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  • 1
    Abstract: Purpose: The classification of medulloblastoma into WNT, SHH, group 3, and group 4 subgroups has become of critical importance for patient risk stratification and subgroup-tailored clinical trials. Here, we aimed to develop a simplified, clinically applicable classification approach that can be implemented in the majority of centers treating patients with medulloblastoma.Experimental Design: We analyzed 1,577 samples comprising previously published DNA methylation microarray data (913 medulloblastomas, 457 non-medulloblastoma tumors, 85 normal tissues), and 122 frozen and formalin-fixed paraffin-embedded medulloblastoma samples. Biomarkers were identified applying stringent selection filters and Linear Discriminant Analysis (LDA) method, and validated using DNA methylation microarray data, bisulfite pyrosequencing, and direct-bisulfite sequencing.Results: Using a LDA-based approach, we developed and validated a prediction method ((Epi)WNT-SHH classifier) based on six epigenetic biomarkers that allowed for rapid classification of medulloblastoma into the clinically relevant subgroups WNT, SHH, and non-WNT/non-SHH with excellent concordance (〉99%) with current gold-standard methods, DNA methylation microarray, and gene signature profiling analysis. The (Epi)WNT-SHH classifier showed high prediction capacity using both frozen and formalin-fixed material, as well as diverse DNA methylation detection methods. Similarly, we developed a classifier specific for group 3 and group 4 tumors, based on five biomarkers ((Epi)G3-G4) with good discriminatory capacity, allowing for correct assignment of more than 92% of tumors. (Epi)WNT-SHH and (Epi)G3-G4 methylation profiles remained stable across tumor primary, metastasis, and relapse samples.Conclusions: The (Epi)WNT-SHH and (Epi)G3-G4 classifiers represent a new simplified approach for accurate, rapid, and cost-effective molecular classification of single medulloblastoma DNA samples, using clinically applicable DNA methylation detection methods. Clin Cancer Res; 24(6); 1355-63. (c)2018 AACR.
    Type of Publication: Journal article published
    PubMed ID: 29351917
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  • 2
    ISSN: 1573-8310
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology , Process Engineering, Biotechnology, Nutrition Technology
    Type of Medium: Electronic Resource
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  • 3
    Publication Date: 2018-09-27
    Description: Brain function depends on interaction of diverse cell types whose gene expression and identity are defined, in part, by epigenetic mechanisms. Neuronal DNA contains two major epigenetic modifications, methylcytosine (mC) and hydroxymethylcytosine (hmC), yet their cell type–specific landscapes and relationship with gene expression are poorly understood. We report high-resolution (h)mC analyses, together with transcriptome and histone modification profiling, in three major cell types in human prefrontal cortex: glutamatergic excitatory neurons, medial ganglionic eminence–derived -aminobutyric acid (GABA)ergic inhibitory neurons, and oligodendrocytes. We detected a unique association between hmC and gene expression in inhibitory neurons that differed significantly from the pattern in excitatory neurons and oligodendrocytes. We also found that risk loci associated with neuropsychiatric diseases were enriched near regions of reduced hmC in excitatory neurons and reduced mC in inhibitory neurons. Our findings indicate differential roles for mC and hmC in regulation of gene expression in different brain cell types, with implications for the etiology of human brain diseases.
    Electronic ISSN: 2375-2548
    Topics: Natural Sciences in General
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  • 4
    Publication Date: 2018-03-16
    Description: Purpose: The classification of medulloblastoma into WNT, SHH, group 3, and group 4 subgroups has become of critical importance for patient risk stratification and subgroup-tailored clinical trials. Here, we aimed to develop a simplified, clinically applicable classification approach that can be implemented in the majority of centers treating patients with medulloblastoma. Experimental Design: We analyzed 1,577 samples comprising previously published DNA methylation microarray data (913 medulloblastomas, 457 non-medulloblastoma tumors, 85 normal tissues), and 122 frozen and formalin-fixed paraffin-embedded medulloblastoma samples. Biomarkers were identified applying stringent selection filters and Linear Discriminant Analysis (LDA) method, and validated using DNA methylation microarray data, bisulfite pyrosequencing, and direct-bisulfite sequencing. Results: Using a LDA-based approach, we developed and validated a prediction method ( Epi WNT-SHH classifier) based on six epigenetic biomarkers that allowed for rapid classification of medulloblastoma into the clinically relevant subgroups WNT, SHH, and non-WNT/non-SHH with excellent concordance (〉99%) with current gold-standard methods, DNA methylation microarray, and gene signature profiling analysis. The Epi WNT-SHH classifier showed high prediction capacity using both frozen and formalin-fixed material, as well as diverse DNA methylation detection methods. Similarly, we developed a classifier specific for group 3 and group 4 tumors, based on five biomarkers ( Epi G3-G4) with good discriminatory capacity, allowing for correct assignment of more than 92% of tumors. Epi WNT-SHH and Epi G3-G4 methylation profiles remained stable across tumor primary, metastasis, and relapse samples. Conclusions: The Epi WNT-SHH and Epi G3-G4 classifiers represent a new simplified approach for accurate, rapid, and cost-effective molecular classification of single medulloblastoma DNA samples, using clinically applicable DNA methylation detection methods. Clin Cancer Res; 24(6); 1355–63. ©2018 AACR .
    Print ISSN: 1078-0432
    Electronic ISSN: 1557-3265
    Topics: Medicine
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