Citations

Abstract INTRODUCTION Recurrent IDH-mutant gliomas pose a significant therapeutic challenge, with limited treatment options following progression after standard therapy. Combining PARP inhibitors with immune checkpoint blockade has been proposed as a synergistic strategy in IDH-mutant high-grade gliomas, leveraging vulnerabilities in homologous recombination repair and increased PD-L1 expression following PARP inhibition. OBJECTIVE This phase II trial (NCT03991832) evaluated the combination of the PARP inhibitor olaparib and the PD-L1 inhibitor durvalumab in patients with recurrent IDH-mutant glioma. We also investigated the potential of the plasma tumor methylome as a non-invasive biomarker of treatment response. METHODS Twenty-nine patients (median age 40.5 years; 41% female) were enrolled between January 2020 and February 2023. All patients received olaparib (300 mg twice daily) and durvalumab (1,500 mg IV every four weeks) until radiographic or clinical progression. Plasma samples were collected at baseline and monthly, and cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) was performed. RESULTS The objective response rate was 10%, and median overall survival was 9.3 months. Longitudinal cfMeDIP-seq profiling enabled development of a circulating methylome classifier that accurately distinguished responders from non-responders. Integration with matched tumor transcriptomic and methylation data revealed enrichment of immune and DNA repair pathways in responders. Whole-exome sequencing identified no consistent mutational correlates. Spatial transcriptomic analysis demonstrated a more interactive, immune-rich tumor microenvironment and reduced malignant cell state diversity in responders. CONCLUSION This study supports the safety of combined PARP and PD-L1 blockade in recurrent IDH-mutant glioma and highlights the plasma methylome as a promising non-invasive biomarker of therapeutic response.

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 BACKGROUND Post-operative prognostication in glioma patients remains a clinical challenge, particularly given the heterogeneity in tumor biology and patient outcomes. We evaluated whether image-derived features from post-operative imaging improve survival modeling beyond established clinicopathologic factors. METHODS We analyzed a cohort of 71 post-surgical glioma patients treated at Princess Margaret Cancer Centre with known clinicopathologic features including age, histological classification, IDH mutation status, MGMT methylation status, extent of resection, and performance status. A baseline Cox proportional hazards model was fit to clinical variables. Radiomic features were extracted using PyRadiomics from post-operative CT and MRI across three regions of interest: whole brain, tumor bed, and peritumoral ring. Features underwent stepwise unsupervised reduction (variance filtering, exclusion of volume-correlated features, and removal of remaining collinear features), followed by dimensionality reduction via principal component analysis. Combined clinical and imaging models were evaluated in the overall cohort and stratified by IDH mutation status. RESULTS In the overall cohort, the clinical model was highly prognostic (C-index 0.74, p ~ 3.2×10⁻⁶). Adding imaging features improved performance (C-index 0.79) but was not significant (p ~ 0.31). Subset analysis revealed differential effects by IDH status. In IDH-mutant gliomas (n=24), neither clinical nor combined models were significant. In IDH wild-type glioblastoma (n=47), the clinical model achieved C-index 0.75 (p=0.04). The addition of imaging features significantly improved model performance (C-index 0.83, p=0.03), with selected imaging components contributing independent prognostic value after adjustment for clinical variables. Post-hoc analysis of radiomic feature importance (random forest ranking) revealed that top contributing features were drawn from both CT and MRI modalities, with most sourced from the whole brain region. CONCLUSIONS In post-surgical patients with IDH wild-type glioblastoma, image-derived features from post-operative imaging can significantly enhance prognostic modeling beyond known clinicopathologic factors. These preliminary findings support the potential role of radiomic biomarkers in refining risk stratification for this patient subgroup.

Abstract CIC-rearranged sarcoma (CRS) is a rare disease driven by a specific fusion protein involving the CIC gene. The occurrence in the brain is 3% in all CRS patients, and these tumors frequently metastasize to the brain. The most common rearrangement is with the double homeobox 4 (DUX4) transcription factor (CIC-DUX4), and others, such as CIC-NUTM1 fusions, have been identified in a subset of pediatric primitive neuroectodermal tumors. However, the molecular mechanisms by which CIC-fusions drive CRS remain unknown. Preliminary data show that CIC-DUX4/NUTM1 fusions activate JAK and its downstream effector STAT1/3. We hypothesize that JAK/STAT1/3 signaling cooperates with CIC-fusions to drive CIC-sarcomas by inducing ETV1/4/5 expression. Patient-derived CRS cell lines showed elevated levels of JAK1/STAT1/3 activation compared to fusion-negative sarcoma lines. Inhibition of JAK1 using Ruxolitinib and Solicitinib reduced STAT1/3 phosphorylation, downregulated ETV1/4/5 expression at both mRNA and protein levels, and diminished ETV5 promoter activity, cell proliferation, and tumorigenicity. Although the mechanism by which CIC-fusions activate oncogenic targets is still under investigation, histone acetylation appears to play a central role. STAT1/3 interacts with p300/CBP to enhance transcription, and STAT1 is necessary for p300 acetyltransferase activity. Ruxolitinib significantly reduced histone acetylation at ETV1/4/5 promoters in hMSC cells expressing CIC-DUX4/NUTM1, as well as in CRS cell lines. Unlike the p300 inhibitor C646, which causes global hypoacetylation, Ruxolitinib’s effects were promoter-specific, indicating its potential as a more targeted and less toxic therapeutic option. Importantly, we found that STAT1/3 binds to ETV1/4/5 promoters only in the presence of CIC-fusions. Luciferase assays confirmed that STAT1/3 alone cannot activate ETV5 transcription without CIC-fusions, revealing a novel cooperative mechanism. In vivo, Ruxolitinib treatment of CRS xenografts led to significant reductions in tumor volume, STAT1/3 activation, and ETV1/4/5 expression. These findings support JAK1/STAT1/3 inhibition as a promising therapeutic strategy for CRS and warrant further preclinical investigation.

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.

Abstract Background Adolescent and young adult (AYA) patients remain underrepresented in neuro-oncology research. Despite being the second most common primary brain tumor in this population, meningiomas have not been studied using age-specific molecular analyses. DNA methylation-based classification and prognostic tools have transformed meningioma care. This study aimed to evaluate the performance of these tools across age groups. Methods We analyzed 1,568 meningiomas with DNA methylation and clinical data, including 18 pediatric patients (<15 years), 195 AYA patients (15-39 years), and 1,355 adult patients (>39 years). Pediatric and AYA (P/AYA) tumors were combined and compared with adult tumors. The performance of established molecular classifiers and recurrence predictors, as well as differences in chromosomal copy number alterations were compared across age groups. Results While histologic grading was comparable between cohorts, P/AYA tumors displayed significantly fewer aggressive molecular groups and lower frequencies of chromosomal arm losses, including 1p, 6q, and 14q. The adult-trained recurrence predictor failed in the P/AYA population (AUC 0.57), despite similar score distributions. Retraining the model on an age-specific cohort using an identical analytic framework improved performance (AUC 0.79) and enabled effective stratification of progression-free survival (P = 0.00054). Importantly, 1p loss retained prognostic significance within the P/AYA group, supporting its clinical utility. Conclusions Molecular tools developed in adult-dominant cohorts do not generalize to younger patients due to both biological divergence and exclusion from model development. These findings underscore the need for age-specific molecular frameworks and highlight the imperative of including P/AYA populations in precision neuro-oncology research to ensure lifespan-equitable care.

Abstract BACKGROUND IDH-mutant gliomas are diffusely infiltrating, slow-growing brain tumors that inevitably undergo malignant progression to grade IV astrocytoma - a highly aggressive and uniformly fatal disease with dismal prognosis. Despite extensive research, the molecular mechanisms underlying this transformation remain poorly understood. While alterations in canonical driver genes such as CDKN2A, PTEN, and PDGFRA have been implicated, the broader landscape of genetic drivers involved in IDH-mutant glioma progression is largely uncharacterized. In contrast to many other malignancies, IDH-mutant gliomas acquire relatively few somatic point mutations during progression - typically in the range of 30 to 40 - suggesting that alternative mechanisms may be driving tumor evolution. Notably, these tumors exhibit widespread chromosomal instability, resulting in a heterogeneous and highly prevalent pattern of copy number alterations (CNAs). This observation raises the possibility that gene dosage changes due to CNAs play a critical role in tumor progression. MATERIAL AND METHODS To investigate this hypothesis, we performed integrative analysis of IDH-mutant glioma cohorts from the Mayo Clinic and TCGA datasets (~1,500 patients), identifying twelve highly recurrent CNAs significantly associated with patient outcomes. We refer to these as malignant IDH-associated CNAs (mIDH CNAs). We hypothesized that these recurrent CNAs harbor driver genes that confer selective growth advantages, thereby contributing to their recurrence and prognostic relevance. To systematically assess the functional impact of genes within these regions, we developed a novel in vivo CRISPR-based screening platform, termed CRISPR-Knock Out and Activation Linked Assay (CRISPR-KOALA). This approach utilizes stereotaxic delivery of lentiviral gRNA libraries - targeting mouse orthologs of ~1,800 genes within recurrently deleted regions and ~1,200 genes within amplified regions - into the brains of a genetically engineered mouse model of IDH-mutant glioma (rs557mut;IDHmut;P53KO;AtrxKO;Cas9GFP: Yanchus et. al Science 2022). RESULTS Mice injected with mIDH CNA-targeting libraries developed tumors significantly more rapidly than control cohorts. Deconvolution of the CRISPR-KOALA screen identified several putative driver genes, including regulators of the NOTCH signaling pathway, lipid metabolism modulators such as THEM6, and the NMDA receptor modulator GRINA - implicating dysregulation of metabolism, immune response, and glutamatergic signaling in the malignant transformation of IDH-mutant gliomas. CONCLUSION Together, these findings provide critical insights into the genetic mechanisms driving the progression of IDH-mutant gliomas and identify new candidate genes and pathways for potential therapeutic targeting.

Abstract BACKGROUND Glioblastoma (GBM) is a highly aggressive brain tumour with limited prognostic tools that are broadly implementable in routine clinical settings. Current models often depend on molecular platforms not universally accessible. We aimed to develop and validate a clinically practical prognostic model for GBM that integrates ICAM1 protein expression, imaging-derived tumor morphometrics, and clinical features. MATERIAL AND METHODS We analyzed ICAM1 expression in GBM at the DNA methylation, RNA, and protein levels using public datasets and institutional cohorts, confirming its association with tumor grade and poor survival. ICAM1 protein levels were then quantified via digital pathology in a multi-institutional cohort of 197 newly diagnosed, treatment-naïve GBM patients. Tumor surface irregularity (SIR) was calculated from pre-operative contrast-enhanced MRI using open-source imaging tools. Age at diagnosis was also recorded. These three variables—ICAM1 expression, SIR, and age—were integrated into a Clinical-Imaging-Molecular (CIM) model using multivariable Cox regression. The model was validated in an independent external GBM cohort (n = 20). Survival prediction was assessed using Kaplan-Meier curves, ROC analysis, and concordance index (c-index). RESULTS ICAM1 expression was associated with DNA hypomethylation and significantly correlated with reduced survival at transcriptomic and proteomic levels. Pathway analysis identified associations with immune activation and angiogenesis. In the clinical cohort, high ICAM1 expression, elevated tumor SIR, and older age independently predicted worse survival. The CIM model combining these factors stratified patients into distinct prognostic groups, achieving strong performance (c-index = 0.779, AUC = 0.778). External validation confirmed the model’s predictive utility (median survival difference: 9.1 months, P = 0.009). CONCLUSION We present a validated and clinically applicable prognostic model for GBM that integrates ICAM1 expression, imaging features, and age. The CIM model is scalable, interpretable, and feasible for implementation across academic and community settings, providing a robust tool for patient stratification, treatment planning, and trial design.

Abstract Glioblastoma is the most common type of malignant primary brain tumor and a major cause of morbidity and mortality. In 2021, the World Health Organization updated the classification of Central Nervous System (CNS) tumors to restrict glioblastomas to isocitrate dehydrogenase-wildtype (IDHwt) tumors, improving understanding of the prognosis and optimal therapy for these tumors. This revision also enables more homogeneous populations of patients to be enrolled in clinical trials, facilitating the evaluation of novel therapies. In this updated consensus review from the Society for Neuro-Oncology (SNO) and the European Association of Neuro-Oncology (EANO), the current management of patients with glioblastoma is discussed. In addition, novel therapies such as immunotherapies, viral therapies, targeted molecular therapies, theranostics, and antibody–drug conjugates will be reviewed, as well as the current challenges and future directions for research.