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

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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

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.

References

  1. 1

    Ferlay, J. et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136, E359–E386 (2015).

    CAS  Article  Google Scholar 

  2. 2

    Thun, M. J., DeLancey, J. O., Center, M. M., Jemal, A. & Ward, E. M. The global burden of cancer: priorities for prevention. Carcinogenesis 31, 100–110 (2010).

    CAS  PubMed  Google Scholar 

  3. 3

    Wu, S., Powers, S., Zhu, W. & Hannun, Y. A. Substantial contribution of extrinsic risk factors to cancer development. Nature 529, 43–47 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4

    Tomasetti, C., Li, L. & Vogelstein, B. Stem cell divisions, somatic mutations, cancer etiology, and cancer prevention. Science 355, 1330–1334 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5

    Wodarz, D. & Zauber, A. G. Cancer: risk factors and random chances. Nature 517, 563–564 (2015).

    CAS  PubMed  Google Scholar 

  6. 6

    Lu, Y. et al. Most common 'sporadic' cancers have a significant germline genetic component. Hum. Mol. Genet. 23, 6112–6118 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7

    Bonder, M. J. et al. Disease variants alter transcription factor levels and methylation of their binding sites. Nat. Genet. 49, 131–138 (2017).

    CAS  PubMed  Google Scholar 

  8. 8

    Burton, H. et al. Public health implications from COGS and potential for risk stratification and screening. Nat. Genet. 45, 349–351 (2013).

    CAS  PubMed  Google Scholar 

  9. 9

    Pashayan, N. et al. Reducing overdiagnosis by polygenic risk-stratified screening: findings from the Finnish section of the ERSPC. Br. J. Cancer 113, 1086–1093 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10

    Pashayan, N. et al. Implications of polygenic risk-stratified screening for prostate cancer on overdiagnosis. Genet. Med. 17, 789–795 (2015).

    PubMed  PubMed Central  Google Scholar 

  11. 11

    Lee, C. H. et al. Risk evaluation for the development of cervical intraepithelial neoplasia: development and validation of risk-scoring schemes. Int. J. Cancer 136, 340–349 (2015).

    CAS  PubMed  Google Scholar 

  12. 12

    Sporn, M. B. & Liby, K. T. Cancer chemoprevention: scientific promise, clinical uncertainty. Nat. Clin. Pract. Oncol. 2, 518–525 (2005).

    CAS  PubMed  Google Scholar 

  13. 13

    Damen, J. A. et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ 353, i2416 (2016).

    PubMed  PubMed Central  Google Scholar 

  14. 14

    Heyn, H. et al. DNA methylation contributes to natural human variation. Genome Res. 23, 1363–1372 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Bergman, Y. & Cedar, H. DNA methylation dynamics in health and disease. Nat. Struct. Mol. Biol. 20, 274–281 (2013).

    CAS  PubMed  Google Scholar 

  16. 16

    Feil, R. & Fraga, M. F. Epigenetics and the environment: emerging patterns and implications. Nat. Rev. Genet. 13, 97–109 (2011).

    Google Scholar 

  17. 17

    Widschwendter, M. et al. Epigenetic stem cell signature in cancer. Nat. Genet. 39, 157–158 (2007).

    CAS  PubMed  Google Scholar 

  18. 18

    Schlesinger, Y. et al. Polycomb-mediated methylation on Lys27 of histone H3 pre-marks genes for de novo methylation in cancer. Nat. Genet. 39, 232–236 (2007).

    CAS  PubMed  Google Scholar 

  19. 19

    Ohm, J. E. et al. A stem cell-like chromatin pattern may predispose tumor suppressor genes to DNA hypermethylation and heritable silencing. Nat. Genet. 39, 237–242 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Gupta, R. A. et al. Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature 464, 1071–1076 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21

    Tsai, M. C. et al. Long noncoding RNA as modular scaffold of histone modification complexes. Science 329, 689–693 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    Rinn, J. L. et al. Functional demarcation of active and silent chromatin domains in human HOX loci by noncoding RNAs. Cell 129, 1311–1323 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    Laugesen, A., Hojfeldt, J. W. & Helin, K. Role of the Polycomb repressive complex 2 (PRC2) in transcriptional regulation and cancer. Cold Spring Harb. Perspect. Med. 6, a026575 (2016).

    PubMed  PubMed Central  Google Scholar 

  24. 24

    Laugesen, A. & Helin, K. Chromatin repressive complexes in stem cells, development, and cancer. Cell Stem Cell 14, 735–751 (2014).

    CAS  PubMed  Google Scholar 

  25. 25

    Vire, E. et al. The Polycomb group protein EZH2 directly controls DNA methylation. Nature 439, 871–874 (2006).

    CAS  PubMed  Google Scholar 

  26. 26

    Teschendorff, A. E. et al. Correlation of smoking-associated DNA methylation changes in buccal cells with DNA methylation changes in epithelial cancer. JAMA Oncol. 1, 476–485 (2015).

    PubMed  Google Scholar 

  27. 27

    Guida, F. et al. Dynamics of smoking-induced genome-wide methylation changes with time since smoking cessation. Hum. Mol. Genet. 24, 2349–2359 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28

    Tsaprouni, L. G. et al. Cigarette smoking reduces DNA methylation levels at multiple genomic loci but the effect is partially reversible upon cessation. Epigenetics 9, 1382–1396 (2014).

    PubMed  PubMed Central  Google Scholar 

  29. 29

    Wienken, M. et al. MDM2 associates with Polycomb repressor complex 2 and enhances stemness-promoting chromatin modifications independent of p53. Mol. Cell 61, 68–83 (2016).

    CAS  PubMed  Google Scholar 

  30. 30

    Zhuang, J. et al. The dynamics and prognostic potential of DNA methylation changes at stem cell gene loci in women's cancer. PLoS Genet. 8, e1002517 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31

    Iliou, M. S. et al. Bivalent histone modifications in stem cells poise miRNA loci for CpG island hypermethylation in human cancer. Epigenetics 6, 1344–1353 (2011).

    CAS  PubMed  Google Scholar 

  32. 32

    Yang, Z. et al. Correlation of an epigenetic mitotic clock with cancer risk. Genome Biol. 17, 205 (2016).

    PubMed  PubMed Central  Google Scholar 

  33. 33

    Klutstein, M., Nejman, D., Greenfield, R. & Cedar, H. DNA methylation in cancer and aging. Cancer Res. 76, 3446–3450 (2016).

    CAS  PubMed  Google Scholar 

  34. 34

    Klutstein, M., Moss, J., Kaplan, T. & Cedar, H. Contribution of epigenetic mechanisms to variation in cancer risk among tissues. Proc. Natl Acad. Sci. USA 114, 2230–2234 (2017).

    CAS  PubMed  Google Scholar 

  35. 35

    Morel, A. P. et al. A stemness-related ZEB1-MSRB3 axis governs cellular pliancy and breast cancer genome stability. Nat. Med. 23, 568–578 (2017).

    CAS  PubMed  Google Scholar 

  36. 36

    Li, Q. et al. The antiproliferative action of progesterone in uterine epithelium is mediated by Hand2. Science 331, 912–916 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    Jones, A. et al. Role of DNA methylation and epigenetic silencing of HAND2 in endometrial cancer development. PLOS Med. 10, e1001551 (2013).

    PubMed  PubMed Central  Google Scholar 

  38. 38

    Horn, L. C., Schnurrbusch, U., Bilek, K., Hentschel, B. & Einenkel, J. Risk of progression in complex and atypical endometrial hyperplasia: clinicopathologic analysis in cases with and without progestogen treatment. Int. J. Gynecol. Cancer 14, 348–353 (2004).

    PubMed  Google Scholar 

  39. 39

    Hanson, J. A. et al. Gene promoter methylation in prostate tumor-associated stromal cells. J. Natl Cancer Inst. 98, 255–261 (2006).

    CAS  PubMed  Google Scholar 

  40. 40

    Valcz, G. et al. Myofibroblast-derived SFRP1 as potential inhibitor of colorectal carcinoma field effect. PLoS ONE 9, e106143 (2014).

    PubMed  PubMed Central  Google Scholar 

  41. 41

    Fiegl, H. et al. Breast cancer DNA methylation profiles in cancer cells and tumor stroma: association with HER-2/neu status in primary breast cancer. Cancer Res. 66, 29–33 (2006).

    CAS  PubMed  Google Scholar 

  42. 42

    Paterson, R. F. et al. Molecular genetic alterations in the laser-capture-microdissected stroma adjacent to bladder carcinoma. Cancer 98, 1830–1836 (2003).

    CAS  PubMed  Google Scholar 

  43. 43

    Lin, H. J. et al. Breast cancer-associated fibroblasts confer AKT1-mediated epigenetic silencing of Cystatin M in epithelial cells. Cancer Res. 68, 10257–10266 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    Widschwendter, M. et al. HOXA methylation in normal endometrium from premenopausal women is associated with the presence of ovarian cancer: a proof of principle study. Int. J. Cancer 125, 2214–2218 (2009).

    CAS  PubMed  Google Scholar 

  45. 45

    Ongen, H. et al. Putative cis-regulatory drivers in colorectal cancer. Nature 512, 87–90 (2014).

    CAS  PubMed  Google Scholar 

  46. 46

    Ehrlich, M., Norris, K. F., Wang, R. Y., Kuo, K. C. & Gehrke, C. W. DNA cytosine methylation and heat-induced deamination. Biosci. Rep. 6, 387–393 (1986).

    CAS  PubMed  Google Scholar 

  47. 47

    Poulos, R. C., Olivier, J. & Wong, J. W. H. The interaction between cytosine methylation and processes of DNA replication and repair shape the mutational landscape of cancer genomes. Nucleic Acids Res. 45, 7786–7795 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48

    Alexandrov, L. B. et al. Clock-like mutational processes in human somatic cells. Nat. Genet. 47, 1402–1407 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49

    Gaunt, T. R. et al. Systematic identification of genetic influences on methylation across the human life course. Genome Biol. 17, 61 (2016).

    PubMed  PubMed Central  Google Scholar 

  50. 50

    Chen, L. et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398–1414.e24 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51

    Bell, J. T. et al. DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biol. 12, R10 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52

    Zeng, H. & Gifford, D. K. Predicting the impact of non-coding variants on DNA methylation. Nucleic Acids Res. 45, e99 (2017).

    PubMed  PubMed Central  Google Scholar 

  53. 53

    Heyn, H. et al. Linkage of DNA methylation quantitative trait loci to human cancer risk. Cell Rep. 7, 331–338 (2014).

    CAS  PubMed  Google Scholar 

  54. 54

    Liu, Y. et al. A mouse model that reproduces the developmental pathways and site specificity of the cancers associated with the human BRCA1 mutation carrier state. EBioMedicine 2, 1318–1330 (2015).

    PubMed  PubMed Central  Google Scholar 

  55. 55

    Widschwendter, M. et al. Osteoprotegerin (OPG), the endogenous inhibitor of receptor activator of NF-kappaB ligand (RANKL), is dysregulated in BRCA mutation carriers. EBioMedicine 2, 1331–1339 (2015).

    PubMed  PubMed Central  Google Scholar 

  56. 56

    Widschwendter, M. et al. The sex hormone system in carriers of BRCA1/2 mutations: a case-control study. Lancet Oncol. 14, 1226–1232 (2013).

    CAS  PubMed  Google Scholar 

  57. 57

    Chodankar, R. et al. Cell-nonautonomous induction of ovarian and uterine serous cystadenomas in mice lacking a functional Brca1 in ovarian granulosa cells. Curr. Biol. 15, 561–565 (2005).

    CAS  PubMed  Google Scholar 

  58. 58

    Hong, H. et al. Changes in the mouse estrus cycle in response to BRCA1 inactivation suggest a potential link between risk factors for familial and sporadic ovarian cancer. Cancer Res. 70, 221–228 (2010).

    CAS  PubMed  Google Scholar 

  59. 59

    Yen, H. Y. et al. Alterations in Brca1 expression in mouse ovarian granulosa cells have short-term and long-term consequences on estrogen-responsive organs. Lab. Invest. 92, 802–811 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60

    Bartlett, T. E. et al. Epigenetic reprogramming of fallopian tube fimbriae in BRCA mutation carriers defines early ovarian cancer evolution. Nat. Commun. 7, 11620 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61

    Benowitz, N. L. et al. Disposition kinetics and metabolism of nicotine and cotinine in African American smokers: impact of CYP2A6 genetic variation and enzymatic activity. Pharmacogenet. Genom. 26, 340–350 (2016).

    CAS  Google Scholar 

  62. 62

    Zhang, Y., Florath, I., Saum, K. U. & Brenner, H. Self-reported smoking, serum cotinine, and blood DNA methylation. Environ. Res. 146, 395–403 (2016).

    CAS  PubMed  Google Scholar 

  63. 63

    Miska, E. A. & Ferguson-Smith, A. C. Transgenerational inheritance: Models and mechanisms of non-DNA sequence-based inheritance. Science 354, 59–63 (2016).

    CAS  PubMed  Google Scholar 

  64. 64

    Pembrey, M., Saffery, R., Bygren, L. O., Network in Epigenetic, E. & Network in Epigenetic, E. Human transgenerational responses to early-life experience: potential impact on development, health and biomedical research. J. Med. Genet. 51, 563–572 (2014).

    PubMed  PubMed Central  Google Scholar 

  65. 65

    Bygren, L. O. et al. Change in paternal grandmothers' early food supply influenced cardiovascular mortality of the female grandchildren. BMC Genet. 15, 12 (2014).

    PubMed  PubMed Central  Google Scholar 

  66. 66

    Northstone, K., Golding, J., Davey Smith, G., Miller, L. L. & Pembrey, M. Prepubertal start of father's smoking and increased body fat in his sons: further characterisation of paternal transgenerational responses. Eur. J. Hum. Genet. 22, 1382–1386 (2014).

    PubMed  PubMed Central  Google Scholar 

  67. 67

    Kuhnen, P. et al. Interindividual variation in DNA methylation at a putative POMC metastable epiallele is associated with obesity. Cell Metab. 24, 502–509 (2016).

    PubMed  Google Scholar 

  68. 68

    Carone, B. R. et al. Paternally induced transgenerational environmental reprogramming of metabolic gene expression in mammals. Cell 143, 1084–1096 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69

    Renehan, A. G., Tyson, M., Egger, M., Heller, R. F. & Zwahlen, M. Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet 371, 569–578 (2008).

    PubMed  PubMed Central  Google Scholar 

  70. 70

    Wahl, S. et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature 541, 81–86 (2017).

    CAS  PubMed  Google Scholar 

  71. 71

    Hoover, R. N. et al. Adverse health outcomes in women exposed in utero to diethylstilbestrol. N. Engl. J. Med. 365, 1304–1314 (2011).

    CAS  PubMed  Google Scholar 

  72. 72

    Bhan, A. et al. Bisphenol-A and diethylstilbestrol exposure induces the expression of breast cancer associated long noncoding RNA HOTAIR in vitro and in vivo. J. Steroid Biochem. Mol. Biol. 141, 160–170 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73

    Bromer, J. G., Wu, J., Zhou, Y. & Taylor, H. S. Hypermethylation of homeobox A10 by in utero diethylstilbestrol exposure: an epigenetic mechanism for altered developmental programming. Endocrinology 150, 3376–3382 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74

    Soto, A. M. & Sonnenschein, C. Environmental causes of cancer: endocrine disruptors as carcinogens. Nat. Rev. Endocrinol. 6, 363–370 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75

    Jorgensen, E. M., Alderman, M. H. 3rd & Taylor, H. S. Preferential epigenetic programming of estrogen response after in utero xenoestrogen (bisphenol-A) exposure. FASEB J. 30, 3194–3201 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76

    Kim, J. Y., Tavare, S. & Shibata, D. Counting human somatic cell replications: methylation mirrors endometrial stem cell divisions. Proc. Natl Acad. Sci. USA 102, 17739–17744 (2005).

    CAS  PubMed  Google Scholar 

  77. 77

    Zhou, D. et al. High fat diet and exercise lead to a disrupted and pathogenic DNA methylome in mouse liver. Epigenetics 12, 55–69 (2017).

    PubMed  Google Scholar 

  78. 78

    Rossi, E. L. et al. Obesity-associated alterations in inflammation, epigenetics, and mammary tumor growth persist in formerly obese mice. Cancer Prev. Res. 9, 339–348 (2016).

    CAS  Google Scholar 

  79. 79

    Li, R. et al. Obesity, rather than diet, drives epigenomic alterations in colonic epithelium resembling cancer progression. Cell Metab. 19, 702–711 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80

    Bhaskaran, K. et al. Body-mass index and risk of 22 specific cancers: a population-based cohort study of 5.24 million UK adults. Lancet 384, 755–765 (2014).

    PubMed  PubMed Central  Google Scholar 

  81. 81

    Harvey, A. E., Lashinger, L. M. & Hursting, S. D. The growing challenge of obesity and cancer: an inflammatory issue. Ann. NY Acad. Sci. 1229, 45–52 (2011).

    CAS  PubMed  Google Scholar 

  82. 82

    O'Hagan, H. M. et al. Oxidative damage targets complexes containing DNA methyltransferases, SIRT1, and polycomb members to promoter CpG Islands. Cancer Cell 20, 606–619 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. 83

    Wang, T. et al. Epigenetic aging signatures in mice livers are slowed by dwarfism, calorie restriction and rapamycin treatment. Genome Biol. 18, 57 (2017).

    PubMed  PubMed Central  Google Scholar 

  84. 84

    Cole, J. J. et al. Diverse interventions that extend mouse lifespan suppress shared age-associated epigenetic changes at critical gene regulatory regions. Genome Biol. 18, 58 (2017).

    PubMed  PubMed Central  Google Scholar 

  85. 85

    Zheng, Y. et al. Blood epigenetic age may predict cancer incidence and mortality. EBioMedicine 5, 68–73 (2016).

    PubMed  PubMed Central  Google Scholar 

  86. 86

    Levine, M. E. et al. DNA methylation age of blood predicts future onset of lung cancer in the women's health initiative. Aging 7, 690–700 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. 87

    Ambatipudi, S. et al. DNA methylome analysis identifies accelerated epigenetic ageing associated with postmenopausal breast cancer susceptibility. Eur. J. Cancer 75, 299–307 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88

    Philibert, R. A., Beach, S. R. & Brody, G. H. Demethylation of the aryl hydrocarbon receptor repressor as a biomarker for nascent smokers. Epigenetics 7, 1331–1338 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 89

    Zeilinger, S. et al. Tobacco smoking leads to extensive genome-wide changes in DNA methylation. PLoS ONE 8, e63812 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. 90

    Joubert, B. R. et al. 450K epigenome-wide scan identifies differential DNA methylation in newborns related to maternal smoking during pregnancy. Environ. Health Perspect. 120, 1425–1431 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 91

    Wan, E. S. et al. Smoking-associated site-specific differential methylation in buccal mucosa in the COPDGene study. Am. J. Respir. Cell. Mol. Biol. 53, 246–254 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. 92

    de Martel, C. et al. Global burden of cancers attributable to infections in 2008: a review and synthetic analysis. Lancet Oncol. 13, 607–615 (2012).

    Google Scholar 

  93. 93

    Schwabe, R. F. & Jobin, C. The microbiome and cancer. Nat. Rev. Cancer 13, 800–812 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94

    Paul, B. et al. Influences of diet and the gut microbiome on epigenetic modulation in cancer and other diseases. Clin. Epigenet. 7, 112 (2015).

    Google Scholar 

  95. 95

    Alenghat, T. Epigenomics and the microbiota. Toxicol. Pathol. 43, 101–106 (2015).

    PubMed  Google Scholar 

  96. 96

    Elinav, E. et al. Inflammation-induced cancer: crosstalk between tumours, immune cells and microorganisms. Nat. Rev. Cancer 13, 759–771 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. 97

    Tang, Y. et al. Jak/Stat3 signaling promotes somatic cell reprogramming by epigenetic regulation. Stem Cells 30, 2645–2656 (2012).

    CAS  PubMed  Google Scholar 

  98. 98

    Munoz, D. P. et al. Activation-induced cytidine deaminase (AID) is necessary for the epithelial-mesenchymal transition in mammary epithelial cells. Proc. Natl Acad. Sci. USA 110, E2977–E2986 (2013).

    CAS  PubMed  Google Scholar 

  99. 99

    Matsumoto, Y. et al. Helicobacter pylori infection triggers aberrant expression of activation-induced cytidine deaminase in gastric epithelium. Nat. Med. 13, 470–476 (2007).

    CAS  PubMed  Google Scholar 

  100. 100

    Wijetunga, N. A. et al. A pre-neoplastic epigenetic field defect in HCV-infected liver at transcription factor binding sites and polycomb targets. Oncogene 36, 2030–2044 (2017).

    CAS  PubMed  Google Scholar 

  101. 101

    Hahn, M. A. et al. Methylation of polycomb target genes in intestinal cancer is mediated by inflammation. Cancer Res. 68, 10280–10289 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. 102

    Atashgaran, V., Wrin, J., Barry, S. C., Dasari, P. & Ingman, W. V. Dissecting the biology of menstrual cycle-associated breast cancer risk. Front. Oncol. 6, 267 (2016).

    PubMed  PubMed Central  Google Scholar 

  103. 103

    Beral, V., Doll, R., Hermon, C., Peto, R. & Reeves, G. Ovarian cancer and oral contraceptives: collaborative reanalysis of data from 45 epidemiological studies including 23,257 women with ovarian cancer and 87,303 controls. Lancet 371, 303–314 (2008).

    CAS  PubMed  Google Scholar 

  104. 104

    Hennessy, B. T., Coleman, R. L. & Markman, M. Ovarian cancer. Lancet 374, 1371–1382 (2009).

    CAS  PubMed  Google Scholar 

  105. 105

    Amant, F. et al. Endometrial cancer. Lancet 366, 491–505 (2005).

    PubMed  Google Scholar 

  106. 106

    Pauklin, S., Sernandez, I. V., Bachmann, G., Ramiro, A. R. & Petersen-Mahrt, S. K. Estrogen directly activates AID transcription and function. J. Exp. Med. 206, 99–111 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  107. 107

    Levine, M. E. et al. Menopause accelerates biological aging. Proc. Natl Acad. Sci. USA 113, 9327–9332 (2016).

    CAS  PubMed  Google Scholar 

  108. 108

    Maldonado-Carceles, A. B. et al. Anogenital distance, a biomarker of prenatal androgen exposure is associated with prostate cancer severity. Prostate 77, 406–411 (2017).

    CAS  PubMed  Google Scholar 

  109. 109

    Rahman, A. A. et al. Hand pattern indicates prostate cancer risk. Br. J. Cancer 104, 175–177 (2011).

    CAS  PubMed  Google Scholar 

  110. 110

    Issa, J. P. Aging and epigenetic drift: a vicious cycle. J. Clin. Invest. 124, 24–29 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. 111

    Tomasetti, C. & Vogelstein, B. Cancer etiology. Variation in cancer risk among tissues can be explained by the number of stem cell divisions. Science 347, 78–81 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  112. 112

    Zhu, L. et al. Multi-organ mapping of cancer risk. Cell 166, 1132–1146.e7 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. 113

    Heyn, H. et al. Distinct DNA methylomes of newborns and centenarians. Proc. Natl Acad. Sci. USA 109, 10522–10527 (2012).

    CAS  PubMed  Google Scholar 

  114. 114

    Ahuja, N., Li, Q., Mohan, A. L., Baylin, S. B. & Issa, J. P. Aging and DNA methylation in colorectal mucosa and cancer. Cancer Res. 58, 5489–5494 (1998).

    CAS  PubMed  Google Scholar 

  115. 115

    Fraga, M. F. et al. Epigenetic differences arise during the lifetime of monozygotic twins. Proc. Natl Acad. Sci. USA 102, 10604–10609 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. 116

    Teschendorff, A. E. et al. Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res. 20, 440–446 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  117. 117

    Zheng, S. C., Widschwendter, M. & Teschendorff, A. E. Epigenetic drift, epigenetic clocks and cancer risk. Epigenomics 8, 705–719 (2016).

    CAS  PubMed  Google Scholar 

  118. 118

    Teschendorff, A. E. et al. Epigenetic variability in cells of normal cytology is associated with the risk of future morphological transformation. Genome Med. 4, 24 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. 119

    Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, R115 (2013).

    PubMed  PubMed Central  Google Scholar 

  120. 120

    Teschendorff, A. E. et al. DNA methylation outliers in normal breast tissue identify field defects that are enriched in cancer. Nat. Commun. 7, 10478 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. 121

    Teschendorff, A. E., Jones, A. & Widschwendter, M. Stochastic epigenetic outliers can define field defects in cancer. BMC Bioinformatics 17, 178 (2016).

    PubMed  PubMed Central  Google Scholar 

  122. 122

    Baba, Y. et al. Epigenetic field cancerization in gastrointestinal cancers. Cancer Lett. 375, 360–366 (2016).

    CAS  PubMed  Google Scholar 

  123. 123

    Yang, B. et al. Methylation profiling defines an extensive field defect in histologically normal prostate tissues associated with prostate cancer. Neoplasia 15, 399–408 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  124. 124

    Klein, R. J. et al. Complement factor H polymorphism in age-related macular degeneration. Science 308, 385–389 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. 125

    Gao, X., Jia, M., Zhang, Y., Breitling, L. P. & Brenner, H. DNA methylation changes of whole blood cells in response to active smoking exposure in adults: a systematic review of DNA methylation studies. Clin. Epigenet. 7, 113 (2015).

    Google Scholar 

  126. 126

    Zhang, Y. et al. Smoking-associated DNA methylation markers predict lung cancer incidence. Clin. Epigenet. 8, 127 (2016).

    Google Scholar 

  127. 127

    Bojesen, S. E., Timpson, N., Relton, C., Davey Smith, G. & Nordestgaard, B. G. AHRR (cg05575921) hypomethylation marks smoking behaviour, morbidity and mortality. Thorax 72, 646–653 (2017).

    PubMed  PubMed Central  Google Scholar 

  128. 128

    Baglietto, L. et al. DNA methylation changes measured in pre-diagnostic peripheral blood samples are associated with smoking and lung cancer risk. Int. J. Cancer 140, 50–61 (2017).

    CAS  PubMed  Google Scholar 

  129. 129

    Zhang, Y. et al. Comparison and combination of blood DNA methylation at smoking-associated genes and at lung cancer related genes in prediction of lung cancer mortality. Int. J. Cancer 139, 2482–2492 (2016).

    CAS  PubMed  Google Scholar 

  130. 130

    Perna, L. et al. Epigenetic age acceleration predicts cancer, cardiovascular, and all-cause mortality in a German case cohort. Clin. Epigenet. 8, 64 (2016).

    Google Scholar 

  131. 131

    Hitchins, M. P. et al. Inheritance of a cancer-associated MLH1 germ-line epimutation. N. Engl. J. Med. 356, 697–705 (2007).

    CAS  PubMed  Google Scholar 

  132. 132

    Suter, C. M., Martin, D. I. & Ward, R. L. Germline epimutation of MLH1 in individuals with multiple cancers. Nat. Genet. 36, 497–501 (2004).

    CAS  PubMed  Google Scholar 

  133. 133

    Cui, H. M. et al. Loss of IGF2 imprinting: a potential marker of colorectal cancer risk. Science 299, 1753–1755 (2003).

    CAS  PubMed  Google Scholar 

  134. 134

    Ito, Y. et al. Somatically acquired hypomethylation of IGF2 in breast and colorectal cancer. Hum. Mol. Genet. 17, 2633–2643 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  135. 135

    Widschwendter, M. et al. Epigenotyping in peripheral blood cell DNA and breast cancer risk: a proof of principle study. PLoS ONE 3, e2656 (2008).

    PubMed  PubMed Central  Google Scholar 

  136. 136

    Teschendorff, A. E. et al. An epigenetic signature in peripheral blood predicts active ovarian cancer. PLoS ONE 4, e8274 (2009).

    PubMed  PubMed Central  Google Scholar 

  137. 137

    Anjum, S. et al. A BRCA1-mutation associated DNA methylation signature in blood cells predicts sporadic breast cancer incidence and survival. Genome Med. 6, 47 (2014).

    PubMed  PubMed Central  Google Scholar 

  138. 138

    Teschendorff, A. E. & Widschwendter, M. Differential variability improves the identification of cancer risk markers in DNA methylation studies profiling precursor cancer lesions. Bioinformatics 28, 1487–1494 (2012).

    CAS  PubMed  Google Scholar 

  139. 139

    Mirabello, L., Savage, S. A., Korde, L., Gadalla, S. M. & Greene, M. H. LINE-1 methylation is inherited in familial testicular cancer kindreds. BMC Med. Genet. 11, 77 (2010).

    PubMed  PubMed Central  Google Scholar 

  140. 140

    Koestler, D. C. et al. Integrative genomic analysis identifies epigenetic marks that mediate genetic risk for epithelial ovarian cancer. BMC Med. Genom. 7, 8 (2014).

    Google Scholar 

  141. 141

    Winham, S. J. et al. Genome-wide investigation of regional blood-based DNA methylation adjusted for complete blood counts implicates BNC2 in ovarian cancer. Genet. Epidemiol. 38, 457–466 (2014).

    PubMed  PubMed Central  Google Scholar 

  142. 142

    Luo, X. et al. Methylation of a panel of genes in peripheral blood leukocytes is associated with colorectal cancer. Sci. Rep. 6, 29922 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  143. 143

    Gupta, S. et al. Methylation of the BRCA1 promoter in peripheral blood DNA is associated with triple-negative and medullary breast cancer. Breast Cancer Res. Treat. 148, 615–622 (2014).

    CAS  PubMed  Google Scholar 

  144. 144

    Flanagan, J. M. et al. Gene-body hypermethylation of ATM in peripheral blood DNA of bilateral breast cancer patients. Hum. Mol. Genet. 18, 1332–1342 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  145. 145

    Langevin, S. M. et al. Peripheral blood DNA methylation profiles are indicative of head and neck squamous cell carcinoma: an epigenome-wide association study. Epigenetics 7, 291–299 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  146. 146

    Liao, L. M. et al. LINE-1 methylation levels in leukocyte DNA and risk of renal cell cancer. PLoS ONE 6, e27361 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  147. 147

    Shen, J. et al. Global methylation of blood leukocyte DNA and risk of melanoma. Int. J. Cancer 140, 1503–1509 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  148. 148

    Pergoli, L. et al. Blood DNA methylation, nevi number, and the risk of melanoma. Melanoma Res. 24, 480–487 (2014).

    CAS  PubMed  Google Scholar 

  149. 149

    Severi, G. et al. Epigenome-wide methylation in DNA from peripheral blood as a marker of risk for breast cancer. Breast Cancer Res. Treat. 148, 665–673 (2014).

    CAS  PubMed  Google Scholar 

  150. 150

    van Veldhoven, K. et al. Epigenome-wide association study reveals decreased average methylation levels years before breast cancer diagnosis. Clin. Epigenet. 7, 67 (2015).

    Google Scholar 

  151. 151

    Xu, Z. et al. Epigenome-wide association study of breast cancer using prospectively collected sister study samples. J. Natl Cancer Inst. 105, 694–700 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  152. 152

    Brennan, K. et al. Intragenic ATM methylation in peripheral blood DNA as a biomarker of breast cancer risk. Cancer Res. 72, 2304–2313 (2012).

    CAS  PubMed  Google Scholar 

  153. 153

    Langevin, S. M. et al. Leukocyte-adjusted epigenome-wide association studies of blood from solid tumor patients. Epigenetics 9, 884–895 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  154. 154

    Marsit, C. J. et al. DNA methylation array analysis identifies profiles of blood-derived DNA methylation associated with bladder cancer. J. Clin. Oncol. 29, 1133–1139 (2011).

    PubMed  PubMed Central  Google Scholar 

  155. 155

    Wu, H. C. et al. Blood DNA methylation markers in prospectively identified hepatocellular carcinoma cases and controls from Taiwan. World J. Hepatol. 8, 301–306 (2016).

    PubMed  PubMed Central  Google Scholar 

  156. 156

    Wu, H. C. et al. Global DNA methylation levels in white blood cells as a biomarker for hepatocellular carcinoma risk: a nested case-control study. Carcinogenesis 33, 1340–1345 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  157. 157

    Noreen, F. et al. Modulation of age- and cancer-associated DNA methylation change in the healthy colon by aspirin and lifestyle. J. Natl Cancer Inst. 106, dju161 (2014).

    PubMed  PubMed Central  Google Scholar 

  158. 158

    Fasanelli, F. et al. Hypomethylation of smoking-related genes is associated with future lung cancer in four prospective cohorts. Nat. Commun. 6, 10192 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  159. 159

    Florath, I., Butterbach, K., Muller, H., Bewerunge-Hudler, M. & Brenner, H. Cross-sectional and longitudinal changes in DNA methylation with age: an epigenome-wide analysis revealing over 60 novel age-associated CpG sites. Hum. Mol. Genet. 23, 1186–1201 (2014).

    CAS  PubMed  Google Scholar 

  160. 160

    Ziller, M. J. et al. Charting a dynamic DNA methylation landscape of the human genome. Nature 500, 477–481 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  161. 161

    Roadmap Epigenomics, C. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Google Scholar 

  162. 162

    Jaffe, A. E. & Irizarry, R. A. Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol. 15, R31 (2014).

    PubMed  PubMed Central  Google Scholar 

  163. 163

    Koestler, D. C. et al. Peripheral blood immune cell methylation profiles are associated with nonhematopoietic cancers. Cancer Epidemiol. Biomarkers Prev. 21, 1293–1302 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  164. 164

    Stunnenberg, H. G., International Human Epigenome, C. & Hirst, M. The International Human Epigenome Consortium: a blueprint for scientific collaboration and discovery. Cell 167, 1145–1149 (2016).

    CAS  PubMed  Google Scholar 

  165. 165

    Adams, D. et al. BLUEPRINT to decode the epigenetic signature written in blood. Nat. Biotechnol. 30, 224–226 (2012).

    CAS  PubMed  Google Scholar 

  166. 166

    Houseman, E. A. et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 13, 86 (2012).

    PubMed  PubMed Central  Google Scholar 

  167. 167

    Teschendorff, A. E., Breeze, C. E., Zheng, S. C. & Beck, S. A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies. BMC Bioinformatics 18, 105 (2017).

    PubMed  PubMed Central  Google Scholar 

  168. 168

    Lehmann-Werman, R. et al. Identification of tissue-specific cell death using methylation patterns of circulating DNA. Proc. Natl Acad. Sci. USA 113, E1826–E1834 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  169. 169

    Kang, S. et al. CancerLocator: non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA. Genome Biol. 18, 53 (2017).

    PubMed  PubMed Central  Google Scholar 

  170. 170

    Hannon, E., Lunnon, K., Schalkwyk, L. & Mill, J. Interindividual methylomic variation across blood, cortex, and cerebellum: implications for epigenetic studies of neurological and neuropsychiatric phenotypes. Epigenetics 10, 1024–1032 (2015).

    PubMed  PubMed Central  Google Scholar 

  171. 171

    Zhang, Y. et al. DNA methylation signatures in peripheral blood strongly predict all-cause mortality. Nat. Commun. 8, 14617 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  172. 172

    Leek, J. T. et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11, 733–739 (2010).

    CAS  PubMed  Google Scholar 

  173. 173

    Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

    PubMed  PubMed Central  Google Scholar 

  174. 174

    Leek, J. T. & Storey, J. D. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 3, 1724–1735 (2007).

    CAS  PubMed  Google Scholar 

  175. 175

    Teschendorff, A. E., Zhuang, J. & Widschwendter, M. Independent surrogate variable analysis to deconvolve confounding factors in large-scale microarray profiling studies. Bioinformatics 27, 1496–1505 (2011).

    CAS  PubMed  Google Scholar 

  176. 176

    Houseman, E. A., Molitor, J. & Marsit, C. J. Reference-free cell mixture adjustments in analysis of DNA methylation data. Bioinformatics 30, 1431–1439 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  177. 177

    van Dongen, J. et al. Epigenetic variation in monozygotic twins: a genome-wide analysis of DNA methylation in buccal cells. Genes 5, 347–365 (2014).

    PubMed  PubMed Central  Google Scholar 

  178. 178

    Slieker, R. C. et al. Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms. Genome Biol. 17, 191 (2016).

    PubMed  PubMed Central  Google Scholar 

  179. 179

    Hansen, K. D. et al. Increased methylation variation in epigenetic domains across cancer types. Nat. Genet. 43, 768–775 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  180. 180

    Rakyan, V. K., Down, T. A., Balding, D. J. & Beck, S. Epigenome-wide association studies for common human diseases. Nat. Rev. Genet. 12, 529–541 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  181. 181

    Krzysztofek, M. Post-reform personal data protection in the European Union: general data protection regulation (EU) 2016/679. (Kluwer Law International B. V., 2017).

    Google Scholar 

  182. 182

    Pashayan, N., Reisel, D. & Widschwendter, M. Integration of genetic and epigenetic markers for risk stratification: opportunities and challenges. Per. Med. 13, 93–95 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  183. 183

    Garcia-Closas, M., Gunsoy, N. B. & Chatterjee, N. Combined associations of genetic and environmental risk factors: implications for prevention of breast cancer. J. Natl Cancer Inst. 106, dju305 (2014).

    PubMed  PubMed Central  Google Scholar 

  184. 184

    Bunnik, E. M., Janssens, A. C. & Schermer, M. H. A tiered-layered-staged model for informed consent in personal genome testing. Eur. J. Hum. Genet. 21, 596–601 (2013).

    PubMed  Google Scholar 

  185. 185

    Ploug, T., Holm, S. & Brodersen, J. To nudge or not to nudge: cancer screening programmes and the limits of libertarian paternalism. J. Epidemiol. Commun. Health 66, 1193–1196 (2012).

    Google Scholar 

  186. 186

    Rothstein, M. A., Cai, Y. & Marchant, G. E. The ghost in our genes: legal and ethical implications of epigenetics. Health Matrix Clevel 19, 1–62 (2009).

    PubMed  PubMed Central  Google Scholar 

  187. 187

    McDowell, M., Rebitschek, F., Gigerenzer, G. & Wegwarth, O. A simple tool for communicating the benefits and harms of health interventions: a guide for creating a fact box. Med. Decision Making Policy Practice 1, 2381468316665365 (2016).

    Google Scholar 

  188. 188

    Steckelberg, A., Berger, B., Köpke, S., Heesen, C. & Mühlhauser, I. Criteria for evidence-based information for patients [German]. Zeitschrift Ärztliche Fortbildung Qualität Gesundheitswesen 99, 343–351 (2005).

    Google Scholar 

  189. 189

    Wegwarth, O., Schwartz, L. M., Woloshin, S., Gaissmaier, W. & Gigerenzer, G. Do physicians understand cancer screening statistics? A national survey of primary care physicians in the U. S. Ann. Intern. Med. 156, 340–349 (2012).

    PubMed  Google Scholar 

  190. 190

    Wegwarth, O. & Gigerenzer, G. “There is nothing to worry about”: Gynecologists' counseling on mammography. Patient Educ. Counsel. 84, 251–256 (2011).

    Google Scholar 

  191. 191

    Wegwarth, O., Gaissmaier, W. & Gigerenzer, G. Deceiving numbers: survival rates and their impact on doctors' risk communication. Med. Decision Making 31, 386–394 (2011).

    Google Scholar 

  192. 192

    Prinz, R., Feufel, M. A., Gigerenzer, G. & Wegwarth, O. What counselors tell low-risk clients about HIV test performance. Curr. HIV Res. 13, 369–380 (2015).

    CAS  PubMed  Google Scholar 

  193. 193

    Gold, M. R., Siegel, J. E., Russell, L. B. & Weinstein, M. C. Cost-Effectiveness in Health and Medicine. (Oxford Univ. Press, 1996).

    Google Scholar 

  194. 194

    Hunink, M. & Glasziou, P. Decision Making in Health and Medicine. Integrating Evidence and Values. (Cambridge Univ. Press, 2001).

    Google Scholar 

  195. 195

    Weinstein, M. C. High-priced technology can be good value for money. Ann. Intern. Med. 130, 857–858 (1999).

    CAS  PubMed  Google Scholar 

  196. 196

    Siebert, U. When should decision-analytic modeling be used in the economic evaluation of health care? [Editorial]. Eur. J. Health Econom. 4, 143–150 (2003).

    Google Scholar 

  197. 197

    Siebert, U. et al. State-transition modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-3. Value Health 15, 812–820 (2012).

    PubMed  Google Scholar 

  198. 198

    Trikalinos, T. A., Siebert, U. & Lau, J. Decision-analytic modeling to evaluate benefits and harms of medical tests: uses and limitations. Med. Decision Making 29, E22–E29 (2009).

    Google Scholar 

  199. 199

    Caro, J. J., Briggs, A. H., Siebert, U. & Kuntz, K. M. Modeling good research practices — overview a report of the ISPOR-SMDM Modeling Good Research Practices Task Force–1. Med. Decision Making 32, 667–677 (2012).

    Google Scholar 

  200. 200

    Hakama, M., Malila, N. & Dillner, J. Randomised health services studies. Int. J. Cancer 131, 2898–2902 (2012).

    CAS  PubMed  Google Scholar 

  201. 201

    Chowdhury, S. et al. Incorporating genomics into breast and prostate cancer screening: assessing the implications. Genet. Med. 15, 423–432 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  202. 202

    Lichtenstein, P. et al. Environmental and heritable factors in the causation of cancer — analyses of cohorts of twins from Sweden, Denmark, and Finland. N. Engl. J. Med. 343, 78–85 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  203. 203

    Mucci, L. A. et al. Familial risk and heritability of cancer among twins in Nordic countries. JAMA 315, 68–76 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  204. 204

    Magnusson, P. K., Lichtenstein, P. & Gyllensten, U. B. Heritability of cervical tumours. Int. J. Cancer 88, 698–701 (2000).

    CAS  PubMed  Google Scholar 

  205. 205

    Chen, D. et al. Analysis of the genetic architecture of susceptibility to cervical cancer indicates that common SNPs explain a large proportion of the heritability. Carcinogenesis 36, 992–998 (2015).

    CAS  PubMed  Google Scholar 

  206. 206

    Al-Tassan, N. A. et al. A new GWAS and meta-analysis with 1000Genomes imputation identifies novel risk variants for colorectal cancer. Sci. Rep. 5, 10442 (2015).

    PubMed  PubMed Central  Google Scholar 

  207. 207

    Wray, N. R., Yang, J., Goddard, M. E. & Visscher, P. M. The genetic interpretation of area under the ROC curve in genomic profiling. PLoS Genet. 6, e1000864 (2010).

    PubMed  PubMed Central  Google Scholar 

  208. 208

    Tyrer, J., Duffy, S. W. & Cuzick, J. A breast cancer prediction model incorporating familial and personal risk factors. Stat. Med. 23, 1111–1130 (2004).

    PubMed  PubMed Central  Google Scholar 

  209. 209

    Pfeiffer, R. M. et al. Risk prediction for breast, endometrial, and ovarian cancer in white women aged 50 y or older: derivation and validation from population-based cohort studies. PLoS Med. 10, e1001492 (2013).

    PubMed  PubMed Central  Google Scholar 

  210. 210

    Roobol, M. J. et al. A risk-based strategy improves prostate-specific antigen-driven detection of prostate cancer. Eur. Urol. 57, 79–85 (2010).

    PubMed  Google Scholar 

  211. 211

    Tammemagi, M. C. et al. Selection criteria for lung-cancer screening. N. Engl. J. Med. 368, 728–736 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  212. 212

    Thrift, A. P. et al. A clinical risk prediction model for Barrett esophagus. Cancer Prev. Res. 5, 1115–1123 (2012).

    Google Scholar 

  213. 213

    Freedman, A. N. et al. Colorectal cancer risk prediction tool for white men and women without known susceptibility. J. Clin. Oncol. 27, 686–693 (2009).

    PubMed  Google Scholar 

  214. 214

    Zhang, Y. et al. F2RL3 methylation, lung cancer incidence and mortality. Int. J. Cancer 137, 1739–1748 (2015).

    CAS  PubMed  Google Scholar 

Download references

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.

Author information

Affiliations

Authors

Consortia

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.

Corresponding author

Correspondence to Martin Widschwendter.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Further reading

Search

Sign up for the Nature Briefing newsletter for a daily update on COVID-19 science.
Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing