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bioRxiv · 10.1101/2025.07.07.663528

AI-powered Deep Visual Proteomics reveals critical molecular transitions in pancreatic cancer precursors

Min, J.·Schweizer, L.·Zonderland, G.·Selvanesan, B. C.·Oldenburg, L.·Bae, S.-W.·Kim, B. J.·Swanson, B. J.·Klute, K. A.·Caffrey, T. C.·Grandgenett, P. M.·Hollingsworth, M. A.·Ummat, I.·Strauss, M. T.·Mund, A.·Maitra, A.
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Abstract

Pancreatic ductal adenocarcinoma (PDAC) evolves through non-invasive precursor lesions, yet its earliest molecular events remain unclear. We established the first spatially resolved proteomic atlas of these lesions using Deep Visual Proteomics (DVP). AI-driven computational pathology classified normal ducts, acinar-ductal metaplasia (ADM), and pancreatic intraepithelial neoplasia (PanIN) from cancer-free organ donors (incidental, "iPanINs") and PDAC patients (cancer-associated, "cPanINs"). Laser microdissection of 96 discrete regions containing as few as 100 phenotypically matched cells and ultrasensitive mass spectrometry quantified a total of 8,512 proteins from formalin-fixed tissues. Distinct molecular signatures stratifying cPanINs from iPanINs, and remarkably, many cancer-associated proteins already marked histologically normal epithelium. Four core programs - stress adaptation, immune engagement, metabolic reprogramming, mitochondrial dysfunction - emerged early and intensified during progression. By integrating DVP with AI-guided tissue annotation, we demonstrate that molecular reprogramming precedes histological transformation, creating opportunities for earlier detection and interception of a near-uniformly lethal cancer. SignificanceOur spatially-resolved proteomics atlas uncovers distinct molecular signatures in pancreatic cancer adjacent precursor lesions, clearly diverging from those in incidental, cancer-free pancreatic lesions. Our deep proteomics dataset offers a valuable resource for identifying novel biomarkers and therapeutic targets, informed by the earliest cancer-associated molecular events in archival pancreatic tissues.

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Min, J., Schweizer, L., Zonderland, G., Selvanesan, B. C., Oldenburg, L., Bae, S.-W., Kim, B. J., Swanson, B. J., Klute, K. A., Caffrey, T. C., Grandgenett, P. M., Hollingsworth, M. A., Ummat, I., Strauss, M. T., Mund, A., Maitra, A.. 2025-07-07. AI-powered Deep Visual Proteomics reveals critical molecular transitions in pancreatic cancer precursors. https://doi.org/10.1101/2025.07.07.663528

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