Colorectal cancer progression is driven by spatially organized interactions within the tumor microenvironment, yet the cellular architecture and molecular interfaces underlying these interactions remain poorly resolved. High-definition spatial transcriptomics platforms, such as Visium HD, now enable transcriptome-wide profiling at near-single-cell resolution, generating datasets with hundreds of thousands of spatially-resolved measurements per tissue section. Extracting biologically meaningful organization from data at this scale exceeds the capacity of conventional statistical approaches and requires deep learning methods that jointly model gene expression and spatial context. In parallel, recent advances in deep learning-based protein structure prediction now allow the three-dimensional modeling of candidate ligand-receptor interfaces directly from sequence, offering a structural basis for interactions inferred from expression data alone. In this project, we will develop and apply deep learning models to deconvolve cellular composition and spatial gene expression patterns within the tumor microenvironment, and combine these with structural modeling of candidate signaling interfaces to assess the biophysical plausibility of predicted cell-cell interactions. This integrated computational framework will be applied across primary and metastatic colorectal cancer specimens to link spatial cellular organization to structurally grounded molecular interaction networks underlying tumor heterogeneity and treatment resistance.