Differentiation of rare brain tumors through unsupervised machine learning: Clinical significance of in-depth methylation and copy number profiling illustrated through an unusual case of IDH wildtype glioblastoma.

Abstract

Methylation profiling has become a mainstay in brain tumor diagnostics since the introduction of the first publicly available classification tool by the German Cancer Research Center in 2017. We demonstrate the capability of this system through an example of a rare case of IDH wildtype glioblastoma diagnosed in a patient previously treated for T-cell acute lymphoblastic leukemia. Our novel in-house diagnostic tool EpiDiP provided hints arguing against a radiation-induced tumor, identified a novel recurrent genetic aberration, and thus informed about a potential therapeutic target.