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.
"It has been proposed that such changes can be utilised to identify patients with tumours, although it is not clear whether these differences in DNA methylation profiles between cancer and healthy individuals may simply be due to underlying inflammation in cancer patients, rather than the cancer itself. Koestler and colleagues  identified 50 differentially methylated regions associated with different leukocyte subtypes and reported that these could be used to discriminate between healthy controls and patients with head and neck squamous cell carcinomas and ovarian cancer. However, such changes were not unique to the cancer type, with both eliciting a similar change in leukocyte populations. "
[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 · 2.30 Impact Factor
"A change commonly observed in peripheral leukocytes of cancer patients is an increase in the number of myeloid cells and a decrease in the number of lymphoid cells (Kuss et al, 2004; Cho et al, 2009; Accomando et al, 2012; Houseman et al, 2012). As different leukocyte subtypes have different DNAm patterns, shifts in leukocyte subpopulations can lead to DNAm alterations in the peripheral whole blood of cancer patients (Koestler et al, 2012; Reinius et al, 2012). However, how and to what extent the population shifts of leukocyte cells could contribute to the DNAm alterations in the peripheral whole blood/leukocytes of cancer patients are still unclear. "
[Show abstract][Hide abstract] ABSTRACT: Background:
Although many DNA methylation (DNAm) alterations observed in peripheral whole blood/leukocytes and serum have been considered as potential diagnostic markers for cancer, their origin and their specificity for cancer (e.g., vs inflammatory diseases) remain unclear.
From publicly available datasets, we identified changes in the methylation of blood-borne DNA for multiple cancers and inflammatory diseases. We compared the identified changes with DNAm difference between myeloid and lymphoid cells extracted from two datasets.
At least 94.7% of the differentially methylated DNA loci (DM loci) observed in peripheral whole blood/leukocytes and serum of cancer patients overlapped with DM loci that distinguish between myeloid and lymphoid cells and >99.9% of the overlapped DM loci had consistent alteration states (hyper- or hypomethylation) in cancer samples compared to normal controls with those in myeloid cells compared to lymphoid cells (binomial test, P-value <2.2 × 10−16). Similar results were observed for DM loci in peripheral whole blood/leukocytes in patients with rheumatoid arthritis or inflammatory bowel diseases. The direct comparison between DM loci observed in the peripheral whole blood/leukocytes of patients with inflammatory diseases and DM loci observed in the peripheral whole blood of patients with cancer showed that DM loci detected from cancer and inflammatory diseases also had significantly consistent alteration states (binomial test, P-value <2.2 × 10−16).
DNAm changes observed in the peripheral whole blood/leukocytes and serum of cancer patients and in the peripheral whole blood/leukocytes of inflammatory disease patients are predominantly determined by the increase of myeloid cells and the decrease of lymphoid cells under the disease conditions, in the sense that their alteration states in disease samples compared to normal controls mainly reflect the DNAm difference between myeloid and lymphoid cells. These analyses highlight the importance of comparing cancer and inflammatory disease directly for the identification of cancer-specific diagnostic biomarkers.
British Journal of Cancer 06/2014; 111(3). DOI:10.1038/bjc.2014.347 · 4.84 Impact Factor
"In the absence of reference data, major components in genome-wide DNA methylation patterns from PBL can be estimated and used as surrogates of cell proportions (46,47), even to the suggested extent that it may be helpful in non-hematopoietic cancers (48). "
[Show abstract][Hide abstract] ABSTRACT: Platform technologies for measurement of CpG methylation at multiple loci across the genome have made ambitious epigenome-wide
association studies affordable and practicable. In contrast to genetic studies, which estimate the effects of structural changes
in DNA, and transcriptomic studies, which measure genomic outputs, epigenetic studies can access states of regulation of genome
function in particular cells and in response to specific stimuli. Although many factors complicate the interpretation of epigenetic
variation in human disease, cell-specific methylation patterns and the cellular heterogeneity present in peripheral blood
and tissue biopsies are anticipated to cause the most problems. In this review, we suggest that the difficulties may be exaggerated
and we explore how cellular heterogeneity may be embraced with appropriate study designs and analytical tools. We further
suggest that systematic mapping of the loci influenced by age, sex and genetic polymorphisms will bring important biological
insights as well as improved control of epigenome-wide association studies.
Human Molecular Genetics 06/2014; 23(R1). DOI:10.1093/hmg/ddu284 · 6.39 Impact Factor
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