Neuro-oncology advances最新文献

Background: The pursuit of automated methods to assess the extent of resection (EOR) in glioblastomas is challenging, requiring precise measurement of residual tumor volume. Many algorithms focus on preoperative scans, making them unsuitable for postoperative studies. Our objective was to develop a deep learning-based model for postoperative segmentation using magnetic resonance imaging (MRI). We also compared our model's performance with other available algorithms.

Methods: To develop the segmentation model, a training cohort from 3 research institutions and 3 public databases was used. Multiparametric MRI scans with ground truth labels for contrast-enhancing tumor (ET), edema, and surgical cavity, served as training data. The models were trained using MONAI and nnU-Net frameworks. Comparisons were made with currently available segmentation models using an external cohort from a research institution and a public database. Additionally, the model's ability to classify EOR was evaluated using the RANO-Resect classification system. To further validate our best-trained model, an additional independent cohort was used.

Results: The study included 586 scans: 395 for model training, 52 for model comparison, and 139 scans for independent validation. The nnU-Net framework produced the best model with median Dice scores of 0.81 for contrast ET, 0.77 for edema, and 0.81 for surgical cavities. Our best-trained model classified patients into maximal and submaximal resection categories with 96% accuracy in the model comparison dataset and 84% in the independent validation cohort.

Conclusions: Our nnU-Net-based model outperformed other algorithms in both segmentation and EOR classification tasks, providing a freely accessible tool with promising clinical applicability.

Background: Brain tumors represent a significant global health challenge, with rising incidence and mortality impacting individuals worldwide and contributing to cancer-related morbidity and mortality. In Africa, this burden is exacerbated by limited access to advanced diagnostics, treatment options, and multidisciplinary care, compounded by the absence of standardized cancer registration and tumor biobanking. The introduction of molecular diagnostics, as outlined in the 2021 World Health Organization central nervous system (CNS) tumor classification, adds complexity to brain tumor management, particularly in regions with scarce resources.

Methods: To address these issues, the Brain Tumor Consortium for Africa (BTCA) was established in 2023, bringing together experts to improve CNS tumor diagnosis, patient care, and research. The initial project, conducted via an electronic questionnaire, aimed to assess neuro-oncology capacity across Sub-Saharan Africa.

Results: The study revealed significant gaps, with a limited number of institutions incorporating molecular subtyping into their diagnostic algorithms. The consortium's efforts focus on enhancing local data use, informing public policy, and promoting collaboration to advance neuro-oncology practices in Africa. By fostering a network enlisting the expertise of collaborators in the fields of neurosurgery, neurology, neuropathology, anatomic pathology, and medical and radiation oncology, the BTCA seeks to improve brain tumor management through better diagnostics, infrastructure, and policy advocacy. Future directions include expanding molecular diagnostic capabilities, standardizing brain tumor biobanking, enhancing data collection, and advocating for improved brain tumor care in national health agendas.

Conclusions: The BTCA represents a pioneering model of collaboration and innovation in addressing the unique challenges of brain tumor care in Africa.

Background: Glioblastoma multiforme (GBM) is an aggressive brain tumor with poor survival rates despite current treatments. The standard of care (SOC) includes surgery, followed by radiotherapy plus concurrent and adjuvant chemotherapy with temozolomide (TMZ). This phase II trial assessed the safety and efficacy of neoadjuvant TMZ (nTMZ) before and during chemoradiotherapy in newly diagnosed GBM patients.

Methods: Newly diagnosed GBM patients who underwent maximal safe resection were randomized into 2 groups. One received nTMZ before standard chemoradiotherapy and adjuvant TMZ (intervention). The other received standard chemoradiotherapy followed by adjuvant TMZ (control). Primary endpoints were progression-free survival (PFS) at 6 and 12 months. Secondary endpoints included overall survival, radiological and clinical responses, and adverse events.

Results: Of 35 patients, 16 were in the intervention group and 19 in the control group. Median PFS was 9 months (95% CI: 3.93-14.06) versus 3 months (95% confidence interval [CI]: 1.98-4.01) in the control and intervention groups (P = .737), with a high progression rate (73.4%) during nTMZ treatment. The 6-month PFS rates were 58% versus 25% (P = .042), and 12-month PFS rates were 26% versus 25% (P = .390) in the control and intervention groups, respectively. Patients with unmethylated O⁶-methylguanine-DNA methyltransferase (MGMT) and those with good performance status (PS) had significantly worse PFS with nTMZ.  However, those who underwent larger extent of resection exhibited significantly better PFS  with nTMZ. Adverse events were similar between groups.

Conclusions: Neoadjuvant TMZ before SOC chemoradiotherapy did not improve outcomes for newly diagnosed GBM patients and is unsuitable for those with unmethylated MGMT and good PS. However, It may benefit patients with near or gross total resection. Further research is needed to refine GBM treatment strategies.

Background: The optimal timing of initiating adjuvant temozolomide (TMZ) chemoradiotherapy after surgery in patients with glioblastoma is contentious. This study aimed to determine whether the timing of adjuvant treatment affects their overall survival (OS).

Methods: Consecutive adult patients with histologically-confirmed newly diagnosed glioblastoma treated with adjuvant TMZ chemoradiotherapy across all neurosurgical centers in Hong Kong between 2006 and 2020 were analyzed. The surgery-to-chemoradiotherapy (S-CRT) interval was defined as the date of the first surgery to the date of initiation of adjuvant TMZ chemoradiotherapy.

Results: Four hundred and forty-one patients were reviewed. The median S-CRT interval was 40 days (interquartile range [IQR]: 33-47) and the median overall survival (mOS) was 16.7 months (95% CI: 15.9-18.2). The median age was 58 years (IQR: 50-63). Multivariable Cox regression with restricted cubic splines identified a nonlinear relationship between the S-CRT interval and mOS. Post hoc analysis-derived S-CRT intervals revealed that early CRT (<5 weeks; adjusted hazard ratio [aHR]: 1.11; 95% CI 0.90-1.37) or late CRT (>9-12 weeks; aHR 1.07; 95% CI 0.67-1.71) were not significantly associated with OS. Subgroup analyses for the extent of resection (EOR) and pMGMT methylation status revealed no significant difference in treatment timing on OS.

Conclusion: The timing of adjuvant TMZ chemoradiotherapy, if commenced within 12 weeks after glioblastoma diagnosis, did not influence OS regardless of EOR or pMGMT methylation status. Clinical judgment should be exercised in optimizing the timing of initiating adjuvant therapy.

Background: Glioblastoma is characterized by rapid tumor growth and high invasiveness. The tumor microenvironment of glioblastoma is highly immunosuppressive with both intrinsic and adaptive resistance mechanisms that result in disease recurrence despite current immunotherapeutic strategies.

Methods: In this systematic review of clinical trials involving immunotherapy for glioblastoma using ClinicalTrials.gov and PubMed databases from 2016 and onward, we explore immunotherapeutic modalities involving immune checkpoint blockade (ICB).

Results: A total of 106 clinical trials were identified, 18 with clinical outcomes. ICB in glioblastoma has failed to improve overall survival compared to the current standard of care, including those therapies inhibiting multiple checkpoints. Among all immune checkpoint trials, targets included programmed cell death protein-1 (PD-1) (35/48), PD-L1 (12/48), cytotoxic T-lymphocyte-associated protein-4 (6/48), TIGIT (2/48), B7-H3 (2/48), and TIM-3 (1/48). Preliminary results from combination immunotherapies (32.1% of all trials) demonstrated improved treatment efficacy compared to monotherapy, specifically those combining checkpoint therapy with another immunotherapy modality.

Conclusions: Clinical trials involving ICB strategies for glioblastoma have not demonstrated improved survival. Comparison of therapeutic efficacy across trials was limited due to heterogeneity in the study population and outcome operationalization. Standardization of future trials could facilitate comparison across immunotherapy modalities for robust meta-analysis. Current immunotherapy trials have shifted focus toward combination strategies; preliminary results suggest that they are more encouraging than mono-modality immunotherapies. Given the intrinsic heterogeneity of glioblastoma, the utilization of immune markers will be key for the development of future immunotherapy approaches.

Background: Radiologically presumed diffuse lower-grade glioma (dLGG) are typically non or minimal enhancing tumors, with hyperintensity in T2w-images. The aim of this study was to test the clinical usefulness of deep learning (DL) in IDH mutation prediction in patients with radiologically presumed dLGG.

Methods: Three hundred and fourteen patients were retrospectively recruited from 6 neurosurgical departments in Sweden, Norway, France, Austria, and the United States. Collected data included patients' age, sex, tumor molecular characteristics (IDH, and 1p19q), and routine preoperative radiological images. A clinical model was built using multivariable logistic regression with the variables age and tumor location. DL models were built using MRI data only, and 4 DL architectures used in glioma research. In the final validation test, the clinical model and the best DL model were scored on an external validation cohort with 155 patients from the Erasmus Glioma Dataset.

Results: The mean age in the recruited and external cohorts was 45.0 (SD 14.3) and 44.3 years (SD 14.6). The cohorts were rather similar, except for sex distribution (53.5% vs 64.5% males, P-value = .03) and IDH status (30.9% vs 12.9% IDH wild-type, P-value <.01). Overall, the area under the curve for the prediction of IDH mutations in the external validation cohort was 0.86, 0.82, and 0.87 for the clinical model, the DL model, and the model combining both models' probabilities.

Conclusions: In their current state, when these complex models were applied to our clinical scenario, they did not seem to provide a net gain compared to our baseline clinical model.

Background: Malignant peripheral nerve sheath tumors (MPNSTs) can arise from atypical neurofibromas (ANF). Loss of the polycomb repressor complex 2 (PRC2) is a common event. Previous studies on PRC2-regulated genes in MPNST used genetic add-back experiments in highly aneuploid MPNST cell lines which may miss PRC2-regulated genes in NF1-mutant ANF-like precursor cells. A set of PRC2-regulated genes in human Schwann cells (SCs) has not been defined. We hypothesized that PRC2 loss has direct and indirect effects on gene expression resulting in MPNST, so we sought to identify PRC2-regulated genes in immortalized human Schwann cells (iHSCs).

Methods: We engineered NF1-deficient iHSCs with loss of function SUZ12 or EED mutations. RNA sequencing revealed 1327 differentially expressed genes to define PRC2-regulated genes. To investigate MPNST pathogenesis, we compared genes in iHSCs to consistent gene expression differences between ANF and MPNSTs. Chromatin immunoprecipitation sequencing was used to further define targets. Methylome and proteomic analyses were performed to further identify enriched pathways.

Results: We identified potential PRC2-regulated drivers of MPNST progression. Pathway analysis indicates many upregulated cancer-related pathways. We found transcriptional evidence for activated Notch and Sonic Hedgehog (SHH) signaling in PRC2-deficient iHSCs. Functional studies confirm that Notch signaling is active in MPNST cell lines, patient-derived xenografts, and transient cell models of PRC2 deficiency. A combination of MEK and γ-secretase inhibition shows synergy in MPNST cell lines.

Conclusions: We identified PRC2-regulated genes and potential drivers of MPNSTs. Our findings support the Notch pathway as a druggable target in MPNSTs. Our identification of PRC2-regulated genes and pathways could result in more novel therapeutic approaches.

Background: Glioblastoma is the most aggressive malignant brain tumor with poor survival due to its invasive nature driven by cell migration, with unclear linkage to transcriptomic information. The aim of this study was to develop a physics-based framework connecting to transcriptomics to predict patient-specific glioblastoma cell migration.

Methods and results: We applied a physics-based motor-clutch model, a cell migration simulator (CMS), to parameterize the migration of glioblastoma cells and define physical biomarkers on a patient-by-patient basis. We reduced the 11-dimensional parameter space of the CMS into 3 principal physical parameters that govern cell migration: motor number-describing myosin II activity, clutch number-describing adhesion level, and F-actin polymerization rate. Experimentally, we found that glioblastoma patient-derived (xenograft) cell lines across mesenchymal (MES), proneural, and classical subtypes and 2 institutions (N = 13 patients) had optimal motility and traction force on stiffnesses around 9.3 kPa, with otherwise heterogeneous and uncorrelated motility, traction, and F-actin flow. By contrast, with the CMS parameterization, we found that glioblastoma cells consistently had balanced motor/clutch ratios to enable effective migration and that MES cells had higher actin polymerization rates resulting in higher motility. The CMS also predicted differential sensitivity to cytoskeletal drugs between patients. Finally, we identified 18 genes that correlated with the physical parameters, suggesting transcriptomic data alone could potentially predict the mechanics and speed of glioblastoma cell migration.

Conclusions: We describe a general physics-based framework for parameterizing individual glioblastoma patients and connecting to clinical transcriptomic data that can potentially be used to develop patient-specific anti-migratory therapeutic strategies.

Background: Gliomas, the most frequent malignant primary brain tumors, lack curative treatments. Understanding glioma-specific molecular alterations is crucial to develop novel therapies. Among them, the biological consequences of the isocitrate dehydrogenase 1 gene mutation (IDH1 ^R132H) remain inconclusive despite its early occurrence and widespread expression.

Methods: We thus employed CRISPR/Cas adenine base editors, which allow precise base pair alterations with minimal undesirable effects, to correct the IDH1 ^R132H mutation.

Results: Successful correction of the IDH1 ^R132H mutation in primary patient-derived cell models led to reduced IDH1 ^R132H protein levels and decreased production of 2-hydroxyglutarate, but increased proliferation. A dual adeno-associated virus split intein system was used to successfully deliver the base editor in vitro and in vivo.

Conclusions: Taken together, our study provides a strategy for a precise genetic intervention to target the IDH1 ^R132H mutation, enabling the development of accurate models to study its impact on glioma biology and serving as a framework for an in vivo gene therapy.

Background: Biological sex is an important risk factor for glioblastoma (GBM), with males having a higher incidence and poorer prognosis. The mechanisms for this sex bias are thought to be both tumor intrinsic and tumor extrinsic. MicroRNAs (miRNAs), key posttranscriptional regulators of gene expression, have been previously linked to sex differences in various cell types and diseases, but their role in the sex bias of GBM remains unknown.

Methods: We leveraged previously published paired miRNA and mRNA sequencing of 39 GBM patients (22 male, 17 female) to identify sex-biased miRNAs. We further interrogated a separate single-cell RNA-sequencing dataset of 110 GBM patients to examine whether differences in miRNA target gene expression were tumor cell-intrinsic or tumor cell extrinsic. Results were validated in a panel of patient-derived cell models.

Results: We identified 10 sex-biased miRNAs (p _adjusted < .1), of which 3 were more highly expressed in males and 7 more highly expressed in females. Of these, miR-644a was higher in females, and increased expression of miR-644a target genes was significantly associated with decreased overall survival (HR 1.3, P = .02). Furthermore, analysis of an independent single-cell RNA-sequencing dataset confirmed sex-specific expression of miR-644a target genes in tumor cells (P < 10^-15). Among patient-derived models, miR-644a was expressed a median of 4.8-fold higher in females compared to males.

Conclusions: Our findings implicate miR-644a as a candidate tumor cell-intrinsic regulator of sex-biased gene expression in GBM.