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Crossing the blood-brain barrier is a crucial, rate-limiting step of brain metastasis. Understanding of the mechanisms of cancer cell extravasation from brain microcapillaries is limited as the underlying cellular and molecular processes cannot be adequately investigated using in vitro models and end-point in vivo experiments. Using ultrastructural and functional imaging, we demonstrate that dynamic changes of activated brain microcapillaries promote the mandatory first steps of brain colonization. Successful extravasation of arrested cancer cells occurred when adjacent capillary endothelial cells (ECs) entered into a distinct remodeling process. After extravasation, capillary loops were formed, which was characteristic of aggressive metastatic growth. Upon cancer cell arrest in brain microcapillaries, matrix-metalloprotease 9 (MMP9) was expressed. Inhibition of MMP2/9 and genetic perturbation of MMP9 in cancer cells, but not the host, reduced EC projections, extravasation, and brain metastasis outgrowth. These findings establish an active role of ECs in the process of cancer cell extravasation, facilitated by crosstalk between the two cell types. This extends our understanding of how host cells can contribute to brain metastasis formation and how to prevent it.

Diffuse gliomas are primary brain tumors associated with a poor prognosis. Cellular and molecular mechanisms driving the invasive growth patterns and therapeutic resistance are incompletely understood. The emerging field of cancer neuroscience offers a novel approach to study these brain tumors in the context of their intricate interactions with the nervous system employing and combining methodological toolsets from neuroscience and oncology. Increasing evidence has shown how neurodevelopmental and neuronal-like mechanisms are hijacked leading to the discovery of multicellular brain tumor networks. Here, we review how gap junction-coupled tumor-tumor-astrocyte networks, as well as synaptic and paracrine neuron-tumor networks drive glioma progression. Molecular mechanisms of these malignant, homo- and heterotypic networks, and their complex interplay are reviewed. Lastly, potential clinical-translational implications and resulting therapeutic strategies are discussed.

Summary Background and Objectives Recent advancements in large language models (LLMs) such as GPT-3.5 and GPT-4 have shown impressive potential in a wide array of applications, including healthcare. While GPT-3.5 and GPT-4 showed heterogeneous results across specialized medical board examinations, the performance of these models in neurology board exams remains unexplored. Methods An exploratory, prospective study was conducted between May 17 and May 31, 2023. The evaluation utilized a question bank approved by the American Board of Psychiatry and Neurology and were validated with a small question cohort by the European Board for Neurology. All questions were categorized into lower-order (recall, understanding) and higher-order (apply, analyze, synthesize) questions based on Bloom Taxonomy for learning and assessment. Performance was assessed in relation to overall scores, question type, and topics, along with the confidence level and reproducibility of answers. Results GPT-4 significantly outperformed GPT-3.5 by correctly answering 85% of 1956 questions compared to GPT-3.5 (66.8%) Notably, GPT-4's performance was above the average human score of 73.8%, effectively achieving near-passing and passing grades in the neurology board exam. GPT-4 outperformed human users in Behavioral, Cognitive and Psych-related questions and demonstrated superior performance to GPT-3.5 in six categories. Both models performed better on lower-order than higher-order questions (GPT4: 790 of 893 (88.5%) vs. 872 of 1063 (82%), GPT-3.5: 639 of 893 (71.6%) vs. 667 of 1063 (62.7%)) with GPT-4 also excelling in both lower-order and higher-order questions. Both models consistently used confident language, even when providing incorrect answers (GPT-4: 99.3%, 294 incorrect answers, GPT-3.5: 100%, 650 incorrect answers). Reproducible answers of GPT-3.5 and GPT-4 (more than 75 % same output across 50 independent queries) were associated with a higher percentage of correct answers (GPT-3.5: 66 of 88 (75%), GPT-4: 78 of 96 (81.3%)) than inconsistent answers, (GPT-3.5: 5 of 13 (38.5%), GPT-4: 1 of 4 (25%)). Discussion Despite the absence of neurology-specific training, GPT-4 demonstrated commendable performance, whereas GPT-3.5 performed slightly below the human average. While higher-order cognitive tasks were more challenging for both models, GPT-4's results were equivalent to passing grades in specialized neurology exams. These findings suggests that LLMs like GPT-4 could have significant applications in clinical neurology and healthcare with further refinements.

Background Glioblastomas are characterized by aggressive and infiltrative growth, and by striking heterogeneity. The aim of this study was to investigate whether tumor cell proliferation and invasion are interrelated, or rather distinct features of different cell populations. Methods Tumor cell invasion and proliferation was longitudinally determined in real time using 3D in vivo two-photon laser scanning microscopy over weeks. Glioblastoma cells expressed fluorescent markers that permitted the identification of their mitotic history or their cycling versus non-cycling cell state. Results Live reporter systems were established that allowed to dynamically determine the invasive behavior, and previous or actual proliferation of distinct glioblastoma cells, in different tumor regions and disease stages over time. Particularly invasive tumor cells that migrated far away from the main tumor mass, when followed over weeks, had a history of marked proliferation and maintained their proliferative capacity during brain colonization. Infiltrating cells showed fewer connections to the multicellular tumor cell network, a typical feature of gliomas. Once tumor cells colonized a new brain region, their phenotype progressively transitioned into tumor microtube-rich, interconnected, slower-cycling glioblastoma cells. Analysis of resected human glioblastomas confirmed a higher proliferative potential of tumor cells from the invasion zone. Conclusion The detection of glioblastoma cells that harbor both particularly high proliferative and invasive capabilities during brain tumor progression provides valuable insights into the interrelatedness of proliferation and migration — two central traits of malignancy in glioma. This contributes to our understanding how the brain is efficiently colonized in this disease.

Ongoing efforts in the field of Cancer Neuroscience seek to improve the understanding of neuron-cancer interactions and their effects on tumor progression. In glioma, synaptic neuronal input to tumor cells drives tumor progression and invasion. Even though neuron-cancer interactions have been described in brain metastases and extracranial tumors as well, the mechanisms of action between the nervous system and tumor cells are incompletely understood. Systematic transcriptomic analyses to detect overarching principles of neuron-tumor interaction have been lacking so far. Here, we performed focused integrative transcriptomic and proteomic analyses in more than 10 different extracranial tumor entities and brain metastases to analyse potential neuron-tumor interactions.Neurotransmitters receptor genes were heterogeneously expressed across extracranial tumor entities and brain metastases. We used Markov Affinity-based Graph Imputation of Cells to recover gene-gene relationships to robustly identify more than 50 initial gene co-expression networks (GCNs) associated with neurotransmitter genes. Molecular pathways that are associated with neurotransmitter receptor gene expression show heterogenous and overlapping neuronal gene expression programs in tumor cell subpopulations. While some GCNs showed overarching expression patterns of neuronal interactions across many entities including brain metastases as well as extracranial tumors, some GCNs were specialised signatures for certain tumor types. To characterise underlying gene regulatory networks, we performed transcription factor analyses. Certain GCNs were clearly associated with specific transcriptional regulators and known tumor biological functions. To validate our results, we further analysed the GCNs in bulk RNA datasets as well as on a protein level. We were able to identify neuronal gene expression patterns that were associated with worse survival across certain tumor entities.The results suggest that brain metastases and extracranial tumors have multiple biologically relevant mechanisms of interaction with the nervous system. These analyses are the foundation for further validation and clinical translation.

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