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Background: Bladder cancer and therapy responses hinge on immune profiles in the tumor microenvironment (TME) and blood, yet studies linking tumor-infiltrating immune cells to peripheral immune profiles are limited. Methods: DNA methylation cytometry quantified TME and matched peripheral blood immune cell proportions. With tumor immune profile data as the input, subjects were grouped by immune infiltration status and consensus clustering. Results: Immune hot and cold groups had different immune compositions in the TME but not in circulating blood. Two clusters of patients identified with consensus clustering had different immune compositions not only in the TME but also in blood. Conclusion: Detailed immune profiling via methylation cytometry reveals the significance of understanding tumor and systemic immune relationships in cancer patients.

BackgroundImmune profiles have been associated with bladder cancer outcomes and may have clinical applications for prognosis. However, associations of detailed immune cell subtypes with patient outcomes remain underexplored and may contribute crucial prognostic information for better managing bladder cancer recurrence and survival.MethodsBladder cancer case peripheral blood DNA methylation was measured using the Illumina HumanMethylationEPIC array. Extended cell-type deconvolution quantified 12 immune cell-type proportions, including memory, naïve T and B cells, and granulocyte subtypes. DNA methylation clocks determined biological age. Cox proportional hazards models tested associations of immune cell profiles and age acceleration with bladder cancer outcomes. The partDSA algorithm discriminated 10-year overall survival groups from clinical variables and immune cell profiles, and a semi-supervised recursively partitioned mixture model (SS-RPMM) with DNA methylation data was applied to identify a classifier for 10-year overall survival.ResultsHigher CD8T memory cell proportions were associated with better overall survival [HR = 0.95, 95% confidence interval (CI) = 0.93-0.98], while higher neutrophil-to-lymphocyte ratio (HR = 1.36, 95% CI = 1.23-1.50), CD8T naïve (HR = 1.21, 95% CI = 1.04-1.41), neutrophil (HR = 1.04, 95% CI = 1.03-1.06) proportions, and age acceleration (HR = 1.06, 95% CI = 1.03-1.08) were associated with worse overall survival in patient with bladder cancer. partDSA and SS-RPMM classified five groups of subjects with significant differences in overall survival.ConclusionsWe identified associations between immune cell subtypes and age acceleration with bladder cancer outcomes.ImpactThe findings of this study suggest that bladder cancer outcomes are associated with specific methylation-derived immune cell-type proportions and age acceleration, and these factors could be potential prognostic biomarkers.

DNA methylation (DNAm)-based cell mixture deconvolution (CMD) has become a quintessential part of epigenome-wide association studies where DNAm is profiled in heterogeneous tissue types. Despite being introduced over a decade ago, detection limits, which represent the smallest fraction of a cell type in a mixed biospecimen that can be reliably detected, have yet to be determined in the context of DNAm-based CMD. Moreover, there has been little attention given to approaches for quantifying the uncertainty associated with DNAm-based CMD. Here, analytical frameworks for determining both cell-specific limits of detection and quantification of uncertainty associated with DNAm-based CMD are described. This work may contribute to improved rigor, reproducibility and replicability of epigenome-wide association studies involving CMD.

BackgroundOver the past decade, DNA methylation (DNAm)-based deconvolution methods that leverage cell-specific DNAm markers of immune cell types have been developed to provide accurate estimates of the proportions of leukocytes in peripheral blood. Immune cell phenotyping using DNAm markers, termed immunomethylomics or methylation cytometry, offers a solution for determining the body's immune cell landscape that does not require fresh blood and is scalable to large sample sizes. Despite significant advances in DNAm-based deconvolution, references at the population level are needed for clinical and research interpretation of these additional immune layers. Here we aim to provide some references for immune populations in a group of multi-ethnic post-menopausal American women.ResultsWe applied DNAm-based deconvolution to a large sample of post-menopausal women enrolled in the Women's Health Initiative (baseline, N = 58) or the ancillary Long Life Study (WHI-LLS, N = 1237) to determine the reference ranges of 58 immune parameters, including proportions and absolute counts for 19 leukocyte subsets and 20 derived cell ratios. Participants were 50-94 years old at the time of blood draw, and N = 898 (69.3%) self-identified as White. Using linear regression models, we observed significant associations between age at blood draw and absolute counts and proportions of naïve B, memory CD4+, naïve CD4+, naïve CD8+, memory CD8+ memory, neutrophils, and natural killer cells. We also assessed the same immune profiles in a subset of paired longitudinal samples collected 14-18 years apart across N = 52 participants. Our results demonstrate high inter-individual variability in rates of change of leukocyte subsets over this time. And, when conducting paired t tests to test the difference in counts and proportions between the baseline visit and LLS visit, there were significant changes in naïve B, memory CD4+, naïve CD4+, naïve CD8+, memory CD8+ cells and neutrophils, similar to the results seen when analyzing the association with age in the entire cohort.ConclusionsHere, we show that derived cell counts largely reflect the immune profile associated with proportions and that these novel methods replicate the known immune profiles associated with age. Further, we demonstrate the value this methylation cytometry approach can add as a potential application in epidemiological studies.

IntroductionThe human brain comprises heterogeneous cell types whose composition can be altered with physiological and pathological conditions. New approaches to discern the diversity and distribution of brain cells associated with neurological conditions would significantly advance the study of brain-related pathophysiology and neuroscience. Unlike single-nuclei approaches, DNA methylation-based deconvolution does not require special sample handling or processing, is cost-effective, and easily scales to large study designs. Existing DNA methylation-based methods for brain cell deconvolution are limited in the number of cell types deconvolved.MethodsUsing DNA methylation profiles of the top cell-type-specific differentially methylated CpGs, we employed a hierarchical modeling approach to deconvolve GABAergic neurons, glutamatergic neurons, astrocytes, microglial cells, oligodendrocytes, endothelial cells, and stromal cells.ResultsWe demonstrate the utility of our method by applying it to data on normal tissues from various brain regions and in aging and diseased tissues, including Alzheimer's disease, autism, Huntington's disease, epilepsy, and schizophrenia.DiscussionWe expect that the ability to determine the cellular composition in the brain using only DNA from bulk samples will accelerate understanding brain cell type composition and cell-type-specific epigenetic states in normal and diseased brain tissues.

BackgroundIdentifying blood-based DNA methylation patterns is a minimally invasive way to detect biomarkers in predicting age, characteristics of certain diseases and conditions, as well as responses to immunotherapies. As microarray platforms continue to evolve and increase the scope of CpGs measured, new discoveries based on the most recent platform version and how they compare to available data from the previous versions of the platform are unknown. The neutrophil dexamethasone methylation index (NDMI 850) is a blood-based DNA methylation biomarker built on the Illumina MethylationEPIC (850K) array that measures epigenetic responses to dexamethasone (DEX), a synthetic glucocorticoid often administered for inflammation. Here, we compare the NDMI 850 to one we built using data from the Illumina Methylation 450K (NDMI 450).ResultsThe NDMI 450 consisted of 22 loci, 15 of which were present on the NDMI 850. In adult whole blood samples, the linear composite scores from NDMI 450 and NDMI 850 were highly correlated and had equivalent predictive accuracy for detecting DEX exposure among adult glioma patients and non-glioma adult controls. However, the NDMI 450 scores of newborn cord blood were significantly lower than NDMI 850 in samples measured with both assays.ConclusionsWe developed an algorithm that reproduces the DNA methylation glucocorticoid response score using 450K data, increasing the accessibility for researchers to assess this biomarker in archived or publicly available datasets that use the 450K version of the Illumina BeadChip array. However, the NDMI850 and NDMI450 do not give similar results in cord blood, and due to data availability limitations, results from sample types of newborn cord blood should be interpreted with care.

BackgroundThere is considerable evidence that epigenetic mechanisms and DNA methylation are critical drivers of immune cell lineage differentiation and activation. However, there has been limited coordinated investigation of common epigenetic pathways among cell lineages. Further, it remains unclear if long-lived memory cell subtypes differentiate distinctly by cell lineages.ResultsWe used the Illumina EPIC array to investigate the consistency of DNA methylation in B cell, CD4 T, and CD8 T naïve and memory cells states. In the process of naïve to memory activation across the three lineages, we identify considerable shared epigenetic regulation at the DNA level for immune memory generation. Further, in central to effector memory differentiation, our analyses revealed specific CpG dinucleotides and genes in CD4 T and CD8 T cells with DNA methylation changes. Finally, we identified unique DNA methylation patterns in terminally differentiated effector memory (TEMRA) CD8 T cells compared to other CD8 T memory cell subtypes.ConclusionsOur data suggest that epigenetic alterations are widespread and essential in generating human lymphocyte memory. Unique profiles are involved in methylation changes that accompany memory genesis in the three subtypes of lymphocytes.

BackgroundNon-muscle-invasive bladder cancer (NMIBC) patients receive frequent monitoring because ≥ 70% will have recurrent disease. However, screening is invasive, expensive, and associated with significant morbidity making bladder cancer the most expensive cancer to treat per capita. There is an urgent need to expand the understanding of markers related to recurrence and survival outcomes of NMIBC.Methods and resultsWe used the Illumina HumanMethylationEPIC array to measure peripheral blood DNA methylation profiles of NMIBC patients (N = 603) enrolled in a population-based cohort study in New Hampshire and applied cell type deconvolution to estimate immune cell-type proportions. Using Cox proportional hazard models, we identified that increasing CD4T and CD8T cell proportions were associated with a statistically significant decreased hazard of tumor recurrence or death (CD4T: HR = 0.98, 95% CI = 0.97-1.00; CD8T: HR = 0.97, 95% CI = 0.95-1.00), whereas increasing monocyte proportion and methylation-derived neutrophil-to-lymphocyte ratio (mdNLR) were associated with the increased hazard of tumor recurrence or death (monocyte: HR = 1.04, 95% CI = 1.00-1.07; mdNLR: HR = 1.12, 95% CI = 1.04-1.20). Then, using an epigenome-wide association study (EWAS) approach adjusting for age, sex, smoking status, BCG treatment status, and immune cell profiles, we identified 2528 CpGs associated with the hazard of tumor recurrence or death (P

BackgroundMyeloid-derived suppressor cells (MDSCs), which include monocytic (mMDSCs) and granulocytic (gMDSCs) cells, are an immunosuppressive, heterogeneous population of cells upregulated in cancer and other pathologic conditions, in addition to normal conditions of stress. The origin of MDSCs is debated, and the regulatory pattern responsible for gMDSC differentiation remains unknown. Since DNA methylation (DNAm) contributes to lineage differentiation, we have investigated whether it contributes to the acquisition of the gMDSC phenotype.ResultsUsing the Illumina EPIC array to measure DNAm of gMDSCs and neutrophils from diverse neonatal and adult blood sources, we found 189 differentially methylated CpGs between gMDSCs and neutrophils with a core of ten differentially methylated CpGs that were consistent across both sources of cells. Genes associated with these loci that are involved in immune responses include VCL, FATS, YAP1, KREMEN2, UBTF, MCC-1, and EFCC1. In two cancer patient groups that reflected those used to develop the methylation markers (head and neck squamous cell carcinoma (HNSCC) and glioma), all of the CpG loci were differentially methylated, reaching statistical significance in glioma cases and controls, while one was significantly different in the smaller HNSCC group.ConclusionsOur findings indicate that gMDSCs have a core of distinct DNAm alterations, informing future research on gMDSC differentiation and function.

BackgroundTumor-based classification of human glioma portends patient prognosis, but considerable unexplained survival variability remains. Host factors (eg, age) also strongly influence survival times, partly reflecting a compromised immune system. How blood epigenetic measures of immune characteristics and age augment molecular classifications in glioma survival has not been investigated. We assess the prognostic impact of immune cell fractions and epigenetic age in archived blood across glioma molecular subtypes for the first time.MethodsWe evaluated immune cell fractions and epigenetic age in archived blood from the University of California San Francisco Adult Glioma Study, which included a training set of 197 patients with IDH-wild type, 1p19q intact, TERT wild type (IDH/1p19q/TERT-WT) glioma, an evaluation set of 350 patients with other subtypes of glioma, and 454 patients without glioma.ResultsIDH/1p19q/TERT-WT patients had lower lymphocyte fractions (CD4+ T, CD8+ T, natural killer, and B cells) and higher neutrophil fractions than people without glioma. Recursive partitioning analysis delineated 4 statistically significantly different survival groups for patients with IDH/1p19q/TERT-WT based on an interaction between chronological age and 2 blood immune factors, CD4+ T cells, and neutrophils. Median overall survival ranged from 0.76 years (95% confidence interval = 0.55-0.99) for the worst survival group (n = 28) to 9.72 years (95% confidence interval = 6.18 to not available) for the best (n = 33). The recursive partitioning analysis also statistically significantly delineated 4 risk groups in patients with other glioma subtypes.ConclusionsThe delineation of different survival groups in the training and evaluation sets based on an interaction between chronological age and blood immune characteristics suggests that common host immune factors among different glioma types may affect survival. The ability of DNA methylation-based markers of immune status to capture diverse, clinically relevant information may facilitate noninvasive, personalized patient evaluation in the neuro-oncology clinic.

The mammalian kidney has extensive repair capacity; however, identifying adult renal stem cells has proven elusive. We applied an epigenetic marker of foetal cell origin (FCO) in diverse human tissues as a probe for developmental cell persistence, finding a 5.4-fold greater FCO proportion in kidney. Normal kidney FCO proportions averaged 49% with extensive interindividual variation. FCO proportions were significantly negatively correlated with immune-related gene expression and positively correlated with genes expressed in the renal medulla, including those involved in renal organogenesis (e.g., FGF2, PAX8, and HOXB7). FCO associated genes also mapped to medullary nephron segments in mouse and rat, suggesting evolutionary conservation of this cellular compartment. Renal cancer patients whose tumours contained non-zero FCO scores survived longer. The kidney appears unique in possessing substantial foetal epigenetic features. Further study of FCO-related gene methylation may elucidate regenerative regulatory programmes in tissues without apparent discrete stem cell compartments.

BackgroundDNA methylation alterations may underlie associations between gestational perfluoroalkyl substances (PFAS) exposure and later-life health outcomes. To the best of our knowledge, no longitudinal studies have examined the associations between gestational PFAS and DNA methylation.ObjectivesWe examined associations of gestational PFAS exposure with longitudinal DNA methylation measures at birth and in adolescence using the Health Outcomes and Measures of the Environment (HOME) Study (2003-2006; Cincinnati, Ohio).MethodsWe quantified serum concentrations of perfluorooctanoate (PFOA), perfluorooctane sulfonate (PFOS), perfluorononanoate (PFNA), and perfluorohexane sulfonate (PFHxS) in mothers during pregnancy. We measured DNA methylation in cord blood (n=266) and peripheral leukocytes at 12 years of age (n=160) using the Illumina HumanMethylation EPIC BeadChip. We analyzed associations between log2-transformed PFAS concentrations and repeated DNA methylation measures using linear regression with generalized estimating equations. We included interaction terms between children's age and gestational PFAS. We performed Gene Ontology enrichment analysis to identify molecular pathways. We used Project Viva (1999-2002; Boston, Massachusetts) to replicate significant associations.ResultsAfter adjusting for covariates, 435 cytosine-guanine dinucleotide (CpG) sites were associated with PFAS (false discovery rate, q

BackgroundCellular compositions of solid tumor microenvironments are heterogeneous, varying across patients and tumor types. High-resolution profiling of the tumor microenvironment cell composition is crucial to understanding its biological and clinical implications. Previously, tumor microenvironment gene expression and DNA methylation-based deconvolution approaches have been shown to deconvolve major cell types. However, existing methods lack accuracy and specificity to tumor type and include limited identification of individual cell types.ResultsWe employed a novel tumor-type-specific hierarchical model using DNA methylation data to deconvolve the tumor microenvironment with high resolution, accuracy, and specificity. The deconvolution algorithm is named HiTIMED. Seventeen cell types from three major tumor microenvironment components can be profiled (tumor, immune, angiogenic) by HiTIMED, and it provides tumor-type-specific models for twenty carcinoma types. We demonstrate the prognostic significance of cell types that other tumor microenvironment deconvolution methods do not capture.ConclusionWe developed HiTIMED, a DNA methylation-based algorithm, to estimate cell proportions in the tumor microenvironment with high resolution and accuracy. HiTIMED deconvolution is amenable to archival biospecimens providing high-resolution profiles enabling to study of clinical and biological implications of variation and composition of the tumor microenvironment.

DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, naïve and memory B cells, naïve and memory CD4 + and CD8 + T cells, natural killer, and T regulatory cells). Including derived variables, our method provides 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data for current and previous platforms. The accuracy of deconvolution estimates obtained using our enhanced libraries was validated using artificial mixtures and whole-blood DNA methylation with known cellular composition from flow cytometry. We applied our libraries to deconvolve cancer, aging, and autoimmune disease datasets. In conclusion, these libraries enable a detailed representation of immune-cell profiles in blood using only DNA and facilitate a standardized, thorough investigation of immune profiles in human health and disease.

Assessing individual responses to glucocorticoid drug therapies that compromise immune status and affect survival outcomes in neuro-oncology is a great challenge. Here we introduce a blood-based neutrophil dexamethasone methylation index (NDMI) that provides a measure of the epigenetic response of subjects to dexamethasone. This marker outperforms conventional approaches based on leukocyte composition as a marker of glucocorticoid response. The NDMI is associated with low CD4 T cells and the accumulation of monocytic myeloid-derived suppressor cells and also serves as prognostic factor in glioma survival. In a non-glioma population, the NDMI increases with a history of prednisone use. Therefore, it may also be informative in other conditions where glucocorticoids are employed. We conclude that DNA methylation remodeling within the peripheral immune compartment is a rich source of clinically relevant markers of glucocorticoid response.

BackgroundUmbilical cord blood (UCB) is commonly used in epigenome-wide association studies of prenatal exposures. Accounting for cell type composition is critical in such studies as it reduces confounding due to the cell specificity of DNA methylation (DNAm). In the absence of cell sorting information, statistical methods can be applied to deconvolve heterogeneous cell mixtures. Among these methods, reference-based approaches leverage age-appropriate cell-specific DNAm profiles to estimate cellular composition. In UCB, four reference datasets comprising DNAm signatures profiled in purified cell populations have been published using the Illumina 450 K and EPIC arrays. These datasets are biologically and technically different, and currently, there is no consensus on how to best apply them. Here, we systematically evaluate and compare these datasets and provide recommendations for reference-based UCB deconvolution.ResultsWe first evaluated the four reference datasets to ascertain both the purity of the samples and the potential cell cross-contamination. We filtered samples and combined datasets to obtain a joint UCB reference. We selected deconvolution libraries using two different approaches: automatic selection using the top differentially methylated probes from the function pickCompProbes in minfi and a standardized library selected using the IDOL (Identifying Optimal Libraries) iterative algorithm. We compared the performance of each reference separately and in combination, using the two approaches for reference library selection, and validated the results in an independent cohort (Generation R Study, n = 191) with matched Fluorescence-Activated Cell Sorting measured cell counts. Strict filtering and combination of the references significantly improved the accuracy and efficiency of cell type estimates. Ultimately, the IDOL library outperformed the library from the automatic selection method implemented in pickCompProbes.ConclusionThese results have important implications for epigenetic studies in UCB as implementing this method will optimally reduce confounding due to cellular heterogeneity. This work provides guidelines for future reference-based UCB deconvolution and establishes a framework for combining reference datasets in other tissues.

Epigenetic clocks comprise a set of CpG sites whose DNA methylation levels measure subject age. These clocks are acknowledged as a highly accurate molecular correlate of chronological age in humans and other vertebrates. Also, extensive research is aimed at their potential to quantify biological aging rates and test longevity or rejuvenating interventions. Here, we discuss key challenges to understand clock mechanisms and biomarker utility. This requires dissecting the drivers and regulators of age-related changes in single-cell, tissue- and disease-specific models, as well as exploring other epigenomic marks, longitudinal and diverse population studies, and non-human models. We also highlight important ethical issues in forensic age determination and predicting the trajectory of biological aging in an individual.

BackgroundDifferentiated cells that arise from stem cells in early development contain DNA methylation features that provide a memory trace of their fetal cell origin (FCO). The FCO signature was developed to estimate the proportion of cells in a mixture of cell types that are of fetal origin and are reminiscent of embryonic stem cell lineage. Here we implemented the FCO signature estimation method to compare the fraction of cells with the FCO signature in tumor tissues and their corresponding nontumor normal tissues.MethodsWe applied our FCO algorithm to discovery data sets obtained from The Cancer Genome Atlas (TCGA) and replication data sets obtained from the Gene Expression Omnibus (GEO) data repository. Wilcoxon rank sum tests, linear regression models with adjustments for potential confounders and non-parametric randomization-based tests were used to test the association of FCO proportion between tumor tissues and nontumor normal tissues. P-values of

Genome-wide methylation arrays are powerful tools for assessing cell composition of complex mixtures. We compare three approaches to select reference libraries for deconvoluting neutrophil, monocyte, B-lymphocyte, natural killer, and CD4+ and CD8+ T-cell fractions based on blood-derived DNA methylation signatures assayed using the Illumina HumanMethylationEPIC array. The IDOL algorithm identifies a library of 450 CpGs, resulting in an average R2 = 99.2 across cell types when applied to EPIC methylation data collected on artificial mixtures constructed from the above cell types. Of the 450 CpGs, 69% are unique to EPIC. This library has the potential to reduce unintended technical differences across array platforms.

Stem cell maturation is a fundamental, yet poorly understood aspect of human development. We devised a DNA methylation signature deeply reminiscent of embryonic stem cells (a fetal cell origin signature, FCO) to interrogate the evolving character of multiple human tissues. The cell fraction displaying this FCO signature was highly dependent upon developmental stage (fetal versus adult), and in leukocytes, it described a dynamic transition during the first 5 yr of life. Significant individual variation in the FCO signature of leukocytes was evident at birth, in childhood, and throughout adult life. The genes characterizing the signature included transcription factors and proteins intimately involved in embryonic development. We defined and applied a DNA methylation signature common among human fetal hematopoietic progenitor cells and have shown that this signature traces the lineage of cells and informs the study of stem cell heterogeneity in humans under homeostatic conditions.

There is emerging evidence that the immune biology associated with lung and other solid tumors, as well as patient immune genetic traits, contributes to individual survival. At this time, dramatic advances in immunologic approaches to the study and management of human cancers are taking place, including lung and head and neck squamous cell carcinoma. However, major obstacles for therapies are the profound immune alterations in blood and in the tumor microenvironment that arise in tandem with the cancer. Although there is a significant current effort underway across the cancer research community to probe the tumor environment to uncover the dynamics of the immune response, little similar work is being done to understand the dynamics of immune alterations in peripheral blood, despite evidence showing the prognostic relevance of the neutrophil/lymphocyte ratio for these cancers. A prominent feature of cancer-associated inflammation is the generation of myeloid-derived suppressor cells, which arise centrally in bone marrow myelopoiesis and peripherally in response to tumor factors. Two classes of myeloid-derived suppressor cells are recognized: granulocytic and monocytic. To date, such immune factors have not been integrated into molecular classification or prognostication. Here, we advocate for a more complete characterization of patient immune profiles, using DNA from archival peripheral blood after application of methylation profiling (immunomethylomics). At the heart of this technology are cell libraries of differentially methylated regions that provide the "fingerprints" of immune cell subtypes. Going forward, opportunities exist to explore aberrant immune profiles in the context of cancer-associated inflammation, potentially adding significantly to prognostic and mechanistic information for solid tumors.

Death from chronic lung disease is increasing and chronic obstructive pulmonary disease has become the third leading cause of death in the United States in the past decade. Both chronic and acute lung diseases disproportionately affect elderly individuals, making it likely that these diseases will become more frequent and severe as the worldwide population ages. Chronic lung diseases are associated with substantial morbidity, frequently resulting in exercise limiting dyspnea, immobilization, and isolation. Therefore, effective strategies to prevent or treat lung disease are likely to increase healthspan as well as life span. This review summarizes the findings of a joint workshop sponsored by the NIA and NHLBI that brought together investigators focused on aging and lung biology. These investigators encouraged the use of genetic systems and aged animals in the study of lung disease and the development of integrative systems-based platforms that can dynamically incorporate data sets that describe the genomics, transcriptomics, epigenomics, metabolomics, and proteomics of the aging lung in health and disease. Further research was recommended to integrate benchmark biological hallmarks of aging in the lung with the pathobiology of acute and chronic lung diseases with divergent pathologies for which advanced age is the most important risk factor.

Background: The peripheral blood neutrophil-to-lymphocyte ratio (NLR) is a cytologic marker of both inflammation and poor outcomes in patients with cancer. DNA methylation is a key element of the epigenetic program defining different leukocyte subtypes and may provide an alternative to cytology in assessing leukocyte profiles. Our aim was to create a bioinformatic tool to estimate NLR using DNA methylation, and to assess its diagnostic and prognostic performance in human populations.Methods: We developed a DNA methylation-derived NLR (mdNLR) index based on normal isolated leukocyte methylation libraries and established cell-mixture deconvolution algorithms. The method was applied to cancer case-control studies of the bladder, head and neck, ovary, and breast, as well as publicly available data on cancer-free subjects.Results: Across cancer studies, mdNLR scores were either elevated in cases relative to controls, or associated with increased hazard of death. High mdNLR values (>5) were strong indicators of poor survival. In addition, mdNLR scores were elevated in males, in nonHispanic white versus Hispanic ethnicity, and increased with age. We also observed a significant interaction between cigarette smoking history and mdNLR on cancer survival.Conclusions: These results mean that our current understanding of mature leukocyte methylomes is sufficient to allow researchers and clinicians to apply epigenetically based analyses of NLR in clinical and epidemiologic studies of cancer risk and survival.Impact: As cytologic measurements of NLR are not always possible (i.e., archival blood), mdNLR, which is computed from DNA methylation signatures alone, has the potential to expand the scope of epigenome-wide association studies. Cancer Epidemiol Biomarkers Prev; 26(3); 328-38. ©2016 AACR.

Previous studies on smokeless tobacco use and head and neck cancer (HNC) have found inconsistent and often imprecise estimates, with limited control for cigarette smoking. Using pooled data from 11 US case-control studies (1981-2006) of oral, pharyngeal, and laryngeal cancers (6,772 cases and 8,375 controls) in the International Head and Neck Cancer Epidemiology (INHANCE) Consortium, we applied hierarchical logistic regression to estimate odds ratios and 95% confidence intervals for ever use, frequency of use, and duration of use of snuff and chewing tobacco separately for never and ever cigarette smokers. Ever use (versus never use) of snuff was strongly associated with HNC among never cigarette smokers (odds ratio (OR) = 1.71, 95% confidence interval (CI): 1.08, 2.70), particularly for oral cavity cancers (OR = 3.01, 95% CI: 1.63, 5.55). Although ever (versus never) tobacco chewing was weakly associated with HNC among never cigarette smokers (OR = 1.20, 95% CI: 0.81, 1.77), analyses restricted to cancers of the oral cavity showed a stronger association (OR = 1.81, 95% CI: 1.04, 3.17). Few or no associations between each type of smokeless tobacco and HNC were observed among ever cigarette smokers, possibly reflecting residual confounding by smoking. Smokeless tobacco use appears to be associated with HNC, especially oral cancers, with snuff being more strongly associated than chewing tobacco.

AimWe examined whether variation in blood-based epigenome-wide association studies could be more completely explained by augmenting existing reference DNA methylation libraries.Materials & methodsWe compared existing and enhanced libraries in predicting variability in three publicly available 450K methylation datasets that collected whole-blood samples. Models were fit separately to each CpG site and used to estimate the additional variability when adjustments for cell composition were made with each library.ResultsCalculation of the mean difference in the CpG-specific residual sums of squares error between models for an arthritis, aging and metabolic syndrome dataset, indicated that an enhanced library explained significantly more variation across all three datasets (p < 10(-3)).ConclusionPathologically important immune cell subtypes can explain important variability in epigenome-wide association studies done in blood.

Natural killer (NK) cells are now recognized to exhibit characteristics akin to cells of the adaptive immune system. The generation of adaptive memory is linked to epigenetic reprogramming including alterations in DNA methylation. The study herein found reproducible genome wide DNA methylation changes associated with human NK cell activation. Activation led predominately to CpG hypomethylation (81% of significant loci). Bioinformatics analysis confirmed that non-coding and gene-associated differentially methylated sites (DMS) are enriched for immune related functions (i.e., immune cell activation). Known DNA methylation-regulated immune loci were also identified in activated NK cells (e.g., TNFA, LTA, IL13, CSF2). Twenty-one loci were designated high priority and further investigated as potential markers of NK activation. BHLHE40 was identified as a viable candidate for which a droplet digital PCR assay for demethylation was developed. The assay revealed high demethylation in activated NK cells and low demethylation in naïve NK, T- and B-cells. We conclude the NK cell methylome is plastic with potential for remodeling. The differentially methylated region signature of activated NKs revealed similarities with T cell activation, but also provided unique biomarker candidates of NK activation, which could be useful in epigenome-wide association studies to interrogate the role of NK subtypes in global methylation changes associated with exposures and/or disease states.

Defects in antigen presenting cell function have been implicated in glioma immunosuppression. We measured peripheral CCL22, a dendritic cell/macrophage derived T cell trafficking chemokine, in sera from 1,208 glioma cases and 976 controls to assess whether it might provide a biomarker of glioma risk, survival and immune dysfunction. Cluster models were used to examine the relationship between CCL22 and glioma risk. Patient survival was assessed using Cox regression models. We also examined the relationship between CCL22 levels and CD4 cell counts, as well as allergy history and IgE levels. CCL22 levels were significantly lower among glioma cases compared with controls (Mean ± SEM: 1.23 ± 0.03 ng/mL in cases vs. 1.60 ± 0.03 ng/mL in controls, p < 0.0001) and this difference remained significant even after controlling for other covariates in the cluster models (highest quartile versus lowest Odds Ratio = 0.21, p < 0.0001). CD4 cell counts were positively correlated with CCL22 in glioma cases (Spearman r(2) = 0.51, p < 0.01) and were significantly lower in cases compared with controls. Higher CCL22 levels were associated with longer survival in all cases combined and in GBM cases (hazard ratio(allcases) = 0.81; 95% CI: 0.72-0.91, p = 0.0003). CCL22 levels were not associated with IgE level or self-reported allergies. Circulating CCL22 levels are related to both glioma risk and survival duration independent of age, histology, grade and IDH mutation status. CCL22 should be considered a marker of immune status with potential prognostic value.

Quantitating the copy number of demethylated CpG promoter sites of the CD3Z gene can be used to estimate the numbers and proportions of T cells in human blood and tissue. Quantitative methylation specific PCR (qPCR) is useful for studying T cells but requires extensive calibration and is imprecise at low copy numbers. Here we compared the performance of a new digital PCR platform (droplet digital PCR or ddPCR) to qPCR using bisulfite converted DNA from 157 blood specimens obtained from ambulatory care controls and patients with primary glioma. We compared both ddPCR and qPCR with conventional flow cytometry (FACS) evaluation of CD3 positive T cells. Repeated measures on the same blood sample revealed ddPCR to be less variable than qPCR. Both qPCR and ddPCR correlated significantly with FACS evaluation of peripheral blood CD3 counts and CD3/total leukocyte values. However, statistical measures of agreement showed that linear concordance was stronger for ddPCR than for qPCR and the absolute values were closer to FACS for ddPCR. Both qPCR and ddPCR could distinguish clinically significant differences in T cell proportions and performed similarly to FACS. Given the higher precision, greater accuracy, and technical simplicity of ddPCR, this approach appears to be a superior DNA methylation based method than conventional qPCR for the assessment of T cells.

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.

Glioblastoma multiforme (GBM) is the most aggressive of all brain tumors, with a median survival of less than 1.5 years. Recently, epigenetic alterations were found to play key roles in both glioma genesis and clinical outcome, demonstrating the need to integrate genetic and epigenetic data in predictive models. To enhance current models through discovery of novel predictive biomarkers, we employed a genome-wide, agnostic strategy to specifically capture both methylation-directed changes in gene expression and alternative associations of DNA methylation with disease survival in glioma. Human GBM-associated DNA methylation, gene expression, IDH1 mutation status, and survival data were obtained from The Cancer Genome Atlas. DNA methylation loci and expression probes were paired by gene, and their subsequent association with survival was determined by applying an accelerated failure time model to previously published alternative and expression-based association equations. Significant associations were seen in 27 unique methylation/expression pairs with expression-based, alternative, and combinatorial associations observed (10, 13, and 4 pairs, respectively). The majority of the predictive DNA methylation loci were located within CpG islands, and all but three of the locus pairs were negatively correlated with survival. This finding suggests that for most loci, methylation/expression pairs are inversely related, consistent with methylation-associated gene regulatory action. Our results indicate that changes in DNA methylation are associated with altered survival outcome through both coordinated changes in gene expression and alternative mechanisms. Furthermore, our approach offers an alternative method of biomarker discovery using a priori gene pairing and precise targeting to identify novel sites for locus-specific therapeutic intervention.

Perfluoroalkyl acids (PFAAs) are persistent, synthetic compounds that are used in a number of consumer products. Perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) have been associated with cardiovascular risk factors, and changes in gene expression and DNA methylation in animals and cellular systems. However, whether PFAA exposure is associated with LINE-1 DNA methylation, a potential marker of cardiovascular risk, in humans remains unknown. We sought to evaluate the cross-sectional associations between serum PFAAs and LINE-1 DNA methylation in a population highly exposed to PFOA. We measured serum PFAAs twice four to five years apart in 685 adult participants (47% male, mean age±SD=42±11years). We measured percent LINE-1 DNA methylation in peripheral blood leukocytes at the second time point (follow-up), and estimated absolute differences in LINE-1 methylation associated with an interquartile (IQR) shift in mean PFAA serum levels. IQR increases in mean serum PFOA, PFOS, perfluorononanoic acid (PFNA), and perfluorohexane sulfonate (PFHxS) were associated with differences of -0.04 (p=0.16), 0.20 (p=0.001), 0.06 (p=0.19), and 0.02 (p=0.57), respectively, in % LINE-1 methylation at follow-up after adjustment for potential confounders. We observed a monotonic increase in LINE-1 DNA methylation across tertiles of PFOS and PFNA (ptrend=0.02 for both associations), but not across tertiles of PFOA or PFHxS (ptrend=0.71 and 0.44, respectively). In summary, serum PFOS was associated with LINE-1 methylation, while serum PFOA, PFHxS, and PFNA were not. Additional research is needed to more precisely determine whether these compounds are epigenetically active.