ReviewAdvances in experimental medicine and biology2025
Classification of Breast Cancer Through the Perspective of Cell Identity Models.
Review in Advances in experimental medicine and biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Review
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The mammary epithelium has an inner luminal layer that contains estrogen receptor (ER)-positive hormone-sensing cells and ER-negative alveolar/secretory cells, and an outer basal layer that contains myoepithelial/stem cells. Most human tumours resemble either hormone-sensing cells or alveolar/secretory cells. The most widely used molecular classification, the Intrinsic classification, assigns hormone-sensing tumours to Luminal A/B and human epidermal growth factor 2-enriched (HER2E)/molecular apocrine (MA)/luminal androgen receptor (LAR)-positive classes, and alveolar/secretory tumours to the Basal-like class. Molecular classification is most useful when tumours have classic invasive carcinoma of no special type (NST) histology. It is less useful for special histological types of breast cancer, such as metaplastic breast cancer and adenoid cystic cancer, which are better described with standard pathology terms. Compared to mice, humans show a strong bias towards making tumours that resemble mammary hormone-sensing cells. This could be caused by the formation in adolescence of der(1;16), a translocation through the centromeres of chromosomes 1 and 16, which only occurs in humans and could trap the cells in the hormone-sensing state.
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Registered trials
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 OpenQuestion graph.