02972nas a2200445 4500008004100000022001400041245010400055210006900159260001600228490000700244520152100251653001901772653001301791653001101804653003201815653001501847653002901862653001901891653002401910100002601934700002201960700002801982700003002010700002802040700002702068700002702095700003002122700002802152700002202180700002002202700001602222700001902238700002802257700002202285700001602307700002402323700002402347700002202371856013302393 2022 eng d a1422-006700aEndoglin and MMP14 Contribute to Ewing Sarcoma Spreading by Modulation of Cell-Matrix Interactions.0 aEndoglin and MMP14 Contribute to Ewing Sarcoma Spreading by Modu c2022 Aug 040 v233 a

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.

10aBone Neoplasms10aEndoglin10aHumans10aMatrix Metalloproteinase 1410aProteomics10aReceptors, Growth Factor10aSarcoma, Ewing10aSignal Transduction1 aPuerto-Camacho, Pilar1 aDiaz-Martin, Juan1 aOlmedo-Pelayo, Joaquín1 aBolado-Carrancio, Alfonso1 aSalguero-Aranda, Carmen1 aJordán-Pérez, Carmen1 aEsteban-Medina, Marina1 aAlamo-Alvarez, Inmaculada1 aDelgado-Bellido, Daniel1 aLobo-Selma, Laura1 aDopazo, Joaquin1 aSastre, Ana1 aAlonso, Javier1 aGrünewald, Thomas, G P1 aBernabeu, Carmelo1 aByron, Adam1 aBrunton, Valerie, G1 aAmaral, Ana, Teresa1 ade Alava, Enrique uhttps://www.clinbioinfosspa.es/content/endoglin-and-mmp14-contribute-ewing-sarcoma-spreading-modulation-cell-matrix-interactions03236nas a2200193 4500008004100000022001400041245011800055210006900173260001600242300000600258490000700264520252200271100002002793700002002813700003002833700002002863700002302883856013602906 2022 eng d a1756-038100aIntegrating pathway knowledge with deep neural networks to reduce the dimensionality in single-cell RNA-seq data.0 aIntegrating pathway knowledge with deep neural networks to reduc c2022 Jan 03 a10 v153 a

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.

1 aGundogdu, Pelin1 aLoucera, Carlos1 aAlamo-Alvarez, Inmaculada1 aDopazo, Joaquin1 aNepomuceno, Isabel uhttps://www.clinbioinfosspa.es/content/integrating-pathway-knowledge-deep-neural-networks-reduce-dimensionality-single-cell-rna-seq00806nas a2200229 4500008004100000022001300041245014700054210006900201260001600270300001600286490000700302100001600309700002300325700001800348700002200366700002000388700002700408700003000435700002400465700002000489856006700509 2021 eng d a2001037000aGenome-scale mechanistic modeling of signaling pathways made easy: A bioconductor/cytoscape/web server framework for the analysis of omic data0 aGenomescale mechanistic modeling of signaling pathways made easy cJan-01-2021 a2968 - 29780 v191 aRian, Kinza1 aHidalgo, Marta, R.1 aCubuk, Cankut1 aFalco, Matias, M.1 aLoucera, Carlos1 aEsteban-Medina, Marina1 aAlamo-Alvarez, Inmaculada1 aPeña-Chilet, Maria1 aDopazo, Joaquin uhttps://linkinghub.elsevier.com/retrieve/pii/S2001037021002038