Multiregion Genomic Analysis of Human Pancreatic Mucinous Cystic Neoplasms

Mucinous cystic neoplasms (MCNs) are precursors to invasive pancreatic cancer. Conflicting clinical management recommendations call for better molecular characterization to improve our understanding of their tumorigenesis and risk assessment. We sampled epithelial tissue from a total of 18 surgically resected MCNs and performed dedicated multiregion analysis of somatic genetic alterations by targeted next-generation sequencing of 25 driver genes. In addition, we performed wholeexome sequencing and immunohistochemistry on selected samples to supplement our analyses. In total, 128 samples of epithelial MCN tissue were sequenced and analyzed, including samples from 13 small MCNs with low-grade (LG) dysplasia, 1 small MCN with high-grade (HG) dysplasia (HGD), and 4 large MCNs with HGD. Eight of 13 (61.5%) comprehensively sampled small LG MCNs lacked somatic driver gene mutations in all tissue blocks. These MCNs were lined by predominantly flat epithelium. In contrast, the majority of MCNs with driver gene mutations were predominantly lined by mucinrich epithelium. No heterogeneity in KRAS mutations was seen across the sampled regions. Multiregion genetic analysis of 4 large MCNs with HGD provide insights into neoplastic progression, with shared somatic alterations suggesting that HGD arises from LG mucin-rich epithelium. These findings were supported by complementary whole-exome sequencing studies in 26 MCN epithelium samples. The neoplastic epithelium in the majority of small MCNs does not harbor somatic mutations in pancreatic driver genes. The genetic findings from multiregion analysis on MCNs contrast previous investigations in other mucin-producing pancreatic cysts, indicating distinct mechanisms in early tumorigenesis. This calls for a more nuanced risk assessment in MCNs, requiring improved preoperative assessment tools. (c) 2025 United States & Canadian Academy of Pathology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.