Chung, Hye‐Jung

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 21,099 CNS tumors included 5139 tumors from AYAs (24%), which showed a mixed pattern, with tumors typical of childhood (e.g. medulloblastoma) as well as those common in older adults (e.g. 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 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 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 Computer vision now enables deep learning models to link histopathologic features to molecular features in a quantitative manner not previously achievable by human pathologists. However, human interpretability is often limited. We trained a deep neural network to predict gene expression from digital whole slide images (WSIs) using 848 central nervous system (CNS) tumors with paired WSI and RNA-sequencing data. We used inferred RNA expression levels as a surrogate for protein expression of the CNS tumor lineage markers GFAP, OLIG2, and SSTR2, as well as the proliferation marker MKI67 (Ki67). We established thresholds for categorizing the inferred expression levels as positive or negative based on levels observed in cross-validation testing in select tumor types to approximate routine clinical interpretation of immunohistochemical (IHC) staining. We tested the sensitivity and specificity of our ‘virtual’ IHC on an independent, multi-institutional cohort of over 2,000 CNS tumors with objective diagnostic labels derived from DNA methylation-based tumor classification. As a dichotomous variable, inferred GFAP expression showed a sensitivity of 74% and a specificity of 99% as a glial marker in a cohort of gliomas and meningiomas. In the same cohort, OLIG2 showed a sensitivity of 75% and a specificity of 97% as a glial marker, while SSTR2 showed a sensitivity of 83% and a specificity of 97% as a marker of meningioma. In a cohort of ependymomas and gliomas, virtual IHC for OLIG2 showed a sensitivity of 75% and a specificity of 82% in distinguishing ependymomas from gliomas. Finally, in a cohort of gliomas, inferred expression of MKI67 demonstrated a trend consistent with what would be expected by actual IHC, with increasing MKI67 expression with increasing tumor grade. Our model provides the basis for a human interpretable and clinically applicable deep neural network to aid human pathologists in the diagnosis and grading of CNS tumors.

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.