Abstract
Collaboration can be challenging; nevertheless, the emerging successes of large, multi-partner, multi-national cooperatives and research networks in the biomedical sector have sustained the appetite of academics and industry partners for developing and fostering new research consortia. This model has percolated down to national funding agencies across the globe, leading to funding for projects that aim to realise the true potential of genomic medicine in the 21st century and to reap the rewards of 'big data'. In this Perspectives article, the experiences of the RA-MAP consortium, a group of more than 140 individuals affiliated with 21 academic and industry organizations that are focused on making genomic medicine in rheumatoid arthritis a reality are described. The challenges of multi-partner collaboration in the UK are highlighted and wide-ranging solutions are offered that might benefit large research consortia around the world.
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RA-MAP, molecular immunological landscapes in early rheumatoid arthritis and healthy vaccine recipients
Scientific Data Open Access 09 May 2022
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Change history
24 January 2018
In the version of this article originally published, the name of one of the authors, Peter Schulz-Knappe, was incorrectly given as Peter Schulze-Knappe. In addition, Mark Coles and James Butler were erroneously omitted from the list of members of the RA-MAP Consortium. These errors have now been corrected in the PDF and HTML versions of the article.
References
Jones, A. & Clifford, L. Drug discovery alliances. Nat. Rev. Drug Discov. 4, 807–808 (2005).
Barnes, M. R. et al. Lowering industry firewalls: pre-competitive informatics initiatives in drug discovery. Nat. Rev. Drug Discov. 8, 701–708 (2009).
Yildirim, O., Gottwald, M., Schüler, P. & Michel, M. C. Opportunities and challenges for drug development: public-private partnerships, adaptive designs and big data. Front. Pharmacol. 7, 461 (2016).
Schmeck, B., Bertrams, W., Lai, X. & Vera, J. Systems medicine for lung diseases: phenotypes and precision medicine in cancer, infection, and allergy. Methods Mol. Biol. 1386, 119–133 (2016).
Stingone, J. A. et al. Big and disparate data: considerations for pediatric consortia. Curr. Opin. Pediatr. 29, 231–239 (2017).
Morrison, M. “A good collaboration is based on unique contributions from each side”: assessing the dynamics of collaboration in stem cell science. Life Sci. Soc. Policy 13, 7 (2017).
Wilan, K. H. Opening up the ivory tower. Cell 129, 847–850 (2007).
Melese, T., Lin, S. M., Chang, J. L. & Cohen, N. H. Open innovation networks between academia and industry: an imperative for breakthrough therapies. Nat. Med. 15, 502–507 (2009).
Schleidgen, S., Klingler, C., Bertram, T., Rogowski, W. H. & Marckmann, G. What is personalized medicine: sharpening a vague term based on a systematic literature review. BMC Med. Ethics 14, 55 (2013).
Association of the British Pharmaceutical Industry. Stratified medicine in the NHS: an assessment of the current landscape and implementation challenges for non-cancer applications. ABPI http://www.abpi.org.uk/our-work/library/medical-disease/Documents/stratified_med_nhs.pdf (2014).
Association of the British Pharmaceutical Industry. The stratification of disease for personalised medicines. Research driven recommendations to strengthen a unified UK strategy through a stakeholder alliance. ABPI http://www.abpi.org.uk/our-work/library/medical-disease/Documents/strat_med.pdf (2014).
Academy of Medical Sciences. Stratified, personalised or P4 medicine: a new direction for placing the patient at the centre of healthcare and health education. Academy of Medical Science s https://acmedsci.ac.uk/download?f=file&i=32644 (2015).
UK Trade and Investment. Unlock your global business potential. UK stratified medicine. GOV.UK https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/301775/UK_Stratified_Medicine.pdf (2013).
Medical Research Council. The Medical Research Council stratified medicine research initiatives. MRC https://www.mrc.ac.uk/research/initiatives/stratified-medicine/ (2017).
Peltonen, L. & McKusick, V. A. Genomics and medicine. Dissecting human disease in the postgenomic era. Science 291, 1224–1229 (2001).
Loscalzo, J., Kohane, I. & Barabasi, A. L. Human disease classification in the postgenomic era: a complex systems approach to human pathobiology. Mol. Syst. Biol. 3, 124 (2007).
Botstein, D. & Risch, N. Discovering genotypes underlying human phenotypes: past successes for Mendelian disease, future approaches for complex disease. Nat. Genet. 33, 228–237 (2003).
Hirschhorn, J. N. & Daly, M. J. Genome-wide association studies for common diseases and complex traits. Nat. Rev. Genet. 6, 95–108 (2005).
Giacomini, K. M. et al. Genome-wide association studies of drug response and toxicity: an opportunity for genome medicine. Nat. Rev. Drug Discov. 16, 70 (2017).
Chatterjee, N., Shi, J. & García-Closas, M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat. Rev. Genet. 17, 392–406 (2016).
Berger, B., Peng, J. & Singh, M. Computational solutions for omics data. Nat. Rev. Genet. 14, 333–346 (2013).
Hood, L., Balling, R. & Auffray, C. Revolutionizing medicine in the 21st century through systems approaches. Biotechnol. J. 7, 992–1001 (2012).
Institute for Safe Medication Practices. The five rights: a destination without a map. Institute for Safe Medication Practices http://www.ismp.org/newsletters/acutecare/articles/20070125.asp (2007).
McInnes, I. B., Buckley, C. D. & Isaacs, J. D. Cytokines in rheumatoid arthritis — shaping the immunological landscape. Nat. Rev. Rheumatol. 12, 63–68 (2016).
Baretta, Z., Mocellin, S., Goldin, E., Olopade, O. I. & Huo, D. Effect of BRCA germline mutations on breast cancer prognosis: a systematic review and meta-analysis. Medicine (Baltimore) 95, e4975 (2016).
Bunting, S. F. & Nussenzweig, A. End-joining, translocations and cancer. Nat. Rev. Cancer 13, 443–454 (2013).
Lieber, M. R. Mechanisms of human lymphoid chromosomal translocations. Nat. Rev. Cancer 16, 387–398 (2016).
Slamon, D. J. et al. Studies of the HER-2/neu proto-oncogene in human breast and ovarian cancer. Science 244, 707–712 (1989).
Pauletti, G., Godolphin, W., Press, M. F. & Slamon, D. J. Detection and quantitation of HER-2/neu gene amplification in human breast cancer archival material using fluorescence in situ hybridization. Oncogene 13, 63–72 (1996).
Maemondo, M. et al. Gefitinib or chemotherapy for non-small-cell lung cancer with mutated EGFR. N. Engl. J. Med. 362, 2380–2388 (2010).
Slamon, D. J. et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N. Engl. J. Med. 344, 783–792 (2001).
Khagi, Y., Kurzrock, R. & Patel, S. P. Next generation predictive biomarkers for immune checkpoint inhibition. Cancer Metastasis Rev. 36, 179–190 (2017).
Siniard, R. C. & Harada, S. Immunogenomics: using genomics to personalize cancer immunotherapy. Virchows Arch. 471, 209–219 (2017).
Ramamurthy, C., Godwin, J. L. & Borghaei, H. Immune checkpoint inhibitor therapy: what line of therapy and how to choose? Curr. Treat. Options Oncol. 18, 33 (2017).
Medical Research Council. MRC Strategic Review of Human Immunology. MRC https://www.mrc.ac.uk/publications/browse/strategic-review-of-human-immunology/ (2007).
Gerlag, D. et al. EULAR recommendations for terminology and research in individuals at risk of rheumatoid arthritis: report from the Study Group for Risk Factors for Rheumatoid Arthritis. Ann. Rheum. Dis. 71, 638–641 (2012).
Innovative Medicines Initiative. The IMI funding model. IMI http://www.imi.europa.eu/about-imi/imi-funding-model (2017).
Johnson, W. E., Rabinovic, A. & Li, C. Adjusting batch effects in microarray expression data using Empirical Bayes methods. Biostatistics 8, 118–127 (2007).
Herzinger, S. et al. SmartR: an open-source platform for interactive visual analytics for translational research data. Bioinformatics 33, 2229–2231 (2017).
Acknowledgements
The programme of research described in this Perspectives article was funded by the Medical Research Council (MRC), UK. The RA-MAP Consortium would particularly like to thank members of the MRC Immunity and Inflammation Stratified Medicine Steering Group and officers of the MRC, who have supported the work of the RA-MAP Consortium with unbridled enthusiasm.
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A.P.C., M.R.B. and J.D.I. wrote the manuscript. A.P.C., S.B., F.B.-C. and A.W.P. researched data for the article. A.P.C., M.R.B., A.B., M.B., S.B., F.B.-C., B.A.F., C.S.G., P.E., M.E.R., N.G., R.H., S.H., M.F.M., I.B.M., S.R., A.W.P., F.P., D.P., R.R., A.R., M.A.S., D.S., B.T. and J.D.I. provided substantial contributions to discussions of content. A.P.C., M.R.B., A.B., S.B., F.B.-C., C.C., B.A.F., C.S.G., M.E.R., N.G., R.H., S.H., S.K., M.L., C.L., M.F.M., I.B.M., C.M.M., G.P., S.R., A.W.P., F.P., D.P., A.R., P.S.-K., M.A.S., D.S., P.C.T., B.T., W.T., D.V. and J.D.I. reviewed and/or edited the manuscript before submission.
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A.P.C. declares that he has acted as a consultant for or received honoraria from BMS, Eisai, GSK, Janssen and Roche. For a full list of competing interests for all co-authors, see Supplementary information S6 (table).
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FURTHER INFORMATION
PowerPoint slides
Supplementary information
Supplementary information S1 (figure)
The RA-MAP Consortium (PDF 95 kb)
Supplementary information S2 (figure)
The RA-MAP Research Programme (PDF 849 kb)
Supplementary information S3 (figure)
Project management and reporting lines (PDF 227 kb)
Supplementary information S4 (figure)
RA-MAP core technologies (PDF 73 kb)
Supplementary information S5 (figure)
Managing 'big data' (PDF 800 kb)
Supplementary information S6 (table)
Contributing authors and their competing interests (PDF 153 kb)
Supplementary information S7 (box)
RA-MAP Consortium Membership (2012 – current) (PDF 266 kb)
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Cope, A., Barnes, M., Belson, A. et al. The RA-MAP Consortium: a working model for academia–industry collaboration. Nat Rev Rheumatol 14, 53–60 (2018). https://doi.org/10.1038/nrrheum.2017.200
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DOI: https://doi.org/10.1038/nrrheum.2017.200
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