Spatial characterization and stratification of colorectal adenomas by Deep Visual Proteomics

Abstract

AbstractColorectal adenomas (CRA) are precursor lesions of the colon that may progress to adenocarcinomas, with patients currently categorized into risk groups by morphological features. We aimed to establish a molecular feature-based risk allocation framework towards improved patient stratification. Deep Visual Proteomics (DVP) is a novel approach that combines image-based artificial intelligence with automated microdissection and ultra-high sensitive mass spectrometry. Here we used DVP on formalin-fixed, paraffin-embedded (FFPE) CRA tissues from nine patients, immunohistologically stained for Caudal-type homeobox 2 (CDX2), a protein implicated in colorectal cancer, enabling the characterization of cellular heterogeneity within distinct tissue regions and across patients. DVP seamlessly integrated with current pathology workflows and equipment, identifying DMBT1, MARCKS and CD99 as correlated with disease recurrence history, making them potential markers of risk stratification. DVP uncovered a metabolic switch towards anaerobic glycolysis in areas of high dysplasia, which was specific for cells with high CDX2 expression. Our findings underscore the potential of spatial proteomics to refine early-stage detection and contribute to personalized patient management strategies and provided novel insights into metabolic reprogramming.