A Multianalyte Panel Consisting of Extracellular Vesicle miRNAs and mRNAs, cfDNA, and CA19-9 Shows Utility for Diagnosis and Staging of Pancreatic Ductal Adenocarcinoma

Abstract

Purpose: To determine whether a multi-analyte liquid biopsy can improve the detection and staging of pancreatic adenocarcinoma (PDAC). Experimental Design: We analyzed plasma from 204 subjects (71 healthy, 44 non-PDAC pancreatic disease, and 89 PDAC) for the following biomarkers: Tumor-associated extra-cellular vesicle (EV) miRNA and mRNA isolated on a nanomagnetic platform that we developed and measured by next-generation sequencing or qPCR, circulating cell-free DNA (ccfDNA) concentration measured by qPCR, ccfDNA KRASG12D/V/R mutations detected by droplet digital PCR, and CA19-9 measured by ECLIA. We applied machine learning to training sets and subsequently evaluated model performance in independent, user-blinded test sets. Results: To identify patients with PDAC versus those without, we generated a classification model using a training set of 47 subjects (20 PDAC and 27 non-cancer). When applied to a blinded test set (N = 136), the model achieved an area under the curve (AUC)of 0.95 and accuracy of 92%, superior to the best individual biomarker, CA19-9 (89%). We next used a cohort of 20 PDAC patients to train our model for disease staging and applied it to a blinded test set of 25 patients clinically staged by imaging as metastasis-free, including 9 subsequently determined to have had occult metastasis. Our workflow achieved significantly higher accuracy for disease staging (84%) than imaging alone (accuracy = 64%; P <0.05). Conclusions: Algorithmically combining blood-based biomarkers may improve PDAC diagnostic accuracy and pre-operative identification of non-metastatic patients best suited for surgery, although larger validation studies are necessary.