Liquid Biopsy in Alzheimer's Disease Patients Reveals Epigenetic Changes in the PRLHR Gene.

In recent years, new DNA methylation variants have been reported in genes biologically relevant to Alzheimer's disease (AD) in human brain tissue. However, this AD-specific epigenetic information remains brain-locked and unreachable during patients' lifetimes. In a previous methylome performed in the hippocampus of 26 AD patients and 12 controls, we found higher methylation levels in AD patients in the promoter region of PRLHR, a gene involved in energy balance regulation. Our aim was to further characterize PRLHR's role in AD and to evaluate if the liquid biopsy technique would provide life access to this brain information in a non-invasive way. First, we extended the methylation mapping of PRLHR and validated previous methylome results via bisulfite cloning sequencing. Next, we observed a positive correlation between PRLHR methylation levels and AD-related neuropathological changes and a decreased expression of PRLHR in AD hippocampus. Then, we managed to replicate the hippocampal methylation differences in plasma cfDNA from an additional cohort of 35 AD patients and 35 controls. The isolation of cfDNA from the plasma of AD patients may constitute a source of potential epigenetic biomarkers to aid AD clinical management.

Detecting aberrant DNA methylation in Illumina DNA methylation arrays: a toolbox and recommendations for its use.

In this study, our goal was to determine probe-specific thresholds for identifying aberrant, or outlying, DNA methylation and to provide guidance on the relative merits of using continuous or outlier methylation data. To construct a reference database, we downloaded Illumina Human 450K array data for more than 2,000 normal samples, characterized the distribution of DNA methylation and derived probe-specific thresholds for identifying aberrations. We made the decision to restrict our reference database to solid normal tissue and morphologically normal tissue found adjacent to solid tumours, excluding blood which has very distinctive patterns of DNA methylation. Next, we explored the utility of our outlier thresholds in several analyses that are commonly performed on DNA methylation data. Outliers are as effective as the full continuous dataset for simple tasks, like distinguishing tumour tissue from normal, but becomes less useful as the complexity of the problem increases. We developed an R package called OutlierMeth containing our thresholds, as well as functions for applying them to data.

Activation of hypermethylated P2RY1 mitigates gastric cancer by promoting apoptosis and inhibiting proliferation.

The P2RY1 receptor is known to cause cancer by activating the ERK signal pathway, and its DNA methylation status and corresponding regulatory mechanism remain unknown. This study used the DNA methylation chip to profile the genome-wide DNA methylation level in gastric cancer tissues. The proliferation and apoptosis of the SGC7901 gastric cancer cell line were determined after treatment with a selective P2RY1 receptor agonist, MRS2365. The promoter region of P2RY1 was found to be highly methylated with four hypermethylated sites (|Δß value| > 0.2) in diffuse gastric cancer and was validated by bioinformatics analysis in the TCGA database. Also, immunohistochemical staining data obtained from the HPA database demonstrated the downregulated expression of proteins encoded by P2RY1 in stomach cancer tissue. The analysis of MRS2365-treated cells by annexin V/propidium iodide staining and caspase-3 activity assays indicated the induction of apoptosis in SGC7901 cells. The P2RY1 receptor activation in human SGC7901 gastric cancer cells via the MRS2365 agonist induced apoptosis and reduced cell growth. High DNA methylation in the promoter region of P2RY1 might have contributed to the reduced expression of P2RY1's mRNA, which was likely responsible for the "aggressive" nature of the diffuse gastric cancer.

Uncertainty quantification of reference-based cellular deconvolution algorithms.

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

DNA methylome in pancreatic cancer identified novel promoter hyper-methylation in NPY and FAIM2 genes associated with poor prognosis in Indian patient cohort.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is one of the leading cancers worldwide and has a poor survival, with a 5-year survival rate of only 8.5%. In this study we investigated altered DNA methylation associated with PDAC severity and prognosis. METHODS: Methylome data, generated using 450 K bead array, was compared between paired PDAC and normal samples in the TCGA cohort (n = 9) and our Indian cohort (n = 7). The total Indian Cohort (n = 75) was split into cohort 1 (n = 7), cohort 2 (n = 22), cohort 3 (n = 26) and cohort 4 (n = 20).Validation of differential methylation (6 selected CpG loci) and associated gene expression for differentially methylated genes (10 selected gDMs) were carried out in separate validation cohorts, using MSP, RT-PCR and IHC correlations between methylation and gene expression were observed in TCGA, GTEx cohorts and in validation cohorts. Kaplan-Meier survival analysis was done to study differential prognosis, during 2-5 years of follow-up. RESULTS: We identified 156 DMPs, mapped to 91 genes (gDMs), in PDAC; 68 (43.5%) DMPs were found to be differentially methylated both in TCGA cohort and our cohort, with significant concordance at hypo- and hyper-methylated loci. Enrichments of "regulation of ion transport", "Interferon alpha/beta signalling", "morphogenesis and development" and "transcriptional dysregulation" pathways were observed among 91 gDMs. Hyper-methylation of NPY and FAIM2 genes with down-regulated expression in PDAC, were significantly associated with poor prognosis in the Indian patient cohort. CONCLUSIONS: Ethnic variations among populations may determine the altered epigenetic landscape in the PDAC patients of the Indian cohort. Our study identified novel differentially methylated genes (mainly NPY and FAIM2) and also validated the previously identified differentially methylated CpG sites associated with PDAC cancer patient's survival. Comparative analysis of our data with TCGA and CPTAC cohorts showed that both NPY and FAIM2 hyper-methylation and down-regulations can be novel epigenetically regulated genes in the Indian patient population, statistically significantly associated with poor survival and advanced tumour stages.

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

BACKGROUND: Identifying blood-based DNA methylation patterns is a minimally invasive way to detect biomarkers in predicting age, characteristics of certain diseases and conditions, as well as responses to immunotherapies. As microarray platforms continue to evolve and increase the scope of CpGs measured, new discoveries based on the most recent platform version and how they compare to available data from the previous versions of the platform are unknown. The neutrophil dexamethasone methylation index (NDMI 850) is a blood-based DNA methylation biomarker built on the Illumina MethylationEPIC (850K) array that measures epigenetic responses to dexamethasone (DEX), a synthetic glucocorticoid often administered for inflammation. Here, we compare the NDMI 850 to one we built using data from the Illumina Methylation 450K (NDMI 450). RESULTS: The NDMI 450 consisted of 22 loci, 15 of which were present on the NDMI 850. In adult whole blood samples, the linear composite scores from NDMI 450 and NDMI 850 were highly correlated and had equivalent predictive accuracy for detecting DEX exposure among adult glioma patients and non-glioma adult controls. However, the NDMI 450 scores of newborn cord blood were significantly lower than NDMI 850 in samples measured with both assays. CONCLUSIONS: We developed an algorithm that reproduces the DNA methylation glucocorticoid response score using 450K data, increasing the accessibility for researchers to assess this biomarker in archived or publicly available datasets that use the 450K version of the Illumina BeadChip array. However, the NDMI850 and NDMI450 do not give similar results in cord blood, and due to data availability limitations, results from sample types of newborn cord blood should be interpreted with care.

Methylation microarray-based detection of clinical copy-number aberrations in CLL benchmarked to standard FISH analysis.

Copy-number aberrations (CNAs) are assessed using FISH analysis in diagnostics of chronic lymphocytic leukemia (CLL), but CNAs can also be extrapolated from Illumina BeadChips developed for genome-wide methylation microarray screening. Increasing numbers of microarray data-sets are available from diagnostic samples, making it useful to assess the potential in CNA diagnostics. We benchmarked the limitations of CNA testing from two Illumina BeadChips (EPIC and 450k) and using two common packages for analysis (conumee and ChAMP) to FISH-based assessment of 11q, 13q, and 17p deletions in 202 CLL samples. Overall, the two packages predicted CNAs with similar accuracy regardless of the microarray type, but lower than FISH-based assessment. We showed that the bioinformatics analysis needs to be adjusted to the specific CNA, as no general settings were identified. Altogether, we were able to predict CNAs using methylation microarray data, however, with limited accuracy, making FISH-based assessment of deletions the superior diagnostic choice.

Epigenetic Signatures of Centrosomes Are Novel Targets in Cancer Diagnosis: Insights from an Analysis of the Cancer Genome Atlas.

The centrosome plays a central role for cellular signaling and is critical for several fundamental cellular processes in human cells. Centrosome abnormalities have been linked to multiple solid tumors and hematological malignancies. We sought to explore the potential role of the DNA methylation, a critical epigenetic modification, of centrosome-related genes in different cancers. The 450K array DNA methylation data and RNA-seq data were downloaded for ~4000 tumor samples and ~500 normal controls from The Cancer Genome Atlas (TCGA) project, covering 11 major cancer types. Cancers with more than 30 normal controls were retained for analysis. Differentially modified CpGs of centrosome genes were identified, and cancer-specific epigenetic models were developed using a machine-learning algorithm for each cancer type. The association between the methylation level of differential CpGs and the corresponding gene expression, as well as the co-localization of the differential CpGs and cis-regulatory elements were evaluated. In total, 2761 CpGs located on 160 centrosome genes for 6 cancers were included in the analysis. Cancer-specific models demonstrated a high accuracy in terms of the area under the receiver operating characteristic (ROC) curve (AUC > 0.9) in five cancers and showed tissue specificity. This study enhanced our understanding of the epigenetic mechanisms underlying the DNA methylation of centrosome-related genes in cancers, and showed the potential of these epigenetic modifications as novel cancer biomarkers.

Prenatal exposure to phthalates and peripheral blood and buccal epithelial DNA methylation in infants: An epigenome-wide association study.

BACKGROUND: Prenatal exposure to phthalates has been associated with adverse health and neurodevelopmental outcomes. DNA methylation (DNAm) alterations may be a mechanism underlying these effects, but prior investigations of prenatal exposure to phthalates and neonatal DNAm profiles are limited to placental tissue and umbilical cord blood. OBJECTIVE: Conduct an epigenome-wide association study (EWAS) of the associations between prenatal exposure to phthalates and DNAm in two accessible infant tissues, venous buffy coat blood and buccal epithelial cells (BECs). METHODS: Participants included 152 maternal-infant pairs from the Alberta Pregnancy Outcomes and Nutrition (APrON) study. Maternal second trimester urine samples were analyzed for nine phthalate metabolites. Blood (n = 74) or BECs (n = 78) were collected from 3-month-old infants and profiled for DNAm using the Infinium HumanMethylation450 (450K) BeadChip. Robust linear regressions were used to investigate the associations between high (HMWPs) and low molecular weight phthalates (LMWPs) and change in methylation levels at variable Cytosine-phosphate-Guanine (CpG) sites in infant tissues, as well as the sensitivity of associations to potential confounders. RESULTS: One candidate CpG in gene RNF39 reported by a previous study examining prenatal exposure to phthalates and cord blood DNAm was replicated. The EWAS identified 12 high-confidence CpGs in blood and another 12 in BECs associated with HMWPs and/or LMWPs. Prenatal exposure to bisphenol A (BPA) associated with two of the CpGs associated with HMWPs in BECs. DISCUSSION: Prenatal exposure to phthalates was associated with DNAm variation at CpGs annotated to genes associated with endocrine hormone activity (i.e., SLCO4A1, TPO), immune pathways and DNA damage (i.e., RASGEF1B, KAZN, HLA-A, MYO18A, DIP2C, C1or109), and neurodevelopment (i.e., AMPH, NOTCH3, DNAJC5). Future studies that characterize the stability of these associations in larger samples, multiple cohorts, across tissues, and investigate the potential associations between these biomarkers and relevant health and neurodevelopmental outcomes are needed.

DNA methylation-based diagnostic, prognostic, and predictive biomarkers in colorectal cancer.

DNA methylation is an epigenetic mechanism regulating gene expression. Changes in DNA methylation were suggested to be useful biomarkers for diagnosis, and for the determination of prognosis and treatment response. Here, we provide an overview of methylation-based biomarkers in colorectal cancer. First, we start with the two methylation-based diagnostic biomarkers already approved for colorectal cancer, SEPT9 and the combination of NDRG4 and BMP3. Then, we provide a list-based overview of new biomarker candidates depending on the sample source including plasma, stool, urine, and surgically removed tumor tissues. The most often identified markers like SDC2, VIM, APC, MGMT, SFRP1, SFRP2, and NDRG4 have distinct functions previously linked to tumor progression. Although numerous studies have identified tumor-specific methylation changes, most of these alterations were observed in a single study only. The lack of validation in independent samples means low reproducibility and is a major limitation. The genome-wide determination of methylation status (methylome) can provide data to solve these issues. In the third section of the review, methylome studies focusing on different aspects related to CRC, including precancerous lesions, CRC-specific changes, molecular subtypes, aging, and chemotherapy response are summarized. Notably, techniques simultaneously analyzing a large set of regions can also uncover epigenetic regulation of genes which have not yet been associated with tumorigenesis previously. A remaining constraint of studies published to date is the low patient number utilized in these preventing the identification of clinically valuable biomarker candidates. Either future large-scale studies or the integration of already available methylome-level data will be necessary to uncover biomarkers sufficiently robust for clinical application.

Epigenome-Wide DNA Methylation Profiling in Colorectal Cancer and Normal Adjacent Colon Using Infinium Human Methylation 450K.

The aims were to profile the DNA methylation in colorectal cancer (CRC) and to explore cancer-specific methylation biomarkers. Fifty-four pairs of CRCs and the adjacent normal tissues were subjected to Infinium Human Methylation 450K assay and analysed using ChAMP R package. A total of 26,093 differentially methylated probes were identified, which represent 6156 genes; 650 probes were hypermethylated, and 25,443 were hypomethylated. Hypermethylated sites were common in CpG islands, while hypomethylated sites were in open sea. Most of the hypermethylated genes were associated with pathways in cancer, while the hypomethylated genes were involved in the PI3K-AKT signalling pathway. Among the identified differentially methylated probes, we found evidence of four potential probes in CRCs versus adjacent normal; HOXA2 cg06786372, OPLAH cg17301223, cg15638338, and TRIM31 cg02583465 that could serve as a new biomarker in CRC since these probes were aberrantly methylated in CRC as well as involved in the progression of CRC. Furthermore, we revealed the potential of promoter methylation ADHFE1 cg18065361 in differentiating the CRC from normal colonic tissue from the integrated analysis. In conclusion, aberrant DNA methylation is significantly involved in CRC pathogenesis and is associated with gene silencing. This study reports several potential important methylated genes in CRC and, therefore, merit further validation as novel candidate biomarker genes in CRC.

Navigating the hydroxymethylome: experimental biases and quality control tools for the tandem bisulfite and oxidative bisulfite Illumina microarrays.

Aim: Tandem bisulfite (BS) and oxidative bisulfite (oxBS) conversion on DNA followed by hybridization to Infinium HumanMethylation BeadChips allows nucleotide resolution of 5-hydroxymethylcytosine genome-wide. Here, the authors compared data quality acquired from BS-treated and oxBS-treated samples. Materials & methods: Raw BeadArray data from 417 pairs of samples across 12 independent datasets were included in the study. Probe call rates were compared between paired BS and oxBS treatments controlling for technical variables. Results: oxBS-treated samples had a significantly lower call rate. Among technical variables, DNA-specific extraction kits performed better with higher call rates after oxBS conversion. Conclusion: The authors emphasize the importance of quality control during oxBS conversion to minimize information loss and recommend using a DNA-specific extraction kit for DNA extraction and an oxBSQC package for data preprocessing.

Lay abstract DNA hydroxymethylation (5-hydroxymethylcytosine [5hmC]) is a chemical modification of the cytosines in the DNA that affects gene transcription. 5hmC has been used as a biomarker for early cancer detection and survival prediction in recent years. 5hmC is measured using tandem bisulfite (BS) and oxidative bisulfite (oxBS) conversion of DNA followed by quantification through DNA methylation microarrays. This study observed a consistent loss of high-quality data in oxBS-treated samples compared with BS-treated samples. The authors offer a bioinformatic tool to evaluate potential quality issues in the process and some technical advice to reduce false signals in the data. Thus, they emphasize the importance of preserving DNA integrity when using tandem BS- and oxBS-treated DNA to measure 5-methylcytosine and 5hmC.

Genome-wide DNA methylation profiling in chronic lymphocytic leukaemia.

Background: DNA methylation aberrations are widespread among the malignant B lymphocytes of patients with chronic lymphocytic leukaemia (CLL), suggesting that DNA methylation might contribute to the pathogenesis of CLL. Aim: We aimed to explore the differentially methylated positions (DMPs) associated with CLL and screen the differentially methylated and expressed genes (DMEGs) by combining public databases. We aimed to observe the direction of each DMEG in CLL based on the DMPs in the promoter and the body region respectively to narrow down DMEGs. We also aimed to explore the methylation heterogeneity of CLL subgroups and the effect of B cells maturation on CLL. Methods: In this population-based case control study, we reported a genome-wide DNA methylation association study using the Infinium HumanMethylation450 BeadChip, profiling the DNA methylation of CD19+ B Cells from 48 CLL cases and 28 healthy controls. By integrating methylation data and expression data from public databases, gene sets were jointly screened, and then the relationship between methylation sites in promoter and body region and expression of each gene was explored. In addition, support vector machine (SVM) classification algorithm was used to identify subgroups of CLL cases based on methylation pattern, and the effect of B-cell differentiation related methylation sites on CLL-related sites was observed. Results: We identified 34,797 DMPs related to CLL across the genome, most of which were hypomethylated; the majority were located in gene body regions. By combining these DMPs with published DNA methylation and RNA sequencing data, we detected 26,244 replicated DMPs associated with 1,130 genes whose expression were significantly different in CLL cases. Among these DMEGs, nine low expressed DMEGs were selected with hypermethylated in promoter and hypomethylated in body region, and 83 high expressed DMEGs were selected with both hypomethylated in promoter and body region. The 48 CLL cases were divided into 3 subgroups based on methylation site by SVM algorithm. Over 92% of CpGs associated with B cell subtypes were found in CLL-related DMPs. Conclusion: The DNA methylation pattern was altered across the genome in CLL patients. The methylation of ZAP70, FMOD, and ADAMTS17 was significantly different between CLL cases and controls. Further studies are warranted to confirm our findings and identify the underlying mechanisms through which these methylation markers are associated with CLL.

Longitudinal DNA methylation dynamics as a practical indicator in clinical epigenetics.

BACKGROUND: One of the fundamental assumptions of DNA methylation in clinical epigenetics is that DNA methylation status can change over time with or without interplay with environmental and clinical conditions. However, little is known about how DNA methylation status changes over time under ordinary environmental and clinical conditions. In this study, we revisited the high frequency longitudinal DNA methylation data of two Japanese males (24 time-points within three months) and characterized the longitudinal dynamics. RESULTS: The results showed that the majority of CpGs on Illumina HumanMethylation450 BeadChip probe set were longitudinally stable over the time period of three months. Focusing on dynamic and stable CpGs extracted from datasets, dynamic CpGs were more likely to be reported as epigenome-wide association study (EWAS) markers of various traits, especially those of immune- and inflammatory-related traits; meanwhile, the stable CpGs were enriched in metabolism-related genes and were less likely to be EWAS markers, indicating that the stable CpGs are stable both in the short-term within individuals and under various environmental and clinical conditions. CONCLUSIONS: This study indicates that CpGs with different stabilities are involved in different functions and traits, and thus, they are potential indicators that can be applied for clinical epigenetic studies to outline underlying mechanisms.

An effective processing pipeline for harmonizing DNA methylation data from Illumina's 450K and EPIC platforms for epidemiological studies.

OBJECTIVE: Illumina BeadChip arrays are commonly used to generate DNA methylation data for large epidemiological studies. Updates in technology over time create challenges for data harmonization within and between studies, many of which obtained data from the older 450K and newer EPIC platforms. The pre-processing pipeline for DNA methylation is not trivial, and influences the downstream analyses. Incorporating different platforms adds a new level of technical variability that has not yet been taken into account by recommended pipelines. Our study evaluated the performance of various tools on different versions of platform data harmonization at each step of pre-processing pipeline, including quality control (QC), normalization, batch effect adjustment, and genomic inflation. We illustrate our novel approach using 450K and EPIC data from the Diabetes Autoimmunity Study in the Young (DAISY) prospective cohort. RESULTS: We found normalization and probe filtering had the biggest effect on data harmonization. Employing a meta-analysis was an effective and easily executable method for accounting for platform variability. Correcting for genomic inflation also helped with harmonization. We present guidelines for studies seeking to harmonize data from the 450K and EPIC platforms, which includes the use of technical replicates for evaluating numerous pre-processing steps, and employing a meta-analysis.

DNA Methylation Alterations in Blood Cells of Toddlers with Down Syndrome.

Recent research has provided evidence on genome-wide alterations in DNA methylation patterns due to trisomy 21, which have been detected in various tissues of individuals with Down syndrome (DS) across different developmental stages. Here, we report new data on the systematic genome-wide DNA methylation perturbations in blood cells of individuals with DS from a previously understudied age group-young children. We show that the study findings are highly consistent with those from the prior literature. In addition, utilizing relevant published data from two other developmental stages, neonatal and adult, we track a quasi-longitudinal trend in the DS-associated DNA methylation patterns as a systematic epigenomic destabilization with age.

Abnormal methylation characteristics predict chemoresistance and poor prognosis in advanced high-grade serous ovarian cancer.

BACKGROUND: Primary or acquired chemoresistance is a key link in the high mortality rate of ovarian cancer. There is no reliable method to predict chemoresistance in ovarian cancer. We hypothesized that specific methylation characteristics could distinguish chemoresistant and chemosensitive ovarian cancer patients. METHODS: In this study, we used 450 K Infinium Methylation BeadChip to detect the different methylation CpGs between ovarian cancer patients. The differential methylation genes were analyzed by GO and KEGG Pathway bioinformatics analysis. The candidate CpGs were confirmed by pyrosequencing. The expression of abnormal methylation gene was identified by QRT-PCR and IHC. ROC analysis confirmed the ability to predict chemotherapy outcomes. Prognosis was evaluated using Kaplan-Meier. RESULTS: In advanced high-grade serous ovarian cancer, 8 CpGs (ITGB6:cg21105318, cg07896068, cg18437633; NCALD: cg27637873, cg26782361, cg16265707; LAMA3: cg20937934, cg13270625) remained hypermethylated in chemoresistant patients. The sensitivity, specificity and AUC of 8 CpGs (ITGB6:cg21105318, cg07896068, cg18437633; NCALD: cg27637873, cg26782361, cg16265707; LAMA3: cg20937934, cg13270625) methylation to predict chemotherapy sensitivity were 63.60-97.00%, 46.40-89.30% and 0.774-0.846. PFS of 6 candidate genes (ITGB6:cg21105318, cg07896068; NCALD: cg27637873, cg26782361, cg16265707; LAMA3: cg20937934) hypermethylation patients was significantly shorter. The expression of NCALD and LAMA3 in chemoresistant patients was lower than that of chemosensitive patients. Spearman analysis showed that NCALD and LAMA3 methylations were negatively correlated with their expression. CONCLUSIONS: As a new biomarker of chemotherapy sensitivity, hypermethylation of NCALD and LAMA3 is associated with poor PFS in advanced high-grade serous ovarian cancer. In the future, further research on NCALD and LAMA3 will be needed to provide guidance for clinical stratification of demethylation therapy.

Epigenome-wide data collection in a case of gliofibroma.

Gliofibroma is a rare tumour entity with glial and mesenchymal histological features. We describe the case of a 30-year-old woman who presented with a short history of intermittent left-sided facial pain and paraesthesia of the left upper extremity. Histologically, the tumour consisted of a mixture of glial fibrillary acidic protein (GFAP)-positive glial cells and collagen-rich stroma. Immunohistochemical and molecular analysis showed no IDH1/2, BRAF, H3F3A mutations or ATP-dependent helicase (ATRX) loss in this tumour. Illumina Infinium HumanMethylation450 BeadChip array (HM450) methylation profile of the tumour was different from typical glioma entities. Genome-wide DNA copy number analysis showed partial loss of chromosome 3 and 8. All previous cases are reviewed. Our data support the classification of gliofibroma as a rare, but distinct brain tumour entity with good prognosis.

Detecting Differentially Methylated Promoters in Genes Related to Disease Phenotypes Using R.

DNA methylation in gene promoters plays a major role in gene expression regulation, and alterations in methylation patterns have been associated with several diseases. In this context, different software suites and statistical methods have been proposed to analyze differentially methylated positions and regions. Among them, the novel statistical method implemented in the mCSEA R package proposed a new framework to detect subtle, but consistent, methylation differences. Here, we provide an easy-to-use pipeline covering all the necessary steps to detect differentially methylated promoters with mCSEA from Illumina 450K and EPIC methylation BeadChips data. This protocol covers the download of data from public repositories, quality control, data filtering and normalization, estimation of cell type proportions, and statistical analysis. In addition, we show the procedure to compare disease vs. normal phenotypes, obtaining differentially methylated regions including promoters or CpG Islands. The entire protocol is based on R programming language, which can be used in any operating system and does not require advanced programming skills.

DNA Methylation Signatures Predict Cytogenetic Subtype and Outcome in Pediatric Acute Myeloid Leukemia (AML).

Pediatric acute myeloid leukemia (AML) is a heterogeneous disease composed of clinically relevant subtypes defined by recurrent cytogenetic aberrations. The majority of the aberrations used in risk grouping for treatment decisions are extensively studied, but still a large proportion of pediatric AML patients remain cytogenetically undefined and would therefore benefit from additional molecular investigation. As aberrant epigenetic regulation has been widely observed during leukemogenesis, we hypothesized that DNA methylation signatures could be used to predict molecular subtypes and identify signatures with prognostic impact in AML. To study genome-wide DNA methylation, we analyzed 123 diagnostic and 19 relapse AML samples on Illumina 450k DNA methylation arrays. We designed and validated DNA methylation-based classifiers for AML cytogenetic subtype, resulting in an overall test accuracy of 91%. Furthermore, we identified methylation signatures associated with outcome in t(8;21)/RUNX1-RUNX1T1, normal karyotype, and MLL/KMT2A-rearranged subgroups (p < 0.01). Overall, these results further underscore the clinical value of DNA methylation analysis in AML.