| Literature DB >> 27390604 |
Xiaofeng Dai1, Liangjian Xiang1, Ting Li1, Zhonghu Bai1.
Abstract
Breast cancer is a complex disease encompassing multiple tumor entities, each characterized by distinct morphology, behavior and clinical implications. Besides estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2, novel biomarkers have shown their prognostic and predictive values, complicating our understanding towards to the heterogeneity of such cancers. Ten cancer hallmarks have been proposed by Weinberg to characterize cancer and its carcinogenesis. By reviewing biomarkers and breast cancer molecular subtypes, we propose that the divergent outcome observed from patients stratified by hormone status are driven by different cancer hallmarks. 'Sustaining proliferative signaling' further differentiates cancers with positive hormone receptors. 'Activating invasion and metastasis' and 'evading immune destruction' drive the differentiation of triple negative breast cancers. 'Resisting cell death', 'genome instability and mutation' and 'deregulating cellular energetics' refine breast cancer classification with their predictive values. 'Evading growth suppressors', 'enabling replicative immortality', 'inducing angiogenesis' and 'tumor-promoting inflammation' have not been involved in breast cancer classification which need more focus in the future biomarker-related research. This review novels in its global view on breast cancer heterogeneity, which clarifies many confusions in this field and contributes to precision medicine.Entities:
Keywords: biomarker; breast cancer; cancer hallmarks; subtype.
Year: 2016 PMID: 27390604 PMCID: PMC4934037 DOI: 10.7150/jca.13141
Source DB: PubMed Journal: J Cancer ISSN: 1837-9664 Impact factor: 4.207
Different immunohistochemical marker combinations used to define the basal phenotype in various publications.
| Journal article | Immunohistochemical marker | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| ER | PR | HER2 | CK5/6 | CK14 | CK17 | CK8/18 | EGFR | Vimentin | P-cadherin | TP-63 | |
| Carey et al., 2006[85] | - | - | - | +/-a | +/-a | ||||||
| Cheang et al., 2008[123] | - | - | - | +/-a | +/-a | ||||||
| Rakha et al., 2009[184] | - | - | - | +/-a | +/-a | +/-a | +/-a | ||||
| Matos et al., 2005[89] | + | + | + | ||||||||
| Nielsen et al., 2004[90] | - | - | +/-a | +/-a | |||||||
| Livasy et al., 2006 [124] | - | - | + | + | + | + | |||||
| Rakha et al., 2007[120] | +/-a | +/-a | |||||||||
| Fulford et al., 2006[13] | + | ||||||||||
a Positivity for at least one of the highlighted markers.
Summary of the breast tumor molecular subtypes.
| Subtype | Alias | Biomarker status | Grade | Outcome | Additional features | PrevalenceΔ | |
|---|---|---|---|---|---|---|---|
| Luminal | Luminal A* | [ER+|PR+]HER2-KI67- | 1|2 | Good | Luminal cytokeratin+, FOXA1+, ADH1B high; cell-cell adhesion genes high | 23.7%[9] | |
| Luminal B* | [ER+|PR+]HER2-KI67+ | 2|3 | Intermediate | Luminal cytokeratin+; TP53-; FGFR1 and ZIC3 amp; ADH1B low; cell-cell adhesion genes high | 38.8%[9] | ||
| [ER+|PR+]HER2+KI67+ | |Poor | 14%[9] | |||||
| HER2 positive | HER2 over-expression* | ER-PR-HER2+ | 2|3 | Poor | TP53-; GRB7 high; cell-cell adhesion genes high | 11.2%[9] | |
| Triple negative | Basal* | ER-PR-HER2-, basal marker+ | 3 | Poor | BRCA1-, TP53-; CDKN2A high; RB1 low; FGFR2 amp; cell-cell adhesion genes high | 10-25%[128] | 12.3%[9] |
| Claudin-low | ER-PR-HER2-, EMT marker+, Stem-cell marker+, claudin- | 3 | Poor | GATA3-regulated genes, cell-cell adhesion genes low; CDH1 low; Claudins low | 7-14%[141] | ||
| Metaplastic breast cancer (MBC) | ER-PR-HER2-, EMT marker+, Stem-cell marker+ | 3 | Poor | GATA3-regulated genes, cell-cell adhesion genes low; PIK3CA-, AKT- or KRAS- | 1%[143] | ||
| Interferon-rich | ER-PR-HER2-, interferon regulated genes+ | 3 | Intermediate | STAT1, SP110 high | ~10%[93] | ||
| Molecular apocrine cancer (MAC) | Molecular apocrine cancer (MAC) | ER-PR-AR+ | 2|3 | Poor | KI67+ | 13.2%[98] | |
* Subtypes with detailed expression patterns and clinical implications discussed in the text, which take the majority of the breast tumor cases and are most commonly referred to.
Δ The percentages could not be added up, as triple negative tumor subtypes are not mutually exclusive and the percentages are taken from different publications.
Δ The prevalences shown here are for all breast tumor cases, which are taken from one particular publication (as indicated in the square brackets) and can vary by different studies.