OpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGA

2026-04-13

AI Summary

This study addresses the lack of large-scale, quantitative, and consistent characterization of the tumor microenvironment (TME) from routine hematoxylin and eosin (H&E)-stained whole-slide images. Leveraging the Atlas foundation model in computational pathology, we developed an AI-driven H&E-TME analysis pipeline to process 3,634 whole slides across five cancer types from The Cancer Genome Atlas (TCGA). The pipeline performs tissue quality control, semantic segmentation, cell detection and classification, and spatial neighborhood analysis, yielding over 4,500 cell-level quantitative features per slide. We constructed and publicly released the OpenTME dataset—the first large-scale, high-resolution, AI-generated quantitative atlas of the TME derived solely from H&E stains. OpenTME is now available on Hugging Face for non-commercial academic research, aiming to advance spatial biology and computational methodology development.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Other Foundations of Computer Vision

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• tumor microenvironment
• H&E-stained histopathology
• quantitative characterization
• large-scale dataset
• spatial biology

Innovation

• tumor microenvironment
• AI-powered pathology
• H&E-stained histopathology
• spatial neighborhood analysis
• foundation models