Citations

Abstract Glioblastoma (GBM) progression and recurrence are often driven by therapy-resistant cellular states emerging within hypoxic tumor niches. While hypoxia is a hallmark of GBM, the spatial transcriptomic heterogeneity that underpins resistance in these regions remains poorly characterized. Here, we employ pimonidazole labeling in conjunction with high-resolution spatial and single cell transcriptomics across 29 primary, treatment-naive human GBM samples, syngeneic mouse models, and patient-derived glioma-initiating cells exposed to hypoxia to define the hypoxia-specific tumor microenvironment and its associated cellular programs. We reveal spatially organized sub-niches within hypoxic regions, each supporting diverse cellular programs yet coexisting tumor lineage states with minimal transitions between cell states. Phylogenetic reconstruction based on inferred copy number variations demonstrates consistent evolutionary trajectories from non-hypoxic to hypoxic tumor loci. Specific copy number alterations were enriched in hypoxic regions and associated with immune infiltration and poor prognosis in MGMT-methylated tumors. Cross-species analyses confirmed the conservation of hypoxia-associated gene programs and spatial architectures. Together, our study delineates the cellular, evolutionary, and spatial complexity of hypoxia in GBM, identifies hypoxia-induced genomic alterations that may promote tumor fitness, and offers insights into new therapeutic vulnerabilities within hypoxic niches.

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 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 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.

Background and aims To validate the applicability of the new histological criteria for autoimmune hepatitis (AIH) proposed by the International AIH Pathology Group (IAIH‐PG) among Chinese patients with AIH and drug‐induced liver injury (DILI). Methods The gold standard for diagnosis relied on clinical response: discontinuing treatment without relapse supported DILI, while relapse or ongoing immunosuppressive treatment confirmed AIH. This two‐centre retrospective cohort study included inpatients with DILI or AIH from January 2002 to March 2023. Cases that underwent liver biopsy were selected according to inclusion and exclusion criteria. The diagnostic performance of the criteria was assessed by an area under the receiver operating characteristic curve (AUROC). Results Out of 69 patients: AIH (41, 59%) and DILI (28, 41%). The accuracy, sensitivity and specificity of the new histological criteria for likely and possible AIH were 70%, 98% and 29%, respectively, with an AUROC of 0.8236 [95% confidence interval (CI): 0.7533–0.8938]. For likely AIH, the accuracy, sensitivity and specificity were 73%, 61% and 89%, respectively, with an AUROC of 0.9177 [95% CI: 0.8757–0.9596]. Moreover, for possible AIH, significant differences were found in serum alanine aminotransferase levels [178.4 (87.0, 435.0) versus 536.5 (206.9, 930.4) U/L] and antinuclear antibody (ANA) ≥1:160 [10 (67%) versus 1 (6%)], as well as in lobular lymphoplasmacytic infiltrate [15 (100%) versus 12 (71%)] and more than mild inflammation [13 (87%)versus 6 (35%)] between AIH and DILI (all P values were <0.05). Conclusion The new histological criteria exhibit good diagnostic efficacy in distinguishing AIH from DILI in China, with high AUROC. Key discriminators include low aminotransferase, ANA ≥1:160, lobular lymphoplasmacytic infiltrate and more than mild inflammation, which may further improve diagnostic accuracy for AIH.

Abstract Background The associations between different types of diabetes, characterized by distinct pathophysiology and genetic architecture, and pancreatic ductal adenocarcinoma (PDAC) risk are not understood. Methods We investigated associations of genetic susceptibility to type 2 diabetes (T2D), 8 T2D mechanistic clusters, type 1 diabetes (T1D), and maturity-onset diabetes of the young (MODY) with PDAC risk. We used genome-wide association study (GWAS) summary-level statistics for T2D (242 283 cases, 1 569 734 controls), T1D (18 942 cases, 501 638 controls), and PDAC (10 244 cases and 360 535 controls) in individuals of European ancestry. Results Two-sample Mendelian randomization (MR) using the Robust Adjusted Profile Score (MR-RAPS) method indicated that genetically predicted T2D was associated with PDAC risk (OR = 1.10; 95% CI = 1.05 to 1.15), particularly the T2D obesity (OR = 1.28; 95% CI = 1.15 to 1.42) and lipodystrophy (OR = 1.25; 95% CI = 1.03 to 1.51) clusters. No association was observed for T1D with PDAC risk (OR = 1.01; 95% CI = 0.99 to 1.02). Pathway/gene-set analysis using the summary-based Adaptive Rank Truncated Product (sARTP) method revealed a significant association between the MODY gene-sets and PDAC risk (P = 1.5 × 10−8), which remained after excluding 20 known PDAC GWAS loci (P = 7.6 × 10−4). HNF1A, FOXA3, and HNF4A were the top contributing genes after excluding the previously identified GWAS loci regions. Conclusions Our results from this genetic association study support that T2D, particularly the obesity and lipodystrophy mechanistic clusters, and MODY genomic susceptibility regions play a role in the etiology of PDAC.