Dataset Information

Practical implementation of DNA methylation and copy-number-based CNS tumor diagnostics: the Heidelberg experience.

ABSTRACT: Recently, we described a machine learning approach for classification of central nervous system tumors based on the analysis of genome-wide DNA methylation patterns [6]. Here, we report on DNA methylation-based central nervous system (CNS) tumor diagnostics conducted in our institution between the years 2015 and 2018. In this period, more than 1000 tumors from the neurosurgical departments in Heidelberg and Mannheim and more than 1000 tumors referred from external institutions were subjected to DNA methylation analysis for diagnostic purposes. We describe our current approach to the integrated diagnosis of CNS tumors with a focus on constellations with conflicts between morphological and molecular genetic findings. We further describe the benefit of integrating DNA copy-number alterations into diagnostic considerations and provide a catalog of copy-number changes for individual DNA methylation classes. We also point to several pitfalls accompanying the diagnostic implementation of DNA methylation profiling and give practical suggestions for recurring diagnostic scenarios.

SUBMITTER: Capper D

PROVIDER: S-EPMC6060790 | biostudies-other | 2018 Aug

REPOSITORIES: biostudies-other

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Practical implementation of DNA methylation and copy-number-based CNS tumor diagnostics: the Heidelberg experience.

Capper David D Stichel Damian D Sahm Felix F Jones David T W DTW Schrimpf Daniel D Sill Martin M Schmid Simone S Hovestadt Volker V Reuss David E DE Koelsche Christian C Reinhardt Annekathrin A Wefers Annika K AK Huang Kristin K Sievers Philipp P Ebrahimi Azadeh A Schöler Anne A Teichmann Daniel D Koch Arend A Hänggi Daniel D Unterberg Andreas A Platten Michael M Wick Wolfgang W Witt Olaf O Milde Till T Korshunov Andrey A Pfister Stefan M SM von Deimling Andreas A

Acta neuropathologica 20180702 2

Recently, we described a machine learning approach for classification of central nervous system tumors based on the analysis of genome-wide DNA methylation patterns [6]. Here, we report on DNA methylation-based central nervous system (CNS) tumor diagnostics conducted in our institution between the years 2015 and 2018. In this period, more than 1000 tumors from the neurosurgical departments in Heidelberg and Mannheim and more than 1000 tumors referred from external institutions were subjected to ...[more]

PMID: 29967940

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Project description:A database of apparently benign copy number variants (bCNVs) detected by a Spectral Genomics Inc./PerkinElmer BAC array platform has been maintained through the University of Utah Comparative Genomic Hybridization laboratory since 2005. The target population for this database represents 1275 patients with abnormal phenotypes, primarily children referred for developmental delay and mental retardation. These bCNVs are independent of any identified copy number abnormality detected. The most common 35 bCNVs observed and their frequencies are reported here, and a subset of ten of the patients studied was evaluated on a new oligonucleotide CNV array set designed by Agilent Technologies. There was a 76% concordance of calls detected by both array platforms in the same patients; the discordant regions may be due to differences in reference DNAs, or false positive/negative results on either array. The higher resolution of the Agilent oligonucleotide array compared to the BAC array allowed for further characterization to determine precise breakpoints of the observed CNVs, in addition to documenting additional CNVs of smaller sizes. As expected, observed CNVs and their frequencies were generally consistent with those of other previously published and available databases, including the Database of Genomic Variants (http://projects.tcag.ca/variation/). The availability of these data should assist other clinical laboratories in the evaluation of CNVs of unknown clinical significance. The Cytogenetics Laboratory at the University of Utah has been using aCGH to evaluate DNA derived from blood of patients with developmental delay, mental retardation and “syndromic” or dysmorphic phenotypes since 2005. From this database, we were able to determine which clones represented sequences which were frequently deleted or duplicated in our patient population. We identified our most common CNV regions and then selected DNAs from ten patients who exhibited copy number changes of those common regions for further study with the Agilent CNV microarray set. Normal male or female DNA from Promega was used as a sex-mismatched control. In addition to the ten clinical specimens, two self-self experiments for two patient samples were analyzed to estimate a false positive rate and to fine tune the parameters used for data analysis. A replicate hybridization was performed on one patient to determine reproducibility.

Dataset Information

Practical implementation of DNA methylation and copy-number-based CNS tumor diagnostics: the Heidelberg experience.

Publications

Practical implementation of DNA methylation and copy-number-based CNS tumor diagnostics: the Heidelberg experience.

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