Mutant p53: a novel target for the treatment of patients with triple-negative breast cancer?
Naoise C SynnottA MurrayP M McGowanM KielyP A KielyN O'DonovanD P O'ConnorW M GallagherJ CrownM J DuffyPublished in: International journal of cancer (2016)
The identification and validation of a targeted therapy for patients with triple-negative breast cancer (TNBC) is currently one of the most urgent needs in breast cancer therapeutics. One of the key reasons for the failure to develop a new therapy for this subgroup of breast cancer patients has been the difficulty in identifying a highly prevalent, targetable molecular alteration in these tumors. Recently however, the p53 gene was found to be mutated in approximately 80% of basal/TNBC, raising the possibility that targeting the mutant p53 protein product might be a new approach for the treatment of this form of breast cancer. In this study, we investigated the anti-cancer activity of PRIMA-1 and PRIMA-1MET (APR-246), two compounds which were previously reported to reactivate mutant p53 and convert it to a form with wild-type (WT) properties. Using a panel of 18 breast cancer cell lines and 2 immortalized breast cell lines, inhibition of proliferation by PRIMA-1 and PRIMA-1MET was found to be cell-line dependent, but independent of cell line molecular subtype. Although response was independent of molecular subtype, p53 mutated cell lines were significantly more sensitive to PRIMA-1MET than p53 WT cells (p = 0.029). Furthermore, response (measured as IC50 value) correlated significantly with p53 protein level as measured by ELISA (p = 0.0089, r=-0.57, n = 19). In addition to inhibiting cell proliferation, PRIMA-1MET induced apoptosis and inhibited migration in a p53 mutant-dependent manner. Based on our data, we conclude that targeting mutant p53 with PRIMA-1MET is a potential new approach for treating p53-mutated breast cancer, including the subgroup with triple-negative (TN) disease.
Keyphrases
- wild type
- induced apoptosis
- signaling pathway
- tyrosine kinase
- endoplasmic reticulum stress
- cell proliferation
- oxidative stress
- randomized controlled trial
- cancer therapy
- genome wide
- machine learning
- dna methylation
- breast cancer risk
- big data
- electronic health record
- open label
- cell death
- deep learning
- artificial intelligence
- phase iii
- mass spectrometry
- atomic force microscopy
- data analysis