Spatial characterization and stratification of colorectal adenomas by Deep Visual Proteomics

Background and Aims: Colorectal adenomas (CRAs) are precursor lesions that can progress to adenocarcinomas. Current clinical guidelines categorize patients into risk groups based on adenoma characteristics observed during index colonoscopy, but this may lead to overtreatment. Our aim was to establish a molecular feature-based risk allocation framework towards improved patient stratification. Methods: 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. Immunohistological staining for Caudal-type homeobox 2 (CDX2), a gene implicated in colorectal cancer, enabled the characterization of cellular heterogeneity within distinct tissue regions and across patients. Results: DVP seamlessly integrated with current pathology workflows and equipment, identifying deleted in malignant brain tumors 1 (DMBT1), myristoylated alanine rich protein kinase C (MARCKS), and cluster of differentiation 99 (CD99) correlated with disease recurrence history, making them potential markers of risk stratification. The spatial and cell type specific capabilities of DVP uncovered a metabolic switch towards anaerobic glycolysis in areas of high dysplasia, which was specific for the cells with high CDX2 expression. Conclusion: The application of spatially resolved proteomics to CRA revealed three new potential markers for early-stage tumor development, and provided novel insights into metabolic reprogramming. Our findings underscore the potential of this technology to refine early-stage detection and contribute to personalized patient management strategies.