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Counteracting high failure rates in oncology drug development and improving therapeutic management of cancer patients requires preclinical models that can account for the complexity and heterogeneity of human tumors. Patient-derived cancer xenografts (PDXs) maintain histopathological features and genetic profiles of the original patient tumors and are increasingly recognized as reliable models to predict treatment efficacy and discover sensitivity and resistance biomarkers with immediate clinical relevance.Launched in 2013, the EurOPDX Consortium now gathers 18 academic research institutions throughout Europe and in the US (www.europdx.eu). The goal of the Consortium is to maximize exploitation of PDXs and other patient-derived models for cancer research by: (i) integrating institutional collections into a multicentre repository; (ii) defining common standards to improve the quality and reproducibility of oncology preclinical data; (iii) sharing models within and outside the consortium to perform collaborative precision oncology “xenopatient” trials. Building on its first successes, EurOPDX is now teaming up with other key academic and SME partners in a four-year project to build the “EurOPDX Distributed Infrastructure for Research on patient-derived Xenografts" (EDIReX project, Horizon 2020 grant no. 731105).This new cutting-edge European infrastructure offers access to PDX resources for academic and industrial cancer researchers through 6 state-of-the-art installations or “nodes”. We will present the specific objectives of the project, including our work towards standardization and optimization of biobanking, quality control and data tracking, and the performance of in vivo drug efficacy experiments. Access to the resource, including the distribution of cryopreserved samples from established models, the structured biobanking of user-developed models and the performance of drug efficacy studies, is offered through a grant application system which last deadline is planned mid-June 2020. Selection of the models by users and browsing of PDXs annotation data is made possible thanks to the newly-developed EurOPDX Data Portal (dataportal.europdx.eu), which will display approximately 1,000 models by April 2020 (including 700+ models of colorectal cancer, 80+ gastric and 80+ breast cancer models).We aim to improve preclinical and translational cancer research and promote innovation in oncology by integrating a European PDX repository and facilitating access to this much-needed resource for European and worldwide researchers. Citation Format: Emilie Vinolo, Joaquin Arribas, Andrea Bertotti, Alejandra Bruna, Annette T. Byrne, Robert B. Clarke, Nathalie Conte, Steven de Jong, Didier Decaudin, Zdenka Dudova, Jos Jonkers, Daniela Krasser, Ales Krenek, Luisa Lanfrancone, Eleonora Leucci, Elisabetta Marangoni, Gunhild Mari Maelandsmo, Michaela Th. Mayrhofer, Terrence F. Meehan, Jens Henrik Norum, Hector G. Palmer, Alejandro Piris Gimenez, Leo Price, Sergio Roman-Roman, Francesca Sarno, Violeta Serra, Laura Soucek, Livio Trusolino, Marieke van de Ven, Luca Vezzadini, Alberto Villanueva, Andrea Wutte, Enzo Medico, on behalf of the EurOPDX Research Infrastructure. The EurOPDX Research Infrastructure: Supporting European and worldwide cancer research with patient-derived xenografts [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1685.

Counteracting high attrition rates in anticancer drug development and providing optimal therapeutic management of cancer patients require preclinical models that properly recapitulate the complexity and diversity of human tumours. Patient-derived cancer xenografts (PDXs), developed by transplanting human tumor fragments into immunodeficient mice, retain the idiosyncratic characteristics of different tumors from different patients. The possibility of population-scale correlations between therapeutic response in PDXs and extensive, multidimensional molecular annotation has enabled the identification of several sensitivity and resistance biomarkers in a number of different tumor types, with immediate clinical relevance. To effectively recapitulate and therapeutically interrogate through patient-derived models the heterogeneity that typifies human cancer, 13 European cancer centres and university hospitals joined forces in 2013 to start EurOPDX, an academic research consortium that now gathers 19 institutions throughout Europe and in the US. EurOPDX includes world-renowned experts at the forefront of research in basic, preclinical, translational and clinical oncology across multiple pathologies, and displays a wide range of expertise in technological platforms. The primary goal of the Consortium is to maximize exploitation of PDXs and other patient-derived cancer models for cancer research by: (i) integrating institutional collections into an organic, multicentre collection of patient-derived models, now reaching 2,000 subcutaneous and orthotopic models for 30+ different pathologies (www.europdx.eu); (ii) defining common operating procedures to improve and standardize molecular and pharmacologic characterization of the models; (iii) sharing models within and outside the consortium to perform collaborative precision oncology “xenopatient” trials, to discover predictive biomarkers, new targets, and new strategies to overcome drug resistance. Towards these objectives, the EC granted 5 million euros under the H2020 programme for a “EurOPDX Distributed Infrastructure for Research on patient-derived Xenografts" - EDIReX EU project n. 731105, starting on February 2018. By teaming up with other key academic, technological and SME partners, our goal with EDIReX is to establish a cutting-edge European infrastructure offering transnational access to PDX resources to academic and industrial cancer researchers, including the distribution of cryopreserved samples to third parties, the structured biobanking of user-developed models, and the performance of drug efficacy studies. We will provide an overview of the current achievements of the Consortium and the objectives of the EDIReX project. Citation Format: Enzo Medico, Mohamed Bentires-Alj, Andrew V. Biankin, Alejandra Bruna, Annette T. Byrne, Carlos Caldas, Robert B. Clarke, Georges Coukos, Olivier Elemento, Manuel Hidalgo, Giorgio Inghirami, Steven de Jong, Jos Jonkers, Ales Krenek, Eleonora Leucci, Gunhild Mari Maelandsmo, Michaela Th. Mayrhofer, Terrence F. Meehan, Simone P. Niclou, Pier Giuseppe Pelicci, Alejandro Piris-Gimenez, Leo Price, Sergio Roman-Roman, Livio Trusolino, Luca Vezzadini, Alberto Villanueva, Emilie Vinolo, on behalf of the EurOPDX Consortium. The EurOPDX EDIReX project: Towards a European research infrastructure on patient-derived cancer models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 985.

Job processing and data transfer are the main computing activities on the WLCG infrastructure. Reliable monitoring of the job processing on the WLCG scope is a complicated task due to the complexity of the infrastructure itself and the diversity of the currently used job submission methods. The paper will describe current status and the new strategy for the job monitoring on the WLCG scope, covering primary information sources, job status changes publishing, transport mechanism and visualization.

Interactions between large biomolecules and smaller bio-active ligands are usually studied through a process called docking. Its aim is to find an energetically favorable orientation of a ligand within an active site of a biomolecule. Chemical reactions take place in active site and the role of the ligand is either to speed up, slow down or change the reaction (e.g., an enzyme catalyzed hydrolysis), which is why it can have huge pharmaceutical or other commercial impact. We present a tool that supports effective management and control of a typical workflow of docking parametric study. Selected subsets of ligands and protein trajectory snapshots can be displayed in three different views and further analyzed. Finally, the application supports spawning and steering underlying computations running on the grid.