Invited Review: DNA methylation‐based classification of paediatric brain tumours

Abstract

2020) Neuropathology and Applied Neurobiology 46, 28-47 DNA methylation-based classification of paediatric brain tumours DNA methylation-based machine learning algorithms represent powerful diagnostic tools that are currently emerging for several fields of tumour classification. For various reasons, paediatric brain tumours have been the main driving forces behind this rapid development and brain tumour classification tools are likely further advanced than in any other field of cancer diagnostics. In this review, we will discuss the main characteristics that were important for this rapid advance, namely the high clinical need for improvement of paediatric brain tumour diagnostics, the robustness of methylated DNA and the consequential possibility to generate high-quality molecular data from archival formalin-fixed paraffin-embedded pathology specimens, the implementation of a single array platform by most laboratories allowing data exchange and data pooling to an unprecedented extent, as well as the high suitability of the data format for machine learning. We will further discuss the four most central output qualities of DNA methylation profiling in a diagnostic setting (tumour classification, tumour sub-classification, copy number analysis and guidance for additional molecular testing) individually for the most frequent types of paediatric brain tumours. Lastly, we will discuss DNA methylation profiling as a tool for the detection of new paediatric brain tumour classes and will give an overview of the rapidly growing family of new tumours identified with the aid of this technique.

Analyses of DNA Methylation Profiling in the Diagnosis of Intramedullary Astrocytomas

“…The Heidelberg Brain Tumor classifier based on DNA methylation profiles has been shown to be a powerful tool even for challenging CNS tumors ( 42 , 43 ). Among the 1155 prospective samples tested, Capper et al ( 35 ) reported 88% of classified cases (with calibrated scores of at least 0.9).…”

Molecular diagnostics in drug‐resistant focal epilepsy define new disease entities

“…Significant limitations at this point include conceptual barriers (the field of epigenetics in epilepsy is still in its infancy), the availability of technical infrastructure at epilepsy centers around the globe, and consequently, the restricted number of samples that have yet been analyzed for DNA methylation. Collaborative research studies will be needed to address these issues and to be able to capture rare and extremely rare disease entities (62) as well as to evaluate the predictive value of DNA methylation for, for example, genotype, disease progression, or treatment response.…”

A rare case of paediatric astroblastoma with concomitant MN1‐GTSE1 and EWSR1‐PATZ1 gene fusions altering management

“…Methylation profiling of the tumour [10] using the Illumina EPIC array platform and analysed using the DKFZ Heidelberg classifier, gave a very low calibration score of <0.9 (specifically 0.0475 at the time of reporting using classifier version MNPv11b4, and 0.0764 with the most recent version MNPv11b6). These scores were too low for reliable classification despite excellent probe hybridisation (only 0.09% of probes failed), suggesting that the tumour was unclassifiable compared with currently recognised tumour entities.…”