Exploring a distinct FGFR2::DLG5 rearrangement in a low-grade neuroepithelial tumor: A case report and mini-review of protein fusions in brain tumors.

We report the novel clinical presentation of a primary brain neoplasm in a 30-year-old man with a mass-like area in the anteromedial temporal lobe. Histopathological analysis revealed a low-grade neuroepithelial tumor with cytologically abnormal neurons and atypical glial cells within the cerebral cortex. Molecular analysis showed a previously undescribed FGFR2::DLG5 rearrangement. We discuss the clinical significance and molecular implications of this fusion event, shedding light on its potential impact on tumor development and patient prognosis. Additionally, an extensive review places the finding in this case in the context of protein fusions in brain tumors in general and highlights their diverse manifestations, underlying molecular mechanisms, and therapeutic implications.

Unsupervised machine learning models reveal predictive clinical markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation.

Background: Glioblastoma is a malignant brain tumor requiring careful clinical monitoring even after primary management. Personalized medicine has suggested the use of various molecular biomarkers as predictors of patient prognosis or factors utilized for clinical decision-making. However, the accessibility of such molecular testing poses a constraint for various institutes requiring identification of low-cost predictive biomarkers to ensure equitable care. Methods: We collected retrospective data from patients seen at Ohio State University, University of Mississippi, Barretos Cancer Hospital (Brazil), and FLENI (Argentina) who were managed for glioblastoma-amounting to 581 patient records documented using REDCap. Patients were evaluated using an unsupervised machine learning approach comprised of dimensionality reduction and eigenvector analysis to visualize the inter-relationship of collected clinical features. Results: We discovered that the serum white blood cell (WBC) count of a patient during baseline planning for treatment was predictive of overall survival with an over 6-month median survival difference between the upper and lower quartiles of WBC count. By utilizing an objective PD-L1 immunohistochemistry quantification algorithm, we were further able to identify an increase in PD-L1 expression in glioblastoma patients with high serum WBC counts. Conclusions: These findings suggest that in a subset of glioblastoma patients the incorporation of WBC count and PD-L1 expression in the brain tumor biopsy as simple biomarkers predicting glioblastoma patient survival. Moreover, machine learning models allow the distillation of complex clinical data sets to uncover novel and meaningful clinical relationships.

Glioblastoma pseudoprogression and true progression reveal spatially variable transcriptional differences.

Post-resection radiologic monitoring to identify areas of new or progressive enhancement concerning for cancer recurrence is critical during patients with glioblastoma follow-up. However, treatment-related pseudoprogression presents with similar imaging features but requires different clinical management. While pathologic diagnosis is the gold standard to differentiate true progression and pseudoprogression, the lack of objective clinical standards and admixed histologic presentation creates the needs to (1) validate the accuracy of current approaches and (2) characterize differences between these entities to objectively differentiate true disease. We demonstrated using an online RNAseq repository of recurrent glioblastoma samples that cancer-immune cell activity levels correlate with heterogenous clinical outcomes in patients. Furthermore, nCounter RNA expression analysis of 48 clinical samples taken from second neurosurgical resection supports that pseudoprogression gene expression pathways are dominated with immune activation, whereas progression is predominated with cell cycle activity. Automated image processing and spatial expression analysis however highlight a failure to apply these broad expressional differences in a subset of cases with clinically challenging admixed histology. Encouragingly, applying unsupervised clustering approaches over our segmented histologic images provides novel understanding of morphologically derived differences between progression and pseudoprogression. Spatially derived data further highlighted polarization of myeloid populations that may underscore the tumorgenicity of novel lesions. These findings not only help provide further clarity of potential targets for pathologists to better assist stratification of progression and pseudoprogression, but also highlight the evolution of tumor-immune microenvironment changes which promote tumor recurrence.

The Cognitive Framework Behind Modern Neuropathology.

CONTEXT.­: In 2021 the World Health Organization distributed a new classification of central nervous system tumors that incorporated modern testing modalities in the diagnosis. Although universally accepted as a scientifically superior system, this schema has created controversy because its deployment globally is challenging in the best of circumstances and impossible in resource-poor health care ecosystems. Compounding this problem is the significant challenge that neuropathologists with expertise in central nervous system tumors are rare. OBJECTIVE.­: To demonstrate diagnostic use of simple unsupervised machine learning techniques using publicly available data sets. I also discuss some potential solutions to the deployment of neuropathology classification in health care ecosystems burdened by this classification schema. DATA SOURCES.­: The Cancer Genome Atlas RNA sequencing data from low-grade and high-grade gliomas. CONCLUSIONS.­: Methylation-based classification will be unable to solve all diagnostic problems in neuropathology. Information theory quantifications generate focused workflows in pathology, resulting in prevention of ordering unnecessary tests and identifying biomarkers that facilitate diagnosis.

Unsupervised machine learning models reveal predictive markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation.

Purpose: Glioblastoma is a malignant brain tumor requiring careful clinical monitoring even after primary management. Personalized medicine has suggested use of various molecular biomarkers as predictors of patient prognosis or factors utilized for clinical decision making. However, the accessibility of such molecular testing poses a constraint for various institutes requiring identification of low-cost predictive biomarkers to ensure equitable care. Methods: We collected retrospective data from patients seen at Ohio State University, University of Mississippi, Barretos Cancer Hospital (Brazil), and FLENI (Argentina) who were managed for glioblastoma-amounting to nearly 600 patient records documented using REDCap. Patients were evaluated using an unsupervised machine learning approach comprised of dimensionality reduction and eigenvector analysis to visualize the inter-relationship of collected clinical features. Results: We discovered that white blood cell count of a patient during baseline planning for treatment was predictive of overall survival with an over 6-month median survival difference between the upper and lower quartiles of white blood cell count. By utilizing an objective PDL-1 immunohistochemistry quantification algorithm, we were further able to identify an increase in PDL-1 expression in glioblastoma patients with high white blood cell counts. Conclusion: These findings suggest that in a subset of glioblastoma patients the incorporation of white blood cell count and PDL-1 expression in the brain tumor biopsy as simple biomarkers predicting glioblastoma patient survival. Moreover, use of machine learning models allows us to visualize complex clinical datasets to uncover novel clinical relationships.

Information theory approaches to improve glioma diagnostic workflows in surgical neuropathology.

AIMS: Resource-strained healthcare ecosystems often struggle with the adoption of the World Health Organization (WHO) recommendations for the classification of central nervous system (CNS) tumors. The generation of robust clinical diagnostic aids and the advancement of simple solutions to inform investment strategies in surgical neuropathology would improve patient care in these settings. METHODS: We used simple information theory calculations on a brain cancer simulation model and real-world data sets to compare contributions of clinical, histologic, immunohistochemical, and molecular information. An image noise assay was generated to compare the efficiencies of different image segmentation methods in H&E and Olig2 stained images obtained from digital slides. An auto-adjustable image analysis workflow was generated and compared with neuropathologists for p53 positivity quantification. Finally, the density of extracted features of the nuclei, p53 positivity quantification, and combined ATRX/age feature was used to generate a predictive model for 1p/19q codeletion in IDH-mutant tumors. RESULTS: Information theory calculations can be performed on open access platforms and provide significant insight into linear and nonlinear associations between diagnostic biomarkers. Age, p53, and ATRX status have significant information for the diagnosis of IDH-mutant tumors. The predictive models may facilitate the reduction of false-positive 1p/19q codeletion by fluorescence in situ hybridization (FISH) testing. CONCLUSIONS: We posit that this approach provides an improvement on the cIMPACT-NOW workflow recommendations for IDH-mutant tumors and a framework for future resource and testing allocation.

Machine learning approaches reveal subtle differences in breathing and sleep fragmentation in Phox2b-derived astrocytes ablated mice.

Modern neurophysiology research requires the interrogation of high-dimensionality data sets. Machine learning and artificial intelligence (ML/AI) workflows have permeated into nearly all aspects of daily life in the developed world but have not been implemented routinely in neurophysiological analyses. The power of these workflows includes the speed at which they can be deployed, their availability of open-source programming languages, and the objectivity permitted in their data analysis. We used classification-based algorithms, including random forest, gradient boosted machines, support vector machines, and neural networks, to test the hypothesis that the animal genotypes could be separated into their genotype based on interpretation of neurophysiological recordings. We then interrogate the models to identify what were the major features utilized by the algorithms to designate genotype classification. By using raw EEG and respiratory plethysmography data, we were able to predict which recordings came from genotype class with accuracies that were significantly improved relative to the no information rate, although EEG analyses showed more overlap between groups than respiratory plethysmography. In comparison, conventional methods where single features between animal classes were analyzed, differences between the genotypes tested using baseline neurophysiology measurements showed no statistical difference. However, ML/AI workflows successfully were capable of providing successful classification, indicating that interactions between features were different in these genotypes. ML/AI workflows provide new methodologies to interrogate neurophysiology data. However, their implementation must be done with care so as to provide high rigor and reproducibility between laboratories. We provide a series of recommendations on how to report the utilization of ML/AI workflows for the neurophysiology community.NEW & NOTEWORTHY ML/AI classification workflows are capable of providing insight into differences between genotypes for neurophysiology research. Analytical techniques utilized in the neurophysiology community can be augmented by implementing ML/AI workflows. Random forest is a robust classification algorithm for respiratory plethysmography data. Utilization of ML/AI workflows in neurophysiology research requires heightened transparency and improved community research standards.

Astrocyte regional heterogeneity revealed through machine learning-based glial neuroanatomical assays.

Evaluation of reactive astrogliosis by neuroanatomical assays represents a common experimental outcome for neuroanatomists. The literature demonstrates several conflicting results as to the accuracy of such measures. We posited that the diverging results within the neuroanatomy literature were due to suboptimal analytical workflows in addition to astrocyte regional heterogeneity. We therefore generated an automated segmentation workflow to extract features of glial fibrillary acidic protein (GFAP) and aldehyde dehydrogenase family 1, member L1 (ALDH1L1) labeled astrocytes with and without neuroinflammation. We achieved this by capturing multiplexed immunofluorescent confocal images of mouse brains treated with either vehicle or lipopolysaccharide (LPS) followed by implementation of our workflows. Using classical image analysis techniques focused on pixel intensity only, we were unable to identify differences between vehicle-treated and LPS-treated animals. However, when utilizing machine learning-based algorithms, we were able to (1) accurately predict which objects were derived from GFAP or ALDH1L1-stained images indicating that GFAP and ALDH1L1 highlight distinct morphological aspects of astrocytes, (2) we could predict which neuroanatomical region the segmented GFAP or ALDH1L1 object had been derived from, indicating that morphological features of astrocytes change as a function of neuroanatomical location. (3) We discovered a statistically significant, albeit not highly accurate, prediction of which objects had come from LPS versus vehicle-treated animals, indicating that although features exist capable of distinguishing LPS-treated versus vehicle-treated GFAP and ALDH1L1-segmented objects, that significant overlap between morphologies exists. We further determined that for most classification scenarios, nonlinear models were required for improved treatment class designations. We propose that unbiased automated image analysis techniques coupled with well-validated machine learning tools represent highly useful models capable of providing insights into neuroanatomical assays.

Machine learning-based data analytic approaches for evaluating post-natal mouse respiratory physiological evolution.

Respiratory parameters change during post-natal development, but the nature of their changes have not been well-described. The advent of commercially available plethysmographic instruments provided improved repeatability of measurements and standardization of measured breathing in mice across laboratories. These technologies thus allowed for exploration of more precise respiratory pattern changes during the post-natal developmental epoch. Current methods to analyze respiratory behavior utilize plethysmography to acquire standing values of frequency, volume and flow at specific time points in murine maturation. These metrics have historically been independently analyzed as a function of time with no further analysis examining the interplay these variables have with each other and in the context of postnatal maturation or during blood gas homeostasis. We posit that machine learning workflows can provide deeper physiological understanding into the postnatal development of respiration. In this manuscript, we delineate a machine learning workflow based on the R-statistical programming language to examine how variation and relationships of frequency (f) and tidal volume (TV) change with respect to inspiratory and expiratory parameters. Our analytical workflows could successfully predict age and found that the variation and relationships between respiratory metrics are dynamically shifting with age and during hypercapnic breathing. Thus, our work demonstrates the utility of high dimensional analyses to provide reliable class label predictions using non-invasive respiratory metrics. These approaches may be useful in large-scale phenotyping across development and in disease.

Hyaluronic acid induces ROCK-dependent amoeboid migration in glioblastoma cells.

Glioblastoma (GBM) is the most aggressive and deadly adult brain tumor, primarily because of its high infiltrative capacity and development of resistance to therapy. Although GBM cells are typically believed to migrate via mesenchymal (e.g., fibroblast-like) migration modes, amoeboid (e.g., leucocyte-like) migration modes have been identified and may constitute a salvage pathway. However, the mesenchymal to amoeboid transition (MAT) process in GB is not well characterized, most likely because most culture models induce MAT via pharmacological or genetic inhibition conditions that are far from physiological. In this study, we examined the ability of hyaluronic acid (HA) content in three-dimensional collagen (Col) hydrogels to induce MAT in U87 GBM cells. HA and Col are naturally-occurring components of the brain extracellular matrix (ECM). In pure Col gels, U87 cells displayed primarily mesenchymal behaviors, including elongated cell morphology, clustered actin and integrin expression, and crawling migration behaviors. Whereas an increasing population of cells displaying amoeboid behaviors, including rounded morphology, cortical actin expression, low/no integrin expression, and squeezing or gliding motility, were observed with increasing HA content (0.1-0.2 wt% in Col). Consistent with amoeboid migration, these behaviors were abrogated by ROCK inhibition with the non-specific small molecule inhibitor Y27632. Toward identification of histological MAT classification criteria, we also examined the correlation between cell and nuclear aspect ratio (AR) in Col and Col-HA gels, finding that nuclear AR has a small variance and is not correlated to cell AR in HA-rich gels. These results suggest that HA may regulate GBM cell motility in a ROCK-dependent manner.

Near field non-invasive electrophysiology of retrotrapezoid nucleus using amperometric cation sensor.

Central chemoreception is the process whereby the brainstem senses blood gas levels and adjusts homeostatic functions such as breathing and cardiovascular tone accordingly. Rodent evidence suggests that the retrotrapezoid nucleus (RTN) is a master regulator of central chemoreception, in particular, through direct sensation of acidosis induced by CO2 levels. The oscillatory dynamics caused by pH changes as sensed by the RTN surface and its relationship to the fluctuations in cation flux is not clearly understood due to the current limitations of electrophysiology tools and this article presents our investigations to address this need. A cation selective sensor fabricated from polypyrrole doped with dodecyl benzenesulfonate (PPy (DBS)) is placed over RTN in an ex-vivo en bloc brain and changes in cation concentration in the diffusion limited region above the RTN is measured due to changes in externally imposed basal pH. The novelty of this technique lies in its feasibility to detect cation fluxes from the cells in the RTN region without having to access either sides of the cell membrane. Owing to the placement of the sensor in close proximity to the tissue, we refer to this technique as near-field electrophysiology. It is observed that lowering the pH in the physiological range (7.4-7.2) results in a significant increase in cation concentration in the vicinity of RTN with a median value of ~5 µM. The utilization of such quantifiable measurement techniques to detect sub-threshold brain activity may help provide a platform for future neural network architectures. Findings from this paper present a quantifiable, sensitive, and robust electrophysiology technique with minimal damage to the underlying tissue.

Neuropathologists play a key role in establishing the extent of COVID-19 in human patients.

SARS-CoV2 infection causes COVID-19, and represents the most emergent health care crisis of our generation. Ample evidence in the scientific literature suggests that SARS-CoV, MERS-CoV, and endemic human coronaviruses infect brain cells. We delineate a rationale for encouraging evaluation of the brain, and in particular the brainstem, in COVID-19 so that potential neuropathological mechanisms can be delineated.

Revisiting the Neuropathology of Sudden Infant Death Syndrome (SIDS).

Background: Sudden infant death syndrome (SIDS) is one of the leading causes of infant mortality in the United States (US). The extent to which SIDS manifests with an underlying neuropathological mechanism is highly controversial. SIDS correlates with markers of poor prenatal and postnatal care, generally rooted in the lack of access and quality of healthcare endemic to select racial and ethnic groups, and thus can be viewed in the context of health disparities. However, some evidence suggests that at least a subset of SIDS cases may result from a neuropathological mechanism. To explain these issues, a triple-risk hypothesis has been proposed, whereby an underlying biological abnormality in an infant facing an extrinsic risk during a critical developmental period SIDS is hypothesized to occur. Each SIDS decedent is thus thought to have a unique combination of these risk factors leading to their death. This article reviews the neuropathological literature of SIDS and uses machine learning tools to identify distinct subtypes of SIDS decedents based on epidemiological data. Methods: We analyzed US Period Linked Birth/Infant Mortality Files from 1990 to 2017 (excluding 1992-1994). Using t-SNE, an unsupervised machine learning dimensionality reduction algorithm, we identified clusters of SIDS decedents. Following identification of these groups, we identified changes in the rates of SIDS at the state level and across three countries. Results: Through t-SNE and distance based statistical analysis, we identified three groups of SIDS decedents, each with a unique peak age of death. Within the US, SIDS is geographically heterogeneous. Following this, we found low birth weight and normal birth weight SIDS rates have not been equally impacted by implementation of clinical guidelines. We show that across countries with different levels of cultural heterogeneity, reduction in SIDS rates has also been distinct between decedents with low vs. normal birth weight. Conclusions: Different epidemiological and extrinsic risk factors exist based on the three unique SIDS groups we identified with t-SNE and distance based statistical measurements. Clinical guidelines have not equally impacted the groups, and normal birth weight infants comprise more of the cases of SIDS even though low birth weight infants have a higher SIDS rate.

Development of a Novel FIJI-Based Method to Investigate Neuronal Circuitry in Neonatal Mice.

The emergence of systems neuroscience tools requires parallel generation of objective analytical workflows for experimental neuropathology. We developed an objective analytical workflow that we used to determine how specific autonomic neural lineages change during postnatal development. While a wealth of knowledge exists regarding postnatal alterations in respiratory neural function, how these neural circuits change and develop in the weeks following birth remains less clear. In this study, we developed our workflow by combining genetic mouse modeling and quantitative immunofluorescent confocal microscopy and used this to examine the postnatal development of neural circuits derived from the transcription factors NKX2.2 and OLIG3 into three medullary respiratory nuclei. Our automated FIJI-based image analysis workflow rapidly and objectively quantified synaptic puncta in user-defined anatomic regions. Using our objective workflow, we found that the density and estimated total number of Nkx2.2-derived afferents into the pre-Bötzinger Complex significantly decreased with postnatal age during the first three weeks of postnatal life. These data indicate that Nkx2.2-derived structures differentially influence pre-Bötzinger Complex respiratory oscillations at different stages of postnatal development.

Micelle-templated, poly(lactic-co-glycolic acid) nanoparticles for hydrophobic drug delivery.

PURPOSE: Poly(lactic-co-glycolic acid) (PLGA) is widely used for drug delivery because of its biocompatibility, ability to solubilize a wide variety of drugs, and tunable degradation. However, achieving sub-100 nm nanoparticles (NPs), as might be desired for delivery via the enhanced permeability and retention effect, is extremely difficult via typical top-down emulsion approaches. METHODS: Here, we present a bottom-up synthesis method yielding PLGA/block copolymer hybrids (ie, "PolyDots"), consisting of hydrophobic PLGA chains entrapped within self-assembling poly(styrene-b-ethylene oxide) (PS-b-PEO) micelles. RESULTS: PolyDots exhibit average diameters <50 nm and lower polydispersity than conventional PLGA NPs. Drug encapsulation efficiencies of PolyDots match conventional PLGA NPs (ie, ~30%) and are greater than those obtained from PS-b-PEO micelles (ie, ~7%). Increasing the PLGA:PS-b-PEO weight ratio alters the drug release mechanism from chain relaxation to erosion controlled. PolyDots are taken up by model glioma cells via endocytotic mechanisms within 24 hours, providing a potential means for delivery to cytoplasm. PolyDots can be lyophilized with minimal change in morphology and encapsulant functionality, and can be produced at scale using electrospray. CONCLUSION: Encapsulation of PLGA within micelles provides a bottom-up route for the synthesis of sub-100 nm PLGA-based nanocarriers with enhanced stability and drug-loading capacity, and tunable drug release, suitable for potential clinical applications.

Automated fluorescent miscroscopic image analysis of PTBP1 expression in glioma.

Multiplexed immunofluorescent testing has not entered into diagnostic neuropathology due to the presence of several technical barriers, amongst which includes autofluorescence. This study presents the implementation of a methodology capable of overcoming the visual challenges of fluorescent microscopy for diagnostic neuropathology by using automated digital image analysis, with long term goal of providing unbiased quantitative analyses of multiplexed biomarkers for solid tissue neuropathology. In this study, we validated PTBP1, a putative biomarker for glioma, and tested the extent to which immunofluorescent microscopy combined with automated and unbiased image analysis would permit the utility of PTBP1 as a biomarker to distinguish diagnostically challenging surgical biopsies. As a paradigm, we utilized second resections from patients diagnosed either with reactive brain changes (pseudoprogression) and recurrent glioblastoma (true progression). Our image analysis workflow was capable of removing background autofluorescence and permitted quantification of DAPI-PTBP1 positive cells. PTBP1-positive nuclei, and the mean intensity value of PTBP1 signal in cells. Traditional pathological interpretation was unable to distinguish between groups due to unacceptably high discordance rates amongst expert neuropathologists. Our data demonstrated that recurrent glioblastoma showed more DAPI-PTBP1 positive cells and a higher mean intensity value of PTBP1 signal compared to resections from second surgeries that showed only reactive gliosis. Our work demonstrates the potential of utilizing automated image analysis to overcome the challenges of implementing fluorescent microscopy in diagnostic neuropathology.

Imaging Cell-Matrix Interactions in 3D Collagen Hydrogel Culture Systems.

3D hydrogels better replicate in vivo conditions, and yield different results from 2D substrates. However, imaging interactions between cells and the hydrogel microenvironment is challenging because of light diffraction and poor focal depth. Here, cryosectioning and vibrating microtomy methods and fixation protocols are compared. Collagen I/III hydrogel sections (20-100 µm) are fixed with paraformaldehyde (2%-4%) and structurally evaluated. Cryosectioning damaged hydrogels, and vibrating microtomy (100 µm, 2%) yielded the best preservation of microstructure and cell integrity. These results demonstrate a potential processing method that preserves hydrogel and cell integrity, permitting imaging of cell interactions with the microenvironment.

Surface topography during neural stem cell differentiation regulates cell migration and cell morphology.

We sought to determine the contribution of scaffold topography to the migration and morphology of neural stem cells by mimicking anatomical features of scaffolds found in vivo. We mimicked two types of central nervous system scaffolds encountered by neural stem cells during development in vitro by constructing different diameter electrospun polycaprolactone (PCL) fiber mats, a substrate that we have shown to be topographically similar to brain scaffolds. We compared the effects of large fibers (made to mimic blood vessel topography) with those of small-diameter fibers (made to mimic radial glial process topography) on the migration and differentiation of neural stem cells. Neural stem cells showed differential migratory and morphological reactions with laminin in different topographical contexts. We demonstrate, for the first time, that neural stem cell biological responses to laminin are dependent on topographical context. Large-fiber topography without laminin prevented cell migration, which was partially reversed by treatment with rock inhibitor. Cell morphology complexity assayed by fractal dimension was inhibited in nocodazole- and cytochalasin-D-treated neural precursor cells in large-fiber topography, but was not changed in small-fiber topography with these inhibitors. These data indicate that cell morphology has different requirements on cytoskeletal proteins dependent on the topographical environment encountered by the cell. We propose that the physical structure of distinct scaffolds induces unique signaling cascades that regulate migration and morphology in embryonic neural precursor cells. J. Comp. Neurol. 524:3485-3502, 2016. © 2016 Wiley Periodicals, Inc.

Progressive multifocal leukoencephalopathy in a patient without apparent immunosuppression.

Progressive multifocal leukoencephalopathy (PML) is a viral demyelinating disease due to the reactivation of the JC virus (JCV), which usually occurs in the context of immunosuppression in HIV infection, malignancy, or in patients on disease modifying therapy for autoimmune diseases, such as multiple sclerosis (MS) and Crohn's disease. Notably, there is growing recognition that PML can occur in patients with transient immune dysfunction. Here, we present a case of a 55-year-old man without history of immunosuppression or evidence of ICL who was diagnosed with PML on brain biopsy. We will discuss the potential etiologies of mild and transient immunosuppression that can lead to PML with non-apparent immunosuppression.