Abstract 153: High-resolution profiling of the tumor microenvironment with spatial ecotypes

Background: Multicellular programs in the tumor microenvironment (TME) drive cancer pathogenesis and response to therapy but remain challenging to identify and profile clinically. Here, we present Spatial EcoTyper, a multimodal machine learning framework for large-scale profiling of spatially dependent cell states and multicellular ecosystems, termed spatial ecotypes (SEs). Methods and Findings: By integrating five million single-cell and spot-level spatial transcriptomes from 10 diverse human neoplasms, including carcinomas and melanomas, we identified a rich tapestry of conserved regional plasticity across nine TME cell types. We then applied Spatial EcoTyper to deeply dissect these data, revealing nine novel SEs with broad conservation - each with unique biology, TME cell states, geospatial features, and clinical outcome associations. SEs were linked to specific biogeographic domains, including the tumor core, margin, and adjacent stroma, and were remarkably well-validated in 235 held-out tumor samples profiled by either single-cell RNA sequencing, sequencing-based spatial transcriptomics (10x Visium), or high-resolution imaging-based spatial transcriptomics (Vizgen MERSCOPE and NanoString CosMx). By deconvolving SEs from over 1,200 bulk tumor RNA-seq profiles, we discovered striking associations between SE levels and response to immune checkpoint inhibitors (ICIs), outperforming nearly 40 potential correlates of ICI response, including previously described signatures of tertiary lymphoid structures and tumor tissue PD-L1 expression. Notably, baseline levels of a proinflammatory SE - localized to the tumor core and defined by a hub of unique T cell, macrophage, fibroblast, and endothelial cell states - strongly forecasted ICI benefit, while another SE - localized to the tumor margin and defined by myofibroblast and hypoxia-associated endothelial cell states - portended ICI resistance. Conclusion: Our data reveal fundamental units of TME organization and demonstrate a platform for large-scale profiling of spatial cellular ecosystems in any tissue, with implications for improved forecasting of immunotherapy response and the development of novel therapeutic strategies. Citation Format: Wubing Zhang,Erin L. Brown,Abul Usmani,Noah Earland,Hyun Soo Jeon,Irfan Alahi,Nicholas P. Semenkovich,Janella C. Schwab,Chloé B. Steen,Chloe M. Sachs,Bilge Gungoren,Faridi Qaium,Peter K. Harris,Jacqueline Mudd,Qingyuan Cai,Antonella Bacchiocchi,Ryan C. Fields,Mario Sznol,Ruth Halaban,David Y. Chen,Aadel A. Chaudhuri,Aaron M. Newman. High-resolution profiling of the tumor microenvironment with spatial ecotypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 153.