Die Neuroonkologie umfasst ein breites Spektrum von Erkrankungen, darunter primäre Tumore und Metastasen im Nervensystem, Tumorprädispositionssyndrome und Tumortherapie-assoziierte neurologische Symptome. Diagnostische Kriterien und Therapiestrategien entwickeln sich dabei stetig weiter. Das vorliegende Werk greift diese Dynamik auf und ist ebenso für Einsteiger wie für erfahrene Kliniker konzipiert. Grundlagenkapitel vermitteln einen kompakten Einblick in relevante Kenntnisse für den klinischen Alltag und über 30 Fallbeispiele behandeln die einzelnen neuroonkologischen Erkrankungen. Die Herausforderungen einer evidenzorientierten und multiprofessionellen Behandlungsstrategie werden dabei systematisch aufgezeigt. Im Zeitalter der Personalisierten Medizin ist auch in der Neuroonkologie ein grundlegender Wandel eingetreten, v.a. durch die Definition prognostischer und prädiktiver molekularer Marker sowie die Biomarker-basierte Gestaltung klinischer Studien. So definiert z.B. die aktuelle WHO-Klassifikation 2021 eine Vielzahl neuer molekularer diagnostischer Kriterien, die in diesem Werk erstmals in deutscher Sprache erläutert werden.

Abstract: Intra-tumoral heterogeneity and cell-state plasticity are key drivers for the therapeutic resistance of glioblastoma. Here, we investigate the association between spatial cellular organization and glioblastoma prognosis. Leveraging single-cell RNA-seq and spatial transcriptomics data, we develop a deep learning model to predict transcriptional subtypes of glioblastoma cells from histology images. Employing this model, we phenotypically analyze 40 million tissue spots from 410 patients and identify consistent associations between tumor architecture and prognosis across two independent cohorts. Patients with poor prognosis exhibit higher proportions of tumor cells expressing a hypoxia-induced transcriptional program. Furthermore, a clustering pattern of astrocyte-like tumor cells is associated with worse prognosis, while dispersion and connection of the astrocytes with other transcriptional subtypes correlate with decreased risk. To validate these results, we develop a separate deep learning model that utilizes histology images to predict prognosis. Applying this model to spatial transcriptomics data reveal survival-associated regional gene expression programs. Overall, our study presents a scalable approach to unravel the transcriptional heterogeneity of glioblastoma and establishes a critical connection between spatial cellular architecture and clinical outcomes

Abstract: When it comes to the human brain, models that closely mimic in vivo conditions are lacking. Living neuronal tissue is the closest representation of the in vivo human brain outside of a living person. Here, we present a method that can be used to maintain therapeutically resected healthy neuronal tissue for prolonged periods without any discernible changes in tissue vitality, evidenced by immunohistochemistry, genetic expression, and electrophysiology. This method was then used to assess glioblastoma (GBM) progression in its natural environment by microinjection of patient-derived tumor cells into cultured sections. The result closely resembles the pattern of de novo tumor growth and invasion, drug therapy response, and cytokine environment. Reactive transformation of astrocytes, as an example of the cellular nonmalignant tumor environment, can be accurately simulated with transcriptional differences similar to those of astrocytes isolated from acute GBM specimens. In a nutshell, we present a simple method to study GBM in its physiological environment, from which valuable insights can be gained. This technique can lead to further advancements in neuroscience, neuro-oncology, and pharmacotherapy

Abstract: Enhanced fatty acid synthesis is a hallmark of tumors, including glioblastoma. SREBF1/2 regulate the expression of enzymes involved in fatty acid and cholesterol synthesis. Yet, little is known about the precise mechanism regulating SREBP gene expression in glioblastoma. Here, we show that a novel interaction between the co-activator/co-repressor CTBP and the tumor suppressor ZBTB18 regulates the expression of SREBP genes. In line with our findings, metabolic assays and glucose tracing analysis confirm the reduction in several phospholipid species upon ZBTB18 expression. Our study identifies CTBP1/2 and LSD1 as co-activators of SREBP genes and indicates that the functional activity of the CTBP-LSD1 complex is altered by ZBTB18. ZBTB18 binding to the SREBP gene promoters is associated with reduced LSD1 demethylase activity of H3K4me2 and H3K9me2 marks. Concomitantly, the interaction between LSD1, CTBP, and ZNF217 is increased, suggesting that ZBTB18 promotes LSD1 scaffolding function. Our results outline a new epigenetic mechanism enrolled by ZBTB18 and its co-factors to regulate fatty acid synthesis that could be targeted to treat glioblastoma patients

Abstract: Molecular precision oncology faces two major challenges: first, to identify relevant and actionable molecular variants in a rapidly changing field and second, to provide access to a broad patient population. Here, we report a four-year experience of the Molecular Tumor Board (MTB) of the Comprehensive Cancer Center Freiburg (Germany) including workflows and process optimizations. This retrospective single-center study includes data on 488 patients enrolled in the MTB from February 2015 through December 2018. Recommendations include individual molecular diagnostics, molecular stratified therapies, assessment of treatment adherence and patient outcomes including overall survival. The majority of MTB patients presented with stage IV oncologic malignancies (90.6%) and underwent an average of 2.1 previous lines of therapy. Individual diagnostic recommendations were given to 487 patients (99.8%). A treatment recommendation was given in 264 of all cases (54.1%) which included a molecularly matched treatment in 212 patients (43.4%). The 264 treatment recommendations were implemented in 76 patients (28.8%). Stable disease was observed in 19 patients (25.0%), 17 had partial response (22.4%) and five showed a complete remission (6.6%). An objective response was achieved in 28.9% of cases with implemented recommendations and for 4.5% of the total population (22 of 488 patients). By optimizing the MTB workflow, case-discussions per session increased significantly while treatment adherence and outcome remained stable over time. Our data demonstrate the feasibility and effectiveness of molecular-guided personalized therapy for cancer patients in a clinical routine setting showing a low but robust and durable disease control rate over time

Abstract: n the past, changes of the Apparent Diffusion Coefficient in glioblastoma multiforme have been shown to be related to specific genes and described as being associated with survival. The purpose of this study was to investigate diffusion imaging parameters in combination with genome-wide expression data in order to obtain a comprehensive characterisation of the transcriptomic changes indicated by diffusion imaging parameters. Diffusion-weighted imaging, molecular and clinical data were collected prospectively in 21 patients. Before surgery, MRI diffusion metrics such as axial (AD), radial (RD), mean diffusivity (MD) and fractional anisotropy (FA) were assessed from the contrast enhancing tumour regions. Intraoperatively, tissue was sampled from the same areas using neuronavigation. Transcriptional data of the tissue samples was analysed by Weighted Gene Co-Expression Network Analysis (WGCNA) thus classifying genes into modules based on their network-based affiliations. Subsequent Gene Set Enrichment Analysis (GSEA) identified biological functions or pathways of the expression modules. Network analysis showed a strong association between FA and epithelial-to-mesenchymal-transition (EMT) pathway activation. Also, patients with high FA had a worse clinical outcome. MD correlated with neural function related genes and patients with high MD values had longer overall survival. In conclusion, FA and MD are associated with distinct molecular patterns and opposed clinical outcomes

Abstract: Single-cell RNA-sequencing (scRNA-seq) is becoming a ubiquitous method in profiling the cellular transcriptomes of both malignant and non-malignant cells from the human brain. Here, we present a protocol to isolate viable tumor cells from human ex vivo glioblastoma cultures for single-cell transcriptomic analysis. We describe steps including surgical tissue collection, sectioning, culturing, primary tumor cells inoculation, growth tracking, fluorescence-based cell sorting, and population-enriched scRNA-seq. This comprehensive methodology empowers in-depth understanding of brain tumor biology at the single-cell level. For complete details on the use and execution of this protocol, please refer to Ravi et al.1