SPOT-Met: Spatially decoding organotropism and immunotherapy response in colorectal cancer from 1,000 multi-omic tumors

Metastasis accounts for over 90% of mortality in colorectal cancer (CRC), yet predicting whether, when, and where it will occur remains a major clinical challenge. Existing tools, including TNM staging, ctDNA, and mutational profiling, cannot anticipate metastatic tropism or guide site-specific surveillance and therapy decisions. Consequently, many patients with stage II-III CRC receive suboptimal postoperative treatment. To address this gap, we established SPOT-Met (Spatial Predictors Of Tropism and Metastasis), a foundation-scale initiative that integrates subcellular-resolution spatial multi-omics with AI-driven modeling to infer the molecular and architectural rules governing organ-specific metastasis. We spatially profiled 1,000 CRC primary tumors and ~100 matched metastatic and adjacent normal tissues using the Singular Genomics G4X platform, generating >300 million same-sample transcriptomic, proteomic, and morphologic cell profiles at submicron resolution. Each case is linked to bulk RNA-seq, qPCR-based mutational data, and detailed clinical metadata encompassing metastasis site, timing, therapy response, and survival. SPOT-Met also incorporates a focused cohort of patients treated with pembrolizumab, enabling the spatial dissection of immunotherapy responses. Comparative analyses of responders and non-responders are underway, revealing emerging spatial differences in immune and stromal architecture. Preliminary data suggest that immune organization and cell-cell topology, rather than total immune content, may distinguish therapeutic outcomes, highlighting the potential of spatial context as a predictive biomarker beyond PD-L1 expression or tumor mutational burden. Early findings further indicate that liver-tropic tumors preferentially form perivascular stromal hubs enriched for metabolic and extracellular matrix signatures, whereas non-metastatic tumors retain compact, immune-regulated crypt structures. Integrating these spatial and molecular features enhances the retrospective classification of metastatic organotropism compared to histopathology alone. SPOT-Met is being developed as a biopsy-compatible diagnostic assay to predict metastatic potential and organotropism at the time of diagnosis, aiming to double current prognostic precision for stage II-III CRC. By uniting spatial multi-omics and AI at unprecedented scale, SPOT-Met transitions metastasis research from retrospective observation to prospective prediction, advancing precision oncology and immunotherapy stratification.