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Mol Cell Proteomics. 2020 Mar;19(3):478-489. doi: 10.1074/mcp.RA119.001886. Epub 2019 Dec 31.

The Challenge of Classifying Metastatic Cell Properties by Molecular Profiling Exemplified with Cutaneous Melanoma Cells and Their Cerebral Metastasis from Patient Derived Mouse Xenografts.

Author information

1
Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna.
2
Department of Inorganic Chemistry, Faculty of Chemistry, University of Vienna.
3
Department of Food Chemistry and Toxicology, Faculty of Chemistry, University of Vienna.
4
Department of Cell Research and Immunology, The George S. Wise Faculty of Life Sciences, Tel Aviv University.
5
Core Facility Multimodal Imaging, Faculty of Chemistry, University of Vienna.
6
Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna christopher.gerner@univie.ac.at.
7
Joint Metabolome Facility, Faculty of Chemistry, University of Vienna.

Abstract

The prediction of metastatic properties from molecular analyses still poses a major challenge. Here we aimed at the classification of metastasis-related cell properties by proteome profiling making use of cutaneous and brain-metastasizing variants from single melanomas sharing the same genetic ancestry. Previous experiments demonstrated that cultured cells derived from these xenografted variants maintain a stable phenotype associated with a differential metastatic behavior: The brain metastasizing variants produce more spontaneous micro-metastases than the corresponding cutaneous variants. Four corresponding pairs of cutaneous and metastatic cells were obtained from four individual patients, resulting in eight cell-lines presently investigated. Label free proteome profiling revealed significant differences between corresponding pairs of cutaneous and cerebellar metastases from the same patient. Indeed, each brain metastasizing variant expressed several apparently metastasis-associated proteomic alterations as compared with the corresponding cutaneous variant. Among the differentially expressed proteins we identified cell adhesion molecules, immune regulators, epithelial to mesenchymal transition markers, stem cell markers, redox regulators and cytokines. Similar results were observed regarding eicosanoids, considered relevant for metastasis, such as PGE2 and 12-HETE. Multiparametric morphological analysis of cells also revealed no characteristic alterations associated with the cutaneous and brain metastasis variants. However, no correct classification regarding metastatic potential was yet possible with the present data. We thus concluded that molecular profiling is able to classify cells according to known functional categories but is not yet able to predict relevant cell properties emerging from networks consisting of many interconnected molecules. The presently observed broad diversity of molecular patterns, irrespective of restricting to one tumor type and two main classes of metastasis, highlights the important need to develop meta-analysis strategies to predict cell properties from molecular profiling data. Such base knowledge will greatly support future individualized precision medicine approaches.

KEYWORDS:

Melanoma; brain metastasis; eicosanoids; metastasis; mouse models; omics; tumor microenvironment; xenograft model

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