Abdullaev, Zied

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 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 Background Astroblastomas are rare brain tumors predominantly affecting children and young adults, for which molecular subtypes and clinical management remain undefined. Methods We analyzed tumor samples, molecular profiles, and clinical data from 200 patients, classified as “Astroblastoma, MN1-altered” under WHO criteria, using DNA methylation profiling, DNA/RNA profiling/sequencing, and survival analyses. Results DNA methylation analyses identified 3 groups: Group A (n = 143, characterized by MN1::BEND2 fusions, predominantly supratentorial location, with striking female predominance and favorable survival); Group B (n = 37, epigenetically and transcriptionally closely related to Group A, but characterized by EWSR1::BEND2 fusions, with spinal and infratentorial locations and poor prognosis); and Group C (n = 20, epigenetically and transcriptionally distinct, characterized by MN1::CXXC5 fusions, exclusively supratentorially located, with favorable survival). Progression-free and overall survival were significantly shorter in Group B (5-year PFS 14%; 10-year OS 54%) compared to A (5-year PFS 47%; 10-year OS 89%) and C (5-year PFS 75%; 10-year OS 89%). Radiotherapy improved PFS in Group B (hazard ratio 0.25), while no clear benefit was identified for Groups A and C. Conclusions Astroblastoma, MN1-altered, comprises 3 molecularly and clinically distinct groups, characterized by different fusion genes, including those without MN1. These new insights, including the identification of potential predictive biomarkers like 14q/16q loss, provide a framework for the development of risk-stratified therapeutic approaches. Importantly, we identified a molecularly defined high-risk group that benefits from radiation therapy. Our findings redefine Astroblastoma as a molecularly diverse tumor type, propose a refined classification, support the development of risk-adapted therapeutic strategies and provide a rational standard of care.

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 The phase III ECOG-ACRIN E3F05 trial tested whether combined therapy with radiation and concomitant followed by adjuvant temozolomide is more effective than radiation alone in patients with grade II gliomas. The study incorporated 1p/19q analysis but predated assessment of IDH mutational status. We previously reported that the addition of temozolomide to radiotherapy significantly improved overall survival in grade II gliomas, with similar hazard ratios in co-deleted (0.56) and non-codeleted (0.53) tumors. Herein we incorporate the results of DNA methylation profiling to examine outcomes in IDHmt, non-codeleted subjects as well as co-deleted ones. METHODS All 172 subjects underwent 1p/19q co-deletion analysis as a stratification factor, 1p and 19q deletion analysis was performed on formalin-fixed paraffin-embedded (FFPE) gliomas by Fluorescent In Situ Hybridization (FISH) following standard methods, 97/172 (56.3%) had tissue with 60% or more tumor content available for DNA methylation profiling using Heidelberg classifier v12 and Bethesda Classifier 2.0 to ascertain classification as IDH mutant glioma and alternative evaluation of 1p/19q co-deletion status. RESULTS Of the 172 subjects, 74 (43.0%) had 1p/19q codeletion by FISH, 38 (22.1%) were IDHmt without codeletion, 14 (8.1%) were IDHwt, and 46 (26.7%) were non-codeleted with unknown IDH status (these include n=4 pts originally classified as 1p/19q undetermined). With a median follow-up of 130 months, the addition of TMZ to RT improved OS in patients with IDHmt, non-codeleted tumors (HR 0.15 (0.03-0.74)) and 10-year OS from 39% to 80%. In codeleted tumors, the addition of TMZ improved OS (HR 0.51 (0.19-1.42)) and 10-year OS from 68% to 90%. CONCLUSION The addition of temozolomide to radiation improves overall survival in both co-deleted and IDHmt, non-co-deleted grade II gliomas.