Reference-free deconvolution, visualization and interpretation of complex DNA methylation data using DecompPipeline, MeDeCom and FactorViz

Zeiner

et al. 2021

J Immunother Cancer

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BackgroundTherapies based on targeting immune checkpoints have revolutionized the treatment of metastatic melanoma in recent years. Still, biomarkers predicting long-term therapy responses are lacking.MethodsA novel approach of reference-free deconvolution of large-scale DNA methylation data enabled us to develop a machine learning classifier based on CpG sites, specific for latent methylation components (LMC), that allowed for patient allocation to prognostic clusters. DNA methylation data were processed using reference-free analyses (MeDeCom) and reference-based computational tumor deconvolution (MethylCIBERSORT, LUMP).ResultsWe provide evidence that DNA methylation signatures of tumor tissue from cutaneous metastases are predictive for therapy response to immune checkpoint inhibition in patients with stage IV metastatic melanoma.ConclusionsThese results demonstrate that LMC-based segregation of large-scale DNA methylation data is a promising tool for classifier development and treatment response estimation in cancer patients under targeted immunotherapy.

Section: Methodsmentioning

confidence: 99%

DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma

Filipski

Zeiner

et al. 2021

J Immunother Cancer

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“…The functional impact of such genetic variants, which can be modulated by QTLs (methQTLs, eQTLs), remains to be investigated. Additionally, DNA methylation data can be used to reliably estimate the proportions of different cell types in the samples, either using a reference data set [ 6 ] or in a reference-free way [ 11 ]; an analysis strategy known as deconvolution. Given the cell-type specificity of a subset of methQTLs identified within this study, a combination of DNA methylation-based deconvolution and identification of methQTLs could be implemented along the line of published, transcriptome-based approaches [ 25 , 53 ].…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, donor genotype has a strong influence on the global DNA methylation state (methylome), especially when a genetic alteration, such as a single nucleotide polymorphism (SNP), occurs at a CpG site. Since bisulfite-based methods can generate unclear and uninterpretable data at annotated or predicted SNPs located at CpG dinucleotides, such positions are typically removed from the analysis of DNA methylation data [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR

Epigenetics & Chromatin

Gasparoni

Rahmouni

et al. 2021

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Background Understanding the influence of genetic variants on DNA methylation is fundamental for the interpretation of epigenomic data in the context of disease. There is a need for systematic approaches not only for determining methylation quantitative trait loci (methQTL), but also for discriminating general from cell type-specific effects. Results Here, we present a two-step computational framework MAGAR (https://bioconductor.org/packages/MAGAR), which fully supports the identification of methQTLs from matched genotyping and DNA methylation data, and additionally allows for illuminating cell type-specific methQTL effects. In a pilot analysis, we apply MAGAR on data in four tissues (ileum, rectum, T cells, B cells) from healthy individuals and demonstrate the discrimination of common from cell type-specific methQTLs. We experimentally validate both types of methQTLs in an independent data set comprising additional cell types and tissues. Finally, we validate selected methQTLs located in the PON1, ZNF155, and NRG2 genes by ultra-deep local sequencing. In line with previous reports, we find cell type-specific methQTLs to be preferentially located in enhancer elements. Conclusions Our analysis demonstrates that a systematic analysis of methQTLs provides important new insights on the influences of genetic variants to cell type-specific epigenomic variation.

“…The functional impact of such genetic variants, which can be modulated by QTLs (methQTLs, eQTLs), remains to be investigated. Additionally, DNA methylation data can be used to reliably estimate the proportions of different cell types in the samples, either using a reference data set [6] or in a reference-free way [52]; an analysis strategy known as deconvolution. Given the celltype specificity of a subset of methQTLs identified within this study, a combination of DNA methylation-based deconvolution and identification of methQTLs could be implemented similarly to transcriptome-based approaches [24,53].…”

Section: Discussionmentioning

confidence: 99%

Identification of tissue-specific and common methylation quantitative trait loci in healthy individuals using MAGAR

Gasparoni

Rahmouni

et al. 2021

Preprint

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Background: Understanding the influence of genetic variants on DNA methylation is fundamental for the interpretation of epigenomic data in the context of disease. There is a need for systematic approaches not only for determining methylation quantitative trait loci (methQTL) but also for discriminating general from cell-type-specific effects. Results: Here, we present a two-step computational framework MAGAR, which fully supports identification of methQTLs from matched genotyping and DNA methylation data, and additionally the identification of quantitative cell-type-specific methQTL effects. In a pilot analysis, we apply MAGAR on data in four tissues (ileum, rectum, T-cells, B-cells) from healthy individuals and demonstrate the discrimination of common from cell-type-specific methQTLs. We experimentally validate both types of methQTLs in an independent dataset comprising additional cell types and tissues. Finally, we validate selected methQTLs (PON1, ZNF155, NRG2) by ultra-deep local sequencing. In line with previous reports, we find cell-type-specific methQTLs to be preferentially located in enhancer elements. Conclusions: Our analysis demonstrates that a systematic analysis of methQTLs provides important new insights on the influences of genetic variants to cell-type-specific epigenomic variation.