Evidence map›Paper›PMID 42637837›Full record

ArticleNature biomedical engineering2026

HisToSpatialCNV: an interpretable deep learning method predicting spatial copy number variations from histopathology images.

Tianao Chen, Thatchayut Unjitwattana, Xuhui Guo, Pooja Thakur, Shu Zhou, Zheng Jing, Yiwen Yang, Yuheng Du, Weiping Zou, Lana X Garmire

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Article in Nature biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Tianao ChenDepartment of Electrical and Computer Engineering, University of Michigan, Ann Arbor, MI, USA.
Thatchayut UnjitwattanaDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0009-0003-2162-519X
Xuhui GuoDepartment of Biomedical Informatics and Data Science, University of Alabama at Birmingham, Birmingham, AL, USA.
Pooja ThakurDepartment of Biomedical Informatics and Data Science, University of Alabama at Birmingham, Birmingham, AL, USA.
Shu ZhouDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Zheng JingDepartment of Applied Statistics, University of Michigan, Ann Arbor, MI, USA.
Yiwen YangDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Yuheng DuDepartment of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Weiping ZouDepartments of Surgery and Pathology, Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, Ann Arbor, MI, USA.
Lana X GarmireDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA. lgarmire@uab.edu.ORCID http://orcid.org/0000-0003-1672-6917

Funding

U.S. Department of Health & Human Services | NIH | NIH Office of the Director (OD) OD039978U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine (NLM) LM012373
6 · The paper itself

Abstract

Copy number variations (CNV) are key drivers of cancer progression, yet methods for predicting spatial CNVs directly from haematoxylin and eosin (H&E) images are currently lacking. We introduce HisToSpatialCNV, a multiscale deep-learning framework that integrates histopathological features from H&E-stained images to infer spatial CNV patterns. Combining interpretable feature extraction with graph neural networks and multihead self-attention, our approach captures both local and global tissue contexts. Applied to HER2+ breast cancer, skin cancer and brain cancer datasets, HisToSpatialCNV outperformed existing methods for spatial gene expression inference and showed strong concordance between predicted CNVs and gene expression. In addition, it enabled tumour subclone identification, phylogenetic reconstruction and detection of pathway alterations linked to tumour progression. HisToSpatialCNV generalized across Visium and Xenium spatial transcriptomics platforms, on HER2+ patients. Applying the HisToSpatialCNV HER2+ model to TCGA HER2+ histopathology data identified spatial-molecular subtypes associated with distinct survival outcomes in TCGA HER2+ patients. By connecting histopathology to spatial genomics, HisToSpatialCNV offers a powerful, cost-effective tool for studying intratumour heterogeneity using only routine pathology images.

Identifiers

PMID42637837

What Socratic holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.