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Mutations in the PRKACA gene are the most frequent cause of cortisol-producing adrenocortical adenomas leading to Cushing’s syndrome. PRKACA encodes for the catalytic subunit α of protein kinase A (PKA). We already showed that PRKACA mutations lead to impairment of regulatory (R) subunit binding. Furthermore, PRKACA mutations are associated with reduced RIIβ protein levels; however, the mechanisms leading to reduced RIIβ levels are presently unknown. Here, we investigate the effects of the most frequent PRKACA mutation, L206R, on regulatory subunit stability. We find that Ser\(^{114}\) phosphorylation of RIIβ is required for its degradation, mediated by caspase 16. Last, we show that the resulting reduction in RIIβ protein levels leads to increased cortisol secretion in adrenocortical cells. These findings reveal the molecular mechanisms and pathophysiological relevance of the R subunit degradation caused by PRKACA mutations, adding another dimension to the deregulation of PKA signaling caused by PRKACA mutations in adrenal Cushing’s syndrome.
Background
Adrenocortical carcinoma (ACC) is a rare endocrine malignancy. Tumor-related glucocorticoid excess is present in similar to 60% of patients and associated with particularly poor prognosis. Results of first clinical trials using immune checkpoint inhibitors were heterogeneous. Here we characterize tumor-infiltrating T lymphocytes (TILs) in ACC in association with glucocorticoids as potential explanation for resistance to immunotherapy.
Methods
We performed immunofluorescence analysis to visualize tumor-infiltrating T cells (CD3\(^+\)), T helper cells (CD3\(^+\)CD4\(^+\)), cytotoxic T cells (CD3\(^+\)CD8\(^+\)) and regulatory T cells (Tregs; CD3\(^+\)CD4\(^+\)FoxP3\(^+\)) in 146 ACC tissue specimens (107 primary tumors, 16 local recurrences, 23 metastases). Quantitative data of immune cell infiltration were correlated with clinical data (including glucocorticoid excess).
Results
86.3% of ACC specimens showed tumor infiltrating T cells (7.7 cells/high power field (HPF)), including T helper (74.0%, 6.7 cells/HPF), cytotoxic T cells (84.3%, 5.7 cells/HPF) and Tregs (49.3%, 0.8 cells/HPF). The number of TILs was associated with better overall survival (HR for death: 0.47, 95% CI 0.25 to 0.87), which was true for CD4\(^+\)- and CD8\(^+\) subpopulations as well. In localized, non-metastatic ACC, the favorable impact of TILs on overall and recurrence-free survival was manifested even independently of ENSAT (European Network for the Study of Adrenal Tumors) stage, resection status and Ki67 index. T helper cells were negatively correlated with glucocorticoid excess (Phi=-0.290, p=0.009). Patients with glucocorticoid excess and low TILs had a particularly poor overall survival (27 vs. 121 months in patients with TILs without glucocorticoid excess).
Conclusion
Glucocorticoid excess is associated with T cell depletion and unfavorable prognosis. To reactivate the immune system in ACC by checkpoint inhibitors, an inhibition of adrenal steroidogenesis might be pivotal and should be tested in prospective studies.
Grünflächen stellen einen der wichtigsten Umwelteinflüsse in der Wohnumwelt der Menschen dar. Einerseits wirken sie sich positiv auf die physische und mentale Gesundheit der Menschen aus, andererseits können Grünflächen auch negative Wirkungen anderer Faktoren abmildern, wie beispielsweise die im Laufe des Klimawandels zunehmenden Hitzeereignisse. Dennoch sind Grünflächen nicht für die gesamte Bevölkerung gleichermaßen zugänglich. Bestehende Forschung im Kontext der Umweltgerechtigkeit (UG) konnte bereits aufzeigen, dass unterschiedliche sozio-ökonomische und demographische Gruppen der deutschen Bevölkerung unterschiedlichen Zugriff auf Grünflächen haben. An bestehenden Analysen von Umwelteinflüssen im Kontext der UG wird kritisiert, dass die Auswertung geographischer Daten häufig auf zu stark aggregiertem Level geschieht, wodurch lokal spezifische Expositionen nicht mehr genau abgebildet werden. Dies trifft insbesondere für großflächig angelegte Studien zu. So werden wichtige räumliche Informationen verloren. Doch moderne Erdbeobachtungs- und Geodaten sind so detailliert wie nie und Methoden des maschinellen Lernens ermöglichen die effiziente Verarbeitung zur Ableitung höherwertiger Informationen.
Das übergeordnete Ziel dieser Arbeit besteht darin, am Beispiel von Grünflächen in Deutschland methodische Schritte der systematischen Umwandlung umfassender Geodaten in relevante Geoinformationen für die großflächige und hochaufgelöste Analyse von Umwelteigenschaften aufzuzeigen und durchzuführen. An der Schnittstelle der Disziplinen Fernerkundung, Geoinformatik, Sozialgeographie und Umweltgerechtigkeitsforschung sollen Potenziale moderner Methoden für die Verbesserung der räumlichen und semantischen Auflösung von Geoinformationen erforscht werden. Hierfür werden Methoden des maschinellen Lernens eingesetzt, um Landbedeckung und -nutzung auf nationaler Ebene zu erfassen. Diese Entwicklungen sollen dazu beitragen bestehende Datenlücken zu schließen und Aufschluss über die Verteilungsgerechtigkeit von Grünflächen zu bieten.
Diese Dissertation gliedert sich in drei konzeptionelle Teilschritte. Im ersten Studienteil werden Erdbeobachtungsdaten der Sentinel-2 Satelliten zur deutschlandweiten Klassifikation von Landbedeckungsinformationen verwendet. In Kombination mit punktuellen Referenzdaten der europaweiten Erfassung für Landbedeckungs- und Landnutzungsinformationen des Land Use and Coverage Area Frame Survey (LUCAS) wird ein maschinelles Lernverfahren trainiert. In diesem Kontext werden verschiedene Vorverarbeitungsschritte der LUCAS-Daten und deren Einfluss auf die Klassifikationsgenauigkeit beleuchtet. Das Klassifikationsverfahren ist in der Lage Landbedeckungsinformationen auch in komplexen urbanen Gebieten mit hoher Genauigkeit abzuleiten. Ein Ergebnis des Studienteils ist eine deutschlandweite Landbedeckungsklassifikation mit einer Gesamtgenauigkeit von 93,07 %, welche im weiteren Verlauf der Arbeit genutzt wird, um grüne Landbedeckung (GLC) räumlich zu quantifizieren.
Im zweiten konzeptionellen Teil der Arbeit steht die differenzierte Betrachtung von Grünflächen anhand des Beispiels öffentlicher Grünflächen (PGS), die häufig Gegenstand der UG-Forschung ist, im Vordergrund. Doch eine häufig verwendete Quelle für räumliche Daten zu öffentlichen Grünflächen, der European Urban Atlas (EUA), wird bisher nicht flächendeckend für Deutschland erhoben. Dieser Studienteil verfolgt einen datengetriebenen Ansatz, die Verfügbarkeit von öffentlichem Grün auf der räumlichen Ebene von Nachbarschaften für ganz Deutschland zu ermitteln. Hierfür dienen bereits vom EUA erfasste Gebiete als Referenz. Mithilfe einer Kombination von Erdbeobachtungsdaten und Informationen aus dem OpenStreetMap-Projekt wird ein Deep Learning -basiertes Fusionsnetzwerk erstellt, welche die verfügbare Fläche von öffentlichem Grün quantifiziert. Das Ergebnis dieses Schrittes ist ein Modell, welches genutzt wird, um die Menge öffentlicher Grünflächen in der Nachbarschaft zu schätzen (𝑅 2 = 0.952).
Der dritte Studienteil greift die Ergebnisse der ersten beiden Studienteile auf und betrachtet die Verteilung von Grünflächen in Deutschland unter Hinzunahme von georeferenzierten Bevölkerungsdaten. Diese exemplarische Analyse unterscheidet dabei Grünflächen nach zwei Typen: GLC und PGS. Zunächst wird mithilfe deskriptiver Statistiken die generelle Grünflächenverteilung in der Bevölkerung Deutschlands beleuchtet. Daraufhin wird die Verteilungsgerechtigkeit anhand gängiger Gerechtigkeitsmetriken bestimmt. Abschließend werden die Zusammenhänge zwischen der demographischen Komposition der Nachbarschaft und der verfügbaren Menge von Grünflächen anhand dreier exemplarischer soziodemographischer Gesellschaftsgruppen untersucht. Die Analyse zeigt starke Unterschiede der Verfügbarkeit von PGS zwischen städtischen und ländlichen Gebieten. Ein höherer Prozentsatz der Stadtbevölkerung hat Zugriff das Mindestmaß von PGS gemessen an der Vorgabe der Weltgesundheitsorganisation. Die Ergebnisse zeigen auch einen deutlichen Unterschied bezüglich der Verteilungsgerechtigkeit zwischen GLC und PGS und verdeutlichen die Relevanz der Unterscheidung von Grünflächentypen für derartige
Untersuchungen. Die abschließende Betrachtung verschiedener Bevölkerungsgruppen arbeitet Unterschiede auf soziodemographischer Ebene auf.
In der Zusammenschau demonstriert diese Arbeit wie moderne Geodaten und Methoden des maschinellen Lernens genutzt werden können bisherige Limitierungen räumlicher Datensätze zu überwinden. Am Beispiel von Grünflächen in der Wohnumgebung der Bevölkerung Deutschlands wird gezeigt, dass landesweite Analysen zur Umweltgerechtigkeit durch hochaufgelöste und lokal feingliedrige geographische Informationen bereichert werden können. Diese Arbeit verdeutlicht, wie die Methoden der Erdbeobachtung und Geoinformatik einen wichtigen Beitrag leisten können, die Ungleichheit der Wohnumwelt der Menschen zu identifizieren und schlussendlich den nachhaltigen Siedlungsbau in Form von objektiven Informationen zu unterstützen und überwachen.
Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with an unfavorable prognosis. Despite the poor prognosis in the majority of patients, no improvements in treatment strategies have been achieved. Therefore, the discovery of new prognostic biomarkers is of enormous interest. Sterol-O-acyl transferase 1 (SOAT1) is involved in cholesterol esterification and lipid droplet formation. Recently, it was demonstrated that SOAT1 inhibition leads to impaired steroidogenesis and cell viability in ACC. To date, no studies have addressed the impact of SOAT1 expression on ACC prognosis and clinical outcomes. We evaluated SOAT1 expression by quantitative real-time polymerase chain reaction and immunohistochemistry in a tissue microarray of 112 ACCs (Weiss score ≥ 3) from adults treated in a single tertiary center in Brazil. Two independent pathologists evaluated the immunohistochemistry results through a semiquantitative approach (0–4). We aimed to evaluate the correlation between SOAT1 expression and clinical, biochemical and anatomopathological parameters, recurrence-free survival (RFS), progression-free survival (PFS), and overall survival (OS). SOAT1 protein expression was heterogeneous in this cohort, 37.5% of the ACCs demonstrated a strong SOAT1 protein expression (score > 2), while 62.5% demonstrated a weak or absent protein expression (score ≤ 2). Strong SOAT1 protein expression correlated with features of high aggressiveness in ACC, such as excessive tumor cortisol secretion (p = 0.01), an advanced disease stage [European Network for the Study of Adrenal Tumors (ENSAT) staging system 3 and 4 (p = 0.011)] and a high Ki67 index (p = 0.002). In multivariate analysis, strong SOAT1 protein expression was an independent predictor of a reduced OS (hazard ratio (HR) 2.15, confidence interval (CI) 95% 1.26–3.66; p = 0.005) in all patients (n = 112), and a reduced RFS (HR 2.1, CI 95% 1.09–4.06; p = 0.027) in patients with localized disease at diagnosis (n = 83). Our findings demonstrated that SOAT1 protein expression has prognostic value in ACC and reinforced the importance of investigating SOAT1 as a possible therapeutic target for patients with ACC.
Human health is known to be affected by the physical environment. Various environmental influences have been identified to benefit or challenge people's physical condition. Their heterogeneous distribution in space results in unequal burdens depending on the place of living. In addition, since societal groups tend to also show patterns of segregation, this leads to unequal exposures depending on social status. In this context, environmental justice research examines how certain social groups are more affected by such exposures. Yet, analyses of this per se spatial phenomenon are oftentimes criticized for using “essentially aspatial” data or methods which neglect local spatial patterns by aggregating environmental conditions over large areas. Recent technological and methodological developments in satellite remote sensing have proven to provide highly detailed information on environmental conditions. This narrative review therefore discusses known influences of the urban environment on human health and presents spatial data and applications for analyzing these influences. Furthermore, it is discussed how geographic data are used in general and in the interdisciplinary research field of environmental justice in particular. These considerations include the modifiable areal unit problem and ecological fallacy. In this review we argue that modern earth observation data can represent an important data source for research on environmental justice and health. Especially due to their high level of spatial detail and the provided large-area coverage, they allow for spatially continuous description of environmental characteristics. As a future perspective, ongoing earth observation missions, as well as processing architectures, ensure data availability and applicability of ’big earth data’ for future environmental justice analyses.
Background
There is a need to establish more cell lines from breast tumors in contrast to immortalized cell lines from metastatic effusions in order to represent the primary tumor and not principally metastatic biology of breast cancer. This investigation describes the simultaneous isolation, characterization, growth and function of primary mammary epithelial cells (MEC), mesenchymal cells (MES) and adipose derived stem cells (ADSC) from four normal breasts, one inflammatory and one triple-negative ductal breast tumors.
Methods
A total of 17 cell lines were established and gene expression was analyzed for MEC and MES (n = 42) and ADSC (n = 48) and MUC1, pan-KRT, CD90 and GATA-3 by immunofluorescence. DNA fingerprinting to track cell line identity was performed between original primary tissues and isolates. Functional studies included ADSC differentiation, tumor MES and MEC invasion co-cultured with ADSC-conditioned media (CM) and MES adhesion and growth on 3D-printed scaffolds.
Results
Comparative analysis showed higher gene expression of EPCAM, CD49f, CDH1 and KRTs for normal MEC lines; MES lines e.g. Vimentin, CD10, ACTA2 and MMP9; and ADSC lines e.g. CD105, CD90, CDH2 and CDH11. Compared to the mean of all four normal breast cell lines, both breast tumor cell lines demonstrated significantly lower ADSC marker gene expression, but higher expression of mesenchymal and invasion gene markers like SNAI1 and MMP2. When compared with four normal ADSC differentiated lineages, both tumor ADSC showed impaired osteogenic and chondrogenic but enhanced adipogenic differentiation and endothelial-like structures, possibly due to high PDGFRB and CD34. Addressing a functional role for overproduction of adipocytes, we initiated 3D-invasion studies including different cell types from the same patient. CM from ADSC differentiating into adipocytes induced tumor MEC 3D-invasion via EMT and amoeboid phenotypes. Normal MES breast cells adhered and proliferated on 3D-printed scaffolds containing 20 fibers, but not on 2.5D-printed scaffolds with single fiber layers, important for tissue engineering.
Conclusion
Expression analyses confirmed successful simultaneous cell isolations of three different phenotypes from normal and tumor primary breast tissues. Our cell culture studies support that breast-tumor environment differentially regulates tumor ADSC plasticity as well as cell invasion and demonstrates applications for regenerative medicine.
Background
Prostate cancer (PCa) is the most frequent cancer in men. The prognosis of PCa is heterogeneous with many clinically indolent tumors and rare highly aggressive cases. Reliable tissue markers of prognosis are lacking. Active cholesteryl ester synthesis has been associated with prostate cancer aggressiveness. Sterol-O-Acyl transferases (SOAT) 1 and 2 catalyze cholesterol esterification in humans.
Objective
To investigate the value of SOAT1 and SOAT2 tissue expression as prognostic markers in high risk PCa.
Patients and Methods
Formalin-fixed paraffin-embedded tissue samples from 305 high risk PCa cases treated with radical prostatectomy were analyzed for SOAT1 and SOAT2 protein expression by semi-quantitative immunohistochemistry. The Kaplan-Meier method and Cox proportional hazards modeling were used to compare outcome.
Main Outcome Measure
Biochemical recurrence (BCR) free survival.
Results
SOAT1 expression was high in 73 (25%) and low in 219 (75%; not evaluable: 13) tumors. SOAT2 was highly expressed in 40 (14%) and at low levels in 249 (86%) samples (not evaluable: 16). By Kaplan-Meier analysis, we found significantly shorter median BCR free survival of 93 months (95% confidence interval 23.6-123.1) in patients with high SOAT1 vs. 134 months (112.6-220.2, Log-rank p < 0.001) with low SOAT1. SOAT2 expression was not significantly associated with BCR. After adjustment for age, preoperative PSA, tumor stage, Gleason score, resection status, lymph node involvement and year of surgery, high SOAT1 but not SOAT2 expression was associated with shorter BCR free survival with a hazard ratio of 2.40 (95% CI 1.57-3.68, p < 0.001). Time to clinical recurrence and overall survival were not significantly associated with SOAT1 and SOAT2 expression CONCLUSIONS: SOAT1 expression is strongly associated with BCR free survival alone and after multivariable adjustment in high risk PCa. SOAT1 may serve as a histologic marker of prognosis and holds promise as a future treatment target.
Unprecedented urbanization in particular in countries of the global south result in informal urban development processes, especially in mega cities. With an estimated 1 billion slum dwellers globally, the United Nations have made the fight against poverty the number one sustainable development goal. To provide better infrastructure and thus a better life to slum dwellers, detailed information on the spatial location and size of slums is of crucial importance. In the past, remote sensing has proven to be an extremely valuable and effective tool for mapping slums. The nature of used mapping approaches by machine learning, however, made it necessary to invest a lot of effort in training the models. Recent advances in deep learning allow for transferring trained fully convolutional networks (FCN) from one data set to another. Thus, in our study we aim at analyzing transfer learning capabilities of FCNs to slum mapping in various satellite images. A model trained on very high resolution optical satellite imagery from QuickBird is transferred to Sentinel-2 and TerraSAR-X data. While free-of-charge Sentinel-2 data is widely available, its comparably lower resolution makes slum mapping a challenging task. TerraSAR-X data on the other hand, has a higher resolution and is considered a powerful data source for intra-urban structure analysis. Due to the different image characteristics of SAR compared to optical data, however, transferring the model could not improve the performance of semantic segmentation but we observe very high accuracies for mapped slums in the optical data: QuickBird image obtains 86–88% (positive prediction value and sensitivity) and a significant increase for Sentinel-2 applying transfer learning can be observed (from 38 to 55% and from 79 to 85% for PPV and sensitivity, respectively). Using transfer learning proofs extremely valuable in retrieving information on small-scaled urban structures such as slum patches even in satellite images of decametric resolution.
Background
Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes.
Methods
A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported.
Results
1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy.
Conclusions
Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models.
Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.
Context
The adrenal cortex produces specific steroid hormones including steroid sulfates such as dehydroepiandrosterone sulfate (DHEAS), the most abundant steroid hormone in the human circulation. Steroid sulfation involves a multistep enzyme machinery that may be impaired by inborn errors of steroid metabolism. Emerging data suggest a role of steroid sulfates in the pathophysiology of adrenal tumors and as potential biomarkers.
Evidence Acquisition
Selective literature search using “steroid,” “sulfat*,” “adrenal,” “transport,” “mass spectrometry” and related terms in different combinations.
Evidence Synthesis
A recent study highlighted the tissue abundance of estrogen sulfates to be of prognostic impact in adrenocortical carcinoma tissue samples using matrix-assisted laser desorption ionization mass spectrometry imaging. General mechanisms of sulfate uptake, activation, and transfer to substrate steroids are reasonably well understood. Key aspects of this pathway, however, have not been investigated in detail in the adrenal; these include the regulation of substrate specificity and the secretion of sulfated steroids. Both for the adrenal and targeted peripheral tissues, steroid sulfates may have relevant biological actions beyond their cognate nuclear receptors after desulfation. Impaired steroid sulfation such as low DHEAS in Cushing adenomas is of diagnostic utility, but more comprehensive studies are lacking. In bioanalytics, the requirement of deconjugation for gas-chromatography/mass-spectrometry has precluded the study of steroid sulfates for a long time. This limitation may be overcome by liquid chromatography/tandem mass spectrometry.
Conclusions
A role of steroid sulfation in the pathophysiology of adrenal tumors has been suggested and a diagnostic utility of steroid sulfates as biomarkers is likely. Recent analytical developments may target sulfated steroids specifically.