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Abstract The tumor microenvironment (TME) is an immensely complex ecosystem^1,2. This complexity underlies difficulties in elucidating principles of spatial organization and using molecular profiling of the TME for clinical use³. Through statistical analysis of 96 spatial transcriptomic (ST-seq) datasets spanning twelve diverse tumor types, we found a conserved distribution of multicellular, transcriptionally covarying units termed ‘Spatial Groups’ (SGs). SGs were either dependent on a hierarchical local spatial context – enriched for cell-extrinsic processes such as immune regulation and signal transduction – or independent from local spatial context – enriched for cell-intrinsic processes such as protein and RNA metabolism, DNA repair, and cell cycle regulation. We used SGs to define a measure of gene spatial heterogeneity – ‘spatial lability’ – and categorized all 96 tumors by their TME spatial lability profiles. The resulting classification captured spatial variation in cell-extrinsic versus cell-intrinsic biology and motivated class-specific strategies for therapeutic intervention. Using this classification to characterize pre-treatment biopsy samples of 16 non-small cell lung cancer (NSCLC) patients outside our database distinguished responders and non-responders to immune checkpoint blockade while programmed death-ligand 1 (PD-L1) status and spatially unaware bulk transcriptional markers did not. Our findings show conserved principles of TME spatial biology that are both biologically and clinically significant. 

Microbial consortia exhibit complex functional properties in contexts ranging from soils to bioreactors to human hosts. Understanding how community composition determines function is a major goal of microbial ecology. Here we address this challenge using the concept of community-function landscapes—analogues to fitness landscapes—that capture how changes in community composition alter collective function. Using datasets that represent a broad set of community functions, from production/degradation of specific compounds to biomass generation, we show that statistically inferred landscapes quantitatively predict community functions from knowledge of species presence or absence. Crucially, community-function landscapes allow prediction without explicit knowledge of abundance dynamics or interactions between species and can be accurately trained using measurements from a small subset of all possible community compositions. The success of our approach arises from the fact that empirical community-function landscapes appear to be not rugged, meaning that they largely lack high-order epistatic contributions that would be difficult to fit with limited data. Finally, we show that this observation holds across a wide class of ecological models, suggesting community-function landscapes can be efficiently inferred across a broad range of ecological regimes. Our results open the door to the rational design of consortia without detailed knowledge of abundance dynamics or interactions.