scholarly journals Molecular tumor classification using DNA methylome analysis

2020 ◽  
Vol 29 (R2) ◽  
pp. R205-R213 ◽  
Author(s):  
Martin Sill ◽  
Christoph Plass ◽  
Stefan M Pfister ◽  
Daniel B Lipka
Keyword(s):  
Dna Methylation ◽  
Genetic Diagnostics ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Liquid Biopsies ◽  
Dna Methylome ◽  
Hematopoietic Malignancies ◽  
Methylome Analysis ◽  
Molecular Patterns ◽  
Molecular Tumor Classification

Abstract Tumor classifiers based on molecular patterns promise to define and reliably classify tumor entities. The high tissue- and cell type-specificity of DNA methylation, as well as its high stability, makes DNA methylation an ideal choice for the development of tumor classifiers. Herein, we review existing tumor classifiers using DNA methylome analysis and will provide an overview on their emerging impact on cancer classification, the detection of novel cancer subentities and patient stratification with a focus on brain tumors, sarcomas and hematopoietic malignancies. Furthermore, we provide an outlook on the enormous potential of DNA methylome analysis to complement classical histopathological and genetic diagnostics, including the emerging field of epigenomic analysis in liquid biopsies.

scholarly journals Calling differential DNA methylation at cell-type resolution: addressing misconceptions and best practices

2021 ◽  
Author(s):  
Elior Rahmani ◽  
Brandon Jew ◽  
Regev Schweiger ◽  
Brooke Rhead ◽  
Lindsey A. Criswell ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Best Practices ◽  
Regression Model ◽  
Association Studies ◽  
Composition Analysis ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Interaction Terms ◽  
Mathematical Explanations ◽  
Cell Type Specific

AbstractWe benchmarked two approaches for the detection of cell-type-specific differential DNA methylation: Tensor Composition Analysis (TCA) and a regression model with interaction terms (CellDMC). Our experiments alongside rigorous mathematical explanations show that TCA is superior over CellDMC, thus resolving recent criticisms suggested by Jing et al. Following misconceptions by Jing and colleagues with modelling cell-type-specificity and the application of TCA, we further discuss best practices for performing association studies at cell-type resolution. The scripts for reproducing all of our results and figures are publicly available at github.com/cozygene/CellTypeSpecificMethylationAnalysis.

scholarly journals DNA methylome analysis of acute lymphoblastic leukemia cells reveals stochastic de novo DNA methylation in CpG islands

Epigenomics ◽  
2016 ◽  
Vol 8 (10) ◽  
pp. 1367-1387 ◽  
Author(s):  
Per Wahlberg ◽  
Anders Lundmark ◽  
Jessica Nordlund ◽  
Stephan Busche ◽  
Amanda Raine ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Acute Lymphoblastic Leukemia ◽  
De Novo ◽  
Lymphoblastic Leukemia ◽  
Cpg Islands ◽  
Leukemia Cells ◽  
Dna Methylome ◽  
Methylome Analysis

scholarly journals Identification of blood autosomal cis-expression quantitative trait methylation (cis-eQTMs) in children

2020 ◽  
Author(s):  
Carlos Ruiz-Arenas ◽  
Carles Hernandez-Ferrer ◽  
Marta Vives-Usano ◽  
Sergi Marí ◽  
Inés Quintela ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Quantitative Trait ◽  
Association Studies ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Biological Interpretation ◽  
Cell Type Specific ◽  
Using Data ◽  
The Relationship

AbstractBackgroundThe identification of expression quantitative trait methylation (eQTMs), defined as correlations between gene expression and DNA methylation levels, might help the biological interpretation of epigenome-wide association studies (EWAS). We aimed to identify autosomal cis-eQTMs in child blood, using data from 832 children of the Human Early Life Exposome (HELIX) project.MethodsBlood DNA methylation and gene expression were measured with the Illumina 450K and the Affymetrix HTA v2 arrays, respectively. The relationship between methylation levels and expression of nearby genes (transcription start site (TSS) within a window of 1 Mb) was assessed by fitting 13.6 M linear regressions adjusting for sex, age, and cohort.ResultsWe identified 63,831 autosomal cis-eQTMs, representing 35,228 unique CpGs and 11,071 unique transcript clusters (TCs, genes). 74.3% of these cis-eQTMs were located at <250 kb, 60.0% showed an inverse relationship and 23.9% had at least one genetic variant associated with the methylation and expression levels. They were enriched for active blood regulatory regions. Adjusting for cellular composition decreased the number of cis-eQTMs to 37.7%, suggesting that some of them were cell type-specific. The overlap of child blood cis-eQTMs with those described in adults was small, and child and adult shared cis-eQTMs tended to be proximal to the TSS, enriched for genetic variants and with lower cell type specificity. Only half of the cis-eQTMs could be captured through annotation to the closest gene.ConclusionsThis catalogue of blood autosomal cis-eQTMs in children can help the biological interpretation of EWAS findings, and is publicly available at https://helixomics.isglobal.org/.

scholarly journals A Differential DNA Methylome Signature of Pulmonary Immune Cells from Individuals Converting to Latent Tuberculosis Infection

2021 ◽  
Author(s):  
Lovisa Karlsson ◽  
Jyotirmoy Das ◽  
Moa Nilsson ◽  
Amanda Tyren ◽  
Isabelle Pehrson ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Immune Cells ◽  
Methylation Status ◽  
Latent Tuberculosis ◽  
Dna Methylome ◽  
T Cell Migration ◽  
Increased Risk ◽  
Before And After ◽  
Methylome Analysis ◽  
Latent Tb

Tuberculosis (TB), caused by Mycobacterium tuberculosis, spreads via aerosols and the first encounter with the immune system is with the pulmonary resident immune cells. The role of epigenetic regulations through DNA methylation in the immune cells is emerging. We have previously shown that capacity to kill M. tuberculosis is reflected in the DNA methylome. The aim of this study was to investigate epigenetic modifications in the pulmonary immune cells in a cohort of medical students with a previously documented increased risk of TB exposure, longitudinally. Sputum samples containing alveolar macrophages (AMs) and T cells were collected before and after study subjects worked in hospital departments with a high-risk of TB exposure. DNA methylome analysis revealed that a unique DNA methylation profile was present already at inclusion in subjects who developed latent TB during the study. The profile was both reflected in different overall DNA methylation distribution as well as more profound alterations in the methylation status of a unique set of CpG-sites. Over-representation analysis of the DMGs showed enrichment in pathways related to metabolic reprograming of macrophages and T cell migration and IFN-γ production. In conclusion, we identified a unique DNA methylation signature in individuals, while still IGRA-negative and who later developed latent TB. Epigenetic regulation was found in pathways that have previously been reported to be important in TB. Together the study suggests that DNA methylation status of pulmonary immune cells can predict IGRA conversion.

scholarly journals DNA methylation and gene expression integration in cardiovascular disease

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Guillermo Palou-Márquez ◽  
Isaac Subirana ◽  
Lara Nonell ◽  
Alba Fernández-Sanlés ◽  
Roberto Elosua
Keyword(s):  
Gene Expression ◽  
Cardiovascular Disease ◽  
Dna Methylation ◽  
Cardiovascular Diseases ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Risk Function ◽  
Predictive Biomarkers ◽  
Independent Study ◽  
Cell Type

Abstract Background The integration of different layers of omics information is an opportunity to tackle the complexity of cardiovascular diseases (CVD) and to identify new predictive biomarkers and potential therapeutic targets. Our aim was to integrate DNA methylation and gene expression data in an effort to identify biomarkers related to cardiovascular disease risk in a community-based population. We accessed data from the Framingham Offspring Study, a cohort study with data on DNA methylation (Infinium HumanMethylation450 BeadChip; Illumina) and gene expression (Human Exon 1.0 ST Array; Affymetrix). Using the MOFA2 R package, we integrated these data to identify biomarkers related to the risk of presenting a cardiovascular event. Results Four independent latent factors (9, 19, 21—only in women—and 27), driven by DNA methylation, were associated with cardiovascular disease independently of classical risk factors and cell-type counts. In a sensitivity analysis, we also identified factor 21 as associated with CVD in women. Factors 9, 21 and 27 were also associated with coronary heart disease risk. Moreover, in a replication effort in an independent study three of the genes included in factor 27 were also present in a factor identified to be associated with myocardial infarction (CDC42BPB, MAN2A2 and RPTOR). Factor 9 was related to age and cell-type proportions; factor 19 was related to age and B cells count; factor 21 pointed to human immunodeficiency virus infection-related pathways and inflammation; and factor 27 was related to lifestyle factors such as alcohol consumption, smoking and body mass index. Inclusion of factor 21 (only in women) improved the discriminative and reclassification capacity of the Framingham classical risk function and factor 27 improved its discrimination. Conclusions Unsupervised multi-omics data integration methods have the potential to provide insights into the pathogenesis of cardiovascular diseases. We identified four independent factors (one only in women) pointing to inflammation, endothelium homeostasis, visceral fat, cardiac remodeling and lifestyles as key players in the determination of cardiovascular risk. Moreover, two of these factors improved the predictive capacity of a classical risk function.

scholarly journals A machine learning case–control classifier for schizophrenia based on DNA methylation in blood

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chathura J. Gunasekara ◽  
Eilis Hannon ◽  
Harry MacKay ◽  
Cristian Coarfa ◽  
Andrew McQuillin ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Population Based ◽  
Case Control ◽  
Training Data ◽  
Polygenic Risk Score ◽  
Reverse Causality ◽  
Cell Type Specificity ◽  
Data Set ◽  
Human Genomic ◽  
Genomic Regions

AbstractEpigenetic dysregulation is thought to contribute to the etiology of schizophrenia (SZ), but the cell type-specificity of DNA methylation makes population-based epigenetic studies of SZ challenging. To train an SZ case–control classifier based on DNA methylation in blood, therefore, we focused on human genomic regions of systemic interindividual epigenetic variation (CoRSIVs), a subset of which are represented on the Illumina Human Methylation 450K (HM450) array. HM450 DNA methylation data on whole blood of 414 SZ cases and 433 non-psychiatric controls were used as training data for a classification algorithm with built-in feature selection, sparse partial least squares discriminate analysis (SPLS-DA); application of SPLS-DA to HM450 data has not been previously reported. Using the first two SPLS-DA dimensions we calculated a “risk distance” to identify individuals with the highest probability of SZ. The model was then evaluated on an independent HM450 data set on 353 SZ cases and 322 non-psychiatric controls. Our CoRSIV-based model classified 303 individuals as cases with a positive predictive value (PPV) of 80%, far surpassing the performance of a model based on polygenic risk score (PRS). Importantly, risk distance (based on CoRSIV methylation) was not associated with medication use, arguing against reverse causality. Risk distance and PRS were positively correlated (Pearson r = 0.28, P = 1.28 × 10−12), and mediational analysis suggested that genetic effects on SZ are partially mediated by altered methylation at CoRSIVs. Our results indicate two innate dimensions of SZ risk: one based on genetic, and the other on systemic epigenetic variants.

scholarly journals Blood-Based Detection of Colorectal Cancer Using Cancer-Specific DNA Methylation Markers

Diagnostics ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 51
Author(s):  
Nam-Yun Cho ◽  
Ji-Won Park ◽  
Xianyu Wen ◽  
Yun-Joo Shin ◽  
Jun-Kyu Kang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Dna Methylation ◽  
Sensitivity And Specificity ◽  
Stage Iv ◽  
High Sensitivity ◽  
Liquid Biopsies ◽  
Blood Leukocytes ◽  
Marker Panel ◽  
A Genome ◽  
Methylight Assay

Cancer tissues have characteristic DNA methylation profiles compared with their corresponding normal tissues that can be utilized for cancer diagnosis with liquid biopsy. Using a genome-scale DNA methylation approach, we sought to identify a panel of DNA methylation markers specific for cell-free DNA (cfDNA) from patients with colorectal cancer (CRC). By comparing DNA methylomes between CRC and normal mucosal tissues or blood leukocytes, we identified eight cancer-specific methylated loci (ADGRB1, ANKRD13, FAM123A, GLI3, PCDHG, PPP1R16B, SLIT3, and TMEM90B) and developed a five-marker panel (FAM123A, GLI3, PPP1R16B, SLIT3, and TMEM90B) that detected CRC in liquid biopsies with a high sensitivity and specificity with a droplet digital MethyLight assay. In a set of cfDNA samples from CRC patients (n = 117) and healthy volunteers (n = 60), a panel of five markers on the platform of the droplet digital MethyLight assay detected stages I–III and stage IV CRCs with sensitivities of 45.9% and 95.7%, respectively, and a specificity of 95.0%. The number of detected markers was correlated with the cancer stage, perineural invasion, lymphatic emboli, and venous invasion. Our five-marker panel with the droplet digital MethyLight assay showed a high sensitivity and specificity for the detection of CRC with cfDNA samples from patients with metastatic CRC.

scholarly journals EMeth: An EM algorithm for cell type decomposition based on DNA methylation data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hanyu Zhang ◽  
Ruoyi Cai ◽  
James Dai ◽  
Wei Sun
Keyword(s):  
Dna Methylation ◽  
Tumor Cells ◽  
T Regulatory Cells ◽  
Simulated Data ◽  
Cell Types ◽  
Computational Method ◽  
Methylation Data ◽  
Cell Type ◽  
A Cell ◽  
Type Decomposition

AbstractWe introduce a new computational method named EMeth to estimate cell type proportions using DNA methylation data. EMeth is a reference-based method that requires cell type-specific DNA methylation data from relevant cell types. EMeth improves on the existing reference-based methods by detecting the CpGs whose DNA methylation are inconsistent with the deconvolution model and reducing their contributions to cell type decomposition. Another novel feature of EMeth is that it allows a cell type with known proportions but unknown reference and estimates its methylation. This is motivated by the case of studying methylation in tumor cells while bulk tumor samples include tumor cells as well as other cell types such as infiltrating immune cells, and tumor cell proportion can be estimated by copy number data. We demonstrate that EMeth delivers more accurate estimates of cell type proportions than several other methods using simulated data and in silico mixtures. Applications in cancer studies show that the proportions of T regulatory cells estimated by DNA methylation have expected associations with mutation load and survival time, while the estimates from gene expression miss such associations.

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