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MethPed: a DNA methylation classifier tool for the identification of pediatric brain tumor subtypes

Artikel i vetenskaplig tidskrift
Författare Anna Danielsson
Szilard Nemes
Magnus Tisell
Birgitta Lannering
Claes Nordborg
Magnus Sabel
Helena Carén
Publicerad i Clinical Epigenetics
Volym 7
Sidor Article Number: 62
ISSN 1868-7083
Publiceringsår 2015
Publicerad vid Institutionen för kliniska vetenskaper, Avdelningen för onkologi
Institutionen för neurovetenskap och fysiologi, sektionen för klinisk neurovetenskap och rehabilitering
Institutionen för biomedicin, avdelningen för patologi
Sahlgrenska Cancer Center
Institutionen för kliniska vetenskaper, Avdelningen för pediatrik
Sidor Article Number: 62
Språk en
Länkar dx.doi.org/10.1186/s13148-015-0103-...
Ämnesord DNA methylation, 450 K, Random forest, PNET, GBM, Medulloblastoma, Ependymoma, Classifier, THERAPEUTIC IMPLICATIONS, MEDULLOBLASTOMA, MUTATIONS, GLIOBLASTOMA, ASTROCYTOMA, EXPRESSION, SUBGROUPS, GLIOMAS, ACVR1, Oncology
Ämneskategorier Cancer och onkologi

Sammanfattning

Background: Classification of pediatric tumors into biologically defined subtypes is challenging, and multifaceted approaches are needed. For this aim, we developed a diagnostic classifier based on DNA methylation profiles. Results: Methylation data generated by the Illumina Infinium HumanMethylation 450 BeadChip arrays were downloaded from the Gene Expression Omnibus (n = 472). Using the data, we built MethPed, which is a multiclass random forest algorithm, based on DNA methylation profiles from nine subgroups of pediatric brain tumors. DNA from 18 regional samples was used to validate MethPed. MethPed was additionally applied to a set of 28 publically available tumors with the heterogeneous diagnosis PNET. MethPed could successfully separate individual histology tumor types at a very high accuracy (kappa = 0.98). Analysis of a regional cohort demonstrated the clinical benefit of MethPed, as confirmation of diagnosis of tumors with clear histology but also identified possible differential diagnoses in tumors with complicated and mixed type morphology. Conclusions: We demonstrate the utility of methylation profiling of pediatric brain tumors and offer MethPed as an easy-to-use toolbox that allows researchers and clinical diagnosticians to test single samples as well as large cohorts for subclass prediction of pediatric brain tumors. This will immediately aid clinical practice and importantly increase our molecular knowledge of these tumors for further therapeutic development.

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