Quezado, Martha

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 Diffuse hemispheric glioma, H3 G34‐mutant (DHG‐H3 G34) has been primarily molecularly characterized by methylation profiling and sequencing studies. We describe an integrated histomolecular evaluation including high‐resolution copy number profiling of a series of 60 DHG‐H3 G34 to further our understanding of the spectrum of genetic changes associated with this tumor type. Cases were clinically tested using an 187‐gene mutation and fusion targeted neuro‐oncology next‐generation sequencing panel ( n = 60) and Oncoscan chromosomal microarray ( n = 26) by a single laboratory (2018–2022). A subset of cases had immunohistochemical results for OLIG2 ( n = 42), p53 ( n = 48), and ATRX ( n = 46), and methylation array data ( n = 8). Median age at testing was 21 years (range, 12–50). No significant difference was noted in clinical, histopathological, and mutational profile between pediatric and adult patients. H3‐3A G34 mutations included G34R ( n = 56; 94%), G34V ( n = 3), and a non‐canonical G34E ( n = 1). Concurrent mutations most often involved TP53 ( n = 55; 92%), ATRX ( n = 50; 83%), and PDGFRA ( n = 34; 57%). A reportedly primary tumor was confirmed to be hypermutant and had a PMS2 mutation. A single case also showed an FGFR3::FAM184B fusion. All cases with available chromosomal microarray data had unbalanced genomes, which were often complex (14/26; 54%). The most frequent recurrent copy number abnormalities were losses involving 3q, 4q, 10q, 13q, and 18q, and 17p copy‐neutral loss of heterozygosity (cnLOH) encompassing TP53 . This copy number profile was reminiscent of that seen in Grade 4 IDH‐mutant astrocytomas. Collectively, a TP53 abnormality at copy number (12/26, all cnLOH), sequence (55/60) and protein expression (46/48) level was detected in all 60 cases. In conclusion, integrated high‐resolution copy number and histomolecular analysis expanded the spectrum of genetic changes associated with DHG‐H3 G34, including the presence of universal TP53 abnormalities with frequent cnLOH—a copy number abnormality that has been largely unrecognized—for this new 2021 World Health Organization central nervous system tumor type.

Abstract Background Intracranial mesenchymal tumors, FET::CREB fusion-positive (ICMT), show fusions involving FET RNA-binding protein family genes (EWSR1 or FUS) and CREB family of transcription factors (ATF1, CREB1, or CREM). The methylation signature(s), gene expression characteristics, and clinical behavior of this important tumor type require further characterization. Methods We study the methylation profiles of 81 ICMT cases (61 newly profiled cases and 20 cases from publicly available sources). Clinicopathologic and genomic data were recorded for each case when available. Results ICMT showed a relatively distinct methylation signature compared to related tumors. Among the 65 cases where fusion types were documented, the identified fusions included EWSR1::ATF1 (25 cases), EWSR1::CREB1 (12 cases), EWSR1::CREM (21 cases), FUS::CREM (3 cases), and SMARCA2::CREM (4 cases). We confirmed the prior description of 2 distinct subgroups of ICMT (subclasses A and B). The majority of the cases belonged to subclass A (n = 69; 85%), which showed a higher median age compared to subclass B patients (26 years vs. 15 years). Subclass B cases (n = 12; 15%) showed shorter progression-free survival (P < 0.01). Gene expression analysis of ICMT showed key overexpressed markers in ICMT, with significant CREM overexpression regardless of fusion type, when compared to either meningioma alone or a larger group of CNS tumors. Conclusions This work provides further characterization of ICMT as an important CNS mesenchymal neoplasm that is prone to tumor recurrence, showing 2 prognostically relevant methylation subclasses and warranting diagnostic distinction from other epigenetically and histologically related tumors. ICMTs show substantial overexpression of the CREM gene, independent of fusion type.

Abstract Molecular testing has become critical components in the diagnostic classification of the central nervous system (CNS) tumors. However, these methods require substantial resources, limiting accessibility for many patients. Recent advances in artificial intelligence (AI) and computer vision empower deep learning models to infer molecular features from histopathology images, which may often be more readily available than molecular testing, to classify CNS tumors. We trained pan-CNS tumor models to predict DNA methylation and gene expression from whole-slide images (WSIs). These molecular predictions were then used in a hierarchical machine learning framework, termed Neuropath-AI, to predict nine broad tumor families and 52 tumor types using a large diverse cohort of 5,715 histopathology samples, of which 2,988 had paired DNA methylation profiling and 848 had paired RNA-sequencing. We evaluated Neuropath-AI on another large independent multi-institutional cohort of 5,516 CNS tumors, for which Neuropath-AI predicted tumor class with an associated confidence score. Neuropath-AI designated 46% of test samples as predictable with high-confidence, which it then accuracy classified in 97% of samples. It made moderate-confidence (and above) predictions for 87% of test samples, for which it achieved a diagnostic accuracy of 80% for the top-1 prediction and 86% accuracy for a top-2 predictions. A comparison with human neuropathologists showed that Neuropath-AI is comparable at classifying tumors. A second test showed that human neuropathologists were more accurate when the Neuropath-AI classifier results were provided to them, testifying to the potential translational benefit of integrating AI classification tools into the pathologist’s workflow. Our model provides the basis for a clinically applicable deep learning assistant to improve human efficiency and accuracy of CNS tumor diagnosis. The model will be made publicly available and can be readily implemented to assist human pathologists in clinical workflows, in further expanded prospective studies.

Abstract Background Methylation class pleomorphic xanthoastrocytoma (mcPXA) comprises tumors with the DNA methylation signature of classical PXA but with a wider histologic spectrum, including overlap with glioblastoma (GBM). Methods To clarify the histologic and molecular scope of mcPXA and characterize its clinical behavior, a cohort of 469 tumor samples from 458 patients matching to mcPXA by the DKFZ classifier (v12.6 score ≥0.85) was interrogated. Results Patient median age was 23 years (range 1-73 years) with a female predominance (259 female/199 male). CDKN2A/B homozygous deletion was observed in 406 of 469 (87%) samples. In samples tested for BRAF p.V600E mutations (n = 279), 240 (86%) harbored the mutation. A chr7+/chr10− pattern was observed in 103 of 469 (22%) samples. Among samples tested for TERT promoter mutations (n = 143), 32 (22%) harbored the mutation. Progression-free and overall survival of patients with mcPXA were comparable to patients with methylation class IDH-mutant astrocytoma, low grade, but a GBM-like subset (ie, cases with a pre-methylation working diagnosis of GBM) showed shorter survival. Histologic features of high grade, including palisading necrosis and microvascular proliferation, were prognostic in mcPXA. Compared to patients with BRAF p.V600E-altered GBM, patients with mcPXA were younger and had a lower frequency of TERT promoter mutations. Conclusion Tumors in mcPXA share molecular characteristics with histologically defined PXA, and high-grade histologic features can help predict their clinical behavior. The use of an epigenetic classification of PXA reveals that this group of tumors is more common than previously appreciated and warrants in-depth study to identify efficacious therapeutic options.

Atypical teratoid/rhabdoid tumor (ATRT) is an aggressive, malignant embryonal tumor with dismal long-term survival despite aggressive multimodal therapy. While this tumor typically presents in infancy or early childhood, there are published case reports of adult-onset ATRT. Making prognostic conclusions or therapeutic decisions for this older patient population remains challenging due to the paucity of these reports. A 25-year-old female with Down syndrome presented with dysphagia and facial droop and was found to have an avidly enhancing, cerebellopontine angle mass. Histology demonstrated sheets of rhabdoid cells with loss of INI1 expression, pathognomonic for ATRT. Further sequencing detected a frameshift SMARCB1 mutation and methylation profiling matched with high confidence to the MYC subclass of ATRT. The patient was treated with subtotal surgical resection and focal proton beam irradiation, followed by chemotherapy on a modified regimen due to concern for heightened risk of treatment-related toxicity. On most recent follow-up 22 months from diagnosis, the patient remains without evidence of disease. This report represents the first known case of ATRT in a young adult patient with Down syndrome, offering unique mechanistic insight into the tumorigenesis of ATRT. Further studies are needed to define an appropriate risk-adapted and standardized therapeutic approach for this patient population.