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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 PURPOSE Age over 40 years is commonly regarded as a “high risk” feature and potential indication for adjuvant chemoradiotherapy for patients with lower grade glioma in most clinical practice guidelines. However, these recommendations are based on retrospective observations and their subsequent incorporation into legacy clinical trials. It is unclear whether age remains a relevant prognostic factor in the molecular era. METHODS The Prospective Gliomas Research [PROGRES] Database contains individual patient-level data from 11 prospective clinical trials or observational registries, including seven international trials from the RTOG, EORTC, or Alliance cooperative groups. We determined the association of age with progression-free survival (PFS) in low grade or anaplastic glioma, stratified by isocitrate dehydrogenase (IDH1/2) status. We validated our findings in a separate multi-institutional retrospective cohort (Retrospective Glioma Research [REGRES] Database). RESULTS We identified 1,619 and 1,271 eligible patients in the PROGRES and REGRES cohorts, respectively. IDH-wildtype tumors were more common in patients >40 years (38% vs, 5%, odds ratio: 11.3 [95%CI 6.5 – 19.7]). Age was associated with PFS in IDH-wildtype (5-year PFS: 6% vs, 24%, P =.003) but not in IDH-mutant glioma (60% vs, 59%, P = 0.22, P value of interaction <.001). In IDH-wildtype tumors, older age predicted aggressive molecular features, including TERT promoter mutation (65% vs, 28%), EGFR amplification (41% vs, 15%), and chromosome +7/-10 alteration (57% vs, 25%). Age was not predictive of a benefit from chemoradiotherapy versus radiotherapy alone in IDH-mutant glioma. CONCLUSION Older age is a putative surrogate for IDH-wildtype status, but was not an independent predictor of progression in IDH-mutant lower grade glioma and did not predict benefit from chemoradiotherapy. In the absence of additional clinical or molecular risk factors, age alone should not be considered an indication for administration or deferral adjuvant treatment. Practice guidelines should be revised to reflect contemporary prognostic factors in the molecular era.

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 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 Cerebrospinal fluid (CSF) has emerged as a valuable liquid biopsy source for glioma biomarker discovery and validation. CSF produced within the ventricles circulates through the subarachnoid space, where the composition of glioma-derived analytes is influenced by the proximity and anatomical location of sampling relative to tumor, in addition to underlying tumor biology. The substantial gradients observed between lumbar and intracranial CSF compartments for tumor-derived analytes underscore the importance of sampling site selection. Moreover, radiographic features, such as tumor-CSF contact and blood-brain barrier disruption, are critical covariates that may affect biomarker detection and the abundance of plasma-derived analytes in CSF, respectively. Longitudinal intracranial CSF sampling, enabled by access devices like Ommaya reservoirs, may offer a window into treatment response and disease progression, though variability in analyte yield, sample volumes, and the dynamic effects of surgical resection pose challenges. This review critically evaluates the anatomic, radiographic, and longitudinal factors, or “time-space continuum,” that impact glioma CSF biomarker abundance. Practical considerations for longitudinal CSF biobanking, including access device placement and collection, are also reviewed. Key takeaways and recommendations for CSF glioma biomarker discovery and validation are provided as a “hitchhiker’s guide” based on our collective experience, along with resources for investigators aiming to develop CSF biobanking at their institutions.

Abstract Background Subsequent to a diagnosis of a brain tumor, psychological distress has been associated with negative effects on mental health as well as suicidality. The magnitude of such impact has been understudied in the literature. We conducted a systematic review to examine the impact of a brain tumor on suicidality (both ideation and attempts). Methods In accordance with the PRISMA guidelines, we searched for relevant peer-reviewed journal articles on PubMed, Scopus, and Web of Science databases from inception to October 20, 2022. Studies investigating suicide ideation and/or attempt among patients with brain tumors were included. Results Our search yielded 1,998 articles which were screened for eligibility. Seven studies consisting of 204,260 patients were included in the final review. Four studies comprising 203,906 patients (99.8%) reported elevated suicidal ideation and suicide attempt incidence compared with the general population. Prevalence of ideation and attempts ranged from 6.0% to 21.5% and 0.03% to 3.33%, respectively. Anxiety, depression, pain severity, physical impairment, glioblastoma diagnosis, male sex, and older age emerged as the primary risk factors associated with increased risk of suicidal ideation and attempts. Conclusion Suicidal ideation and attempts are increased in patients and survivors of brain tumors compared to the general population. Early identification of patients exhibiting these behaviors is crucial for providing timely psychiatric support in neuro-oncological settings to mitigate potential harm. Future research is required to understand pharmacological, neurobiological, and psychiatric mechanisms that predispose brain tumor patients to suicidality.

BACKGROUND: Management of sporadic schwannomas is often dictated by a patient's clinical presentation and the tumor's behavior. For patients who are managed nonsurgically, there are little data available about the expected natural history. OBJECTIVE: To evaluate the natural history and growth patterns of extracranial schwannomas including tumors of the distal peripheral nerves, spine, and brachial plexus. METHODS: A retrospective review was performed to identify patients with nonsyndromic extracranial schwannomas at a single tertiary care institution diagnosed between 2002 and 2019. Patient data and tumor characteristics including volume were recorded. RESULTS: Two hundred twenty-seven patients were identified (mean age 51 years, 42% male, average of 27.8-month follow-up). Tumor location was distal peripheral nerve in 82, brachial plexus in 36, and paraspinal in 109. At the time of diagnosis, peripheral lesions were significantly larger than spinal (59 m 3 vs 13 cm 3 ) and brachial plexus lesions (15 cm 3 ). Distinct growth patterns were seen with both distal peripheral nerve and spinal lesions; 34/82 peripheral nerve lesions had fast growth (β = 0.176%/day), and 48 had slow growth (β = 0.021%/day; P < .01). Spinal schwannomas similarly had 30 fast-growing (β = 0.229%/day), 16 moderate-growing (β = 0.071%/day), and 63 slow-growing (β = 0.022%/day; P = .03) subtypes. The brachial plexus had relatively homogeneous growth patterns (β = 0.065%/day). Females had 2.9 times greater odds of having the fast-growing subtype. CONCLUSION: Distinct growth patterns were seen in extracranial sporadic schwannomas based on tumor location and patient demographics. Fast (>80% volume change per year) vs slow (5%-10% per year) tumor growth can often be ascertained within 2 follow-up images. Awareness of these patterns might have implications for patient counseling and therapeutic decision-making.

Abstract Immunotherapy has revolutionized treatment for many hard-to-treat cancers but has yet to produce significant improvement in outcomes for patients with glioblastoma. This reflects the multiple and unique mechanisms of immune evasion and escape in this highly heterogeneous tumor. Glioblastoma engenders profound local and systemic immunosuppression and is remarkably effective at inducing T-cell dysfunction, posing a challenge to any immunotherapy-based approach. To overcome these mechanisms, multiple disparate modes of immune-oriented therapy will be required. However, designing trials that can evaluate these combinatorial approaches requires careful consideration. In this review, we explore the immunotherapy resistance mechanisms that have been encountered to date and how combinatorial approaches may address these. We also describe the unique aspects of trial design in both preclinical and clinical settings and consider endpoints and markers of response best suited for an intervention involving multiple agents.

Abstract Objectives Two pilot studies of AR‐42, a pan‐histone deacetylase inhibitor, in human neurofibromatosis type 2 (NF2), vestibular schwannomas (VS), and meningiomas are presented. Primary endpoints included safety, and intra‐tumoral pharmacokinetics (PK) and pharmacodynamics (PD). Methods Pilot 1 is a subset analysis of a phase 1 study of AR‐42 in solid tumors, which included NF2 or sporadic meningiomas. Tumor volumes and treatment‐related adverse events (TRAEs) are reported (NCT01129193). Pilot 2 is a phase 0 surgical study of AR‐42 assessing intra‐tumoral PK and PD. AR‐42 was administered for 3 weeks pre‐operatively. Plasma and tumor drug concentrations and p‐AKT expression were measured (NCT02282917). Results Pilot 1: Five patients with NF2 and two with sporadic meningiomas experienced a similar incidence of TRAEs to the overall phase I trial. The six evaluable patients had 15 tumors (8 VS, 7 meningiomas). On AR‐42, tumor volume increased in six, remained stable in eight, and decreased in one tumor. The annual percent growth rate decreased in eight, remained stable in three, and increased in four tumors. Pilot 2: Four patients with sporadic VS and one patient with meningioma experienced no grade 3/4 toxicities. Expression of p‐AKT decreased in three of four VS. All tumors had higher AR‐42 concentrations than plasma. Conclusions AR‐42 is safe. Tumor volumes showed a mixed response, but most slowed growth. On a 40‐mg regimen, drug concentrated in tumors and growth pathways were suppressed in most tumors, suggesting this may be a well‐tolerated and effective dose. A phase 2 study of AR‐42 for NF2‐associated tumors appears warranted. Level of Evidence 1b, 4.

With rapid advances in our understanding of cancer, there is an expanding number of potential novel combination therapies, including novel–novel combinations. Identifying which combinations are appropriate and in which subpopulations are among the most difficult questions in medical research. We conducted a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic review of trials of novel–novel combination therapies involving immunotherapies or molecular targeted therapies in advanced solid tumors. A MEDLINE search was conducted using a modified Cochrane Highly Sensitive Search Strategy for published clinical trials between July 1, 2017, and June 30, 2020, in the top-ranked medical and oncology journals. Trials were evaluated according to a criterion adapted from previously published Food and Drug Administration guidance and other key considerations in designing trials of combinations. This included the presence of a strong biological rationale, the use of a new established or emerging predictive biomarker prospectively incorporated into the clinical trial design, appropriate comparator arms of monotherapy or supportive external data sources and a primary endpoint demonstrating a clinically meaningful benefit. Of 32 identified trials, there were 11 (34%) trials of the novel–novel combination of anti-programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) and anti-cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) therapy, and 10 (31%) trials of anti-PD-1/PD-L1 and anti-vascular endothelial growth factor (VEGF) combination therapy. 20 (62.5%) trials were phase II trials, while 12 (37.5%) were phase III trials. Most (72%) trials lacked significant preclinical evidence supporting the development of the combination in the given indication. A majority of trials (69%) were conducted in biomarker unselected populations or used pre-existing biomarkers within the given indication for patient selection. Most studies (66%) were considered to have appropriate comparator arms or had supportive external data sources such as prior studies of monotherapy. All studies were evaluated as selecting a clinically meaningful primary endpoint. In conclusion, designing trials to evaluate novel–novel combination therapies presents numerous challenges to demonstrate efficacy in a comprehensive manner. A greater understanding of biological rationale for combinations and incorporating predictive biomarkers may improve effective evaluation of combination therapies. Innovative statistical methods and increasing use of external data to support combination approaches are potential strategies that may improve the efficiency of trial design. Designing trials to evaluate novel–novel combination therapies presents numerous challenges to demonstrate efficacy in a comprehensive manner. A greater understanding of biological rationale for combinations and incorporating predictive biomarkers may improve effective evaluation of combination therapies. Innovative statistical methods and increasing use of external data to support combination approaches are potential strategies that may improve the efficiency of trial design.