Harnessing TME depicted by histological images to improve cancer prognosis through a deep learning system
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Abstract
ABSTRACT Interpreting the tumor microenvironment (TME) heterogeneity within solid tumors presents a cornerstone for precise disease diagnosis and prognosis. However, while spatial transcriptomics offers a wealth of data, ranging from gene expression and spatial location to corresponding Hematoxylin and Eosin (HE) images, to explore the TME of various cancers, its high cost and demanding infrastructural needs significantly limit its clinical application, highlighting the need for more accessible alternatives. To bridge this gap, we introduce the Integrated Graph and Image Deep Learning (IGI-DL) model. This innovation, a fusion of Convolutional Neural Networks and Graph Neural Networks, is designed to predict gene spatial expression using HE images. The IGI-DL model outperforms its predecessors in analyzing colorectal cancer (CRC), breast cancer, and cutaneous squamous cell carcinoma (cSCC) by leveraging both pixel intensity and structural features in images. Significantly, across all cancer types, the IGI-DL model enhances the mean correlation of the top five genes by an average of 0.125 in internal and external test sets, rising from 0.306 to 0.431, surpassing existing state-of-the-art (SOTA) models. We further present a novel risk score derived from a super-patch graph, where gene expression predicted by IGI-DL serves as node features. Demonstrating superior prognostic accuracy, this risk score, with a C-index of 0.713 and 0.741 for CRC and breast cancer, supersedes traditional HE-based risk scores. In summary, the approach augments our understanding of the TME from the aspect of histological images, portending a transformation in cancer prognostics and treatment planning and ushering in a new era of personalized and precision oncology.
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Some deep learning approaches have focused on detecting molecular alterations in image data, while most existing studies have centered on inferring spatial transcriptome or bulk transcriptome information from histopathology images.
“While some deep learning approaches have focused on detecting molecular alterations in image data [132], most existing studies have centered on inferring spatial transcriptome or bulk transcriptome information from histopathology images [133–136].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [135]
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HnRNPA2B1 promotes the proliferation of breast cancer MCF‐7 cells via the STAT3 pathway
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SEPAL: Spatial Gene Expression Prediction from Local Graphs
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EPCAM Expression in Colon Adenocarcinoma and its Relationship with TNM Staging
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Integration and Analysis of Heterogeneous Colorectal Cancer Data for Translational Research
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Skin Cancers and the Contribution of Rho GTPase Signaling Networks to Their Progression
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Saito‐Sasaki, N., Sawada, Y., Okada, E., & Nakamura, M. (2021) · Diagnostics · doi:10.3390/diagnostics11050830 · Cited by 10 · see citing works
Human breast cancer in situ capturing transcriptomics
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Anderson, A. N., McClanahan, D. R., Jacobs, J. E., Jeng, S., Vigoda, M., Blucher, A., ZHENG, C. L., Yoo, Y. J., Hale, C., Ouyang, X., et al. (2020) · Molecular Case Studies · doi:10.1101/mcs.a005439 · Cited by 9 · see citing works
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Human squamous cell carcinoma, Visium
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- Authors
- Gao, R., Yuan, X., Ma, Y., Wei, T., Johnston, L., Shao, Y., Lv, W., Zhu, T., Zhang, Y., Zheng, J., Chen, G., Sun, J., Wang, Y. G., & Yu, Z.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Sun, Jing
- Wang, Yu Guang
- Yu, Zhangsheng
- ORCID
- Venue
- Cell Reports Medicine
- Publisher
- Elsevier BV
- Published
- 2024-05
- Volume
- 5
- Issue
- 5
- Pages
- 101536
- ISSN
- 2666-3791 (ISSN-L)
- PMC
- PMC11149411
- OpenAlex
- W4396546360
- Cited by
- 45
- References
- 107
- Citations by year
- 2026: 13 · 2025: 27 · 2024: 5
- FWCI
- 6.64
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cell Reports Medicine (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Shanghai Jiao Tong University (YG2023ZD21, 20190102)
- National Natural Science Foundation of China (12171318)
- Center for High Performance Computing, Shanghai Jiao Tong University
- Science and Technology Commission of Shanghai Municipality (23XD1401900, 23DZ2290600, 21ZR1436300, 20JC1410100)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- tumor microenvironment, spatial transcriptomics, histopathological images, cancer prognosis, survival prediction, graph neural networks, breast cancer, colorectal cancer, deep learning
- MeSH terms
- Deep Learning, Breast Neoplasms, Female, Humans, Neoplasms, Prognosis, Gene Expression Regulation, Neoplastic, Tumor Microenvironment, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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