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.Electronic supplementary materialThe online version of this article (10.1007/s00401-018-1879-y) contains supplementary material, which is available to authorized users.

Ectopic intracranial retinoblastoma in a 3.5‐month‐old infant without eye involvement and without evidence of heritability

“…14 DNA microarray techniques have been successfully applied to study the cancer methylome in a genome-wide manner and have led to the discovery of clearly defined tumor subgroups on the basis of their global DNA methylation patterns, resulting in a recently published DNA-methylation-based classification system of CNS tumors. 15,16 The described case was consistent with the reference methylation class "pineoblastoma group A/intracranial retinoblastoma," together with the histopathological appearance and the evidence of a biallelic inactivation of the RB1 gene leading to the diagnosis of sporadic ectopic retinoblastoma.…”

Genome‐wide DNA methylation analysis of colorectal adenomas with and without recurrence reveals an association between cytosine‐phosphate‐guanine methylation and histological subtypes

“…46 Specialized gastrointestinal histopathologists showed only moderate (kappa = 0.49) concordance in discriminating the different polyp forms of the group of serrated adenomas. 50,51 The same holds true for sarcoma diagnostics and allows a more precise classification of undifferentiated and small blue round cell tumors with array-based DNA-methylation profiling. 48 Additionally, a meta-analysis indicated that hypermethylation of p16 might be an unfavorable biomarker for CRC patients.…”

“…49 Recent studies about brain tumor classification showed that the analysis of methylation patterns could be highly relevant for precise diagnostics and treatment suggestions. 50,51 The same holds true for sarcoma diagnostics and allows a more precise classification of undifferentiated and small blue round cell tumors with array-based DNA-methylation profiling. 52 The here presented finding that adenoma morphology differs by DNA methylation might provide the opportunity for enhancing the accuracy of the up to now mainly morphology-based classification of colonic adenomas.…”

Integrative analysis of gene expression and DNA methylation through one‐class logistic regression machine learning identifies stemness features in medulloblastoma

“…The prognostic copy number alterations in SHH MB, including MYCN amplifications, GLI2 amplifications, and PTEN deletions, were calculated from DNA methylation arrays utilizing the R package 'conumee' with default parameters (http://bioconductor.org/pac kages/conumee) (Capper et al, 2018). P values for the associations between stemness indices and the prognostic copy number alterations of SHH MB were computed using Pearson's correlation coefficient tests followed by multiple testing using the BH method.…”