Differential Methylation Analysis Research Articles

Age-related changes in DNA methylation in a sample of elderly Brazilians

BackgroundAge-related changes in DNA methylation (DNAm) play a critical role in regulating gene expression. However, most epigenome-wide association studies have predominantly focused on individuals of European descent. This study aims to characterize longitudinal changes in DNAm patterns in a cohort of elderly Brazilian participants.MethodsDNAm profiles were collected approximately nine years apart from 23 elderly Brazilian individuals using the Illumina Infinium MethyationEPIC BeadChip. Using mixed-effects models, we examined changes in DNAm patterns using both quantitative age and binary timepoint (e.g., baseline vs. follow-up) as predictors of interest to identify differentially methylated positions (DMPs). Significant DMPs were compared with a list of previously identified age-related DMPs. Differentially methylated regions (DMRs) were also identified using DMRcate. Gene ontology (GO) pathway enrichment analyses were performed to explore the functional significance of identified DMPs and DMRs.ResultsOf the 586,229 autosomal probes included in the differential methylation analyses, 2768 significant (FDR < 0.05) age-associated DMPs (aDMPs) and 2757 significant (FDR < 0.05) timepoint-associated DMPs (tpDMPs) were identified. Of the 2768 aDMPs, 1471 were replicated from previous studies. Of the 1297 non-replicated CpGs, 77.4% were exclusive to the EPIC array. The DMR analyses identified 305 age-associated DMRs (aDMRs) and 372 timepoint-associated DMRs (tpDMRs). Both aDMPs and aDMRs exhibited age-related hypermethylation within CpG islands and promoter regions of the genome, whereas hypomethylation predominantly occurred in interCGI and intergenic regions and introns. The GO enrichment analyses identified several neurological and cognition-related pathways enriched for hypermethylated CpG islands, many of which were mapped near transcription start sites and first exon regions.ConclusionsThis longitudinal study identified age-associated and timepoint-associated DMPs and DMRs in a sample of elderly Brazilians. Most of the non-replicated CpGs were found to be on the new EPIC array, suggesting that more age-related studies using the EPIC array are required to validate these CpGs. The GO pathway enrichment analyses identified age-related enrichment of several gene sets related to cognitive and physical decline in elderly populations. The enrichment of these sites could provide evidence for age-related neurodegeneration and cognitive decline in elderly populations.

The Impact of Air Pollution on Sperm DNA Methylation.

A number of environmental factors have been shown to impact the sperm epigenome. Air pollution is one of the largest health and environmental hazards in the world today and has been implicated in many modern diseases. Recently, air pollution has been shown to alter methylation signatures in some body tissues, indicating that air pollution may also affect the sperm epigenome. The present experiment was conducted to analyze how seasonal air pollution in the Salt Lake Valley may impact DNA methylation patterns in sperm and to establish a relationship between air pollution and sperm epigenetic health as measured by DNA methylation. Sperm DNA methylation patterns were assessed in 74 individuals, who presented at the University of Utah Andrology Clinic for semen analysis, using the Illumina Human MethylationEPIC BeadChip array. Each semen sample collected, as per the fifth edition of WHO reference values for human semen characterization, was deemed normal. Two sample groups from the Salt Lake Valley, Urban Winter (UW, n=20), Urban Summer (US, n=21), and two sample groups east of the Wasatch mountains, Rural Winter (RW, n=19) and Rural Summer (RS, n=14), were compared to assess the effect of air pollution on sperm DNA methylation patterns. Due to seasonal inversions, urban winters are characterized by increased air pollution compared to summer months. Therefore, the UW sample group was designated as treatment and the three remaining groups (US, RW, RS) were designated as control. We conducted multiple differential methylation analyses using a sliding window approach which utilized the USeq software package. A sliding window analysis of UW versus US was conducted first, followed by a confirmatory analysis comparing UW versus RW and RS. Outputs from the USeq analysis were assessed using several tools including the Stanford GREAT analysis and an analysis of methylation instability at key promoter regions in sperm. The sliding window analysis identified six differentially methylated regions (DMRs) between the UW and US groups (Wilcoxon FDR ≥ 40, corresponding p-value of ~0.0001). Three of these six regions were confirmed with the second confirmatory analysis of UW versus RS/RW (Wilcoxon FDR ≥ 20, p-value<0.01). According to a GREAT analysis, each of the identified regions exhibited multiple gene ontology associations. Air pollution subtly alters DNA methylation in sperm, indicating that certain regions of the sperm epigenome may be susceptible to air pollution-induced modification with possible implications for reproductive and offspring health.

Non-invasive diagnosis for urothelial carcinoma using a dual-target DNA methylation biomarker panel.

Urothelial carcinoma (UC) is a common malignancy worldwide. Aberrant DNA methylation is implicated in UC carcinogenesis. This study sought to delineate the DNA methylation landscape in UC and identify DNA methylation-based biomarkers for early detection of UC. Whole genome bisulfite sequencing (WGBS) was conducted on bladder cancer tissues and paired normal tissues. By integrating WGBS data with The Cancer Genome Atlas (TCGA) UBC data, a DNA methylation-based biomarker was identified. When combined with a known UC biomarker AL021918.2, the performance of the dual-target test was evaluated in voided urine samples from 224 UC patients and 419 controls. Notable hypomethylation was observed in UC samples compared to normal samples. Through differential methylation analysis, differential methylation CpG sites, regions, and genes were identified. Of these, Transmembrane protein 106A gene (TMEM106A) was screened as a new UC biomarker. In a dual-target test, using triplex quantitative methylation-specific PCR (qMSP) to examine TMEM106A and AL021918.2 methylation levels, the training set showed a sensitivity of 89.0%, a specificity of 92.9%, and an area under the curve (AUC) value of 0.941 (95% confidence interval [CI]: 0.913-0.969). Similarly, the validation set showed a sensitivity of 90.0%, a specificity of 91.1%, and an AUC value of 0.922 (95% CI: 0.881-0.962). Thus, our dual-target test demonstrated outstanding detection rates for low-grade or early-stage tumors. We provide a comprehensive analysis of DNA methylation profiles in UC, and highlight the promising clinical potential of dual-target urine tests for UC detection.

P0008 A multi-omics approach across multiple chronic inflammatory diseases identifies a distinct type-1-interferon endotype of Inflammatory Bowel Disease

Abstract Background IBD shares many clinical and immunological features with other chronic inflammatory diseases (CIDs) like rheumatoid arthritis (RA) and psoriasis (Pso). Extraintestinal manifestations, particularly in the joints and skin, often overlap with these CIDs. We hypothesize that a systems medicine approach involving omics-driven phenotyping of multiple CIDs can yield a novel, therapy-relevant molecular taxonomy of disease. In this study, we aim to identify IBD-specific molecular signatures and those shared with other CIDs, as well as novel endotypes of IBD. Methods We recruited a cross-sectional cohort of 650 patients with CIDs (CD, UC, RA, Pso, psoriatic arthritis [PsA], and systemic lupus erythematosus [SLE]) along with 202 healthy controls (HC) at the University Hospital Kiel. Active or inactive disease was defined by clinical activity scores (i.e., CD: CDAI &amp;lt;150; UC: partial Mayo ≤2). Blood samples were collected and subjected to RNA sequencing and methylation profiling. Detailed clinical phenotypes and 5 years-follow-up data were obtained by chart review. We conducted differential gene expression and methylation analyses comparing each CID with HCs to identify shared molecular signatures. Core (activity-independent) signatures were defined by the overlap of differential expression in both active vs. HC and inactive disease vs. HC. Biclustering methods (UnPaSt) were used to uncover distinct patient endotypes based on expression profiles. Results IBD shared the most differential signatures with RA, indicating common pathways in their pathogenesis. Core signatures upregulated across all CIDs correlated with DNA methylation changes, reflecting the molecular mechanism of chronicity, and were enriched in pathways related to cytokine signaling, neutrophil chemotaxis, and humoral immune response. While methylation signatures in enhancer regions were shared among most CIDs, disease-specific methylation was mainly located in CpG islands. One disease-overarching subgroup of 71 patients with active disease, identified by biclustering, exhibited upregulation of interleukin-27 and type-1 interferon signaling, including 16 out of 121 active IBD patients with a skewed sex ratio toward females and higher rates of therapy escalations within 5 years (Log-rank test: p=0.0089). Conclusion Our study highlights shared and disease-specific immune dysregulation pathways contributing to the pathogenesis of IBD and other CIDs. We show the clear presence of core signatures of disease regardless of disease activity, which could represent insights into actionable targets for causative treatments and better disease control of CIDs. With our approach, we identified a distinct molecular endotype of IBD with worse prognosis, warranting further validation studies. References This project has received funding from the DFG Cluster of Excellence 2167 "Precision medicine in chronic inflammation", the BMBF (e:Med Network iTREAT 01ZX2202A), the European Union/Horizon2020 (SYSCID, No 733100) and the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 831434 (3TR) and No 853995 (ImmUniverse).The JU receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA. The content provided in this publication reflects the author's views only. Neither the Innovative Medicines Initiative (IMI JU) nor the European Commission are responsible for any use that may be made of the information it contains.

Genome-Wide DNA Methylation Confirms Oral Squamous Cell Carcinomas in Proliferative Verrucous Leukoplakia as a Distinct Oral Cancer Subtype: A Case-Control Study.

Oral cancers in patients with proliferative verrucous leukoplakia (PVL-OSCC) exhibit different clinical and prognostic outcomes from those seen in conventional oral squamous cell carcinomas (cOSSCs). The aim of the present study is to compare the genome-wide DNA methylation signatures in fresh frozen tissues between oral squamous cell carcinomas in patients with PVL and cOSCC using the Illumina Infinium MethylationEPIC BeadChip. This case-control study was carried out at the Stomatology and Maxillofacial Surgery Department of the General University Hospital of Valencia. For the epigenomic study, unsupervised exploratory bioinformatic analyses were performed using principal component and heatmap analysis. Supervised differential methylation analyses were conducted using a rank-based regression model and a penalized logistic regression model to identify potential prognostic biomarkers. The unsupervised analyses of the global methylation profiles did not allow us to differentiate between the distinct oral cancer groups. However, the two supervised analyses confirmed the existence of two oral carcinoma phenotypes. We identified 21 differentially methylated CpGs corresponding to 14 genes. Among them, three CpGs had not been previously assigned to any known gene, and the remaining were associated with genes unrelated to oral cancer. The AGL, WRB, and ARL15 genes were identified as potential prognostic biomarkers. This study emphasizes the significant role of epigenetic dysregulation in OSCC, particularly in cases preceded by PVL. We have provided data on differential methylation genes that could be involved in the molecular carcinogenesis of PVL-OSCC.

EdgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets.

edgeR is an R/Bioconductor software package for differential analyses of sequencing data in the form of read counts for genes or genomic features. Over the past 15years, edgeR has been a popular choice for statistical analysis of data from sequencing technologies such as RNA-seq or ChIP-seq. edgeR pioneered the use of the negative binomial distribution to model read count data with replicates and the use of generalized linear models to analyze complex experimental designs. edgeR implements empirical Bayes moderation methods to allow reliable inference when the number of replicates is small. This article announces edgeR version 4, which includes new developments across a range of application areas. Infrastructure improvements include support for fractional counts, implementation of model fitting in C and a new statistical treatment of the quasi-likelihood pipeline that improves accuracy for small counts. The revised package has new functionality for differential methylation analysis, differential transcript expression, differential transcript and exon usage, testing relative to a fold-change threshold and pathway analysis. This article reviews the statistical framework and computational implementation of edgeR, briefly summarizing all the existing features and functionalities but with special attention to new features and those that have not been described previously.

CYP24A1 DNA Methylation in Colorectal Cancer as Potential Prognostic and Predictive Markers.

The DNA methylation of CYP24A1 can regulate its gene expression and may play a role in the occurrence and progression of colorectal cancer (CRC). However, the association between CYP24A1 DNA methylation and the prognosis of CRC patients has not yet been reported. In this study, differential methylation analysis was conducted in both blood and tissue cohorts, and differential expression analysis was performed in the tissue cohort with in vitro validation. GO and KEGG enrichment analyses were performed on CYP24A1-related genes. A correlation between CYP24A1 promoter methylation and its gene expression was explored. Kaplan-Meier survival and Cox regression analyses were performed to investigate the impact of CYP24A1 DNA methylation on the prognosis of CRC patients. Prognostic risk scores were constructed for survival prediction. Immune infiltration analysis was also conducted. Our results showed that the hypermethylation of cg02712555 in tumor tissues (hazard ratio, 0.48; 95% confidence interval, 0.24-0.94; p = 0.032) and CpG site 41 in peripheral leukocytes (HR, 0.35; 95%CI, 0.14-0.84; p = 0.019) were both associated with decreased overall mortality in CRC patients. Prognostic risk scores showed robust predictive capabilities of these two CpG loci for the prognosis of CRC patients. CYP24A1 hypermethylation was positively correlated with infiltration levels of activated CD4 + T cells, activated CD8 + T cells, activated B cells, activated dendritic cells, and macrophages. Taken together, our findings indicate that the methylation levels of specific CpG sites within the CYP24A1 promoter region in blood leukocytes and tumors are potential prognostic and predictive markers for overall survival in CRC patients.

Differential DNA methylation in the benign and cancerous prostate tissue of African American and European American men.

African American (AA) men are at increased risk of prostate cancer (PCa) compared to men of European ancestry (EA). Biological mechanisms, including epigenetics, likely contribute to this disparity, but prior studies have been limited by sample size, candidate gene approaches, or lack of epigenome-wide DNA methylation (DNAm) data. To improve our understanding of these mechanisms, we compared DNAm features distinguishing tumor and paired histologically benign tissue from 76 AA and 75 EA PCa patients. We measured genome-wide array-based DNAm data and conducted differential methylation analyses comparing tumor and benign tissue in each ancestry group. We then examined the predictive ability of our identified sites and differential methylation by ancestry group in each tissue. We identified 90,747 and 98,929 differentially methylated CpGs (dmCpGs) in AA and EA respectively with 76,400 common to both groups. We identified 6,267 genes with differentially methylated promoters common to both ancestries and 639 and 1,301 genes unique to AA and EA respectively as well as differentially methylated pathways. Only 10 CpGs were needed to distinguish tumor from benign based on a receiver operating characteristic curve (area under the curve, AUC>0.9), with dmCpGs in one ancestry predicting tumor vs benign in the other group. We also identified ancestry-associated CpGs (89 in tumor, 423 in benign). Methylation features distinguishing tumor and benign were similar for EA and AA men; however, subtle differences were identified. Differences in tumor and ancestry-associated CpGs may reveal differential tumor growth strategies, an important area for future disparities research.

Genome-wide DNA methylation analysis reveals a unique methylation pattern for pleural mesothelioma compared to healthy pleura and other lung diseases

BackgroundPleural mesothelioma (PM) is a rare and aggressive cancer type, typically diagnosed at advanced stages. Distinguishing PM from other lung diseases is often challenging. There is an urgent need for biomarkers that can enable early detection. Interest in the field of epigenetics has increased, particularly in the context of tumour development and biomarker discovery. This study aims to identify specific changes in DNA methylation from healthy pleural tissue to PM and to compare these methylation patterns with those found in other lung diseases.ResultsEPIC methylation array data (850 K) were generated for 11 PM and 29 healthy pleura in-house collected samples. This is the first time such a large dataset of healthy pleura samples has been generated. Additional EPIC methylation array data (850 K) for pleural mesothelioma and other lung-related diseases were downloaded from public databases. We conducted pairwise differential methylation analyses across all tissue types, which facilitated the identification of significantly differentially methylated CpG sites. Extensive differential methylation between PM and healthy pleura was observed, identifying 81,968 differentially methylated CpG sites across all genomic regions. Among these, five CpG sites located within four genes (MIR21, RNF39, SPEN and C1orf101) exhibited the most significant and pronounced methylation differences between PM and healthy pleura. Moreover, our analysis delineated distinct methylation patterns specific to PM subtypes. Finally, the methylation profiles of PM were distinctly different from those of other lung cancers, enabling accurate differentiation.ConclusionsDNA methylation analyses provide a robust method for distinguishing PM from healthy pleural tissues, and specific methylation patterns exist within PM subtypes. These methylation differences underscore their importance in understanding disease progression and may serve as viable biomarkers or therapeutic targets. Moreover, differential methylation patterns between PM and other lung cancers highlights its diagnostic potential. These findings necessitate further translational studies to explore their clinical applications.

African origin haplotype protective for AD in APOEe4 carriers: exploring potential mechanisms

AbstractBackgroundThe effect size of APOE4 varies across genetic ancestries with African (AFR) local ancestry conferring a lower risk when compared to other ancestries. Recently, we identified a strong effect of the A allele of rs10423769 (with a minor allele frequency of 0.12 in AFR and 0.003 in Europeans), which lowers the risk of AD by 71% in APOE4 carriers. rs10423769 is 2 MB from APOEe4 in a region of segmental duplications (SD) containing a cluster of pregnancy specific beta‐1 glycoprotein genes (PSGs) and a lncRNA. To gain insights into the mechanism of protection, we characterized the haplotype harboring the variant and investigated differences in methylation and structural variation (SV) between haplotypes.MethodWe used the Alzheimer’s Disease Sequencing Project (ADSP) short read sequencing database to fine map and extend the protective haplotype using Haploview 4.2. In addition, long read sequencing (LRS) with the Oxford Nanopore PromethION was performed on 16 samples of individuals carrying one or two copies of rs10423769_A. LRS was used for SV calling with Sniffles2 and differential methylation analysis using Nanomethphase.ResultThe minimum shared haplotype harboring rs10423769_A allele was ∼21kbp (chr19:43099521‐ 43120243). Though the region is rich in SDs, high quality LRS reads mapped uniquely to the region around the A allele confirming that the haplotype is unique and not duplicated elsewhere in the genome of the sequenced individuals. Moreover, the rs10423769_A haplotype harbors 23 differential CpG sites (fdr&lt;0.05) compared to rs10423769_G, although no differential methylated region was identified. LRS revealed a 140bp insertion of repetitive MEF2 binding motifs. Initial examination demonstrated that the insertion was present in 80% of the A allele carriers.ConclusionWe have identified an AF‐specific haplotype protective against AD risk conferred by APOE ε4. Considering the distance from APOE and the complexity of the highly SD region where the variant is located, we have identified the minimum shared haplotype from AFR origin and are investigating the potential effects of SVs and differential CpG sites. Ongoing analyses include 3D chromatin structure and LRS at higher coverage to assemble and resolve the complex genomic context of the allele.

Navigating Illumina DNA methylation data: biology versus technical artefacts.

Illumina-based BeadChip arrays have revolutionized genome-wide DNA methylation profiling, pushing it into diagnostics. However, comprehensive quality assessment remains challenging within a wide range of available tissue materials and sample preparation methods. This study tackles two critical issues: differentiating between biological effects and technical artefacts in suboptimal quality samples and the impact of the first sample on the Illumina-like normalization algorithm. We introduce three quality control scores based on global DNA methylation distribution (DB-Score), bin distance from copy number variation analysis (BIN-Score) and consistently methylated CpGs (CM-Score) that rely on biological features rather than internal array controls. These scores, designed to be adjustable for different analysis tools and sample cohort characteristics, were explored and benchmarked across independent cohorts. Additionally, we reveal deviations in beta values caused by different sample rankings with the Illumina-like normalization algorithm, verified these with whole-genome methylation sequencing data and showed effects on differential DNA methylation analysis. Our findings underscore the necessity of consistently utilizing a pre-defined normalization sample within the ranking process to boost reproducibility of the Illumina-like normalization algorithm. Overall, our study delivers valuable insights, practical recommendations and R functions designed to enhance reproducibility and quality assurance of DNA methylation analysis, particularly for challenging sample types.

Genome-wide epigenetic profiling and transcriptome analysis in pediatric Obstructive Sleep Apnea: A focus on Black female children

Obstructive Sleep Apnea (OSA) is a common sleep-related breathing disorder characterized by airway obstruction during sleep. Diagnosing pediatric OSA is challenging, particularly in underrepresented populations, leading to disparities in treatment and long-term negative health outcomes. Our study aimed to identify alternative diagnostic tools by investigating genome-wide epigenetic changes and associated transcriptomic alterations in Black female, pediatric patients with OSA. Whole-genome bisulfite sequencing and RNA sequencing were performed on saliva samples from healthy controls and children with OSA. Analysis of differential methylation and gene expression patterns revealed dysregulated inflammation and metabolism pathways in children with OSA. Chromosomes 19 and 22 exhibited elevated methylation signatures in this patient population. Integration of methylation and gene expression data identified specific molecular markers, including NAP1L4, CCR1, and LIF. The study emphasizes the need to consider both genetic and environmental factors in pediatric OSA, and the identified markers may offer avenues for further research.

A two-site combined prediction model based on HOXA9 DNA methylation for early screening of risks of meningioma progression

To establish a recurrence risk prediction model for meningioma based on HOXA9 DNA methylation. Meningioma-related datasets were downloaded from GEO database for screening homeobox genes (HOXs) with prognostic values using differential methylation and ROC curve analysis and Cox regression analysis. The differentially methylated CpG sites with high predictive efficacy were selected to establish the risk prediction model using Lasso-Cox regression analysis, based on which the patients were divided into high- and low-risk groups by the cutoff value. The methylation levels of CpG sites were verified at the cell and tissue levels using methylation-specific PCR (MS-PCR). Clinical meningioma tissue samples were used to validate the predictive efficacy of the model. HOXA9 methylation level was significantly up-regulated in meningiomas (P< 0.001) and showed a high diagnostic efficiency (AUC=0.884) as an independent risk factor for overall survival (P< 0.01) positively correlated with the degree of malignancy and poor prognosis of meningioma (P< 0.05). Risk stratification by HOXA9 methylation was more accurate than WHO grading for predicting recurrence and patient survival time. The AUCs of the sites cg03217995 and cg21001184 were both above 0.8 for meningioma diagnosis and above 0.6 for predicting recurrence. The patients' clinical characteristics differed significantly between the high- and low-risk groups (P< 0.001), and the prediction score of the model was an independent prognostic factor for meningioma (P< 0.05). MSPCR results showed that the methylation levels of the two sites increased significantly in meningioma cells. In clinical samples, the combined model showed a high prediction efficiency (AUC=0.857), and the predicted risk of progression was highly consistent with the patients' actual condition. High HOXA9 methylation level is a predictor for poor prognosis of meningiomas, and the combined prediction model based on its CpG sites provides a new approach to early screening of meningioma patients at risk of progression.

Abstract B033: Identification of cell-specific DNA methylation alterations in the breast tumor microenvironment

Abstract The tumor microenvironment (TME) comprises diverse cell types whose interactions are crucial for cancer progression. Traditional bulk tissue analyses mask cell-type-specific epigenetic changes that may drive disease progression. We used DNA methylation data and a reference- based cell-type deconvolution algorithm — hierarchical tumor immune microenvironment epigenetic deconvolution (HiTIMED) — to quantify cell types in the TME. Then, we employed CellDMC, a statistical interaction testing framework that can use cell type proportions in conjunction with differential methylation analysis to identify cell-specific differential methylation. Genome-scale DNA methylation profiles from breast cancer tissue (n=609) and normal breast tissue (n=230) samples were accessed in GEO and GTEx. Our novel approach leverages bulk DNA methylation data to infer cell-type-specific epigenetic landscapes, circumventing challenges and limitations associated with direct single-cell methylation profiling, such as high costs and technical variability. Reference-based computational tools allow us to dissect complex tissue compositions and reveal distinct methylation patterns and pathways that may be unique to individual cell types, providing insights that are not discernible from traditional bulk tumor analysis approaches. Compared with nontumor normal samples, we identified significant DNA methylation alterations (FDR≤0.05) across tumor, stromal, endothelial, epithelial, lymphocytes, and myeloid cells in breast tumors. Tumor cells exhibited the highest number, 8,510, differentially methylated cytosines (DMCs) with 1,966 of these unique to tumor cells. Stromal, endothelial, and epithelial cells also showed considerable methylation alterations, with a total of 530, 440, 339 DMCs, respectively, with 5 DMCs unique to epithelial cells. Despite lower immune cell proportions in our samples, we identified 104 lymphocyte specific DMCs and 185 myeloid specific DMCs, with 14 and 20 DMCs unique to these cell types respectively. The unique DMCs identified in each cell type suggest distinct epigenetic landscapes that may drive specific cellular functions and interactions within the TME. Importantly, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the annotated cell-type-specific DMCs revealed distinct pathways that were not apparent in the pathway analysis of bulk tumor versus normal tissue, underscoring the value of examining epigenetic alterations at the cell-type level. Our findings highlight the opportunity to leverage DNA-based cell typing with DNA methylation data to discern cell-specific molecular alterations. By focusing on cell-type-specific alterations, we can better understand the epigenetic mechanisms driving TME crosstalk, such as the activation of stromal cells and the modulation of immune responses by tumor cells. Our findings underscore the need for targeted therapeutic strategies that consider the unique epigenetic landscapes of individual cell types within the TME, offering new avenues for improving cancer treatment outcomes. Citation Format: Barbara Karakyriakou, Brock C. Christensen, Lucas A. Salas. Identification of cell-specific DNA methylation alterations in the breast tumor microenvironment [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr B033.

Genome-wide DNA methylation markers associated with metabolic liver cancer.

Metabolic liver disease is the fastest rising cause of hepatocellular carcinoma (HCC) worldwide, but the underlying molecular processes that drive HCC development in the setting of metabolic perturbations are unclear. We investigated the role of aberrant DNA methylation in metabolic HCC development in a multicenter international study. We used a case-control design, frequency-matched on age, sex, and study site. Genome-wide profiling of peripheral blood leukocyte DNA was performed using the 850k EPIC array. Cell type proportions were estimated from the methylation data. The study samples were split 80% and 20% for training and validation. Differential methylation analysis was performed with adjustment for cell type, and we generated area under the receiver-operating curves (ROC-AUC). We enrolled 272 metabolic HCC patients and 316 control patients with metabolic liver disease from six sites. Fifty-five differentially methylated CpGs were identified; 33 hypermethylated and 22 hypomethylated in cases versus controls. The panel of 55 CpGs discriminated between cases and controls with AUC=0.79 (95%CI=0.71-0.87), sensitivity=0.77 (95%CI=0.66-0.89), and specificity=0.74 (95%CI=0.64-0.85). The 55-CpG classifier panel performed better than a base model that comprised age, sex, race, and diabetes mellitus (AUC=0.65, 95%CI=0.55-0.75, sensitivity=0.62 (95%CI=0.49-0.75) and specificity=0.64 (95%CI=0.52-0.75). A multifactorial model that combined the 55 CpGs with age, sex, race, and diabetes, yielded AUC=0.78 (95%CI=0.70-0.86), sensitivity=0.81 (95%CI=0.71-0.92), and specificity=0.67 (95%CI=0.55-0.78). A panel of 55 blood leukocyte DNA methylation markers differentiates patients with metabolic HCC from control patients with benign metabolic liver disease, with a slightly higher sensitivity when combined with demographic and clinical information.

Epigenetic associations with kidney disease in individuals of African ancestry with APOL1 high-risk genotypes and Human Immunodeficiency Virus.

Apolipoprotein L1 (APOL1) high-risk variants are major determinants of chronic kidney disease (CKD) in people of African ancestry. Previous studies have identified epigenetic changes in relation to kidney function and CKD, but not in individuals with APOL1 high-risk genotypes. We conducted an epigenome-wide analysis of CKD and estimated glomerular filtration rate (eGFR) in in people of African ancestry and APOL1 high-risk genotypes with HIV. DNA methylation profiles from peripheral blood mononuclear cells of 119 individuals with APOL1 high-risk genotypes (mean age 48 years, 49% female, median CD4 count 515 cells/mm3, 90% HIV-1 RNA <200 copies/mL, 23% with CKD) were obtained by Illumina MethylationEPIC BeadChip. Differential methylation analysis of CKD considered technical and biological covariates. We also assessed associations with eGFR. Replication was pursued in three independent multi-ancestry cohorts with and without HIV. DNA methylation levels at 14 regions were associated with CKD. The strongest signals were located in SCARB1, DNAJC5B and C4orf50. Seven of the 14 signals also associated with eGFR, and most showed evidence for a genetic basis. Four signals (in SCARB1, FRMD4A, CSRNP1 and RAB38) replicated in other cohorts, and 11 previously reported epigenetic signals for kidney function or CKD replicated in our cohort. We found no significant DNA methylation signals in, or near, the APOL1 promoter region. We report several novel as well as previously reported epigenetic associations with CKD and eGFR in individuals with HIV having APOL1 high-risk genotypes. Further investigation of pathways linking DNA methylation to APOL1 nephropathies is warranted.

Decoding the Methylation Patterns of ABC Transporters in Colorectal Cancer

Colorectal cancer (CRC) is a significant global health concern, posing a major threat to morbidity and mortality rates. The molecular mechanisms that drive CRC, particularly the DNA methylation patterns in ATP-binding cassette (ABC) genes, have attracted considerable attention because of their potential roles in CRC drug resistance, initiation, and progression. This study aimed to investigate the methylation patterns of ABC genes in CRC using a high-throughput microarray technology. Microarray methylation data from CRC and adjacent normal tissues were subjected to preprocessing, differential methylation analysis, and correlation analysis using the MethSurv tool for a detailed study of methylation patterns. The study revealed global hypomethylation of 43 ABC transporters and two pseudogenes in CRC compared to normal tissues. Receiver operating characteristic curve analysis identified 369 CpG sites as potential biomarkers for CRC diagnosis, with area under the curve values ranging from acceptable to outstanding. Survival analysis demonstrated the correlation between DNA methylation of individual CpG sites and patient survival probability in colon and rectal adenocarcinoma datasets from The Cancer Genome Atlas. The study provides insights into the epigenetic landscape of ABC genes in CRC, highlighting their potential roles in drug resistance, disease initiation, and progression. The findings offer opportunities for developing innovative therapeutic approaches and improving patient outcomes in the fight against CRC. Further research is needed to validate these results and investigate the functional implications of ABC gene methylation in CRC pathogenesis and treatment response.

5-hydroxymethylcytosine sequencing in plasma cell-free DNA identifies unique epigenomic features in prostate cancer patients resistant to androgen deprivation therapies.

Currently, no biomarkers are available to identify resistance to androgen-deprivation therapies (ADT) in men with hormone-naive prostate cancer. Since 5-hydroxymethylcytosines (5hmC) in gene body are associated with gene activation, in this study, we evaluated whether 5hmC signatures in cell-free DNA (cfDNA) predicts early resistance to ADT. We collected a total of 139 serial plasma samples from 55 prostate cancer patients receiving ADT at three time points including baseline (prior to initiating ADT, N=55), 3-month (after initiating ADT, N=55), and disease progression (N=15) within 24 months or 24-month if no progression was detected (N=14). To quantify 5hmC abundance across the genome, we used selective chemical labeling sequencing and mapped sequence reads to individual genes. Differential methylation analysis in baseline samples identified significant 5hmC difference in 1,642 of 23,433 genes between patients with and without progression (false discovery rate, FDR<0.1). Patients with disease progression showed significant 5hmC enrichments in multiple hallmark gene sets with androgen responses as top enriched gene set (FDR=1.19E-13). Interestingly, this enrichment was driven by a subgroup of patients featuring a significant 5hmC hypermethylation in the gene sets involving AR , FOXA1 and GRHL2 . To quantify overall activities of these gene sets, we developed a gene set activity scoring algorithm and observed significant association of high activity scores with poor progression-free survival (P<0.05). Longitudinal analysis showed that the high activity scores were significantly reduced after 3-months of initiating ADT (P<0.0001) but returned to higher levels when the disease was progressed (P<0.05). This study demonstrates that 5hmC-based activity scores from gene sets involved in AR , FOXA1 and GRHL2 may be used as biomarkers to determine early treatment resistance, monitor disease progression, and potentially identify patients who would benefit from upfront treatment intensification.

Epigenetic trajectory predicts development of clinical rheumatoid arthritis in ACPA+ individuals: Targeting Immune Responses for Prevention of Rheumatoid Arthritis (TIP-RA).

The presence of autoantibodies to citrullinated protein antigens (ACPAs) in the absence of clinically-apparent inflammatory arthritis (IA) identifies individuals "at-risk" for developing future clinical rheumatoid arthritis (RA). However, it is unclear why some ACPA+ individuals convert to clinical RA while others do not. We explored the possibility in the Targeting Immune Responses for Prevention of Rheumatoid Arthritis (TIP-RA) study that epigenetic remodeling is part of the trajectory from an at-risk state to clinical disease and identifies novel biomarkers associated with conversion to clinical RA. ACPA-Controls, ACPA+ At-Risk, and Early RA individuals were followed for up to 5 years, including obtaining blood samples annually and at RA diagnosis. Peripheral blood mononuclear cells (PBMCs) were separated into CD19+ B cells, memory CD4+ T cells, and naïve CD4+ T cells using antibodies and magnetic beads. Genome-wide methylation within each cell lineage was assayed using the Illumina MethylationEPIC v1.0 beadchip. ACPA+ At-Risk participants who did or did not develop RA were designated "Pre-RA" or "Non-converters", respectively.Differentially methylated loci (DML) were selected using the Limma software package. Using the Caret package, we constructed machine learning models in test and validation cohorts and identified the most predictive loci of clinical RA conversion. Cross-sectional differential methylation analysis at baseline revealed DMLs that distinguish the Pre-RA methylome from ACPA+ Non-converters, the latter which closely resembled ACPA-Controls. Genes overlapping these DMLs correspond to aberrant NOTCH signaling and DNA repair pathways in B cells. Longitudinal analysis showed that ACPA-Control and ACPA+ Non-converter methylomes are relatively constant. In contrast, the Pre-RA methylome remodeled along a dynamic "RA methylome trajectory" characterized by epigenetic changes in active regulatory elements. Clinical conversion to RA, defined based on diagnosis, marked an epigenetic inflection point for cell cycle pathways in B cells and adaptive immunity pathways in naïve T cells. Machine learning revealed individual loci associated with RA conversion. This model significantly outperformed autoantibodies plus acute phase reactants as predictors of RA conversion. DNA methylation is a dynamic process in ACPA+ individuals at-risk for developing RA that eventually transition to clinical disease. In contrast, non-converters and controls have stable methylomes. The accumulation of epigenetic marks over time prior to conversion to clinical RA conforms to pathways that are associated with immunity and can be used to identify potential pathogenic pathways for therapeutic targeting and/or use as prognostic biomarkers.

Exploring the complexities of epigenetics in multiple sclerosis: A study involving meta-analysis of DNA methylation profiles, epigenetic drift, and rare epivariations.

Multiple sclerosis (MS) is an autoimmune condition characterized by inflammatory and neurodegenerative traits. Recently, DNA methylation has emerged as a promising field of investigation for elucidating dynamics characterizing MS development and progression. This study aimed to comprehensively investigate the role of epigenetics in MS by analyzing the methylation profiles from blood and brain tissues from public datasets. Employing a meta-analytical framework for differential methylation analyses, the study extended beyond conventional analyses to explore additional dimensions of epigenetic regulation, including epigenetic drift, age acceleration, and rare epivariations. Results of the differential methylation analysis were in line with previously reported findings. No significant differences were observed in age acceleration or global epigenetic drift between MS cases and controls. However, upon closer analysis at the gene level, distinctive patterns of epigenetic drift emerged, particularly within genes implicated in neural biological functions. These findings underscore the role of epigenetic modifications in shaping MS pathology. Furthermore, the study unveiled the exclusive presence of rare epivariations within the MS cases, some of which involved genes previously linked to MS or other autoimmune diseases. This highlights the potential significance of rare genetic aberrations in driving MS susceptibility and progression.