TY - JOUR A1 - Bothou, Christina A1 - Sharma, Ashish A1 - Oo, Adrian A1 - Kim, Baek A1 - Perge, Pal A1 - Igaz, Peter A1 - Ronchi, Cristina L. A1 - Shapiro, Igor A1 - Hantel, Constanze T1 - Novel insights into the molecular regulation of ribonucleotide reductase in adrenocortical carcinoma treatment JF - Cancers N2 - Current systemic treatment options for patients with adrenocortical carcinomas (ACCs) are far from being satisfactory. DNA damage/repair mechanisms, which involve, e.g., ataxia-telangiectasia-mutated (ATM) and ataxia-telangiectasia/Rad3-related (ATR) protein signaling or ribonucleotide reductase subunits M1/M2 (RRM1/RRM2)-encoded ribonucleotide reductase (RNR) activation, commonly contribute to drug resistance. Moreover, the regulation of RRM2b, the p53-induced alternative to RRM2, is of unclear importance for ACC. Upon extensive drug screening, including a large panel of chemotherapies and molecular targeted inhibitors, we provide strong evidence for the anti-tumoral efficacy of combined gemcitabine (G) and cisplatin (C) treatment against the adrenocortical cell lines NCI-H295R and MUC-1. However, accompanying induction of RRM1, RRM2, and RRM2b expression also indicated developing G resistance, a frequent side effect in clinical patient care. Interestingly, this effect was partially reversed upon addition of C. We confirmed our findings for RRM2 protein, RNR-dependent dATP levels, and modulations of related ATM/ATR signaling. Finally, we screened for complementing inhibitors of the DNA damage/repair system targeting RNR, Wee1, CHK1/2, ATR, and ATM. Notably, the combination of G, C, and the dual RRM1/RRM2 inhibitor COH29 resulted in previously unreached total cell killing. In summary, we provide evidence that RNR-modulating therapies might represent a new therapeutic option for ACC. KW - adrenocortical carcinoma KW - adrenocortical cell line KW - RRM2 KW - RNR KW - COH29 Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-245132 SN - 2072-6694 VL - 13 IS - 16 ER - TY - JOUR A1 - Marquardt, André A1 - Landwehr, Laura-Sophie A1 - Ronchi, Cristina L. A1 - di Dalmazi, Guido A1 - Riester, Anna A1 - Kollmannsberger, Philip A1 - Altieri, Barbara A1 - Fassnacht, Martin A1 - Sbiera, Silviu T1 - Identifying New Potential Biomarkers in Adrenocortical Tumors Based on mRNA Expression Data Using Machine Learning JF - Cancers N2 - 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. KW - adrenocortical carcinoma KW - in silico analysis KW - machine learning KW - bioinformatic clustering KW - biomarker prediction Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-246245 SN - 2072-6694 VL - 13 IS - 18 ER - TY - JOUR A1 - Weigand, Isabel A1 - Ronchi, Cristina L. A1 - Vanselow, Jens T. A1 - Bathon, Kerstin A1 - Lenz, Kerstin A1 - Herterich, Sabine A1 - Schlosser, Andreas A1 - Kroiss, Matthias A1 - Fassnacht, Martin A1 - Calebiro, Davide A1 - Sbiera, Silviu T1 - PKA Cα subunit mutation triggers caspase-dependent RIIβ subunit degradation via Ser\(^{114}\) phosphorylation JF - Science Advances N2 - 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. KW - mutation triggers KW - phosphorylation Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-270445 VL - 7 IS - 8 ER -