Health-RI Conference - Connecting Researchers, Patients ......data in OpenClinica, array-data in GEO, or histological images of tissue microarrays). Data can be examined in more detail

Health-RI Conference - Connecting Researchers, Patients and Enabling Technologies | December 1, 2016 - Amersfoort

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Poster overview 1. Querying, viewing and analyzing colorectal cancer translational research studies in tranSMART - M

Bierkens, W van der Linden, W Weistra, K van Bochove, JAM Beliën, RJA Fijneman, R Azevedo, JW Boiten and GA Meijer

2. tranSMART: towards a FAIR data warehouse - J Bijlard, W Weistra and K van Bochove 3. TraIT: An Integrated Translational Research IT Platform - JW Boiten, R Azevedo, JAM Beliën, M

Cavelaars, A Dekker, RJA Fijneman, W van der Linden, N Stathonikos and GA Meijer 4. SOAR: Scalable OMICS Analysis and Reporting - S Hiltemann, D van Zessen, Y Hoogstrate, R Janssen, J

Hays, G Jenster, P van der Spek, LO Bonino da Silva Santos and A Stubbs 5. Establishing a comprehensive biobank to identify predictive markers for personalized treatment of

high grade serous ovarian cancer - J van Baal, M Timmermans, H Horlings and K van de Vijver 6. Durrer Center for Cardiovascular Research - JF Hermans-van Ast, EPA van Iperen, JP van Tintelen, P

van der Harst and FW Asselbergs 7. AMC Biobank: A central biobank in an academic medical setting - RP Minnaar, JG Wesseling, AC Glas,

EPA van Iperen, JF Hermans-van Ast and J Hamann 8. The Prospective Dutch ColoRectal Cancer cohort (PLCRC): a prospective nationwide observational

cohort study - G Vink, R Coebergh van den Braak, C Punt, H Verkooijen, M de Noo, GA Meijer and M Koopman

9. NFU Data4lifesciences: A shared data infrastructure for biomedical research - program overview - JW Boiten, JAM Beliën, H van den Berg, R Cornelisse, A Dekker, P Drankier, E Flikkenschild, R Hooft, P Jansen, A van der Maas, I Nooren, AJ van Ooijen, PGM van Overveld, H Pijl, R van Schijndel, I Schoonbrood, M Swertz and JJ Uitterdijk

10. NFU Data4lifesciences: HANDS: Handbook for Adequate Natural Data Stewardship - JW Boiten, JAM Beliën, H van den Berg, R Cornelisse, A Dekker, P Drankier, E Flikkenschild, R Hooft, P Jansen, A van der Maas, I Nooren, AJ van Ooijen, PGM van Overveld, H Pijl, R van Schijndel, I Schoonbrood, M Swertz and JJ Uitterdijk

11. NFU Data4lifesciences: Access to experts, training and support - JW Boiten, JAM Beliën, H van den Berg, R Cornelisse, A Dekker, P Drankier, E Flikkenschild, R Hooft, P Jansen, A van der Maas, I Nooren, AJ van Ooijen, PGM van Overveld, H Pijl, R van Schijndel, I Schoonbrood, M Swertz and JJ Uitterdijk

12. NFU Data4lifesciences: Sharing resources in a health research environment - NFU Data4lifesciences WP7 team: Research support staff from AMC, VUmc, LUMC, UMCG, UMCU and ICT infrastructure advisors and community managers from SURF (SURFsara and SURFnet)

13. Towards common IT services for BBMRI-NL - E Adriaanse, J Laros, D van Enckevort, H Mei, A Hiemstra, LO Bonino da Silva Santos, EPA van Iperen, R Azevedo, JAM Beliën, JW Boiten and M Swertz

14. Apps: public access to knowledge - M Swertz, M van Gijn, C Pang, J van der Velde, MOLGENIS development team, JW Boiten, JAM Beliën and E Adriaanse

15. MOLGENIS/connect: towards semi-automatic ‘FAIRification’ for biobank and patient registry data interoperability - C Pang, D van Enckevort, LO Bonino da Silva Santos, N Eklund, K Silander, P Holub, members of BBMRI-ERIC, RD-Connect, EU-BioSHaRE, EU-CORBEL, BBMRI-NL, BBMRI-FI and MOLGENIS teams and M Swertz

16. BBMRI-NL WP5 - Warehouse: get more out of your data - A Hiemstra, E Adriaanse, J Bijlard, The Hyve and MOLGENIS development teams, JW Boiten, JAM Beliën and M Swertz


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17. MOLGENIS Diagnostic Platform for Clinical Genomics - KJ van der Velde, B Charbon, D Hendriksen, M de Haan, C Wijmenga, TJ de Koning, RH Sijmons, MOLGENIS team, RJ Sinke and M Swertz

18. Running image-analysis pipelines in the cloud - A van Opbroek, M Koek and T Kroes 19. BBMRI-OMICS: molecular and clinical profiling -BBMRI-OMICS: a collaboration of: Leiden University

Medical Center, VU University Amsterdam, University Medical Center Groningen, Radboudumc Nijmegen, University of Amsterdam, Erasmus MC: University Medical Center Rotterdam, Maastricht University and University Medical Center Utrecht

20. SURF for life science and health research - I Nooren, P van Dijk and team 21. Stakeholder involvement to ensure sustainability - BBMRI-NL 2.0 - WP6 Sustainable & Interactive

Biobanking: M Boeckhout, JA Bovenberg, EWHM Eijdems, RP Stolk, AHM Willemsen and GA Zielhuis 22. Responsible Health Research Infrastructure: BBMRI Centre of Expertise on Ethical, Legal and Social

Issues - M Boeckhout and GA Zielhuis 23. How to tackle the big data infrastructure –omics challenge? - BBMRI-OMICS: a collaboration of:

Leiden University Medical Center, VU University Amsterdam, University Medical Center Groningen, Radboudumc Nijmegen, University of Amsterdam, Erasmus MC: University Medical Center Rotterdam, Maastricht University and University Medical Center Utrecht

24. Shaping the future of BBMRI-NL2.0 - E Erdtsieck-Ernste, GA Meijer and C Wijmenga 25. Towards FAIR rare disease research infrastructure - D van Enckevort, R Thompson, C Carta, M

Thompson, R Kaliyaperumal, M Wilkinson, M Swertz and M Roos 26. BBMRI-NL Biobank Catalogue: Live updates and linkage with biobanks - D van Enckevort, EPA van

Iperen, A Siezen, R den Ouden, E Flikkenschild, R van der Velde, E Adriaanse, JW Boiten and M Swertz 27. Integration of eScience technologies to tackle scientific challenges in Health-RI - A Gavai, A Mendrik

and L Ridder 28. RADAR CNS - Research Infrastructure for processing wearable data to improve health - J Kurps, M

Moinat, J Borgdorff, F Nobilia, M Kerz, N Mahasivam, I Pulyakhina, M Dümpelmann, H Campos, M Begale, R Dobson and A Folarin

29. 3D Glioma-on-a-Chip Models for Personalized Medicine in OrganoPlates® - Y Habani, HL Lanz, S Venkatesan, T Pierson, M Lamfers, S Leenstra and J Joore

30. Immunowell initiative - T Bezema and AA te Velde 31. LUMC Research ICT Program: A solid basis for research - LUMC 32. Radiomics - Images are more than pictures, they are data - A Dekker, H Aerts and P Lambin 33. Search for the genetic cause of human disease - M van Iterson, M Beekman, M Kattenberg, R

Groenewegen, J Bot, I Nooren and B Heijmans 34. Dutch Techcentre for Life Sciences - DTL 35. Dutch Techcentre for Life Sciences: FAIR data - DTL 36. On the road to EEG markers for individualized prediction of developmental disorders - A Chen, F

Wijnen and H Schnack 37. How old is your brain? Accelerated aging of the brain as a neuroimaging marker of developmental

and psychiatric disorders - H Schnack, N van Haren, M Nieuwenhuis, H Hulshoff Pol, W Cahn and R Kahn

38. A randomised controlled trial of consent procedures for the use of residual tissues for medical research: preferences of and implications for patients, research and clinical practice - S Rebers, E Vermeulen, AP Brandenburg, TJ Stoof, B Zupan-Kajcovski, WJW Bos, MJ Jonker, CJ Bax, WJ van Driel, VJ Verwaal, MW van den Brekel, JC Grutters, RA Tupker, L Plusjé, R de Bree, JH Schagen van Leeuwen, EGJ Vermeulen, RA de Leeuw, RM Brohet, NA Aaronson, FE Van Leeuwen and MK Schmidt


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39. Reporting of evidence for utility of pharmacogenomics: PGx for statins as an example - T Rigter, ME Jansen, W Rodenburg, SWJ Janssen and MC Cornel

40. How to deal with unsolicited findings in research? - P Manders, I Feenstra, HG Yntema and FM van Agt

41. Personalised monitoring of Multiple-Sclerosis - P van Oirschot, B den Teuling and M Martens 42. BBMRI-NL WP5: One entry to all samples you need


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Demo overview 1. tranSMART 2. TraIT - Translational Research IT 3. SOAR - Scalable OMICS Analysis and Reporting 4. BBMRI-NL - Running image-analysis pipelines in the cloud 5. BBMRI-NL - Apps: public access to knowledge 6. BBMRI-NL - Warehouse: get more out of your data 7. BBMRI-NL – BIOS: multi-omics data


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1 - Querying, viewing and analyzing colorectal cancer translational research studies in tranSMART M Bierkens1,*, W van der Linden2, W Weistra3, K van Bochove3, JAM Beliën4, RJA Fijneman1, R Azevedo5, JW Boiten6 and GA Meijer1 1. Department of Pathology, Netherlands Cancer Institute, PO Box 90203, 1006 BE, Amsterdam 2. Philips Research, High Tech Campus 34, 5656 AE, Eindhoven 3. The Hyve, HNK Central Station, Arthur van Schendelstraat 650, 3511 MJ, Utrecht 4. Department of Pathology, VU University Medical Center, PO Box 7057, 1007 MB, Amsterdam 5. TI Pharma - Lygature, Jaarbeursplein 6, 3521 AL, Utrecht 6. Center for Translational Molecular Medicine CTMM - Lygature, Jaarbeursplein 6, 3521 AL, Utrecht * Contact person: Mariska Bierkens - [email protected] 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. This work was financially supported by CTMM- DeCoDe (grant 03O-101) and CTMM-TraIT (grant 05T-401)

http://www.ctmm-trait.nl/


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2 - tranSMART: towards a FAIR data warehouse

J Bijlard1,*, W Weistra1 and K van Bochove1

1. The Hyve, HNK Central Station, Arthur van Schendelstraat 650, 3511 MJ, Utrecht * Contact person: Jochem Bijlard - [email protected] tranSMART is an open source translational research platform used by academic researchers and pharmaceutical companies around the world. For effective data sharing it is important that data is stored and provided in a FAIR way (1). In this poster we discuss what the implications of this upcoming paradigm are for a translational data warehouse. Findable Many instances of the TranSMART data warehouse exist on the internet, hosted by public private research consortia, patient organizations, pharmaceutical companies and commercial providers. A primary goal of tranSMART is to be a study catalogue, a collection of many projects. The first step for tranSMART in a FAIR ecosystem is to add rich metadata with globally unique, persistent identifiers to all studies in tranSMART. This metadata will then be made available through the API or FAIR data point. Doing this makes data inside tranSMART findable. Accessible The tranSMART REST API allows for retrieval of data by their identifier using an open communications protocol. A second step is to add the necessary metadata for (programmatically) retrieving access credentials. This will allow machines and humans to easily apply for data that is often sensitive, and protected. This information could be made available even when tranSMART itself is not via a separate FAIR data endpoint. Interoperable Metadata schemes are slowly developing to be formal, accessible, and broadly applicable representations of knowledge. With the tranSMART 17.1 development project, many improvements are made to make tranSMART ontology aware by allowing to tag any concept with ontology codes. This will greatly improve interoperability between different datasets within tranSMART with external datasets. Reusable As many standards still need to be developed and adopted by the translational research community a data warehouse needs to be flexible in the way it allows storage of its meta(data). By applying the FAIR data principles, to provide clear provenance for accessible data with appropriate licensing, we hope to transform tranSMART from a data warehouse to a data store for reusable data. References: 1. Wilkinson MD et al.: The FAIR Guiding Principles for scientific data management and stewardship

Sci. Data (2016)3: 160018. Doi: 10.1038/sdata.2016.18

mailto:[email protected]


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3 - TraIT: An Integrated Translational Research IT Platform JW Boiten1, R Azevedo1, JAM Beliën2, M Cavelaars3, A Dekker4,5, RJA Fijneman6, W van der Linden7, N Stathonikos8 and GA Meijer5 1. Lygature, Jaarbeursplein 6, 3521 AL, Utrecht 2. Department of Pathology, VU University Medical Center, PO Box 7057, 1007 MB, Amsterdam 3. The Hyve, HNK Central Station, Arthur van Schendelstraat 650, 3511 MJ, Utrecht 4. Maastricht Radiation Oncology (MAASTRO), GROW School for Oncology, University of Maastricht, Dr. Tanslaan 12, 6229 ET,

Maastricht 5. Maastricht University Medical Center+, PO Box 5800, 6202 AZ, Maastricht 6. Department of Pathology, Netherlands Cancer Institute, PO Box 90203, 1006 BE, Amsterdam 7. Philips Research, High Tech Campus 34, 5656 AE, Eindhoven 8. Department of Pathology, University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht * Contact person: Jan-Willem Boiten - [email protected] The TraIT project is a Dutch national consortium providing the Dutch translational research community and its international partners with an easy-to-access and easy-to-use infrastructure for sharing research data, together with a set of tools for further exploration of the collected data. The TraIT project facilitates translational research logistics, data management, data integration and data analysis at a national level through a central and secure infrastructure. TraIT provides the Dutch hub in international networks and closely collaborates with initiatives like BBMRI, NFU, Data4LifeSciences, DTL, and several others. The project adopted a ‘think big, start small, act now’ philosophy, having built a nationally accessible IT infrastructure for translational research in small steps, and preferably based on existing standards and software platforms, therefore ensuring that its databases and tools are open to the wide research community for the foreseeable future. Currently TraIT supports more than 2800 researchers in the Netherlands and abroad, working on more than 250 biomedical translational studies. http://www.trait-platform.org

http://www.trait-platform.org/


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4 - SOAR: Scalable OMICS Analysis and Reporting S Hiltemann1, D van Zessen1,2, Y Hoogstrate1,3, R Janssen1, J Hays4, G Jenster3, P van der Spek1, LO Bonino da Silva Santos5 and A Stubbs1,* 1. Bioinformatics Department, Erasmus MC: University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam 2. Immunology Department, Erasmus MC: University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam 3. Experimental Urological Oncology, Erasmus MC: University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam 4. Medical Microbiology, Erasmus MC: University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam 5. Dutch Techcentre for Life Sciences, PO Box 19245, 3501 DE, Utrecht * Contact person: Andrew Stubbs - [email protected], +31 10 70 44776 Translational research requires the use of high-dimensional clinical data to define phenotypes then to elucidate disease mechanisms and hence treatment options. A major challenge for translational research and personalized medicine is availability of these data and statistical tools to the domain experts who will determine disease mechanisms. To address this challenge we are implementing, SOAR (Secure OMICS Analysis & Reporting), a generalized and scalable open source solution for integrated analysis of clinical and “OMICs” data. SOAR provides biomarker discovery and validation, using the latest “OMICs” tools available via Galaxy (use.galaxy.org), to the medical researchers in clinical and translational research projects. SOAR platform ensures FAIR (Findable, Accessible, Interoperable, and Reusable) data principles where possible. In summary, SOAR provides clinical researchers the ability to create their own FAIR data, to access FAIR data points and use the latest analytical and bioinformatics methods and tools (including R) for clinical research projects and to make the results FAIR for future analysis. SOAR will provide a set of informatics tools essential for FAIR data management and data stewardship required for EU funded projects.


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5 - Establishing a comprehensive biobank to identify predictive markers for personalized treatment of high grade serous ovarian cancer J van Baal1,*, M Timmermans2, H Horlings3 and K van de Vijver3 1. Department of Gynaecologic Oncology, Netherlands Cancer Institute, PO Box 90203, 1006 BE, Amsterdam 2. Integraal Kankercentrum Nederland, Gebouw Janssoenborch - 8e etage, Godebaldkwartier 419, 3511 DT, Utrecht 3. Department of Pathology, Netherlands Cancer Institute, PO Box 90203, 1006 BE, Amsterdam * Contact person: Juliette van Baal - [email protected] Prognosis of patients with epithelial ovarian cancer (EOC) is poor and survival has not changed over the past 20 years, despite extensive research on EOC. Standard treatment for advanced EOC consists of platinum-containing chemotherapy in combination with cytoreductive surgery. Currently, it is unknown which patients may benefit from neo-adjuvant chemotherapy followed by interval cytoreductive surgery and which patients benefit from primary cytoreductive surgery followed by adjuvant chemotherapy. In the present study we aim to identify predicting factors for optimal choice of treatment in patients with advanced EOC. In collaboration with IKNL, 4,956 patients with EOC who underwent either primary cytoreduction or interval cytoreduction in the Netherlands, were identified retrospectively. Based on the Netherlands Cancer Registry (NCR) there were 3,689 patients with high grade serous ovarian cancer (HGSOC). For this study, a selection of HGSOC patients treated within three centers were included (600 patients). Formalin-fixed paraffin embedded (FFPE) tissue blocks are collected for analyses of the tumor microenvironment, chemotherapy response and molecular subtypes. FFPE tissue blocks are requested via the Dutch National Tissue bank Portal (DNTP), a service that connects approximately 60 laboratories in the Netherlands, which facilitates fast and organized requests and transfer of patient materials. Recently, in the NKI-AVL, an electronic biobank system was built (cBioportal), comprising clinical data, digital images, data on pathology and pathology revision, mutational analyses, available materials for research including blood, FFPE tissue and fresh frozen tissue. All data collected in the present study, will be included in cBioportal. In the NKI-AVL yearly approximately 100 patients are treated for EOC, but research on EOC is often hampered because of incomplete biobanking and low number of patients. A sustainable infrastructure and comprehensive biobank is of paramount importance for initiation and continuation of translational studies. In addition, expansion of the use of cBioportal to other clinical institutes in the Netherlands may enable collaboration with other clinics and exchange of data, which is of utmost importance in order to investigate large study populations that is necessary to improve the poor prognosis of patients with EOC and tailor treatment to the individual patient.

mailto:[email protected]


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6 - Durrer Center for Cardiovascular Research JF Hermans-van Ast1, EPA van Iperen1, JP van Tintelen1,2, P van der Harst1,3 and FW Asselbergs1,4 1. Durrer Center for Cardiovascular Research, Netherlands Heart Institute, PO Box 19258, 3501 DG, Utrecht 2. Department of Clinical Genetics, Academic Medical Center, PO Box 22660, 1100 DD, Amsterdam 3. Department of Cardiology, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen 4. Department of Cardiology, University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht * Contact person: Wanda Hermans - van Ast - [email protected] The Durrer Center for Cardiovascular Research (Durrer Center) is a facility that operates within the unique cooperation of the cardiology departments of all eight university medical centers in the Netherlands, called Netherlands Heart Institute. The Netherlands Heart Institute fosters excellent cardiovascular research on national and international level. The goal of this platform is to share knowledge and resources and creating added value for researchers and for patients with cardiovascular disease. Durrer Center facilitates autonomous and secure storage of biosamples and (imaging)data as well as tools for logistic support and the development of registries and standard e-CRFs for clinical data collection (Interoperable and Reusable). A data catalogue is implemented to visualize the collections of data and biomaterials (Findable). This facilitates maximizing its scientific potential by providing a transparent mechanism for sample and data access to benefit the scientific community and society as a whole (Accessible). Durrer Center is already on its way to FAIR-data management. We aim to set up an infrastructure to support cardiovascular researchers in organizing clinical data, imaging data, biomaterials and experimental data in a ‘FAIR’-manner and prepare for Health-RI. Durrer Center closely cooperates with CTMM-TraIT and BBMRI-NL. With Health-RI we hope the cardiovascular (research) data can contribute to implement personalized medicine and health for every Dutch citizen, leading to improvements in healthcare and disease prevention.


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7 - AMC Biobank: A central biobank in an academic medical setting RP Minnaar1, JG Wesseling1, AC Glas1, EPA van Iperen1,2, JF Hermans-van Ast1,2, J Hamann1,* 1. AMC Biobank, Room L01-124, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam 2. Durrer Center for Cardiovascular Research, Netherlands Heart Institute, PO Box 19258, 3501 DG, Utrecht * Contact person: Jörg Hamann - [email protected] The Academic Medical Center is one of the foremost research institutions in the Netherlands, as well as one of its largest hospitals. Over 7.000 people work here to provide integrated patient care, teaching, and fundamental and clinical scientific research. Research is supported by core facilities, including the AMC Biobank, which has been established in 2014. AMC Biobank provides comprehensive storage services for biological materials and related clinical data. The Biobank is part of AMC’s division G (Laboratory Specialisms) and works closely together with the Durrer Center for Cardiovascular Research, hosted by the Netherlands Heart Institute. AMC Biobank and Durrer Center share people, rooms, protocols, and quality standards. AMC Biobank currently houses about 750.000 samples for more than 30 (biobank) studies, including various Parelsnoer collections, shared with other Dutch university medical centers, the HELIUS study on health differences among the residents of Amsterdam with different ethnic origin, and the MARS study on molecular diagnosis and risk stratification of sepsis. A catalog provides metadata of all collections stored at AMC Biobank. By attracting more individual (biobank) studies, building a central site for storage of viable cells in liquid N2, developing study-specific catalogs at sample level, and obtaining ISO9001 certification, AMC Biobank is improving her services for scientific research, using patient material, in the AMC.


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8 - The Prospective Dutch ColoRectal Cancer cohort (PLCRC): a prospective nationwide observational cohort study G Vink1*, R Coebergh van den Braak2, C Punt3, H Verkooijen4, M de Noo5, GA Meijer6 and M Koopman7

1. Integraal Kankercentrum Nederland, Gebouw Janssoenborch - 8e etage, Godebaldkwartier 419, 3511 DT, Utrecht 2. Department of Surgery, Erasmus MC: University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam 3. Department of Medical Oncology, Academic Medical Center, PO Box 22660, 1100 DD, Amsterdam 4. Department of Radiology, University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht 5. Department of Surgery, Deventer Hospital, PO Box 5001, 7400 GC, Deventer 6. Department of Pathology, Netherlands Cancer Institute, PO Box 90203, 1006 BE, Amsterdam 7. Department of Medical Oncology, University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht * Contact person: Geraldine Vink - [email protected] Background The Prospective Dutch ColoRectal Cancer cohort (PLCRC) is a prospective multidisciplinary nationwide observational cohort study in the Netherlands (annual colorectal cancer incidence of 16,000 patients). The goal of the study is to facilitate basic, translational and clinical research in the field of colorectal cancer for national and international research groups. PLCRC provides standardized and validated collection of clinical data, tissue and blood samples, and patient-reported outcome measures, and can serve as an infrastructure for registry based trials. Methods All patients >18 years with histologically proven colorectal cancer are asked to participate. The informed consent includes consent for collection of clinical data (mandatory), optional consent for collection of tissue and blood samples, patient-reported outcome measures and to be invited for future interventional studies according to (amongst others) the cohort multiple randomized controlled trial design. The cohort is set up in close collaboration with other national data collection initiatives and best practices, including the Netherlands Cancer Registry (hosted by IKNL), the national pathology registry PALGA, the national biobanking infrastructure BBMRI-NL, a nationwide PROMs initiative (PROFIEL), and the Dutch Surgical Colorectal Audit (DSCA). Generic IT solutions such as TranSMART for data management and SLIM for patient inclusion management are used. Results Currently 13 centers are open for inclusion, close to 1000 patients have been included in PLCRC, and a total of 10 studies are using the PLCRC infrastructure. Furthermore, a pilot study for the extraction and merging of data using the TranSMART application showed excellent function of the application. Conclusions: PLCRC is a nationwide initiative collecting long-term clinical data, tissue and blood samples, and patient-reported outcome measures of a large cohort of patients with colorectal cancer collaborating with nationwide data collection initiatives using generic solutions developed by national best practices. The available data and material will facilitate basic, translational and clinical research. COI: Corporate-sponsored Research: PLCRC is supported by Bayer, Lilly, Merck, Roche, Servier, Sirtex.


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9-10-11 - NFU Data4lifesciences: A shared data infrastructure for biomedical research JW Boiten1,2,3,*, JAM Beliën3,4, H van den Berg5, R Cornelisse6, A Dekker3,8,9, P Drankier10, E Flikkenschild6,7, R Hooft11, P Jansen12, A van der Maas13, I Nooren14, AJ van Ooijen15, PGM van Overveld6,11, H Pijl12,17, R van Schijndel4, I Schoonbrood9, M Swertz2,16 and JJ Uitterdijk16,17

1. Lygature, Jaarbeursplein 6, 3521 AL, Utrecht 2. BBMRI-NL, Department of Genetics, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen 3. CTMM-TraIT - Lygature, Jaarbeursplein 6, 3521 AL, Utrecht 4. VU University Medical Center, PO Box 7057, 1007 MB, Amsterdam 5. Academic Medical Center, PO Box 22660, 1100 DD, Amsterdam 6. Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden 7. Parelsnoer Institute, Jaarbeursplein 6, 3521 AL, Utrecht 8. Maastricht Radiation Oncology (MAASTRO), GROW School for Oncology, University of Maastricht, Dr. Tanslaan 12, 6229 ET,

Maastricht 9. Maastricht University Medical Center+, PO Box 5800, 6202 AZ, Maastricht 10. Netherlands Federation of University Medical Centres, PO Box 9696, 3506 GR, Utrecht 11. Dutch Techcentre for Life Sciences, PO Box 19245, 3501 DE, Utrecht 12. University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht 13. Radboudumc, PO Box 9101, 6500 HB, Nijmegen 14. SURFsara, PO Box 94613, 1090 GP, Amsterdam 15. Erasmus MC: University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam 16. University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen 17. Mondriaan Research Data Infrastructure, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen * Contact person: Jan-Willem Boiten - [email protected] The aim of the Data4lifesciences program is to connect local initiatives within the Dutch UMCs to national and international infrastructures. This programme is coordinated by the Netherlands Federation of University Medical Centres (NFU), in conjunction with national programs such as TralT, BBMRI-NL, PSI, DTL, AcZie, Mondriaan and SURF. Data4lifesciences is setting up an innovative research data infrastructure at, for, by and between the UMCs and their (national and foreign) partners, and does not only comprise technical facilities, but also focuses on quality assurance processes and expertise development for investigators and administrators. The UMCs will develop and choose data technologies, exchange expertise and experiences to stimulate collaborations. Data4lifesciences is the contribution of the Dutch UMCs to the emerging national research infrastructure for personalized medicine and health research, Health-RI. The Data4lifesciences program will have achieved its objectives when: • Doctors and investigators working at the UMCs use the research data infrastructure to retrieve clinical

and experimental data on all UMC-related patients and make them available to others under well-defined conditions. To this end, we will explore the (re)use of data within patient records for research, in close collaboration with the NFU program ‘Registratie aan de Bron’. The infrastructure will also be used to find and request biological material.

• The infrastructure forms a national virtual collaborative environment in which data is registered, processed, analysed, archived and shared. The infrastructure is accessible, independently of institution or site.

• The data is FAIR - Findable, Accessible, Interoperable and Reusable - and privacy is safeguarded in the whole process. All data is made available in a scalable, distributed environment, in which the computing capacity needed to process the data is available at national and UMC computing facilities.

• Investigators and doctors with questions about handling data have an extensive data expertise network available to answer them. These experts in the UMCs are the first point of contact for these questions, and solutions will be sought both inside and outside the UMCs.

The Data4lifesciences program posters are: No. 9: Data4lifesciences program No. 10: HANDS: Handbook for Adequate Natural Data Stewardship


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No. 11: Access to experts, training and support No. 12: Sharing resources in a health research environment No. 26: Biobank Catalogue, located in the BBMRI-NL corner

For more information: www.data4lifesciences.nl [email protected]


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12 - NFU Data4lifesciences: Sharing resources in a health research environment NFU Data4lifesciences WP7 team: Research support staff from AMC1, VUmc2, LUMC3, UMCG4, UMCU5 as well as ICT infrastructure advisors and community managers from SURF (SURFsara6 and SURFnet7) 1. Academic Medical Center, PO Box 22660, 1100 DD, Amsterdam 2. VU University Medical Center, PO Box 7057, 1007 MB Amsterdam 3. Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden 4. University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen 5. University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht 6. SURFsara, PO Box 94613, 1090 GP, Amsterdam 7. SURFnet, PO Box 19035, 3501 DA, Utrecht * Contact person: Irene Nooren - [email protected] One of the objectives of the NFU Data4LifeSciences program is to harmonize facilities for high-throughput data processing in the Netherlands. This, as well as the sharing of data, requires a federated infrastruture, technically as well as organisationally. The ultimate goal of WP7 is to create a secure, cost-efficient, easyily accessible and scalable data processing infrastructure for end-users in life science & health research without borders between local and national data processing services. End users are able to use HPC capacity on demand, based on a clear governance and cost model, that gives access to shared data and synchronized analysis tools directly available at the processing facilities. An important aspect of a federated infrastructure is the federated authentication and access management of services such as web services, data resources as well as HPC clusters. Secondly, a trusted and secure network enviroment between UMCs and/or other health research institutes is enables to share data in health research. A cloud management portal allows the researcher to choose compute resources on demand. For these ICT infrastructure components Proof of Concepts have been shown, and are based on national SURF services such as SURFconext, and proven concepts form the international e-infrastructure community. Governance and policy models for collaborative ICT infrastructure for health research are currently under development to align with, and manage the technical infrastructure.


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13 - Towards common IT services for BBMRI-NL E Adriaanse1, J Laros2, D van Enckevort1, H Mei2, A Hiemstra3, LO Bonino da Silva Santos4, EPA van Iperen6,7, R Azevedo5, JAM Beliën8, JW Boiten5, and M Swertz1

1. University of Groningen, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen 2. Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden 3. Department of Pathology, Netherlands Cancer Institute, PO Box 90203, 1006 BE, Amsterdam 4. Dutch Techcentre for Life Sciences, PO Box 19245, 3501 DE, Utrecht 5. Lygature, Jaarbeursplein 6, 3521 AL, Utrecht. 6. AMC Biobank, Room L01-124, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam 7. Durrer Center for Cardiovascular Research, Netherlands Heart Institute, PO Box 19258, 3501 DG, Utrecht 8. VU University Medical Center, PO Box 7057, 1007 MB, Amsterdam * Contact person: Edith Adriaanse - [email protected] We deliver the Dutch national IT infrastructure that allows researchers and biobank initiatives to interact optimally, and facilitate the availability of bioresources to all who need them for their research with emphasis on enabling personalized medicine and FAIR data (findable, accessible, interoperable, reusable). For improved findability and access we are delivering: • An expanded biobank catalogue as the public gateway to all data, samples and images available in

BBMRI-NL. • Easy to use request and delivery workflows which will substantially increase (re)use of samples,

images, and clinical data collections. • Publicly accessible research and knowledge apps to provide exciting access knowledge in BBMRI data

for researchers, clinicians and public. To promote use of BBMRI data in large multi-center studies we will deliver: • A secure ‘research environment as a service’ with federated authentication that can scale up to large

storage, network and compute environments. • Professionalized data pipelines for capturing clinical, imaging, and ‘omics’ data that can be used by all

researchers in the Netherlands. • Integrated data management and analysis warehouses to optimally provide integrated data collections

to research studies and consortiums. • Development of linked data methodology to improve semantic interoperability and reuse of biobank

data. To ensure sustainability and use we will deliver: • Professional operating procedures, hosting, support and self-service facilities. This infrastructure will function as the national IT node of BBMRI-NL within BBMRI-ERIC common services for IT and build on collaborations with other national Health-RI partners (DTL, NFU Data4lifesciences, CTMM TraIT, ELIXIR-NL, SURF) and international (ELIXIR, RD-Connect, CORBEL, EGI, EUDAT) research infrastructures and projects.


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14 - Apps: public access to knowledge M Swertz1,2,*, M van Gijn3, C Pang2, J van der Velde2, MOLGENIS development team, JW Boiten1,4, JAM Beliën1,5 and E Adriaanse1,2 1. BBMRI-NL, Department of Genetics, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen 2. University of Groningen, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen 3. University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht 4. Lygature, Jaarbeursplein 6, 3521 AL, Utrecht 5. VU University Medical Center, PO Box 7057, 1007 MB, Amsterdam * Contact person: Morris Swertz - [email protected] The Dutch BBMRI community is generating impressive datasets with high scientific impact and huge potential to also inform clinical decision making. However, currently most of these data often require a long trajectory to request data access in light of privacy constraints of patients/participants and on access bioinformatics wizard to actually query and analyse these data. Therefore BBMRI-NL is developing ‘apps’ that provide ease web user interfaces to query the knowledge in these data. First examples of apps under development include: • VKGL datasharing - enabling answers to the question ‘how often is a DNA variant observed in the

Netherlands’ and ‘what variants where evaluated by clinical experts to be benign or pathogenic’ combining data from diagnostic labs and public sets such as the Genome of the Netherlands

• BiobankUniverse - enabling answers to the question ‘what data items are available in all Dutch biobanks’ and ‘to what extent do these biobanks overlap such that I could integrate the dnkiata’ as basis for making Dutch biobanks F.A.I.R.

• Gaving and GeneNetwork - use BBMRI-NL data and methods to reduce time needed for clinical WGS interpretation by shortlisting ‘what DNA variants are probably pathogenic’ based on accumulating DNA and RNA data in (and outside) BBMRI

More app ideas are underway, in particular to exploit data generated in BBMRI projects such as the BIOS project. We have noticed that it often takes significant effort to convert a great prototype from a PhD student into professional software, and that too often nice ‘apps’ disappear shortly after publication. To enable sustainable development of these apps we have upgraded the MOLGENIS software platform, in particular (a) to function as a ‘backend as a service’ where researchers can easily upload and configure their data including users and permissions (b) rapidly create plug-in fancy user interfaces build with modern HTML and javascript techniques (c) easily integrate existing R statistics and web services as part of their apps. All will be available as BBMRI ‘app store’ and we very much welcome ideas and app submissions.


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15 - MOLGENIS/connect: towards semi-automatic ‘FAIRification’ for biobank and patient registry data interoperability C Pang1,2,3, D van Enckevort1,2,3, LO Bonino da Silva Santos4, N Eklund3,5, K Silander3,5, P Holub3, members of BBMRI-ERIC, RD-Connect, EU-BioSHaRE, EU-CORBEL, BBMRI-NL, BBMRI-FI and MOLGENIS teams, and M Swertz1,2,3 * 1. BBMRI-NL, Department of Genetics, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen 2. University of Groningen, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen 3. BBMRI-ERIC, Neue Stiftingtalstrasse 2/B/6, 8010 Graz, Austria 4. Dutch Techcentre for Life Sciences, PO Box 19245, 3501 DE, Utrecht 5. National Institute for Health and Wellfare (THL), P.O. Box 30, FI-00271 Helsinki, Finland * Contact person: Morris Swertz - [email protected] To enable research across heterogeneous biobanks and registries, we need to standardize data retrospectively, a task that requires much time and effort. MOLGENIS/connect aims to promote FAIR principles for data by enabling researchers to semi-automatically extract, transform and load (ETL) their data into standard models and ontologies. MOLGENIS/connect incorporates SORTA, a tool that can recode data values from free text to ontology terms to improve interoperability. It also incorporates the BiobankConnect tool that can find the most relevant data attributes from thousands of candidates between datasets and map them on one integrated data model to make the data usable for research. Both tools use ontology-based query expansion and lexical matching to overcome variations in terminology. MOLGENIS/connect then can generate data conversion algorithms that transform source attributes to a common target DataSchema. When one applies these algorithms, the heterogeneous data can be automatically converted into the interoperable standard. We have demonstrated use of MOLGENIS/connect based in BioSHaRE, BBMRI-NL, RD-connect, and BBMRI.FI to create and integrated catalogues to federated sample data searches, pool biobanks for integrated studies and recode phenotypes from free text to human phenotype ontology. All software is available as open source within the MOLGENIS system at http://github.com/molgenis/molgenis. See our poster: https://docs.google.com/presentation/d/16M2dTM8x_wXAFFkQ4w5BTu0cU--aCce5y1d_RGWuuXc/edit#slide=id.p4


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16 - BBMRI WP5 - Warehouse: get more out of your data A Hiemstra1, 2, E Adriaanse1,3,*, J Bijlard4, The Hyve and MOLGENIS development teams, JW Boiten1,5, JAM Beliën1,6 and M Swertz 1,3 1. BBMRI-NL, Department of Genetics, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen 2. Department of Pathology, Netherlands Cancer Institute, PO Box 90203, 1006 BE, Amsterdam 3. University of Groningen, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen 4. The Hyve, HNK Central Station, Arthur van Schendelstraat 650, 3511 MJ, Utrecht 5. Lygature, Jaarbeursplein 6, 3521 AL, Utrecht 6. VU University Medical Center, PO Box 7057, 1007 MB, Amsterdam * Contact person: Edith Adriaanse - [email protected] We provide tools and services for structuring and analysis of multiple data collections, from clinical trials to translational research studies. These tools (TranSMART, Molgenis, Xnat) can support a wide range of datatypes, such as phenotype data, physical exams, multi-omics or imaging data. They offer smart solutions for integration of datasets and have standard functionalities for cohort statistics, group comparisons and default plots. For further detailed analyses there are download options. Also you can easily share data with your colleagues in multi-center studies or make it available for public use and reference in papers. In BBMRI we are expanding these tools and connecting them with each other. We do this in close collaboration with researchers, their studies and use cases. As a result, we will also provide best practices, SOPs and services for the handling of the different data types. We are always interested to hear ideas for improvements.


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17 - MOLGENIS Diagnostic Platform for Clinical Genomics KJ van der Velde1,2,*, B Charbon1, D Hendriksen1, M de Haan1, C Wijmenga2, TJ de Koning2, RH Sijmons2, MOLGENIS team1,2, RJ Sinke2 and MA Swertz1,2

1. Genomics Coordination Center, University of Groningen, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen

2. Department of Genetics, University of Groningen, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen * Contact person: Joeri van der Velde - [email protected] With costs of next-generation sequencing decreasing rapidly, we expect thousands of patients to soon have whole-genome profiling but its implementation is a huge challenge for diagnostic laboratories. The primary roadblock is not the data acquisition, but its interpretation. The interpretation process can be sped up using the large amount of reference data collected by diagnostic labs, public databases and biobanks. Unfortunately, these data cannot be explored easily in unison. In addition, many new methods and tools emerge to help analyze these data, but a lack of standardization hinders their implementation, comparison and validation in clinical practice. Here, we report development of MOLGENIS/diagnostic open source platform for clinical genomics (www.molgenis.org/diagnostic), aiming to easily integrate reference data (e.g. ClinVar, ExAC), run best-practice annotation protocols (e.g. CADD, SnpEff) and interpretation tools (e.g. GAVIN), combined with user-friendly reports for rapid translation of knowledge and innovations to clinical practice. We are using it in research and diagnostics at UMC Groningen and data sharing between Dutch labs.

http://www.molgenis.org/diagnostic


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18 - Running image-analysis pipelines in the cloud A van Opbroek1,*, M Koek1 and T Kroes1 1. BBMRI-NL work package 3:

Erasmus MC: University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht Donders Institute for Brain, Cognition and Behaviour, P.O. Box 9101, 6500 HB, Nijmegen

* Contact person: Annegreet van Opbroek - [email protected] At work package 3 of BBMRI-NL2.0 we focus on population imaging and the automatic extraction of imaging biomarkers. In order to achieve this, we are building an image-analysis infrastructure to automatically run image-analysis pipelines on large amounts of medical imaging data. We believe that our infrastructure could greatly benefit personalized medicine by making image-analysis pipelines available for all researchers and clinicians. Also, within our work package we will use the developed pipelines and infrastructure to create a reference database that can be used to compare biomarker values of individuals to those of a healthy population with matching covariates. At the Health-RI meeting we present a demo and accompanying poster where we show a prototype of such an image-analysis infrastructure, where a user can store medical image data, run a fully automated pipeline on it, and inspect progress and results with an interactive viewer.


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19 - BBMRI-OMICS: molecular and clinical profiling BBMRI-OMICS is a collaboration of − Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden − VU University Amsterdam, De Boelelaan 1105 1081 HV Amsterdam − University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen − Radboudumc, PO Box 9101, 6500 HB, Nijmegen − University of Amsterdam, PO Box 19268, 1000 GG, Amsterdam − Erasmus MC: University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam − Maastricht University, PO Box 616, 6200 MD, Maastricht − University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht * Contact person: Marian Beekman - [email protected] The Netherlands is renowned for the solid and sound epidemiological research in biobanks and patient cohorts. BBMRI-OMICS aims to share state-of-the-art large-scale molecular data in eminent Dutch cohorts to support (inter)national collaborative research. Molecular dataset in human cohorts from all over the Netherlands have been collected within the Biobanking and Biomolecular resources Research Infrastructure (BBMRI). The shared datasets currently include whole genome sequencing data (N=750), blood transcriptome (N=4,000), blood epigenome data (N=4,000) and plasma metabolome data (N=25,000). Data were collected from healthy reference panels of individuals as well as diseased individuals. For every individual also a limited amount of health related meta-data amongst others age, sex, cholesterol levels, and blood pressure is shared. To be able to make use of this enormous amount of biological data, BBMRI-OMICS currently generates an infrastructure that allows the access of these data by simple standardized procedures. The infrastructure further encompasses a catalogue of the available data, browsers indicating cross-omics relations, methodology to be applied on these kinds of molecular data and access to computational capacity to perform analyses. All biomedical oriented universities of the Netherlands joint their forces to build the infrastructure as a basis of solid Dutch biomedical science to be shared with the whole scientific community.


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20 - SURF for life science and health research I Nooren1, , P van Dijk2, and team

1. SURFsara, PO Box 94613, 1090 GP, Amsterdam 2. SURFnet, PO Box 19035, 3501 DA, Utrecht * Contact person: Irene Nooren - [email protected] Due to the data-intensive nature of Life Science and Health research, the research community faces a rapidly growing need for suitable ICT services, ICT support and training. As a cooperation of Dutch academic medical centers and universities for ICT services and innovation, SURF (SURFsara, SURFnet, SURFmarket) develops and provides national ICT facilities that are tuned to the needs of the research community. SURF closely collaborates with national projects like CTTM-TraIT, BBMRI and NFU Data4LifeSciences to innovate on authentication, data sharing and storage, computing and online collaboration. Currently, more than 400 TB of research data and 15 million core hours per year are used for data storage and analyzed at SURF facilities. This shows that SURF operates at the heart of ICT research infrastructure innovation to facilitate research. The joint effort in ICT innovation provides opportunities to enhance the development the ICT infrastructure components in a Health RI infrastructure.


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21 - Stakeholder involvement to ensure sustainability BBMRI-NL 2.0 - WP6 Sustainable & Interactive Biobanking: M Boeckhout1, JA Bovenberg1,2, EWHM Eijdems1,3, RP Stolk1,3, AHM Willemsen1,4 and GA Zielhuis1,5,6

1. BBMRI-NL, Department of Genetics, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen 2. Legal Pathways Institute for Health and Bio-Law, Heemstede 3. University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen 4. Netherlands Twin Register, Department of Biological Psychology, VU University Amsterdam, De Boelelaan 1105, 1081 HV,

Amsterdam 5. Radboud Biobank, Radboudumc, PO Box 9101, 6500 HB, Nijmegen 6. Parelsnoer Institute, Jaarbeursplein 6, 3521 AL, Utrecht * Contact person: Lisette Eijdems - [email protected] In WP6, new cost-efficient ways to initiate and manage biobanks are explored. A substantial focus is on participatory biobanking, to investigate and stimulate (inter)active engagement of stakeholders. Consultations and the continually evolving network/sensor technology are explored to connect with and to support stakeholders, including participants, on(biobanking)research and ELSI issues. In five defined tasks, services, guidelines and instruments are developed: 1. MyBiobank - Dr. A.H.M. (Gonneke) Willemsen

A generic portal for use in other data/biobank studies is developed as an important way to sustain participant interaction. For this goal the MyNTR portal, with information, questionnaire and feedback functionalities, is improved and adjusted, based on experiences with MyNTR portal.

2. Privacy Impact Assessment App - Mr. J.A. (Jasper) Bovenberg (WP Leader)

A framework for periodic Privacy Impact Assessments (PIAs) for biobanks is being developed, as required by the EU Data Protection Regulation. The resulting BBMRI-NL-Biobank-PIA Framework provides a PIA that is targeted to the specific context of biobanks, that facilitates the collaboration of biobanks, and that is designed and developed as a ready to use, digital application to assist biobanks in their performance of PIAs.

3. Quantified Self - Dr. E.W.H.M. (Lisette) Eijdems, Prof. dr. R.P. (Ronald) Stolk (WP Leader)

A long-term vision and practical guidelines are being developed for the use of Do-It-Yourself (DIY) measurement in (biobanking)research. DIY measurements can be a cost-effective way of data collection and also of building and maintaining relationships with participants. Useful parameters, requirements, and methods (i.e. sensors, apps) are explored, as well as relevant societal developments and the perspective of the public. The outcome, combined with the on-going study of use cases, will be input for planned pilots.

4. Value Creation - Dr. E.W.H.M. (Lisette) Eijdems, Prof. dr. R.P. (Ronald) Stolk (WP Leader)

Opportunities are explored to exploit the potential of investments and funding more efficiently, as improving supply chain management, access and service fees, and funding 2.0. with suggestions for (new) economic prerequisites. This task will be performed in close alignment with the other Health-RI stakeholders and will make use of Canvas business modelling.

5. Centre of Expertise on Ethical, Legal and Social Issues – Drs. M. (Martin) Boeckhout, Prof.dr. G.A.

(Gerhard) Zielhuis Through the Centre of Expertise on Ethical, Legal and Social Issues, we help biobanking and related health research infrastructure in developing responsible approaches to ethical, legal and social issues and concerns relating. We do so through a number of actions and initiatives: engagement through the public and patient stakeholder forum (in Dutch: Maatschappelijke Adviesraad Biobankonderzoek); debate in the ELSI discussion platform Health RRI; and policy support by providing consultation and expertise to BBMRI and the biobanking community.


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22 - Responsible Health Research Infrastructure: BBMRI Centre of Expertise on Ethical, Legal and Social Issues M Boeckhout1,*, GA Zielhuis1,2,3

1. BBMRI-NL, Department of Genetics, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen 2. Radboud Biobank, Radboudumc, PO Box 9101, 6500 HB, Nijmegen 3. Parelsnoer Institute, Jaarbeursplein 6, 3521 AL, Utrecht * Contact person: Martin Boeckhout - [email protected] Through the BBMRI Centre of Expertise on Ethical, Legal and Social Issues, we help biobanking and Health Research Infrastructure in developing responsible approaches to ethical, legal and social issues and concerns relating. This poster elaborates on our main actions and initiatives as well as the most pertinent policy developments:

• Engagement through the Maatschappelijke Adviesraad Biobankonderzoek aka the public and patient stakeholder forum;

• Expert debate in the ELSI discussion platform Health RRI; • Support, consultation and expertise to BBMRI and the biobanking community; • Proactive policy engagement, together with a.o. Federa-COREON and NFU relating to a number of

themes, including: − The ethical and legal framework for personalized medicine research infrastructure − The EU General data Protection Regulation and its national implementation − The Dutch Human Tissue Act (Wet zeggenschap lichaamsmateriaal)


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23 - How to tackle the big data infrastructure –omics challenge? BBMRI-OMICS is a collaboration of − Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden − VU University Amsterdam, De Boelelaan 1105 1081 HV Amsterdam − University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen − Radboudumc, PO Box 9101, 6500 HB, Nijmegen − University of Amsterdam, PO Box 19268, 1000 GG, Amsterdam − Erasmus MC: University Medical Center Rotterdam, PO Box 2040, 3000 CA, Rotterdam − Maastricht University, PO Box 616, 6200 MD, Maastricht − University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht * Contact person: Hailiang (Leon) Mei – [email protected] Thanks to the rapid decreasing of sequencing cost, more research and clinical institutes are generating Next Generation Sequencing data at an increasing and impressive scale. University Medical Centers in the Netherlands are sequencing thousands patients a year each as part of their routine diagnosis. On the research front, the GoNL project and BIOS project coordinated by the BBMRI-NL consortium have sequenced 770 whole genome DNA samples and over 4000 RNA samples collected from a number of Dutch biobanks. In 2016, the deployment of Illumina X Ten sequencer at the Hartwig Medical Foundation provides a sequencing capacity of 18,000 whole genome DNA samples per year. Processing these petabyte scale datasets requires revolutionary thinking and solutions in the computing and storage infrastructure and the data analysis pipelines. In the BBMRI-NL 2.0 project, a dedicated task group is established to tackle this big data infrastructure challenge. This task group consists of members who led the data processing endeavor in projects like GoNL, BIOS, and population imaging in the Rotterdam Study. The aim is to further improve and disseminate the constructed knowledge and best practice on data management and data processing. Solutions provided by this task group will ensure a maximum level of automation for installing a new scalable and reproducible data analysis infrastructure.


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24 - Shaping the future of BBMRI-NL2.0 E Erdtsieck-Ernste1,2,*, GA Meijer11,3 and C Wijmenga1,4 1. BBMRI-NL, Department of Genetics, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen 2. Lygature, Jaarbeursplein 6, 3521 AL, Utrecht 3. Department of Pathology, Netherlands Cancer Institute, PO Box 90203, 1006 BE, Amsterdam 4. Department of Genetics, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen * Contact person: Erna Erdtsieck-Ernste – [email protected] BBMRI.nl phase 2.0 started in 2015 and is the innovative combination of three complementary national infrastructure initiatives: BBMRI.nl1.0 (biobanking), EPI2 (population imaging), and CTMM-TraIT (Translational research IT) to build an infrastructure that integrates the full range of biobanking, imaging activities, and IT tools in the Netherlands for Precision Medicine. Such a national biobank infrastructure enables ground-breaking biomedical research, resulting in new and improved ways to diagnose and predict disease, and highlight factors critical to disease prevention, healthy ageing or optimal development, and thus to the quality of life. It will cement the Netherlands leading position in biobank-based biomedical research. In 2016 the next step was taken by BBMRI-NL, EATRIS-NL, and DTL/ELIXIR-NL to develop a common vision and roadmap on how The Netherlands can set further course for a comprehensive Nation-wide Personalized Medicine & Health Research infrastructure. The goal is to bundle and connect a wide range of resources including the BBMRI-NL aligned biobanks, IT-technologies, facilities and data collections into one large-scale research infrastructure: Health-RI. With its content ranging from genes, molecules and images to their clinical cognates, Health-RI will provide a unique repository of integrated data that optimally prepares the Netherlands for implementation of precision medicine and precision health for every Dutch citizen, while enhancing quality and reproducibility of research. Health-RI will make BBMRI-NL2.0 a highly visible and attractive partner for international collaborations, including Horizon 2020.

BBMRI-NL [email protected] www.bbmri.nl

Department of Genetics University Medical Center Groningen


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25 - Towards FAIR rare disease research infrastructure D van Enckevort1, R Thompson2, C Carta3, M Thompson4, R Kaliyaperumal4, M Wilkinson5, M Swertz1 and M Roos4 1. University of Groningen, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen 2. Newcastle University, NE1 7RU, Newcastle, United Kingdom 3. National Center for Rare Diseases, Istituto Superiore di Sanità, Via Giano Della Bella 34, 00161, Roma, Italy 4. Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden 5. Universidad Politécnica de Madrid, Calle Ramiro de Maeztu, 7, 28040, Madrid, Spain * Contact person: Marco Roos - [email protected] If we wish to aid the rare disease community in developing robust infrastructure that supports data integration, then we have to take into account that there are over 6000 rare diseases, each with multiple data resources across countries, regions and institutes, and ranging from biobanks, patient or disease registries, and ‘omics data sources. It is of pivotal importance that researchers can combine data from these resources, because of the relative sparsity of the data in this domain. Maintaining a centralized warehouse at this scale, and with this kind of sensitive data, is neither feasible nor ethically or legally acceptable. One challenge is to provide solutions that can scale-up to be adopted by thousands of resources ‘at the source’. A second challenge is to provide solutions that facilitate cross-resource data analytics at the level of the data itself. The latter mitigates the most costly bottleneck for this community: researchers spending too much time reconciling data ambiguities, while previous reconciliation efforts cannot be reused. To address these challenges, we present a ‘rare disease data linkage plan’ written and endorsed by stakeholders and infrastructure experts in the rare disease community. They have committed themselves to making rare disease resources findable, accessible, interoperable, and reusable for humans and computers (FAIR) at the source. The plan is supported, in some cases with generous extra funds, by multiple projects such as RD-Connect, Elixir, BBMRI, FAIRDict, and ODEX4All. Several individuals and institutes kindly commit effort to the plan. Most importantly, a number of rare disease patient organisations have shown strong willingness to invest in the plan. The ambition is to help make at least seven biobanks/registries FAIR, and to study the best approach to make molecular data resources FAIR. We work with the DTL FAIR data team, the international FAIR skunk team, and a collaboration of engineers involved with rare diseases. The FAIR principles delegate design decisions towards their implementation to communities. Therefore, the plan includes providing recommendations for the rare disease community. Early design decisions are to adopt the FAIR data point API for data communication, and to use ontologies and linked data for exchange of interoperable data. We have made a few specific ontology choices, such as the human phenotype ontology, but will use the experience of the data linkage plan to incrementally provide more recommendations. We develop one or more semantic reference models to be reused in the field to further increase data interoperability. The data linkage plan complements work on agreeing to common data elements. We argue that harmonizing how the data are encoded by a shared knowledge representation is possibly easier to achieve than agreeing on common data elements. Finally, we report on four Bring Your Own Data Workshops that we organised in the context of the data linkage plan.


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26 - BBMRI-NL Biobank Catalogue: Live updates and linkage with biobanks D van Enckevort1, EPA van Iperen2,3, A Siezen4, R den Ouden4, E Flikkenschild5,6, R van der Velde5, E Adriaanse1, JW Boiten7 and M Swertz1 1. University of Groningen, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen 2. AMC Biobank, Room L01-124, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam 3. Durrer Center for Cardiovascular Research, Netherlands Heart Institute, PO Box 19258, 3501 DG, Utrecht 4. Radboud Biobank, Radboudumc, PO Box 9101, 6500 HB, Nijmegen 5. Leiden University Medial Center, PO Box 9600, 2300 RC, Leiden 6. Parelsnoer Institute, Jaarbeursplein 6, 3521 AL, Utrecht 7. Lygature, Jaarbeursplein 6, 3521 AL, Utrecht * Contact person: David van Enckevort - [email protected] The BBMRI-NL Biobank Catalogue is an important resource for researchers and biobanks to find eachother and to enable high quality biomedical research using samples collected previously, either for clinical care or for research. However, it is a continuous challenge to provide accurate and up-to-date information about the collections in the catalogue. BBMRI-NL has therefore started two pilots to automate the process of updating the catalogue, one for Parelsnoer and one with Radboud biobank. Together with the section Advanced Data Management of the LUMC we created a process to automatically update the records of the Radboud Biobank and Parelsnoer collections in the catalogue. We implemented two different strategies for Radboud Biobank and Parelsnoer. For Radboud Biobank the catalogue aggregates the data in a streaming process, while for Parelsnoer the catalogue receives pre-aggregated data from the underlying database in Promise. The collected information conforms to the MIABIS 2 core standard for biobank information data exchange, and both processes run in a secured manner protecting the privacy of the participants by suppressing any identifying information. The pilots led to a successful integration of accurate and up-to-date information for Parelsnoer and Radboud Biobank in the BBMRI-NL catalogue and we would like to extend this to more biobanks. In the near future we will also use the same techniques to synchronize the BBMRI-ERIC directory with BBMRI-NL Biobank Catalogue.


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27 - Integration of eScience technologies to tackle scientific challenges in Health-RI A Gavai1,*, A Mendrik1 and L Ridder1 1. Netherlands eScience Center, Science Park 140, 1098 XG, Amsterdam * Contact person: Anand Gavai - [email protected] The Netherlands eScience center (NLeSC) is a multi-disciplinary center, where knowledge from different scientific domains is combined with eScience expertise, for example in natural language processing, data mining and machine learning, semantic web technologies and visualization. eScience research engineers develop open source software tools, which are made freely available via the eStep platform1, and use them to tackle a wide range of scientific questions, e.g. in life sciences and health, environmental sciences, physics and humanities. The Health-RI objectives, to enable personalized medicine and health, will require the integration of a diverse range of expertise’s and technologies. For example, to enhance scientific discovery linked data approaches need to be combined with interactive data visualization and smart methods to search for relationships and patterns in large semantically connected datasets. At NLeSC we are developing unique expertise in generalizing and combining algorithms and software and data tools across domains. Our current life-sciences and eHealth projects include biomarker research, image processing, automatic diagnosis of brain data (EEG) and data of wearables, development of a FAIR data infrastructure and computational workflows for drug development. Based on our experience with this diverse portfolio of life science projects, we are developing a suite of loosely coupled software modules and libraries to tackle various challenges also posed by the Health-RI objectives. References: 1 http://estep.esciencecenter.nl/


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28 - RADAR CNS - Research Infrastructure for processing wearable data to improve health J Kurps1, M Moinat1, J Borgdorff1, F Nobilia2, M Kerz2, N Mahasivam1, I Pulyakhina1, M Dümpelmann4, H Campos5, M Begale6, R Dobson2,3 and A Folarin2,3 1. The Hyve, HNK Central Station, Arthur van Schendelstraat 650, 3511 MJ, Utrecht 2. Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College

London, Box P092, De Crespigny Park, SE5 8AF, London, United Kingdom 3. Farr Institute of Health Informatics Research, UCL Institute of Health Informatics, University College London, WC1E 6BT,

London, United Kingdom 4. Center of Epilepsy, University Hospital Freiburg, Breisacher Str 64, 79106 Freiburg, Germany 5. Goldenarm, 20 Jay Street, Suite 840, 11201 Brooklyn, New York, United States of America 6. Vibrent Health, 12015 Lee Jackson Memorial Highway, Suite 13, 2203 Fairfax, VA, United States of America * Contact person: Julia Kurps - [email protected] Remote Assessment of Disease And Relapse – Central Nervous System (RADAR CNS) is an innovative collaborative research project to evaluate the potential of wearable devices to improve quality of life for patients with epilepsy, multiple sclerosis (MS) and major depression disorder (MDD). As a project in the Innovative Medicine Initiative (IMI) framework, RADAR CNS is built upon close collaboration of patient organizations, clinical partners, research institutes and industry to develop new strategies for treatment of patients with brain disorders. The aim of RADAR CNS is to evaluate how to best leverage innovative technologies like wearable devices and smartphone-based applications for remote monitoring of patients and early predictions of relapse episodes. Together with our data processing partners, The Hyve is building an open source infrastructure to capture, process, manage and analyse data from wearable devices, which will also allow integration with different data from multiple other sources like clinical and -omics data. Our clinical partners will use this data processing pipeline in multiple clinical trials. Sustainability is a focus point during the development of the RADAR platform. Therefore, we develop a generic platform, which will not be limited to brain disorder applications, but will be applicable for subsequent RADAR projects like RADAR Diabetes. Furthermore, we are actively facilitating a striving open source community around the RADAR platform to ensure longevity of the research infrastructure and encourage cross-infrastructure efforts. The RADAR CNS platform is adding an additional dimension to the research infrastructure for personalised medicine and health by allowing new ways to leverage innovative technologies for better care for patients.


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a

Gel Channel (G.C.)

Medium Channel (M.C.)

PhaseGuide M.C. G.C.

b

Calcein NucBlue NucRed

M.C. G.C.

c

29 - 3D Glioma-on-a-Chip Models for Personalized Medicine in OrganoPlates® Y Habani1, HL Lanz1, S Venkatesan2, T Pierson2, M Lamfers2, S Leenstra2 and J Joore1 1. MIMETAS BV, Biopartner Building 2, J.H. Oortweg 19, 2333 CH, Leiden 2. Neurosurgery department, Erasmus MC: University Medical Center Rotterdam, PO Box 2040, 3000

CA, Rotterdam * Contact person: Remko van Vught - [email protected]

www.mimetas.com Treatment of gliomas is complicated by variable response rates of individual patients’ tumors to therapies. The Department of Neurosurgery of the Erasmus MC has developed a 2D culture platform (GLIOscreen) for screening patient-derived glioma tissues with potential therapeutic compounds. Unfortunately, not all patient-derived glioma tissues are amenable to 2D culture, probably caused by tumor heterogeneity, raising the necessity for additional, complementing culture models. Here we show the development of an organotypic glioma model in the OrganoPlate® to establish screenable cellular models for all glioma patients1. The OrganoPlate® is an easy to use, high throughput microfluidic platform enabling 3D cell culture and co-culture options, creating physiologically relevant models with a minimal requirement of cell material, see figure 1a. A close up image of the microfluidic chip and the readout window shows, on top the medium channel and at the bottom the gel channel, see figure 1b. This project is a collaboration with the Erasmus MC and funded by the patient foundation STOPhersentumoren.nl. The 3D glioma model will be used to culture individual patient’s cancer cells for the screening of potential effective (combinatorial) treatments, such as Temozolomide, a first line therapy in glioma treatment. In figure 1c, GLIOscreen-derived glioma cell line GS261 was seeded in BME2 (reduced growth factor ECM), in the OrganoPlate® and cultured for 8 days. On day 8 cells were analyzed by phase contrast imaging and the live/dead cell viability assay (Life Technologies). The cell viability can also be studied in time with RealTime-Glo™ (Promega), a non-toxic cell viability assay. Glioma cells can be cultured in the OrganoPlate® for up to 2 months and are suitable for high-throughput chemotherapeutic drug screening. References: 1) Trietsch, S. J. et al. Microfluidic titer plate for stratified 3D cell culture. Lab Chip 13, 3548–54 (2013).

Live/dead cell viability assay of glioma cell lines in 3D. a) Image of an OrganoPlate®, b) Horizontal view of the readout window and its cross section, c) Live cells are stained with calcein-AM (green), dead cells are stained with ethidiumhomodimer (red), and can be quantified using image analysis software.


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30 - Immunowell initiative T Bezema1,* and AA te Velde1,2,* 1. Immunowell Foundation, Wevelaan 65, 3571 XT, Utrecht , www.immunowell.com 2. Tytgat Institute for Liver and Intestinal Research, Academic Medical Center, PO Box 22660, 1100 DD, Amsterdam * Contact person: T Bezema (founder and chair of Immunowell) and AA te Velde (board member of Immunowell) [email protected] In order to grow old in a healthy manner you need a fit immune system. Only a small amount of chronic diseases that prevent us from ageing healthy is genetically fully penetrant. Most chronic diseases however can have a genetic disposition but whether one gets ill depends on many factors such as your environment, lifestyle, diet, etc. The Immunowell Initiative includes all these factors and combines scientific research with knowledge that is contained in the experiences of patients and doctors. The Immunowell Initiative wants to create a world in 2025 where everyone has the tools to make his or her own immune system more healthy. The initiative encompasses a Platform to collect knowledge from patient experiences, a Human Science Lab, Inspiration Workshops and Education and training on Immune Fitness. In order to scientifically use experiental knowledge we have to discover how to recover knowledge form the stories of experiental experts (being patients or family/friends of patients with a chronic immune disorder). Therefore Immunowell develops Platform ImmunoWeb, where stories will be collected and evaluated. In addition, in the Human Science Lab the immune functioning of a large group of people (patient and healthy) will be followed for a longer period of time and related to their daily lifestyle, their social and physical environment and their individual personality characteristics. In 2017 Immunowell will organize inspiration workshops. Popular subjects related to the immune system will be explained by duo’s consisting of someone from regular and someone from integral medicine. This is followed by an active workshop to generate knowledge for the Immunowell Human Science Lab. The new knowledge from all parts of the Immunowell Initiative will be developed into education and training. Our network is dedicated to establish a healthy immune system for every individual person and is eager to benefit from the Health-RI initiative of existing large infrastructures.

http://www.immunowell.com/beleid/jouw-verhaal-doet-ertoe/


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31 - LUMC Research ICT Program: A solid basis for research LUMC1 1. Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden * Contact person: Rob Cornelisse - [email protected] Project managers: F. Beaumont8, PGM van Overveld1,5, K van de Pal-de Bruin6, M Thompson2, I van Veen6, LA Veltrop-Duits3

Core team: S le Cessie, R Cornelisse4,7, E Flikkenschild, PAC ’t Hoen, ME den Hollander-Gijsman, JW van Ommeren Research ICT Research IT is the ambitious LUMC program set up to improve the data and knowledge exchange between researchers. The program aims to strengthen the foundation for scientific research at the LUMC and prepare it for future developments. The Research ICT program has been started to strengthen the foundation for research at the LUMC and ultimately facilitate personalized patient care. Improving and facilitating procedures, regulations and IT-services enhances the quality of scientific research, while safeguarding the privacy and safety of the patients. By making data findable, accessible, interoperable and reusable, we respond to the growing need for transparency. The projects The Research ICT program embraces several projects: 1. FAIR Data Stewardship: This projects concerns the implementation of FAIR data stewardship at the

LUMC. Guidelines for data management are being developed. 2. Clinical research and the EHR: This project focuses on improving (FAIR) data collection for clinical

research. Also part of this project is to provide a Data Management (DMS) for (clinical) research in the LUMC. A DMS is used to collect and manage research data.

3. Metadata and registration of research: To gain insight into which research projects are conducted within the LUMC, a registration method is being devised.

4. Re-use of data through Medical Intelligence: This project aims to make LUMC data available for re-use. 5. Transparent research: To ensure more transparent research and reusability of data, an electronic lab

notebook (e-lab) is being implemented and a git server for transparent and reproducible data analysis and software development and is in place.

6. Multidisciplinary support: The implementation of a research support desk to help out researchers when they have questions. The research support desk links the questions to the right persons and refers the researcher.

7. IT infrastructure: This project focuses on the IT infrastructure necessary for research, particularly with regard to high performance computing and storage facilities.

8. Training and education: This project ensures up to date courses on topics like data stewardship and FAIR.

Contact Do you have questions about the Research IT program? Then please, do not hesitate to contact the Research ICT team via [email protected] More information: www.lumc.nl/research

http://www.lumc.nl/research


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32 - Radiomics - Images are more than pictures, they are data A Dekker1*, H Aerts1 and P Lambin1 1. Maastricht Radiation Oncology (MAASTRO), GROW School for Oncology, University of Maastricht, Dr. Tanslaan 12, 6229 ET,

Maastricht * Contact person: André Dekker - [email protected] Radiomics is a new scientific field which aims to convert the petabytes of unstructured cancer imaging data, available in hospitals worldwide, into minable, structured data. Radiomics extracts in a high-throughput manner intensity, shape and texture features from standard-of-care imaging. These features can predict treatment response and outcomes (e.g. survival), and correlate with the underlying biology of the cancer. Using tools and experience from TraIT, data from 422 Dutch lung cancer patients were shared including: • Imaging data (Computed Tomography) • Segmentation data (3D) of the gross tumor volume • Gene expression data (for a subset of 88) • Clinical data

− Age, Gender − Histology, Cancer TNM Stage − Survival

Challenges overcome included: • Ethical / legal framework for sharing images of patients • Privacy and de-identification, independently verified by external reviewers • Quality of images, segmentation and terminologies to make sure data reuse is as easy as possible The collection is now freely available to browse, download, and use for scientific and educational purposes. This work was nominated for the RDNL Dutch Data Prize 2016 References: − http://radiomics.org/ − Aerts HJ et al.: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun

(2014) 3(5): 4006. doi:10.1038/ncomms5006


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33 - Search for the genetic cause of human disease M van Iterson1,2, M Beekman1,2, M Kattenberg3, R Groenewegen4, J Bot4, I Nooren4 and B Heijmans1,2 1. Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden 2. BBMRI-NL, Department of Genetics, University Medical Center Groningen, PO Box 30.001, 9700 RB, Groningen 3. VU University Medical Center, PO Box 7057, 1007 MB Amsterdam 4. SURFsara, PO Box 94613, 1090 GP, Amsterdam * Contact person: Maarten van Iterson - [email protected] The mission of the BIOS Consortium is to create a large-scale data infrastructure and to bring together BBMRI researchers focusing on integrative omics studies in Dutch Biobanks. The advent of the genome-wide association study (GWAS) led to the successful identification of thousands of variants that are robustly associated with complex disease phenotypes. Dutch biobanks played a substantial role in these discoveries. To discover new phenotype-genotype relations, the BIOS consortium wants to provide access to data to other researchers. Over 4000 samples from BBMRI-NL biobanks with in-depth information on disease phenotypes and GWAS data have been enriched with RNA-sequencing (>15 M paired end reads) and genome-wide DNA methylation data (Illumina 450k arrays). The same is true for samples with whole-genome sequencing data from GoNL. This unique data infrastructure provides a powerful platform to evaluate key questions in integrative omics from establishing comprehensive eQTL and meQTL catalogues to linking molecular pathways across omics levels to phenotypic outcomes. In collaboration with SURFsara the ICT infrastructure including access management to data resources, software pipelines and HPC facilities, is being developed for researchers.


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34 - Dutch Techcentre for Life Sciences DTL1

1. Dutch Techcentre for Life Sciences, PO Box 19245, 3501 DE, Utrecht * Contact person: team DTL - [email protected] The Dutch Techcentre for Life Sciences (DTL) is a public-private partnership of more than 40 life science organizations in the Netherlands. The majority of Dutch universities and university medical centers are DTL partners, and a growing number of companies are joining the organization. DTL connects scientists, data experts, technical experts and trainers that are specialized in a variety of high-end wet lab and data technologies, and working in life science domains ranging from health to nutrition, agro, biotech and biodiversity. Together, these professionals interconnect and improve their research infrastructure to enable cost-effective cross-technology life science research in national and international collaboration.


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35 - Dutch Techcentre for Life Sciences: FAIR data DTL1

1. Dutch Techcentre for Life Sciences, PO Box 19245, 3501 DE, Utrecht * Contact person: Luiz Olavo Bonino da Silva Santos, CTO FAIR Data - [email protected] A key enabler to achieve international-grade data stewardship is for research data and information to be published in a ‘FAIR’ manner. Data should be: Findable, Accessible, Interoperable and Reusable. DTL actively promotes FAIR Data Stewardship of life science information.


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36 - On the road to EEG markers for individualized prediction of developmental disorders A Chen1, F Wijnen1 and H Schnack2,* 1. Utrecht Institute of Linguistics OTS, Department of Languages, Literature and Communication, Faculty of Humanities, Utrecht

University, PO Box 80125, 3508 TC, Utrecht 2. Neuroimaging Research Group, Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center

Utrecht, PO Box 85500, 3508 GA, Utrecht * Contact person: Hugo Schnack - [email protected] Early prediction of developmental disorders such as autism, developmental dyslexia, and schizophrenia will enable early intervention and may spare affected children from frustrating start of their life. EEG is a non-invasive way to map brain functions with high temporal resolution, and generates large amounts of data as compared to behavioral methods. It is relatively cheap and infant friendly, which makes it a suitable tool for early risk detection among infants. While the use of EEG to study development in health and disease has been well established, its application for making predictions at the individual level is still in its infancy. Advanced statistical methods are needed to discover the patterns in EEG data that are related to (risk of) the disorder. Machine learning techniques are able to find those patterns and make predictions at the level of individuals. Machine learning has been used in combination with EEG before, with aims such as predicting age group (6-months, 12-months, adult), or discriminating healthy adults from patients with ADHD or schizophrenia. Predicting subtle measures such as risk of developmental dyslexia, however, is much more complicated and requires i) large amounts of data and ii) advanced machine learning techniques. Health Research Infrastructure offers a platform to exchange datasets necessary for building and testing individualized prediction models. Our input to Health-RI is expertise in machine learning, pattern recognition and other (statistical) data analysis techniques in brain research. In the poster we will present results from a pilot study on our own dataset.


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37 - How old is your brain? Accelerated aging of the brain as a neuroimaging marker of developmental and psychiatric disorders H Schnack1,*, N van Haren1, M Nieuwenhuis1, H Hulshoff Pol1, W Cahn1 and R Kahn1 1. Neuroimaging Research Group, Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center

Utrecht, PO Box 85500, 3508 GA, Utrecht * Contact person: Hugo Schnack - [email protected] Early prediction of developmental and psychiatric disorders such as schizophrenia, autism spectrum disorder and bipolar disorder will enable early intervention. While there is abundant evidence for brain abnormalities related to these disorders, almost all studies have compared patients and controls at group-level. In the past decade individual prediction studies have been carried out to, e.g., separate healthy subjects from patients with a disorder, and separate patients with schizophrenia from those with bipolar disorder. Some studies have shown that, in adolescents, risk of psychosis can be detected and that transition to psychosis can be predicted to some extent. The problem with predicting risk of, e.g., psychosis is that the subjects for whom we wish to make these predictions vary very much in age and, thus, in brain anatomy. In order to make a useful prediction tool we need to develop a cross-age/developmental stage marker of the disorder. Recently, it has been shown that in patients with different psychiatric disorders, the age of the brain was significantly increased. In subjects at high risk of developing psychosis, brain age was already increased, while we recently showed that in schizophrenia patients the brain is aging at an accelerated pace. These results suggest that brain age could serve as a ‘continuous’ marker for (risk of) the disease throughout development and adulthood. The creation of such brain age models is done by applying machine learning algorithms to MRI brain scan data. These multivariate pattern recognition techniques require large (longitudinal) samples for training and testing the models. Health Research Infrastructure offers a platform to exchange datasets necessary for building and testing individualized prediction models. Our input to Health-RI is expertise in machine learning, pattern recognition and other (statistical) data analysis techniques in brain research, as well as MRI brain data. In the poster we will present very recent results of our longitudinal study of brain age in adults with and without schizophrenia.


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38 - A randomised controlled trial of consent procedures for the use of residual tissues for medical research: preferences of and implications for patients, research and clinical practice S Rebers1*, E Vermeulen1, AP Brandenburg1, TJ Stoof2, B Zupan-Kajcovski3, WJW Bos4, MJ Jonker5, CJ Bax6, WJ van Driel7, VJ Verwaal8, MW van den Brekel9, JC Grutters10,11, RA Tupker12, L Plusjé13, R de Bree14, JH Schagen van Leeuwen15, EGJ Vermeulen16, RA de Leeuw17, RM Brohet18, NA Aaronson1, FE Van Leeuwen1, MK Schmidt1,19

1. Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, PO Box 90203, 1006 BE, Amsterdam 2. Department of Dermatology, VU University Medical Center, PO Box 7057, 1007 MB, Amsterdam 3. Department of Dermatology, Netherlands Cancer Institute, PO Box 90203, 1006 BE, Amsterdam 4. Department of Internal Medicine, St. Antonius ziekenhuis, PO Box 2500, 3430 EM, Nieuwegein 5. Department of Dermatology, Spaarne Gasthuis (locatie Haarlem-Zuid), PO Box 770, 2130 AT, Hoofddorp 6. Department of Obstetrics, VU University Medical Center, PO Box 7057, 1007 MB, Amsterdam 7. Department of Gynaecology, Netherlands Cancer Institute, PO Box 90203, 1006 BE, Amsterdam 8. Department of Surgical Oncology, Netherlands Cancer Institute, PO Box 90203, 1006 BE, Amsterdam 9. Department of Head and Neck Oncology and Surgery, Netherlands Cancer Institute, PO Box 90203, 1006 BE, Amsterdam 10. Department of Pulmonology, St. Antonius ziekenhuis, PO Box 2500, 3430 EM, Nieuwegein 11. Division of Heart and Lungs, University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht 12. Department of Dermatology, St. Antonius ziekenhuis, PO Box 2500, 3430 EM, Nieuwegein 13. Department of Dermatology, Rode Kruis ziekenhuis, PO Box 1074, 1940 EB, Beverwijk 14. Department of Otolaryngology – Head and Neck Surgery, VU University Medical Center, PO Box 7057, 1007 MB, Amsterdam 15. Department of Gynaecology, St. Antonius ziekenhuis, PO Box 2500, 3430 EM, Nieuwegein 16. Department of Surgery, Spaarne Gasthuis (locatie Haarlem-Zuid), PO Box 770, 2130 AT, Hoofddorp 17. Athena Institute for Transdisciplinary Research, VU University Medical Center, PO Box 7057, 1007 MB, Amsterdam 18. Research Center Linnaeus Institute – Scientific Department, Spaarne Gasthuis (locatie Haarlem-Zuid), PO Box 770, 2130 AT,

Hoofddorp 19. Division of Molecular Pathology, Netherlands Cancer Institute, PO Box 90203, 1006 BE, Amsterdam * Contact person: Susanne Rebers - [email protected], 020 512 24 85 Despite much debate, there is little evidence on consequences of consent procedures for residual tissue use. Here, we investigated these consequences for the availability of residual tissue for medical research, clinical practice, and patient informedness. We conducted a randomised clinical trial with three arms in six hospitals. Participants, patients from whom tissue had been removed for diagnosis or treatment, were randomized to one of three arms: informed consent, an opt-out procedure with active information provision (opt-out plus), and an opt-out procedure without active information provision. Participants received a questionnaire six weeks post-intervention; a subsample of respondents was interviewed. Health care providers completed a pre- and post-intervention questionnaire. We assessed percentage of residual tissue samples available for medical research, and patient and health care provider satisfaction and preference. Health care providers and outcome assessors could not be blinded. We randomized 1,319 patients, 440 in the informed consent, 434 in the opt-out plus, and 445 in the opt-out arm; respectively 60.7%, 100%, and 99.8% of patients’ tissue samples could be used for medical research. Of the questionnaire respondents (N=224, 207, and 214 in the informed consent, opt-out plus, and opt-out arms), 71%, 69%, and 31%, respectively, indicated being (very) well informed. By questionnaire, the majority (53%) indicated a preference for informed consent, whereas by interview, most indicated a preference for opt-out plus (37%). Health care providers (N=35) were more likely to be (very) satisfied with opt-out plus than with informed consent (p=0.002) or opt-out (p=0.039); the majority (66%) preferred opt-out plus. We conclude that opt-out with information (opt-out plus) is the best choice to balance the consequences for medical research, patients, and clinical practice, and is therefore the most optimal consent procedure for residual tissue use in Dutch hospitals.


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39 - Reporting of evidence for utility of pharmacogenomics: PGx for statins as an example T Rigter1,2, ME Jansen1,2,*, W Rodenburg2, SWJ Janssen2 and MC Cornel1 1. Department of Clinical Genetics, Section Community Genetics and EMGO Institute for Health and Care Research, VU University

Medical Center, PO Box 7057, 1007 MB, Amsterdam 2. Centre for Health Protection, National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA, Bilthoven * Contact person: Marleen Jansen - [email protected] Advances from pharmacogenomics (PGx) have not been implemented into health care to the expected extent. Several barriers hamper this implementation, such as lack of information on test characteristics, financial hurdles and regulatory issues. One gap that will be addressed in this study is a lack of reporting on relevant outcome measures in literature to enable decision makers and clinicians to evaluate the clinical validity and clinical utility of PGx-tests. A systematic review of current reporting in scientific literature was conducted on publications addressing PGx in the context of statins. 87 articles between 1950 and 2016 were included and information was selected on: study characteristics, reported outcome measures, and accompanying conclusions on potential clinical consequences. Most articles reported odds ratios as the preferred measure for the association between a genetic variant and drug response. Often conclusions on the implementation of a PGx-test were based on this odds ratio, without explicit mention of other measures or factors influencing the clinical validity and clinical utility, such as the positive or negative predictive value. However, to be able to gain insight in the effect on a population and select effective tests, additional outcome measures are needed to estimate the clinical utility.


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40 - How to deal with unsolicited findings in research? P Manders 1,2*, I Feenstra2, HG Yntema2 and FM van Agt3 1. Radboud Biobank, Radboudumc, PO Box 9101, 6500 HB, Nijmegen 2. Department of Human Genetics, Radboudumc, PO Box 9101, 6500 HB, Nijmegen

3. Research Ethics Committee, Radboudumc, PO Box 9101, 6500 HB, Nijmegen * Contact person: Peggy Manders, Radboud Biobank – Radboudumc (830), PO Box 9101, 6500 HB Nijmegen

[email protected], 0031 24 365 36 78 Key words: informed consent, unsolicited findings, patient information, ethics Whole exome sequencing (WES) is increasingly be performed for diagnostic purposes at the Radboudumc. WES leads to a higher diagnostic yield but also unsolicited findings which may be of clinical relevance, but unrelated to the disorder that is under investigation. As part of WES implementation as a diagnostic test, a multidisciplinary committee of experts has been installed to deal with unsolicited findings. A patient information leaflet and informed consent concerning participation in genetic research were developed. Participants have the opportunity to choose for 1) single candidate gene testing or 2) WES, the latter including the possibility of detecting unsolicited findings. In case of an unsolicited finding, the multidisciplinary committee will discuss whether this result should be returned to the investigator/physician involved in the study and provide information on further examination/screening. In our centre, WES has been performed in 1,500 individuals for diagnostic purposes. In 2.3% of all analyses a potential unsolicited finding was detected. In 1.4% there has been a return of the unsolicited finding to the requesting clinical geneticist. The main reason for not returning findings is the uncertainty about the pathogenicity of a mutation and the absence of treatment options for late onset diseases with reduced penetrance. The next step is to find out what research subjects think of the documents and procedures used in diagnostics. Furthermore, the number of WES studies performed for research purposes should be assessed plus the number of potential secondary findings that have been discussed by the expert committee.


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41 - Personalised monitoring of Multiple-Sclerosis P van Oirschot1,*, B den Teuling1 and M Martens2 1. Orikami, Ridderstraat 27, 6511 TM, Nijmegen 2. Drug Target ID, Toernooiveld 200, 6525 EC, Nijmegen * Contact person: Pim van Oirschot - [email protected] Multiple sclerosis (MS) is a highly heterogeneous neurodegenerative disease with a heavy personal, economic and societal burden. No two people have the same combination of symptoms and the rate of progression can not be determined. Currently, clinical features and magnetic resonance imaging (MRI) are used for diagnosis, prognosis and classification of treatment responders versus non-responders. This strategy works for large patient cohorts, but because of the variability of the disease, not for an individual patient. There are 12 different MS medicines approved in the US and all have only moderate effect on the disease, have only effect in a small subset of patients and have substantial side effects. The DiaPro MS-app allows MS patients to self-monitor and it helps medical specialists to offer personalized diagnosis, prognosis and eventually treatment of MS patients.


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42 - BBMRI-NL WP5: One entry to all samples you need No abstract available

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Health-RI Conference - Connecting Researchers, Patients ......data in OpenClinica, array-data in GEO, or histological images of tissue microarrays). Data can be examined in more detail