A deep learning model to predict RNA-Seq expression of tumours from whole-slide images
Open accessAbstract
Deep learning methods for digital pathology analysis are an effective way to address multiple clinical questions, from diagnosis to prediction of treatment outcomes. These methods have also been used to predict gene mutations from pathology images, but no comprehensive evaluation of their potential for extracting molecular features from histology slides has yet been performed. We show that HE2RNA, a model based on the integration of multiple data modes, can be trained to systematically predict RNA-Seq profiles from whole-slide images alone, without expert annotation. Through its interpretable design, HE2RNA provides virtual spatialization of gene expression, as validated by CD3- and CD20-staining on an independent dataset. The transcriptomic representation learned by HE2RNA can also be transferred on other datasets, even of small size, to increase prediction performance for specific molecular phenotypes. We illustrate the use of this approach in clinical diagnosis purposes such as the identification of tumors with microsatellite instability.
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A representative line of work predicts gene expression directly from whole-slide histology by modeling higher-order tissue relationships using hypergraph neural networks, enabling gene-level inference from cost-effective imaging while capturing structured dependencies among multi-scale morphological features.
“A representative line of work predicts gene expression directly from whole-slide histology by modeling higher-order tissue relationships using hypergraph neural networks, enabling gene-level inference from cost-effective imaging while capturing structured dependencies among multi-scale morphological features (13).”
From The role of multimodality in clinical disease diagnosis: advances, challenges, and opportunities (2026) · cites this paper as [13]
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- Authors
- Schmauch, B., Romagnoni, A., Pronier, E., Saillard, C., Maillé, P., Calderaro, J., Kamoun, A., Sefta, M., Toldo, S., Zaslavskiy, M., Clozel, T., Moarii, M., Courtiol, P., & Wainrib, G.
- Affiliations
- Inserm (FR)
- Corresponding author
- Schmauch, Benoît
- Wainrib, Gilles
- ORCID
- Venue
- Nature Communications
- Published
- 2020-08-03
- Volume
- 11
- Issue
- 1
- Pages
- 3877-3877
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC7400514
- OpenAlex
- W3046305306
- Cited by
- 533
- References
- 76
- Citations by year
- 2026: 97 · 2025: 97 · 2024: 104 · 2023: 90 · 2022: 74 · 2021: 65 · 2020: 6
- FWCI
- 23.10
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- whole slide images, digital pathology, multimodal data integration, spatial gene expression, explainable deep learning, transfer learning, microsatellite instability, cancer diagnosis, histology
- MeSH terms
- Deep Learning, RNA-Seq, Algorithms, Humans, Image Processing, Computer-Assisted, Models, Genetic, Neoplasms, Gene Expression Regulation, Neoplastic, Computational Biology, Gene Expression Profiling, Microsatellite Instability
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