Vera Nickl

Rapid advances in computer vision (CV) and artificial intelligence have opened new avenues for digital pathology, including the diagnosis and treatment of central nervous system (CNS) tumors. In addition to reviewing the state-of-the-art in CV-based digital pathology and highlighting its potential to revolutionize the field, this chapter also provides a general introduction to digital pathology and Machine Learning (ML) for neuropathologists. Although currently limited to research, the integration of CV tools into digital pathology already offers significant advantages, such as automating tissue analysis and providing quantitative assessments. The transition from research to clinical application is slowly gaining momentum. To provide neuropathologists with the necessary skills to succeed in digital pathology and ML, the chapter also discusses how physicians and researchers can create custom models and tools tailored to specific needs using tools such as nnU-Net, deepflash2, and PathML. Emphasis is placed on the importance of interdisciplinary collaboration and continued research to fully realize the potential of CV in digital pathology for CNS tumors, to address the challenges of workforce shortages and increased workloads in neuropathology.

Objective: Vestibular schwannomas (VS), benign tumors stemming from the eighth cranial nerve’s Schwann cells, are associated with Merlin gene mutations, inflammation, and the tumor microenvironment (TME), influencing tumor initiation, maintenance, and potential neural dysfunction. Understanding TME composition holds promise for systemic therapeutic interventions, particularly for NF2-related schwannomatosis.Methodology: A retrospective analysis of paraffin-embedded tissue from 40 patients (2013-2020), evenly divided by neurofibromatosis type 2 status, with further stratification based on magnetic resonance imaging (MRI) progression and hearing function. Immunohistochemistry assessed TME components, including T-cell markers (CD4, CD8, CD25), NK cells (CD7), and macrophages (CD14, CD68, CD163, CCR2). Fiji software facilitated image analysis.Results: T-cell markers (CD4, CD8, CD7) exhibited low expression in VS, with no significant NF2-associated vs. sporadic distinctions. Macrophage-related markers (CD14, CD68, CD163, CCR2) showed significantly higher expression (CD14: p = 0.0187, CD68: p < 0.0001, CD163: p = 0.0006, CCR2: p < 0.0001). CCR2 and CD163 significantly differed between NF2-associated and sporadic VS. iNOS, an M1-macrophage marker, was downregulated. CD25, a regulatory T-cell marker, correlated significantly with tumor growth dynamics (p = 0.016).Discussion: Immune cells, notably monocytes and macrophages, crucially contribute to VS pathogenesis in both NF2-associated and sporadic cases. Significant differences in CCR2 and CD163 expression suggest distinct immune responses. Regulatory T-cells may serve as growth dynamic markers. These findings highlight immune cells as potential biomarkers and therapeutic targets for managing VS.

Glioblastoma, IDH-wild type, CNS WHO grade 4 (GBM) is a primary brain tumor associated with poor patient survival despite aggressive treatment. Developing realistic ex vivo models remain challenging. Patient-derived 3-dimensional organoid (PDO) models offer innovative platforms that capture the phenotypic and molecular heterogeneity of GBM, while preserving key characteristics of the original tumors. However, manual dissection for PDO generation is time-consuming, expensive and can result in a number of irregular and unevenly sized PDOs. This study presents an innovative method for PDO production using an automated tissue chopper. Tumor samples from four GBM and one astrocytoma, IDH-mutant, CNS WHO grade 2 patients were processed manually as well as using the tissue chopper. In the manual approach, the tumor material was dissected using scalpels under microscopic control, while the tissue chopper was employed at three different angles. Following culture on an orbital shaker at 37 °C, morphological changes were evaluated using bright field microscopy, while proliferation (Ki67) and apoptosis (CC3) were assessed by immunofluorescence after 6 weeks. The tissue chopper method reduced almost 70% of the manufacturing time and resulted in a significantly higher PDOs mean count compared to the manually processed tissue from the second week onwards (week 2: 801 vs. 601, P = 0.018; week 3: 1105 vs. 771, P = 0.032; and week 4:1195 vs. 784, P < 0.01). Quality assessment revealed similar rates of tumor-cell apoptosis and proliferation for both manufacturing methods. Therefore, the automated tissue chopper method offers a more efficient approach in terms of time and PDO yield. This method holds promise for drug- or immunotherapy-screening of GBM patients.

While glioblastoma (GBM) is still challenging to treat, novel immunotherapeutic approaches have shown promising effects in preclinical settings. However, their clinical breakthrough is hampered by complex interactions of GBM with the tumor microenvironment (TME). Here, we present an analysis of TME composition in a patient-derived organoid model (PDO) as well as in organotypic slice cultures (OSC). To obtain a more realistic model for immunotherapeutic testing, we introduce an enhanced PDO model. We manufactured PDOs and OSCs from fresh tissue of GBM patients and analyzed the TME. Enhanced PDOs (ePDOs) were obtained via co-culture with PBMCs (peripheral blood mononuclear cells) and compared to normal PDOs (nPDOs) and PT (primary tissue). At first, we showed that TME was not sustained in PDOs after a short time of culture. In contrast, TME was largely maintained in OSCs. Unfortunately, OSCs can only be cultured for up to 9 days. Thus, we enhanced the TME in PDOs by co-culturing PDOs and PBMCs from healthy donors. These cellular TME patterns could be preserved until day 21. The ePDO approach could mirror the interaction of GBM, TME and immunotherapeutic agents and may consequently represent a realistic model for individual immunotherapeutic drug testing in the future.

Glioblastoma (GBM) displays a wide range of inter- and intra-tumoral heterogeneity contributing to therapeutic resistance and relapse. Although Tumor Treating Fields (TTFields) are effective for the treatment of GBM, there is a lack of ex vivo models to evaluate effects on patients’ tumor biology or to screen patients for treatment efficacy. Thus, we adapted patient-derived three-dimensional tissue culture models to be compatible with TTFields application to tissue culture. Patient-derived primary cells (PDPC) were seeded onto murine organotypic hippocampal slice cultures (OHSC), and microtumor development with and without TTFields at 200 kHz was observed. In addition, organoids were generated from acute material cultured on OHSC and treated with TTFields. Lastly, the effect of TTFields on expression of the Ki67 proliferation marker was evaluated on cultured GBM slices. Microtumors exhibited increased sensitivity towards TTFields compared to monolayer cell cultures. TTFields affected tumor growth and viability, as the size of microtumors and the percentage of Ki67-positive cells decreased after treatment. Nevertheless, variability in the extent of the response was preserved between different patient samples. Therefore, these pre-clinical GBM models could provide snapshots of the tumor to simulate patient treatment response and to investigate molecular mechanisms of response and resistance.