Validation of Whole Genome Methylation Profiling Classifier for Central Nervous System Tumors

J Mol Diagn. 2022 Aug;24(8):924-934. doi: 10.1016/j.jmoldx.2022.04.009. Epub 2022 May 21.


The 2021 WHO Classification of Tumors of the Central Nervous System includes several tumor types and subtypes for which the diagnosis is at least partially reliant on utilization of whole genome methylation profiling. The current approach to array DNA methylation profiling utilizes a reference library of tumor DNA methylation data, and a machine learning-based tumor classifier. This approach was pioneered and popularized by the German Cancer Research Network (DKFZ) and University Hospital Heidelberg. This research group has kindly made their classifier for central nervous system tumors freely available as a research tool via a web-based portal. However, their classifier is not maintained in a clinical testing environment. Therefore, the Northwestern Medicine (NM) classifier was developed and validated. The NM classifier was validated using the same training and validation data sets as the DKFZ group. Using the DKFZ validation data set, the NM classifier's performance showed high concordance (92%) and comparable accuracy (specificity 94.0% versus 84.9% for DKFZ, sensitivity 88.6% versus 94.7% for DKFZ). Receiver-operator characteristic curves showed areas under the curve of 0.964 versus 0.966 for NM and DKFZ classifiers, respectively. In addition, in-house validation was performed and performance was compared using both classifiers. The NM classifier performed comparably well and is currently offered for clinical testing.

MeSH terms

  • Central Nervous System
  • Central Nervous System Neoplasms* / diagnosis
  • Central Nervous System Neoplasms* / genetics
  • DNA Methylation / genetics
  • Humans
  • Machine Learning