering individualized treatments to patients with PDAC, but predicting recurrence following surgery remains clinically challenging. We analyzed 210 plasma and serum specimens from 4 cohorts of PDAC patients. Using a discovery cohort (n = 25), we performed genome-wide sequencing to identify candidate exosomal miRNAs (exo-miRNAs). Subsequently, we trained and validated the predictive performance of the exo-miRNAs in two clinical cohorts (training cohort: n = 82, validation cohort: n = 57) without neoadjuvant therapy (NAT), followed by a post-NAT clinical cohort (n = 46) as additional validation. We performed exo-miRNA expression profiling in plasma specimens obtained before any treatment in a discovery cohort. Subsequently we optimized and trained a 6-exo-miRNA risk-prediction model, which robustly discriminated patients with recurrence [area under the curve (AUC): 0.81, 95% confidence interval (CI): 0.70-0.89] and relapse-free survival (RFS, P < 0.01) in the training cohort. The identified exo-miRNA panel was successfully validated in an independent validation cohort (AUC: 0.78, 95% CI: 0.65- 0.88, RFS: P < 0.01), where it exhibited comparable performance in the post-NAT cohort (AUC: 0.72, 95% CI: 0.57-0.85, RFS: P < 0.01) and emerged as an independent predictor for RFS (hazard ratio: 2.84, 95% CI: 1.30-6.20). We identified a novel, noninvasive exo-miRNA signature that robustly predicts recurrence following surgery in patients with PDAC; highlighting its potential clinical impact for optimized patient selection and improved individualized treatment strategies.