Hawkins, Cynthia

Abstract Purpose: Knowledge of prognostic factors and long-term survival in patients with diffuse hemispheric glioma, H3 G34–mutant (DHG, H3 G34), remains limited in this tumor with a poor prognosis. Experimental Design: This retrospective, multi-institutional study investigated prognostic variables for patients with DHG, H3 G34, and their association with progression-free survival (PFS) and overall survival (OS). Uni- and multivariable Cox proportional hazard models were applied with multiple imputed datasets. Results: A total of 153 patients (142 G34R, 9 G34V, 2 via DNA methylation) were included. The median age at diagnosis was 17 years (range, 2–45). Initial gross/near total resection (GTR/NTR) was achieved in 43% of patients. Radiation was given in 91% (85% focal irradiation), and initial chemotherapy was given in 87% [70% temozolomide-based (TMZ), 25% TMZ/lomustine, 5% non-TMZ]. Median OS was 24 months [interquartile range (IQR), 22–28] with a median PFS of 14 months (IQR, 12–19). Twelve patients (8%) were found to be long-term survivors (≥5 years). Exploratory multivariable analysis showed that adjuvant radiotherapy [HR, 0.076; 95% confidence interval (CI), 0.033–0.17] and achieving GTR/NTR compared with < NTR (HR, 0.51; 95% CI, 0.33–0.78) were associated with improved PFS. Multivariable analysis showed improved OS with increasing age at diagnosis (HR, 0.70; 95% CI, 0.57–0.87), initial radiotherapy (HR, 0.38; 95% CI, 0.15–0.96), and initial GTR/NTR compared with < NTR (HR, 0.60; 95% CI, 0.37–0.97). Conclusions: This cohort highlights prognostic factors for patients with DHG, H3 G34, and describes relapse patterns and therapy approaches. Clinical trials and prospective registries are needed to improve outcomes.

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.

PURPOSE Diagnosis of Mismatch Repair Deficiency (MMRD) is crucial for tumor management and early detection in patients with the cancer predisposition syndrome constitutional mismatch repair deficiency (CMMRD). Current diagnostic tools are cumbersome and inconsistent both in childhood cancers and in determining germline MMRD. PATIENTS AND METHODS We developed and analyzed a functional Low-pass Genomic Instability Characterization (LOGIC) assay to detect MMRD. The diagnostic performance of LOGIC was compared with that of current established assays including tumor mutational burden, immunohistochemistry, and the microsatellite instability panel. LOGIC was then applied to various normal tissues of patients with CMMRD with comprehensive clinical data including age of cancer presentation. RESULTS Overall, LOGIC was 100% sensitive and specific in detecting MMRD in childhood cancers (N = 376). It was more sensitive than the microsatellite instability panel (14%, P = 4.3 × 10 −12 ), immunohistochemistry (86%, P = 4.6 × 10 −3 ), or tumor mutational burden (80%, P = 9.1 × 10 −4 ). LOGIC was able to distinguish CMMRD from other cancer predisposition syndromes using blood and saliva DNA ( P < .0001, n = 277). In normal cells, MMRDness scores differed between tissues (GI > blood > brain), increased over time in the same individual, and revealed genotype-phenotype associations within the mismatch repair genes. Importantly, increased MMRDness score was associated with younger age of first cancer presentation in individuals with CMMRD ( P = 2.2 × 10 −5 ). CONCLUSION LOGIC was a robust tool for the diagnosis of MMRD in multiple cancer types and in normal tissues. LOGIC may inform therapeutic cancer decisions, provide rapid diagnosis of germline MMRD, and support tailored surveillance for individuals with CMMRD.