Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA

Genome wide identification of novel DNA methylation driven prognostic markers in colorectal cancer

Colorectal cancer (CRC) stands as a major contributor to cancer-related fatalities within China. There is an urgent need to identify accurate biomarkers for recurrence predicting in CRC. Reduced representation bisulfite sequencing was used to perform a comparative analysis of methylation profiles in tissue samples from 30 recurrence to 30 non-recurrence patients with CRC. Least absolute shrinkage and selection operator method was performed to select the differential methylation regions (DMRs) and built a DNA methylation classifier for predicting recurrence. Based on the identified top DMRs, a methylation classifier was built and consisted of eight hypermethylated DMRs in CRC. The DNA methylation classifier showed high accuracy for predicting recurrence with an area under the receiver operator characteristic curve of 0.825 (95% CI 0.680-0.970). The Kaplan-Meier survival analysis demonstrated that CRC patients with high methylation risk score, evaluated by the DNA methylation classifier, had poorer survival than low risk score (Hazard Ratio 4.349; 95% CI 1.783-10.61, P = 0.002). And only CRC patients with low methylation risk score could acquire benefit from adjuvant therapy. The DNA methylation classifier has been proved as crucial biomarkers for predicting recurrence and exhibited promising prognostic value after curative surgery in patients with CRC.

Tissue of origin detection for cancer tumor using low-depth cfDNA samples through combination of tumor-specific methylation atlas and genome-wide methylation density in graph convolutional neural networks

BACKGROUND

Cell free DNA (cfDNA)-based assays hold great potential in detecting early cancer signals yet determining the tissue-of-origin (TOO) for cancer signals remains a challenging task. Here, we investigated the contribution of a methylation atlas to TOO detection in low depth cfDNA samples.

METHODS

We constructed a tumor-specific methylation atlas (TSMA) using whole-genome bisulfite sequencing (WGBS) data from five types of tumor tissues (breast, colorectal, gastric, liver and lung cancer) and paired white blood cells (WBC). TSMA was used with a non-negative least square matrix factorization (NNLS) deconvolution algorithm to identify the abundance of tumor tissue types in a WGBS sample. We showed that TSMA worked well with tumor tissue but struggled with cfDNA samples due to the overwhelming amount of WBC-derived DNA. To construct a model for TOO, we adopted the multi-modal strategy and used as inputs the combination of deconvolution scores from TSMA with other features of cfDNA.

RESULTS

Our final model comprised of a graph convolutional neural network using deconvolution scores and genome-wide methylation density features, which achieved an accuracy of 69% in a held-out validation dataset of 239 low-depth cfDNA samples.

CONCLUSIONS

In conclusion, we have demonstrated that our TSMA in combination with other cfDNA features can improve TOO detection in low-depth cfDNA samples.

Methylated circulating tumor DNA in hepatocellular carcinoma: A comprehensive analysis of biomarker potential and clinical implications

The intricate epigenetic landscape of hepatocellular carcinoma (HCC) is profoundly influenced by alterations in DNA methylation patterns. Understanding these alterations is crucial for unraveling the molecular mechanisms underlying HCC pathogenesis. Methylated circulating tumor DNA (ctDNA) presents itself as an encouraging avenue for biomarker discovery and holds substantial clinical implications in HCC management. This review comprehensively outlines the studies concerning DNA methylation in HCC and underscores the significance of methylated ctDNA within this context. Moreover, a variety of cfDNA methylation-based methodologies, such as 5hmC profiling, bisulfite-based, restriction enzyme-dependent, and enrichment-based methods, provide in-depth insights into the molecular pathology of HCC. Additionally, the integration of methylated ctDNA analysis into clinical practice represents a significant advancement in personalized HCC management. By facilitating cancer screening, prognosis assessment, and treatment response prediction, the utilization of methylated ctDNA signifies a pivotal stride toward enhancing patient care and outcomes in HCC.

Single-stranded pre-methylated 5mC adapters uncover the methylation profile of plasma ultrashort Single-stranded cell-free DNA

Whole-genome bisulfite sequencing (BS-Seq) measures cytosine methylation changes at single-base resolution and can be used to profile cell-free DNA (cfDNA). In plasma, ultrashort single-stranded cfDNA (uscfDNA, ∼50 nt) has been identified together with 167 bp double-stranded mononucleosomal cell-free DNA (mncfDNA). However, the methylation profile of uscfDNA has not been described. Conventional BS-Seq workflows may not be helpful because bisulfite conversion degrades larger DNA into smaller fragments, leading to erroneous categorization as uscfDNA. We describe the '5mCAdpBS-Seq' workflow in which pre-methylated 5mC (5-methylcytosine) single-stranded adapters are ligated to heat-denatured cfDNA before bisulfite conversion. This method retains only DNA fragments that are unaltered by bisulfite treatment, resulting in less biased uscfDNA methylation analysis. Using 5mCAdpBS-Seq, uscfDNA had lower levels of DNA methylation (∼15%) compared to mncfDNA and was enriched in promoters and CpG islands. Hypomethylated uscfDNA fragments were enriched in upstream transcription start sites (TSSs), and the intensity of enrichment was correlated with expressed genes of hemopoietic cells. Using tissue-of-origin deconvolution, we inferred that uscfDNA is derived primarily from eosinophils, neutrophils, and monocytes. As proof-of-principle, we show that characteristics of the methylation profile of uscfDNA can distinguish non-small cell lung carcinoma from non-cancer samples. The 5mCAdpBS-Seq workflow is recommended for any cfDNA methylation-based investigations.

MethPhaser: methylation-based long-read haplotype phasing of human genomes

The assignment of variants across haplotypes, phasing, is crucial for predicting the consequences, interaction, and inheritance of mutations and is a key step in improving our understanding of phenotype and disease. However, phasing is limited by read length and stretches of homozygosity along the genome. To overcome this limitation, we designed MethPhaser, a method that utilizes methylation signals from Oxford Nanopore Technologies to extend Single Nucleotide Variation (SNV)-based phasing. We demonstrate that haplotype-specific methylations extensively exist in Human genomes and the advent of long-read technologies enabled direct report of methylation signals. For ONT R9 and R10 cell line data, we increase the phase length N50 by 78%-151% at a phasing accuracy of 83.4-98.7% To assess the impact of tissue purity and random methylation signals due to inactivation, we also applied MethPhaser on blood samples from 4 patients, still showing improvements over SNV-only phasing. MethPhaser further improves phasing across HLA and multiple other medically relevant genes, improving our understanding of how mutations interact across multiple phenotypes. The concept of MethPhaser can also be extended to non-human diploid genomes. MethPhaser is available at https://github.com/treangenlab/methphaser .

Lung tumor discrimination by deep neural network model CanDo via DNA methylation in bronchial lavage

Bronchoscopic-assisted discrimination of lung tumors presents challenges, especially in cases with contraindications or inaccessible lesions. Through meta-analysis and validation using the HumanMethylation450 database, this study identified methylation markers for molecular discrimination in lung tumors and designed a sequencing panel. DNA samples from 118 bronchial washing fluid (BWF) specimens underwent enrichment via multiplex PCR before targeted methylation sequencing. The Recursive Feature Elimination Cross-Validation and deep neural network algorithm established the CanDo classification model, which incorporated 11 methylation features (including 8 specific to the TBR1 gene), demonstrating a sensitivity of 98.6% and specificity of 97.8%. In contrast, bronchoscopic rapid on-site evaluation (bronchoscopic-ROSE) had lower sensitivity (87.7%) and specificity (80%). Further validation in 33 individuals confirmed CanDo's discriminatory potential, particularly in challenging cases for bronchoscopic-ROSE due to pathological complexity. CanDo serves as a valuable complement to bronchoscopy for the discriminatory diagnosis and stratified management of lung tumors utilizing BWF specimens.

Circulating Tumor DNA in Genitourinary Cancers: Detection, Prognostics, and Therapeutic Implications

CtDNA is emerging as a non-invasive clinical detection method for several cancers, including genitourinary (GU) cancers such as prostate cancer, bladder cancer, and renal cell carcinoma (RCC). CtDNA assays have shown promise in early detection of GU cancers, providing prognostic information, assessing real-time treatment response, and detecting residual disease and relapse. The ease of obtaining a "liquid biopsy" from blood or urine in GU cancers enhances its potential to be used as a biomarker. Interrogating these "liquid biopsies" for ctDNA can then be used to detect common cancer mutations, novel genomic alterations, or epigenetic modifications. CtDNA has undergone investigation in numerous clinical trials, which could address clinical needs in GU cancers, for instance, earlier detection in RCC, therapeutic response prediction in castration-resistant prostate cancer, and monitoring for recurrence in bladder cancers. The utilization of liquid biopsy for ctDNA analysis provides a promising method of advancing precision medicine within the field of GU cancers.

LABS: linear amplification-based bisulfite sequencing for ultrasensitive cancer detection from cell-free DNA

Methylation-based liquid biopsies show promises in detecting cancer using circulating cell-free DNA; however, current limitations impede clinical application. Most assays necessitate substantial DNA inputs, posing challenges. Additionally, underrepresented tumor DNA fragments may go undetected during exponential amplification steps of traditional sequencing methods. Here, we report linear amplification-based bisulfite sequencing (LABS), enabling linear amplification of bisulfite-treated DNA fragments in a genome-wide, unbiased fashion, detecting cancer abnormalities with sub-nanogram inputs. Applying LABS to 100 patient samples revealed cancer-specific patterns, copy number alterations, and enhanced cancer detection accuracy by identifying tissue-of-origin and immune cell composition.

Early Diagnosis of Colorectal Cancer Based on Bisulfite-free Site-specific Methylation Identification PCR Strategy: High-Sensitivity, Accuracy, and Primary Medical Accessibility

Due to its decade-long progression, colorectal cancer (CRC) is most suitable for population screening to achieve a significant reduction in its incidence and mortality. DNA methylation has emerged as a potential marker for the early detection of CRC. However, the current mainstream methylation detection method represented by bisulfite conversion has issues such as tedious operation, DNA damage, and unsatisfactory sensitivity. Herein, a new high-performance CRC screening tool based on the promising specific terminal-mediated polymerase chain reaction (STEM-PCR) strategy is developed. CRC-related methylation-specific candidate CpG sites are first prescreened through The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases using self-developed bioinformatics. Next, 9 homebrew colorectal cancer DNA methylated STEM‒PCR assays (ColoC-mSTEM) with high sensitivity (0.1%) and high specificity are established to identify candidate sites. The clinical diagnostic performance of these selected methylation sites is confirmed and validated by a case-control study. The optimized diagnostic model has an overall sensitivity of 94.8% and a specificity of 95.0% for detecting early-stage CRC. Taken together, ColoC-mSTEM, based on a single methylation-specific site, is a promising diagnostic approach for the early detection of CRC which is perfectly suitable for the screening needs of CRC in primary healthcare institutions.

CelFiE-ISH: a probabilistic model for multi-cell type deconvolution from single-molecule DNA methylation haplotypes

Deconvolution methods infer quantitative cell type estimates from bulk measurement of mixed samples including blood and tissue. DNA methylation sequencing measures multiple CpGs per read, but few existing deconvolution methods leverage this within-read information. We develop CelFiE-ISH, which extends an existing method (CelFiE) to use within-read haplotype information. CelFiE-ISH outperforms CelFiE and other existing methods, achieving 30% better accuracy and more sensitive detection of rare cell types. We also demonstrate the importance of marker selection and of tailoring markers for haplotype-aware methods. While here we use gold-standard short-read sequencing data, haplotype-aware methods will be well-suited for long-read sequencing.

Advances in the joint profiling technologies of 5mC and 5hmC

DNA cytosine methylation, a crucial epigenetic modification, involves the dynamic interplay of 5-methylcytosine (5mC) and its oxidized form, 5-hydroxymethylcytosine (5hmC), generated by ten-eleven translocation (TET) DNA dioxygenases. This process is central to regulating gene expression, influencing critical biological processes such as development, disease progression, and aging. Recognizing the distinct functions of 5mC and 5hmC, researchers often employ restriction enzyme-based or chemical treatment methods for their simultaneous measurement from the same genomic sample. This enables a detailed understanding of the relationship between these modifications and their collective impact on cellular function. This review focuses on summarizing the technologies for detecting 5mC and 5hmC together but also discusses the limitations and potential future directions in this evolving field.

Electrically Oriented Antibodies on Transistor for Monitoring Several Copies of Methylated DNA

An antibody transistor is a promising biosensing platform for the diagnosis and monitoring of various diseases. Nevertheless, the low concentration and short half-life of biomarkers require biodetection at the trace-molecule level, which remains a challenge for existing antibody transistors. Herein, we demonstrate a graphene field-effect transistor (gFET) with electrically oriented antibody probes (EOA-gFET) for monitoring several copies of methylated DNA. The electric field confines the orientation of antibody probes on graphene and diminishes the distance between graphene and methylated DNAs captured by antibodies, generating more induced charges on graphene and amplifying the electric signal. EOA-gFET realizes a limit of detection (LoD) of ∼0.12 copy μL-1, reaching the lowest LoD reported before. EOA-gFET shows a distinguishable signal for liver cancer clinical serum samples within ∼6 min, which proves its potential as a powerful tool for disease screening and diagnosis.

Age Prediction Using DNA Methylation Heterogeneity Metrics

Dynamic changes in genomic DNA methylation patterns govern the epigenetic developmental programs and accompany the organism's aging. Epigenetic clock (eAge) algorithms utilize DNA methylation to estimate the age and risk factors for diseases as well as analyze the impact of various interventions. High-throughput bisulfite sequencing methods, such as reduced-representation bisulfite sequencing (RRBS) or whole genome bisulfite sequencing (WGBS), provide an opportunity to identify the genomic regions of disordered or heterogeneous DNA methylation, which might be associated with cell-type heterogeneity, DNA methylation erosion, and allele-specific methylation. We systematically evaluated the applicability of five scores assessing the variability of methylation patterns by evaluating within-sample heterogeneity (WSH) to construct human blood epigenetic clock models using RRBS data. The best performance was demonstrated by the model based on a metric designed to assess DNA methylation erosion with an MAE of 3.686 years. We also trained a prediction model that uses the average methylation level over genomic regions. Although this region-based model was relatively more efficient than the WSH-based model, the latter required the analysis of just a few short genomic regions and, therefore, could be a useful tool to design a reduced epigenetic clock that is analyzed by targeted next-generation sequencing.

BISCUIT: an efficient, standards-compliant tool suite for simultaneous genetic and epigenetic inference in bulk and single-cell studies

Data from both bulk and single-cell whole-genome DNA methylation experiments are under-utilized in many ways. This is attributable to inefficient mapping of methylation sequencing reads, routinely discarded genetic information, and neglected read-level epigenetic and genetic linkage information. We introduce the BISulfite-seq Command line User Interface Toolkit (BISCUIT) and its companion R/Bioconductor package, biscuiteer, for simultaneous extraction of genetic and epigenetic information from bulk and single-cell DNA methylation sequencing. BISCUIT's performance, flexibility and standards-compliant output allow large, complex experimental designs to be characterized on clinical timescales. BISCUIT is particularly suited for processing data from single-cell DNA methylation assays, with its excellent scalability, efficiency, and ability to greatly enhance mappability, a key challenge for single-cell studies. We also introduce the epiBED format for single-molecule analysis of coupled epigenetic and genetic information, facilitating the study of cellular and tissue heterogeneity from DNA methylation sequencing.

Circulating, cell-free methylated DNA indicates cellular sources of allograft injury after liver transplant

Post-transplant complications reduce allograft and recipient survival. Current approaches for detecting allograft injury non-invasively are limited and do not differentiate between cellular mechanisms. Here, we monitor cellular damages after liver transplants from cell-free DNA (cfDNA) fragments released from dying cells into the circulation. We analyzed 130 blood samples collected from 44 patients at different time points after transplant. Sequence-based methylation of cfDNA fragments were mapped to patterns established to identify cell types in different organs. For liver cell types DNA methylation patterns and multi-omic data integration show distinct enrichment in open chromatin and regulatory regions functionally important for the respective cell types. We find that multi-tissue cellular damages post-transplant recover in patients without allograft injury during the first post-operative week. However, sustained elevation of hepatocyte and biliary epithelial cfDNA beyond the first week indicates early-onset allograft injury. Further, cfDNA composition differentiates amongst causes of allograft injury indicating the potential for non-invasive monitoring and timely intervention.

Blood leukocytes as a non-invasive diagnostic tool for thyroid nodules: a prospective cohort study

BACKGROUND

Thyroid nodule (TN) patients in China are subject to overdiagnosis and overtreatment. The implementation of existing technologies such as thyroid ultrasonography has indeed contributed to the improved diagnostic accuracy of TNs. However, a significant issue persists, where many patients undergo unnecessary biopsies, and patients with malignant thyroid nodules (MTNs) are advised to undergo surgery therapy.

METHODS

This study included a total of 293 patients diagnosed with TNs. Differential methylation haplotype blocks (MHBs) in blood leukocytes between MTNs and benign thyroid nodules (BTNs) were detected using reduced representation bisulfite sequencing (RRBS). Subsequently, an artificial intelligence blood leukocyte DNA methylation (BLDM) model was designed to optimize the management and treatment of patients with TNs for more effective outcomes.

RESULTS

The DNA methylation profiles of peripheral blood leukocytes exhibited distinctions between MTNs and BTNs. The BLDM model we developed for diagnosing TNs achieved an area under the curve (AUC) of 0.858 in the validation cohort and 0.863 in the independent test cohort. Its specificity reached 90.91% and 88.68% in the validation and independent test cohorts, respectively, outperforming the specificity of ultrasonography (43.64% in the validation cohort and 47.17% in the independent test cohort), albeit with a slightly lower sensitivity (83.33% in the validation cohort and 82.86% in the independent test cohort) compared to ultrasonography (97.62% in the validation cohort and 100.00% in the independent test cohort). The BLDM model could correctly identify 89.83% patients whose nodules were suspected malignant by ultrasonography but finally histological benign. In micronodules, the model displayed higher specificity (93.33% in the validation cohort and 92.00% in the independent test cohort) and accuracy (88.24% in the validation cohort and 87.50% in the independent test cohort) for diagnosing TNs. This performance surpassed the specificity and accuracy observed with ultrasonography. A TN diagnostic and treatment framework that prioritizes patients is provided, with fine-needle aspiration (FNA) biopsy performed only on patients with indications of MTNs in both BLDM and ultrasonography results, thus avoiding unnecessary biopsies.

CONCLUSIONS

This is the first study to demonstrate the potential of non-invasive blood leukocytes in diagnosing TNs, thereby making TN diagnosis and treatment more efficient in China.

Circulating cell-free DNA methylation-based multi-omics analysis allows early diagnosis of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with a 5-year survival rate of 7.2% in China. However, effective approaches for diagnosis of PDAC are limited. Tumor-originating genomic and epigenomic aberration in circulating free DNA (cfDNA) have potential as liquid biopsy biomarkers for cancer diagnosis. Our study aims to assess the feasibility of cfDNA-based liquid biopsy assay for PDAC diagnosis. In this study, we performed parallel genomic and epigenomic profiling of plasma cfDNA from Chinese PDAC patients and healthy individuals. Diagnostic models were built to distinguish PDAC patients from healthy individuals. Cancer-specific changes in cfDNA methylation landscape were identified, and a diagnostic model based on six methylation markers achieved high sensitivity (88.7% for overall cases and 78.0% for stage I patients) and specificity (96.8%), outperforming the mutation-based model significantly. Moreover, the combination of the methylation-based model with carbohydrate antigen 19-9 (CA19-9) levels further improved the performance (sensitivity: 95.7% for overall cases and 95.5% for stage I patients; specificity: 93.3%). In conclusion, our findings suggest that both methylation-based and integrated liquid biopsy assays hold promise as non-invasive tools for detection of PDAC.

DNA methylation haplotype block signatures responding to Staphylococcus aureus subclinical mastitis and association with production and health traits

BACKGROUND

DNA methylation has been documented to play vital roles in diseases and biological processes. In bovine, little is known about the regulatory roles of DNA methylation alterations on production and health traits, including mastitis.

RESULTS

Here, we employed whole-genome DNA methylation sequencing to profile the DNA methylation patterns of milk somatic cells from sixteen cows with naturally occurring Staphylococcus aureus (S. aureus) subclinical mastitis and ten healthy control cows. We observed abundant DNA methylation alterations, including 3,356,456 differentially methylated cytosines and 153,783 differential methylation haplotype blocks (dMHBs). The DNA methylation in regulatory regions, including promoters, first exons and first introns, showed global significant negative correlations with gene expression status. We identified 6435 dMHBs located in the regulatory regions of differentially expressed genes and significantly correlated with their corresponding genes, revealing their potential effects on transcriptional activities. Genes harboring DNA methylation alterations were significantly enriched in multiple immune- and disease-related pathways, suggesting the involvement of DNA methylation in regulating host responses to S. aureus subclinical mastitis. In addition, we found nine discriminant signatures (differentiates cows with S. aureus subclinical mastitis from healthy cows) representing the majority of the DNA methylation variations related to S. aureus subclinical mastitis. Validation of seven dMHBs in 200 cows indicated significant associations with mammary gland health (SCC and SCS) and milk production performance (milk yield).

CONCLUSIONS

In conclusion, our findings revealed abundant DNA methylation alterations in milk somatic cells that may be involved in regulating mammary gland defense against S. aureus infection. Particularly noteworthy is the identification of seven dMHBs showing significant associations with mammary gland health, underscoring their potential as promising epigenetic biomarkers. Overall, our findings on DNA methylation alterations offer novel insights into the regulatory mechanisms of bovine subclinical mastitis, providing further avenues for the development of effective control measures.

Multi-omics integration identifies regulatory factors underlying bovine subclinical mastitis

BACKGROUND

Mastitis caused by multiple factors remains one of the most common and costly disease of the dairy industry. Multi-omics approaches enable the comprehensive investigation of the complex interactions between multiple layers of information to provide a more holistic view of disease pathogenesis. Therefore, this study investigated the genomic and epigenomic signatures and the possible regulatory mechanisms underlying subclinical mastitis by integrating RNA sequencing data (mRNA and lncRNA), small RNA sequencing data (miRNA) and DNA methylation sequencing data of milk somatic cells from 10 healthy cows and 20 cows with naturally occurring subclinical mastitis caused by Staphylococcus aureus or Staphylococcus chromogenes.

RESULTS

Functional investigation of the data sets through gene set analysis uncovered 3458 biological process GO terms and 170 KEGG pathways with altered activities during subclinical mastitis, provided further insights into subclinical mastitis and revealed the involvement of multi-omics signatures in the altered immune responses and impaired mammary gland productivity during subclinical mastitis. The abundant genomic and epigenomic signatures with significant alterations related to subclinical mastitis were observed, including 30,846, 2552, 1276 and 57 differential methylation haplotype blocks (dMHBs), differentially expressed genes (DEGs), lncRNAs (DELs) and miRNAs (DEMs), respectively. Next, 5 factors presenting the principal variation of differential multi-omics signatures were identified. The important roles of Factor 1 (DEG, DEM and DEL) and Factor 2 (dMHB and DEM), in the regulation of immune defense and impaired mammary gland functions during subclinical mastitis were revealed. Each of the omics within Factors 1 and 2 explained about 20% of the source of variation in subclinical mastitis. Also, networks of important functional gene sets with the involvement of multi-omics signatures were demonstrated, which contributed to a comprehensive view of the possible regulatory mechanisms underlying subclinical mastitis. Furthermore, multi-omics integration enabled the association of the epigenomic regulatory factors (dMHBs, DELs and DEMs) of altered genes in important pathways, such as 'Staphylococcus aureus infection pathway' and 'natural killer cell mediated cytotoxicity pathway', etc., which provides further insights into mastitis regulatory mechanisms. Moreover, few multi-omics signatures (14 dMHBs, 25 DEGs, 18 DELs and 5 DEMs) were identified as candidate discriminant signatures with capacity of distinguishing subclinical mastitis cows from healthy cows.

CONCLUSION

The integration of genomic and epigenomic data by multi-omics approaches in this study provided a better understanding of the molecular mechanisms underlying subclinical mastitis and identified multi-omics candidate discriminant signatures for subclinical mastitis, which may ultimately lead to the development of more effective mastitis control and management strategies.

The association between DNA methylation and human height and a prospective model of DNA methylation-based height prediction

As a vital anthropometric characteristic, human height information not only helps to understand overall developmental status and genetic risk factors, but is also important for forensic DNA phenotyping. We utilized linear regression analysis to test the association between each CpG probe and the height phenotype. Next, we designed a methylation sequencing panel targeting 959 CpGs and subsequent height inference models were constructed for the Chinese population. A total of 11,730 height-associated sites were identified. By employing KPCA and deep neural networks, a prediction model was developed, of which the cross-validation RMSE, MAE and R2 were 5.62 cm, 4.45 cm and 0.64, respectively. Genetic factors could explain 39.4% of the methylation level variance of sites used in the height inference models. Collectively, we demonstrated an association between height and DNA methylation status through an EWAS analysis. Targeted methylation sequencing of only 959 CpGs combined with deep learning techniques could provide a model to estimate human height with higher accuracy than SNP-based prediction models.

Variant Allele Frequency Analysis of Circulating Tumor DNA as a Promising Tool in Assessing the Effectiveness of Treatment in Non-Small Cell Lung Carcinoma Patients

Despite the different possible paths of treatment, lung cancer remains one of the leading causes of death in oncological patients. New tools guiding the therapeutic process are under scientific investigation, and one of the promising indicators of the effectiveness of therapy in patients with NSCLC is variant allele frequency (VAF) analysis. VAF is a metric characterized as the measurement of the specific variant allele proportion within a genomic locus, and it can be determined using methods based on NGS or PCR. It can be assessed using not only tissue samples but also ctDNA (circulating tumor DNA) isolated from liquid biopsy. The non-invasive characteristic of liquid biopsy enables a more frequent collection of material and increases the potential of VAF analysis in monitoring therapy. Several studies have been performed on patients with NSCLC to evaluate the possibility of VAF usage. The research carried out so far demonstrates that the evaluation of VAF dynamics may be useful in monitoring tumor progression, remission, and recurrence during or after treatment. Moreover, the use of VAF analysis appears to be beneficial in making treatment decisions. However, several issues require better understanding and standardization before VAF testing can be implemented in clinical practice. In this review, we discuss the difficulties in the application of ctDNA VAF analysis in clinical routine, discussing the diagnostic and methodological challenges in VAF measurement in liquid biopsy. We highlight the possible applications of VAF-based measurements that are under consideration in clinical trials in the monitoring of personalized treatments for patients with NSCLC.

Circulating cell-free DNA-based multi-cancer early detection

Patients benefit considerably from early detection of cancer. Existing single-cancer tests have various limitations, which could be effectively addressed by circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED). With sensitive detection and accurate localization of multiple cancer types at a very low and fixed false-positive rate (FPR), MCED has great potential to revolutionize early cancer detection. Herein, we review state-of-the-art approaches for cfDNA-based MCED and their limitations and discuss both technical and clinical challenges in the development and application of MCED tests. Given the constant improvements in technology and understanding of cancer biology, we propose that a cfDNA-based targeted sequencing assay that integrates multimodal features should be optimized for MCED.

A diagnostic model based on DNA methylation haplotype block characteristics for identifying papillary thyroid carcinoma from thyroid adenoma

Papillary thyroid carcinoma (PTC) is the most prevalent form of thyroid cancer. Methylation of some genes plays a crucial role in the tendency to malignancy as well as poor prognosis of thyroid cancer, suggesting that methylation features can serve as complementary markers for molecular diagnosis. In this study, we aimed to develop and validate a diagnostic model for PTC based on DNA methylation markers. A total of 142 thyroid nodule tissue samples containing 84 cases of PTC and 58 cases of thyroid adenoma (TA) were collected for reduced representation bisulfite sequencing (RRBS) and subsequent analysis. The diagnostic model was constructed by the logistic regression (LR) method followed by 5-cross validation and based on 94 tissue methylation haplotype block (MHB) markers. The model achieved an area under the receiver operating characteristic curve (AUROC) of 0.974 (95% CI, 0.964-0.981) on 108 training samples and 0.917 (95% CI, 0.864-0.973) on 27 independent testing samples. The diagnostic model scores showed significantly high in males (P = 0.0016), age ≤ 45 years (P = 0.026), high body mass index (BMI) (P = 0.040), lymph node metastasis (P = 0.00052) and larger nodules (P = 0.0017) in the PTC group, and the risk score of this diagnostic model showed significantly high in recurrent PTC group (P = 0.0005). These results suggest that the diagnostic model can be expected to be a powerful tool for PTC diagnosis and there are more potential clinical applications of methylation markers to be excavated.

The correlation between high-sensitivity troponin-T and cell-free cardiac DNA in the blood of patients undergoing noncardiac, predominantly vascular surgery

OBJECTIVE

To present a novel method that uses an epigenetic fingerprint to measure changes in plasma concentrations of cardiac-specific cell-free DNA (CS-cfDNA) as a marker of myocardial cell death.

METHODS

This prospective, analytic, observational comparative study included patients with heart disease or multiple risk factors for heart disease undergoing major noncardiac, mostly vascular surgery, requiring an arterial-line, and at least 24 h hospitalization in the post anaesthesia care unit or critical care unit after surgery. Blood samples were collected at least four times per patient to measure troponin-T (via high-sensitivity troponin-T test) and CS-cfDNA pre- and postoperatively.

RESULTS

A total of 117 patients were included (group 1, 77 patients [66%] with low preoperative and postoperative troponin-T; group 2, 18 patients [15%] with low preoperative but increased postoperative troponin-T; group 3, 16 patients [14%] with high troponin-T both preoperatively and postoperatively; and group 4, six patients [5%] with elevated preoperative troponin-T that decreased postoperatively). The increase in CS-cfDNA after surgery was statistically significant only in group 2, which correlated with an increase in troponin-T in the same group.

CONCLUSIONS

CS-cfDNA increased early postoperatively, particularly in patients with silent postoperative troponin elevation, and was correlated with an increase in troponin-T. These results may suggest that, in the subgroup of patients with postoperative elevated troponin, cardiomyocyte death indeed occurred.

Emergency myelopoiesis distinguishes multisystem inflammatory syndrome in children from pediatric severe COVID-19

BACKGROUND

Multisystem inflammatory syndrome in children (MIS-C) is a hyperinflammatory condition caused by recent SARS-CoV-2 infection, but the underlying immunological mechanisms driving this distinct syndrome are unknown.

METHODS

We utilized high dimensional flow cytometry, cell-free (cf) DNA, and cytokine and chemokine profiling to identify mechanisms of critical illness distinguishing MIS-C from severe acute COVID-19 (SAC).

RESULTS

Compared to SAC, MIS-C patients demonstrated profound innate immune cell death and features of emergency myelopoiesis (EM), an understudied phenomenon observed in severe inflammation. EM signatures were characterized by fewer mature myeloid cells in the periphery and decreased expression of HLA-DR and CD86 on antigen presenting cells. IL-27, a cytokine known to drive hematopoietic stem cells towards EM, was increased in MIS-C, and correlated with immature cell signatures in MIS-C. Upon recovery, EM signatures decreased, and IL-27 plasma levels returned to normal levels. Despite profound lymphopenia, we report a lack of cfDNA released by adaptive immune cells and increased CCR7 expression on T cells indicative of egress out of peripheral blood.

CONCLUSIONS

Immune cell signatures of EM combined with elevated innate immune cell-derived cfDNA levels distinguish MIS-C from SAC in children and provide mechanistic insight into dysregulated immunity contributing towards MIS-C, offering potential diagnostic and therapeutic targets.

Circulating cell-free DNA-based methylation pattern in plasma for early diagnosis of esophagus cancer

With the increased awareness of early tumor detection, the importance of detecting and diagnosing esophageal cancer in its early stages has been underscored. Studies have consistently demonstrated the crucial role of methylation levels in circulating cell-free DNA (cfDNA) in identifying and diagnosing early-stage cancer. cfDNA methylation pertains to the methylation state within the genomic scope of cfDNA and is strongly associated with cancer development and progression. Several research teams have delved into the potential application of cfDNA methylation in identifying early-stage esophageal cancer and have achieved promising outcomes. Recent research supports the high sensitivity and specificity of cfDNA methylation in early esophageal cancer diagnosis, providing a more accurate and efficient approach for early detection and improved clinical management. Accordingly, this review aims to present an overview of methylation-based cfDNA research with a focus on the latest developments in the early detection of esophageal cancer. Additionally, this review summarizes advanced analytical technologies for cfDNA methylation that have significantly benefited from recent advancements in separation and detection techniques, such as methylated DNA immunoprecipitation sequencing (MeDIP-seq). Recent findings suggest that biomarkers based on cfDNA methylation may soon find successful applications in the early detection of esophageal cancer. However, large-scale prospective clinical trials are required to identify the potential of these biomarkers.

A novel approach toward optimal workflow selection for DNA methylation biomarker discovery

DNA methylation is a major epigenetic modification involved in many physiological processes. Normal methylation patterns are disrupted in many diseases and methylation-based biomarkers have shown promise in several contexts. Marker discovery typically involves the analysis of publicly available DNA methylation data from high-throughput assays. Numerous methods for identification of differentially methylated biomarkers have been developed, making the need for best practices guidelines and context-specific analyses workflows exceedingly high. To this end, here we propose TASA, a novel method for simulating methylation array data in various scenarios. We then comprehensively assess different data analysis workflows using real and simulated data and suggest optimal start-to-finish analysis workflows. Our study demonstrates that the choice of analysis pipeline for DNA methylation-based marker discovery is crucial and different across different contexts.

mHapBrowser: a comprehensive database for visualization and analysis of DNA methylation haplotypes

DNA methylation acts as a vital epigenetic regulatory mechanism involved in controlling gene expression. Advances in sequencing technologies have enabled characterization of methylation patterns at single-base resolution using bisulfite sequencing approaches. However, existing methylation databases have primarily focused on mean methylation levels, overlooking phased methylation patterns. The methylation status of CpGs on individual sequencing reads represents discrete DNA methylation haplotypes (mHaps). Here, we present mHapBrowser, a comprehensive database for visualizing and analyzing mHaps. We systematically processed data of diverse tissues in human, mouse and rat from public repositories, generating mHap format files for 6366 samples. mHapBrowser enables users to visualize eight mHap metrics across the genome through an integrated WashU Epigenome Browser. It also provides an online server for comparing mHap patterns across samples. Additionally, mHap files for all samples can be downloaded to facilitate local processing using downstream analysis toolkits. The utilities of mHapBrowser were demonstrated through three case studies: (i) mHap patterns are associated with gene expression; (ii) changes in mHap patterns independent of mean methylation correlate with differential expression between lung cancer subtypes; and (iii) the mHap metric MHL outperforms mean methylation for classifying tumor and normal samples from cell-free DNA. The database is freely accessible at http://mhap.sibcb.ac.cn/.

External quality assessment for detection of colorectal cancer by Septin9 DNA methylation in clinical laboratories

BACKGROUND AND AIMS

The incidence and mortality rate of colorectal cancer (CRC) are increasing worldwide. Septin9 methylated (mSEPT9) DNA in circulation can be used as a non-invasive detection method to assist in the early diagnosis of CRC; however, the detection methods and procedures are complicated. This study aimed to evaluate the ability of clinical laboratories to detect Septin9 methylation in plasma cell-free DNA (cfDNA).

MATERIALS AND METHODS

We prepared a sample panel consisting of positive and negative Septin9 methylation cells and CRC cells. Three positive samples with different methylation levels, one negative sample and one duplicate sample, two samples containing interference, three different CRC cell samples, and a fictitious case report were included. The panel was distributed to 59 laboratories for mSEPT9 analysis, result comparison, and scoring.

RESULTS

The sample panel, validated by National Medical Products Administration (NMPA)-approved tests and targeted bisulfite sequencing, met expectations and could be used for external quality assessment (EQA). Among the 59 laboratories, 55 (93.22%) correctly reported the mSEPT9 results for all samples, while four (6.79%) reported 15 false negatives and were considered improvable. All false negatives originated from four laboratories using laboratory-developed tests (LDTs), with three failing to detect weakly positive samples, samples containing interference, and samples from different CRC cells, and one reported erroneous results on all positive samples.

CONCLUSION

Our results illustrated that the detection of mSEPT9 in cfDNA is satisfactory in China. EQA is indispensable because it can help improve the diagnostic capability and quality management of the laboratories, and provide suggestions for the problems existing in mSEPT9 detection.

A Novel Tissue-Free Method to Estimate Tumor-Derived Cell-Free DNA Quantity Using Tumor Methylation Patterns

Estimating the abundance of cell-free DNA (cfDNA) fragments shed from a tumor (i.e., circulating tumor DNA (ctDNA)) can approximate tumor burden, which has numerous clinical applications. We derived a novel, broadly applicable statistical method to quantify cancer-indicative methylation patterns within cfDNA to estimate ctDNA abundance, even at low levels. Our algorithm identified differentially methylated regions (DMRs) between a reference database of cancer tissue biopsy samples and cfDNA from individuals without cancer. Then, without utilizing matched tissue biopsy, counts of fragments matching the cancer-indicative hyper/hypo-methylated patterns within DMRs were used to determine a tumor methylated fraction (TMeF; a methylation-based quantification of the circulating tumor allele fraction and estimate of ctDNA abundance) for plasma samples. TMeF and small variant allele fraction (SVAF) estimates of the same cancer plasma samples were correlated (Spearman's correlation coefficient: 0.73), and synthetic dilutions to expected TMeF of 10-3 and 10-4 had estimated TMeF within two-fold for 95% and 77% of samples, respectively. TMeF increased with cancer stage and tumor size and inversely correlated with survival probability. Therefore, tumor-derived fragments in the cfDNA of patients with cancer can be leveraged to estimate ctDNA abundance without the need for a tumor biopsy, which may provide non-invasive clinical approximations of tumor burden.

Hierarchical classification-based pan-cancer methylation analysis to classify primary cancer

Hierarchical classification offers a more specific categorization of data and breaks down large classification problems into subproblems, providing improved prediction accuracy and predictive power for undefined categories, while also mitigating the impact of poor-quality data. Despite these advantages, its application in predicting primary cancer is rare. To leverage the similarity of cancers and the specificity of methylation patterns among them, we developed the Cancer Hierarchy Classification Tool (CHCT) using the idea of hierarchical classification, with methylation data from 30 cancer types and 8239 methylome samples downloaded from publicly available databases (The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO)). We used unsupervised clustering to divide the classification subproblems and screened differentially methylated sites using Analysis of variance (ANOVA) test, Tukey-kramer test, and Boruta algorithms to construct models for each classifier module. After validation, CHCT accurately classified 1568 out of 1660 cases in the test set, with an average accuracy of 94.46%. We further curated an independent validation cohort of 677 cancer samples from GEO and assigned a diagnosis using CHCT, which showed high diagnostic potential with generally high accuracies (an average accuracy of 91.40%). Moreover, CHCT demonstrates predictive capability for additional cancer types beyond its original classifier scope as demonstrated in the medulloblastoma and pituitary tumor datasets. In summary, CHCT can hierarchically classify primary cancer by methylation profile, by splitting a large-scale classification of 30 cancer types into ten smaller classification problems. These results indicate that cancer hierarchical classification has the potential to be an accurate and robust cancer classification method.

A DNA methylation haplotype block landscape in human tissues and preimplantation embryos reveals regulatory elements defined by comethylation patterns

DNA methylation and associated regulatory elements play a crucial role in gene expression regulation. Previous studies have focused primarily on the distribution of mean methylation levels. Advances in whole-genome bisulfite sequencing (WGBS) have enabled the characterization of DNA methylation haplotypes (MHAPs), representing CpG sites from the same read fragment on a single chromosome, and the subsequent identification of methylation haplotype blocks (MHBs), in which adjacent CpGs on the same fragment are comethylated. Using our expert-curated WGBS data sets, we report comprehensive landscapes of MHBs in 17 representative normal somatic human tissues and during early human embryonic development. Integrative analysis reveals MHBs as a distinctive type of regulatory element characterized by comethylation patterns rather than mean methylation levels. We show the enrichment of MHBs in open chromatin regions, tissue-specific histone marks, and enhancers, including super-enhancers. Moreover, we find that MHBs tend to localize near tissue-specific genes and show an association with differential gene expression that is independent of mean methylation. Similar findings are observed in the context of human embryonic development, highlighting the dynamic nature of MHBs during early development. Collectively, our comprehensive MHB landscapes provide valuable insights into the tissue specificity and developmental dynamics of DNA methylation.

Nanopore sequencing reveals methylation changes associated with obesity in circulating cell-free DNA from Göttingen Minipigs

Profiling of circulating cell-free DNA (cfDNA) by tissue-specific base modifications, such as 5-methylcytosines (5mC), may enable the monitoring of ongoing pathophysiological processes. Nanopore sequencing allows genome-wide 5mC detection in cfDNA without bisulphite conversion. The aims of this study were: i) to find differentially methylated regions (DMRs) of cfDNA associated with obesity in Göttingen minipigs using Nanopore sequencing, ii) to validate a subset of the DMRs using methylation-specific PCR (MSP-PCR), and iii) to compare the cfDNA DMRs with those from whole blood genomic DNA (gDNA). Serum cfDNA and gDNA were obtained from 10 lean and 7 obese Göttingen Minipigs both with experimentally induced myocardial infarction and sequenced using Oxford Nanopore MinION. A total of 1,236 cfDNA DMRs (FDR<0.01) were associated with obesity. In silico analysis showed enrichment of the adipocytokine signalling, glucagon signalling, and cellular glucose homoeostasis pathways. A strong cfDNA DMR was discovered in PPARGC1B, a gene linked to obesity and type 2 diabetes. The DMR was validated using MSP-PCR and correlated significantly with body weight (P < 0.05). No DMRs intersected between cfDNA and gDNA, suggesting that cfDNA originates from body-wide shedding of DNA. In conclusion, nanopore sequencing detected differential methylation in minute quantities (0.1-1 ng/µl) of cfDNA. Future work should focus on translation into human and comparing 5mC from somatic tissues to pinpoint the exact location of pathology.

Circulating tumor DNA as liquid biopsy in lung cancer: Biological characteristics and clinical integration

Lung cancer maintains high morbidity and mortality rate globally despite significant advancements in diagnosis and treatment in the era of precision medicine. Pathological analysis of tumor tissue, the current gold standard for lung cancer diagnosis, is intrusive and intrinsically confined to evaluating the limited amount of tissues that could be physically extracted. However, tissue biopsy has several limitations, including the invasiveness of the procedure and difficulty in obtaining samples for patients at advanced stages., there Additionally,has been no major breakthrough in tumor biomarkers with high specificity and sensitivity, particularly for early-stage lung cancer. Liquid biopsy has been considered a feasible auxiliary tool for tearly dianosis, evaluating treatment responses and monitoring prognosis of lung cancer. Circulating tumor DNA (ctDNA), an ideal biomarker of liquid biopsy, has emerged as one of the most reliable tools for monitoring tumor processes at molecular levels. Herein, this review focuses on tumor heterogeneity to elucidate the superiority of liquid biopsy and retrospectively discussdeciphersolution. We systematically elaborate ctDNA biological characteristics, introduce methods for ctDNA detection, and discuss the current role of plasma ctDNA in lung cancer management. Finally, we summarize the drawbacks of ctDNA analysis and highlight its potential clinical application in lung cancer.

Estimating genome-wide DNA methylation heterogeneity with methylation patterns

BACKGROUND

In a heterogeneous population of cells, individual cells can behave differently and respond variably to the environment. This cellular diversity can be assessed by measuring DNA methylation patterns. The loci with variable methylation patterns are informative of cellular heterogeneity and may serve as biomarkers of diseases and developmental progression. Cell-to-cell methylation heterogeneity can be evaluated through single-cell methylomes or computational techniques for pooled cells. However, the feasibility and performance of these approaches to precisely estimate methylation heterogeneity require further assessment.

RESULTS

Here, we proposed model-based methods adopted from a mathematical framework originally from biodiversity, to estimate genome-wide DNA methylation heterogeneity. We evaluated the performance of our models and the existing methods with feature comparison, and tested on both synthetic datasets and real data. Overall, our methods have demonstrated advantages over others because of their better correlation with the actual heterogeneity. We also demonstrated that methylation heterogeneity offers an additional layer of biological information distinct from the conventional methylation level. In the case studies, we showed that distinct profiles of methylation heterogeneity in CG and non-CG methylation can predict the regulatory roles between genomic elements in Arabidopsis. This opens up a new direction for plant epigenomics. Finally, we demonstrated that our score might be able to identify loci in human cancer samples as putative biomarkers for early cancer detection.

CONCLUSIONS

We adopted the mathematical framework from biodiversity into three model-based methods for analyzing genome-wide DNA methylation heterogeneity to monitor cellular heterogeneity. Our methods, namely MeH, have been implemented, evaluated with existing methods, and are open to the research community.

Early detection of hepatocellular carcinoma via no end-repair enzymatic methylation sequencing of cell-free DNA and pre-trained neural network

BACKGROUND

Early detection of hepatocellular carcinoma (HCC) is important in order to improve patient prognosis and survival rate. Methylation sequencing combined with neural networks to identify cell-free DNA (cfDNA) carrying aberrant methylation offers an appealing and non-invasive approach for HCC detection. However, some limitations exist in traditional methylation detection technologies and models, which may impede their performance in the read-level detection of HCC.

METHODS

We developed a low DNA damage and high-fidelity methylation detection method called No End-repair Enzymatic Methyl-seq (NEEM-seq). We further developed a read-level neural detection model called DeepTrace that can better identify HCC-derived sequencing reads through a pre-trained and fine-tuned neural network. After pre-training on 11 million reads from NEEM-seq, DeepTrace was fine-tuned using 1.2 million HCC-derived reads from tumor tissue DNA after noise reduction, and 2.7 million non-tumor reads from non-tumor cfDNA. We validated the model using data from 130 individuals with cfDNA whole-genome NEEM-seq at around 1.6X depth.

RESULTS

NEEM-seq overcomes the drawbacks of traditional enzymatic methylation sequencing methods by avoiding the introduction of unmethylation errors in cfDNA. DeepTrace outperformed other models in identifying HCC-derived reads and detecting HCC individuals. Based on the whole-genome NEEM-seq data of cfDNA, our model showed high accuracy of 96.2%, sensitivity of 93.6%, and specificity of 98.5% in the validation cohort consisting of 62 HCC patients, 48 liver disease patients, and 20 healthy individuals. In the early stage of HCC (BCLC 0/A and TNM I), the sensitivity of DeepTrace was 89.6 and 89.5% respectively, outperforming Alpha Fetoprotein (AFP) which showed much lower sensitivity in both BCLC 0/A (50.5%) and TNM I (44.7%).

CONCLUSIONS

By combining high-fidelity methylation data from NEEM-seq with the DeepTrace model, our method has great potential for HCC early detection with high sensitivity and specificity, making it potentially suitable for clinical applications. DeepTrace: https://github.com/Bamrock/DeepTrace.

Cancer signature ensemble integrating cfDNA methylation, copy number, and fragmentation facilitates multi-cancer early detection

Cell-free DNA (cfDNA) sequencing has demonstrated great potential for early cancer detection. However, most large-scale studies have focused only on either targeted methylation sites or whole-genome sequencing, limiting comprehensive analysis that integrates both epigenetic and genetic signatures. In this study, we present a platform that enables simultaneous analysis of whole-genome methylation, copy number, and fragmentomic patterns of cfDNA in a single assay. Using a total of 950 plasma (361 healthy and 589 cancer) and 240 tissue samples, we demonstrate that a multifeature cancer signature ensemble (CSE) classifier integrating all features outperforms single-feature classifiers. At 95.2% specificity, the cancer detection sensitivity with methylation, copy number, and fragmentomic models was 77.2%, 61.4%, and 60.5%, respectively, but sensitivity was significantly increased to 88.9% with the CSE classifier (p value < 0.0001). For tissue of origin, the CSE classifier enhanced the accuracy beyond the methylation classifier, from 74.3% to 76.4%. Overall, this work proves the utility of a signature ensemble integrating epigenetic and genetic information for accurate cancer detection.

Unlocking the secrets: the power of methylation-based cfDNA detection of tissue damage in organ systems

BACKGROUND

Detecting organ and tissue damage is essential for early diagnosis, treatment decisions, and monitoring disease progression. Methylation-based assays offer a promising approach, as DNA methylation patterns can change in response to tissue damage. These assays have potential applications in early detection, monitoring disease progression, evaluating treatment efficacy, and assessing organ viability for transplantation. cfDNA released into the bloodstream upon tissue or organ injury can serve as a biomarker for damage. The epigenetic state of cfDNA, including DNA methylation patterns, can provide insights into the extent of tissue and organ damage.

CONTENT

Firstly, this review highlights DNA methylation as an extensively studied epigenetic modification that plays a pivotal role in processes such as cell growth, differentiation, and disease development. It then presents a variety of highly precise 5-mC methylation detection techniques that serve as powerful tools for gaining profound insights into epigenetic alterations linked with tissue damage. Subsequently, the review delves into the mechanisms underlying DNA methylation changes in organ and tissue damage, encompassing inflammation, oxidative stress, and DNA damage repair mechanisms. Next, it addresses the current research status of cfDNA methylation in the detection of specific organ tissues and organ damage. Finally, it provides an overview of the multiple steps involved in identifying specific methylation markers associated with tissue and organ damage for clinical trials. This review will explore the mechanisms and current state of research on cfDNA methylation-based assay detecting organ and tissue damage, the underlying mechanisms, and potential applications in clinical practice.

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.

Methylation haplotypes of the insulin gene promoter in children and adolescents with type 1 diabetes: Can a dimensionality reduction approach predict the disease?

DNA methylation of cytosine-guanine sites (CpGs) is associated with type 1 diabetes (T1D). The sequence of methylated and non-methylated sites in a specific genetic region constitutes its methyl-haplotype. The aim of the present study was to identify insulin gene promoter (IGP) methyl-haplotypes among children and adolescents with T1D and suggest a predictive model for the discrimination of cases and controls according to methyl-haplotypes. A total of 40 individuals (20 T1D) participated. The IGP region from peripheral whole blood DNA of 40 participants (20 T1D) was sequenced using next-generation sequencing, sequences were read using FASTQ files and methylation status was calculated by python-based pipeline for targeted deep bisulfite sequenced amplicons (ampliMethProfiler). Methylation profile at 10 CpG sites proximal to transcription start site of the IGP was recorded and coded as 0 for unmethylation or 1 for methylation. A single read could result in '1111111111' methyl-haplotype (all methylated), '000000000' methyl-haplotype (all unmethylated) or any other combination. Principal component analysis was applied to the generated methyl-haplotypes for dimensionality reduction, and the first three principal components were employed as features with five different classifiers (random forest, decision tree, logistic regression, Naive Bayes, support vector machine). Naive Bayes was the best-performing classifier, with 0.9 accuracy. Predictive models were evaluated using receiver operating characteristics (AUC 0.96). Methyl-haplotypes '1111111111', '1111111011', '1110111111', '1111101111' and '1110101111' were revealed to be the most significantly associated with T1D according to the dimensionality reduction method. Methylation-based biomarkers such as IGP methyl-haplotypes could serve to identify individuals at high risk for T1D.

Genome-wide single-molecule analysis of long-read DNA methylation reveals heterogeneous patterns at heterochromatin that reflect nucleosome organisation

High-throughput sequencing technology is central to our current understanding of the human methylome. The vast majority of studies use chemical conversion to analyse bulk-level patterns of DNA methylation across the genome from a population of cells. While this technology has been used to probe single-molecule methylation patterns, such analyses are limited to short reads of a few hundred basepairs. DNA methylation can also be directly detected using Nanopore sequencing which can generate reads measuring megabases in length. However, thus far these analyses have largely focused on bulk-level assessment of DNA methylation. Here, we analyse DNA methylation in single Nanopore reads from human lymphoblastoid cells, to show that bulk-level metrics underestimate large-scale heterogeneity in the methylome. We use the correlation in methylation state between neighbouring sites to quantify single-molecule heterogeneity and find that heterogeneity varies significantly across the human genome, with some regions having heterogeneous methylation patterns at the single-molecule level and others possessing more homogeneous methylation patterns. By comparing the genomic distribution of the correlation to epigenomic annotations, we find that the greatest heterogeneity in single-molecule patterns is observed within heterochromatic partially methylated domains (PMDs). In contrast, reads originating from euchromatic regions and gene bodies have more ordered DNA methylation patterns. By analysing the patterns of single molecules in more detail, we show the existence of a nucleosome-scale periodicity in DNA methylation that accounts for some of the heterogeneity we uncover in long single-molecule DNA methylation patterns. We find that this periodic structure is partially masked in bulk data and correlates with DNA accessibility as measured by nanoNOMe-seq, suggesting that it could be generated by nucleosomes. Our findings demonstrate the power of single-molecule analysis of long-read data to understand the structure of the human methylome.

Accurate age prediction from blood using a small set of DNA methylation sites and a cohort-based machine learning algorithm

Chronological age prediction from DNA methylation sheds light on human aging, health, and lifespan. Current clocks are mostly based on linear models and rely upon hundreds of sites across the genome. Here, we present GP-age, an epigenetic non-linear cohort-based clock for blood, based upon 11,910 methylomes. Using 30 CpG sites alone, GP-age outperforms state-of-the-art models, with a median accuracy of ∼2 years on held-out blood samples, for both array and sequencing-based data. We show that aging-related changes occur at multiple neighboring CpGs, with implications for using fragment-level analysis of sequencing data in aging research. By training three independent clocks, we show enrichment of donors with consistent deviation between predicted and actual age, suggesting individual rates of biological aging. Overall, we provide a compact yet accurate alternative to array-based clocks for blood, with applications in longitudinal aging research, forensic profiling, and monitoring epigenetic processes in transplantation medicine and cancer.

DARESOME enables concurrent profiling of multiple DNA modifications with restriction enzymes in single cells and cell-free DNA

5-Methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) are the most abundant DNA modifications that have important roles in gene regulation. Detailed studies of these different epigenetic marks aimed at understanding their combined effects and dynamic interconversion are, however, hampered by the inability of current methods to simultaneously measure both modifications, particularly in samples with limited quantities. We present DNA analysis by restriction enzyme for simultaneous detection of multiple epigenomic states (DARESOME), an assay based on modification-sensitive restriction digest and sequential tag ligation that can concurrently perform quantitative profiling of unmodified cytosine, 5mC, and 5hmC in CCGG sites genome-wide. DARESOME reveals the opposing roles of 5mC and 5hmC in gene expression regulation as well as their interconversion during aging in mouse brain. Implementation of DARESOME in single cells demonstrates pronounced 5hmC strand bias that reflects the semiconservative replication of DNA. Last, we showed that DARESOME enables integrative genomic, 5mC, and 5hmC profiling of cell-free DNA that uncovered multiomics cancer signatures in liquid biopsy.

DNA methylation profiling to determine the primary sites of metastatic cancers using formalin-fixed paraffin-embedded tissues

Identifying the primary site of metastatic cancer is critical to guiding the subsequent treatment. Approximately 3-9% of metastatic patients are diagnosed with cancer of unknown primary sites (CUP) even after a comprehensive diagnostic workup. However, a widely accepted molecular test is still not available. Here, we report a method that applies formalin-fixed, paraffin-embedded tissues to construct reduced representation bisulfite sequencing libraries (FFPE-RRBS). We then generate and systematically evaluate 28 molecular classifiers, built on four DNA methylation scoring methods and seven machine learning approaches, using the RRBS library dataset of 498 fresh-frozen tumor tissues from primary cancer patients. Among these classifiers, the beta value-based linear support vector (BELIVE) performs the best, achieving overall accuracies of 81-93% for identifying the primary sites in 215 metastatic patients using top-k predictions (k = 1, 2, 3). Coincidentally, BELIVE also successfully predicts the tissue of origin in 81-93% of CUP patients (n = 68).

DNA methylation markers in the detection of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and has a poor prognosis. Epigenetic modification has been shown to be deregulated during HCC development by dramatically impacting the differentiation, proliferation, and function of cells. One important epigenetic modification is DNA methylation during which methyl groups are added to cytosines without changing the DNA sequence itself. Studies found that methylated DNA markers can be specific for detection of HCC. On the basis of these findings, the utility of methylated DNA markers as novel biomarkers for early-stage HCC has been measured in blood, and indeed superior sensitivity and specificity have been found in several studies when compared to current surveillance methods. However, a variety of factors currently limit the immediate application of these exciting biomarkers. In this review, we provide a detailed rationalisation of the approach and basis for the use of methylation biomarkers for HCC detection and summarise recent studies on methylated DNA markers in HCC focusing on the importance of the aetiological cause of liver disease in the mechanisms leading to cancer.

Value of ctDNA methylation biomarkers in diagnosis of colorectal tumors

Tweetable abstract DNA methylation alterations have been identified as promising biological markers for early-stage colorectal cancer detection. Here, the authors highlight some recent advances in DNA methylation and its role in the early diagnosis and overall disease course management of colorectal tumors. New insights into DNA methylation biomarkers for colorectal cancer early diagnosis and management are discussed.

'Longing' for the Next Generation of Liquid Biopsy: The Diagnostic Potential of Long Cell-Free DNA in Oncology and Prenatal Testing

Liquid biopsy using cell-free DNA (cfDNA) has gained global interest as a molecular diagnostic tool. However, the analysis of cfDNA in cancer patients and pregnant women has been focused on short DNA molecules (e.g., ≤ 600 bp). With the detection of long cfDNA in the plasma of pregnant women and cancer patients in two recent studies, a new avenue of long cfDNA-based liquid biopsy has been opened. In this review, we summarize our current knowledge in this nascent field of long cfDNA analysis, focusing on the fragmentomic and epigenetic features of long cfDNA. In particular, long-read sequencing enabled single-molecule methylation analysis and subsequent determination of the tissue-of-origin of long cfDNA, which has promising clinical potential in prenatal and cancer testing. We also examine some of the limitations that may hinder the immediate clinical applications of long cfDNA analysis and the current efforts involved in addressing them. With concerted efforts in this area, it is hoped that long cfDNA analysis will add to the expanding armamentarium of liquid biopsy.

Genomic and Transcriptomic Research in the Discovery and Application of Colorectal Cancer Circulating Markers

Colorectal cancer (CRC) is the most frequently occurring malignancy in the world. However, the mortality from CRC can be reduced through early diagnostics, selection of the most effective treatment, observation of the therapy success, and the earliest possible diagnosis of recurrences. A comprehensive analysis of genetic and epigenetic factors contributing to the CRC development is needed to refine diagnostic, therapeutic, and preventive strategies and to ensure appropriate decision making in managing specific CRC cases. The liquid biopsy approach utilizing circulating markers has demonstrated its good performance as a tool to detect the changes in the molecular pathways associated with various cancers. In this review, we attempted to brief the main tendencies in the development of circulating DNA and RNA-based markers in CRC such as cancer-associated DNA mutations, DNA methylation changes, and non-coding RNA expression shifts. Attention is devoted to the existing circulating nucleic acid-based CRC markers, the possibility of their application in clinical practice today, and their future improvement. Approaches to the discovery and verification of new markers are described, and the existing problems and potential solutions for them are highlighted.

Circulating cell-free methylated DNA reveals tissue-specific, cellular damage from radiation treatment

Radiation therapy is an effective cancer treatment, although damage to healthy tissues is common. Here we analyzed cell-free, methylated DNA released from dying cells into the circulation to evaluate radiation-induced cellular damage in different tissues. To map the circulating DNA fragments to human and mouse tissues, we established sequencing-based, cell-type-specific reference DNA methylation atlases. We found that cell-type-specific DNA blocks were mostly hypomethylated and located within signature genes of cellular identity. Cell-free DNA fragments were captured from serum samples by hybridization to CpG-rich DNA panels and mapped to the DNA methylation atlases. In a mouse model, thoracic radiation-induced tissue damage was reflected by dose-dependent increases in lung endothelial and cardiomyocyte methylated DNA in serum. The analysis of serum samples from patients with breast cancer undergoing radiation treatment revealed distinct dose-dependent and tissue-specific epithelial and endothelial responses to radiation across multiple organs. Strikingly, patients treated for right-sided breast cancers also showed increased hepatocyte and liver endothelial DNA in the circulation, indicating the impact on liver tissues. Thus, changes in cell-free methylated DNA can uncover cell-type-specific effects of radiation and provide a readout of the biologically effective radiation dose received by healthy tissues.