TY  - JOUR
A1  - Marquardt, André
A1  - Kollmannsberger, Philip
A1  - Krebs, Markus
A1  - Argentiero, Antonella
A1  - Knott, Markus
A1  - Solimando, Antonio Giovanni
A1  - Kerscher, Alexander Georg
T1  - Visual clustering of transcriptomic data from primary and metastatic tumors — dependencies and novel pitfalls
JF  - Genes
N2  - Personalized oncology is a rapidly evolving area and offers cancer patients therapy options that are more specific than ever. However, there is still a lack of understanding regarding transcriptomic similarities or differences of metastases and corresponding primary sites. Applying two unsupervised dimension reduction methods (t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP)) on three datasets of metastases (n = 682 samples) with three different data transformations (unprocessed, log10 as well as log10 + 1 transformed values), we visualized potential underlying clusters. Additionally, we analyzed two datasets (n = 616 samples) containing metastases and primary tumors of one entity, to point out potential familiarities. Using these methods, no tight link between the site of resection and cluster formation outcome could be demonstrated, or for datasets consisting of solely metastasis or mixed datasets. Instead, dimension reduction methods and data transformation significantly impacted visual clustering results. Our findings strongly suggest data transformation to be considered as another key element in the interpretation of visual clustering approaches along with initialization and different parameters. Furthermore, the results highlight the need for a more thorough examination of parameters used in the analysis of clusters.
KW  - visual clustering
KW  - t-SNE
KW  - UMAP
KW  - transcriptomic analysis
KW  - cancer
KW  - metastasis
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-281872
SN  - 2073-4425
VL  - 13
IS  - 8
ER  - 
TY  - JOUR
A1  - Mainz, Laura
A1  - Sarhan, Mohamed A. F. E.
A1  - Roth, Sabine
A1  - Sauer, Ursula
A1  - Maurus, Katja
A1  - Hartmann, Elena M.
A1  - Seibert, Helen-Desiree
A1  - Rosenwald, Andreas
A1  - Diefenbacher, Markus E.
A1  - Rosenfeldt, Mathias T.
T1  - Autophagy blockage reduces the incidence of pancreatic ductal adenocarcinoma in the context of mutant Trp53
JF  - Frontiers in Cell and Developmental Biology
N2  - Macroautophagy (hereafter referred to as autophagy) is a homeostatic process that preserves cellular integrity. In mice, autophagy regulates pancreatic ductal adenocarcinoma (PDAC) development in a manner dependent on the status of the tumor suppressor gene Trp53. Studies published so far have investigated the impact of autophagy blockage in tumors arising from Trp53-hemizygous or -homozygous tissue. In contrast, in human PDACs the tumor suppressor gene TP53 is mutated rather than allelically lost, and TP53 mutants retain pathobiological functions that differ from complete allelic loss. In order to better represent the patient situation, we have investigated PDAC development in a well-characterized genetically engineered mouse model (GEMM) of PDAC with mutant Trp53 (Trp53\(^{R172H}\)) and deletion of the essential autophagy gene Atg7. Autophagy blockage reduced PDAC incidence but had no impact on survival time in the subset of animals that formed a tumor. In the absence of Atg7, non-tumor-bearing mice reached a similar age as animals with malignant disease. However, the architecture of autophagy-deficient, tumor-free pancreata was effaced, normal acinar tissue was largely replaced with low-grade pancreatic intraepithelial neoplasias (PanINs) and insulin expressing islet β-cells were reduced. Our data add further complexity to the interplay between Atg7 inhibition and Trp53 status in tumorigenesis.
KW  - pancreatic cancer
KW  - autophagy
KW  - p53
KW  - metastasis
KW  - ATG7
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-266005
SN  - 2296-634X
VL  - 10
ER  -