Many malignancies display heterogeneous features that support cancer progression. Emerging high-resolution methods provide a view of heterogeneity that recognizes the influence of diverse cell types and cell states of the tumor microenvironment. Here we outline a hierarchical organization of tumor heterogeneity from a genomic perspective, summarize the origins of spatially patterned metabolic features, and review recent developments in single-cell and spatially resolved techniques for genome-wide study of multicellular tissues. We also discuss how integrating these approaches can yield new insights into human cancer and emerging immune therapies. Applying these technologies for the analysis of primary tumors, patient-derived xenografts, and in vitro systems holds great promise for understanding the hierarchical structure and environmental influences that underlie tumor ecosystems.