Abstract 3166: Querying, viewing and analyzing colorectal cancer translational research studies in tranSMART
Authors: Bierkens Mariska , van der Linden Wim , Weistra Ward , van Bochove Kees , Beliën Jeroen A.M. , Fijneman Remond J.A. , Azevedo Rita , Boiten Jan-Willem , Meijer Gerrit A. 
Published: July 2016

All translational research projects basically share the same design, which is correlating variation in disease phenotype to variation in underlying biology. Typical questions to be addressed are: ‘Which (out of thousands) biomarkers predict good/bad outcome?’ and ‘Which (out of thousands) biomarkers predict whether a patient will benefit from a particular therapy?’. In line with this concept, many research teams have generated large amounts of experimental molecular data from patient samples, yet generally this information is inaccessible for examination due to local storage of both (meta)data and the data processing workflows used. Alternatively, data stored in central databases may only be available for exploration and interpretation by data specialists, provided that the processing workflow has been published and is available. Thus, if data is not recorded and easily retrievable, validation of obtained results (e.g. promising biomarkers) may be virtually impossible. Additionally, it will be difficult to query existing data sets to answer new questions, which may lead to experiments being unnecessarily repeated and biological materials being wasted. In the Netherlands, the Translational Research IT (TraIT) project initiated by the Center for Translational Molecular Medicine (CTMM, www.ctmm-trait.nl) aims to provide IT solutions to support translational research from start to end, including sustainable management and analysis of data. These data types involve the clinical, biomedical imaging, biobanking, (molecular) experimental data domains and their associated workflows. In addition to domain-specific solutions to manage these data types, the processed or ‘final’ data of these different domains will become available in the data-integration platform tranSMART for querying, visualization and analysis. To ensure sustainable data stewardship and provide easy access to existing data for the CTMM colorectal cancer project ‘Decrease in Colorectal Cancer Death (DeCoDe)’, data has been made available in tranSMART and more datasets are being added. The data encompasses both clinical and (molecular) experimental data from non-high-throughput molecular profiling (NHTMP) assays and array-based profiling techniques.

It is now possible with tranSMART to explore the respective DeCoDe datasets and perform various analyses (survival, Fisher-exact tests, ANOVA, aCGH tests etc.), without needing deep bioinformatics expertise. Through metadata tags it is possible to trace back raw or pre-processed data in other tools (e.g. clinical data in OpenClinica, array-data in GEO, or histological images of tissue microarrays). Data can be examined in more detail by domain experts using tools they are familiar with or other tools provided by CTMM-TraIT. Thus, existing rich data are made findable, accessible, interoperable and reusable (FAIR) and source data can be traced back for customized processing.

Citation Format: Mariska Bierkens, Wim van der Linden, Ward Weistra, Kees van Bochove, Jeroen A.M. Beliën, Remond J.A. Fijneman, Rita Azevedo, Jan-Willem Boiten, Gerrit A. Meijer. Querying, viewing and analyzing colorectal cancer translational research studies in tranSMART. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3166.