Transcriptomic Profiling and Demographic Analysis of Appalachian Patients with Pancreatic Ductal Adenocarcinoma using the Purity Independent Subtyping of Tumors (PurIST) Classifier
Year: 2026
Type: preprint
Study design: Observational Study
Abstract: PURPOSE Pancreatic ductal adenocarcinoma (PDAC) is a deadly malignancy with poor prognosis. A transcriptomic-based algorithm called Purity Independent Subtyping of Tumors (PurIST) relies on expression profiling of a defined set of genes to stratify PDAC into classical and basal-like molecular subtypes and assigns a confidence level to the subtypes as either “strong,” “likely,” or “lean.” In this study, we apply PurIST to Appalachian and non-Appalachian patient cohorts and assess demographic variables between groups that may influence treatment outcomes. PATIENTS AND METHODS This 13-site study includes 780 PDAC patients. Patients were divided into Appalachian and non-Appalachian cohorts according to Appalachian Regional Commission guidelines, and differences in PurIST classification, tobacco use, and alcohol use were analyzed. RESULTS Individuals from Appalachian regions were more likely to receive a subtype classification from PurIST with “strong” confidence as opposed to “likely” or “lean” ( P =0.0175; odds ratio=1.66). We did not observe any significant difference between basal and classical subtype classification in Appalachia ( P =0.1457), yet an exploratory analysis showed that individuals aged ≥85 were significantly more likely to have basal-like tumor classifications ( P =0.005817; odds ratio=3.79). Appalachian patients were more likely to have a history of smoking (59.6% vs 49.9%; P =0.01756) and less likely to report alcohol consumption (50.5% vs 65.6%; P =0.000229). CONCLUSION We conducted the largest comparison of the PurIST algorithm in Appalachia to date and found PurIST is more likely to have higher confidence in molecular subtype classifications for Appalachian PDAC tumors compared to non-Appalachian tumors. If PurIST is to be considered as a prognostic or decision-making tool for PDAC patients, we must account for variability in different populations and model the likelihood that tumors will have a strong molecular signature.
Source: medRxiv
Authors: Chelsey M. Williams, Reema A. Patel, Nicole Lisek, Chandra Kakarala, Michael J. Cavnar, Joseph Kim, Prakash K. Pandalai, Emily Baiyee Toegel, Tiago Biachi de Castria, Ashish Manne, Robert J. Rounbehler, Patrick M Boland, Carlos H. F. Chan, Muneeb Rehman, Deepak Vadehra, Dae Won Kim, Michelle L Churchman, Derek B. Allison, Justin B. Miller
Institutions: University of Kentucky, Markey Cancer Center, University of Colorado Anschutz, Moffitt Cancer Center, The Ohio State University Wexner Medical Center, Rutgers Cancer Institute, University of Iowa, University of Iowa Hospitals and Clinics, University of Virginia Cancer Center, University of Virginia, Roswell Park Comprehensive Cancer Center
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7214122393