Epigenomic Signatures Research Articles

Epigenomic signatures in age-related macular degeneration: Focus on their role as disease modifiers and therapeutic targets.

Epigenetics is characterized by molecular modifications able to shape gene expression profiles in response to inner and external stimuli. Therefore, epigenetic elements are able to provide intriguing and useful information for the comprehension and management of different human conditions, including aging process, and diseases. On this subject, Age-related Macular Degeneration (AMD) represents one of the most frequent age-related disorders, dramatically affecting the quality of life of older adults worldwide. The etiopathogenesis is characterized by an interplay among multiple genetic and non-genetic factors, which have been extensively studied. Nevertheless, a deeper dissection of molecular machinery associated with risk, onset, progression and effectiveness of therapies is still missing. In this regard, epigenetic signals may be further explored to disentangle disease etiopathogenesis, the possible therapeutic avenues and the differential response to AMD treatment. This review will discuss the epigenomic signatures mostly investigated in AMD, which could be applied to improve the knowledge of disease mechanisms and to set-up novel or modified treatment options.

Development and validation of genomic and epigenomic signatures associated with tumor immune microenvironment in hepatoblastoma

BackgroundThis study aimed to probe and verify aberrantly methylated and expressed genes in hepatoblastoma and to analyze their interactions with tumor immune microenvironment.MethodsAberrantly methylated and expressed genes were obtained by comprehensively analyzing gene expression and DNA methylation profiles from GSE81928, GSE75271 and GSE78732 datasets. Their biological functions were predicted by the STRING and Metascape databases. CIBERSORT was utilized for inferring the compositions of tumor-infiltrating immune cells (TIICs) in each sample. Correlation between hub genes and immune cells was then analyzed. Hub genes were validated in hepatoblastoma tissues via western blot or immunohistochemistry. After transfection with sh-NOTUM, migration and invasion of HuH-6 and HepG2 cells were investigated. The nude mouse tumorigenesis model was constructed.ResultsTotally, 83 aberrantly methylated and expressed genes were determined in hepatoblastoma, which were mainly involved in metabolic and cancer-related pathways. Moreover, their expression was liver-specific. 13 hub genes were screened, which were closely related to immune cells in hepatoblastoma tissues. Among them, it was confirmed that AXIN2, LAMB1 and NOTUM were up-regulated and SERPINC1 was down-regulated in hepatoblastoma than normal tissues. NOTUM knockdown distinctly weakened migration and invasion of HuH-6 and HepG2 cells and tumor growth in vivo.ConclusionsThis study identified aberrantly methylated and expressed signatures that were in relation to immune microenvironment in hepatoblastoma. Targeting NOTUM hub gene could suppress migration and invasion of hepatoblastoma cells. Thus, these aberrantly methylated and expressed genes might act as therapeutic agents in hepatoblastoma therapy.

Multiscale and integrative single-cell Hi-C analysis with Higashi

Single-cell Hi-C (scHi-C) can identify cell-to-cell variability of three-dimensional (3D) chromatin organization, but the sparseness of measured interactions poses an analysis challenge. Here we report Higashi, an algorithm based on hypergraph representation learning that can incorporate the latent correlations among single cells to enhance overall imputation of contact maps. Higashi outperforms existing methods for embedding and imputation of scHi-C data and is able to identify multiscale 3D genome features in single cells, such as compartmentalization and TAD-like domain boundaries, allowing refined delineation of their cell-to-cell variability. Moreover, Higashi can incorporate epigenomic signals jointly profiled in the same cell into the hypergraph representation learning framework, as compared to separate analysis of two modalities, leading to improved embeddings for single-nucleus methyl-3C data. In an scHi-C dataset from human prefrontal cortex, Higashi identifies connections between 3D genome features and cell-type-specific gene regulation. Higashi can also potentially be extended to analyze single-cell multiway chromatin interactions and other multimodal single-cell omics data.

Fine-mapping of Parkinson's disease susceptibility loci identifies putative causal variants.

Recent genome-wide association studies have identified 78 loci associated with Parkinson’s disease susceptibility but the underlying mechanisms remain largely unclear. To identify likely causal variants for disease risk, we fine-mapped these Parkinson’s-associated loci using four different fine-mapping methods. We then integrated multi-assay cell type–specific epigenomic profiles to pinpoint the likely mechanism of action of each variant, allowing us to identify Consensus single nucleotide polymorphism (SNPs) that disrupt LRRK2 and FCGR2A regulatory elements in microglia, an MBNL2 enhancer in oligodendrocytes, and a DYRK1A enhancer in neurons. This genome-wide functional fine-mapping investigation of Parkinson’s disease substantially advances our understanding of the causal mechanisms underlying this complex disease while avoiding focus on spurious, non-causal mechanisms. Together, these results provide a robust, comprehensive list of the likely causal variants, genes and cell-types underlying Parkinson’s disease risk as demonstrated by consistently greater enrichment of our fine-mapped SNPs relative to lead GWAS SNPs across independent functional impact annotations. In addition, our approach prioritized an average of 3/85 variants per locus as putatively causal, making downstream experimental studies both more tractable and more likely to yield disease-relevant, actionable results. Large-scale studies comparing individuals with Parkinson’s disease to age-matched controls have identified many regions of the genome associated with the disease. However, there is widespread correlation between different parts of the genome, making it difficult to tell which genetic variants cause Parkinson’s and which are simply co-inherited with causal variants. We therefore applied a suite of statistical models to identify the most likely causal genetic variants (i.e. fine-mapping). We then linked these genetic variants with epigenomic and gene expression signatures across a wide variety of tissues and cell types to identify how these variants cause disease. Therefore, this study provides a comprehensive and robust list of cellular and molecular mechanisms that may serve as targets in the development of more effective Parkinson’s therapeutics.

From GWAS variant to function: A study of ∼148,000 variants for blood cell traits.

SummaryGenome-wide association studies (GWASs) have identified hundreds of thousands of genetic variants associated with complex diseases and traits. However, most variants are noncoding and not clearly linked to genes, making it challenging to interpret these GWAS signals. We present a systematic variant-to-function study, prioritizing the most likely functional elements of the genome for experimental follow-up, for >148,000 variants identified for hematological traits. Specifically, we developed VAMPIRE: Variant Annotation Method Pointing to Interesting Regulatory Effects, an interactive web application implemented in R Shiny. This tool efficiently integrates and displays information from multiple complementary sources, including epigenomic signatures from blood-cell-relevant tissues or cells, functional and conservation summary scores, variant impact on protein and gene expression, chromatin conformation information, as well as publicly available GWAS and phenome-wide association study (PheWAS) results. Leveraging data generated from independently performed functional validation experiments, we demonstrate that our prioritized variants, genes, or variant-gene links are significantly more likely to be experimentally validated. This study not only has important implications for systematic and efficient revelation of functional mechanisms underlying GWAS variants for hematological traits but also provides a prototype that can be adapted to many other complex traits, paving the path for efficient variant-to-function (V2F) analyses.

TIGER: The gene expression regulatory variation landscape of human pancreatic islets.

SUMMARYGenome-wide association studies (GWASs) identified hundreds of signals associated with type 2 diabetes (T2D). To gain insight into their underlying molecular mechanisms, we have created the translational human pancreatic islet genotype tissue-expression resource (TIGER), aggregating >500 human islet genomic datasets from five cohorts in the Horizon 2020 consortium T2DSystems. We impute genotypes using four reference panels and meta-analyze cohorts to improve the coverage of expression quantitative trait loci (eQTL) and develop a method to combine allele-specific expression across samples (cASE). We identify >1 million islet eQTLs, 53 of which colocalize with T2D signals. Among them, a low-frequency allele that reduces T2D risk by half increases CCND2 expression. We identify eight cASE colocalizations, among which we found a T2D-associated SLC30A8 variant. We make all data available through the TIGER portal (http://tiger.bsc.es), which represents a comprehensive human islet genomic data resource to elucidate how genetic variation affects islet function and translates into therapeutic insight and precision medicine for T2D.

Integrative analysis of epigenetics data identifies gene-specific regulatory elements.

Understanding how epigenetic variation in non-coding regions is involved in distal gene-expression regulation is an important problem. Regulatory regions can be associated to genes using large-scale datasets of epigenetic and expression data. However, for regions of complex epigenomic signals and enhancers that regulate many genes, it is difficult to understand these associations. We present StitchIt, an approach to dissect epigenetic variation in a gene-specific manner for the detection of regulatory elements (REMs) without relying on peak calls in individual samples. StitchIt segments epigenetic signal tracks over many samples to generate the location and the target genes of a REM simultaneously. We show that this approach leads to a more accurate and refined REM detection compared to standard methods even on heterogeneous datasets, which are challenging to model. Also, StitchIt REMs are highly enriched in experimentally determined chromatin interactions and expression quantitative trait loci. We validated several newly predicted REMs using CRISPR-Cas9 experiments, thereby demonstrating the reliability of StitchIt. StitchIt is able to dissect regulation in superenhancers and predicts thousands of putative REMs that go unnoticed using peak-based approaches suggesting that a large part of the regulome might be uncharted water.

DNA methylation affects photoperiodic tuberization in potato (Solanum tuberosum L.) by mediating the expression of genes related to the photoperiod and GA pathways

Overcoming short-day-dependent tuberization to adapt to long-day conditions is critical for the widespread geographical success of potato. The genetic pathways of photoperiodic tuberization are similar to those of photoperiodic flowering. DNA methylation plays an important role in photoperiodic flowering. However, little is known about how DNA methylation affects photoperiodic tuberization in potato. Here, we verified the effect of a DNA methylation inhibitor on photoperiodic tuberization and compared the DNA methylation levels and differentially methylated genes (DMGs) in the photoperiodic tuberization process between photoperiod-sensitive and photoperiod-insensitive genotypes, aiming to dissect the role of DNA methylation in the photoperiodic tuberization of potato. We found that a DNA methylation inhibitor could promote tuber initiation in strict short-day genotypes. Whole-genome DNA methylation sequencing showed that the photoperiod-sensitive and photoperiod-insensitive genotypes had distinct DNA methylation modes in which few differentially methylated genes were shared. Transcriptome analysis confirmed that the DNA methylation inhibitor regulated the expression of the key genes involved in the photoperiod and GA pathways to promote tuber initiation in the photoperiod-sensitive genotype. Comparison of the DNA methylation levels and transcriptome levels identified 52 candidate genes regulated by DNA methylation that were predicted to be involved in photoperiodic tuberization. Our findings provide a new perspective for understanding the relationship between photoperiod-dependent and GA-regulated tuberization. Uncovering the epigenomic signatures of these pathways will greatly enhance potato breeding for adaptation to a wide range of environments.

Towards epigenomic and metabolomic profiles of chronic organophosphate exposure in residents of California’ Central Valley

BACKGROUND AND AIM: Organophosphates (OP) are widely used in the agricultural Central Valley region of California. This study aimed to elucidate metabolomic and epigenetic changes induced by chronic ambient OP exposure, as well as possible interactions between different molecular layers. METHODS: We conducted high-resolution metabolomic profiling (liquid chromatography with high-resolution mass spectrometry) and genome-wide DNA methylation profiling (Illumina 450 k) of blood samples from 176 older adults living in the California Central Valley. Cumulative OP exposure from ambient sources at homes and workplaces over a ten-year period was estimated using a geographic information system (GIS)-based model. Biweighted midcorrelation (bicor) and partial least squares regression were used to identify metabolomic features and CpGs associated with OPs. Potential confounders, including age, sex, race/ethnicity, education, and cell compositions, were adjusted a prior. Functional annotation and pathway enrichment analyses were conducted for biological interpretation. We also integrated the methylome and metabolome by investigating the correlation structures existing between OP-related CpGs and metabolites and all other features via bicor. RESULTS:The single-omic analyses showed both epigenomic and metabolomic signatures of OP as being enriched in the glycosphingolipid (GSL) biosynthesis pathway. Besides this common pathway, the metabolome and epigenome also exhibited distinct responses to OPs, with differently methylated CpGs being involved in intracellular membrane transport, cell adhesion, and carcinogenesis; and OP-related metabolites being involved in aromatic amino acids metabolism, neurotransmitter precursors, oxidative stress, and mitochondria function. Moreover, we illustrate possible interactions between these two molecular layers through metabolic processes and nutrient-sensing pathways when integrating the epigenomic and metabolomic signals. CONCLUSIONS:In summary, we linked GIS-modeled chronic low-level OP pesticide data with blood metabolomic and epigenomic data in an older adult population. Our findings suggest that it seems feasible to use multi-omics integration in studies of chronic environmental exposures in humans to better understand the pathophysiology involved in pesticide-related chronic health outcomes. KEYWORDS: Organophosphates, multi-omics, metabolomics, epigenomics, glycosphingolipid

Blood DNA Methylation and Incident Coronary Heart Disease

American Indian communities experience a high burden of coronary heart disease (CHD). Strategies are needed to identify individuals at risk and implement preventive interventions. To investigate the association of blood DNA methylation (DNAm) with incident CHD using a large number of methylation sites (cytosine-phosphate-guanine [CpG]) in a single model. This prospective study, including a discovery cohort (the Strong Heart Study [SHS]) and 4 additional cohorts (the Women's Health Initiative [WHI], the Framingham Heart Study [FHS], the Atherosclerosis Risk in Communities Study ([ARIC]-Black, and ARIC-White), evaluated 12 American Indian communities in 4 US states; African American women, Hispanic women, and White women throughout the US; White men and White women from Massachusetts; and Black men and women and White men and women from 4 US communities. A total of 2321 men and women (mean [SD] follow-up, 19.1 [9.2] years) were included in the SHS, 1874 women (mean [SD] follow-up, 15.8 [5.9] years) in the WHI, 2128 men and women (mean [SD] follow-up, 7.7 [1.8] years) in the FHS, 2114 men and women (mean [SD] follow-up, 20.9 [7.2] years) in the ARIC-Black, and 931 men and women (mean [SD] follow-up, 20.9 [7.2] years) in the ARIC-White. Data were collected from May 1989 to December 2018 and analyzed from February 2019 to May 2021. Blood DNA methylation. Using a high-dimensional time-to-event elastic-net model for the association of 407 224 CpG sites with incident CHD in the SHS (749 events), this study selected the differentially methylated CpG positions (DMPs) selected in the SHS and evaluated them in the WHI (531 events), FHS (143 events), ARIC-Black (350 events), and ARIC-White (121 events) cohorts. The median (IQR) age of participants in SHS was 55 (49-62) years, and 1359 participants (58.6%) were women. Elastic-net models selected 505 DMPs associated with incident CHD in the SHS beyond established risk factors, center, blood cell counts, and genetic principal components. Among those DMPs, 33 were commonly selected in 3 or 4 of the other cohorts and the pooled hazard ratios from the standard Cox models were significant at P < .05 for 10 of the DMPs. For example, the hazard ratio (95% CI) for CHD comparing the 90th and 10th percentiles of differentially methylated CpGs was 0.86 (0.78-0.95) for cg16604233 (tagged to COL11A2) and 1.23 (1.08-1.39) for cg09926486 (tagged to FRMD5). Some of the DMPs were consistent in the direction of the association; others showed associations in opposite directions across cohorts. Untargeted independent elastic-net models of CHD showed distinct DMPs, genes, and network of genes in the 5 cohorts. In this multi-cohort study, blood-based DNAm findings supported an association between a complex blood epigenomic signature and CHD that was largely different across populations.

Comparing the Predictivity of Human Placental Gene, microRNA, and CpG Methylation Signatures in Relation to Perinatal Outcomes.

Molecular signatures are being increasingly integrated into predictive biology applications. However, there are limited studies comparing the overall predictivity of transcriptomic versus epigenomic signatures in relation to perinatal outcomes. This study set out to evaluate mRNA and microRNA (miRNA) expression and cytosine-guanine dinucleotide (CpG) methylation signatures in human placental tissues and relate these to perinatal outcomes known to influence maternal/fetal health; namely, birth weight, placenta weight, placental damage, and placental inflammation. The following hypotheses were tested: (1) different molecular signatures will demonstrate varying levels of predictivity towards perinatal outcomes, and (2) these signatures will show disruptions from an example exposure (ie, cadmium) known to elicit perinatal toxicity. Multi-omic placental profiles from 390 infants in the Extremely Low Gestational Age Newborns cohort were used to develop molecular signatures that predict each perinatal outcome. Epigenomic signatures (ie, miRNA and CpG methylation) consistently demonstrated the highest levels of predictivity, with model performance metrics including R2 (predicted vs observed) values of 0.36-0.57 for continuous outcomes and balanced accuracy values of 0.49-0.77 for categorical outcomes. Top-ranking predictors included miRNAs involved in injury and inflammation. To demonstrate the utility of these predictive signatures in screening of potentially harmful exogenous insults, top-ranking miRNA predictors were analyzed in a separate pregnancy cohort and related to cadmium. Key predictive miRNAs demonstrated altered expression in association with cadmium exposure, including miR-210, known to impact placental cell growth, blood vessel development, and fetal weight. These findings inform future predictive biology applications, where additional benefit will be gained by including epigenetic markers.

Abstract 2105: Cell-free DNA fragments inform epigenomic mechanisms for early detection of breast cancer

Abstract Introduction: Chromatin accessibility and cell-free DNA fragmentation patterns can be used to identify epigenomic mechanisms (Sharma et al. 2010) and infer cell-types contributing to cfDNA in pathological states such as cancer (Snyder et al. 2016; Ulz et al. 2017). We describe results from a novel blood-based cell-free DNA (cfDNA) assay using epigenomic signatures that have high sensitivity for detecting early stages of breast cancer, a cancer type that is characterized by low tumor burden (Phallen et al. 2017). We present the results from a prospective, case-control study demonstrating improved sensitivity to the screening mammogram and other published blood-based assays. Methods: Assay performance was evaluated using a case-control study design enrolling 123 total subjects (58% Healthy, 18% Stage I, 13% Stage II, 11% Stage III). Cases were defined as subjects with a confirmatory diagnosis of invasive breast cancer, at any stage, by tissue biopsy. Controls were composed of subjects with either a negative finding by mammography (BI-RADS 1 or 2) or self-declared cancer-free. Whole blood samples were collected in Streck BCT tubes and shipped to a central laboratory for processing. Total cell-free DNA was extracted from plasma and prepped for next-generation sequencing. Sequencing libraries were enriched using a custom panel targeting genomic regions with distinct epigenomic activity in breast cancer. We trained a neural net to predict regulatory events in each of these regions, and then identified those events that were predictive of the presence of breast cancer. Final classification was performed by logistic regression over the predicted regulatory events. Results: Performance was tested using a held-out test set and achieved an overall sensitivity of 92.5% (95% CI: 88.1%, 97%) at specificity of 88.9% with an overall AUC of 95.8%. Performance of screening mammography is reported to be 86.9% (95% CI: 86.3%, 87.6%) sensitive at 88.9% specificity on data obtained from six Breast Cancer Surveillance Consortium (BCSC) registries on 792808 women (Lehman et al. 2017). Conclusion: These results support the utility for detecting epigenomic signals from cell-free DNA to enhance early detection of breast cancer. A prospective breast cancer screening study in a larger cohort is needed to further validate performance. Citation Format: Erik Gafni, Adam Harvey, Artur Jaroszewicz, Omid Shams Solari, Jane Landolin, Mouadh Barbirou, Amanda Miller, Peter J. Tonellato, Anshul Kundaje, Stefanie S. Jeffrey, Christina Curtis, George W. Sledge, Paul Giresi, Nathan Boley. Cell-free DNA fragments inform epigenomic mechanisms for early detection of breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2105.

Toward an understanding of the detection and function of R-loops in plants.

Although lagging behind studies in humans and other mammals, studies of R-loops in plants have recently entered an exciting stage in which the roles of R-loops in gene expression, genome stability, epigenomic signatures, and plant development and stress responses are being elucidated. Here, we review the strengths and weaknesses of existing methodologies, which were largely developed for R-loop studies in mammals, and then discuss the potential challenges of applying these methodologies to R-loop studies in plants. We then focus on recent advances in the functional characterization of R-loops in Arabidopsis thaliana and rice. Recent studies in plants indicate that there are coordinated relationships between R-loops and gene expression, and between R-loops and epigenomic signatures that depend, in part, on the types of R-loops involved. Finally, we discuss the emerging roles of R-loops in plants and directions for future research.

Differential Epigenetic Signature of Corticospinal Motor Neurons in ALS

Corticospinal motor neurons (CSMN) are an indispensable neuron population for the motor neuron circuitry. They are excitatory projection neurons, which collect information from different regions of the brain and transmit it to spinal cord targets, initiating and controlling motor function. CSMN degeneration is pronounced cellular event in motor neurons diseases, such as amyotrophic lateral sclerosis (ALS). Genetic mutations contribute to only about ten percent of ALS. Thus understanding the involvement of other factors, such as epigenetic controls, is immensely valuable. Here, we investigated epigenomic signature of CSMN that become diseased due to misfolded SOD1 toxicity and TDP-43 pathology, by performing quantitative analysis of 5-methylcytosine (5mC) and 5-hydroxymethycytosine (5hmC) expression profiles during end-stage of the disease in hSOD1G93A, and prpTDP-43A315T mice. Our analysis revealed that expression of 5mC was specifically reduced in CSMN of both hSOD1G93A and prpTDP-43A315T mice. However, 5hmC expression was increased in the CSMN that becomes diseased due to misfolded SOD1 and decreased in CSMN that degenerates due to TDP-43 pathology. These results suggest the presence of a distinct difference between different underlying causes. These differential epigenetic events might modulate the expression profiles of select genes, and ultimately contribute to the different paths that lead to CSMN vulnerability in ALS.

Genetic and Epigenetic Contributions to Child Heath Outcomes in the STRONG Kids 2 Cohort Study

ObjectivesChildhood obesity is becoming a significant health concern in the United States, and the prevalence of excess weight increases continuously throughout the growing years. It has been proposed that genetic variation partly contributes to the susceptibility of obesity and its development during the early onset. Thus, this study focuses on the determination of the genome-wide signatures (GWAS) and epigenomic signatures (EWAS) of divergent weight for length z scores (WFLZ) in the first year of life. In addition, it attempts to relate the molecular findings to body composition, BMI, adipose tissue deposition, in the first 5 years of life. Our objective is to generate novel preliminary data on the contributions to child health outcomes. Our approach involves integrating genetic and epigenetic variations with factors such as diet and growth. MethodsInitially, saliva samples of 426 children from SKP cohort were collected. Next, height and weight measurements were collected at intervals of 6 weeks, 3, 12, and 18 months, and 2, 3, 4 and 5 years. Measures such as maternal height and weight were also collected at the time points mentioned above. Furthermore, children were separated into three groups based on their WFLZ score trajectories (low-slow raising growth, mid stable growth and low-high rising growth) and it was demonstrated that there was a biological variation within the groups to investigate the genetic regulation of development in early life. For genomic analysis, DNA was extracted from infant saliva samples, fixed and quantified to used for genomic analysis. ResultsAfter statistical analysis 96 samples were chosen and genotyping were carried out using Illimuna Systems. Further results will be discussed at Nutrition 2021 Online. For the epigenetic analysis, EWAS analysis will be conducted and 850,000 methylation sites further will be investigated. We hypothesize that divergent growth factors in the first year of life will represent a unique epigenomic pattern and reveal new genetic targets modified by environmental variables. ConclusionsThis cohort study will provide valuable information about growth trajectories and genome and epigenome in the first 5 y of life. Funding SourcesUniversity of Illinois at Urbana-Champaign, FIRE Grant

Analysis of epigenomic signatures in cell-free DNA (cfDNA) from cancer patients and high-risk controls: A blinded test cohort of THUNDER-II study.

e22518 Background: Early detection of cancer has the potential to provide a clinical benefit to patients, especially for those with hard-to-treat diseases like ovarian and pancreatic cancer. The THUNDER (the unintrusive detection of early-stage cancers) program was designed to develop and validate a multicancer detection blood test (ELSA-seq) through a series of prospective, observational case-control studies. The second sub-study of THUNDER program (THUNDER-II) includes a discovery, training/validation, and single-blind test (SBT) phase. This new analysis, comprising data from the SBT set of THUNDER-II, evaluated performance of ELSA-seq in an independent group of participants, including individuals at high risk of certain cancer types for the first time. Methods: In the SBT group reported here, all participants were recruited from sites that were not represented in training or validation. The cancer group focused on six deadly cancer types (lung, colorectal, liver, pancreatic, esophageal, and ovarian cancer), and the non-cancer controls were stratified to ‘healthy’ and ‘high-risk’ subgroups based on examination results of low-dose spiral chest CT scan (LDCT), abdominal ultrasound, and Hepatitis B serologic test (HBsAg). 10ml of blood from each participant was collected and methylation-sequencing libraries were prepared by ELSA-seq as described previously. The detection results were automatically reported by a locked computational pipeline to mimic a real-world test scenario (Burning Rock Biotech, China). All samples were delinked from the donors’ cancer status and experiments were performed by blinded personnel. Results: In general, the cancer and non-cancer groups were comparable with respect to age, sex, and tobacco/alcohol-consumption history. The assessment is still in progress, and we have studied 360 eligible participants including 202 cancer cases and 158 non-cancer controls (76 ‘healthy’; 82 ‘high-risk’). Overall, ELSA-seq achieved a specificity of 98.1% (95%CI: 94.1-99.5%) and a sensitivity of 74.8% (95%CI: 68.1-80.5%), consistent with the performance in training and validation1. Among six pre-specified cancer types, sensitivity was 53.0% (95%CI: 40.4-76.3%) in stage I, 73.3% (95%CI: 57.8-84.9%) in stage II, 90.4% (95%CI: 78.2-96.4%) in stage III, and 92.3% (95%CI: 78.0-98.0%) in stage IV. In terms of identifying the diseased organ(s), 80.8% (95%CI: 73.4-86.6%) of the predications matched the true TOO status. Conclusions: ELSA-seq demonstrated consistent results in sites that did not contribute to training, supporting that performance was not subject to sample-/site-dependent bias. It also showed a very low false positive rate in asymptomatic individuals with or without certain cancer-prone conditions, suggesting a potential applicability to broad populations.

Multiomic, plasma-only ctDNA NGS assay for minimal residual disease (MRD) detection in solid tumors.

3045 Background: Detection of minimal residual disease (MRD) by circulating tumor DNA (ctDNA) post-curative intent treatment is predictive of recurrence in many solid tumors. Due to biological challenges with low ctDNA shed in early-stage disease and potential to detect non-tumor derived alterations in plasma (e.g. CHIP), most ctDNA MRD assays are dependent on a priori knowledge of genomic alterations from tumor tissue to achieve high sensitivity and specificity. Tissue-dependent methods limit the clinical application of a MRD assay, especially in cancer types where tissue biopsy is challenging, neoadjuvant therapy (NAT) is standard of care, and/or rapid turnaround times are needed for clinical decision making. We previously validated an assay (Guardant Reveal) that combines somatic and epigenomic analysis to detect ctDNA from early-stage colorectal tumors without tumor tissue or peripheral blood cells. Here we describe the expansion of this assay to detect MRD across multiple tumor types. Methods: Cell-free DNA (cfDNA) fragments are extracted from patient plasma, partitioned based on methylation fraction, enriched using a panel to target informative genomic and epigenomic regions, barcoded, and pooled for sequencing. Methylation status is determined non-destructively and with minimal loss of molecules, allowing sensitive genomic and epigenomic analysis of the same cfDNA fragments. A single assay with a total panel size of 5.3 Mb was developed for MRD analysis in multiple cancer types, including CRC, Lung and Bladder. A “ctDNA detected” result is defined by the de novo identification of tumor-derived somatic variants and/or the observation of a tumor-specific methylation profile exceeding predefined thresholds. Results: The assay performance was tested using 163 pre-treatment clinical samples from patients with early-stage non small cell lung cancer (NSCLC) and bladder cancer and 133 self-declared healthy donors. Sensitivity for pre-treatment detection in NSCLC was 68.9% at 95% specificity (20/42 Stage I: 47.6%, 25/30 Stage II: 83.3%, 19/21 Stage III: 90.5%). Sensitivity for bladder cancer was 44.2% at 95% specificity (13/43 NMIBC: 30.2%, 18/27 MIBC: 66.7%). Additional development from larger cohorts and other tumor types is ongoing and data will be presented as available. Conclusions: Using cancer-specific genomic and epigenomic signals combined with learning-based classifiers, we developed a highly specific method for detecting the presence of ctDNA in early-stage NSCLC and bladder cancer patients from plasma without the need for tumor tissue but with comparable sensitivities to tissue-dependent approaches. A plasma-only MRD assay will allow for greater clinical impact by overcoming the challenges of tissue procurement, particularly following NAT, and enabling faster time to results.

Cell-specific epigenetic changes in atherosclerosis.

Atherosclerosis is a disease of large and medium arteries that can lead to life-threatening cerebrovascular and cardiovascular consequences such as heart failure and stroke and is a major contributor to cardiovascular-related mortality worldwide. Atherosclerosis development is a complex process that involves specific structural, functional and transcriptional changes in different vascular cell populations at different stages of the disease. The application of single-cell RNA sequencing (scRNA-seq) analysis has discovered not only disease-related cell-specific transcriptomic profiles but also novel subpopulations of cells once thought as homogenous cell populations. Vascular cells undergo specific transcriptional changes during the entire course of the disease. Epigenetics is the instruction-set-architecture in living cells that defines and maintains the cellular identity by regulating the cellular transcriptome. Although different cells contain the same genetic material, they have different epigenomic signatures. The epigenome is plastic, dynamic and highly responsive to environmental stimuli. Modifications to the epigenome are driven by an array of epigenetic enzymes generally referred to as writers, erasers and readers that define cellular fate and destiny. The reversibility of these modifications raises hope for finding novel therapeutic targets for modifiable pathological conditions including atherosclerosis where the involvement of epigenetics is increasingly appreciated. This article provides a critical review of the up-to-date research in the field of epigenetics mainly focusing on in vivo settings in the context of the cellular role of individual vascular cell types in the development of atherosclerosis.

Impact of Epigenomic Hypermethylation at TP53 on Allogeneic Hematopoietic Cell Transplantation Outcomes for Myelodysplastic Syndromes

Myelodysplastic syndromes (MDS) are a heterogeneous group of hematopoietic stem cell disorders for which allogeneic hematopoietic cell transplantation (HCT) is currently the sole curative treatment. Epigenetic lesions are considered a major pathogenetic determinant in many cancers, and in MDS, epigenetic-based interventions have emerged as life-prolonging therapies. However, the impact of epigenomic aberrations on HCT outcomes among patients with MDS are not well understood. We hypothesized that epigenomic signatures in MDS patients before undergoing HCT serve as a novel prognostic indicator of the risk of post-HCT MDS relapse. To evaluate these epigenomic signatures in MDS patients, we analyzed reduced representation bisulfite sequencing profiles in a matched case-control population of 94 patients. Among these HCT recipients, 47 patients with MDS who relapsed post-HCT (cases) were matched 1:1 to patients with MDS who did not relapse (controls). Only patients with wild-type TP53, RAS pathway, and JAK2 mutations were included in this study to promote the discovery of novel factors. Cases were matched with controls based on conditioning regimen intensity, age, sex, Revised International Prognostic Scoring System, Karnofsky Performance Status, graft type, and donor type. Pre-HCT whole-blood samples from cases and matched controls were obtained from the Center for International Blood and Marrow Transplant Research repository. We comprehensively identified differentially methylated regions (DMRs) by comparing the methylation patterns among matched cases and controls. Our findings show that cases displayed more hyper-DMRs pretransplantation compared with controls, even after adjusting for pre-HCT use of hypomethylating agents. Hyper-DMRs specific to cases were mapped to the transcription start site of 218 unique genes enriched in 5 different signaling pathways that may serve as regions of interest and factors to consider as prognostic determinants of post-HCT relapse in MDS patients. Interestingly, although patients selected for this cohort were wild-type for the TP53 gene, cases showed significantly greater levels of methylation at TP53 compared with controls. These findings indicate that previously identified prognostic genes for MDS, such as TP53, may affect disease relapse not only through genetic mutation, but also through epigenetic methylation mechanisms.

Hookah Smoke Mediates Cancer-Associated Epigenomic and Transcriptomic Signatures in Human Respiratory Epithelial Cells

IntroductionAlthough communal smoking of hookah by means of water pipes is perceived to be a safe alternative to cigarette smoking, the effects of hookah smoke in respiratory epithelia have not been well characterized. This study evaluated epigenomic and transcriptomic effects of hookah smoke relative to cigarette smoke in human respiratory epithelial cells. MethodsPrimary normal human small airway epithelial cells from three donors and cdk4 and hTERT-immortalized small airway epithelial cells and human bronchial epithelial cells were cultured for 5 days in normal media with or without cigarette smoke condensates (CSCs) or water pipe condensates (WPCs). Cell count, immunoblot, RNA sequencing, quantitative real-time reverse-transcriptase polymerase chain reaction, methylation-specific polymerase chain reaction, and quantitative chromatin immunoprecipitation techniques were used to compare effects of hookah and cigarette smoke on cell proliferation, global histone marks, gene expression, and promoter-related chromatin structure. ResultsCSC and WPC decreased global H4K16ac and H4K20me3 histone marks and mediated distinct and overlapping cancer-associated transcriptome signatures and pathway modulations that were cell line dependent and stratified across lung cancer cells in a histology-specific manner. Epiregulin encoding a master regulator of EGFR signaling that is overexpressed in lung cancers was up-regulated, whereas FILIP1L and ABI3BP encoding mediators of senescence that are repressed in lung cancers were down-regulated by CSC and WPC. Induction of epiregulin and repression of FILIP1L and ABI3BP by these condensates coincided with unique epigenetic alterations within the respective promoters. ConclusionsThese findings support translational studies to ascertain if hookah-mediated epigenomic and transcriptomic alterations in cultured respiratory epithelia are detectable and clinically relevant in hookah smokers.