Transcriptional dissection of melanoma identifies a high-risk subtype underlying TP53 family genes and epigenome deregulation
Brateil Badal, … , Benjamin D. Greenbaum, Julide Tok Celebi
Brateil Badal, … , Benjamin D. Greenbaum, Julide Tok Celebi
Published May 4, 2017
Citation Information: JCI Insight. 2017;2(9):e92102. https://doi.org/10.1172/jci.insight.92102.
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Clinical Medicine Dermatology Oncology

Transcriptional dissection of melanoma identifies a high-risk subtype underlying TP53 family genes and epigenome deregulation

Abstract

BACKGROUND. Melanoma is a heterogeneous malignancy. We set out to identify the molecular underpinnings of high-risk melanomas, those that are likely to progress rapidly, metastasize, and result in poor outcomes.

METHODS. We examined transcriptome changes from benign states to early-, intermediate-, and late-stage tumors using a set of 78 treatment-naive melanocytic tumors consisting of primary melanomas of the skin and benign melanocytic lesions. We utilized a next-generation sequencing platform that enabled a comprehensive analysis of protein-coding and -noncoding RNA transcripts.

RESULTS. Gene expression changes unequivocally discriminated between benign and malignant states, and a dual epigenetic and immune signature emerged defining this transition. To our knowledge, we discovered previously unrecognized melanoma subtypes. A high-risk primary melanoma subset was distinguished by a 122-epigenetic gene signature (“epigenetic” cluster) and TP53 family gene deregulation (TP53, TP63, and TP73). This subtype associated with poor overall survival and showed enrichment of cell cycle genes. Noncoding repetitive element transcripts (LINEs, SINEs, and ERVs) that can result in immunostimulatory signals recapitulating a state of “viral mimicry” were significantly repressed. The high-risk subtype and its poor predictive characteristics were validated in several independent cohorts. Additionally, primary melanomas distinguished by specific immune signatures (“immune” clusters) were identified.

CONCLUSION. The TP53 family of genes and genes regulating the epigenetic machinery demonstrate strong prognostic and biological relevance during progression of early disease. Gene expression profiling of protein-coding and -noncoding RNA transcripts may be a better predictor for disease course in melanoma. This study outlines the transcriptional interplay of the cancer cell’s epigenome with the immune milieu with potential for future therapeutic targeting.

FUNDING. National Institutes of Health (CA154683, CA158557, CA177940, CA087497-13), Tisch Cancer Institute, Melanoma Research Foundation, the Dow Family Charitable Foundation, and the Icahn School of Medicine at Mount Sinai.

Authors

Brateil Badal, Alexander Solovyov, Serena Di Cecilia, Joseph Minhow Chan, Li-Wei Chang, Ramiz Iqbal, Iraz T. Aydin, Geena S. Rajan, Chen Chen, Franco Abbate, Kshitij S. Arora, Antoine Tanne, Stephen B. Gruber, Timothy M. Johnson, Douglas R. Fullen, Leon Raskin, Robert Phelps, Nina Bhardwaj, Emily Bernstein, David T. Ting, Georg Brunner, Eric E. Schadt, Benjamin D. Greenbaum, Julide Tok Celebi

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Figure 1

Gene expression–based classification of primary melanomas and benign melanocytic nevi.

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Gene expression–based classification of primary melanomas and benign mel...
(A) Schematic diagram summarizing the study design. (B) Tumor thickness distribution of the primary melanoma cases of the discovery cohort (blue line) versus the primary melanomas of TCGA (yellow line). (C) The top bar indicates subtypes: nevus (light blue) and primary melanoma clusters (dark blue). In the heatmap, expression scores are median centered and clustered by complete linkage hierarchical clustering. Each row indicates a differentially expressed gene and each column represents a tumor sample. The heatmap is color coded on the basis of median-centered log2 gene expression levels. The bars at the bottom represent molecular parameters (nonsynonymous mutational load and BRAFV600E genotype) and clinical characteristics of the patient cohort using AJCC staging and classification for melanoma (age, gender, tumor thickness, ulceration, stage at diagnosis, and patient outcome at last follow-up). The variables are color coded as indicated. (D) Principal component analysis of the melanocytic tumors (nevus, light blue; primary melanoma, dark blue). (E) Gene expression classifier distinguishing nevus and primary melanoma groups.