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.

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.

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.

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.

A multi-resolution textural approach to diagnostic neuropathology reporting.

We present a computer aided diagnostic workflow focusing on two diagnostic branch points in neuropathology (intraoperative consultation and p53 status in tumor biopsy specimens) by means of texture analysis via discrete wavelet frames decomposition. For intraoperative consultation, our methodology is capable of classifying glioblastoma versus metastatic cancer by extracting textural features from the non-nuclei region of cytologic preparations based on the imaging characteristics of glial processes, which appear as anisotropic thin linear structures. For metastasis, these are homogeneous in appearance, thus suitable and extractable texture features distinguish the two tissue types. Experiments on 53 images (29 glioblastomas and 24 metastases) resulted in average accuracy as high as 89.7 % for glioblastoma, 87.5 % for metastasis and 88.7 % overall. For p53 interpretation, we detect and classify p53 status by classifying staining intensity into strong, moderate, weak and negative sub-classes. We achieved this by developing a novel adaptive thresholding for detection, a two-step rule based on weighted color and intensity for the classification of positively and negatively stained nuclei, followed by texture classification to classify the positively stained nuclei into the strong, moderate and weak intensity sub-classes. Our detection method is able to correctly locate and distinguish the four types of cells, at 85 % average precision and 88 % average sensitivity rate. These classification methods on the other hand recorded 81 % accuracy in classifying the positive and negative cells, and 60 % accuracy in further classifying the positive cells into the three intensity groups, which is comparable with neuropathologists' markings.

Glioma-astrocyte interactions on white matter tract-mimetic aligned electrospun nanofibers.

Gliomas are highly invasive forms of brain cancer comprising more than 50% of brain tumor cases in adults, and astrocytomas account for ∼60-70% of all gliomas. As a result of multiple factors, including enhanced migratory properties and extracellular matrix remodeling, even with current standards of care, mean survival time for patients is only ∼12 months. Because glioblastoma multiforme (GBM) cells arise from astrocytes, there is great interest in elucidating the interactions of these two cell types in vivo. Previous work performed on two-dimensional assays (i.e., tissue culture plastic and Boyden chamber assays) utilizes substrates that lack the complexities of the natural microenvironment. Here, we employed a three-dimensional, electrospun poly-(caprolactone) (PCL) nanofiber system (NFS) to mimic some features of topographical properties evidenced in vivo. Co-cultures of human GBM cells and rat astrocytes, as performed on the NFS, showed a significant increase in astrocyte GFAP expression, particularly in the presence of extracellular matrix (ECM) deposited by GBM cells. In addition, GBM migration increased in the presence of astrocytes or soluble factors (i.e., conditioned media). However, the presence of fixed astrocytes acted as an antagonist, lowering GBM migration rates. This data suggests that astrocytes and GBM cells interact through a multitude of pathways, including soluble factors and direct contact. This work demonstrates the potential of the NFS to duplicate some topographical features of the GBM tumor microenvironment, permitting analysis of topographical effects in GBM migration.

Morphological analysis of CDC2 and glycogen synthase kinase 3ß phosphorylation as markers of g2 → m transition in glioma.

G2 → M transition is a strategic target for glioma chemotherapy. Key players in G2 → M transition include CDC2 and glycogen synthase kinase 3ß (GSK3ß), which are highly regulated by posttranslational phosphorylation. This report is a morphological analysis of CDC2 and GSK3ß phosphorylation using immunohistochemistry in gliomas with different biological properties. GBM showed a 2.8-fold and 5.6-fold increase in number of cells positive for pThr161CDC2 and a 4.2- and 6.9-fold increase in number of cells positive for pTyr15CDC2 relative to oligodendroglioma and ependymoma, respectively. Elevated labeling for inhibited phospho-CDC2 (pTyr15CDC) correlates with elevated levels of phosphorylated glycogen synthase kinase 3ß (GSK3ß). 71% of the GBM cases showed intermediate to high intensity staining for pSer9SGK3ß 53% of oligodendroglioma, and 73% of ependymoma showed low intensity staining. CDC2 gene amplification correlates with increased survival in glioblastoma multiforme (GBM) and astrocytoma WHO grades II-III, but not in oligodendroglioma WHO grades II-III.

OLIG2 is differentially expressed in pediatric astrocytic and in ependymal neoplasms.

The bHLH transcription factor, OLIG2, is universally expressed in adult human gliomas and, as a major factor in the development of oligodendrocytes, is expressed at the highest levels in low-grade oligodendroglial tumors. In addition, it is functionally required for the formation of high-grade astrocytomas in a genetically relevant murine model. The pediatric gliomas have genomic profiles that are different from the corresponding adult tumors and accordingly, the expression of OLIG2 in non-oligodendroglial pediatric gliomas is not well documented within specific tumor types. In the current study, the pattern of OLIG2 expression in a spectrum of 90 non-oligodendroglial pediatric gliomas varied from very low levels in the ependymomas (cellular and tanycytic) to high levels in pilocytic astrocytoma, and in the diffuse-type astrocytic tumors (WHO grades II-IV). With dual-labeling, glioblastoma had the highest percentage of OLIG2 expressing cells that were also Ki-67 positive (mean = 16.3%) whereas pilocytic astrocytoma WHO grade I and astrocytoma WHO grade II had the lowest (0.9 and 1%, respectively); most of the Ki-67 positive cells in the diffuse-type astrocytomas (WHO grade II-III) were also OLIG2 positive (92-94%). In contrast to the various types of pediatric astrocytic tumors, all ependymomas WHO grade II, regardless of site of origin, showed at most minimal OLIG2 expression, suggesting that OLIG2 function in pediatric gliomas is cell lineage dependent.

Embryonic stem cells overexpressing Pitx2c engraft in infarcted myocardium and improve cardiac function.

This study investigated the effects on cardiomyocyte differentiation of embryonic stem cells by the overexpression of the transcription factor, Pitx2c, and examined the effects of transplantation of these differentiated cells on cardiac function in a mouse model of myocardial infarction. Pitx2c overexpressing embryonic stem cells were characterized for cardiac differentiation by immunocytochemistry, RNA analysis, and electrophysiology. Differentiated cells were transplanted by directed injection into the infarcted murine myocardium and functional measurements of blood pressure, contractility, and relaxation were performed. Histochemistry and FISH analysis performed on these mice confirmed the engraftment and cardiac nature of the transplanted cells. Pitx2c overexpressing embryonic stem cells robustly differentiated into spontaneously contracting cells which acquired cardiac protein markers and exhibited action potentials resembling that of cardiomyocytes. These cells could also be synchronized to an external pacemaker. Significant improvements (P < 0.01) in blood pressure (56%), contractility (57%), and relaxation (59%) were observed in infarcted mice with transplants of these differentiated cells but not in mice which were transplanted with control cells. The Pitx2c overexpressing cells secrete paracrine factors which when adsorbed onto a heparinated gel and injected into the infarcted myocardium produce a comparable and significant (P < 0.01) functional recovery. Pitx2c overexpression is a valuable method for producing cardiomyocytes from embryonic stem cells, and transplantation of these cardiomyocytes into infracted myocardium restores cardiac function through multiple mechanisms.

Beta-catenin signaling is required for neural differentiation of embryonic stem cells.

Culture of embryonic stem (ES) cells at high density inhibits both beta-catenin signaling and neural differentiation. ES cell density does not influence beta-catenin expression, but a greater proportion of beta-catenin is targeted for degradation in high-density cultures. Moreover, in high-density cultures, beta-catenin is preferentially localized to the membrane further reducing beta-catenin signaling. Increasing beta-catenin signaling by treatment with Wnt3a-conditioned medium, by overexpression of beta-catenin, or by overexpression of a dominant-negative form of E-cadherin promotes neurogenesis. Furthermore, beta-catenin signaling is sufficient to induce neurogenesis in high-density cultures even in the absence of retinoic acid (RA), although RA potentiates the effects of beta-catenin. By contrast, RA does not induce neurogenesis in high-density cultures in the absence of beta-catenin signaling. Truncation of the armadillo domain of beta-catenin, but not the C terminus or the N terminus, eliminates its proneural effects. The proneural effects of beta-catenin reflect enhanced lineage commitment rather than proliferation of neural progenitor cells. Neurons induced by beta-catenin overexpression either alone or in association with RA express the caudal neuronal marker Hoxc4. However, RA treatment inhibits the beta-catenin-mediated generation of tyrosine hydroxylase-positive neurons, suggesting that not all of the effects of RA are dependent upon beta-catenin signaling. These observations suggest that beta-catenin signaling promotes neural lineage commitment by ES cells, and that beta-catenin signaling may be a necessary co-factor for RA-mediated neuronal differentiation. Further, enhancement of beta-catenin signaling with RA treatment significantly increases the numbers of neurons generated from ES cells, thus suggesting a method for obtaining large numbers of neural species for possible use in for ES cell transplantation.