ArticleNature communications2023
Single-cell morphological and topological atlas reveals the ecosystem diversity of human breast cancer.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
- Precision neoadjuvant treatment with AI-assisted subtyping in HR+/HER2-breast cancer: A randomized, phase 2 trial FASCINATE-N.Cell reports. Medicine · 2026Trial
- Spatial organization of the TNBC tumor microenvironment: multicellular niches, T-cell bottlenecks, and therapeutic opportunities.Journal of translational medicine · 2026Review
- Prognostic RNA-splicing archetypes in breast cancer identified by extended pre-training of histopathology foundation models.Nature communications · 2026Article
- Single cell spatial transcriptomics track the evolutionary hierarchy and microenvironment remodeling during breast carcinoma invasion.Nature communications · 2026Article
- Spatially interpretable artificial intelligence framework to tailored neoadjuvant dual HER2 blockade in HER2-positive breast cancer.Signal transduction and targeted therapy · 2026Article
- Deep learning prediction of pathological complete response in breast cancer using Mamba architecture.NPJ digital medicine · 2026Article
- Radiomics: Current Applications and Future Directions.MedComm · 2026Review
- Decoding the breast cancer microenvironment by spatial multi-omics: from architecture to clinical translation.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- MuTriM: A multiscale deep learning model integrating longitudinal radiomics and pathomic features for predicting recurrence and adjuvant radiation benefit in breast cancer.European journal of cancer (Oxford, England : 1990) · 2026Article
- Integrated multi-regional multiomic profiling of breast phyllodes tumours reveals peritumoural immune activation and stromal remodelling.Clinical and translational medicine · 2026Article
- Radiomics and Deep Learning: Bridging Breast Cancer Imaging Phenotypes and Genomic Heterogeneity.Breast cancer (Dove Medical Press) · 2026Review
- Pathogenomic analysis reveals clinically relevant epithelial-mesenchymal plasticity in esophageal squamous cell carcinoma.Theranostics · 2026Article
- Multimodal Deep Learning with Routine Clinical Data for Recurrence Risk Stratification in HRResearch (Washington, D.C.) · 2026Article
- Predicting MammaPrint Recurrence Risk from Breast Cancer Pathological Images Using a Weakly Supervised Transformer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Quantifying the Single-Cell Morphological Landscape of Cellular Transdifferentiation through Force Field Reconstruction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Distinct transcriptomic features of tumor and stromal cells in direct contact in luminal and triple-negative breast cancers.Exploration of targeted anti-tumor therapy · 2026Article
- AI-powered programmable virtual humans toward human physiologically-based drug discovery.Drug discovery today · 2025Review
- Deep learning-based fusion of nuclear segmentation features for microsatellite instability and tumor mutational burden prediction in digestive tract cancers: a multicenter validation study.Briefings in bioinformatics · 2025Article
- Development and validation of an artificial intelligence system for triple-negative breast cancer identification and prognosis prediction: a multicentre retrospective study.EClinicalMedicine · 2025Article
- Detection, localization, and staging of breast cancer lymph node metastasis in digital pathology whole slide images using selective neighborhood attention-based deep learning.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
18 authors.
Funding
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Abstract
Digital pathology allows computerized analysis of tumor ecosystem using whole slide images (WSIs). Here, we present single-cell morphological and topological profiling (sc-MTOP) to characterize tumor ecosystem by extracting the features of nuclear morphology and intercellular spatial relationship for individual cells. We construct a single-cell atlas comprising 410 million cells from 637 breast cancer WSIs and dissect the phenotypic diversity within tumor, inflammatory and stroma cells respectively. Spatially-resolved analysis identifies recurrent micro-ecological modules representing locoregional multicellular structures and reveals four breast cancer ecotypes correlating with distinct molecular features and patient prognosis. Further analysis with multiomics data uncovers clinically relevant ecosystem features. High abundance of locally-aggregated inflammatory cells indicates immune-activated tumor microenvironment and favorable immunotherapy response in triple-negative breast cancers. Morphological intratumor heterogeneity of tumor nuclei correlates with cell cycle pathway activation and CDK inhibitors responsiveness in hormone receptor-positive cases. sc-MTOP enables using WSIs to characterize tumor ecosystems at the single-cell level.
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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.