<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">López-Sánchez, Macarena</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discovering potential interactions between rare diseases and COVID-19 by combining mechanistic models of viral infection with statistical modeling.</style></title><secondary-title><style face="normal" font="default" size="100%">Hum Mol Genet</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Hum Mol Genet</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2022 Jan 12</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Recent studies have demonstrated a relevant role of the host genetics in the COVID-19 prognosis. Most of the 7000 rare diseases described to date have a genetic component, typically highly penetrant. However, this vast spectrum of genetic variability remains yet unexplored with respect to possible interactions with COVID-19. Here, a mathematical mechanistic model of the COVID-19 molecular disease mechanism has been used to detect potential interactions between rare disease genes and the COVID-19 infection process and downstream consequences. Out of the 2518 disease genes analyzed, causative of 3854 rare diseases, a total of 254 genes have a direct effect on the COVID-19 molecular disease mechanism and 207 have an indirect effect revealed by a significant strong correlation. This remarkable potential of interaction occurs for more than 300 rare diseases. Mechanistic modeling of COVID-19 disease map has allowed a holistic systematic analysis of the potential interactions between the loss of function in known rare disease genes and the pathological consequences of COVID-19 infection. The results identify links between disease genes and COVID-19 hallmarks and demonstrate the usefulness of the proposed approach for future preventive measures in some rare diseases.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Casimiro-Soriguer, Carlos S</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer.</style></title><secondary-title><style face="normal" font="default" size="100%">Sci Rep</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Sci Rep</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2022 Jan 10</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">450</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Gut microbiome is gaining interest because of its links with several diseases, including colorectal cancer (CRC), as well as the possibility of being used to obtain non-intrusive predictive disease biomarkers. Here we performed a meta-analysis of 1042 fecal metagenomic samples from seven publicly available studies. We used an interpretable machine learning approach based on functional profiles, instead of the conventional taxonomic profiles, to produce a highly accurate predictor of CRC with better precision than those of previous proposals. Moreover, this approach is also able to discriminate samples with adenoma, which makes this approach very promising for CRC prevention by detecting early stages in which intervention is easier and more effective. In addition, interpretable machine learning methods allow extracting features relevant for the classification, which reveals basic molecular mechanisms accounting for the changes undergone by the microbiome functional landscape in the transition from healthy gut to adenoma and CRC conditions. Functional profiles have demonstrated superior accuracy in predicting CRC and adenoma conditions than taxonomic profiles and additionally, in a context of explainable machine learning, provide useful hints on the molecular mechanisms operating in the microbiota behind these conditions.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martorell-Marugán, Jordi</style></author><author><style face="normal" font="default" size="100%">López-Domínguez, Raúl</style></author><author><style face="normal" font="default" size="100%">García-Moreno, Adrián</style></author><author><style face="normal" font="default" size="100%">Toro-Domínguez, Daniel</style></author><author><style face="normal" font="default" size="100%">Villatoro-García, Juan Antonio</style></author><author><style face="normal" font="default" size="100%">Barturen, Guillermo</style></author><author><style face="normal" font="default" size="100%">Martín-Gómez, Adoración</style></author><author><style face="normal" font="default" size="100%">Troule, Kevin</style></author><author><style face="normal" font="default" size="100%">Gómez-López, Gonzalo</style></author><author><style face="normal" font="default" size="100%">Al-Shahrour, Fátima</style></author><author><style face="normal" font="default" size="100%">González-Rumayor, Víctor</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Saez-Rodriguez, Julio</style></author><author><style face="normal" font="default" size="100%">Alarcón-Riquelme, Marta E</style></author><author><style face="normal" font="default" size="100%">Carmona-Sáez, Pedro</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A comprehensive database for integrated analysis of omics data in autoimmune diseases.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Autoimmune Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Factual</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 Jun 24</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">22</style></volume><pages><style face="normal" font="default" size="100%">343</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Here, we present Autoimmune Diseases Explorer ( https://adex.genyo.es ), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;This is the first omics database focused on autoimmune diseases. This resource incorporates homogeneously processed data to facilitate integrative analyses among studies.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/34167460?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ostaszewski, Marek</style></author><author><style face="normal" font="default" size="100%">Niarakis, Anna</style></author><author><style face="normal" font="default" size="100%">Mazein, Alexander</style></author><author><style face="normal" font="default" size="100%">Kuperstein, Inna</style></author><author><style face="normal" font="default" size="100%">Phair, Robert</style></author><author><style face="normal" font="default" size="100%">Orta-Resendiz, Aurelio</style></author><author><style face="normal" font="default" size="100%">Singh, Vidisha</style></author><author><style face="normal" font="default" size="100%">Aghamiri, Sara Sadat</style></author><author><style face="normal" font="default" size="100%">Acencio, Marcio Luis</style></author><author><style face="normal" font="default" size="100%">Glaab, Enrico</style></author><author><style face="normal" font="default" size="100%">Ruepp, Andreas</style></author><author><style face="normal" font="default" size="100%">Fobo, Gisela</style></author><author><style face="normal" font="default" size="100%">Montrone, Corinna</style></author><author><style face="normal" font="default" size="100%">Brauner, Barbara</style></author><author><style face="normal" font="default" size="100%">Frishman, Goar</style></author><author><style face="normal" font="default" size="100%">Monraz Gómez, Luis Cristóbal</style></author><author><style face="normal" font="default" size="100%">Somers, Julia</style></author><author><style face="normal" font="default" size="100%">Hoch, Matti</style></author><author><style face="normal" font="default" size="100%">Kumar Gupta, Shailendra</style></author><author><style face="normal" font="default" size="100%">Scheel, Julia</style></author><author><style face="normal" font="default" size="100%">Borlinghaus, Hanna</style></author><author><style face="normal" font="default" size="100%">Czauderna, Tobias</style></author><author><style face="normal" font="default" size="100%">Schreiber, Falk</style></author><author><style face="normal" font="default" size="100%">Montagud, Arnau</style></author><author><style face="normal" font="default" size="100%">Ponce de Leon, Miguel</style></author><author><style face="normal" font="default" size="100%">Funahashi, Akira</style></author><author><style face="normal" font="default" size="100%">Hiki, Yusuke</style></author><author><style face="normal" font="default" size="100%">Hiroi, Noriko</style></author><author><style face="normal" font="default" size="100%">Yamada, Takahiro G</style></author><author><style face="normal" font="default" size="100%">Dräger, Andreas</style></author><author><style face="normal" font="default" size="100%">Renz, Alina</style></author><author><style face="normal" font="default" size="100%">Naveez, Muhammad</style></author><author><style face="normal" font="default" size="100%">Bocskei, Zsolt</style></author><author><style face="normal" font="default" size="100%">Messina, Francesco</style></author><author><style face="normal" font="default" size="100%">Börnigen, Daniela</style></author><author><style face="normal" font="default" size="100%">Fergusson, Liam</style></author><author><style face="normal" font="default" size="100%">Conti, Marta</style></author><author><style face="normal" font="default" size="100%">Rameil, Marius</style></author><author><style face="normal" font="default" size="100%">Nakonecnij, Vanessa</style></author><author><style face="normal" font="default" size="100%">Vanhoefer, Jakob</style></author><author><style face="normal" font="default" size="100%">Schmiester, Leonard</style></author><author><style face="normal" font="default" size="100%">Wang, Muying</style></author><author><style face="normal" font="default" size="100%">Ackerman, Emily E</style></author><author><style face="normal" font="default" size="100%">Shoemaker, Jason E</style></author><author><style face="normal" font="default" size="100%">Zucker, Jeremy</style></author><author><style face="normal" font="default" size="100%">Oxford, Kristie</style></author><author><style face="normal" font="default" size="100%">Teuton, Jeremy</style></author><author><style face="normal" font="default" size="100%">Kocakaya, Ebru</style></author><author><style face="normal" font="default" size="100%">Summak, Gökçe Yağmur</style></author><author><style face="normal" font="default" size="100%">Hanspers, Kristina</style></author><author><style face="normal" font="default" size="100%">Kutmon, Martina</style></author><author><style face="normal" font="default" size="100%">Coort, Susan</style></author><author><style face="normal" font="default" size="100%">Eijssen, Lars</style></author><author><style face="normal" font="default" size="100%">Ehrhart, Friederike</style></author><author><style face="normal" font="default" size="100%">Rex, Devasahayam Arokia Balaya</style></author><author><style face="normal" font="default" size="100%">Slenter, Denise</style></author><author><style face="normal" font="default" size="100%">Martens, Marvin</style></author><author><style face="normal" font="default" size="100%">Pham, Nhung</style></author><author><style face="normal" font="default" size="100%">Haw, Robin</style></author><author><style face="normal" font="default" size="100%">Jassal, Bijay</style></author><author><style face="normal" font="default" size="100%">Matthews, Lisa</style></author><author><style face="normal" font="default" size="100%">Orlic-Milacic, Marija</style></author><author><style face="normal" font="default" size="100%">Senff Ribeiro, Andrea</style></author><author><style face="normal" font="default" size="100%">Rothfels, Karen</style></author><author><style face="normal" font="default" size="100%">Shamovsky, Veronica</style></author><author><style face="normal" font="default" size="100%">Stephan, Ralf</style></author><author><style face="normal" font="default" size="100%">Sevilla, Cristoffer</style></author><author><style face="normal" font="default" size="100%">Varusai, Thawfeek</style></author><author><style face="normal" font="default" size="100%">Ravel, Jean-Marie</style></author><author><style face="normal" font="default" size="100%">Fraser, Rupsha</style></author><author><style face="normal" font="default" size="100%">Ortseifen, Vera</style></author><author><style face="normal" font="default" size="100%">Marchesi, Silvia</style></author><author><style face="normal" font="default" size="100%">Gawron, Piotr</style></author><author><style face="normal" font="default" size="100%">Smula, Ewa</style></author><author><style face="normal" font="default" size="100%">Heirendt, Laurent</style></author><author><style face="normal" font="default" size="100%">Satagopam, Venkata</style></author><author><style face="normal" font="default" size="100%">Wu, Guanming</style></author><author><style face="normal" font="default" size="100%">Riutta, Anders</style></author><author><style face="normal" font="default" size="100%">Golebiewski, Martin</style></author><author><style face="normal" font="default" size="100%">Owen, Stuart</style></author><author><style face="normal" font="default" size="100%">Goble, Carole</style></author><author><style face="normal" font="default" size="100%">Hu, Xiaoming</style></author><author><style face="normal" font="default" size="100%">Overall, Rupert W</style></author><author><style face="normal" font="default" size="100%">Maier, Dieter</style></author><author><style face="normal" font="default" size="100%">Bauch, Angela</style></author><author><style face="normal" font="default" size="100%">Gyori, Benjamin M</style></author><author><style face="normal" font="default" size="100%">Bachman, John A</style></author><author><style face="normal" font="default" size="100%">Vega, Carlos</style></author><author><style face="normal" font="default" size="100%">Grouès, Valentin</style></author><author><style face="normal" font="default" size="100%">Vazquez, Miguel</style></author><author><style face="normal" font="default" size="100%">Porras, Pablo</style></author><author><style face="normal" font="default" size="100%">Licata, Luana</style></author><author><style face="normal" font="default" size="100%">Iannuccelli, Marta</style></author><author><style face="normal" font="default" size="100%">Sacco, Francesca</style></author><author><style face="normal" font="default" size="100%">Nesterova, Anastasia</style></author><author><style face="normal" font="default" size="100%">Yuryev, Anton</style></author><author><style face="normal" font="default" size="100%">de Waard, Anita</style></author><author><style face="normal" font="default" size="100%">Turei, Denes</style></author><author><style face="normal" font="default" size="100%">Luna, Augustin</style></author><author><style face="normal" font="default" size="100%">Babur, Ozgun</style></author><author><style face="normal" font="default" size="100%">Soliman, Sylvain</style></author><author><style face="normal" font="default" size="100%">Valdeolivas, Alberto</style></author><author><style face="normal" font="default" size="100%">Esteban-Medina, Marina</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Rian, Kinza</style></author><author><style face="normal" font="default" size="100%">Helikar, Tomáš</style></author><author><style face="normal" font="default" size="100%">Puniya, Bhanwar Lal</style></author><author><style face="normal" font="default" size="100%">Modos, Dezso</style></author><author><style face="normal" font="default" size="100%">Treveil, Agatha</style></author><author><style face="normal" font="default" size="100%">Olbei, Marton</style></author><author><style face="normal" font="default" size="100%">De Meulder, Bertrand</style></author><author><style face="normal" font="default" size="100%">Ballereau, Stephane</style></author><author><style face="normal" font="default" size="100%">Dugourd, Aurélien</style></author><author><style face="normal" font="default" size="100%">Naldi, Aurélien</style></author><author><style face="normal" font="default" size="100%">Noël, Vincent</style></author><author><style face="normal" font="default" size="100%">Calzone, Laurence</style></author><author><style face="normal" font="default" size="100%">Sander, Chris</style></author><author><style face="normal" font="default" size="100%">Demir, Emek</style></author><author><style face="normal" font="default" size="100%">Korcsmaros, Tamas</style></author><author><style face="normal" font="default" size="100%">Freeman, Tom C</style></author><author><style face="normal" font="default" size="100%">Augé, Franck</style></author><author><style face="normal" font="default" size="100%">Beckmann, Jacques S</style></author><author><style face="normal" font="default" size="100%">Hasenauer, Jan</style></author><author><style face="normal" font="default" size="100%">Wolkenhauer, Olaf</style></author><author><style face="normal" font="default" size="100%">Wilighagen, Egon L</style></author><author><style face="normal" font="default" size="100%">Pico, Alexander R</style></author><author><style face="normal" font="default" size="100%">Evelo, Chris T</style></author><author><style face="normal" font="default" size="100%">Gillespie, Marc E</style></author><author><style face="normal" font="default" size="100%">Stein, Lincoln D</style></author><author><style face="normal" font="default" size="100%">Hermjakob, Henning</style></author><author><style face="normal" font="default" size="100%">D'Eustachio, Peter</style></author><author><style face="normal" font="default" size="100%">Saez-Rodriguez, Julio</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Valencia, Alfonso</style></author><author><style face="normal" font="default" size="100%">Kitano, Hiroaki</style></author><author><style face="normal" font="default" size="100%">Barillot, Emmanuel</style></author><author><style face="normal" font="default" size="100%">Auffray, Charles</style></author><author><style face="normal" font="default" size="100%">Balling, Rudi</style></author><author><style face="normal" font="default" size="100%">Schneider, Reinhard</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">COVID-19 Disease Map Community</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.</style></title><secondary-title><style face="normal" font="default" size="100%">Mol Syst Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Mol Syst Biol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Antiviral Agents</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Graphics</style></keyword><keyword><style  face="normal" font="default" size="100%">COVID-19</style></keyword><keyword><style  face="normal" font="default" size="100%">Cytokines</style></keyword><keyword><style  face="normal" font="default" size="100%">Data Mining</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Factual</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Host Microbial Interactions</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Immunity, Cellular</style></keyword><keyword><style  face="normal" font="default" size="100%">Immunity, Humoral</style></keyword><keyword><style  face="normal" font="default" size="100%">Immunity, Innate</style></keyword><keyword><style  face="normal" font="default" size="100%">Lymphocytes</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolic Networks and Pathways</style></keyword><keyword><style  face="normal" font="default" size="100%">Myeloid Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Interaction Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">SARS-CoV-2</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Viral Proteins</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 10</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">e10387</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/34664389?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Roldán, Gema</style></author><author><style face="normal" font="default" size="100%">Perez-Florido, Javier</style></author><author><style face="normal" font="default" size="100%">Ortuno, Francisco M</style></author><author><style face="normal" font="default" size="100%">Carmona, Rosario</style></author><author><style face="normal" font="default" size="100%">Aquino, Virginia</style></author><author><style face="normal" font="default" size="100%">López-López, Daniel</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Fernandez-Rueda, Jose L</style></author><author><style face="normal" font="default" size="100%">Gallego, Asunción</style></author><author><style face="normal" font="default" size="100%">Garcia-Garcia, Francisco</style></author><author><style face="normal" font="default" size="100%">González-Neira, Anna</style></author><author><style face="normal" font="default" size="100%">Pita, Guillermo</style></author><author><style face="normal" font="default" size="100%">Núñez-Torres, Rocío</style></author><author><style face="normal" font="default" size="100%">Santoyo-López, Javier</style></author><author><style face="normal" font="default" size="100%">Ayuso, Carmen</style></author><author><style face="normal" font="default" size="100%">Minguez, Pablo</style></author><author><style face="normal" font="default" size="100%">Avila-Fernandez, Almudena</style></author><author><style face="normal" font="default" size="100%">Corton, Marta</style></author><author><style face="normal" font="default" size="100%">Moreno-Pelayo, Miguel Ángel</style></author><author><style face="normal" font="default" size="100%">Morin, Matías</style></author><author><style face="normal" font="default" size="100%">Gallego-Martinez, Alvaro</style></author><author><style face="normal" font="default" size="100%">Lopez-Escamez, Jose A</style></author><author><style face="normal" font="default" size="100%">Borrego, Salud</style></author><author><style face="normal" font="default" size="100%">Antiňolo, Guillermo</style></author><author><style face="normal" font="default" size="100%">Amigo, Jorge</style></author><author><style face="normal" font="default" size="100%">Salgado-Garrido, Josefa</style></author><author><style face="normal" font="default" size="100%">Pasalodos-Sanchez, Sara</style></author><author><style face="normal" font="default" size="100%">Morte, Beatriz</style></author><author><style face="normal" font="default" size="100%">Carracedo, Ángel</style></author><author><style face="normal" font="default" size="100%">Alonso, Ángel</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors><translated-authors><author><style face="normal" font="default" size="100%">Spanish Exome Crowdsourcing Consortium</style></author></translated-authors></contributors><titles><title><style face="normal" font="default" size="100%">CSVS, a crowdsourcing database of the Spanish population genetic variability.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Alleles</style></keyword><keyword><style  face="normal" font="default" size="100%">Chromosome Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">Crowdsourcing</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Exome</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Frequency</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetic Variation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genetics, Population</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Precision Medicine</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Spain</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 01 08</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">49</style></volume><pages><style face="normal" font="default" size="100%">D1130-D1137</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The knowledge of the genetic variability of the local population is of utmost importance in personalized medicine and has been revealed as a critical factor for the discovery of new disease variants. Here, we present the Collaborative Spanish Variability Server (CSVS), which currently contains more than 2000 genomes and exomes of unrelated Spanish individuals. This database has been generated in a collaborative crowdsourcing effort collecting sequencing data produced by local genomic projects and for other purposes. Sequences have been grouped by ICD10 upper categories. A web interface allows querying the database removing one or more ICD10 categories. In this way, aggregated counts of allele frequencies of the pseudo-control Spanish population can be obtained for diseases belonging to the category removed. Interestingly, in addition to pseudo-control studies, some population studies can be made, as, for example, prevalence of pharmacogenomic variants, etc. In addition, this genomic data has been used to define the first Spanish Genome Reference Panel (SGRP1.0) for imputation. This is the first local repository of variability entirely produced by a crowdsourcing effort and constitutes an example for future initiatives to characterize local variability worldwide. CSVS is also part of the GA4GH Beacon network. CSVS can be accessed at: http://csvs.babelomics.org/.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">D1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32990755?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Moura, David S</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Cordero Varela, Juan Antonio</style></author><author><style face="normal" font="default" size="100%">Alvarez-Alegret, Ramiro</style></author><author><style face="normal" font="default" size="100%">Agra-Pujol, Carolina</style></author><author><style face="normal" font="default" size="100%">Izquierdo, Francisco</style></author><author><style face="normal" font="default" size="100%">Ramos, Rafael</style></author><author><style face="normal" font="default" size="100%">Ortega-Medina, Luis</style></author><author><style face="normal" font="default" size="100%">Martin-Davila, Francisco</style></author><author><style face="normal" font="default" size="100%">Castilla-Ramirez, Carolina</style></author><author><style face="normal" font="default" size="100%">Hernandez-Leon, Carmen Nieves</style></author><author><style face="normal" font="default" size="100%">Romagosa, Cleofe</style></author><author><style face="normal" font="default" size="100%">Vaz Salgado, Maria Angeles</style></author><author><style face="normal" font="default" size="100%">Lavernia, Javier</style></author><author><style face="normal" font="default" size="100%">Bagué, Silvia</style></author><author><style face="normal" font="default" size="100%">Mayodormo-Aranda, Empar</style></author><author><style face="normal" font="default" size="100%">Vicioso, Luis</style></author><author><style face="normal" font="default" size="100%">Hernández Barceló, Jose Emilio</style></author><author><style face="normal" font="default" size="100%">Rubio-Casadevall, Jordi</style></author><author><style face="normal" font="default" size="100%">de Juan, Ana</style></author><author><style face="normal" font="default" size="100%">Fiaño-Valverde, Maria Concepcion</style></author><author><style face="normal" font="default" size="100%">Hindi, Nadia</style></author><author><style face="normal" font="default" size="100%">Lopez-Alvarez, Maria</style></author><author><style face="normal" font="default" size="100%">Lacerenza, Serena</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Gutierrez, Antonio</style></author><author><style face="normal" font="default" size="100%">Alvarez, Rosa</style></author><author><style face="normal" font="default" size="100%">Valverde, Claudia</style></author><author><style face="normal" font="default" size="100%">Martinez-Trufero, Javier</style></author><author><style face="normal" font="default" size="100%">Martin-Broto, Javier</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A DNA damage repair gene-associated signature predicts responses of patients with advanced soft-tissue sarcoma to treatment with trabectedin.</style></title><secondary-title><style face="normal" font="default" size="100%">Mol Oncol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Mol Oncol</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 12</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">3691-3705</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Predictive biomarkers of trabectedin represent an unmet need in advanced soft-tissue sarcomas (STS). DNA damage repair (DDR) genes, involved in homologous recombination or nucleotide excision repair, had been previously described as biomarkers of trabectedin resistance or sensitivity, respectively. The majority of these studies only focused on specific factors (ERCC1, ERCC5, and BRCA1) and did not evaluate several other DDR-related genes that could have a relevant role for trabectedin efficacy. In this retrospective translational study, 118 genes involved in DDR were evaluated to determine, by transcriptomics, a predictive gene signature of trabectedin efficacy. A six-gene predictive signature of trabectedin efficacy was built in a series of 139 tumor samples from patients with advanced STS. Patients in the high-risk gene signature group showed a significantly worse progression-free survival compared with patients in the low-risk group (2.1 vs 6.0 months, respectively). Differential gene expression analysis defined new potential predictive biomarkers of trabectedin sensitivity (PARP3 and CCNH) or resistance (DNAJB11 and PARP1). Our study identified a new gene signature that significantly predicts patients with higher probability to respond to treatment with trabectedin. Targeting some genes of this signature emerges as a potential strategy to enhance trabectedin efficacy.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">12</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33983674?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rian, Kinza</style></author><author><style face="normal" font="default" size="100%">Hidalgo, Marta R.</style></author><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Falco, Matias M.</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Esteban-Medina, Marina</style></author><author><style face="normal" font="default" size="100%">Alamo-Alvarez, Inmaculada</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genome-scale mechanistic modeling of signaling pathways made easy: A bioconductor/cytoscape/web server framework for the analysis of omic data</style></title><secondary-title><style face="normal" font="default" size="100%">Computational and Structural Biotechnology Journal</style></secondary-title><short-title><style face="normal" font="default" size="100%">Computational and Structural Biotechnology Journal</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-01-2021</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://linkinghub.elsevier.com/retrieve/pii/S2001037021002038</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">19</style></volume><pages><style face="normal" font="default" size="100%">2968 - 2978</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brozos-Vázquez, Elena María</style></author><author><style face="normal" font="default" size="100%">Díaz-Peña, Roberto</style></author><author><style face="normal" font="default" size="100%">García-González, Jorge</style></author><author><style face="normal" font="default" size="100%">León-Mateos, Luis</style></author><author><style face="normal" font="default" size="100%">Mondelo-Macía, Patricia</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">López-López, Rafael</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Immunotherapy in nonsmall-cell lung cancer: current status and future prospects for liquid biopsy.</style></title><secondary-title><style face="normal" font="default" size="100%">Cancer Immunol Immunother</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Cancer Immunol Immunother</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Biomarkers, Tumor</style></keyword><keyword><style  face="normal" font="default" size="100%">Carcinoma, Non-Small-Cell Lung</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell-Free Nucleic Acids</style></keyword><keyword><style  face="normal" font="default" size="100%">Exosomes</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Immunotherapy</style></keyword><keyword><style  face="normal" font="default" size="100%">Liquid Biopsy</style></keyword><keyword><style  face="normal" font="default" size="100%">Lung Neoplasms</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 May</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">70</style></volume><pages><style face="normal" font="default" size="100%">1177-1188</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Immunotherapy has been one of the great advances in the recent years for the treatment of advanced tumors, with nonsmall-cell lung cancer (NSCLC) being one of the cancers that has benefited most from this approach. Currently, the only validated companion diagnostic test for first-line immunotherapy in metastatic NSCLC patients is testing for programmed death ligand 1 (PD-L1) expression in tumor tissues. However, not all patients experience an effective response with the established selection criteria and immune checkpoint inhibitors (ICIs). Liquid biopsy offers a noninvasive opportunity to monitor disease in patients with cancer and identify those who would benefit the most from immunotherapy. This review focuses on the use of liquid biopsy in immunotherapy treatment of NSCLC patients. Circulating tumor cells (CTCs), cell-free DNA (cfDNA) and exosomes are promising tools for developing new biomarkers. We discuss the current application and future implementation of these parameters to improve therapeutic decision-making and identify the patients who will benefit most from immunotherapy.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rian, Kinza</style></author><author><style face="normal" font="default" size="100%">Esteban-Medina, Marina</style></author><author><style face="normal" font="default" size="100%">Hidalgo, Marta R</style></author><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Falco, Matias M</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Gunyel, Devrim</style></author><author><style face="normal" font="default" size="100%">Ostaszewski, Marek</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mechanistic modeling of the SARS-CoV-2 disease map.</style></title><secondary-title><style face="normal" font="default" size="100%">BioData Min</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BioData Min</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 Jan 21</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">5</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Here we present a web interface that implements a comprehensive mechanistic model of the SARS-CoV-2 disease map. In this framework, the detailed activity of the human signaling circuits related to the viral infection, covering from the entry and replication mechanisms to the downstream consequences as inflammation and antigenic response, can be inferred from gene expression experiments. Moreover, the effect of potential interventions, such as knock-downs, or drug effects (currently the system models the effect of more than 8000 DrugBank drugs) can be studied. This freely available tool not only provides an unprecedentedly detailed view of the mechanisms of viral invasion and the consequences in the cell but has also the potential of becoming an invaluable asset in the search for efficient antiviral treatments.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33478554?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Millán-Esteban, David</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">García-Casado, Zaida</style></author><author><style face="normal" font="default" size="100%">Manrique-Silva, Esperanza</style></author><author><style face="normal" font="default" size="100%">Requena, Celia</style></author><author><style face="normal" font="default" size="100%">Bañuls, José</style></author><author><style face="normal" font="default" size="100%">Lopez-Guerrero, Jose Antonio</style></author><author><style face="normal" font="default" size="100%">Rodríguez-Hernández, Aranzazu</style></author><author><style face="normal" font="default" size="100%">Traves, Víctor</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Virós, Amaya</style></author><author><style face="normal" font="default" size="100%">Kumar, Rajiv</style></author><author><style face="normal" font="default" size="100%">Nagore, Eduardo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mutational Characterization of Cutaneous Melanoma Supports Divergent Pathways Model for Melanoma Development.</style></title><secondary-title><style face="normal" font="default" size="100%">Cancers (Basel)</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Cancers (Basel)</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 Oct 18</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">13</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;According to the divergent pathway model, cutaneous melanoma comprises a nevogenic group with a propensity to melanocyte proliferation and another one associated with cumulative solar damage (CSD). While characterized clinically and epidemiologically, the differences in the molecular profiles between the groups have remained primarily uninvestigated. This study has used a custom gene panel and bioinformatics tools to investigate the potential molecular differences in a thoroughly characterized cohort of 119 melanoma patients belonging to nevogenic and CSD groups. We found that the nevogenic melanomas had a restricted set of mutations, with the prominently mutated gene being . The CSD melanomas, in contrast, showed mutations in a diverse group of genes that included , , , and . We thus provide evidence that nevogenic and CSD melanomas constitute different biological entities and highlight the need to explore new targeted therapies.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">20</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Esteban-Medina, Marina</style></author><author><style face="normal" font="default" size="100%">Muñoyerro-Muñiz, Dolores</style></author><author><style face="normal" font="default" size="100%">Villegas, Román</style></author><author><style face="normal" font="default" size="100%">López-Miranda, José</style></author><author><style face="normal" font="default" size="100%">Rodríguez-Baño, Jesús</style></author><author><style face="normal" font="default" size="100%">Túnez, Isaac</style></author><author><style face="normal" font="default" size="100%">Bouillon, Roger</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Quesada Gomez, Jose Manuel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Real world evidence of calcifediol or vitamin D prescription and mortality rate of COVID-19 in a retrospective cohort of hospitalized Andalusian patients.</style></title><secondary-title><style face="normal" font="default" size="100%">Sci Rep</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Sci Rep</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Calcifediol</style></keyword><keyword><style  face="normal" font="default" size="100%">COVID-19</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Kaplan-Meier Estimate</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Retrospective Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Spain</style></keyword><keyword><style  face="normal" font="default" size="100%">Survival Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Vitamin D</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 12 03</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">23380</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;COVID-19 is a major worldwide health problem because of acute respiratory distress syndrome, and mortality. Several lines of evidence have suggested a relationship between the vitamin D endocrine system and severity of COVID-19. We present a survival study on a retrospective cohort of 15,968 patients, comprising all COVID-19 patients hospitalized in Andalusia between January and November 2020. Based on a central registry of electronic health records (the Andalusian Population Health Database, BPS), prescription of vitamin D or its metabolites within 15-30 days before hospitalization were recorded. The effect of prescription of vitamin D (metabolites) for other indication previous to the hospitalization was studied with respect to patient survival. Kaplan-Meier survival curves and hazard ratios support an association between prescription of these metabolites and patient survival. Such association was stronger for calcifediol (Hazard Ratio, HR = 0.67, with 95% confidence interval, CI, of [0.50-0.91]) than for cholecalciferol (HR = 0.75, with 95% CI of [0.61-0.91]), when prescribed 15 days prior hospitalization. Although the relation is maintained, there is a general decrease of this effect when a longer period of 30 days prior hospitalization is considered (calcifediol HR = 0.73, with 95% CI [0.57-0.95] and cholecalciferol HR = 0.88, with 95% CI [0.75, 1.03]), suggesting that association was stronger when the prescription was closer to the hospitalization.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Garrido-Rodriguez, Martín</style></author><author><style face="normal" font="default" size="100%">López-López, Daniel</style></author><author><style face="normal" font="default" size="100%">Ortuno, Francisco M</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Muñoz, Eduardo</style></author><author><style face="normal" font="default" size="100%">Calzado, Marco A</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A versatile workflow to integrate RNA-seq genomic and transcriptomic data into mechanistic models of signaling pathways.</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS Comput Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">PLoS Comput Biol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Line, Tumor</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Factual</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">High-Throughput Nucleotide Sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Theoretical</style></keyword><keyword><style  face="normal" font="default" size="100%">mutation</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA-seq</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptome</style></keyword><keyword><style  face="normal" font="default" size="100%">whole exome sequencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Workflow</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021 02</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">e1008748</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;MIGNON is a workflow for the analysis of RNA-Seq experiments, which not only efficiently manages the estimation of gene expression levels from raw sequencing reads, but also calls genomic variants present in the transcripts analyzed. Moreover, this is the first workflow that provides a framework for the integration of transcriptomic and genomic data based on a mechanistic model of signaling pathway activities that allows a detailed biological interpretation of the results, including a comprehensive functional profiling of cell activity. MIGNON covers the whole process, from reads to signaling circuit activity estimations, using state-of-the-art tools, it is easy to use and it is deployable in different computational environments, allowing an optimized use of the resources available.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33571195?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Esteban-Medina, Marina</style></author><author><style face="normal" font="default" size="100%">Rian, Kinza</style></author><author><style face="normal" font="default" size="100%">Falco, Matias M</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Drug repurposing for COVID-19 using machine learning and mechanistic models of signal transduction circuits related to SARS-CoV-2 infection.</style></title><secondary-title><style face="normal" font="default" size="100%">Signal Transduct Target Ther</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Signal Transduct Target Ther</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computational Chemistry</style></keyword><keyword><style  face="normal" font="default" size="100%">COVID-19</style></keyword><keyword><style  face="normal" font="default" size="100%">drug repositioning</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Docking Simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Targeted Therapy</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">SARS-CoV-2</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 12 11</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">290</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33311438?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Palomero, Luis</style></author><author><style face="normal" font="default" size="100%">Galván-Femenía, Ivan</style></author><author><style face="normal" font="default" size="100%">de Cid, Rafael</style></author><author><style face="normal" font="default" size="100%">Espín, Roderic</style></author><author><style face="normal" font="default" size="100%">Barnes, Daniel R</style></author><author><style face="normal" font="default" size="100%">Blommaert, Eline</style></author><author><style face="normal" font="default" size="100%">Gil-Gil, Miguel</style></author><author><style face="normal" font="default" size="100%">Falo, Catalina</style></author><author><style face="normal" font="default" size="100%">Stradella, Agostina</style></author><author><style face="normal" font="default" size="100%">Ouchi, Dan</style></author><author><style face="normal" font="default" size="100%">Roso-Llorach, Albert</style></author><author><style face="normal" font="default" size="100%">Violan, Concepció</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Extremera, Ana Isabel</style></author><author><style face="normal" font="default" size="100%">García-Valero, Mar</style></author><author><style face="normal" font="default" size="100%">Herranz, Carmen</style></author><author><style face="normal" font="default" size="100%">Mateo, Francesca</style></author><author><style face="normal" font="default" size="100%">Mereu, Elisabetta</style></author><author><style face="normal" font="default" size="100%">Beesley, Jonathan</style></author><author><style face="normal" font="default" size="100%">Chenevix-Trench, Georgia</style></author><author><style face="normal" font="default" size="100%">Roux, Cecilia</style></author><author><style face="normal" font="default" size="100%">Mak, Tak</style></author><author><style face="normal" font="default" size="100%">Brunet, Joan</style></author><author><style face="normal" font="default" size="100%">Hakem, Razq</style></author><author><style face="normal" font="default" size="100%">Gorrini, Chiara</style></author><author><style face="normal" font="default" size="100%">Antoniou, Antonis C</style></author><author><style face="normal" font="default" size="100%">Lázaro, Conxi</style></author><author><style face="normal" font="default" size="100%">Pujana, Miquel Angel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Immune Cell Associations with Cancer Risk.</style></title><secondary-title><style face="normal" font="default" size="100%">iScience</style></secondary-title><alt-title><style face="normal" font="default" size="100%">iScience</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 Jul 24</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">23</style></volume><pages><style face="normal" font="default" size="100%">101296</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Proper immune system function hinders cancer development, but little is known about whether genetic variants linked to cancer risk alter immune cells. Here, we report 57 cancer risk loci associated with differences in immune and/or stromal cell contents in the corresponding tissue. Predicted target genes show expression and regulatory associations with immune features. Polygenic risk scores also reveal associations with immune and/or stromal cell contents, and breast cancer scores show consistent results in normal and tumor tissue. SH2B3 links peripheral alterations of several immune cell types to the risk of this malignancy. Pleiotropic SH2B3 variants are associated with breast cancer risk in BRCA1/2 mutation carriers. A retrospective case-cohort study indicates a positive association between blood counts of basophils, leukocytes, and monocytes and age at breast cancer diagnosis. These findings broaden our knowledge of the role of the immune system in cancer and highlight promising prevention strategies for individuals at high risk.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32622267?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Falco, Matias M</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Hidalgo, Marta R</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mechanistic models of signaling pathways deconvolute the glioblastoma single-cell functional landscapeAbstract</style></title><secondary-title><style face="normal" font="default" size="100%">NAR Cancer</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-06-2020</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://academic.oup.com/narcancer/article/doi/10.1093/narcan/zcaa011/5862620http://academic.oup.com/narcancer/article-pdf/2/2/zcaa011/33428092/zcaa011.pdfhttp://academic.oup.com/narcancer/article-pdf/2/2/zcaa011/33428092/zcaa011.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">2</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cubuk, Cankut</style></author><author><style face="normal" font="default" size="100%">Can, Fatma E</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mechanistic Models of Signaling Pathways Reveal the Drug Action Mechanisms behind Gender-Specific Gene Expression for Cancer Treatments.</style></title><secondary-title><style face="normal" font="default" size="100%">Cells</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Cells</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Neoplastic</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 06 29</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">9</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Despite the existence of differences in gene expression across numerous genes between males and females having been known for a long time, these have been mostly ignored in many studies, including drug development and its therapeutic use. In fact, the consequences of such differences over the disease mechanisms or the drug action mechanisms are completely unknown. Here we applied mechanistic mathematical models of signaling activity to reveal the ultimate functional consequences that gender-specific gene expression activities have over cell functionality and fate. Moreover, we also used the mechanistic modeling framework to simulate the drug interventions and unravel how drug action mechanisms are affected by gender-specific differential gene expression. Interestingly, some cancers have many biological processes significantly affected by these gender-specific differences (e.g., bladder or head and neck carcinomas), while others (e.g., glioblastoma or rectum cancer) are almost insensitive to them. We found that many of these gender-specific differences affect cancer-specific pathways or in physiological signaling pathways, also involved in cancer origin and development. Finally, mechanistic models have the potential to be used for finding alternative therapeutic interventions on the pathways targeted by the drug, which lead to similar results compensating the downstream consequences of gender-specific differences in gene expression.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32610626?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martin-Broto, Javier</style></author><author><style face="normal" font="default" size="100%">Hindi, Nadia</style></author><author><style face="normal" font="default" size="100%">Grignani, Giovanni</style></author><author><style face="normal" font="default" size="100%">Martinez-Trufero, Javier</style></author><author><style face="normal" font="default" size="100%">Redondo, Andres</style></author><author><style face="normal" font="default" size="100%">Valverde, Claudia</style></author><author><style face="normal" font="default" size="100%">Stacchiotti, Silvia</style></author><author><style face="normal" font="default" size="100%">Lopez-Pousa, Antonio</style></author><author><style face="normal" font="default" size="100%">D'Ambrosio, Lorenzo</style></author><author><style face="normal" font="default" size="100%">Gutierrez, Antonio</style></author><author><style face="normal" font="default" size="100%">Perez-Vega, Herminia</style></author><author><style face="normal" font="default" size="100%">Encinas-Tobajas, Victor</style></author><author><style face="normal" font="default" size="100%">de Alava, Enrique</style></author><author><style face="normal" font="default" size="100%">Collini, Paola</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Carrasco-Garcia, Irene</style></author><author><style face="normal" font="default" size="100%">Lopez-Alvarez, Maria</style></author><author><style face="normal" font="default" size="100%">Moura, David S</style></author><author><style face="normal" font="default" size="100%">Lopez-Martin, Jose A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Nivolumab and sunitinib combination in advanced soft tissue sarcomas: a multicenter, single-arm, phase Ib/II trial.</style></title><secondary-title><style face="normal" font="default" size="100%">J Immunother Cancer</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Immunother Cancer</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Antineoplastic Agents, Immunological</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Nivolumab</style></keyword><keyword><style  face="normal" font="default" size="100%">Sarcoma</style></keyword><keyword><style  face="normal" font="default" size="100%">Sunitinib</style></keyword><keyword><style  face="normal" font="default" size="100%">Young Adult</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 11</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Sarcomas exhibit low expression of factors related to immune response, which could explain the modest activity of PD-1 inhibitors. A potential strategy to convert a cold into an inflamed microenvironment lies on a combination therapy. As tumor angiogenesis promotes immunosuppression, we designed a phase Ib/II trial to test the double inhibition of angiogenesis (sunitinib) and PD-1/PD-L1 axis (nivolumab).&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;This single-arm, phase Ib/II trial enrolled adult patients with selected subtypes of sarcoma. Phase Ib established two dose levels: level 0 with sunitinib 37.5 mg daily from day 1, plus nivolumab 3 mg/kg intravenously on day 15, and then every 2 weeks; and level -1 with sunitinib 37.5 mg on the first 14 days (induction) and then 25 mg per day plus nivolumab on the same schedule. The primary endpoint was to determine the recommended dose for phase II (phase I) and the 6-month progression-free survival rate, according to Response Evaluation Criteria in Solid Tumors 1.1 (phase II).&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;From May 2017 to April 2019, 68 patients were enrolled: 16 in phase Ib and 52 in phase II. The recommended dose of sunitinib for phase II was 37.5 mg as induction and then 25 mg in combination with nivolumab. After a median follow-up of 17 months (4-26), the 6-month progression-free survival rate was 48% (95% CI 41% to 55%). The most common grade 3-4 adverse events included transaminitis (17.3%) and neutropenia (11.5%).&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Sunitinib plus nivolumab is an active scheme with manageable toxicity in the treatment of selected patients with advanced soft tissue sarcoma, with almost half of patients free of progression at 6 months. NCT03277924.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/33203665?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martin-Broto, Javier</style></author><author><style face="normal" font="default" size="100%">Cruz, Josefina</style></author><author><style face="normal" font="default" size="100%">Penel, Nicolas</style></author><author><style face="normal" font="default" size="100%">Le Cesne, Axel</style></author><author><style face="normal" font="default" size="100%">Hindi, Nadia</style></author><author><style face="normal" font="default" size="100%">Luna, Pablo</style></author><author><style face="normal" font="default" size="100%">Moura, David S</style></author><author><style face="normal" font="default" size="100%">Bernabeu, Daniel</style></author><author><style face="normal" font="default" size="100%">de Alava, Enrique</style></author><author><style face="normal" font="default" size="100%">Lopez-Guerrero, Jose Antonio</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Gutierrez, Antonio</style></author><author><style face="normal" font="default" size="100%">Collini, Paola</style></author><author><style face="normal" font="default" size="100%">Karanian, Marie</style></author><author><style face="normal" font="default" size="100%">Redondo, Andres</style></author><author><style face="normal" font="default" size="100%">Lopez-Pousa, Antonio</style></author><author><style face="normal" font="default" size="100%">Grignani, Giovanni</style></author><author><style face="normal" font="default" size="100%">Diaz-Martin, Juan</style></author><author><style face="normal" font="default" size="100%">Marcilla, David</style></author><author><style face="normal" font="default" size="100%">Fernandez-Serra, Antonio</style></author><author><style face="normal" font="default" size="100%">Gonzalez-Aguilera, Cristina</style></author><author><style face="normal" font="default" size="100%">Casali, Paolo G</style></author><author><style face="normal" font="default" size="100%">Blay, Jean-Yves</style></author><author><style face="normal" font="default" size="100%">Stacchiotti, Silvia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pazopanib for treatment of typical solitary fibrous tumours: a multicentre, single-arm, phase 2 trial.</style></title><secondary-title><style face="normal" font="default" size="100%">Lancet Oncol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Lancet Oncol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Follow-Up Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Indazoles</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasm Metastasis</style></keyword><keyword><style  face="normal" font="default" size="100%">Prognosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Prospective Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Kinase Inhibitors</style></keyword><keyword><style  face="normal" font="default" size="100%">Pyrimidines</style></keyword><keyword><style  face="normal" font="default" size="100%">Response Evaluation Criteria in Solid Tumors</style></keyword><keyword><style  face="normal" font="default" size="100%">Solitary Fibrous Tumors</style></keyword><keyword><style  face="normal" font="default" size="100%">Sulfonamides</style></keyword><keyword><style  face="normal" font="default" size="100%">Survival Rate</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020 03</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">456-466</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Solitary fibrous tumour is an ultra-rare sarcoma, which encompasses different clinicopathological subgroups. The dedifferentiated subgroup shows an aggressive course with resistance to pazopanib, whereas in the malignant subgroup, pazopanib shows higher activity than in previous studies with chemotherapy. We designed a trial to test pazopanib activity in two different cohorts of solitary fibrous tumour: the malignant-dedifferentiated cohort, which was previously published, and the typical cohort, which is presented here.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;In this single-arm, phase 2 trial, adult patients (aged ≥18 years) diagnosed with confirmed metastatic or unresectable typical solitary fibrous tumour of any location, who had progressed in the previous 6 months (by Choi criteria or Response Evaluation Criteria in Solid Tumors [RECIST]) and an Eastern Cooperative Oncology Group (ECOG) performance status of 0-2 were enrolled at 11 tertiary hospitals in Italy, France, and Spain. Patients received pazopanib 800 mg once daily, taken orally, until progression, unacceptable toxicity, withdrawal of consent, non-compliance, or a delay in pazopanib administration of longer than 3 weeks. The primary endpoint was proportion of patients achieving an overall response measured by Choi criteria in patients who received at least 1 month of treatment with at least one radiological assessment. All patients who received at least one dose of the study drug were included in the safety analyses. This study is registered in ClinicalTrials.gov, NCT02066285, and with the European Clinical Trials Database, EudraCT 2013-005456-15.&lt;/p&gt;&lt;p&gt;&lt;b&gt;FINDINGS: &lt;/b&gt;From June 26, 2014, to Dec 13, 2018, of 40 patients who were assessed, 34 patients were enrolled and 31 patients were included in the response analysis. Median follow-up was 18 months (IQR 14-34), and 18 (58%) of 31 patients had a partial response, 12 (39%) had stable disease, and one (3%) showed progressive disease according to Choi criteria and central review. The proportion of overall response based on Choi criteria was 58% (95% CI 34-69). There were no deaths caused by toxicity, and the most frequent adverse events were diarrhoea (18 [53%] of 34 patients), fatigue (17 [50%]), and hypertension (17 [50%]).&lt;/p&gt;&lt;p&gt;&lt;b&gt;INTERPRETATION: &lt;/b&gt;To our knowledge, this is the first prospective trial of pazopanib for advanced typical solitary fibrous tumour. The manageable toxicity and activity shown by pazopanib in this cohort suggest that this drug could be considered as first-line treatment for advanced typical solitary fibrous tumour.&lt;/p&gt;&lt;p&gt;&lt;b&gt;FUNDING: &lt;/b&gt;Spanish Group for Research on Sarcomas (GEIS), Italian Sarcoma Group (ISG), French Sarcoma Group (FSG), GlaxoSmithKline, and Novartis.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/32066540?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Esteban-Medina, Marina</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models.</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></secondary-title><alt-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Databases, Factual</style></keyword><keyword><style  face="normal" font="default" size="100%">Fanconi Anemia</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Phenotype</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Transduction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 Jul 02</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">370</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;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.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;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.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/31266445?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martin-Broto, Javier</style></author><author><style face="normal" font="default" size="100%">Stacchiotti, Silvia</style></author><author><style face="normal" font="default" size="100%">Lopez-Pousa, Antonio</style></author><author><style face="normal" font="default" size="100%">Redondo, Andres</style></author><author><style face="normal" font="default" size="100%">Bernabeu, Daniel</style></author><author><style face="normal" font="default" size="100%">de Alava, Enrique</style></author><author><style face="normal" font="default" size="100%">Casali, Paolo G</style></author><author><style face="normal" font="default" size="100%">Italiano, Antoine</style></author><author><style face="normal" font="default" size="100%">Gutierrez, Antonio</style></author><author><style face="normal" font="default" size="100%">Moura, David S</style></author><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Diaz-Martin, Juan</style></author><author><style face="normal" font="default" size="100%">Biscuola, Michele</style></author><author><style face="normal" font="default" size="100%">Taron, Miguel</style></author><author><style face="normal" font="default" size="100%">Collini, Paola</style></author><author><style face="normal" font="default" size="100%">Ranchere-Vince, Dominique</style></author><author><style face="normal" font="default" size="100%">Garcia Del Muro, Xavier</style></author><author><style face="normal" font="default" size="100%">Grignani, Giovanni</style></author><author><style face="normal" font="default" size="100%">Dumont, Sarah</style></author><author><style face="normal" font="default" size="100%">Martinez-Trufero, Javier</style></author><author><style face="normal" font="default" size="100%">Palmerini, Emanuela</style></author><author><style face="normal" font="default" size="100%">Hindi, Nadia</style></author><author><style face="normal" font="default" size="100%">Sebio, Ana</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author><author><style face="normal" font="default" size="100%">Dei Tos, Angelo Paolo</style></author><author><style face="normal" font="default" size="100%">LeCesne, Axel</style></author><author><style face="normal" font="default" size="100%">Blay, Jean-Yves</style></author><author><style face="normal" font="default" size="100%">Cruz, Josefina</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pazopanib for treatment of advanced malignant and dedifferentiated solitary fibrous tumour: a multicentre, single-arm, phase 2 trial.</style></title><secondary-title><style face="normal" font="default" size="100%">Lancet Oncol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Lancet Oncol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Angiogenesis Inhibitors</style></keyword><keyword><style  face="normal" font="default" size="100%">Antineoplastic Agents</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Indazoles</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Multivariate Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Pyrimidines</style></keyword><keyword><style  face="normal" font="default" size="100%">Response Evaluation Criteria in Solid Tumors</style></keyword><keyword><style  face="normal" font="default" size="100%">Soft Tissue Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Solitary Fibrous Tumors</style></keyword><keyword><style  face="normal" font="default" size="100%">Sulfonamides</style></keyword><keyword><style  face="normal" font="default" size="100%">Survival Analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019 01</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">134-144</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;A solitary fibrous tumour is a rare soft-tissue tumour with three clinicopathological variants: typical, malignant, and dedifferentiated. Preclinical experiments and retrospective studies have shown different sensitivities of solitary fibrous tumour to chemotherapy and antiangiogenics. We therefore designed a trial to assess the activity of pazopanib in a cohort of patients with malignant or dedifferentiated solitary fibrous tumour. The clinical and translational results are presented here.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;In this single-arm, phase 2 trial, adult patients (aged ≥ 18 years) with histologically confirmed metastatic or unresectable malignant or dedifferentiated solitary fibrous tumour at any location, who had progressed (by RECIST and Choi criteria) in the previous 6 months and had an ECOG performance status of 0-2, were enrolled at 16 third-level hospitals with expertise in sarcoma care in Spain, Italy, and France. Patients received pazopanib 800 mg once daily, taken orally without food, at least 1 h before or 2 h after a meal, until progression or intolerance. The primary endpoint of the study was overall response measured by Choi criteria in the subset of the intention-to-treat population (patients who received at least 1 month of treatment with at least one radiological assessment). All patients who received at least one dose of the study drug were included in the safety analyses. This study is registered with ClinicalTrials.gov, number NCT02066285, and with the European Clinical Trials Database, EudraCT number 2013-005456-15.&lt;/p&gt;&lt;p&gt;&lt;b&gt;FINDINGS: &lt;/b&gt;From June 26, 2014, to Nov 24, 2016, of 40 patients assessed, 36 were enrolled (34 with malignant solitary fibrous tumour and two with dedifferentiated solitary fibrous tumour). Median follow-up was 27 months (IQR 16-31). Based on central radiology review, 18 (51%) of 35 evaluable patients had partial responses, nine (26%) had stable disease, and eight (23%) had progressive disease according to Choi criteria. Further enrolment of patients with dedifferentiated solitary fibrous tumour was stopped after detection of early and fast progressions in a planned interim analysis. 51% (95% CI 34-69) of 35 patients achieved an overall response according to Choi criteria. Ten (29%) of 35 patients died. There were no deaths related to adverse events and the most frequent grade 3 or higher adverse events were hypertension (11 [31%] of 36 patients), neutropenia (four [11%]), increased concentrations of alanine aminotransferase (four [11%]), and increased concentrations of bilirubin (three [8%]).&lt;/p&gt;&lt;p&gt;&lt;b&gt;INTERPRETATION: &lt;/b&gt;To our knowledge, this is the first trial of pazopanib for treatment of malignant solitary fibrous tumour showing activity in this patient group. The manageable toxicity profile and the activity shown by pazopanib suggests that this drug could be an option for systemic treatment of advanced malignant solitary fibrous tumour, and provides a benchmark for future trials.&lt;/p&gt;&lt;p&gt;&lt;b&gt;FUNDING: &lt;/b&gt;Spanish Group for Research on Sarcomas (GEIS), Italian Sarcoma Group (ISG), French Sarcoma Group (FSG), GlaxoSmithKline, and Novartis.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/30578023?dopt=Abstract</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Peña-Chilet, Maria</style></author><author><style face="normal" font="default" size="100%">Esteban-Medina, Marina</style></author><author><style face="normal" font="default" size="100%">Falco, Matias M.</style></author><author><style face="normal" font="default" size="100%">Rian, Kinza</style></author><author><style face="normal" font="default" size="100%">Hidalgo, Marta R.</style></author><author><style face="normal" font="default" size="100%">Loucera, Carlos</style></author><author><style face="normal" font="default" size="100%">Dopazo, Joaquin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Using mechanistic models for the clinical interpretation of complex genomic variation</style></title><secondary-title><style face="normal" font="default" size="100%">Scientific Reports</style></secondary-title><short-title><style face="normal" font="default" size="100%">Sci Rep</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan-12-2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.nature.com/articles/s41598-019-55454-7http://www.nature.com/articles/s41598-019-55454-7.pdfhttp://www.nature.com/articles/s41598-019-55454-7.pdfhttp://www.nature.com/articles/s41598-019-55454-7</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">9</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record></records></xml>