Visualization of DNA methylation profiling data as a quality control tool for molecular classification of CNS tumors

Considering the importance of genome methylation profile assessment in distinguishing molecular classes within different types of cancer, microarray-based DNA methylation analysis has become routine in modern pathomorphological diagnosis of CNS tumors.

The most recent version (5th edition) of the WHO classification of CNS tumors includes current consensus about DNA methylation-based molecular groups. It is advised that morphological diagnosis should take methylation studies into

account since this information is important for risk stratification, prognosis, treatment strategy, as well as for enrollment in clinical trials.

Novel computational tools — machine learning-based online tumor classifiers — were designed to facilitate easier interpretation of DNA methylation data and increase diagnostic precision. While being a valuable resource in many cases, the existing online classifiers still have limitations and are not always conclusive about tumor methylation classes due to individual sample features or lack of similar samples in the reference cohort. Addressing these limitations is possible by introducing additional graphical methods of methylation data analysis, which would confirm the correspondence between methylation profiles in tumor subgroups and support evidence for a particular diagnosis.

In this work, we demonstrate an interactive tool we developed to visualize the results of DNA methylation analysis, compare histological findings with molecular classes and check the similarities between various tumor types for 470 CNS tumor samples available in our database. This tool provides a beneficial option for a morphologist to achieve a better quality diagnosis in controversial cases where other methods are insufficient or misleading.

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