A translational framework for high-plex spatial profiling and complexity reduction toward diagnostic assay development in colorectal polyps

Background: Accurate risk stratification of colorectal polyps is essential for reducing unnecessary surveillance while ensuring that high-risk patients receive timely intervention. Pathology workflows rely on morphology from H&E slides, while emerging immune-profiling techniques such as multiplex immunofluorescence (mIF) offer deeper biological resolution but are often too complex and costly for routine clinical deployment. To address this, we propose a diagnostic-assay development framework that integrates high-plex spatial profiling with computational complexity-reduction strategies to derive a clinically practical biomarker panel. Methods: We designed a 20-plex mIF panel to characterize immune cell populations and spatial interactions within colorectal polyp microenvironments. Corresponding H&E whole-slide images were analyzed to extract epithelial, stromal, and architectural features using area-based models and computational morphology descriptors. These multimodal data were integrated into a unified predictive modeling pipeline for stratifying patients into low- and elevated-risk groups. To support translation into a deployable assay, we implemented a complexity-reduction framework incorporating iterative feature selection, redundancy elimination, model pruning, and simulation of assay-ready marker subsets. Results: An initial dataset of 200 mIF and H&E slides was used for model fine-tuning, biomarker feature extraction, and preliminary integration of immune and morphological signatures. Early-stage mIF-based models captured >10 immune cell populations, distinguished epithelial subtypes, and localized key microenvironmental interactions. H&E-based models identified colorectal compartments, stromal-epithelial organization, inflammatory patterns, and dysplasia-related features. This groundwork enabled refinement of feature sets, assessment of model stability, and establishment of the multimodal fusion strategy guiding downstream predictive modeling and assay simplification later to be verified on a larger ~1000 sample cohort. Conclusions: We present a scalable framework that unifies high-plex mIF discovery with H&E-based computational morphology to support biomarker identification, feature reduction, and diagnostic assay development for colorectal polyp risk stratification. This platform provides the foundation for forthcoming clinical validation and deployment within colorectal surveillance programs.