Analysis Of DNA Methylation Data Research Articles

Integrative Analysis of DNA Methylation Data and Transcriptome Data Identified a DNA Methylation-Dysregulated Four-LncRNA Signature for Predicting Prognosis in Head and Neck Squamous Cell Carcinoma.

Increasing evidence has demonstrated the crosstalk between DNA epigenetic alterations and aberrant expression of long non-coding RNAs (lncRNAs) during carcinogenesis. However, epigenetically dysregulated lncRNAs and their functional and clinical roles in Head and Neck Squamous Cell Carcinoma (HNSCC) are still not explored. In this study, we performed an integrative analysis of DNA methylation data and transcriptome data and identified a DNA methylation-dysregulated four-lncRNA signature (DNAMeFourLncSig) from 596 DNA methylation-dysregulated lncRNAs using a machine-learning-based feature selection method, which classified the patients of the discovery cohort into two risk groups with significantly different survival including overall survival, disease-specific survival, and progression-free survival. Then the DNAMeFourLncSig was implemented to another two HNSCC patient cohorts and showed similar prognostic values in both. Results from multivariable Cox regression analysis revealed that the DNAMeFourLncSig might be an independent prognostic factor. Furthermore, the DNAMeFourLncSig was substantially correlated with the complete response rate of chemotherapy and may predict chemotherapy response. Functional in silico analysis found that DNAMeFourLncSig-related mRNAs were mainly enriched in cell differentiation, tissue development and immune-related pathways. Overall, our study will improve our understanding of underlying transcriptional and epigenetic mechanisms in HNSCC carcinogenesis and provided a new potential biomarker for the prognosis of patients with HNSCC.

A region-based method for causal mediation analysis of DNA methylation data

ABSTRACT Exposure to environmental factors can affect DNA methylation at a 5'-cytosine-phosphate-guanine-3' (CpG) site or a genomic region, which can then affect an outcome. In other words, environmental effects on an outcome could be mediated by DNA methylation. To date, single CpG-site-based mediation analysis has been employed extensively. More recently, however, there has been considerable interest in studying differentially methylated regions (DMRs), both because DMRs are more likely to have functional effects than single CpG sites and because testing DMRs reduces multiple testing. In this report, we propose a novel causal mediation approach under the counterfactual framework to test the significance of total (TE), direct (DE), and indirect effects (IE) of predictors on response variable with a methylated region (MR) as the mediator (denoted as MR-Mediation). Functional linear transformation is used to reduce the possible high dimension of the CpG sites in a predefined MR and to account for their location information. In our simulation studies, MR-Mediation retained the desired Type I error rates for TE, DE, and IE tests. Furthermore, MR-Mediation had better power performance than testing mean methylation level as the mediator in most considered scenarios, especially for IE (i.e., mediated effect) test, which could be more interesting than the other two effect tests. We further illustrate our proposed method by analysing the methylation mediated effect of exposure to gun violence on total immunoglobulin E or atopic asthma among participants in the Epigenetic Variation and Childhood Asthma in Puerto Ricans study.

Identification and Analysis of An Epigenetically Regulated Five-lncRNA Signature Associated With Outcome and Chemotherapy Response in Ovarian Cancer.

The deregulation of long non-coding RNAs (lncRNAs) by epigenetic alterations has been implicated in cancer initiation and progression. However, the epigenetically regulated lncRNAs and their association with clinical outcome and therapeutic response in ovarian cancer (OV) remain poorly investigated. This study performed an integrative analysis of DNA methylation data and transcriptome data and identified 419 lncRNAs as potential epigenetically regulated lncRNAs. Using machine-learning and multivariate Cox regression analysis methods, we identified and developed an epigenetically regulated lncRNA expression signature (EpiLncRNASig) consisting of five lncRNAs from the list of 17 epigenetically regulated lncRNAs significantly associated with outcome. The EpiLncRNASig could stratify patients into high-risk groups and low-risk groups with significantly different survival and chemotherapy response in different patient cohorts. Multivariate Cox regression analyses, after adjusted by other clinical features and treatment response, demonstrated the independence of the DEpiLncSig in predicting survival. Functional analysis for relevant protein-coding genes of the DEpiLncSig indicated enrichment of known immune-related or cancer-related biological pathways. Taken together, our study not only provides a promising prognostic biomarker for predicting outcome and chemotherapy response but also will improve our understanding of lncRNA epigenetic regulation mechanisms in OV.

Ependymoma-like tumor with mesenchymal differentiation harboring C11orf95-NCOA1/2 or -RELA fusion: A hitherto unclassified tumor related to ependymoma.

Recurrent fusion genes involving C11orf95, C11orf95‐RELA, have been identified only in supratentorial ependymomas among primary CNS tumors. Here, we report hitherto histopathologically unclassifiable high‐grade tumors, under the tentative label of “ependymoma‐like tumors with mesenchymal differentiation (ELTMDs),” harboring C11orf95‐NCOA1/2 or ‐RELA fusion. We examined the clinicopathological and molecular features in five cases of ELTMDs. Except for one adult case (50 years old), all cases were in children ranging from 1 to 2.5 years old. All patients presented with a mass lesion in the cerebral hemisphere. Histologically, all cases demonstrated a similar histology with a mixture of components. The major components were embryonal‐appearing components forming well‐delineated tumor cell nests composed of small uniform cells with high proliferative activity, and spindle‐cell mesenchymal components with a low‐ to high‐grade sarcoma‐like appearance. The embryonal‐appearing components exhibited minimal ependymal differentiation including a characteristic EMA positivity and tubular structures, but histologically did not fit with ependymoma because they lacked perivascular pseudorosettes, a histological hallmark of ependymoma, formed well‐delineated nests, and had diffuse and strong staining for CAM5.2. Molecular analysis identified C11orf95‐NCOA1, ‐NCOA2, and ‐RELA in two, one, and two cases, respectively. t‐distributed stochastic neighbor embedding analysis of DNA methylation data from two cases with C11orf95‐NCOA1 or ‐NCOA2 and a reference set of 380 CNS tumors revealed that these two cases were clustered together and were distinct from all subgroups of ependymomas. In conclusion, although ELTMDs exhibited morphological and genetic associations with supratentorial ependymoma with C11orf95‐RELA, they cannot be regarded as ependymoma. Further analyses of more cases are needed to clarify their differences and similarities.

LGG-33. ISOMORPHIC DIFFUSE GLIOMA HAS RECURRENT GENE FUSIONS OF MYBL1 OR MYB AND CAN BE DISTINGUISHED FROM OTHER MYB/MYBL1 ALTERED GLIOMAS BASED ON A DISTINCT MORPHOLOGY AND DNA METHYLATION PROFILE

Isomorphic diffuse glioma (IDG) was first described in 2004 as an epilepsy-associated supratentorial diffuse glioma with low cellularity, low proliferation and very monomorphic tumour cells. Most patients had seizures since childhood but were operated on as adults. To study the position of these lesions among brain tumours we histologically, molecularly and clinically analysed 26 histologically typical IDGs. Tumour cells were GFAP-positive, MAP2-, OLIG2- and CD34-negative and the nuclear ATRX-expression was retained. Proliferation was very low. Sequencing of 24 cases revealed an IDH-wildtype status. Cluster analyses of DNA methylation data showed that IDG has a DNA methylation profile distinct from those of different glial/glio-neuronal brain tumours and normal hemispheric tissue. About half of IDGs had copy number alterations of MYBL1 or MYB (13/25) and half of the cases analysed by RNA-sequencing had gene fusions of MYBL1 or MYB with various gene partners (11/22), often associated with an increased RNA-expression of the respective MYB-family gene. Integrating all data available, 77% of IDGs had either MYBL1 (54%) or MYB (23%) alterations. All patients had a good outcome and most were seizure-free after surgery. In summary, we show that isomorphic diffuse glioma is a distinct benign tumour in the family of MYB/MYBL1-altered gliomas. DNA methylation analysis is very helpful for their identification. More recent analyses of a large cohort of MYB/MYBL1-altered brain tumours suggest the presence of a third methylation group that primarily contains paediatric cases and seems to be distinct from IDG and angiocentric gliomas. Further histological, molecular and clinical analyses are ongoing.

EPEN-18. CROSS-SPECIES GENOMICS IDENTIFIES GLI2 AS AN ONCOGENE OF C11orf95 FUSION-POSITIVE SUPRATENTORIAL EPENDYMOMA

The majority of supratentorial ependymomas (ST-EPN) are driven by fusions between RELA and a zinc finger containing gene, C11orf95. Apart from fusions to the Hippo effector YAP1, which affects a small group of infant patients, the oncogenic mechanism of remaining ST-EPNs is unclear. Aiming at refining the molecular classification of ST-EPNs, we analyzed methylation profiles, RNA and DNA sequencing results as well as clinical data in a cohort of 617 ST-EPNs. Unsupervised clustering analysis of DNA methylation data revealed four distinct clusters that formed in addition to the known molecular groups ST-EPN-RELA and –YAP1. Tumors within these additional clusters were characterized by fusions of C11orf95 to numerous fusion partners different from RELA, e.g. MAML2, MAML3, NCOA2 and SS18, suggesting a general role of C11orf95 in tumorigenesis of ST-EPN. Transforming capacity of newly identified fusion genes was validated using an electroporation-based in vivo gene transfer technology. All fusion genes were sufficient to drive malignant transformation in the cerebral cortex of mice and resulting tumors faithfully recapitulated molecular characteristics of their human counterparts. We found that both, the partner gene and the zinc finger DNA binding domain of C11orf95, were essential to exert tumorigenesis. When exploring genes commonly upregulated in C11orf95 fusion-expressing tumors of human and murine origin, the Sonic Hedgehog effector gene Gli2 was identified as a promising downstream target. Subsequent co-expression of C11orf95:RELA and a dominant negative form of Gli2 indeed hampered tumorigenesis. We thus propose GLI2 as a potential therapeutic downstream target of C11orf95 fusion-dependent oncogenic signaling in ST-EPN.

Integrative Analysis of DNA Methylation Identified 12 Signature Genes Specific to Metastatic ccRCC.

Background: Abnormal epigenetic alterations can contribute to the development of human malignancies. Identification of these alterations for early screening and prognosis of clear cell renal cell carcinoma (ccRCC) has been a highly sought-after goal. Bioinformatic analysis of DNA methylation data provides broad prospects for discovery of epigenetic biomarkers. However, there is short of exploration of methylation-driven genes of ccRCC.Methods: Gene expression data and DNA methylation data in metastatic ccRCC were sourced from the Gene Expression Omnibus (GEO) database. Differentially methylated genes (DMGs) at 5′-C-phosphate-G- 3′ (CpG) sites and differentially expressed genes (DEGs) were screened and the overlapping genes in DMGs and DEGs were then subject to gene set enrichment analysis. Next, the weighted gene co-expression network analysis (WGCNA) was used to search hub DMGs associated with ccRCC. Cox regression and ROC analyses were performed to screen potential biomarkers and develop a prognostic model based on the screened hub genes.Results: Three hundred and fourteen overlapping DMGs were obtained from two independent GEO datasets. The turquoise module contained 79 hub DMGs, which represent the most significant module screened by WGCNA. Furthermore, a total of 12 hub genes (CETN3, DCAF7, GPX4, HNRNPA0, NUP54, SERPINB1, STARD5, TRIM52, C4orf3, C12orf51, and C17orf65) were identified in the TCGA database by multivariate Cox regression analyses. All the 12 genes were then used to generate the model for diagnosis and prognosis of ccRCC. ROC analysis showed that these genes exhibited good diagnostic efficiency for metastatic and non-metastatic ccRCC. Furthermore, the prognostic model with the 12 methylation-driven genes demonstrated a good prediction of 5-year survival rates for ccRCC patients.Conclusion: Integrative analysis of DNA methylation data identified 12 signature genes, which could be used as epigenetic biomarkers for prognosis of metastatic ccRCC. This prognostic model has a good prediction of 5-year survival for ccRCC patients.

EpigenCentral: Portal for DNA methylation data analysis and classification in rare diseases.

Epigenetic processes play a key role in regulating gene expression. Genetic variants that disrupt chromatin-modifying proteins are associated with a broad range of diseases, some of which have specific epigenetic patterns, such as aberrant DNA methylation (DNAm), which may be used as disease biomarkers. While much of the epigenetic research has focused on cancer, there is a paucity of resources devoted to neurodevelopmental disorders (NDDs), which include autism spectrum disorder and many rare, clinically overlapping syndromes. To address this challenge, we created EpigenCentral, a free web resource for biomedical researchers, molecular diagnostic laboratories, and clinical practitioners to perform the interactive classification and analysis of DNAm data related to NDDs. It allows users to search for known disease-associated patterns in their DNAm data, classify genetic variants as pathogenic or benign to assist in molecular diagnostics, or analyze patterns of differential methylation in their data through a simple web form. EpigenCentral is freely available at http://epigen.ccm.sickkids.ca/.

Methylation data imputation performances under different representations and missingness patterns

BackgroundHigh-throughput technologies enable the cost-effective collection and analysis of DNA methylation data throughout the human genome. This naturally entails missing values management that can complicate the analysis of the data. Several general and specific imputation methods are suitable for DNA methylation data. However, there are no detailed studies of their performances under different missing data mechanisms –(completely) at random or not- and different representations of DNA methylation levels (β and M-value).ResultsWe make an extensive analysis of the imputation performances of seven imputation methods on simulated missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR) methylation data. We further consider imputation performances on the popular β- and M-value representations of methylation levels. Overall, β-values enable better imputation performances than M-values. Imputation accuracy is lower for mid-range β-values, while it is generally more accurate for values at the extremes of the β-value range. The MAR values distribution is on the average more dense in the mid-range in comparison to the expected β-value distribution. As a consequence, MAR values are on average harder to impute.ConclusionsThe results of the analysis provide guidelines for the most suitable imputation approaches for DNA methylation data under different representations of DNA methylation levels and different missing data mechanisms.

Isomorphic diffuse glioma is a morphologically and molecularly distinct tumour entity with recurrent gene fusions of MYBL1 or MYB and a benign disease course.

The "isomorphic subtype of diffuse astrocytoma" was identified histologically in 2004 as a supratentorial, highly differentiated glioma with low cellularity, low proliferation and focal diffuse brain infiltration. Patients typically had seizures since childhood and all were operated on as adults. To define the position of these lesions among brain tumours, we histologically, molecularly and clinically analysed 26 histologically prototypical isomorphic diffuse gliomas. Immunohistochemically, they were GFAP-positive, MAP2-, OLIG2- and CD34-negative, nuclear ATRX-expression was retained and proliferation was low. All 24 cases sequenced were IDH-wildtype. In cluster analyses of DNA methylation data, isomorphic diffuse gliomas formed a group clearly distinct from other glial/glio-neuronal brain tumours and normal hemispheric tissue, most closely related to paediatric MYB/MYBL1-altered diffuse astrocytomas and angiocentric gliomas. Half of the isomorphic diffuse gliomas had copy number alterations of MYBL1 or MYB (13/25, 52%). Gene fusions of MYBL1 or MYB with various gene partners were identified in 11/22 (50%) and were associated with an increased RNA-expression of the respective MYB-family gene. Integrating copy number alterations and available RNA sequencing data, 20/26 (77%) of isomorphic diffuse gliomas demonstrated MYBL1 (54%) or MYB (23%) alterations. Clinically, 89% of patients were seizure-free after surgery and all had a good outcome. In summary, we here define a distinct benign tumour class belonging to the family of MYB/MYBL1-altered gliomas. Isomorphic diffuse glioma occurs both in children and adults, has a concise morphology, frequent MYBL1 and MYB alterations and a specific DNA methylation profile. As an exclusively histological diagnosis may be very challenging and as paediatric MYB/MYBL1-altered diffuse astrocytomas may have the same gene fusions, we consider DNA methylation profiling very helpful for their identification.

Epigenome-wide association study of narcolepsy-affected lateral hypothalamic brains, and overlapping DNA methylation profiles between narcolepsy and multiple sclerosis

Narcolepsy with cataplexy is a sleep disorder caused by a deficiency in hypocretin neurons in the lateral hypothalamus (LH). Here we performed an epigenome-wide association study (EWAS) of DNA methylation for narcolepsy and replication analyses using DNA samples extracted from two brain regions: LH (Cases: N = 4; Controls: N = 4) and temporal cortex (Cases: N = 7; Controls: N = 7). Seventy-seven differentially methylated regions (DMRs) were identified in the LH analysis, with the top association of a DMR in the myelin basic protein (MBP) region. Only five DMRs were detected in the temporal cortex analysis. Genes annotated to LH DMRs were significantly associated with pathways related to fatty acid response or metabolism. Two additional analyses applying the EWAS data were performed: (1) investigation of methylation profiles shared between narcolepsy and other disorders and (2) an integrative analysis of DNA methylation data and a genome-wide association study for narcolepsy. The results of the two approaches, which included significant overlap of methylated positions associated with narcolepsy and multiple sclerosis, indicated that the two diseases may partly share their pathogenesis. In conclusion, DNA methylation in LH where loss of orexin-producing neurons occurs may play a role in the pathophysiology of the disease.

RMDB: An Integrated Database of Single-cytosine-resolution DNA Methylation in Oryza Sativa

Background: Previous studies have revealed that DNA methylation plays a crucial role in eukaryotic growth and development via involvement in the regulation of gene expression and chromosomal instability. With the advancement of biotechnology, next-generation sequencing (NGS) is emerging as a popular method to explore the functions of DNA methylation, and an increasing number of genome-scale DNA methylation datasets have been published. Several DNA methylation databases, including MethDB, NGSmethDB and MENT have been developed for storing and analyzing the DNA methylation data. However, no public resource dedicated to DNA methylation of Oryza sativa is available to date. Methods & Results: We built a comprehensive database (RMDB) for integration and analysis of DNA methylation data of Oryza sativa. A couple of functional modules were developed to identify the connections between DNA methylation and phenotypes. Moreover, rich graphical visualization tools were employed to facilitate data presentation and interpretation. Conclusion: RMDB is an integrated database dedicated to rice DNA methylation. To the best of our knowledge, this is the first integrated rice DNA methylation database. We believe that RMDB will be helpful to understand the epigenetic mechanisms of Oryza sativa. RMDB is freely available at http://admis.fudan.edu.cn/rmdb.

DNA methylation analysis in plants: review of computational tools and future perspectives.

Genome-wide DNA methylation studies have quickly expanded due to advances in next-generation sequencing techniques along with a wealth of computational tools to analyze the data. Most of our knowledge about DNA methylation profiles, epigenetic heritability and the function of DNA methylation in plants derives from the model species Arabidopsis thaliana. There are increasingly many studies on DNA methylation in plants-uncovering methylation profiles and explaining variations in different plant tissues. Additionally, DNA methylation comparisons of different plant tissue types and dynamics during development processes are only slowly emerging but are crucial for understanding developmental and regulatory decisions. Translating this knowledge from plant model species to commercial crops could allow the establishment of new varieties with increased stress resilience and improved yield. In this review, we provide an overview of the most commonly applied bioinformatics tools for the analysis of DNA methylation data (particularly bisulfite sequencing data). The performances of a selection of the tools are analyzed for computational time and agreement in predicted methylated sites for A. thaliana, which has a smaller genome compared to the hexaploid bread wheat. The performance of the tools was benchmarked on five plant genomes. We give examples of applications of DNA methylation data analysis in crops (with a focus on cereals) and an outlook for future developments for DNA methylation status manipulations and data integration.

DNA methylation profiling distinguishes Ewing-like sarcoma with EWSR1-NFATc2 fusion from Ewing sarcoma.

Recent studies revealed divergent gene expression patterns in Ewing sarcoma (EwS) with canonical EWSR1-ETS gene fusions and undifferentiated round cell sarcomas (URCS) with EWSR1 rearrangements fused to the non-ETS gene NFATc2. Thus, the question arises whether the latter tumors really belong to EwS. We collected five cases matching the group of URCS with EWSR1-NFATc2 fusion and performed DNA methylation and copy number profiling. Results were compared to methylation data of 30 EwS with various EWSR1-ETS fusions and one EwS with FUS-ERG fusion, 16 URCS with CIC rearrangement and 10 URCS with BCOR alteration and a total of 81 EWSR1-associated soft tissue sarcomas including 7 angiomatoid fibrous histiocytomas, 7 clear cell sarcomas of the soft tissue, 28 desmoplastic small round cell tumors, 10 extraskeletal myxoid chondrosarcomas and 29 myxoid liposarcomas. Unsupervised hierarchical clustering and t-distributed stochastic neighbor embedding analysis of DNA methylation data revealed a homogeneous methylation cluster for URCS with EWSR1-NFATc2 fusion, which clearly segregated from EwS and the other subtypes. Copy number profiles of EWSR1-NFATc2 cases showed recurrent losses on chromosome 9q and segmental gains on 20q13 and 22q12 involving the EWSR1 and NFATc2 loci, respectively. In summary, URCS with EWSR1-NFATc2 fusion share a distinct DNA methylation signature and carry characteristic copy number alterations, which emphasizes that these sarcomas should be considered separately from EwS.

A distinctive DNA methylation pattern in insufficient sleep

Short sleep duration or insomnia may lead to an increased risk of various psychiatric and cardio-metabolic conditions. Since DNA methylation plays a critical role in the regulation of gene expression, studies of differentially methylated positions (DMPs) might be valuable for understanding the mechanisms underlying insomnia. We performed a cross-sectional genome-wide analysis of DNA methylation in relation to self-reported insufficient sleep in individuals from a community-based sample (79 men, aged 39.3 ± 7.3), and in relation to shift work disorder in an occupational cohort (26 men, aged 44.9 ± 9.0). The analysis of DNA methylation data revealed that genes corresponding to selected DMPs form a distinctive pathway: “Nervous System Development” (FDR P value < 0.05). We found that 78% of the DMPs were hypomethylated in cases in both cohorts, suggesting that insufficient sleep may be associated with loss of DNA methylation. A karyoplot revealed clusters of DMPs at various chromosomal regions, including 12 DMPs on chromosome 17, previously associated with Smith-Magenis syndrome, a rare condition comprising disturbed sleep and inverse circadian rhythm. Our findings give novel insights into the DNA methylation patterns associated with sleep loss, possibly modifying processes related to neuroplasticity and neurodegeneration. Future prospective studies are needed to confirm the observed associations.

An integrated package for bisulfite DNA methylation data analysis with Indel-sensitive mapping

BackgroundDNA methylation plays crucial roles in most eukaryotic organisms. Bisulfite sequencing (BS-Seq) is a sequencing approach that provides quantitative cytosine methylation levels in genome-wide scope and single-base resolution. However, genomic variations such as insertions and deletions (indels) affect methylation calling, and the alignment of reads near/across indels becomes inaccurate in the presence of polymorphisms. Hence, the simultaneous detection of DNA methylation and indels is important for exploring the mechanisms of functional regulation in organisms.ResultsThese problems motivated us to develop the algorithm BatMeth2, which can align BS reads with high accuracy while allowing for variable-length indels with respect to the reference genome. The results from simulated and real bisulfite DNA methylation data demonstrated that our proposed method increases alignment accuracy. Additionally, BatMeth2 can calculate the methylation levels of individual loci, genomic regions or functional regions such as genes/transposable elements. Additional programs were also developed to provide methylation data annotation, visualization, and differentially methylated cytosine/region (DMC/DMR) detection. The whole package provides new tools and will benefit bisulfite data analysis.ConclusionBatMeth2 improves DNA methylation calling, particularly for regions close to indels. It is an autorun package and easy to use. In addition, a DNA methylation visualization program and a differential analysis program are provided in BatMeth2. We believe that BatMeth2 will facilitate the study of the mechanisms of DNA methylation in development and disease. BatMeth2 is an open source software program and is available on GitHub (https://github.com/GuoliangLi-HZAU/BatMeth2/).

Gene Expression and Methylation Analysis in Melanomas and Melanocytes From the Same Patient: Loss of NPM2 Expression Is a Potential Immunohistochemical Marker for Melanoma.

DNA methylation is considered the primary epigenetic mechanism underlying the development of malignant melanoma. Since DNA methylation can be influenced by environmental factors, it is preferable to compare cancer and normal cells from the same patient. In order to compare the methylation status in melanoma tissues and melanocytes from the same individuals, we employed a novel epidermal sheet cultivation technique to isolate normal melanocytes from unaffected sites of melanoma patients. We also analyzed primary and metastatic melanoma samples, three commercially available melanocytes, and four melanoma cell lines. Cluster analysis of DNA methylation data classified freshly isolated melanomas and melanocytes into the same group, whereas the four melanoma cell lines were clustered together in a distant clade. Moreover, our analysis discovered methylation at several novel loci (KRTCAP3, AGAP2, ZNF490), in addition to those identified in previous studies (COL1A2, GPX3); however, the latter two were not observed in fresh melanoma samples. Subsequent studies revealed that NPM2 was hypermethylated and downregulated in melanomas, which was consistent with previous reports. In many normal melanocytes, NPM2 showed distinct immunohistochemical staining, while its expression was lost in malignant melanoma cells. In particular, intraepithelial lesions of malignant melanoma, an important challenge in clinical practice, could be distinguished from benign nevi. The present findings indicate the importance of using fresh melanoma samples, not melanoma cell lines and melanocytes in epigenetic studies. In addition, NPM2 immunoreactivity could be used to differentiate melanomas from normal melanocytes or benign disease.

Relationship of DNA methylation to mutational changes and transcriptional organization in fusion-positive and fusion-negative rhabdomyosarcoma.

Our previous study of DNA methylation in the pediatric soft tissue tumor rhabdomyosarcoma (RMS) demonstrated that fusion-positive (FP) and fusion-negative (FN) RMS tumors exhibit distinct DNA methylation patterns. To further examine the significance of DNA methylation differences in RMS, we investigated genome-wide DNA methylation profiles in discovery and validation cohorts. Unsupervised analysis of DNA methylation data identified novel distinct subsets associated with the specific fusion subtype in FP RMS and with RAS mutation status in FN RMS. Furthermore, the methylation pattern in normal muscle is most similar to the FN subset with wild-type RAS mutation status. Several biologically relevant genes were identified with methylation and expression differences between the two fusion subtypes of FP RMS or between the RAS wild-type and mutant subsets of FN RMS. Genomic localization studies showed that promoter and intergenic regions were hypomethylated and the 3' untranslated regions were hypermethylated in FP compared to FN tumors. There was also a significant difference in the distribution of PAX3-FOXO1 binding sites between genes with and without differential methylation. Moreover, genes with PAX3-FOXO1 binding sites and promoter hypomethylation exhibited the highest frequency of overexpression in FP tumors. Finally, a comparison of RMS model systems revealed that patient-derived xenografts most closely recapitulate the DNA methylation patterns found in human RMS tumors compared to cell lines and cell line-derived xenografts. In conclusion, these findings highlight the interaction of epigenetic changes with mutational alterations and transcriptional organization in RMS tumors, and contribute to improved molecular categorization of these tumors.

Application of novel and existing methods to identify genes with evidence of epigenetic association: results from GAW20

BackgroundThe rise in popularity and accessibility of DNA methylation data to evaluate epigenetic associations with disease has led to numerous methodological questions. As part of GAW20, our working group of 8 research groups focused on gene searching methods.ResultsAlthough the methods were varied, we identified 3 main themes within our group. First, many groups tackled the question of how best to use pedigree information in downstream analyses, finding that (a) the use of kinship matrices is common practice, (b) ascertainment corrections may be necessary, and (c) pedigree information may be useful for identifying parent-of-origin effects. Second, many groups also considered multimarker versus single-marker tests. Multimarker tests had modestly improved power versus single-marker methods on simulated data, and on real data identified additional associations that were not identified with single-marker methods, including identification of a gene with a strong biological interpretation. Finally, some of the groups explored methods to combine single-nucleotide polymorphism (SNP) and DNA methylation into a single association analysis.ConclusionsA causal inference method showed promise at discovering new mechanisms of SNP activity; gene-based methods of summarizing SNP and DNA methylation data also showed promise. Even though numerous questions still remain in the analysis of DNA methylation data, our discussions at GAW20 suggest some emerging best practices.

Robust joint score tests in the application of DNA methylation data analysis

BackgroundRecently differential variability has been showed to be valuable in evaluating the association of DNA methylation to the risks of complex human diseases. The statistical tests based on both differential methylation level and differential variability can be more powerful than those based only on differential methylation level. Anh and Wang (2013) proposed a joint score test (AW) to simultaneously detect for differential methylation and differential variability. However, AW’s method seems to be quite conservative and has not been fully compared with existing joint tests.ResultsWe proposed three improved joint score tests, namely iAW.Lev, iAW.BF, and iAW.TM, and have made extensive comparisons with the joint likelihood ratio test (jointLRT), the Kolmogorov-Smirnov (KS) test, and the AW test. Systematic simulation studies showed that: 1) the three improved tests performed better (i.e., having larger power, while keeping nominal Type I error rates) than the other three tests for data with outliers and having different variances between cases and controls; 2) for data from normal distributions, the three improved tests had slightly lower power than jointLRT and AW. The analyses of two Illumina HumanMethylation27 data sets GSE37020 and GSE20080 and one Illumina Infinium MethylationEPIC data set GSE107080 demonstrated that three improved tests had higher true validation rates than those from jointLRT, KS, and AW.ConclusionsThe three proposed joint score tests are robust against the violation of normality assumption and presence of outlying observations in comparison with other three existing tests. Among the three proposed tests, iAW.BF seems to be the most robust and effective one for all simulated scenarios and also in real data analyses.