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  - Solimando, Antonio G.
A1  - Bittrich, Max
A1  - Shahini, Endrit
A1  - Albanese, Federica
A1  - Fritz, Georg
A1  - Krebs, Markus
T1  - Determinants of COVID-19 disease severity – lessons from primary and secondary immune disorders including cancer
JF  - International Journal of Molecular Sciences
N2  - At the beginning of the COVID-19 pandemic, patients with primary and secondary immune disorders — including patients suffering from cancer — were generally regarded as a high-risk population in terms of COVID-19 disease severity and mortality. By now, scientific evidence indicates that there is substantial heterogeneity regarding the vulnerability towards COVID-19 in patients with immune disorders. In this review, we aimed to summarize the current knowledge about the effect of coexistent immune disorders on COVID-19 disease severity and vaccination response. In this context, we also regarded cancer as a secondary immune disorder. While patients with hematological malignancies displayed lower seroconversion rates after vaccination in some studies, a majority of cancer patients’ risk factors for severe COVID-19 disease were either inherent (such as metastatic or progressive disease) or comparable to the general population (age, male gender and comorbidities such as kidney or liver disease). A deeper understanding is needed to better define patient subgroups at a higher risk for severe COVID-19 disease courses. At the same time, immune disorders as functional disease models offer further insights into the role of specific immune cells and cytokines when orchestrating the immune response towards SARS-CoV-2 infection. Longitudinal serological studies are urgently needed to determine the extent and the duration of SARS-CoV-2 immunity in the general population, as well as immune-compromised and oncological patients.
KW  - COVID-19
KW  - SARS-CoV-2
KW  - disorder of immunity
KW  - cancer
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-319412
SN  - 1422-0067
VL  - 24
IS  - 10
ER  -