TY  - JOUR
A1  - Marquardt, André
A1  - Hartrampf, Philipp
A1  - Kollmannsberger, Philip
A1  - Solimando, Antonio G.
A1  - Meierjohann, Svenja
A1  - Kübler, Hubert
A1  - Bargou, Ralf
A1  - Schilling, Bastian
A1  - Serfling, Sebastian E.
A1  - Buck, Andreas
A1  - Werner, Rudolf A.
A1  - Lapa, Constantin
A1  - Krebs, Markus
T1  - Predicting microenvironment in CXCR4- and FAP-positive solid tumors — a pan-cancer machine learning workflow for theranostic target structures
JF  - Cancers
N2  - (1) Background: C-X-C Motif Chemokine Receptor 4 (CXCR4) and Fibroblast Activation Protein Alpha (FAP) are promising theranostic targets. However, it is unclear whether CXCR4 and FAP positivity mark distinct microenvironments, especially in solid tumors. (2) Methods: Using Random Forest (RF) analysis, we searched for entity-independent mRNA and microRNA signatures related to CXCR4 and FAP overexpression in our pan-cancer cohort from The Cancer Genome Atlas (TCGA) database —  representing n = 9242 specimens from 29 tumor entities. CXCR4- and FAP-positive samples were assessed via StringDB cluster analysis, EnrichR, Metascape, and Gene Set Enrichment Analysis (GSEA). Findings were validated via correlation analyses in n = 1541 tumor samples. TIMER2.0 analyzed the association of CXCR4 / FAP expression and infiltration levels of immune-related cells. (3) Results: We identified entity-independent CXCR4 and FAP gene signatures representative for the majority of solid cancers. While CXCR4 positivity marked an immune-related microenvironment, FAP overexpression highlighted an angiogenesis-associated niche. TIMER2.0 analysis confirmed characteristic infiltration levels of CD8+ cells for CXCR4-positive tumors and endothelial cells for FAP-positive tumors. (4) Conclusions: CXCR4- and FAP-directed PET imaging could provide a non-invasive decision aid for entity-agnostic treatment of microenvironment in solid malignancies. Moreover, this machine learning workflow can easily be transferred towards other theranostic targets.
KW  - machine learning
KW  - tumor microenvironment
KW  - immune infiltration
KW  - angiogenesis
KW  - mRNA
KW  - miRNA
KW  - transcriptome
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-305036
SN  - 2072-6694
VL  - 15
IS  - 2
ER  - 
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  -