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Spinocerebellar ataxia type 2 (SCA2) is caused by polyglutamine expansion in Ataxin-2 (ATXN2). This factor binds RNA/proteins to modify metabolism after stress, and to control calcium (Ca2+) homeostasis after stimuli. Cerebellar ataxias and corticospinal motor neuron degeneration are determined by gain/loss in ATXN2 function, so we aimed to identify key molecules in this atrophic process, as potential disease progression markers. Our Atxn2-CAG100-Knock-In mouse faithfully models features observed in patients at pre-onset, early and terminal stages. Here, its cerebellar global RNA profiling revealed downregulation of signaling cascades to precede motor deficits. Validation work at mRNA/protein level defined alterations that were independent of constant physiological ATXN2 functions, but specific for RNA/aggregation toxicity, and progressive across the short lifespan. The earliest changes were detected at three months among Ca2+ channels/transporters (Itpr1, Ryr3, Atp2a2, Atp2a3, Trpc3), IP3 metabolism (Plcg1, Inpp5a, Itpka), and Ca2+-Calmodulin dependent kinases (Camk2a, Camk4). CaMKIV–Sam68 control over alternative splicing of Nrxn1, an adhesion component of glutamatergic synapses between granule and Purkinje neurons, was found to be affected. Systematic screening of pre/post-synapse components, with dendrite morphology assessment, suggested early impairment of CamKIIα abundance together with the weakening of parallel fiber connectivity. These data reveal molecular changes due to ATXN2 pathology, primarily impacting excitability and communication.
Background: Definite diagnosis and therapeutic management of cholangiocarcinoma (CCA) remains a challenge. The aim of the current study was to investigate feasibility and potential impact on clinical management of targeted sequencing of intraductal biopsies.
Methods: Intraductal biopsies with suspicious findings from 16 patients with CCA in later clinical course were analyzed with targeted sequencing including tumor and control benign tissue (n = 55 samples). A CCA-specific sequencing panel containing 41 genes was designed and a dual strand targeted enrichment was applied.
Results: Sequencing was successfully performed for all samples. In total, 79 mutations were identified and a mean of 1.7 mutations per tumor sample (range 0–4) as well as 2.3 per biopsy (0–6) were detected and potentially therapeutically relevant genes were identified in 6/16 cases. In 14/18 (78%) biopsies with dysplasia or inconclusive findings at least one mutation was detected. The majority of mutations were found in both surgical specimen and biopsy (68%), while 28% were only present in biopsies in contrast to 4% being only present in the surgical tumor specimen.
Conclusion: Targeted sequencing from intraductal biopsies is feasible and potentially improves the diagnostic yield. A profound genetic heterogeneity in biliary dysplasia needs to be considered in clinical management and warrants further investigation.
Translational impact: The current study is the first to demonstrate the feasibility of sequencing of intraductal biopsies which holds the potential to impact diagnostic and therapeutical management of patients with biliary dysplasia and neoplasia.
Classical Hodgkin lymphoma (cHL) is one of the most common malignant lymphomas in Western Europe. The nodular sclerosing subtype of cHL (NS cHL) is characterized by a proliferation of fibroblasts in the tumor microenvironment, leading to fibrotic bands surrounding the lymphoma infiltrate. Several studies have described a crosstalk between the tumour cells of cHL, the Hodgkin- and Reed-Sternberg (HRS) cells, and cancer-associated fibroblasts. However, to date a deep molecular characterization of these fibroblasts is lacking. Thus, the aim of the present study is a comprehensive characterization of these fibroblasts. Gene expression profiling and methylation profiles of fibroblasts isolated from primary lymph node suspensions revealed persistent differences between fibroblasts obtained from NS cHL and lymphadenitis. NS cHL derived fibroblasts exhibit a myofibroblastic phenotype characterized by myocardin (MYOCD) expression. Moreover, TIMP3, an inhibitor of matrix metalloproteinases, was strongly upregulated in NS cHL fibroblasts, likely contributing to the accumulation of collagen in sclerotic bands of NS cHL. As previously shown for other types of cancer-associated fibroblasts, treatment by luteolin could reverse this fibroblast phenotype and decrease TIMP3 secretion. NS cHL fibroblasts showed enhanced proliferation when they were exposed to soluble factors released from HRS cells. For HRS cells, soluble factors from fibroblasts were not sufficient to protect them from Brentuximab-Vedotin induced cell death. However, HRS cells adherent to fibroblasts were protected from Brentuximab-Vedotin induced injury. In summary, we confirm the importance of fibroblasts for HRS cell survival and identify TIMP3 which probably contributes as a major factor to the typical fibrosis observed in NS cHL.
Objectives: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. Conclusions: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance.
The pathogenesis of nodular lymphocyte–predominant Hodgkin lymphoma (NLPHL) and its relationship to other lymphomas are largely unknown. This is partly because of the technical challenge of analyzing its rare neoplastic lymphocytic and histiocytic (L&H) cells, which are dispersed in an abundant nonneoplastic cellular microenvironment. We performed a genome-wide expression study of microdissected L&H lymphoma cells in comparison to normal and other malignant B cells that indicated a relationship of L&H cells to and/or that they originate from germinal center B cells at the transition to memory B cells. L&H cells show a surprisingly high similarity to the tumor cells of T cell–rich B cell lymphoma and classical Hodgkin lymphoma, a partial loss of their B cell phenotype, and deregulation of many apoptosis regulators and putative oncogenes. Importantly, L&H cells are characterized by constitutive nuclear factor {kappa}B activity and aberrant extracellular signal-regulated kinase signaling. Thus, these findings shed new light on the nature of L&H cells, reveal several novel pathogenetic mechanisms in NLPHL, and may help in differential diagnosis and lead to novel therapeutic strategies.
Zur genomweiten Genexpressionsanalyse werden Microarray-Experimente verwendet. Ziel dieser Arbeit ist es, Methoden zur Präprozessierung von Microarrays der Firma Affymetrix zu evaluieren und die VSN-Methode für Experimente mit weniger als 1000 Zellen zu verbessern. Bei dieser Technologie wird die Expression jedes Gens durch mehrere Probessets gemessen. Jedes Probeset besteht aus einem Perfect-Match (PM) und einem dazugehörigen Mismatch (MM). Der Expressionswert pro Gen wird durch ein vierstufiges Verfahren aus den einzelnen Probe-Werten berechnet: Hintergrundkorrektur, Normalisierung, PM-Adjustierung und Aggregation. Für jeden dieser Schritte existieren mehrere Algorithmen. Dazu dienten die im affy-Paket des Bioconductor implementierten Methoden MAS5, RMA, VSN und die Methode sRMA von Cope et al. [Cope et al., 2006] in Kombination mit der Methode VSN von Huber et al. [Huber et al., 2002]. Den ersten Teil dieser Arbeit bildet die Reanalyse der Datensätze von Küppers et al. [Küppers et al., 2003] und Piccaluga et al. [Piccaluga et al., 2007] mit der VSN-Methode. Dabei konnte gezeigt werden, dass die VSN-Methode gegenüber Klein et al. [Klein et al., 2001] Vorteile zeigt. Bei beiden Datensätzen wurden zusätzliche Gene gefunden, die für die Pathogenese der jeweiligen Tumorarten wichtig sein können. Einige der zusätzlich gefunden Gene wurden durch andere wissenschaftliche Arbeiten bestätigt. Die Gene, die bisher in keinem Zusammenhang mit der untersuchten Tumorart stehen, sind eine Möglichkeit für die weitere Forschung. Vor allem der Zytokine/Zytokine Signalweg wurde bei beiden Reanalysen als überrepräsentiert erkannt. Da für einige Microarray-Experimente die Anzahl der Zellen und damit die Menge an mRNA nur begrenzt zur Verfügung stehen, müssen die Laborarbeit und die statistischen Analysen angepasst werden. Hierzu werden fünf Methoden für die Präprozessierung untersucht, um zu evaluieren, welche Methode geeignet ist, derartige Expressionsdaten zu verrechnen. Auf Basis eines Testdatensatzes der bereits zur Etablierung des Laborprozesses diente werden Expressionswerte durch empirische Verteilung, Gammaverteilung und ein linear gemischtes Modell simuliert. Die Simulation lässt sich in vier Schritte einteilen: Wahl der Verteilung, Simulation der Expressionsmatrix, Simulation der differentiellen Expression, Sortierung der Probes innerhalb des Probesets. Anschließend werden die fünf Präprozessierungsmethoden mit diesen simulierten Expressionsdaten auf ihre Sensitivität und Spezifität untersucht. Während sich bei den empirisch und gammaverteilt simulierten Expressionsdaten kein eindeutiges Ergebnis abzeichnet, hat sVSN bei den Daten aus dem linear gemischten Modell die größte Sensitivität und die größte Spezifität. Der in dieser Arbeit entwickelte sVSN-Algorithmus wurde zum ersten Mal angewendet und bewertet. Abschließend wird ein Teildatensatz von Brune et al. verwendet und hinsichtlich der fünf Präprozessierungsmethoden untersucht. Die Ergebnisse der sVSN-Methode wird im Detail weiter verfolgt. Die zusätzlich gefunden Gene können durch bereits veröffentlichte Arbeiten bestätigt werden. Letztendlich zeigt sich, dass neuere statistische Methoden (wie das im Rahmen dieser Arbeit entwickelte sVSN) bei der Analyse von Affymetrix Microarrays einen Vorteil bringen. Die sVSN und sRMA Methoden zeigen Vorteile, da die Probes nach der Normalisierung gewichtet werden, bevor diese aggregiert werden. Die MAS5-Methode schneidet am schlechtesten ab und sollte bei geringen Zellmengen nicht eingesetzt werden. Für die Analyse mit geringer Menge an mRNA müssen weitere Untersuchungen vorgenommen werden, um eine geeignete statistische Methode für die Analyse der Expressionsdaten zu finden.
In gastric cancer (GC), there are four molecular subclasses that indicate whether patients respond to chemotherapy or immunotherapy, according to the TCGA. In clinical practice, however, not every patient undergoes molecular testing. Many laboratories have used well-implemented in situ techniques (IHC and EBER-ISH) to determine the subclasses in their cohorts. Although multiple stains are used, we show that a staining approach is unable to correctly discriminate all subclasses. As an alternative, we trained an ensemble convolutional neuronal network using bagging that can predict the molecular subclass directly from hematoxylin–eosin histology. We also identified patients with predicted intra-tumoral heterogeneity or with features from multiple subclasses, which challenges the postulated TCGA-based decision tree for GC subtyping. In the future, deep learning may enable targeted testing for molecular subtypes and targeted therapy for a broader group of GC patients. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
The membrane bound NADPH oxidase involved in the synthesis of reactive oxygen species (ROS) is a multi-protein enzyme encoded by CYBA, CYBB, NCF1, NCF2 and NCF4 genes. Growing evidence suggests a role of ROS in the modulation of signaling pathways of non-phagocytic cells, including differentiation and proliferation of B-cell progenitors. Transcriptional downregulation of the CYBB gene has been previously reported in cell lines of the B-cell derived classical Hodgkin lymphoma (cHL). Thus, we explored functional consequences of CYBB downregulation on the NADPH complex. Using flow cytometry to detect and quantify superoxide anion synthesis in cHL cell lines we identified recurrent loss of superoxide anion production in all stimulated cHL cell lines in contrast to stimulated non-Hodgkin lymphoma cell lines. As CYBB loss proved to exert a deleterious effect on the NADPH oxidase complex in cHL cell lines, we analyzed the CYBB locus in Hodgkin and Reed-Sternberg (HRS) cells of primary cHL biopsies by in situ hybridisation and identified recurrent deletions of the gene in 8/18 cases. Immunohistochemical analysis to 14 of these cases revealed a complete lack of detectable CYBB protein expression in all HRS cells in all cases studied. Moreover, by microarray profiling of cHL cell lines we identified additional alterations of NADPH oxidase genes including CYBA copy number loss in 3/7 cell lines and a significant downregulation of the NCF1 transcription (p=0.006) compared to normal B-cell subsets. Besides, NCF1 protein was significantly downregulated (p<0.005) in cHL compared to other lymphoma cell lines. Together this findings show recurrent alterations of the NADPH oxidase encoding genes that result in functional inactivation of the enzyme and reduced production of superoxide anion in cHL.
Anaplastic large cell lymphoma (ALCL) and classical Hodgkin lymphoma (cHL) are lymphomas that contain CD30-expressing tumor cells and have numerous pathological similarities. Whereas ALCL is usually diagnosed at an advanced stage, cHL more frequently presents with localized disease. The aim of the present study was to elucidate the mechanisms underlying the different clinical presentation of ALCL and cHL. Chemokine and chemokine receptor expression were similar in primary ALCL and cHL cases apart from the known overexpression of the chemokines CCL17 and CCL22 in the Hodgkin and Reed-Sternberg (HRS) cells of cHL. Consistent with the overexpression of these chemokines, primary cHL cases encountered a significantly denser T cell microenvironment than ALCL. Additionally to differences in the interaction with their microenvironment, cHL cell lines presented a lower and less efficient intrinsic cell motility than ALCL cell lines, as assessed by time-lapse microscopy in a collagen gel and transwell migration assays. We thus propose that the combination of impaired basal cell motility and differences in the interaction with the microenvironment hamper the dissemination of HRS cells in cHL when compared with the tumor cells of ALCL.
In pathology, tissue images are evaluated using a light microscope, relying on the expertise and experience of pathologists. There is a great need for computational methods to quantify and standardize histological observations. Computational quantification methods become more and more essential to evaluate tissue images. In particular, the distribution of tumor cells and their microenvironment are of special interest. Here, we systematically investigated tumor cell properties and their spatial neighborhood relations by a new application of statistical analysis to whole slide images of Hodgkin lymphoma, a tumor arising in lymph nodes, and inflammation of lymph nodes called lymphadenitis. We considered properties of more than 400, 000 immunohistochemically stained, CD30-positive cells in 35 whole slide images of tissue sections from subtypes of the classical Hodgkin lymphoma, nodular sclerosis and mixed cellularity, as well as from lymphadenitis. We found that cells of specific morphology exhibited significant favored and unfavored spatial neighborhood relations of cells in dependence of their morphology. This information is important to evaluate differences between Hodgkin lymph nodes infiltrated by tumor cells (Hodgkin lymphoma) and inflamed lymph nodes, concerning the neighborhood relations of cells and the sizes of cells. The quantification of neighborhood relations revealed new insights of relations of CD30-positive cells in different diagnosis cases. The approach is general and can easily be applied to whole slide image analysis of other tumor types.