Integration of pathomics and single-cell omics in pancreatic ductal adenocarcinoma: Applications and clinical translation prospects

Pancreatic ductal adenocarcinoma (PDAC), the predominant pathological subtype of pancreatic cancer, presents significant challenges in early diagnosis and treatment due to its high degree of heterogeneity. The emergence of single-cell omics and pathomics are providing powerful new tools and insights that are advancing PDAC research. Single-cell omics elucidates the molecular profiles of malignant epithelial cells, immune cells, and stromal cells within the PDAC tumor microenvironment, uncovering key pathways and cellular subpopulations that drive PDAC progression and drug resistance. In contrast, pathomics quantitatively extracts subtle morphological features from digitized whole-slide images, employing machine and deep learning to build diagnostic and prognostic prediction models. The multi-omics integration based on single-cell and pathology data provides deeper insights into tumor microenvironment. This integrated approach not only enables the prediction of molecular subtypes and immune status from routine hematoxylin and eosin-stained images, providing a low-cost and rapid auxiliary diagnostic tool for clinical practice, but also accurately identifies therapeutic targets, predicts drug responses, and screens potential beneficiaries for immunotherapy. This minireview aims to dissect PDAC from a multi-omics perspective, with the objectives of fostering greater integration and exploration across these fields and thereby deepening the molecular and spatial understanding of PDAC and laying the groundwork for future precision medicine approaches.