OpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGA
For cancer researchers, this provides a large-scale, standardized TME dataset from routine histopathology, but it is an incremental resource rather than a novel method or breakthrough.
The authors created OpenTME, a dataset of AI-derived tumor microenvironment profiles from 3,634 H&E-stained whole-slide images across five cancer types from TCGA, providing over 4,500 quantitative readouts per slide. This resource aims to facilitate biomarker discovery and spatial biology research.
The tumor microenvironment (TME) plays a central role in cancer progression, treatment response, and patient outcomes, yet large-scale, consistent, and quantitative TME characterization from routine hematoxylin and eosin (H&E)-stained histopathology remains scarce. We introduce OpenTME, an open-access dataset of pre-computed TME profiles derived from 3,634 H&E-stained whole-slide images across five cancer types (bladder, breast, colorectal, liver, and lung cancer) from The Cancer Genome Atlas (TCGA). All outputs were generated using Atlas H&E-TME, an AI-powered application built on the Atlas family of pathology foundation models, which performs tissue quality control, tissue segmentation, cell detection and classification, and spatial neighborhood analysis, yielding over 4,500 quantitative readouts per slide at cell-level resolution. OpenTME is available for non-commercial academic research on Hugging Face. We will continue to expand OpenTME over time and anticipate it will serve as a resource for biomarker discovery, spatial biology research, and the development of computational methods for TME analysis.