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High MITF Expression Is Associated with Super-Enhancers and Suppressed by CDK7 Inhibition in Melanoma

  • Author Footnotes
    10 These authors contributed equally to this work as co-first authors.
    Philip Eliades
    Footnotes
    10 These authors contributed equally to this work as co-first authors.
    Affiliations
    Wellman Center for Photomedicine and Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA

    Department of Dermatology, Weill Cornell Medical College, New York, New York, USA

    Signature Healthcare Brockton Hospital, Brockton, Massachusetts, USA
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    10 These authors contributed equally to this work as co-first authors.
    Brian J. Abraham
    Footnotes
    10 These authors contributed equally to this work as co-first authors.
    Affiliations
    Whitehead Institute for Biomedical Research, Cambridge, Massachusetts, USA
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  • Author Footnotes
    10 These authors contributed equally to this work as co-first authors.
    Zhenyu Ji
    Footnotes
    10 These authors contributed equally to this work as co-first authors.
    Affiliations
    Wellman Center for Photomedicine and Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
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  • David M. Miller
    Affiliations
    Wellman Center for Photomedicine and Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA

    Division of Hematology/Oncology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
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  • Camilla L. Christensen
    Affiliations
    Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA
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  • Nicholas Kwiatkowski
    Affiliations
    Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
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  • Raj Kumar
    Affiliations
    Wellman Center for Photomedicine and Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
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  • Ching Ni Njauw
    Affiliations
    Wellman Center for Photomedicine and Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
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  • Michael Taylor
    Affiliations
    Wellman Center for Photomedicine and Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
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  • Benchun Miao
    Affiliations
    Wellman Center for Photomedicine and Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
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  • Tinghu Zhang
    Affiliations
    Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

    Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, USA
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  • Kwok-Kin Wong
    Affiliations
    Division of Hematology/Oncology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
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  • Author Footnotes
    11 These authors contributed equally to this work as co-senior authors.
    Nathanael S. Gray
    Footnotes
    11 These authors contributed equally to this work as co-senior authors.
    Affiliations
    Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

    Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts, USA
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  • Author Footnotes
    11 These authors contributed equally to this work as co-senior authors.
    Richard A. Young
    Footnotes
    11 These authors contributed equally to this work as co-senior authors.
    Affiliations
    Whitehead Institute for Biomedical Research, Cambridge, Massachusetts, USA

    Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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  • Author Footnotes
    11 These authors contributed equally to this work as co-senior authors.
    Hensin Tsao
    Correspondence
    Correspondence: Hensin Tsao, Wellman Center for Photomedicine, Department of Dermatology, Massachusetts General Hospital, 50 Blossom Street, Boston, Massachusetts 02114, USA.
    Footnotes
    11 These authors contributed equally to this work as co-senior authors.
    Affiliations
    Wellman Center for Photomedicine and Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
    Search for articles by this author
  • Author Footnotes
    10 These authors contributed equally to this work as co-first authors.
    11 These authors contributed equally to this work as co-senior authors.
Open ArchivePublished:February 02, 2018DOI:https://doi.org/10.1016/j.jid.2017.09.056
      Cutaneous melanoma is an aggressive tumor that accounts for most skin cancer deaths. Among the physiological barriers against therapeutic success is a strong survival program driven by genes such as MITF that specify melanocyte identity, a phenomenon known in melanoma biology as lineage dependency. MITF overexpression is occasionally explained by gene amplification, but here we show that super-enhancers are also important determinants of MITF overexpression in some melanoma cell lines and tumors. Although compounds that directly inhibit MITF are unavailable, a covalent CDK7 inhibitor, THZ1, has recently been shown to potently suppress the growth of various cancers through the depletion of master transcription-regulating oncogenes and the disruption of their attendant super-enhancers. We also show that melanoma cells are highly sensitive to CDK7 inhibition both in vitro and in vivo and that THZ1 can dismantle the super-enhancer apparatus at MITF and SOX10 in some cell lines, thereby extinguishing their intracellular levels. Our results show a dimension to MITF regulation in melanoma cells and point to CDK7 inhibition as a potential strategy to deprive oncogenic transcription and suppress tumor growth in melanoma.

      Abbreviations:

      ChIP-seq (chromatin immunoprecipitation sequencing), CTD (carboxy-terminal domain), GI50 (concentration at which half maximal inhibition of cell proliferation is achieved), GSEA (gene set enrichment analysis), MITF-hi (high MITF expression level), MITF-lo (low MITF expression level), Pol II (polymerase II), SE (super-enhancer), Ser (serine), siRNA (small interfering RNA)

      Introduction

      Melanoma is an extremely aggressive form of skin cancer that originates from melanocytes, which are neural crest-derived pigment cells that migrate widely during embryogenesis to take up residence in a variety of anatomical compartments. Central to the biology of melanocytes and melanoma cells is MITF, a master transcription factor-encoding gene, which is essential for maintaining melanoblast proliferation, driving the differentiation of the melanocyte lineage in early development, and regulating the transcriptional program necessary for melanin synthesis (
      • Opdecamp K.
      • Nakayama A.
      • Nguyen M.T.
      • Hodgkinson C.A.
      • Pavan W.J.
      • Arnheiter H.
      Melanocyte development in vivo and in neural crest cell cultures: crucial dependence on the Mitf basic-helix-loop-helix-zipper transcription factor.
      ,
      • Wellbrock C.
      • Arozarena I.
      Microphthalmia-associated transcription factor in melanoma development and MAP-kinase pathway targeted therapy.
      ,
      • Yasumoto K.
      • Yokoyama K.
      • Shibata K.
      • Tomita Y.
      • Shibahara S.
      Microphthalmia-associated transcription factor as a regulator for melanocyte-specific transcription of the human tyrosinase gene.
      ). The importance of MITF as a critical cell identity gene in melanocytes is preserved in melanomas, where it is often overexpressed and functions as a oncogenic transcription factor important for maintaining tumor survival, enhancing proliferation, and promoting differentiation (
      • Cirenajwis H.
      • Ekedahl H.
      • Lauss M.
      • Harbst K.
      • Carneiro A.
      • Enoksson J.
      • et al.
      Molecular stratification of metastatic melanoma using gene expression profiling: prediction of survival outcome and benefit from molecular targeted therapy.
      ,
      • Harbst K.
      • Staaf J.
      • Lauss M.
      • Karlsson A.
      • Masback A.
      • Johansson I.
      • et al.
      Molecular profiling reveals low- and high-grade forms of primary melanoma.
      ,
      • Hsiao J.J.
      • Fisher D.E.
      The roles of microphthalmia-associated transcription factor and pigmentation in melanoma.
      ). However, the underlying mechanism by which MITF levels are sustained is not fully understood, because only 10–20% of melanoma tumor specimens exhibit amplification of the MITF locus (
      • Wellbrock C.
      • Arozarena I.
      Microphthalmia-associated transcription factor in melanoma development and MAP-kinase pathway targeted therapy.
      ).
      Recently identified regulatory domains termed super-enhancers (SEs) provide insight into possible epigenetic mechanisms affecting MITF expression plasticity in melanoma. SEs are clusters of enhancers bound by an extreme density of transcription factors and cofactors, including CDK7, and tend to be associated with genes that control and define cell identity. SEs are also acquired by tumor cells at key oncogenes, are capable of energizing gene expression, and, importantly, are exquisitely sensitive to transcriptional disruption (
      • Hnisz D.
      • Abraham B.J.
      • Lee T.I.
      • Lau A.
      • Saint-Andre V.
      • Sigova A.A.
      • et al.
      Super-enhancers in the control of cell identity and disease.
      ,
      • Loven J.
      • Hoke H.A.
      • Lin C.Y.
      • Lau A.
      • Orlando D.A.
      • Vakoc C.R.
      • et al.
      Selective inhibition of tumor oncogenes by disruption of super-enhancers.
      ,
      • Whyte W.A.
      • Orlando D.A.
      • Hnisz D.
      • Abraham B.J.
      • Lin C.Y.
      • Kagey M.H.
      • et al.
      Master transcription factors and mediator establish super-enhancers at key cell identity genes.
      ). This last characteristic of SEs makes them an ideal proxy target for their attendant genes. Transcription factors that seem to offer therapeutic opportunities, such as MITF, have historically been difficult to target with small molecule inhibitors, but an alternative approach is to selectively down-regulate the expression of these proto-oncogenic transcription factors by targeting enzymatic cofactors central to transcriptional regulation.
      Recently, various groups have demonstrated the ability to preferentially affect expression of key tumor identity and oncogenic transcription factors using a first-in-class covalent CDK7 inhibitor, THZ1 (
      • Chipumuro E.
      • Marco E.
      • Christensen C.L.
      • Kwiatkowski N.
      • Zhang T.
      • Hatheway C.M.
      • et al.
      CDK7 inhibition suppresses super-enhancer-linked oncogenic transcription in MYCN-driven cancer.
      ,
      • Christensen C.L.
      • Kwiatkowski N.
      • Abraham B.J.
      • Carretero J.
      • Al-Shahrour F.
      • Zhang T.
      • et al.
      Targeting transcriptional addictions in small cell lung cancer with a covalent CDK7 inhibitor.
      ,
      • Kwiatkowski N.
      • Zhang T.
      • Rahl P.B.
      • Abraham B.J.
      • Reddy J.
      • Ficarro S.B.
      • et al.
      Targeting transcription regulation in cancer with a covalent CDK7 inhibitor.
      ,
      • Wang Y.
      • Zhang T.
      • Kwiatkowski N.
      • Abraham B.J.
      • Lee T.I.
      • Xie S.
      • et al.
      CDK7-dependent transcriptional addiction in triple-negative breast cancer.
      ). Unique among the CDK family, CDK7 serves as a critical regulator of the cell cycle and gene transcription (
      • Fisher R.P.
      The CDK Network: linking cycles of cell division and gene expression.
      ,
      • Schachter M.M.
      • Fisher R.P.
      The CDK-activating kinase Cdk7: taking yes for an answer.
      ). In the nucleus, CDK7 forms the kinase core of the RNA polymerase II (Pol II) general transcription factor IIH and phosphorylates the Pol II carboxy-terminal domain (CTD) at serine (Ser)5/Ser7, thereby promoting transcriptional initiation (
      • Akhtar M.S.
      • Heidemann M.
      • Tietjen J.R.
      • Zhang D.W.
      • Chapman R.D.
      • Eick D.
      • et al.
      TFIIH kinase places bivalent marks on the carboxy-terminal domain of RNA polymerase II.
      ,
      • Fisher R.P.
      Secrets of a double agent: CDK7 in cell-cycle control and transcription.
      ,
      • Glover-Cutter K.
      • Larochelle S.
      • Erickson B.
      • Zhang C.
      • Shokat K.
      • Fisher R.P.
      • et al.
      TFIIH-associated Cdk7 kinase functions in phosphorylation of C-terminal domain Ser7 residues, promoter-proximal pausing, and termination by RNA.
      ). CDK7 may also indirectly promote elongation via phosphorylation of CDK9, a subunit of P-TEFb that regulates transcriptional elongation by phosphorylating Pol II CTD at Ser2 (
      • Larochelle S.
      • Amat R.
      • Glover-Cutter K.
      • Sanso M.
      • Zhang C.
      • Allen J.J.
      • et al.
      Cyclin-dependent kinase control of the initiation-to-elongation switch of RNA polymerase II.
      ).
      In this study, we investigate the role SEs play in driving MITF expression in melanomas that lack high-level amplification of the MITF locus. Furthermore, we show the therapeutic potential of abrogating up-regulated MITF transcription in MITF-dependent melanoma cells by disrupting the SE complexes via covalent targeting of CDK7, an important component of the transcriptional apparatus.

      Results

      MITF expression in tumors and melanoma cell lines

      To explore the relationship between MITF copy number and RNA expression, we examined 287 melanoma tumor specimens from The Cancer Genome Atlas and found that only 3 of the 15 tumors exhibiting high MITF expression harbored MITF amplification (i.e., >4 copies) (Figure 1a). Cases of elevated MITF in the absence of amplification imply alternative mechanisms of MITF up-regulation. Recently, heightened transcription of key oncogenes in tumor cells has been linked to the presence of SEs, which have been shown to both influence cell identity and promote the expression of master oncogenes and networks of oncogenic transcription factors (
      • Mansour M.R.
      • Abraham B.J.
      • Anders L.
      • Berezovskaya A.
      • Gutierrez A.
      • Durbin A.D.
      • et al.
      An oncogenic super-enhancer formed through somatic mutation of noncoding intergenic element.
      ). We thus set out to determine if SEs could be operative in some MITF-overexpressing lines.
      Figure 1
      Figure 1Super-enhancers are associated with MITF in cell lines that overexpress MITF in the absence of gene amplification. (a) The Cancer Genome Atlas melanoma samples ranked according to MITF mRNA expression and MITF amplification status. Red dots indicate melanoma samples with MITF copy number gain > 4 copies. (b) Melanoma cell lines (n = 18) ranked by normalized level of MITF mRNA relative to GUSB mRNA. Red and blue lines denote log2 = 2 and log2 = –2, respectively. (c) Gene tracks of H3K27ac ChIP-seq signal occupancy at MITF in melanoma cell lines; super-enhancers denoted by bars. Signal of ChIP-seq occupancy (y-axis) is in units of reads per million. (d) Super-enhancer swooshes show total H3K27ac ChIP-seq signal (length × density) in enhancer regions for all stitched enhancers in SK-MEL-5, UACC257, SK-MEL-30, and LOXIMVI cells. Enhancers are ranked (position in parentheses) by increasing H3K27ac ChIP-seq signal. (e) Gene tracks of H3K27ac ChIP-seq signal occupancy at MITF in melanoma samples (
      • Verfaillie A.
      • Imrichova H.
      • Atak Z.K.
      • Dewaele M.
      • Rambow F.
      • Hulselmans G.
      • et al.
      (2015) Decoding the regulatory landscape of melanoma reveals TEADS as regulators of the invasive cell state.
      ). AMP, amplified; ChIP-seq, chromatin immunoprecipitation sequencing; rpm, reads per million; SE, super-enhancer.
      To identify a set of MITF high- and low-expressing cells, we performed quantitative real-time reverse transcriptase–PCR analysis on 18 melanoma lines and found 8 lines with high MITF (MITF-hi) (>2-fold mean normalized MITF levels) and 5 lines with low MITF (MITF-lo) (<¼-fold mean normalized MITF levels) expression levels (Figure 1b). Analysis of MITF copy number in these lines showed only two lines with evidence of MITF gene amplification (Figure 1b, red circles). H3K27ac chromatin immunoprecipitation sequencing (ChIP-seq) profiling, which marks active enhancers, indicated the presence of SEs at the MITF gene locus in MITF-hi cells and substantially less enhancer signal at the same location in an MITF-lo cell line (LOXIMVI) and a normal human dermal fibroblast line (Figure 1c) (
      • Kaufman C.K.
      • Mosimann C.
      • Fan Z.P.
      • Yang S.
      • Thomas A.J.
      • Ablain J.
      • et al.
      A zebrafish melanoma model reveals emergence of neural crest identity during melanoma initiation.
      ). Western blot analysis also showed higher protein levels of MITF in SK-MEL-5, UACC257, and SK-MEL-30 compared with the MITF-lo LOXIMVI and BJ fibroblast lines (see Supplementary Figure S1 online). Among all genes in the MITF-hi lines, relative ranking of enhancer size (Figure 1d) indicated that the MITF locus harbored some of the largest SEs in these cells (rank #2 in UACC257, #4 in SK-MEL-5, and #11 in SK-MEL-30) (see Supplementary Tables S1–S8 online). Using a public dataset, SEs were found to exist proximal to MITF in 4 of 10 short-passage cultures derived from melanoma specimens, providing a clinical correlate to our cell line data (Figure 1e, and see Supplementary Tables S5–S8 online) (
      • Verfaillie A.
      • Imrichova H.
      • Atak Z.K.
      • Dewaele M.
      • Rambow F.
      • Hulselmans G.
      • et al.
      (2015) Decoding the regulatory landscape of melanoma reveals TEADS as regulators of the invasive cell state.
      ).
      We next examined whether SEs existed near SOX10, which is a known transcriptional regulator of MITF that has also been implicated as an important driver of melanoma proliferation and survival (
      • Bondurand N.
      • Pingault V.
      • Goerich D.E.
      • Lemort N.
      • Sock E.
      • Le Caignec C.
      • et al.
      Interaction among SOX10, PAX3 and MITF, three genes altered in Waardenburg syndrome.
      ,
      • Cronin J.C.
      • Watkins-Chow D.E.
      • Incao A.
      • Hasskamp J.H.
      • Schonewolf N.
      • Aoude L.G.
      • et al.
      SOX10 ablation arrests cell cycle, induces senescence, and suppresses melanomagenesis.
      ,
      • Graf S.A.
      • Busch C.
      • Bosserhoff A.K.
      • Besch R.
      • Berking C.
      SOX10 promotes melanoma cell invasion by regulating melanoma inhibitory activity.
      ,
      • Harris M.L.
      • Baxter L.L.
      • Loftus S.K.
      • Pavan W.J.
      Sox proteins in melanocyte development and melanoma.
      ,
      • Kubic J.D.
      • Young K.P.
      • Plummer R.S.
      • Ludvik A.E.
      • Lang D.
      Pigmentation PAX-ways: the role of Pax3 in melanogenesis, melanocyte stem cell maintenance, and disease.
      ,
      • Shakhova O.
      • Zingg D.
      • Schaefer S.M.
      • Hari L.
      • Civenni G.
      • Blunschi J.
      • et al.
      Sox10 promotes the formation and maintenance of giant congenital naevi and melanoma.
      ,
      • Wellbrock C.
      • Arozarena I.
      Microphthalmia-associated transcription factor in melanoma development and MAP-kinase pathway targeted therapy.
      ). In all three MITF-hi cell lines, but not the MITF-lo LOXIMVI line, SOX10 appeared to be associated with exceptionally large enhancers (see Supplementary Figure S2 online) (
      • Kaufman C.K.
      • Mosimann C.
      • Fan Z.P.
      • Yang S.
      • Thomas A.J.
      • Ablain J.
      • et al.
      A zebrafish melanoma model reveals emergence of neural crest identity during melanoma initiation.
      ), although only in UACC257 and SK-MEL-30 met the criteria for SE designation. Protein levels of SOX10 were also elevated in these lines compared with the LOXIMVI and BJ lines (see Supplementary Figure S1). These results suggest that SOX10 and MITF could fuel a transcriptional dependency in the MITF-hi lines. To test this possibility, we individually suppressed these two genes in SK-MEL-5, SK-MEL-30, and UACC257 (Figure 2a, and see Supplementary Figure S3 online) and investigated cellular responses. Depletion of SOX10 led to a notable loss of MITF in SK-MEL-5 and SK-MEL-30, and suppression of MITF resulted in a partial depletion of SOX10 (Figure 2a, and see Supplementary Figure S3). Consistent with the loss of these key transcription factors, cell growth (Figure 2b) and colony formation (Figure 2c) of MITF-hi cell lines were significantly impaired when either MITF or SOX10 was depleted; this was not observed in the MITF-lo LOXIMVI line (see Supplementary Figure S4 online). These results indicate that MITF and SOX10 are dependency genes associated with SEs in MITF-hi melanomas.
      Figure 2
      Figure 2MITF and SOX10 depletion inhibits melanoma growth. Pooled (n = 4, ON-TARGET plus SMARTpool) siRNAs against MITF and SOX10 were used to deplete respective transcription factors in MITF-hi cell lines. Compared with control siRNA (siNTC), siMITF and siSOX10 suppress (a) protein expression, as evidenced by Western blots of SOX10, MITF, and GAPDH as control (densitometry in ); (b) cell viability, as evidenced by counts of cells in SK-MEL-5, UACC257, and SK-MEL-30 cell lines (5 days after siRNA transfection); and (c) colony formation, as evidenced by crystal violet staining and quantification of three replicates (2 weeks after siRNA transfection). Error bars represent the standard deviation of triplicate samples, and the three cell lines represent independent biologic replicates. ∗∗P < 0.01 by Student t test. MITF-hi, high MITF expression level; NTC, nontemplate control; si, small interfering.

      THZ1 suppresses melanoma growth in vitro and in vivo

      In other malignancies, the presence of SE-associated dependency genes has provided a therapeutic opportunity to target master oncogenes through CDK7 inhibition with the first-in-class CDK7 inhibitor THZ1 (
      • Chipumuro E.
      • Marco E.
      • Christensen C.L.
      • Kwiatkowski N.
      • Zhang T.
      • Hatheway C.M.
      • et al.
      CDK7 inhibition suppresses super-enhancer-linked oncogenic transcription in MYCN-driven cancer.
      ,
      • Christensen C.L.
      • Kwiatkowski N.
      • Abraham B.J.
      • Carretero J.
      • Al-Shahrour F.
      • Zhang T.
      • et al.
      Targeting transcriptional addictions in small cell lung cancer with a covalent CDK7 inhibitor.
      ,
      • Kwiatkowski N.
      • Zhang T.
      • Rahl P.B.
      • Abraham B.J.
      • Reddy J.
      • Ficarro S.B.
      • et al.
      Targeting transcription regulation in cancer with a covalent CDK7 inhibitor.
      ,
      • Wang Y.
      • Zhang T.
      • Kwiatkowski N.
      • Abraham B.J.
      • Lee T.I.
      • Xie S.
      • et al.
      CDK7-dependent transcriptional addiction in triple-negative breast cancer.
      ). Because MITF-hi cells appear to rely on SE-mediated MITF transcription for survival, we examined the effect of THZ1 in the MITF-hi lines more closely. Given THZ1’s known specificity for CDK7 and its known effects on transcription, we first sought evidence of intracellular Pol II targeting by THZ1. The compound diminished the phosphorylation of initiation-associated Ser5/7 and elongation-associated Ser2 phospho-residues on Pol II CTD by 6 hours (see Supplementary Figure S5 online). CDK7-mediated phosphorylation of CDK9’s activation loop (Thr186) was also inhibited by 24 hours. For both Pol II and CDK9 targets, there were concomitant decreases in protein levels by 24 hours at the highest dose level (500 nmol/L), which may be due to a generalized suppression of transcription. The reduction in Pol II CTD Ser2 phosphorylation at 6 hours despite sustained CDK9 Thr186 phosphorylation may be due to partial cross-reactivity of THZ1 with CDK12/13 (
      • Kwiatkowski N.
      • Zhang T.
      • Rahl P.B.
      • Abraham B.J.
      • Reddy J.
      • Ficarro S.B.
      • et al.
      Targeting transcription regulation in cancer with a covalent CDK7 inhibitor.
      ).
      We next tested the sensitivity of the MITF-hi and MITF-lo cells to CDK7 inhibition. For the MITF-hi lines, SK-MEL-5, SK-MEL-30, and UACC257 were in general highly sensitive to THZ1 in vitro (Figure 3a); the concentrations at which half maximal inhibition of cell proliferation was achieved (GI50) ranged from 18.5 nmol/L to 44.4 nmol/L. THZ1 led to a strong induction of apoptosis as determined by increases in subG1 cells and annexin-V staining after THZ1 treatment (Figure 3b). To verify that CDK7 blockade can lead to growth suppression, we subjected SK-MEL-5, SK-MEL-30, and UACC257 cells to small interfering RNA (siRNA) against CDK7 and observed consistent decreases in cell growth (see Supplementary Figure S6 online) compared with siRNA nontargeting controls. Cell lines with lower MITF levels (LOXIMVI, A375, SK-MEL-63) (see Supplementary Figure S7 online) were also sensitive to THZ1, exhibited appropriate CDK7 targeting, and showed an apoptotic response when exposed to the agent. These results suggest that sensitivity to THZ1 is unlikely to be gene specific but, rather, is linked to transcriptional activity at one or more SE-dependent survival-related genes (i.e., “Achilles cluster”) (
      • Wang Y.
      • Zhang T.
      • Kwiatkowski N.
      • Abraham B.J.
      • Lee T.I.
      • Xie S.
      • et al.
      CDK7-dependent transcriptional addiction in triple-negative breast cancer.
      ), which may vary between cells and cancer types.
      Figure 3
      Figure 3THZ1 suppresses melanoma in vitro and in vivo. (a) Dose-response curves for representative cell lines exposed to increasing concentrations of THZ1 for 72 hours; cell viability was analyzed using the CellTiter-Glo (Promega) luminescence assay. Mean GI50 values were calculated compared with DMSO-treated cells. Error bars show standard deviation. (b) Demonstration of cytostatic and apoptotic effects induced by THZ1 using propidium iodide (left panel) and FITC-annexin V (right panel). Error bars indicate standard deviation. (c) SK-MEL-5 xenografts in nude mice were randomized to treatment with either 10 mg/kg THZ1 twice daily or vehicle twice daily, given by intraperitoneal administration. Tumor volume was measured weekly from treatment. Each data point represents mean ± standard deviation. P < 0.05 by analysis of variance. (d) THZ1-treated tumors showed necrotic areas, and TUNEL staining showed apoptotic cells with significant nuclear condensation. For hematoxylin and eosin, scale bar = 20 μm; for TUNEL and nuclear stains, scale bar = 1 μm. GI50, concentration at which half maximal inhibition of cell proliferation was achieved; H/E, hematoxylin and eosin; Hr, hour; M, mol/L.
      THZ1 significantly suppressed tumor growth in vivo compared with vehicle by 5 weeks (P < 0.05 by analysis of variance) (Figure 3c). Only one animal was killed because of adverse effects (abdominal bloating); this occurred after 10 weeks of treatment. Histopathologic evaluation showed that THZ1-treated tumors had high levels of necrosis and cellular debris (Figure 3d, hematoxylin and eosin staining). Additional staining indicated higher TUNEL positivity in treated tumors and more nuclear condensation, suggesting that apoptosis was induced (Figure 3d). Comparable to our in vitro results, Pol II CTD and Pol II CTD (Ser2/5/7) phosphorylation levels were reduced in the THZ1-treated tumors compared with untreated tumors (see Supplementary Figure S8 online).

      Disruption of MITF’s lineage programming contributes to THZ1-induced growth suppression

      To better understand the transcriptional vulnerability to THZ1 in melanoma cells, we performed genome-wide expression profiling before and after THZ1 treatment. Consistent with other tumor models, THZ1 treatment led to decreased global gene expression in the MITF-hi lines and in the MITF-lo LOXIMVI line (Figure 4a). On a programmatic level, transcription or regulation of transcription were among the most significant gene ontology categories affected by THZ1 in the MITF-hi melanoma cells, further validating THZ1’s inhibitory effects on CDK7-dependent production of transcriptional regulators (Figure 4b). In the LOXIMVI cells, DNA repair and double-stranded break repair via homologous recombination were the most significant categories affected by THZ1, perhaps because of targeting of CDK12/13, which has been reported in Ewing sarcomas (

      Balboni A, Stolte B, Kalev P, Kwiatkowski N, Zhang T, Abraham B, et al. CDK12/13 inhibition cooperates with the Ewing sarcoma oncoprotein EWS/FLI to attenuate homologous recombination repair in Ewing sarcoma cells. In: Proc. 2016 American Association for Cancer Research conference; 2016.

      ). In general, transcripts from SE-associated genes were more sensitive to THZ1 compared with those genes associated with typical enhancers (Figure 4c, left panel). Additionally, gene set enrichment analysis (GSEA) confirmed that SE-associated targets were significantly enriched (P < 0.001) among the genes most down-regulated by THZ1 (Figure 4c, right panel).
      Figure 4
      Figure 4Super-enhancer–associated genes and genes involved in transcription are particularly sensitive to THZ1. (a) SK-MEL-5, UACC257, SK-MEL-30 (MITF-hi), and LOXIMVI (MITF-lo) cells were treated with THZ1 (5 × GI50) for 6 hours. Heatmap displays the log2 fold change in gene expression versus DMSO for the set of expressed transcripts. (b) Expressed gene transcripts were ranked from most to least sensitive to THZ1, and the top 10% of THZ1-sensitive transcripts were subjected to gene ontology enrichment using DAVID (Laboratory of Human Retrovirology and Immunoinformatics) functional annotation software analysis. DAVID-supplied Benjamini-Hochberg adjusted P-values are shown. (c) Left panel: comparison of transcript levels after THZ1 exposure based on all transcripts (All), typical enhancers (TE) or super-enhancers (SE). P-value compares levels of change between TE and SE. Right panel: gene set enrichment analysis of super-enhancer–associated gene transcripts. Along the x-axis, SK-MEL-5, UACC257, SK-MEL-30, and LOXIMVI expressed genes are ranked from least to most (left to right) sensitive to THZ1. Super-enhancer–associated genes identified by H3K27ac signal are designated by black lines and assessed for their level of enrichment among THZ1-sensitive genes. Super-enhancer–associated genes showed significant enrichment in THZ1-responsive genes. Gene set enrichment analysis-supplied P-value < 0.001 for all three lines. ER, endoplasmic reticulum; GI50, concentration at which half maximal inhibition of cell proliferation was achieved; GPI, glycophosphatidylinositol.
      Transcriptional dependencies that are sustained by SEs, such as those for master oncogenes, appear to be selectively targeted by CDK7 inhibition (
      • Chipumuro E.
      • Marco E.
      • Christensen C.L.
      • Kwiatkowski N.
      • Zhang T.
      • Hatheway C.M.
      • et al.
      CDK7 inhibition suppresses super-enhancer-linked oncogenic transcription in MYCN-driven cancer.
      ,
      • Christensen C.L.
      • Kwiatkowski N.
      • Abraham B.J.
      • Carretero J.
      • Al-Shahrour F.
      • Zhang T.
      • et al.
      Targeting transcriptional addictions in small cell lung cancer with a covalent CDK7 inhibitor.
      ,
      • Loven J.
      • Hoke H.A.
      • Lin C.Y.
      • Lau A.
      • Orlando D.A.
      • Vakoc C.R.
      • et al.
      Selective inhibition of tumor oncogenes by disruption of super-enhancers.
      ,
      • Wang Y.
      • Zhang T.
      • Kwiatkowski N.
      • Abraham B.J.
      • Lee T.I.
      • Xie S.
      • et al.
      CDK7-dependent transcriptional addiction in triple-negative breast cancer.
      ). We thus examined the impact of THZ1 specifically on MITF and SOX10. As shown in Figure 5a, there was a reduction in the amounts of both RNA (Figure 5a, and see Supplementary Figure S9a online) and protein (see Supplementary Figure S9b) for these genes in all three MITF-hi lines. To better understand the structural mechanism that underpins THZ1’s biological effects, we performed, in parallel, comparative ChIP-seq analysis in SK-MEL-5 cells. Consistent with the loss of MITF and SOX10 gene expression, we observed concomitant loss of Pol II ChIP-seq signal at gene promoters and their associated SEs (Figure 5b).
      Figure 5
      Figure 5Effect of THZ1 on MITF locus. (a) Plots of MITF (red line) and SOX10 (blue line) transcript levels in response to 5 × GI50 THZ1 for 6 hours; other transcripts shown in grey. (b) Gene tracks of Pol II (Pol2) ChIP-seq signal occupancy at MITF and SOX10 loci in SK-MEL-5 cells. Pol II occupancy is shown after the treatment with 250 nmol/L THZ1 or DMSO vehicle for 6 hours. Signal of ChIP-seq occupancy is in units of reads per million. (c) SK-MEL-5 cells were treated with siRNA directed against MITF for 48 hours. Heatmap displays the log2 fold change in gene expression versus control siRNA for the set of 14,214 expressed transcripts. (d) MITF down-regulation recapitulates THZ1’s transcriptional effects. Along the x-axis, SK-MEL-5 expressed gene transcripts are ranked from least to most (left to right) sensitive to 250 nmol/L THZ1. All genes for which expression was down-regulated by siRNA-mediated MITF knockdown (>1.5 × log2-fold) are represented by black lines and assessed for their level of enrichment among THZ1-sensitive genes. siMITF-sensitive gene transcripts show significant enrichment in THZ1-responsive genes in SK-MEL-5 cells. Gene set enrichment analysis-supplied P-value < 0.001. ChIP-seq, chromatin immunoprecipitation sequencing; chr, chromosome; GI50, concentration at which half maximal inhibition of cell proliferation was achieved; Pol 2, polymerase II; Pol II, polymerase II; rpm, reads per million; siRNA, small interfering RNA.
      Next, we surmised that if MITF represents a critical target of THZ1, then the MITF transcriptional program should be preferentially depleted by THZ1. To prove this, we subjected SK-MEL-5 cells to siRNA-mediated suppression of MITF, followed by genome-wide expression analysis. With exogenous RNA spike-in controls, there appeared to be a slight drop in global gene expression (Figure 5c), which to our knowledge has not been previously reported but is consonant with prior spike-in analyses with master regulators such as c-MYC (
      • Loven J.
      • Orlando D.A.
      • Sigova A.A.
      • Lin C.Y.
      • Rahl P.B.
      • Burge C.B.
      • et al.
      Revisiting global gene expression analysis.
      ). To verify that siMITF depletion selectively diminished known MITF targets, we assessed the effect of siMITF in SK-MEL-5 on a set of 110 known high-confidence MITF targets (
      • Hoek K.S.
      • Schlegel N.C.
      • Eichhoff O.M.
      • Widmer D.S.
      • Praetorius C.
      • Einarsson S.O.
      • et al.
      Novel MITF targets identified using a two-step DNA microarray strategy.
      ). As shown in Supplementary Figure S10 online, 98 of these Hoek MITF targets (177 probes) were expressed and, in aggregate, showed a significantly greater degree of suppression relative to all other non-MITF target genes (log2-fold difference, –1.14 ± 0.029 (n = 177) vs. –0.81 ± 0.003 (n = 14,035), respectively; P = 4.42 × 10–36, Student t test); this supports the notion that the siMITF knockdown did selectively deplete known MITF targets. We next subjected the top siMITF-suppressed genes in SK-MEL-5 to GSEA against the set of THZ1-diminished genes and detected a strong association (P < 0.001) between genes that were maximally depleted by siMITF (>2.82-fold, i.e., 1.5 × log2-fold) and those that were most down-regulated by THZ1 (Figure 5d); there were 114 genes that were shared among the most down-regulated genes (>2-fold) by siMITF and by THZ1 (see Supplementary Figure S11 online). The siMITF depletion signature was also cross-validated on THZ1 gene sets for UACC257 and SK-MEL-30, and a significant association was found for UACC257 (P < 0.001) (see Supplementary Figure S12 online) but not SK-MEL-30 (P = 0.52) (see Supplementary Figure S12). These results provide evidence that an MITF-sustained program is preferentially depleted by THZ1 at least in a subset of MITF-hi lines.
      Finally, to determine if elevated MITF levels can rescue cells from THZ1, we overexpressed MITF using a tetracycline-inducible system in several melanoma lines with lower MITF levels (A375 and SK-MEL-63). As shown in Supplementary Figure S13 online, controlled expression of MITF led to significant increases in the GI50 for THZ1 in both cell lines. Taken together, these results suggest that THZ1 can effectively exploit an inherent vulnerability of MITF-hi melanoma cells because of dependency on SE-driven oncogenes such as MITF or SOX10.

      Discussion

      With this study, we provide preliminary evidence that SEs at the MITF locus exist in at least a subset of human melanomas that harbor high-level MITF expression. Although “lineage addiction” through MITF amplification has already been described in about 5% of cutaneous melanoma specimens (
      • Cancer Genome Atlas Network
      Genomic classification of cutaneous melanoma.
      ,
      • Garraway L.A.
      • Widlund H.R.
      • Rubin M.A.
      • Getz G.
      • Berger A.J.
      • Ramaswamy S.
      • et al.
      Integrative genomic analyses identify MITF as a lineage survival oncogene amplified in malignant melanoma.
      ), SE formation may explain some of the high-MITF tumors that lack ostensible gene amplification. Additionally, a key regulator of MITF, SOX10, is also marked with SEs in some high-MITF–expressing melanoma lines, which may indicate a pro-survival partnership that enhances cellular dependency on MITF. These findings complement and reinforce recent zebrafish studies, which point to the emergence of a neural crest identity during melanoma initiation through SOX10 (
      • Kaufman C.K.
      • Mosimann C.
      • Fan Z.P.
      • Yang S.
      • Thomas A.J.
      • Ablain J.
      • et al.
      A zebrafish melanoma model reveals emergence of neural crest identity during melanoma initiation.
      ).
      The presence of SEs at MITF also presented an opportunity for selective “oncogene starvation.” We found that targeting transcription with the CDK7 inhibitor THZ1 (i) potently induces apoptosis of melanoma cells, (ii) effectively suppresses melanoma growth both in vitro and in vivo, and (iii) disrupts the transcriptional apparatus at the MITF and SOX10 loci. Although THZ1 is known to preferentially deplete SE-associated transcriptional units, it does not appear to be gene selective, that is, MITF-lo melanomas are also sensitive to THZ1. Since the initial demonstration of RUNX1 disruption in T-cell acute lymphoblastic leukemia (
      • Kwiatkowski N.
      • Zhang T.
      • Rahl P.B.
      • Abraham B.J.
      • Reddy J.
      • Ficarro S.B.
      • et al.
      Targeting transcription regulation in cancer with a covalent CDK7 inhibitor.
      ), THZ1 has also been found to be highly effective in suppressing neuroblastoma cells driven by MYCN (
      • Chipumuro E.
      • Marco E.
      • Christensen C.L.
      • Kwiatkowski N.
      • Zhang T.
      • Hatheway C.M.
      • et al.
      CDK7 inhibition suppresses super-enhancer-linked oncogenic transcription in MYCN-driven cancer.
      ), small-cell lung cancer cells dependent on lineage-specific factors (
      • Christensen C.L.
      • Kwiatkowski N.
      • Abraham B.J.
      • Carretero J.
      • Al-Shahrour F.
      • Zhang T.
      • et al.
      Targeting transcriptional addictions in small cell lung cancer with a covalent CDK7 inhibitor.
      ), and triple-negative breast cancers (
      • Wang Y.
      • Zhang T.
      • Kwiatkowski N.
      • Abraham B.J.
      • Lee T.I.
      • Xie S.
      • et al.
      CDK7-dependent transcriptional addiction in triple-negative breast cancer.
      ). In each of these studies, as well as ours, it has been shown that covalent inactivation of CDK7 with THZ1 is effective at extinguishing transcriptional dependency on critical oncogenes that the cancer requires for survival and that the cell uses to support lineage identity (
      • Loven J.
      • Hoke H.A.
      • Lin C.Y.
      • Lau A.
      • Orlando D.A.
      • Vakoc C.R.
      • et al.
      Selective inhibition of tumor oncogenes by disruption of super-enhancers.
      ,
      • Whyte W.A.
      • Orlando D.A.
      • Hnisz D.
      • Abraham B.J.
      • Lin C.Y.
      • Kagey M.H.
      • et al.
      Master transcription factors and mediator establish super-enhancers at key cell identity genes.
      ). High-level expression of these oncogenes is supported by the concentration of transcriptional regulatory apparatus, including CDK7, at proximal SE regulatory elements, a consequence of which is to endow their expression with extreme sensitivity to CDK7 inhibition. Thus, selective targeting of SE elements creates a unique context of vulnerability for melanoma.
      Although beyond the scope of this investigation, it is likely that THZ1’s effects are mediated by variable oncogene groupings driving different melanoma genotypes/phenotypes. An advantage of THZ1 is that multiple lineage members can be simultaneously targeted if they are dependent on their associated SEs. Thus, THZ1 has the potential to be used as a chemical tool to refine the network of transcriptional addictions in a range of melanomas through a transcriptional analysis combining SE profiling with THZ1 vulnerability studies.
      In conclusion, these experiments show the existence of an SE-associated lineage network in MITF-hi human melanomas. Furthermore, concurrent depletion of multiple oncogenic, lineage-specific transcription factors through CDK7 inhibition can lead to profound growth suppression and induction of apoptosis in these cells.

      Materials and Methods

      Chromatin immunoprecipitation

      Cells were cross-linked for 10 minutes at room temperature by the addition of one tenth of the volume of 11% formaldehyde solution (11% formaldehyde; 50 mmol/L HEPES, pH 7.3; 100 mmol/L NaCl; 1 mmol/L EDTA, pH 8.0; 0.5 mmol/L EGTA, pH 8.0) to the growth medium followed by 5 minutes of quenching with 100 mmol/L glycine. Cells were washed twice with phosphate-buffered saline; then the supernatant was aspirated, and the cell pellet was flash frozen in liquid nitrogen. Frozen cross-linked cells were stored at –80°C. Next, 50 μl of Dynal magnetic beads (Sigma-Aldrich, St. Louis, MO) were blocked with 0.5% bovine serum albumin (weight/volume) in phosphate-buffered saline. Magnetic beads were bound with 5 μg of H3K27ac (Abcam, Cambridge, UK; catalog no. AB4729A) or RNA Pol II antibody (Santa Cruz Biotechnology, Dallas, TX; catalog no. sc-899). Cross-linked cells were lysed with lysis buffer 1 (50 mmol/L HEPES, pH 7.5; 140 mmol/L NaCl; 1 mmol/L EDTA; 10% glycerol; 0.5% NP-40; and 0.25% Triton X-100), pelleted, and resuspended in lysis buffer 2 (10 mmol/L TrisHCl, pH 8.0; 200 mmol/L NaCl; 1 mmol/L EDTA; 0.5 mmol/L EGTA). The subsequent pellet was resuspended in and sonicated in sonication buffer (50 mmol/L HEPES, pH 7.5; 140 mmol/L NaCl; 1 mmol/L EDTA, pH 8.0; 1 mmol/L EGTA; 0.1% Na-deoxycholate; 0.1% SDS; 1% Triton X-100). Cells were sonicated for 10 cycles at 30 seconds each on ice (18–21 W) with 60 seconds on ice between cycles. Sonicated lysates were cleared and incubated overnight at 4°C with magnetic beads bound with antibody to enrich for DNA fragments bound by the indicated factor. Beads were washed two times with sonication buffer, one time with sonication buffer with 500 mmol/L NaCl, one time with LiCl wash buffer (10 mmol/L TrisHCl, pH 8.0; 1 mmol/L EDTA; 250 mmol/L LiCl; 0.5% NP-40; 0.5% Na-deoxycholate), and one time with Tris-EDTA. DNA was eluted in elution buffer (50 mmol/L TrisHCl, pH 8.0; 10 mmol/L EDTA; 1% SDS). Cross-links were reversed overnight. RNA and protein were digested using RNase A and Proteinase K, respectively, and DNA was purified with phenol chloroform extraction and ethanol precipitation. Data have been submitted to Gene Expression Omnibus under submission GSE75820.

      ChIP-seq analysis

      Illumina (San Diego, CA) sequencing libraries were generated, and data were processed according to
      • Lin C.Y.
      • Lovén J.
      • Rahl P.B.
      • Paranal R.M.
      • Burge C.B.
      • Bradner J.E.
      • et al.
      Transcriptional amplification in tumor cells with elevated c-Myc.
      . In brief, libraries were generated for ChIP samples following the Illumina TruSeq DNA Sample Preparation v2 kit protocol with minor changes. All ChIP-seq data sets were aligned using Bowtie (
      • Langmead B.
      • Trapnell C.
      • Pop M.
      • Salzberg S.L.
      Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
      ) to build NCBI36/hg19 of the human genome with parameters –k 2, –m 2, –best, –sam, and –l set to read length. Wiggle files for gene tracks were created using MACS 1.4 (
      • Zhang Y.
      • Liu T.
      • Meyer C.A.
      • Eeckhoute J.
      • Johnson D.S.
      • Bernstein B.E.
      • et al.
      Model-based analysis of ChIP-Seq (MACS).
      ) with options –w –S –space=50 to count reads in 50-base pair bins, were divided by the number of treatment reads to normalize to mapped reads per million, and were displayed in the University of California, Santa Cruz genome browser.

      SE identification and assignment

      SEs were identified as described previously (
      • Christensen C.L.
      • Kwiatkowski N.
      • Abraham B.J.
      • Carretero J.
      • Al-Shahrour F.
      • Zhang T.
      • et al.
      Targeting transcriptional addictions in small cell lung cancer with a covalent CDK7 inhibitor.
      ). Briefly, peaks of H3K27ac were identified using MACS. Peaks were identified twice using –p 1e-9 –keep-dup=1 or –p 1e-9 –keep-dup=all. Peaks were identified using input DNA samples as control. The collapsed union of peaks identified in the separate methods was used as input for SE identification by ROSE (https://github.com/BradnerLab/pipeline) with parameters –s 12500 and –t 2000. ROSE was applied to cell line data using input control. Enhancers were assigned to their single most proximal expressed RefSeq gene, where expression is defined by being in the top two thirds of promoter H3K27ac density. The 1,000-base pair promoters were centered on the transcription start site, and density was calculated using bamToGFF (https://github.com/BradnerLab/pipeline) with parameters –e 200 –m 1 –r –d to get reads per million-normalized read densities of reads extended to 200 base pairs.

      Two-dimensional THZ1 cell viability assays

      Melanoma cells were plated in 96-well, white-walled, tissue culture plates at a density of 1.5 × 103 to 2 × 103 cells/well; all treatments were performed in triplicate, and dosing studies were replicated. Cells were exposed to THZ1 for 72 hours, beginning 24 hours after cells were plated. Cell viability was measured with the CellTiter-Glo luminescence assay (Promega, Madison, WI); in brief, 30 μl of reconstituted reagent was added to each well; plates were incubated for 10 minutes at room temperature, protected from light, on a shaking platform; and luminescence (total light emission) was measured on either a Molecular Devices (Sunnyvale, CA) Spectramax M5 or Spectramax Plus 384 plate reader using the preprogrammed CellTiter-Glo protocol. Raw data were normalized to DMSO-treated controls, and GI50 values were calculated via nonlinear regression curve fit, with error bars representing standard error of the mean (GraphPad Prism 6, La Jolla, CA).

      Murine xenograft studies

      All studies were performed in accordance with an animal protocol approved by the Dana Farber Cancer Institute (Boston, MA). Human melanoma xenografts were established via subcutaneous injection of 5 × 106 SK-MEL-5 cells suspended in Matrigel (Corning Life Sciences, Tewksbury, MA) into the left and right flanks of nude mice (Charles River Laboratories, Wilmington, MA). After visual tumor nodules appeared, they were measured weekly using a caliper. Once tumor volumes reached 100–300 mm3, mice were divided into two treatment cohorts: vehicle (10% DMSO in 5% dextrose water) twice daily or 10 mg/kg THZ1 twice daily. The vehicle and treatment cohorts included 15 and 16 tumors, respectively. Tumor volumes and body weights of mice were measured weekly during treatment. Mice were killed when tumors grew beyond 1,200 mm3, if tumors ulcerated, or if mice became symptomatic from either disease burden or THZ1 toxicity. Tumor tissues were fixed in phosphate-buffered formalin, embed in paraffin, cut to 4-μm thickness, and applied to slides. TUNEL staining was performed using a DeadEnd Fluorometric TUNEL kit according to manufacturer’s instructions (Promega), and then slides were incubated with Hoechst stain for 10 minutes to stain nuclei. The slides were analyzed and photographed using a fluorescent microscope.

      siRNA knockdown

      For siRNA, ON-TARGETplus SMARTpool small interference RNAs against MITF, SOX10, and CDK7 and nontargeting control siRNA were purchased from GE Healthcare Dharmacon (Cambridge, MA). The SMARTpool is a mixture of four siRNAs against these individual targets and has been used to reproducibly suppress MITF and SOX10 (
      • Esumi N.
      • Kachi S.
      • Campochiaro P.A.
      • Zack D.J.
      VMD2 promoter requires two proximal E-box sites for its activity in vivo and is regulated by the MITF-TFE family.
      ,
      • Ford K.M.
      • D’Amore P.A.
      Molecular regulation of vascular endothelial growth factor expression in the retinal pigment epithelium.
      ,
      • Laurette P.
      • Strub T.
      • Koludrovic D.
      • Keime C.
      • Le Gras S.
      • Seberg H.
      • et al.
      (2015) Transcription factor MITF and remodeller BRG1 define chromatin organisation at regulatory elements in melanoma cells.
      ,
      • Lv X.B.
      • Wu W.
      • Tang X.
      • Wu Y.
      • Zhu Y.
      • Liu Y.
      • et al.
      Regulation of SOX10 stability via ubiquitination-mediated degradation by Fbxw7alpha modulates melanoma cell migration.
      ,
      • Quinn D.A.
      • Robinson D.
      • Hales C.A.
      Intravenous injection of propylene glycol causes pulmonary hypertension in sheep.
      ). To transfect melanoma cells, siRNA complex was made by mixing siRNA and RNAiMAX from Thermo Fisher (Waltham, MA) and then added to melanoma cell suspension. Cells were collected at various time points after transfection to extract RNA and protein.

      Statistics

      The data points represent the mean, and error bars display standard deviation, unless otherwise specified. Two-way Student t test and analysis of variance were used; all tests were two sided, and P-values less than 0.05 were considered statistically significant. Statistical analyses were performed using GraphPad Prism 6, GSEA (Broad Institute, Cambridge MA), and DAVID functional annotation software analysis (Laboratory of Human Retrovirology and Immunoinformatics, Frederick, MD). Additional methods are presented in the Supplementary Materials and Methods.

      Conflict of Interest

      NSG, NK, and TZ are inventors on a patent application covering THZ1. NSG and RAY are scientific founders of Syros Pharmaceuticals, a company that has licensed THZ1 intellectual property from the Dana-Farber Cancer Institute. BJA is a shareholder in Syros Pharmaceuticals.

      Acknowledgments

      We thank the staff of the Wellman Center for Photomedicine Photopathology Core and the Whitehead Institute Genome Technology Core for their technical assistance and use of instruments. This work was supported in part by the National Institutes of Health (K24-CA149202, P01-CA163222, T32-CA071345), the Melanoma Research Alliance, the American Dermatological Association, the American Skin Association, Hope Funds, and the generous philanthropic donors to the Massachusetts General Hospital Millennium Melanoma and Innovations in Melanoma Care funds. BJA is the Hope Funds for Cancer Research Grillo-Marxuach Family Fellow.

      Supplementary Material

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