Y J. Li, Z Li, Ryan Quinton, Yuanfeng Ji, Xi Zhang, Jinxi Xiang, Xiyue Wang, Sen Yang, Feyisope Eweje, Yijiang Chen, Xiangde Luo, Y J. Li, Jonathan Mulholland, Siwei Chen, Colin Bergstrom, T Kim, Francesca Olguin, Sierra Willens, S H Lin, Jeffrey Nirschl, Robert West, Joel Neal, Maximilian Diehn, Ruijiang Li
The tumor microenvironment (TME) critically shapes disease progression and therapeutic resistance. However, a comprehensive understanding of its spatial architecture remains elusive, and clinical translation is challenging. Here, we present cellular architecture and neighborhood-informed virtual AI-driven spatial profiling (CANVAS), an artificial intelligence platform that infers tumor ecological habitats from hematoxylin and eosin (H&E) histopathology. Built on an atlas of over 18 million cells profiled by 41-plex spatial proteomics across 457 patients with non-small cell lung cancer, CANVAS establishes 10 reproducible cellular neighborhoods (CNs) capturing conserved spatial organization of the TME. Through multimodal alignment and foundation-model-based morphological encoding, CANVAS predicts CN-anchored habitat structures from H&E slides and enables clinical evaluation in over 5,000 patients spanning 9 cancer types. Across patient cohorts, CANVAS supports prognostic modeling, spatial ecotype stratification, and immunotherapy outcome prediction. These results establish CANVAS as a clinically scalable platform for spatial profiling, bridging single-cell analysis to population-level insight and enabling precision oncology.