From genomic databases to translation: a call to action

Knoppers BM, Harris JR, Burton PR, Murtagh M, Cox D, Deschênes M, Fortier I, Hudson TJ, Kaye J, Lindpaintner K. From genomic databases to translation: a call to action. Journal of medical ethics. 2011 Aug 1;37(8):515-6.

From genomic databases to translation: a call to action 1

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4 5 5 Bartha Maria Knoppers, Jennifer R Harris, Paul R Burton, Madeleine Murtagh, David Cox, Myl`ene Deschˆenes, Isabel Fortier, Thomas 8 6 7 J Hudson, Jane Kaye, Klaus Lindpaintner 1

Department of Human Genetics, McGill University, Montreal, Quebec, Canada 2Norwegian Institute of Public Health, Oslo, Norway 3Department of Health Sciences, University of Leicester, Leicester, UK 4Biotherapeutics and Innovation Center, Pfizer Inc., Cambridge, Massachusetts, USA 5 Public Population Project in Genomics (P3G), Montreal, Quebec, Canada 6Department of Medical Genetics and Microbiology, University of Toronto, Toronto, Ontario, Canada 7 Department of Public Health, University of Oxford, Oxford, UK 8Strategic Diagnostics Inc. (SDI), Newark, New Jersey, USA

ABSTRACT The rapid rise of international collaborative science has enabled access to genomic data. In this article, it is argued that to move beyond mapping genomic variation to understanding its role in complex disease aetiology and treatment will require extending data sharing for the purposes of clinical research translation and implementation.

‘We, as funders of health research, intend to work together to increase the availability to the scientific community of the research data we fund that is collected from populations for the purpose of health research and to promote the efficient use of those data to accelerate improvements in public health.’ Sharing Research Data to Improve Public Health, The Wellcome Trust and 16 other funders of health research, January 10, 2011.

The human genome project ushered in an era of data sharing that accelerated the pace of science. But what is now needed so that this new data- intensive science1 can best be brought to bear on the advancement of clinical research? The International Human Genome Sequencing Consortium (which included the non-profit and private sectors) sought to ensure that genome sequences were rapidly and freely made available to the scientific community. To that end, scientists and funding agencies behind this Consortium met in Bermuda in 1996 to discuss prepublication release of sequence data and drafted their ideas into a consensus document called the Bermuda Princi- ples.2 This approach was reaffirmed in 2003 and expanded upon to develop and encompass the concept of a ‘community resource project’. In recognition of the essential role of diverse stake- holders, the tripartite responsibility of data users, data producers and funding agencies was also addressed. 3 The principles of rapid prepublication data release were then extended to proteomics data in 2008 at a meeting in Amsterdam.4 Renewed discussions continued at the Data Release Workshop in Toronto in May 2009 where there was agreement that rapid prepublication data release should apply more widely to other types of data that have broad utility, including population studies and clinical trials with proper attention given to participant consent and privacy protection. 5 The recent Biomarkers Consortium6 illustrates that crosscompany precompetitive collaboration is a powerful approach and augers well for widening the pre-emptive sharing spectrum to clinical trials. In short, the principles that enabled access to genomic data clearly accelerated the reach of science and pace of discoveries by enabling the data to be used by a large community of scientists in diverse disciplinary settings to query the data in ways that could not be possible if the data were only available to the data producers. This process continues to enhance greatly the value of data collected in individual laboratories around the world. For example, the recent release of the map of human genome variation from population-scale sequencing by the 1000 Genomes Project has already enabled more accurate imputation of variants in many genome-wide association (GWA) studies.7 There is now an emerging consensus that moving beyond mapping human genomic variation to understanding its role in complex disease aeti- ology and treatment will also require broad access to more comprehensive data. This includes data on exposures, health outcomes including treatment response and adverse effects, biochemical and other biomarker data as well as demographic, behavioural and social measures. Thus today, we stand in a critical position whereby we can fuel contempo- rary bioscience and therapies through the extension of the data sharing principles to implementation studies and public health. This requires cooperation between funders and a range of actors involved in the production, dissemination and translation of data as well as the efficient access and use of most data types collected in the context of health administrative databases, tissue banks, population and disease studies and clinical trials whether in the public or private sectors. In order to enable this translation, we need to expand data sharing. We recognise that such a broader data sharing initiative would: build on current harmonisation efforts to ensure the inter- operability of biomedical and personal information derived from a variety of sources and nations;8e10 promote the quality of stored tissues samples,11 and most importantly, support international ethical policies to foster access to clinical information and associated tissue samples and sociodemographic data. 12 Such a call to action parallels the precompetitive data sharing approach of the international sequencing Consortium that funded and inspired the mapping effort. Now, it is time to activate this ‘commons’ into data sharing for translation. Competing interests None. Provenance and peer review Not commissioned; not externally peer reviewed.

REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

Buchan I, Winn J, Biship C. A unified modeling approach to data-intensive healthcare. In: Hey T, Tansley S, Tolle K, eds. The Fourth Paradigm: Data-Intensive Scientific Discovery. Washington, DC: Microsoft Research, 2009:91e8. Summary of Principles Agreed at the First International Strategy Meeting on Human Genome Sequencing; 25e28 February 1996. Bermuda: Human Genome Organization, 1996. [cited 1 Feb 2011]. http://www.ornl.gov/ sci/techresources/Human_Genome/research/bermuda.shtml (accessed 14 Mar 2011). Fort Lauderdale report, Sharing Data from Large-Scale Biological Research Projects: A System of Tripartite Responsibility; 14e15 January 2003. Fort Lauderdale: Wellcome Trust, 2003. [cited 1 Feb 2011]. http://www.genome.gov/ Pages/ Research/WellcomeReport0303.pdf (accessed 14 Mar 2011). Rodriguez H, Snyder M, Uhl´en M, et al. Recommendations from the 2008 International Summit on Proteomics Data Release and Sharing Policy: The Amsterdam Principles. J Proteome Res 2009;8:3689e92. Birney E, Hudson TJ, Green ED, et al. Prepublication data sharing. Nature 2009;461:168e70. Wagner JA, Prince M, Wright EC, et al. The Biomarkers Consortium: practice and pitfalls of open-source precompetitive collaboration. Clin Pharmacol Ther 2010;87:539e42. Durbin RM, Abecasis GR, Altshuler DL, et al; The 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature 2010;467:1061e73. Public Population Project in Genomics (P3G). http://www.p3g.org (accessed 14 Mar 2011). European Network for Genetic and Genomic Epidemiology (ENGAGE). http:// www.euengage.org (accessed 14 Mar 2011). BioSHaRE. http://www.bioshare.eu (accessed 14 Mar 2011). Biobanking and Biomolecular Resources Research Infrastructure (BBMRI). http://www.bbmri.eu (accessed 14 Mar 2011). International Cancer Genome Consortium (ICGC). http://www.icgc.org (accessed 14 Mar 2011).