Proteomics-Enhanced AI-Digital Pathology in Metastatic Mucinous Colorectal Carcinoma: A Case Report

Abstract Mucinous colorectal carcinoma (CRC) is a distinct histomorphological subtype characterized by abundant extracellular mucin that may promote immune evasion and chemoresistance. We describe a metastatic mucinous CRC case integrating digital pathology and proteomics to investigate disease progression and therapy resistance. Formalin-fixed paraffin-embedded samples from the primary tumor, peritoneal metastasis, and hepatoduodenal ligament metastasis of a 56-year-old patient were analyzed. Whole-slide imaging with QuPath-based AI enabled detailed histological annotation, while data-independent acquisition mass spectrometry identified over 6,000 proteins. Digital pathology revealed extensive mucin pools, architectural evolution from heterogeneous glandular patterns in the primary tumor to cribriform morphology in advanced metastases, and immune cell exclusion from mucin-rich regions. Proteomics revealed metabolic reprogramming, suppressed antigen presentation, and stage-specific activation of inflammatory, angiogenic, EMT, and PI3K/AKT/mTOR–MYC signaling pathways, consistent with proliferative and therapy-resistant phenotypes.Integration of AI-assisted histopathology with spatial proteomics highlighted the mucin barrier as a key mediator of immune evasion and chemoresistance. These findings support a personalized therapeutic framework targeting mucin-associated mechanisms alongside pathway-directed inhibitors, suggesting that spatial multi-omics may guide precision management strategies for aggressive mucinous colorectal cancer.