Literature DB >> 28469092

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

Brateil Badal1,2,3, Alexander Solovyov1,3,4, Serena Di Cecilia1,2,3, Joseph Minhow Chan1,2,3, Li-Wei Chang1,2,3, Ramiz Iqbal1,2,3, Iraz T Aydin1,2,3, Geena S Rajan1,2,3, Chen Chen1, Franco Abbate1,2,3, Kshitij S Arora5, Antoine Tanne4, Stephen B Gruber6, Timothy M Johnson7, Douglas R Fullen8, Leon Raskin9, Robert Phelps1,2, Nina Bhardwaj4,10, Emily Bernstein2,3,10, David T Ting5, Georg Brunner11, Eric E Schadt12, Benjamin D Greenbaum1,3,4,10, Julide Tok Celebi1,2,3,10.   

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.

Entities:  

Keywords:  Dermatology; Oncology

Year:  2017        PMID: 28469092      PMCID: PMC5414564          DOI: 10.1172/jci.insight.92102

Source DB:  PubMed          Journal:  JCI Insight        ISSN: 2379-3708


  57 in total

1.  Inhibiting DNA Methylation Causes an Interferon Response in Cancer via dsRNA Including Endogenous Retroviruses.

Authors:  Katherine B Chiappinelli; Pamela L Strissel; Alexis Desrichard; Huili Li; Christine Henke; Benjamin Akman; Alexander Hein; Neal S Rote; Leslie M Cope; Alexandra Snyder; Vladimir Makarov; Sadna Budhu; Sadna Buhu; Dennis J Slamon; Jedd D Wolchok; Drew M Pardoll; Matthias W Beckmann; Cynthia A Zahnow; Taha Merghoub; Taha Mergoub; Timothy A Chan; Stephen B Baylin; Reiner Strick
Journal:  Cell       Date:  2015-08-27       Impact factor: 41.582

2.  Melanoma-intrinsic β-catenin signalling prevents anti-tumour immunity.

Authors:  Stefani Spranger; Riyue Bao; Thomas F Gajewski
Journal:  Nature       Date:  2015-05-11       Impact factor: 49.962

Review 3.  Staging and prognosis of cutaneous melanoma.

Authors:  Paxton V Dickson; Jeffrey E Gershenwald
Journal:  Surg Oncol Clin N Am       Date:  2011-01       Impact factor: 3.495

4.  Clinicopathologic predictors of sentinel lymph node metastasis in thin melanoma.

Authors:  Dale Han; Jonathan S Zager; Yu Shyr; Heidi Chen; Lynne D Berry; Sanjana Iyengar; Mia Djulbegovic; Jaimie L Weber; Suroosh S Marzban; Vernon K Sondak; Jane L Messina; John T Vetto; Richard L White; Barbara Pockaj; Nicola Mozzillo; Kim James Charney; Eli Avisar; Robert Krouse; Mohammed Kashani-Sabet; Stanley P Leong
Journal:  J Clin Oncol       Date:  2013-11-04       Impact factor: 44.544

5.  p73 suppresses polyploidy and aneuploidy in the absence of functional p53.

Authors:  Flaminia Talos; Alice Nemajerova; Elsa R Flores; Oleksi Petrenko; Ute M Moll
Journal:  Mol Cell       Date:  2007-08-17       Impact factor: 17.970

6.  Transcriptome profiling identifies HMGA2 as a biomarker of melanoma progression and prognosis.

Authors:  Leon Raskin; Douglas R Fullen; Thomas J Giordano; Dafydd G Thomas; Marcus L Frohm; Kelly B Cha; Jaeil Ahn; Bhramar Mukherjee; Timothy M Johnson; Stephen B Gruber
Journal:  J Invest Dermatol       Date:  2013-04-30       Impact factor: 8.551

7.  Histone Variant H2A.Z.2 Mediates Proliferation and Drug Sensitivity of Malignant Melanoma.

Authors:  Chiara Vardabasso; Alexandre Gaspar-Maia; Dan Hasson; Sebastian Pünzeler; David Valle-Garcia; Tobias Straub; Eva C Keilhauer; Thomas Strub; Joanna Dong; Taniya Panda; Chi-Yeh Chung; Jonathan L Yao; Rajendra Singh; Miguel F Segura; Barbara Fontanals-Cirera; Amit Verma; Matthias Mann; Eva Hernando; Sandra B Hake; Emily Bernstein
Journal:  Mol Cell       Date:  2015-06-04       Impact factor: 17.970

8.  Machine learning for neuroimaging with scikit-learn.

Authors:  Alexandre Abraham; Fabian Pedregosa; Michael Eickenberg; Philippe Gervais; Andreas Mueller; Jean Kossaifi; Alexandre Gramfort; Bertrand Thirion; Gaël Varoquaux
Journal:  Front Neuroinform       Date:  2014-02-21       Impact factor: 4.081

9.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

Authors:  Mark D Robinson; Davis J McCarthy; Gordon K Smyth
Journal:  Bioinformatics       Date:  2009-11-11       Impact factor: 6.937

10.  The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote.

Authors:  Yang Liao; Gordon K Smyth; Wei Shi
Journal:  Nucleic Acids Res       Date:  2013-04-04       Impact factor: 16.971

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  21 in total

1.  FBXW7 regulates a mitochondrial transcription program by modulating MITF.

Authors:  Franco Abbate; Brateil Badal; Karen Mendelson; Iraz T Aydin; Madhavika N Serasinghe; Ramiz Iqbal; Jarvier N Mohammed; Alexander Solovyov; Benjamin D Greenbaum; Jerry E Chipuk; Julide T Celebi
Journal:  Pigment Cell Melanoma Res       Date:  2018-04-29       Impact factor: 4.693

2.  Novel immune signatures associated with dysplastic naevi and primary cutaneous melanoma in human skin.

Authors:  Bernice Y Yan; Sandra Garcet; Nicholas Gulati; Felix Kiecker; Judilyn Fuentes-Duculan; Patricia Gilleaudeau; Mary Sullivan-Whalen; Avner Shemer; Hiroshi Mitsui; James G Krueger
Journal:  Exp Dermatol       Date:  2018-12-21       Impact factor: 3.960

3.  Biological Validation of RNA Sequencing Data from Formalin-Fixed Paraffin-Embedded Primary Melanomas.

Authors:  Lawrence N Kwong; Mariana Petaccia De Macedo; Lauren Haydu; Aron Y Joon; Michael T Tetzlaff; Tiffany L Calderone; Chiang-Jun Wu; Man Kam Kwong; Jason Roszik; Kenneth R Hess; Michael A Davies; Alexander J Lazar; Jeffrey E Gershenwald
Journal:  JCO Precis Oncol       Date:  2018-06-14

Review 4.  Melanoma: Genetic Abnormalities, Tumor Progression, Clonal Evolution and Tumor Initiating Cells.

Authors:  Ugo Testa; Germana Castelli; Elvira Pelosi
Journal:  Med Sci (Basel)       Date:  2017-11-20

5.  Common Nevus and Skin Cutaneous Melanoma: Prognostic Genes Identified by Gene Co-Expression Network Analysis.

Authors:  Lingge Yang; Yu Xu; Yan Yan; Peng Luo; Shiqi Chen; Biqiang Zheng; Wangjun Yan; Yong Chen; Chunmeng Wang
Journal:  Genes (Basel)       Date:  2019-09-25       Impact factor: 4.096

6.  CDK7 and MITF repress a transcription program involved in survival and drug tolerance in melanoma.

Authors:  Pietro Berico; Max Cigrang; Guillaume Davidson; Cathy Braun; Jeremy Sandoz; Stephanie Legras; Bujamin Hektor Vokshi; Nevena Slovic; François Peyresaubes; Carlos Mario Gene Robles; Jean-Marc Egly; Emmanuel Compe; Irwin Davidson; Frederic Coin
Journal:  EMBO Rep       Date:  2021-07-23       Impact factor: 9.071

Review 7.  A Next-Generation Sequencing Primer-How Does It Work and What Can It Do?

Authors:  Yuriy O Alekseyev; Roghayeh Fazeli; Shi Yang; Raveen Basran; Thomas Maher; Nancy S Miller; Daniel Remick
Journal:  Acad Pathol       Date:  2018-05-06

8.  Dual suppression of inner and outer mitochondrial membrane functions augments apoptotic responses to oncogenic MAPK inhibition.

Authors:  Madhavika N Serasinghe; Jesse D Gelles; Kent Li; Lauren Zhao; Franco Abbate; Marie Syku; Jarvier N Mohammed; Brateil Badal; Cuahutlehuanitzin A Rangel; Kyle L Hoehn; Julide Tok Celebi; Jerry Edward Chipuk
Journal:  Cell Death Dis       Date:  2018-01-18       Impact factor: 8.469

9.  A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data.

Authors:  Gaurav Pandey; Om P Pandey; Angela J Rogers; Mehmet E Ahsen; Gabriel E Hoffman; Benjamin A Raby; Scott T Weiss; Eric E Schadt; Supinda Bunyavanich
Journal:  Sci Rep       Date:  2018-06-11       Impact factor: 4.379

10.  Global Cancer Transcriptome Quantifies Repeat Element Polarization between Immunotherapy Responsive and T Cell Suppressive Classes.

Authors:  Alexander Solovyov; Nicolas Vabret; Kshitij S Arora; Alexandra Snyder; Samuel A Funt; Dean F Bajorin; Jonathan E Rosenberg; Nina Bhardwaj; David T Ting; Benjamin D Greenbaum
Journal:  Cell Rep       Date:  2018-04-10       Impact factor: 9.423

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