DNA methylation subgroups in melanoma are associated with proliferative and immunological processes

BMC Medical Genomics

Göran Jönsson¹  Johan Staaf¹  Per Eystein Lønning³  Jürgen Geisler²  Christian Busch⁴  Katja Harbst¹  Anna Karlsson¹  Markus Ringnér¹  Martin Lauss¹ 

[1]Department of Oncology and Pathology, Clinical Sciences, Lund University Hospital, Lund University, Lund 221 85, Sweden

[2]Present Address: Department of Clinical Molecular Biology and Laboratory Sciences, Akershus University Hospital, Lørenskog, Norway

[3]Institute of Clinical Medicine, University of Oslo, Oslo, Norway

[4]Department of Clinical Oncology, Haukeland University Hospital, Bergen, Norway

关键词: Polycomb;    Molecular subtypes;    Melanocytes;    Gene expression;    DNA methylation;    Melanoma;   

Others  :  1233843
DOI  :  10.1186/s12920-015-0147-4

 received in 2015-05-22, accepted in 2015-10-28,  发布年份 2015

【 摘 要 】

Background

DNA methylation at CpG dinucleotides is modified in tumorigenesis with potential impact on transcriptional activity.

Methods

We used the Illumina 450 K platform to evaluate DNA methylation patterns of 50 metastatic melanoma tumors, with matched gene expression data.

Results

We identified three different methylation groups and validated the groups in independent data from The Cancer Genome Atlas. One group displayed hypermethylation of a developmental promoter set, genome-wide demethylation, increased proliferation and activity of the SWI/SNF complex. A second group had a methylation pattern resembling stromal and leukocyte cells, over-expressed an immune signature and had improved survival rates in metastatic tumors (p < 0.05). A third group had intermediate methylation levels and expressed both proliferative and immune signatures. The methylation groups corresponded to some degree with previously identified gene expression phenotypes.

Conclusions

Melanoma consists of divergent methylation groups that are distinguished by promoter methylation, proliferation and content of immunological cells.

【 授权许可】

   
2015 Lauss et al.

【 图 表 】

Fig. 1.

Fig. 2.

Fig. 3.

Fig. 4.

【 参考文献 】

• [1]Hodis E, Watson IR, Kryukov GV, Arold ST, Imielinski M, Theurillat J-P, et al.: A landscape of driver mutations in melanoma. Cell 2012, 150:251-263.
• [2]Krauthammer M, Kong Y, Ha BH, Evans P, Bacchiocchi A, McCusker JP, et al.: Exome sequencing identifies recurrent somatic RAC1 mutations in melanoma. Nat Genet 2012, 44:1006-1014.
• [3]Pleasance ED, Cheetham RK, Stephens PJ, McBride DJ, Humphray SJ, Greenman CD, et al.: A comprehensive catalogue of somatic mutations from a human cancer genome. Nature 2010, 463:191-196.
• [4]Robert C, Thomas L, Bondarenko I, O’Day S, Garbe C, et al.: M D JW: Ipilimumab plus dacarbazine for previously untreated metastatic melanoma. N Engl J Med 2011, 364:2517-2526.
• [5]Wolchok JD, Kluger H, Callahan MK, Postow MA, Rizvi NA, Lesokhin AM, et al.: Nivolumab plus ipilimumab in advanced melanoma. N Engl J Med 2013, 369:122-133.
• [6]Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, et al.: Mutational landscape and significance across 12 major cancer types. Nature 2013, 502:333-339.
• [7]van den Hurk K, Niessen HEC, Veeck J, van den Oord JJ, van Steensel MAM, Zur HA, et al.: Genetics and epigenetics of cutaneous malignant melanoma: A concert out of tune. Biochimica Et Biophysica Acta 2012, 1826:89-102.
• [8]Tanemura A, Terando AM, Sim M-S, van Hoesel AQ, de Maat MFG, Morton DL, et al.: CpG island methylator phenotype predicts progression of malignant melanoma. Clin Cancer Res 2009, 15:1801-1807.
• [9]Noushmehr H, Weisenberger DJ, Diefes K, Phillips HS, Pujara K, Berman BP, et al.: Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell 2010, 17:510-522.
• [10]Weisenberger DJ, Siegmund KD, Campan M, Young J, Long TI, Faasse MA, et al.: CpG island methylator phenotype underlies sporadic microsatellite instability and is tightly associated with BRAF mutation in colorectal cancer. Nat Genet 2006, 38:787-793.
• [11]Furuta J, Nobeyama Y, Umebayashi Y, Otsuka F, Kikuchi K, Ushijima T: Silencing of Peroxiredoxin 2 and aberrant methylation of 33 CpG islands in putative promoter regions in human malignant melanomas. Cancer Res 2006, 66:6080-6086.
• [12]Koga Y, Pelizzola M, Cheng E, Krauthammer M, Sznol M, Ariyan S, et al.: Genome-wide screen of promoter methylation identifies novel markers in melanoma. Genome Res 2009, 19:1462-1470.
• [13]Conway K, Edmiston SN, Khondker ZS, Groben PA, Zhou X, Chu H, et al.: DNA-methylation profiling distinguishes malignant melanomas from benign nevi. Pigment Cell Melanoma Res 2011, 24:352-360.
• [14]Bonazzi VF, Nancarrow DJ, Stark MS, Moser RJ, Boyle GM, Aoude LG, et al.: Cross-platform array screening identifies COL1A2, THBS1, TNFRSF10D and UCHL1 as genes frequently silenced by methylation in melanoma. PLoS One 2011, 6:e26121.
• [15]Gao L, Smit MA, van den Oord JJ, Goeman JJ, Verdegaal EME, van der Burg SH, et al.: Genome-wide promoter methylation analysis identifies epigenetic silencing of MAPK13 in primary cutaneous melanoma. Pigment Cell Melanoma Res 2013, 26:542-554.
• [16]Hou P, Liu D, Dong J, Xing M: The BRAF(V600E) causes widespread alterations in gene methylation in the genome of melanoma cells. Cell Cycle 2012, 11:286-295.
• [17]Lian CG, Xu Y, Ceol C, Wu F, Larson A, Dresser K, et al.: Loss of 5-hydroxymethylcytosine is an epigenetic hallmark of melanoma. Cell 2012, 150:1135-1146.
• [18]Marzese DM, Scolyer RA, Roqué M, Vargas-Roig LM, Huynh JL, Wilmott JS, et al.: DNA methylation and gene deletion analysis of brain metastases in melanoma patients identifies mutually exclusive molecular alterations. Neuro Oncol 2014, 16:1499-1509.
• [19]Marzese DM, Scolyer RA, Huynh JL, Huang SK, Hirose H, Chong KK, et al. Epigenome-wide DNA methylation landscape of melanoma progression to brain metastasis reveals aberrations on homeobox D cluster associated with prognosis. Hum Mol Genet. 2014;23:226–38.
• [20]Ecsedi S, Hernandez-Vargas H, Lima SC, Vizkeleti L, Toth R, Lazar V, et al.: DNA methylation characteristics of primary melanomas with distinct biological behaviour. PLoS One 2014, 9:e96612.
• [21]Li J-L, Mazar J, Zhong C, Faulkner GJ, Govindarajan SS, Zhang Z, et al.: Genome-wide methylated CpG island profiles of melanoma cells reveal a melanoma coregulation network. Sci Rep 2013, 3:2962.
• [22]Dahl C, Abildgaard C, Riber-Hansen R, Steiniche T, Lade-Keller J, Guldberg P. KIT Is a Frequent Target for Epigenetic Silencing in Cutaneous Melanoma. J Invest Dermatol. 2015;135:516–24.
• [23]Gao L, van den Hurk K, Moerkerk PTM, Goeman JJ, Beck S, Gruis NA, et al.: Promoter CpG Island Hypermethylation in Dysplastic Nevus and Melanoma: CLDN11 as an Epigenetic Biomarker for Malignancy. J Invest Dermatol 2014, 134(12):2957-2966.
• [24]Thomas NE, Slater NA, Edmiston SN, Zhou X, Kuan P-F, Groben PA, et al. DNA methylation profiles in primary cutaneous melanomas are associated with clinically significant pathologic features. Pigment Cell Melanoma Res. 2014;27:1097–105.
• [25]Sigalotti L, Covre A, Fratta E, Parisi G, Sonego P, Colizzi F, et al.: Whole genome methylation profiles as independent markers of survival in stage IIIC melanoma patients. J Transl Med 2012, 10:185. BioMed Central Full Text
• [26]Jönsson G, Busch C, Knappskog S, Geisler J, Miletic H, Ringnér M, et al.: Gene expression profiling-based identification of molecular subtypes in stage IV melanomas with different clinical outcome. Clin Cancer Res 2010, 16:3356-3367.
• [27]Harbst K, Staaf J, Lauss M, Karlsson A, Måsbäck A, Johansson I, et al.: Molecular profiling reveals low- and high-grade forms of primary melanoma. Clin Cancer Res 2012, 18:4026-4036.
• [28]Sandoval J, Heyn H, Moran S, Serra-Musach J, Pujana MA, Bibikova M, et al.: Validation of a DNA methylation microarray for 450,000 CpG sites in the human genome. Epigenetics 2011, 6:692-702.
• [29]Lauss M, Haq R, Cirenajwis H, Phung B, Harbst K, Staaf J, et al.: Genome-Wide DNA Methylation Analysis in Melanoma Reveals the Importance of CpG Methylation in MITF Regulation. J Invest Dermatol 2015, 135(7):1820-1828.
• [30]Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al.: Roadmap Epigenomics Consortium: Integrative analysis of 111 reference human epigenomes. Nature 2015, 518:317-330.
• [31]Easwaran H, Johnstone SE, Van Neste L, Ohm J, Mosbruger T, Wang Q, et al.: A DNA hypermethylation module for the stem/progenitor cell signature of cancer. Genome Res 2012, 22:837-849.
• [32]Widschwendter M, Fiegl H, Egle D, Mueller-Holzner E, Spizzo G, Marth C, et al.: Epigenetic stem cell signature in cancer. Nat Genet 2007, 39:157-158.
• [33]Yoshihara K, Shahmoradgoli M, Martínez E, Vegesna R, Kim H, Torres-Garcia W, et al.: Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun 2013, 4:2612.
• [34]Sproul D, Kitchen RR, Nestor CE, Dixon JM, Sims AH, Harrison DJ, et al.: Tissue of origin determines cancer-associated CpG island promoter hypermethylation patterns. Genome Biol 2012, 13:R84. BioMed Central Full Text
• [35]Turcan S, Rohle D, Goenka A, Walsh LA, Fang F, Yilmaz E, et al.: IDH1 mutation is sufficient to establish the glioma hypermethylator phenotype. Nature 2012, 483:479-83.
• [36]Shibata T, Kokubu A, Miyamoto M, Sasajima Y, Yamazaki N: Mutant IDH1 confers an in vivo growth in a melanoma cell line with BRAF mutation. Am J Pathol 2011, 178:1395-1402.
• [37]Long GV, Stroyakovskiy D, Gogas H, Levchenko E, de Braud F, Larkin J, et al.: Combined BRAF and MEK Inhibition versus BRAF Inhibition Alone in Melanoma. N Engl J Med 2014, 371:1877-1888.
• [38]Kwong LN, Davies MA: Navigating the therapeutic complexity of PI3K pathway inhibition in melanoma. Clin Cancer Res 2013, 19:5310-5319.
• [39]Shain AH, Pollack JR: The spectrum of SWI/SNF mutations, ubiquitous in human cancers. PLoS One 2013, 8:e55119.
• [40]Helming KC, Wang X, Roberts CWM: Vulnerabilities of Mutant SWI/SNF Complexes in Cancer. Cancer Cell 2014, 26:309-317.
• [41]Ehrlich M: DNA methylation in cancer: too much, but also too little. Oncogene 2002, 21:5400-5413.
• [42]Witte T, Plass C, Gerhauser C: Pan-cancer patterns of DNA methylation. Genome Med 2014, 6:66. BioMed Central Full Text
• [43]Bernstein BE, Mikkelsen TS, Xie X, Kamal M, Huebert DJ, Cuff J, et al.: A bivalent chromatin structure marks key developmental genes in embryonic stem cells. Cell 2006, 125:315-326.
• [44]Lauss M, Aine M, Sjödahl G, Veerla S, Patschan O, Gudjonsson S, et al.: DNA methylation analyses of urothelial carcinoma reveal distinct epigenetic subtypes and an association between gene copy number and methylation status. Epigenetics 2012, 7:858-867.
• [45]Tumeh PC, Harview CL, Yearley JH, Shintaku IP, Taylor EJM, Robert L, et al.: PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 2014, 515:568-571.
• [46]Herbst RS, Soria J-C, Kowanetz M, Fine GD, Hamid O, Gordon MS, et al.: Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 2014, 515:563-567.
• [47]Gubin MM, Zhang X, Schuster H, Caron E, Ward JP, Noguchi T, et al.: Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens. Nature 2014, 515:577-581.
• [48]Snyder A, Makarov V, Merghoub T, Yuan J, Zaretsky JM, Desrichard A, et al.: Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med 2014, 371:2189-2199.
• [49]Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, et al.: Missing value estimation methods for DNA microarrays. Bioinformatics 2001, 17:520-525.
• [50]Dedeurwaerder S, Defrance M, Calonne E, Denis H, Sotiriou C, Fuks F: Evaluation of the Infinium Methylation 450 K technology. Epigenomics 2011, 3:771-784.
• [51]Smyth GK: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004, 3:Article3.
• [52]Ernst J, Kheradpour P, Mikkelsen TS, Shoresh N, Ward LD, Epstein CB, et al.: Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 2011, 473:43-49.
• [53]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. Oncotarget. 2015;6:12297–309.
• [54]Lauss M, Visne I, Kriegner A, Ringnér M, Jönsson G, Höglund M: Monitoring of technical variation in quantitative high-throughput datasets. Cancer Inform 2013, 12:193-201.