ProNGF promotes brain metastasis through TrkA/EphA2 induced Src activation in triple negative breast cancer cells.

BACKGROUND: Triple-Negative Breast Cancer is particularly aggressive, and its metastasis to the brain has a significant psychological impact on patients' quality of life, in addition to reducing survival. The development of brain metastases is particularly harmful in triple-negative breast cancer (TNBC). To date, the mechanisms that induce brain metastasis in TNBC are poorly understood. METHODS: Using a human blood-brain barrier (BBB) in vitro model, an in vitro 3D organotypic extracellular matrix, an ex vivo mouse brain slices co-culture and in an in vivo xenograft experiment, key step of brain metastasis were recapitulated to study TNBC behaviors. RESULTS: In this study, we demonstrated for the first time the involvement of the precursor of Nerve Growth Factor (proNGF) in the development of brain metastasis. More importantly, our results showed that proNGF acts through TrkA independent of its phosphorylation to induce brain metastasis in TNBC. In addition, we found that proNGF induces BBB transmigration through the TrkA/EphA2 signaling complex. More importantly, our results showed that combinatorial inhibition of TrkA and EphA2 decreased TBNC brain metastasis in a preclinical model. CONCLUSIONS: These disruptive findings provide new insights into the mechanisms underlying brain metastasis with proNGF as a driver of brain metastasis of TNBC and identify TrkA/EphA2 complex as a potential therapeutic target.

An Evaluation of Computational Learning-based Methods for the Segmentation of Nuclei in Cervical Cancer Cells from Microscopic Images.

BACKGROUND: The manual segmentation of cellular structures on Z-stack microscopic images is time-consuming and often inaccurate, highlighting the need to develop auto-segmentation tools to facilitate this process. OBJECTIVE: This study aimed to compare the performance of three different machine learning architectures, including random forest (RF), AdaBoost, and multi-layer perceptron (MLP), for the autosegmentation of nuclei in proliferating cervical cancer cells on Z-Stack cellular microscopy proliferation images provided by the HCS Pharma. The impact of using post-processing techniques, such as the StarDist plugin and majority voting, was also evaluated. METHODS: The RF, AdaBoost, and MLP algorithms were used to auto-segment the nuclei of cervical cancer cells on microscopic images at different Z-stack positions. Post-processing techniques were then applied to each algorithm. The performance of all algorithms was compared by an expert to globally generated ground truth by calculating the accuracy detection rate, the Dice coefficient, and the Jaccard index. RESULTS: RF achieved the best accuracy, followed by the AdaBoost and then the MLP. All algorithms achieved good pixel classifications except in regions whereby the nuclei overlapped. The majority voting and StarDist plugin improved the accuracy of the segmentation but did not resolve the nuclei overlap issue. The Z-Stack analysis revealed similar segmentation results to the Z-stack layer used to train the image. However, a worse performance was noted for segmentations performed on different Z-stack positions, which were not used to train the algorithms. CONCLUSION: All machine learning architectures provided a good segmentation of nuclei in cervical cancer cells but did not resolve the problem of overlapping nuclei and Z-stack segmentation. Further research should therefore evaluate the combined segmentation techniques and deep learning architectures to resolve these issues.

In vitro differentiation modifies the neurotoxic response of SH-SY5Y cells.

The SH-SY5Y cell line is commonly used for the assessment of neurotoxicity in drug discovery. These neuroblastoma-derived cells can be differentiated into neurons using many methods. The present study has compared 24 of these differentiation methods on SH-SY5Y cells. After morphologic selection of the three most differentiating media (retinoic acid in 10% fetal bovine serum (FBS), staurosporine in 1% FBS medium, and cyclic adenosine monophosphate (cAMP) in B21-supplemented neurobasal medium), cells were analyzed for pan-neuronal and specific neuronal protein expression by fluorescent automated imaging. The response of SH-SY5Y to a set of compounds of known toxicity was examined in these culture conditions performed in 2D, and also in a 3D hyaluronic acid-based hydroscaffold™ which mimics the extracellular matrix. The extent of neuronal markers expression and the sensitivity to neurotoxic compounds varied according to the differentiation medium. The cAMP B21-supplemented neurobasal medium led to the higher neuronal differentiation, and the higher sensitivity to neurotoxic compounds. The culture in 3D modified the neurotoxic response, through a lower sensitivity of cells compared to the 2D culture. The in vitro differentiation environment influences the neurotoxic response of SH-SY5Y cells and thus should be considered carefully in research as well as in drug discovery.