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Epigenome-wide studies of DNA methylation using blood-derived DNA from cancer patients are complicated by the heterogeneity of cell types within blood and the associated cell lineage specification of DNA methylation signatures. Here, we applied a novel set of analytic approaches to assess the association between cancer case-status and DNA methylation adjusted for leukocyte variation using blood specimens from three case-control cancer studies (bladder: 223 cases, 205 controls; head and neck: 92 cases, 92 controls; and ovarian: 131 cases, 274 controls). Using previously published data on leukocyte-specific CpG loci and a recently described approach to deconvolute subject-specific blood composition, we performed an epigenome-wide analysis to examine the association between blood-based DNA methylation patterns and each of the three aforementioned solid tumor types adjusted for cellular heterogeneity in blood. After adjusting for leukocyte profile in our epigenome-wide analysis, the omnibus association between case-status and methylation was significant for all three studies (bladder cancer: P = 0.047; HNSCC: P = 0.013; ovarian cancer: P = 0.0002). Subsequent analyses revealed that CpG sites associated with cancer were enriched for transcription factor binding motifs involved with cancer-associated pathways. These results support the existence of cancer-associated DNA methylation profiles in the blood of solid tumor patients that are independent of alterations in normal leukocyte distributions. Adoption of the methods developed here will make it feasible to rigorously assess the influence of variability of normal leukocyte profiles when investigating cancer related changes in blood-based epigenome-wide association studies.

Immune factors are thought to influence glioma risk and outcomes, but immune profiling studies to further our understanding of the immune response are limited by current immunodiagnostic methods. We developed a new assay to capture glioma immune biology based on quantitative methylation specific PCR (qMSP) of two T-cell genes (CD3Z: T-cells, and FOXP3: Tregs). Flow cytometry of T-cells correlated well with the CD3Z demethylation assay (r = 0.93; p < 2.2 × 10 (-16) ), demonstrating the validity of the assay. Furthermore, there was a high correlation between qMSP and immunohistochemistry (IHC) in quantifying tumor infiltrating T-cells (r = 0.85; p = 3.4 × 10 (-11) ). Applying our qMSP methods to archival whole blood from 65 glioblastoma multiforme (GBM) cases and 94 non-diseased controls, GBM cases had highly statistically significantly lower T-cells (p = 1.7 × 10 (-9) ) as well as Tregs (p = 5.2 × 10 (-11) ) and a modestly lower ratio of Tregs/T-cells (p = 0.024). Applying the methods to 120 excised glioma tumors, we observed that tumor infiltrating CD3+ T-cells were positively correlated with glioma tumor grade (p = 5.7 × 10 (-7) ), and that Tregs were enriched in tumors compared with peripheral blood indicating active chemoattraction of suppressive Tregs into the tumor compartment. Poorer patient survival was correlated with higher levels of tumor infiltrating T-cells (p = 0.01) and Tregs (p = 0.04). DNA methylation based immunodiagnostics represent a new generation of powerful laboratory tools offering many advantages over conventional methods that will facilitate large clinical epidemiologic studies and capitalize on stored archival blood and tissue banks.

PurposeNatural killer (NK) cells are a key element of the innate immune system implicated in human cancer. To examine NK cell levels in archived bloods from a study of human head and neck squamous cell carcinoma (HNSCC), a new DNA-based quantification method was developed.Experimental designNK cell-specific DNA methylation was identified by analyzing DNA methylation and mRNA array data from purified blood leukocyte subtypes (NK, T, B, monocytes, granulocytes), and confirmed via pyrosequencing and quantitative methylation specific PCR (qMSP). NK cell levels in archived whole blood DNA from 122 HNSCC patients and 122 controls were assessed by qMSP.ResultsPyrosequencing and qMSP confirmed that a demethylated DNA region in NKp46 distinguishes NK cells from other leukocytes, and serves as a quantitative NK cell marker. Demethylation of NKp46 was significantly lower in HNSCC patient bloods compared with controls (P < 0.001). Individuals in the lowest NK tertile had over 5-fold risk of being a HNSCC case, controlling for age, gender, HPV16 status, cigarette smoking, alcohol consumption, and BMI (OR = 5.6, 95% CI, 2.0 to 17.4). Cases did not show differences in NKp46 demethylation based on tumor site or stage.ConclusionsThe results of this study indicate a significant depression in NK cells in HNSCC patients that is unrelated to exposures associated with the disease. DNA methylation biomarkers of NK cells represent an alternative to conventional flow cytometry that can be applied in a wide variety of clinical and epidemiologic settings including archival blood specimens.

BackgroundBlood 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.MethodsWe 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.ResultsA 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).ConclusionsThese results suggest that shifts in leukocyte subpopulations may account for a considerable proportion of variability in peripheral blood DNA methylation patterns of solid tumors.ImpactThis 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.

Epigenetic alterations are a common event in lung cancer and their identification can serve to inform on the carcinogenic process and provide clinically relevant biomarkers. Using paired tumor and non-tumor lung tissues from 146 individuals from three independent populations we sought to identify common changes in DNA methylation associated with the development of non-small cell lung cancer. Pathologically normal lung tissue taken at the time of cancer resection was matched to tumorous lung tissue and together were probed for methylation using Illumina GoldenGate arrays in the discovery set (n = 47 pairs) followed by bisulfite pyrosequencing for validation sets (n = 99 pairs). For each matched pair the change in methylation at each CpG was calculated (the odds ratio), and these ratios were averaged across individuals and ranked by magnitude to identify the CpGs with the greatest change in methylation associated with tumor development. We identified the top gene-loci representing an increase in methylation (HOXA9, 10.3-fold and SOX1, 5.9-fold) and decrease in methylation (DDR1, 8.1-fold). In replication testing sets, methylation was higher in tumors for HOXA9 (p < 2.2 × 10 (-16) ) and SOX1 (p < 2.2 × 10 (-16) ) and lower for DDR1 (p < 2.2 × 10 (-16) ). The magnitude and strength of these changes were consistent across squamous cell and adenocarcinoma tumors. Our data indicate that the identified genes consistently have altered methylation in lung tumors. Our identified genes should be included in translational studies that aim to develop screening for early disease detection.

Abstract Background There has been a long-standing need in biomedical research for a method that quantifies the normally mixed composition of leukocytes beyond what is possible by simple histological or flow cytometric assessments. The latter is restricted by the labile nature of protein epitopes, requirements for cell processing, and timely cell analysis. In a diverse array of diseases and following numerous immune-toxic exposures, leukocyte composition will critically inform the underlying immuno-biology to most chronic medical conditions. Emerging research demonstrates that DNA methylation is responsible for cellular differentiation, and when measured in whole peripheral blood, serves to distinguish cancer cases from controls. Results Here we present a method, similar to regression calibration, for inferring changes in the distribution of white blood cells between different subpopulations (e.g. cases and controls) using DNA methylation signatures, in combination with a previously obtained external validation set consisting of signatures from purified leukocyte samples. We validate the fundamental idea in a cell mixture reconstruction experiment, then demonstrate our method on DNA methylation data sets from several studies, including data from a Head and Neck Squamous Cell Carcinoma (HNSCC) study and an ovarian cancer study. Our method produces results consistent with prior biological findings, thereby validating the approach. Conclusions Our method, in combination with an appropriate external validation set, promises new opportunities for large-scale immunological studies of both disease states and noxious exposures.

Approximately 500,000 individuals diagnosed with bladder cancer in the U.S. require routine cystoscopic follow-up to monitor for disease recurrences or progression, resulting in over $2 billion in annual expenditures. Identification of new diagnostic and monitoring strategies are clearly needed, and markers related to DNA methylation alterations hold great promise due to their stability, objective measurement, and known associations with the disease and with its clinical features. To identify novel epigenetic markers of aggressive bladder cancer, we utilized a high-throughput DNA methylation bead-array in two distinct population-based series of incident bladder cancer (n = 73 and n = 264, respectively). We then validated the association between methylation of these candidate loci with tumor grade in a third population (n = 245) through bisulfite pyrosequencing of candidate loci. Array based analyses identified 5 loci for further confirmation with bisulfite pyrosequencing. We identified and confirmed that increased promoter methylation of HOXB2 is significantly and independently associated with invasive bladder cancer and methylation of HOXB2, KRT13 and FRZB together significantly predict high-grade non-invasive disease. Methylation of these genes may be useful as clinical markers of the disease and may point to genes and pathways worthy of additional examination as novel targets for therapeutic treatment.

Although tumor size and lymph node involvement are the current cornerstones of breast cancer prognosis, they have not been extensively explored in relation to tumor methylation attributes in conjunction with other tumor and patient dietary and hormonal characteristics. Using primary breast tumors from 162 (AJCC stage I-IV) women from the Kaiser Division of Research Pathways Study and the Illumina GoldenGate methylation bead-array platform, we measured 1,413 autosomal CpG loci associated with 773 cancer-related genes and validated select CpG loci with Sequenom EpiTYPER. Tumor grade, size, estrogen and progesterone receptor status, and triple negative status were significantly (Q-values

Epigenetic control of gene transcription is critical for normal human development and cellular differentiation. While alterations of epigenetic marks such as DNA methylation have been linked to cancers and many other human diseases, interindividual epigenetic variations in normal tissues due to aging, environmental factors, or innate susceptibility are poorly characterized. The plasticity, tissue-specific nature, and variability of gene expression are related to epigenomic states that vary across individuals. Thus, population-based investigations are needed to further our understanding of the fundamental dynamics of normal individual epigenomes. We analyzed 217 non-pathologic human tissues from 10 anatomic sites at 1,413 autosomal CpG loci associated with 773 genes to investigate tissue-specific differences in DNA methylation and to discern how aging and exposures contribute to normal variation in methylation. Methylation profile classes derived from unsupervised modeling were significantly associated with age (P

BackgroundEpigenetics is the study of heritable changes in gene function that cannot be explained by changes in DNA sequence. One of the most commonly studied epigenetic alterations is cytosine methylation, which is a well recognized mechanism of epigenetic gene silencing and often occurs at tumor suppressor gene loci in human cancer. Arrays are now being used to study DNA methylation at a large number of loci; for example, the Illumina GoldenGate platform assesses DNA methylation at 1505 loci associated with over 800 cancer-related genes. Model-based cluster analysis is often used to identify DNA methylation subgroups in data, but it is unclear how to cluster DNA methylation data from arrays in a scalable and reliable manner.ResultsWe propose a novel model-based recursive-partitioning algorithm to navigate clusters in a beta mixture model. We present simulations that show that the method is more reliable than competing nonparametric clustering approaches, and is at least as reliable as conventional mixture model methods. We also show that our proposed method is more computationally efficient than conventional mixture model approaches. We demonstrate our method on the normal tissue samples and show that the clusters are associated with tissue type as well as age.ConclusionOur proposed recursively-partitioned mixture model is an effective and computationally efficient method for clustering DNA methylation data.