TY  - JOUR
A1  - Sbiera, Silviu
A1  - Kunz, Meik
A1  - Weigand, Isabel
A1  - Deutschbein, Timo
A1  - Dandekar, Thomas
A1  - Fassnacht, Martin
T1  - The new genetic landscape of Cushing’s disease: deubiquitinases in the spotlight
JF  - Cancers
N2  - Cushing’s disease (CD) is a rare condition caused by adrenocorticotropic hormone (ACTH)-producing adenomas of the pituitary, which lead to hypercortisolism that is associated with high morbidity and mortality. Treatment options in case of persistent or recurrent disease are limited, but new insights into the pathogenesis of CD are raising hope for new therapeutic avenues. Here, we have performed a meta-analysis of the available sequencing data in CD to create a comprehensive picture of CD’s genetics. Our analyses clearly indicate that somatic mutations in the deubiquitinases are the key drivers in CD, namely USP8 (36.5%) and USP48 (13.3%). While in USP48 only Met415 is affected by mutations, in USP8 there are 26 different mutations described. However, these different mutations are clustering in the same hotspot region (affecting in 94.5% of cases Ser718 and Pro720). In contrast, pathogenic variants classically associated with tumorigenesis in genes like TP53 and BRAF are also present in CD but with low incidence (12.5% and 7%). Importantly, several of these mutations might have therapeutic potential as there are drugs already investigated in preclinical and clinical setting for other diseases. Furthermore, network and pathway analyses of all somatic mutations in CD suggest a rather unified picture hinting towards converging oncogenic pathways.
KW  - Cushing’s disease
KW  - pathogenesis
KW  - somatic mutations
KW  - deubiquitinases
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193194
SN  - 2072-6694
VL  - 11
IS  - 11
ER  - 
TY  - JOUR
A1  - Vey, Johannes
A1  - Kapsner, Lorenz A.
A1  - Fuchs, Maximilian
A1  - Unberath, Philipp
A1  - Veronesi, Giulia
A1  - Kunz, Meik
T1  - A toolbox for functional analysis and the systematic identification of diagnostic and prognostic gene expression signatures combining meta-analysis and machine learning
JF  - Cancers
N2  - The identification of biomarker signatures is important for cancer diagnosis and prognosis. However, the detection of clinical reliable signatures is influenced by limited data availability, which may restrict statistical power. Moreover, methods for integration of large sample cohorts and signature identification are limited. We present a step-by-step computational protocol for functional gene expression analysis and the identification of diagnostic and prognostic signatures by combining meta-analysis with machine learning and survival analysis. The novelty of the toolbox lies in its all-in-one functionality, generic design, and modularity. It is exemplified for lung cancer, including a comprehensive evaluation using different validation strategies. However, the protocol is not restricted to specific disease types and can therefore be used by a broad community. The accompanying R package vignette runs in ~1 h and describes the workflow in detail for use by researchers with limited bioinformatics training.
KW  - bioinformatics tool
KW  - R package
KW  - machine learning
KW  - meta-analysis
KW  - biomarker signature
KW  - gene expression analysis
KW  - survival analysis
KW  - functional analysis
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193240
SN  - 2072-6694
VL  - 11
IS  - 10
ER  -