| Literature DB >> 35203574 |
Amal Qattan1, Taher Al-Tweigeri2, Kausar Suleman2.
Abstract
Triple-negative breast cancers (HER2-, ER-, PR-) continue to present a unique treatment challenge and carry unfavorable prognoses. The elucidation of novel therapeutic targets has necessitated the re-evaluation of stratification approaches to best predict prognosis, treatment response and theranostic and prognostic markers. Androgen receptor expression and function have important implications on proliferation, tumor progression, immunity and molecular signaling in breast cancer. Accordingly, there has been increasing support for classification of androgen receptor-negative triple-negative breast cancer or quadruple-negative breast cancer (QNBC). QNBC has unique molecular, signaling and expression regulation profiles, particularly those affected by microRNA regulatory networks. microRNAs are now known to regulate AR-related targets and pathways that are dysregulated in QNBC, including immune checkpoint inhibitors (ICIs), SKP2, EN1, ACSL4 and EGFR. In this review, we explore and define the QNBC tumor subtype, its molecular and clinical distinctions from other subtypes, miRNA dysregulation and function in QNBC, and knowledge gaps in the field. Potential insights into clinical and translational implications of these dysregulated networks in QNBC are discussed.Entities:
Keywords: TNBC; androgen receptor; microRNA; quadruple-negative breast cancer (QNBC)
Year: 2022 PMID: 35203574 PMCID: PMC8962346 DOI: 10.3390/biomedicines10020366
Source DB: PubMed Journal: Biomedicines ISSN: 2227-9059
Lehmann classification 2011 and 2016. Pathways and molecular targets in TNBC subtypes. Classical molecular and cellular TNBC subtype classifications are listed, along with their distinctive dysregulated signaling pathways and potential molecular targets. Potential targeted therapies associated with classifications and mutations are listed in parentheses. BL, basal-like; IM, immunomodulatory; M, mesenchymal, MSL, mesenchymal stem-like; LAR, luminal androgen receptor.
| Expression-Based Molecular Classification | ||||||
|---|---|---|---|---|---|---|
| BL1 (17.9%) | BL2 (11.1%) | IM (21.1%) | M (20.8%) | MSL (6.5%) | LAR (9.2%) | Unstable (13.5%) |
|
| ||||||
| PARP1,CHEK1, | EGFR, mTOR, MET,EPHA2 | JAK1/2, STAT,BTK, NFκB,LYN,IRF1 | PI3K,IGF1R mTOR, SRC, FGFR PDGFR | PI3K, mTOR, MEK1/2, SRC, IGF1R, FGFR, PDGFR, NFkB | AR, HSP90, PI3K, FGFR4 | PARP1, RAD51, PLK1, AURKA/BTTK,CHEK1 |
|
| ||||||
| Antimitotic agents | TKI,mTORi, | Anti-androgen | ||||
|
| ||||||
| BRCA1/2, PARPi | PIK3CA | PD-L1 | CDK4/6 | TP53 | PTEN | EGFR |
|
| ||||||
| Basal-like | Claudin-high | Claudin-low | LAR | |||
|
| ||||||
| Androgen receptor-positive | Androgen receptor-negative | |||||
|
| ||||||
| Cell metabolism | acyl-CoA synthetase4 (ASCL4) | |||||
| Tumor immune microenvironment | Tumor-infiltrating lymphocytes (TIL), Tumor necrosis factor superfamily member 10 (TNFSF10), Programmed death ligand 1 (PD-L1) | |||||
| Cell growth and proliferation | EGFR,HER4, CK5/6,CDK6,PTEN,ki-67 | |||||
Figure 1TNBC-relevant androgen receptor signaling and miRNA regulation. Cytoplasmic androgen receptors (AR), chaperoned by heat-shock proteins (HSPs), are activated to dimerize and cross the nuclear membrane upon engagement with androgen ligand. Thereafter, activated AR dimers complex with co-regulators to regulate androgen response elements within target genes, including EGFR and Cyclin D1. Growth factor signaling, particularly that involving the AKT signaling axis, promotes crosstalk and feedback with AR signaling as described in further detail in the text.
Figure 2Network analysis of miRNAs found to be upregulated in QNBC in African American women by Angajala et al. using miRNet 2.0 with minimum network filtering. The miRNAs identified by this study and the genes they are known to regulate were analyzed for relatedness and common nodes, revealing prevalent regulation networks and regulated genes in this population.
Figure 3Network analysis of miRNAs found to be upregulated in QNBC by Bhattari et al. using miRNet 2.0 with minimum network filtering. The miRNAs identified by this study and the genes they are known to regulate were analyzed for relatedness and common nodes, revealing prevalent regulation networks and regulated genes in this population.