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SOX5 is involved in balanced MITF regulation in human melanoma cells
- Theresa Kordaß1, 2,
- Claudia E. M. Weber1,
- Marcus Oswald2, 3,
- Volker Ast2, 3,
- Mathias Bernhardt4, 5,
- Daniel Novak4, 5,
- Jochen Utikal4, 5,
- Stefan B. Eichmüller†1 and
- Rainer König†2, 3, 6Email author
© Kordaß et al. 2016
Received: 7 August 2015
Accepted: 21 February 2016
Published: 29 February 2016
Melanoma is a cancer with rising incidence and new therapeutics are needed. For this, it is necessary to understand the molecular mechanisms of melanoma development and progression. Melanoma differs from other cancers by its ability to produce the pigment melanin via melanogenesis; this biosynthesis is essentially regulated by microphthalmia-associated transcription factor (MITF). MITF regulates various processes such as cell cycling and differentiation. MITF shows an ambivalent role, since high levels inhibit cell proliferation and low levels promote invasion. Hence, well-balanced MITF homeostasis is important for the progression and spread of melanoma. Therefore, it is difficult to use MITF itself for targeted therapy, but elucidating its complex regulation may lead to a promising melanoma-cell specific therapy.
We systematically analyzed the regulation of MITF with a novel established transcription factor based gene regulatory network model. Starting from comparative transcriptomics analysis using data from cells originating from nine different tumors and a melanoma cell dataset, we predicted the transcriptional regulators of MITF employing ChIP binding information from a comprehensive set of databases. The most striking regulators were experimentally validated by functional assays and an MITF-promoter reporter assay. Finally, we analyzed the impact of the expression of the identified regulators on clinically relevant parameters of melanoma, i.e. the thickness of primary tumors and patient overall survival.
Our model predictions identified SOX10 and SOX5 as regulators of MITF. We experimentally confirmed the role of the already well-known regulator SOX10. Additionally, we found that SOX5 knockdown led to MITF up-regulation in melanoma cells, while double knockdown with SOX10 showed a rescue effect; both effects were validated by reporter assays. Regarding clinical samples, SOX5 expression was distinctively up-regulated in metastatic compared to primary melanoma. In contrast, survival analysis of melanoma patients with predominantly metastatic disease revealed that low SOX5 levels were associated with a poor prognosis.
MITF regulation by SOX5 has been shown only in murine cells, but not yet in human melanoma cells. SOX5 has a strong inhibitory effect on MITF expression and seems to have a decisive clinical impact on melanoma during tumor progression.
KeywordsSOX5 MITF SOX10 Melanoma Mixed Integer Linear Programming Regulatory models
Normal melanocytes located in the stratum basale of the epidermis are beneficial cells that are capable of producing the pigment melanin; these cells transfer melanin to keratinocytes and by this means prevent DNA damage which can be caused by ultraviolet radiation. However, melanocytes can transform into malignant cells. Melanoma cells exhibit an imbalanced regulation that allows for abnormally high proliferation rates, reduced apoptosis and the potential to form metastases. Melanoma is the most lethal form of skin cancer and causes approximately 75 % of all skin cancer deaths, with a rising incidence rate in the last three decades [1–3]. Although the resection of early diagnosed melanoma yields very high curation rates, for progressed melanoma, no effective therapy is currently available. Common tumor treatments like radiotherapy and chemotherapy often fail for the treatment of patients with metastatic melanoma, and the average survival rate for these patients is less than 1 year [3, 4]. To improve treatment for therapy, it is mandatory to better understand the molecular pathways and transcriptional regulation involved in melanoma formation. In particular, changes in the transcriptional regulation driving melanoma progression and metastasis are crucial to find new strategies to cure melanoma patients [5, 6].
We focused our work on the so-called master regulator of melanocytes and melanoma cells, microphthalmia-associated transcription factor (MITF) . MITF is a basic-helix-loop helix leucine zipper transcription factor that binds as a dimer to conserved sequences of the E-box (CATGTG) and M-box (AGTCATGTGCT) motifs in the promoter region of its target genes. MITF regulates several genes involved in melanocyte differentiation, proliferation and it also regulates the expression of the two pacemaker enzymes of melanogenesis, tyrosinase (TYR) and dopachrome tautomerase (DCT) [8, 9]. Most melanoma cancer cells maintain their ability to produce melanin, and often genes of melanogenesis are highly expressed. These characteristics distinguish melanoma cells from other cancer cells and melanogenesis is a discussed target for chemotherapy .
Different MITF expression levels have been shown to result in very divergent clinical courses in melanoma patients. Low MITF expression levels can be observed in invasive melanoma and are therefore associated with a low survival rate . On the contrary, high MITF expression levels can slow down the proliferation of melanoma cells . Cancer cells are characterized by an abnormally high proliferation rate and they circumvent cell cycle stagnancy and apoptosis. A strategy of melanoma cells to gain a high proliferation rate is to avoid high MITF expression levels, which have an anti-proliferative effect. Besides this, many melanoma tumors (~50 %) exhibit a driving mutation in the serine/threonine-protein kinase B-RRAF (BRAF) [13, 14]. The mutation results in a constantly activated kinase that permanently stimulates extracellular-signal regulated protein kinase 2 (ERK2), which in turn phosphorylates and targets MITF proteins for ubiquitin-dependent degradation via the proteasomal pathway  and thereby decreases the activity of MITF. Hoek and coworkers found that MITF levels can be used as a marker to distinguish proliferative and invasive phenotypes of melanoma cell lines with low MITF levels marking the invasive state [12, 16, 17].
The aim of this study was to further investigate the regulation of MITF and the impact of MITF regulators on melanoma progression. The transcriptional regulation of MITF is very complex, involving numerous activating and inhibiting factors. For example, SRY (sex determining region Y)-box 10 (SOX10), paired box 3 (PAX3)  and one cut homeobox 2 (ONECUT2)  activate MITF expression, whereas zinc finger E-box-binding homeobox 1 (ZEB1)  and GLI family zinc finger 2 (GLI2)  repress MITF expression.
We applied a computational approach we developed earlier  to identify MITF transcriptional regulators that could predict changes in MITF expression levels over a set of different cancer types (NCI-60 panel) and a set of melanoma samples. The next step was to verify the effect of the obtained regulators on MITF mRNA levels using siRNA transfection and an MITF-promoter reporter assay. Finally, we unraveled the relationships between the obtained transcription factor expression levels and central clinical parameters like overall survival and the Breslow thickness.
Gene regulatory network models to identify regulators of MITF
With the use of a branch and cut based optimization program (Gurobi™ 5.5, http://www.gurobi.com/) to solve the Mixed Integer Linear Programming (MILP) problem, the β-coefficients were calculated in order to minimize the sum of differences between measured and predicted MITF expression for all samples (objective function). The MITF model was restricted to a defined number of regulators from the set of all putative regulators (19 TFs). We applied a bottom-up approach to identify the most important regulators of the model, starting with restricting the model to one regulator. Within each of the following runs, one additional regulator was added to the model. The optimizer selected independently in every run the best regulators in order to minimize the objective function. The prediction performance of each model was estimated by the correlation between real and predicted MITF expression in the test data (unseen data, not used for learning the model) based on a leave-one-out cross validation (LOO-CV). For details regarding the MILP model and activity definition see Schacht et al. .
As described previously , we used several sources to assess TF binding information. From the database MetaCoreTM (http://thomsonreuters.com/metacore/) human TF-target gene interactions were selected, of both of the categories direct and indirect. Additionally, we used z-scores of the Total Binding Affinity (TBA) which are calculated TF binding profiles for the whole promoter based on position weight matrices [24, 25]. Moreover, human entries of the CHIP Enrichment Analysis (ChEA) database were used containing large data sets of high-throughput chromatin immunoprecipitation experiments . At the date of analysis (July 2013) the ChEA database for man comprised of 83 transcription factors, 20,035 genes and 131,996 total entries. In addition, we used chromatin immunoprecipitation data from the ENCODE project (http://www.genome.gov/Encode/). We used binding information of cell lines for which the most comprehensive set of regulation information was available (Tier 1). Binding of a transcription factor to a target gene as listed in Encode, was scored as “1” or if absent, as “0”, respectively. Target genes occurring more than once, were combined in single rows containing consistent (intersecting) hits and transcription factors showing up multiple times were assembled into one column as the union of hits. Information on regulatory transcription factor/target gene interaction was considered reliable if (i) this pair was found in Metacore with the annotation “direct”, or if (ii) this pair was found in at least two of the datasets Metacore “indirect”, CheA, Encode and TBA with a value greater or equal to one. For these TF/target gene pairs, their putative regulatory interaction was denoted edge strength est,i between TF t and target gene i, and set to the number of occurrences of the specific TF/target gene combinations among the datasets CheA, Metacore “direct” activation, Metacore “direct inhibition”, Metacore “indirect activation” and Metacore “indirect inhibition”. TBA values greater or equal to one were added to the edge strength. For all TF/target gene pairs missing criteria (i) or (ii), the edge strength was set to zero, i.e. this TF/target gene pair was not considered by our prediction algorithms. The binding information of SOX5/MITF interaction was taken from Metacore where it was annotated as “direct inhibition”. In addition, the z-score of TBA of SOX5 binding to the human MITF promoter was strongly positive (z = 1.5, see Additional file 1: Figure S1).
Gene expression data
To identify prominent transcription factors of MITF with our regulatory network model, we used the gene expression profiles of 59 cancer cell lines from the National Cancer Institute (NCI-60 panel), which comprises 60 cancer cell lines from nine different cancer types (breast, central nervous system, colon, kidney, leukemia, lung, melanoma, ovary and prostate). The data were downloaded from CellMiner and based on an integration of five different microarray platforms (5-Platform, Affymetrix HG-U95, HG-U133, HG-U133 Plus 2.0, GH Exon 1.0 ST, and Agilent WHG) yielding a z-score for each gene of each sample (details, see ). Missing values were replaced by the mean expression values of the according genes. The cell line SF 539 was excluded from our analysis because of a large number (N = 10,404) of undefined entries. Subsequently, we continued the analysis of MITF’s TFs on a second, independent dataset, to see whether our findings are consistent and reproducible. Therefore, we used gene expression data from melanoma cells taken from a study by Hoek et al. [16, 17]. In brief, melanoma cells were released from tissue sections of melanoma metastases. Cells were cultured, total RNA was extracted, labeled and their transcriptome profiled using Affymetrix HG-U133 plus 2.0 oligonucleotide microarrays. Raw intensity signals were normalized employing Affymetrix MAS 5.0. Values below 0.01 were set to 0.01 and each value was divided by the 50th percentile of all values in that sample. Each expression value was divided by the median of its values in all samples. Finally, expression values were z-normalized for each gene. For our analysis, we used expression data from 33 samples from the Mannheim cohort of the study by Hoek and coworkers (subsequently denoted as the Mannheim cohort). Cell lines from this panel were also used for our in vitro experiments. For inferring clinical and expression data, we used skin cutaneous melanoma (SKCM) samples from the Cancer Genome Atlas (TCGA; http://cancergenome.nih.gov/). Clinical as well as MITF, SOX5 and SOX10 mRNA expression (RNA Seq V2 RSEM) data were downloaded from the cBio portal (http://www.cbioportal.org/). The SKCM expression data were z-normalized. For the comparison of expression levels between non-survived and survived subgroups Wilcoxon rank sum tests were applied, because the distribution of the expression levels was not normally distributed. All data sets used are publically available.
Five melanoma cell lines used in the Hoek and coworkers analysis [16, 17] MaMel-122, MaMel-86b, MaMel-61e and MaMel-79b (own laboratory) as well as A375 purchased from ATCC were cultured at 37 °C and 5 % CO2 in RPMI 1640 medium (Gibco, Carlsbad, CA, USA) + 10 % FCS in general without antibiotics. MaMel-122-pMITF-GFP was cultured in medium containing 0.5 μg/ml puromycin (Sigma-Aldrich, Steinheim, Germany). These cell lines were chosen because they exhibit substantial expression of MITF, SOX5 and SOX10.
siRNA transfection and qRT-PCR
To investigate the effects of SOX5 and SOX10 on MITF expression levels, Ambion® Silencer® Select Pre-designed (Inventoried) siRNAs (Life Technologies, Carlsbad, CA, USA) were utilized to knock down these transcription factors. For the knock-down of SOX5 or SOX10 siRNA s13303 (Antisense sequence, no overhangs: UCCUUUCACACCGUAAGUG) and siRNA s13308 (Antisense, no overhangs: UCCUUCUUCAGAUCGGGCU) were used, respectively.
MITF-promoter reporter assay
Stable transfection of MaMel-122 cells was performed to generate a cell line that expresses the green fluorescence protein (GFP) gene downstream of the MITF promoter.
MITF prom forward → CGCATCGATAGGCCGTTAGAAACATGATC
MITF prom reverse → CGCTCTAGACAATCCAGTGAGAGACGGTAG
The amplified promoter was cloned into pLenti CMV GFP Puro (pLenti CMV GFP Puro (658-5) was a gift from Eric Campeau; Addgene plasmid # 17448) . For this purpose, the CMV promoter was cut from pLenti CMV GFP Puro with ClaI and XbaI and the MITF promoter was introduced at the same position. A plasmid map of the used vector MITFP-pLenti can be found in the supplement (Additional file 1: Figure S2). Functional validation of the vector was performed in primary human melanocytes in comparison to human fibroblasts (Additional file 1: Figure S3). Melanocytes and fibroblasts were isolated following standard protocols from skin remainings after operations such as foreskins after circumcisions of healthy donors. Successfully MITFP-pLenti transfected cells were positively selected using 0.5 μg/ml puromycin.
The generated cell line was denoted by MaMel-122-pMITF and was constantly kept under selective pressure. MaMel-122-pMITF was used to investigate the role of SOX10 and SOX5 in regulating MITF at the transcriptional level. siRNA transfection experiments were performed analogous to the qRT-PCR analyses. 1 · 105 cells were seeded in 24-well plates and cultured for 24 h. Then, the wells were transfected with either SOX10 siRNA, SOX5 siRNA, non-targeting control siRNA or a mixture of both, i.e. SOX10 and SOX5 siRNA. The final concentration of each siRNA per well was 25 nM. The cells were harvested 72 h after transfection. The cells were detached from each well with 50 μl trypsin and resuspended in 150 μl of medium. After centrifugation, the cell pellets were washed once with 200 μl PBS and three times with 1 ml ice cold FACS buffer. Finally, the pellets were dissolved in 200 μl FACS-buffer and fluorescence measurements were performed using a BD FACSCaliburTM (BD Biosciences) flow cytometer using channel FL-1 to detect GFP. Unstained MaMel-122 cells were included in each individual measurement as a negative control. The analysis of the flow cytometry data was conducted using FlowJo version 9.6.4 (http://www.flowjo.com/).
The effect of SOX5 on cell viability was assessed using CellTiter-Glo Luminescent Cell Viability Assay (Promega, Fitchburg, WI, USA) after transfection with 10 nM control or SOX5 siRNA pools. 1 x 104 cells (fast growing) and 2 x 104 cells (slow growing) cells were seeded per well in 96 well black/clear flat bottom plates (Corning, Corning NY). Viability was measured according to the manufactures instructions 24, 48 and 72 h after transfection. Three biological replicates were performed for each condition.
Invasion assays were performed 48 h after transfection with SOX5 or control siRNA pool (10 nM) in 24 well plate format. Therefore, 5 x 104 cells resuspended in 50 μl serum-free medium (three technical replicates) were pipetted into the upper insert of a 96 well transwell plate (Corning, Corning NY) coated with 50 ng matrigel/well. The lower chambers were filled with 150 μl medium + 10 % FCS as a chemoattractant. After 24 h, invaded cells were detached from the membrane, washed, stained with calcein AM (Thermo Fisher Scientific, Waltham, MA) and analyzed with a fluorometer according to the manufactures protocol.
Statistical significance was calculated using the one-sided two-sample Student’s t-test and a Wilcoxon rank sum test was performed for non-normally distributed populations (comparing the distribution of SOX5 expression between different subgroups of SKCM data (survived vs. non-survived, thin vs. thick)). P-values less than 0.05 were considered statistically significant. For the Kaplan-Meier analysis the cutoffs for low and high expression were determined by a 10-fold cross-validation approach using the R-package maxstat . The median cutoff was used to classify samples into low and high expression subgroups and nonparametric log-rank tests were used to assess significance. All statistical analyses were performed using R version 3.0.1 (http://www.r-project.org/) and Microsoft Excel 2013.
Prediction of Breslow thickness
with β 0 as an additive offset, β TF as the optimization parameter for the TF (SOX5/SOX10/MITF) and eff TF,j as the estimated effect of a TF in sample j. As effect eff Tf,j , we used the gene expression of the TF in sample j.
Identifying the regulators of MITF in silico
Results of the bottom-up approach for modeling MITF regulation using Mixed Integer Linear Programming
No. of TFs
ESR2, PAX2, SOX5
ESR2, NFKB1.1, PAX2, SOX5
ESR2, NFKB1.1, PAX2, SOX5, ZEB1
ESR2, NFKB1.1, ONECUT2, POU3F2, SOX5, ZEB1
ESR2, NFKB1.1, ONECUT2, PAX2, POU3F2, SOX5, ZEB1
ESR2, GLI2, NFKB1.1, ONECUT2, PAX3, POU3F2, SOX5, ZEB1
ESR2, GLI2, NFKB1.1, ONECUT2, PAX2, PAX3, POU3F2, SOX5, ZEB1
ESR2, GLI2, IRF1, NFKB1.1, ONECUT2, PAX2, PAX3, POU3F2,
BHLHE40, ESR2, GLI2, IRF1, NFKB1.1, ONECUT2, PAX2, PAX3, POU3F2, SOX5, ZEB1
ESR2, LEF1, NFKB1.1, ONECUT2, PAX2, PAX3, PAX6, PDX1,
POU3F2, SOX5, SOX9, ZEB1
ESR2, LEF1, NFKB1.1, ONECUT2, PAX2, PAX3, PAX6, PDX1,
POU3F2, SOX5, SOX9, TCF4, ZEB1
BHLHE40, ESR2, LEF1, NFKB1.1, ONECUT2, PAX2, PAX3, PAX6, PDX1, POU3F2, SOX5, SOX9, TCF4, ZEB1
BHLHE40, ESR2, GLI2, LEF1, NFKB1.1, ONECUT2, PAX2, PAX3, PAX6, PDX1, POU3F2, SOX5, SOX9, TCF4, ZEB1
BHLHE40, ESR2, GLI2, LEF1, NFKB1.1, ONECUT2, PAX2, PAX3, PAX6, PDX1, POU3F2, SOX10, SOX5, SOX9, TCF4, ZEB1
BHLHE40, ESR2, GLI2, LEF1, NFKB1.1, ONECUT2, PAX2, PAX3, PAX6, PDX1, POU3F2, SOX10, SOX2, SOX5, SOX9, TCF4, ZEB1
BHLHE40, ESR2, GLI2, IRF1, LEF1, NFKB1.1, ONECUT2, PAX2, PAX3, PAX6, PDX1, POU3F2, SOX10, SOX2, SOX5, SOX9, TCF4, ZEB1
BHLHE40, CREB1, ESR2, GLI2, IRF1, LEF1, NFKB1.1, ONECUT2, PAX2, PAX3, PAX6, PDX1, POU3F2, SOX10, SOX2, SOX5, SOX9, TCF4, ZEB1
The regulatory network model and the estimated activity values revealed SOX5 and SOX10 as important regulators of MITF. In agreement with our findings, SOX10 is a commonly known activating regulator of MITF [18, 31] in human. MITF regulation by SOX5 has only been shown in murine cells so far  and hence we were interested in the regulatory effect of SOX5 on MITF in human melanoma cells and tumors, and its regulatory effect in combination with SOX10. Thus, we performed functional assays to validate our in silico predictions in human melanoma cells and investigated the expression signatures of MITF, SOX5 and SOX10 in respect to clinically relevant parameters.
Phenotypic effects of SOX5 knockdown
Effects of SOX5 siRNA on cell viability and invasion
Prediction of Breslow thickness for SKCM melanoma samples using the regression model of SOX5/MITF/SOX10
Number of samples
< 1 mm
1 – 4 mm
> 4 mm
High percentages of melanoma tumor cells show mutations in the BRAF locus  Due to the fact, that in melanoma hyper-activated BRAF often suppresses MITF [13–17], we compared the BRAF mutation status with the expression of MITF, SOX10 and SOX5. Investigating the SKCM dataset, SOX10 and MITF expression tended to be higher in melanoma samples with normal BRAF (p = 0.06 and p = 0.08, two sided Student’s T-test). Strikingly, SOX5 was significantly overexpressed in samples with mutated BRAF (p = 0.006). We observed a weakly positive correlation of SOX5 and MITF expression in the BRAF wildtype subgroup (Pearson’s correlation r = 0.18), and weakly negative correlation in the BRAF mutated subgroup (PCC r = -0.13) hinting for a stronger regulatory involvement of SOX5 on MITF expression in the tumor cells with BRAF mutation. Comparing tumor subgroups of NRAS mutated with NRAS wildtype, no significant expression differences were found except for SOX5 which was significantly overexpressed in NRAS mutated samples (p = 0.05). Taken together, SOX5, SOX10 and MITF seem to have a crucial clinical impact and our developed linear regression based expression signature of these three genes associated in particular with melanomas with a small Breslow thickness.
In this study, a transcription factor network was constructed based on chromatin immunoprecipitation binding data from several data repositories and a motif analysis. Using our established regression model (MILP model) and the defined activity, we found the transcription factors SOX5 and SOX10 with which the model could predict best the gene expression values of MITF in various melanoma cell lines. Indeed, both transcription factors were capable to explain the differences in MITF expression levels when trained with a dataset of cells from different tumors and applied to a different dataset, i.e. a dataset of melanoma cell lines. In particular, SOX5 was found to be a very informative predictor, exhibiting the highest correlation of its calculated activity with MITF expression. We confirmed experimentally that SOX5 and SOX10 have an effect on MITF expression levels in melanoma cell lines; SOX5 down-regulation increases MITF expression, hinting at an inhibitory effect, while vice versa, SOX10 down-regulation led to MITF up-regulation. In addition, our model predicted a combined regulation in which MITF transcription is activated by SOX10 and inhibited by SOX5. In line with this, after investigating the expression profiles of the melanoma cell lines (Mannheim cohort of ), we observed a correlation between SOX5 and SOX10 expression (PCC r = 0.43) and an even stronger correlation between the activity of SOX5 and SOX10 (PCC r = 0.75). SOX10 is a well-known transcriptional activator of MITF [18, 31]. In addition to this, we found SOX5 to be a novel regulator of MITF in human melanoma cells. Stolt and coworkers found the involvement of SOX5 in melanocyte development by altering SOX10 activity in mouse models. In mice, SOX5 and SOX10 can bind to the same locus on their target genes Mitf and Dct. It was shown in B16 mouse melanoma cells that SOX5 prevents the activation of these target genes through site competition with SOX10 [32, 36]. We observed a similar effect in human melanoma cells: A double knockdown of SOX5 and SOX10 partially rescued MITF expression compared to a single knockdown of SOX10. We assume that SOX5 regulates MITF via direct binding to the MITF promoter as (i) Stolt and coworkers showed in mice , and (ii) as we observed a strong binding profile of the sequence motif in SOX5 to the MITF promoter (see Additional file 1: Figure S1 in the supplementary material); however direct binding remains to be shown with e.g. ChIP experiments.
In addition to SOX5 and SOX10, to a lesser extent, also SOX2 and SOX9 were among our predicted candidates of selected transcription factors (in the models with 12 or more predicted regulators, see Table 1). It is known, that also other SOX family members are involved in melanocyte development . Shakova and coworkers observed an efficient reduction of tumorigenesis in animal models and in human melanoma cells when reducing SOX10 expression levels and for this anti-tumoric effect they found SOX9 to be required as a functional antagonistic regulator of SOX10 . Besides this, Liu and Lefebvre found that the regulatory trio of SOX9, SOX5 and SOX6 cooperatively work together to activate super-enhancers in a genome-wide way in rat chondrosarcoma cells . Taken together, these observations are in line with our observation that SOX5 and SOX10 have an opposing effect on regulation of the central transcription factor MITF. When investigating the SKCM dataset, we found SOX9, SOX2 as well as SOX6 to be down-regulated in the tumor subgroup of low SOX5 expression compared to the tumor subgroup of high SOX5 expression (see Additional file 1: Table S2). For the future, it could be intriguing to disentangle the fine grained interplay between these SOX family members and their involvement in tumor progression. The analysis of clinical tumor data (SKCM) revealed that higher SOX5 expression was a significant indicator for longer survival (Fig. 5). Accordingly, we observed a tendency towards longer survival of patients with tumors showing lower expression of MITF (Additional file 1: Figure S12).
We observed a higher SOX5 expression in metastatic melanoma compared to primary melanoma (Additional file 1: Figure S8), although the survival analysis revealed that very low SOX5 expression is associated with poor prognosis (Fig. 5). This might point towards a dual functional role of SOX5 depending on primary versus metastatic tumor stage. We speculate that SOX5 could be an important factor during the transition from primary to metastatic melanoma, as SOX5 knockdown resulted in reduced invasion (Table 2). As only ten primary melanoma samples from patients who succumbed to disease were available, we performed a correlation analysis on SOX5 expression and survival time resulting in a strongly negative (r = -0.65) correlation, whereas in metastatic melanoma samples only a weak correlation could be observed (r = -0.12; not shown). This is in line with Riker et al., who observed in their analyses that SOX5 expression is strongly increased in thick versus intermediate melanoma samples (Breslow’s thickness), associated with onset of metastatic phenotype . In contrast, our survival analysis revealed a worse prognosis for patients with tumors expressing low-level of SOX5. Notably, this analysis included mainly metastatic tumor samples and only ten samples from primary tumors. We speculate that in metastatic melanoma the anti-proliferative effect of very low SOX5 and thus high MITF levels might lead to a diminished susceptibility to chemotherapy and thus to a worse prognosis.
Regulation of MITF expression is highly complex and mediated by various activating and inhibiting intra- and extracellular processes. Although high MITF levels have an anti-proliferative effect, MITF expression is detectable in almost all melanoma tumors. It seems that a basal level of MITF expression is necessary for melanoma cells and therefore MITF expression and activity is not entirely down-regulated, which is in line with the observation that almost all melanoma cells maintain their ability to synthesize melanin. Wellbrock and coworkers proposed that a low basal MITF level could be important for the survival of melanoma cells and also for their proliferation through regulation of cyclin-dependent kinase 2 (CDK2) and B-cell CLL/lymphoma 2 (BCL2) . They proposed that an intermediate, well-balanced MITF level is important for melanoma cells to survive and proliferate. We add to this the notion that a well-tuned interplay of SOX5 and SOX10 could be crucial for this homeostasis of MITF expression, avoiding too high as well as too low MITF expression.
We found that up-regulation of SOX5 expression co-occurs with BRAF mutations. It might be favorable for the tumor to suppress MITF expression with different strategies like increased BRAF activity that leads to MITF degradation, or increased inhibition of MITF transcription due to SOX5 blocking the binding site of SOX10. In future studies, it would be interesting to investigate whether increased SOX5 expression is a downstream effect of BRAF mutation or whether it is rather an independent control mechanism for MITF regulation. Interestingly, the prediction of Breslow thickness using all of the investigated regulators (SOX5, SOX10, and MITF) showed good prediction performance only for thin melanoma tumors (<1 mm) and was rather poor for thick melanomas. This may indicate a transition point in melanoma progression. Indeed, Riker and coworkers reported of a transition point of melanoma progression; they observed that most genes up-regulated in more advanced melanoma exhibit the highest change of their expression level during the transition of intermediate to thick lesions . We also observed a similar transition by modeling Breslow thickness with the three TFs, SOX5, SOX10 and MITF. Interestingly, we identified a bimodal distribution of SOX5 expression in tumor samples and also in the melanoma cell lines. Cells of a potential subset of melanoma, which is indicated by the bimodal distribution, may use the up-regulation of SOX5 to repress MITF in order to prevent its inhibitory effect on proliferation.
To conclude, we applied a computational approach to infer transcriptional regulation of MITF in human melanoma cells employing microarray expression profiles. Besides SOX10, we identified SOX5 regulating MITF in human melanoma cells and validated its inhibitory effect experimentally by functional and reporter assays. We found low SOX5 expression to be an indicator for shorter survival of patients with melanoma tumors. In the future, SOX5 might play an important role when entangling the fine grained interplay of MITF regulation and its impact on tumorigenesis. SOX5 may suit as a prognostic marker in combination with other biomarkers involved in regulation of MITF.
Ethics approval and consent
TCGA data used in this study are publically available. Melanoma cell lines were generated at the DKFZ Skin Cancer Unit with the approval by the Ethics Committee II of Heidelberg University and have been published previously . Melanocytes and fibroblasts used to test MITFP-Lenti vector were obtained from healthy donors and this study was approved by the Ethics Committee II of Heidelberg University (approval number: 2009-350 N-MA) and written informed consent was obtained from donors or their parents, if donors were under the age of 16 years.
Availability of data and materials
All used data sets are publically available. For the SKCM samples expression and clinical data were obtained from TCGA. The clinical data was downloaded from cancergenome.nih.gov and the expression data was downloaded from cbioportal.org [41, 42]. NCI-60 data were obtained from CellMiner . Cell line expression data from Hoek study (Mannheim cohort) can be accessed via NCBIs Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) with GEO Series accession GSE4845 [17, 18].
This work was supported by the Federal Ministry of Education and Research (BMBF), Germany, FKZ 0316168D (SysMet-BC), FKZ 01ZX1302B (CancerTelSys) and FKZ: 01EO1002 (CSCC). We thank Dr. Ballotti for providing the pMI plasmid and Dr. Eric Campeau for providing pLenti CMV GFP Puro. We acknowledge the TCGA research network http://cancergenome.nih.gov/ for making the multiplatform genomic data for various cancers publicly available.
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