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2. eTRANSAFE: data science to empower translational safety assessment

Author

Sanz, Ferran, Pognan, François, Steger-Hartmann, Thomas, Díaz, Carlos, Asakura, Shoji, Amberg, Alexander, Bécourt-Lhote, Nathalie, Blomberg, Niklas, Bosc, Nicolas, Briggs, Katharine, Bringezu, Frank, Brulle-Wohlhueter, Claire, Brunak, Søren, Bueters, Ruud, Callegaro, Giulia, Capella-Gutierrez, Salvador, Centeno, Emilio, Corvi, Javier, Cronin, Mark T. D., Drew, Philip, Duchateau-Nguyen, Guillemette, Ecker, Gerhard F., Escher, Sylvia, Felix, Eloy, Ferreiro, Miguel, Frericks, Markus, Furlong, Laura I., Geiger, Robert, George, Catherine, Grandits, Melanie, Ivanov-Draganov, Dragomir, Kilgour-Christie, Jean, Kiziloren, Tevfik, Kors, Jan A., Koyama, Naoki, Kreuchwig, Annika, Leach, Andrew R., Mayer, Miguel-Angel, Monecke, Peter, Muster, Wolfgang, Nakazawa, Chihiro Miyamoto, Nicholson, Gavin, Parry, Rowan, Pastor, Manuel, Piñero, Janet, Oberhauser, Nils, Ramírez-Anguita, Juan Manuel, Rodrigo, Adrián, Smajic, Aljosa, Schaefer, Markus, Schieferdecker, Sebastian, Soininen, Inari, Terricabras, Emma, Trairatphisan, Panuwat, Turner, Sean C., Valencia, Alfonso, van de Water, Bob, van der Lei, Johan L., van Mulligen, Erik M., Vock, Esther, and Wilkinson, David

Published
2023
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3. Contributors

Author

Arroyo, Liliana, primary, Buslón, Nataly, additional, Caduff, Ladina, additional, Catuara-Solarz, Silvina, additional, Cirillo, Davide, additional, Confalonieri, Roberto, additional, Cortés, Atia, additional, Costa-jussà, Marta R., additional, Diana, Gianluca, additional, Dubreuil-Vall, Laura, additional, Eyre, Harris A., additional, Furlong, Laura I., additional, Gonen, Hila, additional, Guney, Emre, additional, Kutterer, Cornelia, additional, Laabs, Tracy L., additional, Lucchesi, Federico, additional, Maffei, Giovanni, additional, Mavridis, Nikolaos, additional, Mellino, Simona, additional, Morey, Czuee, additional, Papasotiriou, Spyridon, additional, Piella, Gemma, additional, Piñero, Janet, additional, Quevenco, Frances-Catherine, additional, Racionero-Plaza, Sandra, additional, Rementeria, María José, additional, Rohner, Colin, additional, Santus, Enrico, additional, Smith, Erin, additional, Subirats, Laia, additional, Valencia, Alfonso, additional, and Villegas, Marta, additional

Published
2022
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6. Mining the modular structure of protein interaction networks

Author

Berenstein, Ariel, Piñero, Janet, Furlong, Laura Ines, and Chernomoretz, Ariel

Subjects
Quantitative Biology - Molecular Networks
Abstract

Cluster-based descriptions of biological networks have received much attention in recent years fostered by accumulated evidence of the existence of meaningful correlations between topological network clusters and biological functional modules. Several well-performing clustering algorithms exist to infer topological network partitions. However, due to respective technical idiosyncrasies they might produce dissimilar modular decompositions of a given network. In this contribution, we aimed to analyze how alternative modular descriptions could condition the outcome of follow-up network biology analysis. We considered a human protein interaction network and two paradigmatic cluster recognition algorithms, namely: the Clauset-Newman-Moore and the infomap procedures. We analyzed at what extent both methodologies yielded different results in terms of granularity and biological congruency. In addition, taking into account Guimera cartographic role characterization of network nodes, we explored how the adoption of a given clustering methodology impinged on the ability to highlight relevant network meso-scale connectivity patterns. As a case study we considered a set of aging related proteins, and showed that only the high-resolution modular description provided by infomap, could unveil statistically significant associations between them and inter-intra modular cartographic features. Besides reporting novel biological insights that could be gained from the discovered associations, our contribution warns against possible technical concerns that might affect the tools used to mine for interaction patterns in network biology studies. In particular our results suggested that sub-optimal partitions from the strict point of view of their modularity levels might still be worth being analyzed when meso-scale features were to be explored in connection with external source of biological knowledge., Comment: (v2 35 pages, 11 figures, including Sup Mat)

Published
2014
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7. Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

Author

Niarakis, Anna, Ostaszewski, Marek, Mazein, Alexander, Kuperstein, Inna, Kutmon, Martina, Gillespie, Marc E., Funahashi, Akira, Acencio, Marcio Luis, Hemedan, Ahmed, Aichem, Michael, Klein, Karsten, Czauderna, Tobias, Burtscher, Felicia, Yamada, Takahiro G., Hiki, Yusuke, Hiroi, Noriko F., Hu, Finterly, Pham, Nhung, Ehrhart, Friederike, Willighagen, Egon L., Valdeolivas, Alberto, Dugourd, Aurelien, Messina, Francesco, Esteban-Medina, Marina, Peña-Chilet, Maria, Rian, Kinza, Soliman, Sylvain, Aghamiri, Sara Sadat, Puniya, Bhanwar Lal, Naldi, Aurélien, Helikar, Tomáš, Singh, Vidisha, Fernández, Marco Fariñas, Bermudez, Viviam, Tsirvouli, Eirini, Montagud, Arnau, Noël, Vincent, Ponce-de-Leon, Miguel, Maier, Dieter, Bauch, Angela, Gyori, Benjamin M., Bachman, John A., Luna, Augustin, Piñero, Janet, Furlong, Laura I., Balaur, Irina, Rougny, Adrien, Jarosz, Yohan, Overall, Rupert, Phair, Robert, Perfetto, Livia, Matthews, Lisa, Rex, Devasahayam Arokia Balaya, Orlic-Milacic, Marija, Gomez, Luis Cristobal Monraz, De Meulder, Bertrand, Ravel, Jean Marie, Jassal, Bijay, Satagopam, Venkata, Wu, Guanming, Golebiewski, Martin, Gawron, Piotr, Calzone, Laurence, Beckmann, Jacques S., Evelo, Chris T., D’Eustachio, Peter, Schreiber, Falk, Saez-Rodriguez, Julio, Dopazo, Joaquin, Kuiper, Martin, Valencia, Alfonso, Wolkenhauer, Olaf, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, Schneider, Reinhard, Niarakis, Anna, Ostaszewski, Marek, Mazein, Alexander, Kuperstein, Inna, Kutmon, Martina, Gillespie, Marc E., Funahashi, Akira, Acencio, Marcio Luis, Hemedan, Ahmed, Aichem, Michael, Klein, Karsten, Czauderna, Tobias, Burtscher, Felicia, Yamada, Takahiro G., Hiki, Yusuke, Hiroi, Noriko F., Hu, Finterly, Pham, Nhung, Ehrhart, Friederike, Willighagen, Egon L., Valdeolivas, Alberto, Dugourd, Aurelien, Messina, Francesco, Esteban-Medina, Marina, Peña-Chilet, Maria, Rian, Kinza, Soliman, Sylvain, Aghamiri, Sara Sadat, Puniya, Bhanwar Lal, Naldi, Aurélien, Helikar, Tomáš, Singh, Vidisha, Fernández, Marco Fariñas, Bermudez, Viviam, Tsirvouli, Eirini, Montagud, Arnau, Noël, Vincent, Ponce-de-Leon, Miguel, Maier, Dieter, Bauch, Angela, Gyori, Benjamin M., Bachman, John A., Luna, Augustin, Piñero, Janet, Furlong, Laura I., Balaur, Irina, Rougny, Adrien, Jarosz, Yohan, Overall, Rupert, Phair, Robert, Perfetto, Livia, Matthews, Lisa, Rex, Devasahayam Arokia Balaya, Orlic-Milacic, Marija, Gomez, Luis Cristobal Monraz, De Meulder, Bertrand, Ravel, Jean Marie, Jassal, Bijay, Satagopam, Venkata, Wu, Guanming, Golebiewski, Martin, Gawron, Piotr, Calzone, Laurence, Beckmann, Jacques S., Evelo, Chris T., D’Eustachio, Peter, Schreiber, Falk, Saez-Rodriguez, Julio, Dopazo, Joaquin, Kuiper, Martin, Valencia, Alfonso, Wolkenhauer, Olaf, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, and Schneider, Reinhard

Abstract

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies., Peer Reviewed

Published
2024

10. Genopyc: a Python library for investigating the functional effects of genomic variants associated to complex diseases.

Author

Gualdi, Francesco, Oliva, Baldomero, and Piñero, Janet

Subjects
GENOME-wide association studies, INTERVERTEBRAL disk, LIBRARY design & construction, GENETIC variation, RESEARCH personnel
Abstract

Motivation Integrative Biomedicl Informatics, Research Program on Biomedical Informatics (IBI - GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Universitat Pompeu Fabra (UPF) C/ del Dr. Aiguader 88 Barcelona 08003 Spain. Understanding the genetic basis of complex diseases is one of the main challenges in modern genomics. However, current tools often lack the versatility to efficiently analyze the intricate relationships between genetic variations and disease outcomes. To address this, we introduce Genopyc, a novel Python library designed for comprehensive investigation of how the variants associated to complex diseases affects downstream pathways. Genopyc offers an extensive suite of functions for heterogeneous data mining and visualization, enabling researchers to delve into and integrate biological information from large-scale genomic datasets. Results In this work, we present the Genopyc library through application to real-world genome wide association studies variants. Using Genopyc to investigate the functional consequences of variants associated to intervertebral disc degeneration enabled a deeper understanding of the potential dysregulated pathways involved in the disease, which can be explored and visualized by exploiting the functionalities featured in the package. Genopyc emerges as a powerful asset for researchers, facilitating the investigation of complex diseases paving the way for more targeted therapeutic interventions. Availability and implementation Genopyc is available on pip https://pypi.org/project/genopyc/.The source code of Genopyc is available at https://github.com/freh-g/genopyc. A tutorial notebook is available at https://github.com/freh-g/genopyc/blob/main/tutorials/Genopyc%5ftutorial%5fnotebook.ipynb. Finally, a detailed documentation is available at: https://genopyc.readthedocs.io/en/latest/. [ABSTRACT FROM AUTHOR]

Published
2024
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12. A dynamic model of the intestinal epithelium integrates multiple sources of preclinical data and enables clinical translation of drug‐induced toxicity

Author

Gall, Louis, primary, Jardi, Ferran, additional, Lammens, Lieve, additional, Piñero, Janet, additional, Souza, Terezinha M., additional, Rodrigues, Daniela, additional, Jennen, Danyel G. J., additional, de Kok, Theo M., additional, Coyle, Luke, additional, Chung, Seung‐Wook, additional, Ferreira, Sofia, additional, Jo, Heeseung, additional, Beattie, Kylie A., additional, Kelly, Colette, additional, Duckworth, Carrie A., additional, Pritchard, D. Mark, additional, and Pin, Carmen, additional

Published
2023
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13. ELIXIR-CONVERGE D5.5 Report on the remaining four DMP processes

Author

Portell-Silva, Laura, Haurheeram, Vanita, Adam-Blondon, Anne-Françoise, Capella-Gutierrez, Salvador, Popleteeva, Marina, Bianchini, Federico, Åberg, Espen, Hooft, Rob, Schuánek, Marek, Slifka, Jan, Hospital, Adam, Willassen, Nils-Peder, Piñero, Janet, Sanz, Ferran, Ramirez-Anguita, Juan Manuel, Pastor, Manuel, Angel Mayer, Miguel, Picardi, Ernesto, Alper, Pinar, Ded, Vilém, Djenaba Barry, Nene, Lieby, Paulette, D'Altri, Teresa, Faria, Daniel, Le Floch, Erwin, Rocca-Serra, Philippe, Droesbeke, Bert, Bösl, Korbinian, Vidak, Marko, Pommier, Cyril, Beier, Sebastian, Lange, Matthias, Arend, Daniel, Zlender, Nadja, and Alic, Isabelle

Subjects
RDMKit, DMPs, ELIXIR-CONVERGE, Data Management Plans, ELIXIR, Data Management
Abstract

The main objective of this deliverable is to provide resources that can assist life sciences researchers and data stewards in creating reference Data Management Plans (DMPs) for their research projects. The resources provided in this deliverable are intended to promote good research data management practices across the EU research landscape. To achieve this objective, a range of activities were undertaken, with a particular focus on the needs of the different domains covered by the demonstrator use-cases. In order to create these resources, the RDMkit pages were extended to include domain-specific information, which can be used as a reference when developing DMPs for different research projects. These pages fall under the "Your Domain" category and provide specific information on the data management needs and considerations for each domain. They also highlight challenges that are specific to each domain, such as data types, species, or areas, and offer solutions and considerations to overcome these challenges. In this deliverable, the RDMkit page for the Toxicology data demonstrator use-case was completed and added to the existing RDMkit pages for the other demonstrator use-cases. Additionally, a new Tool Assembly was added to the RDMkit corresponding to the Plant Sciences demonstrator use-case, covering the entire life cycle of experimental plant phenotyping data. In addition, the general Knowledge Models (KMs) of the Data Stewardship Wizard (DSW) were adapted to address the specific DMP questions needed for each demonstrator use-case. The DSW is a collaborative tool that enables data stewards and researchers to efficiently create DMPs for their research projects and it is designed with a hierarchical KM that guides users through the creation of DMPs. Since the relevant information for DMPs can vary across different domains, these KMs can be modified to contain the information relevant for each demonstrator use-case. For this deliverable, special focus was put on two of the demonstrator use-cases, namely Toxicology and Epitranscriptomics data. Additionally, related to the Human Data use-case, separate efforts are underway to enhance the sensitive data section of the KM system to ensure the proper management of such data. The improvements and new question suggestions that were found during these sessions were incorporated into the DSW KM by the DSW team. Furthermore, DMP templates were created in DSW for the demonstrator use-case using two standard approaches: creating a KM or a project template (PT). When creating a PT, a set of answers is saved and can be used to generate a partially pre-filled questionnaire for a new project. In the ideal case scenario, the two methods can be used together to provide domain-specific recommendations by answering questions that better reflect a scientific domain, such as metadata standards. In conclusion, this deliverable provides several valuable resources for life sciences researchers and data stewards, including extended RDMkit pages, customised DSW KMs, domain-specific DMP templates, and a new KM for creating DPIAs. These resources are designed to encourage good research data management practices across the EU research landscape, ensuring that valuable research data is effectively managed before, during, and after a project.

Published
2023
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14. Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

Author

Sanofi, Instituto de Salud Carlos III, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), German Research Foundation, Ministero della Salute, European Commission, Generalitat de Catalunya, National Institutes of Health (US), Klaus Tschira Foundation, National Library of Medicine (US), Niarakis, Anna, Ostaszewski, Marek, Mazein, Alexander, Kuperstein, Inna, Kutmon, Martina, Gillespie, Marc E., Funahashi, Akira, Acencio, Marcio Luis, Hemedan, Ahmed, Aichem, Michael, Klein, Karsten, Czauderna, Tobias, Burtscher, Felicia, Yamada, Takahiro G., Hiki, Yusuke, Hiroi, Noriko F., Hu, Finterly, Pham, Nhung, Ehrhart, Friederike, Willighagen, Egon L., Valdeolivas, Alberto, Dugourd, Aurelien, Messina, Francesco, Esteban-Medina, Marina, Peña-Chilet, María, Rian, Kinza, Soliman, Sylvain, Aghamiri, Sara Sadat, Lal Puniya, Bhanwar, Naldi, Aurelien, Helikar, Tomas, Singh, Vidisha, Fariñas Fernández, Marco, Bermudez, Viviam, Tsirvouli, Eirini, Montagud, Arnau, Noël, Vincent, Ponce de León, Miguel, Maier, Dieter, Bauch, Angela, Gyori, Benjamin M., Bachman, John A., Luna, Augustin, Piñero, Janet, Furlong, Laura I., Balaur, Irina BalaurIrina, Rougny, Adrien, Jarosz, Yohan, Overall, Rupert W., Phair, Robert, Perfetto, Livia, Matthews, Lisa, Balaya Rex, Devasahayam Arokia, Orlic-Milacic, Marija, Monraz Gómez, Luis Cristóbal, De Meulder, Bertrand, Ravel, Jean Marie, Jassal, Bijay, Satagopam, Venkata, Wu, Guanming, Golebiewski, Martin, Gawron, Piotr, Calzone, Laurence, Beckmann, Jacques S., Evelo, Chris T., D’Eustachio, Peter, Schreiber, Falk, Sáez-Rodríguez, Julio, Dopazo, Joaquín, Kuiper, Martin, Valencia, Alfonso, Wolkenhauer, Olaf, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, Schneider, Reinhard, COVID- Disease Map Community the COVID-19 Disease Map Community, Sanofi, Instituto de Salud Carlos III, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), German Research Foundation, Ministero della Salute, European Commission, Generalitat de Catalunya, National Institutes of Health (US), Klaus Tschira Foundation, National Library of Medicine (US), Niarakis, Anna, Ostaszewski, Marek, Mazein, Alexander, Kuperstein, Inna, Kutmon, Martina, Gillespie, Marc E., Funahashi, Akira, Acencio, Marcio Luis, Hemedan, Ahmed, Aichem, Michael, Klein, Karsten, Czauderna, Tobias, Burtscher, Felicia, Yamada, Takahiro G., Hiki, Yusuke, Hiroi, Noriko F., Hu, Finterly, Pham, Nhung, Ehrhart, Friederike, Willighagen, Egon L., Valdeolivas, Alberto, Dugourd, Aurelien, Messina, Francesco, Esteban-Medina, Marina, Peña-Chilet, María, Rian, Kinza, Soliman, Sylvain, Aghamiri, Sara Sadat, Lal Puniya, Bhanwar, Naldi, Aurelien, Helikar, Tomas, Singh, Vidisha, Fariñas Fernández, Marco, Bermudez, Viviam, Tsirvouli, Eirini, Montagud, Arnau, Noël, Vincent, Ponce de León, Miguel, Maier, Dieter, Bauch, Angela, Gyori, Benjamin M., Bachman, John A., Luna, Augustin, Piñero, Janet, Furlong, Laura I., Balaur, Irina BalaurIrina, Rougny, Adrien, Jarosz, Yohan, Overall, Rupert W., Phair, Robert, Perfetto, Livia, Matthews, Lisa, Balaya Rex, Devasahayam Arokia, Orlic-Milacic, Marija, Monraz Gómez, Luis Cristóbal, De Meulder, Bertrand, Ravel, Jean Marie, Jassal, Bijay, Satagopam, Venkata, Wu, Guanming, Golebiewski, Martin, Gawron, Piotr, Calzone, Laurence, Beckmann, Jacques S., Evelo, Chris T., D’Eustachio, Peter, Schreiber, Falk, Sáez-Rodríguez, Julio, Dopazo, Joaquín, Kuiper, Martin, Valencia, Alfonso, Wolkenhauer, Olaf, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, Schneider, Reinhard, and COVID- Disease Map Community the COVID-19 Disease Map Community

Abstract

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing., Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors., Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19., Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

Published
2023

15. eTRANSAFE:data science to empower translational safety assessment

Author

Sanz, Ferran, Pognan, François, Steger-Hartmann, Thomas, Díaz, Carlos, Asakura, Shoji, Amberg, Alexander, Bécourt-Lhote, Nathalie, Blomberg, Niklas, Bosc, Nicolas, Briggs, Katharine, Bringezu, Frank, Brulle-Wohlhueter, Claire, Brunak, Søren, Bueters, Ruud, Callegaro, Giulia, Capella-Gutierrez, Salvador, Centeno, Emilio, Corvi, Javier, Cronin, Mark T.D., Drew, Philip, Duchateau-Nguyen, Guillemette, Ecker, Gerhard F., Escher, Sylvia, Felix, Eloy, Ferreiro, Miguel, Frericks, Markus, Furlong, Laura I., Geiger, Robert, George, Catherine, Grandits, Melanie, Ivanov-Draganov, Dragomir, Kilgour-Christie, Jean, Kiziloren, Tevfik, Kors, Jan A., Koyama, Naoki, Kreuchwig, Annika, Leach, Andrew R., Mayer, Miguel Angel, Monecke, Peter, Muster, Wolfgang, Nakazawa, Chihiro Miyamoto, Nicholson, Gavin, Parry, Rowan, Pastor, Manuel, Piñero, Janet, Oberhauser, Nils, Ramírez-Anguita, Juan Manuel, Rodrigo, Adrián, Smajic, Aljosa, Schaefer, Markus, Schieferdecker, Sebastian, Soininen, Inari, Terricabras, Emma, Trairatphisan, Panuwat, Turner, Sean C., Valencia, Alfonso, van de Water, Bob, van der Lei, Johan L., van Mulligen, Erik M., Vock, Esther, Wilkinson, David, Sanz, Ferran, Pognan, François, Steger-Hartmann, Thomas, Díaz, Carlos, Asakura, Shoji, Amberg, Alexander, Bécourt-Lhote, Nathalie, Blomberg, Niklas, Bosc, Nicolas, Briggs, Katharine, Bringezu, Frank, Brulle-Wohlhueter, Claire, Brunak, Søren, Bueters, Ruud, Callegaro, Giulia, Capella-Gutierrez, Salvador, Centeno, Emilio, Corvi, Javier, Cronin, Mark T.D., Drew, Philip, Duchateau-Nguyen, Guillemette, Ecker, Gerhard F., Escher, Sylvia, Felix, Eloy, Ferreiro, Miguel, Frericks, Markus, Furlong, Laura I., Geiger, Robert, George, Catherine, Grandits, Melanie, Ivanov-Draganov, Dragomir, Kilgour-Christie, Jean, Kiziloren, Tevfik, Kors, Jan A., Koyama, Naoki, Kreuchwig, Annika, Leach, Andrew R., Mayer, Miguel Angel, Monecke, Peter, Muster, Wolfgang, Nakazawa, Chihiro Miyamoto, Nicholson, Gavin, Parry, Rowan, Pastor, Manuel, Piñero, Janet, Oberhauser, Nils, Ramírez-Anguita, Juan Manuel, Rodrigo, Adrián, Smajic, Aljosa, Schaefer, Markus, Schieferdecker, Sebastian, Soininen, Inari, Terricabras, Emma, Trairatphisan, Panuwat, Turner, Sean C., Valencia, Alfonso, van de Water, Bob, van der Lei, Johan L., van Mulligen, Erik M., Vock, Esther, and Wilkinson, David

Published
2023

17. Assessing network-based methods in the context of system toxicology.

Author

Valls-Margarit, Jordi, Piñero, Janet, Füzi, Barbara, Cerisier, Natacha, Taboureau, Olivier, and Furlong, Laura I.

Subjects
TOXICOLOGY, DRUG toxicity, VALPROIC acid, PHARMACODYNAMICS, SYSTEMS biology, COMMUNITIES
Abstract

Introduction: Network-based methods are promising approaches in systems toxicology because they can be used to predict the effects of drugs and chemicals on health, to elucidate the mode of action of compounds, and to identify biomarkers of toxicity. Over the years, the network biology community has developed a wide range of methods, and users are faced with the task of choosing the most appropriate method for their own application. Furthermore, the advantages and limitations of each method are difficult to determine without a proper standard and comparative evaluation of their performance. This study aims to evaluate different network-based methods that can be used to gain biological insight into the mechanisms of drug toxicity, using valproic acid (VPA)-induced liver steatosis as a benchmark. Methods: We provide a comprehensive analysis of the results produced by each method and highlight the fact that the experimental design (how the method is applied) is relevant in addition to the method specifications. We also contribute with a systematic methodology to analyse the results of the methods individually and in a comparative manner. Results: Our results show that the evaluated tools differ in their performance against the benchmark and in their ability to provide novel insights into the mechanism of adverse effects of the drug. We also suggest that aggregation of the results provided by different methods provides a more confident set of candidate genes and processes to further the knowledge of the drug's mechanism of action. Discussion: By providing a detailed and systematic analysis of the results of different network-based tools, we aim to assist users in making informed decisions about the most appropriate method for systems toxicology applications. [ABSTRACT FROM AUTHOR]

Published
2023
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20. The eTRANSAFE project on translational safety assessment through integrative knowledge management: achievements and perspectives

Author

Barcelona Supercomputing Center, Pognan, François, Steger-Hartmann, Thomas, Díaz, Carlos, Blomberg, Niklas, Bringezu, Frank, Briggs, Katharine, Callegaro, Giulia, Capella Gutiérrez, Salvador, Centeno, Emilio, Corvi, Javier, Drew, Philip, Drewe, William C., Fernández González, Jose María, Furlong, Laura I., Guney, Emre, Kors, Jan A., Mayer, Miguel Angel, Pastor, Manuel, Piñero, Janet, Ramírez-Anguita, Juan Manuel, Ronzano, Francesco, Rowell, Philip, Saüch-Pitarch, Josep, Valencia, Alfonso, Water, Bob van de, Lei, Johan van der, Mulligen, Erik van, Sanz, Ferran, Barcelona Supercomputing Center, Pognan, François, Steger-Hartmann, Thomas, Díaz, Carlos, Blomberg, Niklas, Bringezu, Frank, Briggs, Katharine, Callegaro, Giulia, Capella Gutiérrez, Salvador, Centeno, Emilio, Corvi, Javier, Drew, Philip, Drewe, William C., Fernández González, Jose María, Furlong, Laura I., Guney, Emre, Kors, Jan A., Mayer, Miguel Angel, Pastor, Manuel, Piñero, Janet, Ramírez-Anguita, Juan Manuel, Ronzano, Francesco, Rowell, Philip, Saüch-Pitarch, Josep, Valencia, Alfonso, Water, Bob van de, Lei, Johan van der, Mulligen, Erik van, and Sanz, Ferran

Abstract

eTRANSAFE is a research project funded within the Innovative Medicines Initiative (IMI), which aims at developing integrated databases and computational tools (the eTRANSAFE ToxHub) that support the translational safety assessment of new drugs by using legacy data provided by the pharmaceutical companies that participate in the project. The project objectives include the development of databases containing preclinical and clinical data, computational systems for translational analysis including tools for data query, analysis and visualization, as well as computational models to explain and predict drug safety events., This research received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreements eTRANSAFE (777365). This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA companies in kind contribution. The Research Programme on Biomedical Informatics (GRIB) of IMIM and DCEXS-UPF is a member of the Spanish National Bioinformatics Institute (INB), PRB2-ISCIII and is supported by grant PT13/0001/0023, of the PE I + D + i 2013–2016, funded by ISCIII and FEDER. The GRIB also receive support from Agència de Gestió D’ajuts Universitaris i de Recerca Generalitat de Catalunya (AGAUR, ref.: 2017SGR01020). The DCEXS is a “Unidad de Excelencia María de Maeztu”, funded by the MINECO (ref: MDM-2014-0370)., Peer Reviewed, Postprint (published version)

Published
2021

21. DOME: recommendations for supervised machine learning validation in biology

Author

Walsh, Ian, Fishman, Dmytro, Garcia-Gasulla, Dario, Titma, Tiina, Pollastri, Gianluca, Capriotti, Emidio, Casadio, Rita RC, Capella-Gutierrez, Salvador, Cirillo, Davide, Del Conte, Alessio, Dimopoulos, Alexandros A.C., Del Angel, Victoria Dominguez, Dopazo, Joaquin, Fariselli, Piero, Fernández, José Maria, Huber, Florian, Kreshuk, Anna, Lenaerts, Tom, Martelli, Pier Luigi, Navarro, Arcadi, Broin, Pilib, Piñero, Janet, Piovesan, Damiano, Reczko, Martin, Ronzano, Francesco, Satagopam, Venkata, Savojardo, Castrense, Spiwok, Vojtěch, Tangaro, Marco Antonio, Tartari, Giacomo, Salgado, David, Valencia, Alfonso, Zambelli, Federico, Harrow, Jennifer, Psomopoulos, Fotis F.E., Tosatto, Silvio S.C.E., Walsh, Ian, Fishman, Dmytro, Garcia-Gasulla, Dario, Titma, Tiina, Pollastri, Gianluca, Capriotti, Emidio, Casadio, Rita RC, Capella-Gutierrez, Salvador, Cirillo, Davide, Del Conte, Alessio, Dimopoulos, Alexandros A.C., Del Angel, Victoria Dominguez, Dopazo, Joaquin, Fariselli, Piero, Fernández, José Maria, Huber, Florian, Kreshuk, Anna, Lenaerts, Tom, Martelli, Pier Luigi, Navarro, Arcadi, Broin, Pilib, Piñero, Janet, Piovesan, Damiano, Reczko, Martin, Ronzano, Francesco, Satagopam, Venkata, Savojardo, Castrense, Spiwok, Vojtěch, Tangaro, Marco Antonio, Tartari, Giacomo, Salgado, David, Valencia, Alfonso, Zambelli, Federico, Harrow, Jennifer, Psomopoulos, Fotis F.E., and Tosatto, Silvio S.C.E.

Abstract

In the version of this Comment initially published, an error appeared in the “Specificity” equation displayed in the middle-right panel of Fig. 2. Originally reading “ fp/fp+tn”, the equation has been corrected to read: “ tn/tn+fp”. The error has been corrected in the online version of the Article. *A list of authors and their affiliations appears online., SCOPUS: no.j, SCOPUS: er.j, info:eu-repo/semantics/published

Published
2021

22. The etransafe project on translational safety assessment through integrative knowledge management:Achievements and perspectives

Author

Pognan, François, Steger‐hartmann, Thomas, Díaz, Carlos, Blomberg, Niklas, Bringezu, Frank, Briggs, Katharine, Callegaro, Giulia, Capella‐gutierrez, Salvador, Centeno, Emilio, Corvi, Javier, Drew, Philip, Drewe, William C., Fernández, José M., Furlong, Laura I., Guney, Emre, Kors, Jan A., Mayer, Miguel Angel, Pastor, Manuel, Piñero, Janet, Ramírez‐anguita, Juan Manuel, Ronzano, Francesco, Rowell, Philip, Saüch‐pitarch, Josep, Valencia, Alfonso, van de Water, Bob, van der Lei, Johan, van Mulligen, Erik, Sanz, Ferran, Pognan, François, Steger‐hartmann, Thomas, Díaz, Carlos, Blomberg, Niklas, Bringezu, Frank, Briggs, Katharine, Callegaro, Giulia, Capella‐gutierrez, Salvador, Centeno, Emilio, Corvi, Javier, Drew, Philip, Drewe, William C., Fernández, José M., Furlong, Laura I., Guney, Emre, Kors, Jan A., Mayer, Miguel Angel, Pastor, Manuel, Piñero, Janet, Ramírez‐anguita, Juan Manuel, Ronzano, Francesco, Rowell, Philip, Saüch‐pitarch, Josep, Valencia, Alfonso, van de Water, Bob, van der Lei, Johan, van Mulligen, Erik, and Sanz, Ferran

Abstract

eTRANSAFE is a research project funded within the Innovative Medicines Initiative (IMI), which aims at developing integrated databases and computational tools (the eTRANSAFE ToxHub) that support the translational safety assessment of new drugs by using legacy data provided by the pharmaceutical companies that participate in the project. The project objectives include the development of databases containing preclinical and clinical data, computational systems for translational analysis including tools for data query, analysis and visualization, as well as computational models to explain and predict drug safety events.

Published
2021

23. SARS-CoV-2 sculpts the immune system to induce sustained virus-specific naïve-like and memory B cell responses

Author

de Campos-Mata, Leire, primary, Vaquero, Sonia Tejedor, additional, Tachó-Piñot, Roser, additional, Piñero, Janet, additional, Grasset, Emilie K., additional, Aldea, Itziar Arrieta, additional, Melero, Natalia Rodrigo, additional, Carolis, Carlo, additional, Horcajada, Juan P., additional, Cerutti, Andrea, additional, Villar-García, Judit, additional, and Magri, Giuliana, additional

Published
2021
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24. The eTRANSAFE Project on Translational Safety Assessment through Integrative Knowledge Management: Achievements and Perspectives

Author

Pognan, François, primary, Steger-Hartmann, Thomas, additional, Díaz, Carlos, additional, Blomberg, Niklas, additional, Bringezu, Frank, additional, Briggs, Katharine, additional, Callegaro, Giulia, additional, Capella-Gutierrez, Salvador, additional, Centeno, Emilio, additional, Corvi, Javier, additional, Drew, Philip, additional, Drewe, William C., additional, Fernández, José M., additional, Furlong, Laura I., additional, Guney, Emre, additional, Kors, Jan A., additional, Mayer, Miguel Angel, additional, Pastor, Manuel, additional, Piñero, Janet, additional, Ramírez-Anguita, Juan Manuel, additional, Ronzano, Francesco, additional, Rowell, Philip, additional, Saüch-Pitarch, Josep, additional, Valencia, Alfonso, additional, van de Water, Bob, additional, van der Lei, Johan, additional, van Mulligen, Erik, additional, and Sanz, Ferran, additional

Published
2021
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25. SARS‐CoV‐2 sculpts the immune system to induce sustained virus‐specific naïve‐like and memory B‐cell responses

Author

de Campos‐Mata, Leire, primary, Tejedor Vaquero, Sonia, additional, Tachó‐Piñot, Roser, additional, Piñero, Janet, additional, Grasset, Emilie K, additional, Arrieta Aldea, Itziar, additional, Rodrigo Melero, Natalia, additional, Carolis, Carlo, additional, Horcajada, Juan P, additional, Cerutti, Andrea, additional, Villar‐García, Judit, additional, and Magri, Giuliana, additional

Published
2021
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28. SARS-CoV-2-Specific Antibody Profiles Distinguish Patients with Moderate from Severe COVID-19

Author

de Campos Mata, Leire, primary, Piñero, Janet, additional, Vaquero, Sonia Tejedor, additional, Tachó-Piñot, Roser, additional, Kuksin, Maria, additional, Aldea, Itziar Arrieta, additional, Melero, Natalia Rodrigo, additional, Carolis, Carlo, additional, Furlong, Laura, additional, Cerutti, Andrea, additional, Villar-García, Judit, additional, and Magri, Giuliana, additional

Published
2020
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29. The ELIXIR Human Copy Number Variations Community: building bioinformatics infrastructure for research

Author

Salgado, David, primary, Armean, Irina M., additional, Baudis, Michael, additional, Beltran, Sergi, additional, Capella-Gutierrez, Salvador, additional, Carvalho-Silva, Denise, additional, Dominguez Del Angel, Victoria, additional, Dopazo, Joaquin, additional, Furlong, Laura I., additional, Gao, Bo, additional, Garcia, Leyla, additional, Gerloff, Dietlind, additional, Gut, Ivo, additional, Gyenesei, Attila, additional, Habermann, Nina, additional, Hancock, John M., additional, Hanauer, Marc, additional, Hovig, Eivind, additional, Johansson, Lennart F., additional, Keane, Thomas, additional, Korbel, Jan, additional, Lauer, Katharina B., additional, Laurie, Steve, additional, Leskošek, Brane, additional, Lloyd, David, additional, Marques-Bonet, Tomas, additional, Mei, Hailiang, additional, Monostory, Katalin, additional, Piñero, Janet, additional, Poterlowicz, Krzysztof, additional, Rath, Ana, additional, Samarakoon, Pubudu, additional, Sanz, Ferran, additional, Saunders, Gary, additional, Sie, Daoud, additional, Swertz, Morris A., additional, Tsukanov, Kirill, additional, Valencia, Alfonso, additional, Vidak, Marko, additional, Yenyxe González, Cristina, additional, Ylstra, Bauke, additional, and Béroud, Christophe, additional

Published
2020
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31. ResMarkerDB: a database of biomarkers of response to antibody therapy in breast and colorectal cancer

Author

Pérez-Granado, Judith, Piñero, Janet, and Furlong, Laura I

Subjects
Male, Databases, Factual, Antibodies, Neoplasm, Colorectal cancer, MEDLINE, Breast Neoplasms, Translational research, Context (language use), Disease, computer.software_genre, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, Breast cancer, Biomarkers, Tumor, medicine, Humans, 030304 developmental biology, 0303 health sciences, Database, business.industry, 030302 biochemistry & molecular biology, Cancer, medicine.disease, 3. Good health, Database Tool, Còlon -- Càncer, Marcadors bioquímics, Mama -- Càncer, Biomarker (medicine), Female, Colorectal Neoplasms, General Agricultural and Biological Sciences, business, computer, Information Systems
Abstract

The clinical efficacy of therapeutic monoclonal antibodies for breast and colorectal cancer has greatly contributed to the improvement of patients' outcomes by individualizing their treatments according to their genomic background. However, primary or acquired resistance to treatment reduces its efficacy. In this context, the identification of biomarkers predictive of drug response would support research and development of new alternative treatments. Biomarkers play a major role in the genomic revolution, supporting disease diagnosis and treatment decision-making. Currently, several molecular biomarkers of treatment response for breast and colorectal cancer have been described. However, information on these biomarkers is scattered across several resources, and needs to be identified, collected and properly integrated to be fully exploited to inform monitoring of drug response in patients. Therefore, there is a need of resources that offer biomarker data in a harmonized manner to the user to support the identification of actionable biomarkers of response to treatment in cancer. ResMarkerDB was developed as a comprehensive resource of biomarkers of drug response in colorectal and breast cancer. It integrates data of biomarkers of drug response from existing repositories, and new data extracted and curated from the literature (referred as ResCur). ResMarkerDB currently features 266 biomarkers of diverse nature. Twenty-five percent of these biomarkers are exclusive of ResMarkerDB. Furthermore, ResMarkerDB is one of the few resources offering non-coding DNA data in response to drug treatment. The database contains more than 500 biomarker-drug-tumour associations, covering more than 100 genes. ResMarkerDB provides a web interface to facilitate the exploration of the current knowledge of biomarkers of response in breast and colorectal cancer. It aims to enhance translational research efforts in identifying actionable biomarkers of drug response in cancer. Instituto de Salud Carlos III-Fondo Europeo de Desarrollo Regional [grant numbers: PIE15/00008, CP10/00524, CPII16/00026]; Instituto de Salud Carlos III-Fondo Social Europeo [FI18/00034]; and the European Commission Horizon 2020 Programme 2014–2020 under grant agreements MedBioinformatics [grant number: 634143] and Elixir-Excelerate [grant number: 676559]. The Research Programme on Biomedical Informatics is a member of the Spanish National Bioinformatics Institute, Plataforma de Recursos Biomoleculares y Bioinformáticos-Instituto de Salud Carlos III [grant number: PT13/0001/0023], of the PE I + D + i 2013–2016, funded by Instituto de Salud Carlos III and Fondo Europeo de Desarrollo Regional. The Departamento de Ciencias Experimentales y de la Salud is a Unidad de Excelencia María de Maeztu, funded by the Ministerio de Economía y Competitividad (reference number: MDM-2014-0370).

Published
2019

33. Pancreatic cancer and autoimmune diseases: An association sustained by computational and epidemiological case–control approaches

Author

Universidad de Alicante. Departamento de Fisiología, Genética y Microbiología, Gomez‐Rubio, Paulina, Piñero, Janet, Molina‐Montes, Esther, Gutiérrez‐Sacristán, Alba, Marquez, Mirari, Rava, Marta, Michalski, Christoph W., Farré, Antoni, Molero, Xavier, Löhr, Matthias, Perea, José, Greenhalf, William, O'Rorke, Michael, Tardón, Adonina, Gress, Thomas, Barberá, Víctor Manuel, Crnogorac‐Jurcevic, Tatjana, Muñoz‐Bellvís, Luís, Domínguez‐Muñoz, Enrique, Balsells, Joaquim, Costello, Eithne, Yu, Jingru, Iglesias, Mar, Ilzarbe, Lucas, Kleeff, Jörg, Kong, Bo, Mora, Josefina, Murray, Liam, O'Driscoll, Damian, Poves, Ignasi, Lawlor, Rita T., Ye, Weimin, Hidalgo, Manuel, Scarpa, Aldo, Sharp, Linda, Carrato, Alfredo, Real, Francisco X., Furlong, Laura I., Malats, Núria, PanGenEU Study Investigators, Universidad de Alicante. Departamento de Fisiología, Genética y Microbiología, Gomez‐Rubio, Paulina, Piñero, Janet, Molina‐Montes, Esther, Gutiérrez‐Sacristán, Alba, Marquez, Mirari, Rava, Marta, Michalski, Christoph W., Farré, Antoni, Molero, Xavier, Löhr, Matthias, Perea, José, Greenhalf, William, O'Rorke, Michael, Tardón, Adonina, Gress, Thomas, Barberá, Víctor Manuel, Crnogorac‐Jurcevic, Tatjana, Muñoz‐Bellvís, Luís, Domínguez‐Muñoz, Enrique, Balsells, Joaquim, Costello, Eithne, Yu, Jingru, Iglesias, Mar, Ilzarbe, Lucas, Kleeff, Jörg, Kong, Bo, Mora, Josefina, Murray, Liam, O'Driscoll, Damian, Poves, Ignasi, Lawlor, Rita T., Ye, Weimin, Hidalgo, Manuel, Scarpa, Aldo, Sharp, Linda, Carrato, Alfredo, Real, Francisco X., Furlong, Laura I., Malats, Núria, and PanGenEU Study Investigators

Abstract

Deciphering the underlying genetic basis behind pancreatic cancer (PC) and its associated multimorbidities will enhance our knowledge toward PC control. The study investigated the common genetic background of PC and different morbidities through a computational approach and further evaluated the less explored association between PC and autoimmune diseases (AIDs) through an epidemiological analysis. Gene‐disease associations (GDAs) of 26 morbidities of interest and PC were obtained using the DisGeNET public discovery platform. The association between AIDs and PC pointed by the computational analysis was confirmed through multivariable logistic regression models in the PanGen European case–control study population of 1,705 PC cases and 1,084 controls. Fifteen morbidities shared at least one gene with PC in the DisGeNET database. Based on common genes, several AIDs were genetically associated with PC pointing to a potential link between them. An epidemiologic analysis confirmed that having any of the nine AIDs studied was significantly associated with a reduced risk of PC (Odds Ratio (OR) = 0.74, 95% confidence interval (CI) 0.58–0.93) which decreased in subjects having ≥2 AIDs (OR = 0.39, 95%CI 0.21–0.73). In independent analyses, polymyalgia rheumatica, and rheumatoid arthritis were significantly associated with low PC risk (OR = 0.40, 95%CI 0.19–0.89, and OR = 0.73, 95%CI 0.53–1.00, respectively). Several inflammatory‐related morbidities shared a common genetic component with PC based on public databases. These molecular links could shed light into the molecular mechanisms underlying PC development and simultaneously generate novel hypotheses. In our study, we report sound findings pointing to an association between AIDs and a reduced risk of PC.

Published
2019

41. Pancreatic cancer and autoimmune diseases: An association sustained by computational and epidemiological case–control approaches.

Author

Gomez‐Rubio, Paulina, Piñero, Janet, Molina‐Montes, Esther, Gutiérrez‐Sacristán, Alba, Marquez, Mirari, Rava, Marta, Michalski, Christoph W., Farré, Antoni, Molero, Xavier, Löhr, Matthias, Perea, José, Greenhalf, William, O'Rorke, Michael, Tardón, Adonina, Gress, Thomas, Barberá, Victor M., Crnogorac‐Jurcevic, Tatjana, Muñoz‐Bellvís, Luís, Domínguez‐Muñoz, Enrique, and Balsells, Joaquim

Abstract

Deciphering the underlying genetic basis behind pancreatic cancer (PC) and its associated multimorbidities will enhance our knowledge toward PC control. The study investigated the common genetic background of PC and different morbidities through a computational approach and further evaluated the less explored association between PC and autoimmune diseases (AIDs) through an epidemiological analysis. Gene‐disease associations (GDAs) of 26 morbidities of interest and PC were obtained using the DisGeNET public discovery platform. The association between AIDs and PC pointed by the computational analysis was confirmed through multivariable logistic regression models in the PanGen European case–control study population of 1,705 PC cases and 1,084 controls. Fifteen morbidities shared at least one gene with PC in the DisGeNET database. Based on common genes, several AIDs were genetically associated with PC pointing to a potential link between them. An epidemiologic analysis confirmed that having any of the nine AIDs studied was significantly associated with a reduced risk of PC (Odds Ratio (OR) = 0.74, 95% confidence interval (CI) 0.58–0.93) which decreased in subjects having ≥2 AIDs (OR = 0.39, 95%CI 0.21–0.73). In independent analyses, polymyalgia rheumatica, and rheumatoid arthritis were significantly associated with low PC risk (OR = 0.40, 95%CI 0.19–0.89, and OR = 0.73, 95%CI 0.53–1.00, respectively). Several inflammatory‐related morbidities shared a common genetic component with PC based on public databases. These molecular links could shed light into the molecular mechanisms underlying PC development and simultaneously generate novel hypotheses. In our study, we report sound findings pointing to an association between AIDs and a reduced risk of PC. What's new? Deaths from pancreatic cancer are increasing making it a public health emergency to define the molecular causes of this deadly disease. Here the authors show that autoimmune diseases share genetic components with pancreatic cancer and further corroborate this association in a case‐control study. This could bring new mechanistic understanding of pancreatic cancer, potentially impacting its prevention and treatment in the future. [ABSTRACT FROM AUTHOR]

Published
2019
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42. A systems approach identifies time-dependent associations of multimorbidities with pancreatic cancer risk

Author

Universidad de Alicante. Departamento de Fisiología, Genética y Microbiología, Gomez-Rubio, Paulina, Rosato, V., Marquez, Mirari, Bosetti, C., Molina-Montes, Esther, Rava, Marta, Piñero, Janet, Michalski, Christoph W., Farré, Antoni, Molero, Xavier, Löhr, Matthias, Ilzarbe, L., Perea, José, Greenhalf, William, O’Rorke, Michael, Tardón, Adonina, Gress, Thomas, Barberá, Víctor Manuel, Crnogorac-Jurcevic, Tatjana, Muñoz-Bellvís, Luís, Domínguez-Muñoz, Enrique, Gutiérrez-Sacristán, Alba, Balsells, Joaquim, Costello, Eithne, Guillén-Ponce, Carmen, Huang, J., Iglesias, Mar, Kleeff, Jörg, Kong, Bo, Mora, Josefina, Murray, Liam, O’Driscoll, Damian, Peláez, P., Poves, Ignasi, Lawlor, Rita T., Carrato, Alfredo, Hidalgo, Manuel, Scarpa, Aldo, Sharp, Linda, Furlong, Laura I., Real, Francisco X., La Vecchia, C., Malats, Núria, Universidad de Alicante. Departamento de Fisiología, Genética y Microbiología, Gomez-Rubio, Paulina, Rosato, V., Marquez, Mirari, Bosetti, C., Molina-Montes, Esther, Rava, Marta, Piñero, Janet, Michalski, Christoph W., Farré, Antoni, Molero, Xavier, Löhr, Matthias, Ilzarbe, L., Perea, José, Greenhalf, William, O’Rorke, Michael, Tardón, Adonina, Gress, Thomas, Barberá, Víctor Manuel, Crnogorac-Jurcevic, Tatjana, Muñoz-Bellvís, Luís, Domínguez-Muñoz, Enrique, Gutiérrez-Sacristán, Alba, Balsells, Joaquim, Costello, Eithne, Guillén-Ponce, Carmen, Huang, J., Iglesias, Mar, Kleeff, Jörg, Kong, Bo, Mora, Josefina, Murray, Liam, O’Driscoll, Damian, Peláez, P., Poves, Ignasi, Lawlor, Rita T., Carrato, Alfredo, Hidalgo, Manuel, Scarpa, Aldo, Sharp, Linda, Furlong, Laura I., Real, Francisco X., La Vecchia, C., and Malats, Núria

Abstract

Background: Pancreatic ductal adenocarcinoma (PDAC) is usually diagnosed in late adulthood; therefore, many patients suffer or have suffered from other diseases. Identifying disease patterns associated with PDAC risk may enable a better characterization of high-risk patients. Methods: Multimorbidity patterns (MPs) were assessed from 17 self-reported conditions using hierarchical clustering, principal component, and factor analyses in 1705 PDAC cases and 1084 controls from a European population. Their association with PDAC was evaluated using adjusted logistic regression models. Time since diagnosis of morbidities to PDAC diagnosis/recruitment was stratified into recent (<3 years) and long term (≥3 years). The MPs and PDAC genetic networks were explored with DisGeNET bioinformatics-tool which focuses on gene-diseases associations available in curated databases. Results: Three MPs were observed: gastric (heartburn, acid regurgitation, Helicobacter pylori infection, and ulcer), metabolic syndrome (obesity, type-2 diabetes, hypercholesterolemia, and hypertension), and atopic (nasal allergies, skin allergies, and asthma). Strong associations with PDAC were observed for ≥2 recently diagnosed gastric conditions [odds ratio (OR), 6.13; 95% confidence interval CI 3.01–12.5)] and for ≥3 recently diagnosed metabolic syndrome conditions (OR, 1.61; 95% CI 1.11–2.35). Atopic conditions were negatively associated with PDAC (high adherence score OR for tertile III, 0.45; 95% CI, 0.36–0.55). Combining type-2 diabetes with gastric MP resulted in higher PDAC risk for recent (OR, 7.89; 95% CI 3.9–16.1) and long-term diagnosed conditions (OR, 1.86; 95% CI 1.29–2.67). A common genetic basis between MPs and PDAC was observed in the bioinformatics analysis. Conclusions: Specific multimorbidities aggregate and associate with PDAC in a time-dependent manner. A better characterization of a high-risk population for PDAC may help in the early diagnosis of this cancer. The common genetic basis b

Published
2017

50. Network medicine analysis of COPD multimorbidities

Author

Grosdidier, Solène, primary, Ferrer, Antoni, additional, Faner, Rosa, additional, Piñero, Janet, additional, Roca, Josep, additional, Cosío, Borja, additional, Agustí, Alvar, additional, Gea, Joaquim, additional, Sanz, Ferran, additional, and Furlong, Laura I, additional

Published
2014
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