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. 2019 Apr 1;20(1):164.
doi: 10.1186/s12859-019-2748-y.

Data and knowledge management in translational research: implementation of the eTRIKS platform for the IMI OncoTrack consortium

Wei Gu  1 , Reha Yildirimman  2 , Emmanuel Van der Stuyft  3 , Denny Verbeeck  3 , Sascha Herzinger  1 , Venkata Satagopam  1 , Adriano Barbosa-Silva  1 , Reinhard Schneider  1 , Bodo Lange  2 , Hans Lehrach  2   4   5 , Yike Guo  6 , David Henderson  7 , Anthony Rowe  8 ; IMI OncoTrack and the IMI eTRIKS consortia
Affiliations

Data and knowledge management in translational research: implementation of the eTRIKS platform for the IMI OncoTrack consortium

Wei Gu et al. BMC Bioinformatics. .

Abstract

Background: For large international research consortia, such as those funded by the European Union's Horizon 2020 programme or the Innovative Medicines Initiative, good data coordination practices and tools are essential for the successful collection, organization and analysis of the resulting data. Research consortia are attempting ever more ambitious science to better understand disease, by leveraging technologies such as whole genome sequencing, proteomics, patient-derived biological models and computer-based systems biology simulations.

Results: The IMI eTRIKS consortium is charged with the task of developing an integrated knowledge management platform capable of supporting the complexity of the data generated by such research programmes. In this paper, using the example of the OncoTrack consortium, we describe a typical use case in translational medicine. The tranSMART knowledge management platform was implemented to support data from observational clinical cohorts, drug response data from cell culture models and drug response data from mouse xenograft tumour models. The high dimensional (omics) data from the molecular analyses of the corresponding biological materials were linked to these collections, so that users could browse and analyse these to derive candidate biomarkers.

Conclusions: In all these steps, data mapping, linking and preparation are handled automatically by the tranSMART integration platform. Therefore, researchers without specialist data handling skills can focus directly on the scientific questions, without spending undue effort on processing the data and data integration, which are otherwise a burden and the most time-consuming part of translational research data analysis.

Keywords: Data management; Oncology; Precision medicine; Translational medicine.

PubMed Disclaimer

Conflict of interest statement

Ethics approval and consent to participate

The research conducted by the OncoTrack consortium has been approved by the medical ethics committees of Charité – Universitätsmedizin Berlin (Berlin, Germany) and Medizinische Universität Graz (Graz, Austria). All participating patients gave written informed consent before participating in the research programme.

Consent for publication

Not applicable.

Competing interests

Anthony Rowe is a full time employee and shareholder of Johnson and Johnson. Emmanuel Van der Stuyft is a full time employee and shareholder of Johnson and Johnson. Denny Verbeeck is a full time employee of Johnson and Johnson. David Henderson is a part time employee and shareholder of Bayer AG. Bodo Lange is a full time employee and CEO of Alacris Theranostics GmbH. Hans Lehrach is chairman of the company board of Alacris Theranostics GmbH.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
The components of the OncoTrack data coordination operation. The platform comprises three major components: the Electronic Data Capture System (EDC, OpenClinica), the Central Data Repository (OncoTrack DB), and the Data Integration System (tranSMART). The OpenClinica EDC system is used to collect medical history and observational patient data from clinical sites during the studies and feeds the structured data to the Central Data Repository. The Central Data Repository, OncoTrack DB is a sample indexed content management system. Data and results generated in the laboratories (before integration) are deposited and exchanged here. In order to link the different data types and layers, the data collected in the OncoTrack DB are integrated in the Data Integration System, tranSMART. The tranSMART data warehouse provides deep linking and integration between the clinical and laboratory data and a set of tools for the exploratory analysis of the integrated data
Fig. 2
Fig. 2
The OncoTrack dataset structure. a The complex OncoTrack data hierarchy with OMICS datasets directly generated from patient material and datasets generated from patient derived pre-clinical in vivo, in vitro and in silico models. b Due to constraints in tranSMART (v16.1) unable to represent this hierarchical use of samples, data has been organised as a series of different independent collections. One collection for data derived directly from patient samples and other collections for data derived from the pre-clinical models. c A solution we provided with linkage back to human subject and a tool to automatically map data using this linkage
Fig. 3
Fig. 3
Integration of OncoTrack data into tranSMART: (1) Left panel: Overall data representation in the TranSMART data tree. Right panel: easy customized cohort building with drag-and-drop. (2) Cascaded querying with cohort linking/selection tool PatientMapper. (3) Generating summary statistics of a miRNA of choice by dragging the miRNA-Seq node to the right panel and providing miRNA ID using the HiDome plugin. (4) Performing miRNA-ome wide heatmap analysis between the two sub-cohorts (here responder vs. non-responder for a selected drug treatment) using SmartR workflows
Fig. 4
Fig. 4
An overview of OncoTrack data that have been modelled, curated and loaded in the OncoTrack tranSMART Server
See this image and copyright information in PMC

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