Data from AI-Powered Deep Visual Proteomics Reveals Critical Molecular Transitions in Pancreatic Cancer Precursors

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Abstract Pancreatic ductal adenocarcinoma (PDAC) evolves through precursors, yet the protein programs governing early progression remain poorly defined. We applied Deep Visual Proteomics (DVP)—integrating computational pathology, laser microdissection, and mass spectrometry (MS)—to profile normal ducts, acinar-to-ductal metaplasia (ADM), low-grade (LG) and high-grade (HG) pancreatic intraepithelial neoplasia (PanIN), and invasive carcinoma from organ donors and patients with PDAC. Quantifying 9,181 proteins from ∼100 cells per region, we uncovered a molecular field effect in histologically normal ducts and proteomic divergence of LG-PanINs by cancer context. We identified four stage-associated molecular programs. Stress adaptation and immune engagement emerged early in cancer-associated normal ducts. Metabolic reprogramming initiated in normal ducts and intensified across PanIN progression. Mitochondrial remodeling became prominent in HG-PanINs before invasion. MS detected KRAS hotspot mutant peptides within incidental precursor lesions from cancer-free individuals. These findings demonstrate that molecular reprogramming precedes histologic transformation, creating opportunities for earlier detection of lethal cancer. Significance: Artificial intelligence (AI)–guided DVP represents the first in-depth assessment of the proteomic landscapes observed during the multistep progression of pancreatic adenocarcinoma, including histologically normal ducts, ADM, and LG- and HG-PanIN lesions. These data represent a unique resource of candidate biomarkers and interception targets against this lethal disease. See related commentary by Yang and Fan, p. 1255

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