J Gastroenterol. 2025 Nov 26. doi: 10.1007/s00535-025-02328-7. Online ahead of print.

ABSTRACT

Intraductal papillary mucinous neoplasms (IPMNs) of the pancreas are bona fide cystic precursor lesions to pancreatic ductal adenocarcinoma (PDAC), which is the cancer type with the most dismal prognosis. Since IPMNs are detectable by imaging, they offer a rare window of opportunity for early intervention for PDAC development. Despite their clinical visibility, the molecular pathogenesis of IPMNs remained incompletely understood, and no effective non-surgical therapeutic strategies have been established to date. In the past few decades, however, substantial progress has been made in elucidating their molecular pathology. Next-generation sequencing technologies demonstrated the comprehensive genetic mutation profile of IPMNs in the early 2010s. Elucidation of these mutation profiles enabled the establishment of genetically engineered mouse models, successfully recapitulating the natural development of human IPMNs and their progression to invasive cancer. Rapid evolution of "omics" technologies in recent years has facilitated the application of mass spectrometry, single-cell sequencing and spatial transcriptomics to IPMNs, significantly advancing our understanding of their pathophysiology. These techniques elucidated the changes in transcriptome, proteome, metabolome, microbiome, and tumor microenvironment associated with IPMN development and progression. This review summarizes current insights into the molecular and cellular landscapes of IPMN tumorigenesis, with particular emphasis on the mechanisms driving malignant progression.

PMID:41296011 | DOI:10.1007/s00535-025-02328-7

bioRxiv [Preprint]. 2025 Nov 4:2025.11.03.686234. doi: 10.1101/2025.11.03.686234.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) arises from heterogeneous precursor lesions, including intraductal papillary mucinous neoplasms (IPMNs), but the features distinguishing indolent from progressive lesions remain unclear. We performed an integrative analysis of transcriptomic, genomic, and microenvironmental profiles of IPMNs to define multi-omic phenotypes. Using transfer learning, we projected IPMN-derived transcriptional programs onto spatial transcriptomic datasets from IPMNs and pancreatic intraepithelial neoplasias (PanINs). We identified two major phenotypes: one associated with cancer-associated fibroblasts and epithelial-to-mesenchymal transition, shared across IPMN, PanIN, and PDAC; and a second, glycolysis-enriched phenotype with a unique somatic mutation profile specific to IPMN. Spatial mapping further revealed grade-specific enrichment of transcriptional programs and distinct interactions with stromal and immune subtypes, underscoring the role of the precancer microenvironment in progression. These findings establish multi-omic phenotypes that unify genetic, transcriptional, and microenvironmental heterogeneity, providing a framework for distinguishing progressive from indolent precancers and a web-based public atlas for future exploration of these data and transcriptional phenotypes.

PMID:41279473 | PMC:PMC12637499 | DOI:10.1101/2025.11.03.686234

bioRxiv [Preprint]. 2025 Jun 8:2025.06.08.658506. doi: 10.1101/2025.06.08.658506.

ABSTRACT

Advances in spatial profiling have resulted in the generation of multi-omic atlases that span biological scales. In general, multiple workflows are required for image registration, coordinate registration, and spot deconvolution to integrate modalities. To improve the throughput of registration of multi-omic cohorts, we introduce PIVOT, a user-friendly and open-source interface for streamlined nonlinear registration. We demonstrate PIVOT's strengths through registration of three multi-omic datasets, and show comparison of its performance to existing workflows.

PMID:40661390 | PMC:PMC12259011 | DOI:10.1101/2025.06.08.658506