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  • 1
    Abstract: We collated data from 157 unpublished cases of pediatric high-grade glioma and diffuse intrinsic pontine glioma and 20 publicly available datasets in an integrated analysis of 〉1,000 cases. We identified co-segregating mutations in histone-mutant subgroups including loss of FBXW7 in H3.3G34R/V, TOP3A rearrangements in H3.3K27M, and BCOR mutations in H3.1K27M. Histone wild-type subgroups are refined by the presence of key oncogenic events or methylation profiles more closely resembling lower-grade tumors. Genomic aberrations increase with age, highlighting the infant population as biologically and clinically distinct. Uncommon pathway dysregulation is seen in small subsets of tumors, further defining the molecular diversity of the disease, opening up avenues for biological study and providing a basis for functionally defined future treatment stratification.
    Type of Publication: Journal article published
    PubMed ID: 28966033
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  • 2
    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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  • 3
    ISSN: 1437-9813
    Keywords: Prenatal diagnosis ; Neuroblastoma ; Adrenal tumor
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine
    Notes: Abstract A right retroperitoneal mass with mixed solid and cystic pattern was detected sonographically in a fetus in the 36th week of pregnancy. After term delivery radiologic, sonographic, and CT studies confirmed the presence of a spherical, 5 × 4-cm mass above the right kidney with mixed echogenicity. At operation an enlarged adrenal gland containing hemorrhagic spaces was removed. Histological studies revealed adrenal neuroblastoma. This prenatal sonographic pattern was suggestive of neuroblastoma because it was identical to that of the only similar case reported so far. Prenatal detection is therefore possible and should be regarded as desirable since early diagnosis and treatment provide the best chances for cure.
    Type of Medium: Electronic Resource
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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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