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

UNLABELLED: 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.