Citations

Abstract Background DNA methylation profiling can be used to robustly predict postsurgical outcomes and response to radiotherapy (RT) for meningioma patients. To allow for seamless integration of these complementary models into clinical practice, a practical framework is needed. Methods We leveraged a cohort of nearly 2000 surgically-treated meningiomas with DNA methylation profiling and clinical outcomes data. Existing methylation-based prediction models were dichotomized to yield four risk groups: low and high recurrent risk, each with RT sensitive and resistant subgroups. Risk groups were correlated with progression-free survival in the context of existing biomarkers including extent of resection and WHO grade. Results We first demonstrated that all risk groups benefit from gross total resection. All “high-risk, RT sensitive” tumors (n = 306, 15.7%) also benefited from adjuvant RT: after GTR, median PFS increased from 4.68 (4.13-9.48) years to not reached (P = .003); after subtotal resection (STR), from 2.12 (1.59-3.02) to 4.09 (3.41-not reached) years (P = .004). “Low-risk, RT sensitive cases” (n = 1207, 61.8%) also benefited from RT after STR (median PFS 7.39 (6.66-12.8) vs. 16.53 (10.35-not reached) years, P = .03), suggesting that RT be considered in these patients. Neither “low-risk RT resistant” (n = 84, 4.3%) nor “high-risk RT resistant” (n = 356, 18.2%) cases benefited from RT, and the latter group was associated with universally poor outcomes. Conclusions We identify methylation-defined risk groups of meningioma for which additional benefit is gained from adjuvant RT, leading to a clinical decision-making framework for straightforward integration of molecular models into clinical practice.

Abstract Recurrent or refractory IDH-mutant (mt) gliomas represent a population with significant unmet clinical need. The Notch ligand delta-like 3 (DLL3) is a tumor-associated antigen that is highly and homogeneously expressed in IDH-mt gliomas but absent in normal brain tissue, making it an attractive therapeutic target. Tarlatamab is a half-life extended bispecific T-cell engager that targets DLL3 and CD3 to redirect cytotoxic T cells toward DLL3-expressing tumor cells. Given the specific expression of DLL3 in IDH-mt gliomas and promising activity of tarlatamab in other DLL3-positive tumors, this study investigates the safety, efficacy, and intracranial activity of tarlatamab in this patient population. TARGID is an open-label, phase II trial enrolling adult patients with histologically confirmed IDH1 or IDH2-mt astrocytoma or oligodendroglioma following up to two prior lines of systemic therapies. The study comprises two cohorts. Cohort 1 (n=10) includes patients with resectable disease who will receive up to three cycles of tarlatamab prior to surgery; postoperative continuation is permitted. The primary endpoint for this cohort is the change in CD8+ T-cell infiltration in tumor tissue and blood. Cohort 2 (n=34) includes patients with unresectable disease and uses a Simon’s optimal two-stage design (H0: P ≤ 0.1 versus H1: P ≥ 0.25). In the 1st stage, 13 patients will be accrued. If 2 or more responses are observed, 21 additional patients will be enrolled. Tarlatamab is administered intravenously at 10 mg every two weeks, following a 1 mg priming dose on Cycle 1 Day 1. The primary endpoint is objective response rate by RANO 2.0. Exploratory endpoints include DLL3 expression, circulating biomarkers, and immune correlatives. The trial is currently in activation, with first patient enrollment anticipated in Q3 2025. The study is supported by the provision of study drug from Amgen as an investigator-initiated trial.

Abstract BACKGROUND While most meningiomas are classified as low-grade tumors with indolent behavior, a subset is characterized by a more aggressive clinical course and patients undergo repeated resections and courses of radiation. Once these therapies fail, prognosis is poor and progression-free survival (PFS) rates at 6 months (PFS-6) are estimated to only be 43.6% for WHO grade 1 and 38% for WHO grade 2/3 meningiomas. Effective medical therapies are limited. Somatostatin receptor 2 (SSTR2) is expressed in a majority of meningiomas and represents a suitable treatment target. [177Lu]Lu-DOTA-TATE (177Lu-Dotatate) is an SSTR2-targeted radioligand therapy, established as standard of care in the treatment of certain neuroendocrine tumors. Two ongoing non-randomized studies of 177Lu-Dotatate in meningiomas have reported encouraging efficacy compared to historical benchmarks. METHODS/STUDY DESIGN The MOMENTUM-1 study (RTOG 3523, NCT#06955169) is a multicenter, randomized, open-label study of 177Lu-Dotatate in adult patients with progressive intracranial WHO grade 1-3 meningiomas. Up to 130 eligible subjects are randomized in a 2:1 fashion to 177Lu-Dotatate at 7.4 GBq (200 mCi) every 4 weeks for 4 (+2 optional) doses or standard of care (SOC) medical therapy as defined by the protocol and deemed appropriate by the local investigator. Treatment responses are assessed by gadolinium-enhanced MRI brain and [68Ga]Ga-DOTA-TATE (68Ga-Dotatate) PET imaging. At time of progression, cross-over from the SOC to the interventional treatment arm is allowed. The primary endpoint is PFS. Secondary endpoints include PFS-6, OS-12, OS, response assessments (ORR, DCR), safety/tolerability, and patient-reported outcomes. RESULTS/CONCLUSION MOMENTUM-1 is a multicenter, randomized, open-label, phase II study of 177Lu-Dotatate in adult patients with progressive WHO grade 1-3 meningioma. This clinical trial is expected to start enrolling patients in the United States in the third quarter of 2025. Details on study design, eligibility criteria for subject enrollment and study site eligibility criteria will be presented.

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 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.

Background: Post-treatment prognosis and monitoring are critical for determining the timing of salvage treatment in glioblastoma patients but has been challenging due to difficulties differentiating progression from treatment effects in conventional images. This exploratory study aimed to establish the correlation of radiomics image features from time series of amino acid tracer 18F-DOPA PET images, with outcomes, using machine learning and dimension reduction analysis. Methods: 18F-DOPA PET images were collected for a patient cohort with wild-type IDH and unmethylated MGMT who underwent dose-escalated radiation therapy. Quantitative features were derived from the high uptake region (T/N > 2.0) in pre- and post-radiation therapy follow-up 18F-DOPA PET images. A customized workflow was utilized for pre-selecting predictive features, followed by manifold learning. Machine learning algorithms were employed to establish associations between imaging features and remaining survival (RS), defined as the time between a follow-up scan and date of death. Results: The ML models exhibited 81–83% ROC_AUC in predicting RS evaluated on an independent test dataset. A RS map is proposed for monitoring tumor alterations through serial 18F-DOPA PET scans, demonstrating superior sensitivity and better correlation with survival compared to the RANO criteria. Conclusions: Our study demonstrates that ML models utilizing FU 18F-DOPA PET images have the potential to effectively predict future survival outcomes in patients with glioblastoma treated with dose-escalated radiation therapy. The capability to assess changes in tumor over time through imaging can potentially assist in patient stratification and the selection of salvage treatments, while also aiding in distinguishing treatment effects from genuine tumor progression.

Background: Spontaneous Coronary Artery Dissection (SCAD) is a cause of acute coronary syndrome, particularly among women, and is associated with psychological distress, including depression, anxiety, and post-traumatic stress (PTSD) symptoms. Fibromuscular dysplasia (FMD), a vascular condition that alters the arteries, is often found in patients with SCAD, adding complexity and uncertainty with a second serious diagnosis. This study examined the differences in mental health symptoms between SCAD survivors with and without FMD to better understand their unique psychosocial needs and inform interventions to enhance patient outcomes. Methods: Patients diagnosed with SCAD within the past 3 years were recruited from 6 Canadian cardiac hospitals. Participants completed questionnaires assessing socio-demographics, comorbidities, and validated psychological measures, including Patient Health Questionnaire-9 (PHQ-9) for depression, Generalized Anxiety Disorder-7 (GAD-7) for generalized anxiety, Cardiac Anxiety Questionnaire (CAQ) for cardiac-related anxiety, and PTSD Checklist (PCL-5) for PTSD symptoms. Differences in mental health outcomes between SCAD survivors with and without FMD were analyzed using t-tests for continuous variables and chi-square tests for categorical variables. Results: The study included 326 participants (92.9% female; mean age 53.3 years), with 67 participants (20.6%) having a confirmed diagnosis of FMD at the time of questionnaire completion (39.3% FMD at study completion). Post-SCAD, patients with FMD reported significantly higher scores than patients without FMD on the GAD-7 (6.4±5.9 vs. 4.7±4.9, p = .028), CAQ (29.9±13.1 vs. 26.0±12.3, p = .023), and PCL-5 (17.2±15.4 vs. 13.3± 14.0, p = .048). The proportion of SCAD survivors with FMD scoring in the clinically diagnostic range was significantly higher than those in patients without FMD: 32.8% vs. 20.8% for depression, 31.3% vs. 17.4% for anxiety, 64.2% vs. 49.2% for cardiac anxiety, and 25.4% vs. 11.6% for PTSD (all p < .05). Conclusions: Our findings suggest that SCAD survivors with comorbid FMD experience significantly higher distress —depression, generalized anxiety, cardiac-related anxiety, and PTSD symptoms—compared to those without FMD. Longitudinal research is needed to explore the role of these mental health concerns in long-term mental health and cardiac outcomes. Enhanced psychological interventions for those with FMD may be required.

Importance The effect of cornea donor diabetes on endothelial cell density (ECD) and morphometry after Descemet membrane endothelial keratoplasty (DMEK) is not known. Objective To determine whether endothelial cell loss (ECL) and morphometric changes 1 year after successful DMEK are related to cornea donor’s diabetes status. Design, Setting, and Participants This was a multicenter, double-masked, randomized clinical trial conducted from February 2022 to July 2024 at 28 US clinical sites (46 surgeons) and 13 eye banks. Included in the trial were the eyes of recipients (some of whom received tissue from donors without diabetes and others who received tissue from donors with diabetes) who underwent successful DMEK, primarily for Fuchs endothelial corneal dystrophy, and had at least 1 analyzable postoperative endothelial image. Intervention DMEK performed with a cornea from a donor without or with diabetes, assigned using a minimization procedure to achieve an approximate 2:1 distribution. Main Outcomes and Measures ECD, ECL, coefficient of variation in cell area (CV), and percentage of hexagonal cells (HEX) at 1 year from eye bank and postoperative specular central endothelial images. Results A total 1274 eyes of 982 recipients (mean [SD] age, 70 [8] years; 569 female [57.9%]; 816 [64.1%] with tissue from donors without diabetes and 458 [35.9%] with tissue from donors with diabetes) were included in the study. Preoperatively, mean (SD) central ECD in tissue from donors without diabetes and with diabetes were 2676 (290) cells/mm 2 and 2671 (286) cells/mm 2 , respectively. At 1 year, mean (SD) ECL was 28.3% (16.1%) and 28.0% (17.0%), in the donor groups without and with diabetes, respectively (adjusted mean difference = −0.4%; 95% CI, −2.3% to 1.4%), resulting in a mean (SD) 1-year ECD of 1927 (498) cells/mm 2 and 1920 (496) cells/mm 2 , respectively (adjusted mean difference = 10 cells/mm 2 ; 95% CI, −36 to 56; P = .95). No difference in ECD at 1 year associated with diabetes severity was noted ( P = .97). Mean (SD) CV did not differ at 1 year between the 2 groups of eyes (31.5% [4.1%] vs 31.4% [4.1%]; adjusted mean difference = −0.4%; 95% CI, −0.9% to 0.1%; P = .51), and mean (SD) HEX did not differ at 1 year between the 2 groups of eyes (57.7% [5.8%] vs 57.2% [5.8%]; adjusted mean difference = 0.1%, 95% CI, −0.8% to 0.9%; P = .33). Conclusions and Relevance This randomized clinical trial found that ECL and morphometry 1 year after DMEK were not affected by cornea donor diabetes status. With comparable 1-year graft success with tissue from donors with and without diabetes demonstrated in this trial, these findings support the use of corneas from donors with diabetes for endothelial keratoplasty procedures. Trial Registration ClinicalTrials.gov Identifier: NCT05134480