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This study used deep-learning image analysis to quantify pancreatic tissue composition and precancerous lesions (PanIN) in 410 pancreas samples from donors aged 21-70 years. While 83% of donors had some PanIN present, the actual burden of these lesions was very low in most people, with high PanIN burden concentrated in approximately 10% of donors who were disproportionately likely to have type 2 diabetes and hypertension. These high-burden cases also showed increased stromal fibrosis and immune cell infiltration, suggesting that quantifying PanIN burden rather than simply detecting its presence may better identify individuals at elevated risk for pancreatic cancer.
Why it matters
The findings suggest that measuring the amount of precancerous tissue, not just its presence, could improve identification of people at high risk for pancreatic cancer, particularly those with metabolic conditions like diabetes. This quantitative approach could refine screening strategies to focus on the minority of individuals who carry disproportionate precancer burden.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Background & Aims: Pancreatic intraepithelial neoplasia (PanIN) is the primary precursor to invasive pancreatic ductal adenocarcinoma (PDAC). While PanIN is common in the general population, PDAC is rare. PDAC risk rises with age, diabetes mellitus, and obesity, but whether this increased risk reflects a greater precancer burden is unknown, as previous studies assessed PanIN presence and grade. We aimed to quantify pancreatic composition and PanIN burden across the adult lifespan to identify demographics of donors carrying disproportionate precancer burden. Methods: We applied CODAvision, a deep-learning segmentation pipeline, to 410 high-quality pancreas sections from Genotype-Tissue Expression (GTEx) donors aged 21 to 70 years. We screened twenty-seven donor phenotypes against 14 compositional measures, applying Benjamini-Hochberg correction for multiple testing. We further related tissue composition and donor phenotype to bulk gene expression in 281 donors to understand which demographics are associated with a compositional or transcriptional fingerprint. Results: PanIN was present in 83% of donors. Acinar proportion declined with age, offset by fat and stroma. Median PanIN burden did not change with age, though the fitted 90th percentile rose from 0.21% to 0.72% of exocrine tissue (uncorrected P=.002). The 39 sections (10%) with high-outlier PanIN burden came disproportionately from donors with type 2 diabetes mellitus and hypertension and had greater stromal proportion and higher fibroblast and immune-cell densities. Both cell types were denser in peri-PanIN stroma than in the surrounding pancreatic parenchyma. PanIN abundance was associated with a gastric transcriptional signature including VSIG1, TFF1, and MUC5AC. Conclusions: Most donors carried little PanIN at any age, whereas high burden was concentrated in a minority of donors more likely to have type 2 diabetes mellitus, hypertension, and inflamed, fibrotic stroma. Because PanIN was present in most donors, its presence did not distinguish high-risk groups, whereas quantitative burden did, concentrating on the metabolic phenotypes already under consideration for pancreatic surveillance.