Exploring new pathways in endocrine-resistant breast cancer.
Inês Soares de PinhoCatarina AbreuInês GomesSandra CasimiroTeresa Raquel PachecoRita Teixeira de SousaLuis CostaPublished in: Exploration of targeted anti-tumor therapy (2022)
The most common breast cancer (BC) subtypes are hormone-dependent, being either estrogen receptor-positive (ER + ), progesterone receptor-positive (PR + ), or both, and altogether comprise the luminal subtype. The mainstay of treatment for luminal BC is endocrine therapy (ET), which includes several agents that act either directly targeting ER action or suppressing estrogen production. Over the years, ET has proven efficacy in reducing mortality and improving clinical outcomes in metastatic and nonmetastatic BC. However, the development of ET resistance promotes cancer survival and progression and hinders the use of endocrine agents. Several mechanisms implicated in endocrine resistance have now been extensively studied. Based on the current clinical and pre-clinical data, the present article briefly reviews the well-established pathways of ET resistance and continues by focusing on the three most recently uncovered pathways, which may mediate resistance to ET, namely receptor activator of nuclear factor kappa B ligand (RANKL)/receptor activator of nuclear factor kappa B (RANK), nuclear factor kappa B (NFκB), and Notch. It additionally overviews the evidence underlying the approval of combined therapies to overcome ET resistance in BC, while highlighting the relevance of future studies focusing on putative mediators of ET resistance to uncover new therapeutic options for the disease.
Keyphrases
- nuclear factor
- toll like receptor
- estrogen receptor
- inflammatory response
- small cell lung cancer
- type diabetes
- stem cells
- squamous cell carcinoma
- systematic review
- cell proliferation
- randomized controlled trial
- cardiovascular events
- signaling pathway
- electronic health record
- cardiovascular disease
- drug delivery
- papillary thyroid
- young adults
- big data
- deep learning
- combination therapy
- binding protein
- lymph node metastasis
- mesenchymal stem cells
- lps induced
- breast cancer risk