Over 150,000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50,000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. CRC tumors are removed en bloc with surrounding vasculature and lymphatics. Examination of regional lymph nodes at the time of surgical resection is essential for prognostication. Developing alternative approaches to indirectly assess recurrence risk would have utility in cases where lymph node yield is incomplete or inadequate. Spatially dependent, immune cell-specific (e.g., Tumor Infiltrating Lymphocytes- TILs), proteomic, and transcriptomic expression patterns inside and around the tumor - the tumor immune microenvironment (TIME) - can predict nodal/distant metastasis and probe the coordinated immune response from the primary tumor site. The comprehensive characterization of TILs and other immune infiltrates is possible using highly multiplexed spatial omics technologies, such as the GeoMX Digital Spatial Profiler (DSP). In this study, machine learning and differential co-expression analyses helped identify biomarkers from DSP-assayed protein expression patterns inside, at the invasive margin, and away from the tumor, associated with extracellular matrix remodeling (e.g., GZMB, fibronectin), immune suppression (e.g., FOXP3), exhaustion and cytotoxicity (e.g., CD8), PD-L1 expressing dendritic cells, neutrophil proliferation, amongst other concomitant alterations. Further investigation of these biomarkers may reveal independent risk factors of CRC metastasis that can be formulated into low-cost, widely available assays.
