TY - JOUR T1 - Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. JF - Nat Commun Y1 - 2019 A1 - Menden, Michael P A1 - Wang, Dennis A1 - Mason, Mike J A1 - Szalai, Bence A1 - Bulusu, Krishna C A1 - Guan, Yuanfang A1 - Yu, Thomas A1 - Kang, Jaewoo A1 - Jeon, Minji A1 - Wolfinger, Russ A1 - Nguyen, Tin A1 - Zaslavskiy, Mikhail A1 - Jang, In Sock A1 - Ghazoui, Zara A1 - Ahsen, Mehmet Eren A1 - Vogel, Robert A1 - Neto, Elias Chaibub A1 - Norman, Thea A1 - Tang, Eric K Y A1 - Garnett, Mathew J A1 - Veroli, Giovanni Y Di A1 - Fawell, Stephen A1 - Stolovitzky, Gustavo A1 - Guinney, Justin A1 - Dry, Jonathan R A1 - Saez-Rodriguez, Julio KW - ADAM17 Protein KW - Antineoplastic Combined Chemotherapy Protocols KW - Benchmarking KW - Biomarkers, Tumor KW - Cell Line, Tumor KW - Computational Biology KW - Datasets as Topic KW - Drug Antagonism KW - Drug Resistance, Neoplasm KW - Drug Synergism KW - Genomics KW - Humans KW - Molecular Targeted Therapy KW - mutation KW - Neoplasms KW - pharmacogenetics KW - Phosphatidylinositol 3-Kinases KW - Phosphoinositide-3 Kinase Inhibitors KW - Treatment Outcome AB -

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

VL - 10 IS - 1 U1 - https://www.ncbi.nlm.nih.gov/pubmed/31209238?dopt=Abstract ER - TY - JOUR T1 - Interoperability with Moby 1.0--it's better than sharing your toothbrush! JF - Brief Bioinform Y1 - 2008 A1 - Wilkinson, Mark D A1 - Senger, Martin A1 - Kawas, Edward A1 - Bruskiewich, Richard A1 - Gouzy, Jerome A1 - Noirot, Celine A1 - Bardou, Philippe A1 - Ng, Ambrose A1 - Haase, Dirk A1 - Saiz, Enrique de Andres A1 - Wang, Dennis A1 - Gibbons, Frank A1 - Gordon, Paul M K A1 - Sensen, Christoph W A1 - Carrasco, Jose Manuel Rodriguez A1 - Fernández, José M A1 - Shen, Lixin A1 - Links, Matthew A1 - Ng, Michael A1 - Opushneva, Nina A1 - Neerincx, Pieter B T A1 - Leunissen, Jack A M A1 - Ernst, Rebecca A1 - Twigger, Simon A1 - Usadel, Bjorn A1 - Good, Benjamin A1 - Wong, Yan A1 - Stein, Lincoln A1 - Crosby, William A1 - Karlsson, Johan A1 - Royo, Romina A1 - Párraga, Iván A1 - Ramírez, Sergio A1 - Gelpi, Josep Lluis A1 - Trelles, Oswaldo A1 - Pisano, David G A1 - Jimenez, Natalia A1 - Kerhornou, Arnaud A1 - Rosset, Roman A1 - Zamacola, Leire A1 - Tárraga, Joaquín A1 - Huerta-Cepas, Jaime A1 - Carazo, Jose María A1 - Dopazo, Joaquin A1 - Guigó, Roderic A1 - Navarro, Arcadi A1 - Orozco, Modesto A1 - Valencia, Alfonso A1 - Claros, M Gonzalo A1 - Pérez, Antonio J A1 - Aldana, Jose A1 - Rojano, M Mar A1 - Fernandez-Santa Cruz, Raul A1 - Navas, Ismael A1 - Schiltz, Gary A1 - Farmer, Andrew A1 - Gessler, Damian A1 - Schoof, Heiko A1 - Groscurth, Andreas KW - Computational Biology KW - Database Management Systems KW - Databases, Factual KW - Information Storage and Retrieval KW - Internet KW - Programming Languages KW - Systems Integration AB -

The BioMoby project was initiated in 2001 from within the model organism database community. It aimed to standardize methodologies to facilitate information exchange and access to analytical resources, using a consensus driven approach. Six years later, the BioMoby development community is pleased to announce the release of the 1.0 version of the interoperability framework, registry Application Programming Interface and supporting Perl and Java code-bases. Together, these provide interoperable access to over 1400 bioinformatics resources worldwide through the BioMoby platform, and this number continues to grow. Here we highlight and discuss the features of BioMoby that make it distinct from other Semantic Web Service and interoperability initiatives, and that have been instrumental to its deployment and use by a wide community of bioinformatics service providers. The standard, client software, and supporting code libraries are all freely available at http://www.biomoby.org/.

VL - 9 IS - 3 U1 - https://www.ncbi.nlm.nih.gov/pubmed/18238804?dopt=Abstract ER -