Prostate Cancer Diagnosis and Treatment Enhancement Through the Power of Big Data in Europe (PIONEER) is a European network of excellence for big data in prostate cancer, consisting of 32 private and public stakeholders from 9 countries across Europe. Launched by the Innovative Medicines Initiative 2 and part of the Big Data for Better Outcomes Programme (BD4BO), the overarching goal of PIONEER is to provide high-quality evidence on prostate cancer management by unlocking the potential of big data. The project has identified critical evidence gaps in prostate cancer care, via a detailed prioritization exercise including all key stakeholders. By standardizing and integrating existing high-quality and multidisciplinary data sources from patients with prostate cancer across different stages of the disease, the resulting big data will be assembled into a single innovative data platform for research. Based on a unique set of methodologies, PIONEER aims to advance the field of prostate cancer care with a particular focus on improving prostate-cancer-related outcomes, health system efficiency by streamlining patient management, and the quality of health and social care delivered to all men with prostate cancer and their families worldwide.
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Smith-Palmer, J., Takizawa, C. & Valentine, W. Literature review of the burden of prostate cancer in Germany, France, the United Kingdom and Canada. BMC Urol. 19, 19 (2019).
Ferlay, J. et al. Cancer incidence and mortality patterns in Europe: estimates for 40 countries and 25 major cancers in 2018. Eur. J. Cancer 103, 356–387 (2018).
Patel, A. R. & Klein, E. A. Risk factors for prostate cancer. Nat. Clin. Pract. Urol. 6, 87–95 (2009).
Luengo-Fernandez, R., Leal, J., Gray, A. & Sullivan, R. Economic burden of cancer across the European Union: a population-based cost analysis. Lancet Oncol. 14, 1165–1174 (2013).
Cancer Research UK. CRUK. https://www.cancerresearchuk.org/ (2019).
Chinegwundoh, F. et al. Risk and presenting features of prostate cancer amongst African-Caribbean, South Asian and European men in North-east London. BJU Int. 98, 1216–1220 (2006).
Campi, R. et al. Impact of metabolic diseases, drugs, and dietary factors on prostate cancer risk, recurrence, and survival: a systematic review by the European Association of Urology section of oncological urology. Eur. Urol. Focus 5, 1029–1057 (2018).
Eggener, S. E., Rumble, R. B. & Beltran, H. Molecular biomarkers in localized prostate cancer: ASCO guideline summary. JCO Oncol. Pract https://doi.org/10.1200/JOP.19.00752 (2020).
Kohaar, I. P. G. & Srivastava, S. A rich array of prostate cancer molecular biomarkers: opportunities and challenges. Int. J. Mol. Sci. 20, 1813 (2019).
Gondos, A., Krilaviciute, A., Smailyte, G., Ulys, A. & Brenner, H. Cancer surveillance using registry data: results and recommendations for the Lithuanian national prostate cancer early detection programme. Eur. J. Cancer 51, 1630–1637 (2015).
Ilic, D. et al. Prostate cancer screening with prostate-specific antigen (PSA) test: a systematic review and meta-analysis. BMJ. 362, k3519 (2018).
Brookman-May, S. D. et al. Latest evidence on the impact of smoking, sports, and sexual activity as modifiable lifestyle risk factors for prostate cancer incidence, recurrence, and progression: a systematic review of the literature by the european association of urology section of oncological urology (ESOU). Eur. Urol. Focus 5, 756–787 (2018).
Health Europa. Available from: https://www.healtheuropa.eu/enhance-prostate-cancer-care/85990/ (2019).
PIONEER. Prostate Cancer Diagnosis and Treatment Enhancement through the Power of Big Data in Europe. Available from: https://prostate-pioneer.eu (2019).
Domecq, J. P. et al. Patient engagement in research: a systematic review. BMC Health Serv. Res. 14, 89 (2014).
Kamphuis B. A. B., et al. RWE in Europe Paper V — policy challenges around real world evidence adoption in Europe 2020. http://www.lse.ac.uk/business-and-consultancy/consulting/assets/documents/rwe-in-europe-paper-v.pdf (2019).
Dasgupta, P. et al. Geographical variations in prostate cancer outcomes: a systematic review of international evidence. Front. Oncol. 9, 238 (2019).
UCAN. UCAN Aberdeen 2020. https://www.ucanaberdeen.com (2019).
Europa Uomo. https://www.europa-uomo.org (2019).
EAPM. European Alliance for Personalised Medicine. https://www.euapm.eu (2019).
IMI. Innovative Medicines Initiative (IMI). www.imi.europa.eu (2019).
BD4BO. Big Data for Better Outcomes. http://bd4bo.eu (2019).
MacLennan, S. et al. A core outcome set for localised prostate cancer effectiveness trials. BJU Int. 120, E64–E79 (2017).
Auffray, C. et al. Making sense of big data in health research: towards an EU action plan. Genome Med. 8, 71 (2016).
Scheufele, E. et al. tranSMART: an open source knowledge management and high content data analytics platform. AMIA Jt. Summits Transl. Sci. Proc. 2014, 96–101 (2014).
SAS. SAS analytics software solutions 2019. https://www.sas.com/en_us/solutions/analytics.html (2019).
OHDSI. Observational Health Data Sciences and Informatics. https://github.com/OHDSI (2019).
EMIF. European Medical Information Framework. http://www.emif.eu (2019).
EISBM. European Institute for Systems Biology and Medicine. http://www.eisbm.org/ (2019).
Imperial College London, Data Science Institue. https://www.imperial.ac.uk/data-science/about-the-institute/ (2019).
U-BIOPRED. Unbiased BIOmarkers in PREDiction of respiratory disease outcomes 2019. https://www.europeanlung.org/en/projects-and-research/projects/u-biopred/home (2019).
eTRIKS. eTRIKS Harmonisation Services. https://www.etriks.org/etriks-harmonisation-services/ (2019).
Martin, N. E. et al. Defining a standard set of patient-centered outcomes for men with localized prostate cancer. Eur. Urol. 67, 460–467 (2015).
Morgans, A. K. et al. Development of a standardized set of patient-centered outcomes for advanced prostate cancer: an international effort for a unified approach. Eur. Urol. 68, 891–898 (2015).
Shamseer, L. et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ 350, g7647 (2015).
Dodd, S. et al. A taxonomy has been developed for outcomes in medical research to help improve knowledge discovery. J. Clin. Epidemiol. 96, 84–92 (2018).
Williamson, P. R. et al. The COMET Handbook: version 1.0. Trials. 18 (Suppl 3), 280 (2017).
Bruinsma, S. M. et al. Expert consensus document: semantics in active surveillance for men with localized prostate cancer - results of a modified Delphi consensus procedure. Nat. Rev. Urol. 14, 312–322 (2017).
HARMONY. Big Data to Enable Better and Faster Treatments for Patients with Hematological Malignancies. https://www.harmony-alliance.eu/ (2019).
OHDSI. Observational Health Data Sciences and Informatics (OHDSI) - Genomic CDM Subgroup. https://www.ohdsi.org/web/wiki/doku.php?id=projects:workgroups:genetics-sg (2019).
Wang, B. et al. Similarity network fusion for aggregating data types on a genomic scale. Nat. Methods 11, 333–337 (2014).
Lum, P. Y. et al. Extracting insights from the shape of complex data using topology. Sci. Rep. 3, 1236 (2013).
Le Cao K. A., et al. mixOmics: Omics Data Integration Project. 2016. p. R package version 6.1.
Lowe, W. L. Jr. & Reddy, T. E. Genomic approaches for understanding the genetics of complex disease. Genome Res. 25, 1432–1441 (2015).
Kuhn M., et al caret: Classification and Regression Training. 5.15-044 ed2012.
Brockwell, S. E. & Gordon, I. R. A comparison of statistical methods for meta-analysis. Stat. Med. 20, 825–840 (2001).
Thompson, S. G. & Higgins, J. P. T. How should meta-regression analyses be undertaken and interpreted? Stat. Med. 21, 1559–1573 (2002).
Reimand, J., Arak, T. & Vilo, J. g:Profiler — a web server for functional interpretation of gene lists (2011 update). Nucleic Acids Res. 39, W307–W315 (2011).
Rappaport, N. et al. MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search. Nucleic Acids Res. 45(D1), D877–D887 ‑(2017).
Szklarczyk, D. et al. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 45(D1), D362–D368 (2017).
Steyerberg E. W. Clinical Prediction Models: a Practical Approach to Development, Validation, and Updating. York S-VN, editor2009.
Alberts A. R., et al. Characteristics of prostate cancer found at fifth screening in the European Randomized Study of Screening for Prostate Cancer Rotterdam: can we selectively detect high-grade prostate cancer with upfront multivariable risk stratification and magnetic resonance imaging? Eur. Urol. 73, 343–350 (2018).
Brains4Brain. https://www.brains4brain.eu (2019).
Parmar, A. & Chan, K. K. W. Health technology assessment methodology in metastatic renal cell carcinoma. Nat. Rev. Urol. 17, 3–5 (2020).
Makady, A. et al. What is real-world data? A review of definitions based on literature and stakeholder interviews. Value Health 20, 858–865 (2017).
Sacchini, D., Virdis, A., Refolo, P., Pennacchini, M. & de Paula, I. C. Health technology assessment (HTA): ethical aspects. Med. Health Care Philos. 12, 453–457 (2009).
EFPIA. What are Medicines Adaptive Pathways to Patients (MAPPs)? https://www.efpia.eu/about-medicines/development-of-medicines/regulations-safety-supply/mapps/ (2019).
U.S. Food and Drug Administration. Framework for FDA’s Real-World Evidence Program. 2018. https://www.fda.gov/media/120060/download (2019).
Noura, M. & Gaedke, A. M. Interoperability in internet of things: taxonomies and open challenges. Mob. Netw. Appl. 24, 796–809 (2019).
Project Data Sphere. https://projectdatasphere.org/projectdatasphere/html/home (2019).
EUR-Lex. Access to European Union law. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32016R0679 (2019).
GDPR. General Data Protection Regulations 2020. https://gdpr-info.eu/ (2019).
PIONEER is funded through the IMI2 Joint Undertaking and is listed under grant agreement No. 777492. This joint undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA.
The authors declare no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Europa Uomo: https://www.europa-uomo.org/
European Alliance for Personalized Medicine (EAPM): https://www.euapm.eu/
European Association of Urology (EAU): http://uroweb.org
European Commission: https://ec.europa.eu/info/index_en
European Federation of Pharmaceutical Industry Associations (EFPIA): https://www.efpia.eu/
Innovative Medicines Initiative 2 (IMI2): https://www.imi.europa.eu/about-imi
Urological Cancer Charity (UCAN): https://www.ucanaberdeen.com/
- Adaptive clinical trials
In adaptive clinical trials, the parameters of the trials could be modified during the course of the study, based upon the observations of participants.
- Big data
Big data describes very-high-volume data that could be difficult to process using traditional methods, database and software techniques.
COMET’s objective is to encourage the development of evidence-based core outcome sets.
- Delphi consensus-building process
Delphi consensus-building is a structured approach to decision-making.
- European Medical Information Framework
(EMIF). A central metadata catalogue that is a unified platform to support a wide range of studies.
- Europa Uomo
An organization supporting patients with prostate diseases in general and prostate cancer in particular.
- European Alliance for Personalized Medicine
(EAPM). An alliance that brings together various stakeholders including health-care experts, health-care organizations and institutions, and patient advocates.
- European Commission
One of the executive arms of the European Union. The role of the European Commission is to propose and enforce legislation, as well as implement policies and the European Union budget.
- Federated data model
In a federated data model, the data are converted into a standard format within the environment of the data provider and made available for research upon request.
- Health-technology assessment
A systematic process to assess both the intended and unintended effects of health technology, comparing the balance of benefits and harms.
An organization committed to developing standard sets of condition-specific outcomes.
A data observatory enabling data visualization from various angles.
- Medical Adaptive Pathways to Patients
(MAPPs). Prospectively planned processes, starting with the early authorization of a medicine in a restricted patient population, followed by iterative phases of evidence gathering and adaptations of the marketing authorization to expand access to the medicine to broader patient populations.
An international network of researchers and observational health databases.
- Observational Medical Outcomes Partnership (OMOP) Common Data Model
Allows systematic analysis of disparate observational databases.
The private or public entity that pays for health-care services. Examples include private insurance companies or a government-supported single public system (for example, the National Health Service in the UK).
- Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines
A checklist of 27 items containing reporting standards in systematic reviews and meta-analysis.
- R package
A software environment for statistical computing.
- SAS open Platform
A data analytics platform.
- SPARK infrastructure
A unified analytics engine for big data processing.
A knowledge management and big data analytics platform.
A urological cancer charity dedicated to providing support to patients and their families.
- Williamson–Clarke taxonomy
Consists of 38 items and was developed to provide a robust scope to classify core outcome sets that are listed in the COMET database in a way that classifies what the outcome is at the conceptual level rather than how the outcomes are measured.
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Omar, M.I., Roobol, M.J., Ribal, M.J. et al. Introducing PIONEER: a project to harness big data in prostate cancer research. Nat Rev Urol 17, 351–362 (2020). https://doi.org/10.1038/s41585-020-0324-x