IRAK2 Downregulation in Triple-Negative Breast Cancer Cells Decreases Cellular Growth In Vitro and Delays Tumour Progression in Murine Models.

Breast cancer stem cells (BCSCs) are responsible for tumour recurrence and therapy resistance. We have established primary BCSC cultures from human tumours of triple-negative breast cancer (TNBC), a subgroup of breast cancer likely driven by BCSCs. Primary BCSCs produce xenografts that phenocopy the tumours of origin, making them an ideal model for studying breast cancer treatment options. In the TNBC cell line MDA-MB-468, we previously screened kinases whose depletion elicited a differentiation response, among which IRAK2 was identified. Because primary BCSCs are enriched in IRAK2, we wondered whether IRAK2 downregulation might affect cellular growth. IRAK2 was downregulated in primary BCSCs and MDA-MB-468 by lentiviral delivery of shRNA, causing a decrease in cellular proliferation and sphere-forming capacity. When orthotopically transplanted into immunocompromised mice, IRAK2 knockdown cells produced smaller xenografts than control cells. At the molecular level, IRAK2 downregulation reduced NF-κB and ERK phosphorylation, IL-6 and cyclin D1 expression, ERN1 signalling and autophagy in a cell line-dependent way. Overall, IRAK2 downregulation decreased cellular aggressive growth and pathways often exploited by cancer cells to endure stress; therefore, IRAK2 may be considered an interesting target to compromise TNBC progression.

Tumor Necrosis Factor-α (TNFα) Stimulate Triple-Negative Breast Cancer Stem Cells to Promote Intratumoral Invasion and Neovasculogenesis in the Liver of a Xenograft Model.

TNBC represents the most aggressive breast cancer subtype. Although cancer stem cells (CSCs) are a minor fraction of all cancer cells, they are highly cancerous when compared to their non-stem counterparts, playing a major role in tumor recurrence and metastasis. Angiogenic stimuli and the tumor environment response are vital factors in cancer metastasis. However, the causes and effects of tumor angiogenesis are still poorly understood. In this study, we demonstrate TNFα effects on primary triple-negative breast cancer stem cells (BCSCs). TNFα stimulation increased the mesenchymality of BCSCs in an intermediate epithelial-to-mesenchymal transition (EMT) state, enhanced proliferation, self-renewal, and invasive capacity. TNFα-treatment elicited BCSC signaling on endothelial networks in vitro and increased the network forming capacity of the endothelial cells. Our findings further demonstrate that TNFα stimulation in BCSCs has the ability to instigate distinct cellular communication within the tumor microenvironment, inducing intra-tumoral stromal invasion. Further, TNFα-treatment in BCSCs induced a pre-metastatic niche through breast-liver organ crosstalk by inducing vascular cell adhesion molecule-1 (VCAM-1) enriched neovasculogenesis in the liver of tumor-bearing mice. Overall, TNFα is an important angiogenic target to be considered in breast cancer progression to attenuate any angiogenic response in the tumor environment that could lead to secondary organ metastasis.

Deep learning-based meta-classifier approach for COVID-19 classification using CT scan and chest X-ray images.

Literature survey shows that convolutional neural network (CNN)-based pretrained models have been largely used for CoronaVirus Disease 2019 (COVID-19) classification using chest X-ray (CXR) and computed tomography (CT) datasets. However, most of the methods have used a smaller number of data samples for both CT and CXR datasets for training, validation, and testing. As a result, the model might have shown good performance during testing, but this type of model will not be more effective on unseen COVID-19 data samples. Generalization is an important term to be considered while designing a classifier that can perform well on completely unseen datasets. Here, this work proposes a large-scale learning with stacked ensemble meta-classifier and deep learning-based feature fusion approach for COVID-19 classification. The features from the penultimate layer (global average pooling) of EfficientNet-based pretrained models were extracted and the dimensionality of the extracted features reduced using kernel principal component analysis (PCA). Next, a feature fusion approach was employed to merge the features of various extracted features. Finally, a stacked ensemble meta-classifier-based approach was used for classification. It is a two-stage approach. In the first stage, random forest and support vector machine (SVM) were applied for prediction, then aggregated and fed into the second stage. The second stage includes logistic regression classifier that classifies the data sample of CT and CXR into either COVID-19 or Non-COVID-19. The proposed model was tested using large CT and CXR datasets, which are publicly available. The performance of the proposed model was compared with various existing CNN-based pretrained models. The proposed model outperformed the existing methods and can be used as a tool for point-of-care diagnosis by healthcare professionals.

Investigation of Endogenous Retrovirus Sequences in the Neighborhood of Genes Up-regulated in a Neuroblastoma Model after Treatment with Hypoxia-Mimetic Cobalt Chloride.

Human endogenous retroviruses (ERVs) have been found to be associated with different diseases, e.g., multiple sclerosis (MS). Most human ERVs integrated in our genome are not competent to replicate and these sequences are presumably silent. However, transcription of human ERVs can be reactivated, e.g., by hypoxia. Interestingly, MS has been linked to hypoxia since decades. As some patterns of demyelination are similar to white matter ischemia, hypoxic damage is discussed. Therefore, we are interested in the association between hypoxia and ERVs. As a model, we used human SH-SY5Y neuroblastoma cells after treatment with the hypoxia-mimetic cobalt chloride and analyzed differences in the gene expression profiles in comparison to untreated cells. The vicinity of up-regulated genes was scanned for endogenous retrovirus-derived sequences. Five genes were found to be strongly up-regulated in SH-SY5Y cells after treatment with cobalt chloride: clusterin, glutathione peroxidase 3, insulin-like growth factor 2, solute carrier family 7 member 11, and neural precursor cell expressed developmentally down-regulated protein 9. In the vicinity of these genes we identified large (>1,000 bp) open reading frames (ORFs). Most of these ORFs showed only low similarities to proteins from retro-transcribing viruses. However, we found very high similarity between retrovirus envelope sequences and a sequence in the vicinity of neural precursor cell expressed developmentally down-regulated protein 9. This sequence encodes the human endogenous retrovirus group FRD member 1, the encoded protein product is called syncytin 2. Transfection of syncytin 2 into the well-characterized Ewing sarcoma cell line A673 was not able to modulate the low immunostimulatory activity of this cell line. Future research is needed to determine whether the identified genes and the human endogenous retrovirus group FRD member 1 might play a role in the etiology of MS.