Neuro-oncology advances最新文献

Background: Isocitrate dehydrogenase (IDH) mutation status is a diagnostic requirement for glioma with associated prognostic and therapeutic implications. Clinical routine visual assessment of tissue is insufficient to determine IDH status conclusively, mandating molecular workup that is unavailable everywhere.

Methods: We developed an interpretable Artificial Intelligence (AI)-based approach for determining IDH status directly from H&E-stained glioma slides. Our study is based on 2442 multi-institutional whole slide images (WSIs) from 3 independent retrospective glioma collections, following their reclassification according to the WHO 2021 criteria: (1) TCGA-GBM/TCGA-LGG (n _WSI = 1534, n _patients = 799), (2) University of Pennsylvania Health System collection (UPHS, n _WSI = n _patients = 114), and (3) EBRAINS (n _WSI = n _patients = 794). Method development is based on TCGA, whereas UPHS and EBRAINS are independent hold-out datasets for model validation. Six pathology-specific foundation AI models and an ImageNet-pretrained AI model facilitate robust feature extraction. Features are aggregated into slide-level representations via an interpretable multiple-instance learning (MIL) mechanism to differentiate IDH-wildtype from IDH-mutant cases and generate attention heatmaps correlating with identifiable morphologic characteristics.

Results: Our approach yields AUC_TCGA = 0.96 over a 10-fold cross-validation schema and generalizable performance on independent validation (AUC_UPHS = 0.97, AUC_EBRAINS = 0.95). Interpretability analysis reveals high attention regions in IDH-wildtype tumors exhibiting significant pleomorphism and microvascular proliferation, while IDH-mutant tumors show dense nodular cell concentrations, microcysts, and gemistocytic cells.

Conclusions: Accurate H&E-based determination of glioma IDH mutation status can expedite conclusive diagnosis and clinical decision-making and even facilitate it in underserved regions. Finally, interpretability analysis of distilled human-identifiable features can further improve our understanding of the disease.

Background: The p90 ribosomal S6 kinase (RSK) family, downstream target of Ras/ERK signaling, encompasses four human isoforms (RSK1-4). Glioblastomas (GBMs) predominantly express RSK1 and RSK2, whereby RSK1 is markedly upregulated in a subset of GBMs associated with dismal prognosis and immune infiltration, while RSK2 expression is constant. RSKs were proposed as regulators of mRNA translation through the activation of mTORC1 and other factors, such as eIF4B, but nothing is known about their effect on the translatome of GBM cells.

Methods: Through the generation of RSK1 and RSK2 knockout as well as double knockout (DKO) GBM cells, we investigated RSK isoform-specific functions in cell signaling, followed by the identification of their distinct transcriptome and translatome.

Results: We find that mTORC1 is not activated by RSK isoforms and that eIF4B phosphorylation at S422 is more potently targeted by RSK1 than mTORC1/S6K in GBM cells. Intriguingly, RSK isoforms display differential effects on translation, with RSK1 specifically sustaining translation of a subset of mRNAs upon mTORC1 inhibition. We demonstrate that RSK1 modulates expression in the translatome of mRNAs encoding proteins affecting cell cycle, DNA replication, and repair, while RSK2 impacts mitochondria-related functions. Notably, DKO cells exhibit compounded phenotypes, underscoring the existence of isoform-specific gene regulation.

Conclusions: Our findings offer mechanistic insights into the role of RSK in GBMs and provide evidence for a mTORC1-independent and RSK1-dependent translation regulatory program.

Background: Stereotactic radiotherapy (SRT) is the main treatment for patients with 1 to 3-5 brain metastases (BMs) but with the compromise of a higher risk of distant brain failure (DBF) in comparison with whole brain radiotherapy. Early DBF has a significant negative impact on overall survival (OS). We propose to build and validate a prediction model of early DBF.

Methods: Early DBF was defined as the appearance of unknown BMs on the first post-SRT magnetic resonance imaging (MRI). A nomogram was built for the prediction of early DBF using patients from a cohort of 537 SRT courses. The nomogram was evaluated for early DBF classification using the chi-squared test. Prediction of DBF-free survival and OS was tested using Kaplan-Meier curves and the log-rank test. The model was validated on an external cohort of 160 subsequently delivered SRT courses.

Results: In the cohort of 537 SRT courses, early DBF occurred in 17% cases. The number of BMs and the DS-GPA score were significant predictors of early DBF. The nomogram demonstrated robust performances for early DBF classification (χ² 23.7, P < .0001), DBF-free survival (χ² 35.5 P < .0001, Figure 1), and OS (χ² 38.9, P < .0001). On the validation cohort, the same nomogram achieved a χ² of 15.3, P = .0005 for DBF-free survival and a χ² of 8.6, P = .01 for OS.

Conclusion: Our study provides a robust predictive model for early DBF, validated in an independent cohort. This nomogram could improve clinical outcomes and treatment personalization.

Background: This phase II clinical trial evaluated the safety and efficacy of nilotinib in patients with recurrent, platelet-derived growth factor receptor alpha (PDGFRA)-enriched high-grade gliomas.

Methods: Thirty-four adult patients with PDGFRA-enriched recurrent high-grade gliomas were enrolled. Study treatment consisted of nilotinib 400 mg administered twice daily in 28-day cycles. Safety and clinical activity were evaluated.

Results: Median lines of prior therapy were 2 (range 1-7) and 9 of 34 (26%) patients received prior bevacizumab. Four patients had PDGFRA gene amplification, and 30 had PDGFRA overexpression by immunohistochemistry. Overall, nilotinib was well tolerated. The most common treatment-related toxicities were increased ALT, joint pain, and hyponatremia. No treatment-related grade 4 or 5 adverse events occurred. The best response was stable disease (SD) for 8 patients and complete response (CR) for one patient with glioblastoma. The median PFS was 1.45 months (95% CI 0.986-2.07) and the median OS was 6.6 months (95% CI 4.9-18.3). The patient with a CR was an MGMT-unmethylated GBM with PDGFRA overexpression by IHC, and maintained a durable response for over 5 years.

Conclusion: Nilotinib was well tolerated with limited benefit in this enriched population of patients. Further studies are warranted to determine the clinical benefit in patients in earlier lines of treatment. Trial registration number: NCT01140568, registered 08 June 2010.

Background: MR-guided laser interstitial thermal therapy (LITT) is a minimally invasive neurosurgical treatment used for managing brain tumors and drug-resistant epilepsy. This study investigates the temporal pattern of blood-brain barrier (BBB) permeability following LITT in patients with tumors and epilepsy.

Methods: Twenty-three patients undergoing LITT (11 with brain tumors, 12 with non-tumor epilepsy) were enrolled. Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) and blood levels of glial fibrillary acidic protein (GFAP) were used to assess BBB permeability one day before and on days 1, 15, 30, and 45 post-LITT.

Results: Quantitative analysis using DCE-MRI demonstrated significant increases in perilesional BBB permeability on days 15, 30, and 45 postoperatively (P < .005), with no notable changes on the first postoperative day. The findings were robust with regards to region of interest selection, showing consistent increases in BBB permeability in patients with both brain tumors and epilepsy. Additionally, GFAP levels peaked significantly above baseline on the first postoperative day, maintaining elevated levels through day 45.

Conclusions: Quantitative DCE-MRI and GFAP blood levels demonstrate a prolonged window of increased perilesional BBB permeability following LITT, potentially enhancing the delivery and efficacy of therapeutic agents in patients with brain tumors.

Background: The CCDC26 germline variant rs55705857 is causal for development of IDH mutant (IDHmut) adult glioma. However, ~60% of IDHmut patients do not carry the rs55705857 risk allele. We aimed to identify variants associated with developing IDHmut glioma among patients that do not have the rs55705857 risk allele and to further understand development of IDHwt glioma.

Methods: We used three datasets that included 1216 IDHmut and 1442 IDHwt glioma patients and a case-case design to perform genome-wide association (GWAS) analyses comparing IDHmut versus IDHwt glioma. Analyses were performed overall and stratified by rs55705857 genotype and sex. Multivariable logistic regression and regression trees were used to develop models to predict IDH status using germline variants, age, and contrast enhancement on MRI.

Results: Three regions were identified comparing IDHmut versus IDHwt: rs55705857 (meta P-value [P] = 1.35 × 10^-43), PHLDB1 (rs7125115, P = 3.46 × 10^-17), and D2HGDH (rs71430382, P = 2.43 × 10^-12). When analyzing only patients that do not have the rs55705857 risk allele, PHLDB1 (rs7125115, P = 1.73 × 10^-13) and D2HGDH (rs71430382, P = 8.86 × 10^-10) were identified. Among patients who have the rs55705857 risk allele, four variants between ROBO1 and ROBO2 (rs4680975, P = 4.65 × 10^-7) increased the likelihood of having an IDHwt tumor. Tumor expression of ROBO1 was associated with rs4680975 genotype in IDHwt patients that have the rs55705857 risk allele (P = 0.034). Multivariable logistic analysis demonstrated that rs55705857, rs71430382 (D2HGDH), and age predicted IDH mutation status.

Conclusions: To understand the development of adult glioma, we demonstrate that D2HGDH and PHLDB1 should be prioritized for functional studies in IDHmut tumors. The ROBO1 region warrants further investigation in IDHwt tumors.

Background: Pediatric low-grade gliomas (pLGGs) have heterogeneous clinical presentations, and given the morbidity of treatment, some patients receive observation with magnetic resonance (MR). The natural histories of untreated pLGGs remain understudied. We leveraged deep learning-based volumetrics to analyze longitudinal growth trajectories and progression risk factors for untreated pLGGs.

Methods: We conducted a pooled, retrospective study of radiographically diagnosed pLGG patients from two institutions diagnosed between 1992 and 2020 who were surveilled for at least 1 year post-diagnosis. Tumor segmentation was applied to longitudinal T2-weighted MR to calculate 3D tumor volumes. We assessed volume trajectories, disease progression, and associated risk factors using Cox-Hazards regression, survival analysis, and time-series forecasting with autoregressive integrated moving average (ARIMA). Patients were categorized based on volumetric changes into progression (≥25%), regression (≤-25%), or stability.

Results: Of 99 patients (970 scans; median follow-up: 7.0 years; median diagnosis age: 12.0 years), 55 (55.5%) had tumors that volumetrically progressed, 28 (28.3%) remained stable, and 16 (16.2%) regressed. 42 (42.4%) patients initiated treatment. Risk factors associated with progression included infancy/preschool age, cortical location, and female sex (p ≤ 0.05 for each). Most progressions occurred within five years of diagnosis (80.0%), most commonly in school-aged children (7-13 years old). Time-series forecasting predicted future tumor volume with a mean absolute error of 2.04 cm³.

Conclusion: Deep learning enables systematic, longitudinal, pLGG growth tracking and characterization of patients on surveillance, yielding insights into untreated tumor trajectories and progression risk. This pipeline is useful at population-level to study growth trends and at patient-level to guide personalized management.

Background: Glioblastoma (GBM) is a highly aggressive malignancy and the most common primary malignant brain tumor in adults, with significant variations in incidence and outcomes across different populations. Despite extensive research in the United States (U.S.), there is limited data on GBM epidemiology in Puerto Rico, a U.S. commonwealth with a unique demographic and healthcare system. This study aims to provide the first comprehensive population-based analysis of GBM in Puerto Rico, focusing on incidence, demographics, and geographic distribution.

Methods: We performed a retrospective study of 1,423 GBM cases diagnosed in Puerto Rico from 2000 to 2020, including 1,334 cases with histopathologically confirmed diagnoses, utilizing data from the Puerto Rico Central Cancer Registry. Demographic, clinical, and geographic variables were analyzed to identify epidemiological patterns and treatment trends. Statistical analyses included age-adjusted incidence rates, temporal trends, and geolocation mapping.

Results: The average age-adjusted incidence rate (AAAIR) of GBM in Puerto Rico was 1.78 per 100,000 people. The highest incidence was observed in the 65-74 age group (6.71 per 100,000). Municipalities such as Moca, Cayey, and San Sebastián exhibited the highest incidence rates, while Culebra reported no cases. A significant upward trend in GBM incidence was observed, with an annual percentage change (APC) of 4.85% (95% CI: 3.66%-6.04%).

Conclusion: This study highlights unique epidemiological patterns of GBM in Puerto Rico, including lower incidence rates compared to the U.S. mainland and significant geographic variations. The findings underscore the need for further research into environmental, genetic, and socioeconomic factors influencing GBM in this population.