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Differential histone acetylation and super-enhancer regulation underlie melanoma cell dedifferentiation
Karen Mendelson, Tiphaine C. Martin, Christie B. Nguyen, Min Hsu, Jia Xu, Claudia Lang, Reinhard Dummer, Yvonne Saenger, Jane L. Messina, Vernon K. Sondak, Garrett Desman, Dan Hasson, Emily Bernstein, Ramon E. Parsons, Julide Tok Celebi
Karen Mendelson, Tiphaine C. Martin, Christie B. Nguyen, Min Hsu, Jia Xu, Claudia Lang, Reinhard Dummer, Yvonne Saenger, Jane L. Messina, Vernon K. Sondak, Garrett Desman, Dan Hasson, Emily Bernstein, Ramon E. Parsons, Julide Tok Celebi
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Research Article Dermatology

Differential histone acetylation and super-enhancer regulation underlie melanoma cell dedifferentiation

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Abstract

Dedifferentiation or phenotype switching refers to the transition from a proliferative to an invasive cellular state. We previously identified a 122-gene epigenetic gene signature that classifies primary melanomas as low versus high risk (denoted as Epgn1 or Epgn3). We found that the transcriptomes of the Epgn1 low-risk and Epgn3 high-risk cells are similar to the proliferative and invasive cellular states, respectively. These signatures were further validated in melanoma tumor samples. Examination of the chromatin landscape revealed differential H3K27 acetylation in the Epgn1 low-risk versus Epgn3 high-risk cell lines that corroborated with a differential super-enhancer and enhancer landscape. Melanocytic lineage genes (MITF, its targets and regulators) were associated with super-enhancers in the Epgn1 low-risk state, whereas invasiveness genes were linked with Epgn3 high-risk status. We identified the ITGA3 gene as marked by a super-enhancer element in the Epgn3 invasive cells. Silencing of ITGA3 enhanced invasiveness in both in vitro and in vivo systems, suggesting it as a negative regulator of invasion. In conclusion, we define chromatin landscape changes associated with Epgn1/Epgn3 and phenotype switching during early steps of melanoma progression that regulate transcriptional reprogramming. This super-enhancer and enhancer-driven epigenetic regulatory mechanism resulting in major changes in the transcriptome could be important in future therapeutic targeting efforts.

Authors

Karen Mendelson, Tiphaine C. Martin, Christie B. Nguyen, Min Hsu, Jia Xu, Claudia Lang, Reinhard Dummer, Yvonne Saenger, Jane L. Messina, Vernon K. Sondak, Garrett Desman, Dan Hasson, Emily Bernstein, Ramon E. Parsons, Julide Tok Celebi

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Figure 2

Epgn1/Epgn3 gene signature classifies primary melanoma tumor samples into low- versus high-risk groups.

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Epgn1/Epgn3 gene signature classifies primary melanoma tumor samples int...
(A) Heatmap of a cohort of primary cutaneous melanoma samples (n = 205). The heatmap depicts expression of 118 epigenetic genes from our signature along with TP53 family and housekeeping genes. Each row of the heatmap indicates a differentially expressed gene (n = 118), and each column represents a tumor sample (n = 205). The status bar indicates the classifier: Epgn1 (gold) and Epgn3 (light blue). The overall survival (OS) risk score, AJCC thickness, ulceration, stage, and OS (number of months) is color-coded as indicated. (B) Kaplan-Meier survival curves for Epgn1 (gold) and Epgn3 (blue) subgroups. (C) Heatmap showing the minimum number of epigenetic genes (HIST1H2BL, MGEA5, TFB2M) that correlated with OS. Correlation with our OS risk score allows for the identification of our 2 groups Epgn1 (gold) and Epgn3 (blue). TP53, TP63, and TP73 gene expression are depicted. Cox regression model was used. (D) The AJCC tumor thickness in the Epgn1 group versus the Epgn3 group (P = 0.0108). (E) Ulceration in the Epgn1 group versus the Epgn3 group (P = 0.0238). (F) Stage in the Epgn1 group versus the Epgn3 group (P = 0.00132). One-way ANOVA test was used (D–F). (G) Kaplan-Meier curve showing the Epgn1/Epgn3 risk classifier in discriminating better versus poor OS by Stage. (H) Kaplan-Meier curve showing the Epgn1/Epgn3 risk classifier in discriminating better versus poor OS for patients with T3 tumors (P = 0.0002).

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