An exosome-based transcriptomic signature for noninvasive, early detection of patients with pancreatic ductal adenocarcinoma : A multicenter cohort study

Background & Aims: Pancreatic ductal adenocarcinoma (PDAC) incidence is rising worldwide, and majority of patients present with an unresectable disease at initial diagnosis. Measurement of carbohydrate antigen 19-9 (CA19-9) levels lack adequate sensitivity and specificity for early detection; hence, there is an unmet need to develop alternate molecular diagnostic biomarkers for PDAC. Emerging evidence suggests that tumor-derived exosomal cargo, particularly miRNAs, offer an attractive platform for the development of cancer-specific biomarkers. Herein, genomewide profiling in blood specimens was performed to develop an exosome-based transcriptomic signature for noninvasive and early detection of PDAC.

Methods: Small RNA-sequencing was undertaken in a cohort of 44 patients with an early-stage PDAC and 57 non-disease controls. Using machine-learning algorithms, a panel of cell-free (cf) and exosomal (exo) miRNAs was prioritized that discriminated PDAC patients from control subjects. Subsequently, the performance of the biomarkers was trained and validated in independent cohorts (n=191) using quantitative real time PCR (qRT-PCR) assays.

Results: The sequencing analysis initially identified a panel of 30 overexpressed miRNAs in PDAC. Subsequently using qRT-PCR assays, the panel was reduced to 13 markers (5 cf- and 8 exo-miRNAs), which successfully identified patients with all stages of PDAC (AUC=0.98 training cohort; AUC=0.93 validation cohort); but more importantly, was equally robust for the identification of early-stage PDAC (stages 1&II; AUC=0.93). Furthermore, this transcriptomic signature successfully identified CA19-9 negative cases (<37 U/ml; AUC=0.96), when analyzed in combination with CA19-9 levels, significantly improved the overall diagnostic accuracy (AUC=0.99 vs. AUC=0.86 for CA19-9 alone).

Conclusions: In this study, an exosome-based liquid-biopsy signature for the noninvasive and robust detection of patients with PDAC was developed.