RT-PCR assay to detect FGFR3::TACC3 fusions in formalin-fixed, paraffin-embedded glioblastoma samples.

Background: One targeted treatment option for isocitrate dehydrogenase (IDH)-wild-type glioblastoma focuses on tumors with fibroblast growth factor receptor 3::transforming acidic coiled-coil-containing protein 3 (FGFR3::TACC3) fusions. FGFR3::TACC3 fusion detection can be challenging, as targeted RNA next-generation sequencing (NGS) is not routinely performed, and immunohistochemistry is an imperfect surrogate marker. Fusion status can be determined using reverse transcription polymerase chain reaction (RT-PCR) on fresh frozen (FF) material, but sometimes only formalin-fixed, paraffin-embedded (FFPE) tissue is available. Aim: To develop an RT-PCR assay to determine FGFR3::TACC3 status in FFPE glioblastoma samples. Methods: Twelve tissue microarrays with 353 historical glioblastoma samples were immunohistochemically stained for FGFR3. Samples with overexpression of FGFR3 (n = 13) were subjected to FGFR3::TACC3 RT-PCR on FFPE, using 5 primer sets for the detection of 5 common fusion variants. Fusion-negative samples were additionally analyzed with NGS (n = 6), FGFR3 Fluorescence In Situ Hybridization (n = 6), and RNA sequencing (n = 5). Results: Using RT-PCR on FFPE material of the 13 samples with FGFR3 overexpression, we detected an FGFR3::TACC3 fusion in 7 samples, covering 3 different fusion variants. For 5 of these FF was available, and the presence of the fusion was confirmed through RT-PCR on FF. With RNA sequencing, 1 additional sample was found to harbor an FGFR3::TACC3 fusion (variant not covered by current RT-PCR for FFPE). The frequency of FGFR3::TACC3 fusion in this cohort was 9/353 (2.5%). Conclusions: RT-PCR for FGFR3::TACC3 fusions can successfully be performed on FFPE material, with a specificity of 100% and (due to limited primer sets) a sensitivity of 83.3%. This assay allows for the identification of potential targeted treatment options when only formalin-fixed tissue is available.

NnU-Net versus mesh growing algorithm as a tool for the robust and timely segmentation of neurosurgical 3D images in contrast-enhanced T1 MRI scans.

PURPOSE: This study evaluates the nnU-Net for segmenting brain, skin, tumors, and ventricles in contrast-enhanced T1 (T1CE) images, benchmarking it against an established mesh growing algorithm (MGA). METHODS: We used 67 retrospectively collected annotated single-center T1CE brain scans for training models for brain, skin, tumor, and ventricle segmentation. An additional 32 scans from two centers were used test performance compared to that of the MGA. The performance was measured using the Dice-Sørensen coefficient (DSC), intersection over union (IoU), 95th percentile Hausdorff distance (HD95), and average symmetric surface distance (ASSD) metrics, with time to segment also compared. RESULTS: The nnU-Net models significantly outperformed the MGA (p < 0.0125) with a median brain segmentation DSC of 0.971 [95CI: 0.945-0.979], skin: 0.997 [95CI: 0.984-0.999], tumor: 0.926 [95CI: 0.508-0.968], and ventricles: 0.910 [95CI: 0.812-0.968]. Compared to the MGA's median DSC for brain: 0.936 [95CI: 0.890, 0.958], skin: 0.991 [95CI: 0.964, 0.996], tumor: 0.723 [95CI: 0.000-0.926], and ventricles: 0.856 [95CI: 0.216-0.916]. NnU-Net performance between centers did not significantly differ except for the skin segmentations Additionally, the nnU-Net models were faster (mean: 1139 s [95CI: 685.0-1616]) than the MGA (mean: 2851 s [95CI: 1482-6246]). CONCLUSIONS: The nnU-Net is a fast, reliable tool for creating automatic deep learning-based segmentation pipelines, reducing the need for extensive manual tuning and iteration. The models are able to achieve this performance despite a modestly sized training set. The ability to create high-quality segmentations in a short timespan can prove invaluable in neurosurgical settings.

Preserving the ability to discriminate between left and right; A case study.

Left-right orientation, a function related to the parietal lobe, is important for many daily activities. Here, we describe a left-handed patient with a right parietal brain tumour. During awake surgery, electric stimulation of the right inferior parietal lobe resulted in mistakes in his left-right orientation. Postoperatively our patient had no problems in discriminating left right. This case report shows that monitoring of left-right orientation during awake brain tumour surgery is feasible so that this function can be preserved.

Evaluation Metrics for Augmented Reality in Neurosurgical Preoperative Planning, Surgical Navigation, and Surgical Treatment Guidance: A Systematic Review.

BACKGROUND AND OBJECTIVE: Recent years have shown an advancement in the development of augmented reality (AR) technologies for preoperative visualization, surgical navigation, and intraoperative guidance for neurosurgery. However, proving added value for AR in clinical practice is challenging, partly because of a lack of standardized evaluation metrics. We performed a systematic review to provide an overview of the reported evaluation metrics for AR technologies in neurosurgical practice and to establish a foundation for assessment and comparison of such technologies. METHODS: PubMed, Embase, and Cochrane were searched systematically for publications on assessment of AR for cranial neurosurgery on September 22, 2022. The findings were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. RESULTS: The systematic search yielded 830 publications; 114 were screened full text, and 80 were included for analysis. Among the included studies, 5% dealt with preoperative visualization using AR, with user perception as the most frequently reported metric. The majority (75%) researched AR technology for surgical navigation, with registration accuracy, clinical outcome, and time measurements as the most frequently reported metrics. In addition, 20% studied the use of AR for intraoperative guidance, with registration accuracy, task outcome, and user perception as the most frequently reported metrics. CONCLUSION: For quality benchmarking of AR technologies in neurosurgery, evaluation metrics should be specific to the risk profile and clinical objectives of the technology. A key focus should be on using validated questionnaires to assess user perception; ensuring clear and unambiguous reporting of registration accuracy, precision, robustness, and system stability; and accurately measuring task performance in clinical studies. We provided an overview suggesting which evaluation metrics to use per AR application and innovation phase, aiming to improve the assessment of added value of AR for neurosurgical practice and to facilitate the integration in the clinical workflow.

Longitudinal characteristics of T2-FLAIR mismatch in IDH-mutant astrocytomas: Relation to grade, histopathology, and overall survival in the GLASS-NL cohort.

Background: The T2-FLAIR mismatch sign is defined by signal loss of the T2-weighted hyperintense area with Fluid-Attenuated Inversion Recovery (FLAIR) on magnetic resonance imaging, causing a hypointense region on FLAIR. It is a highly specific diagnostic marker for IDH-mutant astrocytoma and is postulated to be caused by intercellular microcystic change in the tumor tissue. However, not all IDH-mutant astrocytomas show this mismatch sign and some show the phenomenon in only part of the lesion. The aim of the study is to determine whether the T2-FLAIR mismatch phenomenon has any prognostic value beyond initial noninvasive molecular diagnosis. Methods: Patients initially diagnosed with histologically lower-grade (2 or 3) IDH-mutant astrocytoma and with at least 2 surgical resections were included in the GLASS-NL cohort. T2-FLAIR mismatch was determined, and the growth pattern of the recurrent tumor immediately before the second resection was annotated as invasive or expansive. The relation between the T2-FLAIR mismatch sign and tumor grade, microcystic change, overall survival (OS), and other clinical parameters was investigated both at first and second resection. Results: The T2-FLAIR mismatch sign was significantly related to Grade 2 (80% vs 51%), longer post-resection median OS (8.3 vs 5.2 years), expansive growth, and lower age at second resection. At first resection, no relation was found between the mismatch sign and OS. Microcystic change was associated with areas of T2-FLAIR mismatch. Conclusions: T2-FLAIR mismatch in IDH-mutant astrocytomas is correlated with microcystic change in the tumor tissue, favorable prognosis, and Grade 2 tumors at the time of second resection.

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks.

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.

Multi-class glioma segmentation on real-world data with missing MRI sequences: comparison of three deep learning algorithms.

This study tests the generalisability of three Brain Tumor Segmentation (BraTS) challenge models using a multi-center dataset of varying image quality and incomplete MRI datasets. In this retrospective study, DeepMedic, no-new-Unet (nn-Unet), and NVIDIA-net (nv-Net) were trained and tested using manual segmentations from preoperative MRI of glioblastoma (GBM) and low-grade gliomas (LGG) from the BraTS 2021 dataset (1251 in total), in addition to 275 GBM and 205 LGG acquired clinically across 12 hospitals worldwide. Data was split into 80% training, 5% validation, and 15% internal test data. An additional external test-set of 158 GBM and 69 LGG was used to assess generalisability to other hospitals' data. All models' median Dice similarity coefficient (DSC) for both test sets were within, or higher than, previously reported human inter-rater agreement (range of 0.74-0.85). For both test sets, nn-Unet achieved the highest DSC (internal = 0.86, external = 0.93) and the lowest Hausdorff distances (10.07, 13.87 mm, respectively) for all tumor classes (p < 0.001). By applying Sparsified training, missing MRI sequences did not statistically affect the performance. nn-Unet achieves accurate segmentations in clinical settings even in the presence of incomplete MRI datasets. This facilitates future clinical adoption of automated glioma segmentation, which could help inform treatment planning and glioma monitoring.

TRIB1 confers therapeutic resistance in GBM cells by activating the ERK and Akt pathways.

GBM (Glioblastoma) is the most lethal CNS (Central nervous system) tumor in adults, which inevitably develops resistance to standard treatments leading to recurrence and mortality. TRIB1 is a serine/threonine pseudokinase which functions as a scaffold platform that initiates degradation of its substrates like C/EBPα through the ubiquitin proteasome system and also activates MEK and Akt signaling. We found that increased TRIB1 gene expression associated with worse overall survival of GBM patients across multiple cohorts. Importantly, overexpression of TRIB1 decreased RT/TMZ (radiation therapy/temozolomide)-induced apoptosis in patient derived GBM cell lines in vitro. TRIB1 directly bound to MEK and Akt and increased ERK and Akt phosphorylation/activation. We also found that TRIB1 protein expression was maximal during G2/M transition of cell cycle in GBM cells. Furthermore, TRIB1 bound directly to HDAC1 and p53. Importantly, mice bearing TRIB1 overexpressing tumors had worse overall survival. Collectively, these data suggest that TRIB1 induces resistance of GBM cells to RT/TMZ treatments by activating the cell proliferation and survival pathways thus providing an opportunity for developing new targeted therapeutics.

Study protocol for a multicenter randomised controlled trial on the (cost)effectiveness of biopsy combined with same-session MR-guided LITT versus biopsy alone in patients with primary irresectable glioblastoma (EMITT trial).

BACKGROUND: Glioblastoma (GBM) is the most common primary, malignant brain tumour with a 5-year survival of 5%. If possible, a glioblastoma is resected and further treated with chemoradiation therapy (CRT), but resection is not feasible in about 30% of cases. Current standard of care in these cases is a biopsy followed by CRT. Magnetic resonance (MR) imaging-guided laser interstitial thermal therapy (LITT) has been suggested as a minimally invasive alternative when surgery is not feasible. However, high-quality evidence directly comparing LITT with standard of care is lacking, precluding any conclusions on (cost-)effectiveness. We therefore propose a multicenter randomized controlled study to assess the (cost-)effectiveness of MR-guided LITT as compared to current standard of care (EMITT trial). METHODS AND ANALYSIS: The EMITT trial will be a multicenter pragmatic randomized controlled trial in the Netherlands. Seven Dutch hospitals will participate in this study. In total 238 patients will be randomized with 1:1 allocation to receive either biopsy combined with same-session MR-guided LITT therapy followed by CRT or the current standard of care being biopsy followed by CRT. The primary outcomes will be health-related quality of life (HR-QoL) (non-inferiority) using EORTC QLQ-C30 + BN20 scores at 5 months after randomization and overall survival (superiority). Secondary outcomes comprise cost-effectiveness (healthcare and societal perspective) and HR-QoL of life over an 18-month time horizon, progression free survival, tumour response, disease specific survival, longitudinal effects, effects on adjuvant treatment, ablation percentage and complication rates. DISCUSSION: The EMITT trial will be the first RCT on the effectiveness of LITT in patients with glioblastoma as compared with current standard of care. Together with the Dutch Brain Tumour Patient association, we hypothesize that LITT may improve overall survival without substantially affecting patients' quality of life. TRIAL REGISTRATION: This trial is registered at ClinicalTrials.gov (NCT05318612).

PET Imaging and Protein Expression of Prostate-Specific Membrane Antigen in Glioblastoma: A Multicenter Inventory Study.

Upregulation of prostate-specific membrane antigen (PSMA) in neovasculature has been described in glioblastoma multiforme (GBM), whereas vasculature in nonaffected brain shows hardly any expression of PSMA. It is unclear whether PSMA-targeting tracer uptake on PET is based on PSMA-specific binding to neovasculature or aspecific uptake in tumor. Here, we quantified uptake of various PSMA-targeting tracers in GBM and correlated this with PSMA expression in tumor biopsy samples from the same patients. Methods: Fourteen patients diagnosed with de novo (n = 8) or recurrent (n = 6) GBM underwent a preoperative PET scan after injection of 1.5 MBq/kg [68Ga]Ga-PSMA-11 (n = 7), 200 MBq of [18F]DCFpyl (n = 3), or 200 MBq of [18F]PSMA-1007 (n = 4). Uptake in tumor and tumor-to-background ratios, with contralateral nonaffected brain as background, were determined. In a subset of patients, PSMA expression levels from different regions in the tumor tissue samples (n = 40), determined using immunohistochemistry (n = 35) or RNA sequencing (n = 13), were correlated with tracer uptake on PET. Results: Moderate to high (SUVmax, 1.3-20.0) heterogeneous uptake was found in all tumors irrespective of the tracer type used. Uptake in nonaffected brain was low, resulting in high tumor-to-background ratios (6.1-359.0) calculated by dividing SUVmax of tumor by SUVmax of background. Immunohistochemistry showed variable PSMA expression on endothelial cells of tumor microvasculature, as well as on dispersed individual cells (of unknown origin), and granular staining of the neuropil. No correlation was found between in vivo uptake and PSMA expression levels (for immunohistochemistry, r = -0.173, P = 0.320; for RNA, r = -0.033, P = 0.915). Conclusion: Our results indicate the potential use of various PSMA-targeting tracers in GBM. However, we found no correlation between PSMA expression levels on immunohistochemistry and uptake intensity on PET. Whether this may be explained by methodologic reasons, such as the inability to measure functionally active PSMA with immunohistochemistry, tracer pharmacokinetics, or the contribution of a disturbed blood-brain barrier to tracer retention, should still be investigated.

Cosmetic satisfaction and patient-reported outcome measures following cranioplasty after craniectomy - A prospective cohort study.

Introduction: Evaluating patient-reported outcomes (PROMs) helps optimize preoperative counseling and psychosocial care for patients who underwent cranioplasty. Research question: This study aimed to evaluate cosmetic satisfaction, level of self-esteem, and fear of negative evaluation (FNE) of patients who underwent cranioplasty. Material and methods: Patients who underwent cranioplasty from 1 January 2014 to 31 December 2020 â€‹at University Medical Center Utrecht and a control group consisting of our center' employees were invited to fill out the Craniofacial Surgery Outcomes Questionnaire (CSO-Q), consisting of an assessment of cosmetic satisfaction, the Rosenberg Self-Esteem Scale (RSES), and the FNE scale. To test for differences in results, chi-square tests and T-tests were performed. Logistic regression was used to study the effect of cranioplasty-related variables on cosmetic satisfaction. Results: Cosmetic satisfaction was seen in 44/80 patients (55.0%) and 52/70 controls (74.3%) (p â€‹= â€‹0.247). Thirteen patients (16.3%) and 8 controls (11.4%) had high self-esteem (p â€‹= â€‹0.362), 51 patients (63.8%) and 59 controls (84.3%) had normal self-esteem (p â€‹= â€‹0.114), and 7 patients (8.8%) and 3 controls (4.3%) had low self-esteem (p â€‹= â€‹0.337). Forty-nine patients (61.3%) and 39 controls (55.7%) had low FNE (p â€‹= â€‹0.012), 8 patients (10.0%) and 18 controls (25.7%) had average FNE (p â€‹= â€‹0.095), and 6 patients (7.5%) and 13 controls (18.6%) had high FNE (p â€‹= â€‹0.215). Cosmetic satisfaction was associated with glass fiber-reinforced composite implants (OR 8.20, p-value â€‹= â€‹0.04). Discussion and conclusion: This study prospectively evaluated PROMs following cranioplasty, for which we found favorable results.

Development of a Prediction Model for Cranioplasty Implant Survival Following Craniectomy.

BACKGROUND: Cranioplasty after craniectomy can result in high rates of postoperative complications. Although determinants of postoperative outcomes have been identified, a prediction model for predicting cranioplasty implant survival does not exist. Thus, we sought to develop a prediction model for cranioplasty implant survival after craniectomy. METHODS: We performed a retrospective cohort study of patients who underwent cranioplasty following craniectomy between 2014 and 2020. Missing data were imputed using multiple imputation. For model development, multivariable Cox proportional hazards regression analysis was performed. To test whether candidate determinants contributed to the model, we performed backward selection using the Akaike information criterion. We corrected for overfitting using bootstrapping techniques. The performance of the model was assessed using discrimination and calibration. RESULTS: A total of 182 patients were included (mean age, 43.0 ± 19.7 years). Independent determinants of cranioplasty implant survival included the indication for craniectomy (compared with trauma-vascular disease: hazard ratio [HR], 0.65 [95% confidence interval (CI), 0.36-1.17]; infection: HR, 0.76 [95% CI, 0.32-1.80]; tumor: HR, 1.40 [95% CI, 0.29-6.79]), cranial defect size (HR, 1.01 per cm2 [95% CI, 0.73-1.38]), use of an autologous bone flap (HR, 1.63 [95% CI, 0.82-3.24]), and skin closure using staples (HR, 1.42 [95% CI, 0.79-2.56]). The concordance index of the model was 0.60 (95% CI, 0.47-0.73). CONCLUSIONS: We have developed the first prediction model for cranioplasty implant survival after craniectomy. The findings from our study require external validation and deserve further exploration in future studies.

Transcriptome analysis reveals tumor microenvironment changes in glioblastoma.

A better understanding of transcriptional evolution of IDH-wild-type glioblastoma may be crucial for treatment optimization. Here, we perform RNA sequencing (RNA-seq) (n = 322 test, n = 245 validation) on paired primary-recurrent glioblastoma resections of patients treated with the current standard of care. Transcriptional subtypes form an interconnected continuum in a two-dimensional space. Recurrent tumors show preferential mesenchymal progression. Over time, hallmark glioblastoma genes are not significantly altered. Instead, tumor purity decreases over time and is accompanied by co-increases in neuron and oligodendrocyte marker genes and, independently, tumor-associated macrophages. A decrease is observed in endothelial marker genes. These composition changes are confirmed by single-cell RNA-seq and immunohistochemistry. An extracellular matrix-associated gene set increases at recurrence and bulk, single-cell RNA, and immunohistochemistry indicate it is expressed mainly by pericytes. This signature is associated with significantly worse survival at recurrence. Our data demonstrate that glioblastomas evolve mainly by microenvironment (re-)organization rather than molecular evolution of tumor cells.

Risk Factors and Management of Incisional Cerebrospinal Fluid Leakage After Craniotomy: A Retrospective International Multicenter Study.

BACKGROUND: Incisional cerebrospinal fluid (iCSF) leakage is a serious complication after intradural cranial surgery. OBJECTIVE: To determine the incidence and risk factors of iCSF leakage after craniotomy. Secondarily, the complications after iCSF leakage and the success rate of iCSF leakage treatment was studied. METHODS: All patients who underwent an intradural cranial surgery from 2017 to 2018 at 5 neurosurgical centers were retrospectively included. Data were retrieved from medical records with 2 months of follow-up. First, univariate regression analyses were performed. Subsequently, identified risk factors were evaluated in a multivariate regression analysis. RESULTS: In total 2310 consecutive patients were included. Total iCSF leakage rate was 7.1% (n = 165). Younger age, male, higher body mass index, smoking, infratentorial surgery, and use of a dural substitute were associated with increased iCSF leakage risk, and use of a sealant reduced that risk. The odds for developing a wound infection and/or meningitis were 15 times higher in patients with iCSF leakage compared with patients without leakage. Initial conservative iCSF leakage treatment failed in 48% of patients. In 80% of cases, external cerebrospinal fluid drainage ceased the iCSF leakage. A total of 32% of patients with iCSF leakage required wound revision surgery. CONCLUSION: iCSF leakage risk increases by younger age, higher body mass index, smoking, infratentorial craniotomy, and dural substitute use, whereas sealant use reduced the risk for iCSF leakage. The leak increases the risk of postoperative infections. When iCSF leakage occurs, immediate external cerebrospinal fluid drainage or wound revision should be considered.

Occurrence, Risk Factors, and Consequences of Postoperative Ischemia After Glioma Resection: A Retrospective Study.

BACKGROUND: Postoperative ischemia can lead to neurological deficits and is a known complication of glioma resection. There is inconsistency in documented incidence of ischemia after glioma resection, and the precise cause of ischemia is often unknown. OBJECTIVE: To assess the incidence of postoperative ischemia and neurological deficits after glioma resection and to evaluate their association with potential risk factors. METHODS: One hundred thirty-nine patients with 144 surgeries between January 2012 and September 2014 for World Health Organization (WHO) 2016 grade II-IV diffuse supratentorial gliomas with postoperative MRI within 72 hours were retrospectively included. Patient, tumor, and perioperative data were extracted from the electronic patient records. Occurrence of postoperative confluent ischemia, defined as new confluent areas of diffusion restriction, and new or worsened neurological deficits were analyzed univariably and multivariably using logistic regression models. RESULTS: Postoperative confluent ischemia was found in 64.6% of the cases. Occurrence of confluent ischemia was associated with an insular location ( P = .042) and intraoperative administration of vasopressors ( P = .024) in multivariable analysis. Glioma location in the temporal lobe was related to an absence of confluent ischemia ( P = .01). Any new or worsened neurological deficits occurred in 30.6% and 20.9% at discharge from the hospital and at first follow-up, respectively. Occurrence of ischemia was significantly associated with the presence of novel neurological deficits at discharge ( P = .013) and after 3 months ( P = .024). CONCLUSION: Postoperative ischemia and neurological deficit were significantly correlated. Intraoperative administration of vasopressors, insular glioma involvement, and absence of temporal lobe involvement were significantly associated with postoperative ischemia.

Limited Effects of Class II Transactivator-Based Immunotherapy in Murine and Human Glioblastoma.

BACKGROUND: The major histocompatibility complex type II is downregulated in glioblastoma (GB) due to the silencing of the major transcriptional regulator class II transactivator (CIITA). We investigated the pro-immunogenic potential of CIITA overexpression in mouse and human GB. METHODS: The intracerebral growth of wildtype GL261-WT cells was assessed following contralateral injection of GL261-CIITA cells or flank injections with GL261-WT or GL261-CIITA cells. Splenocytes obtained from mice implanted intracerebrally with GL261-WT, GL261-CIITA cells or phosphate buffered saline (PBS) were transferred to other mice and subsequently implanted intracerebrally with GL261-WT. Human GB cells and (syngeneic) GB-infiltrating immune cells were isolated from surgical samples and co-cultured with GB cells expressing CIITA or not, followed by RT-qPCR assessment of the expression of key immune regulators. RESULTS: Intracerebral vaccination of GL261-CIITA significantly reduced the subsequent growth of GL261-WT cells implanted contralaterally. Vaccination with GL261-WT or -CIITA subcutaneously, however, equivalently retarded the intracerebral growth of GL261 cells. Adoptive cell transfer experiments showed a similar antitumor potential of lymphocytes harvested from mice implanted intracerebrally with GL261-WT or -CIITA. Human GB-infiltrating myeloid cells and lymphocytes were not activated when cultured with CIITA-expressing GB cells. Tumor-infiltrating NK cells remained mostly inactivated when in co-culture with GB cells, regardless of CIITA. CONCLUSION: these results question the therapeutic potential of CIITA-mediated immunotherapy in glioblastoma.

Study protocol of the GLOW study: maximising treatment options for recurrent glioblastoma patients by whole genome sequencing-based diagnostics-a prospective multicenter cohort study.

BACKGROUND: Glioblastoma (GBM), the most common glial primary brain tumour, is without exception lethal. Every year approximately 600 patients are diagnosed with this heterogeneous disease in The Netherlands. Despite neurosurgery, chemo -and radiation therapy, these tumours inevitably recur. Currently, there is no gold standard at time of recurrence and treatment options are limited. Unfortunately, the results of dedicated trials with new drugs have been very disappointing. The goal of the project is to obtain the evidence for changing standard of care (SOC) procedures to include whole genome sequencing (WGS) and consequently adapt care guidelines for this specific patient group with very poor prognosis by offering optimal and timely benefit from novel therapies, even in the absence of traditional registration trials for this small volume cancer indication. METHODS: The GLOW study is a prospective diagnostic cohort study executed through collaboration of the Hartwig Medical Foundation (Hartwig, a non-profit organisation) and twelve Dutch centers that perform neurosurgery and/or treat GBM patients. A total of 200 patients with a first recurrence of a glioblastoma will be included. Dual primary endpoint is the percentage of patients who receive targeted therapy based on the WGS report and overall survival. Secondary endpoints include WGS report success rate and number of targeted treatments available based on WGS reports and number of patients starting a treatment in presence of an actionable variant. At recurrence, study participants will undergo SOC neurosurgical resection. Tumour material will then, together with a blood sample, be sent to Hartwig where it will be analysed by WGS. A diagnostic report with therapy guidance, including potential matching off-label drugs and available clinical trials will then be sent back to the treating physician for discussing of the results in molecular tumour boards and targeted treatment decision making. DISCUSSION: The GLOW study aims to provide the scientific evidence for changing the SOC diagnostics for patients with a recurrent glioblastoma by investigating complete genome diagnostics to maximize treatment options for this patient group. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05186064.

STAT5b is a marker of poor prognosis, rather than a therapeutic target in glioblastomas.

The copy number and mRNA expression of STAT5b were assessed in samples from the TCGA repository of glioblastomas (GBM). The activation of this transcription factor was analyzed on tissue microarrays comprising 392 WHO 2016 GBM samples from our clinical practice. These data were correlated with patient survival using multivariable Cox analysis and, for a subset of 167 tumors, with signs of tumor invasiveness on the MRI. The effects of STAT5b knockdown by siRNA were assessed on the growth, therapeutic resistance, invasion and migration of GBM cell lines U87, U87­EGFRVIII and LN18 and primary cultures GM2 and GM3. The activation, but not the copy number or the mRNA expression of nuclear transcription factor STAT5b expression correlated inversely with patient survival independently of IDH1R132H status, age, Karnofsky Performance Score, treatment and tumor volume. STAT5b inhibition neither altered the cell proliferation nor reduced the clonogenic proliferative potency of GBM cells, and did not sensitize them to the cytotoxic effect of ionizing radiation and temozolomide in vitro. STAT5b inhibition significantly increased GBM cell migration, but decreased the invasion of some GBM cells in vitro. There was no correlation between the activation of STAT5b in clinical tumors and the extent of invasion on MRI OF patients. In conclusion, STAT5b is frequently activated in GBM and correlates inversely with patient survival. It does not contribute to the growth and resistance of these tumors, and is thus rather a potential prognostic marker than a therapeutic target in these tumors.

Determining glioma cell invasion and proliferation in ex vivo organotypic mouse brain slices using whole-mount immunostaining and tissue clearing.

The ex vivo organotypic brain slice invasion model is commonly used to study the growth dynamics of gliomas, primary brain tumors that are known for their invasive behavior. Here, we describe a protocol where the ex vivo organotypic mouse brain slice invasion model is combined with whole-mount immunostaining, tissue clearing, and 3D reconstruction, to visualize and quantify the invasion of glioma cells. In addition, we describe an approach to determine the proliferation rate of the cells within this model. For complete details on the use and execution of this protocol, please refer to Uceda-Castro et al. (2022).

Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting.

For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and automatic methods for tumor detection and generation of clinical reports, incorporating a wide range of tumor characteristics, represents a major hurdle. In this study, we investigate the most occurring brain tumor types: glioblastomas, lower grade gliomas, meningiomas, and metastases, through four cohorts of up to 4,000 patients. Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols. Segmentation performances were assessed in-depth using a wide-range of voxel and patient-wise metrics covering volume, distance, and probabilistic aspects. Finally, two software solutions have been developed, enabling an easy use of the trained models and standardized generation of clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances were quite homogeneous across the four different brain tumor types, with an average true positive Dice ranging between 80 and 90%, patient-wise recall between 88 and 98%, and patient-wise precision around 95%. In conjunction to Dice, the identified most relevant other metrics were the relative absolute volume difference, the variation of information, and the Hausdorff, Mahalanobis, and object average symmetric surface distances. With our Raidionics software, running on a desktop computer with CPU support, tumor segmentation can be performed in 16-54 s depending on the dimensions of the MRI volume. For the generation of a standardized clinical report, including the tumor segmentation and features computation, 5-15 min are necessary. All trained models have been made open-access together with the source code for both software solutions and validation metrics computation. In the future, a method to convert results from a set of metrics into a final single score would be highly desirable for easier ranking across trained models. In addition, an automatic classification of the brain tumor type would be necessary to replace manual user input. Finally, the inclusion of post-operative segmentation in both software solutions will be key for generating complete post-operative standardized clinical reports.