@article{LacombeSoaresMarianietal.2020,
  author    = {Lacombe, Amanda Meneses Ferreira and Soares, Iber{\^e} Cauduro and Mariani, Beatriz Marinho de Paula and Nishi, Mirian Yumie and Bezerra-Neto, Jo{\~a}o Evangelista and Charchar, Helaine da Silva and Brondani, Vania Balderrama and Tanno, Fabio and Srougi, Victor and Chambo, Jos{\´e} Luiz and Costa de Freitas, Ricardo Miguel and Mendonca, Berenice Bilharinho and Hoff, Ana O. and Almeida, Madson Q. and Weigand, Isabel and Kroiss, Matthias and Zerbini, Maria Claudia Nogueira and Fragoso, Maria Candida Barisson Villares},
  title     = {Sterol O-acyl transferase 1 as a prognostic marker of adrenocortical carcinoma},
  series = {Cancers},
  volume    = {12},
  journal   = {Cancers},
  number    = {1},
  issn      = {2072-6694},
  doi       = {10.3390/cancers12010247},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-200857},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@article{WeigandWurmDechetal.2019,
  author    = {Weigand, Matthias and Wurm, Michael and Dech, Stefan and Taubenb{\"o}ck, Hannes},
  title     = {Remote sensing in environmental justice research—a review},
  series = {ISPRS International Journal of Geo-Information},
  volume    = {8},
  journal   = {ISPRS International Journal of Geo-Information},
  number    = {1},
  issn      = {2220-9964},
  doi       = {10.3390/ijgi8010020},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-196950},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}
@article{WeigandBoosTasbihietal.2016,
  author    = {Weigand, Annika and Boos, Anja M. and Tasbihi, Kereshmeh and Beier, Justus P. and Dalton, Paul D. and Schrauder, Michael and Horch, Raymund E. and Beckmann, Matthias W. and Strissel, Pamela L. and Strick, Reiner},
  title     = {Selective isolation and characterization of primary cells from normal breast and tumors reveal plasticity of adipose derived stem cells},
  series = {Breast Cancer Research},
  volume    = {18},
  journal   = {Breast Cancer Research},
  number    = {32},
  doi       = {10.1186/s13058-016-0688-2},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-164759},
  year      = {2016},
  abstract  = {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.},
  language  = {en}
}
@article{EckhardtSbieraKrebsetal.2022,
  author    = {Eckhardt, Carolin and Sbiera, Iuliu and Krebs, Markus and Sbiera, Silviu and Spahn, Martin and Kneitz, Burkhard and Joniau, Steven and Fassnacht, Martin and K{\"u}bler, Hubert and Weigand, Isabel and Kroiss, Matthias},
  title     = {High expression of Sterol-O-Acyl transferase 1 (SOAT1), an enzyme involved in cholesterol metabolism, is associated with earlier biochemical recurrence in high risk prostate cancer},
  series = {Prostate Cancer and Prostatic Diseases},
  volume    = {25},
  journal   = {Prostate Cancer and Prostatic Diseases},
  number    = {3},
  issn      = {1476-5608},
  doi       = {10.1038/s41391-021-00431-3},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-271819},
  pages     = {484-490},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@article{WeigandRonchiVanselowetal.2021,
  author    = {Weigand, Isabel and Ronchi, Cristina L. and Vanselow, Jens T. and Bathon, Kerstin and Lenz, Kerstin and Herterich, Sabine and Schlosser, Andreas and Kroiss, Matthias and Fassnacht, Martin and Calebiro, Davide and Sbiera, Silviu},
  title     = {PKA Cα subunit mutation triggers caspase-dependent RIIβ subunit degradation via Ser\(^{114}\) phosphorylation},
  series = {Science Advances},
  volume    = {7},
  journal   = {Science Advances},
  number    = {8},
  doi       = {10.1126/sciadv.abd4176},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-270445},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}
@article{LandwehrAltieriSchreineretal.2020,
  author    = {Landwehr, Laura-Sophie and Altieri, Barbara and Schreiner, Jochen and Sbiera, Iuliu and Weigand, Isabel and Kroiss, Matthias and Fassnacht, Martin and Sbiera, Silviu},
  title     = {Interplay between glucocorticoids and tumor-infiltrating lymphocytes on the prognosis of adrenocortical carcinoma},
  series = {Journal for ImmunoTherapy of Cancer},
  volume    = {8},
  journal   = {Journal for ImmunoTherapy of Cancer},
  doi       = {10.1136/jitc-2019-000469},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-229893},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@phdthesis{Weigand2024,
  author    = {Weigand, Matthias Johann},
  title     = {Fernerkundung und maschinelles Lernen zur Erfassung von urbanem Gr{\"u}n - Eine Analyse am Beispiel der Verteilungsgerechtigkeit in Deutschland},
  doi       = {10.25972/OPUS-34961},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-349610},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2024},
  abstract  = {Gr{\"u}nfl{\"a}chen stellen einen der wichtigsten Umwelteinfl{\"u}sse in der Wohnumwelt der Menschen dar. Einerseits wirken sie sich positiv auf die physische und mentale Gesundheit der Menschen aus, andererseits k{\"o}nnen Gr{\"u}nfl{\"a}chen auch negative Wirkungen anderer Faktoren abmildern, wie beispielsweise die im Laufe des Klimawandels zunehmenden Hitzeereignisse. Dennoch sind Gr{\"u}nfl{\"a}chen nicht f{\"u}r die gesamte Bev{\"o}lkerung gleichermaßen zug{\"a}nglich. Bestehende Forschung im Kontext der Umweltgerechtigkeit (UG) konnte bereits aufzeigen, dass unterschiedliche sozio-{\"o}konomische und demographische Gruppen der deutschen Bev{\"o}lkerung unterschiedlichen Zugriff auf Gr{\"u}nfl{\"a}chen haben. An bestehenden Analysen von Umwelteinfl{\"u}ssen im Kontext der UG wird kritisiert, dass die Auswertung geographischer Daten h{\"a}ufig auf zu stark aggregiertem Level geschieht, wodurch lokal spezifische Expositionen nicht mehr genau abgebildet werden. Dies trifft insbesondere f{\"u}r großfl{\"a}chig angelegte Studien zu. So werden wichtige r{\"a}umliche Informationen verloren. Doch moderne Erdbeobachtungs- und Geodaten sind so detailliert wie nie und Methoden des maschinellen Lernens erm{\"o}glichen die effiziente Verarbeitung zur Ableitung h{\"o}herwertiger Informationen. Das {\"u}bergeordnete Ziel dieser Arbeit besteht darin, am Beispiel von Gr{\"u}nfl{\"a}chen in Deutschland methodische Schritte der systematischen Umwandlung umfassender Geodaten in relevante Geoinformationen f{\"u}r die großfl{\"a}chige und hochaufgel{\"o}ste Analyse von Umwelteigenschaften aufzuzeigen und durchzuf{\"u}hren. An der Schnittstelle der Disziplinen Fernerkundung, Geoinformatik, Sozialgeographie und Umweltgerechtigkeitsforschung sollen Potenziale moderner Methoden f{\"u}r die Verbesserung der r{\"a}umlichen und semantischen Aufl{\"o}sung von Geoinformationen erforscht werden. Hierf{\"u}r werden Methoden des maschinellen Lernens eingesetzt, um Landbedeckung und -nutzung auf nationaler Ebene zu erfassen. Diese Entwicklungen sollen dazu beitragen bestehende Datenl{\"u}cken zu schließen und Aufschluss {\"u}ber die Verteilungsgerechtigkeit von Gr{\"u}nfl{\"a}chen 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 f{\"u}r 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 gr{\"u}ne Landbedeckung (GLC) r{\"a}umlich zu quantifizieren. Im zweiten konzeptionellen Teil der Arbeit steht die differenzierte Betrachtung von Gr{\"u}nfl{\"a}chen anhand des Beispiels {\"o}ffentlicher Gr{\"u}nfl{\"a}chen (PGS), die h{\"a}ufig Gegenstand der UG-Forschung ist, im Vordergrund. Doch eine h{\"a}ufig verwendete Quelle f{\"u}r r{\"a}umliche Daten zu {\"o}ffentlichen Gr{\"u}nfl{\"a}chen, der European Urban Atlas (EUA), wird bisher nicht fl{\"a}chendeckend f{\"u}r Deutschland erhoben. Dieser Studienteil verfolgt einen datengetriebenen Ansatz, die Verf{\"u}gbarkeit von {\"o}ffentlichem Gr{\"u}n auf der r{\"a}umlichen Ebene von Nachbarschaften f{\"u}r ganz Deutschland zu ermitteln. Hierf{\"u}r 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 verf{\"u}gbare Fl{\"a}che von {\"o}ffentlichem Gr{\"u}n quantifiziert. Das Ergebnis dieses Schrittes ist ein Modell, welches genutzt wird, um die Menge {\"o}ffentlicher Gr{\"u}nfl{\"a}chen in der Nachbarschaft zu sch{\"a}tzen (𝑅 2 = 0.952). Der dritte Studienteil greift die Ergebnisse der ersten beiden Studienteile auf und betrachtet die Verteilung von Gr{\"u}nfl{\"a}chen in Deutschland unter Hinzunahme von georeferenzierten Bev{\"o}lkerungsdaten. Diese exemplarische Analyse unterscheidet dabei Gr{\"u}nfl{\"a}chen nach zwei Typen: GLC und PGS. Zun{\"a}chst wird mithilfe deskriptiver Statistiken die generelle Gr{\"u}nfl{\"a}chenverteilung in der Bev{\"o}lkerung Deutschlands beleuchtet. Daraufhin wird die Verteilungsgerechtigkeit anhand g{\"a}ngiger Gerechtigkeitsmetriken bestimmt. Abschließend werden die Zusammenh{\"a}nge zwischen der demographischen Komposition der Nachbarschaft und der verf{\"u}gbaren Menge von Gr{\"u}nfl{\"a}chen anhand dreier exemplarischer soziodemographischer Gesellschaftsgruppen untersucht. Die Analyse zeigt starke Unterschiede der Verf{\"u}gbarkeit von PGS zwischen st{\"a}dtischen und l{\"a}ndlichen Gebieten. Ein h{\"o}herer Prozentsatz der Stadtbev{\"o}lkerung hat Zugriff das Mindestmaß von PGS gemessen an der Vorgabe der Weltgesundheitsorganisation. Die Ergebnisse zeigen auch einen deutlichen Unterschied bez{\"u}glich der Verteilungsgerechtigkeit zwischen GLC und PGS und verdeutlichen die Relevanz der Unterscheidung von Gr{\"u}nfl{\"a}chentypen f{\"u}r derartige Untersuchungen. Die abschließende Betrachtung verschiedener Bev{\"o}lkerungsgruppen arbeitet Unterschiede auf soziodemographischer Ebene auf. In der Zusammenschau demonstriert diese Arbeit wie moderne Geodaten und Methoden des maschinellen Lernens genutzt werden k{\"o}nnen bisherige Limitierungen r{\"a}umlicher Datens{\"a}tze zu {\"u}berwinden. Am Beispiel von Gr{\"u}nfl{\"a}chen in der Wohnumgebung der Bev{\"o}lkerung Deutschlands wird gezeigt, dass landesweite Analysen zur Umweltgerechtigkeit durch hochaufgel{\"o}ste und lokal feingliedrige geographische Informationen bereichert werden k{\"o}nnen. Diese Arbeit verdeutlicht, wie die Methoden der Erdbeobachtung und Geoinformatik einen wichtigen Beitrag leisten k{\"o}nnen, die Ungleichheit der Wohnumwelt der Menschen zu identifizieren und schlussendlich den nachhaltigen Siedlungsbau in Form von objektiven Informationen zu unterst{\"u}tzen und {\"u}berwachen.},
  subject      = {Geografie},
  language  = {de}
}