OpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGA
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911591388151808 |
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| author | Galama, Maaike Kozar-Gillan, Nina Embacher, Christina Dembo, Todd Böhm, Cornelius Ramberger, Evelyn Ribbat-Idel, Julika Krupar, Rosemarie Aumiller, Verena Hägele, Miriam Standvoss, Kai Erdmann, Gerrit Pablos, Blanca Angelo, Ari Schallenberg, Simon Norgan, Andrew Matyas, Viktor Müller, Klaus-Robert Alber, Maximilian Ruff, Lukas Klauschen, Frederick |
| author_facet | Galama, Maaike Kozar-Gillan, Nina Embacher, Christina Dembo, Todd Böhm, Cornelius Ramberger, Evelyn Ribbat-Idel, Julika Krupar, Rosemarie Aumiller, Verena Hägele, Miriam Standvoss, Kai Erdmann, Gerrit Pablos, Blanca Angelo, Ari Schallenberg, Simon Norgan, Andrew Matyas, Viktor Müller, Klaus-Robert Alber, Maximilian Ruff, Lukas Klauschen, Frederick |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_12075 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | OpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGA Galama, Maaike Kozar-Gillan, Nina Embacher, Christina Dembo, Todd Böhm, Cornelius Ramberger, Evelyn Ribbat-Idel, Julika Krupar, Rosemarie Aumiller, Verena Hägele, Miriam Standvoss, Kai Erdmann, Gerrit Pablos, Blanca Angelo, Ari Schallenberg, Simon Norgan, Andrew Matyas, Viktor Müller, Klaus-Robert Alber, Maximilian Ruff, Lukas Klauschen, Frederick Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Quantitative Methods 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. |
| title | OpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGA |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2604.12075 |