Peripheral Blood Immune Cell Methylation Profiles Are Associated with Nonhematopoietic Cancers
ABSTRACT Blood leukocytes from patients with solid tumors exhibit complex and distinct cancer-associated patterns of DNA methylation. However, the biologic mechanisms underlying these patterns remain poorly understood. Because epigenetic biomarkers offer significant clinical potential for cancer detection, we sought to address a mechanistic gap in recently published works, hypothesizing that blood-based epigenetic variation may be due to shifts in leukocyte populations.
We identified differentially methylated regions (DMR) among leukocyte subtypes using epigenome-wide DNA methylation profiling of purified peripheral blood leukocyte subtypes from healthy donors. These leukocyte-tagging DMRs were then evaluated using epigenome-wide blood methylation data from three independent case-control studies of different cancers.
A substantial proportion of the top 50 leukocyte DMRs were significantly differentially methylated among head and neck squamous cell carcinoma (HNSCC) cases and ovarian cancer cases compared with cancer-free controls (48 and 47 of 50, respectively). Methylation classes derived from leukocyte DMRs were significantly associated cancer case status (P < 0.001, P < 0.03, and P < 0.001) for all three cancer types: HNSCC, bladder cancer, and ovarian cancer, respectively and predicted cancer status with a high degree of accuracy (area under the curve [AUC] = 0.82, 0.83, and 0.67).
These results suggest that shifts in leukocyte subpopulations may account for a considerable proportion of variability in peripheral blood DNA methylation patterns of solid tumors.
This illustrates the potential use of DNA methylation profiles for identifying shifts in leukocyte populations representative of disease, and that such profiles may represent powerful new diagnostic tools, applicable to a range of solid tumors.
- SourceAvailable from: Timothy Barrow
Article: Epigenetic Epidemiology of Cancer.[Show abstract] [Hide abstract]
ABSTRACT: Epigenetic epidemiology includes the study of variation in epigenetic traits and the risk of disease in populations. Its application to the field of cancer has provided insight into how lifestyle and environmental factors influence the epigenome and how epigenetic events may be involved in carcinogenesis. Furthermore, it has the potential to bring benefit to patients through the identification of diagnostic markers that enable the early detection of disease and prognostic markers that can inform upon appropriate treatment strategies. However, there are a number of challenges associated with the conduct of such studies, and with the identification of biomarkers that can be applied to the clinical setting. In this review, we delineate the challenges faced in the design of epigenetic epidemiology studies in cancer, including the suitability of blood as a surrogate tissue and the capture of genome-wide DNA methylation. We describe how epigenetic epidemiology has brought insight into risk factors associated with lung, breast, colorectal and bladder cancer and review relevant research. We discuss recent findings on the identification of epigenetic diagnostic and prognostic biomarkers for these cancers.Biochemical and Biophysical Research Communications 08/2014; 455(1-2). DOI:10.1016/j.bbrc.2014.08.002 · 2.28 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Background: Previous genome-wide association studies (GWAS) have identified a large number of genetic variants for obesity and its related traits, representing a group of potential key genes in the etiology of obesity. Emerging evidence suggests that epigenetics may play an important role in obesity. It has not been explored whether the GWAS-identified loci contribute to obesity through epigenetics (e.g., DNA (deoxyribonucleic acid) methylation) in addition to genetics. Method: A multi-stage cross-sectional study was designed. We did a literature search and identified 117 genes discovered by GWAS for obesity and its related traits. Then we analyzed whether the methylation levels of these genes were also associated with obesity in two genome-wide methylation panels. We examined an initial panel of seven adolescent obese cases and seven age-matched lean controls, followed by a second panel of 48 adolescent obese cases and 48 age- and gender-matched lean controls. The validated CpG sites were further replicated in two independent replication panels of youth (46 vs. 46 and 230 cases vs. 413 controls, respectively) and a general population of youth, including 703 healthy subjects. Results: One CpG site in the lymphocyte antigen 86 (LY86) gene, which showed higher methylation in the obese in both the initial (p = .009) and second genome-wide DNA methylation panel (p = .008), was further validated in both replication panels (meta p = .00016). Moreover, in the general population of youth, the methylation levels of this region were significantly correlated with adiposity indices (p ≤ .02), insulin resistance (p = .001), and inflammatory markers (p < .001). Conclusion: By focusing on recent GWAS findings in genome-wide methylation profiles, we identified a solid association between LY86 gene DNA methylation and obesity.Twin Research and Human Genetics 04/2014; 17(3):1-9. DOI:10.1017/thg.2014.22 · 1.92 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Epigenetic studies are commonly conducted on DNA from tissue samples. However, tissues are ensembles of cells that may each have their own epigenetic profile, and therefore inter-individual cellular heterogeneity may compromise these studies. Here, we explore the potential for such confounding on DNA methylation measurement outcomes when using DNA from whole blood. DNA methylation was measured using pyrosequencing-based methodology in whole blood (n = 50-179) and in two white blood cell fractions (n = 20), isolated using density gradient centrifugation, in four CGIs (CpG Islands) located in genes HHEX (10 CpG sites assayed), KCNJ11 (8 CpGs), KCNQ1 (4 CpGs) and PM20D1 (7 CpGs). Cellular heterogeneity (variation in proportional white blood cell counts of neutrophils, lymphocytes, monocytes, eosinophils and basophils, counted by an automated cell counter) explained up to 40% (p<0.0001) of the inter-individual variation in whole blood DNA methylation levels in the HHEX CGI, but not a significant proportion of the variation in the other three CGIs tested. DNA methylation levels in the two cell fractions, polymorphonuclear and mononuclear cells, differed significantly in the HHEX CGI; specifically the average absolute difference ranged between 3.4-15.7 percentage points per CpG site. In the other three CGIs tested, methylation levels in the two fractions did not differ significantly, and/or the difference was more moderate. In the examined CGIs, methylation levels were highly correlated between cell fractions. In summary, our analysis detects region-specific differential DNA methylation between white blood cell subtypes, which can confound the outcome of whole blood DNA methylation measurements. Finally, by demonstrating the high correlation between methylation levels in cell fractions, our results suggest a possibility to use a proportional number of a single white blood cell type to correct for this confounding effect in analyses.PLoS ONE 10/2012; 7(10):e46705. DOI:10.1371/journal.pone.0046705 · 3.53 Impact Factor