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Epigenome-based cancer risk prediction: rationale, opportunities and challenges

Nature Reviews Clinical Oncology volume 15, pages 292–309 (2018)Cite this article

Subjects

Key Points

  • Epigenetic misprogramming is an essential component of cancer development.

  • DNA methylation-based risk-prediction models provide novel opportunities for risk-tailored screening and prevention of cancer.

  • Multidisciplinary collaborative research is needed to overcome the scientific challenges associated with the discovery of DNA methylation markers for risk-prediction, such as identifying surrogate tissues and developing novel analytical methods.

  • Implementation of epigenome-based risk-tailored screening and prevention programmes requires several ethical, legal, social, organizational and economic challenges to be addressed in addition to the engagement of policymakers, health-care professionals and the public.

Abstract

The incidence of cancer is continuing to rise and risk-tailored early diagnostic and/or primary prevention strategies are urgently required. The ideal risk-predictive test should: integrate the effects of both genetic and nongenetic factors and aim to capture these effects using an approach that is both biologically stable and technically reproducible; derive a score from easily accessible biological samples that acts as a surrogate for the organ in question; and enable the effectiveness of risk-reducing measures to be monitored. Substantial evidence has accumulated suggesting that the epigenome and, in particular, DNA methylation-based tests meet all of these requirements. However, the development and implementation of DNA methylation-based risk-prediction tests poses considerable challenges. In particular, the cell type specificity of DNA methylation and the extensive cellular heterogeneity of the easily accessible surrogate cells that might contain information relevant to less accessible tissues necessitates the use of novel methods in order to account for these confounding issues. Furthermore, the engagement of the scientific community with health-care professionals, policymakers and the public is required in order to identify and address the organizational, ethical, legal, social and economic challenges associated with the routine use of epigenetic testing.

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Figure 1: Multicellular predictors of epigenetic risk.
Figure 2: Examples illustrating how epigenetic alterations contribute to cancer development.
Figure 3: The use of epigenomics in adjusting for intrasample heterogeneity.
Figure 4: Organizational, ethical, legal and social issues to be considered when implementing epigenome-based risk predictors.
Figure 5: Decision analysis to evaluate the consequences of DNA methylation test-based intervention strategies.

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Acknowledgements

The authors' research is supported by the European Union's Horizon 2020 Programme (H2020/2014-2020) under grant agreement number 634570 (Project FORECEE: www.forecee.eu/). M.W. also receives support from the European Research Council (ERC Advanced Grant ERC-BRCA) and The Eve Appeal (www.eveappeal.org.uk/). The authors acknowledge the support of the National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre.

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Authors and Affiliations

  1. Department of Women's Cancer, Institute for Women's Health, University College London, London, UK

    Martin Widschwendter, Allison Jones, Iona Evans, Daniel Reisel & Andrew E. Teschendorff

  2. Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden

    Joakim Dillner & Karin Sundström

  3. Karolinska University Laboratory, Karolinska University Hospital, Stockholm, Sweden

    Joakim Dillner & Karin Sundström

  4. Department of Public Health, Center for Medical Decision Sciences, Erasmus MC, Rotterdam, Netherlands

    Ewout W. Steyerberg & Yvonne Vergouwe

  5. Department of Biomedical Data Sciences, LUMC, Leiden, Netherlands

    Ewout W. Steyerberg

  6. Max Planck Institute for Human Development, Harding Center for Risk Literacy, Berlin, Germany

    Odette Wegwarth & Felix G. Rebitschek

  7. Max Planck Institute for Human Development, Center for Adaptive Rationality, Berlin, Germany

    Odette Wegwarth

  8. Department of Public Health, Institute of Public Health, Medical Decision Making and Health Technology Assessment, Health Services Research, and HTA, UMIT-University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria

    Uwe Siebert & Gaby Sroczynski

  9. Department of Health Policy and Management, Harvard T. C. Chan School of Public Health, Center for Health Decision Science, Boston, MA, USA

    Uwe Siebert

  10. Oncotyrol: Center for Personalized Medicine, Innsbruck, Austria

    Uwe Siebert

  11. Department of Medical Ethics and Philosophy of Medicine, Erasmus Medical Center, Rotterdam, Netherlands

    Inez D. de Beaufort & Ineke Bolt

  12. Department of Obstetrics and Gynaecology, First Medical Faculty of the Charles University and General Faculty Hospital, Prague, Czech Republic

    David Cibula & Michal Zikan

  13. Department of Obstetrics and Gynecology, Department of Clinical Science, Haukeland University Hospital, and Centre for Cancer Biomarkers, University of Bergen, Bergen, Norway

    Line Bjørge

  14. European Institute of Oncology and University Milan-Bicocca, Milan, Italy

    Nicoletta Colombo

  15. Department of Gynaecology and Obstetrics, Breast Center, University of Munich (LMU), Munich, Germany

    Nadia Harbeck

  16. Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK

    Frank Dudbridge

  17. Department of Health Sciences, University of Leicester, Leicester, UK

    Frank Dudbridge

  18. Public Population Project in Genomics and Society, McGill University and Genome Quebec Innovation Centre, Montreal, Canada

    Anne-Marie Tasse

  19. Centre of Genomics and Policy, McGill University, Montreal, Canada

    Bartha M. Knoppers & Yann Joly

  20. Department of Applied Health Research, Institute of Epidemiology and Healthcare, University College, London, UK

    Nora Pashayan

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on behalf of the FORECEE (4C) Consortium

Contributions

M.W. and K.S. researched data for this manuscript, M.W., A.J., I.E., D.R., J.D., K.S., E.W.S., O.W., F.G.R., G.S., I.D.d.B., I.B., D.C., M.Z., L.B., N.H., F.D., A.-M.T., B.M.K., Y.J., A.E.T. and N.P. made a contribution to discussions of content, M.W. wrote the manuscript, and M.W., A.J., I.E., D.R., J.D., K.S., E.W.S., Y.V., O.W., U.S., G.S., I.D.d.B., I.B., M.Z., L.B., N.C., N.H., F.D., A.-M.T., B.M.K., Y.J., A.E.T. and N.P. edited and/or reviewed the manuscript before submission.

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Correspondence to Martin Widschwendter.

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Widschwendter, M., Jones, A., Evans, I. et al. Epigenome-based cancer risk prediction: rationale, opportunities and challenges. Nat Rev Clin Oncol 15, 292–309 (2018). https://doi.org/10.1038/nrclinonc.2018.30

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