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.
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.
Subscribe to Journal
Get full journal access for 1 year
only $17.42 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Ferlay, J. et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136, E359–E386 (2015).
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).
Wu, S., Powers, S., Zhu, W. & Hannun, Y. A. Substantial contribution of extrinsic risk factors to cancer development. Nature 529, 43–47 (2016).
Tomasetti, C., Li, L. & Vogelstein, B. Stem cell divisions, somatic mutations, cancer etiology, and cancer prevention. Science 355, 1330–1334 (2017).
Wodarz, D. & Zauber, A. G. Cancer: risk factors and random chances. Nature 517, 563–564 (2015).
Lu, Y. et al. Most common 'sporadic' cancers have a significant germline genetic component. Hum. Mol. Genet. 23, 6112–6118 (2014).
Bonder, M. J. et al. Disease variants alter transcription factor levels and methylation of their binding sites. Nat. Genet. 49, 131–138 (2017).
Burton, H. et al. Public health implications from COGS and potential for risk stratification and screening. Nat. Genet. 45, 349–351 (2013).
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).
Pashayan, N. et al. Implications of polygenic risk-stratified screening for prostate cancer on overdiagnosis. Genet. Med. 17, 789–795 (2015).
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).
Sporn, M. B. & Liby, K. T. Cancer chemoprevention: scientific promise, clinical uncertainty. Nat. Clin. Pract. Oncol. 2, 518–525 (2005).
Damen, J. A. et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ 353, i2416 (2016).
Heyn, H. et al. DNA methylation contributes to natural human variation. Genome Res. 23, 1363–1372 (2013).
Bergman, Y. & Cedar, H. DNA methylation dynamics in health and disease. Nat. Struct. Mol. Biol. 20, 274–281 (2013).
Feil, R. & Fraga, M. F. Epigenetics and the environment: emerging patterns and implications. Nat. Rev. Genet. 13, 97–109 (2011).
Widschwendter, M. et al. Epigenetic stem cell signature in cancer. Nat. Genet. 39, 157–158 (2007).
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).
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).
Gupta, R. A. et al. Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature 464, 1071–1076 (2010).
Tsai, M. C. et al. Long noncoding RNA as modular scaffold of histone modification complexes. Science 329, 689–693 (2010).
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).
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).
Laugesen, A. & Helin, K. Chromatin repressive complexes in stem cells, development, and cancer. Cell Stem Cell 14, 735–751 (2014).
Vire, E. et al. The Polycomb group protein EZH2 directly controls DNA methylation. Nature 439, 871–874 (2006).
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).
Guida, F. et al. Dynamics of smoking-induced genome-wide methylation changes with time since smoking cessation. Hum. Mol. Genet. 24, 2349–2359 (2015).
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).
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).
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).
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).
Yang, Z. et al. Correlation of an epigenetic mitotic clock with cancer risk. Genome Biol. 17, 205 (2016).
Klutstein, M., Nejman, D., Greenfield, R. & Cedar, H. DNA methylation in cancer and aging. Cancer Res. 76, 3446–3450 (2016).
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).
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).
Li, Q. et al. The antiproliferative action of progesterone in uterine epithelium is mediated by Hand2. Science 331, 912–916 (2011).
Jones, A. et al. Role of DNA methylation and epigenetic silencing of HAND2 in endometrial cancer development. PLOS Med. 10, e1001551 (2013).
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).
Hanson, J. A. et al. Gene promoter methylation in prostate tumor-associated stromal cells. J. Natl Cancer Inst. 98, 255–261 (2006).
Valcz, G. et al. Myofibroblast-derived SFRP1 as potential inhibitor of colorectal carcinoma field effect. PLoS ONE 9, e106143 (2014).
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).
Paterson, R. F. et al. Molecular genetic alterations in the laser-capture-microdissected stroma adjacent to bladder carcinoma. Cancer 98, 1830–1836 (2003).
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).
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).
Ongen, H. et al. Putative cis-regulatory drivers in colorectal cancer. Nature 512, 87–90 (2014).
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).
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).
Alexandrov, L. B. et al. Clock-like mutational processes in human somatic cells. Nat. Genet. 47, 1402–1407 (2015).
Gaunt, T. R. et al. Systematic identification of genetic influences on methylation across the human life course. Genome Biol. 17, 61 (2016).
Chen, L. et al. Genetic drivers of epigenetic and transcriptional variation in human immune cells. Cell 167, 1398–1414.e24 (2016).
Bell, J. T. et al. DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines. Genome Biol. 12, R10 (2011).
Zeng, H. & Gifford, D. K. Predicting the impact of non-coding variants on DNA methylation. Nucleic Acids Res. 45, e99 (2017).
Heyn, H. et al. Linkage of DNA methylation quantitative trait loci to human cancer risk. Cell Rep. 7, 331–338 (2014).
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).
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).
Widschwendter, M. et al. The sex hormone system in carriers of BRCA1/2 mutations: a case-control study. Lancet Oncol. 14, 1226–1232 (2013).
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).
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).
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).
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).
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).
Zhang, Y., Florath, I., Saum, K. U. & Brenner, H. Self-reported smoking, serum cotinine, and blood DNA methylation. Environ. Res. 146, 395–403 (2016).
Miska, E. A. & Ferguson-Smith, A. C. Transgenerational inheritance: Models and mechanisms of non-DNA sequence-based inheritance. Science 354, 59–63 (2016).
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).
Bygren, L. O. et al. Change in paternal grandmothers' early food supply influenced cardiovascular mortality of the female grandchildren. BMC Genet. 15, 12 (2014).
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).
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).
Carone, B. R. et al. Paternally induced transgenerational environmental reprogramming of metabolic gene expression in mammals. Cell 143, 1084–1096 (2010).
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).
Wahl, S. et al. Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity. Nature 541, 81–86 (2017).
Hoover, R. N. et al. Adverse health outcomes in women exposed in utero to diethylstilbestrol. N. Engl. J. Med. 365, 1304–1314 (2011).
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).
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).
Soto, A. M. & Sonnenschein, C. Environmental causes of cancer: endocrine disruptors as carcinogens. Nat. Rev. Endocrinol. 6, 363–370 (2010).
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).
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).
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).
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).
Li, R. et al. Obesity, rather than diet, drives epigenomic alterations in colonic epithelium resembling cancer progression. Cell Metab. 19, 702–711 (2014).
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).
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).
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).
Wang, T. et al. Epigenetic aging signatures in mice livers are slowed by dwarfism, calorie restriction and rapamycin treatment. Genome Biol. 18, 57 (2017).
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).
Zheng, Y. et al. Blood epigenetic age may predict cancer incidence and mortality. EBioMedicine 5, 68–73 (2016).
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).
Ambatipudi, S. et al. DNA methylome analysis identifies accelerated epigenetic ageing associated with postmenopausal breast cancer susceptibility. Eur. J. Cancer 75, 299–307 (2017).
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).
Zeilinger, S. et al. Tobacco smoking leads to extensive genome-wide changes in DNA methylation. PLoS ONE 8, e63812 (2013).
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).
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).
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).
Schwabe, R. F. & Jobin, C. The microbiome and cancer. Nat. Rev. Cancer 13, 800–812 (2013).
Paul, B. et al. Influences of diet and the gut microbiome on epigenetic modulation in cancer and other diseases. Clin. Epigenet. 7, 112 (2015).
Alenghat, T. Epigenomics and the microbiota. Toxicol. Pathol. 43, 101–106 (2015).
Elinav, E. et al. Inflammation-induced cancer: crosstalk between tumours, immune cells and microorganisms. Nat. Rev. Cancer 13, 759–771 (2013).
Tang, Y. et al. Jak/Stat3 signaling promotes somatic cell reprogramming by epigenetic regulation. Stem Cells 30, 2645–2656 (2012).
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).
Matsumoto, Y. et al. Helicobacter pylori infection triggers aberrant expression of activation-induced cytidine deaminase in gastric epithelium. Nat. Med. 13, 470–476 (2007).
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).
Hahn, M. A. et al. Methylation of polycomb target genes in intestinal cancer is mediated by inflammation. Cancer Res. 68, 10280–10289 (2008).
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).
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).
Hennessy, B. T., Coleman, R. L. & Markman, M. Ovarian cancer. Lancet 374, 1371–1382 (2009).
Amant, F. et al. Endometrial cancer. Lancet 366, 491–505 (2005).
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).
Levine, M. E. et al. Menopause accelerates biological aging. Proc. Natl Acad. Sci. USA 113, 9327–9332 (2016).
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).
Rahman, A. A. et al. Hand pattern indicates prostate cancer risk. Br. J. Cancer 104, 175–177 (2011).
Issa, J. P. Aging and epigenetic drift: a vicious cycle. J. Clin. Invest. 124, 24–29 (2014).
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).
Zhu, L. et al. Multi-organ mapping of cancer risk. Cell 166, 1132–1146.e7 (2016).
Heyn, H. et al. Distinct DNA methylomes of newborns and centenarians. Proc. Natl Acad. Sci. USA 109, 10522–10527 (2012).
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).
Fraga, M. F. et al. Epigenetic differences arise during the lifetime of monozygotic twins. Proc. Natl Acad. Sci. USA 102, 10604–10609 (2005).
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).
Zheng, S. C., Widschwendter, M. & Teschendorff, A. E. Epigenetic drift, epigenetic clocks and cancer risk. Epigenomics 8, 705–719 (2016).
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).
Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, R115 (2013).
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).
Teschendorff, A. E., Jones, A. & Widschwendter, M. Stochastic epigenetic outliers can define field defects in cancer. BMC Bioinformatics 17, 178 (2016).
Baba, Y. et al. Epigenetic field cancerization in gastrointestinal cancers. Cancer Lett. 375, 360–366 (2016).
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).
Klein, R. J. et al. Complement factor H polymorphism in age-related macular degeneration. Science 308, 385–389 (2005).
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).
Zhang, Y. et al. Smoking-associated DNA methylation markers predict lung cancer incidence. Clin. Epigenet. 8, 127 (2016).
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).
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).
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).
Perna, L. et al. Epigenetic age acceleration predicts cancer, cardiovascular, and all-cause mortality in a German case cohort. Clin. Epigenet. 8, 64 (2016).
Hitchins, M. P. et al. Inheritance of a cancer-associated MLH1 germ-line epimutation. N. Engl. J. Med. 356, 697–705 (2007).
Suter, C. M., Martin, D. I. & Ward, R. L. Germline epimutation of MLH1 in individuals with multiple cancers. Nat. Genet. 36, 497–501 (2004).
Cui, H. M. et al. Loss of IGF2 imprinting: a potential marker of colorectal cancer risk. Science 299, 1753–1755 (2003).
Ito, Y. et al. Somatically acquired hypomethylation of IGF2 in breast and colorectal cancer. Hum. Mol. Genet. 17, 2633–2643 (2008).
Widschwendter, M. et al. Epigenotyping in peripheral blood cell DNA and breast cancer risk: a proof of principle study. PLoS ONE 3, e2656 (2008).
Teschendorff, A. E. et al. An epigenetic signature in peripheral blood predicts active ovarian cancer. PLoS ONE 4, e8274 (2009).
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).
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).
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).
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).
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).
Luo, X. et al. Methylation of a panel of genes in peripheral blood leukocytes is associated with colorectal cancer. Sci. Rep. 6, 29922 (2016).
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).
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).
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).
Liao, L. M. et al. LINE-1 methylation levels in leukocyte DNA and risk of renal cell cancer. PLoS ONE 6, e27361 (2011).
Shen, J. et al. Global methylation of blood leukocyte DNA and risk of melanoma. Int. J. Cancer 140, 1503–1509 (2017).
Pergoli, L. et al. Blood DNA methylation, nevi number, and the risk of melanoma. Melanoma Res. 24, 480–487 (2014).
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).
van Veldhoven, K. et al. Epigenome-wide association study reveals decreased average methylation levels years before breast cancer diagnosis. Clin. Epigenet. 7, 67 (2015).
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).
Brennan, K. et al. Intragenic ATM methylation in peripheral blood DNA as a biomarker of breast cancer risk. Cancer Res. 72, 2304–2313 (2012).
Langevin, S. M. et al. Leukocyte-adjusted epigenome-wide association studies of blood from solid tumor patients. Epigenetics 9, 884–895 (2014).
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).
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).
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).
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).
Fasanelli, F. et al. Hypomethylation of smoking-related genes is associated with future lung cancer in four prospective cohorts. Nat. Commun. 6, 10192 (2015).
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).
Ziller, M. J. et al. Charting a dynamic DNA methylation landscape of the human genome. Nature 500, 477–481 (2013).
Roadmap Epigenomics, C. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
Jaffe, A. E. & Irizarry, R. A. Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biol. 15, R31 (2014).
Koestler, D. C. et al. Peripheral blood immune cell methylation profiles are associated with nonhematopoietic cancers. Cancer Epidemiol. Biomarkers Prev. 21, 1293–1302 (2012).
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).
Adams, D. et al. BLUEPRINT to decode the epigenetic signature written in blood. Nat. Biotechnol. 30, 224–226 (2012).
Houseman, E. A. et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 13, 86 (2012).
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).
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).
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).
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).
Zhang, Y. et al. DNA methylation signatures in peripheral blood strongly predict all-cause mortality. Nat. Commun. 8, 14617 (2017).
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).
Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).
Leek, J. T. & Storey, J. D. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 3, 1724–1735 (2007).
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).
Houseman, E. A., Molitor, J. & Marsit, C. J. Reference-free cell mixture adjustments in analysis of DNA methylation data. Bioinformatics 30, 1431–1439 (2014).
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).
Slieker, R. C. et al. Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms. Genome Biol. 17, 191 (2016).
Hansen, K. D. et al. Increased methylation variation in epigenetic domains across cancer types. Nat. Genet. 43, 768–775 (2011).
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).
Krzysztofek, M. Post-reform personal data protection in the European Union: general data protection regulation (EU) 2016/679. (Kluwer Law International B. V., 2017).
Pashayan, N., Reisel, D. & Widschwendter, M. Integration of genetic and epigenetic markers for risk stratification: opportunities and challenges. Per. Med. 13, 93–95 (2016).
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).
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).
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).
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).
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).
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).
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).
Wegwarth, O. & Gigerenzer, G. “There is nothing to worry about”: Gynecologists' counseling on mammography. Patient Educ. Counsel. 84, 251–256 (2011).
Wegwarth, O., Gaissmaier, W. & Gigerenzer, G. Deceiving numbers: survival rates and their impact on doctors' risk communication. Med. Decision Making 31, 386–394 (2011).
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).
Gold, M. R., Siegel, J. E., Russell, L. B. & Weinstein, M. C. Cost-Effectiveness in Health and Medicine. (Oxford Univ. Press, 1996).
Hunink, M. & Glasziou, P. Decision Making in Health and Medicine. Integrating Evidence and Values. (Cambridge Univ. Press, 2001).
Weinstein, M. C. High-priced technology can be good value for money. Ann. Intern. Med. 130, 857–858 (1999).
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).
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).
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).
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).
Hakama, M., Malila, N. & Dillner, J. Randomised health services studies. Int. J. Cancer 131, 2898–2902 (2012).
Chowdhury, S. et al. Incorporating genomics into breast and prostate cancer screening: assessing the implications. Genet. Med. 15, 423–432 (2013).
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).
Mucci, L. A. et al. Familial risk and heritability of cancer among twins in Nordic countries. JAMA 315, 68–76 (2016).
Magnusson, P. K., Lichtenstein, P. & Gyllensten, U. B. Heritability of cervical tumours. Int. J. Cancer 88, 698–701 (2000).
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).
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).
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).
Tyrer, J., Duffy, S. W. & Cuzick, J. A breast cancer prediction model incorporating familial and personal risk factors. Stat. Med. 23, 1111–1130 (2004).
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).
Roobol, M. J. et al. A risk-based strategy improves prostate-specific antigen-driven detection of prostate cancer. Eur. Urol. 57, 79–85 (2010).
Tammemagi, M. C. et al. Selection criteria for lung-cancer screening. N. Engl. J. Med. 368, 728–736 (2013).
Thrift, A. P. et al. A clinical risk prediction model for Barrett esophagus. Cancer Prev. Res. 5, 1115–1123 (2012).
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).
Zhang, Y. et al. F2RL3 methylation, lung cancer incidence and mortality. Int. J. Cancer 137, 1739–1748 (2015).
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.
The authors declare no competing financial interests.
About this article
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
Journal of Oncology (2020)
Identifying epigenetic biomarkers of established prognostic factors and survival in a clinical cohort of individuals with oropharyngeal cancer
Clinical Epigenetics (2020)
Life Sciences (2020)
Determination of trace elements Fe, cu and Zn in the Algerian cancerous plasma using X‐ray fluorescence (XRF)
X-Ray Spectrometry (2020)