Endoglin and MMP14 Contribute to Ewing Sarcoma Spreading by Modulation of Cell-Matrix Interactions.

Endoglin (ENG) is a mesenchymal stem cell (MSC) marker typically expressed by active endothelium. This transmembrane glycoprotein is shed by matrix metalloproteinase 14 (MMP14). Our previous work demonstrated potent preclinical activity of first-in-class anti-ENG antibody-drug conjugates as a nascent strategy to eradicate Ewing sarcoma (ES), a devastating rare bone/soft tissue cancer with a putative MSC origin. We also defined a correlation between ENG and MMP14 expression in ES. Herein, we show that ENG expression is significantly associated with a dismal prognosis in a large cohort of ES patients. Moreover, both ENG/MMP14 are frequently expressed in primary ES tumors and metastasis. To deepen in their functional relevance in ES, we conducted transcriptomic and proteomic profiling of in vitro ES models that unveiled a key role of ENG and MMP14 in cell mechano-transduction. Migration and adhesion assays confirmed that loss of ENG disrupts actin filament assembly and filopodia formation, with a concomitant effect on cell spreading. Furthermore, we observed that ENG regulates cell-matrix interaction through activation of focal adhesion signaling and protein kinase C expression. In turn, loss of MMP14 contributed to a more adhesive phenotype of ES cells by modulating the transcriptional extracellular matrix dynamics. Overall, these results suggest that ENG and MMP14 exert a significant role in mediating correct spreading machinery of ES cells, impacting the aggressiveness of the disease.

Integrating pathway knowledge with deep neural networks to reduce the dimensionality in single-cell RNA-seq data.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) data provide valuable insights into cellular heterogeneity which is significantly improving the current knowledge on biology and human disease. One of the main applications of scRNA-seq data analysis is the identification of new cell types and cell states. Deep neural networks (DNNs) are among the best methods to address this problem. However, this performance comes with the trade-off for a lack of interpretability in the results. In this work we propose an intelligible pathway-driven neural network to correctly solve cell-type related problems at single-cell resolution while providing a biologically meaningful representation of the data. RESULTS: In this study, we explored the deep neural networks constrained by several types of prior biological information, e.g. signaling pathway information, as a way to reduce the dimensionality of the scRNA-seq data. We have tested the proposed biologically-based architectures on thousands of cells of human and mouse origin across a collection of public datasets in order to check the performance of the model. Specifically, we tested the architecture across different validation scenarios that try to mimic how unknown cell types are clustered by the DNN and how it correctly annotates cell types by querying a database in a retrieval problem. Moreover, our approach demonstrated to be comparable to other less interpretable DNN approaches constrained by using protein-protein interactions gene regulation data. Finally, we show how the latent structure learned by the network could be used to visualize and to interpret the composition of human single cell datasets. CONCLUSIONS: Here we demonstrate how the integration of pathways, which convey fundamental information on functional relationships between genes, with DNNs, that provide an excellent classification framework, results in an excellent alternative to learn a biologically meaningful representation of scRNA-seq data. In addition, the introduction of prior biological knowledge in the DNN reduces the size of the network architecture. Comparative results demonstrate a superior performance of this approach with respect to other similar approaches. As an additional advantage, the use of pathways within the DNN structure enables easy interpretability of the results by connecting features to cell functionalities by means of the pathway nodes, as demonstrated with an example with human melanoma tumor cells.

Genome-scale mechanistic modeling of signaling pathways made easy: A bioconductor/cytoscape/web server framework for the analysis of omic data.

Genome-scale mechanistic models of pathways are gaining importance for genomic data interpretation because they provide a natural link between genotype measurements (transcriptomics or genomics data) and the phenotype of the cell (its functional behavior). Moreover, mechanistic models can be used to predict the potential effect of interventions, including drug inhibitions. Here, we present the implementation of a mechanistic model of cell signaling for the interpretation of transcriptomic data as an R/Bioconductor package, a Cytoscape plugin and a web tool with enhanced functionality which includes building interpretable predictors, estimation of the effect of perturbations and assessment of the effect of mutations in complex scenarios.