bioRxiv2025
BackgroundPancreatic ductal adenocarcinoma (PDAC) arises from distinct cellular origins, yet the extent to which DNA methylation patterns from normal pancreatic cells are preserved in tumor cells remains unclear. Identifying cell-of-origin signatures may enhance PDAC classification and therapeutic stratification. ObjectiveTo determine whether DNA methylation signatures in normal acinar and ductal pancreatic cells are retained in PDAC cell lines and to develop a robust classifier for distinguishing tumor origins. DesignWe performed DNA methylation profiling using the Illumina Infinium Mouse MethylationEPIC array on normal acinar and ductal cells and their PDAC derivatives in genetically engineered mouse models (GEMMs). Differential methylation analysis, and hierarchical clustering were used to identify and validate a conserved cell-of-origin DNA methylation signature. A logistic regression model was developed for classification. ResultsWe identified 178 CpG sites that remain preserved during tumorigenesis and effectively distinguished acinar- and ductal-derived PDAC cell lines. This signature was validated across independent sample sets, primary tumors, and orthotopic allografts. It successfully classified cell lines of unknown origin, including PDAC samples from KPC mice, and revealed the impact of oncogenic mutations on tumor fate. A logistic regression model supported these findings, confirming the robustness of the classification approach. Furthermore, the cell of origin influenced key PDAC characteristics, including treatment response, highlighting its potential role in molecular subtyping and patient stratification. ConclusionA preserved DNA methylation signature during pancreatic carcinogenesis distinguishes PDAC origins and influences tumor behavior. These results highlight the potential of DNA methylation profiling for tumor classification and personalized treatment strategies. They also raise important questions about the relevance of KPC mice as a preclinical model and the mechanisms driving PDAC heterogeneity. What is already known on this topicO_LIHuman PDAC exhibits significant heterogeneity, with molecular subtyping (classical vs basal-like) providing some insights into tumor behavior and clinical outcomes. C_LIO_LIMouse acinar and ductal cells can give rise to PDAC, influencing tumor characteristics and survival outcomes. C_LIO_LIDNA methylation is a powerful tool for tracing cellular identity and distinguishing cancer subtypes based on epigenetic profiles. C_LI What this study addsO_LIA cell-of-origin methylation signature is preserved during mouse carcinogenesis and across diverse experimental settings, providing a reliable tool for tumor classification. C_LIO_LIA DNA methylation-based classification system reliably distinguishes between acinar- and ductal-origin PDAC, filling a critical gap in methods to trace tumor lineage. C_LIO_LIAcinar- and ductal-derived PDACs exhibit distinct methylation patterns that correlate with differences in tumor behavior, such as chemoresistance, highlighting the biological relevance of cellular origin in PDAC. C_LIO_LIThe study provides new insights into how cell-of-origin influences PDAC heterogeneity and could lead to more precise therapeutic strategies tailored to the tumors lineage. C_LI How this study might affect research, practice or policyO_LIThis study provides a new, reliable method for classifying PDAC based on its cellular origin, which could significantly improve tumor classification in both preclinical and clinical settings, aiding in more accurate diagnoses and prognostic predictions. C_LIO_LIThe identification of distinct methylation patterns linked to tumor behavior offers valuable insights for developing personalized treatment strategies, as therapies could be tailored based on the tumors cellular origin and associated molecular characteristics. C_LIO_LIThe preservation of cell-of-origin methylation signature suggests the potential for developing a universal biomarker for PDAC classification, which could guide future clinical trials, therapeutic targeting, and patient stratification in PDAC care. C_LI