Nasrallah, MacLean

Abstract A working group of cIMPACT-NOW evaluated the literature on IDH-mutant gliomas for opportunities to improve pathology-based risk stratification and grading, especially at the interface of CNS WHO grades 2 and 3, since therapeutic decisions depend on risk assessment for clinical management. The group also evaluated newly described IDH-mutant glioma subgroups and considered multidisciplinary aspects of therapeutic decision-making. PDGFRA amplification was associated with highly aggressive clinical behavior of IDH-mutant astrocytomas, consistent with CNS WHO grade 4. Other genetic alterations associated with intermediate risk and most consistent with CNS WHO grade 3 included PIK3 mutations; EGFR alteration; MYCN amplification; and specific retinoblastoma pathway alterations, including CDK4, CDK6, and CCND2 amplification, RB1 homozygous deletion or mutation, and mutation of CDKN2A. DNA methylation signatures of G-CIMP-low or A_IDH_HG were consistent with CNS WHO grade 3 or 4, depending upon other grading criteria. We suggest a mitotic count of ≥3 per 2.4 mm2 could be considered as a CNS WHO grade 3 criterion. Our review did not uncover features to improve the grading of oligodendroglioma, IDH-mutant and 1p/19q-codeleted, although CNS WHO grade 3 tumors with elevated mitotic rates, yet lacking necrosis, microvascular proliferation, CDKN2A/B homozygous deletion, and MRI contrast enhancement, may be associated with extended survival. Implementing evidence-based criteria for risk stratification and grading will improve guidance for clinical decision-making.

Abstract Background Approximately 12 000 adolescents and young adults (ages 15-39, AYAs) are diagnosed with a primary central nervous system (CNS) tumor each year. DNA methylation profiling has transformed CNS tumor classification by refining diagnostic accuracy and identifying biologically distinct subtypes, but its application has not been systematically evaluated in the AYA population. In this study, we examined the spectrum of CNS tumor types across age groups, with a focus on the AYA population. Methods We assembled a large dataset of CNS tumor samples with age annotations and methylation profiles matching with high confidence to a CNS tumor type using the NCI/Bethesda classifier. Prevalence of tumor type, methylation class and DNA copy number aberrations were compared across age strata (pediatric, AYA, and adult). Results The cohort of 21 712 CNS tumors included 5351 tumors from AYAs (25%), which showed a mixed pattern, with tumors typical of childhood (eg, medulloblastoma) as well as those common in older adults (eg, glioblastoma). Several tumor types were specifically enriched in the AYA, including IDH-mutant astrocytoma, pleomorphic xanthoastrocytoma, posterior fossa group B ependymoma, and diffuse hemispheric glioma, H3 G34-mutant, among others. Distinct methylation subclasses of multiple tumor types were observed in the AYA, and patterns of genomic aberrations showed age-specific distributions. Conclusion Large-scale methylation profiling revealed unique classification patterns of CNS tumors in AYAs, with specific tumor types, subclasses, and genomic alterations enriched in this population. These data may serve as a valuable reference resource for better understanding the spectrum of CNS tumors affecting AYA patients.

Abstract Genome-wide DNA methylation signatures correlate with and distinguish central nervous system (CNS) tumor types. Since the publication of the initial CNS tumor DNA methylation classifier in 2018, this platform has been increasingly used as a diagnostic tool for CNS tumors, with multiple studies showing the value and utility of DNA methylation-based classification of CNS tumors. A Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) Working Group was therefore convened to describe the current state of the field and to provide advice based on lessons learned to date. Here, we provide recommendations for the use of DNA methylation-based classification in CNS tumor diagnostics, emphasizing the attributes and limitations of the modality. We emphasize that the methylation classifier is one diagnostic tool to be used alongside previously established diagnostic tools in a fully integrated fashion. In addition, we provide examples of the inclusion of DNA methylation data within the layered diagnostic reporting format endorsed by the World Health Organization (WHO) and the International Collaboration on Cancer Reporting. We emphasize the need for backward compatibility of future platforms to enable accumulated data to be compatible with new versions of the array. Finally, we outline the specific connections between methylation classes and CNS WHO tumor types to aid in the interpretation of classifier results. It is hoped that this update will assist the neuro-oncology community in the interpretation of DNA methylation classifier results to facilitate the accurate diagnosis of CNS tumors and thereby help guide patient management.