Abstract
Metastatic melanoma is hallmarked by its ability to switch oncogenic MITF expression. Here we tested the impact of STAT3 on melanoma onset and progression in association with MITF expression levels. We established a mouse melanoma model for deleting Stat3 specifically in melanocytes with specific expression of human hyperactive NRASQ61K in an Ink4a deficient background. Mice with tissue specific Stat3 deletion showed an early onset of disease, but displayed significantly diminished lung metastases. Whole genome expression profiling also revealed a reduced invasion phenotype, which was functionally confirmed in 3D melanoma model systems. Notably, loss or knockdown of STAT3 in mouse or human cells resulted in up-regulation of MITF and induction of cell proliferation. Mechanistically we show that STAT3 induced CEBPa/b expression was sufficient to suppress MITF transcription. Epigenetic analysis by ATAC-seq confirmed that STAT3 enabled CEBPa/b binding to the MITF enhancer region thereby silencing it. We conclude that STAT3 is a metastasis driver in melanoma able to antagonize the MITF oncogene via direct induction of CEBP family member transcription facilitating RAS-RAF-driven melanoma metastasis.
List of Abbreviations
ATAC-seq, Assay for Transposase-Accessible Chromatin using sequencing; CEBP, CAAT Box Enhancer Binding Protein; CRE, Cre recombinase; EGF, Epidermal Growth Factor; GEO, Gene Expression Omnibus; GSEA, Gene Set Enrichment Analysis; HSC70, Heat Shock 70 kDa protein; IHC, Immunohistochemistry; IL-6, Interleukin-6; JAK, Janus Kinase; MITF, Microphthalmia-Associated Transcription Factor; NSG, NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ mice; OSM, Oncostatin M; PDGF, Platelet-Derived Growth Factor; pS, phosphoserine; pY, phosphotyrosine; RAS, Rat Sarcoma; RAF, Rapidly Accelerated Fibrosarcoma; RTK, Receptor Tyrosine Kinase; RT-PCR, Reverse Transcription Polymerase Chain Reaction; S100b, Calcium Binding Protein S100 beta; shRNA, short hairpin RNA; SOX10, Sex Determining Region Y-10; STAT, Signal Transducer and Activator of Transcription; TCGA, The Cancer Genome Atlas; TMA, Tissue Micro Array
Introduction
Melanoma is a very aggressive form of skin cancer, with >76.000 new cases diagnosed annually in the US 1. Stage I and stage II lesions can be successfully removed by surgery, but metastasized melanomas are challenging to treat, leading to an estimated 10.000 deaths in the US annually 1. The current prognosis of advanced melanoma remains poor despite the success of immune- and targeted therapy 2. This phenomenon can be attributed to the plasticity of melanoma, which describes the ability of melanoma cells to switch multiple times from a proliferative to an invasive state without the need for additional mutations 3,4. A key player involved in phenotype switching of melanoma is the Microphthalmia-associated Transcription Factor (MITF). MITF is essential for melanocyte development, homeostasis and pigmentation response 5,6. This oncoprotein controls differentiation, survival and proliferation. High MITF expression marks melanoma cells with a proliferative, but non-invasive phenotype. In contrast, melanoma cells expressing low MITF protein represent increased invasive and metastatic capacity 3,7-9. Overall, the regulation of MITF by NRAS and BRAF oncogenes in context of STAT3 signaling is unclear 7,10.
Interestingly, about 50% of melanoma patients display prominent and enhanced tyrosine phosphorylation of Signal Transducer and Activator of Transcription 3 (STAT3) catalyzed via JAK, SRC or growth factor tyrosine kinase family members 11-14. So far, reports on inhibition of STAT3 by siRNA or expression of a dominant negative form of STAT3 in xenograft models have postulated an oncogenic role of STAT3 in melanoma progression, but detailed genetic studies are still missing 12,15. Contrary, treatment with Oncostatin M (OSM) or IL-6 resulted in phosphotyrosine-mediated STAT3 (pYSTAT3) activation and decreased proliferation in melanoma cell line studies, suggesting a tumor suppressor role for STAT3 in melanoma progression 14,16. In prostate, lung and colorectal carcinomas, tumor suppressive roles were associated with STAT3 function depending on the mutational context or STAT1 expression levels, a known heterodimerization partner/balancer of oncogenic STAT3 17-20. A specific role for STAT3 in melanoma has not yet been established and it is not clear whether STAT3 plays a role in phenotype-switching towards invasive melanoma.
Here, we utilized a genetic model of spontaneous melanoma formation. To closely mimic human melanoma, transgenic mice have been used carrying melanocyte-specific expression of the NRASQ61K oncogene in an INK4A-deficient background 21. Additionally, the mouse model allows for conditional melanocyte-specific deletion of Stat3 22. We show that melanoma lacking Stat3 expression have an accelerated tumor onset in vivo and exert higher proliferation rate. In contrast, metastasis formation from Stat3 knockout primary tumors was severely impaired. STAT3 function was evaluated mechanistically using tumor derived cell lines, where we performed whole genome expression analysis combined with ATAC-seq profiling. We found, that STAT3 antagonizes MITF expression through elevated expression of CAAT Box Enhancer Binding Protein (CEBP) family members. Remarkably, data mining of melanoma patient samples also revealed a negative correlation of CEBPa/b with MITF. STAT3 knockdowns in human melanoma confirmed these observations. We conclude that STAT3 is associated with melanoma metastasis and MITF is associated with melanoma growth. Therefore, concurrent low STAT3 and high MITF expression marks worsened prognosis for melanoma patients.
Results
Loss of STAT3 enhanced pigmentation, enabled earlier tumor onset, but reduced metastasis
To study the effect of STAT3 loss in melanoma we used a mouse model that allows the conditional deletion of the Stat3 gene by Cre-loxP technology. We investigated how deletion of the Stat3 gene influences progression of melanoma in a genetic mouse model driven by melanocyte specific dominant-active human NRASQ61K. Furthermore, the INK4a tumor suppressor locus was deleted, hence the model closely mimics human cutaneous melanoma progression. Melanoma that carry NRASQ61K, lost p16INK4A and p19ARF and deleted Stat3 (Tyr∷NRASQ61KInk4a-/-Stat3flox/floxTyr∷Cre) are further termed Stat3Δ, control melanoma expressing STAT3 are termed Stat3fl (Fig. 1 A). Melanocyte specific CRE expression was described to recombine loxP sites from E10.5 onwards in development 23. Hence, STAT3 was lost in mouse skin melanomas as evaluated by immunohistochemistry (IHC, Fig. 1 B). PCNA staining revealed that primary Stat3Δ tumors displayed increased cell cycle activity compared to Stat3fl tumors (Fig. 1 C). In line with this, we observed significantly accelerated tumor onset in the Stat3Δ group (Fig. 1 D). Notably, despite earlier onset of disease, the overall survival was comparable (Fig. S1 A). Stat3Δ tumors appeared more pigmented compared to Stat3fl tumors and Melanin measurements of isolated and cultured melanocytes confirmed this finding (Fig. 1 E). Since loss of Stat3 could impact expression of the nearby Stat5 locus, we tested STAT5 expression and localization by antibody staining but no compensation occurred (Fig. S1 B and C).
Next, lungs of 40 week old mice were stained for S100b to visualize micrometastases. Importantly, Stat3 deletion significantly reduced the overall number of metastatic lung colonies (Fig. 1 F). These findings indicate that albeit tumor growth was accelerated in the Stat3Δ group, Stat3 expression enhanced the metastatic process. Next, we investigated the mechanism responsible for reduced metastasis of Stat3Δ melanomas.
Expression profiling revealed MITF pathway induction upon loss of STAT3
We isolated tumor cells from melanoma positive lymph nodes of the Stat3fl and Stat3Δ mice and selected for melanoma cells by continuous culturing for 10 passages. Cells were uniformly positive for the melanoma marker S100b (Fig. S1 D). They showed basal or enhanced STAT3 activity after IL-6 stimulation, while Stat3Δ cells showed complete loss of STAT3 expression as measured by STAT3 tyrosine and serine phosphorylation using specific antibody detection by Western blotting (Fig. 2 A). Then we performed Affymetrix whole transcriptome microarray mRNA analysis followed by gene set enrichment analysis (GSEA) under basal growth conditions or stimulation with murine IL-6 or OSM. Loss of Stat3 resulted in significant reduction of Stat3 target gene expression (Fig. 2 B). Importantly, Stat3Δ cells displayed augmented MITF pathway activation, which was amongst the top up-regulated pathways (Fig. 2 B). To our surprise, cytokine stimulation neither influenced the overall MITF pathway regulation by activated STAT3 (Fig. 2 C, D and Fig. S2), nor changed the overall gene expression after two hours of stimulation significantly. We conclude that low pYSTAT3 is actively engaged for broad Stat3 target gene transcription by oncogenic RAS-RAF signaling to reprogram melanoma cells, independent of a cytokine stimulus.
Invasion and EMT-like phenotype are suppressed upon loss of Stat3
Gene set enrichment analysis, including proliferative and invasive melanoma signatures, were performed to identify STAT3 related phenotypes (Fig. 3 A). We found that Stat3Δ cells resembled MITF-driven proliferative gene signatures, which were identified in two independent large melanoma cohort studies 3,9. In contrast, Stat3fl cells are closely related to the described invasive signatures. Performing in vitro assays, we found enhanced 3D-proliferation, but abrogated invasion and migration in Stat3Δ cells (Fig. 3 B and C). Our data implies that STAT3 fulfills an important function in RAS-RAF-transformed melanoma promoting invasion and migration. Tumor xenografts of Stat3Δ cells were more compact, with higher cellular density and showed little to no invasion of epidermal tissue (Fig. 3 D). In contrast Stat3fl-derived tumors were de-differentiated with fibroblastoid morphology and showed high invasion into murine epidermis. Tumor formation and growth rates of the two melanoma groups after xenotransplantation were comparable (Fig. S3 A).
Beside the prominent MITF pathway expression, we found a set of five important de-regulated receptor tyrosine kinases: Three of them, platelet-derived growth factor receptor alpha and beta (PDGFRa/b) and epidermal growth factor receptor (EGFR). These are known targets of STAT3 and accordingly displayed decreased mRNA expression in the Stat3Δ group (Fig. 4 A and Figure S3 B). For the two other receptor tyrosine kinases, Met and cKit, expression was in contrast increased. Both are oncogenes and act as important inducers of MITF pathway activity and Melanin production in normal melanocytes. Accordingly, PDGFRb and EGFR were highly expressed in immunostainings of Stat3fl primary mouse melanomas, whereas Stat3Δ tumors showed increased expression of MET and cKIT (Fig. 4 B and C).
To dissect the regulatory basis of the changes in the cell transcriptomes, we performed chromatin profiling using ATAC-seq with a focus on the cKit and Pdgfrb locus in Stat3fl and Stat3Δ melanoma cells. Chromatin accessibility at the cKit promoter region was increased only in Stat3Δ and conversely the Pdgfrb promoter was only accessible in Stat3fl cells (Fig. 4 D and E). This indicates, that the loss of STAT3 leads to epigenetic changes accompanied by the observed strong changes in gene expression patterns.
Reciprocal expression between CEBPs and MITF
To investigate the mechanisms underlying the regulation of MITF by STAT3, we reasoned that STAT3 or its downstream targets could repress MITF. To test this hypothesis, we investigated regulatory elements of the proximal Mitf promoter region by ATAC seq. We found that Stat3fl cells displayed chromatin accessibility in a regulatory element in close proximity of the Mitf gene which contained a CEBPa/b binding element (Fig. 5 A and B). Access at this site was specifically lost in Stat3Δ cells. Contrary, Stat3Δ cells displayed an increased accessibility at a regulatory element containing a SOX10 binding element that was absent in Stat3fl cells arguing for a SOX10-mediated establishment of a stable Mitf high status (Fig. 5 B).
The CEBP transcription factors are well known downstream targets of STAT3 and we could identify strong inhibition of Cebpa/b/d mRNA expression after Stat3 knockout (Fig. 2 C, underlined). Also on protein level we found CEBPa/b proteins to be significantly expressed in a STAT3-dependent way and we could confirm reciprocal expression with MITF and SOX10 (Fig. 5 C). We introduced a 4-OH-tamoxifen inducible STAT3ERT2 construct 24 into Stat3Δ murine melanoma cells. Addition of 4-OH-tamoxifen led to an increased expression of Cebpa/b mRNA as measured by RT-PCR analysis (Fig. 5 D). Expression of the STAT3ERT2 fusion protein was confirmed by Western blotting (Fig. S3 C). Induced STAT3 activity led to slightly increased CEBP expression and a reciprocal down-regulation of MITF pathway associated genes (Mitf, Met, cKit, Tyr were all significantly repressed upon 4-OH-tamoxifen induced STAT3 activation) (Fig. 5 D). Importantly, exogenous Cebpa expression in Stat3Δ melanoma cells and subsequent mRNA quantification lead to a significant reduction in Mitf and Met expression, altering levels of these genes to approximately 50% of control cells (Fig. 5 E). In line with these results, murine and human melanoma cells devoid of STAT3, but ectopically overexpressing CEBPa or CEBPb, showed reduced MITF protein expression which validated the above ATAC seq findings (Fig. 5 F and Figure S4 and S5). We conclude that STAT3-regulated CEBP proteins are responsible for repression of MITF at mRNA and protein levels.
To test, whether our findings also apply for human melanoma cell lines, we performed stable lentiviral shRNA mediated knockdown of Stat3 in 451Lu, WM793B and WM35 cells. Evaluation of the knockdown was performed via RT-PCR and Western blot (Fig. 6 A and C). Consistent with our murine data, we observed up-regulation of MITF, MET, SOX10 and TYR on mRNA level, as well as MITF and SOX10 up-regulation on protein level, upon Stat3 knockdown (Fig. 6 B and C). Furthermore, cells devoid of Stat3 showed an increased proliferation as quantified by Ki67 positive cells (Fig. 6 D). To further validate and to address the clinical relevance of our findings, we evaluated publically available expression data sets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. This analysis revealed a strong negative correlation between MITF and CEBPA as well as between MITF and CEBPB expression (Fig. 6 E).
Validation of STAT3 expression signatures in human clinical samples
To assess the clinical relevance of the relationship between MITF and CEBPa/b, we performed Kaplan-Meier survival analysis on publicly available datasets (GEO accession GSE19234 and TCGA data from Cutaneous Melanoma). Samples were sorted according to the expression level of STAT3, CEBPA, CEBPB and MITF to generate equal sized low- and high-risk groups (Fig. 7 A). The high-risk group was defined by high levels of MITF in combination with low STAT3, CEBPA and CEBPB levels (Fig. 7 B). To validate our findings in human patient samples we analyzed a cohort of 25 primary melanoma tumors and 96-130 melanoma metastases for STAT3 and MITF antibody mediated staining intensities. Primary tumors compared to metastases showed higher levels of STAT3 protein, while MITF levels were higher in metastases than in primary tumors (Fig. 7 C and Fig. S6). A representative example of consecutive sections of a primary melanoma and a metastatic lesion, stained for STAT3 and MITF is shown (Fig. 7 D).
In summary, we conclude that expression of CEBP family members depends on availability of STAT3. Upon Stat3 deletion, Cebpa, Cebpb and Cebpd levels decreased, resulting in increased expression of Mitf, which abrogated invasion/migration and led to diminished metastasis formation (Fig. 7 E). In summary, the prognostic relevance is underlined by our findings that low STAT3 and high MITF expression exert a worsened prognosis for melanoma patients.
Discussion
We identified STAT3 as a critical regulator to promote melanoma progression in a NRAS-driven mouse cutaneous melanoma model, devoid of INK4A tumor suppressors. STAT3 expression interferes with malignant melanoma growth through antagonizing MITF action via CEBPa/b/d family member induction. Analysis of publically available human mRNA expression datasets showed that high MITF, but low STAT3 and CEBPa/b levels are associated with a worsened prognosis for patients with metastasis. Genetic loss or knockdown of STAT3 in mouse or human cells resulted in up-regulation of MITF and induction of cell proliferation. Tumor invasion and migration activity was abrogated upon STAT3 knockdown or loss, reminiscent to an EMT-like phenotype. Loss of STAT3 caused an earlier disease onset in mice consistent with a pronounced proliferative signature marked by augmented MITF expression. Rescuing Stat3Δ melanoma cells by reactivation of the STAT3 pathway or by forced expression of CEBPa/b repressed Mitf mRNA production. We conclude that STAT3 is a major transcriptomic regulator able to establish persistent epigenetic changes and functionally promoting invasion and metastasis in melanoma.
Hence, targeting of STAT3 in melanoma therapy would drive tumors towards the melanocytic lineage and it would enhance proliferation. Targeting STAT3 only, could result in a worsened outcome, but targeting both oncogenic MITF and STAT3 could be beneficial for patients since both transcription factors control melanoma biology. Recent data showed that MITF inversely regulated the amount of EGFR, which was important to establish Vemuravenib resistance 25. Our data confirm this inverse association since we show that STAT3 induced MITF repression lead to EGFR and PDGFR accumulation. These findings display that MITF and STAT3 are tightly interwoven, best exemplified by tyrosine kinase receptor expression changes. While PDGFRa/b and EGFR were down-regulated in the Stat3Δ group, MET and cKIT receptor showed strong up-regulation. These five receptor tyrosine kinases can all prominently activate STAT3 and their mode of action will depend on ligand-induced ligation via stromal components suggesting cell-cell interaction complexity, but also pointing towards the interdependence of the YIN/YANG STAT3/MITF dualism. MET and cKIT receptors are described as target genes of MITF and they could facilitate melanoma growth, invasion and angiogenesis 26,27. In addition, MET expression was associated with dependency of melanoma cells with a malignant phenotype 28. High cKIT levels are frequently found in benign skin lesions and primary melanomas associated with a less invasive phenotype 29. These reports support our finding that high MITF activity results in faster proliferation of melanomas to suppress migration and invasion. Interestingly, PDGFR expression in epithelial cells can be regarded as a marker for the EMT phenotype 30,31. The low response rate of patients towards anti-melanoma therapy was attributed to the persistence of a pronounced EMT-like phenotype 32,33. This could be due to the fact, that EMT in tumor patients is considered as a major factor in the development of resistance towards tyrosine kinase blockers 34 and for escaping PD-1 immunotherapy treatment 35. Our conclusion regarding STAT3 as a metastasis driver is paralleled by recent reports which describe STAT3 activity in melanoma cells as a driver for up-regulation of invasion-related genes and as a major factor in the transition towards a mesenchymal phenotype during EMT-like processes 36. Consistently, STAT3 inhibition was shown to reduce invasion and migration of cells 12,37. Since Stat3Δ cells display a MITF-driven phenotype, it is interesting to note that HDAC inhibitors (HDACi) like Panobinostat have been shown to suppress MITF 38. Suppression of MITF activity by HDACi was pioneered as therapy for melanoma patients with high levels of MITF expression 39. HDAC inhibitors Panobinostat and Vorinostat are currently being tested as adjuvant therapy (NCT02032810), or as stand-alone treatment (NCT02836548) after BRAFV600E inhibitor resistance (data from clinicaltrials.gov).
CEBP family members are well established downstream targets of STAT3 40,41. Additionally, CEBPa/b/d DNA binding sites were reported in the murine Mitf promoter 42. Loss of STAT3 diminished expression of CEBP family members and CEBP binding motifs were less accessible as determined by ATAC-seq at the Mitf locus. Importantly, expression of CEBPa/b was sufficient to repress MITF, similar as reported in myeloid cells 42. Our study highlights the prognostic significance of viewing high or low levels of MITF or STAT3 and CEBP transcription factors in the etiology and progression of melanomas. Interestingly, when STAT3, CEBPA, CEBPB and MITF are used as criteria to split patient data into a low and high risk group, the low risk group reassembled the STAT3, CEBPA, CEBPB high and MITF low group, while the high risk group fits to the expression data resembling the MITF high expressing cohort. These findings are supported by studies which identified an impact of the proliferative status of melanoma on patient survival by quantifying Ki67, Proliferating Cell Nuclear Antigen (PCNA) and Cyclin D1 43,44. This indicates, that patient survival times in melanoma are not determined by the degree of invasiveness, but rather by the amount of proliferation.
Our findings imply that STAT3 is a vulnerable node for fighting melanoma migration and invasion. We provide a rationale for clinical exploration of MITF inhibitors in combination with STAT3 inhibition, since MITF high expression is associated with poor survival. Importantly, here we identified STAT3 as a critical regulator of the MITF pathway, which has been recently shown to be the key pathway activated after acquired resistance to mitogen-activated protein kinase (MAPK)-targeted therapy 45,46. We conclude that a balanced YIN/YANG expression of STAT3/MITF controls melanoma fate and we propose that the main role of STAT3 is to promote invasion and metastatic spread.
Materials and Methods
Animals and generation of transgenic mice
Mice carrying the Stat3floxed allele 22 were crossed with transgenic mice carrying melanocyte-specific expression of the NRASQ61K oncogene in an INK4A-deficient background 21,47 and tyrsosinase Cre 48. Human NRASQ61K and Cre are targeted to the melanocyte linage by tyrosinase regulatory sequences. Compound Tyr∷NRASQ61K Ink4a-/- ; Stat3flox/flox; Tyr∷Cre mice were termed Stat3Δ for simplified reading and maintained on a C57BL/6Jx129/Sv background. In all experiments described, littermates lacking Tyr∷Cre were used as controls termed Stat3fl. Immunocompromised NOD.CgPrkdcscid Il2rgtm1Wjl/SzJ mice (NSG) recipients were purchased from Harlan. For all analyses male and female mice were used and analyzed in gender-specific ways.
Xenograft experiments
A total of 1×106 cells were injected into the left and right flank of 8–10 weeks old NSG mice. Tumor volumes were evaluated every 48 hours by measuring two perpendicular diameters with calipers. Tumor volume was calculated using the following equation: (width*width*length)/2. The experiment was stopped when tumors exceeded a calculated size of 600 mm3.
Cell lines
Mouse melanoma cell lines were established from lymph nodes of diseased mice (25-30 weeks of age) by single cell dissociation and culturing. Multiple individual primary pools of cell lines per Stat3fl and Stat3Δ melanoma cell genotype were generated, upon which we selected two pools each for further detailed analysis. Human BRAF mutated cell lines WM793B, 451Lu and WM35 were bought from ATCC, all three devoid of INK4A proteins. The human NRAS mutated cell line WC125 (WM3854) was bought from the Coriell Institute (Wistar Collection) also devoid of INK4A proteins. All cell lines were cultivated under standard conditions (95% humidity, 5% CO2, 37°C) and maintained in standard medium (DMEM) supplied with 10% Fetal Bovine Serum (FBS), 10 U/ml Penicillin, 10 µg/ml Streptomycin, 2 mM L-Glutamine (all from Gibco) and 2 µg/ml Ciprofloxacin (Sigma). Cell stimulations were performed with human OSM (20 ng/ml), human IL-6 (20 ng/ml) or murine IL-6 (20 ng/ml; all from Immunotools). Murine OSM (20 ng/ml) was purchased from R&D. For transfection of murine melanoma cells, the cells were seeded in 6-well plates and starved overnight in 2% DMEM without antibiotics. Cells were transfected with 2 µg CEBPa, CEBPb or STAT3ERT2 plasmid using lipofectamine 3000 (Invitrogen) according to the manufacturer’s protocol and analyzed after 24 hours. After transfection, the cells carrying the STAT3ERT2 plasmid 24 were selected for four weeks in 1 µg/ml puromycin (Sigma) and expression was tested by Western blot (Fig. S3 C).
3D proliferation and in vitro migration assays
2500 melanoma cells were seeded in a 96 well round bottom plate and the diameter of formed spheroids was evaluated with the ImageJ software after 72 hours (Rasband, W.S., ImageJ, US National Institutes of Health, Bethesda, Maryland, USA, http://imagej.nih.gov/ij/). 30 spheroids, each consisting of 2.500 melanoma cells were formed in a 96 well round bottom plate. After four days of formation the spheroids were embedded into a 3D rattail-collagen matrix and grown in DMEM for 20 hours. Invasion was evaluated as described by 49. For the in vitro cell migration assay 3×104 cells were seeded in serum free DMEM onto 8 µm pore transwell inserts (BD Biosciences). As attractant DMEM containing 10% FBS was used. Cells were allowed to migrate 8 hours, then the cells were fixed with 4% formaldehyde, the cells on the upper side of the transwell were wiped away and migrated cells were stained with 0.1% crystal violet (all from Sigma). Six fields of view were taken with microscope images using a 10x magnification with cell counting.
RNA interference and lentiviral transduction
RNA interference and lentiviral transduction experiments were performed as described in 50. The following shRNA constructs selected from the Mission TRC shRNA library (Sigma) were used: shRNA STAT3 (TRCN0000071456) and scrambled control shRNA (SHC002). Transduced cells were selected for puromycin resistance prior to further analysis. The functionality of shRNAs was validated by realtime-PCR and Western blot analysis using antibodies listed in Table S3.
RNA isolation and real-time PCR
RNA was isolated by TriZol (Life Technologies) extraction. Complementary DNA transcription was performed by RevertAid™ H Minus First Strand cDNA Synthesis Kit (Fermentas) according to the manufacturer’s instruction. Primer pairs are listed in Table S1. Real-time PCR was performed in triplicates on a RealPlex Master-cycler (Eppendorf). Values were normalized to either murine or human ACTB mRNA expression. Results were quantified using the Delta C(T) method 51.
Western blotting
Protein lysates, SDS–PAGE and Western blotting were performed according to standard protocols. Nitrocellulose membranes (Amersham) were incubated with antibodies listed in Table S3. Semi-quantitative densitometric analysis of bands was performed with the ImageJ software according to standard procedures.
Melanin absorption assay
Absorption of Melanin in mouse melanoma cells was measured at 410 nm in an Infinite Nanoquant photometer (Tecan) using the supernatant of 5×105 cells 48 hours after seeding. Two biological replicates with six independent measurements were performed.
Immunohistochemistry, immunofluorescence and tissue microarrays
Mouse tissue was fixed overnight in 4% phosphate buffered formaldehyde solution, dehydrated, paraffin embedded and cut. 3-µm-FFPE tissue sections were stained with Hematoxylin (Merck) and Eosin G (Roth). Immunohistochemistry and immunofluorescence was performed using antibodies listed in Table S2. For immunohistochemical analysis of cutaneous mouse melanomas, tissue microarrays were built including 28 cutaneous melanomas and 28 positive lymph nodes, each represented by duplicate core biopsies. Normal skin and lymph nodes served as controls. For quantification, high-power field sections (x200 objective) were obtained for each sample and analyzed with HistoQuest (TissueGnostics) (Fig. 1 B and C, Fig. S1 B) quantification software or Tissue Studio Software® (Definiens) (Fig. 4 B and 7 C) as described 52,53. In brief, cut-offs (to differentiate between positive and negative cells) were set for all samples and the resulting positive intensities were normalized to Hemalaun staining intensity. Images were taken with a Zeiss Imager Z1 microscope. Immunofluorescence images were taken with a Leica TCS SP8 Microscope.
Immunocytochemistry
To characterize murine cells as melanoma cells, they were trypsinized and 2×105 cells were centrifuged using the Thermo Scientific Shandon Cytospin (Shandon Scientific). The samples were fixed in methanol (10’, -20°C) and dried for one hour at room temperature. Samples were stained by S100b by standard methods (Fig. S1 B, Table S2). For immunofluorescence staining on murine melanoma cells, 1.5x105 cells were seeded on 4-well chamber slides (NUNC). Cells were grown in Lab-Tek® 4-well chamber slides. Cells were fixed in 4% phosphate buffered formaldehyde solution and stained using antibodies from Table S2.
Whole transcript expression profiling, and gene-set enrichment analysis
Total RNA was isolated using the RNAeasy Kit (Qiagen), pooled and hybridized to GeneChip Mouse Gene 1.0 ST array (Affymetrix). 24 hybridizations were performed (see GEO submission) comprising 12 replicates from each genetically defined cell line (two cell lines per Stat3fl or Stat3Δ murine melanoma cell line group including individual cytokine stimulation with either IL-6 or OSM). RNA was isolated from cells that were either unstimulated (full media without cytokine) or stimulated with fresh media containing either 5 ng/ml murine IL-6 or 10 ng/ml murine OSM for 2 hours. The command-line version of GSEA was used for gene-set enrichment analysis (GSEA) 54,55, with normalized log2 transformed expression values as input and “Signal2Noise” as ranking metric. Further non-default settings were -collapse false, - permute gene-set, and -set_min 3. In case of gene-sets with human gene ids, murine gene symbols were converted with the help of an orthologue list downloaded from the Ensembl gene browser web site (v75; GRCh37.p13). Heatmaps show hierarchical clustering of log-transformed gene expression values (Euclidean distance metric). Gene-sets were compiled from literature. Whole genome expression data was deposited at GEO (GSE95063).
ATAC-seq
Chromatin accessibility mapping was performed using the ATAC-seq method as previously described 56 with minor adaptions. Duplicates of two different Stat3fl and Stat3Δ cell lines were used for the assay. A volume containing 50.000 cells were harvested with the aid of an automated cell counter (TC20 Bio-Rad) and spun at 500g for 5 minutes at 4°C in PBS. After centrifugation, the pellet was carefully resuspended in the transposase reaction mix (12.5 µl 2xTD buffer, 2 µl TDE1 (Illumina), and 10.25 µl nuclease-free water, 0.25 µl 1% Digitonin (Sigma)) for 30 min at 37 °C. Following DNA purification with the MiniElute kit eluting in 12 µl, 1 µl of the eluted DNA was used in a quantitative PCR reaction to estimate the optimum number of amplification cycles. Library amplification was followed by SPRI size selection to exclude fragments larger than 1,200 bp. DNA and concentration was measured with a Qubit fluorometer (Life Technologies). Library amplification was performed using custom Nextera primers 57. The libraries were sequenced by the Biomedical Sequencing Facility at CeMM using the Illumina HiSeq 3000/4000 platform and the 50-bp single-end configuration. Whole genome sequencing data was deposited at GEO (GSE119540).
Preprocessing and analysis of ATAC-seq data
ATAC-seq reads were trimmed using Skewer 58 and aligned to the GRCh37/hg19 assembly of the human genome using Bowtie2 59 with the ‘-very-sensitive’ parameter. Duplicate reads were removed using the sambamba 60 ‘markdup’ command, and reads with mapping quality >30 and alignment to the nuclear genome were kept. All downstream analyses were performed on these filtered reads. Genome browser tracks were created with the genomeCoverageBed command in BEDTools 61 and normalized such that each value represents the read count per base pair per million mapped and filtered reads. Finally, the UCSC Genome Browser’s bedGraphToBigWig tool was used to produce a bigWig file. Peak calling was performed with MACS2 62 using the ‘-nomodel’ and ‘-extsize 147’ parameters, and peaks overlapping blacklisted features defined by the ENCODE project 63 were discarded. We created a consensus region set by merging the called peaks from all samples across cell lines, and we quantified the accessibility of each region in each sample by counting the number of reads from the filtered BAM file that overlapped each region. To normalize the chromatin accessibility signal across samples, we first performed quantile normalization using the R implementation in the preprocessCore package (‘normalize.quantiles’ function).
Statistics
All values are given as means±standard error of the mean (s.e.m.) or standard deviation (s.d.) as indicated. Data were analyzed by GraphPad Prism® 5. In vitro data, Western blots, RT-PCRs and viability assays were repeated at least three times. Scatter plots were calculated from the publically available GSE19234 and the TCGA study on cutaneous melanoma 64,65. RNA expression data sets were generated with SPSS v21. Pearson correlation analysis was performed to calculate the P-values of the graphs. Numbers of animals are stated in each figure or figure legend. Differences were assessed for statistical significance by an unpaired two-tailed t test and by the Log-rank (Mantel-Cox) test (for Kaplan-Meier plots). P<0.05 was accepted as statistically significant and P values are considered as follows: *P<0.05; **P<0.01; and ***P<0.001.
Study approval
Human tissue samples were collected after signed, informed consent was provided and approval for studies was obtained from Ethics Committee of the Medical University of Vienna, EK 405/2006, extension 11/10/2016. All mice were bred and maintained under standardized conditions at the Decentralized Biomedical Facility of the Medical University Vienna according to an ethical animal license protocol complying with the Austrian law and approved by the “Bundesministerium für Wissenschaft und Forschung” (BMWF-66009/0281-I/3b/2012).
Author’s contribution
Authorship note: R. Moriggl and M. Mikula contributed equally to this work.
Initial design and work was performed by RM, MM and AS. RS, AS, MM and RM wrote the original draft of the manuscript. RS and AS designed, performed and analyzed experiments. MV performed in vitro 3D proliferation and invasion experiments. KK performed in vitro migration experiments and quantified xenograft and evaluated patient TMA stainings. BW, HTTP, RZ and BK helped designing experiments and provided major technical support. JH performed and analyzed xenograft experiments. CS and FA performed shRNA knockdown. DSc, AFR and TK analyzed in silico ATAC-seq or melanoma patient data. PP provided and stained human melanoma TMAs. SK and TK significantly assisted in data analysis. DSt, LK, LL, VP, FB, TY, SK, FA and MH reviewed and edited the manuscript. All authors have given approval to the final version of the manuscript.
Conflict of Interest
The authors declare no conflict of interest.
Acknowledgments
We thank Safia Zahma, Birgit Schütz, Eva Bauer, Michaela Schlederer and Karin Neumüller for excellent technical assistance. We thank Anna Ringler and Bernd Boidol for excellent assistance with the inhibitor screening library. We also thank the Biomedical Sequencing Facility at CeMM for assistance with next generation sequencing. VP was supported by the grant AIRC IG16930. RM and AS were supported by two network grants SFB-F061 and SFB-F047 and a private melanoma research donation from Liechtenstein. RS, KK, MV and MM were supported by P 25336-B13, all from the Austrian Science Fund (FWF).