Proteomic identification of a marker signature for MAPK i resistance in melanoma
2019; Springer Nature; Volume: 38; Issue: 15 Linguagem: Inglês
10.15252/embj.201695874
ISSN1460-2075
AutoresVerena Paulitschke, Ossia M. Eichhoff, Christopher Gerner, Philipp Paulitschke, Andrea Bileck, Thomas Mohr, Phil F. Cheng, Alexander Leitner, Emmanuella Guenova, Ieva Saulīte, Sandra N. Freiberger, Anja Irmisch, Bernhard Knapp, Nina Zila, Theodora‐Pagona Chatziisaak, Jürgen Stephan, Joanna Mangana, Rainer Kunstfeld, Hubert Pehamberger, Ruedi Aebersold, Reinhard Dummer, Mitchell P. Levesque,
Tópico(s)vaccines and immunoinformatics approaches
ResumoArticle26 June 2019free access Transparent process Proteomic identification of a marker signature for MAPKi resistance in melanoma Verena Paulitschke Department of Dermatology, Medical University of Vienna, Vienna, Austria Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland Search for more papers by this author Ossia Eichhoff Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland Search for more papers by this author Christopher Gerner Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria Search for more papers by this author Philipp Paulitschke Institute of Physics, Center for NanoScience, Ludwig Maximilians University, Munich, Germany Search for more papers by this author Andrea Bileck Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria Search for more papers by this author Thomas Mohr Department of Medicine I, Institute of Cancer Research and Comprehensive Cancer Center, Medical University Vienna, Vienna, Austria Search for more papers by this author Phil F Cheng Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland Search for more papers by this author Alexander Leitner orcid.org/0000-0003-4126-0725 Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland Search for more papers by this author Emmanuella Guenova Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland Search for more papers by this author Ieva Saulite Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland Search for more papers by this author Sandra N Freiberger Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland Search for more papers by this author Anja Irmisch Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland Search for more papers by this author Bernhard Knapp Department of Statistics, Protein Informatics Group, University of Oxford, Oxford, UK Search for more papers by this author Nina Zila Department of Dermatology, Medical University of Vienna, Vienna, Austria Search for more papers by this author Theodora-Pagona Chatziisaak Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland Search for more papers by this author Jürgen Stephan Institute of Physics, Center for NanoScience, Ludwig Maximilians University, Munich, Germany Search for more papers by this author Joanna Mangana Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland Search for more papers by this author Rainer Kunstfeld Department of Dermatology, Medical University of Vienna, Vienna, Austria Search for more papers by this author Hubert Pehamberger Department of Dermatology, Medical University of Vienna, Vienna, Austria Search for more papers by this author Ruedi Aebersold orcid.org/0000-0002-9576-3267 Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland Faculty of Science, University of Zurich, Zurich, Switzerland Search for more papers by this author Reinhard Dummer Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland Search for more papers by this author Mitchell P Levesque Corresponding Author [email protected] orcid.org/0000-0001-5902-9420 Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland Search for more papers by this author Verena Paulitschke Department of Dermatology, Medical University of Vienna, Vienna, Austria Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland Search for more papers by this author Ossia Eichhoff Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland Search for more papers by this author Christopher Gerner Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria Search for more papers by this author Philipp Paulitschke Institute of Physics, Center for NanoScience, Ludwig Maximilians University, Munich, Germany Search for more papers by this author Andrea Bileck Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria Search for more papers by this author Thomas Mohr Department of Medicine I, Institute of Cancer Research and Comprehensive Cancer Center, Medical University Vienna, Vienna, Austria Search for more papers by this author Phil F Cheng Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland Search for more papers by this author Alexander Leitner orcid.org/0000-0003-4126-0725 Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland Search for more papers by this author Emmanuella Guenova Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland Search for more papers by this author Ieva Saulite Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland Search for more papers by this author Sandra N Freiberger Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland Search for more papers by this author Anja Irmisch Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland Search for more papers by this author Bernhard Knapp Department of Statistics, Protein Informatics Group, University of Oxford, Oxford, UK Search for more papers by this author Nina Zila Department of Dermatology, Medical University of Vienna, Vienna, Austria Search for more papers by this author Theodora-Pagona Chatziisaak Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland Search for more papers by this author Jürgen Stephan Institute of Physics, Center for NanoScience, Ludwig Maximilians University, Munich, Germany Search for more papers by this author Joanna Mangana Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland Search for more papers by this author Rainer Kunstfeld Department of Dermatology, Medical University of Vienna, Vienna, Austria Search for more papers by this author Hubert Pehamberger Department of Dermatology, Medical University of Vienna, Vienna, Austria Search for more papers by this author Ruedi Aebersold orcid.org/0000-0002-9576-3267 Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland Faculty of Science, University of Zurich, Zurich, Switzerland Search for more papers by this author Reinhard Dummer Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland Search for more papers by this author Mitchell P Levesque Corresponding Author [email protected] orcid.org/0000-0001-5902-9420 Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland Search for more papers by this author Author Information Verena Paulitschke1,2,3, Ossia Eichhoff2, Christopher Gerner4, Philipp Paulitschke5, Andrea Bileck4, Thomas Mohr6, Phil F Cheng2, Alexander Leitner3, Emmanuella Guenova2, Ieva Saulite2, Sandra N Freiberger2, Anja Irmisch2, Bernhard Knapp7, Nina Zila1, Theodora-Pagona Chatziisaak2, Jürgen Stephan5, Joanna Mangana2, Rainer Kunstfeld1, Hubert Pehamberger1, Ruedi Aebersold3,8, Reinhard Dummer2 and Mitchell P Levesque *,2 1Department of Dermatology, Medical University of Vienna, Vienna, Austria 2Department of Dermatology, University of Zurich Hospital, University of Zurich, Zurich, Switzerland 3Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland 4Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria 5Institute of Physics, Center for NanoScience, Ludwig Maximilians University, Munich, Germany 6Department of Medicine I, Institute of Cancer Research and Comprehensive Cancer Center, Medical University Vienna, Vienna, Austria 7Department of Statistics, Protein Informatics Group, University of Oxford, Oxford, UK 8Faculty of Science, University of Zurich, Zurich, Switzerland *Corresponding author. Tel: +41 (0) 44 556 3262; E-mail: [email protected] EMBO J (2019)38:e95874https://doi.org/10.15252/embj.201695874 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract MAPK inhibitors (MAPKi) show outstanding clinical response rates in melanoma patients harbouring BRAF mutations, but resistance is common. The ability of melanoma cells to switch from melanocytic to mesenchymal phenotypes appears to be associated with therapeutic resistance. High-throughput, subcellular proteome analyses and RNAseq on two panels of primary melanoma cells that were either sensitive or resistant to MAPKi revealed that only 15 proteins were sufficient to distinguish between these phenotypes. The two proteins with the highest discriminatory power were PTRF and IGFBP7, which were both highly upregulated in the mesenchymal-resistant cells. Proteomic analysis of CRISPR/Cas-derived PTRF knockouts revealed targets involved in lysosomal activation, endocytosis, pH regulation, EMT, TGFβ signalling and cell migration and adhesion, as well as a significantly reduced invasive index and ability to form spheres in 3D culture. Overexpression of PTRF led to MAPKi resistance, increased cell adhesion and sphere formation. In addition, immunohistochemistry of patient samples showed that PTRF expression levels were a significant biomarker of poor progression-free survival, and IGFBP7 levels in patient sera were shown to be higher after relapse. Synopsis Therapy resistance towards BRAF inhibition and relapse in skin cancer are frequently associated with cellular phenotype switching, but the molecular control of this plasticity and discriminating markers remain unclear. Multidimensional expression profiling of resistant and sensitive primary melanoma reveals functional biomarkers and potential targets associated with poor progression-free survival. Combined proteome and transcriptome analyses identify a core set of 15 MAPKi resistance-associated proteins. PTRF and IGFBP7 are upregulated in resistant mesenchymal-type melanoma cells. Depletion of PTRF results in downregulation of lysosomal endocytosis targets and EMT signaling. Overexpression of PTRF induces MAPKi-resistance, increased cell adhesion and sphere formation. PTRF expression correlates with poor progression-free survival under MAPKi treatment. Introduction BRAF inhibitors (BRAFi) are used routinely for the treatment of metastatic or inoperable melanoma harbouring a BRAFV600E mutation (Chapman et al, 2011; Hauschild et al, 2012). Although BRAFi achieves clinical responses in 50% of patients, acquired drug resistance frequently develops (Wagle et al, 2011; McArthur et al, 2014). The mechanisms of BRAFi resistance have been investigated intensively and have identified MEK reactivation as a critical node. This resulted in the development of BRAFi (i.e. vemurafenib, dabrafenib) and MEKi (i.e. cobimetinib, trametinib) combination therapy, which improved therapeutic response, lowered toxicity, and prolonged progression-free survival (PFS) and overall survival (OS; Long et al, 2014; McArthur et al, 2014; Robert et al, 2015). However, acquired resistance also develops from BRAFi/MEKi combinatorial therapy and has been shown to be caused by a set of genetic aberrations similar to those responsible for BRAFi resistance (Villanueva et al, 2013; Long et al, 2014; Wagle et al, 2014; Moriceau et al, 2015). In mono- or combination therapy, resistance might be due to the plasticity and heterogeneity of melanoma cells (Zipser et al, 2011). Phenotype switching may allow for multiple adaptive mechanisms that enable melanoma cell survival independent of MAPK pathway activation (e.g. EMT like, and differences in proliferation, motility or stem cell-like characteristics) (Dummer & Flaherty, 2012). Very few proteomic studies have investigated MAPKi resistance phenotype switching in melanoma to get insight into the cellular mechanisms of melanoma drug resistance and to extract clinically relevant features (Koomen & Smalley, 2011; Straussman et al, 2012; Parker et al, 2014, 2015; Fleuren et al, 2016). Proteomics has been used to predict pleiotropic acidophilic protein kinase CK2 (CK2) as a potential drug target, demonstrating that CK2 blockade potentiated the antiproliferative effects of BRAF and MEK inhibition in BRAF-mutated cancers (Parker et al, 2014). Phosphoproteomics has shown changes in cytoskeletal regulation, GTP/GDP exchange, protein kinase C, insulin growth factor (IGF) signalling and melanosome maturation after transition to a drug-resistant phenotype (Parker et al, 2015). In a previous feasibility study, we showed in cisplatin-sensitive versus cisplatin-resistant melanoma cell lines that proteome profiling facilitates the identification of drug resistance mechanisms (Paulitschke et al, 2013). Recently, we reported that acquired BRAFi resistance is associated with a loss of differentiation, an enhanced expression of the lysosomal compartment, an increased potential for metastasis and epithelial–mesenchymal transition (EMT; Paulitschke et al, 2015). These features are associated with pronounced changes in cellular morphology in line with the model of “phenotype switching” from a melanocytic to a mesenchymal state during progression and resistance (Hoek et al, 2008; Zipser et al, 2011; Paulitschke et al, 2015; Spranger et al, 2015; Hugo et al, 2016), summarized in Paulitschke et al (2016). Mesenchymal melanoma cells are resistant to BRAFi treatment and tend to downregulate lineage-specific genes (e.g. MelanA, MITF) while upregulating factors known to be involved in drug resistance (e.g. Wnt5a; Weeraratna et al, 2002; Hoek et al, 2008; Eichhoff et al, 2010; Zipser et al, 2011). Chronic exposure of proliferative melanoma cells to TGFβ causes a phenotype switch which involves the activation of PI3K signalling, downregulation of E-cadherin and the loss of tissue-specific marker gene expression, which is a process similar to EMT and contributes to melanoma heterogeneity (Schlegel et al, 2015). Recently, proteomic and phosphoproteomic changes of cultured human keratinocytes undergoing EMT and cell cycle arrest in response to stimulation with TGFβ were demonstrated by SMAD-dependent and SMAD-independent pathways (D'Souza et al, 2014). The goal of the present study was to identify a proteomic signature of MAPKi resistance in melanoma cells, to mechanistically dissect the role of genes in that signature and to validate the most informative features on patient biopsies prior to MAPKi therapy. By applying high-throughput techniques such as subcellular shotgun proteome analyses in two different mass spectrometry (MS) centres and RNAseq, we identified two proteins associated with a mesenchymal phenotype and demonstrated the involvement of PTRF in the invasive phenotype associated with MAPKi resistance in melanoma. Further validation in patient biopsies suggests that PTRF expression is significantly highly expressed in patients who fail to respond to targeted therapy. The study design is highlighted in Fig EV1. To understand the subcellular contribution and to identify possible secreted proteins as serum biomarkers to MAPKi resistance, we first established subcellular fractionation of two sensitive and four resistant cells and obtained a resistance signature by proteome analysis of subcellular compartments (1st proteome cohort). In a next step, we used a larger cohort of seven sensitive and 13 resistant cells and a second MS centre for validation of the signature, generalization of the data by using different MS centres and for additional information for BRAFi and MEKi resistance (2nd proteome cohort). Comparison of RNAseq data (six sensitive and 10 resistant cell lines) to the proteome resistance signature validated our results. The most informative protein PTRF was used for mechanistic analysis and clinical correlation (Fig EV1). Click here to expand this figure. Figure EV1. Overview over experimental and bioinformatic design of the study Download figure Download PowerPoint Results Patient-derived BRAFi-resistant short-term melanoma cultures have an EMT phenotype Two metastatic primary melanoma cell cultures (S1, S2) sensitive to the BRAFi LGX818 (Encorafenib) and PLX4032 (Vemurafenib), and four BRAFi-resistant primary cell cultures (R1, R2, R3, R4), were used as short-term term cultures, characterized (Fig 1A) and processed for shotgun proteomics. Figure 1. In vivo derived melanoma cultures are resistant to BRAFi treatment and have an EMT phenotype EM image of S1, S2 and R1–4, Ruler: 50 μM, 5 μM (below). IC50 values to PLX4032 and LGX818 of S1, S2 and R1–4. For each cell culture and targeted therapy, we performed the viability analysis in triplicates (n = 3) and depicted the mean values of each concentration (±SD). The normalized data were merged, and the average IC50 was calculated using GraphPad Prism software. We show the 95% confident interval (CI95%) as a measurement of data distribution around the IC50 value of each cell culture. Cell adhesion assay of S1, S2 and R1–4, ECM-mediated cell adhesion was quantified at OD 560 nm after extraction. Each bar graph represents the mean of two independent experiments for each cell culture (±SD). We performed a one-tailed unpaired Student's t-test to analyse significance of two groups (sensitive versus resistant cell cultures) (*< 0.05; ***< 0.005). Zymography assay of S1, R1, M (marker) and MMP2 C (MMP2-positive control). Western blotting of N-cadherin of S1 and R1; tubulin serves as a positive control. Download figure Download PowerPoint The two sensitive cultures, S1 and S2, were established from skin metastases of treatment-naïve patients. Four resistant cultures, R1-R4, were taken from different patients who received BRAFi and progressed under treatment. Resistant melanoma cultures R2 and R3 were established shortly before treatment initiation, suggesting that resistant melanoma cells were already present in these tumours (e.g. intrinsic resistance). The other resistant cultures (R1 and R4) were established from relapsed tumours after an initially successful BRAFi treatment (acquired resistance). Cell morphology was examined by electron microscopy, revealing sensitive cells that were usually present as smaller cells with a ball-like structure, whereas resistant cells were larger and flatter with a fibroblast-like morphology (Fig 1A), consistent with our previous results of melanoma cells with induced resistance (Paulitschke et al, 2015). All melanoma cells were BRAFV600E mutated, except R3 which has a BRAFV600K mutation. R1 and R4 were shown by single-cell cloning and following Sanger sequencing to be double mutated for BRAFV600E and NRASQ61K or NRASQ61H, respectively (Raaijmakers et al, 2016). The IC50 to PLX4032 was the lowest in S2, followed by S1, R2, R4, R3 and R1 with the highest IC50. The IC50 to LGX818 was the lowest in S2 followed by S1, R3, R1, R2 and R4 with the highest IC50 (Fig 1B). The IC50 to MEK162 was highest for R1 (Table 1). Table 1. Category of primary cells and the IC50 to MAPKi treatment IC50 LGX (nM) IC50 PLX (nM) IC50 MEK162 (nM) Sensitive BRAF mutated MM000921 (S1) 3 210 50 MM980513 (S2) 1 100 10 MM990922 10 1,032 100 MM991104 3 200 100 MM951004 7 413 100 MM990802 3 176 20 MM990706 2 137 10 BRAF mut MM130604 (R2) 1,980 520 20 MM130820 (R3) 80 2,690 20 BRAF/NRAS mut MM140307 (DR1) 200 > 10,000 > 1,000 MM121224 (R1)/DMSO 980 3,960 100 MM140906 (DR2) >200 > 10,000 > 1,000 MM121224_250 nM MEK162 980 3,960 > 10,000 MM121224_500 nM MEK162 980 3,960 1,924 MM130903 (R4) 3,220 970 20 MM150423 (DR 3) 554 > 10,000 1,000 Intrinsic resistant MM111031 (IR) 1670 140 30 MM150325 8*107 8*106 951.3 MM150405 13,321.5 36,954 5,663 As published previously, induced drug-resistant melanoma cells show an increased adherence to extracellular matrix proteins (Paulitschke et al, 2015). Similarly, the patient-derived drug-resistant melanoma cells compared to sensitive ones have a higher adherence in a cell adhesion assay (Fig 1C). Comparing the sensitive (S1) and resistant (R1) melanoma cells by zymography, we detected an increase in MMP2 (matrix metalloproteinase 2) activity in the resistant cells (R1; Fig 1D), a feature associated with invasiveness in EMT (Bae et al, 2013). Consistently, N-cadherin levels were increased in culture R1 (Fig 1E), which is both in line with the proteome data and our previous publication (Paulitschke et al, 2015). Shotgun proteomic analysis of primary MAPKi-sensitive and MAPKi-resistant cells We enriched for subcellular fractions of all cells and performed shotgun proteomic analysis of every fraction. In total, 4,052 proteins in the cytoplasmic, 1,007 proteins in the supernatant and 3,463 proteins in the nuclear fraction were detected. Using Perseus software, we generated heatmaps for every fraction analysed and compared protein expression of sensitive versus resistant cells. In the cytoplasm and nuclei, the functional classes “nucleic acid binding or metabolic process”, “cellular nitrogen compound metabolic process” and “RNA processing” were commonly and significantly upregulated in the sensitive cells, whereas “COPI vesicle coat” and “calcium ion binding” were commonly and significantly upregulated in the resistant cells (Fig EV2). Click here to expand this figure. Figure EV2. Proteome characterization of sensitive (S1,2) and resistant (R1,2,3,4) melanoma cells (cytoplasmic fraction)Left: Hierarchical clustering of Z-scored expression values for significantly changed protein expression, revealing differences between sensitive and resistant cells. Right: Profiles of the four main clusters. Red: upregulated in resistant cells, green: downregulated in sensitive cells. Download figure Download PowerPoint Groups significantly upregulated in the cytoplasmic fraction of the resistant cultures revealed, e.g. “antigen processing and presentation”, “MHC class I protein complex”, “threonine-type peptidase activity”, “lysosomal membrane and lumen”, “cell adhesion”, “regulation of cytokine production”, “regulation of angiogenesis” and “immune processes” (Fig EV2). In the secretome, the identified proteins with significantly different abundance revealed to be upregulated in the resistant cells (Fig EV3). Since many proteins that were upregulated in resistant cells could not be assigned significantly to a specific process, we examined individual proteins that were the most highly differentially expressed. The most highly and significantly upregulated proteins in the resistant secretome were insulin-like growth factor-binding protein 7 (IGFBP7), procollagen C-endopeptidase enhancer 1 (POLCE), nicotinamide phosphoribosyltransferase (NAMPT), nidogen-1 (NID1), thrombospondin-2 (THBS2) or interleukin 8 (IL8). Only one pathway was upregulated in the secretome of the sensitive cells: “very-low-density lipoprotein particle” (Fig EV3). In the nuclear compartment of the resistant cells, the functional groups “locomotion”, “eukaryotic translation initiation factors”, “proton-transporting V-type ATPase”, “clathrin adaptor complex”, “insulin receptor signalling pathway” and “glial cell development” were significantly upregulated (Fig EV4). Click here to expand this figure. Figure EV3. Proteome characterization of sensitive and resistant melanoma cells of secretomeLeft: Hierarchical clustering of Z-scored expression values for significantly changed proteins, revealing differences. Right: Profiles of the one cluster and additional proteins. Red: upregulated in resistant cells, green: downregulated in sensitive cells. Download figure Download PowerPoint Click here to expand this figure. Figure EV4. Proteome characterization of sensitive and resistant melanoma cells of nuclear fractionLeft: Hierarchical clustering of Z-scored expression values for significantly changed proteins, revealing differences. Right: Profiles of the four clusters. Red: upregulated in resistant cells, green: downregulated in sensitive cells. Download figure Download PowerPoint BRAFi and MEKi resistance in KEGG pathway analysis In a next step, we enhanced the cohort of primary cells by using cell pellets of additional patients of the same cohort to confirm the data, integrating relevant new features such as BRAFi and MEKi double-resistant primary melanoma cells and intrinsically double-resistant melanoma cells, in order to analyse MAPKi (BRAFi and MEKi) resistance. This second dataset was comprised of 20 primary melanoma cell cultures with seven MAPKi-sensitive, four BRAFi-resistant and five MAPKi-resistant primary cells including a cell line with increasing in vitro derived MEKi resistance, and three MAPKi intrinsically resistant primary cells. All IC50 values to LGX, PLX and MEK162 and the mutational status of BRAF and NRAS were determined prior to analysis (Table 1). Cell pellets were made, and the MS analysis was conducted at a different institute in order to confirm the earlier findings in a different experimental setting. To get an insight into the pathways generally upregulated in drug-resistant cells, a KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis was performed for both proteome cohorts, which are highly comparable. In Fig 3, KEGG pathways of the cytoplasmic fraction (Fig 2A and C) and the 2nd proteome cohort (Fig 2B and D) are exemplified by the pathways “cell adhesion molecules” (Fig 2A and B) and “ECM receptor interaction” (Fig 2C and D), where almost every protein is upregulated (red colour). In addition, gene set enrichment analysis (GSEA) confirmed that these pathways are significantly enriched in both proteome cohorts (Fig 2A’–D’). Figure 2. GSEA (gene set enrichment analysis) and KEGG pathway visualization of main regulated pathways Cell adhesion molecules 1st proteome cohort. Cell adhesion molecules 2nd proteome cohort. ECM receptor interaction in the 1st proteome cohort. ECM receptor interaction in the 2nd proteome cohort is highly enriched and upregulated as visualized KEGG pathway visualization and by GSEA (A′–D′) of the two cell culture cohorts. Heatmap of significant regulated EIFs in ne (nucleus) and sn (supernatant), pellets. red: upregulated in resistant cells, grey: not identified, * multiparameter correction, s, sensitive; dr, double resistant; r, BRAF resistant; ir, intrinsic resistant; ar, all resistant; vs, versus. KEGG pathway visualization of log fold change (FC) regulated EIFs in ne, sn, cyt, cell pellets, red: upregulated, green/blue: downregulated in resistant cells. Barplot of proteins involved in drug elimination strategies, regulation is depicted as upregulation in resistant cells by log2FC, order of compartment: cyt, ne, sn, all significant, * multiparameter correction. Measurement of the concentration of H+ in the resistant cell cultures and endosomal activity by Molecular Probes® pHrodo® dye in S1, S2 and R1–4. Fluorescence was quantified compared to background using ImageJ software. Five images per cell culture were analysed, and the experiment was performed in triplicates (n = 15). Each measurement was depicted on the dot plot as one single point (mean). One-tailed unpaired Student's t-test was performed and revealed statistical significance (P < 0.0001****) of group S against group R. Download figure Download PowerPoint To take advantage of the different fractions, shifts of proteins assigned to specific functions can be visualized. Interestingly, the ribosomal compartment is enhanced in the nucleus and secretome, whereas the proteasomal compartment is enhanced in the cytoplasm and secretome. This shift in the resistant cells of the ribosomal compartment in the nucleus/ER and the proteasomal compartment in the cytoplasm is also observed to be significant in the GSEA (Fig EV5). In addition, components of both compartments are significantly upregulated in the secretome of the resistant cells (Fig EV5). Click here to expand this figure. Figure EV5. GSEA (gene set enrichment analysis) and KEGG pathway visualization for the differential expression of ribosomal and proteasomal proteins KEGG pathway visualization of logFC regulated ribosomal and proteasomal proteins in cyt, sn and ne, red: upregulated green: downregulated in resistant cells Download figure Download PowerPoint In conclusion, here we provide evidence that BRAFi and MAPKi resistance show common features on the pathway level. Resistant cells upregulate translation initiation factors and have a significantly lower pH than sensitive cells Based on a recent publication (Boussemart et al, 2014), the expression of eukaryotic translation initiation factors (EIFs) was also analysed in our cohorts. In the nucleus, all 37 identi
Referência(s)