Search

Background: Survival is variable in patients with glioblastoma IDH wild-type (GBM), even after comparable surgical resection of radiographically detectable disease, highlighting the limitations of radiographic assessment of infiltrative tumor anatomy. The majority of postsurgical progressive events are failures within 2 cm of the resection margin, motivating supramaximal resection strategies to improve local control. However, which patients benefit from such radical resections remains unknown., Methods: We developed a predictive model to identify which IDH wild-type GBMs are amenable to radiographic gross-total resection (GTR). We then investigated whether GBM survival heterogeneity following GTR is correlated with microscopic tumor burden by analyzing tumor cell content at the surgical margin with a rapid qPCR-based method for detection of TERT promoter mutation., Results: Our predictive model for achievable GTR, developed on retrospective radiographic and molecular data of GBM patients undergoing resection, had an area under the curve of 0.83, sensitivity of 62%, and specificity of 90%. Prospective analysis of this model in 44 patients found that 89% of patients were correctly predicted to achieve a residual volume (RV) < 4.9cc. Of the 44 prospective patients undergoing rapid qPCR TERT promoter mutation analysis at the surgical margin, 7 had undetectable TERT mutation, of which 5 also had a GTR (RV < 1cc). In these 5 patients at 30 months follow-up, 75% showed no progression, compared to 0% in the group with TERT mutations detected at the surgical margin (P = .02)., Conclusions: These findings identify a subset of patients with GBM that may derive local control benefits from radical resection to undetectable molecular margins., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: We recently conducted a phase 2 trial (NCT028865685) evaluating intracranial efficacy of pembrolizumab for brain metastases (BM) of diverse histologies. Our study met its primary efficacy endpoint and illustrates that pembrolizumab exerts promising activity in a select group of patients with BM. Given the importance of aberrant vasculature in mediating immunosuppression, we explored the relationship between immune checkpoint inhibitor (ICI) efficacy and vascular architecture in the hopes of identifying potential mechanisms of intracranial ICI response or resistance for BM., Methods: Using Vessel Architectural Imaging, a histologically validated quantitative metric for in vivo tumor vascular physiology, we analyzed dual-echo DSC/DCE MRI for 44 patients on trial. Tumor and peri-tumor cerebral blood volume/flow, vessel size, arterial and venous dominance, and vascular permeability were measured before and after treatment with pembrolizumab., Results: BM that progressed on ICI were characterized by a highly aberrant vasculature dominated by large-caliber vessels. In contrast, ICI-responsive BM possessed a more structurally balanced vasculature consisting of both small and large vessels, and there was a trend toward a decrease in under-perfused tissue, suggesting a reversal of the negative effects of hypoxia. In the peri-tumor region, the development of smaller blood vessels, consistent with neo-angiogenesis, was associated with tumor growth before radiographic evidence of contrast enhancement on anatomical MRI., Conclusions: This study, one of the largest functional imaging studies for BM, suggests that vascular architecture is linked with ICI efficacy. Studies identifying modulators of vascular architecture, and effects on immune activity, are warranted and may inform future combination treatments., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Chimeric antigen receptor (CAR) T-cells targeting CD19 have been established as a leading engineered T-cell therapy for B-cell lymphomas; however, data for patients with central nervous system (CNS) involvement are limited., Methods: We retrospectively report on CNS-specific toxicities, management, and CNS response of 45 consecutive CAR T-cell transfusions for patients with active CNS lymphoma at the Massachusetts General Hospital over a 5-year period., Results: Our cohort includes 17 patients with primary CNS lymphoma (PCNSL; 1 patient with 2 CAR T-cell transfusions) and 27 patients with secondary CNS lymphoma (SCNSL). Mild ICANS (grade 1-2) was observed after 19/45 transfusions (42.2%) and severe immune effector cell-associated neurotoxicity syndrome (ICANS) (grade 3-4) after 7/45 transfusions (15.6%). A larger increase in C-reactive protein (CRP) levels and higher rates of ICANS were detected in SCNSL. Early fever and baseline C-reactive protein levels were associated with ICANS occurrence. CNS response was seen in 31 cases (68.9%), including a complete response of CNS disease in 18 cases (40.0%) which lasted for a median of 11.4 ± 4.5 months. Dexamethasone dose at time of lymphodepletion (but not at or after CAR T-cell transfusion) was associated with an increased risk for CNS progression (hazard ratios [HR] per mg/d: 1.16, P = .031). If bridging therapy was warranted, the use of ibrutinib translated into favorable CNS-progression-free survival (5 vs. 1 month, HR 0.28, CI 0.1-0.7; P = .010)., Conclusions: CAR T-cells exhibit promising antitumor effects and a favorable safety profile in CNS lymphoma. Further evaluation of the role of bridging regimens and corticosteroids is warranted., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Activating mutations in the pathway of phosphatidylinositol-3 kinase (PI3K)/Akt/mammalian target of rapamycin (mTOR) occur in 43-70% of breast cancer brain metastasis patients. To date, the treatment of these patients presents an ongoing challenge, mainly because of the lack of targeted agents that are able to sufficiently penetrate the blood-brain barrier. GDC-0068 is a pan-Akt inhibitor that has shown to be effective in various preclinical tumor models as well as in clinical trials. The purpose of this study was to analyze the efficacy of GDC-0068 in a breast cancer brain metastases model., Methods: In in vitro studies, antitumor activity of GDC-0068 was assessed in breast cancer cells of phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA)-mutant and PIK3CA-wildtype breast cancer cell lines using cell viability and apoptosis assays, cell cycle analysis, and western blots. In vivo, the efficacy of GDC-0068 was analyzed in a PIK3CA-mutant breast cancer brain metastasis orthotopic xenograft mouse model and evaluated by repeated bioluminescent imaging and immunohistochemistry., Results: GDC-0068 decreased cell viability, induced apoptosis, and inhibited phosphorylation of proline rich Akt substrate 40 kDa and p70 S6 kinase in a dose-dependent manner in PIK3CA-mutant breast cancer brain metastatic cell lines compared with PIK3CA-wildtype cell lines. In vivo, treatment with GDC-0068 notably inhibited the growth of PIK3CA-mutant tumors and resulted in a significant survival benefit compared with sham, whereas no effect was detected in a PIK3CA-wildtype model., Conclusions: This study suggests that the Akt inhibitor GDC-0068 may be an encouraging targeted treatment strategy for breast cancer brain metastasis patients with activating mutations in the PI3K pathway. These data provide a rationale to further evaluate the efficacy of GDC-0068 in patients with brain metastases., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Cancer therapy-induced adverse effects on the brain are a major challenge in neuro-oncology. Brain tissue necrosis (treatment necrosis [TN]) as a consequence of brain directed cancer therapy remains an insufficiently characterized condition with diagnostic and therapeutic difficulties and is frequently associated with significant patient morbidity. A better understanding of the underlying mechanisms, improvement of diagnostic tools, development of preventive strategies, and implementation of evidence-based therapeutic practices are pivotal to improve patient management. In this comprehensive review, we address existing challenges associated with current TN-related clinical and research practices and highlight unanswered questions and areas in need of further research with the ultimate goal to improve management of patients affected by this important neuro-oncological condition., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Epidermal growth factor receptor variant III (EGFRvIII) is a driver mutation and potential therapeutic target in glioblastoma. Non-invasive in vivo EGFRvIII determination, using clinically acquired multiparametric MRI sequences, could assist in assessing spatial heterogeneity related to EGFRvIII, currently not captured via single-specimen analyses. We hypothesize that integration of subtle, yet distinctive, quantitative imaging/radiomic patterns using machine learning may lead to non-invasively determining molecular characteristics, and particularly the EGFRvIII mutation., Methods: We integrated diverse imaging features, including the tumor's spatial distribution pattern, via support vector machines, to construct an imaging signature of EGFRvIII. This signature was evaluated in independent discovery (n = 75) and replication (n = 54) cohorts of de novo glioblastoma, and compared with the EGFRvIII status obtained through an assay based on next-generation sequencing., Results: The cross-validated accuracy of the EGFRvIII signature in classifying the mutation status in individual patients of the independent discovery and replication cohorts was 85.3% (specificity = 86.3%, sensitivity = 83.3%, area under the curve [AUC] = 0.85) and 87% (specificity = 90%, sensitivity = 78.6%, AUC = 0.86), respectively. The signature was consistent with EGFRvIII+ tumors having increased neovascularization and cell density, as well as a distinctive spatial pattern involving relatively more frontal and parietal regions compared with EGFRvIII- tumors., Conclusions: An imaging signature of EGFRvIII was found, revealing a complex, yet distinct macroscopic glioblastoma phenotype. By non-invasively capturing the tumor in its entirety, the proposed methodology can assist in evaluating the tumor's spatial heterogeneity, hence overcoming common spatial sampling limitations of tissue-based analyses. This signature can preoperatively stratify patients for EGFRvIII-targeted therapies, and potentially monitor dynamic mutational changes during treatment.

Background: MRI characteristics of brain gliomas have been used to predict clinical outcome and molecular tumor characteristics. However, previously reported imaging biomarkers have not been sufficiently accurate or reproducible to enter routine clinical practice and often rely on relatively simple MRI measures. The current study leverages advanced image analysis and machine learning algorithms to identify complex and reproducible imaging patterns predictive of overall survival and molecular subtype in glioblastoma (GB)., Methods: One hundred five patients with GB were first used to extract approximately 60 diverse features from preoperative multiparametric MRIs. These imaging features were used by a machine learning algorithm to derive imaging predictors of patient survival and molecular subtype. Cross-validation ensured generalizability of these predictors to new patients. Subsequently, the predictors were evaluated in a prospective cohort of 29 new patients., Results: Survival curves yielded a hazard ratio of 10.64 for predicted long versus short survivors. The overall, 3-way (long/medium/short survival) accuracy in the prospective cohort approached 80%. Classification of patients into the 4 molecular subtypes of GB achieved 76% accuracy., Conclusions: By employing machine learning techniques, we were able to demonstrate that imaging patterns are highly predictive of patient survival. Additionally, we found that GB subtypes have distinctive imaging phenotypes. These results reveal that when imaging markers related to infiltration, cell density, microvascularity, and blood-brain barrier compromise are integrated via advanced pattern analysis methods, they form very accurate predictive biomarkers. These predictive markers used solely preoperative images, hence they can significantly augment diagnosis and treatment of GB patients., (© The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)