The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Deconstructing the molecular portraits of breast cancer
Open accessAbstract
Breast cancer is a heterogeneous disease in terms of histology, therapeutic response, dissemination patterns to distant sites, and patient outcomes. Global gene expression analyses using high‐throughput technologies have helped to explain much of this heterogeneity and provided important new classifications of cancer patients. In the last decade, genomic studies have established five breast cancer intrinsic subtypes (Luminal A, Luminal B, HER2‐enriched, Claudin‐low, Basal‐like) and a Normal Breast‐like group. In this review, we dissect the most recent data on this genomic classification of breast cancer with a special focus on the Claudin‐low subtype, which appears enriched for mesenchymal and stem cell features. In addition, we discuss how the combination of standard clinical‐pathological markers with the information provided by these genomic entities might help further understand the biological complexity of this disease, increase the efficacy of current and novel therapies, and ultimately improve outcomes for breast cancer patients.
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Cites
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Cited by
Garrido-Castro, A. C., Lin, N. U., & Polyak, K. (2019) · Cancer Discovery · doi:10.1158/2159-8290.cd-18-1177 · Cited by 1,451
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Pathogenesis of Triple-Negative Breast Cancer
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Self-citationBiology and Management of Patients With Triple-Negative Breast Cancer
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Identification of synergistic drug combinations using breast cancer patient-derived xenografts
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Metadata
- Authors
- Prat, A., & Perou, C. M.
- Venue
- Molecular Oncology
- Publisher
- Wiley
- Published
- 2010-11-24
- Volume
- 5
- Issue
- 1
- Pages
- 5-23
- ISSN
- 1574-7891 (ISSN-L)
- 1878-0261
- OpenAlex
- W2166311434
- Cited by
- 1,343
- References
- 12
- FWCI
- 20.23
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
