Citations

Abstract A new mouse model for the classical subtype of human glioblastoma (GBM) has been bred using Cre-mediated EGFRvIII overexpression and homozygous p19-ARF deletion, the mouse homolog of human p14ARF/CDKN2A, in GFAP expressing cells. Transgenic mice develop intraparenchymal and/or leptomeningeal brain lesions with some spinal cord invasion as early as 1 month old and 95% of mice die by 6 months due to hydrocephalus and/or paralysis. Mice with high grade tumors have worse survival and similar features to human classical GBM such as necrosis, high levels of mitosis and infiltration of tumor cells into normal brain. Immunohistochemical analysis confirms EGFRvIII overexpression and p19-ARF loss in Cre-expressing cells, along with patchy positive GFAP and high proliferation by Ki67. Bulk RNA sequencing reveals GEC3 tumors cluster closely with human classical GBM and have significant upregulation of downstream markers in the JAK/STAT and P13K/AKT pathways. Adherent and neurosphere primary culture of dissociated tumors indicate that tumor cells maintain EGFRvIII expression in culture and generate xenograft tumors by 3 weeks after intracranial injections into NODSCID mice. Xenograft tumors are reminiscent of the primary tumor, with similar histopathological features and immunohistochemical staining. Our spontaneous glioma model is a powerful tool for future studies focused on the role of the immune microenvironment on glioma recurrence and resistance.

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 Pediatric, adolescent and young adult patients (Ped/AYA; 0–39 years) with meningiomas are underrepresented in neuro-oncology research, despite meningiomas being the second most common primary brain tumor in this age group. While DNA methylation-based models have advanced classification and prognostication in adults, their utility in younger populations remains uncertain. To address this gap, we aimed to evaluate the performance of current DNA methylation-based classification and recurrence prediction models in Ped/AYA patients with meningiomas and identify age-associated molecular differences. As part of this study, we analyzed 1,568 meningioma samples with DNA methylation and clinical data, including subsets with whole-exome and RNA sequencing. Patients were grouped into Ped/AYA (≤39 years, n = 213) and adult (>39 years, n = 1,355) cohorts. We assessed classifier performance, performed subgroup-specific model retraining, and compared molecular profiles across age groups. Our results showed that molecular classification differed significantly between age groups, with Ped/AYA tumors less frequently belonging to the most aggressive Proliferative Molecular Group. The DNA methylation-based recurrence model performed well in adults (AUC = 0.83) but poorly in the Ped/AYA cohort (AUC = 0.57). However, retraining the model on only younger patients improved performance (AUC = 0.75). In addition, Ped/AYA tumors exhibited significantly fewer chromosomal alterations, including lower frequencies of 1p, 6q, and 14q loss (p<0.05). Taken together, these findings indicate that predominantly adult-trained methylation models underperform in younger meningioma patients due to both biological divergence and exclusion from training datasets. Our findings underscore the need for age-specific molecular tools to improve prognostication and support personalized care across the lifespan in neuro-oncology.

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.

Abstract INTRODUCTION Brain metastases (BM) are the most common intracranial tumor, with a poor prognosis of 10-16 months, and lung adenocarcinoma (LUAD) is the most common source. Currently, we cannot reliably predict which patients will develop BMs clinically, so most are detected after they develop and growing to cause deficits. Here, we use tumor DNA methylomes to predict which patients are likely to develop BM through the course of disease and we use both cerebrospinal fluid (CSF) and plasma methylomes for BM liquid biopsy, enabling their early detection and treatment. METHODS DNA methylomes were acquired from 419 tumor and plasma samples from 363 LUAD and BM patients. We built DNA methylation-based models that predict BM development and that also identify BMs non-invasively from CSF and plasma in discovery data subsets. A nomogram was built that predicts BM risk using methylation scores and clinical stage together. Models were evaluated in independent validation sets. RESULTS Our model predicting BM development was accurate (Univariable Cox: HR=5.7, 95% CI=1.9-17.2), p=0.0023), independent of clinical factors (Multivariable Cox: HR=8.9, 95% CI=2.0-40.5, p=0.0046), and more predictive than cancer staging used currently clinically (AUC=0.81 versus 0.65). Our combinatorial nomogram using methylation and staging data together showed enhanced BM prediction (Univariable Cox: HR=17.2, 95% CI=4.1-71.3, p<0.0001; AUROC=0.82). Our models identifying BM through biofluid methylomes accurately distinguished BM from differential diagnoses of gliomas and lymphomas in plasma (AUROC=0.80, 95% CI=0.68-0.93) and with greater accuracy in CSF (AUROC=0.93, 95% CI=0.71-1.0). CONCLUSION Overall, we show the first approaches for robust molecular prediction of BM development and for reliable liquid biopsy of BM. This work is expected to transform cancer management by identifying high-risk lung cancer patients after initial treatment for inclusion in screening protocols using neuroimaging and liquid biopsy, to identify BM development early with less advanced disease and more optimal therapeutic outcomes.

Abstract Meningiomas are the most common primary intracranial tumors and exhibit clinical heterogeneity. Radiotherapy (RT) remains the only adjuvant therapy, but response is variable and biomarkers are limited. RTOG-0539 is the first prospective phase 2 trial to stratify meningioma patients for adjuvant RT based on clinical risk. Here, we apply modern molecular tools to this cohort and uncover correlates of RT response. To do so, tumor tissue from 100 RTOG-0539 patients was profiled using DNA methylation arrays, RNA sequencing, and whole-exome sequencing. Recurrence scores, Molecular Groups, gene expression, and copy number alterations were compared across clinical groups and between RT responders and non-responders. Using this approach, modern grading criteria, including brain invasion, TERT mutation, CDKN2A/B deletion and 1p/1q status, would reclassify 10% of tumors and alter treatment group assignment in 7%. Notably, non-responders to RT had more frequent 1p and 14q loss, and greater copy number burden. In addition, transcriptomic and epigenetic profiling revealed immune-related signatures in RT responders and upregulation of cell cycle–related pathways in RT non-responders, several of which overlapped with targets of vorinostat, a histone deacetylase inhibitor validated in aggressive meningioma models. Of particular importance, the Proliferative Molecular Group was an independent predictor of post-RT recurrence in multivariable analysis, outperforming WHO grade. In conclusion, multi-omic analysis of the RTOG-0539 cohort shows that updated WHO grading criteria, incorporating molecular and cytogenetic features, improve risk stratification. However, molecular classification, particularly the Proliferative group, remains an independent and stronger predictor of RT response. These findings support integrating molecular biomarkers alongside modern grading frameworks to guide treatment and trial design in meningioma.

Abstract INTRODUCTION While TERT promoter mutation (TPM) has been established as a marker of clinically aggressive meningiomas, this alteration is rare and found in less than 5% of all cases. However, a larger subset of meningiomas may exhibit aberrant TERT expression in the absence of TPMs. This study investigated the effect of TERT gene expression on clinical outcome in meningioma patients. METHODS Clinical and molecular data were retrospectively collected on 1241 meningiomas, split into a Toronto discovery cohort and a multi-institutional validation cohort. Sanger sequencing and bulk RNA sequencing were used to determine TPM status and TERT gene expression. The effect of TERT expression on progression-free survival (PFS) was assessed using Kaplan-Meier and Cox regression analysis. RESULTS While meningiomas with TPM showed expectedly higher TERT gene expression compared to wildtype (TP-WT) cases (p<0.0001), TERT expression was still detected in 28.7% (157/547) of TP-WT meningiomas. Meningiomas with TERT expression showed significantly worse PFS compared to meningiomas without any TERT expression. In fact, WHO grade 1 meningiomas with TERT expression had PFS outcomes resembling WHO grade 2 meningiomas lacking TERT expression (p=0.59). In turn, WHO grade 2 meningiomas with TERT expression had clinical outcomes similar to WHO grade 3 meningiomas without TERT expression (p=0.42). Furthermore, the proportion of meningiomas expressing TERT as well as overall TERT expression levels increased with increasing WHO grade. Multivariable analysis showed that TERT expression was significantly associated with worse PFS even when controlling for other known predictors of clinical outcome including TPM, CDKN2A/B loss, 1p/22q status and WHO grade (HR 1.85 [95% CI 1.33-2.57], p=0.00024). CONCLUSION TERT expression is a novel independent biomarker of outcome for meningiomas identifiable in up to one-third of cases that may be utilized to reclassify tumours to a higher WHO grade.

Importance Treatment for cognitive dysfunction due to postacute sequelae of long COVID (ie, symptoms of fatigue, malaise, weakness, confusion that persist beyond 12 weeks after an initial COVID infection) remains a significant unmet need. Objective To test evidence-based rehabilitation strategies for improving cognitive symptoms in persons with long COVID. Design, Setting, and Participants This was a 5-arm, multicenter, randomized clinical trial of 3 remotely delivered interventions conducted between August 17, 2023, and June 10, 2024. The study took place at 22 trial sites and included the screening of individuals with cognitive long COVID. Interventions Participants were randomized to 1 of 5 arms: adaptive computerized cognitive training (BrainHQ [Posit Science]), cognitive-behavioral rehabilitation involving both group and individual counseling sessions (PASC-Cognitive Recovery [PASC-CoRE]) paired with BrainHQ, and transcranial direct current stimulation (tDCS) paired with BrainHQ. Two comparator arms were included as follows: unstructured computer puzzles and games (active comparator) and sham tDCS paired with BrainHQ. The interventions occurred 5 times per week over 10 weeks. Main Outcomes and Measures Cognitive and behavioral in-person assessments were performed at baseline, midintervention, at the end of intervention, and 3 months after the end of the intervention. The primary outcome measure was the modified Everyday Cognition Scale 2 (ECog2) completed at the end of the intervention compared to the baseline visit based on participant self-report looking back over the prior 7 days. Results A total of 378 individuals were screened, from which there were 328 participants (median [IQR] age, 48.0 [37.0-58.0] years; 241 female [73.5%]; race: 15 Asian [4.6%], 47 Black [14.3%], and 235 White [71.6%]; ethnicity: 52 Hispanic [15.9%]). None of the 3 active interventions demonstrated benefits on the modified ECog2 in the intention-to-treat population by the end of the intervention period. The adjusted differences in mean change were 0.0 (95% CI, −0.2 to 0.2) for BrainHQ vs active comparator, 0.1 (95% CI, −0.1 to 0.3) for PASC-CoRE + BrainHQ vs active comparator, 0.0 (95% CI, −0.2 to 0.2) for tDCS-active + BrainHQ vs tDCS-sham + BrainHQ, and 0.1 (95% CI, −0.1 to 0.3) for PASC-CoRE + BrainHQ vs BrainHQ alone. Secondary participant-reported outcomes and neuropsychological tests showed no differential benefits for any treatment arm. All 5 arms demonstrated some improvements over time on the modified ECog2 and on secondary outcomes. There were no serious adverse events attributable to the interventions. Conclusions and Relevance This phase 2 randomized clinical trial failed to demonstrate differential benefits for online cognitive training, a structured cognitive rehabilitation program, and tDCS for cognitive long COVID. Trial Registration ClinicalTrials.gov Identifier: NCT05965739 .