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Mismatch repair-deficient (MMRD) brain tumors are rare among primary brain tumors and can be induced by germline or sporadic mutations. Here, we report 13 MMRD-associated (9 sporadic and 4 Lynch syndrome) primary brain tumors to determine clinicopathological and molecular characteristics and biological behavior. Our 13 MMRD brain tumors included glioblastoma (GBM) IDH-wildtype (n = 9) including 1 gliosarcoma, astrocytoma IDH-mutant WHO grade 4 (n = 2), diffuse midline glioma (DMG) H3 K27M-mutant (n = 1), and pleomorphic xanthoastrocytoma (PXA) (n = 1). Next-generation sequencing using a brain tumor-targeted gene panel, microsatellite instability (MSI) testing, Sanger sequencing for germline MMR gene mutation, immunohistochemistry of MMR proteins, and clinicopathological and survival analysis were performed. There were many accompanying mutations, suggesting a high tumor mutational burden (TMB) in 77%, but TMB was absent in one case of GBM, IDH-wildtype, DMG, and PXA, respectively. MSH2, MLH1, MSH6, and PMS2 mutations were found in 31%, 31%, 31% and 7% of patients, respectively. MSI-high and MSI-low were found in 50% and 8% of these gliomas, respectively and 34% was MSI-stable. All Lynch syndrome-associated GBMs had MSI-high. In addition, 77% (10/13) had histopathologically multinucleated giant cells. The progression-free survival tended to be poorer than the patients with no MMRD gliomas, but the number and follow-up duration of our patients were insufficient to get statistical significance. In the present study, we found that the most common MMRD primary brain tumor was GBM IDH-wildtype. The genetic profile of MMRD GBM was different from that of conventional GBM. MMRD gliomas with TMB and MSI-H may be sensitive to immunotherapy but resistant to temozolomide. Our findings can help develop better treatment options. The authors examined 13 mismatch repair-deficient (MMRD)-associated primary brain tumors to determine molecular characteristics and biological behavior. MMRD occurred after chemotherapy and radiotherapy in two cases. The genetic profile of MMRD glioblastoma was different from that of conventional glioblastoma. Half of the MMRD-gliomas and all Lynch syndrome-associated GBMs were high in microsatellite instability. These tumors had high mutational burdens and tended to have shorter progression-free survival than glioma without MMRD.

When glioblastoma multiforme (GBM) is treated with anti-vascular endothelial growth factor (VEGF) agents, it commonly exhibits tumor progression due to the development of resistance, which results in a dismal survival rate. GBM tumors contain a large number of monocytes/macrophages, which have been shown to be resistant to the effects of bevacizumab. It has been reported that tumor-associated macrophages (TAMs) promote resistance to bevacizumab treatment. Therefore, it is important to target TAMs in the GBM microenvironment. TAMs, which depend on chemokine ligand 2 (CCL2) for differentiation and survival, induce the expression of proangiogenic factors such as VEGF. Dynamic susceptibility contrast (DSC)-MR imaging is an advanced technique that provides information on tumor blood volume and can potentially predict the response to several treatments, including anti-angiogenic agents such as bevacizumab, in human GBM. In this study, we used a CCL2 inhibitor, mNOX-E36, to suppress the recruitment of TAMs in a CCL2-expressing rat GBM model and investigated the effect of combination therapy with bevacizumab using DSC-MR imaging. We demonstrated that the inhibition of CCL2 blocked macrophage recruitment and angiogenesis, which resulted in decreased tumor volume and blood volume in CCL2-expressing GBM in a rat model. Our results provide direct evidence that CCL2 expression can increase the resistance to bevacizumab, which can be assessed noninvasively with the DSC-MR imaging technique. This study shows that the suppression of CCL2 can play an important role in increasing the efficacy of anti-angiogenic treatment in GBM by inhibiting the recruitment of CCL2-dependent macrophages.

Cancer immunotherapy has emerged as a novel cancer treatment, although recent immunotherapy trials have produced suboptimal outcomes, with durable responses seen only in a small number of patients. The tumor microenvironment (TME) has been shown to be responsible for tumor immune escape and therapy failure. The vital component of the TME is tumor-associated macrophages (TAMs), which are usually associated with poor prognosis and drug resistance, including immunotherapies, and have emerged as promising targets for cancer immunotherapy. Recently, nanoparticles, because of their unique physicochemical characteristics, have emerged as crucial translational moieties in tackling tumor-promoting TAMs that amplify immune responses and sensitize tumors to immunotherapies in a safe and effective manner. In this review, we mainly described the current potential nanomaterial-based therapeutic strategies that target TAMs, including restricting TAMs survival, inhibiting TAMs recruitment to tumors and functionally repolarizing tumor-supportive TAMs to antitumor type. The current understanding of the origin and polarization of TAMs, their crucial role in cancer progression and prognostic significance was also discussed in this review. We also highlighted the recent evolution of chimeric antigen receptor (CAR)-macrophage cell therapy.

Iron-oxide-based contrast agents for magnetic resonance imaging (MRI) had been clinically approved in the United States and Europe, yet most of these nanoparticle products were discontinued owing to failures to meet rigorous clinical requirements. Significant advances have been made in the synthesis of magnetic nanoparticles and their biomedical applications, but several major challenges remain for their clinical translation, in particular large-scale and reproducible synthesis, systematic toxicity assessment, and their preclinical evaluation in MRI of large animals. Here, we report the results of a toxicity study of iron oxide nanoclusters of uniform size in large animal models, including beagle dogs and the more clinically relevant macaques. We also show that iron oxide nanoclusters can be used as T 1 MRI contrast agents for high-resolution magnetic resonance angiography in beagle dogs and macaques, and that dynamic MRI enables the detection of cerebral ischaemia in these large animals. Iron oxide nanoclusters show clinical potential as next-generation MRI contrast agents. Uniform iron oxide nanoparticle clusters are highly biocompatible and can be used as contrast agents for high-resolution magnetic resonance angiography of large animals.

Although there have been a plethora of radiogenomics studies related to glioblastoma (GBM), most of them only used genomic information from tumor cells. In this study, we used radiogenomics profiling to identify MRI-associated immune cell markers in GBM, which was also correlated with prognosis. Expression levels of immune cell markers were correlated with quantitative MRI parameters in a total of 60 GBM patients. Fourteen immune cell markers (i.e., CD11b, CD68, CSF1R, CD163, CD33, CD123, CD83, CD63, CD49d and CD117 for myeloid cells, and CD4, CD3e, CD25 and CD8 for lymphoid cells) were selected for RNA-level analysis using quantitative RT-PCR. For MRI analysis, quantitative MRI parameters from FLAIR, contrast-enhanced (CE) T1WI, dynamic susceptibility contrast perfusion MRI and diffusion-weighted images were used. In addition, PFS associated with interesting mRNA data was performed by Kaplan-Meier survival analysis. CD163, which marks tumor associated microglia/macrophages (TAMs), showed the highest expression level in GBM patients. CD68 (TAMs), CSF1R (TAMs), CD33 (myeloid-derived suppressor cell) and CD4 (helper T cell, regulatory T cell) levels were highly positively correlated with nCBV values, while CD3e (helper T cell, cytotoxic T cell) and CD49d showed a significantly negative correlation with apparent diffusion coefficient (ADC) values. Moreover, regardless of any other molecular characteristics, CD49d was revealed as one independent factor for PFS of GBM patients by Cox proportional-hazards regression analysis ( P  = 0.0002). CD49d expression level CD49d correlated with ADC can be considered as a candidate biomarker to predict progression of GBM patients.

ObjectivesPrediction of progression-free survival (PFS) and overall survival (OS) and early identification of molecular biomarkers with prognostic information are clinically important in glioblastoma (GBM) patients. We aimed to explore the utility of arterial spin labeling perfusion-weighted imaging (ASL-PWI) in the prediction of molecular biomarkers and survival in GBM patients.MethodsWe retrospectively analyzed 149 consecutive GBM patients, who had undergone maximal surgical resection or biopsy followed by concurrent chemoradiotherapy and adjuvant chemotherapy using temozolomide between November 2010 and June 2016. On preoperative ASL-PWI, cerebral blood flow (CBF) within contrast-enhancing (CE) and nonenhancing (NE) portions were evaluated both qualitatively (perfusion pattern[CE] and perfusion pattern[NE]) and quantitatively (nCBFCE and nCBFNE). ASL-PWI findings were correlated with molecular biomarkers, including isocitrate dehydrogenase (IDH) and O6-methylguanine-DNA methyltransferase (MGMT) methylation statuses, and survival, using the Mann-Whitney U-test, Spearman rank correlation, Kaplan-Meier analysis, and receiver operating characteristics analysis.ResultsnCBFCE was significantly higher in the IDH wild-type group than in the IDH mutant group (p = .013) and in the MGMT unmethylated group than in the methylated group (p = .047). Areas under the receiver operating characteristic curve were 0.678 for IDH mutation (p = .022) and 0.601 for MGMT promoter methylation (p = .043). Hyperperfusion was associated with the shortest median PFS for both perfusion pattern[CE] (7.6 months) and perfusion pattern[NE] (4.0 months). The perfusion pattern[NE] remained an independent predictor for PFS and OS even after adjusting for clinical and molecular predictors, unlike perfusion pattern[CE].ConclusionsASL-PWI can aid to predict survival and molecular biomarkers including IDH mutation and MGMT promoter methylation statuses in GBM patients.Key Points• ASL-PWI can aid to predict survival in GBM patients.• ASL-PWI can aid to predict IDH and MGMT promoter methylation statuses in GBM.

ObjectivesTo assess the association between MR imaging features and major genomic profiles in glioblastoma.MethodsQualitative and quantitative imaging features such as volumetrics and histogram analysis from normalised CBV (nCBV) and ADC (nADC) were evaluated based on both T2WI and CET1WI. The imaging parameters of different genetic profile groups were compared and regression analyses were used for identifying imaging-molecular associations. Progression-free survival (PFS) was analysed by a Kaplan-Meier test and Cox proportional hazards model.ResultsAn IDH mutation was observed in 18/176 patients, and ATRX loss was positive in 17/158 of the IDH-wt cases. The IDH-mut group showed a larger volume on T2WI and a higher volume ratio between T2WI and CET1WI than the IDH-wt group (p < 0.05). In the IDH-mut group, higher mean nADC values were observed compared with the IDH-wt tumours (p < 0.05). Among the IDH-wt tumours, IDH-wt, ATRX-loss tumours revealed higher 5th percentile nADC values than the IDH-wt, ATRX-noloss tumours (p = 0.03). PFS was the longest in the IDH-mut group, followed by the IDH-wt, ATRX-loss groups and the IDH-wt, ATRX-noloss groups, consecutively (p < 0.05). We found significant associations of PFS with the genetic profiles and imaging parameters.ConclusionMajor genetic profiles of glioblastoma showed a significant association with MR imaging features, along with some genetic profiles, which are independent prognostic parameters for GBM.Key Points• Significant correlation exists between radiological parameters such as volumetric and ADC values and major genomic profiles such as IDH mutation and ATRX loss status• Radiological parameters such as the ADC value were feasible predictors of glioblastoma patients’ prognosis• Imaging features can predict major genomic profiles of the tumours and the prognosis of glioblastoma patients

Glioblastoma remains the most devastating brain tumor despite optimal treatment, because of the high rate of recurrence. Distant recurrence has distinct genomic alterations compared to local recurrence, which requires different treatment planning both in clinical practice and trials. To date, perfusion-weighted MRI has revealed that perfusional characteristics of tumor are associated with prognosis. However, not much research has focused on recurrence patterns in glioblastoma: namely, local and distant recurrence. Here, we propose two different neural network models to predict the recurrence patterns in glioblastoma that utilizes high-dimensional radiomic profiles based on perfusion MRI: area under the curve (AUC) (95% confidence interval), 0.969 (0.903–1.000) for local recurrence; 0.864 (0.726–0.976) for distant recurrence for each patient in the validation set. This creates an opportunity to provide personalized medicine in contrast to studies investigating only group differences. Moreover, interpretable deep learning identified that salient radiomic features for each recurrence pattern are related to perfusional intratumoral heterogeneity. We also demonstrated that the combined salient radiomic features, or “radiomic risk score”, increased risk of recurrence/progression (hazard ratio, 1.61; p  = 0.03) in multivariate Cox regression on progression-free survival.

ObjectivesTo evaluate the diagnostic performance of arterial spin labelling perfusion weighted images (ASL-PWIs) to differentiate primary CNS lymphoma (PCNSL) from glioblastoma (GBM).MethodsASL-PWIs of pathologically confirmed PCNSL (n = 21) or GBM (n = 93) were analysed. For qualitative analysis, tumours were visually scored into five categories based on ASL-CBF maps. For quantitative analysis, normalised CBF values were derived by contralateral grey matter (GM) in intra- and peritumoral areas (nCBFintratumoral and nCBFperitumoral, respectively). Visual scoring scales and quantitative parameters from PCNSL and GBM were compared. In addition, the area under the receiver-operating characteristic (ROC) curve was used to determine the diagnostic accuracy of ASL-PWI for differentiating PCNSL from GBM. Weighted kappa or intraclass correlation coefficients (ICCs) were used to assess reliability between two observers.ResultsIn qualitative analysis, scores 5 (CBFintratumoral>CBFGM, 68.8% [64/93]) and 4 (CBFintratumoral ≈ CBFGM, 47.6% [10/21]) were the most frequently reported scores for GBM and PCNSL, respectively. In quantitative analysis, both nCBFintratumoral and nCBFperitumoral in PCNSL were significantly lower than those in the GBM (nCBFintratumoral, 0.89 ± 0.59 [mean and SD] vs. 2.68 ± 1.89, p < 0.001; nCBFperitumoral, 0.17 ± 0.08 vs. 0.45 ± 0.28, p < 0.001). nCBFperitumoral demonstrated the best diagnostic performance (area under the ROC curve: visual scoring, 0.814; nCBFintratumoral, 0.849; nCBFperitumoral, 0.908; p < 0.001 for all). Interobserver agreements for visual scoring (weighted kappa = 0.869), nCBFintratumoral_GM (ICC = 0.958) and nCBFperitumoral_GM (ICC = 0.947) were all excellent.ConclusionsASL-PWI performs well in differentiating PCNSL from GBM in both qualitative and quantitative analyses.Key Points• ASL-PWI performs well (AUC > 0.8) in differentiating PCNSL from GBM.• The visual scoring template demonstrated good diagnostic performance, similar to quantitative analysis.• nCBFperitumoraldemonstrated better diagnostic performance than nCBFintratumoralor visual scoring.

Objective To develop a radiomics risk score based on dynamic contrast-enhanced (DCE) MRI for prognosis prediction in patients with glioblastoma. Materials and Methods One hundred and fifty patients (92 male [61.3%]; mean age ± standard deviation, 60.5 ± 13.5 years) with glioblastoma who underwent preoperative MRI were enrolled in the study. Six hundred and forty-two radiomic features were extracted from volume transfer constant (Ktrans), fractional volume of vascular plasma space (Vp), and fractional volume of extravascular extracellular space (Ve) maps of DCE MRI, wherein the regions of interest were based on both T1-weighted contrast-enhancing areas and non-enhancing T2 hyperintense areas. Using feature selection algorithms, salient radiomic features were selected from the 642 features. Next, a radiomics risk score was developed using a weighted combination of the selected features in the discovery set (n = 105); the risk score was validated in the validation set (n = 45) by investigating the difference in prognosis between the “radiomics risk score” groups. Finally, multivariable Cox regression analysis for progression-free survival was performed using the radiomics risk score and clinical variables as covariates. Results 16 radiomic features obtained from non-enhancing T2 hyperintense areas were selected among the 642 features identified. The radiomics risk score was used to stratify high- and low-risk groups in both the discovery and validation sets (both pp = 0.004 and hazard ratio, 0.34; p = 0.022, respectively). Conclusion We developed and validated the “radiomics risk score” from the features of DCE MRI based on non-enhancing T2 hyperintense areas for risk stratification of patients with glioblastoma. It was associated with progression-free survival independently of IDH mutation status.