Exploring DNA Methylation for Prognosis and Analyzing the Tumor Microenvironment in Pleomorphic Xanthoastrocytoma

Tang, Karen; Kurland, David B.; Vasudevaraja, Varshini; Serrano, Jonathan; DeLorenzo, Michael; Radmanesh, Alireza; Thomas, Cheddhi; Spino, Marissa; Gardner, Sharon; Allen, Jeffrey C.; Nicolaides, Theodore; Osorio, Diana S; Finlay, Jonathan L.; Boué, Daniel R.; Snuderl, Matija

doi:10.1093/jnen/nlaa051

Cited by 12 publications

(12 citation statements)

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“…To further classify these tumors and establish a more robust molecular spatial profile, we analyzed the top 10,000 most different methylation probes across the entire genome [ 41 ], and performed unsupervised hierarchical clustering (Fig. 5 A).…”

Section: Resultsmentioning

confidence: 99%

“…Further analysis of differential methylation between clusters using MethylCIBERSORT allowed us to explore the tumor microenvironment by deconvolving the signatures of different types of tumor-associated cells [ 6 , 41 ]. We found measurable differences in immune epigenetic signatures between the three clusters (Additional file 1 : Figure S5).…”

Section: Resultsmentioning

confidence: 99%

“…The data were analyzed using the R package ( http://www.R-project.org/ ) in Bioconductor. Unsupervised hierarchical clustering of the top 10,000 differently expressed DNA methylation regions was performed on each sample [ 41 ]. Differential methylation analysis between each pair of clusters (C1: sample 1; C2: samples 2, 3, 4, 9; C3: samples 5, 6, 7, 8) was performed by pairwise comparison in order to identify differentially methylated sequences with FDR < 0.01.…”

Section: Methodsmentioning

confidence: 99%

“…Gene set enrichment analysis (GSEA) was performed using the probes matching gene body and promoter regions for each methylation cluster, to identify differentially activated pathways between clusters ( https://www.gsea-msigdb.org/gsea/index.jsp ) [ 40 ]. MethyCIBERSORT was used to deconvolve the cell populations in the microenvironment of each cluster, including endothelial, fibroblast, and immune cell populations, as previously described [ 6 , 41 ].…”

Section: Methodsmentioning

confidence: 99%

“…Genome-wide DNA methylation profiling has proven to be a particularly robust and reproducible tool in central nervous system (CNS) tumor classification and has been increasingly utilized as a marker of cell development and a surrogate representation of gene expression, which is particularly helpful in cases where the histology is unusual or non-specific and/or other molecular testing is inconclusive [ 16 , 30 ]. DNA methylation profiling measures epigenetic alterations in which methyl groups are transferred to the 5′ position of the cytosine ring, forming CpG islands throughout the genome in patterns that can be measured to classify CNS tumors [ 5 , 41 ]. Additionally, DNA methylation can inactivate tumor suppressor genes and/or cause genomic instability and push cells toward malignant progression, and may also partially explain the molecular heterogeneity found in GBM [ 10 , 18 , 23 , 29 , 37 ].…”

Section: Introductionmentioning

confidence: 99%

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Spatial progression and molecular heterogeneity of IDH-mutant glioblastoma determined by DNA methylation-based mapping

Lyon

Vasudevaraja

Mirchia

et al. 2021

acta neuropathol commun

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Glioblastoma (GBM) is the most common malignant primary central nervous system (CNS) neoplasm in adults, and has an almost universally poor prognosis. Recently, an emphasis on genetic and epigenetic profiling has revealed a number of molecular features useful in the diagnostic and prognostic classification of GBM, advancing our understanding of the underlying features that make these tumors so aggressive and providing the rationale for the creation of better targeted therapeutics. One such method, DNA methylation profiling, has recently emerged as an important technique for the classification of CNS tumors, with diagnostic accuracy in some cases surpassing traditional methods. However, how DNA methylation profiles change with the course of the disease remains less understood. Here, we present a case of a 30-year-old male with primary IDH-mutant GBM with widespread recurrence and death two years later. Using unsupervised hierarchical clustering of methylation probes, we created a phylogenetic map to trace the tumor path as it spread from the initial biopsy site throughout the right hemisphere, across the corpus callosum to the contralateral hemisphere, and into the brainstem. We identified molecular divergence between the right and left hemisphere GBM samples marked by distinct copy number profile alterations, alterations in specific methylation sites, and regional loss of MGMT promoter methylation, providing a potential mechanism for treatment resistance in this case. In summary, this case both highlights the molecular diversity in GBM, and illustrates a novel use for methylation profiling in establishing a phylogenetic profile to allow for spatial mapping of tumor progression.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%