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

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.

Inter-Tumor Heterogeneity—Melanomas Respond Differently to GM-CSF-Mediated Activation

“…These results may constitute a relevant example of inter-tumor heterogeneity [ 44 ] reported recently by us to occur in melanoma cells derived from 4 individual melanoma patients [ 11 ]. Cutaneous and brain metastatic variant pairs from these melanomas, sharing the same genetic ancestry, were subjected to proteome profiling aiming to identify shared molecular pathways leading to brain metastasis.…”

“…Each pair of variants originated thus from an identical genetic lineage [ 10 ]. Comparing transcriptomic [ 10 ], proteomic [ 11 ] and epigenomic [ 12 ] profiles of these variants, we identified and characterized a number of tumor-intrinsic and microenvironmental signaling factors as well as downstream factors. These three types of factors were involved in driving or inhibiting MBM formation, survival and preservation [ 7 , 8 , 9 , 10 , 13 , 14 , 15 , 16 , 17 , 18 ].…”

Endoplasmic Reticulum Adaptation and Autophagic Competence Shape Response to Fluid Shear Stress in T24 Bladder Cancer Cells

“…After a subsequent washing step, the stained cells were kept in Live Cell Imaging Solution and imaged within 1 h. Cells were imaged using a Zeiss LSM710 laser scanning confocal microscope (ELYRA PS.1 system) equipped with a 63X/1.2 water immersion objective (Zeiss Microscopy GmbH, Germany) or with the Lionheart FX Automated Microscope (BioTek Instruments, Winooski, VT, United States). For image analysis, the software ZEN 2012 Black Edition (Zeiss Microscopy GmbH, Germany), Gen5 (BioTek Instruments, Winooski, VT, United States), and the free software ImageJ were used as previously described ( Del Favero et al, 2018b ; Neuditschko et al, 2020 ). For the microfluidic slides, at least eight images were taken with the phase contrast, GFP (469/525 nm), and DAPI (377/447 nm) channels resulting in minimum 24 different optical fields for quantification.…”

“…To detect the morphological changes of the cells before and after the biomechanical stimulation, bright field images were acquired using an Olympus CKX53 Inverted Microscope. For every experiment, at least three images were acquired before (static controls) and after the biomechanical stimulation protocol (shear stress), resulting in a minimum of nine different optical fields for subsequent single-cell morphometric quantification ( Neuditschko et al, 2020 ).…”

Schwann cell plasticity regulates neuroblastic tumor cell differentiation via epidermal growth factor-like protein 8

“…For the identification and label free quantification of proteins, the MaxQuant software package (version 1.6.1.0) 97 was used 98 . The human UniProt database (version 03/2018, restricted to reviewed entries only) with 20316 entries was used for the search, and the false discovery rate (FDR) was set to 0.01 on both peptide and protein level.…”