Early Diagnosis of Pancreatic Cancer Via Deciphering Multi-modal Immunological Signatures

Prospective inclusion of 1000 patients with pancreatic cancer (early-stage pancreatic cancer accounts for approximately 75% of cases), 1000 patients with benign pancreatic diseases, and 1000 healthy individuals as controls. Peripheral blood samples were collected from newly diagnosed pancreatic cancer patients and healthy individuals. Using techniques such as plasma TCR/BCR-seq, CyTOF, and plasma proteomics, multi-modal individual immune characteristics were obtained and analyzed along with clinical information. An artificial intelligence predictive model was built based on these multi-modal individual immune characteristics to establish an early screening technique for pancreatic cancer. The sensitivity and specificity of this artificial intelligence model for early pancreatic cancer diagnosis were evaluated using an external multicenter sample test set.