@article{FussOtherHeinzeetal.2021,
  author    = {Fuss, Carmina Teresa and Other, Katharina and Heinze, Britta and Landwehr, Laura-Sophie and Wiegering, Armin and Kalogirou, Charis and Hahner, Stefanie and Fassnacht, Martin},
  title     = {Expression of the chemokine receptor CCR7 in the normal adrenal gland and adrenal tumors and its correlation with clinical outcome in adrenocortical carcinoma},
  series = {Cancers},
  volume    = {13},
  journal   = {Cancers},
  number    = {22},
  issn      = {2072-6694},
  doi       = {10.3390/cancers13225693},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-250112},
  year      = {2021},
  abstract  = {Background: The chemokine receptor CCR7 is crucial for an intact immune function, but its expression is also associated with clinical outcome in several malignancies. No data exist on the expression of CCR7 in adrenocortical tumors. Methods: CCR7 expression was investigated by qRT-PCR and immunohistochemistry in 4 normal adrenal glands, 59 adrenocortical adenomas, and 181 adrenocortical carcinoma (ACC) samples. Results: CCR7 is highly expressed in the outer adrenocortical zones and medulla. Aldosterone-producing adenomas showed lower CCR7 protein levels (H-score 1.3 ± 1.0) compared to non-functioning (2.4 ± 0.5) and cortisol-producing adenomas (2.3 ± 0.6), whereas protein expression was variable in ACC (1.8 ± 0.8). In ACC, CCR7 protein expression was significantly higher in lymph node metastases (2.5 ± 0.5) compared to primary tumors (1.8±0.8) or distant metastases (2.0 ± 0.4; p < 0.01). mRNA levels of CCR7 were not significantly different between ACCs, normal adrenals, and adrenocortical adenomas. In contrast to other tumor entities, neither CCR7 protein nor mRNA expression significantly impacted patients' survival. Conclusion: We show that CCR7 is expressed on mRNA and protein level across normal adrenals, benign adrenocortical tumors, as well as ACCs. Given that CCR7 did not influence survival in ACC, it is probably not involved in tumor progression, but it could play a role in adrenocortical homeostasis.},
  language  = {en}
}
@article{MarquardtLandwehrRonchietal.2021,
  author    = {Marquardt, Andr{\´e} and Landwehr, Laura-Sophie and Ronchi, Cristina L. and di Dalmazi, Guido and Riester, Anna and Kollmannsberger, Philip and Altieri, Barbara and Fassnacht, Martin and Sbiera, Silviu},
  title     = {Identifying New Potential Biomarkers in Adrenocortical Tumors Based on mRNA Expression Data Using Machine Learning},
  series = {Cancers},
  volume    = {13},
  journal   = {Cancers},
  number    = {18},
  issn      = {2072-6694},
  doi       = {10.3390/cancers13184671},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-246245},
  year      = {2021},
  abstract  = {Simple Summary Using a visual-based clustering method on the TCGA RNA sequencing data of a large adrenocortical carcinoma (ACC) cohort, we were able to classify these tumors in two distinct clusters largely overlapping with previously identified ones. As previously shown, the identified clusters also correlated with patient survival. Applying the visual clustering method to a second dataset also including benign adrenocortical samples additionally revealed that one of the ACC clusters is more closely located to the benign samples, providing a possible explanation for the better survival of this ACC cluster. Furthermore, the subsequent use of machine learning identified new possible biomarker genes with prognostic potential for this rare disease, that are significantly differentially expressed in the different survival clusters and should be further evaluated. Abstract Adrenocortical carcinoma (ACC) is a rare disease, associated with poor survival. Several "multiple-omics" studies characterizing ACC on a molecular level identified two different clusters correlating with patient survival (C1A and C1B). We here used the publicly available transcriptome data from the TCGA-ACC dataset (n = 79), applying machine learning (ML) methods to classify the ACC based on expression pattern in an unbiased manner. UMAP (uniform manifold approximation and projection)-based clustering resulted in two distinct groups, ACC-UMAP1 and ACC-UMAP2, that largely overlap with clusters C1B and C1A, respectively. However, subsequent use of random-forest-based learning revealed a set of new possible marker genes showing significant differential expression in the described clusters (e.g., SOAT1, EIF2A1). For validation purposes, we used a secondary dataset based on a previous study from our group, consisting of 4 normal adrenal glands and 52 benign and 7 malignant tumor samples. The results largely confirmed those obtained for the TCGA-ACC cohort. In addition, the ENSAT dataset showed a correlation between benign adrenocortical tumors and the good prognosis ACC cluster ACC-UMAP1/C1B. In conclusion, the use of ML approaches re-identified and redefined known prognostic ACC subgroups. On the other hand, the subsequent use of random-forest-based learning identified new possible prognostic marker genes for ACC.},
  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}
}