Neuro-oncology advances最新文献

Background: To enhance the quality of care available for children with central nervous system (CNS) tumors across the world, a systematic evaluation of capacity is needed to identify gaps and prioritize interventions. To that end, we created the pediatric neuro-oncology (PNO) resource assessment aid (PANORAMA) tool.

Methods: The development of PANORAMA encompassed 3 phases: operationalization, consensus building, and piloting. PANORAMA aimed to capture the elements of the PNO care continuum through domains with weighted assessments reflecting their importance. Responses were ordinally scored to reflect the level of satisfaction. PANORAMA was revised based on feedback at various phases to improve its relevance, usability, and clarity.

Results: The operationalization phase identified 14 domains by using 252 questions. The consensus phase involved 15 experts (6 pediatric oncologists, 3 radiation oncologists, 2 neurosurgeons, 2 radiologists, and 2 pathologists). The consensus phase validated the identified domains, questions, and scoring methodology. The PANORAMA domains included national context, hospital infrastructure, organization and service integration, human resources, financing, laboratory, neurosurgery, diagnostic imaging, pathology, chemotherapy, radiotherapy, supportive care, and patient outcomes. PANORAMA was piloted at 13 institutions in 12 countries, representing diverse patient care contexts. Face validity was assessed by examining the correlation between the estimated score by respondents and calculated PANORAMA scores for each domain (r = 0.67, P < .0001).

Conclusions: PANORAMA was developed through a systematic, collaborative approach, ensuring its relevance to evaluate core elements of PNO service capacity. Distribution of PANORAMA will enable quantitative service evaluations across institutions, facilitating benchmarking and the prioritization of interventions.

Background: Glioblastoma (GB) is the most frequent malignant brain tumor and has a dismal prognosis. In other cancers, antibiotic use has been associated with severity of chemotherapy-induced toxicity and outcome. We investigated if these mechanisms are also involved in GB.

Methods: We selected a cohort of 78 GB patients who received combined radiochemotherapy. We investigated if exposure to prediagnostic antibiotic use is associated with clinical side effects and laboratory changes during adjuvant therapy as well as overall survival (OS) and progression-free survival (PFS) using chi-square test, binary logistic regression, Kaplan-Meyer analysis, and multivariable Cox regression.

Results: Seventeen patients (21.8%) received at least one course of prediagnostic antibiotics and 61 (78.2%) received no antibiotics. We found a higher incidence of loss of appetite (23.5% vs. 4.9%; P = .018) and myelosuppression (41.2% vs. 18.0%; P = .045) in the antibiotic group. Multivariable logistic regression analysis revealed antibiotics to be a predictor for nausea (OR = 6.94, 95% CI: 1.09-44.30; P = .041) and myelosuppression (OR = 9.75, 95% CI: 1.55-61.18; P = .015). Furthermore, lymphocytopenia was more frequent in the antibiotic group (90.0% vs. 56.1%, P = .033). There were no significant differences in OS (P = .404) and PFS (P = .844). Multivariable Cox regression showed a trend toward shorter survival time (P = .089) in the antibiotic group.

Conclusions: Our study suggests that antibiotic use affects symptoms and lab values in GB patients. Larger prospective studies are required to investigate if prediagnostic antibiotic use could be a prognostic factor in GB patients.

[This corrects the article DOI: 10.1093/noajnl/vdae128.].

Background: Glioma, the most prevalent primary brain tumor, poses challenges in prognosis, particularly in the high-grade subclass, despite advanced treatments. The recent shift in tumor classification underscores the crucial role of isocitrate dehydrogenase (IDH) mutation status in the clinical care of glioma patients. However, conventional methods for determining IDH status, including biopsy, have limitations. Exploring the use of machine learning (ML) on magnetic resonance imaging to predict IDH mutation status shows promise but encounters challenges in generalizability and translation into clinical practice because most studies either use single institution or homogeneous datasets for model training and validation. Our study aims to bridge this gap by using multi-institution data for model validation.

Methods: This retrospective study utilizes data from large, annotated datasets for internal (377 cases from Yale New Haven Hospitals) and external validation (207 cases from facilities outside Yale New Haven Health). The 6-step research process includes image acquisition, semi-automated tumor segmentation, feature extraction, model building with feature selection, internal validation, and external validation. An extreme gradient boosting ML model predicted the IDH mutation status, confirmed by immunohistochemistry.

Results: The ML model demonstrated high performance, with an Area under the Curve (AUC), Accuracy, Sensitivity, and Specificity in internal validation of 0.862, 0.865, 0.885, and 0.713, and external validation of 0.835, 0.851, 0.850, and 0.847.

Conclusions: The ML model, built on a heterogeneous dataset, provided robust results in external validation for the prediction task, emphasizing its potential clinical utility. Future research should explore expanding its applicability and validation in diverse global healthcare settings.

Background: It is imperative to differentiate true progression (TP) from pseudoprogression (PsP) in glioblastomas (GBMs). We sought to investigate the potential of physiologically sensitive quantitative parameters derived from diffusion and perfusion magnetic resonance imaging (MRI), and molecular signature combined with machine learning in distinguishing TP from PsP in GBMs in the present study.

Methods: GBM patients (n = 93) exhibiting contrast-enhancing lesions within 6 months after completion of standard treatment underwent 3T MRI. Final data analyses were performed on 75 patients as O⁶-methylguanine-DNA-methyltransferase (MGMT) status was available only from these patients. Subsequently, patients were classified as TP (n = 55) or PsP (n = 20) based on histological features or mRANO criteria. Quantitative parameters were computed from contrast-enhancing regions of neoplasms. PsP datasets were artificially augmented to achieve balanced class distribution in 2 groups (TP and PsP). A random forest algorithm was applied to select the optimized features. The data were randomly split into training and testing subsets in an 8:2 ratio. To develop a robust prediction model in distinguishing TP from PsP, several machine-learning classifiers were employed. The cross-validation and receiver operating characteristic (ROC) curve analyses were performed to determine the diagnostic performance.

Results: The quadratic support vector machine was found to be the best classifier in distinguishing TP from PsP with a training accuracy of 91%, cross-validation accuracy of 86%, and testing accuracy of 85%. Additionally, ROC analysis revealed an accuracy of 85%, sensitivity of 70%, and specificity of 100%.

Conclusions: Machine learning using quantitative multiparametric MRI may be a promising approach to distinguishing TP from PsP in GBMs.

Background: This phase 1 (NCT04396717) open-label, multicenter study, evaluated Pritumumab, a IgG1 monoclonal antibody, in patients with gliomas and brain metastases. The primary objective was to evaluate the safety and/or tolerability and to identify a recommended phase 2 dose (RP2D) of Pritumumab.

Methods: Adult patients with recurrent gliomas or brain metastases were enrolled in the dose cohort that was open at the time of their consent. Study treatment consisted of pritumumab administered intravenously weekly on days 1, 8, 15, and 22 in 28-day cycles. Safety, pharmacokinetics (PK), pharmacodynamics (PD), and clinical activity were evaluated.

Results: Fifteen patients received Pritumumab in the recurrent setting. Pritumumab was well tolerated, with no serious adverse events related to Pritumumab reported. The most common drug-related toxicities were constipation and fatigue. There were no dose-limiting toxicities observed, and a maximum tolerable dose was not reached. Thus, the maximum feasible dose and recommended phase 2 dose of Pritumumab was established at 16.2 mg/kg weekly. Out of eleven patients evaluated for efficacy, one patient (9.1%) demonstrated partial response based on response assessment in neuro-oncology criteria, and disease stabilization was seen in 3 patients (27.3%).

Conclusions: Pritumumab was well tolerated with no DLTs observed up to 16.2 mg/kg weekly. Further studies are warranted to determine clinical benefit in patients.

Background: Glioblastoma (GBM) is a dreadful brain tumor, with a particular relationship to the adult subventricular zone (SVZ) that has been described as relevant to disease initiation, progression, and recurrence.

Methods: We propose a novel strategy for the detection and tracking of xenografted GBM cells that are located in the SVZ, based on an intracerebroventricular (icv) recombinant adeno-associated virus (AAV)-mediated color conversion method. We used different patient-derived GBM stem-like cells (GSCs), which we transduced first with a retroviral vector (LRLG) that included a lox-dsRed-STOP-lox cassette, upstream of the eGFP gene, then with rAAVs expressing the Cre-recombinase. Red and green fluorescence is analyzed in vitro and in vivo using flow cytometry and fluorescence microscopy.

Results: After comparing the efficiency of diverse rAAV serotypes, we confirmed that the in vitro transduction of GSC-LRLG with rAAV-Cre induced a switch from red to green fluorescence. In parallel, we verified that rAAV transduction was confined to the walls of the lateral ventricles. We, therefore, applied this conversion approach in 2 patient-derived orthotopic GSC xenograft models and showed that the icv injection of an rAAV-DJ-Cre after GSC-LRLG tumor implantation triggered the conversion of red GSCs to green, in the periventricular region. Green GSCs were also found at distant places, including the migratory tract and the tumor core.

Conclusions: This study not only sheds light on the putative outcome of SVZ-nested GBM cells but also shows that icv injection of rAAV vectors allows to transduce and potentially modulate gene expression in hard-to-reach GBM cells of the periventricular area.

Atypical teratoid/rhabdoid tumors (AT/RTs) are aggressive brain tumors primarily observed in infants. The only characteristic, recurrent genetic aberration of AT/RTs is biallelic inactivation of SMARCB1 (or SMARCA4). These genes are members of the mSWI/SNF chromatin-remodeling complex, which regulates various developmental processes, including neural differentiation. This review explores AT/RT subgroups regarding their distinct SMARCB1 loss-of-function mechanisms, molecular features, and patient characteristics. Additionally, it addresses the ongoing debate about the oncogenic relevance of cell-of-origin, examining the influence of developmental stage and lineage commitment of the seeding cell on tumor malignancy and other characteristics. Epigenetic dysregulation, particularly through the regulation of histone modifications and DNA hypermethylation, has been shown to play an integral role in AT/RTs' malignancy and differentiation blockage, maintaining cells in a poorly differentiated state via the insufficient activation of differentiation-related genes. Here, the differentiation blockage and its contribution to malignancy are also explored in a cellular context. Understanding these mechanisms and AT/RT heterogeneity is crucial for therapeutic improvements against AT/RTs.