bioRxiv preprint doi: https://doi.org/10.1101/2020.09.24.311431; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

TFIIH kinase CDK7 antagonizes phenotype switching and emergence of drug

tolerance in melanoma

Pietro Berico1, 2, 3, 4, Max Cigrang1, 2, 3, 4, Cathy Braun1, 2, 3, 4, Guillaume Davidson1, 2, 3, 4,

Jeremy Sandoz1, 2, 3, 4, Stephanie Legras1, 2, 3, 4, François Peyresaubes1, 2, 3, 4, Carlos Mario

Gene Robles1, 2, 3, 4, Jean-Marc Egly1, 2, 3, 4, Emmanuel Compe1, 2, 3, 4, Irwin Davidson1, 2, 3, 4, 5

and Frederic Coin1, 2, 3, 4, 5.

1 Institut de Génétique et de Biologie Moléculaire et Cellulaire, Equipe Labélisée Ligue

contre le Cancer. BP 163, 67404 Illkirch Cedex, C.U. Strasbourg, France

2 Centre National de la Recherche Scientifique, UMR7104, 67404 Illkirch, France

3 Institut National de la Santé et de la Recherche Médicale, U1258, 67404 Illkirch, France

4 Université de Strasbourg, 67404 Illkirch, France

5 Corresponding authors: [email protected], [email protected]

Keywords: CDK7, TFIIH, MITF, melanoma, GATA6

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Abstract

Melanoma cells switch back-and-forth between phenotypes of proliferation and invasion in

response to changing microenvironment, driving metastatic progression. We show that

inhibition of the TFIIH kinase CDK7 (CDK7i) results in a melanocytic to mesenchymal

phenotype switching and acquisition of targeted therapy tolerance. We identify a gene

expression program controlled by the transcription factor GATA6, which participates in

drug tolerance in mesenchymal-like cells and which is antagonized by CDK7 in melanocytic-

like cells. This program emerges concomitantly with loss of melanocyte lineage-specific

MITF protein following CDK7i. By dissecting the underlying mechanism, we observe that

CDK7 accumulates at the super-enhancer regulating MITF to drive its expression. MITF

itself binds to a intronic region of GATA6 to transcriptionally repress it. This molecular

cascade antagonizes expression of the GATA6 regulon that only emerges in MITF-low cells

of metastatic melanoma. Our work reveals a role for CDK7 in counteracting phenotype

switching and activation of a gene expression program mediating multidrug tolerance in

melanoma cells.

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Introduction

Among the protein complexes essential for gene expression in eukaryotes, the basal

transcription factor TFIIH is unique due to its various enzymatic activities, including

helicase, translocase and kinase functions (Berico and Coin, 2018; Villicana et al., 2014).

TFIIH comprises 7 core forming subunits (composed of the XPB translocase, the XPD

helicase together with the p62, p52, p44, p34, and TTDA subunits) that associate with the

CDK-Activating-Kinase (CAK) sub-complex (containing the CDK7 kinase together with MAT1

and Cyclin H) (Compe and Egly, 2016). The CDK7 kinase is a positive kinase that

phosphorylates transcription factors to promote gene expression (Fisher, 2019). The main

target of CDK7 is the carboxyl-terminal domain (CTD) of the largest subunit of RNA

polymerase II (RNAPII) at serine 2 (ser-2) and 7 (ser-7) contributing to the proper

progression of transcription initiation (Eick and Geyer, 2013; Fisher, 2019). Surprisingly,

inhibition of the CDK7 kinase activity (CDK7i) has led to impressive responses in various

cancers (Cao and Shilatifard, 2014; Christensen et al., 2014; Kwiatkowski et al., 2014)

probably due to the participation of the TFIIH kinase to super-enhancer-linked oncogene

transcription (Chipumuro et al., 2014).

Malignant melanoma is responsible for 70% of skin cancer deaths in western countries

(Eggermont et al., 2014). The 5-year survival rate is greater than 90% for localized

melanomas, but drops to 16% for distant-stage disease demonstrating that metastases are

responsible for patient mortality (Balch et al., 2011). Somatic gain-of-function mutations in

the proto-oncogene kinase BRAF are the commonest mutations (60%) and the T → A

transversion underlying BRAFV600E comprises the majority of BRAF mutations (Brose et al.,

2002; Davies et al., 2002). As an alternative to BRAF mutations, human melanomas

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commonly carry NRAS and NF1 mutations (35%), while a number of patients show no

mutations of these three genes (Triple-wt) (5%) (Hodis et al., 2012).

Melanoma is notorious for its heterogeneity based on co-existing melanoma cell

phenotypes. In cultured melanoma cells, gene expression analyses have established two

main and distinct signatures defined as either proliferative (melanocytic-type) or invasive

(mesenchymal-like) melanoma cell states (Carreira et al., 2006; Verfaillie et al., 2015;

Widmer et al., 2012). At the transcriptional level, the differentiated melanocytic-type

melanoma cells display high levels of lineage-specific transcription factors, including the

microphthalmia-associated transcription factor (MITF) and SRY-box 10 (SOX10), while the

undifferentiated mesenchymal-like melanoma cells show low levels of MITF and SOX10 but

high expression of markers such as the AXL receptor tyrosine kinase (AXL) and SRY-Box 9

(SOX9). The discovery of cells with intermediate signatures supports the initial concept of

phenotypic plasticity, called phenotype switching, driving melanoma progression through

conversion from one phenotype into another in response to external cues and which is

reminiscent to epithelial-mesenchymal transition (EMT) (Ennen et al., 2017; Hoek et al.,

2008; Rambow et al., 2018).

Treatment options for patients with metastatic melanoma have changed dramatically in

the past 10 years. Combination therapies with inhibitors targeting BRAF (i.e Vemurafenib

and Dabrafenib) and MEK (i.e Trametinib) kinases (BRAFi and MEKi, respectively) have

emerged and show high efficiency but are limited by development of resistance and

subsequent progression (Menzies and Long, 2014). Studies have shown that tolerance to

targeted therapies can involve various phenotype changes including the EMT-like process

of melanocytic to mesenchymal phenotype switching (Arozarena and Wellbrock, 2019;

Kemper et al., 2014; Rambow et al., 2019; Rambow et al., 2018). Therefore, understanding

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the molecular detail of phenotypic plasticity of melanoma cells is crucial for the

development of future therapeutic approaches.

Here we identify the CDK7 kinase activity of TFIIH as a repressor of phenotype switching in

melanoma, antagonizing the emergence of transcription program involved in drug

tolerance. By dissecting the link between CDK7, phenotype switching and drug tolerance,

we identified a new transcriptional program in mesenchymal-like cells, under the control of

the GATA-binding factor 6 protein (GATA6), which we show to be involved in multidrug

resistance and to be antagonized by CDK7 activity in melanocytic-type melanoma cells.

Mechanistically, we unveil a negative molecular cascade in which CDK7 drives the

expression of MITF that binds to an intronic regulatory sequence of the GATA6 locus to

transcriptionally repress it. Therefore, the loss of MITF during CDK7i and phenotype

switching promotes the progressive activation of the GATA6-dependent transcription

program. Consistently, we observe that the GATA6 regulon emerges in MITF-

low mesenchymal-like melanoma cells of human metastatic melanoma.

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Results

Melanoma cultures exhibit distinct sensitivity to CDK7i

To study the role of CDK7 in melanoma, we first explored the sensitivity of melanoma cells

to the loss of CDK7 kinase activity by using unique patient-derived 2D cultures (MM011,

MM029, MM047, MM074, MM099 and MM117) (Gembarska et al., 2012; Verfaillie et al.,

2015) together with one long-term frequently used melanoma cell line (501mel). MM029

(BRAFV600K), MM047 (NRASQ61R) and MM099 (BRAFV600E) showed a mesenchymal-like gene

expression signature that included low levels of the lineage-specific transcription factor

MITF and high levels of SOX9 and c-Jun (Figure 1a) (Verfaillie et al., 2015; Widmer et al.,

2012). In contrast, the melanocytic-type melanoma cells 501mel (BRAFV600E), MM011

(NRASQ61K), MM074 (BRAFV600E) and MM117 (Triple-wt) exhibited moderate to high

expression of MITF and SOX10 together with undetectable levels of expression of SOX9 and

c-Jun (Verfaillie et al., 2015; Widmer et al., 2012) (Figure 1a).

We next observed that the melanocytic-type 501mel, MM011, MM074, MM117 cells

together with the mesenchymal-like MM047 cells were sensitive to low concentrations of

the first-in-class CDK7 inhibitor THZ1 (Kwiatkowski et al., 2014) (Figure 1b) (IC50 ranging

from 10 to 70nM). In marked contrast, the mesenchymal-like MM099 and MM029 cells

were insensitive to CDK7i, even at high concentrations of the drug (IC50>500nM) (Figure

1b).

Because CDK7 plays a positive role in transcription initiation, we next measured the impact

of CDK7i on global transcription by labeling de novo synthesized RNA with 5-ethynyl uridine

(5EU) (Alekseev et al., 2017). While a global inhibition of RNA synthesis occurred in CDK7i-

sensitive 501mel cells at low drug concentration, no transcriptional alteration was

observed in CDK7i-insensitive MM029 cells after similar treatment (Figure 1c). Consistently,

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phosphorylation of initiation-associated ser-5 and ser-7 and elongation-associated ser-2 of

the CTD of RNAPII was impaired in 501mel, while persisting in MM029 cells (Figure 1d).

CDK7i was accompanied by an anti-proliferative effect on the 501mel and MM011 cells

together with a slight induction of apoptosis, which was not observed in CDK7i-insensitive

MM029 and MM099 melanoma cells (Figures 1e-f). Taken together, these observations

demonstrated that melanocytic-type melanoma cells displayed high sensitivity to CDK7i,

regardless of their driver mutation, while mesenchymal-like melanoma cells were mostly

insensitive to it.

CDK7 activity antagonizes melanoma phenotype switching

To unveil the molecular mechanisms controlling the sensitivity/insensitivity of melanoma

cells to CDK7i, we generated THZ1 (CDK7i) or Vemurafenib (BRAFi) resistant cell lines ex

vivo. To this end, we chronically exposed the melanocytic-type and CDK7i/BRAFi sensitive

MM074(BRAFV600E) cells to escalating doses of the corresponding drug over several weeks.

These treatments were carried out until the cells proliferated in drug concentrations equal

to at least 5 times the IC50 values of the original cells, allowing us to generate the stable

MM074CDK7i-R and MM074BRAFi-R cell lines, respectively (Supplemental Figures 1a-b). In

parallel, the CDK7i-sensitive and mesenchymal-like MM047 cells (NRASQ61R) were

chronically exposed to THZ1 following the same protocol to generate stable MM047CDK7i-R

cells (Supplemental Figure 1c). Interestingly, establishment of CDK7i resistance decreased

the sensitivity of the MM074CDK7i-R cells to BRAFi (Vemurafenib) and MEKi (Trametinib)

(Supplemental Figures 1b and d). In apparent contrast, the BRAFi-resistant MM074BRAFi-R

cells remained sensitive to both CDK7i and MEKi (Supplemental Figure 1a and d). Acquired

resistance to CDK7i correlated with maintenance of global transcription synthesis of

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MM047CDK7i-R and MM074CDK7i-R cells upon CDK7i treatment when compared to their

respective parental cells (Supplemental Figure 1e-f).

Using RNA-seq, we next analyzed the transcriptome of the CDK7i, BRAFi and parental cells.

We first observed a pronounced modification of the transcriptional programs of

MM074CDK7i-R and MM074BRAFi-R cells compared to MM074 cells and a less pronounced

modification of the MM047CDK7i-R cells compared to MM047 cells (Figure 2a). Analyses of

the CDK7i-resistant cells showed deregulation of more than 6.000 genes in MM074CDK7i-R

cells compared to MM074 cells and 1.000 genes in MM047CDK7i-R cells compared to MM047

cells (Figure 2b). Interestingly, despite the fact that the parental lines were of different cell

types, 261 genes were commonly upregulated in the two CDK7i-resistant cell cultures

(Figure 2b and Supplemental Data 1). As shown by Gene Ontology (GO) analysis, these

genes were involved in epithelial cell differentiation or in the transport of small molecules,

which could explain the resistance of melanoma cells to CDK7i (Supplemental Figure 2a).

We next conducted a bioinformatics approach to cluster the cell lines based on the

expression of a hundred genes corresponding to previously described signatures of

melanocytic vs. mesenchymal transcriptional cell states (Widmer et al., 2012). With this

approach and in agreement with the literature (Verfaillie et al., 2015), the 501mel and

MM074 cells showed a melanocytic-type transcriptional signature (Figure 2c, lanes 1-2),

while the MM047, MM099 and MM029 cells showed a mesenchymal-like signature (lanes

5-7). Interestingly, we observed a mesenchymal-like signature in MM074CDK7i-R cells, which

differed from the melanocytic-type signature of the parental cells (compare lanes 2 and 4)

and which correlated with an increased invasion capacity (Supplemental Figure 2b). In

apparent contrast with MM074CDK7i-R, the melanocytic-type signature of the parental

MM074 persisted in MM074BRAFi-R cells in which we observed a significant increase in the

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expression of a set of bona fide pigmentation genes (Figure 2c, compare lanes 2 and 3),

including MLANA (Supplemental Figure 2c), which correlated with higher cellular

pigmentation (Supplemental Figure 2d).

The transcriptomic data prompted us to compare the amount of several protein markers of

the melanocytic-type and mesenchymal-like states in the MM074, MMO74CDK7i-R and

MM074BRAFi-R cells. We observed that the MM074BRAFi-R cells exhibited higher amounts of

the melanocytic lineage-specific proteins MITF, SOX10, TFAP2A and PAX3 compared to

MM074 cells (Figure 2d). In contrast, MM074CDK7i-R cells showed a dramatic loss of these

proteins together with the emergence of SOX9 (Figure 2d).

In summary, melanoma cells chronically exposed to CDK7i adopted (for MM74CDK7i-R) or

even heightened (for MM47CDK7i-R) an undifferentiated mesenchymal-like cell state, while

cells exposed to BRAFi acquired a highly pigmented hyper-differentiated cell state.

The GATA6 regulon is expressed in mesenchymal-like melanoma cells

Next, we analyzed the 261 common up-regulated genes in CDK7i-resistant cells

(Supplemental Data 1) to identify a gene expression signature reflecting the observed

CDK7i resistance. We first merged these genes with a list of annotated transcription factors

and identified 16 common up-regulated transcription factors (TFs) in MM074CDK7i-R and

MM047CDK7i-R cells (Supplemental Figure 3a). We next analyzed the expression of these TFs

in melanoma cells in vivo using data from scRNA-seq performed on 674 cells derived from

one drug-naive PDX tumor (Rambow et al., 2018). Seven out of the 16 TFs were expressed

in a significant fraction of cells from the PDX (Supplemental Figure 3b). However, only the

transcription factor GATA6 was significantly overexpressed in primary melanoma compared

to nevi (Supplemental Figure 3c). In line with these data, we observed high levels of GATA6

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mRNA and GATA6 protein in MM074CDK7i-R but also in the CDK7i-insensitive MM029 and

MM099 cells, compared to CDK7i-sensitive 501mel, MM011, MM117, MM074 cells

(Figures 3a-b). We noticed low level of GATA6 protein in MM047, which however slightly

increased in MM047CDK7i-R (Figure 3b).

We next used pySCENIC (Single-Cell rEgulatory Network Inference and Clustering) through

SCOPE (scope.aertslab.org) on genome-wide transcriptome data recently obtained by

scRNA-seq from shorted-cultured cells (Wouters et al., 2020). We observed that the GATA6

regulon, containing 359 genes (Supplemental Data 2), was highly enriched in the

mesenchymal-like MM099 and MM029 cells (Figure 3c). Strikingly, depletion of GATA6 using

short-interference RNA (siRNA) in MM029 and MM099 cells decreased expression of genes

that were defined as part of the GATA6 regulon by pySCENIC, such as the efflux pump ATP

Binding Cassette Subfamily G member 2 (ABCG2) and the Vasoactive Intestinal Peptide

Receptor 1 (VIPR1) (Figures 3d-f). Furthermore, MM029 proliferation was significantly

impacted upon GATA6 silencing compared to melanocytic-type MM011 that did not

express GATA6 (Figures 3g-h).

The efflux pump ABCG2 is involved in tolerance to CDK7i and BRAFi

We then compared the 261 common up-regulated genes in MM047CDK7i-R and MM074CDK7i-R with

the GATA6 regulon and found 35 genes in common between the two signatures, amongst which

was the efflux pump ABCG2 (Supplemental data 1 and Supplemental Figure 4a). ABCG2

belongs to the family of ATP-binding cassette (ABC) transporters playing a role in multi-

drug resistance (Robey et al., 2018). Analysis of RNA-seq data from melanoma tumors or in

situ mRNA hybridization of melanoma tumor sections demonstrated higher expression of

ABCG2 in cutaneous metastatic melanoma compared to primary tumors (Supplemental

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Figures 4b-c). In addition, three ABC transporters (ABCB1, ABCC3 and ABCG2) were up-

regulated in MM047CDK7i-R and/or MM074CDK7i-R cells (Supplemental Figures 4d-e) but only

ABCG2 was significantly over-expressed in mesenchymal-like CDK7i-resistant MM099 and

MM029 cells (Figures 4a-b). Depletion of ABCG2 using siRNA (Supplemental Figure 4f)

significantly sensitized MM099 and MM029 to CDK7i (Figures 4c-d). Interestingly, depletion

of ABCG2 also significantly sensitized the MM099 cells to BRAFi (Figure 4e), showing the

pleiotropic impact of this efflux pump on drug resistance in these cells. Rationally, MM029

cells bearing the Vemurafenib-resistance associated BRAFV600K mutation were insensitive to

BRAFi with or without ABCG2 (Figure 4f). Taken together, these data suggested that the

ABC transporter ABCG2 played a significant role in the tolerance to CDK7i and BRAFi in

melanoma cells.

Loss of MITF induces expression of GATA6

We next sought to understand how CDK7 activity could counteract the emergence of the

GATA6 regulon in melanocytic-like melanoma cells. Previous work suggested that CDK7

may occupy SE regulating MITF and SOX10 expression in melanoma cells {Eliades, 2018

#8337}. Using 501mel in which the two alleles coding for CDK7 were tagged with a Biotin-

3xFlag tag by genome editing (501melBIO-FLAG:CDK7) (Supplemental Figures 5a-b), we first

aimed at identifying the chromatin binding profile of CDK7 in the melanocyte-type

melanoma cells. Following FLAG Chip-seq, we identified numerous CDK7-binding sites

throughout the locus of the lineage-specific MITF oncogene and of its transcriptional

activator SOX10 (Figure 5a-b). Binding of CDK7 occurred together with marks of H3K27ac,

binding of MITF and/or of SOX10, BRG1 or H2AZ, all characterizing super-enhancer-type

elements. In agreement with a role of CDK7 in MITF-SOX10 expression, a short 24 h CDK7i

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treatment impaired the expression of MITF and SOX10 in 501mel cells (Figure 5c).

Interestingly, loss of SOX10 and MITF occurred in parallel with an increased expression of

GATA6 (Figure 5c). To strengthen this observation we calculated the correlation of GATA6

with MITF or CDK7 expression in published RNA-seq data from human skin cutaneous

melanoma (TCGA) and identified a consistent anti-correlation (r=-0.250 for GATA6 vs. MITF

and r=-0.181 for GATA6 vs. CDK7) between these genes (Figure 5d).

To assess if loss of MITF could mimic CDK7i and induces GATA6 expression, we directly

depleted MITF or its transcriptional activator SOX10 with siRNA in 501mel cells and

observed a significant induction of GATA6 expression (Figure 6a). To analyse the expression

of the whole GATA6 regulon after loss of MITF, we conducted a bioinformatic approach to

exploit published scRNA-seq performed at different times after depletion of the MITF

activator SOX10 in the CDK7i-sensitive MM074 cells (Wouters et al., 2020). We observed

that the progressive down-regulation of the SOX10 regulon, containing MITF

(Supplemental data 3), correlated with the progressive up-regulation of that of GATA6

(Figure 6b and Supplemental Figure 6a). We observed similar results with the melanocytic-

type MM087 (Triple-wt) cells (Figure 6b and Supplemental Figure 6b). Altogether these

data demonstrate an antagonism between GATA6 and MITF-CDK7 in melanoma both ex

vivo and in vivo.

MITF transcriptionally represses GATA6

The above observations prompted us to hypothesize a mechanistic link between MITF and

the repression of GATA6 in melanocytic-like cells. We analysed our published MITF ChIP-

seq data set (Laurette et al., 2015) to determine potential MITF binding profile in the

genomic region of GATA6. In an intronic region of the GATA6 gene body, we identified a

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MITF-binding site containing also 3 E-box motifs that are, with M-box motifs, one of the

two DNA recognition sequences of MITF (Figure 7a)(Laurette et al., 2015). This binding site

was enriched in H3K27ac, BRG1 and H2AZ, marks of enhancer elements. Interestingly, in

mesenchymal-like MM099 cells where MITF is not expressed, intronic H3K27ac was lost,

but we observed a strong H3K27ac deposition at the GATA6 promoter, which correlates

with its high expression in these cells (Figure 7a). ChIP-qPCR of MITF and H3K27ac mark

confirmed their enrichment in the E-box rich intronic region of the GATA6 gene body in

501mel cells (Figure 7b).

To determine whether MITF was able to transcriptionally repress GATA6, we generated the

MM099MITF-SOX10-PAX3 cell line in which MITF, SOX10 and PAX3 expression could be induced

by doxycycline treatment (Figure 7c, left panel). Following induction of MITF-SOX10-PAX3,

we observed a repression of GATA6 mRNA level and a loss of GATA6 protein, which

correlated with the repression of ABCG2 and VIPR1, belonging to the GAAT6 regulon(Figure

7c).

In an attempt to demonstrate the repressive role of the regulatory intronic GATA6 region,

the 500bp sequence encompassing the three E-box recognized by MITF (hereafter called

“intGATA6r”) was subcloned upstream of the CMV promoter of the pcDNA-CMV vector to

replace the immediate early CMV enhancer (ieCMVenh) in the context of a GFP reporter

vector (Figure 7d, left panel). The reporter construct was transiently transfected into

MM099MITF-SOX10-PAX3 expressing or not MITF-SOX10-PAX3. While the expression of the CMV

under the ieCMVenh sequence was barely affected by the induction of MITF-SOX10-PAX3

expression, the presence of the intGATA6r element upstream the promoter decreased its

activity ∼ 85%, compared to cells that did not expressed MITF-SOX10-PAX3 (Figure 7d, right

panel). Altogether, these data demonstrate that MITF transcriptionally represses GATA6 by

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binding to a negative transcriptional regulatory sequence located in an intronic region of

GATA6.

GATA6 is enriched in undifferentiated melanoma cells in vivo

The above data imply that the expression of the GATA6 regulon correlates with MITF-low

undifferentiated melanoma cells in tumors. To test this hypothesis, we first performed an

immunohistological examination of human tumor samples and observed that GATA6 was

highly expressed in a subpopulation of cells in cutaneous metastases, which did not express

SOX10 (Figure 8a, panels e-f). In contrast, GATA6 was hardly observed in nevi and primary

melanomas, which showed high expression of SOX10 (Figure 8a, panels a-d). Consistently,

we observed higher expression of GATA6 in metastatic melanoma compared with primary

melanoma by analyzing public DNA microarray (Xu et al., 2008) or RNA-seq (TCGA) data

(Supplemental Figure 7).

To further decipher which subtypes of melanoma cells may express GATA6 and its regulon

in a tumor, we used the Seurat script to conduct an unsupervised gene clustering analysis

of published scRNA-seq data of the drug-naive PDX tumour mentioned above (Rambow et

al., 2018). Our analyses allowed us to separate the 674 single cells in 9 different clusters

(Figure 8b and Supplemental Figures 8a-b). According to the GO analysis performed for

each cluster, a significant percentage of GATA6 positive cells in the PDX belonged to cluster

7, which was characterized by genes involved in cell migration or RTK signalling pathways

expressed in undifferentiated mesenchymal-like melanoma cells (Supplemental Figure 8b-

c). In agreement, expression of the GATA6 regulon merged significantly with cluster 7 of

mesenchymal-like melanoma cells (Figures 8c-d). Finally, using the TCGA RNA-seq cohort of

patient melanoma mentioned above, we observed a positive correlation between the

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expression of GATA6 and specific mesenchymal-like (cluster 7) and neural-crest cell (cluster

8) markers, expressed in undifferentiated melanoma cells (Figure 8e and Supplemental

data 4). Altogether, these data establish that, as predicted from our ex vivo data, GATA6

and its regulon are significantly enriched in MITF-low undifferentiated melanoma cells in

vivo, such as the mesenchymal-like or neural-crest cells.

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Discussion

Here our report provides new insights into understanding the processes that govern gene

regulatory network expression in melanoma by showing that a negative molecular cascade

involving CDK7, MITF and GATA6 promotes the repression of a transcription program

involved in drug tolerance in melanoma. To demonstrate this, we first studied CDK7i

sensitivity of melanoma cells using patient-derived 2D-cultures that represent a better

model system than classical long-term melanoma cell lines. This approach allowed us to

observe variability in the CDK7i sensitivity of melanoma cells, which is independent of

driver mutation status, but rather reflects their phenotype. Melanocytic-type melanoma

cells are highly sensitive to CDK7i, which potently prevents their growth at low

concentrations of THZ1. Contrary to melanocytic-type cells, mesenchymal-like melanoma

cells were largely insensitive to CDK7i. The establishment of acquired CDK7i-resistant

melanoma cells further allowed us to define a set of 260 genes that are commonly up-

regulated upon CDK7i exposure. The altered expression of these genes, involved in

epithelial cell and skin differentiation or in the transport of small molecules, reflects an

adaptation to CDK7i treatment. Functional genomics allowed us to show that, among

these genes, CDK7i provokes the emergence of a new transcription program in

melanoma cells controlled by the transcription factor GATA6. We observed that the

emergence of the GATA6 regulon following CDK7i exposure is instigated by

decommission of the super-enhancers regulating the expression of the lineage-specific

factor MITF and its regulator SOX10 (Eliades et al., 2018). Indeed, we observed

accumulation of CDK7 at these super-enhancers and we were able to mimic the effect of

CDK7i on the emergence of the GATA6 regulon by depleting individually SOX10 or MITF

16 bioRxiv preprint doi: https://doi.org/10.1101/2020.09.24.311431; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

proteins. Reciprocally, ectopic expression of MITF in mesenchymal-like cells led to the

inhibition of GATA6 expression, indicating a direct impact of MITF on GATA6 expression.

Ultimately, we unveiled the molecular detail of this inhibition cascade. Indeed, we

observed that MITF binds to a short regulatory sequence located in an intron of the

GATA6 locus. When introduced upstream of a reporter gene, the regulatory sequence

repressed its transcription only in the presence of MITF. Then, and although the

regulatory element displays the characteristics of an enhancer, it rather appears to be a

negative regulatory element of GATA6 expression in melanoma cells when it is bound by

MITF (Riesenberg et al., 2015). Of note, this is the first cloning of a bona fide negative

regulatory sequence involving MITF. How might MITF exert a negative regulatory

function by binding to the intGATA6r element remains to be determined but it seems

likely that repressors interact with MITF in these conditions to turn-off the expression of

GATA6. In any cases, our work reveals a molecular cascade of inhibition involving CDK7,

MITF and GATA6 validated by the anti-correlation observed between MITF and GATA6 but

also between CDK7 and GATA6 in tumors. Since MITF has been shown to participate to the

stabilization of CDK7 in melanocyte-type melanoma cells (Seoane et al., 2019), our data

suggest that the progressive loss of MITF during phenotype switching in vivo provokes the

loss of CDK7 protein, which in turn will amplify the loss of MITF to result in the release of

GATA6 expression in MITF/CDK7-low mesenchymal melanoma cells.

GATA6 is one of six members of the mammalian GATA family of transcriptional

regulators and is expressed in various normal tissues derived from the mesoderm and

endoderm (Almalki and Agrawal, 2016). An oncogenic role for GATA6 has been proposed

in various cancers including pancreatic cancer where its knockdown reduced cell

proliferation and cell-cycle progression (Sun and Yan, 2020). In line with these

17 bioRxiv preprint doi: https://doi.org/10.1101/2020.09.24.311431; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

observations, we show that the loss of GATA6 impairs proliferation of mesenchymal-like

cells. Since GATA6 is expressed in normal adult tissues, it is unlikely that its targeting

would lead to efficient therapy. However, this new transcription program may help to

identify important molecular targets in mesenchymal-like melanoma cells, which could

be exploited therapeutically to prevent acquisition of metastatic and drug resistance

potential. Among these targets, we observed that the multidrug transporter ABCG2 is

responsible for cross-resistance to targeted therapies in mesenchymal-like cells. Because

we observed that the GATA6 regulon, including ABCG2, is significantly overexpressed in

metastatic melanoma tumors compared with primary tumors, these genes may mediate

ubiquitous cross-resistance to targeted therapies clinically.

Together with the de-repression of gene expression programs, our results clearly

established that melanocytic-type melanoma cells exposed to CDK7i progressively switch

from a melanocytic to a mesenchymal-like cell state, suggesting a role for CDK7 in cell

plasticity. It has been suggested that cellular phenotype changes, such as cell morphology

or invasion capacity, leading to an invasive behavior of melanoma cells are rapid and

appear before full establishment of the mesenchymal-like signature at the transcriptional

level. For instance, the over-expression of the mesenchymal-like marker SOX9 induces

increased invasion in vitro, but this is only accompanied by a partial enrichment of invasive

signature genes (Cheng et al., 2015). In acquired CDK7i-resistant melanoma cells, we

detected an established mesenchymal-like transcriptional signature with, for instance,

the acquisition of programs responsible for invasion. These observations show that

these cells undergo a complete and stable phenotype switching characterized by a

transcriptional reprogramming reminiscent of EMT.

18 bioRxiv preprint doi: https://doi.org/10.1101/2020.09.24.311431; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

In apparent contrast, we observed that the acquired resistance of melanocytic

melanoma cells to BRAFi was not accompanied by a loss of lineage-specific transcription

factors. Different outcomes of chronic exposure of melanoma cells to BRAFi exist in the

literature (Rambow et al., 2019), depending probably on the starting cells but also on

the strategy that is used to obtain the resistant cells (escalating doses vs. a single killing

dose). In our hands, and as previously observed in vitro as well as in tumors (Smith et al.,

2016)-(Haq et al., 2013), the chronic exposure of melanocytic-type melanoma cells to

escalating doses of BRAFi switched them to a highly pigmented state characterized by an

increased expression of pigmentation pathway-associated genes. Such hyper-

differentiation is a likely consequence of the increased MITF expression that we

observed in these cells (Khaled et al., 2010). Nevertheless, our data indicate that similar

chronic exposure of a unique melanocytic-type melanoma cell culture to escalating doses

of CDK7i or BRAFi promotes opposite effects with hyper-differentiation for BRAFi and

undifferentiation for CDK7i.

Finally, many studies identified CDK7 as a cancer therapeutic target (Fisher, 2019; Sava

et al., 2020). However, the phenotype switching observed during prolonged exposure of

melanoma cells to CDK7i illustrates the potential danger of targeting this kinase in

cancers in which EMT exists, a point that has never been investigated so far. Therefore,

further studies should take into consideration the potential emergence, during CDK7i

exposure of tumor cells, of mesenchymal-like cells able to resist to conventional

therapies.

19 bioRxiv preprint doi: https://doi.org/10.1101/2020.09.24.311431; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Acknowledgments

We thank the IGBMC antibody and cell culture facilities, Prof D. Lipsker and the staff of the

Strasbourg University Hospital dermatology clinic for tumour sections and Dr Ghanem

Ghanem for providing us the MM0-series melanoma cultures. This study was supported by

the INCA (2017-11537), the Ligue contre le Cancer (Equipe labélisée 2019, FC and équipe

labélisée 2018, ID) and the ANR-10-LABX-0030-INRT, a French State fund managed by the

Agence Nationale de la Recherche under the frame program Investissements d’Avenir ANR-

10- IDEX-0002-02. Sequencing was performed by the IGBMC GenomEast platform, a

member of the “France Génomique” consortium (ANR-10-INBS-0009). P.B is supported by

the Ligue contre le Cancer. M.C is supported by the Fondation pour la Recherche Médicale.

Author Contributions

P.B., I.D and F.C. conceived the study. P.B., I.D, F.C and E.C analyzed the data. P.B.

generated the resistant cells, performed the RNA-seq and the IH staining. S.L., G.D. and

P.B., performed the bioinformatics analyses. P.B and C.M.G.R performed the survival

assays. E.C, F.P, P.B., and M.C., performed the RT-qPCR, and the WB. J.S performed the EU

staining. C.B provided a valuable technical assistance. J.M.E provided valuable materials.

F.C wrote the manuscript with input from I.D and E.C.

Competing Financial Interests

Authors declare no competing financial interests.

20 bioRxiv preprint doi: https://doi.org/10.1101/2020.09.24.311431; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Figure legends

Figure 1: Mesenchymal-like melanoma cells resist to CDK7i

a. Protein lysates from either melanocytic-like (proliferative) 501mel, MM011, MM074 and

MM117 or mesenchymal-like (invasive) MM029, MM047 and MM099 melanoma cell

cultures were immuno-blotted for proteins as indicated. Molecular sizes of the proteins are

indicated (kDa).

b. Melanoma cell cultures were treated with increasing concentrations of THZ1 for 72 h.

Mean growth is shown relative to vehicle (DMSO)-treated cells. Error bars indicate mean

values + Standard Deviation (SD) for three biological triplicates. IC50 for each cell line is

indicated.

c. Melanocytic-type 501mel and mesenchymal-like MM029 melanoma cells were treated 4

h with either vehicle (DMSO, grey boxes) or THZ1 (50nM, black boxes). Transcribed RNAs

were labelled by 5EU incorporation. Error bars indicate mean values + SD for three

biological triplicates. N=∼2500 cells were analyzed in each experiment (P-value ***<0.005,

ns=non significant (>0.5), Student’s t-test).

d. Protein lysates from melanocytic-type 501mel and mesenchymal-like MM029 melanoma

cells, treated with vehicle (DMSO) or THZ1 (50nM) for 4 h, were immuno-blotted for

proteins and for phosphorylation of ser-2, ser-5 and ser-7 of the CTD. Molecular sizes (KDa)

of the proteins are indicated in kDa.

e-f. Melanoma cells were treated with either vehicle (DMSO) or THZ1 (50nM) for 72 h. Cell

proliferation and apoptosis were analysed using CellTrace (e) and Annexin V (f) staining

respectively, followed by flow cytometry.

Figure 2: Exposure to CDK7i induces phenotype switching

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a. Volcano plots were used to demonstrate differentially expressed genes as determined by

RNA-seq in either MM047CDK7i-R vs MM047 (top), MM074CDK7i-R vs MM074 (middle) or

MM074BRAFi-R vs MM074 cells (bottom). Red dots show significantly over-(top) or under-

(bottom) represented mRNA in drug-resistant cells compared to parental cells. All data

were evaluated with the DESeq2 R package. The value for a given gene is the normalized

gene expression value relative to the mean of all samples belonging to the same condition.

b. Venn diagram indicating the number of up- and downregulated genes in MM047CDK7i-R

and MM074CDK7i-R compared to the parental MM047 and MM074 cell lines. The number of

genes overlapping between the four data sets is indicated.

c. Genes characterizing the melanocytic-type and mesenchymal-like transcription

signatures (Widmer et al., 2012) have been plotted in relation to their expression in

different melanoma cell lines through a Heatmap where the RPKM values are represented

as z-score. The group of genes related to pigmentation has been highlighted in red to show

their over-expression in MM074BRAFi-R. Left, the color key showing the log2 expression

values. Yellow color stands for high expression and dark violet for low expression. For

reference, several genes including MITF, SOX10, MLANA, and SOX9, JUN are highlighted in

blue.

d. Protein lysates either from MM074, MM074BRAFi-R or MM074CDK7i-R were immuno-blotted

for indicated proteins. Molecular sizes of the proteins are indicated (kDa).

Figure 3: Differential expression of GATA6 regulon in melanoma cells

a. qRT-PCR analysis showing average TBP-normalized expression of GATA6 in the indicated

cell lines. Cells expressing GATA6 correspond to CDK7i-resistant cells (in green). Error bars

indicate mean values + SD for three biological triplicates.

22 bioRxiv preprint doi: https://doi.org/10.1101/2020.09.24.311431; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

b. Protein lysates from the indicated cell lines were immuno-blotted for the indicated

proteins. Molecular sizes of the proteins are indicated in kDa.

c. Seurat T-distributed stochastic neighbor embedding (t-SNE1/2) plots of the indicated

melanoma cells colored based on the expressional activity of genes forming the GATA6

regulon. Expression was retrieved from scRNA-seq of the melanoma cell cultures

(GSE134432) (Wouters et al., 2020) and visualized with SCOPE. MM01 (Triple-wt), MM011,

MM031 (BRAFV600E), MM057 (NRASQ61L), MM074, MM087 (Triple-wt) and A-375 (BRAFV600E)

are melanocytic-type melanoma cells. MM099, MM029 and MM047 are mesenchymal-like

melanoma cells. MM099 and MM029 are marked by dashed ellipses.

d-f. qRT-PCR analysis showing average TBP-normalized expression of GATA6 (d), ABCG2 (e)

and VIPR1 (f) in the indicated cell lines treated with either siCTL or siGATA6 for 72 h. Error

bars indicate mean values + SD for three biological triplicates.

g-h. Melanocytic-type MM011 and mesenchymal-like MM029 melanoma cultures were

treated with either siCTL or siGATA6 for 72 h. qRT-PCR analysis shows average TBP-

normalized expression of GATA6 in the indicated cell lines (g). Cell proliferation was

analysed using CellTrace staining and flow cytometry in the indicated cell lines (h). Error

bars indicate mean values + SD for three biological triplicates.

Figure 4: ABCG2 is involved in multidrug tolerance in melanoma cells

a. qRT-PCR analysis showing average TBP-normalized expression of ABCB1, ABCC3 and

ABCG2 in the indicated cell lines. Error bars indicate mean values + SD for three biological

triplicates.

b. Protein lysates from the indicated cell lines were immuno-blotted for the indicated

proteins. Molecular sizes of the proteins are indicated in kDa.

23 bioRxiv preprint doi: https://doi.org/10.1101/2020.09.24.311431; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

c-d. MM029 (c) and MM099 (d) melanoma cells were pre-treated with either siCTL or

siABCG2 as indicated and treated with increasing concentrations of THZ1 for 72 h. Mean

growth is shown relative to vehicle (DMSO)-treated cells. Error bars indicate mean values +

SD for three biological triplicates. IC50 for each cell line is indicated.

e-f. MM029 (e) and MM099 (f) melanoma cells were pre-treated with either siCTL or

siABCG2 as indicated and treated with increasing doses of Vemurafenib for 72 h. Mean

growth is shown relative to vehicle (DMSO)-treated cells. Error bars indicate mean values +

SD for three biological triplicates. IC50 for each cell line is indicated.

Figure 5: MITF and GATA6 expressions are anticorrelated

a-b. Gene track of CDK7 occupancy at MITF (a) and its transcriptional regulator SOX10 (b)

loci in 501melBIO-FLAG:CDK7 cell line. Gene tracks of H2A.Z, BRG1, MITF, SOX10 and H3K27ac

(GSE94488 and GSE61967) at the same loci in parental 501mel are indicated. SE is denoted

by a red opened square. H3K27ac deposition is also shown in Hair Follicle Melanocytes

(HFM) (GSE94488).

c. qRT-PCR analysis showing average TBP-normalized fold expression of SOX10, MITF,

GATA6 and ERCC2 in melanocytic-type 501mel cells treated either with DMSO or THZ1

(50nM) for 24 h. Error bars indicate mean values + SD for three biological triplicates. p-

value, **<0.05, ns=non significant (>0.5), Student’s t-test. Expression of ERCC2 is used as a

control unaffected by CDK7i treatment.

d. Scatter plot expression values (in log[RPKM]) of GATA6 against MITF (left) or CDK7 (right)

shows inverse relationship between GATA6 and these two genes in human skin cutaneous

melanoma. These data were extracted from The Cancer Genome Atlas bulk RNA-seq data

24 bioRxiv preprint doi: https://doi.org/10.1101/2020.09.24.311431; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

on a cohort of patient melanomas (n=363, Pan-Cancer Atlas). The line of best fit, the

Spearman’s rank correlation coefficient (r) and the P-value (T-test) are indicated.

Figure 6: Loss of MITF releases GATA6 expression

a. qRT-PCR analysis showing average TBP-normalized fold expression of MITF, SOX10 and

GATA6 in melanocytic-type 501mel cells treated either with siCTL, siSOX10 or siMITF for 48

h. Error bars indicate mean values + SD for three biological triplicates. p-value siCTL vs

siSOX10 or siMITF, **<0.05, Student’s t-test.

b. Diffusion map (DM1/2) with melanocytic-type MM074 (left panel) and MM087 cells

(right panel) colored by expression of SOX10- (green) or GATA6- (red) regulons. The graphs

show the trajectory that melanoma cells follow from 0 h (left) to 72h (right) post-siSOX10

treatment as determined by scRNAseq at 0, 24, 48 and 72 h (GSE116237) (Wouters et al.,

2020). An experimental setup is indicated on the left.

Figure 7: MITF binds GATA6 locus and transcriptionally represses it

a. Gene track of 3HA-MITF signal occupancy showing a significant MITF binding peak (P1) in

the GATA6 gene body in the 501mel cell line (GSE61967). Additional tracks indicate

potential regulatory regions highlighted by ATAC-seq and H3K27ac, BRG1, SOX10 and

H2A.Z deposition (GSE94488 and GSE61967). H3K27ac deposition is also shown in

mesenchymal-like MM099 cells at the GATA6 locus. The scale bar indicates the size of the

genomic region in kilobases (Kb).

b. ChIP experiment monitoring the fold enrichment (compare to control IgG) of MITF

protein and H3K27ac mark at the P1 GATA6 intronic region identified above. Proteamine 1

25 bioRxiv preprint doi: https://doi.org/10.1101/2020.09.24.311431; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

(PRM1) and Tyrosinase (TYR) regulatory regions were used as negative and positive

controlled, respectively (Laurette et al., 2015).

c. Left panel; Mesenchymal-like MM099MITF-SOX10-PAX3 cells expressing inducible MITF-

SOX10-PAX3 genes were treated or not with Doxycycline (1µg/ml) for 24 h and protein

lysates were immunoblotted for the indicated protein. Right panel; qRT-PCR analysis

showing average TBP-normalized fold expression of GATA6, ABCG2 and VIPR1 in

Mesenchymal-like MM099 cells treated or not with Doxycycline (1µg/ml) for 24 h. Error

bars indicate mean values + SD for three biological triplicates. p-value **<0.05, ns=non

significant (>0.5), Student’s t-test.

d. Left panel; Schematic representation of pCDNA-ieCMVenh-CMV-GFP (C1) or pCDNA-

intGATA6r-CMV-GFP (C2) reporter vectors. The ieCMVenh sequence in C1 was replaced by

the intGATA6r sequence to generate C2. Right panel; qRT-PCR analysis showing average

TBP-normalized fold expression of GFP in Mesenchymal-like MM099MITF-SOX10-PAX3

transfected with C1 or C2 vectors for 48 h before treatment or not with Doxycycline

(1µg/ml) for 24 h. Error bars indicate mean values + SD for three technical triplicates. p-

value ***<0.005, ns=non significant (>0.5), Student’s t-test.

Figure 8: The GATA6 regulon is expressed in undifferentiated cells of melanoma tumours

a. Tumor sections were immuno-labelled with anti-GATA6 (red) and anti-SOX10 (green)

antibodies and images were captured by confocal microscopy at the indicated

magnification.

b. Seurat t-SNE1/2 with cells colored according to their cluster. Data was retrieved from

scRNA-seq performed on 674 cells from a single drug-naïve PDX (GSE116237) (Rambow et

al., 2018) and visualized with SCOPE.

26 bioRxiv preprint doi: https://doi.org/10.1101/2020.09.24.311431; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

c. Seurat t-SNE1/2 with cells colored in red scale according to the level of expression of the

gene set forming the GATA6 regulon. Expression was measured by AUCell from scRNA-seq

of PDX data used in (b) and visualized with SCOPE.

d. The mean expression of the gene set forming the GATA6 regulon for each cluster is

represented. Expression was measured by AUCell from scRNA-seq of PDX data used in (b)

and reported on the graph. Error bars indicate mean values + SD. The P-value (One-way

ANOVA) of cluster 7 vs. all is indicated.

e. Heatmap illustrating the Spearman’s rank correlation coefficient between expression of

GATA6 and that of the indicated cluster markers extracted from TCGA RNA-seq of a cohort

of patient melanomas (skin cutaneous melanoma, n=444) (Pan-Cancer Atlas)

(Supplemental data 4). Yellow color stands for high correlation and dark violet for low

correlation.

MATERIAL AND METHODS

A full list of reagents including antibodies, commercial kits and oligonucleotides is supplied

in Supplemental Table 1.

Access numbers for data generated in this paper are: GSE158118 and GSE158119

Patients

Gene expressions in tumors and nevi were retrieved from several previously published

datasets (including TCGA) indicated in the figure legends.

Cell culture and treatment. Cells were grown in 5% CO2 at 37°C in HAM-F10 (Gibco,

Invitrogen) supplemented with 10% FCS and Penicillin-Streptomycin. Melanoma cell line

501mel was grown in 5% CO2 at 37°C in RPMI w/o HEPES (Gibco, Invitrogen) supplemented

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with 10% FCS and Gentamycin. Melanocyte cell line Hermes3A was grown in 10% CO2 at

37°C in RPMI w/o HEPES, supplemented with 10% FCS, Penicillin-Streptomycin, 200nM TPA

(Sigma Aldrich), 200pM Cholera Toxin (Sigma Aldrich), 10ng/mL hSCF (Life Technologies),

10nM EDN-1 (Sigma Aldrich) and 2mM Glutamine (Invitrogen).

Cells were transfected with Lipofectamine RNAiMAX following the manufacture

instructions with 25nM of siRNA ON-TARGETplus SMARTPool (Horizon Discovery) and cells

were harvested 48 and/or 72h after transfection. All cell lines used were mycoplasm

negative.

MMO99MITF-SOX10-PAX3 cells were generated as followed. Lentiviral vector pTET-SMP encoding

human untagged MITF, SOX10 and PAX3 proteins was transduced in MM099 in presence of

Polybrene and cells were selected with 3µg/ml of Puromycin. Conditional expression of

pTET vector was carried out by adding 1µg/ml of Doxycycline in the medium for at least

24h.

CRISPR/Cas9 editing of 501melBIO-FLAG:CDK7. 501mel were co-transfected with vector px738

(encoding Cas9-HF-GFP and two guide RNAs targeting CDK7 locus and construct m599

(linear DNA fragment carrying homology regions to CDK7 locus and Puromycin-P2A-BIO-

FLAG-CDK7N-termsequence (Key Resources Table) with transfection reagent FuGENE6. 24

h later single cells GFP positive were sorted in P96 well plates in presence of Puromycin

(3µg/ml) with cell sorter. Single clones were let grown and selected for 4-6 weeks and

surviving ones screened for gene editing through PCR using Phusion High-Fidelity TAQ

Polymerase using different combination of primers (F1, F5, R3, R4, R5, see Key Resources

Table). PCR positive clones were finally further amplified to perform western blot and Co-

IPs validation.

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Cell growth, cell death and senescence assay. To measure cell proliferation, cell death and

senescence, cells were incubated first with CellTrace Violet according to the manufacture

instructions. At the end of transfection and/or drug treatment, cells were incubated 1h

with 100nM BafilomycinA1 and 2h with C12FDG; afterwards cells were rinsed and incubated

15 min with AnnexinV-APC. Cell proliferation, cell death and senescence were detected on

a FORTESSA BD Biosciences Cytofluorometer. Data were analyzed with FlowJo software.

Reporter assay. The intGATA6r element was isolated by genomic PCR using Phusion High-

Fidelity TAQ polymerase (ThermoFisher) with specific primers (see Key Resources Table).

To allow the cloning within pCDNA-GFP vector, the first PCR product was further amplified

by PCR with primers carrying MluI and SmaI restriction sites at the 5’ and 3’ respectively

(MluI_F and SmaI_R primers in Key Resources Table). The immediate early CMV enhancer

(ieEnh) in the pCDNA-GFP vector (pCDNA-ieEnh-CMV-GFP) was then replaced with the

intGATA6r element (pCDNA-intGATA6r-CMV-GFP).

MITF-SOX10-PAX3 expression was induced in MMO99MITF-SOX10-PAX3 cells with doxycycline for

48h and cells were subsequently transfected with pCDNA-ieEnh-CMV-GFP or pCDNA-

intGATA6r-CMV-GFP vectors for 24 h with FuGENE6 following manufacture instructions.

RNA was then collected for qPCR and GFP protein signal was detected on cytofluorometer.

FACS data were analyzed with FlowJo software.

Histology. Human tissue sections were de-paraffinized and dehydrated with Histosol and

dilutions of ethanol (100%, 90%, 70% and 30%) and then rehydrated with demineralized

water. Subsequently, sections were boiled in Sodium Citrate buffer (0.1M Citric acid, 0.1M

Sodium citrate) for 15 min to unmask antigens. Alternatively, cells were growth on glass

slides and fixed with 4% formaldehyde. Both tissues and cells were permeabilized with PBS

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and 0.1% TritoX-100. Blocking was done with 10% Fetal Bovin Serum before incubation

with primary antibodies.

In situ hybridization of ABCG2 mRNA was performed using the RNAscope assay according

to manufacturer’s instructions (ACDBio). Cells and tissue sections were counterstained with

DAPI and visualized using confocal microscope Spinning disk Leica CSU W1. Probes’

sequences were not provided by the manufacture.

EU incorporation assay. RNA labeling by EU incorporation was performed with Click-iT RNA

Imaging kit following the manufacturer protocol. EU signal intensity was quantified using

imaging system.

Cell survival assay. Normal or transfected cells were seeded at 5000 cells/well in a 96well

plate and treated with increasing concentrations of THZ1, Vemurafenib or Trametinib.

After 72h of incubation, cells were treated with PrestoBlue reagent according to

manufacture instructions. The absorbance per well was measured with a microplate

reader. The data were then analyzed using Prism8.

RT-qPCR. Total RNA was isolated from cells using a GenElute Mammalian Total RNA

Miniprep kit (Sigma) and reverse transcribed with SuperScript IV reverse transcriptase

(Invitrogen). The quantitative PCR was done using Lightcycler. The primer sequences for

the different genes used in qPCR are indicated in Key Resources Table. The mRNA

expression of the various analysed genes represents the ratio between values obtained

from treated and untreated cells normalized with the housekeeping genes mRNA.

ChIP. Cells were grown on 15cm plates and, once reached 80% of confluence, were fixed

with PBS + 0.4% formaldehyde solution for 10min. Fixation reaction was stopped with 2M

Glycin pH 8. Cells were then pellet and suspended in lysis buffer (EDTA 10mM, Tris HCl pH8

50mM, SDS 1%) and sonicated with Covaris E220 AFA power 200Hz 6 cycles 200s to get a

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DNA fragmentation between 500 and 200bp. Chromatin was then diluted in 60ug aliquots

with 8 volumes of ChIP dilution buffer (Tris HCl pH8 16.7mM, EDTA 1.2mM, NaCl 167mM,

Triton X-100 1.1%, SDS 0.01%). The immuno-precipitations were done as following. 1-5ug

of antibody was incubated overnight with chromatin and the complex antibody-chromatin

was then captured with G protein sepharose beads (Sigma Aldrich) 1h at 4°C. Beads were

washed 2 times with Low Salt Buffer (Trish HCl pH8 20mM,Ò EDTA 2mM, NaCl 150mM,

Triton X-100 1%, SDS 0.1%), High Salt Buffer (Trish HCl pH8 20mM, EDTA 2mM, NaCl

500mM, Triton X-100 1%, SDS 0.1%), LiCl buffer (Tris HCl pH8 500mM, EDTA 1mM, Na

deoxycholate 1%, NP40 1%, LiCl 0.25M), TE buffer (Tris HCl pH8 10mM, EDTA 1mM) and

DNA was eluted 30min at room temperature with Elution buffer (NaHCO3 0.1M, SDS 1%).

DNA was finally purified through phenol-chloroform, re-suspended in 100uL of water and

analysed by qPCR using a set of primers indicated in Key Resources Table.

RNA-seq. Rna-seq was performed like previously described (Laurette et al., 2019). For

RNA-seq performed in this study, reads were preprocessed in order to remove adapter and

low-quality sequences (Phred quality score below 20). After this preprocessing, reads

shorter than 40 bases were discarded for further analysis. These preprocessing steps were

performed using cutadapt version 1.10. Reads were mapped to rRNA sequences using

bowtie version 2.2.8, and reads mapping to rRNA sequences were removed for further

analysis. Reads were mapped onto the hg19 assembly of Homo sapiens genome using STAR

version 2.5.3a. Gene expression quantification was performed from uniquely aligned reads

using htseq-count version 0.6.1p1, with annotations from Ensembl version 75 and “union”

mode. Only non-ambiguously assigned reads have been retained for further analyses. Read

counts have been normalized across samples with the median-of-ratios method (Anders

and Huber, 2010). Differential gene expression analysis was performed using the

31 bioRxiv preprint doi: https://doi.org/10.1101/2020.09.24.311431; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

methodology implemented in the Bioconductor package DESeq2 version 1.16.1 (Love et al.,

2014). P-values were adjusted for multiple testing by the method proposed by Benjamini

and Hochberg (Benjamini and Hochberg, 1995). Deregulated genes were defined as genes

with log2(foldchange) >1 or <-1 and adjusted p-value <0.05. Heatmaps were generated

with the R package pheatmap v1.0.12.

ChIP-seq. Purified DNA fragments for ChIP-seq were prepared by using the ChIP-IT High

Sensitivity Kit (Active Motif) and the related antibodies. ChIP-seq was performed on an

Illumina sequencer as single-end 50 base reads following Illumina’s instructions. Image

analysis and base calling were performed using RTA 1.17.20 and CASAVA 1.8.2. Reads were

mapped onto the hg19 assembly of the human genome. Peak detection was performed

using MACS (http://liulab.dfci.harvard.edu/MACS/) under settings where the input fraction

was used as negative control. Peaks detected were annotated using HOMER

(http://biowhat.ucsd.edu/homer/ngs/annotation.html) as well as TSS protein enrichment

comparison. Quantitative comparison of RNA Pol II gene body enrichment was performed

using seqMINER (http://bips.u-strasbg.fr/seqminer/). As reference coordinates, we used

the MACS-determined peaks or the annotated TSS/TTS of human genes as defined by

RefSeq database. Sequence enrichment was performed using RSAT (http://rsat.sb-

roscoff.fr) with MACS-determined peaks as reference.

Bioinformatics. Expression matrix with row reads counts for the single cell experiment was

retrieved from GEO (GSE116237). Then, data were normalized and clustered using the

Seurat software package version 3.1.4 (Butler et al., 2018) in R version 3.6.1. Data were

filtered and only genes detected in at least 3 cells and cells with at least 350 detected

genes were kept for further analysis. Expression of 26,661 transcripts in 674 cells was

quantified. To cluster cells, read counts were normalized using the method “LogNormalize”

32 bioRxiv preprint doi: https://doi.org/10.1101/2020.09.24.311431; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

of the Seurat function NormalizeData. It divides gene expression counts by the total

expression, multiplies this by a scale factor (10,000 was used), and log-transforms the

result. Then, 2000 variable features were selected with the variance stabilizing

transformation method using the Seurat function FindVariableGenes with default

parameters. Integrated expression matrices were scaled (linear transformation) followed

by principal component analysis (PCA) for linear dimensional reduction. The first 20

principal components (PCs) were used to cluster the cells with a resolution of 0.5 and as

input to tSNE to visualize the dataset in two dimensions. The Bioconductor package AUCell

v 1.6.1 (Aibar et al., 2017) was used to assess whether some cells from the Rambow

dataset were enriched in gene sets of interest.

Gene Ontology: Gene ontology was performed using Metascape software developed by

(Zhou et al., 2019).

QUANTIFICATION AND STATISTICAL ANALYSIS

Statistical details of experimental can be found in figure legends or in the methods details

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36 bioRxiv preprint doi: https://doi.org/10.1101/2020.09.24.311431; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Figure 1

a. b. 501mel, IC50=45nM MM029, IC50>1μM Melanocytic-type Mesenchymal-like MM011, IC50=35nM MM099, IC50=630nM MM074, IC50=70nM MM047, IC50=60nM 1.2 MM117, IC50=11nM 501mel MM011 MM074 MM117 MM029 MM047 MM099

53 MITF 1.0

47 SOX10 0.8

51 SOX9 0.6 Cell viability 0.4 34 c-Jun

0.2 113 Vinculin 0.0 1 2 3 4 5 6 7 0101 102 103 104 log[THZ1] (nM) c. d.

501mel MM029 120 DMSO + - + - *** ns THZ1 - + - + 100 250 RPB1 80 250 pSer-2 60 DMSO THZ1 250 pSer-5 40 EU fluorescent intensity

20 250 pSer-7

0 501mel MM029 113 Vinculin Melanocytic-type Mesenchymal-like 1 2 3 4 e. f.

Normal proliferation Slow proliferation Normal Apoptosis 100 100

80 80

60 60

% of cells 40 40 % of cells

20 20

0 0 DMSO THZ1 DMSO THZ1 DMSO THZ1 DMSO THZ1 DMSO THZ1 DMSO THZ1 DMSO THZ1 DMSO THZ1

501melMM011 MM029 MM099 501mel MM011 MM029 MM099

Melanocytic-type Mesenchymal-like Melanocytic-type Mesenchymal-like bioRxiv preprint doi: https://doi.org/10.1101/2020.09.24.311431; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Figure 2 a. c.

-R

DK7i BRAFi-R C CDK7i-R

10 501mel MM074 MM074 MM074 MM099 MM029 MM047 MM047 GPRC5B CAPN3 PLXNC1 2 CDH1 SOX10 APOE 5 TYRP1 1 ACP5 CEACAM1

vs MM047 RGS20 MICAL1 0 NR4A3 SIRPA

CDK7i-R 0 RAB38 SEMA6A -1 SLC45A2 ADCY2 ST3GAL6 NTM -2

MM047 GYG2 PMEL -5 HPS4 MBP

Estimated log2 fold change STX7 CDK2 GPR143 RRAGD CDK5K1 MYO1D -10 TNFRSF14 GNPTAB 100 10000 BIRC7 IVNS1ABP Mean of normalized counts MLPH INPP4B PIR

Melanocytic Signature GPM6B OCA2 DAPK1 IRF4 DCT WDR91 PHACTR1 MITF TRPM1 10 RHOQ ASAH1 GALNT3

vs MM074 FAM174B MLANA TYR 5 ZFYVE16

CDK7i-R C21orf91 RAB27A TNC EGFR

BIRC3 Pigmented Signature 0 CDK14 OSMR MM074 TPBG ITGA3 NRP1 Estimated log2 fold change TEAD4 -5 ADAM12 F2RL1 FST MYL12B AMOTL2 FOSL2 101000 100000 ZEB1 SOX9 AXL Mean of normalized counts FGF2 JUN CRIM1 MYOF 15 EFEMP1 NUAK1 TPM1 BGN TGM2 10 FOXD1 THBS1 TCF4 ADAM19 HS3ST3A1

vs MM074 5 CRISPLD2 FLNB FZD2

Mesenchymal Signature PDGFC SYNJ2 BRAFi-R 0 S100A2 TRAM12 WNT5A CYR61 TNFRSF11B KONM1 MM074 -10 LOXL2 SLIT2 COL13A1 Estimated log2 fold change HEG1 TLE4 -15 CDH13 DKK3 PODXL ITGA2 DPYD 101000 100000 SLC22A4 1 2 3 4 5 6 7 8 Mean of normalized counts b. d.

MM074 + - - MM074BRAFi-R -+- MM047CDK7i-R Down (559) MM074CDK7i-R Up (3153) MM074CDK7i-R - - + 47 SOX10 190 2778 53 MITF 254 114 261

2762 0 0 196 51 SOX9

0 0 1 55 TFAP2A 00 48 165 PAX3

MM074CDK7i-R Down (3181) MM047CDK7i-R Up (623) 113 Vinculin

1 2 3 bioRxiv preprint doi: https://doi.org/10.1101/2020.09.24.311431; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Figure 3

a. b. c.

0.6 MM057

0.5 MM047 R i-R MM031 CDK7i- CDK7 0.4 MM087 MM011 501mel MM029 MM099 MM074 MM074 MM047 MM047 0.3 60 GATA6 MM001 0.2 MM099 42 Actin GATA6 relative expression GATA6 MM029 0.1 1 2 3 4 5 6 7 A375 GATA6 regulon

t-SNE 2 MM074 0.0 0 1 0.5 R t-SNE 1

BRAFi-R CDK7i- CDK7i-R 501mel MM011 MM117 MM074 MM047 MM099MM029

MM074 MM074 MM047 d. e. f. g. h. 1.5 1.2 11.2 1.2 100 Normal proliferation *** ** *** *** ** Slow proliferation 1.0 11.0 1.0 80 *** *** 1.0 0.8 00.8 0.8 60 0.6 00.6 0.6

% of cells %f ll 40 0.5 0.4 00.4 0.4 VIPR1 relative expression GATA6 relative expression GATA6 relative expression GATA6 ABCG2 relative expression 20 0.2 00.2 0.2

0 0 0 0 0 MM029 +-+-MM029 +-+-MM029 +-+-MM029 +-+-MM029 +-+- MM099 - -++ MM099 -+-+MM099 - -++ MM011 - -++ MM011 -+-+ siCTL ++--siCTL ++--siCTL ++--siCTL ++--siCTL ++-- siGATA6 --++siGATA6 --++siGATA6 --++siGATA6 --++siGATA6 --++ bioRxiv preprint doi: https://doi.org/10.1101/2020.09.24.311431; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Figure 4

a. b. CDK7i-resistant cells

14 ABCG2 ABCB1 12 CDK7i-resistant cells ABCC3 10

8

Hermes 3A 501mel MM011 MM117 MM099 MM047 MM029 6 72 ABCG2

Relative expression 4

2 55 βTubulin

0 1 2 3 4 5 6 7

Mel501 MM117 MM099 MM074 MM047 MM029 MM011 Hermes3A

c. 1.4 d. 1.2

1.2 1

1 0.8 0.8 0.6 0.6 0.4 MM029 cell viability MM099 cell viability 0.4

0.2 0.2 siCTL, IC50=650nM siCTL, IC50>1μM siABCG2, IC50=200nM siABCG2, IC50=600nM 0 0 1 2 3 4 0101 102 103 104 01010 10 10 log[THZ1] (nM) log[THZ1] (nM)

e. 1.2 f. 1.2

1 1

0.8 0.8

0.6 0.6

0.4 0.4 MM029 cell viability MM099 cell viability

0.2 0.2 siCTL, IC50>2μM siCTL, IC50>5μM siABCG2, IC50=200nM siABCG2, IC50>5μM 0 0 0101 102 103 104 0101 102 103 104 log[Vemur] (nM) log[Vemur] (nM) bioRxiv preprint doi: https://doi.org/10.1101/2020.09.24.311431; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Figure 5

a. b. HG19: chr3:69,731,360-70,074,714 HG19: chr22:38,313,324-38,435,533

135 90

H2AZ H2AZ 150 150

BRG1 BRG1 100 480 MITF MITF

100 140 501mel 501mel SOX10 SOX10

100 200 CDK7 CDK7 400 500 H3K27ac H3K27ac

160 150 H3K27ac H3K27ac HFM HFM

MITF SOX10 c. d. 2.0 2.0 2.0 ** 2.0 20 TCGA, n=363 15 TCGA, n=363

1.5 1.5 1.5 1.5 15 ns 10 ** ** 1.0 1.0 1.0 1.0 10 relative expression log[RPKM]

log[RPKM] 5 0.5 0.5 5 0.5 0.5 CDK7 expression MITF expression r= -0,250 r= -0,181 ERCC2 MITF relative expression relative expression GATA6 SOX10 relative expression pValue<0.0001 pValue<0.0001 0.0 0.0 0.0 0.0 0 0 DMSO THZ1(24 h) DMSO THZ1(24 h) DMSO THZ1(24 h) DMSO THZ1(24 h) 0 5 10 15 516 7 89 011 GATA6 expression GATA6 expression log[RPKM] log[RPKM] bioRxiv preprint doi: https://doi.org/10.1101/2020.09.24.311431; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Figure 6

a. 4 siCTL siSOX10 siMITF ** 3

2

** **

Relative expression 1

0 SOX10 MITF GATA6

501mel

SOX10 knockdown b. 0 h 24 h 48 h 72 h

scRNA-seq MM074

GATA6 regulon 0 0.35

DM2 SOX10 regulon 01

DM1 MM087 bioRxiv preprint doi: https://doi.org/10.1101/2020.09.24.311431; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Figure 7 a. HG19: chr18:19,716,316-19,815,579 50

ATAC

80

H2AZ

150

BRG1 100 501mel P1 MITF

100 SOX10

131 H3K27ac

227 H3K27ac MM099

GATA6 b.

25 500

20 400

15 300

10 200

5 100 MITF (fold enrichment)

0 H3K27ac (fold enrichment) 0 PRM1 TYR GATA6 (P1) PRM1 TYR GATA6 (P1)

c. MM099MITF-SOX10-PAX3 Dox -+ 53 1.2 MITF ** ** ** 1.0 GATA6 60 0.8 Dox(-) Dox(+) 0.6 113 Vinculin 0.4 47 SOX10 Relative expression 0.2

0 113 Vinculin GATA6 ABGC2 VIPR1 1 2 1.2 d. ns *** 1.0 C1 ieCMVenh CMV GFP 0.8

0.6

0.4 C2 intGATA6r CMV GFP 0.2 GFP relative expression

0 C1 + + - - C2 - - + + Dox - + - + bioRxiv preprint doi: https://doi.org/10.1101/2020.09.24.311431; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Figure 8

a. d.

Nevi Primary Melanoma Cutaneous Metastases

40x ace40x 40x 0.20 GATA6 regulon P-value<0.0001

0.15 ) -3

0.10 AUCell (10

0.05 100x bd100x 100x f + SOX10 DAPI + GATA6 0 084321 765

Clusters

GATA6

e. SOSTDC1 0 SERPIN2 ZC3H13 AMD1 C1QTNF3 ANXA2 1 PCSK2 CRYAB TUBA1A 8 CNN3 b. c. MT-CO2 2 RPS12 RPS28 RPLP2 7 7 RPS27A 6 RPL30 3 3 RPL11 COXAI1 RPS5 2 1 CORO2B KIF20A CCNA2 4 TOP2A 2 TPX2 0 Clusters NUSAP1 DCT 1 MLANA 5 PMEL SNAI2 CDH3 BST2 IFI27 5 6 IFITM1 7 MX1 7 IFI6 COL3A1 COL1A1 4 7 IGFBP3 ANGPTL4 BGN Clusters TMEM176B 0 12345678 GATA6 regulon TMEM176A 8 GFRA2 t-SNE 2 t-SNE 2 0 0.35 AQP1 t-SNE 1 GFRA3

t-SNE 1 t-SNE 1 Correlation

-0.2 0 0.2