Recurrent adamantinomatous craniopharyngiomas show MAPK pathway activation, clonal evolution and rare TP53-loss-mediated malignant progression.

The two types of craniopharyngioma, adamantinomatous (ACP) and papillary (PCP), are clinically relevant tumours in children and adults. Although the biology of primary craniopharyngioma is starting to be unravelled, little is known about the biology of recurrence. To fill this gap in knowledge, we have analysed through methylation array, RNA sequencing and pERK1/2 immunohistochemistry a cohort of paired primary and recurrent samples (32 samples from 14 cases of ACP and 4 cases of PCP). We show the presence of copy number alterations and clonal evolution across recurrence in 6 cases of ACP, and analysis of additional whole genome sequencing data from the Children's Brain Tumour Network confirms chromosomal arm copy number changes in at least 7/67 ACP cases. The activation of the MAPK/ERK pathway, a feature previously shown in primary ACP, is observed in all but one recurrent cases of ACP. The only ACP without MAPK activation is an aggressive case of recurrent malignant human craniopharyngioma harbouring a CTNNB1 mutation and loss of TP53. Providing support for a functional role of this TP53 mutation, we show that Trp53 loss in a murine model of ACP results in aggressive tumours and reduced mouse survival. Finally, we characterise the tumour immune infiltrate showing differences in the cellular composition and spatial distribution between ACP and PCP. Together, these analyses have revealed novel insights into recurrent craniopharyngioma and provided preclinical evidence supporting the evaluation of MAPK pathway inhibitors and immunomodulatory approaches in clinical trials in against recurrent ACP.

The course of tumor-related epilepsy in glioblastoma patients: A retrospective analysis.

PURPOSE: Many patients with glioblastoma suffer from tumor-related seizures. However, there is limited data on the characteristics of tumor-related epilepsy achieving seizure freedom. The aim of this study was to characterize the course of epilepsy in patients with glioblastoma and the factors that influence it. METHODS: We retrospectively analyzed the medical records of glioblastoma patients treated at the University Hospital Erlangen between 01/2006 and 01/2020. RESULTS: In the final cohort of patients with glioblastoma (n = 520), 292 patients (56.2 %) suffered from tumor-related epilepsy (persons with epilepsy, PWE). Levetiracetam was the most commonly used first-line antiseizure medication (n = 245, 83.9 % of PWE). The onset of epilepsy was preoperative in 154/292 patients (52.7 %). 136 PWE (46.6 %) experienced only one single seizure while 27/292 PWE (9.2 %) developed drug-resistant epilepsy. Status epilepticus occurred in 48/292 patients (16.4 %). Early postoperative onset (within 30 days of surgery) of epilepsy and total gross resection (compared with debulking) were independently associated with a lower risk of further seizures. We did not detect dose-dependent pro- or antiseizure effects of radiochemotherapy. CONCLUSION: Tumor-related epilepsy occurred in more than 50% of our cohort, but drug-resistant epilepsy developed in less than 10% of cases. Epilepsy usually started before tumor surgery.

Lineage specification in glioblastoma is regulated by METTL7B.

Glioblastomas are the most common malignant brain tumors in adults; they are highly aggressive and heterogeneous and show a high degree of plasticity. Here, we show that methyltransferase-like 7B (METTL7B) is an essential regulator of lineage specification in glioblastoma, with an impact on both tumor size and invasiveness. Single-cell transcriptomic analysis of these tumors and of cerebral organoids derived from expanded potential stem cells overexpressing METTL7B reveal a regulatory role for the gene in the neural stem cell-to-astrocyte differentiation trajectory. Mechanistically, METTL7B downregulates the expression of key neuronal differentiation players, including SALL2, via post-translational modifications of histone marks.

Age-dependent increase of perineuronal nets in the human hippocampus and precocious aging in epilepsy.

OBJECTIVE: Perineuronal nets (PNN) are specialized extracellular matrix (ECM) components of the central nervous system, frequently accumulating at the surface of inhibitory GABAergic interneurons. While an altered distribution of PNN has been observed in neurological disorders including Alzheimer's disease, schizophrenia and epilepsy, their anatomical distribution also changes during physiological brain maturation and aging. Such an age-dependent shift was experimentally associated also with hippocampal engram formation during brain maturation. Our aim was to histopathologically assess PNN in the hippocampus of adult and pediatric patients with temporal lobe epilepsy (TLE) compared to age-matched post-mortem control subjects and to compare PNN-related changes with memory impairment observed in our patient cohort. METHODS: Sixty-six formalin-fixed and paraffin-embedded tissue specimens of the human hippocampus were retrieved from the European Epilepsy Brain Bank. Twenty-nine patients had histopathologically confirmed hippocampal sclerosis (HS), and eleven patients suffered from TLE without HS. PNN were immunohistochemically visualized using an antibody directed against aggrecan and manually counted from hippocampus subfields and the subiculum. RESULTS: PNN density increased with age in both human controls and TLE patients. However, their density was significantly higher in all HS patients compared to age-matched controls. Intriguingly, TLE patients presented presurgically with better memory when their hippocampal PNN density was higher (p < 0.05). SIGNIFICANCE: Our results were compatible with age-dependent ECM specialization in the human hippocampus and its precocious aging in the epileptic condition. These observations confirm recent experimental animal models and also support the notion that PNN play a role in memory formation in the human brain. PLAIN LANGUAGE SUMMARY: "Perineuronal nets" (PNN) are a specialized compartment of the extracellular matrix (ECM), especially surrounding highly active neurons of the mammalian brain. There is evidence that PNN play a role in memory formation, brain maturation, and in some pathologies like Alzheimer's disease, schizophrenia or epilepsy. In this study, we investigated the role of PNN in patients suffering from drug-resistant focal epilepsy compared to controls. We found that with increasing age, more neurons are surrounded by PNN. Similarly, all epilepsy patients but especially patients with better memory performance also had more PNN. This study raises further interest in studying ECM molecules in the human brain under physiological and pathophysiological conditions.

Artificial intelligence in histopathological image analysis of central nervous system tumours: A systematic review.

The convergence of digital pathology and artificial intelligence could assist histopathology image analysis by providing tools for rapid, automated morphological analysis. This systematic review explores the use of artificial intelligence for histopathological image analysis of digitised central nervous system (CNS) tumour slides. Comprehensive searches were conducted across EMBASE, Medline and the Cochrane Library up to June 2023 using relevant keywords. Sixty-eight suitable studies were identified and qualitatively analysed. The risk of bias was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST) criteria. All the studies were retrospective and preclinical. Gliomas were the most frequently analysed tumour type. The majority of studies used convolutional neural networks or support vector machines, and the most common goal of the model was for tumour classification and/or grading from haematoxylin and eosin-stained slides. The majority of studies were conducted when legacy World Health Organisation (WHO) classifications were in place, which at the time relied predominantly on histological (morphological) features but have since been superseded by molecular advances. Overall, there was a high risk of bias in all studies analysed. Persistent issues included inadequate transparency in reporting the number of patients and/or images within the model development and testing cohorts, absence of external validation, and insufficient recognition of batch effects in multi-institutional datasets. Based on these findings, we outline practical recommendations for future work including a framework for clinical implementation, in particular, better informing the artificial intelligence community of the needs of the neuropathologist.

Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2-4 glioma patients: a deep Radiomics Approach.

BACKGROUND: The role of isocitrate dehydrogenase (IDH) mutation status for glioma stratification and prognosis is established. While structural magnetic resonance image (MRI) is a promising biomarker, it may not be sufficient for non-invasive characterisation of IDH mutation status. We investigated the diagnostic value of combined diffusion tensor imaging (DTI) and structural MRI enhanced by a deep radiomics approach based on convolutional neural networks (CNNs) and support vector machine (SVM), to determine the IDH mutation status in Central Nervous System World Health Organization (CNS WHO) grade 2-4 gliomas. METHODS: This retrospective study analyzed the DTI-derived fractional anisotropy (FA) and mean diffusivity (MD) images and structural images including fluid attenuated inversion recovery (FLAIR), non-enhanced T1-, and T2-weighted images of 206 treatment-naïve gliomas, including 146 IDH mutant and 60 IDH-wildtype ones. The lesions were manually segmented by experienced neuroradiologists and the masks were applied to the FA and MD maps. Deep radiomics features were extracted from each subject by applying a pre-trained CNN and statistical description. An SVM classifier was applied to predict IDH status using imaging features in combination with demographic data. RESULTS: We comparatively assessed the CNN-SVM classifier performance in predicting IDH mutation status using standalone and combined structural and DTI-based imaging features. Combined imaging features surpassed stand-alone modalities for the prediction of IDH mutation status [area under the curve (AUC) = 0.846; sensitivity = 0.925; and specificity = 0.567]. Importantly, optimal model performance was noted following the addition of demographic data (patients' age) to structural and DTI imaging features [area under the curve (AUC) = 0.847; sensitivity = 0.911; and specificity = 0.617]. CONCLUSIONS: Imaging features derived from DTI-based FA and MD maps combined with structural MRI, have superior diagnostic value to that provided by standalone structural or DTI sequences. In combination with demographic information, this CNN-SVM model offers a further enhanced non-invasive prediction of IDH mutation status in gliomas.

Isocitrate Dehydrogenase 1/2 Wildtype Adult Astrocytoma with WHO Grade 2/3 Histological Features: Molecular Re-Classification, Prognostic Factors, Clinical Outcomes.

BACKGROUND: Isocitrate Dehydrogenase 1/2 (IDH 1/2)-wildtype (WT) astrocytomas constitute a heterogeneous group of tumors and have undergone a series of diagnostic reclassifications over time. This study aimed to investigate molecular markers, clinical, imaging, and treatment factors predictive of outcomes in WHO grade 2/3 IDH-WT astrocytomas ('early glioblastoma'). METHODOLOGY: Patients with WHO grade 2/3 IDH-WT astrocytomas were identified from the hospital archives. They were cross-referenced with the electronic medical records systems, including neuroimaging. The expert neuro-pathology team retrieved data on molecular markers-MGMT, TERT, IDH, and EGFR. Tumors with a TERT mutation and/or EGFR amplification were reclassified as glioblastoma. RESULTS: Fifty-four patients were identified. Sixty-three percent of the patients could be conclusively reclassified as glioblastoma based on either TERT mutation, EGFR amplification, or both. On imaging, 65% showed gadolinium enhancement on MRI. Thirty-nine patients (72%) received long-course radiotherapy, of whom 64% received concurrent chemotherapy. The median follow-up of the group was 16 months (range: 2-90), and the median overall survival (OS) was 17.3 months. The 2-year OS of the whole cohort was 31%. On univariate analysis, older age, worse performance status (PS), and presence versus absence of contrast enhancement on diagnostic MRI were statistically significant for poorer OS. CONCLUSION: IDH-WT WHO grade 2/3 astrocytomas are a heterogeneous group of tumors with poor clinical outcomes. The majority can be reclassified as glioblastoma, based on current WHO classification criteria, but further understanding of the underlying biology of these tumors and the discovery of novel targeted agents are needed for better outcomes.

Topographical anatomy of the subthalamic region with special interest in the human medial forebrain bundle.

OBJECTIVE: The medial forebrain bundle (MFB) is a novel promising deep brain stimulation (DBS) target in severe affective disorders that courses through the subthalamic region according to tractography studies. Its potential therapeutic role arose in connection with the development of hypomania during stimulation of the subthalamic nucleus (STN) in Parkinson's disease, offering an alternative explanation for the occurrence of this side effect. However, until now its course exclusively described by tractography had not yet been confirmed by any anatomical method. The aim of this study was to fill this gap as well as to provide a detailed description of the fiber tracts surrounding the STN to facilitate a better understanding of the background of side effects occurring during STN DBS. METHODS: Ten human cadaveric brains (20 hemispheres) and 100 healthy subjects (200 hemispheres) from the S500 Release of the Human Connectome Project were involved in this study. Nineteen hemispheres were dissected according to Klingler's method. One additional hemisphere was prepared for histological examinations to validate the macroscopical results and stained with neurofibril silver impregnation according to Krutsay. The authors also aimed to reconstruct the MFB using tractography and correlated the results with their dissections and histological findings. RESULTS: The white matter connections coursing through the subthalamic region were successfully dissected. The ansa lenticularis, lenticular fasciculus, thalamic fasciculus, ipsi- and contralateral cerebellar fibers, and medial lemniscus were revealed as closely related fiber tracts to the STN. However, the existence of a distinct fiber bundle corresponding to the MFB described by tractography could not be identified. Using tractography, the authors showed that the depiction of the streamlines representing the MFB was also strongly dependent on the threshold parameters. CONCLUSIONS: According to this study's findings, the streamlines of the MFB described by tractography arise from the limitations of the diffusion-weighted MRI fiber tracking method and actually correspond to subthalamic fiber bundles, especially the ansa lenticularis and lenticular fasciculus, which erroneously continue in the anterior limb of the internal capsule, toward the prefrontal cortex.

Defining benchmark outcomes for mesial temporal lobe epilepsy surgery: A global multicenter analysis of 1119 cases.

OBJECTIVE: Benchmarking has been proposed to reflect surgical quality and represents the highest standard reference values for desirable results. We sought to determine benchmark outcomes in patients after surgery for drug-resistant mesial temporal lobe epilepsy (MTLE). METHODS: This retrospective multicenter study included patients who underwent MTLE surgery at 19 expert centers on five continents. Benchmarks were defined for 15 endpoints covering surgery and epilepsy outcome at discharge, 1 year after surgery, and the last available follow-up. Patients were risk-stratified by applying outcome-relevant comorbidities, and benchmarks were calculated for low-risk ("benchmark") cases. Respective measures were derived from the median value at each center, and the 75th percentile was considered the benchmark cutoff. RESULTS: A total of 1119 patients with a mean age (range) of 36.7 (1-74) years and a male-to-female ratio of 1:1.1 were included. Most patients (59.2%) underwent anterior temporal lobe resection with amygdalohippocampectomy. The overall rate of complications or neurological deficits was 14.4%, with no in-hospital death. After risk stratification, 377 (33.7%) benchmark cases of 1119 patients were identified, representing 13.6%-72.9% of cases per center and leaving 742 patients in the high-risk cohort. Benchmark cutoffs for any complication, clinically apparent stroke, and reoperation rate at discharge were ≤24.6%, ≤.5%, and ≤3.9%, respectively. A favorable seizure outcome (defined as International League Against Epilepsy class I and II) was reached in 83.6% at 1 year and 79.0% at the last follow-up in benchmark cases, leading to benchmark cutoffs of ≥75.2% (1-year follow-up) and ≥69.5% (mean follow-up of 39.0 months). SIGNIFICANCE: This study presents internationally applicable benchmark outcomes for the efficacy and safety of MTLE surgery. It may allow for comparison between centers, patient registries, and novel surgical and interventional techniques.

Interictal Electrical Source Imaging.

SUMMARY: Interictal electrical source imaging (ESI) determines the neuronal generators of epileptic activity in EEG occurring outside of seizures. It uses computational models to take anatomic and neuronal characteristics of the individual patient into account. The presented article provides an overview of application and clinical value of interictal ESI in patients with pharmacoresistant focal epilepsies undergoing evaluation for surgery. Neurophysiological constraints of interictal data are discussed and technical considerations are summarized. Typical indications are covered as well as issues of integration into clinical routine. Finally, an outlook on novel markers of epilepsy for interictal source analysis is presented. Interictal ESI provides diagnostic performance on par with other established methods, such as MRI, PET, or SPECT. Although its accuracy benefits from high-density recordings, it provides valuable information already when applied to EEG with only a limited number of electrodes with complete coverage. Novel oscillatory markers and the integration of frequency coupling and connectivity may further improve accuracy and efficiency.

Nerve biopsy in T-cell lymphoma with neurolymphomatosis: where and when.

Peripheral T-cell lymphomas are rare heterogeneous haematological malignancies that may also involve peripheral nerves in a very small subset of cases. We report a patient with a diagnostically challenging cutaneous T-cell lymphoma and multifocal mononeuropathies in whom a targeted nerve biopsy identified lymphomatous infiltration of nerves and expedited combination treatment with chemotherapy and an autologous stem cell transplant. She showed an excellent response with a complete metabolic response on positron emission tomography imaging and significant clinical improvement, maintained 5 years post-treatment.

Extracting structured information from unstructured histopathology reports using generative pre-trained transformer 4 (GPT-4).

Deep learning applied to whole-slide histopathology images (WSIs) has the potential to enhance precision oncology and alleviate the workload of experts. However, developing these models necessitates large amounts of data with ground truth labels, which can be both time-consuming and expensive to obtain. Pathology reports are typically unstructured or poorly structured texts, and efforts to implement structured reporting templates have been unsuccessful, as these efforts lead to perceived extra workload. In this study, we hypothesised that large language models (LLMs), such as the generative pre-trained transformer 4 (GPT-4), can extract structured data from unstructured plain language reports using a zero-shot approach without requiring any re-training. We tested this hypothesis by utilising GPT-4 to extract information from histopathological reports, focusing on two extensive sets of pathology reports for colorectal cancer and glioblastoma. We found a high concordance between LLM-generated structured data and human-generated structured data. Consequently, LLMs could potentially be employed routinely to extract ground truth data for machine learning from unstructured pathology reports in the future. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Direct image to subtype prediction for brain tumors using deep learning.

Background: Deep Learning (DL) can predict molecular alterations of solid tumors directly from routine histopathology slides. Since the 2021 update of the World Health Organization (WHO) diagnostic criteria, the classification of brain tumors integrates both histopathological and molecular information. We hypothesize that DL can predict molecular alterations as well as WHO subtyping of brain tumors from hematoxylin and eosin-stained histopathology slides. Methods: We used weakly supervised DL and applied it to three large cohorts of brain tumor samples, comprising N = 2845 patients. Results: We found that the key molecular alterations for subtyping, IDH and ATRX, as well as 1p19q codeletion, were predictable from histology with an area under the receiver operating characteristic curve (AUROC) of 0.95, 0.90, and 0.80 in the training cohort, respectively. These findings were upheld in external validation cohorts with AUROCs of 0.90, 0.79, and 0.87 for prediction of IDH, ATRX, and 1p19q codeletion, respectively. Conclusions: In the future, such DL-based implementations could ease diagnostic workflows, particularly for situations in which advanced molecular testing is not readily available.