AB186 Inhibits Migration of Triple-Negative Breast Cancer Cells and Interacts with α-Tubulin.

Breast cancer is one of the leading causes of cancer-related death among females worldwide. A major challenge is to develop innovative therapy in order to treat breast cancer subtypes resistant to current treatment. In the present study, we examined the effects of two Troglitazone derivatives Δ2-TGZ and AB186. Previous studies showed that both compounds induce apoptosis, nevertheless AB186 was a more potent agent. The kinetic of cellular events was investigated by real-time cell analysis system (RTCA) in MCF-7 (hormone dependent) and MDA-MB-231 (triple negative) breast cancer (TNBC) cells, followed by cell morphology analysis by immuno-localization. Both compounds induced a rapid modification of both impedance-based signals and cellular morphology. This process was associated with an inhibition of cell migration measured by wound healing and transwell assays in TNBC MDA-MB-231 and Hs578T cells. In order to identify cytoplasmic targets of AB186, we performed surface plasmon resonance (SPR) and pull-down analyses. Subsequently, 6 cytoskeleton components were identified as potential targets. We further validated α-tubulin as one of the direct targets of AB186. In conclusion, our results suggested that AB186 could be promising to develop novel therapeutic strategies to treat aggressive forms of breast cancer such as TNBC.

Identification of dynamical biological systems based on random effects models.

System identification is a data-driven modeling approach more and more used in biology and biomedicine. In this application context, each assay is always repeated to estimate the response variability. The inference of the modeling conclusions to the whole population requires to account for the inter-individual variability within the modeling procedure. One solution consists in using random effects models but up to now no similar approach exists in the field of dynamical system identification. In this article, we propose a new solution based on an ARX (Auto Regressive model with eXternal inputs) structure using the EM (Expectation-Maximisation) algorithm for the estimation of the model parameters. Simulations show the relevance of this solution compared with a classical procedure of system identification repeated for each subject.