Patients with H3K27M-mutant diffuse midline glioma (DMG) have no proven effective therapies. ONC201 has recently demonstrated efficacy in these patients, but the mechanism behind this finding remains unknown. We assessed clinical outcomes, tumor sequencing, and tissue/cerebrospinal fluid (CSF) correlate samples from patients treated in two completed multisite clinical studies. Patients treated with ONC201 following initial radiation but prior to recurrence demonstrated a median overall survival of 21.7 months, whereas those treated after recurrence had a median overall survival of 9.3 months. Radiographic response was associated with increased expression of key tricarboxylic acid cycle–related genes in baseline tumor sequencing. ONC201 treatment increased 2-hydroxyglutarate levels in cultured H3K27M-DMG cells and patient CSF samples. This corresponded with increases in repressive H3K27me3 in vitro and in human tumors accompanied by epigenetic downregulation of cell cycle regulation and neuroglial differentiation genes. Overall, ONC201 demonstrates efficacy in H3K27M-DMG by disrupting integrated metabolic and epigenetic pathways and reversing pathognomonic H3K27me3 reduction. Significance: The clinical, radiographic, and molecular analyses included in this study demonstrate the efficacy of ONC201 in H3K27M-mutant DMG and support ONC201 as the first monotherapy to improve outcomes in H3K27M-mutant DMG beyond radiation. Mechanistically, ONC201 disrupts integrated metabolic and epigenetic pathways and reverses pathognomonic H3K27me3 reduction. This article is featured in Selected Articles from This Issue, p. 2293

Accurate CNS tumor diagnosis can be challenging, and methylation profiling can serve as an adjunct to classify diagnostically difficult cases. An integrated diagnostic approach was employed for a consecutive series of 1258 surgical neuropathology samples obtained primarily in a consultation practice over 2-year period. DNA methylation profiling and classification using the DKFZ/Heidelberg CNS tumor classifier was performed, as well as unsupervised analyses of methylation data. Ancillary testing, where relevant, was performed. Among the received cases in consultation, a high-confidence methylation classifier score (>0.84) was reached in 66.4% of cases. The classifier impacted the diagnosis in 46.7% of these high-confidence classifier score cases, including a substantially new diagnosis in 26.9% cases. Among the 289 cases received with only a descriptive diagnosis, methylation was able to resolve approximately half (144, 49.8%) with high-confidence scores. Additional methods were able to resolve diagnostic uncertainty in 41.6% of the low-score cases. Tumor purity was significantly associated with classifier score (P = 1.15e−11). Deconvolution demonstrated that suspected glioblastomas (GBMs) matching as control/inflammatory brain tissue could be resolved into GBM methylation profiles, which provided a proof-of-concept approach to resolve tumor classification in the setting of low tumor purity. This work assesses the impact of a methylation classifier and additional methods in a consultative practice by defining the proportions with concordant vs change in diagnosis in a set of diagnostically challenging CNS tumors. We address approaches to low-confidence scores and confounding issues of low tumor purity.

Recent years have witnessed a shift to more objective and biologically-driven methods for central nervous system (CNS) tumor classification. The 2016 world health organization (WHO) classification update (“blue book”) introduced molecular diagnostic criteria into the definitions of specific entities as a response to the plethora of evidence that key molecular alterations define distinct tumor types and are clinically meaningful. While in the past such diagnostic alterations included specific mutations, copy number changes, or gene fusions, the emergence of DNA methylation arrays in recent years has similarly resulted in improved diagnostic precision, increased reliability, and has provided an effective framework for the discovery of new tumor types. In many instances, there is an intimate relationship between these mutations/fusions and DNA methylation signatures. The adoption of methylation data into neuro-oncology nosology has been greatly aided by the availability of technology compatible with clinical diagnostics, along with the development of a freely accessible machine learning-based classifier. In this review, we highlight the utility of DNA methylation profiling in CNS tumor classification with a focus on recently described novel and rare tumor types, as well as its contribution to refining existing types.

Childhood posterior fossa group A ependymomas demonstrate integrated metabolic and epigenetic pathways that can be disrupted by metformin.