@article {763, title = {Endoglin and MMP14 Contribute to Ewing Sarcoma Spreading by Modulation of Cell-Matrix Interactions.}, journal = {Int J Mol Sci}, volume = {23}, year = {2022}, month = {2022 Aug 04}, abstract = {

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.

}, keywords = {Bone Neoplasms, Endoglin, Humans, Matrix Metalloproteinase 14, Proteomics, Receptors, Growth Factor, Sarcoma, Ewing, Signal Transduction}, issn = {1422-0067}, doi = {10.3390/ijms23158657}, author = {Puerto-Camacho, Pilar and Diaz-Martin, Juan and Olmedo-Pelayo, Joaqu{\'\i}n and Bolado-Carrancio, Alfonso and Salguero-Aranda, Carmen and Jord{\'a}n-P{\'e}rez, Carmen and Esteban-Medina, Marina and Alamo-Alvarez, Inmaculada and Delgado-Bellido, Daniel and Lobo-Selma, Laura and Dopazo, Joaquin and Sastre, Ana and Alonso, Javier and Gr{\"u}newald, Thomas G P and Bernabeu, Carmelo and Byron, Adam and Brunton, Valerie G and Amaral, Ana Teresa and de Alava, Enrique} } @article {610, title = {Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models.}, journal = {BMC Bioinformatics}, volume = {20}, year = {2019}, month = {2019 Jul 02}, pages = {370}, abstract = {

BACKGROUND: In spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases.

RESULTS: The application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets.

CONCLUSIONS: The use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.

}, keywords = {Databases, Factual, Fanconi Anemia, Genomics, Humans, Machine Learning, Phenotype, Proteins, Signal Transduction}, issn = {1471-2105}, doi = {10.1186/s12859-019-2969-0}, author = {Esteban-Medina, Marina and Pe{\~n}a-Chilet, Maria and Loucera, Carlos and Dopazo, Joaquin} }