Citations

Abstract Background Biological sex and endocrine signaling influence cancer biology, immune response, and therapeutic outcomes. Recent evidence suggests that testosterone signaling may exert brain-context-dependent protective effects in glioblastoma through the hypothalamic-pituitary-adrenal axis, reduced glucocorticoid-mediated immune suppression, and altered tumor-immune interactions. We assessed whether testosterone replacement therapy (TRT) exposure was associated with survival in solid tumor central nervous system (CNS) metastases and glioblastoma (GBM, IDH-wildtype, WHO grade 4), settings in which post-diagnosis survival and TRT timing can be clinically defined. Methods We performed a retrospective Mayo Clinic cohort study of adult patients with molecularly confirmed glioblastoma and solid tumor CNS metastases confirmed from neuroimaging reports using large language model-assisted adjudication. TRT exposure was defined by testosterone-specific prescription evidence within prespecified peri-diagnostic windows. Overall survival was evaluated using propensity score-matched Cox models, 24-month administratively censored Cox models, time-dependent Cox sensitivity analyses, and 24-month restricted mean survival time. Results In the pooled solid tumor CNS metastasis cohort, TRT exposure was associated with improved overall survival after propensity score matching (HR 0.80, 95% CI 0.65–0.98, p = 0.029) and a 3.22-month improvement in 24-month restricted mean survival time. In glioblastoma, TRT exposure was similarly associated with improved overall survival after propensity score matching (HR 0.38, 95% CI 0.14-0.98, p = 0.046) and a 5.81-month improvement in 24-month restricted mean survival time. Conclusions TRT exposure was associated with improved survival in CNS metastases and glioblastoma. These hypothesis-generating findings support prospective studies incorporating TRT timing, hormone levels, corticosteroid exposure, immune correlates, and tumor-specific stratification.

Abstract Background Biological sex and endocrine signaling influence cancer biology, immune response, and therapeutic outcomes. Recent evidence suggests that testosterone signaling may exert brain-context-dependent protective effects in glioblastoma through the hypothalamic-pituitary-adrenal axis, reduced glucocorticoid-mediated immune suppression, and altered tumor-immune interactions. We assessed whether testosterone replacement therapy (TRT) exposure was associated with survival in solid tumor central nervous system (CNS) metastases and glioblastoma (GBM, IDH-wildtype, WHO grade 4), settings in which post-diagnosis survival and TRT timing can be clinically defined. Methods We performed a retrospective Mayo Clinic cohort study of adult patients with molecularly confirmed glioblastoma and solid tumor CNS metastases confirmed from neuroimaging reports using large language model-assisted adjudication. TRT exposure was defined by testosterone-specific prescription evidence within prespecified peri-diagnostic windows. Overall survival was evaluated using propensity score-matched Cox models, 24-month administratively censored Cox models, time-dependent Cox sensitivity analyses, and 24-month restricted mean survival time. Results In the pooled solid tumor CNS metastasis cohort, TRT exposure was associated with improved overall survival after propensity score matching (HR 0.80, 95% CI 0.65–0.98, p=0.029) and a 3.22-month improvement in 24-month restricted mean survival time. In glioblastoma, TRT exposure was similarly associated with improved overall survival after propensity score matching (HR 0.56, 95% CI 0.38–0.82, p=0.003) and a 5.81-month improvement in 24-month restricted mean survival time. Conclusions TRT exposure was associated with improved survival in CNS metastases and glioblastoma. These hypothesis-generating findings support prospective studies incorporating TRT timing, hormone levels, corticosteroid exposure, immune correlates, and tumor-specific stratification. Key points TRT exposure is associated with improved survival in aggressive brain tumors. Survival associations persist after propensity score matching. Prospective studies should define TRT timing, hormone levels, and immune effects. Importance of Study This study evaluates testosterone replacement therapy (TRT) in aggressive intracranial tumors with clinically interpretable survival timelines. Building on recent evidence that androgen signaling may have protective brain-specific effects in glioblastoma, we extend this question to CNS metastases. In a multi-site Mayo Clinic cohort, TRT exposure was associated with improved survival in CNS metastases and glioblastoma, supporting androgen biology as an underrecognized factor in neuro-oncology outcomes. These findings provide rationale for prospective clinical trials incorporating TRT timing, hormone levels, corticosteroid exposure, immune profiling, and tumor-specific stratification.

Abstract Background Glioblastoma (GB) exhibits marked tumor microenvironmental heterogeneity, contributing to both therapy resistance and poor survival outcomes. Intercellular adhesion molecule 1 (ICAM1) is an inflammation-associated adhesion molecule implicated in immune-stromal interactions, but its clinical and biological significance in GB remains incompletely defined. Methods We performed ICAM1 immunohistochemistry, bulk RNA sequencing, DNA methylation analysis, single-nucleus RNA sequencing, and preoperative MRI-derived morphologic feature analysis across multi-institutional cohorts of treatment-naïve IDH-wildtype GB. Results High ICAM1 expression at both transcript and protein levels is associated with shorter overall survival, particularly within the mesenchymal transcriptional subtype. ICAM1 expression is inversely associated with promoter methylation. Single-nucleus RNA-sequencing analysis shows that ICAM1-high tumors are enriched for mesenchymal, hypoxia, stress-associated, and immunosuppressive myeloid programs, suggesting that ICAM1 reflects a multicompartment inflammatory tumor ecosystem. ICAM1 expression, tumor surface irregularity, and patient age are independently prognostic and minimally correlated, capturing complementary clinical, imaging, and molecular features of GB biology. When combined, our Clinical-Imaging-Molecular (CIM) framework refines outcome prediction across independent GB cohorts using routinely available clinical data, conventional imaging, and standard immunohistochemistry. Conclusions ICAM1 characterizes an immune-remodeled, stress-adapted GB ecosystem associated with poor clinical outcomes. Integrating patient age, surface irregularity, and ICAM1 expression yields a scalable and clinically accessible prognostic framework for GB, supporting tailored outcome prediction, particularly in low- and middle-resource settings without routine access to advanced molecular profiling.

Abstract Background Meningiomas exhibit clinical heterogeneity. Radiotherapy (RT) remains the only adjuvant therapy, but tumor-control is variable, and biomarkers are limited. NRG/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 identify correlates of RT response. Methods 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; non-response was defined as progression or death within 3 years. Results 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%. Non-responders to RT exhibited more frequent 1p and 14q loss, and more copy number alterations. Transcriptomic and epigenetic profiling revealed immune-related signatures in responders and cell cycle–related pathways in non-responders, several of which overlapped with targets of vorinostat, a histone deacetylase inhibitor previously validated in aggressive meningioma models. The Proliferative Molecular Group was an independent predictor of post-RT recurrence in multivariable analysis, outperforming WHO grade. Conclusion Multi-omic analysis of the NRG/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. Clinical Trial Information NCT00895622.

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 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 Glioblastoma (GBM) is a highly aggressive brain tumor with poor survival outcomes and limited tools for accurate prediction of individual patient prognosis. While many prognostic models rely on advanced molecular testing, such methods are not routinely accessible across most clinical settings. In this study, we identify a practical and clinically applicable approach to estimate prognosis for GBM patients using three objective and readily accessible features: the patient’s age, tumor surface irregularity determined from MRI scans, and the expression of ICAM1 measured by standard immunohistochemistry. We show that higher ICAM1 levels are linked to worse survival and a more inflamed but immunosuppressive tumor microenvironment. These findings are functionally validated in ICAM1-wildtype and knockout mouse models using single-cell proteomics via cytometry by time-of-flight, and an immune-comprehensive panel of antibodies. By combining age, ICAM1, and MRI-derived shape features into a single Clinical-Imaging-Molecular (CIM) score, we developed a model that can reliably separate patients with a GBM into higher- and lower-risk groups. This score was validated across multiple international institutions and patient populations and does not require genomic sequencing or specialized technologies. The CIM model could serve as a supplement for care providers to better tailor treatment plans and follow-up strategies for individual patients, making personalized care more achievable even in resource-limited settings.

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.