RESUMO
The Yes-associated protein (YAP) oncoprotein has been linked to both metastases and resistance to targeted therapy of lung cancer cells. We aimed to investigate the effect of YAP pharmacological inhibition, using YAP/TEA domain (TEAD) transcription factor interaction inhibitors in chemo-resistant lung cancer cells. YAP subcellular localization, as a readout for YAP activation, cell migration, and TEAD transcription factor functional transcriptional activity were investigated in cancer cell lines with up-regulated YAP, with and without YAP/TEAD interaction inhibitors. Parental (A549) and paclitaxel-resistant (A549R) cell transcriptomes were analyzed. The half-maximal inhibitory concentration (IC50) of paclitaxel or trametinib, which are Mitogen-Activated protein kinase and Erk Kinase (MEK) inhibitors, combined with a YAP/TEAD inhibitor (IV#6), was determined. A three-dimensional (3D) microfluidic culture device enabled us to study the effect of IV#6/paclitaxel combination on cancer cells isolated from fresh resected lung cancer samples. YAP activity was significantly higher in paclitaxel-resistant cell lines. The YAP/TEAD inhibitor induced a decreased YAP activity in A549, PC9, and H2052 cells, with reduced YAP nuclear staining. Wound healing assays upon YAP inhibition revealed impaired cell motility of lung cancer A549 and mesothelioma H2052 cells. Combining YAP pharmacological inhibition with trametinib in K-Ras mutated A549 cells recapitulated synthetic lethality, thereby sensitizing these cells to MEK inhibition. The YAP/TEAD inhibitor lowered the IC50 of paclitaxel in A549R cells. Differential transcriptomic analysis of parental and A549R cells revealed an increased YAP/TEAD transcriptomic signature in resistant cells, downregulated upon YAP inhibition. The YAP/TEAD inhibitor restored paclitaxel sensitivity of A549R cells cultured in a 3D microfluidic system, with lung cancer cells from a fresh tumor efficiently killed by YAP/TEAD inhibitor/paclitaxel doublet. Evidence of the YAP/TEAD transcriptional program's role in chemotherapy resistance paves the way for YAP therapeutic targeting.
RESUMO
One of the major problems in bioimaging, often highly underestimated, is whether features extracted for a discrimination or regression task will remain valid for a broader set of similar experiments or in the presence of unpredictable perturbations during the image acquisition process. Such an issue is even more important when it is addressed in the context of deep learning features due to the lack of a priori known relationship between the black-box descriptors (deep features) and the phenotypic properties of the biological entities under study. In this regard, the widespread use of descriptors, such as those coming from pre-trained Convolutional Neural Networks (CNNs), is hindered by the fact that they are devoid of apparent physical meaning and strongly subjected to unspecific biases, i.e., features that do not depend on the cell phenotypes, but rather on acquisition artifacts, such as brightness or texture changes, focus shifts, autofluorescence or photobleaching. The proposed Deep-Manager software platform offers the possibility to efficiently select those features having lower sensitivity to unspecific disturbances and, at the same time, a high discriminating power. Deep-Manager can be used in the context of both handcrafted and deep features. The unprecedented performances of the method are proven using five different case studies, ranging from selecting handcrafted green fluorescence protein intensity features in chemotherapy-related breast cancer cell death investigation to addressing problems related to the context of Deep Transfer Learning. Deep-Manager, freely available at https://github.com/BEEuniroma2/Deep-Manager , is suitable for use in many fields of bioimaging and is conceived to be constantly upgraded with novel image acquisition perturbations and modalities.