American Association for Cancer Research
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Figure S1 from A Multianalyte Panel Consisting of Extracellular Vesicle miRNAs and mRNAs, cfDNA, and CA19-9 Shows Utility for Diagnosis and Staging of Pancreatic Ductal Adenocarcinoma

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journal contribution
posted on 2023-03-31, 22:01 authored by Zijian Yang, Michael J. LaRiviere, Jina Ko, Jacob E. Till, Theresa Christensen, Stephanie S. Yee, Taylor A. Black, Kyle Tien, Andrew Lin, Hanfei Shen, Neha Bhagwat, Daniel Herman, Andrew Adallah, Mark H. O'Hara, Charles M. Vollmer, Bryson W. Katona, Ben Z. Stanger, David Issadore, Erica L. Carpenter

Figure S1. Sample cohort of this study, which included 204 subjects in total. Workflow shows patient cohorts involved in each classification. * indicated 8 patients repeated in the discovery set and the training set. ** indicated 32 locally advanced patients that were not used in the occult metastases detection as training set did not have locally advanced PDAC patients.


National Institute of Health

American Cancer Society

Congressionally Directed Medical Research Programs



To determine whether a multianalyte liquid biopsy can improve the detection and staging of pancreatic ductal adenocarcinoma (PDAC). We analyzed plasma from 204 subjects (71 healthy, 44 non-PDAC pancreatic disease, and 89 PDAC) for the following biomarkers: tumor-associated extracellular vesicle 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 KRAS G12D/V/R mutations detected by droplet digital PCR, and CA19-9 measured by electrochemiluminescence immunoassay. We applied machine learning to training sets and subsequently evaluated model performance in independent, user-blinded test sets. To identify patients with PDAC versus those without, we generated a classification model using a training set of 47 subjects (20 PDAC and 27 noncancer). When applied to a blinded test set (N = 136), the model achieved an AUC of 0.95 and accuracy of 92%, superior to the best individual biomarker, CA19-9 (89%). We next used a cohort of 20 patients with PDAC 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). Algorithmically combining blood-based biomarkers may improve PDAC diagnostic accuracy and preoperative identification of nonmetastatic patients best suited for surgery, although larger validation studies are necessary.