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Aim The role of chemokines and their receptors, which regulate trafficking and homing of leucocytes to inflamed organs in human or murine autoimmune neuritis, has not yet been elucidated in detail, Therefore, the role of the chemokine receptors CXCR4 and CXCR7 and their ligand CXCL12 was studied in autoimmune-mediated inflammation of the peripheral nervous system. Methods CXCL12/ CXCR4 and/or CXCL12/ CXCR7 interactions were specifically inhibited by the compounds AMD3100 or CCX771, respectively, in experimental autoimmune neuritis ( EAN) of C57 BL/6 J mice immunized with P0106-125 peptide. Results Disease activity was significantly suppressed by blocking CXCR7 while antagonization of CXCR4 enhanced disease activity. Enhanced disease activity was accompanied by significantly increased transcription of IFN-γ, IL-12 and TNF-α mRNA in regional lymph nodes and spleen as well as by increased serum levels of IFN-γ. Furthermore, by blocking CXCR4, expression of the cell adhesion molecules ICAM-1 and VCAM-1 was upregulated on vascular endothelial cells of the sciatic nerve, which coincided with significantly increased infiltration of the sciatic nerve by CD4+ T cells and macrophages. Remarkably, combined antagonization of both CXCR4 and CXCR7 significantly suppressed disease activity. This was accompanied by increased frequencies of activated and highly IFN-γ-expressing, P0106-125-specific T cells in regional lymph nodes and spleen; however, these cells were unable to infiltrate the sciatic nerve. Conclusion These data suggest differential and hierarchically ordered roles for CXCR4/ CXCL12- vs. CXCR7/ CXCL12-dependent effects during EAN: CXCR7/ CXCL12 interaction is a gatekeeper for pathogenic cells, regardless of their CXCR4/ CXCL12-dependent state of activation. [ABSTRACT FROM AUTHOR]

Background: Stimulated Raman histology (SRH) is a label-free optical imaging method for rapid intraoperative analysis of fresh tissue samples. Analysis of SRH images using Convolutional Neural Networks (CNN) has shown promising results for predicting the main histopathological classes of neurooncological tumors. Due to the relatively low number of rare tumor representations in CNN training datasets, a valid prediction of rarer entities remains limited. To develop new reliable analysis tools, larger datasets and greater tumor variety are crucial. One way to accomplish this is through research biobanks storing frozen tumor tissue samples. However, there is currently no data available regarding the pertinency of previously frozen tissue samples for SRH analysis. The aim of this study was to assess image quality and perform a comparative reliability analysis of artificial intelligence-based tumor classification using SRH in fresh and frozen tissue samples., Methods: In a monocentric prospective study, tissue samples from 25 patients undergoing brain tumor resection were obtained. SRH was acquired in fresh and defrosted samples of the same specimen after varying storage durations at -80 °C. Image quality was rated by an experienced neuropathologist, and prediction of histopathological diagnosis was performed using two established CNNs., Results: The image quality of SRH in fresh and defrosted tissue samples was high, with a mean image quality score of 1.96 (range 1-5) for both groups. CNN analysis showed high internal consistency for histo-(Cα 0.95) and molecular (Cα 0.83) pathological tumor classification. The results were confirmed using a dataset with samples from the local tumor biobank (Cα 0.91 and 0.53)., Conclusions: Our results showed that SRH appears comparably reliable in fresh and frozen tissue samples, enabling the integration of tumor biobank specimens to potentially improve the diagnostic range and reliability of CNN prediction tools.

CIC fusions have been described in two different central nervous system (CNS) tumor entities. On one hand, fusions of CIC or ATXN1 genes belonging to the same complex of transcriptional repressors, were reported in the CIC-rearranged, sarcoma (SARC-CIC). The diagnosis of this tumor type, which was recently added to the World Health Organization (WHO) Classification of CNS tumors, is difficult mainly because the data concerning its histopathology (as compared to its soft tissue counterpart), immunoprofile, and clinical as well as radiological characteristics are scarce in the literature. On the other hand, a recent study, based on DNA-methylation profiling, has identified a novel high-grade neuroepithelial tumor characterized by recurrent CIC fusions (HGNET-CIC). The aim of this multicentric study was to characterize a cohort of 15 primary CNS tumors harboring a CIC or ATXN1 fusion in terms of clinical, radiological, histopathological, immunophenotypical, and epigenetic characteristics. According to the integrated diagnoses, 14/15 tumors corresponded to SARC-CIC, and only one to HGNET-CIC. The tumors showed similar clinical (mainly pediatric), radiological (mostly supratentorial, cystic, and contrast enhancing), immunophenotypical (common expression of glioneuronal markers), and genetic (similar spectrum of fusions) profiles but their histopathological appearance was clearly distinct. Moreover, we found a novel fusion transcript (CIC::EWSR1) in a SARC-CIC. Most DNA methylation profiles using the Heidelberg Brain Tumor Classifier (v12.8) annotated the samples to the methylation class "SARC-CIC" (9/14 tumors with available data). By using uniform manifold approximation and projection analysis, four other samples were classified as SARC-CIC and another clustered within the methylation class of HGNET-CIC. Our findings confirm that CNS CIC-fused tumors do not represent a single molecular tumor entity. Further analyses are needed to characterize HGNET-CIC in more detail. These results may help to refine the essential diagnostic criteria for SARC-CIC and their terminology (with a suggested consensual name of sarcoma, CIC/ATXN1-complex rearranged)., (© 2024 The Author(s). Brain Pathology published by John Wiley & Sons Ltd on behalf of International Society of Neuropathology.)

Purpose: Recent artificial intelligence algorithms aided intraoperative decision-making via stimulated Raman histology (SRH) during craniotomy. This study assesses deep learning algorithms for rapid intraoperative diagnosis from SRH images in small stereotactic-guided brain biopsies. It defines a minimum tissue sample size threshold to ensure diagnostic accuracy., Experimental Design: A prospective single-center study examined 121 SRH images from 84 patients with unclear intracranial lesions undergoing stereotactic brain biopsy. Unprocessed, label-free samples were imaged using a portable fiber laser Raman scattering microscope. Three deep learning models were tested to (i) identify tumorous/nontumorous tissue as qualitative biopsy control; (ii) subclassify into high-grade glioma (central nervous system World Health Organization grade 4), diffuse low-grade glioma (central nervous system World Health Organization grades 2-3), metastases, lymphoma, or gliosis; and (iii) molecularly subtype IDH and 1p/19q statuses of adult-type diffuse gliomas. Model predictions were evaluated against frozen section analysis and final neuropathologic diagnoses., Results: The first model identified tumorous/nontumorous tissue with 91.7% accuracy. Sample size on slides impacted accuracy in brain tumor subclassification (81.6%, κ = 0.72 frozen section; 73.9%, κ = 0.61 second model), with SRH images being smaller than hematoxylin and eosin images (4.1 ± 2.5 mm2 vs. 16.7 ± 8.2 mm2, P < 0.001). SRH images with more than 140 high-quality patches and a mean squeezed sample of 5.26 mm2 yielded 89.5% accuracy in subclassification and 93.9% in molecular subtyping of adult-type diffuse gliomas., Conclusions: Artificial intelligence-based SRH image analysis is non-inferior to frozen section analysis in detecting and subclassifying brain tumors during small stereotactic-guided biopsies once a critical squeezed sample size is reached. Beyond frozen section analysis, it enables valid molecular glioma subtyping, allowing faster treatment decisions in the future; however, refinement is needed for long-term application., (©2024 American Association for Cancer Research.)

Despite their unique histologic features, gliosarcomas belong to the group of glioblastomas and are treated according to the same standards. Fibroblast activation protein (FAP) is a component of a tumor-specific subpopulation of fibroblasts that plays a critical role in tumor growth and invasion. Some case studies suggest an elevated expression of FAP in glioblastoma and a particularly strong expression in gliosarcoma attributed to traits of predominant mesenchymal differentiation. However, the prognostic impact of FAP and its diagnostic and therapeutic potential remain unclear. Here, we investigate the clinical relevance of FAP expression in gliosarcoma and glioblastoma and how it correlates with 68 Ga-FAP inhibitor (FAPI)-46 PET uptake. Methods: Patients diagnosed with gliosarcoma or glioblastoma without sarcomatous differentiation with an overall survival of less than 2.5 y were enrolled. Histologic examination included immunohistochemistry and semiquantitative scoring of FAP (0-3, with higher values indicating stronger expression). Additionally, 68 Ga-FAPI-46 PET scans were performed in a subset of glioblastomas without sarcomatous differentiation patients. The clinical SUVs were correlated with FAP expression levels in surgically derived tumor tissue and relevant prognostic factors. Results: Of the 61 patients who were enrolled, 13 of them had gliosarcoma. Immunohistochemistry revealed significantly more FAP in gliosarcomas than in glioblastomas without sarcomatous differentiation of tumor tissue ( P < 0.0001). In the latter, FAP expression was confined to the perivascular space, whereas neoplastic cells additionally expressed FAP in gliosarcoma. A significant correlation of immunohistochemical FAP with SUV mean and SUV peak of 68 Ga-FAPI-46 PET indicates that clinical tracer uptake represents FAP expression of the tumor. Although gliosarcomas express higher levels of FAP than do glioblastomas without sarcomatous differentiation, overall survival does not significantly differ between the groups. Conclusion: The analysis reveals a significant correlation between SUV mean and SUV peak in 68 Ga-FAPI-46 PET and immunohistochemical FAP expression. This study indicates that FAP expression is much more abundant in the gliosarcoma subgroup of glioblastomas. This could open not only a diagnostic but also a therapeutic gap, since FAP could be explored as a theranostic target to enhance survival in a distinct subgroup of high-risk brain tumor patients with poor survival prognosis., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

Background: Primary central nervous system lymphomas (PCNSL) pose a challenge as they may mimic gliomas on magnetic resonance imaging (MRI) imaging, compelling precise differentiation for appropriate treatment. This study focuses on developing an automated MRI-based workflow to distinguish between PCNSL and gliomas., Methods: MRI examinations of 240 therapy-naive patients (141 males and 99 females, mean age: 55.16 years) with cerebral gliomas and PCNSLs (216 gliomas and 24 PCNSLs), each comprising a non-contrast T1-weighted, fluid-attenuated inversion recovery (FLAIR), and contrast-enhanced T1-weighted sequence were included in the study. HD-GLIO, a pre-trained segmentation network, was used to generate segmentations automatically. To validate the segmentation efficiency, 237 manual segmentations were prepared (213 gliomas and 24 PCNSLs). Subsequently, radiomics features were extracted following feature selection and training of an XGBoost algorithm for classification., Results: The segmentation models for gliomas and PCNSLs achieved a mean Sørensen-Dice coefficient of 0.82 and 0.80 for whole tumors, respectively. Three classification models were developed in this study to differentiate gliomas from PCNSLs. The first model differentiated PCNSLs from gliomas, with an area under the curve (AUC) of 0.99 (F1-score: 0.75). The second model discriminated between high-grade gliomas and PCNSLs with an AUC of 0.91 (F1-score: 0.6), and the third model differentiated between low-grade gliomas and PCNSLs with an AUC of 0.95 (F1-score: 0.89)., Conclusions: This study serves as a pilot investigation presenting an automated virtual biopsy workflow that distinguishes PCNSLs from cerebral gliomas. Prior to clinical use, it is necessary to validate the results in a prospective multicenter setting with a larger number of PCNSL patients., Competing Interests: The authors declare that there is no conflict of interest to disclose., (© The Author(s) 2024. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

Background: The aim of this study was to assess health-related quality of life (HRQOL) before and after treatment for intracerebral low-grade glioma., Methods: Patients with low-grade glioma who underwent surgical tumor removal between 2012 and 2018 were eligible for this study. All individuals and their closest relatives received thorough preoperative (

Abstract: State-of-the-art response assessment of central nervous system lymphoma (CNSL) by magnetic resonance imaging is challenging and an insufficient predictor of treatment outcomes. Accordingly, the development of novel risk stratification strategies in CNSL is a high unmet medical need. We applied ultrasensitive circulating tumor DNA (ctDNA) sequencing to 146 plasma and cerebrospinal fluid (CSF) samples from 67 patients, aiming to develop an entirely noninvasive dynamic risk model considering clinical and molecular features of CNSL. Our ultrasensitive method allowed for the detection of CNSL-derived mutations in plasma ctDNA with high concordance to CSF and tumor tissue. Undetectable plasma ctDNA at baseline was associated with favorable outcomes. We tracked tumor-specific mutations in plasma-derived ctDNA over time and developed a novel CNSL biomarker based on this information: peripheral residual disease (PRD). Persistence of PRD after treatment was highly predictive of relapse. Integrating established baseline clinical risk factors with assessment of radiographic response and PRD during treatment resulted in the development and independent validation of a novel tool for risk stratification: molecular prognostic index for CNSL (MOP-C). MOP-C proved to be highly predictive of outcomes in patients with CNSL (failure-free survival hazard ratio per risk group of 6.60; 95% confidence interval, 3.12-13.97; P < .0001) and is publicly available at www.mop-c.com. Our results highlight the role of ctDNA sequencing in CNSL. MOP-C has the potential to improve the current standard of clinical risk stratification and radiographic response assessment in patients with CNSL, ultimately paving the way toward individualized treatment., (© 2024 American Society of Hematology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.)

We briefly present machine learning approaches for designing better biological experiments. These approaches build on machine learning predictors and provide additional tools to guide scientific discovery. There are two different kinds of objectives when designing better experiments: to improve the predictive model or to improve the experimental outcome. We survey five different approaches for adaptive experimental design that iteratively search the space of possible experiments while adapting to measured data. The approaches are Bayesian optimization, bandits, reinforcement learning, optimal experimental design, and active learning. These machine learning approaches have shown promise in various areas of biology, and we provide broad guidelines to the practitioner and links to further resources., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Adaptive plasticity to the standard chemotherapeutic temozolomide (TMZ) leads to glioblastoma progression. Here, we examine early stages of this process in patient-derived cellular models, exposing the human lysine-specific demethylase 5B (KDM5B) as a prospective indicator for subclonal expansion. By integration of a reporter, we show its preferential activity in rare, stem-like ALDH1A1+ cells, immediately increasing expression upon TMZ exposure. Naive, genetically unmodified KDM5B high cells phosphorylate AKT (pAKT) and act as slow-cycling persisters under TMZ. Knockdown of KDM5B reverses pAKT levels, simultaneously increasing PTEN expression and TMZ sensitivity. Pharmacological inhibition of PTEN rescues the effect. Interference with KDM5B subsequent to TMZ decreases cellular vitality, and clonal tracing with DNA barcoding demonstrates high individual levels of KDM5B to predict subclonal expansion already before TMZ exposure. Thus, KDM5B high treatment-naive cells preferentially contribute to the dynamics of drug resistance under TMZ. These findings may serve as a cornerstone for future biomarker-assisted clinical trials., Competing Interests: The authors declare no conflicts of interest., (© 2023 The Author(s).)

Purpose: To evaluate a correlation between an MRI-specific marker for cellular density [apparent diffusion coefficient (ADC)] and the expression of Somatostatin Receptors (SSTR) in patients with meningioma of the skull plane and orbital space., Methods: 68 Ga-DOTATOC PET/MR imaging was performed in 60 Patients with suspected or diagnosed meningiomas of the skull base and eye socket. Analysis of ADC values succeeded in 32 patients. ADC values (ADC mean and ADC min ) were analyzed using a polygonal region of interest. Tracer-uptake of target lesions was assessed according to corresponding maximal (SUV max ) and mean (SUV mean ) values. Correlations between assessed parameters were evaluated using the Pearson correlation coefficient., Results: One out of 32 patients (3%) was diagnosed with lymphoma by histopathological examination and therefore excluded from further analysis. Median ADC mean amounted to 822 × 10 -5  mm²/s -1 (95% CI: 570-1497) and median ADC min was 493 × 10 -5 mm 2 /s -1 (95% CI: 162-783). There were no significant correlations between SUV max and ADC min (r = 0.60; P  = 0.76) or ADC mean (r = -0.52; P  = 0.79), respectively. However, Pearson's test showed a weak, inverse but insignificant correlation between ADC mean and SUV mean (r = -0.33; P  = 0.07)., Conclusion: The presented data displays no relevant correlations between increased SSTR expression and cellularity in patients with meningioma of the skull base. SSTR-PET and DWI thus may offer complementary information on tumor characteristics of meningioma., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Plexiform neurofibromas are the hallmark of neurofibromatosis type 1 (NF1) and significantly contribute to the overall burden of disease. While surgical excision has long been the only available therapy, the MEK inhibitor (MEKi) selumetinib has been approved as a non-surgical treatment option for these tumors in 2020 (USA) and 2021 (Europe), respectively. However, selumetinib will result in tumor shrinkage only after several months of therapy and might not prevent malignant transformation of a plexiform neurofibroma that occurs with a frequency of 10-15%. Here, we demonstrate that surgical excision might be the therapy of choice in some plexiform neurofibromas despite the availability of MEKi therapy., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)