Triple-Negative Breast Cancer: A Review of Conventional and Advanced Therapeutic Strategies.
Mauricio A MedinaGoldie OzaAshutosh SharmaL G ArriagaJosé Manuel Hernández HernándezVincent M RotelloJose Tapia RamirezPublished in: International journal of environmental research and public health (2020)
Triple-negative breast cancer (TNBC) cells are deficient in estrogen, progesterone and ERBB2 receptor expression, presenting a particularly challenging therapeutic target due to their highly invasive nature and relatively low response to therapeutics. There is an absence of specific treatment strategies for this tumor subgroup, and hence TNBC is managed with conventional therapeutics, often leading to systemic relapse. In terms of histology and transcription profile these cancers have similarities to BRCA-1-linked breast cancers, and it is hypothesized that BRCA1 pathway is non-functional in this type of breast cancer. In this review article, we discuss the different receptors expressed by TNBC as well as the diversity of different signaling pathways targeted by TNBC therapeutics, for example, Notch, Hedgehog, Wnt/b-Catenin as well as TGF-beta signaling pathways. Additionally, many epidermal growth factor receptor (EGFR), poly (ADP-ribose) polymerase (PARP) and mammalian target of rapamycin (mTOR) inhibitors effectively inhibit the TNBCs, but they face challenges of either resistance to drugs or relapse. The resistance of TNBC to conventional therapeutic agents has helped in the advancement of advanced TNBC therapeutic approaches including hyperthermia, photodynamic therapy, as well as nanomedicine-based targeted therapeutics of drugs, miRNA, siRNA, and aptamers, which will also be discussed. Artificial intelligence is another tool that is presented to enhance the diagnosis of TNBC.
Keyphrases
- epidermal growth factor receptor
- artificial intelligence
- tyrosine kinase
- cell proliferation
- photodynamic therapy
- small molecule
- cancer therapy
- signaling pathway
- induced apoptosis
- epithelial mesenchymal transition
- machine learning
- advanced non small cell lung cancer
- stem cells
- deep learning
- big data
- randomized controlled trial
- dna damage
- drug delivery
- transforming growth factor
- cell death
- dna repair