Novel epigenetic biomarkers for hematopoietic cancer found in twins
DOI:
https://doi.org/10.2340/1651-226X.2024.40700Keywords:
DNA methylation, EWAS, hematopoietic malignancy, survival analysis, twin study, FinnGenAbstract
Background and purpose: This article aims to identify epigenetic markers and detect early development of hematopoietic malignancies through an epigenome wide association study of DNA methylation data.
Materials and methods: This register-based study includes 1,085 Danish twins with 31 hematopoietic malignancies and methylation levels from 450,154 5’-C-phospate-G-3’ (CpG) sites. Associations between methylation levels and incidence of hematopoietic malignancy is studied through time-to-event regression. The matched case-cotwin design, where one twin has a malignancy and the cotwin does not, is applied to enhance control for unmeasured shared confounding and false discoveries. Predictive performance is validated in the independent Older Finnish Twin Cohort.
Results and interpretation: We identified 67 epigenetic markers for hematopoietic malignancies of which 12 are linked to genes associated with hematologic malignancies. For some markers, we discovered a 2–3-fold relative risk difference for high versus low methylation. The identification of these 67 sites enabled the formation of a predictor demonstrating a cross-validated time-varying area under the curve (AUC) of 92% 3 years after individual blood sampling and persistent performance above 70% up to 6 years after blood sampling. This predictive performance was to a large extent recovered in the validation sample showing an overall Harrell’s C of 73%.
In conclusion, from a large population representative twin study on hematopoietic cancers, novel epigenetic markers were identified that may prove useful for early diagnosis.
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NORDCAN: cancer incidence, mortality, prevalence and survival in the Nordic countries, version 9.3 (02.10.2023) [Internet]. Association of the Nordic Cancer Registries. Cancer Registry of Norway; 2023 [cited 16-11-2023]. Available from: https://nordcan.iarc.fr/
Engholm G, Ferlay J, Christensen N, et al. NORDCAN-a Nordic tool for cancer information, planning, quality control and research. Acta Oncol. 2010;49(5):725–36.
https://doi.org/10.3109/02841861003782017 DOI: https://doi.org/10.3109/02841861003782017
Clemmensen SB, Harris JR, Mengel-From J, et al. Familial risk and heritability of hemato-logic malignancies in the Nordic twin study of cancer. Cancers (Basel). 2021;13(12):3023.
https://doi.org/10.3390/cancers13123023 DOI: https://doi.org/10.3390/cancers13123023
Hu D, Shilatifard A. Epigenetics of hematopoiesis and hematological malignancies. Genes Dev. 2016;30(18):2021–41.
https://doi.org/10.1101/gad.284109.116 DOI: https://doi.org/10.1101/gad.284109.116
Bueno-Costa A, Piñeyro D, Soler M, et al. B-cell leukemia transdifferentiation to macro-phage involves reconfiguration of DNA methylation for long-range regulation. Leukemia. 2020;34(4):1158–62.
https://doi.org/10.1038/s41375-019-0643-1 DOI: https://doi.org/10.1038/s41375-019-0643-1
Espín-Pérez A, Brennan K, Ediriwickrema AS, et al. Peripheral blood DNA methylation pro-files predict future development of B-cell Non-Hodgkin Lymphoma. NPJ Precis Oncol. 2022;6(1):53.
https://doi.org/10.1038/s41698-022-00295-3 DOI: https://doi.org/10.1038/s41698-022-00295-3
Blecua P, Martinez-Verbo L, Esteller M. The DNA methylation landscape of hematological malignancies: an update. Mol Oncol. 2020;14(8):1616–39.
https://doi.org/10.1002/1878-0261.12744 DOI: https://doi.org/10.1002/1878-0261.12744
Sham P. Statistics in human genetics. Arnold; London. 1998.
Wang J, Van Den Berg D, Hwang AE, et al. DNA methylation patterns of adult survivors of adolescent/young adult Hodgkin lymphoma compared to their unaffected monozygotic twin. Leuk Lymphoma. 2019;60(6):1429–37.
https://doi.org/10.1080/10428194.2018.1533128 DOI: https://doi.org/10.1080/10428194.2018.1533128
Christensen K, Holm NV, Mcgue M, et al. A Danish population-based twin study on gen-eral health in the elderly. J Aging Health. 1999;11(1):49–64.
https://doi.org/10.1177/089826439901100103 DOI: https://doi.org/10.1177/089826439901100103
Gaist D, Bathum L, Skytthe A, et al. Strength and anthropometric measures in identical and fraternal twins: no evidence of masculinization of females with male co-twins. Epidemiology. 2000;11(3):340–3.
https://doi.org/10.1097/00001648-200005000-00020 DOI: https://doi.org/10.1097/00001648-200005000-00020
Frost M, Petersen I, Brixen K, et al. Adult glucose metabolism in extremely birthweight-discordant monozygotic twins. Diabetologia. 2012;55(12):3204–12.
https://doi.org/10.1007/s00125-012-2695-x DOI: https://doi.org/10.1007/s00125-012-2695-x
Skytthe A, Harris JR, Czene K, et al. Cancer incidence and mortality in 260,000 Nordic twins with 30,000 prospective cancers. Twin Res Hum Genet. 2019;22:99–107.
https://doi.org/10.1017/thg.2019.10 DOI: https://doi.org/10.1017/thg.2019.10
Svane AM, Soerensen M, Lund J, et al. DNA methylation and all-cause mortality in middle-aged and elderly Danish twins. Genes (Basel). 2018;9(2):78.
https://doi.org/10.3390/genes9020078 DOI: https://doi.org/10.3390/genes9020078
van Iterson M, Tobi EW, Slieker RC, et al. MethylAid: visual and interactive quality control of large Illumina 450k datasets. Bioinformatics. 2014;30(23):3435–7.
https://doi.org/10.1093/bioinformatics/btu566 DOI: https://doi.org/10.1093/bioinformatics/btu566
Aryee MJ, Jaffe AE, Corrada-Bravo H, et al. Minfi: a flexible and comprehensive bioconduc-tor package for the analysis of infinium DNA methylation microarrays. Bioin-formatics. 2014;30(10):1363–9.
https://doi.org/10.1093/bioinformatics/btu049 DOI: https://doi.org/10.1093/bioinformatics/btu049
Soerensen M, Li W, Debrabant B, et al. Epigenome-wide exploratory study of monozygotic twins suggests differentially methylated regions to associate with hand grip strength. Biogerontology. 2019;20(5):627–47.
https://doi.org/10.1007/s10522-019-09818-1 DOI: https://doi.org/10.1007/s10522-019-09818-1
Debrabant B, Soerensen M, Christiansen L, et al. DNA methylation age and perceived age in elderly Danish twins. Mech Ageing Dev. 2018;169:40–4.
https://doi.org/10.1016/j.mad.2017.09.004 DOI: https://doi.org/10.1016/j.mad.2017.09.004
Bode HF, Heikkinen A, Lundgren S, et al. Differences in DNA methylation-based age pre-diction within twin pairs discordant for cancer. Twin Res Hum Genet. 2022;25(4–5):171–9.
https://doi.org/10.1017/thg.2022.32 DOI: https://doi.org/10.1017/thg.2022.32
Kaprio J, Bollepalli S, Buchwald J, et al. The older Finnish twin cohort – 45 years of follow-up. Twin Res Hum Genet. 2019;22(4):240–54.
https://doi.org/10.1017/thg.2019.54 DOI: https://doi.org/10.1017/thg.2019.54
Min JL, Hemani G, Davey Smith G, et al. Meffil: efficient normalization and analysis of very large DNA methylation datasets. Bioinformatics. 2018;34(23):3983–9.
https://doi.org/10.1093/bioinformatics/bty476 DOI: https://doi.org/10.1093/bioinformatics/bty476
Heikkinen A, Esser VFC, Lundgren S, et al. Twin pair analysis uncovers novel links be-tween DNA methylation, mitochondrial DNA quantity and obesity. medRxiv. 2024.
https://doi.org/10.1101/2024.04.02.24304959 DOI: https://doi.org/10.1101/2024.04.02.24304959
R Core Team. R: a language and environment for statistical computing. Vienna: R Founda-tion for Statistical Computing; 2021.
Therneau T. Survival: a package for survival analysis in R. R package version 3.5-5 ed2023.
Holst KK, Scheike TH, Hjelmborg JB. The liability threshold model for censored twin data. Comput Stat Data Anal. 2016;93:324–35.
https://doi.org/10.1016/j.csda.2015.01.014 DOI: https://doi.org/10.1016/j.csda.2015.01.014
Scheike TH, Holst KK, Hjelmborg JB. Estimating heritability for cause specific mortality based on twin studies. Lifetime Data Anal. 2014;20(2):210–33.
https://doi.org/10.1007/s10985-013-9244-x DOI: https://doi.org/10.1007/s10985-013-9244-x
Kurki MI, Karjalainen J, Palta P, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;613(7944):508–18.
https://doi.org/10.1038/s41586-022-05473-8 DOI: https://doi.org/10.1038/s41586-022-05473-8
Pearl J, Glymour M, Jewell NP. Causal inference in statistics: a primer. Chichester: John Wiley & Sons Ltd; 2016.
Frisell T, Öberg S, Kuja-Halkola R, et al. Sibling comparison designs: bias from non-shared confounders and measurement error. Epidemiology. 2012;23(5):713–20.
https://doi.org/10.1097/EDE.0b013e31825fa230 DOI: https://doi.org/10.1097/EDE.0b013e31825fa230
Rasmussen SHR, Ludeke S, Hjelmborg JVB. A major limitation of the direction of causa-tion model: non-shared environmental confounding. Twin Res Hum Genet. 2019;22(1):14–26.
https://doi.org/10.1017/thg.2018.67 DOI: https://doi.org/10.1017/thg.2018.67
Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33(1):1–22.
https://doi.org/10.18637/jss.v033.i01 DOI: https://doi.org/10.18637/jss.v033.i01
Blanche P, Dartigues J-F, Jacqmin-Gadda H. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med. 2013;32(30):5381–97.
https://doi.org/10.1002/sim.5958 DOI: https://doi.org/10.1002/sim.5958
Liu J, Huang B, Ding F, Li Y. Environment factors, DNA methylation, and cancer. Environ Geochem Health. 2023;45(11):7543–68.
https://doi.org/10.1007/s10653-023-01749-8 DOI: https://doi.org/10.1007/s10653-023-01749-8
Timms JA, Relton CL, Rankin J, et al. DNA methylation as a potential mediator of environ-mental risks in the development of childhood acute lymphoblastic leukemia. Epigenomics. 2016;8(4):519–36.
https://doi.org/10.2217/epi-2015-0011 DOI: https://doi.org/10.2217/epi-2015-0011
Guillamot M, Cimmino L, Aifantis I. The impact of DNA methylation in hematopoietic ma-lignancies. Trends Cancer. 2016;2(2):70–83.
https://doi.org/10.1016/j.trecan.2015.12.006 DOI: https://doi.org/10.1016/j.trecan.2015.12.006
Jiang H, Ou Z, He Y, et al. DNA methylation markers in the diagnosis and prognosis of common leukemias. Signal Transduct Target Ther. 2020;5(1):3.
https://doi.org/10.1038/s41392-019-0090-5 DOI: https://doi.org/10.1038/s41392-019-0090-5
Sud A, Chattopadhyay S, Thomsen H, et al. Analysis of 153115 patients with hematologi-cal malignancies refines the spectrum of familial risk. Blood. 2019;134(12):960–9.
https://doi.org/10.1182/blood.2019001362 DOI: https://doi.org/10.1182/blood.2019001362
Wang J, Conti DV, Epeldegui M, et al. Lymphoma-associated biomarkers are increased in current smokers in twin pairs discordant for smoking. Cancers (Basel). 2021;13(21):5395.
https://doi.org/10.3390/cancers13215395 DOI: https://doi.org/10.3390/cancers13215395
Laurent C, Nicolae A, Laurent C, et al. Gene alterations in epigenetic modifiers and JAK-STAT signaling are frequent in breast implant-associated ALCL. Blood. 2020;135(5):360–70.
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Copyright (c) 2023 Signe B. Clemmensen, Henrik Frederiksen, Jonas Mengel-From, Aino Heikkinen, Jaakko Kaprio, Jacob vB Hjelmborg
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