A suite of DNA methylation markers that can detect most common human cancers

DNA methylation biomarker panels for differentiating various liver adenocarcinomas, including hepatocellular carcinoma, cholangiocarcinoma, colorectal liver metastases and pancreatic adenocarcinoma liver metastases

BACKGROUND

DNA methylation biomarkers are one of the most promising tools for the diagnosis and differentiation of adenocarcinomas of the liver, which are among the most common malignancies worldwide. Their differentiation is important because of the different prognoses and treatment options. This study aimed to validate previously identified DNA methylation biomarkers that successfully differentiate between liver adenocarcinomas, including the two most common primary liver cancers, hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), as well as two common metastatic liver cancers, colorectal liver metastases (CRLM) and pancreatic ductal adenocarcinoma liver metastases (PCLM), and translate them to the methylation-sensitive high-resolution melting (MS-HRM) and digital PCR (dPCR) platforms.

METHODS

Our study included a cohort of 149 formalin-fixed, paraffin-embedded tissue samples, including 19 CRLMs, 10 PCLMs, 15 HCCs, 15 CCAs, 15 colorectal adenocarcinomas (CRCs), 15 pancreatic ductal adenocarcinomas (PDACs) and their paired normal tissue samples. The methylation status of the samples was experimentally determined by MS-HRM and methylation-specific dPCR. Previously determined methylation threshold were adjusted according to dPCR data and applied to the same DNA methylation array datasets (provided by The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO)) used to originally identify the biomarkers for the included cancer types and additional CRLM projects. The sensitivities, specificities and diagnostic accuracies of the panels for individual cancer types were calculated.

RESULTS

In the dPCR experiment, the DNA methylation panels identified HCC, CCA, CRC, PDAC, CRLM and PCLM with sensitivities of 100%, 66.7%, 100%, 86.7%, 94.7% and 80%, respectively. The panels differentiate between HCC, CCA, CRLM, PCLM and healthy liver tissue with specificities of 100%, 100%, 97.1% and 94.9% and with diagnostic accuracies of 100%, 94%, 97% and 93%, respectively. Reevaluation of the same bioinformatic data with new additional CRLM projects demonstrated that the lower dPCR methylation threshold still effectively differentiates between the included cancer types. The bioinformatic data achieved sensitivities for HCC, CCA, CRC, PDAC, CRLM and PCLM of 88%, 64%, 97.4%, 75.5%, 80% and 84.6%, respectively. Specificities between HCC, CCA, CRLM, PCLM and healthy liver tissue were 98%, 93%, 86.6% and 98.2% and the diagnostic accuracies were 94%, 91%, 86% and 98%, respectively. Moreover, we confirmed that the methylation of the investigated promoters is preserved from primary CRC and PDAC to their liver metastases.

CONCLUSIONS

The cancer-specific methylation biomarker panels exhibit high sensitivities, specificities and diagnostic accuracies and enable differentiation between primary and metastatic adenocarcinomas of the liver using methylation-specific dPCR. High concordance was achieved between MS-HRM, dPCR and bioinformatic data, demonstrating the successful translation of bioinformatically identified methylation biomarkers from the Illumina Infinium HumanMethylation450 BeadChip (HM450) and lllumina MethylationEPIC BeadChip (EPIC) platforms to the simpler MS-HRM and dPCR platforms.

Simultaneous assessment of human genome and methylome data in a single experiment using limited deamination of methylated cytosine

Multiomics require concerted recording of independent information, ideally from a single experiment. In this study, we introduce RIMS-seq2, a high-throughput technique to simultaneously sequence genomes and overlay methylation information while requiring only a small modification of the experimental protocol for high-throughput DNA sequencing to include a controlled deamination step. Importantly, the rate of deamination of 5-methylcytosine is negligible and thus does not interfere with standard DNA sequencing and data processing. Thus, RIMS-seq2 libraries from whole- or targeted-genome sequencing show the same germline variation calling accuracy and sensitivity compared with standard DNA-seq. Additionally, regional methylation levels provide an accurate map of the human methylome.

Detection and Characterization of Methylated Circulating Tumor DNA in Gastric Cancer

Gastric cancer is the fifth most common disease in the world and the fourth most common cause of death. It is diagnosed through esophagogastroduodenoscopy with biopsy; however, there are limitations in finding lesions in the early stages. Recently, research has been actively conducted to use liquid biopsy to diagnose various cancers, including gastric cancer. Various substances derived from cancer are reflected in the blood. By analyzing these substances, it was expected that not only the presence or absence of cancer but also the type of cancer can be diagnosed. However, the amount of these substances is extremely small, and even these have various variables depending on the characteristics of the individual or the characteristics of the cancer. To overcome these, we collected methylated DNA fragments using MeDIP and compared them with normal plasma to characterize gastric cancer tissue or patients' plasma. We attempted to diagnose gastric cancer using the characteristics of cancer reflected in the blood through the cancer tissue and patients' plasma. As a result, we confirmed that the consistency of common methylated fragments between tissue and plasma was approximately 41.2% and we found the possibility of diagnosing and characterizing cancer using the characteristics of the fragments through SFR and 5'end-motif analysis.

Cancer screening with multicancer detection tests: A translational science review

Multicancer detection (MCD) tests use a single, easily obtainable biospecimen, such as blood, to screen for more than one cancer concurrently. MCD tests can potentially be used to improve early cancer detection, including cancers that currently lack effective screening methods. However, these tests have unknown and unquantified benefits and harms. MCD tests differ from conventional cancer screening tests in that the organ responsible for a positive test is unknown, and a broad diagnostic workup may be necessary to confirm the location and type of underlying cancer. Among two prospective studies involving greater than 16,000 individuals, MCD tests identified those who had some cancers without currently recommended screening tests, including pancreas, ovary, liver, uterus, small intestine, oropharyngeal, bone, thyroid, and hematologic malignancies, at early stages. Reported MCD test sensitivities range from 27% to 95% but differ by organ and are lower for early stage cancers, for which treatment toxicity would be lowest and the potential for cure might be highest. False reassurance from a negative MCD result may reduce screening adherence, risking a loss in proven public health benefits from standard-of-care screening. Prospective clinical trials are needed to address uncertainties about MCD accuracy to detect different cancers in asymptomatic individuals, whether these tests can detect cancer sufficiently early for effective treatment and mortality reduction, the degree to which these tests may contribute to cancer overdiagnosis and overtreatment, whether MCD tests work equally well across all populations, and the appropriate diagnostic evaluation and follow-up for patients with a positive test.

Alteration in DNA methylation patterns: Epigenetic signatures in gastrointestinal cancers

Abnormalities in epigenetic modifications can cause malignant transformations in cells, leading to cancers of the gastrointestinal (GI) tract, which accounts for 20% of all cancers worldwide. Among the epigenetic alterations, DNA hypomethylation is associated with genomic instability. In addition, CpG methylation and promoter hypermethylation have been recognized as biomarkers for different malignancies. In GI cancers, epigenetic alterations affect genes responsible for cell cycle control, DNA repair, apoptosis, and tumorigenic-specific signaling pathways. Understanding the pattern of alterations in DNA methylation in GI cancers could help scientists discover new molecular-based pharmaceutical treatments. This study highlights alterations in DNA methylation in GI cancers. Understanding epigenetic differences among GI cancers may improve targeted therapies and lead to the discovery of new diagnostic biomarkers.

Inhibition of cytosine 5-hydroxymethylation during progression of cancer precursor lesions in the uterine cervix

Methylation and hydroxymethylation of cytosine moieties in CpG islands of specific genes are epigenetic processes shown to be involved in the development of cervical (pre)neoplastic lesions. We studied global (hydroxy)methylation during the subsequent steps in the carcinogenic process of the uterine cervix by using immunohistochemical protocols for the detection of 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC) in paraffin-embedded tissues of the normal epithelia and (pre)malignant lesions. This approach allowed obtaining spatially resolved information of (epi)genetic alterations for individual cell populations in morphologically heterogeneous tissue samples. The normal ectocervical squamous epithelium showed a high degree of heterogeneity for both modifications, with a major positivity for 5-mC in the basal and parabasal layers in the ectocervical region, while 5-hmC immunostaining was even more restricted to the cells in the basal layer. Immature squamous metaplasia, characterized by expression of SOX17, surprisingly showed a decrease of 5-hmC in the basal compartments and an increase in the more superficial layers of the epithelium. The normal endocervical glandular epithelium showed a strong immunostaining reactivity for both modifications. At the squamocolumnar junctions, a specific 5-hmC pattern was observed in the squamous epithelium, resembling that of metaplasia, with the typical weak to negative reaction for 5-hmC in the basal cell compartment. The reserve cells underlying the glandular epithelium were also largely negative for 5-hmC but showed immunostaining for 5-mC. While the overall methylation status remained relatively constant, about 20% of the high-grade squamous lesions showed a very low immunostaining reactivity for 5-hmC. The (pre)malignant glandular lesions, including adenocarcinoma in situ (AIS) and adenocarcinoma showed a progressive decrease of hydroxymethylation with advancement of the lesion, resulting in cases with regions that were negative for 5-hmC immunostaining. These data indicate that inhibition of demethylation, which normally follows cytosine hydroxymethylation, is an important epigenetic switch in the development of cervical cancer.

Blood-Based DNA Methylation Analysis by Multiplexed OBBPA-ddPCR to Verify Indications for Prostate Biopsies in Suspected Prostate Cancer Patients

Current prostate carcinoma (PCa) biomarkers, including total prostate-specific antigen (tPSA), have unsatisfactory diagnostic sensitivity and specificity resulting in overdiagnosis and overtreatment. Previously, we described an optimised bias-based preamplification-digital droplet PCR (OBBPA-ddPCR) technique, which detects tumour DNA in blood-derived cell-free DNA (cfDNA) of cancer patients. The current study investigated the performance of newly developed OBBPA-ddPCR-based biomarkers. Blood plasma samples from healthy individuals (n = 90, controls) and PCa (n = 39) and benign prostatic hyperplasia patients (BPH, n = 40) were analysed. PCa and BPH patients had tPSA values within a diagnostic grey area of 2-15 ng/mL, for whom further diagnostic validation is most crucial. Methylation levels of biomarkers RASSF1A, MIR129-2, NRIP3, and SOX8 were found significantly increased in PCa patients compared to controls. By combining classical PCa risk factors (percentage of free PSA compared to tPSA (QfPSA) and patient's age) with cfDNA-based biomarkers, we developed PCa risk scores with improved sensitivity and specificity compared to established tPSA and QfPSA single-marker analyses. The diagnostic specificity was increased to 70% with 100% sensitivity for clinically significant PCa patients. Thus, prostate biopsies could be avoided for 28 out of 40 BPH patients. In conclusion, the newly developed risk scores may help to confirm the clinical decision and prevent unnecessary prostate biopsy.

Discovery of novel DNA methylation biomarker panels for the diagnosis and differentiation between common adenocarcinomas and their liver metastases

Differentiation between adenocarcinomas is sometimes challenging. The promising avenue for discovering new biomarkers lies in bioinformatics using DNA methylation analysis. Utilizing a 2853-sample identification dataset and a 782-sample independent verification dataset, we have identified diagnostic DNA methylation biomarkers that are hypermethylated in cancer and differentiate between breast invasive carcinoma, cholangiocarcinoma, colorectal cancer, hepatocellular carcinoma, lung adenocarcinoma, pancreatic adenocarcinoma and stomach adenocarcinoma. The best panels for cancer type exhibit sensitivity of 77.8-95.9%, a specificity of 92.7-97.5% for tumors, a specificity of 91.5-97.7% for tumors and normal tissues and a diagnostic accuracy of 85.3-96.4%. We have shown that the results can be extended from the primary cancers to their liver metastases, as the best panels diagnose and differentiate between pancreatic adenocarcinoma liver metastases and breast invasive carcinoma liver metastases with a sensitivity and specificity of 83.3-100% and a diagnostic accuracy of 86.8-91.9%. Moreover, the panels could detect hypermethylation of selected regions in the cell-free DNA of patients with liver metastases. At the same time, these were unmethylated in the cell-free DNA of healthy donors, confirming their applicability for liquid biopsies.

Cell-free DNA methylation analysis as a marker of malignancy in pleural fluid

Diagnosis of malignant pleural effusion (MPE) is made by cytological examination of pleural fluid or histological examination of pleural tissue from biopsy. Unfortunately, detection of malignancy using cytology has an overall sensitivity of 50%, and is dependent upon tumor load, volume of fluid assessed, and cytopathologist experience. The diagnostic yield of pleural fluid cytology is also compromised by low abundance of tumor cells or when morphology is obscured by inflammation or reactive mesothelial cells. A reliable molecular marker that may complement fluid cytology for the diagnosis of malignant pleural effusion is needed. The purpose of this study was to establish a molecular diagnostic approach based on pleural effusion cell-free DNA methylation analysis for the differential diagnosis of malignant pleural effusion and benign pleural effusion. This was a blind, prospective case-control biomarker study. We recruited 104 patients with pleural effusion for the study. We collected pleural fluid from patients with: MPE (n = 48), indeterminate pleural effusion in subjects with known malignancy or IPE (n = 28), and benign PE (n = 28), and performed the Sentinel-MPE liquid biopsy assay. The methylation level of Sentinel-MPE was markedly higher in the MPE samples compared to BPE control samples (p < 0.0001) and the same tendency was observed relative to IPE (p = 0.004). We also noted that the methylation signal was significantly higher in IPE relative to BPE (p < 0.001). We also assessed the diagnostic efficiency of the Sentinel-MPE test by performing receiver operating characteristic analysis (ROC). For the ROC analysis we combined the malignant and indeterminate pleural effusion groups (n = 76) and compared against the benign group (n = 28). The detection sensitivity and specificity of the Sentinel-MPE test was high (AUC = 0.912). The Sentinel-MPE appears to have better performance characteristics than cytology analysis. However, combining Sentinel-MPE with cytology analysis could be an even more effective approach for the diagnosis of MPE. The Sentinel-MPE test can discriminate between BPE and MPE. The Sentinel-MPE liquid biopsy test can detect aberrant DNA in several different tumor types. The Sentinel-MPE test can be a complementary tool to cytology in the diagnosis of MPE.

Early Cancer Detection in Li-Fraumeni Syndrome with Cell-Free DNA

People with Li-Fraumeni syndrome (LFS) harbor a germline pathogenic variant in the TP53 tumor suppressor gene, face a near 100% lifetime risk of cancer, and routinely undergo intensive surveillance protocols. Liquid biopsy has become an attractive tool for a range of clinical applications, including early cancer detection. Here, we provide a proof-of-principle for a multimodal liquid biopsy assay that integrates a targeted gene panel, shallow whole-genome, and cell-free methylated DNA immunoprecipitation sequencing for the early detection of cancer in a longitudinal cohort of 89 LFS patients. Multimodal analysis increased our detection rate in patients with an active cancer diagnosis over uni-modal analysis and was able to detect cancer-associated signal(s) in carriers prior to diagnosis with conventional screening (positive predictive value = 67.6%, negative predictive value = 96.5%). Although adoption of liquid biopsy into current surveillance will require further clinical validation, this study provides a framework for individuals with LFS.

SIGNIFICANCE

By utilizing an integrated cell-free DNA approach, liquid biopsy shows earlier detection of cancer in patients with LFS compared with current clinical surveillance methods such as imaging. Liquid biopsy provides improved accessibility and sensitivity, complementing current clinical surveillance methods to provide better care for these patients. See related commentary by Latham et al., p. 23. This article is featured in Selected Articles from This Issue, p. 5.

Performance of DNA methylation analysis in the detection of high-grade cervical intraepithelial neoplasia or worse (CIN3+): a cross-sectional study

It is commonly accepted that host genes show high methylation in cervical intraepithelial neoplasia 3 (CIN3) or worse (CIN3+). However, study quality varies, as does the clinical performance of markers in different populations. We aimed to validate candidate gene DNA methylation with standardized testing methods in the same batch of samples. We first compared the performance of 16 DNA methylation markers for detecting CIN3+ in the 82-sample training set, including 24 subjects with ≤ CIN1, 10 subjects with CIN2, 23 subjects with CIN3, and 25 subjects with cervical cancer (CC). Then five methylation markers were selected and subsequently validated among an independent set of 74 subjects, including 47 subjects with ≤ CIN1, 13 subjects with CIN2, 6 subjects with CIN3, and 8 subjects with CC. The results in the validation set revealed that methylation analysis of the SOX1 (SOX1m) showed a superior level of clinical performance (AUC = 0.879; sensitivity = 85.7%; specificity = 90.0%). SOX1m had better accuracy than cytology, with a reduced referral rate (23.0% vs. 31.4%) and a lower number of overtreatment (5 vs. 13) cases among high-risk human papillomavirus (hrHPV)-positive women. Importantly, among hrHPV-positive and SOX1m-negative women, only 1 CIN3 patient was at risk for follow-up after 1 year, whereas 1 CIN3 patient and 1 CC patient were at risk among hrHPV-positive and cytology-negative women. In this investigation, we screened 16 reported methylation markers to provide a basis for future studies related to potential precancerous lesion/cancer methylation markers in the Chinese population. The study also revealed that SOX1m has optimal CIN3+ detection performance, suggesting that it may be a promising biomarker for detecting CIN3+ in the Chinese population.

Potential applications of DNA methylation testing technology in female tumors and screening methods

DNA methylation is a common epigenetic modification, and the current commonly used methods for DNA methylation detection include methylation-specific PCR, methylation-sensitive restriction endonuclease-PCR, and methylation-specific sequencing. DNA methylation plays an important role in genomic and epigenomic studies, and combining DNA methylation with other epigenetic modifications, such as histone modifications, may lead to better DNA methylation. DNA methylation also plays an important role in the development of disease, and analyzing changes in individual DNA methylation patterns can provide individualized diagnostic and therapeutic solutions. Liquid biopsy techniques are also increasingly well established in clinical practice and may provide new methods for early cancer screening. It is important to find new screening methods that are easy to perform, minimally invasive, patient-friendly, and affordable. DNA methylation mechanisms are thought to have an important role in cancer and have potential applications in the diagnosis and treatment of female tumors. This review discussed early detection targets and screening methods for common female tumors such as breast, ovarian, and cervical cancers and discussed advances in the study of DNA methylation in these tumors. Although existing screening, diagnostic, and treatment modalities exist, the high morbidity and mortality rates of these tumors remain challenging.

Accurate prediction of pan-cancer types using machine learning with minimal number of DNA methylation sites

DNA methylation analysis has been applied to determine the primary site of cancer; however, robust and accurate prediction of cancer types with a minimum number of sites is still a significant scientific challenge. To build an accurate and robust cancer type prediction tool with a minimum number of DNA methylation sites, we internally benchmarked different DNA methylation site selection and ranking procedures, as well as different classification models. We used The Cancer Genome Atlas dataset (26 cancer types with 8296 samples) to train and test models and used an independent dataset (17 cancer types with 2738 samples) for model validation. A deep neural network model using a combined feature selection procedure (named MethyDeep) can predict 26 cancer types using 30 methylation sites with superior performance compared with the known methods for both primary and metastatic cancers in independent validation datasets. In conclusion, MethyDeep is an accurate and robust cancer type predictor with the minimum number of DNA methylation sites; it could help the cost-effective clarification of cancer of unknown primary patients and the liquid biopsy-based early screening of cancers.

Integrative analysis of DNA methylomes reveals novel cell-free biomarkers in lung adenocarcinoma

Lung cancer is a leading cause of cancer-related deaths worldwide, with a low 5-year survival rate due in part to a lack of clinically useful biomarkers. Recent studies have identified DNA methylation changes as potential cancer biomarkers. The present study identified cancer-specific CpG methylation changes by comparing genome-wide methylation data of cfDNA from lung adenocarcinomas (LUAD) patients and healthy donors in the discovery cohort. A total of 725 cell-free CpGs associated with LUAD risk were identified. Then XGBoost algorithm was performed to identify seven CpGs associated with LUAD risk. In the training phase, the 7-CpGs methylation panel was established to classify two different prognostic subgroups and showed a significant association with overall survival (OS) in LUAD patients. We found that the methylation of cg02261780 was negatively correlated with the expression of its representing gene GNA11. The methylation and expression of GNA11 were significantly associated with LAUD prognosis. Based on bisulfite PCR, the methylation levels of five CpGs (cg02261780, cg09595050, cg20193802, cg15309457, and cg05726109) were further validated in tumor tissues and matched non-malignant tissues from 20 LUAD patients. Finally, validation of the seven CpGs with RRBS data of cfDNA methylation was conducted and further proved the reliability of the 7-CpGs methylation panel. In conclusion, our study identified seven novel methylation markers from cfDNA methylation data which may contribute to better prognosis for LUAD patients.

Plasma cell-free DNA methylome profiling in pre- and post-surgery oral cavity squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC), a highly heterogeneous disease that involves multiple anatomic sites, is a leading cause of cancer-related mortality worldwide. Although the utility of noninvasive biomarkers based on circulating cell-free DNA (cfDNA) methylation profiling has been widely recognized, limited studies have been reported so far regarding the dynamics of cfDNA methylome in oral cavity squamous cell carcinoma (OCSCC). It is hypothesized in this study that comparison of methylation profiles in pre- and postsurgery plasma samples will reveal OCSCC-specific prognostic and diagnostic biomarkers. As a strategy to further prioritize tumor-specific targets, top differential methylated regions (DMRs) were called by reanalyzing methylation data from paired tumor and normal tissue collected in the the cancer genome atlas head-neck squamous cell carcinoma (TCGA) head and neck cancer cohort. Matched plasma samples from eight patients with OCSCC were collected at Moffitt Cancer Center before and after surgical resection. Plasma-derived cfDNA was analyzed by cfMBD-seq, which is a high-sensitive methylation profiling assay. Differential methylation analysis was then performed based on the matched samples profiled. In the top 200 HNSCC-specific DMRs detected based on the TCGA data set, a total of 23 regions reached significance in the plasma-based DMR test. The top five validated DMR regions (ranked by the significance in the plasma study) are located in the promoter regions of genes PENK, NXPH1, ZIK1, TBXT, and CDO1, respectively. The genome-wide cfDNA DMR analysis further highlighted candidate biomarkers located in genes SFRP4, SOX1, IRF4, and PCDH17. The prognostic relevance of candidate genes was confirmed by survival analysis using the TCGA data. This study supports the utility of cfDNA-based methylome profiling as a promising noninvasive biomarker source for OCSCC and HNSCC.

Shifting the Cancer Screening Paradigm: The Rising Potential of Blood-Based Multi-Cancer Early Detection Tests

Cancer remains a leading cause of death worldwide, partly owing to late detection which entails limited and often ineffective therapeutic options. Most cancers lack validated screening procedures, and the ones available disclose several drawbacks, leading to low patient compliance and unnecessary workups, adding up the costs to healthcare systems. Hence, there is a great need for innovative, accurate, and minimally invasive tools for early cancer detection. In recent years, multi-cancer early detection (MCED) tests emerged as a promising screening tool, combining molecular analysis of tumor-related markers present in body fluids with artificial intelligence to simultaneously detect a variety of cancers and further discriminate the underlying cancer type. Herein, we aim to provide a highlight of the variety of strategies currently under development concerning MCED, as well as the major factors which are preventing clinical implementation. Although MCED tests depict great potential for clinical application, large-scale clinical validation studies are still lacking.

The Role of Cell-Free DNA in Cancer Treatment Decision Making

The aim of this review is to evaluate the present status of the use of cell-free DNA and its fraction of circulating tumor DNA (ctDNA) because this year July 2022, an ESMO guideline was published regarding the application of ctDNA in patient care. This review is for clinical oncologists to explain the concept, the terms used, the pros and cons of ctDNA; thus, the technical aspects of the different platforms are not reviewed in detail, but we try to help in navigating the current knowledge in liquid biopsy. Since the validated and adequately sensitive ctDNA assays have utility in identifying actionable mutations to direct targeted therapy, ctDNA may be used for this soon in routine clinical practice and in other different areas as well. The cfDNA fragments can be obtained by liquid biopsy and can be used for diagnosis, prognosis, and selecting among treatment options in cancer patients. A great proportion of cfDNA comes from normal cells of the body or from food uptake. Only a small part (<1%) of it is related to tumors, originating from primary tumors, metastatic sites, or circulating tumor cells (CTCs). Soon the data obtained from ctDNA may routinely be used for finding minimal residual disease, detecting relapse, and determining the sites of metastases. It might also be used for deciding appropriate therapy, and/or emerging resistance to the therapy and the data analysis of ctDNA may be combined with imaging or other markers. However, to achieve this goal, further clinical validations are inevitable. As a result, clinicians should be aware of the limitations of the assays. Of course, several open questions are still under research and because of it cfDNA and ctDNA testing are not part of routine care yet.

Plasma Methylated RNF180 for Noninvasive Diagnosis of Gastric Cancer

Background

RNF180 is a tumor suppressor gene involved in cell development, proliferation, and apoptosis. Methylation of RNF180 (mRNF180) leads to low expression of RNF180, which is closely related to the occurrence and development of gastric cancer (GC). This study was designed to evaluate the potential performance of plasma mRNF180 as noninvasive biomarker for the diagnosis of GC.

Methods

A total of 156 participants, including 60 patients with GC, 39 with chronic superficial gastritis (CSG), 27 with chronic atrophic gastritis (CAG), and 30 with gastric ulcer (GU) were recruited for this study. Plasma mRNF180 level was measured using real-time polymerase chain reaction.

Results

As a diagnostic target, mRNF180 had a sensitivity of 71.67% (95% CI: 58.36%-82.18%) and specificity of 59.38% (95% CI: 48.85%-69.14%). The area under the ROC curve value of mRNF180 was 0.731 (95% CI: 0.648%-0.813%) for differentiation of GC from benign gastric diseases (BGD). The effectiveness of mRNF180 was superior to that of CEA, CA199, and CA724. mRNF180 was positively correlated with age, tumor size, T stage, N stage, M stage, and clinical stage of patients with GC.

Conclusions

Plasma mRNF180 might serve as a useful and noninvasive biomarker for the diagnosis of GC and can be used to evaluate its prognosis.

Density Control over MBD2 Receptor-Coated Surfaces Provides Superselective Binding of Hypermethylated DNA

Using the biomarker hypermethylated DNA (hmDNA) for cancer detection requires a pretreatment to isolate or concentrate hmDNA from nonmethylated DNA. Affinity chromatography using a methyl binding domain-2 (MBD2) protein can be used, but the relatively low enrichment selectivity of MBD2 limits its clinical applicability. Here, we developed a superselective, multivalent, MBD2-coated platform to improve the selectivity of hmDNA enrichment. The multivalent platform employs control over the MBD2 surface receptor density, which is shown to strongly affect the binding of DNA with varying degrees of methylation, improving both the selectivity and the affinity of DNAs with higher numbers of methylation sites. Histidine-10-tagged MBD2 was immobilized on gold surfaces with receptor density control by tuning the amount of nickel nitrilotriacetic acid (NiNTA)-functionalized thiols in a thiol-based self-assembled monolayer. The required MBD2 surface receptor densities for DNA surface binding decreases for DNA with higher degrees of methylation. Both higher degrees of superselectivity and surface coverages were observed upon DNA binding at increasing methylation levels. Adopting the findings of this study into hmDNA enrichment of clinical samples has the potential to become more selective and sensitive than current MBD2-based methods and, therefore, to improve cancer diagnostics.

Diagnostic classification of cancers using DNA methylation of paracancerous tissues

The potential role of DNA methylation from paracancerous tissues in cancer diagnosis has not been explored until now. In this study, we built classification models using well-known machine learning models based on DNA methylation profiles of paracancerous tissues. We evaluated our methods on nine cancer datasets collected from The Cancer Genome Atlas (TCGA) and utilized fivefold cross-validation to assess the performance of models. Additionally, we performed gene ontology (GO) enrichment analysis on the basis of the significant CpG sites selected by feature importance scores of XGBoost model, aiming to identify biological pathways involved in cancer progression. We also exploited the XGBoost algorithm to classify cancer types using DNA methylation profiles of paracancerous tissues in external validation datasets. Comparative experiments suggested that XGBoost achieved better predictive performance than the other four machine learning methods in predicting cancer stage. GO enrichment analysis revealed key pathways involved, highlighting the importance of paracancerous tissues in cancer progression. Furthermore, XGBoost model can accurately classify nine different cancers from TCGA, and the feature sets selected by XGBoost can also effectively predict seven cancer types on independent GEO datasets. This study provided new insights into cancer diagnosis from an epigenetic perspective and may facilitate the development of personalized diagnosis and treatment strategies.

Tumor DNA Methylation Profiles Enable Diagnosis, Prognosis Prediction, and Screening for Cervical Cancer

Background

DNA-methylation-based machine learning algorithms have demonstrated powerful diagnostic capabilities, and these tools are currently emerging in many fields of tumor diagnosis and patient prognosis prediction. This work aimed to identify novel DNA methylation diagnostic biomarkers for differentiating cervical cancer (CC) from normal tissues, as well as a prognostic prediction model to predict survival of CC patients.

Methods

The methylation profiles with the available clinical characteristics were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) program. We first screened out the differential methylation sites in CC and normal tissues and performed multiple statistical analyses to discover DNA methylation diagnostic markers that are used to distinguish CC and normal control. Then, we developed a methylation-based survival model to improve risk stratification.

Results

A diagnostic prediction panel consists of five CpG markers that could predict cervical cancer versus normal tissue with highly correct rate of 100%, and cg16428251, cg22341310, and cg23316360 which in diagnostic prediction panel all could yield high sensitivity and specificity for detection of CC and normal in six cohorts (area under curve [AUC] > 0.8), in addition to excellent performance in discriminating between CC and normal sample. The diagnostic marker panel also effectively predicted the CIN3 versus normal tissue with high accuracy in two datasets (AUC = 0.80, 0.789, respectively). Furthermore, a prognostic prediction model aggregated two CpG markers that effectively stratified the prognosis of high-risk and low-risk groups (training cohort: hazard ratio [HR] 4, 95% CI: 1.7–9.6, P = 0.0021; testing cohort: hazard ratio [HR] 1.9, 95% CI: 1.2–3.1, P = 0.0072).

Conclusion

The findings of our study showed that DNA methylation markers are of great value in the diagnosis and prognosis of CC.

Genome-wide methylation profiling of early colorectal cancer using an Illumina Infinium Methylation EPIC BeadChip

BACKGROUND

DNA methylation is a part of epigenetic modification, that is closely related to the growth and development of colorectal cancer (CRC). Specific methylated genes and methylated diagnostic models of tumors have become current research focuses. The methylation status of circulating DNA in plasma might serve as a potential biomarker for CRC.

AIM

To investigate genome-wide methylation pattern in early CRC using the Illumina Infinium Human Methylation 850K BeadChip.

METHODS

The 850K Methylation BeadChip was used to analyze the genome-wide methylation status of early CRC patients (n = 5) and colorectal adenoma patients (n = 5). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analyses were performed on the selected differentially methylated sites to further discover candidate methylation biomarkers in plasma.

RESULTS

A total of 1865 methylated CpG sites with significant differences were detected, including 676 hypermethylated sites and 1189 hypomethylated sites. The distribution of these sites covered from the 1^st to 22^nd chromosomes and are mainly distributed on the gene body and gene promoter region. GO and KEGG enrichment analysis showed that the functions of these genes were related to biological regulation, molecular binding, transcription factor activity and signal transduction pathway.

CONCLUSION

The study demonstrated that the Illumina Infinium Human Methylation 850K BeadChip can be used to investigate genome-wide methylation status of plasma DNA in early CRC and colorectal adenoma patients.

Stage-differentiated ensemble modeling of DNA methylation landscapes uncovers salient biomarkers and prognostic signatures in colorectal cancer progression

Background

Aberrant DNA methylation acts epigenetically to skew the gene transcription rate up or down, contributing to cancer etiology. A gap in our understanding concerns the epigenomics of stagewise cancer progression. In this study, we have developed a comprehensive computational framework for the stage-differentiated modelling of DNA methylation landscapes in colorectal cancer (CRC).

Methods

The methylation β-matrix was derived from the public-domain TCGA data, converted into M-value matrix, annotated with AJCC stages, and analysed for stage-salient genes using an ensemble of approaches involving stage-differentiated modelling of methylation patterns and/or expression patterns. Differentially methylated genes (DMGs) were identified using a contrast against controls (adjusted p-value <0.001 and |log fold-change of M-value| >2), and then filtered using a series of all possible pairwise stage contrasts (p-value <0.05) to obtain stage-salient DMGs. These were then subjected to a consensus analysis, followed by matching with clinical data and performing Kaplan–Meier survival analysis to evaluate the impact of methylation patterns of consensus stage-salient biomarkers on disease prognosis.

Results

We found significant genome-wide changes in methylation patterns in cancer cases relative to controls agnostic of stage. The stage-differentiated models yielded the following consensus salient genes: one stage-I gene (FBN1), one stage-II gene (FOXG1), one stage-III gene (HCN1) and four stage-IV genes (NELL1, ZNF135, FAM123A, LAMA1). All the biomarkers were significantly hypermethylated in the promoter regions, indicating down-regulation of expression and implying a putative CpG island Methylator Phenotype (CIMP) manifestation. A prognostic signature consisting of FBN1 and FOXG1 survived all the analytical filters, and represents a novel early-stage epigenetic biomarker / target.

Conclusions

We have designed and executed a workflow for stage-differentiated epigenomic analysis of colorectal cancer progression, and identified several stage-salient diagnostic biomarkers, and an early-stage prognostic biomarker panel. The study has led to the discovery of an alternative CIMP-like signature in colorectal cancer, reinforcing the role of CIMP drivers in tumor pathophysiology.

Liquid biopsy, using a novel DNA methylation signature, distinguishes pancreatic adenocarcinoma from benign pancreatic disease

We tested the ability of a novel DNA methylation biomarker set to distinguish metastatic pancreatic cancer cases from benign pancreatic cyst patients and to monitor tumor dynamics using quantitative DNA methylation analysis of cell-free DNA (cfDNA) from blood samples. The biomarkers were able to distinguish malignant cases from benign disease with high sensitivity and specificity (AUC = 0.999). Furthermore, the biomarkers detected a consistent decline in tumor-derived cfDNA in samples from patients undergoing chemotherapy. The study indicates that our liquid biopsy assay could be useful for management of pancreatic cancer patients.

The insertion and dysregulation of transposable elements in osteosarcoma and their association with patient event-free survival

The dysregulation of transposable elements (TEs) has been explored in a variety of cancers. However, TE activities in osteosarcoma (OS) have not been extensively studied yet. By integrative analysis of RNA-seq, whole-genome sequencing (WGS), and methylation data, we showed aberrant TE activities associated with dysregulations of TEs in OS tumors. Specifically, expression levels of LINE-1 and Alu of different evolutionary ages, as well as subfamilies of SVA and HERV-K, were significantly up-regulated in OS tumors, accompanied by enhanced DNA repair responses. We verified the characteristics of LINE-1 mediated TE insertions, including target site duplication (TSD) length (centered around 15 bp) and preferential insertions into intergenic and AT-rich regions as well as intronic regions of longer genes. By filtering polymorphic TE insertions reported in 1000 genome project (1KGP), besides 148 tumor-specific somatic TE insertions, we found most OS patient-specific TE insertions (3175 out of 3326) are germline insertions, which are associated with genes involved in neuronal processes or with transcription factors important for cancer development. In addition to 68 TE-affected cancer genes, we found recurrent germline TE insertions in 72 non-cancer genes with high frequencies among patients. We also found that +/− 500 bps flanking regions of transcription start sites (TSS) of LINE-1 (young) and Alu showed lower methylation levels in OS tumor samples than controls. Interestingly, by incorporating patient clinical data and focusing on TE activities in OS tumors, our data analysis suggested that higher TE insertions in OS tumors are associated with a longer event-free survival time.

Genome-wide DNA methylation profiling and identification of potential pan-cancer and tumor-specific biomarkers

DNA methylation alterations have already been linked to cancer, and their usefulness for therapy and diagnosis has encouraged research into the human epigenome. Several biomarker studies have focused on identifying cancer types individually, yet common cancer and multi-cancer markers are still underexplored. We used The Cancer Genome Atlas (TCGA) to investigate genome-wide methylation profiles of 14 different cancer types and developed a three-step computational approach to select candidate biomarker CpG sites. In total, 1,991 pan-cancer and between 75 and 1,803 cancer-specific differentially methylated CpG sites were discovered. Differentially methylated blocks and regions were also discovered for the first time on such a large-scale. Through a three-step computational approach, a combination of four pan-cancer CpG markers was identified from these sites and externally validated (AUC = 0.90), maintaining comparable performance across tumor stages. Additionally, 20 tumor-specific CpG markers were identified and made up the final type-specific prediction model, which could accurately differentiate tumor types (AUC = 0.87-0.99). Our study highlights the power of the methylome as a rich source of cancer biomarkers, and the signatures we identified provide a new resource for understanding cancer mechanisms on the wider genomic scale with strong applicability in the context of new minimally invasive cancer detection assays.

Cancer Detection and Classification by CpG Island Hypermethylation Signatures in Plasma Cell-Free DNA

Simple Summary

The detection of DNA methylation changes in blood has emerged as a promising approach for cancer diagnosis and management. Our group has previously optimized a blood DNA methylation profiling technology that is based on affinity capture of methylated DNA, termed cfMBD-seq. The aim of this study was to assess the potential clinical feasibility of cfMBD-seq. We applied cfMBD-seq to the blood samples of cancer patients and identified methylation signatures that can not only discriminate cancer patients from cancer-free individuals but can also enable accurate multi-cancer classification. Our findings will help to expand on existing blood-based molecular diagnostic tests and identify novel methylation biomarkers for early cancer detection and classification.

Abstract

Cell-free DNA (cfDNA) methylation has emerged as a promising biomarker for early cancer detection, tumor type classification, and treatment response monitoring. Enrichment-based cfDNA methylation profiling methods such as cfMeDIP-seq have shown high accuracy in the classification of multiple cancer types. We have previously optimized another enrichment-based approach for ultra-low input cfDNA methylome profiling, termed cfMBD-seq. We reported that cfMBD-seq outperforms cfMeDIP-seq in the enrichment of high-CpG-density regions, such as CpG islands. However, the clinical feasibility of cfMBD-seq is unknown. In this study, we applied cfMBD-seq to profiling the cfDNA methylome using plasma samples from cancer patients and non-cancer controls. We identified 1759, 1783, and 1548 differentially hypermethylated CpG islands (DMCGIs) in lung, colorectal, and pancreatic cancer patients, respectively. Interestingly, the vast majority of DMCGIs were overlapped with aberrant methylation changes in corresponding tumor tissues, indicating that DMCGIs detected by cfMBD-seq were mainly driven by tumor-specific DNA methylation patterns. From the overlapping DMCGIs, we carried out machine learning analyses and identified a set of discriminating methylation signatures that had robust performance in cancer detection and classification. Overall, our study demonstrates that cfMBD-seq is a powerful tool for sensitive detection of tumor-derived epigenomic signals in cfDNA.

Guidelines for pre-analytical conditions for assessing the methylation of circulating cell-free DNA

Methylation analysis of circulating cell-free DNA (cirDNA), as a liquid biopsy, has a significant potential to advance the detection, prognosis, and treatment of cancer, as well as many genetic disorders. The role of epigenetics in disease development has been reported in several hereditary disorders, and epigenetic modifications are regarded as one of the earliest and most significant genomic aberrations that arise during carcinogenesis. Liquid biopsy can be employed for the detection of these epigenetic biomarkers. It consists of isolation (pre-analytical) and detection (analytical) phases. The choice of pre-analytical variables comprising cirDNA extraction and bisulfite conversion methods can affect the identification of cirDNA methylation. Indeed, different techniques give a different return of cirDNA, which confirms the importance of pre-analytical procedures in clinical diagnostics. Although novel techniques have been developed for the simplification of methylation analysis, the process remains complex, as the steps of DNA extraction, bisulfite treatment, and methylation detection are each carried out separately. Recent studies have noted the absence of any standard method for the pre-analytical processing of methylated cirDNA. We have therefore conducted a comprehensive and systematic review of the important pre-analytical and analytical variables and the patient-related factors which form the basis of our guidelines for analyzing methylated cirDNA in liquid biopsy.

Using Cell Type-Specific Genes to Identify Cell-Type Transitions Between Different in vitro Culture Conditions

In vitro differentiation or expansion of stem and progenitor cells under chemical stimulation or genetic manipulation is used for understanding the molecular mechanisms of cell differentiation and self-renewal. However, concerns around the cell identity of in vitro-cultured cells exist. Bioinformatics methods, which rely heavily on signatures of cell types, have been developed to estimate cell types in bulk samples. The Tabula Muris Senis project provides an important basis for the comprehensive identification of signatures for different cell types. Here, we identified 46 cell type-specific (CTS) gene clusters for 83 mouse cell types. We conducted Gene Ontology term enrichment analysis on the gene clusters and revealed the specific functions of the relevant cell types. Next, we proposed a simple method, named CTSFinder, to identify different cell types between bulk RNA-Seq samples using the 46 CTS gene clusters. We applied CTSFinder on bulk RNA-Seq data from 17 organs and from developing mouse liver over different stages. We successfully identified the specific cell types between organs and captured the dynamics of different cell types during liver development. We applied CTSFinder with bulk RNA-Seq data from a growth factor-induced neural progenitor cell culture system and identified the dynamics of brain immune cells and nonimmune cells during the long-time cell culture. We also applied CTSFinder with bulk RNA-Seq data from reprogramming induced pluripotent stem cells and identified the stage when those cells were massively induced. Finally, we applied CTSFinder with bulk RNA-Seq data from in vivo and in vitro developing mouse retina and captured the dynamics of different cell types in the two development systems. The CTS gene clusters and CTSFinder method could thus serve as promising toolkits for assessing the cell identity of in vitro culture systems.

Cell-free DNA methylome profiling by MBD-seq with ultra-low input

Methylation signatures in cell-free DNA (cfDNA) have shown great sensitivity and specificity in the characterization of tumour status and classification of tumour types, as well as the response to therapy and recurrence. Currently, most cfDNA methylation studies are based on bisulphite conversion, especially targeted bisulphite sequencing, while enrichment-based methods such as cfMeDIP-seq are beginning to show potential. Here, we report an enrichment-based ultra-low input cfDNA methylation profiling method using methyl-CpG binding proteins capture, termed cfMBD-seq. We optimized the conditions for cfMBD capture by adjusting the amount of MethylCap protein along with using methylated filler DNA. Our data show high correlation between low input cfMBD-seq and standard MBD-seq (>1000 ng input). When compared to cfMEDIP-seq, cfMBD-seq demonstrates higher sequencing data quality with more sequenced reads passed filter and less duplicate rate. cfMBD-seq also outperforms cfMeDIP-seq in the enrichment of CpG islands. This new bisulphite-free ultra-low input methylation profiling technology has great potential in non-invasive and cost-effective cancer detection and classification.

Liquid Biopsy of Methylation Biomarkers in Cell-Free DNA

Liquid biopsies, in particular, analysis of cell-free DNA (cfDNA), have emerged as a promising noninvasive diagnostic approach in oncology. Abnormal distribution of DNA methylation is one of the hallmarks of many cancers and methylation changes occur early during carcinogenesis. Systemic analysis of cfDNA methylation profiles is being developed for cancer early detection, monitoring for minimal residual disease (MRD), predicting treatment response and prognosis, and tracing the tissue origin. This review highlights the advantages and disadvantages of ctDNA profiling for noninvasive diagnosis of early-stage cancers and explores recent advances in the clinical application of ctDNA methylation assays. We also summarize the technologies for ctDNA methylation analysis and provide a brief overview of the bioinformatic approaches for analyzing DNA methylation sequencing data.

Analysis of DNA methylation and TP53 mutational status for differentiating feline oral squamous cell carcinoma from non-neoplastic mucosa: A preliminary study

Feline oral squamous cell carcinoma (FOSCC) is characterized by high local invasiveness and early bone lysis. The late diagnosis largely limits the efficacy of therapy and increases treatment-related morbidity. The aim of this exploratory study was to assess the methylation pattern of 10 candidate genes and TP53 mutational status in histologic samples of FOSCC. Results were compared with normal oral mucosa and oral inflammatory lesions, in order to establish a gene panel for FOSCC detection. For 10 cats, the above analyses were also performed on oral brushing samples, in order to explore the utility of these methods for screening purposes. Thirty-one FOSCC, 25 chronic inflammatory lesions and 12 controls were included. TP53 mutations were significantly more frequent in the FOSCC (68%) than in the non-neoplastic oral mucosa (3%; P <.001). Based on lasso regression analysis, a step-wise algorithm including TP53, FLI1, MiR124-1, KIF1A and MAGEC2 was proposed. The algorithm allowed to differentiate FOSCC with 94% sensitivity and 100% specificity (accuracy, 97%). When applying the proposed algorithm on 10 brushing samples, accuracy decreased to 80%. These results indicate that the altered DNA methylation of specific genes is present in FOSCC, together with a significant proportion of TP53 mutations. Such alterations are infrequent in normal oral mucosa and chronic stomatitis in cats, suggesting their involvement in feline oral carcinogenesis and their utility as diagnostic biomarkers. Further studies on a high number of brushing samples will be needed to assess the utility of a screening test for the early detection of FOSCC.

Leveraging the urban-rural divide for epigenetic research

Urbanization coincides with a complex change in environmental exposure and a rapid increase in noncommunicable diseases (NCDs). Epigenetics, including DNA methylation (DNAm), is thought to mediate part of the association between genetic/environmental exposure and NCDs. The urban-rural divide provides a unique opportunity to investigate the effect of the combined presence of multiple forms of environmental exposure on DNAm and the related increase in disease risk. This review evaluates the ability of three epidemiological study designs (migration, income-comparative and urban-rural designs) to investigate the role of DNAm in the association between urbanization and the rise in NCD prevalence. We also discuss the ability of each study design to address the gaps in the current literature, including the complex methylation-mediated risk attributable to the cluster of forms of exposure characterizing urban and rural living, while providing a platform for developing countries to leverage their demographic discrepancies in future research ventures.

Alu Methylation and Risk of Cancer: A Meta-analysis

BACKGROUND

The association between Alu methylation and risk of cancer remains uncertain. This meta-analysis was conducted to elucidate this issue.

MATERIALS AND METHODS

PubMed and Web of Science up to December 31, 2018, and the reference lists of studies, as well as those presented in relevant meta-analyses and reviews were systematically searched. Standardized mean difference (SMD) in Alu methylation level between cases and controls were pooled using random effects model and assessed heterogeneity between strata by stratified factors using meta-regression model. Sensitivity analysis and publication bias test were also conducted.

RESULTS

Twenty-five articles, including 2719 cases and 3018 controls were included in the meta-analysis. The significant difference in Alu methylation level between cancer cases and controls was greater in tissue (SMD = -1.89, 95% CI: -2.72, -1.05) than blood (SMD = -0.46, 95% CI: -0.82, -0.09), and heterogeneity was found in materials (P = 0.038). In tissue samples, Alu hypomethylation was found in carcinoma (SMD = -2.50, 95% CI: -3.51, -1.48), while not in non-carcinoma. The inverse associations were consistently found in subgroups stratified by data sources and quality score in tissue samples, and publication year was considered to be the potential source of between-study heterogeneity. Moreover, reduced Alu methylation level was found in the European subgroup, detection method of SIRPH and COBRA, and original data source in blood samples.

CONCLUSIONS

Alu hypomethylation was associated with increased risk of cancer, which could be a potential biomarker for cancer.

DNA methylation changes in biomarker loci occur early in cancer progression

Tumor-specific DNA methylation can be used for cancer diagnostics and monitoring.  We have recently reported a set of DNA methylation biomarkers that can distinguish plasma samples from lung cancer patients versus healthy controls with high sensitivity and specificity.  Furthermore, the DNA methylation signal from the biomarker loci detected in plasma samples correlated with tumor size and decreased after surgical resection of lung tumors.  In order to determine the timing of DNA methylation of these loci during carcinogenesis and thus the potential of the biomarkers to detect early stages of the disease we analyzed the DNA methylation of the biomarker loci in five precancerous conditions using available data from the GEO database.  We found that the DNA methylation of the biomarker loci is gained early in carcinogenesis since most of the precancerous conditions already have biomarker loci hypermethylated.  Moreover, these DNA methylation biomarkers are able to distinguish between precancerous lesions with malignant potential and those that stay benign where data is available.  Taken together, the biomarkers have the potential to detect the earliest cancer stages; the only limitation to detection of cancer from plasma samples or other liquid biopsies is the timing when tumors start to shed enough DNA into body fluids.

DNA methylation biomarkers discovered in silico detect cancer in liquid biopsies from non-small cell lung cancer patients

Identification of cancer-specific methylation of DNA released by tumours can be used for non-invasive diagnostics and monitoring. We previously reported in silico identification of DNA methylation loci specifically hypermethylated in common human cancers that could be used as epigenetic biomarkers. Using DNA methylation specific qPCR we now clinically tested a group of these cancer-specific loci on cell-free DNA (cfDNA) extracted from the plasma fraction of blood samples from healthy controls and non-small cell lung cancer (NSCLC) patients. These DNA methylation biomarkers distinguish lung cancer cases from controls with high sensitivity and specificity (AUC = 0.956), and furthermore, the signal from the markers correlates with tumour size and decreases after surgical resection of lung tumours. Presented observations suggest the clinical value of these DNA methylation biomarkers for NSCLC diagnostics and monitoring. Since we successfully validated the biomarkers using independent DNA methylation data from multiple additional common carcinoma cohorts (bladder, breast, colorectal, oesophageal, head and neck, pancreatic or prostate cancer) we predict that these DNA methylation biomarkers will detect additional carcinoma types from plasma samples as well.

Cell-Free DNA Methylation Profiling Analysis-Technologies and Bioinformatics

Analysis of circulating nucleic acids in bodily fluids, referred to as “liquid biopsies”, is rapidly gaining prominence. Studies have shown that cell-free DNA (cfDNA) has great potential in characterizing tumor status and heterogeneity, as well as the response to therapy and tumor recurrence. DNA methylation is an epigenetic modification that plays an important role in a broad range of biological processes and diseases. It is well known that aberrant DNA methylation is generalizable across various samples and occurs early during the pathogenesis of cancer. Methylation patterns of cfDNA are also consistent with their originated cells or tissues. Systemic analysis of cfDNA methylation profiles has emerged as a promising approach for cancer detection and origin determination. In this review, we will summarize the technologies for DNA methylation analysis and discuss their feasibility for liquid biopsy applications. We will also provide a brief overview of the bioinformatic approaches for analysis of DNA methylation sequencing data. Overall, this review provides informative guidance for the selection of experimental and computational methods in cfDNA methylation-based studies.

DNA Methylation Markers for Pan-Cancer Prediction by Deep Learning

For cancer diagnosis, many DNA methylation markers have been identified. However, few studies have tried to identify DNA methylation markers to diagnose diverse cancer types simultaneously, i.e., pan-cancers. In this study, we tried to identify DNA methylation markers to differentiate cancer samples from the respective normal samples in pan-cancers. We collected whole genome methylation data of 27 cancer types containing 10,140 cancer samples and 3386 normal samples, and divided all samples into five data sets, including one training data set, one validation data set and three test data sets. We applied machine learning to identify DNA methylation markers, and specifically, we constructed diagnostic prediction models by deep learning. We identified two categories of markers: 12 CpG markers and 13 promoter markers. Three of 12 CpG markers and four of 13 promoter markers locate at cancer-related genes. With the CpG markers, our model achieved an average sensitivity and specificity on test data sets as 92.8% and 90.1%, respectively. For promoter markers, the average sensitivity and specificity on test data sets were 89.8% and 81.1%, respectively. Furthermore, in cell-free DNA methylation data of 163 prostate cancer samples, the CpG markers achieved the sensitivity as 100%, and the promoter markers achieved 92%. For both marker types, the specificity of normal whole blood was 100%. To conclude, we identified methylation markers to diagnose pan-cancers, which might be applied to liquid biopsy of cancers.

Identification of prognosis markers for endometrial cancer by integrated analysis of DNA methylation and RNA-Seq data

Endometrial cancer is highly malignant and has a poor prognosis in the advanced stage, thus, prediction of its prognosis is important. DNA methylation has rapidly gained clinical attention as a biomarker for diagnostic, prognostic and predictive purposes in various cancers. In present study, differentially methylated positions and differentially expressed genes were identified according to DNA methylation and RNA-Seq data. Functional analyses and interaction network were performed to identify hub genes, and overall survival analysis of hub genes were validated. The top genes were evaluated by immunohistochemical staining of endometrial cancer tissues. The gene function was evaluated by cell growth curve after knockdown CDC20 and CCNA2 of endometrial cancer cell line. A total of 329 hypomethylated highly expressed genes and 359 hypermethylated lowly expressed genes were identified, and four hub genes were obtained according to the interaction network. Patients with low expression of CDC20 and CCNA2 showed better overall survival. The results also were demonstrated by the immunohistochemical staining. Cell growth curve also demonstrated that knockdown CDC20 and CCNA2 can suppress the cell proliferation. We have identified two aberrantly methylated genes, CDC20 and CCNA2 as novel biomarkers for precision diagnosis in EC.

DNA hypermethylation in disease: mechanisms and clinical relevance

Increasing numbers of studies implicate abnormal DNA methylation in cancer and many non-malignant diseases. This is consistent with numerous findings about differentiation-associated changes in DNA methylation at promoters, enhancers, gene bodies, and sites that control higher-order chromatin structure. Abnormal increases or decreases in DNA methylation contribute to or are markers for cancer formation and tumour progression. Aberrant DNA methylation is also associated with neurological diseases, immunological diseases, atherosclerosis, and osteoporosis. In this review, I discuss DNA hypermethylation in disease and its interrelationships with normal development as well as proposed mechanisms for the origin of and pathogenic consequences of disease-associated hypermethylation. Disease-linked DNA hypermethylation can help drive oncogenesis partly by its effects on cancer stem cells and by the CpG island methylator phenotype (CIMP); atherosclerosis by disease-related cell transdifferentiation; autoimmune and neurological diseases through abnormal perturbations of cell memory; and diverse age-associated diseases by age-related accumulation of epigenetic alterations.

DNA methylation signatures in mendelian developmental disorders as a diagnostic bridge between genotype and phenotype

Epigenetic and genetic mechanisms regulate the establishment and maintenance of gene expression in its proper context. Recent genome-wide mapping approaches have identified DNA methylation (DNAm) signatures in patients clinically diagnosed with syndromes manifesting as developmental disabilities with intellectual impairments. Here, we review recent studies in which these DNA methylation signatures have enabled highly sensitive and specific screening of such individuals and have clarified ambiguous cases where subjects present with genetic sequence variants of unknown clinical significance (VUS). We propose that these episignatures be considered as echoes and/or legacies of the initiating mutational events within proteins of the so-called epigenetic machinery. As well, we discuss approaches to directly confirm the functional consequences and the implications of these episignatures to patient management and treatment.

A two-gene methylation signature for the diagnosis of bladder cancer in urine

AIM

To analyze the potential of 14 cancer-associated genes, including six miRNAs, for bladder cancer (BC) diagnosis in urine.

PATIENTS & METHODS

DNA methylation levels of 14 genes were analyzed in urine of 72 BC patients and 75 healthy controls using quantitative methylation-specific PCR. Multivariate logistic regression analysis was used to determine an optimal marker panel.

RESULTS

Ten genes were significantly hypermethylated in BC patients. The GHSR/MAL combination showed the best diagnostic performance, reaching a sensitivity of 92% (95% CI: 86-99) and a specificity of 85% (95% CI: 76-94).

CONCLUSION

We identified a novel two-gene panel with a high diagnostic accuracy for BC that can be applied in a noninvasive, urine-based test.

Diagnostic and prognostic biomarkers of common urological cancers based on aberrant DNA methylation

AIM

We intended to construct DNA methylation-based models for the diagnosis and prognosis of three common urological cancers including prostate adenocarcinoma, renal clear cell carcinoma and bladder urothelial carcinoma.

MATERIALS & METHODS

Total 450K methylation array data from the cancer genome atlas and gene expression omnibus datasets were downloaded. Moderated t-statistics and least absolute shrinkage and selection operator method were used to build diagnosis and prognosis models.

RESULTS

Our diagnostic panels including 128 CpG sites had high sensitivity and accuracy in distinguishing samples and could identify lymphatic metastases in prostate adenocarcinoma patients. The prognostic models with 19 CpG sites for renal clear cell carcinoma and 21 CpG sites for bladder urothelial carcinoma were able to distinguish high- and low-risk patients and improve the predictive ability of the tumor node metastasis staging system.

CONCLUSION

DNA methylation may afford reliable biomarkers in the diagnosis and prognosis of common urological cancers.