New 3-alkylpyridine marine alkaloid analogues as promising antitumor agents against the CD44+/high /CD24-/low subset of triple-negative breast cancer cell line.
Aline Brito de LimaCamila de Souza BarbosaAlessandra Mirtes Marques Neves GonçalvesFabio Vieira Dos SantosGustavo Henrique Ribeiro VianaFernando de Pilla VarottiLuciana Maria SilvaPublished in: Chemical biology & drug design (2017)
Triple-negative breast cancer (TNBC) is one of the most aggressive cancers in women. Additionally, presence of residual cancer stem cells (CSC) in TNBC has challenged the efficacy of chemotherapy. Thus, the development of new molecules with potential action against CSC is fundamental. In this study, six synthetic analogues of theonelladin C, a 3-alkylpyridine marine alkaloid, were tested for cytotoxic activity against human TNBC cell line (BT-549) and tumorspheres derived from BT-549. Cytotoxicity assay was performed by sulforhodamine B (SRB). BT-549 and tumorspheres were examined for CD44+/high /CD24-/low markers, indicative of CSC profile, by flow cytometry. Clonogenic assay was performed to verify inhibiting growth of tumorspheres by the synthetic analogues. Cell death by apoptosis was investigated employing annexin V assay. SRB assay on BT-549 cells revealed that compounds 1c and 2c were the most active of the series, with IC50 values of 18.66 and 9.8 μm, respectively. Compounds 1c and 2c were able to reduce both CSC-like population (CD44+/high /CD24-/low ) and non-CSC population (CD44+/high /CD24+/high ) in tumorsphere model. Clonogenic and annexin V assays confirmed the ability of 1c and 2c to induce growth inhibition and apoptosis in BT-549 cells and tumorspheres. These preliminary data indicate that these compounds are a promising class for development of anticancer agents.
Keyphrases
- cell death
- cell cycle arrest
- high throughput
- nk cells
- induced apoptosis
- oxidative stress
- flow cytometry
- squamous cell carcinoma
- endothelial cells
- radiation therapy
- metabolic syndrome
- adipose tissue
- machine learning
- cancer stem cells
- signaling pathway
- skeletal muscle
- big data
- deep learning
- molecular dynamics simulations
- pregnancy outcomes
- atomic force microscopy
- rectal cancer