ArticleNature communications2024
Unsupervised representation learning of chromatin images identifies changes in cell state and tissue organization in DCIS.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep representation learning for temporal inference in cancer omics: a systematic literature review.Briefings in bioinformatics · 2026Pooled it
- Detecting chromatin state alterations in PBMCs associated with Type 2 Diabetes Mellitus.Communications medicine · 2026Article
- Partially shared multi-modal embedding learns holistic representation of cell state.Nature computational science · 2026Article
- Artificial intelligence for breast cancer management.Communications medicine · 2026Review
- Deep learning in chromatin organization: from super-resolution microscopy to clinical applications.Cellular and molecular life sciences : CMLS · 2025Review
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
- Male Breast Cancer: Epidemiology, Diagnosis, Molecular Mechanisms, Therapeutics, and Future Prospective.Oncology research · 2025Review
- Unsupervised representation learning of chromatin images identifies changes in cell state and tissue organization in DCIS.Nature communications · 2024Article
- Molecular and modular intricacies of precision oncology.Frontiers in immunology · 2024Review
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Authors and funding
7 authors.
Funding
Abstract
Ductal carcinoma in situ (DCIS) is a pre-invasive tumor that can progress to invasive breast cancer, a leading cause of cancer death. We generate a large-scale tissue microarray dataset of chromatin images, from 560 samples from 122 female patients in 3 disease stages and 11 phenotypic categories. Using representation learning on chromatin images alone, without multiplexed staining or high-throughput sequencing, we identify eight morphological cell states and tissue features marking DCIS. All cell states are observed in all disease stages with different proportions, indicating that cell states enriched in invasive cancer exist in small fractions in normal breast tissue. Tissue-level analysis reveals significant changes in the spatial organization of cell states across disease stages, which is predictive of disease stage and phenotypic category. Taken together, we show that chromatin imaging represents a powerful measure of cell state and disease stage of DCIS, providing a simple and effective tumor biomarker.
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