ARTICLE

Received 15 Sep 2014 | Accepted 16 Feb 2015 | Published 9 Apr 2015 DOI: 10.1038/ncomms7683 OPEN Decoding the regulatory landscape of reveals TEADS as regulators of the invasive cell state

Annelien Verfaillie1,*, Hana Imrichova1,*, Zeynep Kalender Atak1,*, Michael Dewaele2,3, Florian Rambow2,3, Gert Hulselmans1, Valerie Christiaens1, Dmitry Svetlichnyy1, Flavie Luciani2,3, Laura Van den Mooter3,4, Sofie Claerhout3,4, Mark Fiers3, Fabrice Journe5, Ghanem-Elias Ghanem5, Carl Herrmann6, Georg Halder3,4, Jean-Christophe Marine2,3 & Stein Aerts1

Transcriptional reprogramming of proliferative melanoma cells into a phenotypically distinct invasive cell subpopulation is a critical event at the origin of metastatic spreading. Here we generate transcriptome, open chromatin and histone modification maps of melanoma cultures; and integrate this data with existing transcriptome and DNA methylation profiles from tumour biopsies to gain insight into the mechanisms underlying this key reprogramming event. This shows thousands of genomic regulatory regions underlying the proliferative and invasive states, identifying SOX10/MITF and AP-1/TEAD as regulators, respectively. Knockdown of TEADs shows a previously unrecognized role in the invasive network and establishes a causative link between these transcription factors, cell invasion and sensitivity to MAPK inhibitors. Using regulatory landscapes and in silico analysis, we show that transcriptional reprogramming underlies the distinct cellular states present in melanoma. Furthermore, it reveals an essential role for the TEADs, linking it to clinically relevant mechanisms such as invasion and resistance.

1 Laboratory of Computational Biology, Center for Genetics, University of Leuven, 3000 Leuven, Belgium. 2 Laboratory for Molecular Cancer Biology, Center for Human Genetics, University of Leuven, 3000 Leuven, Belgium. 3 VIB Center for the Biology of Disease, 3000 Leuven, Belgium. 4 Laboratory of Growth Control and Cancer Research, Center for Human Genetics, University of Leuven, 3000 Leuven, Belgium. 5 Medical Oncology Clinic, Institut Jules Bordet, Universite´ Libre de Bruxelles, 1000 Brussels, Belgium. 6 Department of Theoretical Bioinformatics, DKFZ Heidelberg, 69117 Heidelberg, Germany. * These authors contributed equally to this work. Correspondence and requests for materials should be addressed to S.A. (email: [email protected]).

NATURE COMMUNICATIONS | 6:6683 | DOI: 10.1038/ncomms7683 | www.nature.com/naturecommunications 1 & 2015 Macmillan Publishers Limited. All rights reserved. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms7683

elanoma is one of the most aggressive cancers and, Results although investigation into the genetic underpinnings Proliferative and invasive gene signatures in tumour samples. Mof melanoma have led to promising therapeutics, The invasive and proliferative transcriptional cell states have thus clinical outcome remains poor, with most patients rapidly far only been described in vitro. We asked whether the tran- acquiring resistance1. The difficulty in eradicating melanoma scriptome of these two distinct cell populations could be observed lies in its high degree of heterogeneity and plasticity. Melanoma in clinical samples. To this end, we assembled three compendia comprises multiple phenotypically distinct subpopulations of of publicly available melanoma data cancer cells, all with a potentially variable sensitivity to therapy2. (Supplementary Table 1). Unsupervised clustering revealed that However, the mechanisms evoking this heterogeneity are largely each of the three compendia clustered into three similar-sized uncharacterized. clusters, two exhibiting features reminiscent of the proliferative Gene expression profiling of cultured melanoma cell lines3–5 and invasive cell states and a third exhibiting an immune-related identified two types of cultures characterized by very distinct signature presumably due to the presence of a high number of transcriptomes. Samples of the ‘proliferative’ type express high tumour-infiltrating lymphocytes (Fig. 1a,b and Supplementary levels of the -lineage-specific transcription factor (TF) Figs 1 and 2). Accordingly, the gene signatures derived for both MITF6 as well as SOX10 and PAX3 (ref. 7,8). In contrast, samples the proliferative and invasive clusters show very significant of the ‘invasive’ type express low levels of MITF, high levels of the overlap with the Hoek3 gene signatures representing curated gene epithelial-to-mesenchymal transition (EMT)-related TF ZEB1 lists for proliferative and invasive melanoma states in culture (ref. 5,9) and involved in TGF- signalling. It has been (Supplementary Fig. 3a). As expected, samples in the invasive proposed that melanoma invasion is triggered by the appearance cluster have high expression of genes identified as miR-200 of clusters of MITF-low/ZEB1-high cells at the edge of the targets or implicated in TGF- and JNK signalling, cell migration, primary lesions5. These cells acquire migratory properties stemness and EMT (Fig. 1b). Genes that are upregulated in this allowing them to invade the dermis, enter the blood stream and cluster include ZEB1, SNAI1 and TGFB2. In contrast, genes eventually contribute to metastatic dissemination. Interestingly, with high expression in the proliferative sample cluster are MITF-positive cells are also found at metastatic sites, suggesting significantly enriched for cell cycle, proliferation and melanocytic an ability of melanoma cells to switch back and forth between processes. Genes upregulated in this cluster include known these transcriptional states. While several models have been markers of the melanocyte lineage and melanoma such as SOX10, proposed to explain these observations, the initial event always MITF and PAX3 (Supplementary Fig. 3b). Consistently, when the involves a transition in the primary tumour from a proliferative entire gene expression pattern of a sample is visualized using self- to an invasive cell state. This (reversible) transition is likely organizing maps (SOMs)13 some of the invasive and proliferative caused by dynamic transcriptional changes driven by differential samples show remarkable similarities (Fig. 1c and Supplementary chromatin architecture, and changes in the activity of Note 2). In addition, these transcriptomes are highly similar to master regulators and gene regulatory networks4,10. In support the transcriptomes of the previously defined invasive and of this, no ‘metastasis-driving’ mutations have thus far proliferative melanoma cultures. These observations indicate been found in primary and metastatic tumours from the same that the clinical samples cluster into distinct groups and that these patient. represent cellular subpopulations in either the proliferative or the Importantly, it has been proposed that distinct invasive cell state. However, whether mutations or transcriptional transcriptional cell states characterized by variable MITF reprogramming forms the driver of these subpopulations is a or SOX10 activity influence resistance to MAPK pathway matter of debate. Our analysis of the exome re-sequencing data inhibitors1,11. Interestingly, enforcing MITF expression from the same TCGA cohort did not identify specific ‘pushes’ cells towards a different cell state12, which could then enrichments for mutations in BRAF, NRAS or any of the other be exploited therapeutically. This illustrates how a better well-established melanoma driver genes (n ¼ 116) in either of the understanding of the molecular processes underlying the clusters (Supplementary Note 1). This observation favours the proliferative-to-invasive transition can be used to overcome possibility that these very distinct cell states are driven largely by drug resistance and improve current therapies. As these transcriptional reprogramming rather than by common genetic processes are largely driven by changes in gene-regulatory mutations. networks, new insight may be gained by genome-wide mapping Together, these data indicate that, despite the fact that all of and decoding of the chromatin landscapes and the master these samples originate from different human patients with very regulators that control the distinct transcriptomic states in divergent mutational profiles, most tumours fall into one of these melanoma. two dominant states. Note that when analysing the immune- In this study, we first provide evidence that the cell infiltrated cluster separately, the samples also show a tendency states described in vitro are also recapitulated in microarray towards either of these two states, suggesting that the immune and RNA-seq data sets across tumour biopsies. Next, signature may, at least partly, represent a layer that confounds the we map the transcriptome and chromatin landscape of underlying melanoma states (Supplementary Fig. 4). 10 short-term melanoma cultures and find thousands of genomic regulatory regions underlying the proliferative and invasive states. Using an integrated approach for motif and Changes in the chromatin landscape underlie cellular states. track discovery, we confirm SOX10/MITF as master regulators The above findings support in vitro short-term cultures as a valid of the proliferative gene network and identify AP-1/TEAD as model system that can be exploited to decipher the chromatin new master regulators of the invasive gene network. We landscapes and regulatory networks underlying these two tran- experimentally validate chromatin interactions upstream of scriptional cell states. Therefore, we profiled the transcriptome SOX9 by 4C-seq, and we test the TEAD-predicted network and chromatin landscape of 10 short-passage melanoma cultures using knockdown (KD) experiments. These experiments previously described14 and one classical melanoma cell line establish a previously unrecognized role for the TEADs (SK-MEL-5; see accession code for data availability15). The in the invasive gene network and reveal a causative link transcriptome of all 11 samples was compared with publicly between these TFs, cell invasion and sensitivity to MAPK available gene expression data from melanoma cultures and with inhibitors. the clusters of tumour biopsies described above using SOMs

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(Fig. 1c). This comparison indicated that cultures MM047 our data with the Hoek gene signatures3,16 identified 100% of and MM099 are in an invasive transcriptional state, while Hoek’s proliferative genes upregulated in our proliferative the remaining harbour a transcriptome reminiscent of samples, while 100% of Hoek’s invasive genes are upregulated the proliferative state (Supplementary Table 2). This in our invasive samples (Supplementary Fig. 3a). Importantly, the correspondence was further supported by a significant cells with an invasive transcriptional profile do exhibit enhanced enrichment of the invasive and proliferative gene signatures capabilities to invade in a Matrigel assay compared with the cell from Hoek et al.3, and by the high expression of invasive marker lines with a transcriptional proliferative state (Supplementary genes such as ZEB1, SOX9 and WNT5A (Supplementary Figs 3, 5 Fig. 8). In addition, similar to the results obtained using the and6). In contrast, these samples have undetectable SOX10 and TCGA cohort, the proliferative versus invasive split is not MITF, while the other nine samples express high SOX10 and correlated with any specific mutations in known melanoma driver MITF levels (Fig. 2, Supplementary Figs 3a and 7). Using these genes, such as BRAF (Supplementary Fig. 9). Again, this is data we established a new gene signature for each state, consisting consistent with the view that acquisition of the invasive cell state of 772 and 643 genes for the proliferative and invasive is likely to be a consequence of transcriptional reprogramming phenotypes, respectively (Supplementary Data 1). Comparing rather than being driven by any specific genetic alterations.

Consensus map for NMF on SKCM RNA-seq data

30,000 ZEB1 ZEB1 TLE4 22,500 CDH13 Invasive MITF OSMR 15,000 NTM n=123 PDGFC FST 7,500 CDK14 Normailzed SLC22A4 0 SLIT2 expression values expression TCF4 –4 WNT5A FGF2 PODXL Immune ADAM12 infiltrated ITGA2 NRP1 n=116 HS3ST3A1 4 KCNMA1 LOXL2

375 patients CRIM1 COL13A1 F2RL1 TPBG BIRC7 IVNS1ABP Proliferative CDK5R1 PLXNC1 n=136 INPP4B CDH1 TYR WDR91 CAPN3 GALNT3 FAM174B 375 patients C21orf91 TRPM1 Invasive Immune response present Proliferative RAB27A PHACTR1 MLANA KAZ IRF4 MITF RRAGD TNFRSF14 All shown enrichments are significant at FDR 0.05 level SLC45A2 ADCY2 MBP GPR143 Hoek invasive signature (L) RAB38 SIRPA HPS4 Cell migration (L + GO) MICAL1 CDK2 SEMA6A Stem cell signature (L) MYO1D Invasive samples Proliferative samples TYRP1 NR4A3 Mesenchymal transition signature (L) OCA2

Extracellular matrix (GO + KEGG)

Cell/cell-cell/adherens/gap junction (GO + R)

FRA1 and FRA2 targets (L) TCGA.D3.A2J8.06 TCGA.EE.A2MC.06 TCGA.EE.A2GK.06 TCGA.D3.A2J6.06 TCGA.FS.A1ZG.06 TCGA.D9.A6EG.06 TGF beta signalling pathway (KEGG) . SKCM MiR-200 targets (T) et al Hippo signalling (L + R)

JNK pathway targets (L) GSM109051 GSM109091 GSM109065 GSM170862 GSM109082 GSM109058 Telomere maintanance (R) maintanance (R) Cell cycle (KEGG + R)

Pigment metabolic process (GO) GSM207959 GSM316844 GSM316848 GSM207967 GSM316859 GSM316891 MYC target signature (L) DNA repair/replication (R + KEGG) Oxidative phosphorylation (KEGG) TCA cycle (R + L + KEGG) GSM938874 GSM183276 GSM169524 GSM700743 GSM700745 GSM170979 Mitotic CIN attractor (L) MITF targets (L)

Hoek proliferative signature (L) This study Compendium B Compendium A Hoek

MM047 MM099 MM001 MM011 MM031 32131 2 High in proliferative High in invasive Normalized enrichment score Figure 1 | Proliferative and invasive cellular states in melanoma biopsies and cultures. (a) Non-negative matrix factorization on TCGA-SKCM RNA-seq data results in three sample clusters. (b) Functional characteristics of two states revealed by GSEA on the invasive and the proliferative meta-rankings integrated across SKCM RNA-seq and two microarray compendia, using various sources of functional data (L) Literature; (R) Reactome; (KEGG) KEGG pathways; (GO) ; (T) TargetScan. (c) Expression heatmap for TCGA samples showing a core subset of invasive and proliferative gene signatures (the GSEA overlap between the Hoek signatures and our ranking). The samples are ranked according to MITF expression, and the expression levels of both MITF and ZEB1 are indicated on top of the heatmap. Below the heatmap are mosaic plots of several samples. Each mosaic shows the expression of all variable genes in a 25 Â 26 grid, whereby each field contains one or more genes. Genes and clusters with similar expression profiles across the cohort are placed close to each other in the grid. The mosaics show a high similarity among the invasive samples, and a strong difference between invasive and proliferative samples. SKCM is RNA-seq data from TCGA; Hoek et al.16, is microarray data from melanoma cultures; Compendium A and B are melanoma microarray data from GEO (see Supplementary Table 1 for accession numbers used).

NATURE COMMUNICATIONS | 6:6683 | DOI: 10.1038/ncomms7683 | www.nature.com/naturecommunications 3 & 2015 Macmillan Publishers Limited. All rights reserved. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms7683

Scale 50 kb hg19 chr22: 38,330,000 38,340,000 38,350,000 38,360,000 38,370,000 38,380,000 38,390,000 38,400,000 38,410,000 38,420,000

POLR2F MIR4534 C22orf23 MIR6820 SOX10 3,070 MM047 0 RNA-seq 3,070 MM011 0 1,000 MM047 0 H3K27ac 1,000 MM011 0 90 MM047 0 H3K27me3 90 MM011 0 40 MM047 0 FAIRE-seq 40 MM011 0

10 kb hg19 10 kb hg19 Scale 10 kb hg19 Scale Scale 10 kb hg19 Scale 38,375,000 38,380,000 38,385,000 38,375,000 38,380,000 38,385,000 chr22: 38,375,000 38,380,000 38,385,000 38,375,000 38,380,000 38,385,000 chr22: chr22: chr22:

SOX10 SOX10 SOX10 5,130 SOX10 1,250 90 52 MM047 0 0 0 0 5,130 1,250 90 52 Invasive MM099 0 0 0 0 5,130 1,250 90 52 MM001 0 0 0 0 5,130 1,250 90 52 MM011 0 0 0 0 5,130 1,250 90 52 MM031 0 0 0 0 5,130 1,250 90 52 MM034 0 0 0 0 5,130 1,250 90 52 MM057 0 0 0 0 Proliferative 5,130 1,250 90 52 MM074 0 0 0 0 5,130 1,250 90 52 MM087 0 0 0 0 5,130 1,250 90 52 MM118 0 0 0 0 5,130 1,250 90 52 SKMEL 0 0 0 0 1,250 90 Input 0 0 RNA-seq H3K27ac H3K27me3 FAIRE-seq

Figure 2 | Transcriptome and epigenome profiling in 11 melanoma cell cultures. RNA-seq, FAIRE-seq and ChIP-seq against H3K27Ac and H3K27me3 across 10 short-passage melanoma cultures and one melanoma cell line SK-MEL-5. The SOX10 gene shows high expression and its upstream regions contain high H3K27ac and FAIRE but low H3K27me3 signal in the nine proliferative (blue) samples. In the two invasive (orange) samples, there is no SOX10 expression, no H3K27Ac and FAIRE peaks but high H3K27me3 peaks. Upper panel shows one invasive sample (MM047) and one proliferative sample (MM011). Lower panels showing zoom in around the promoter region of SOX10 with tracks for all 11 samples for each of the four data types. Vertical axes represent normalized coverage for each data track. Arrows indicate regions of interest that are different between proliferative and invasive states. Other genes are illustrated in Supplementary Figs 4 and 5 and in the UCSC Genome Browser using our Melanoma Track Hub (see Methods).

The two expression profiles we identified likely arise through of RNA-seq data. For example, the H3K27ac and FAIRE-seq gene regulation by cis-regulatory modules. Therefore, we next tracks indicate an active and open SOX10 promoter, respectively, investigated active cis-regulatory regions underlying the invasive in the nine proliferative samples with high SOX10 expression. In and proliferative transcriptional states using open chromatin contrast, the SOX10 promoter lacks activating marks, but carries profiling (FAIRE-seq17) and ChIP-seq against two important repressing H3K27me3 marks in the two invasive cultures (Fig. 2). histone modifications representing activated (H3K27ac) and This reciprocity is a genome-wide property since the two invasive repressed (H3K27me3) chromatin marks. Interestingly, the samples clearly segregate from the other samples in a invasive samples are clustered separately on the basis of their multidimensional scaling unsupervised analysis of the H3K27ac chromatin activity profiles, similarly to the clustering on the basis or FAIRE-seq peaks (but not H3K27me3; Fig. 3a). An overview of

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Correlation between H3K27ac and expression MDS 20 Using 10,000 most variable genes/regions INVASIVE samples Proliferative samples RNA 10 2,000 MM047 MM099 MM001 MM011 MM057 MM087 MM031 MM118 SKMEL5 MM034 MM074 Proliferative 1,000 0 H3K27ac Invasive samples 0 –10 –1,000 FAIRE –20 –2,000

H3K27me3 –30

–3,000 Invasive Max. diff. H3K27ac peak fold change R = 0.82, P < 0.001 –5,000 –2,500 0 2,500 –5 05 H3K27ac TSSs of invasive genes TSSs of proliferative genes Invasive Proliferative 1.5 RNA fold change (log2FC>=|1| and adj.P<0.05) 20 30 1.0 Invasive samples 15 Correlation between H3K27me3 and expression 0.5 20 5 0.0 H3K27ac 10 –0.5

10 Proliferative 5 –1.0 0

–2 –1 0 1 0 0 FAIRE –5,000–2,500 0 2,500 5,000 –5,000–2,500 0 2,500 5,000 12.5 –5

10.0 15 0.5 R = –0.51, P < 0.001

Invasive –10 Max. diff. H3K27me3 peak fold change FAIRE 7.5 10 –5 0 5 0.0 Invasive