AI-powered vaccine breakthroughs: Targeting pancreatic cancer with neoantigens and combination therapies

Abstract

The five-year survival rate for Pancreatic Ductal Adenocarcinoma (PDAC) remains below 10 %, primarily due to the limited efficacy of conventional chemotherapy and immune checkpoint inhibitors against its triple-immune-sequestered, low-TMB tumor microenvironment(TME). This situation has been furter exacerbated by the stagnation of traditional vaccine development, driven by inefficient antigen screening and high tumor heterogeneity.

Artificial intelligence (AI) exhibits remarkable advantages in the design of pancreatic ductal adenocarcinoma (PDAC) vaccines. It can integrate multi – omics data to efficiently unearth cryptic neoantigens from low - tumor mutation burden (TMB) samples, significantly enhancing the screening efficiency. Through dynamic modeling, AI can rationally plan the timing of combined vaccine therapies, effectively reducing the degree of T - cell exhaustion. By leveraging the digital twin model, AI can remarkably improve the matching accuracy between antigens and human leukocyte antigen (HLA). Additionally, it can construct a monitoring system to provide early warnings of antigen loss risks, thus gaining adjustment time for clinical treatments.This review aims to accomplish three primary objectives: demonstrate AI's potential in breaking the therapeutic impasse to overcome manufacturing-related treatment delays for 25–30 % of patients; further delineates the logical progression of AI from concept to clinical application; thereby provides a translational framework to bridge the gap between research and patient benefit.