Genome-Wide DNA Methylation Analysis of Patients with Myelodysplastic Syndrome After Azacitidine Treatment.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 600-600
Author(s):  
Hyang-Min Byun ◽  
Timothy Triche ◽  
Hyeoung-Joon Kim ◽  
Hee Nam Kim ◽  
Yeo-Kyeoung Kim ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Bone Marrow ◽  
Who Classification ◽  
Methylation Pattern ◽  
Methylation Analysis ◽  
Cpg Sites ◽  
Genome Wide ◽  
Methylation Patterns ◽  
Dna Methylation Analysis ◽  
Hypermethylated Genes

Abstract Abstract 600 Background: Azacitidine is hypothesized to exert its therapeutic effect in patients with myelodysplastic syndrome (MDS) through inhibition of DNA methylation. However to date no genomic DNA methylation pattern has been shown to predict response to azacitidine in patients with MDS, and no aberrantly silenced gene or group of genes has been shown to be reactivated by azacitidine that can be clearly linked to the beneficial clinical effect. We sought to identify the gene or group of aberrantly hypermethylated genes that are responsible for the therapeutic effect of azacitidine by retrospectively analyzing genome-wide DNA methylation profiles from bone marrow samples of a cohort of 113 patients with MDS treated with the DNA methylation inhibitor, azacitidine. Methods: Bone marrow aspirates were collected at time of diagnosis prior to treatment, after 4 cycles of azacitidine therapy and 8 cycles of therapy. DNA was isolated and bisulfite treated with the EZ-96 DNA Methylation-Gold Kit. DNA methylation analysis was performed on 27,578 CpG sites representing 14,475 genes (almost ¾ of known genes) using the Infinium Bead Array system for samples at the time of diagnosis, 4 and 8 cycles of therapy. Only 19,662 CpG sites were used for further analysis due to exclusion of CpG sites that were on the × chromosome, sites suspected of containing single nucleotide polymorphisms (SNP), and sites within DNA repeats. In total 91 samples were analyzed from 43 patients with MDS, which were selected to represent different disease classifications and responses to therapy, and bone marrow aspirates from 10 healthy control subjects without MDS. Results: Two-way hierarchical cluster analysis showed clear clustering of bone marrow samples taken from subjects without MDS. DNA methylation patterns from healthy controls clustered together, and pre and post azacitidine treatment samples from the same subject clustered together as well. Samples did not cluster by DNA methylation patterns for WHO classification, International Prognostic Scoring System (IPSS), cytogenetic abnormalities, or response to azacitidine. Supervised cluster analysis is ongoing. Global decreases in DNA methylation as measured by the average methylation for all 19,662 loci assayed did decrease with treatment and there was a trend for a larger decrease in DNA methylation in those patients who responded to azacitidine. Conclusion: In this pilot study of genome-wide DNA methylation analysis of MDS patients treated with azacitidine we find global decreases of DNA methylation. We were unable to identify a DNA methylation pattern or group of hypermethylated genes that would predict response to azacitidine. MDS samples did not cluster by WHO classification, IPSS or response to azacitidine. Larger translational studies are needed, but the possibility that DNA methylation decreases in patients treated with azacitidine serve as a pharmacological marker rather than a therapeutic target should also be considered Disclosures: Laird: Celgene: Consultancy. Yang:Celgene: Honoraria, Research Funding, Speakers Bureau.

Abstract LB-181: Genome-wide DNA methylation analysis reveals novel hypermethylated genes in astrocytic tumors

2011 ◽  
Author(s):  
Shani A. Mulholland ◽  
Rifat A. Hamoudi ◽  
Deborah S. Malley ◽  
V. Peter Collins ◽  
Koichi Ichimura
Keyword(s):  
Dna Methylation ◽  
Astrocytic Tumors ◽  
Methylation Analysis ◽  
Genome Wide ◽  
Dna Methylation Analysis ◽  
Hypermethylated Genes

Genome-Wide DNA Methylation Analysis Shows Enrichment of Differential Methylation in “Open Seas” and Enhancers and Reveals Hypomethylation in DNMT3A Mutated Cytogenetically Normal AML (CN-AML)

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 653-653 ◽  
Author(s):  
Ying Qu ◽  
Andreas Lennartsson ◽  
Verena I. Gaidzik ◽  
Stefan Deneberg ◽  
Sofia Bengtzén ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Cpg Island ◽  
Cpg Islands ◽  
Differential Methylation ◽  
Methylation Analysis ◽  
Cpg Sites ◽  
Genome Wide ◽  
Genomic Regions ◽  
Methylation Patterns

Abstract Abstract 653 DNA methylation is involved in multiple biologic processes including normal cell differentiation and tumorigenesis. In AML, methylation patterns have been shown to differ significantly from normal hematopoietic cells. Most studies of DNA methylation in AML have previously focused on CpG islands within the promoter of genes, representing only a very small proportion of the DNA methylome. In this study, we performed genome-wide methylation analysis of 62 AML patients with CN-AML and CD34 positive cells from healthy controls by Illumina HumanMethylation450K Array covering 450.000 CpG sites in CpG islands as well as genomic regions far from CpG islands. Differentially methylated CpG sites (DMS) between CN-AML and normal hematopoietic cells were calculated and the most significant enrichment of DMS was found in regions more than 4kb from CpG Islands, in the so called open sea where hypomethylation was the dominant form of aberrant methylation. In contrast, CpG islands were not enriched for DMS and DMS in CpG islands were dominated by hypermethylation. DMS successively further away from CpG islands in CpG island shores (up to 2kb from CpG Island) and shelves (from 2kb to 4kb from Island) showed increasing degree of hypomethylation in AML cells. Among regions defined by their relation to gene structures, CpG dinucleotide located in theoretic enhancers were found to be the most enriched for DMS (Chi χ2<0.0001) with the majority of DMS showing decreased methylation compared to CD34 normal controls. To address the relation to gene expression, GEP (gene expression profiling) by microarray was carried out on 32 of the CN-AML patients. Totally, 339723 CpG sites covering 18879 genes were addressed on both platforms. CpG methylation in CpG islands showed the most pronounced anti-correlation (spearman ρ =-0.4145) with gene expression level, followed by CpG island shores (mean spearman rho for both sides' shore ρ=-0.2350). As transcription factors (TFs) have shown to be crucial for AML development, we especially studied differential methylation of an unbiased selection of 1638 TFs. The most enriched differential methylation between CN-AML and normal CD34 positive cells were found in TFs known to be involved in hematopoiesis and with Wilms tumor protein-1 (WT1), activator protein 1 (AP-1) and runt-related transcription factor 1 (RUNX1) being the most differentially methylated TFs. The differential methylation in WT 1 and RUNX1 was located in intragenic regions which were confirmed by pyro-sequencing. AML cases were characterized with respect to mutations in FLT3, NPM1, IDH1, IDH2 and DNMT3A. Correlation analysis between genome wide methylation patterns and mutational status showed statistically significant hypomethylation of CpG Island (p<0.0001) and to a lesser extent CpG island shores (p<0.001) and the presence of DNMT3A mutations. This links DNMT3A mutations for the first time to a hypomethylated phenotype. Further analyses correlating methylation patterns to other clinical data such as clinical outcome are ongoing. In conclusion, our study revealed that non-CpG island regions and in particular enhancers are the most aberrantly methylated genomic regions in AML and that WT 1 and RUNX1 are the most differentially methylated TFs. Furthermore, our data suggests a hypomethylated phenotype in DNMT3A mutated AML. Disclosures: No relevant conflicts of interest to declare.

Genome-Wide DNA Methylation Analysis Reveals Biological and Clinical Insights In Relapsed Childhood Acute Lymphoblastic Leukemia: A Report From The COG ALL Target Project

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 3736-3736
Author(s):  
Huimin Geng ◽  
Mignon L. Loh ◽  
Richard C. Harvey ◽  
I-Ming Chen ◽  
Meenakshi Devidas ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Acute Lymphoblastic Leukemia ◽  
Lymphoblastic Leukemia ◽  
Cpg Islands ◽  
Gene Promoters ◽  
Methylation Analysis ◽  
Cpg Sites ◽  
Genome Wide ◽  
Organismal Development ◽  
Dna Methylation Analysis

Abstract Although survival of children with B-cell acute lymphoblastic leukemia (B-ALL) has improved substantially over time, 15% to 20% of patients will relapse, and most of those who experience a bone marrow relapse will die. A better understanding of genetic and epigenetic aberrations in relapsed ALL will facilitate new strategies for risk stratification and targeted therapy. In this collaborative study with the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) project, we performed high resolution genome-wide DNA methylation profiling using the HELP (HpaII tiny fragment Enrichment by Ligation-mediated PCR) array on a total of 178 (110 diagnosis, 68 relapse) leukemia samples from 111 patients with childhood B-ALL enrolled on the Children’s Oncology Group (COG) clinical trials who experienced relapsed, and 12 normal preB samples isolated from the bone marrows of 12 healthy individuals. The HELP array covers 117,521 CpG sites, annotated to ∼22,000 gene promoters. For eight diagnosis/relapse pairs, base-pair resolution DNA methylation using the eRRBS (enhanced Reduced Representation Bisulfite Sequencing) method was also performed on Illumina HiSeq2000. The median relapse time for the 111 patients was 21.8 months (range 2.1 to 56.2). Unsupervised clustering analysis using the HELP data revealed seven clusters: one cluster contained only the 12 normal preB samples; four clusters were enriched with MLLr, ETV6/RUNX1, Trisomy 4+10, and TCF3/PBX1 samples, respectively. The sixth cluster was not enriched for specific cytogenetic cases, but interestingly, all cases in this cluster were NCI High Risk (age>10 years or WBC>=50,000; p<0.0001, Fisher’s Exact test) while the seventh cluster has a mixture of other cases. Supervised analysis of HELP profiles between paired relapse/diagnosis samples (n=67) revealed a markedly aberrant DNA methylation signature (1011 probesets, 888 genes, FDR<0.01 and methylation difference dx >25%, paired t-test), with 70% of the genes hyper- and 30% hypo-methylated in relapse samples. Using a Bayesian predictor and leave-one-out cross validation, this methylation signature could predict a sample as diagnosis or relapse with 95.3% accuracy. When comparing early (<36 months; n=50) versus late relapses (>=36 months; n=18), we detected a profound hypermethylation signature in early relapse (96.6% of the 610 probesets, 544 genes, FDR<0.01, dx >25%). Finally, we identified 1800 probesets (1658 genes) as differentially methylated within all cytogenetic subtypes described above compared to the normal preB samples (Dunnett’s test with normal preB as reference, FDR<0.01, dx>25%). Again the majority (70%) of those genes were hypermethylated in relapse as compared to diagnostic and normal preB. The base-pair resolution and more comprehensive eRRBS methylation analysis for the eight pairs of samples identified 39,679 CpG sites as differentially methylated (dx >25%, FDR<0.01), with 78.2% CpG sites hyper- and 21.2% hypo-methylated in relapse samples. Remarkably, the hypermethylated CpGs are primarily in promoter regions (50%, defined as +/-1kb to TSS), followed by intergenic (26%), then intragenic (14%), and exonic (10%) regions. In contrast, the hypomethylated CpGs are mainly in intragenic (48%), followed by intergenic (31%), exonic (14%) and promoter (7%) regions. The hypermethylated CpGs were mainly in CpG islands (86%) or CpG shores (10%), while hypomethylated CpGs were not (CpG islands: 8%, CpG shores: 27%). We further identified 3040 differentially methylated regions (DMRs) with a median size 426 bp. 78.4% of those DMRs were hyper- (1362 gene promoters) and 21.6% hypo-methylated (98 promoters) in relapse compared to diagnostic samples. Gene set enrichment and Ingenuity pathway analysis showed epigenetically disrupted pathways that are highly involved in cell signaling, and embryonic and organismal development. Taken together, our genome-wide high resolution DNA methylation analysis on a large cohort of relapsed childhood B-ALL from the COG trial identified unique methylation signatures that correlated with relapse and with specific genetic subsets. Those methylation signatures featured prevailing promoter hypermethylation and to a lesser extent, intrageneic hypomethylation. Epigenetically dysregulated gene networks in those relapse samples involved cell signaling, and embryonic and organismal development. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Genome-wide DNA methylation analysis pre- and post-lenalidomide treatment in patients with myelodysplastic syndrome with isolated deletion (5q)

2021 ◽  
Author(s):  
Anna Hecht ◽  
Julia A. Meyer ◽  
Johann-Christoph Jann ◽  
Katja Sockel ◽  
Aristoteles Giagounidis ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Myelodysplastic Syndrome ◽  
Target Genes ◽  
Methylation Status ◽  
Response To Treatment ◽  
Methylation Analysis ◽  
Relevant Effect ◽  
Genome Wide ◽  
Methylation Patterns ◽  
Dna Methylation Analysis

AbstractMyelodysplastic syndrome (MDS) with isolated deletion of chromosome 5q (MDS del5q) is a distinct subtype of MDS with quite favorable prognosis and excellent response to treatment with lenalidomide. Still, a relevant percentage of patients do not respond to lenalidomide and even experience progression to acute myeloid leukemia (AML). In this study, we aimed to investigate whether global DNA methylation patterns could predict response to lenalidomide. Genome-wide DNA methylation analysis using Illumina 450k methylation arrays was performed on n=51 patients with MDS del5q who were uniformly treated with lenalidomide in a prospective multicenter trial of the German MDS study group. To study potential direct effects of lenalidomide on DNA methylation, 17 paired samples pre- and post-treatment were analyzed. Our results revealed no relevant effect of lenalidomide on methylation status. Furthermore, methylation patterns prior to therapy could not predict lenalidomide response. However, methylation clustering identified a group of patients with a trend towards inferior overall survival. These patients showed hypermethylation of several interesting target genes, including genes of relevant signaling pathways, potentially indicating the evaluation of novel therapeutic targets.

scholarly journals Human blood DNA methylation patterns reflect individual lifestyle

2021 ◽  
Author(s):  
Ireen Klemp ◽  
Anne Hoffmann ◽  
Luise Mueller ◽  
Tobias Hagemann ◽  
Kathrin Horn ◽  
...  
Keyword(s):  
Physical Activity ◽  
Dna Methylation ◽  
Population Based ◽  
Methylation Pattern ◽  
Unhealthy Lifestyle ◽  
Genome Wide ◽  
Intersection Analysis ◽  
Methylation Patterns ◽  
Dna Methylation Analysis ◽  
Modifiable Lifestyle Factors

Obesity is driven by modifiable lifestyle factors whose effects may be mediated by epigenetics. Therefore, we investigated lifestyle effects (diet, physical activity, smoking and alcohol) on blood DNA methylation in participants of the LIFE-Adult study, a well-characterized population-based cohort from Germany. Fifty subjects with an extremely healthy and 50 with an extremely unhealthy lifestyle were selected for genome-wide DNA methylation analysis in blood samples. Whereas obesity was only marginally related to variability in DNA methylation pattern, comparisons between lifestyle categories resulted in 145 Differentially Methylated Positions (DMPs) and 4682 Differentially Methylated Regions (DMRs) annotated to 4426 unique genes. Intersection analysis showed that diet, physical activity, smoking and alcohol intake are equally contributing to the observed differences, which particularly affects pathways related to glutamatergic synapse and axon guidance. DNA methylation patterns help discriminate individuals with a healthy vs. unhealthy lifestyle, which may mask subtle methylation differences derived from obesity.

scholarly journals Estimands in epigenome-wide association studies

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Jochen Kruppa ◽  
Miriam Sieg ◽  
Gesa Richter ◽  
Anne Pohrt
Keyword(s):  
Dna Methylation ◽  
Statistical Analysis ◽  
Biological Effect ◽  
Association Studies ◽  
Theoretical Problem ◽  
Methylation Analysis ◽  
Cpg Sites ◽  
Cpg Site ◽  
Genome Wide ◽  
Dna Methylation Analysis

Abstract Background In DNA methylation analyses like epigenome-wide association studies, effects in differentially methylated CpG sites are assessed. Two kinds of outcomes can be used for statistical analysis: Beta-values and M-values. M-values follow a normal distribution and help to detect differentially methylated CpG sites. As biological effect measures, differences of M-values are more or less meaningless. Beta-values are of more interest since they can be interpreted directly as differences in percentage of DNA methylation at a given CpG site, but they have poor statistical properties. Different frameworks are proposed for reporting estimands in DNA methylation analysis, relying on Beta-values, M-values, or both. Results We present and discuss four possible approaches of achieving estimands in DNA methylation analysis. In addition, we present the usage of M-values or Beta-values in the context of bioinformatical pipelines, which often demand a predefined outcome. We show the dependencies between the differences in M-values to differences in Beta-values in two data simulations: a analysis with and without confounder effect. Without present confounder effects, M-values can be used for the statistical analysis and Beta-values statistics for the reporting. If confounder effects exist, we demonstrate the deviations and correct the effects by the intercept method. Finally, we demonstrate the theoretical problem on two large human genome-wide DNA methylation datasets to verify the results. Conclusions The usage of M-values in the analysis of DNA methylation data will produce effect estimates, which cannot be biologically interpreted. The parallel usage of Beta-value statistics ignores possible confounder effects and can therefore not be recommended. Hence, if the differences in Beta-values are the focus of the study, the intercept method is recommendable. Hyper- or hypomethylated CpG sites must then be carefully evaluated. If an exploratory analysis of possible CpG sites is the aim of the study, M-values can be used for inference.

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