Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

PIONEER big data platform for prostate cancer: lessons for advancing future real-world evidence research

Abstract

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. PIONEER brings together 34 private and public stakeholders from 9 countries in one multidisciplinary research consortium with the aim of positively transforming the field of prostate cancer clinical care by answering pressing questions related to prostate cancer screening, diagnosis and treatment. PIONEER has developed a unique state-of-the-art big data analytic platform by integrating existing data sources from patients with prostate cancer. PIONEER leveraged this platform to address prioritized research questions, filling knowledge gaps in the characterization, management and core outcomes of prostate cancer across the different disease stages. The network has benefited from sustained patient and stakeholder involvement and engagement, but many challenges remain when using real-world data for big data projects. To continue to advance prostate cancer care, data need to be available, suitable methodologies should be selected and mechanisms for knowledge sharing must be in place. Now acting as the prostate cancer arm of the European Association of Urology’s new endeavour, UroEvidenceHub, PIONEER maintains its goal of maximizing the potential of big data to improve prostate cancer care.

Key points

  • Prostate Cancer Diagnosis and Treatment Enhancement through the Power of Big Data in Europe (PIONEER), a European network of 34 private and public stakeholders, developed a big data platform integrating existing real-world prostate cancer data sources to answer key research questions.

  • So far, 14 research questions have been or are currently being addressed using the PIONEER platform.

  • Ongoing multi-stakeholder engagement and involvement ensure that research questions addressed by PIONEER are relevant and important to patients.

  • Substantial challenges remain in the process of executing big data projects: outcome heterogeneity, lack of suitable data sources, and high-quality disease-specific data.

  • Core outcome sets and common data models offer solutions to big data project challenges, but widespread implementation is needed.

  • PIONEER will continue its work as the prostate cancer arm of the European Association of Urology’s new endeavour, UroEvidenceHub.

This is a preview of subscription content, access via your institution

Access options

Buy this article

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of PIONEER work packages and their interconnectivities.
Fig. 2: Overview of PIONEER’s data sources to date.

Similar content being viewed by others

References

  1. Global Cancer Observatory. Cancer Today. International Agency for Research on Cancer https://gco.iarc.fr/today/en (2023).

  2. Gandaglia, G. et al. Epidemiology and prevention of prostate cancer. Eur. Urol. Oncol. 4, 877–892 (2021).

    Article  PubMed  Google Scholar 

  3. Yamada, Y. & Beltran, H. The treatment landscape of metastatic prostate cancer. Cancer Lett. 519, 20–29 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Omar, M. I. et al. Introducing PIONEER: a project to harness big data in prostate cancer research. Nat. Rev. Urol. 17, 351–362 (2020).

    Article  PubMed  Google Scholar 

  5. Kalkman, S., Mostert, M., Udo-Beauvisage, N., van Delden, J. J. & van Thiel, G. J. Responsible data sharing a big data-driven translational research platform: lessons learned. BMC Med. Inf. Decis. Mak. 19, 283 (2019).

    Article  CAS  Google Scholar 

  6. Hulsen, T. et al. From big data to precision medicine. Front. Med. 6, 34 (2019).

    Article  Google Scholar 

  7. Jiang, P. et al. Big data in basic and translational cancer research. Nat. Rev. Can. 22, 625–639 (2022).

    Article  CAS  Google Scholar 

  8. Saesen, R. et al. Defining the role of real-world data in cancer clinical research: the position of the European Organisation for Research and Treatment of Cancer. Eur. J. Cancer 186, 52–61 (2023).

    Article  PubMed  Google Scholar 

  9. PIONEER. Our Mission. PIONEER https://prostate-pioneer.eu/about-us-2/our-mission/ (2023).

  10. Omar, M. I. et al. Unanswered questions in prostate cancer — findings of an international multi-stakeholder consensus by the PIONEER consortium. Nat. Rev. Urol. 20, 494–501 (2023).

    Article  PubMed  Google Scholar 

  11. PIONEER. Data Protection & Data Processing. PIONEER https://prostate-pioneer.eu/big-data-platform/pioneer-data-processing/ (2023).

  12. Beyer, K. et al. Updating and integrating core outcome sets for localised, locally advanced, metastatic, and nonmetastatic castration-resistant prostate cancer: an update from the PIONEER consortium. Eur. Urol. 81, 501–514 (2022).

    Article  Google Scholar 

  13. Williamson, P. R. et al. The COMET Handbook: version 1.0. Trials 18 (Suppl. 3), 280 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Hughes, N. et al. Evaluating a novel approach to stimulate open science collaborations: a case series of “study-a-thon” events within the OHDSI and European IMI communities. JAMIA Open 5, ooac100 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Arraras et al. EORTC QLQ-COMU26: a questionnaire for the assessment of communication between patients and professionals. Phase III of the module development in ten countries. Support. Care Cancer 25, 1485–1494 (2017).

    Article  PubMed  Google Scholar 

  16. Maskrey, N. Shared decision making: why the slow progress? An essay by Neal Maskrey. BMJ 367, l6762 (2019).

    Article  PubMed  Google Scholar 

  17. Ratti, M. M. et al. Standardising the assessment of patient-reported outcome measures in localised prostate cancer. a systematic review. Eur. Urol. Oncol. 5, 151–163 (2022).

    Google Scholar 

  18. Gandaglia, G. et al. Clinical characterization of patients diagnosed with prostate cancer and undergoing conservative management: a PIONEER analysis based on big data. Eur. Urol. 85, 457–467 (2023).

    Article  PubMed  Google Scholar 

  19. Remmers, S. et al. Development and validation of patient-level prediction models for symptoms, hospitalization, and treatment initiation amongst prostate cancer patients on watchful waiting. Protocol Exchange https://doi.org/10.21203/rs.3.pex-1525/v1 (2021).

  20. Siltari, A. L. et al. How well do polygenic risk scores identify men at high risk for prostate cancer? Systematic review and meta-analysis. Clin. Genitourin. Cancer 21, 316 (2022).

    Google Scholar 

  21. Gómez-Rivas, J. et al. Research protocol to identify progression and death amongst patients with metastatic hormone-sensitive prostate cancer treated with available treatments: PIONEER IMI’s “big data for better outcomes” program. Int. J. Surg. Protoc. 27, 122–129 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Hoffman, K., Schou, L. H., Piil, K. & Jarden, M. Current trends in patient and public involvement in cancer research: a systematic review. Health Expectations 22, 3–20 (2018).

    Google Scholar 

  23. N’Dow, J. PIONEER: prostate cancer diagnosis and treatment enhancement through the power of big data in Europe. Health Europa Q. 8, 140–142 (2019).

    Google Scholar 

  24. European Commission. IDEA4RC Project Fact Sheet. European Commission https://cordis.europa.eu/project/id/101057048 (2022).

  25. Innovative Medicines Initiative. HARMONY Project Fact Sheet. IMI https://www.imi.europa.eu/projects-results/project-factsheets/harmony (2023).

  26. Kent, S. et al. Common problems, common data model solutions: evidence generation for health technology assessment. Pharmacoeconomics 39, 275–285 (2021).

    Article  PubMed  Google Scholar 

  27. Beyer et al. Diagnostic and prognostic factors in patients with prostate cancer: a systematic review. BMJ Open 12, e058267 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Anker, S. et al. Big data in cardiovascular disease. Eur. Heart J. 38, 1863–1865 (2017).

    Article  PubMed  Google Scholar 

  29. Batko, K. & Ślęzak, A. The use of big data analytics in healthcare. J. Big Data 9, 3 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Tsai, C.-W., Lai, C-F., Chao, H-C. & Vasilakos, A.V. Big data analytics: a survey. J. Big Data https://doi.org/10.1186/s40537-015-0030-3 (2015).

  31. Hariri, R.H., Fredericks, E.M. & Bowers, K.M. Uncertainty in big data analytics: survey, opportunities, and challenges. J. Big Data 6, 44 (2019).

    Article  Google Scholar 

  32. Beyer, K. et al. Secondary treatment for men with localized prostate cancer: a pooled analysis of PRIAS and ERSPC-Rotterdam data within the PIONEER data platform. J. Pers. Med. 12, 751 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

PIONEER is funded through the IMI2 Joint Undertaking and is listed under grant agreement no. 777492. IMI2 receives support from the European Union’s Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Associations (EFPIA). IMI supports collaborative research projects and builds networks of industrial and academic experts in order to boost pharmaceutical innovation in Europe. The views communicated within are those of PIONEER. The IMI, the European Union, the EFPIA, or any Associated Partners are not responsible for any use that may be made of the information contained herein.

Author information

Authors and Affiliations

Authors

Consortia

Contributions

A.L., C.S., B.D.M., J.G.R., G.G., R.N. and E.J.S. researched data for the article. A.L., K.B., C.S., B.D.M., M.I.O., E.J.S., J.N.’D., M.M., T.A., S.E.-A. and M.V.H. contributed substantially to discussion of the content. A.L., T.A. and S.E.-A. wrote the article. All authors reviewed and/or edited the manuscript before submission.

Corresponding author

Correspondence to Ailbhe Lawlor.

Ethics declarations

Competing interests

S.E.-A., J.B. and J.Z. are employees of Bayer; T.H. is an employee of Philips; R.S. and K.P. are employees of Astellas Pharma; T.A. was an employee of Astellas Pharma during the PIONEER project prior to retirement; A.B. has been a consultant and adviser for Accord, Astellas Pharma, AstraZeneca, Bayer, J&J and Pfizer and a board member, officer and trustee for Glactone Pharma, has received lecture honoraria for Accord, Astellas, AstraZeneca, Bayer, Ipsen, J&J and Merck, has participated in trials run by Astellas, Bayer, Ferring, Janssen and Pfizer, and holds stock in Glactone Pharma, LIDDS Pharma and WntResearch. All companies make products used to treat prostate cancer. The other authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Urology thanks Caroline Moore and Fred Saad for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

BD4BO: https://bd4bo.eu/

Big data analytic platform: https://prostate-pioneer.eu/big-data-platform/

Big Data for Better Outcomes and IMI: https://prostate-pioneer.eu/about-us-2/bd4bo-imi/

EAU: https://uroweb.org/

EAUN: https://nurses.uroweb.org/

ecancer: https://ecancer.org/en/

ECPC: https://ecpc.org/

EHDEN: https://www.ehden.eu/

EUROPA Uomo: https://www.europa-uomo.org/

Innovative Medicines Initiative 2: https://www.imi.europa.eu/about-imi

OHDSI: https://ohdsi.org/

Online search tool for diagnostic and prognostic biomarkers: https://pioneer.elixir-luxembourg.org/

PIONEER: https://prostate-pioneer.eu/Innovative

Prostate Cancer UK: https://prostatecanceruk.org/

UCAN: https://www.ucanaberdeen.com/

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lawlor, A., Beyer, K., Russell, B. et al. PIONEER big data platform for prostate cancer: lessons for advancing future real-world evidence research. Nat Rev Urol 22, 116–124 (2025). https://doi.org/10.1038/s41585-024-00925-4

Download citation

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41585-024-00925-4

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing