Dysfunctional epigenetic aging of the normal colon and colorectal cancer risk

Ting Wang, Sean K Maden, Georg E Luebeck, Christopher I Li, Polly A Newcomb, Cornelia M Ulrich, Ji-Hoon E Joo, Daniel D Buchanan, Roger L Milne, Melissa C Southey, Kelly T Carter, Amber R Willbanks, Yanxin Luo, Ming Yu, William M Grady, Ting Wang, Sean K Maden, Georg E Luebeck, Christopher I Li, Polly A Newcomb, Cornelia M Ulrich, Ji-Hoon E Joo, Daniel D Buchanan, Roger L Milne, Melissa C Southey, Kelly T Carter, Amber R Willbanks, Yanxin Luo, Ming Yu, William M Grady

Abstract

Background: Chronological age is a prominent risk factor for many types of cancers including colorectal cancer (CRC). Yet, the risk of CRC varies substantially between individuals, even within the same age group, which may reflect heterogeneity in biological tissue aging between people. Epigenetic clocks based on DNA methylation are a useful measure of the biological aging process with the potential to serve as a biomarker of an individual's susceptibility to age-related diseases such as CRC.

Methods: We conducted a genome-wide DNA methylation study on samples of normal colon mucosa (N = 334). Subjects were assigned to three cancer risk groups (low, medium, and high) based on their personal adenoma or cancer history. Using previously established epigenetic clocks (Hannum, Horvath, PhenoAge, and EpiTOC), we estimated the biological age of each sample and assessed for epigenetic age acceleration in the samples by regressing the estimated biological age on the individual's chronological age. We compared the epigenetic age acceleration between different risk groups using a multivariate linear regression model with the adjustment for gender and cell-type fractions for each epigenetic clock. An epigenome-wide association study (EWAS) was performed to identify differential methylation changes associated with CRC risk.

Results: Each epigenetic clock was significantly correlated with the chronological age of the subjects, and the Horvath clock exhibited the strongest correlation in all risk groups (r > 0.8, p < 1 × 10-30). The PhenoAge clock (p = 0.0012) revealed epigenetic age deceleration in the high-risk group compared to the low-risk group.

Conclusions: Among the four DNA methylation-based measures of biological age, the Horvath clock is the most accurate for estimating the chronological age of individuals. Individuals with a high risk for CRC have epigenetic age deceleration in their normal colons measured by the PhenoAge clock, which may reflect a dysfunctional epigenetic aging process.

Keywords: Biological/epigenetic age; Colorectal cancer; DNA methylation; Epigenetic age acceleration; Epigenetic clock.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Correlation of four epigenetic age estimates (Hannum, Horvath, PhenoAge, and EpiTOC) in the normal colon with the chronological age of the individuals providing the normal colon samples. Different colors represent different groups based on the CRC risk status
Fig. 2
Fig. 2
Distribution of epigenetic age acceleration in the three CRC risk groups. The y-axis shows the epigenetic age acceleration after adjusting for gender and cell-type fractions (i.e., residual of regressing the epigenetic age acceleration on gender and cell-type fractions). Standardized effect size (i.e., Cohen’s d) and p value for the significant association (p value < 0.01) is shown above the corresponding line

References

    1. Stryker SJ, et al. Natural history of untreated colonic polyps. Gastroenterology. 1987;93(5):1009–1013. doi: 10.1016/0016-5085(87)90563-4.
    1. Brenner DE, Normolle DP. Biomarkers for cancer risk, early detection, and prognosis: the validation conundrum. Cancer Epidemiol Biomarkers Prev. 2007;16(10):1918–1920. doi: 10.1158/1055-9965.EPI-07-2619.
    1. Rashid A, et al. CpG island methylation in colorectal adenomas. Am J Pathol. 2001;159(3):1129–1135. doi: 10.1016/S0002-9440(10)61789-0.
    1. Maekita T, et al. High levels of aberrant DNA methylation in Helicobacter pylori-infected gastric mucosae and its possible association with gastric cancer risk. Clin Cancer Res. 2006;12(3 Pt 1):989–995. doi: 10.1158/1078-0432.CCR-05-2096.
    1. Ahuja N, et al. Aging and DNA methylation in colorectal mucosa and cancer. Cancer Res. 1998;58(23):5489–5494.
    1. Shen L, et al. MGMT promoter methylation and field defect in sporadic colorectal cancer. J Natl Cancer Inst. 2005;97(18):1330–1338. doi: 10.1093/jnci/dji275.
    1. Kim YH, et al. CpG island methylation of genes accumulates during the adenoma progression step of the multistep pathogenesis of colorectal cancer. Genes Chromosomes Cancer. 2006;45(8):781–789. doi: 10.1002/gcc.20341.
    1. Bird A. The essentials of DNA methylation. Cell. 1992;70(1):5–8. doi: 10.1016/0092-8674(92)90526-I.
    1. Worthley DL, et al. DNA methylation within the normal colorectal mucosa is associated with pathway-specific predisposition to cancer. Oncogene. 2010;29(11):1653–1662. doi: 10.1038/onc.2009.449.
    1. Hiraoka S, et al. Methylation status of normal background mucosa is correlated with occurrence and development of neoplasia in the distal colon. Hum Pathol. 2010;41(1):38–47. doi: 10.1016/j.humpath.2009.06.002.
    1. Ally MS, Al-Ghnaniem R, Pufulete M. The relationship between gene-specific DNA methylation in leukocytes and normal colorectal mucosa in subjects with and without colorectal tumors. Cancer Epidemiol Biomarkers Prev. 2009;18(3):922–928. doi: 10.1158/1055-9965.EPI-08-0703.
    1. Kawakami K, et al. DNA hypermethylation in the normal colonic mucosa of patients with colorectal cancer. Br J Cancer. 2006;94(4):593–598. doi: 10.1038/sj.bjc.6602940.
    1. Belshaw NJ, et al. Patterns of DNA methylation in individual colonic crypts reveal aging and cancer-related field defects in the morphologically normal mucosa. Carcinogenesis. 2010;31(6):1158–1163. doi: 10.1093/carcin/bgq077.
    1. Belshaw NJ, et al. Profiling CpG island field methylation in both morphologically normal and neoplastic human colonic mucosa. Br J Cancer. 2008;99(1):136–142. doi: 10.1038/sj.bjc.6604432.
    1. Ferlitsch M, et al. Sex-specific prevalence of adenomas, advanced adenomas, and colorectal cancer in individuals undergoing screening colonoscopy. JAMA. 2011;306(12):1352–1358. doi: 10.1001/jama.2011.1362.
    1. Walker RF. Developmental theory of aging revisited: focus on causal and mechanistic links between development and senescence. Rejuvenation Res. 2011;14(4):429–436. doi: 10.1089/rej.2011.1162.
    1. Field AE, et al. DNA methylation clocks in aging: categories, causes, and consequences. Mol Cell. 2018;71(6):882–895. doi: 10.1016/j.molcel.2018.08.008.
    1. Horvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet. 2018;19(6):371–384. doi: 10.1038/s41576-018-0004-3.
    1. Luebeck GE, et al. Implications of epigenetic drift in colorectal neoplasia. Cancer Res. 2019;79(3):495–504. doi: 10.1158/0008-5472.CAN-18-1682.
    1. Bocklandt S, et al. Epigenetic predictor of age. PLoS One. 2011;6(6):e14821. doi: 10.1371/journal.pone.0014821.
    1. Hannum G, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49(2):359–367. doi: 10.1016/j.molcel.2012.10.016.
    1. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):R115. doi: 10.1186/gb-2013-14-10-r115.
    1. Levine ME, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY) 2018;10(4):573–591. doi: 10.18632/aging.101414.
    1. Yang Z, et al. Correlation of an epigenetic mitotic clock with cancer risk. Genome Biol. 2016;17(1):205. doi: 10.1186/s13059-016-1064-3.
    1. Lin Q, Wagner W. Epigenetic aging signatures are coherently modified in cancer. PLoS Genet. 2015;11(6):e1005334. doi: 10.1371/journal.pgen.1005334.
    1. Horvath S. Erratum to: DNA methylation age of human tissues and cell types. Genome Biol. 2015;16:96. doi: 10.1186/s13059-015-0649-6.
    1. Liesenfeld DB, et al. Metabolomics and transcriptomics identify pathway differences between visceral and subcutaneous adipose tissue in colorectal cancer patients: the ColoCare study. Am J Clin Nutr. 2015;102(2):433–443. doi: 10.3945/ajcn.114.103804.
    1. Barault L, et al. Discovery of methylated circulating DNA biomarkers for comprehensive non-invasive monitoring of treatment response in metastatic colorectal cancer. Gut. 2018;67(11):1995–2005. doi: 10.1136/gutjnl-2016-313372.
    1. Luo Y, et al. Differences in DNA methylation signatures reveal multiple pathways of progression from adenoma to colorectal cancer. Gastroenterology. 2014;147(2):418–429. doi: 10.1053/j.gastro.2014.04.039.
    1. Aryee MJ, et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30:1363–1369. doi: 10.1093/bioinformatics/btu049.
    1. Triche TJ, Jr, et al. Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res. 2013;41(7):e90. doi: 10.1093/nar/gkt090.
    1. Fortin JP, et al. Functional normalization of 450k methylation array data improves replication in large cancer studies. Genome Biol. 2014;15(12):503. doi: 10.1186/s13059-014-0503-2.
    1. Fan S, et al. Integrative analysis with expanded DNA methylation data reveals common key regulators and pathways in cancers. NPJ Genom Med. 2019;4:2. doi: 10.1038/s41525-019-0077-8.
    1. Chen W, et al. An epigenome-wide association study of total serum IgE in Hispanic children. J Allergy Clin Immunol. 2017;119:1291–1301.
    1. Chen YA, et al. Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics. 2013;8(2):203–209. doi: 10.4161/epi.23470.
    1. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8(1):118–127. doi: 10.1093/biostatistics/kxj037.
    1. Willer CJ, Li Y, Abecasis GAR. METAL: Fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190–2191. doi: 10.1093/bioinformatics/btq340.
    1. Teschendorff AE, et al. A comparison of reference-based algorithms for correcting cell-type heterogeneity in epigenome-wide association studies. BMC Bioinformatics. 2017;18(1):105. doi: 10.1186/s12859-017-1511-5.
    1. McEwen LM, et al. Systematic evaluation of DNA methylation age estimation with common preprocessing methods and the Infinium MethylationEPIC BeadChip array. Clin Epigenetics. 2018;10(1):123. doi: 10.1186/s13148-018-0556-2.
    1. Leek JT, et al. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28(6):882–883. doi: 10.1093/bioinformatics/bts034.
    1. Dennis G, Jr, et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003;4(5):P3. doi: 10.1186/gb-2003-4-5-p3.
    1. Milne RL, et al. Cohort Profile: The Melbourne Collaborative Cohort Study (Health 2020) Int J Epidemiol. 2017;46(6):1757–1757i. doi: 10.1093/ije/dyx085.
    1. Calvanese V, et al. The role of epigenetics in aging and age-related diseases. Ageing Res Rev. 2009;8(4):268–276. doi: 10.1016/j.arr.2009.03.004.
    1. Horvath S, et al. An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease. Genome Biol. 2016;17(1):171. doi: 10.1186/s13059-016-1030-0.
    1. Levine ME, et al. Epigenetic age of the pre-frontal cortex is associated with neuritic plaques, amyloid load, and Alzheimer’s disease related cognitive functioning. Aging (Albany NY) 2015;7(12):1198–1211. doi: 10.18632/aging.100864.
    1. Eshleman JR, et al. Increased mutation rate at the hprt locus accompanies microsatellite instability in colon cancer. Oncogene. 1995;10(1):33–37.
    1. Valko M, et al. Free radicals, metals and antioxidants in oxidative stress-induced cancer. Chem Biol Interact. 2006;160(1):1–40. doi: 10.1016/j.cbi.2005.12.009.
    1. Reya T, et al. Stem cells, cancer, and cancer stem cells. Nature. 2001;414(6859):105–111. doi: 10.1038/35102167.
    1. Collado M, Blasco MA, Serrano M. Cellular senescence in cancer and aging. Cell. 2007;130(2):223–233. doi: 10.1016/j.cell.2007.07.003.
    1. Zheng C, Li L, Xu R. Association of epigenetic clock with consensus molecular subtypes and overall survival of colorectal cancer. Cancer Epidemiol Biomarkers Prev. 2019;28(10):1720–1724. doi: 10.1158/1055-9965.EPI-19-0208.
    1. Marwitz S, et al. Fountain of youth for squamous cell carcinomas? On the epigenetic age of non-small cell lung cancer and corresponding tumor-free lung tissues. Int J Cancer. 2018;143(12):3061–3070. doi: 10.1002/ijc.31641.
    1. Lu AT, et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging (Albany NY) 2019;11(2):303–327. doi: 10.18632/aging.101684.

Source: PubMed

3
Subscribe