DNA methylation profiling of primary neuroblastoma tumors using methyl-CpG-binding domain sequencing

Scientific Data

Volumn 3, Issue , 2016, Pages

  Decock, Anneleen ^a,b   Ongenaert, Maté ^a   Van Criekinge, Wim ^c,d   Speleman, Frank ^a,b   Vandesompele, Jo ^a,b,e  

^a GHENT UNIVERSITY HOSPITAL   (Belgium)

^b CANCER RESEARCH INSTITUTE GHENT CRIG   (Belgium)

^c GHENT UNIVERSITY   (Belgium)

^d MDxHealth   (Belgium)

^e Bioinformatics Institute Ghent from Nucleotides to Networks (BIG N2N)   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

DNA; TUMOR MARKER;

CPG ISLAND; DNA FINGERPRINTING; DNA METHYLATION; GENETICS; HUMAN; NEUROBLASTOMA;

BIOMARKERS, TUMOR; CPG ISLANDS; DNA FINGERPRINTING; DNA METHYLATION; DNA, NEOPLASM; HUMANS; NEUROBLASTOMA;

EID: 84960982231     PISSN: None     EISSN: 20524463     Source Type: Journal    
DOI: 10.1038/sdata.2016.4     Document Type: Article

References (38)

1. Brodeur, G. M. Neuroblastoma: biological insights into a clinical enigma. Nat. Rev. Cancer 3, 203-216 (2003).
2. Molenaar, J. J. et al. Sequencing of neuroblastoma identifies chromothripsis and defects in neuritogenesis genes. Nature 483, 589-593 (2012).
3. Valentijn, L. J. et al. TERT rearrangements are frequent in neuroblastoma and identify aggressive tumors. Nat. Genet. 47, 1411-1414 (2015).
4. Peifer, M. et al. Telomerase activation by genomic rearrangements in high-risk neuroblastoma. Nature 526, 700-704 (2015).
5. Decock, A., Ongenaert, M., Vandesompele, J. & Speleman, F. Neuroblastoma epigenetics: from candidate gene approaches to genome-wide screenings. Epigenetics 6, 962-970 (2011).
6. Carén, H. et al. Identification of epigenetically regulated genes that predict patient outcome in neuroblastoma. BMC Cancer 11, 66 (2011).
7. Decock, A. et al. Genome-wide promoter methylation analysis in neuroblastoma identifies prognostic methylation biomarkers. Genome Biol. 13, R95 (2012).
8. Duijkers, F. A. et al. Epigenetic drug combination induces genome-wide demethylation and altered gene expression in neuro-ectodermal tumor-derived cell lines. Cell. Oncol. 36, 351-362 (2013).
9. Mayol, G. et al. DNA hypomethylation affects cancer-related biological functions and genes relevant in neuroblastoma pathogenesis. PLoS ONE 7, e48401 (2012).
10. Yáñez, Y. et al. Two independent epigenetic biomarkers predict survival in neuroblastoma. Clin. Epigenetics 7, 1-14 (2015).
11. Gómez, S. et al. DNA methylation fingerprint of neuroblastoma reveals new biological and clinical insights. Epigenomics 7, 1137-1153 (2015).
12. Schramm, A. et al. Mutational dynamics between primary and relapse neuroblastomas. Nat. Genet. 47, 872-877 (2015).
13. Stirzaker, C., Taberlay, P. C., Statham, A. L. & Clark, S. J. Mining cancer methylomes: prospects and challenges. Trends Genet. 30, 75-84 (2014).
14. Decock, A. et al. Methyl-CpG-binding domain sequencing reveals a prognostic methylation signature in neuroblastoma. Oncotarget 7, 1960-1972 (2015).
15. Brodeur, G. M. et al. Revisions of the international criteria for neuroblastoma diagnosis, staging, and response to treatment. J. Clin. Oncol. 11, 1466-1477 (1993).
16. Mestdagh, P. et al. The miR-17-92 microRNA cluster regulates multiple components of the TGF-β pathway in neuroblastoma. Mol. Cell 40, 762-773 (2010).
17. Eschenburg, G., Eggert, A., Schramm, A., Lode, H. N. & Hundsdoerfer, P. Smac mimetic LBW242 sensitizes XIAP-overexpressing neuroblastoma cells for TNF-α-independent apoptosis. Cancer Res. 72, 2645-2656 (2012).
18. Kumps, C. et al. Focal DNA copy number changes in neuroblastoma target MYCN regulated genes. PLoS ONE 8, e52321 (2013).
19. Sante, T. et al. ViVar: a comprehensive platform for the analysis and visualization of structural genomic variation. PLoS ONE 9, e113800 (2014).
20. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357-359 (2012).
21. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078-2079 (2009).
22. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841-842 (2010).
23. Lassmann, T., Hayashizaki, Y. & Daub, C. O. SAMStat: monitoring biases in next generation sequencing data. Bioinformatics 27, 130-131 (2011).
24. De Meyer, T. et al. Quality evaluation of methyl binding domain based kits for enrichment DNA-methylation sequencing. PLoS ONE 8, e59068 (2013).
25. Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621-628 (2008).
26. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).
27. Thorvaldsdóttir, H., Robinson, J. T. & Mesirov, J. P. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief. Bioinform. 14, 178-192 (2013).
28. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).
29. Minoche, A. E., Dohm, J. C. & Himmelbauer, H. Evaluation of genomic high-throughput sequencing data generated on Illumina HiSeq and genome analyzer systems. Genome Biol. 12, R112 (2011).
30. Yu, Y. et al. Comprehensive RNA-Seq transcriptomic profiling across 11 organs, 4 ages, and 2 sexes of Fischer 344 rats. Sci. Data 1, 140013 (2014).
31. Banelli, B. et al. A pyrosequencing assay for the quantitative methylation analysis of the PCDHB gene cluster, the major factor in neuroblastoma methylator phenotype. Lab. Invest. 92, 458-465 (2012).
32. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754-1760 (2009).
33. Feng, X., Grossman, R. & Stein, L. PeakRanger: a cloud-enabled peak caller for ChIP-seq data. BMC Bioinformatics 12, 139 (2011).
34. Lan, X. et al. High resolution detection and analysis of CpG dinucleotides methylation using MBD-seq technology. PLoS ONE 6, e22226 (2011).
35. Lienhard, M., Grimm, C., Morkel, M., Herwig, R. & Chavez, L. MEDIPS: Genome-wide differential coverage analysis of sequencing data derived from DNA enrichment experiments. Bioinformatics 30, 284-286 (2014).
36. Stark, R. & Brown, G. D. DiffBind: differential binding analysis of ChIP-seq peak data. http://bioconductor.org/packages/release/ bioc/vignettes/DiffBind/inst/doc/DiffBind.pdf (2011).
37. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139-140 (2010).
38. Hong, F. et al. RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis. Bioinformatics 22, 2825-2827 (2006).