Tumors consist not only of cancer cells, but also stromal and immune cells that constitute the tumor microenvironment. Clinical outcome and response to therapy depend on the complex interplay between these cell populations within the tumor microenvironment. Beyond numerical values, spatial organization of cells within tumors (and tumor-draining lymph nodes) also impacts biological behavior. These can now be collectively addressed via a quantitative image analysis approach that incorporates 1) multi-color tissue staining (Opal, Perkin Elmer), 2) high-resolution, automated whole-slide spectral imaging (Vectra, Perkin Elmer), 3) image analysis algorithms that utilize machine-learning to identify cell types and locations (InForm, Perkin Elmer), and 4) spatial statistical analysis to understand relationships between cell populations within tissue samples. This novel approach provides objective assessment of immune-stromal-cancer interactions within tumors and tumor-draining lymph nodes, and data generated are of prognostic and mechanistic value.