Quantitative reconstruction of leukocyte subsets using DNA methylation

Spatial deconvolution from bulk DNA methylation profiles determines intratumoral epigenetic heterogeneity

BACKGROUND

Intratumoral heterogeneity emerges from accumulating genetic and epigenetic changes during tumorigenesis, which may contribute to therapeutic failure and drug resistance. However, the lack of a quick and convenient approach to determine the intratumoral epigenetic heterogeneity (eITH) limit the application of eITH in clinical settings. Here, we aimed to develop a tool that can evaluate the eITH using the DNA methylation profiles from bulk tumors.

METHODS

Genomic DNA of three laser micro-dissected tumor regions, including digestive tract surface, central bulk, and invasive front, was extracted from formalin-fixed paraffin-embedded sections of colorectal cancer patients. The genome-wide methylation profiles were generated with methylation array. The most variable methylated probes were selected to construct a DNA methylation-based heterogeneity (MeHEG) estimation tool that can deconvolve the proportion of each reference tumor region with the support vector machine model-based method. A PCR-based assay for quantitative analysis of DNA methylation (QASM) was developed to specifically determine the methylation status of each CpG in MeHEG assay at single-base resolution to realize fast evaluation of epigenetic heterogeneity.

RESULTS

In the discovery set with 79 patients, the differentially methylated CpGs among the three tumor regions were found. The 7 most representative CpGs were identified and subsequently selected to develop the MeHEG algorithm. We validated its performance of deconvolution of tumor regions in an independent cohort. In addition, we showed the significant association of MeHEG-based epigenetic heterogeneity with the genomic heterogeneity in mutation and copy number variation in our in-house and TCGA cohorts. Besides, we found that the patients with higher MeHEG score had worse disease-free and overall survival outcomes. Finally, we found dynamic change of epigenetic heterogeneity based on MeHEG score in cancer cells under the treatment of therapeutic drugs.

CONCLUSION

By developing a 7-loci panel using a machine learning approach combined with the QASM assay for PCR-based application, we present a valuable method for evaluating intratumoral heterogeneity. The MeHEG algorithm offers novel insights into tumor heterogeneity from an epigenetic perspective, potentially enriching current knowledge of tumor complexity and providing a new tool for clinical and research applications in cancer biology.

DNA methylation in necrotizing enterocolitis

Epigenetic modifications, such as DNA methylation, are enzymatically regulated processes that directly impact gene expression patterns. In early life, they are central to developmental programming and have also been implicated in regulating inflammatory responses. Research into the role of epigenetics in neonatal health is limited, but there is a growing body of literature related to the role of DNA methylation patterns and diseases of prematurity, such as the intestinal disease necrotizing enterocolitis (NEC). NEC is a severe intestinal inflammatory disease, but the key factors that precede disease development remain to be determined. This knowledge gap has led to a failure to design effective targeted therapies and identify specific biomarkers of disease. Recent literature has identified altered DNA methylation patterns in the stool and intestinal tissue of neonates with NEC. These findings provide the foundation for a new avenue in NEC research. In this review, we will provide a general overview of DNA methylation and then specifically discuss the recent literature related to methylation patterns in neonates with NEC. We will also discuss how DNA methylation is used as a biomarker for other disease states and how, with further research, methylation patterns may serve as potential biomarkers for NEC.

Quantifying the proportion of different cell types in the human cortex using DNA methylation profiles

BACKGROUND

Due to interindividual variation in the cellular composition of the human cortex, it is essential that covariates that capture these differences are included in epigenome-wide association studies using bulk tissue. As experimentally derived cell counts are often unavailable, computational solutions have been adopted to estimate the proportion of different cell types using DNA methylation data. Here, we validate and profile the use of an expanded reference DNA methylation dataset incorporating two neuronal and three glial cell subtypes for quantifying the cellular composition of the human cortex.

RESULTS

We tested eight reference panels containing different combinations of neuronal- and glial cell types and characterised their performance in deconvoluting cell proportions from computationally reconstructed or empirically derived human cortex DNA methylation data. Our analyses demonstrate that while these novel brain deconvolution models produce accurate estimates of cellular proportions from profiles generated on postnatal human cortex samples, they are not appropriate for the use in prenatal cortex or cerebellum tissue samples. Applying our models to an extensive collection of empirical datasets, we show that glial cells are twice as abundant as neuronal cells in the human cortex and identify significant associations between increased Alzheimer's disease neuropathology and the proportion of specific cell types including a decrease in NeuNNeg/SOX10Neg nuclei and an increase of NeuNNeg/SOX10Pos nuclei.

CONCLUSIONS

Our novel deconvolution models produce accurate estimates for cell proportions in the human cortex. These models are available as a resource to the community enabling the control of cellular heterogeneity in epigenetic studies of brain disorders performed on bulk cortex tissue.

Oligodendroglioma patient survival is associated with circulating B-cells and age

Background

Variations in survival among patients with oligodendroglioma are unexplained by known prognostic factors. To assess the impact of peripheral immune profiles on prognosis, we applied immunomethylomics analyses-DNA methylation of archived whole blood samples, to characterize immune cells.

Methods

We compared the proportions of immune cells from patients with oligodendroglioma to other glioma subtypes and controls. We used recursive partitioning analysis (RPA) within the oligodendrogliomas to correlate with survival.

Results

Patients with oligodendrogliomas (141) were median age at diagnosis of 44 years; 57% male; 75% White; 60% prior chemotherapy; and 25% on dexamethasone at sample collection. Patients with oligodendrogliomas had immune profiles more similar to controls than other glioma subtypes, though with notably lower B-cells. RPA of patients with oligodendrogliomas delineated 2 survival groups based on an interaction between age and B-naïve cells. Patients with longer survival (median 24.2 years) were ≤42 years of age with higher B-naïve cells versus worse survival (median 16.9 years) who were ≤42 years of age with lower B-naïve cells or >42 years of age (P = .00032). Patients with worse survival also had lower CD4- and CD8-naïve T-cells. Similar immune profiles were observed in an independent cohort of oligodendroglioma patients prior to surgery.

Conclusions

Peripheral blood immune profiles in oligodendroglioma suggested that younger patients with lower B-naïve cells experienced shorter survival. Though our findings lack of validation cohort and use a heterogenous patient population, they suggest peripheral blood immune profiles may be prognostic for patients with glioma and warrant further investigation.

Uncertainty quantification of reference-based cellular deconvolution algorithms

The majority of epigenetic epidemiology studies to date have generated genome-wide profiles from bulk tissues (e.g., whole blood) however these are vulnerable to confounding from variation in cellular composition. Proxies for cellular composition can be mathematically derived from the bulk tissue profiles using a deconvolution algorithm; however, there is no method to assess the validity of these estimates for a dataset where the true cellular proportions are unknown. In this study, we describe, validate and characterize a sample level accuracy metric for derived cellular heterogeneity variables. The CETYGO score captures the deviation between a sample's DNA methylation profile and its expected profile given the estimated cellular proportions and cell type reference profiles. We demonstrate that the CETYGO score consistently distinguishes inaccurate and incomplete deconvolutions when applied to reconstructed whole blood profiles. By applying our novel metric to >6,300 empirical whole blood profiles, we find that estimating accurate cellular composition is influenced by both technical and biological variation. In particular, we show that when using a common reference panel for whole blood, less accurate estimates are generated for females, neonates, older individuals and smokers. Our results highlight the utility of a metric to assess the accuracy of cellular deconvolution, and describe how it can enhance studies of DNA methylation that are reliant on statistical proxies for cellular heterogeneity. To facilitate incorporating our methodology into existing pipelines, we have made it freely available as an R package (https://github.com/ds420/CETYGO).

Targeted DNA Methylation Analysis Facilitates Leukocyte Counts in Dried Blood Samples

BACKGROUND

Cell-type specific DNA methylation (DNAm) can be employed to determine the numbers of leukocyte subsets in blood. In contrast to conventional methods for leukocyte counts, which are based on cellular morphology or surface marker protein expression, the cellular deconvolution based on DNAm levels is applicable for frozen or dried blood. Here, we further enhanced targeted DNAm assays for leukocyte counts in clinical application.

METHODS

DNAm profiles of 40 different studies were compiled to identify CG dinucleotides (CpGs) with cell-type specific DNAm using a computational framework, CimpleG. DNAm levels at these CpGs were then measured with digital droplet PCR in venous blood from 160 healthy donors and 150 patients with various hematological disorders. Deconvolution was further validated with venous blood (n = 75) and capillary blood (n = 31) that was dried on Whatman paper or on Mitra microsampling devices.

RESULTS

In venous blood, automated cell counting or flow cytometry correlated well with epigenetic estimates of relative leukocyte counts for granulocytes (r = 0.95), lymphocytes (r = 0.97), monocytes (r = 0.82), CD4 T cells (r = 0.84), CD8 T cells (r = 0.94), B cells (r = 0.96), and NK cells (r = 0.72). Similar correlations and precisions were achieved for dried blood samples. Spike-in with a reference plasmid enabled accurate epigenetic estimation of absolute leukocyte counts from dried blood samples, correlating with conventional venous (r = 0.86) and capillary (r = 0.80) blood measurements.

CONCLUSIONS

The advanced selection of cell-type specific CpGs and utilization of digital droplet PCR analysis provided accurate epigenetic blood counts. Analysis of dried blood facilitates self-sampling with a finger prick, thereby enabling easier accessibility to testing.

The Origin of Highly Elevated Cell-Free DNA in Healthy Individuals and Patients with Pancreatic, Colorectal, Lung, or Ovarian Cancer

Cell-free DNA (cfDNA) concentrations from patients with cancer are often elevated compared with those of healthy controls, but the sources of this extra cfDNA have never been determined. To address this issue, we assessed cfDNA methylation patterns in 178 patients with cancers of the colon, pancreas, lung, or ovary and 64 patients without cancer. Eighty-three of these individuals had cfDNA concentrations much greater than those generally observed in healthy subjects. The major contributor of cfDNA in all samples was leukocytes, accounting for ∼76% of cfDNA, with neutrophils predominating. This was true regardless of whether the samples were derived from patients with cancer or the total plasma cfDNA concentration. High levels of cfDNA observed in patients with cancer did not come from either neoplastic cells or surrounding normal epithelial cells from the tumor's tissue of origin. These data suggest that cancers may have a systemic effect on cell turnover or DNA clearance.

SIGNIFICANCE

The origin of excess cfDNA in patients with cancer is unknown. Using cfDNA methylation patterns, we determined that neither the tumor nor the surrounding normal tissue contributes this excess cfDNA-rather it comes from leukocytes. This finding suggests that cancers have a systemic impact on cell turnover or DNA clearance. See related commentary by Thierry and Pisareva, p. 2122. This article is featured in Selected Articles from This Issue, p. 2109.

Mouse methylation profiles for leukocyte cell types, and estimation of leukocyte fractions in inflamed gastrointestinal DNA samples

Precise analysis of tissue DNA and RNA samples is often hampered by contaminating non-target cells whose amounts are highly variable. DNA methylation profiles are specific to cell types, and can be utilized for assessment of the fraction of such contaminating non-target cells. Here, we aimed 1) to identify methylation profiles specific to multiple types of mouse leukocytes, and 2) to estimate the fraction of leukocytes infiltrating inflamed tissues using DNA samples. First, genome-wide DNA methylation analysis was conducted for three myeloid-lineage cells and four lymphoid-lineage cells isolated by fluorescence-activated cell sorting after magnetic-activated cell sorting from leukocytes in the spleen. Clustering analysis using CpG sites within enhancers separated the three myeloid-lineage cells and four lymphoid-lineage cells while that using promoter CpG islands (TSS200CGIs) did not. Among the 266,108 CpG sites analyzed, one CpG site was specifically hypermethylated (β value ≥ 0.7) in B cells, and four, seven, 183, and 34 CpG sites were specifically hypomethylated (β value < 0.2) in CD4+ T cells, CD8+ T cells, B cells, and NK cells, respectively. Importantly, cell type-specific hypomethylated CpG sites were located at genes involved in cell type-specific biological functions. Then, marker CpG sites to estimate the leukocyte fraction in a tissue with leukocyte infiltration were selected, and an estimation algorithm was established. The fractions of infiltrating leukocytes were estimated to be 1.6-12.4% in the stomach (n = 10) with Helicobacter pylori-induced inflammation and 1.5-4.3% in the colon with dextran sulfate sodium-induced colitis (n = 4), and the fractions were highly correlated with those estimated histologically using Cd45-stained tissue sections [R = 0.811 (p = 0.004)]. These results showed that mouse methylation profiles at CpG sites within enhancers reflected leukocyte cell lineages, and the use of marker CpG sites successfully estimated the leukocyte fraction in inflamed gastric and colon tissues.

Association of immune cell composition with the risk factors and incidence of acute coronary syndrome

BACKGROUND

Although immune cells are involved in acute coronary syndrome (ACS), few studies have explored the association of incident ACS with the relative immune cell proportions. We aimed to investigate the association of immune cell proportions with the incidence and risk factors of ACS in the Dongfeng-Tongji cohort.

METHODS

We conducted the analyses with 38,295 subjects from the first follow-up of the Dongfeng-Tongji cohort, including DNA methylation profiles for 1570 individuals. The proportions of immune cell types were observed from routine blood tests or estimated from DNA methylation profiles. For both observed and estimated immune cell proportions, we tested their associations with risk factors of ACS by multivariable linear regression models. In addition, the association of each immune cell proportion with incident ACS was assessed by the Cox regression model and conditional logistic regression model, respectively, adjusting for the risk factors of ACS.

FINDINGS

The proportions of lymphocytes, monocytes, and neutrophils showed strong associations with sex, followed by diabetes. Moreover, sex and current smoking were the two factors with strongest association with the proportions of lymphocyte subtypes. The hazard ratio (HR) and 95% confidence interval (CI) of incident ACS per standard deviation (SD) increase in proportions of lymphocytes and neutrophils were 0.91 (0.85-0.96) and 1.10 (1.03-1.16), respectively. Furthermore, the OR (95% CI) of incident ACS per SD increase in proportions of NK cells, CD4⁺ T cells, and B cells were 0.88 (0.78-0.99), 1.15 (1.03-1.30), and 1.13 (1.00-1.26), respectively.

INTERPRETATION

The proportions of immune cells were associated with several risk factors of ACS, including sex, diabetes, and current smoking. In addition, proportion of neutrophils had a risk effect, while proportion of lymphocytes had a protective effect on the incidence of ACS. The protective effect of lymphocytes was probably driven by NK cells.

The methylation profile of IL4, IL5, IL10, IFNG and FOXP3 associated with environmental exposures differed between Polish infants with the food allergy and/or atopic dermatitis and without the disease

Objectives

Epigenetic dynamics has been indicated to play a role in allergy development. The environmental stimuli have been shown to influence the methylation processes. This study investigated the differences in CpGs methylation rate of immune-attached genes between healthy and allergic infants. The research was aimed at finding evidence for the impact of environmental factors on methylation-based regulation of immunological processes in early childhood.

Methods

The analysis of methylation level of CpGs in the IL4, IL5, IL10, IFNG and FOXP3 genes was performed using high resolution melt real time PCR technology. DNA was isolated from whole blood of Polish healthy and allergic infants, with food allergy and/or atopic dermatitis, aged under six months.

Results

The significantly lower methylation level of FOXP3 among allergic infants compared to healthy ones was reported. Additional differences in methylation rates were found, when combining with environmental factors. In different studied groups, negative correlations between age and the IL10 and FOXP3 methylation were detected, and positive - in the case of IL4. Among infants with different allergy symptoms, the decrease in methylation level of IFNG, IL10, IL4 and FOXP3 associated with passive smoke exposure was observed. Complications during pregnancy were linked to different pattern of the IFNG, IL5, IL4 and IL10 methylation depending on allergy status. The IFNG and IL5 methylation rates were higher among exclusively breastfed infants with atopic dermatitis compared to the non-breastfed. A decrease in the IFNG methylation was noted among allergic patients fed exclusively with milk formula. In different study groups, a negative correlation between IFNG, IL5 methylation and maternal BMI or IL5 methylation and weight was noted. Some positive correlations between methylation rate of IL10 and child's weight were found. A higher methylation of IL4 was positively correlated with the number of family members with allergy.

Conclusion

The FOXP3 methylation in allergic infants was lower than in the healthy ones. The methylation profile of IL4, IL5, IL10, IFNG and FOXP3 associated with environmental exposures differed between the studied groups. The results offer insights into epigenetic regulation of immunological response in early childhood.

DNA methylation variations and epigenetic aging in telomere biology disorders

Telomere Biology Disorders (TBDs) are characterized by mutations in telomere-related genes leading to short telomeres and premature aging but with no strict correlation between telomere length and disease severity. Epigenetic alterations are also markers of aging and we aimed to evaluate whether DNA methylation (DNAm) could be part of the pathogenesis of TBDs. In blood from 35 TBD cases, genome-wide DNAm were analyzed and the cases were grouped based on relative telomere length (RTL): short (S), with RTL close to normal controls, and extremely short (ES). TBD cases had increased epigenetic age and DNAm alterations were most prominent in the ES-RTL group. Thus, the differentially methylated (DM) CpG sites could be markers of short telomeres but could also be one of the mechanisms contributing to disease phenotype since DNAm alterations were observed in symptomatic, but not asymptomatic, cases with S-RTL. Furthermore, two or more DM-CpGs were identified in four genes previously linked to TBD or telomere length (PRDM8, SMC4, VARS, and WNT6) and in three genes that were novel in telomere biology (MAS1L, NAV2, and TM4FS1). The DM-CpGs in these genes could be markers of aging in hematological cells, but they could also be of relevance for the progression of TBD.

Investigating the effects of maltreatment and acute stress on the concordance of blood and DNA methylation methods of estimating immune cell proportions

BACKGROUND

Immune cell proportions can be used to detect pathophysiological states and are also critical covariates in genomic analyses. The complete blood count (CBC) is the most common method of immune cell proportion estimation, but immune cell proportions can also be estimated using whole-genome DNA methylation (DNAm). Although the concordance of CBC and DNAm estimations has been validated in various adult and clinical populations, less is known about the concordance of existing estimators among stress-exposed individuals. As early life adversity and acute psychosocial stress have both been associated with unique DNAm alterations, the concordance of CBC and DNAm immune cell proportion needs to be validated in various states of stress.

RESULTS

We report the correlation and concordance between CBC and DNAm estimates of immune cell proportions using the Illumina EPIC DNAm array within two unique studies: Study 1, a high-risk pediatric cohort of children oversampled for exposure to maltreatment (N = 365, age 8 to 14 years), and Study 2, a sample of young adults who have participated in an acute laboratory stressor with four pre- and post-stress measurements (N = 28, number of observations = 100). Comparing CBC and DNAm proportions across both studies, estimates of neutrophils (r = 0.948, p < 0.001), lymphocytes (r = 0.916, p < 0.001), and eosinophils (r = 0.933, p < 0.001) were highly correlated, while monocyte estimates were moderately correlated (r = 0.766, p < 0.001) and basophil estimates were weakly correlated (r = 0.189, p < 0.001). In Study 1, we observed significant deviations in raw values between the two approaches for some immune cell subtypes; however, the observed differences were not significantly predicted by exposure to child maltreatment. In Study 2, while significant changes in immune cell proportions were observed in response to acute psychosocial stress for both CBC and DNAm estimates, the observed changes were similar for both approaches.

CONCLUSIONS

Although significant differences in immune cell proportion estimates between CBC and DNAm exist, as well as stress-induced changes in immune cell proportions, neither child maltreatment nor acute psychosocial stress alters the concordance of CBC and DNAm estimation methods. These results suggest that the agreement between CBC and DNAm estimators of immune cell proportions is robust to exposure to child maltreatment and acute psychosocial stress.

Genome-wide DNA methylation profile analysis identifies an individualized predictive signature for melanoma immune response

PURPOSE

The current evaluation methods for tumor infiltrating lymphocytes (TILs), particularly CD8 + TILs, mainly rely on semiquantitative immunohistochemistry with high variability. We aimed to construct an individualized DNA methylation-based signature for CD8 + TILs (CD8 + MeTIL) that may characterize melanoma immune microenvironment and guide therapeutic selection.

METHODS

The transcriptome profiles and DNA methylation data of 457 melanoma patients from The Cancer Genome Atlas (TCGA) database were analyzed. Differential methylation analysis between groups with high and low CD8 + TILs was performed to select differentially methylated positions (DMPs) and define CD8 + MeTIL. The prognostic value of CD8 + MeTIL and its predictive value for immunotherapy response were investigated using multiple melanoma cohorts.

RESULTS

We successfully constructed the CD8 + MeTIL signature based on four DMPs. The survival analyses showed that higher CD8 + MeTIL score was associated with worse survival outcomes in TCGA-SKCM and GSE144487 cohorts. The ROC curve for the predictive analysis revealed that the survival prediction of CD8 + MeTIL score was superior compared with CD8 + TILs (CIBERSORT) and CD8B mRNA expression. Furthermore, we founded that tumors with higher CD8 + MeTIL score were marked with immunosuppressive characteristics, including low immune score and downregulated immune-related pathways. More importantly, the CD8 + MeTIL score showed a potential predictive value for the benefit from immunotherapy in two published cohorts. When combined CD8 + MeTIL with PD-L1 expression, the patient classification showed significantly different immunotherapy response rates and long-term survival outcomes.

CONCLUSIONS

The CD8 + MeTIL signature might be as a novel method to evaluate CD8 + TILs and guide immunotherapy approaches.

Evaluation of cross-platform compatibility of a DNA methylation-based glucocorticoid response biomarker

Background

Identifying 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).

Results

The 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.

Conclusions

We 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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01352-1.

Tumor microenvironment: barrier or opportunity towards effective cancer therapy

Tumor microenvironment (TME) is a specialized ecosystem of host components, designed by tumor cells for successful development and metastasis of tumor. With the advent of 3D culture and advanced bioinformatic methodologies, it is now possible to study TME’s individual components and their interplay at higher resolution. Deeper understanding of the immune cell’s diversity, stromal constituents, repertoire profiling, neoantigen prediction of TMEs has provided the opportunity to explore the spatial and temporal regulation of immune therapeutic interventions. The variation of TME composition among patients plays an important role in determining responders and non-responders towards cancer immunotherapy. Therefore, there could be a possibility of reprogramming of TME components to overcome the widely prevailing issue of immunotherapeutic resistance. The focus of the present review is to understand the complexity of TME and comprehending future perspective of its components as potential therapeutic targets. The later part of the review describes the sophisticated 3D models emerging as valuable means to study TME components and an extensive account of advanced bioinformatic tools to profile TME components and predict neoantigens. Overall, this review provides a comprehensive account of the current knowledge available to target TME.

DNA methylation as a pharmacodynamic marker of glucocorticoid response and glioma survival

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.

Pre-surgery immune profiles of adult glioma patients

INTRODUCTION

Although immunosuppression is a known characteristic of glioma, no previous large studies have reported peripheral blood immune cell profiles prior to patient surgery and chemoradiation. This report describes blood immune cell characteristics and associated variables prior to surgery among typical glioma patients seen at a large University practice.

METHODS

We analyzed pre-surgery blood samples from 139 glioma patients diagnosed with a new or recurrent grade II/III glioma (LrGG, n = 64) or new glioblastoma (GBM, n = 75) and 454 control participants without glioma. Relative cell fractions of CD4, CD8, B-cells, Natural Killer cells, monocytes, and neutrophils, were estimated via a validated deconvolution algorithm from blood DNA methylation measures from Illumina EPIC arrays.

RESULTS

Dexamethasone use at time of blood draw varied by glioma type being highest among patients with IDH wild-type (wt) GBM (75%) and lowest for those with oligodendroglioma (14%). Compared to controls, glioma patients showed statistically significant lower cell fractions for all immune cell subsets except for neutrophils which were higher (all p-values < 0.001), in part because of the higher prevalence of dexamethasone use at time of blood draw for IDHwt GBM. Patients who were taking dexamethasone were more likely to have a low CD4 count (< 200, < 500), increased neutrophils, low absolute lymphocyte counts, higher total cell count and higher NLR.

CONCLUSION

We show that pre-surgery blood immune profiles vary by glioma subtype, age, and more critically, by use of dexamethasone. Our results highlight the importance of considering dexamethasone exposures in all studies of immune profiles and of obtaining immune measures prior to use of dexamethasone, if possible.

Harmonization of transcriptomic and methylomic analysis in environmental epidemiology studies for potential application in chemical risk assessment

Recent efforts have posited the utility of transcriptomic-based approaches to understand chemical-related perturbations in the context of human health risk assessment. Epigenetic modification (e.g., DNA methylation) can influence gene expression changes and is known to occur as a molecular response to some chemical exposures. Characterization of these methylation events is critical to understand the molecular consequences of chemical exposures. In this context, a novel workflow was developed to interrogate publicly available epidemiological transcriptomic and methylomic data to identify relevant pathway level changes in response to chemical exposure, using inorganic arsenic as a case study. Gene Set Enrichment Analysis (GSEA) was used to identify causal methylation events that result in concomitant downstream transcriptional deregulation. This analysis demonstrated an unequal distribution of differentially methylated regions across the human genome. After mapping these events to known genes, significant enrichment of a subset of these pathways suggested that arsenic-mediated methylation may be both specific and non-specific. Parallel GSEA performed on matched transcriptomic samples determined that a substantially reduced subset of these pathways are enriched and that not all chemically-induced methylation results in a downstream alteration in gene expression. The resulting pathways were found to be representative of well-established molecular events known to occur in response to arsenic exposure. The harmonization of enriched transcriptional patterns with those identified from the methylomic platform promoted the characterization of plausibly causal molecular signaling events. The workflow described here enables significant gene and methylation-specific pathways to be identified from whole blood samples of individuals exposed to environmentally relevant chemical levels. As future efforts solidify specific causal relationships between these molecular events and relevant apical endpoints, this novel workflow could aid risk assessments by identifying molecular targets serving as biomarkers of hazard, informing mechanistic understanding, and characterizing dose ranges that promote relevant molecular/epigenetic signaling events occuring in response to chemical exposures.

Interactions of Age and Blood Immune Factors and Noninvasive Prediction of Glioma Survival

BACKGROUND

Tumor-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.

METHODS

We 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.

RESULTS

IDH/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.

CONCLUSIONS

The 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.

How to Translate DNA Methylation Biomarkers Into Clinical Practice

Recent advances in sequencing technologies provide unprecedented opportunities for epigenetic biomarker development. Particularly the DNA methylation pattern-which is modified at specific sites in the genome during cellular differentiation, aging, and disease-holds high hopes for a wide variety of diagnostic applications. While many epigenetic biomarkers have been described, only very few of them have so far been successfully translated into clinical practice and almost exclusively in the field of oncology. This discrepancy might be attributed to the different demands of either publishing a new finding or establishing a standardized and approved diagnostic procedure. This is exemplified for epigenetic leukocyte counts and epigenetic age-predictions. To ease later clinical translation, the following hallmarks should already be taken into consideration when designing epigenetic biomarkers: 1) Identification of best genomic regions, 2) pre-analytical processing, 3) accuracy of DNA methylation measurements, 4) identification of confounding parameters, 5) accreditation as diagnostic procedure, 6) standardized data analysis, 7) turnaround time, and 8) costs and customer requirements. While the initial selection of relevant genomic regions is usually performed on genome wide DNA methylation profiles, it might be advantageous to subsequently establish targeted assays that focus on specific genomic regions. Development of an epigenetic biomarker for clinical application is a long and cumbersome process that is only initiated with the identification of an epigenetic signature.

Immune profiles and DNA methylation alterations related with non-muscle-invasive bladder cancer outcomes

BACKGROUND

Non-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 RESULTS

We 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 < 0.005). Among these CpGs, the 1572 were associated with an increased hazard and were significantly enriched in open sea regions; the 956 remaining CpGs were associated with a decreased hazard and were significantly enriched in enhancer regions and DNase hypersensitive sites.

CONCLUSIONS

Our results expand on the knowledge of immune profiles and methylation alteration associated with NMIBC outcomes and represent a first step toward the development of DNA methylation-based biomarkers of tumor recurrence.

Epigenetic quantification of circulating immune cells in peripheral blood of triple-negative breast cancer patients

BACKGROUND

A shift in the proportions of blood immune cells is a hallmark of cancer development. Here, we investigated whether methylation-derived immune cell type ratios and methylation-derived neutrophil-to-lymphocyte ratios (mdNLRs) are associated with triple-negative breast cancer (TNBC).

METHODS

Leukocyte subtype-specific unmethylated/methylated CpG sites were selected, and methylation levels at these sites were used as proxies for immune cell type proportions and mdNLR estimation in 231 TNBC cases and 231 age-matched controls. Data were validated using the Houseman deconvolution method. Additionally, the natural killer (NK) cell ratio was measured in a prospective sample set of 146 TNBC cases and 146 age-matched controls.

RESULTS

The mdNLRs were higher in TNBC cases compared with controls and associated with TNBC (odds ratio (OR) range (2.66-4.29), all P_adj. < 1e-04). A higher neutrophil ratio and lower ratios of NK cells, CD4 + T cells, CD8 + T cells, monocytes, and B cells were associated with TNBC. The strongest association was observed with decreased NK cell ratio (OR range (1.28-1.42), all P_adj. < 1e-04). The NK cell ratio was also significantly lower in pre-diagnostic samples of TNBC cases compared with controls (P = 0.019).

CONCLUSION

This immunomethylomic study shows that a shift in the ratios/proportions of leukocyte subtypes is associated with TNBC, with decreased NK cell showing the strongest association. These findings improve our knowledge of the role of the immune system in TNBC and point to the possibility of using NK cell level as a non-invasive molecular marker for TNBC risk assessment, early detection, and prevention.

High-throughput and affordable genome-wide methylation profiling of circulating cell-free DNA by methylated DNA sequencing (MeD-seq) of LpnPI digested fragments

Background

DNA methylation detection in liquid biopsies provides a highly promising and much needed means for real-time monitoring of disease load in advanced cancer patient care. Compared to the often-used somatic mutations, tissue- and cancer-type specific epigenetic marks affect a larger part of the cancer genome and generally have a high penetrance throughout the tumour. Here, we describe the successful application of the recently described MeD-seq assay for genome-wide DNA methylation profiling on cell-free DNA (cfDNA). The compatibility of the MeD-seq assay with different types of blood collection tubes, cfDNA input amounts, cfDNA isolation methods, and vacuum concentration of samples was evaluated using plasma from both metastatic cancer patients and healthy blood donors (HBDs). To investigate the potential value of cfDNA methylation profiling for tumour load monitoring, we profiled paired samples from 8 patients with resectable colorectal liver metastases (CRLM) before and after surgery.

Results

The MeD-seq assay worked on plasma-derived cfDNA from both EDTA and CellSave blood collection tubes when at least 10 ng of cfDNA was used. From the 3 evaluated cfDNA isolation methods, both the manual QIAamp Circulating Nucleic Acid Kit (Qiagen) and the semi-automated Maxwell® RSC ccfDNA Plasma Kit (Promega) were compatible with MeD-seq analysis, whereas the QiaSymphony DSP Circulating DNA Kit (Qiagen) yielded significantly fewer reads when compared to the QIAamp kit (p < 0.001). Vacuum concentration of samples before MeD-seq analysis was possible with samples in AVE buffer (QIAamp) or water, but yielded inconsistent results for samples in EDTA-containing Maxwell buffer. Principal component analysis showed that pre-surgical samples from CRLM patients were very distinct from HBDs, whereas post-surgical samples were more similar. Several described methylation markers for colorectal cancer monitoring in liquid biopsies showed differential methylation between pre-surgical CRLM samples and HBDs in our data, supporting the validity of our approach. Results for MSC, ITGA4, GRIA4, and EYA4 were validated by quantitative methylation specific PCR.

Conclusions

The MeD-seq assay provides a promising new method for cfDNA methylation profiling. Potential future applications of the assay include marker discovery specifically for liquid biopsy analysis as well as direct use as a disease load monitoring tool in advanced cancer patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01177-4.

What can clinical immunology learn from inborn errors of epigenetic regulators?

The epigenome is at the interface between environmental factors and the genome, regulating gene transcription, DNA repair, and replication. Epigenetic modifications play a crucial role in establishing and maintaining cell identity and are especially crucial for neurology, musculoskeletal integrity, and the function of the immune system. Mutations in genes encoding for the components of the epigenetic machinery lead to the development of distinct disorders, especially involving the central nervous system and host defense. In this review, we focus on the role of epigenetic modifications for the function of the immune system. By studying the immune phenotype of patients with monogenic mutations in components of the epigenetic machinery (inborn errors of epigenetic regulators), we demonstrate the importance of DNA methylation, histone modifications, chromatin remodeling, noncoding RNAs, and mRNA processing for immunity. Moreover, we give a short overview on therapeutic strategies targeting the epigenome.

Comprehensive Analysis of CDCAs Methylation and Immune Infiltrates in Hepatocellular Carcinoma

Background

As essential components of cycle growth, the cell division cycle-associated family genes (CDCAs) have crucial roles in tumor development and progression, especially in hepatocellular carcinoma (HCC). However, due to the tumor heterogeneity of HCC, little is known about the methylation variability of CDCAs in mediating phenotypic changes (e.g., immune infiltrates) in HCC. Presently, we aim to comprehensively explore the expression and prognosis of CDCAs methylation with regard to immune infiltrates of HCC.

Methods

We first identified the correlating differentially expressed genes (co-DEGs) among 19 different types of cancer cohorts (a total of 7,783 patients) and then constructed the weighted gene co-expressed and co-methylated networks. Applying the clustering analysis, significant modules of DEGs including CDCAs were selected and their functional bioinformatics analyses were performed. Besides, using DiseaseMeth and TIMER, the correlation between the methylation levels of CDCAs and tumor immune infiltrates was also analyzed. In final, to assess the influence of CDCAs methylation on clinical prognosis, Kaplan-Meier and Cox regression analysis were carried out.

Result

A total of 473 co-DEGs are successfully identified, while seven genes of CDCAs (CDCA1–3 and CDCA5–8) have significant over-expression in HCC. Co-expressed and co-methylated networks reveal the strong positive correlations in mRNA expression and methylation levels of CDCAs. Besides, the biological enrichment analysis of CDCAs demonstrates that they are significantly related to the immune function regulation of infiltrating immune cells in HCC. Also, the methylation analysis of CDCAs depicts the strong association with the tumor immunogenicity, i.e., low-methylation of CDCA1, CDCA2, and CDCA8 dramatically reduced the immune infiltrate levels of T cells and cytotoxic lymphocytes. Additionally, CDCA1–6 and CDCA8 with low-methylation levels significantly deteriorate the overall survival of patients in HCC.

Conclusions

The co-expressed and co-methylated gene networks of CDCAs show a powerful association with immune function regulation. And the methylation levels of CDCAs suggesting the prognostic value and infiltrating immune differences could be a novel and predictive biomarker for the response of immunotherapy.

Cell-free DNA and RNA-measurement and applications in clinical diagnostics with focus on metabolic disorders

Circulating cell-free DNA (cfDNA) and RNA (cfRNA) hold enormous potential as a new class of biomarkers for the development of noninvasive liquid biopsies in many diseases and conditions. In recent years, cfDNA and cfRNA have been studied intensely as tools for noninvasive prenatal testing, solid organ transplantation, cancer screening, and monitoring of tumors. In obesity, higher cfDNA concentration indicates accelerated cellular turnover of adipocytes during expansion of adipose mass and may be directly involved in the development of adipose tissue insulin resistance by inducing inflammation. Furthermore, cfDNA and cfRNA have promising diagnostic value in a range of obesity-related metabolic disorders, such as nonalcoholic fatty liver disease, type 2 diabetes, and diabetic complications. Here, we review the current and future applications of cfDNA and cfRNA within clinical diagnostics, discuss technical and analytical challenges in the field, and summarize the opportunities of using cfDNA and cfRNA in the diagnostics and prognostics of obesity-related metabolic disorders.

Methylation pattern of urinary DNA as a marker of kidney function decline in diabetes

INTRODUCTION

Renal tubular injury contributes to the decline in kidney function in patients with diabetes. Cell type-specific DNA methylation patterns have been used to calculate proportions of particular cell types. In this study, we developed a method to detect renal tubular injury in patients with diabetes by detecting exfoliated tubular cells shed into the urine based on tubular cell-specific DNA methylation patterns.

RESEARCH DESIGN AND METHODS

We identified DNA methylation patterns specific for human renal proximal tubular cells through compartment-specific methylome analysis. We next determined the methylation levels of proximal tubule-specific loci in urine sediment of patients with diabetes and analyzed correlation with clinical variables.

RESULTS

We identified genomic loci in SMTNL2 and G6PC to be selectively unmethylated in human proximal tubular cells. The methylation levels of SMTNL2 and G6PC in urine sediment, deemed to reflect the proportion of exfoliated proximal tubular cells due to injury, correlated well with each other. Methylation levels of SMTNL2 in urine sediment significantly correlated with the annual decline in estimated glomerular filtration rate. Moreover, addition of urinary SMTNL2 methylation to a model containing known risk factors significantly improved discrimination of patients with diabetes with faster estimated glomerular filtration rate decline.

CONCLUSIONS

This study demonstrates that patients with diabetes with continual loss in kidney function may be stratified by a specific DNA methylation signature through epigenetic urinalysis and provides further evidence at the level of exfoliated cells in the urine that injury of proximal tubular cells may contribute to pathogenesis of diabetic kidney disease.

Phenotypes from cell-free DNA

Cell-free DNA (cfDNA) has the potential to enable non-invasive detection of disease states and progression. Beyond its sequence, cfDNA also represents the nucleosomal landscape of cell(s)-of-origin and captures the dynamics of the epigenome. In this review, we highlight the emergence of cfDNA epigenomic methods that assess disease beyond the scope of mutant tumour genotyping. Detection of tumour mutations is the gold standard for sequencing methods in clinical oncology. However, limitations inherent to mutation targeting in cfDNA, and the possibilities of uncovering molecular mechanisms underlying disease, have made epigenomics of cfDNA an exciting alternative. We discuss the epigenomic information revealed by cfDNA, and how epigenomic methods exploit cfDNA to detect and characterize cancer. Future applications of cfDNA epigenomic methods to act complementarily and orthogonally to current clinical practices has the potential to transform cancer management and improve cancer patient outcomes.

MethylResolver-a method for deconvoluting bulk DNA methylation profiles into known and unknown cell contents

Bulk tissue DNA methylation profiling has been used to examine epigenetic mechanisms and biomarkers of complex diseases such as cancer. However, heterogeneity of cellular content in tissues complicates result interpretation and utility. In silico deconvolution of cellular fractions from bulk tissue data offers a fast and inexpensive alternative to experimentally measuring such fractions. In this study, we report the design, implementation, and benchmarking of MethylResolver, a Least Trimmed Squares regression-based method for inferring leukocyte subset fractions from methylation profiles of tumor admixtures. Compared to previous approaches MethylResolver is more accurate as unknown cellular content in the mixture increases and is able to resolve tumor purity-scaled immune cell-type fractions without a cancer-specific signature. We also present a pan-cancer deconvolution of TCGA, recapitulating that high eosinophil fraction predicts improved cervical carcinoma survival and identifying elevated B cell fraction as a previously unreported predictor of poor survival for papillary renal cell carcinoma.

DNA Methylation Signature for EZH2 Functionally Classifies Sequence Variants in Three PRC2 Complex Genes

Weaver syndrome (WS), an overgrowth/intellectual disability syndrome (OGID), is caused by pathogenic variants in the histone methyltransferase EZH2, which encodes a core component of the Polycomb repressive complex-2 (PRC2). Using genome-wide DNA methylation (DNAm) data for 187 individuals with OGID and 969 control subjects, we show that pathogenic variants in EZH2 generate a highly specific and sensitive DNAm signature reflecting the phenotype of WS. This signature can be used to distinguish loss-of-function from gain-of-function missense variants and to detect somatic mosaicism. We also show that the signature can accurately classify sequence variants in EED and SUZ12, which encode two other core components of PRC2, and predict the presence of pathogenic variants in undiagnosed individuals with OGID. The discovery of a functionally relevant signature with utility for diagnostic classification of sequence variants in EZH2, EED, and SUZ12 supports the emerging paradigm shift for implementation of DNAm signatures into diagnostics and translational research.

Computational methods in tumor immunology

The remarkable success of cancer immunotherapies, especially the checkpoint blocking antibodies, in a subset of patients has reinvigorated the study of tumor-immune crosstalk and its role in heterogeneity of response. High-throughput sequencing and imaging technologies can help recapitulate various aspects of the tumor ecosystem. Computational approaches provide an arsenal of tools to efficiently analyze, quantify and integrate multiple parameters of tumor immunity mined from these diverse but complementary high-throughput datasets. This chapter describes numerous such computational approaches in tumor immunology that leverage high-throughput data from diverse sources (genomic, transcriptomics, epigenomics and digitized histopathology images) to systematically interrogate tumor immunity in context of its microenvironment, and to identify mechanisms that confer resistance or sensitivity to cancer therapies, in particular immunotherapy.

Differential DNA methylation in experienced meditators after an intensive day of mindfulness-based practice: Implications for immune-related pathways

The human methylome is dynamically influenced by psychological stress. However, its responsiveness to stress management remains underexplored. Meditation practice has been shown to significantly reduce stress level, among other beneficial neurophysiological outcomes. Here, we evaluated the impact of a day of intensive meditation practice (t2-t1 = 8 h) on the methylome of peripheral blood mononuclear cells in experienced meditators (n = 17). In parallel, we assessed the influence of a day of leisure activities in the same environment on the methylome of matched control subjects with no meditation experience (n = 17). DNA methylation profiles were analyzed using the Illumina 450 K beadchip array. We fitted for each methylation site a linear model for multi-level experiments which adjusts the variation between t1 and t2 for baseline differences. No significant baseline differences in methylation profiles was detected between groups. In the meditation group, we identified 61 differentially methylated sites (DMS) after the intervention. These DMS were enriched in genes mostly associated with immune cell metabolism and ageing and in binding sites for several transcription factors involved in immune response and inflammation, among other functions. In the control group, no significant change in methylation level was observed after the day of leisure activities. These results suggest that a short meditation intervention in trained subjects may rapidly influence the epigenome at sites of potential relevance for immune function and provide a better understanding of the dynamics of the human methylome over short time windows.

Prediagnostic Immune Cell Profiles and Breast Cancer

IMPORTANCE

Higher overall leukocyte counts in women may be associated with increased risk of breast cancer, but the association of specific leukocyte subtypes with breast cancer risk remains unknown.

OBJECTIVE

To determine associations between circulating leukocyte subtypes and risk of breast cancer.

DESIGN, SETTING, AND PARTICIPANTS

Between 2003 and 2009, the Sister Study enrolled 50 884 women who had a sister previously diagnosed with breast cancer but were themselves breast cancer free. A case-cohort subsample was selected in July 2014 from the full Sister Study cohort. Blood samples were obtained at baseline, and women were followed up through October 2016. Data analysis was performed in April 2019.

MAIN OUTCOMES AND MEASURES

The main outcome was the development of breast cancer in women. Whole-blood DNA methylation was measured, and methylation values were deconvoluted using the Houseman method to estimate proportions of 6 leukocyte subtypes (B cells, natural killer cells, CD8+ and CD4+ T cells, monocytes, and granulocytes). Leukocyte subtype proportions were dichotomized at their population median value, and Cox proportional hazard models were used to estimate associations with breast cancer.

RESULTS

Among 2774 non-Hispanic white women included in the analysis (mean [SD] age at enrollment, 56.6 [8.8] years), 1295 women were randomly selected from the full cohort (of whom 91 developed breast cancer) along with an additional 1479 women who developed breast cancer during follow-up (mean [SD] time to diagnosis, 3.9 [2.2] years). Circulating proportions of B cells were positively associated with later breast cancer (hazard ratio [HR], 1.17; 95% CI, 1.01-1.36; P = .04). Among women who were premenopausal at blood collection, the association between B cells and breast cancer was significant (HR, 1.38; 95% CI, 1.05-1.82; P = .02), and an inverse association for circulating proportions of monocytes was found (HR, 0.75; 95% CI, 0.57-0.99; P = .05). Among all women, associations between leukocyte subtypes and breast cancer were time dependent: higher monocyte proportions were associated with decreased near-term risk (within 1 year of blood collection, HR, 0.62; 95% CI, 0.43-0.89; P = .01), whereas higher B cell proportions were associated with increased risk 4 or more years after blood collection (HR, 1.38; 95% CI, 1.15-1.67; P = .001).

CONCLUSIONS AND RELEVANCE

Circulating leukocyte profiles may be altered before clinical diagnoses of breast cancer and may be time-dependent markers for breast cancer risk, particularly among premenopausal women.

Immunomethylomics: A Novel Cancer Risk Prediction Tool

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.

Virtual methylome dissection facilitated by single-cell analyses

Background

Numerous cell types can be identified within plant tissues and animal organs, and the epigenetic modifications underlying such enormous cellular heterogeneity are just beginning to be understood. It remains a challenge to infer cellular composition using DNA methylomes generated for mixed cell populations. Here, we propose a semi-reference-free procedure to perform virtual methylome dissection using the nonnegative matrix factorization (NMF) algorithm.

Results

In the pipeline that we implemented to predict cell-subtype percentages, putative cell-type-specific methylated (pCSM) loci were first determined according to their DNA methylation patterns in bulk methylomes and clustered into groups based on their correlations in methylation profiles. A representative set of pCSM loci was then chosen to decompose target methylomes into multiple latent DNA methylation components (LMCs). To test the performance of this pipeline, we made use of single-cell brain methylomes to create synthetic methylomes of known cell composition. Compared with highly variable CpG sites, pCSM loci achieved a higher prediction accuracy in the virtual methylome dissection of synthetic methylomes. In addition, pCSM loci were shown to be good predictors of the cell type of the sorted brain cells. The software package developed in this study is available in the GitHub repository (https://github.com/Gavin-Yinld).

Conclusions

We anticipate that the pipeline implemented in this study will be an innovative and valuable tool for the decoding of cellular heterogeneity.

Unravelling cellular relationships during development and regeneration using genetic lineage tracing

Tracking the progeny of single cells is necessary for building lineage trees that recapitulate processes such as embryonic development and stem cell differentiation. In classical lineage tracing experiments, cells are fluorescently labelled to allow identification by microscopy of a limited number of cell clones. To track a larger number of clones in complex tissues, fluorescent proteins are now replaced by heritable DNA barcodes that are read using next-generation sequencing. In prospective lineage tracing, unique DNA barcodes are introduced into single cells through genetic manipulation (using, for example, Cre-mediated recombination or CRISPR-Cas9-mediated editing) and tracked over time. Alternatively, in retrospective lineage tracing, naturally occurring somatic mutations can be used as endogenous DNA barcodes. Finally, single-cell mRNA-sequencing datasets that capture different cell states within a developmental or differentiation trajectory can be used to recapitulate lineages. In this Review, we discuss methods for prospective or retrospective lineage tracing and demonstrate how trajectory reconstruction algorithms can be applied to single-cell mRNA-sequencing datasets to infer developmental or differentiation tracks. We discuss how these approaches are used to understand cell fate during embryogenesis, cell differentiation and tissue regeneration.

Epigenetic immune cell counting in human blood samples for immunodiagnostics

Immune cell profiles provide valuable diagnostic information for hematologic and immunologic diseases. Although it is the most widely applied analytical approach, flow cytometry is limited to liquid blood. Moreover, either analysis must be performed with fresh samples or cell integrity needs to be guaranteed during storage and transport. We developed epigenetic real-time quantitative polymerase chain reaction (qPCR) assays for analysis of human leukocyte subpopulations. After method establishment, whole blood from 25 healthy donors and 97 HIV⁺ patients as well as dried spots from 250 healthy newborns and 24 newborns with primary immunodeficiencies were analyzed. Concordance between flow cytometric and epigenetic data for neutrophils and B, natural killer, CD3⁺ T, CD8⁺ T, CD4⁺ T, and FOXP3⁺ regulatory T cells was evaluated, demonstrating substantial equivalence between epigenetic qPCR analysis and flow cytometry. Epigenetic qPCR achieves both relative and absolute quantifications. Applied to dried blood spots, epigenetic immune cell quantification was shown to identify newborns suffering from various primary immunodeficiencies. Using epigenetic qPCR not only provides a precise means for immune cell counting in fresh-frozen blood but also extends applicability to dried blood spots. This method could expand the ability for screening immune defects and facilitates diagnostics of unobservantly collected samples, for example, in underdeveloped areas, where logistics are major barriers to screening.

Computational approaches for characterizing the tumor immune microenvironment

Recent advances in high-throughput molecular profiling technologies and multiplexed imaging platforms have revolutionized our ability to characterize the tumor immune microenvironment. As a result, studies of tumor-associated immune cells increasingly involve complex data sets that require sophisticated methods of computational analysis. In this review, we present an overview of key assays and related bioinformatics tools for analyzing the tumor-associated immune system in bulk tissues and at the single-cell level. In parallel, we describe how data science strategies and novel technologies have advanced tumor immunology and opened the door for new opportunities to exploit host immunity to improve cancer clinical outcomes.

Detection of cell-type-specific risk-CpG sites in epigenome-wide association studies

In epigenome-wide association studies, the measured signals for each sample are a mixture of methylation profiles from different cell types. Current approaches to the association detection claim whether a cytosine-phosphate-guanine (CpG) site is associated with the phenotype or not at aggregate level and can suffer from low statistical power. Here, we propose a statistical method, HIgh REsolution (HIRE), which not only improves the power of association detection at aggregate level as compared to the existing methods but also enables the detection of risk-CpG sites for individual cell types.

Cellular heterogeneity is one of the major confounding factors in EWAS studies. Here the authors present a statistical method, HIgh REsolution (HIRE), which enables the detection of risk-CpG sites for individual cell types.

Insights from deconvolution of cell subtype proportions enhance the interpretation of functional genomic data

Cell subtype proportion variability between samples contributes significantly to the variation of functional genomic properties such as gene expression or DNA methylation. Although the impact of the variation of cell subtype composition on measured genomic quantities is recognized, and some innovative tools have been developed for the analysis of heterogeneous samples, most functional genomics studies using samples with mixed cell types still ignore the influence of cell subtype proportion variation, or just deal with it as a nuisance variable to be eliminated. Here we demonstrate how harvesting information about cell subtype proportions from functional genomics data can provide insights into cellular changes associated with phenotypes. We focused on two types of mixed cell populations, human blood and mouse kidney. Cell type prediction is well developed in the former, but not currently in the latter. Estimating the cellular repertoire is easier when a reference dataset from purified samples of all cell types in the tissue is available, as is the case for blood. However, reference datasets are not available for most other tissues, such as the kidney. In this study, we showed that the proportion of alterations attributable to changes in the cellular composition varies strikingly in the two disorders (asthma and systemic lupus erythematosus), suggesting that the contribution of cell subtype proportion changes to functional genomic properties can be disease-specific. We also showed that a reference dataset from a single-cell RNA-seq study successfully estimated the cell subtype proportions in mouse kidney and allowed us to distinguish altered cell subtype differences between two different knock-out mouse models, both of which had reported a reduced number of glomeruli compared to their wild-type counterparts. These findings demonstrate that testing for changes in cell subtype proportions between conditions can yield important insights in functional genomics studies.

Cell lineage-specific genome-wide DNA methylation analysis of patients with paediatric-onset systemic lupus erythematosus

Patients with paediatric-onset systemic lupus erythematosus (SLE) often present with more severe clinical courses than adult-onset patients. Although genome-wide DNA methylation (DNAm) profiling has been performed in adult-onset SLE patients, parallel data on paediatric-onset SLE are not available. Therefore, we undertook a genome-wide DNAm study in paediatric-onset SLE patients across multiple blood cell lineages. The DNAm profiles of four purified immune cell lineages (CD4 + T cells, CD8 + T cells, B cells and neutrophils) and whole blood were compared in 16 Chinese patients with paediatric-onset SLE and 13 healthy controls using the Illumina HumanMethylationEPIC BeadChip. Comparison of DNAm in whole blood and within each independent cell lineage identified a consistent pattern of loss of DNAm at 21 CpG sites overlapping 15 genes, which represented a robust, disease-specific DNAm signature for paediatric-onset SLE in our cohort. In addition, cell lineage-specific changes, involving both loss and gain of DNAm, were observed in both novel genes and genes with well-described roles in SLE pathogenesis. This study also highlights the importance of studying DNAm changes in different immune cell lineages rather than only whole blood, since cell type-specific DNAm changes facilitated the elucidation of the cell type-specific molecular pathophysiology of SLE.

Novel Assay for Quantitative Analysis of DNA Methylation at Single-Base Resolution

BACKGROUND

The DNA methylation profile provides valuable biological information with potential clinical utility. Several methods, such as quantitative methylation-specific PCR (qMSP), have been developed to examine methylation of specific CpG sites. Existing qMSP-based techniques fail to examine the genomic methylation at a single-base resolution, particularly for loci in gene bodies or extensive CpG open seas lacking flanking CpGs. Therefore, we established a novel assay for quantitative analysis of single-base methylation.

METHODS

To achieve a robust single-base specificity, we developed a PCR-based method using paired probes following bisulfite treatment. The 6-carboxyfluorescein- and 2'-chloro-7'phenyl-1,4-dichloro-6-carboxy-fluorescein-labeled probes conjugated with minor groove binder were designed to specifically bind to the methylated and unmethylated allele of targeted single CpGs at their 3' half regions, respectively. The methylation percentage was calculated by values of methylation / (methylation + unmethylation).

RESULTS

In the detection of single CpGs within promoters or bodies of 4 human genes, the quantitative analysis of the single-base methylation assay showed a detection capability in the 1 to 1:10000 dilution experiments with linearity over 4 orders of magnitude (R ² = 0.989-0.994; all P < 0.001). In a cohort of 10 colorectal cancer samples, the assay showed a comparable detection performance with bisulfite pyrosequencing (R ² = 0.875-0.990; all P < 0.001), which was better than conventional qMSP methods normalized by input control reaction (R ² = 0.841 vs 0.769; P = 0.002 vs 0.009).

CONCLUSIONS

This assay is highly specific and sensitive for determining single-base methylation and, thus, is potentially useful for methylation-based panels in diagnostic and prognostic applications.

Epigenetic subtyping of white blood cells using a thermoplastic elastomer-based microfluidic emulsification device for multiplexed, methylation-specific digital droplet PCR

Digital droplet PCR for epigenetic leukocyte subtyping from clinically relevant samples is implemented using a thermoplastic elastomer microfluidic droplet generator as a first step towards an economical, customizable and easily deployable system.

Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease

Methylation patterns of circulating cell-free DNA (cfDNA) contain rich information about recent cell death events in the body. Here, we present an approach for unbiased determination of the tissue origins of cfDNA, using a reference methylation atlas of 25 human tissues and cell types. The method is validated using in silico simulations as well as in vitro mixes of DNA from different tissue sources at known proportions. We show that plasma cfDNA of healthy donors originates from white blood cells (55%), erythrocyte progenitors (30%), vascular endothelial cells (10%) and hepatocytes (1%). Deconvolution of cfDNA from patients reveals tissue contributions that agree with clinical findings in sepsis, islet transplantation, cancer of the colon, lung, breast and prostate, and cancer of unknown primary. We propose a procedure which can be easily adapted to study the cellular contributors to cfDNA in many settings, opening a broad window into healthy and pathologic human tissue dynamics.

The methylation status of circulating cell-free DNA (cfDNA) can be informative about recent cell death events. Here the authors present an approach to determine the tissue origins of cfDNA, using a reference methylation atlas of 25 human tissues and cell types, and find that cfDNA from patients reveals tissue contributions that agree with clinical findings.