ABSTRACT
The goal of the methylation classifier in brain tumor classification is to accurately classify tumors based on their methylation profiles. Accurate brain tumor diagnosis is the first step for healthcare professionals to predict tumor prognosis and establish personalized treatment plans for patients. The methylation classifier can be used to perform classification on tumor samples with diagnostic difficulties due to ambiguous histology or mismatch between histopathology and molecular signatures, i.e., not otherwise specified (NOS) cases or not elsewhere classified (NEC) cases, aiding in pathological decision-making. Here, the authors elucidate upon the application of a methylation classifier as a tool to mitigate the inherent complexities associated with the pathological evaluation of brain tumors, even when pathologists are experts in histopathological diagnosis and have access to enough molecular genetic information. Also, it should be emphasized that methylome cannot classify all types of brain tumors, and it often produces erroneous matches even with high matching scores, so, excessive trust is prohibited. The primary issue is the considerable difficulty in obtaining reference data regarding the methylation profile of each type of brain tumor. This challenge is further amplified when dealing with recently identified novel types or subtypes of brain tumors, as such data are not readily accessible through open databases or authors of publications. An additional obstacle arises from the fact that methylation classifiers are primarily research-based, leading to the unavailability of charging patients. It is important to note that the application of methylation classifiers may require specialized laboratory techniques and expertise in DNA methylation analysis.
ABSTRACT
The aim of this study is to investigate the genetic profiles and methylation-based classifications of Embryonal tumor with multilayered rosettes (ETMR), with a specific focus on differentiating between C19MC amplified and C19MC-not amplified groups, including cases with DICER1 mutations. To achieve this, next-generation sequencing using a targeted gene panel for brain tumors and methylation class studies using the Epic850K microarray were performed to identify tumor subclasses and their clinicopathological characteristics. The study cohort consisted of four patients, including 3 children (a 4-months/F, a 9-months/M, and a 2 y/F), and one adult (a 30 y/Male). All three tumors in the pediatric patients originated in the posterior fossa and exhibited TTYH1:C19MC fusion and C19MC amplification. The fourth case in the adult patient involved the cerebellopontine angle with biallelic DICER1 mutation. Histopathological examination revealed typical embryonal features characterized by multilayered rosettes and abundant neuropils in all cases, while the DICER1-mutant ETMR also displayed cartilage islands in addition to the classic ETMR pathology. All four tumors showed positive staining for LIN28A. The t-SNE clustering analysis demonstrated that the first three cases clustered with known subtypes of ETMR, specifically C19MC amplified, while the fourth case clustered separately to non-C19MC amplified subclass. During the follow-up period of 6~12 months, leptomeningeal dissemination of the tumor occurred in all patients. Considering the older age of onset in DICER1-mutant ETMR, genetic counseling should be recommended due to the association of DICER1 mutations with germline and second-hit somatic mutations in cancer.