ArticleCommunications medicine2023
Deep learning and transfer learning identify breast cancer survival subtypes from single-cell imaging data.
Article in Communications medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Innovative Approaches to EMT-Related Biomarker Identification in Breast Cancer: Multi-Omics and Machine Learning Methods.Biotech (Basel (Switzerland)) · 2025Review
- Modelling metastasis in a dish:Pathology oncology research : POR · 2025Review
- Article
- Keratins 6, 16, and 17 in Health and Disease: A Summary of Recent Findings.Current issues in molecular biology · 2024Review
- Deep learning and transfer learning identify breast cancer survival subtypes from single-cell imaging data.Communications medicine · 2023Article
- Abdominal myosteatosis measured with computed tomography predicts poor outcomes in patients with glioblastoma.Neuro-oncology advancesArticle
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5 authors.
Funding
Abstract
backgroundSingle-cell multiplex imaging data have provided new insights into disease subtypes and prognoses recently. However, quantitative models that explicitly capture single-cell resolution cell-cell interaction features to predict patient survival at a population scale are currently missing.
methodsWe quantified hundreds of single-cell resolution cell-cell interaction features through neighborhood calculation, in addition to cellular phenotypes. We applied these features to a neural-network-based Cox-nnet survival model to identify survival-associated features. We used non-negative matrix factorization (NMF) to identify patient survival subtypes. We identified atypical subpopulations of triple-negative breast cancer (TNBC) patients with moderate prognosis and Luminal A patients with poor prognosis and validated these subpopulations by label transferring using the UNION-COM method.
resultsThe neural-network-based Cox-nnet survival model using all cellular phenotype and cell-cell interaction features is highly predictive of patient survival in the test data (Concordance Index > 0.8). We identify seven survival subtypes using the top survival features, presenting distinct profiles of epithelial, immune, and fibroblast cells and their interactions. We reveal atypical subpopulations of TNBC patients with moderate prognosis (marked by GATA3 over-expression) and Luminal A patients with poor prognosis (marked by KRT6 and ACTA2 over-expression and CDH1 under-expression). These atypical subpopulations are validated in TCGA-BRCA and METABRIC datasets.
conclusionsThis work provides an approach to bridge single-cell level information toward population-level survival prediction.
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