Citations

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 The WHO 2021 classification of central nervous system tumors incorporated molecular markers to better define diffuse glioma subgroups. We analyzed array-based copy number, targeted next-generation sequencing, germline genotyping, and Illumina EPIC array methylation data from 404 primary IDH-mutant astrocytomas to further understand the relationship between copy number and outcomes. Loss of chromosome arm 13q was associated with poor overall survival after adjusting for age, sex, tumor grade, and treatment status (hazard ratio [HR]=2.04 (95% confidence interval: 1.25-3.33), p=0.004). Chromosome 13q loss was also significantly associated with the total number of copy number alterations (p<0.001). Loss of arm 11p was associated with poor survival in the adjusted analysis (HR=2.29 (1.38-3.79), p=0.001). Unsupervised clustering of the raw copy number data identified 12 clusters, where each cluster was defined by distinct copy number alteration(s). Clusters that contained 13q loss exhibited worse overall survival compared to other clusters. The clusters were significantly associated with age at diagnosis (p=0.015), tumor grade (p<0.001) and copy number complexity (p<0.001). The clusters with a high proportion of grade 4 tumors and with the highest complexity had the poorest overall survival. These findings highlight the prognostic relevance of chromosome 13q and 11p losses in IDH-mutant astrocytomas and support the integration of additional copy number alterations into molecular classification frameworks of IDH-mutant astrocytomas to improve risk stratification and patient management.

Abstract Brain metastases (BMs) are a common and often fatal progression of systemic cancers, affecting up to 25% of patients. Despite advances in surgical resection and radiotherapy, the median survival remains limited to 10–16 months. While genomic profiling has enabled the development of targeted therapies, there remains a paucity of prognostic tools for clinical use. To address this, we explored the utility of DNA methylation profiling—an epigenetic marker increasingly recognized for its diagnostic and prognostic value. We profiled fresh-frozen tissue from 327 surgically resected BM samples of lung, breast, melanoma, and gastrointestinal (GI) origin using the Illumina Infinium MethylationEPIC BeadChip array. Unsupervised analysis via partitioning around medoids (PAM) clustering identified five robust groups. Four of these correlated closely with primary tumor origin. However, a distinct “Poly-Origin Cluster” emerged, comprising tumors from multiple primary sites with a shared epigenetic signature. Poly-Origin tumors showed improved survival outcomes compared to origin-aligned clusters (20.2 vs. 10.1 months, p=0.0018), independent of clinical covariates. Notably, these tumors were enriched for immune cell infiltration—including CD14+ macrophages, CD19+ B-cells, and CD56+ NK cells—based on deconvolution analysis, and exhibited reduced genomic instability as measured by copy number variation. These tumors were enriched for pathways related to cell signaling. We validated the prognostic significance of the Poly-Origin methylation signature in a publicly available independent cohort (n=96), and found that high signature concordance correlated with extended survival. Finally, we demonstrated the feasibility of detecting this signature in plasma-derived cell-free DNA using cfMeDIP-seq, with high classification accuracy (AUC = 0.98). These findings reveal a clinically meaningful subtypes of BM not captured by tissue origin or mutation status alone. Our work suggests that DNA methylation profiling may be a powerful tissue- and liquid biopsy–based tool that holds promise as a non-invasive prognostic biomarker in the management of brain metastasis.

Abstract A major barrier in understanding adult diffuse gliomas lies in the inability to track tumor evolution in real time. To address this, we employed a novel method known as continuous lineage tracing, which integrates CRISPR/Cas9-based expressed DNA barcoding with single-cell RNA sequencing, enabling a phylogenetic approach to studying tumor development. Patient-derived glioma-initiating cell lines were engineered with expressed barcodes targeted by CRISPR/Cas9 and implanted into mice to create intracranial xenografts. Tumors underwent single-cell RNA sequencing; expressed barcodes were used to infer clonal relationships, and transcriptomic profiles enabled cell state classification. Phylogenetic lineage trees were reconstructed using lineage inference algorithms to characterize cell fitness, expansion, and plasticity. Our analysis uncovered extensive intra-clonal cell state heterogeneity, indicating active phenotype switching prior to therapy. We identified consistent transcriptional programs associated with tumor engraftment and in vivo clonal advantage. Lineage tracing revealed gene expression signatures linked to key phenotypes: fitness, enriched for neural-mesenchymal and injury-response pathways; expansion, associated with RNA splicing; and plasticity, correlated with cell cycle and DNA repair programs. Glioma stem cells appeared to span a transcriptional continuum from undifferentiated, high-fitness states to more differentiated, low-fitness states, with high-fitness cells potentially representing transitional phenotypes. We then validated these findings in a cohort of 185 surgically resected gliomas with matched bulk RNA and proteomic data. Phylogenetic gene signatures differed markedly between IDH-mutant and IDH-wildtype tumors. When stratified by tumor type, both fitness and expansion signatures were significantly associated with overall survival in GBM (median 12.6 months, p=0.041) and oligodendrogliomas (median 66 months, p=0.027). This study demonstrates the utility of continuous lineage tracing to reconstruct tumor evolution and identify transcriptional programs linked to tumor growth and prognosis. Our approach provides a powerful framework for dissecting glioma biology and identifying potential therapeutic vulnerabilities.

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 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.